Jade Vanbuis
email: jade.vanbuis@eseo.fr

Mathieu Feuilloy

Guillaume Baffet

Nicole Meslier

Frédéric Gagnadoux

Jean-Marc Girault

Lucile Riaboff

Alain Le Duff

Towards a user-friendly sleep staging system for polysomnography part I: Automatic classification based on medical knowledge

Keywords:

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Sleep-related disorders nearly affect the third of the population and are increasingly recognized as real public health problems [START_REF] Croft | CDC's Public Health Surveillance of Sleep Health[END_REF]. The need of sleep diagnosis has then increased during the last few years [START_REF] Peppard | Increased Prevalence of Sleep-Disordered Breathing in Adults[END_REF][START_REF] Franklin | Obstructive sleep apnea is a common disorder in the population-a review on the epidemiology of sleep apnea[END_REF]. Gold-standard procedure for sleep diagnosis consists of electrophysiological and respiratory signals recording with the use of a polysomnograph. With all those signals, medical staff manually scores both sleep events and sleep stages. Sleep events, as apneas and hypopneas, are detected through respiratory signals. On the other hand, sleep stages are scored based on the electrophysiological signals. Among them, electroencephalograms (EEG), electrooculograms (EOG) and electromyograms (EMG) are used for the measurement of cerebral, ocular and muscular activities, respectively. Sleep scoring consists on the classification of wakefulness, stage N1 and stage N2 (both light sleep), stage N3 (deep sleep) and REM sleep (Rapid Eye Movement, also called R stage or paradoxical sleep: stage with active brain but reduced muscle tone) in 30-second sections, also called epochs. Besides being a time-consuming task, sleep scoring requires specific medical knowledge. A manual of recommendations published by the American Academy of Sleep Medicine (AASM) in 2007 [START_REF] Berry | The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications[END_REF] describes the temporal and spectral contents of each sleep stage, as well as sleep patterns that can be rec-ognized by the scorer and the possible transitions from one sleep stage to another.

There was a growing number of automatic sleep scoring algorithms developed those last few years, spread out into three categories: deep learning [START_REF] Biswal | Expert-level sleep scoring with deep neural networks[END_REF][START_REF] Zhang | Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks[END_REF], machine learning [START_REF] Enshaeifar | Quaternion Singular Spectrum Analysis of Electroencephalogram With Application in Sleep Analysis[END_REF][START_REF] Lajnef | Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multiclass support vector machines[END_REF][START_REF] Mohammadi | Improving time-frequency domain sleep EEG classification via singular spectrum analysis[END_REF][START_REF] Charbonnier | Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging[END_REF][START_REF] Garcia-Molina | Automated NREM sleep staging using the Electro-oculogram: A pilot study[END_REF][START_REF] Fraiwan | Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier[END_REF][START_REF] Zokaeinikoo | Automatic Sleep Stages Classification[END_REF] and hybrid approaches [START_REF] Ugon | Fusion Symbolique et Données Polysomnographiques[END_REF][START_REF] Chen | An e-health system for personalized automatic sleep stages classification[END_REF][START_REF] Chen | Towards a Hybrid Expert System Based on Sleep Event's Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm[END_REF]. Despite the increasing number of models based on artificial intelligence (AI), only a few are routinely employed by sleep specialists. Several reasons for that have been identified and detailed in the companion paper [REF] and in [START_REF] Fiorillo | Automated sleep scoring: A review of the latest approaches[END_REF]. One of them is the lack of transparency of the developed approaches. Indeed, deep learning approaches, which often reach the best scores, are opaque and their lack of transparency raises skepticism among physicians. With opaque approaches, medical practitioners must accept to loose their control upon the task that is realized by the algorithm. It could prevent them to properly react when dealing with an unusual pathology (that was not necessarily represented when training the model). For this reason, some researchers attempt to improve the interpretability of their models [START_REF] Doshi-Velez | Towards A Rigorous Science of Interpretable Machine Learning[END_REF], and provide some concrete elements for the practitioner to relate to. Hybrid approaches [START_REF] Ugon | Fusion Symbolique et Données Polysomnographiques[END_REF][START_REF] Chen | An e-health system for personalized automatic sleep stages classification[END_REF][START_REF] Chen | Towards a Hybrid Expert System Based on Sleep Event's Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm[END_REF] were then designed to overcome several identified limitations, including the lack of transparency of the implemented method.

The present article is the second part of a two-part paper. In the companion paper [REF], a novel hybrid approach replicating the steps of a manual scoring was developed and presented. This hybrid system is composed of several functions, including one dedicated to features extraction, necessary for classification. Called SATUD, this features extractor will be fully detailed in the following section, since it is the core of the present paper. Extracted features describe each epoch. They are qualitative and represent concrete elements mentioned in the AASM guidelines. However, the extraction of such features can be problematic for classification if not correctly carried out. Indeed, there is a strong variability within subjects. This variability is one of the major difficulties encountered by sleep specialists while manually scoring sleep. To score sleep stages appropriately, medical practitioners generally need to get familiar with the recording specificities. To do so, they start with a quick visualization of the entire recording, before doing the scoring. However, amongst the many automatic scoring algorithms developed the last few years [START_REF] Biswal | Expert-level sleep scoring with deep neural networks[END_REF][START_REF] Zhang | Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks[END_REF][START_REF] Enshaeifar | Quaternion Singular Spectrum Analysis of Electroencephalogram With Application in Sleep Analysis[END_REF][START_REF] Lajnef | Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multiclass support vector machines[END_REF][START_REF] Mohammadi | Improving time-frequency domain sleep EEG classification via singular spectrum analysis[END_REF][START_REF] Charbonnier | Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging[END_REF][START_REF] Garcia-Molina | Automated NREM sleep staging using the Electro-oculogram: A pilot study[END_REF][START_REF] Fraiwan | Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier[END_REF][START_REF] Zokaeinikoo | Automatic Sleep Stages Classification[END_REF][START_REF] Ugon | Fusion Symbolique et Données Polysomnographiques[END_REF][START_REF] Chen | An e-health system for personalized automatic sleep stages classification[END_REF][START_REF] Chen | Towards a Hybrid Expert System Based on Sleep Event's Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm[END_REF], only a few were adapted to the variability between subjects [START_REF] Ugon | Fusion Symbolique et Données Polysomnographiques[END_REF][START_REF] Chen | An e-health system for personalized automatic sleep stages classification[END_REF][START_REF] Chen | Towards a Hybrid Expert System Based on Sleep Event's Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm[END_REF]. Furthermore, adaptation to each recording specificities implies having a sufficient number of recordings from patients with various pathologies, which is rarely the case.

This study presents the Self-Adaptative Thresholding Using Descriptors (SATUD) method. Developed for the extraction of subject-dependent features, the SATUD reduces the impact of subjects variability. Features values are adjusted to each patient, as sleep specialists naturally do when scoring sleep. The SATUD principle is explained in the following section, and its ability to correctly adjust thresholds is then evaluated and compared with other thresholding methods.

Material and method

In this section, the data used for the implementation of the SATUD is first presented (2.1). The chosen approach is then detailed in Section 2.2.

Data

Patients with various sleep pathologies underwent onenight polysomnography (PSG) in Angers sleep laboratory (University Hospital, INSERM UMR1063, Angers, FRANCE). PSG is a sleep diagnostic device acquiring electrophysiological signals, respiratory signals, body movements and position. Recordings were part of the "Institut de Recherche en Santé Respiratoire des Pays de la Loire" [IRSR] sleep cohort. Approval was obtained from the University of Angers ethics committee and from the "Comité Consultative sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé" [CCTIRS] (07.207bis). All patients included in the IRSR sleep cohort have given their written informed consent. Recordings were anonymous. Sleep stages and events were recorded and scored following the AASM recommendations [START_REF] Berry | The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications[END_REF] using CID102L8D polysomnographs. Besides those standard polysomnographic signals, tracheal sounds were recorded using a PneaVoX ® device for enhanced ventilatory events recognition [START_REF] Penzel | The use of tracheal sounds for the diagnosis of sleep apnoea[END_REF]. A total of 60 anonymous patients recordings was scored by sleep specialists (three sleep specialists were involved in the study, but each recording was scored by a single scorer). In this study, the automatic algorithm only employed EEG, EOG and EMG signals used by physicians to score sleep stages. The sequence of wake, N1, N2, N3 and REM sleep in epochs of 30 seconds is called hypnogram and constitutes our reference.

Method

The hypothesis made in this paper is that concrete features can help overcome the model lack of transparency. Qualitative (ordinal) features allow close translation of medical knowledge (AASM recommendations). Their estimation requires the discretization of quantitative features. For example, the quantitative feature amplitudeEEG1 is discretized into qualitative features as amplitudeEEGHigh and ampli-tudeEEGLow, employing a certain number of thresholds needing to be estimated. For this example, the problem is to know the threshold for which we can consider having a low or high EEG amplitude. The SATUD algorithm aims to adjust those thresholds automatically, for each recording. The integration of the SATUD in the sleep staging system implemented in this study is schematized in Figure 1. The architecture is composed of several main functions: F1, F2, F3 and F3.A.

A detailed example can be found in Appendix A.

SYSTEM

The proposed system aims to provide a set of patientspecific qualitative features. As described in Figure 1, it was composed of three main functions (F1, F2, F3). For applications concerning sleep staging, the system inputs were:

• a priori knowledge giving information about sleep scoring through AASM manual. For example, this manual indicates that N3 sleep stage can be recognized using low-frequency (0.5-2 Hz) high-amplitude (> 75 ) EEG frontal waves. A high proportion of those waves called 'Slow wave activity', indicates N3;

• electrophysiological signals obtained from the recorded polysomnographies, and more precisely three electroencephalograms (EEG), two electrooculograms (EOG) and the chin electromyogram (EMG).

F1 -Sleep stages description

F1 aims to build one list of properties for each sleep stage. The a priori knowledge from AASM manual (as detailed in SYSTEM) was the input of the first function F1. In order to build the list, we carefully studied the AASM manual for all sleep stages, and translated subsequently into lists of properties which represent time and frequency information used to differentiate sleep stages. The upper part of the Figure 2 shows an example of two properties that were used to describe sleep stage N3. The lists of properties associated with each sleep stage are essential for the SATUD proper functioning, as they replace labelled data.

F2 -Features extraction

F2 aims to extract the quantitative features corresponding to the properties identified in function F1. Using the electrophysiological channels split into epochs, 13 quantitative features were extracted. Those quantitative features are listed in the 1st column of Table 1. Those quantitative features can reflect temporal or spectral content, but also a combination of both temporal and spectral content. They were identified in the AASM guidelines as being required for sleep scoring. In the middle part of Figure 2 are presented two quantitative features identified thanks to the previous properties.

F3 -SATUD

The SATUD aims to deduce patient-specific information from quantitative features using specific knowledge. It decreases subjects variability with the estimation of patientspecific thresholds. Thresholds were applied on the 13 quantitative features obtained in F2 to generate 41 qualitative features. The qualitative features and the associated number of thresholds were chosen in agreement with the properties described in F1. To do so, for each quantitative feature from F2, and depending on the properties from F1 (which translate the AASM guidelines), the appropriate qualitative features were extracted 2 . Those qualitative features are listed in the 3rd column of Table 1. Different ways of defining initial thresholds were tested. In this application, initial thresholds were chosen using a statistical approach (percentiles). Thresholds values were then adjusted in order to minimize the cost function described in F3.A. To do so, several meta-heuristics were tested: global search algorithms such as simulated annealing [START_REF] Kirkpatrick | Optimization by Simulated Annealing[END_REF] and genetic algorithms [START_REF] Holland | Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence[END_REF][START_REF] Goldberg | Genetic Algorithms and Machine Learning[END_REF] and also local algorithms such as gradient descent methods [START_REF] Byrd | An Interior Point Algorithm for Large-Scale Nonlinear Programming[END_REF][START_REF] Waltz | An interior algorithm for nonlinear optimization that combines line search and trust region steps[END_REF]. For our application, the use of local search algorithms alone was ineffective due to the number of thresholds to adjust. We thus chose to use a global search algorithm to initialize the search zone, but still combined it with a final local search algorithm for better precision. As indicated in the pseudo-code reported in Appendix B, the method chosen for this study was the combination of simulated annealing followed by a gradient descent.

As there is a high variability between subjects, optimal thresholds values could be very different from one patient to another. Thresholds adaptation to each patient is explained in the following section. 2 Only qualitative features which correspond to sleep stages properties described in the AASM guidelines were computed, even if the number of thresholds allowed the estimation of more features. For example, 2 thresholds (Low → Mid and Mid → High) can generate 6 qualitative features: low, low or mid, mid, mid or high, high and low or mid or high. However, the study of the AASM guidelines rarely indicates there is a need for all those features. Only few of them can be sufficient to translate the guidelines.

Table 1

List of the 41 qualitative features extracted for sleep scoring, depending on the 13 quantitative features extracted in F2 (ranging from EEG amplitude to Substracted EOG instability) and the number of associated thresholds.

F3.A -Cost estimation

The final cost was defined as a weighted sum of the costs associated to each class (sleep stage in this study):

finalCost = ∑ =1 × cost (1)
where is the number of classes, and and cost are the weight and cost associated to the ℎ class respectively. Weights are optional. In our application, they have been chosen to promote sleep stages that are difficult to identify or demote the ones that occur rarely (N1 sleep stage only represents approximately 5 % of the night). cost was defined as:

cost = 1 ( P1 , P2 , ..., PN ) × (antiScore ) (2)
with:

antiScore = ∑ =1 × Pi (3)
where is the number of properties of class , Pi is a binary variable representing the respect of the ℎ property of class (as explained hereafter), and is the weight associated to the ℎ property of class . This time, weights have been chosen to better translate the AASM guidelines. Indeed, some properties are clearly indicated as being more important for sleep staging. The cost function is minimized for each recording individually. Defined as above, it is equivalent to i) maximize the similarity between the epochs of a same sleep stage of the same patient, hereinafter referred to as intra-class similarity and ii) maximize the differences between the epochs belonging to different sleep stages of the same patient, hereinafter referred to as inter-class dissimilarity.

i) maximize intra-class similarity: intra-class similarity is optimized by maximizing ( P1 , P2 , ..., PN ). This term represents the concordance between the respect of the properties used to describe class . The respect of a property is assessed using the corresponding qualitative feature. For our application, the concordance between the respect of the properties was estimated using the Fleiss' Kappa [START_REF] Fleiss | Measuring nominal scale agreement among many raters[END_REF]. This is a statistical measure used to assess the reliability of agreement between several categorical vectors. Indeed, we can consider that when a patient is in a specific sleep stage, most of the properties related to this sleep stage are respected. When the sleep stage changes, a certain number of those properties will no longer be respected. If we focus on the properties related to a particular sleep stage, the transitions to and from this sleep stage will be highlighted by a simultaneous change of the respect of its properties. Those simultaneous changes can be evaluated by assessing its properties respects interdependency. Therefore, a high interdependency will indicate a general concordance between thresholds. Thresholds were adjusted unsupervisingly, until they agreed and confirmed each other, leading to a higher concordance measure (Fleiss' Kappa). This is illustrated in Figure 3.

After thresholds adjustment

Before thresholds adjustment Figure 3: Simplified example of the properties respects behaviour before and after thresholds adjustment. Class is described by only two properties (P1 and P2). The interdependency between properties respect P1 and P2 increases when thresholds are being adjusted.

In this example, that has a length of 26 epochs, class presence is unknown for thresholds adjustment (unsupervised functioning). The better the thresholds are, the more properties are respected ( P1 and P2 ) when and only when the patient is in class . Before thresholds adjustment, 18 epochs among the 26 were in concordance with each other (highlighted areas). This number increased from 18 to 23 after thresholds adjustment, leading to a better Fleiss' Kappa.

ii) maximize inter-class dissimilarity: inter-class dissimilarity is optimized by maximizing (antiScore ). The anti-score function is defined as a weighted sum of the no-respect of a class properties (Equation 3). Weights are optional. For our application, they were empirically chosen to translate the degree of importance according to the AASM manual.

(antiScore ) represents the fluctuation (using standard deviation value) of class anti-score function. The antiscore function of a particular sleep stage varies from 0, when all the properties associated to this sleep stage are respected, to 1 when none of the properties are respected. When thresholds are being adjusted, the anti-score values are getting closer to its limits (0 when in the sleep stage and 1 when in another sleep stage), with fewer values in between. Consequently, the anti-score standard deviation on the entire recording increases. This is illustrated in Figure 4, which pursues the example presented in Figure 3. In this example, the anti-score standard deviation increased from 0.41 to 0.47 with thresholds adjustment.

Yes

To summarize, the cost depends on the intra-class similarity and inter-class dissimilarity, both associated with the respect of each class properties. The respect of each class properties are evaluated from the qualitative features values. Those values are directly related to the thresholds. For each recording individually, and without using the manual scoring as the reference, the thresholds are thus adjusted by minimizing the cost. At the end of the process, the patientspecific qualitative features are extracted (using the final thresholds) and can be used for classification.

SATUD evaluation methods

This section is dedicated to the assessment of the SATUD performance, which was evaluated from the totality of the 60 recordings (since the method is unsupervised). The SATUD was employed to extract qualitative features that are understandable and represent concrete information from the AASM guidelines. It was thus compared with other thresholding methods employed the same way. The complete system reported in Figure 1 was replaced by GST (General Statistical Thresholding) and IST (Individual Statistical Thresholding) as described thereafter.

• General Statistical Thresholding (GST): with this method, thresholds were adjusted using statistical information of the entire database, as percentiles (thresholds were not patient-dependent).

• Individual Statistical Thresholding (IST): with this method, thresholds were adjusted for each patient depending on statistical information (thresholds were patient-dependent).

Impact on the obtained qualitative features was first estimated (3.1). The SATUD behaviour in presence of noise or artifacts was then tested (3.2) and, eventually, the impact on classification was evaluated (3.3).

SATUD impact on qualitative features

Using the lists describing each sleep stage, we quantified the agreement between each epoch qualitative features and the properties of the associated sleep stage. For example if there were 10 properties in the list describing sleep stage Wake, then a Wake epoch with qualitative features respecting only 5 of them had a global respect value of 50 % with its sleep stage properties. Outcomes will be presented in Section 4.1.

Robustness test

To evaluate the robustness of the SATUD, we assessed the quantity of epochs highly respecting the properties associated to their sleep stage under several situations:

• noise amplification: white Gaussian noise was added to all raw electrophysiological signals. Raw signals presented a native Signal-to-Noise Ratio (SNR) of approximately 46 dB. Several SNR levels were tested, ranging from 30 dB to 0 dB, in 10 dB intervals.

• artifacts addition: several artifact types are usually present on electrophysiological signals. All artifacts were kept in our recordings to evaluate our algorithm under real-life conditions. To further test the robustness of the method, we added artificial artifacts to the signals. In this study, we chose sweat artifacts, considered as the most disruptive ones. Indeed, during such artifacts, both temporal and spectral information are compromised or even lost. Artificial sweat artifacts were created using random slopes and durations within intervals defined after visualization of several natural sweat artifacts. A duration limit was used to prevent an entire 30-seconds segment to be exclusively composed of sweat artifacts. Natural sweat artifacts occur more often in some sleep stages compared to others and their quantity is generally limited [START_REF] Arnardottir | Sleep-related sweating in obstructive sleep apnoea: association with sleep stages and blood pressure[END_REF][START_REF] Broughton | The electrodermogram (Tarchanoff effect) during sleep[END_REF]. For this reason, we tested the results with the addition of artificial artifacts on 0 % (raw signals) to 100 % of the epochs composing recordings. Figure 5 presents natural (5a) and artificial (5b) sweat artifacts examples.

Outcomes will be presented in Section 4.2.

SATUD impact on classification

Patient-dependent qualitative features described in the previous section are used for sleep stages classification. To properly estimate the impact of the SATUD, a first simple classification model was tested in this paper and compared with manual sleep scoring. The implementation of an advanced classifier was presented in the companion paper [REF]. However, it is necessary to assess the SATUD efficiency by avoiding any bias that could be linked to the use of powerful classification models. The classifier described in the current paper was built to be relatively transparent and easy to understand. It did not required training and testing steps. Indeed, using the adjusted thresholds, percentages of agreement with each sleep stage list of properties were evaluated for each epoch. The sleep stage with the higher agreement rate between the properties and its patient-dependent qualitative features was the chosen one. In the current paper, results were simply expressed in term of accuracy rate with the manual scoring, named Acc:

= TP + TN TP + TN + FP + FN
where TP, FN, FP and TN represent the number of true positives, false negatives, false positives and true negatives, respectively.

Outcomes will be presented in Section 4.3.

Results

The SATUD performance was estimated through the qualitative features generated (see Section 4.1) and compared with the two other thresholding methods: GST and IST. Robustness to noise or artifacts tests results were then reported in Section 4.2. The impact on classification were evaluated in Section 4.3.

SATUD impact on qualitative features

Figure 6 shows the number of epochs that respect at least 0 % to 100 % of their sleep stage properties, according to the thresholding method used. Of course, all epochs respected at least 0 % of their sleep stage properties whatever the method used. With the SATUD, the number of epochs respecting at least 60 % to 100 % of the properties associated with its sleep stage were higher than with GST and IST. It means that qualitative features obtained with the SATUD better respect the properties expected for their sleep stage. There were indeed relative increases of 81 % ( 8135-4506

4506

) and 89 % ( 8135-4306 4306 ) of the number of epochs that perfectly respect the properties associated with their sleep stage when using the SATUD compared to GST and IST, respectively. When focusing on epochs that highly (60 % < ≤ 80 %) or almost perfectly ( > 80 %) respect the properties associated with their sleep stage, we compared the different methods depending on each sleep stage. Figure 7 shows the impact of the SATUD on those epochs compared to GST and IST. When comparing the SATUD with GST and IST, we registered improvements for sleep stages W, N2, N3 and REM sleep but not for N1 sleep stage.

Robustness test

Robustness tests described in Section 3.2 were conducted on GST, IST and the SATUD methods. We first studied noise and sweat artifacts impact on qualitative features. The quantity of epochs that almost perfectly respect the properties associated with their sleep stage ( > 80 %) were evaluated. Results were compared for different levels of noise and artifacts for GST, IST and the SATUD (Figure 8). The SATUD behaviour to noise was globally similar than GST and IST, but with higher scores. Almost no N1 and N3 epochs respected almost perfectly the N1 and N3 properties. The quantity of epochs that almost perfectly respect the properties associated with their sleep stage dropped from a SNR level of 10 dB for all sleep stages except for Wake, where it remained quite constant with GST and IST, and slowly decreased for the SATUD. Considering the addition of artificial sweat artifacts, the use of the SATUD once again positively impacts the respect of sleep stages with their properties. However this time, it behaved differently than GST and IST. For those last ones, artifacts had a very small impact, with a slight increase for Wake and N2 epochs and a progressive decrease for REM sleep. Using the SATUD, N2 and N3 epochs underwent an improvement whereas adding sweat artifacts until they are applied on 30 % -40 % of the recording epochs. Afterwards, it decreased progressively. The SATUD obtained better results than GST and IST in terms of quantity of epochs respecting almost perfectly properties associated with their sleep stage. While remaining superior, its behaviour in noise and artifact situations was globally similar to GST and IST thresholding methods.

Table 2

Accuracy rate with the manual scoring according to the different thresholding methods while testing robustnes.

SATUD impact on classification

Classification global accuracy rates were estimated for all three methods. For raw signals, the SATUD obtained the best agreement with the reference with = 55 %, versus = 45 % and = 46 % for GST and IST respectively (Table 2). Confusion matrices are shown in Figure 9. The SATUD confusion matrix (Figure 9a) registered significant improvements for sleep stages N2 and N3, if compared to IST and GST (Figure 9b and Figure 9c). Results for different levels of noise and artifacts are reported in Table 2. The SATUD obtained the best results for all levels of signals degradation. For IST and the SATUD, sweat artifacts did not have an important impact on the classification scores.

Discussion

The method developed in our study, called SATUD (Self-Adaptative Thresholding Using Descriptors algorithm), aims to facilitate data-dependent features obtainment to be used for classification. It automatically and unsupervisingly adjust thresholds by minimizing a cost function. The cost function was determined based on mathematical reasoning and translation of the expert knowledge. For our application, the SATUD was employed to reduce the impact of subjects variability before automatic sleep staging. It reproduces the first screening done by experts when manually scoring sleep.

This pre-processing step proved its value since the SATUD showed an improvement of epoch agreement with the properties associated with their sleep stage, compared to two other thresholding methods. Compared to GST (General Statistical Thresholding) and IST (Individual Statistical Thresholding), the quantity of epochs almost perfectly respecting their sleep stage properties increased from less than 3000 to more than 7300 epochs with the use of the SATUD for sleep stage N2. This stage had the best classification improvement. We can consider that the SATUD highlighted this sleep stage, probably because it represents almost 50 % of a night. On the opposite, and for all methods, N1 was the sleep stage with the worst results. This could be explained by the fact that N1 is a transitional stage that occurs during only 5 % of the night. This limitation does not have a significant impact on the classification performance because N1 appears to be rare, and N1 errors are common in literature, even within manual scorers [START_REF] Rosenberg | The American Academy of Sleep Medicine Inter-scorer Reliability Program: Sleep Stage Scoring[END_REF]. The primary classification tool tested also showed higher results for the SATUD than GST and IST. Improvements were more important for the N3 sleep stage. The proposed method also showed a good performance regarding noise and artifacts. The SATUD was not sensitive to additional white noise until the SNR level of 10 dB was reached. Note that this is an important noise level that would be difficult to reach using sensors as used in polysomnog- raphy. Moreover such noise would also make the manual scoring a lot more complicated, leading to a probable invalidation of the recording. Artificial sweat artifacts did not have an important impact on the SATUD and IST classification agreement rate. Regarding the SATUD, they however tended to increase the number of epochs almost perfectly respecting the properties associated with their sleep stage for sleep stages N2 and N3, until a limit around 30 %-40%. It could be explained by the fact that one property of those sleep stages is the high amplitude, more often respected with the addition of sweat artifacts. Also, the number of Wake epochs almost perfectly respecting Wake properties increases with sweat artifacts when using the SATUD. The reason would be that agitated wake can make saturation appear on signals, like in presence of sweat artifacts. The proposed method showed its efficiency, especially since the database used is composed of many patients with various pathologies.

Based on medical knowledge, this method was built to contribute to the development of a user-friendly sleep staging system. As discussed in the companion paper [REF], very few sleep staging systems are considered by physicians because of several limitations. One of them is the lack of transparency of models, often considered as black boxes. To overcome this limitation, a methodology replicating the manual scoring process was implemented in the companion paper [REF]. To give the medical practitioner concrete elements to relate to, qualitative features were chosen as the input of the classifier. However, such features can be problematic if not adjusted to each patient. The SATUD was designed to extract patient-dependent qualitative features, without the need of previous partial scoring by a sleep specialist. The SATUD method is potentially transposable to other applications. It however requires a good knowledge of the specific field, with the identification of a maximum of rules describing each class. Those numerous rules are key points for the SATUD functioning, enabling the use without the need of knowledge on the classification output. For this reason, this algorithm can run under real time conditions.

Conclusion

This paper presents an approach to extract features from a dataset. This approach, called SATUD, is at the core of the companion paper [REF], devoted to an automatic sleep staging for polysomnographic recordings.

The SATUD method was the outcome of an approach combining mathematical reasoning and medical knowledge. Compared to other thresholding method, the SATUD allowed a better generalization of features depending on each sleep stage. The performance proved that resulting features are significantly in agreement with the expected sleep stage properties. A straightforward classification model was also developed (to test the SATUD without being biased by the abilities of a complex classifier). Reported results were better for the SATUD, compared to the other thresholding methods. This performance, obtained on 60 patients with various sleep pathologies, confirmed that this approach is suitable for sleep staging. Tests with noise and artifacts showed this algorithm had a sufficient robustness for the application.

The companion paper [REF] presented an entire and userfriendly classification model based on the extracted features. A detailed analysis of obtained classification impact on each patient diagnostic is also worthy of investigation.

A. Simplified example of the SATUD employment

This appendix presents a simplified version of the use of the SATUD for sleep stage classification. We thus consider only two patients, Patient1 and Patient2 and we admit they had a similar night. For a better understanding, we merged sleep stages N1, N2 and N3 into the so-called NREM sleep. We also considered in this appendix that wake was only composed of calm wake with eyes closed. The simplified hypnogram is represented in The goal is then to retrieve the hypnogram using each patient signals, knowing that there is a variability between each patient. A highly simplified version of sleep stages description (F1) is presented in Table A.1. For this example, only three properties were used to describe wake, REM sleep and NREM sleep. A.1, we understand that even with perfectly adjusted thresholds, there is not always 100 % interdependency between properties respects. This is due to the possibility of some properties to be respected in different sleep stages.

Properties respects interdependancy

The SATUD aims to estimate the thresholds following the properties indicated in Table A.1. Then, a classification tool will be able to rebuild the hypnogram. For both Patient1 and Patient2, thresholds have to be adjusted until H -L -L ↔ Wake, M -MH -M ↔ REM and L -L -H ↔ NREM.

If thresholds are well adjusted, each patient properties will be respected, as in Figure A.2. Because of the rapid eye movements that is Low during wake but also during NREM, we can see that the properties respects interdependency is not at 100 % for Wake and NREM sleep. If thresholds are not correctly settled, and for example the rapid eye movements threshold is too high, the property rapid eye movements is Mid or High associated to REM sleep will never be respected. The result on the properties respects interdependency of REM sleep would then be deteriorated, as shown in Figure A.3a. For each sleep stage, the properties respects interdependency has to be maximized to find the best thresholds. However thresholds that are too far from correct values (highly erroneous) can generate a situation where the properties respects interdependency would be maximum as in Figure A.3b. To prevent this situation, fluctuation has been considered. Indeed, we can assume that all sleep stages will appear during a whole night. To estimated fluctuation, anti-scores were created using a weighted sum of the disrespect of each property. local search algorithm (l. [START_REF] Lajnef | Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multiclass support vector machines[END_REF][START_REF] Mohammadi | Improving time-frequency domain sleep EEG classification via singular spectrum analysis[END_REF][START_REF] Charbonnier | Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging[END_REF][START_REF] Garcia-Molina | Automated NREM sleep staging using the Electro-oculogram: A pilot study[END_REF][START_REF] Fraiwan | Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier[END_REF]. 'FinalCost' function is evaluated as described in Equation 1, with weights defined empirically.

Data:

quantFeat: quantitative features propLists: properties lists associated to each class

Result:

thresholds: patient-dependent thresholds adjusted with the SATUD qualFeat: qualitative features 1 Function SATUD(quantFeat, propLists):

F2Figure 1 :

 1 Figure 1: Functional architecture composed of three main functions (F1, F2 and F3). Medical knowledge from the AASM manual and electrophysiological signals are used as inputs. The output is a set of qualitative features with less vulnerability to patients specificities.

Figure 2 :

 2 Figure 2: Simplified example illustrating the links between sleep stage N3 properties (defined in F1), quantitative features (defined in F2) and qualitative features (defined in F3).

2 Substracted EOG instability 2 3 Total 41 a

 22341 Low | Mid or High | High Number of Thresholds employed. b Number of qualitative features used for each quantitative feature.

RRFigure 4 :

 4 Figure 4: Simplified example of the anti-score behaviour before and after thresholds adjustment. Class is described by only two properties (P1 and P2 ). The anti-score is a weighted sum of the respect of each property ( P1 and P2 ). Its standard deviation value increases when thresholds are being adjusted.

Figure 5 :

 5 Figure 5: Examples of EEG signals during 30-seconds epochs: (a) an epoch with natural sweat artifacts and (b) an epoch with added simulated sweat artifacts.

Figure 6 :

 6 Figure6: Evolution of the number of epochs respecting at least 0 % (the total number of epochs is obtained) to 100 % of the properties associated to their sleep stage, depending on the method used.

Figure 7 :

 7 Figure 7: For each sleep stage, quantity of epochs highly (60 % < ≤ 80 %) or almost perfectly ( > 80 %) respecting the properties associated with their sleep stage, according to the method used.

Figure 8 :

 8 Figure8: Evolution of the number of epochs with an almost perfect agreement ( > 80 %) with the properties associated to their sleep stage for: (a) the SATUD thresholding tested with increasing noise, (b) the SATUD thresholding tested with increasing number of artifacts, (c) IST thresholding tested with increasing noise, (d) IST thresholding tested with increasing number of artifacts, (e) GST thresholding tested with increasing noise and (f) GST thresholding tested with increasing number of artifacts.

Figure 9 :

 9 Figure 9: Classification agreement rate (percentage) between the predicted and true labels for: (a) the SATUD thresholding method, (b) IST thresholding method and (c) GST thresholding method.

Figure

Figure A. 1 :

 1 Figure A.1: Simplified hypnogram presenting the nights of patients Patient1 and Patient2.

Figure A. 2 :

 2 Figure A.2: Example of properties respects for each sleep stage, when thresholds are perfectly adjusted. Using TableA.1, we understand that even with perfectly adjusted thresholds, there is not always 100 % interdependency between properties respects. This is due to the possibility of some properties to be respected in different sleep stages.

2 thresholds ← Stats(quantFeat) 3 while global stopping criterion * not respected do 4 thresholds ← thresholds + changes * 5 qualFeat← 6 cost← 7 end 8 while local stopping criterion † not respected do 9 thresholds 13 return

 2345678913 Thresholding(quantFeat, thresholds) FinalCost(propLists, qualFeat) ← thresholds + changes † 10 qualFeat ← Thresholding(quantFeat, thresholds) 11 cost ← FinalCost(propLists, qualFeat) 12 end thresholds and qualFeat * Depends on the global search algorithm used. † Depends on the local search algorithm used. Algorithm B.1: Pseudo-code of the SATUD.

Table A . 1

 A1 Simplified sleep stage description.

		Alpha waves	Rapid eye movements	EEG amplitude
	Wake a	High	Low	Low
	REM	Mid	Mid or High	Mid
	NREM	Low	Low	High

a Only wake with eyes closed was considered for this example.

Note the signals were recorded with a bit-depth of at least 8 bits.

Acknowledgment

The authors would like to thank Christelle Gosselin and Jean-Louis Racineux, from the Institut de Recherche en Santé Respiratoire des Pays de La Loire. We thank Julien Godey, Laetitia Moreno and Marion Vincent, sleep technicians in the Department of Respiratory and Sleep Medicine of Angers University Hospital. We thank Roberto Longo for its contribution.

Funding

This work was supported by grants from the Institut de Recherche en Santé Respiratoire des Pays de La Loire.

SATUD

IST GST Here are this example anti-scores: We can see that anti-score fluctuation is higher when thresholds are well adjusted, and null when thresholds are highly erroneous. For each sleep stage, the anti-score fluctuation has to be maximized to find the best thresholds.

Robustness to noise test results

Raw

In order to maximize properties respects interdependency and anti-score fluctuation for each sleep stage, costs have been evaluated as: In this example, the total cost could have been defined as:

In this example, the weight associated to wake and REM sleep are higher than the weight associated to NREM sleep. Indeed, we estimated that wake and REM sleep were more complicated to detect and decided to bring them more emphasis. Equation A.10 was then minimized using several global and local search algorithms as explained in F3.

B. Pseudo-code of the SATUD

The SATUD pseudo-code is presented in Algorithm B.1. It uses here a global search algorithm (l.3-7) followed by a


M.D Loïc Sentilhes
email: loicsentilhes@hotmail.com

MD Fanny De Marcillac

MD Charlotte Jouffrieau

MD, PhD Pierre Kuhn

MD Vincent Thuet

MD, PhD Yves Hansmann

MD Yvon Ruch

MD, PhD Samira Fafi-Kremer

MD Philippe Deruelle

COVID-19 in pregnancy was associated with maternal morbidity and preterm birth

Keywords: COVID-19 infection or pneumonia, pregnancy, maternal and neonatal outcomes, maternal morbidity, preterm birth, respiratory failure, intubation, extracorporeal membrane oxygenation

Condensation

Among symptomatic pregnant women with COVID-19, 24.1% (13/54) required oxygen support, and 14.3% (3/21) had very preterm deliveries indicated for their severe COVID-19-related conditions.

AJOG at a Glance:

A. Why was this study conducted?

Although the available data about maternal outcomes for pregnant women with COVID-19 infection are reassuring, some limited case series report adverse maternal outcomes justifying further investigation. This study was conducted to report the maternal characteristics and clinical outcomes of these women.

B. What are the key findings?

Among the pregnant women with COVID-19 infection, 24.1% (13/54) received oxygen support, with 5.5% (3/54) requiring invasive mechanical ventilation. Among the 21 women who gave birth, 23.8%

(5/21) had deliveries ─ 14.3% (3/21) very preterm ─ indicated for severe maternal condiIons related to their COVID-19 disease.

C. What does this study add to what is already known?

In contrast to most existing data, this study shows COVID-19 in pregnancy is associated with maternal morbidity and preterm birth.

Introduction

Since the beginning of the severe acute respiratory syndrome (SARS-CoV-2) or COVID-19 outbreak, it has been argued that pregnant women are at increased risk of severe infection. [START_REF] Rasmussen | Coronavirus Disease 2019 (COVID-19) and Pregnancy: Responding to a Rapidly Evolving Situation[END_REF]2 Various experts and authorities rapidly published articles of advice, guidance, and information to aid professionals in caring for pregnant women infected by COVID-19. [START_REF]Practice advisory: novel coronavirus 2019 (COVID-19)[END_REF][START_REF] Poon | ISUOG Interim Guidance on 2019 novel coronavirus infection during pregnancy and puerperium: information for healthcare professionals[END_REF][START_REF] Dashraath | Coronavirus Disease 2019 (COVID-19) Pandemic and Pregnancy[END_REF][START_REF] Rasmussen | Coronavirus Disease 2019 (COVID-19) and Pregnancy: What obstetricians need to know[END_REF] Most of the early reports, mainly from the epicenter of the pandemic in China, [START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Wang | A case of 2019 Novel Coronavirus in a pregnant woman with preterm delivery[END_REF][START_REF] Zhu | Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia[END_REF][START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF][START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF][START_REF] Chen | Clinical analysis of pregnant women with 2019 novel coronavirus pneumonia[END_REF][START_REF] Yu | Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study[END_REF][START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF][START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF][START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF] have suggested that pregnant women are not more severely affected than the general population. [START_REF] Rasmussen | Coronavirus Disease 2019 (COVID-19) and Pregnancy: Responding to a Rapidly Evolving Situation[END_REF][START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Wang | A case of 2019 Novel Coronavirus in a pregnant woman with preterm delivery[END_REF][START_REF] Zhu | Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia[END_REF][START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF][START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF][START_REF] Chen | Clinical analysis of pregnant women with 2019 novel coronavirus pneumonia[END_REF][START_REF] Yu | Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study[END_REF][START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF][START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF][START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF][START_REF] Iqbal | An Uncomplicated Delivery in a Patient with Covid-19 in the United States[END_REF][START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF][START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF][START_REF] Zaigham | Maternal and Perinatal Outcomes with COVID-19: a systematic review of 108 pregnancies[END_REF][START_REF] Gatta | COVID19 during pregnancy: a systematic review of reported cases[END_REF] Case reports and limited case series of pregnant women from United States, [START_REF] Hirshberg | Care of critically ill pregnant patients with COVID-19: a case series[END_REF] Iran, 2,[START_REF]Practice advisory: novel coronavirus 2019 (COVID-19)[END_REF] and South America [START_REF] Amorim | Maternal Deaths with Covid19: a different outcome from mid to low resource countries?[END_REF] have contradicted these reassuring preliminary findings, however. They have instead described women with COVID-19 disease who require invasive mechanical ventilation including extracorporeal membrane oxygenation (ECMO) and even maternal death. These contradictory findings and the potential variation in disease severity between countries means that additional information is urgently needed, especially from areas besides China, to determine whether or not pregnant women with COVID-19 are likely to experience severe cases with pneumonia.

We aim to present the clinical features and course of consecutive pregnant women with COVID-19 managed at a single French referral center, located in one of the main pandemic epicenters in France.

Methods

Study design and patients

This single-center retrospective study includes all consecutive pregnant women with COVID-19 infection, regardless of gestational age at diagnosis, admitted to Strasbourg University Hospital (Strasbourg, France), a tertiary referral hospital, from March 1, 2020, to April 3, 2020. During the study period, only symptomatic women were tested by qRT-PCR for COVID-19. Women with an ongoing pregnancy and a time from illness onset to April 3 shorter than 14 days 25,26, were excluded. COVID-19 infection was diagnosed according to the Chinese management guidelines (version 7•0), [START_REF]Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment[END_REF] which distinguishes confirmed cases, defined as all cases with respiratory tract samples tested positive for SARS-CoV-2 by quantitative reverse transcription polymerase chain reaction (qRT-PCR), from suspected cases, which include an epidemiological history (Strasbourg is a community with numerous COVID-19 cases) and at least two of the following clinical manifestations: (i) fever and/or respiratory syndrome, (ii) imaging features of COVID-19 pneumonia, and (iii) in early-stage disease, a decreased or normal total number of leukocytes and lymphocyte counts. [START_REF]Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment[END_REF] The imaging features of COVID-19 pneumonia used were typical radiographic features on chest computed tomography (CT), owing to their high diagnostic sensitivity and accuracy for this diagnosis. [START_REF]Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment[END_REF][START_REF] Ai | Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases[END_REF][START_REF] Wang | Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area[END_REF] The Ethics Committee of Strasbourg University Hospital approved this study under a waiver of informed consent (RNI 2020 -HUS N°7794).

Data collection

Maternal characteristics, laboratory findings, CT scans, management, and maternal and fetal outcomes were obtained from patients' medical records.

All laboratory tests were performed at Strasbourg University Hospital, including the laboratory confirmation of SARS-CoV-2 by qRT-PCR tests for SARS-CoV2 nucleic acid on nasopharyngeal swabs. 29 Primer and probe sequences target two regions on the RdRp gene and are specific to SARS-CoV2.

Assay sensitivity is around 10 copies/reaction. For women with multiple RT-PCT assays, diagnosis of COVID-19 was confirmed when any one nucleic acid test result was positive. Typical radiographic features of COVID-19 on chest CT were consolidation, ground-glass opacity, or bilateral pulmonary infiltration. [START_REF] Ai | Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases[END_REF] Neonatal throat and rectal swab samples were also collected for SARS-CoV-2 RT-PCR assays at birth or on day 1, again on day 3 in term newborns , and also on days 7 and 14 in preterm neonates. Skin-to-skin contact between mothers and neonates and breastfeeding were both permitted at birth.

Definitions

Fever was defined as an axillary temperature of at least 37•8°C (100•0 F). The severity of COVID-19 was defined according to the Chinese management guidelines (version 7•0) as mild (mild clinical symptoms and no sign of pneumonia), moderate (patients have fever and respiratory symptoms; radiologic assessments found signs of pneumonia), severe (adults meeting any of the following criteria: shortness of breath, respiratory rate 30 times/min or oxygen saturation ≤93% at rest or alveolar oxygen partial pressure/fraction of inspiration 02 ≤ 300 mm Hg), and critical (any one or more of mechanical ventilation, shock, or organ failure that needed intensive care unit (ICU) monitoring and treatment). [START_REF]Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment[END_REF] Acute respiratory distress syndrome (ARDS) was diagnosed according to the Berlin definition in three mutually exclusive categories based on degree of hypoxemia: mild (200 mm Hg < PaO2/FIO2 ≤ 300 mm Hg), moderate (100 mm Hg < PaO2/FIO2 ≤ 200 mm Hg), and severe (PaO2/FIO2 ≤ 100 mm Hg). [START_REF] Ranieri | Acute respiratory distress syndrome: the Berlin Definition[END_REF]

Statistical analysis

Descriptive characteristics were calculated for the variables of interest. Statistical analysis included determination of rates with their 95% confidence intervals (CIs), performed with R software (R Foundation for Statistical Computing, Vienna, Austria).

Results

The study included 54 symptomatic pregnant women with COVID-19 infection during the study period; 38 had positive RT-PCR results, while 16 had negative RT-PCR results but met the Chinese management guideline criteria for suspected COVID-19 (version 7•0). [START_REF] Sentilhes | Postpartum haemorrhage: prevention and treatment[END_REF] Among these 54 women, 32 had an ongoing pregnancy more than 14 days after the onset of their illness, one had a miscarriage, and 21 had live births by vaginal or cesarean delivery (Figure 1).

Maternal clinical characteristics

Table 1 summarizes maternal clinical characteristics. Their ages ranged from 19 to 42 years. Ten (18.5%) women had at least one comorbidity before pregnancy: four had asthma and one preexisting chronic hypertension, four were obese, and four had another comorbidity. No woman had a multiple pregnancy, and only 15 (27.3%) were nulliparous. The mean gestational age at the COVID-19 diagnosis was 30.4 ± 9.0 weeks, while the mean time from illness onset to hospital admission was 3.5 ± 3.2 days. Fourteen (25.9%) had fever, 36 (66.7%) a cough, 22 (40.7%) shortness of breath, 9 (16.7%) digestive disorders, and 20 (37.0%) anosmia or ageusia. Chest CT was performed on 12 (22.2%) women and showed typical features of COVID-19 infection for 10 (83.3).

Laboratory tests

Data from laboratory tests showed that 24 (44.4%) women had lymphopenia; C-reactive protein was elevated in 22 (40.7%) and aspartate and/or alanine aminotransferases in 11 (20.4%). None had thrombocytopenia (Table 2).

Clinical course of pregnancy

Thirteen (24.1%) women received oxygen support. Two of them (15.4%) required high flow oxygen, one after and one before delivery. The latter subsequently required noninvasive mechanical ventilation and has not yet given birth. Three more of the 13 women who received oxygen (23.1%) subsequently had invasive mechanical ventilation in the postpartum. One (1.9%) woman later required extracorporeal membrane oxygenation (ECMO). Three (5.6%) received antiviral therapy (lopinavir/ritonavir 400 mg/100 mg twice a day for 5 days) (Table 3).

Among the 21 women who gave birth by April 17, 9 (42.9%) had cesarean and 12 (57.1%) vaginal deliveries. The mean gestational age at delivery was 37.4 ± 4.7 weeks, but five women (23.8%) gave birth before 37, three (14.3%) before 32 and two (9.5%) before 28 weeks' gestation. These five premature births were medically indicated for severe maternal conditions related to COVID-19 infection, and two women were transferred to the ICU after delivery. Five women had postpartum hemorrhages (9.3%), four (7.4%) necessitating a blood transfusion and one (1.9%) arterial embolization. Five (9.3%) women were admitted to the ICU, all due to COVID-19 respiratory symptoms. No thromboembolic event nor any other significant maternal morbidity occurred (Table 4).

No neonatal acidosis was observed, nor was there any fetal or neonatal death. Three neonates, two born at 27 weeks' gestation and one at 28 weeks, underwent endotracheal intubation in the labor ward and were admitted to the neonatal ICU for problems related to their extremely or very preterm birth. All 21 neonates were tested for SARS-CoV-2 by RT-PCR of throat and rectal swab specimens, and SARS-CoV-2 was never detected (Table 5).

By the end of the follow-up (April 17), one woman (woman 2, Table S1) remained in the ICU, 27 days after her transfer there. She had a secondary infection by Klebsiella pneumoniae and has required ECMO since April 9. Another woman (woman 13, Table S1) remained hospitalized with an ongoing pregnancy, but was transferred back to conventional care after six days in the ICU. She required high flow oxygen therapy and transient noninvasive mechanical ventilation. All the other women have been discharged from the hospital. Eighteen neonates were also discharged, healthy and with no respiratory symptoms or fever. The remaining three newborns were still hospitalized for care related to their prematurity.

Clinical outcomes of the 13 women who required oxygen support

The characteristics and clinical course of the 13 (24.1%) pregnant women with COVID-19 infection who required oxygen support are described in Table S1. All but three had lymphopenia (<1.0 × 10 9 cells per L) or elevated concentrations of C-reactive protein (>10 mg/L). Four of them were still pregnant, while nine had had cesarean deliveries. Among the latter, three women aged 35 or older with positive COVID-19 RT-PCR had medically indicated deliveries before 29 weeks' gestation due to severe maternal respiratory symptoms. Before delivery, all three (women 1, 2, and 9) had respiratory failure even though they were receiving 5 liters of oxygen per minute. Preterm delivery was decided because physicians thought that maternal respiratory status would improve and oxygenation would be facilitated after birth.

One of these women (woman 2) needed invasive mechanical ventilation and then ECMO after delivery. All had at least one comorbidity: class III obesity (woman 1), overweight and gestational hypertension (woman 2), and overweight and gestational diabetes mellitus (woman 9).

Comment

Principal findings of the study

This is a descriptive study of the clinical characteristics and outcomes of 54 pregnant women with either laboratory-confirmed (n=38) or suspected (n=16) COVID-19 infection; 32 pregnancies are ongoing and 22 completed. Oxygen support was required for 24.1% (13/54) of pregnant women with COVID-19 including 1.9% (1/54) who required ECMO, and 14.3% (3/21) of the births were very preterm deliveries indicated for severe COVID-19-related maternal conditions. The latter three women were aged 35 years or older, and all had at least one comorbidity.

Results of the study in the context of what is known

Most of the data about the maternal, fetal, and neonatal outcomes of pregnant women infected by COVID-19 have been globally reassuring. [START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Wang | A case of 2019 Novel Coronavirus in a pregnant woman with preterm delivery[END_REF][START_REF] Zhu | Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia[END_REF][START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF][START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF][START_REF] Chen | Clinical analysis of pregnant women with 2019 novel coronavirus pneumonia[END_REF][START_REF] Yu | Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study[END_REF][START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF][START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF][START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF][START_REF] Iqbal | An Uncomplicated Delivery in a Patient with Covid-19 in the United States[END_REF][START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF][START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF][START_REF] Zaigham | Maternal and Perinatal Outcomes with COVID-19: a systematic review of 108 pregnancies[END_REF][START_REF] Gatta | COVID19 during pregnancy: a systematic review of reported cases[END_REF] Despite the unavailability of numerous details about maternal characteristics, including body mass index and preexisting disease, or about the third stage of labor, [START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF][START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF] the authors considered that the women's outcomes were "good" [START_REF] Yu | Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study[END_REF] or "satisfactory" [START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF] or that there is "no evidence to suggest that COVID-19 pneumonia causes severe maternal and neonatal complications." [START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF] Zaigham and Andersson summarized these reports in a systematic review up to April 1 that included 108 pregnant women with COVID-19 from 18 case reports or series, mostly from China. [START_REF] Zaigham | Maternal and Perinatal Outcomes with COVID-19: a systematic review of 108 pregnancies[END_REF] They underlined that 3% (3/108) of the women were admitted to ICUs and noted that severe maternal morbidity could therefore not be ruled out in COVID-19 infection during pregnancy, [START_REF] Zaigham | Maternal and Perinatal Outcomes with COVID-19: a systematic review of 108 pregnancies[END_REF] while Della Gatta et al pointed out in a systematic review of 6 Chinese studies totaling 51 pregnant women [START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Zhu | Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia[END_REF][START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF] that preterm ─ mainly indicated ─deliveries were frequent (31%) in women with COVID-19 disease. [START_REF] Gatta | COVID19 during pregnancy: a systematic review of reported cases[END_REF] Finally, on April 17, 2020, Chen and colleagues published a case series of 118 pregnant women with COVID-19 from 50 hospitals in Wuhan city. [START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF] It reported 3 spontaneous abortions, 2 ectopic pregnancies, 4 induced abortions, 41 ongoing pregnancies, and 68 deliveries, 63 of them cesarean (93%) because of concern about the effect of COVID-19 on the pregnancy. No neonatal asphyxia or death occurred. Among the 14 (21%) preterm deliveries, half (10.5%) were related to COVID-19 infection. Overall, 109 (92%) women had mild disease and 9 (8%) severe disease (hypoxemia); only one (0.8%) received noninvasive mechanical ventilation. The only maternal characteristics described were age, parity, and singleton or multiple pregnancy, with no details about comorbidities, in particular body mass index, preexisting conditions, or pregnancy-related disease. The authors concluded that their results "do not suggest an increased risk of severe disease among pregnant women." [START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF] Nevertheless, other authors have previously reported severe maternal complications related to COVID-19 in pregnant women, consistently with our findings. [START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF][START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF][START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF] Y. Liu and colleagues retrospectively reported the clinical outcome of 13 pregnant women with COVID-19, including 3 ongoing pregnancies. [START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF] Details were scarce, but the authors stated that 12 pregnant women were discharged without obvious complications. The remaining woman was admitted to the ICU with multiple organ dysfunction syndrome including acute respiratory distress syndrome, necessitating an emergency cesarean. The neonate died. The woman required intubation, mechanical ventilation, and finally ECMO, but her final outcome was not reported. [START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF] In a retrospective case series of 43 pregnant women with PCR-confirmed COVID-19 infection from two American centers, most of the women were obese, and 18 had an additional comorbidity. [START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF] Among these 43 women, 18 gave birth, 10 by uncomplicated vaginal deliveries and 8 by cesareans for obstetric reasons unrelated to COVID. Nonetheless, two (4.7%) women were admitted to the ICU. [START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF][START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF] The first was 38 years-old with a body mass index of 38 kg/m 2 , diabetes mellitus, and intrahepatic cholestasis of pregnancy. Severe postpartum hemorrhage related to uterine atony occurred during a cesarean that followed failed induction of labor. Because she had fever and respiratory symptoms during delivery, a chest CT was performed and revealed ground-glass opacities in the lungs. RT-PCR was subsequently positive for COVID-19. She spent only eight hours in ICU and was discharged four days later. [START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF] The second woman admitted to the ICU was 33 years old, with a body mass index of 47 kg/m 2 , chronic hypertension, type 2 diabetes mellitus, and mild intermittent asthma. Twenty-five hours after a cesarean for failed induction of labor, she had coughing, respiratory distress, high fever, and reduced oxygen saturation. Although her respiratory status improved, she was admitted to the ICU after developing severe hypertension (blood pressure reaching 200/90 mm Hg). RT-PCR showed COVID-19. After five days, when the report concluded, she remained hospitalized, had acute kidney injury, and still needed oxygen supplementation. [START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF] In neither of these cases, however, does COVID-19 infection appear to have been the -or at least the only -reason for ICU admission, as the first case involved a severe postpartum hemorrhage and the second severe hypertension.

Moreover, Yan and colleagues reported a retrospective series expanded from four previous small case series, 7,9,10 totaling 116 pregnant women with COVID-19 pneumonia from 25 hospitals in China.

Preterm delivery before 34 and 37 weeks gestation occurred in 2% and 21.2% of cases: [START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF] 6.9% (8/116) of the women were admitted to ICU, 5.2% (6/116) required noninvasive and 1.7% (2/116) invasive ventilation, and 0.9% (1/116) ECMO. While these results were consistent with ours, the authors surprisingly concluded that "there is no evidence that pregnant women with COVID-19 are more prone to develop severe pneumonia, in comparison to nonpregnant patients." [START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF] Interestingly, since April 22, 2020, three different teams from distinct parts of the world have also alerted healthcare professionals about the possible detrimental effect of COVID-19 among pregnant women. Hirshberg and colleagues described the clinical course of five American women, all requiring mechanical ventilation, including ECMO for one. [START_REF] Hirshberg | Care of critically ill pregnant patients with COVID-19: a case series[END_REF] All had at least one of the following comorbidities: overweight or obesity, chronic kidney disease, hypertension, insulin-dependent diabetes, obstructive sleep apnea, and mild asthma. [START_REF] Hirshberg | Care of critically ill pregnant patients with COVID-19: a case series[END_REF] Finally, maternal deaths have been reported in pregnant women infected by COVID-19. [START_REF] Hantoushzadeh | Maternal Death Due to COVID-19 Disease[END_REF][START_REF] Amorim | Maternal Deaths with Covid19: a different outcome from mid to low resource countries?[END_REF] Ramos Amorim and colleagues reported the cases of nine women; the details were unfortunately limited, as the sources were mainly local media or the Brazilian or Mexican ministries of health. [START_REF] Amorim | Maternal Deaths with Covid19: a different outcome from mid to low resource countries?[END_REF] An Iranian case series reported the clinical outcomes of nine pregnant women with severe COVID-19 disease. Seven of them died, [START_REF] Hantoushzadeh | Maternal Death Due to COVID-19 Disease[END_REF] three aged 35 years or older.

Nevertheless, no details were provided about any comorbidities related to these maternal deaths, including body mass index. [START_REF] Hantoushzadeh | Maternal Death Due to COVID-19 Disease[END_REF] Thus, due to the considerable methodological limitations of the latter two reports, their principal information is that maternal deaths do occur in pregnant women infected by COVID-19. [START_REF] Hantoushzadeh | Maternal Death Due to COVID-19 Disease[END_REF][START_REF] Amorim | Maternal Deaths with Covid19: a different outcome from mid to low resource countries?[END_REF] Clinical and research implications Our case series confirms that COVID-19 infection during pregnancy may be responsible for severe maternal morbidity that may require very or extremely preterm elective delivery. In our series, 5.5% of the women (3/54) had such medically indicated premature births, 24.1% (13/54) required oxygen support, including invasive mechanical ventilation or ECMO for 5.5% (3/54), and 9.2% (5/54) were admitted to the ICU. Interestingly, except for one of the two women who required ECMO and for whom very few details were available, [START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF] the most severe cases reported came from countries outside China, that is, from the United States, [START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF] France (our series), South America, [START_REF] Amorim | Maternal Deaths with Covid19: a different outcome from mid to low resource countries?[END_REF] and Iran, [START_REF] Hantoushzadeh | Maternal Death Due to COVID-19 Disease[END_REF] where maternal characteristics and the prevalence of preexisting conditions may differ from those in China.

Most of the French and American women had one or more maternal characteristics that are known to be associated with severe maternal morbidity in pregnant women, including an age of 35 years or older, overweight or obesity, gestational hypertension or diabetes, and preexisting asthma. [START_REF] Freese | Population-attributable fraction of risk factors for severe maternal morbidity[END_REF] These new findings should alert physicians and help them to manage and follow up pregnant women with COVID-19 infections.

Another interesting finding of our study is the high rate of postpartum hemorrhage (9.3%) and blood transfusions (7.4%) observed in pregnant women with COVID-19, much higher than that reported in the general population of pregnant women. [START_REF] Sentilhes | Postpartum haemorrhage: prevention and treatment[END_REF] The literature thus far unfortunately fails to detail these women's third stage of labor. [START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Wang | A case of 2019 Novel Coronavirus in a pregnant woman with preterm delivery[END_REF][START_REF] Zhu | Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia[END_REF][START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF][START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF][START_REF] Chen | Clinical analysis of pregnant women with 2019 novel coronavirus pneumonia[END_REF][START_REF] Yu | Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study[END_REF][START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF][START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF][START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF][START_REF] Iqbal | An Uncomplicated Delivery in a Patient with Covid-19 in the United States[END_REF][START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF][START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF][START_REF] Zaigham | Maternal and Perinatal Outcomes with COVID-19: a systematic review of 108 pregnancies[END_REF][START_REF] Gatta | COVID19 during pregnancy: a systematic review of reported cases[END_REF] Despite the overall low numbers and the single limited experience they come from, our findings warrant further investigation to examine whether COVID-19 infection might increase the risk of blood loss after delivery. Hemostasis changes may occur in these women, especially as clinically significant coagulopathy with antiphospholipid antibodies has been reported in nonpregnant patients with COVID-19 pneumonia. [START_REF] Zhang | Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19[END_REF] Finally, all the neonates we tested were negative for COVID-19, consistently with previous reports, [START_REF] Chen | Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women : a retrospective review of medical records[END_REF][START_REF] Wang | A case of 2019 Novel Coronavirus in a pregnant woman with preterm delivery[END_REF][START_REF] Zhu | Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia[END_REF][START_REF] Liu | Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy[END_REF][START_REF] Liu | Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis[END_REF][START_REF] Chen | Clinical analysis of pregnant women with 2019 novel coronavirus pneumonia[END_REF][START_REF] Yu | Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study[END_REF][START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF][START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF][START_REF] Yan | Coronavirus disease 2019 (COVID-19) in pregnant women: A report based on 116 cases[END_REF][START_REF] Iqbal | An Uncomplicated Delivery in a Patient with Covid-19 in the United States[END_REF][START_REF] Breslin | COVID-19 in pregnancy: early lessons[END_REF][START_REF] Breslin | COVID-19 infection among asymptomatic and symptomatic pregnant women: Two weeks of confirmed presentations to an affiliated pair of New York City hospitals[END_REF][START_REF] Zaigham | Maternal and Perinatal Outcomes with COVID-19: a systematic review of 108 pregnancies[END_REF][START_REF] Gatta | COVID19 during pregnancy: a systematic review of reported cases[END_REF] although no conclusion can be drawn from our results about the possibility of intrauterine vertical transmission.

Strengths and limitations of the study

Our retrospective study is one of the largest series of pregnant women with confirmed and suspected cases of COVID-19 infection, and it provides the most detailed look at maternal characteristics and maternal outcomes. It is one of the first to suggest that COVID-19 infection may be responsible for severe maternal morbidity that can require medically indicated extremely preterm delivery. Nevertheless, several limitations of our study must be underlined. The first is its retrospective design, common to all the studies that have thus far assessed maternal outcome in pregnant women with COVID-19. All the flaws of retrospective analyses apply. Moreover, the reported rates should be interpreted cautiously; an overestimation of severe cases is possible as Strasbourg University Hospital is the referral center for the management of pregnant women with COVID-19 in the Alsace region. Moreover, generalizability is limited to regions with COVID-19 prevalence similar to that in the region where our hospital is located, which is one of the principal pandemic hot spots in France (along with the Paris metropolitan region). Moreover, like previous authors, [START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF][START_REF] Chen | Clinical Characteristics of Pregnant Women with Covid-19 in Wuhan, China[END_REF] we have included suspected cases with typical chest CT findings but with RT-PCR testing negative for COVID-19, in accordance with the Chinese management guideline (version 7.0). [START_REF]Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment[END_REF] Given the frequency of false negatives for COVID-19 cases, due to low virus titers, inappropriate swabbing sites, and sampling at late disease stages, [START_REF] Yang | Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections[END_REF] as well as the limited testing capacity in France during our study period (which also explains why only symptomatic women were tested during the study period), limiting inclusion to laboratory-confirmed cases would likely result in missing some COVID-19 cases. [START_REF] Li | Maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia: a case-control study[END_REF] Nonetheless, all five women with premature births, including the three with the most severe illness in our study, those that required indicated extremely or very preterm cesarean delivery, had positive RT-PCR findings for COVID-19. Finally, even though our series is one of the largest so far reported, the small sample size should be taken into account when interpreting the results.

Conclusions

COVID-19 infection may be associated with severe maternal outcomes with hypoxemic respiratory failure despite oxygen support and can require an indicated -and sometimes very or extremely preterm -delivery. Healthcare providers should be aware that many of the standard risk factors associated with severe maternal morbidity without COVID-19 infection, such as maternal age above 35 years, overweight or obesity, preexisting and/or gestational hypertension or diabetes, may also increase the risk of severe maternal morbidity for pregnant women infected by COVID-19. Pregnant women with ongoing pregnancy N= 32

List of figures and tables

Women who required admission to hospital due to COVID-19 infection N=1

Women who were discharged home N=31

1 *Five women had more than one oxygen support procedure: One (woman 2, Table S1) had invasive mechanical ventilation and then extracorporeal membrane oxygenation; two (women 3 and 4, Table S1) had invasive mechanical ventilation; one (woman 7, Table S1) received high flow oxygen. The last one (woman 13, Table S1) received high flow oxygen and then required noninvasive mechanical ventilation.

Figure 1 :

 1 Figure 1: Flow chart of the study population

Figure 1 :

 1 Figure 1: Flow chart of the study population

  Management of COVID-19 pneumonia after deliveryOxygen support (nasal cannula) -no. (

Table 1 .

 1 Women's characteristics at admission

Table 2 .

 2 Clinical laboratory results

Table 3 .

 3 Management of COVID-19 pneumonia

Table 4 .

 4 Maternal and neonatal outcomes of the pregnant women with confirmed or suspected COVD-19 infection

Table 5 .

 5 Clinical characteristics and outcomes of the 13 women with confirmed or suspected COVID-19 infection who required oxygen support.

Table 1 .

 1 Women's characteristics at admission* Body mass index is weight in kilograms divided by the square of the height in meters. ‡ Other preexisting chronic disease means disease other than body-mass index > 25 kg/m 2 , preexisting chronic hypertension or diabetes mellitus and asthma. It included one woman with sicklecell anemia, one with chronic alcohol abuse, one with chronic hepatitis B, and one with nephropathy $ Contact history of epidemic area means exposure to the relevant environment (Alsace region) or

			Pregnant women	Pregnant women
		All women	with confirmed	with suspected
	Characteristics	n = 54	COVID-19 infection	COVID-19 infection
			n = 38	n = 16
	Age -years	30.6 ± 6.2	31.1 ± 6.4	29.6 ± 5.9
	Age > 35 years -no. (%)	15 (27.8)	12 (31.6)	3 (18.8)
	Geographical origin-no. (%)
	Europe	34 (63.0)	22 (57.9)	12 (75.0)
	Sub-Saharan Africa	10 (18.5)	10 (26.3)	0
	North Africa	10 (18.5)	6 (15.8)	4 (25.0)
	Asia	0	0	0
	Non-French nationality -no. (%)	21 (38.9)	18 (47.4)	3 (18.8)
	Body mass index before pregnancy †	25.3 ± 4.7	25.6 ± 5.1	24.2 ± 3.3
	Body mass index > 25 kg/m 2 -no. (%)	20 (37.0)	16 (42.1)	4 (25)
	Body mass index > 30 kg/m 2 -no. (%)	4 (7.4)	4 (10.5)	0
	Preexisting chronic hypertension -no. (%)	1 (1.9)	0	1 (6.25)
	Asthma -no. (%)	5 (9.3)	1 (2.6)	4 (25.0)
	Preexisting diabetes mellitus -no. (%)	0	0	0
	Other preexisting chronic disease-no. (%) ‡	4 (7.4)	3 (7.9)	1 (6.25)
	Nulliparous -no. (%)	15 (27.3)	9 (23.7)	6 (37.5)
	Singleton pregnancy -no. (%)	54 (100)	38 (100)	16 (100)
	Previous cesarean delivery-no. (%)	9 (16.7)	6 (15.8)	3 (18.8)
	History of postpartum hemorrhage-no. (%)	2 (3.7)	2 (5.3)	0
	History of preterm delivery	0	0	0
	Tobacco use during pregnancy -no. (%)	1 (1.9)	0	1 (6.3)

* Plus-minus values are mean ±SD.

3 †

Table 2 .

 2 Clinical laboratory results * CoV-2 by quantitative reverse transcription polymerase chain reaction (qRT-PCR), from suspected cases, which include an epidemiological history (Strasbourg is a community with numerous COVID-19 cases) and at least two of the following clinical manifestations: (i) fever and/or respiratory syndrome, (ii) imaging features of COVID-19 pneumonia, and (iii) in early-stage disease, a decreased or normal total number of leukocytes, and lymphocyte counts.[START_REF]Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment[END_REF]

				Pregnant women	Pregnant women
		Reference		with confirmed	with suspected
			All women
	Laboratory characteristics	range		COVID-19	COVID-19
			n = 54
				infection †	infection †
				n = 38	n = 16
	White blood cell count (×10 9 cells per L)	4.02-11.42	8.9 ± 3.7	8.4 ± 3.7	10.2 ± 3.7
	Lymphocyte count (× 10⁹ cells per L)	1.24-3.56	1.6 ± 0.7	1.5 ± 0.7	1.7 ± 0.7
	Lymphopenia (<1.5 x 10⁹ cells per L) -no. (%)	NA	24 (44.4)	17 (44.7)	7 (43.8)
	C-reactive protein concentration (mg/L)	< 5	25.7 ± 36.9	26.4 ± 37.6	7.5 ± 36.9
	Elevated C-reactive protein (>10 mg/L) -no. (%)	NA	22 (40.7)	15 (41.7)	6 (37.5)
	Elevated ALT (>45 U/L) or AST (>35 U/L) -no. (%)	NA	11 (20.4)	10 (27.0)	1 (6.3)
	ALT (U/L)	6-55	26.6 ± 17.1	29.1 ± 19.0	19.9 ± 8.4
	AST (U/L)	5-34	25.7 ± 20.4	27.4 ± 19.7	21.5 ± 22.3
	Hemoglobin (g/dL)	11.5-14.9	11.2 ± 1.3	11.1 ± 1.3	11.2 ± 1.4
	Hematocrit (%)	34.4-43.9	36.4 ± 10.8	34.3 ± 4.0	41.2 ± 18.2
	Thrombocytopenia < 100 000/dL -no. (%)	NA	0	0	0
	Blood urea nitrogen (mmol/L)	2.5-6.7	2.7 ± 1.0	2.5 ± 1.0	3.0 ± 1.1
	Creatinine (µmol/L)	49-90	48.4 ± 10.2	47.3 ± 8.3	50.9 ± 13.7
	Prothrombin ratio < 80% -no. (%)	NA	2 (3.7)	1 (2.7)	1 (6.3)
	Prolonged aPTT -no. (%)	ratio > 1.19	6 (11.1)	5 (13.2)	1 (6.3)
	* Plus-minus values are mean ±SD. AST, Aspartate aminotransferase; ALT, Alanine aminotransferase;
	aPTT, activated partial thromboplastin time; NA, not applicable.
	† COVID-19 infection was diagnosed according to the Chinese management guidelines (version 7•0), 26
	which distinguishes confirmed cases, defined as all cases with respiratory tract samples tested
	positive for SARS-

Table 3 .

 3 Management of COVID-19 pneumonia

		Pregnant women	Pregnant women
	All women	with confirmed	with suspected
	n = 54	COVID-19 infection	COVID-19 infection
		n = 38	n = 16

Loic Sentilhes carried out consultancy work and was a lecturer for Ferring

Laboratories in the previous 3 years. This study was funded by the Strasbourg University Hospital.