Yves Gillet
email: yves.gillet@chu-lyon.fr

Anne Tristan

Jean-Philippe Rasigade

Mitra Saadatian-Elahi

Coralie Bouchiat

Michele Bes

Oana Dumitrescu

Marie Leloire

Céline Dupieux

Frédéric Laurent

Gérard Lina

Jerome Etienne

Philippe Vanhems

Laurent Argaud

MD Francois Vandenesch
email: francois.vandenesch@univ-lyon1.fr

Prognostic factors of severe community-acquired staphylococcal pneumonia in France

Keywords: Community-acquired pneumonia, Staphylococcus aureus, Panton Valentine leucocidin, Sepsis, Severity, Mortality Plain Language summary

In severe community-acquired staphylococcal pneumonia, Panton-Valentine leukocidin is associated with pleuropneumonia in toddlers and, in olders patients, with increased mortality independent of methicillin resistance and other virulence factors.

Abstract. (250)

Purpose. Staphylococcus aureus causes severe forms of community-acquired pneumonia (CAP), namely staphylococcal pleuropneumonia in young children and staphylococcal necrotising pneumonia in older patients. Methicillin resistance, the Panton-Valentine leukocidin (PVL) toxin, as well as less specific factors have been associated with poor outcome in severe CAP, but their respective roles are unclear.

Methods

A prospective multicentre cohort study of severe staphylococcal CAP was conducted in 77 paediatric and adult intensive care units in France between January 2011 and December 2016.

After age-clustering, risk factors for mortality, including pre-existing conditions, clinical presentation, laboratory features, strain genetic lineage, PVL, other virulence factors, and methicillin resistance were assessed using univariate and multivariable Cox and LASSO regressions.

Results

Of 163 included patients, aged one month to 87 years, 85 (52.1%) had PVL-positive CAP; there were 20 (12.3%) patients aged <3 a ( a ), among whom 19 (95%) had PVL-positive CAP. The features of PVL-positive CAP in toddlers matched with the historical description of staphylococcal pleuropneumonia, with a lower mortality (n=3/19, 15%) compared to PVL-positive CAP in older patients (n=31/66, 47%). Mortality in older patients was independently predicted by PVL-positivity (hazard ratio 1.81, 95% CI, 1.03 to 3.17) and methicillin resistance (2.37, 95% CI 1.29 to 4.34) independently from S. aureus lineages and the presence of other virulence determinants.

Conclusion

PVL was associated with staphylococcal pleuropneumonia in toddlers and was a risk factor for mortality in older patients with severe CAP, independently of methicillin resistance, S. aureus genetic background and other virulence factors.

Introduction

Staphylococcal necrotising pneumonia caused by Staphylococcus aureus was first described in 1919 in adults during the influenza pandemic [1]. It is associated with airway haemorrhage, epithelial necrosis, and a high fatality rate in otherwise healthy patients [2]. It was seldom reported during the XX th century [3,4] until the epidemiological and pathophysiological links with Panton-Valentine Leucocidin (PVL) were discovered at the turn of the century [2,5].

Subsequent reports confirmed the high fatality rates (40-50%) of PVL-associated pneumonia in adults [6 9]. Independently of staphylococcal necrotising pneumonia, staphylococcal pleuropneumonia in young children was described in the late 1950s and 1960s as a specific clinical entity characterised by round-shaped lung infiltrations evolving towards bullous lesions, purulent pleural effusion, and a lack of airway haemorrhage or epithelial necrosis [10,11]. Recent case series of pleuropneumonia involving PVL-positive community-acquired S. aureus [12,13] pointed to a possible relationship between pleuropneumonia and PVL; mortality was below 5% in these series.

The striking differences between pleuropneumonia and necrotising pneumonia, both associated with PVL but exhibiting contrasting mortality rates, may have contributed to controversies regarding the impact of PVL on the severity of staphylococcal pneumonia [14 16]. In this context, whether age affects both the clinical picture and the outcome of community-acquired PVL-positive staphylococcal pneumonia, which factors are associated with severity and whether PVL is an independent factor of severity, are questions that remain to be answered and which are addressed herein.

Patients and methods

Ethics.

The regional ethics committee approved the study (number: A11-39). Written informed consent was obtained from all patients or their parents.

Study design and participants. This is a prospective, multicentre, observational cohort of patients with S. aureus communityacquired pneumonia (CAP) requiring intensive care. All French adult and paediatric intensive care units (ICU) were contacted via the main learned medical societies (Société de Réanimation de Langue Française, ca 200 adult ICU; and Groupe Francophone de R anima i n e d Urgence Pédiatrique of the Société Française de Pédiatrie, 41 paediatric ICU) and invited to participate. Patients with S. aureus CAP (see criteria in supplementary methods) and admitted to a participating ICU were included between January 2011 and December 2016. Exclusion criteria were HIV-positive status, hospital-acquired pneumonia, or admission to hospital in the past 3 months. Clinical, laboratory, and therapeutic data were collected prospectively at admission to ICU and on day 1, 3, and 7. Severity was evaluated using age-adapted severity scores (see details in the supplementary methods).

Microbiology.

The causative staphylococcal isolates were transmitted to the French National Reference Centre for Staphylococci for full characterisation and DNA array genotyping (supplementary methods) [START_REF] Tristan | Rise of CC398 lineage of Staphylococcus aureus among Infective endocarditis isolates revealed by two consecutive population-based studies in France[END_REF].

Statistical analysis.

We determined whether the previous descriptions of different PVL-associated diseases, namely pleuropneumonia in children and staphylococcal necrotising pneumonia in adults, correlated with identifiable age groups in patients with severe CAP. To this aim, patterns in patient age distribution were detected using a clustering procedure (supplementary figure S1).

Following this, disease presentation and outcome in toddlers (<3 years of age, as determined by the age clustering procedure) were compared to that found in older patients. The comparison of PVL-positive and PVL-negative cases was restricted to older patients ( 3 a of age) because 95% of toddlers had PVL-positive CAP. Between-group comparisons used F a S -test, as appropriate. Possibly non-linear changes of mortality rates in function of patient age were visualised using kernel density estimation with bootstrap confidence intervals (supplementary methods). Potential predictors of mortality were investigated using univariate and multivariable Cox proportional hazards models. To avoid bias due to row-wise deletion of cases containing missing data in multivariate models [18], all missing data were imputed prior to analysis using a non-linear, random forest-based multiple imputation technique. Imputation uncertainty was accounted for by replacing binary factors (such as, for example, leukopenia) by a probability estimate between 0 (absence) and 1

(presence).

To further examine the relationship between microbiological characteristics and outcome, we a a a a a , a using DNA arrays, predicted mortality and major clinical characteristics at admission, namely, rash, haemoptysis and leukopenia. These analyses were restricted to adult patients.

All models included the Charlson score as a covariate to account for the diversity of age and baseline health in the cohort. Models with death as the response variable used Cox regression, with coefficients reported as hazard ratios. Models with rash, haemoptysis or leukopenia as the response variable used logistic regression, with coefficients reported as odds ratios. In a first series of models, each outcome was regressed on the clonal complex, treated as a categorical variable, to identify associations of the bacterial lineage with clinical presentation.

Given the strong genetic diversity in our collection, lineages with less than 10 isolates were a O a a a . In a second series of models, we examined a near-exhaustive array of virulence determinants for associations with death, rash, haemoptysis and leukopenia. A subset of the 332 loci and alleles, as determined using DNA arrays, was selected a priori based on their plausible role in virulence (e.g. excluding antimicrobial resistance determinants other than mecA) and non-redundancy (e.g. excluding the lukF component of pvl which is virtually always present with the lukS component;

supplementary table S8). The alleles defining agr groups I to IV were also included, but not the lineage (clonal complex) which would be redundant with the genotypic profile. Variables present or absent in less than 10 isolates were excluded. A final set of 28 non-constant genotypic determinants were included in the final analyses (Supplementary Table S8).

Determinants were coded as 0 when absent and 1 when present. Ambiguous DNA array results (missing data, 0.8% of results) were imputed using the random forest procedure described above. Given the large number of determinants, we used sparse regression (LASSO) as implemented in the glmnet R package, to identify the determinants retained in sparse models [19]. Briefly, LASSO models are built sequentially by modifying a L1 penalisation constraint such that more penalised models include fewer predictors. The bestfitting models (using death, rash, haemoptysis or leukopenia as the response variable) were selected using leave-one-out cross-validation.

Analyses were conducted using R software v3.6.0 (R Core PVL is associated with specific symptoms and is a severity factor in patients aged 3 years.

Among the 143 patients aged 3 a , ( .2%) had PVL-positive CAP. Compared to PVL-negative CAP, patients with PVL-positive CAP were younger and less likely to have an underlying condition (table 1). At admission, they had more frequently airway haemorrhage and cutaneous eruption or rash evoking immune system reaction. PVL-positive CAP patients were also more likely to present with septic shock, leukopenia, elevated blood lactate and SOFA severity score, and to require extra corporeal membrane oxygenation (ECMO).

Appropriate initial antibiotic treatment, defined as 1 drug active against the corresponding strain, was equally likely in PVL-positive and PVL-negative CAP. However, the initial treatment was less frequently appropriate in MRSA cases (odds ratio 0.027, 95% CI 0.006 to 0.097). See the supplementary results for additional information on antibiotic treatment.

Most of the differences observed between patients with PVL-positive and -negative CAP at admission persisted during the first week in ICU (table 2 & 3). The higher SOFA score at admission persisted at day 1, as did the higher median blood lactate. Temperature >39°C, airway haemorrhage, and rash remained significantly more common in PVL-positive cases, who received more frequently inhaled nitric oxide and required ECMO support.

Survival.

We used survival analysis to examine the influence of microbiological and clinical factors on lethal outcome in the 143 patients aged 3 years. Kaplan-Meier survival curves suggested that PVL-positivity and methicillin resistance contributed additively to mortality (Fig. 2 To further decipher the relationships between virulence and CAP outcome, we examined 28 virulence determinants for associations with death, rash, haemoptysis, and leukopenia (see Supplementary Figure S5 for correlation between the selected predictors). The best-fitting LASSO regression models for each outcome, selected using leave-one-out cross-validation, are reported in table 6 (see supplementary figure S5 and supplementary tables S4 to S7 for details of LASSO models). This analysis confirmed that both mecA and pvl were independent predictors of death, even when all other virulence markers were considered. The pvl was also an independent predictor of haemoptysis and leukopenia, but not rash. Rash was mainly predicted by an agrIV background, in line with the association of rash with lineage CC121 of the agrIV background (table 5). Of note, PVL remained among the top-ranking predictors not included in the best-fitting model (Supplementary Table S5).

The LASSO models of haemoptysis and leukopenia yielded additional insights into the interplay of virulence factors with CAP presentation. In addition to PVL, edinB (encoding the epidermal differentiation inhibitor) was associated with both conditions, and the staphylococcal enterotoxin A gene (seA) gene predicted leukopenia.

Discussion

The present results improve our understanding of the role of PVL in different clinical presentation and severity of staphylococcal pneumonia. In addition, we identify the role of other severity-associated factors including bacteriological ones, and their link with PVL.

PVL-positive CAP and its severity was not evenly distributed according to age. PVL-negative CAP was virtually absent in toddlers and mortality was low. Many infectious processes vary in terms of presentation between young and older subjects, but the differences we observed exceed the expected variations due to age alone. For instance, radiological findings in pneumonia are usually similar at all ages [22], however, we observed substantial radiological differences, notably regarding pleural effusion, pneumothorax and unilateral involvement that were all more frequent in toddlers. It was also unexpected to observe a lower mortality in very young children compared to young adults without underlying conditions. The lower severity associated with PVL in toddlers could be due to: (i) immunological immaturity, considering that local inflammation plays a central role in PVL-induced lesions [23], (ii) passive protection by maternal antibodies, (iii) the route of infection the bullous lesions in toddlers point towards the involvement of the supporting tissue, suggesting a haematogenous route of infection as opposed to a likely inhalation route in adolescent and adults leading to direct necrosis of the respiratory epithelium. The observed specific symptoms and lower severity in toddlers might reflect a combination of one or more of the factors described above, but also other yet to be identified features (see additional comments in the supplement). Thus, despite similarities in terms of site of infection and bacteriology, staphylococcal CAP in young children (i.e. pleuropneumonia) and staphylococcal necrotising pneumonia in adults should be considered as two distinct entities.

As we uncovered major differences according to age in PVL-positive staphylococcal pneumonia, the lack of consideration of age-specificity of the disease severity may at least partially explain some conflicting results reported in the literature [START_REF] Shallcross | The role of the Panton-Valentine leucocidin toxin in staphylococcal disease: a systematic review and meta-analysis[END_REF]24]. Considering that PVL-negative staphylococcal pneumonia is almost absent in the youngest patients, toddlers should be excluded from analysis to assess the role of PVL in severity.

We observed that PVL was associated with specific symptoms in staphylococcal pneumonia of adolescents and adults. Previously described features strongly associated with PVL [2,7] were observed herein among patients aged 3 years: PVL-positive S. aureus pneumonia occurred mainly in younger people without underlying condition and was associated with cutaneous rash, airway haemorrhage, and leukopenia. However, logistic regression revealed that rash was most strongly predicted by the agrIV-CC121 genetic background, independently of PVL or the presence of superantigens. The basis of the association of PVL with rash appears elusive although rash as a symptom remains a relevant marker of PVL-positive CAP.

Airway haemorrhage is a consequence of respiratory epithelial necrosis, as described in lung autopsies [1,2]. Haemorrhage is an indirect effect of PVL since the presence of This later association is novel in humans; it potentially results from extravasation of cells in a

Vbeta-unrestricted manner as demonstrated experimentally in rabbits for another major superantigen, the TSST-1 [30].

One objective of the present study was to assess the link between PVL and severity, considering that such an association has been controversial since the first description of PVLassociated pneumonia in 2002 [2]. The frequency of PVL in severe CAP in adults herein contrasts with the low frequency of PVL (<5%) in the staphylococcal carriage population in France [START_REF] Tristan | Rise of CC398 lineage of Staphylococcus aureus among Infective endocarditis isolates revealed by two consecutive population-based studies in France[END_REF] and this striking difference constitutes the initial evidence for an association of PVL with severity. We observed that PVL, especially but not exclusively when associated with methicillin resistance, was an independent factor of mortality in staphylococcal CAP even when other virulence markers were considered, outweighing protective factors such as young age and the absence of underlying conditions. The present study indicates that PVL is associated with non-specific severity markers of infection such as elevated lactate or presence of septic shock at admission and absence of reduction in lactate between admission and day 1.

All these factors are associated with a higher mortality in sepsis, irrespective of the bacteria involved [31, 32], and their higher frequency in the PVL-positive cases confirms greater severity in such patients. Surrogate markers of respiratory failure, such as the need for nitric oxide and for ECMO, were more frequent in PVL-positive cases, which also indicates greater severity in accordance with recent studies [START_REF] Gijón | Factors associated with severity in invasive community-acquired Staphylococcus aureus infections in children: a prospective European multicentre study[END_REF][START_REF] Jacquot | Epidemiology of post-influenza bacterial pneumonia due to Panton-Valentine leucocidin positive Staphylococcus aureus in intensive care units: a retrospective nationwide study[END_REF]. The major symptoms associated with PVL (i.e. airway haemorrhage and leukopenia) were associated with mortality in the multivariable prediction model (Table 4), strongly suggesting that these factors mediate the link between PVL and death.

Since its first description [2], PVL-positive S. aureus pneumonia has been reported worldwide [34 37]. A particular situation is observed in the USA where the description of necrotising pneumonia coincided with the emergence of the PVL-positive CA-MRSA USA300 lineage [4,[START_REF] Finelli | Influenza-associated pediatric mortality in the United States: increase of Staphylococcus aureus coinfection[END_REF]. PVL has been also associated with the CA-MRSA ST-80 lineage in Europe [START_REF] Dufour | Community-acquired methicillin-resistant Staphylococcus aureus infections in France: emergence of a single clone that produces Panton-Valentine leukocidin[END_REF].

However, only 11 and five strains of the present study belong to these clones, respectively, and PVL was distributed within a large diversity of genetic background (i.e. 13 CCs) with a majority of MSSA, ruling out a possible clonal bias associated with PVL-positive cases. The lack of genetic diversity of CA-MRSA in the US may have initially led to confusion in the understanding of the determinant of severity associated with staphylococcal CAP. Thus, methicillin resistance was thought to be the prominent determinant for severity, presumably by inducing a delay in initiation of appropriate antibiotics, in line with our observations. However, this higher mortality in MRSA was present both in PVL-positive and PVLnegative-pneumonia patients.

Mortality increased above 60 years (markedly in PVL-negative cases), which is anticipated for bacterial pneumonia; however, mortality reached a plateau in PVL-positive at ~30 years, an observation for which we have only hypothetical and non-exhaustive explanations.

Mortality results from a balance of protective and deleterious factors. Protection is likely conferred by a gradual acquisition of toxin neutralising-and opsonising-antibodies against S. aureus. Conversely, enhanced severity may be caused by a highly reactive innate immune system (typically lacking in toddlers). Alternatively, the age-dependent acquisition of autoantibodies against cytokines and chemokines involved in S. aureus response (IL-1b, TNF-a, MIP-1a, GMCSF, IL-17A, IFN-g [START_REF] Armentrout | T Cell Immunity and the Quest for Protective Vaccines against Staphylococcus aureus Infection[END_REF]) may impair protective immune responses in a subset of young adults, mimicking the immunosuppression associated with autoantibodies against type I IFNs in patients with severe SARS-CoV2 [START_REF] Bastard | Autoantibodies against type I IFNs in patients with life-threatening COVID-19[END_REF].

We acknowledge limitations of the present study, most notably the observational design whereby all participating centres were encouraged to enrol patients fulfilling the inclusion criteria. We cannot exclude under-reporting, and a higher reporting of the most severe cases may have occurred, but this is unlikely to have affected directly the comparison between PVL-positive and PVL-negative cases. The number of missing data were limited and exhaustivity was reached for all major parameters. Furthermore, the study was restricted to

France but covers almost entirely the French territory with 77 participating centres, thus limiting possible local epidemiological bias.

In conclusion, the present study demonstrates the association of PVL with two distinct facets of staphylococcal CAP with marked differences between toddlers and adolescents/adults regarding clinical presentation and outcome. In toddlers, PVL appears to be prominent in staphylococcal pneumonia, presentation matches with pleuropneumonia, and standard of care in modern ICU appears to be sufficient for favourable outcome. In contrast, PVL-positive CAP in adolescents and adults remains extremely severe despite aggressive management; it deserves further research to develop tailored therapeutic approaches.

Tables

List of variables

The clinical, laboratory, and microbiological variables were recorded for each included patient through a standardised case report form. The reported variables were:

Patient characteristics

Age /sex Body weight and Body Mass Index (BMI) for adults Underlying conditions including immunosuppression, alcohol abuse, and smoking

Cha l c e

Personal and familial history of furuncles or other staphylococcal infection

Flu-like syndrome in the preceding days

Microbiological data

Staphylococcus aureus lineage, PVL status,

Methicillin resistance

Clinical, radiological, and treatment data

The following clinical data were fully assessed at ICU admission (day 0) as well as at day 1, 3, and 7:

Fever >39°C

Producing cough and/or purulent expectoration Airway haemorrhage, Cutaneous rash, Therapeutic procedures including ventilator support, prone ventilation, nitric oxide inhalation, need for curare, pleural drainage, antibiotics, and need for inotropic support.

Radi l gical fea e i h M a c e

Severity score (PELOD for children <18 years and SOFA for adults) with detailed organ dysfunction according to score.

Laboratory data

The following laboratory data were assessed at admission and at day one: total leucocytes and PMN cell count, blood lactate level with change in lactate level between day 0 and day 1, CRP and/or PCT value. If a laboratory value was measured more than once in the corresponding day, the worse value was retained.

Analysis of antibiotic treatment

Due to the observational design of the study, antibiotic treatment regimen varied considerably between patients (>10 different drugs, variously associated, used for initial treatment). Therefore, the potential role of antibiotics in the evolution could not be assessed precisely, nor the effects of potential associations. We could only determine whether at least one drug recommended for treatment of pneumonia was active in vitro according to the antibiogram of the corresponding strain ( a ia e e f he treatment) and whether a drug with toxin-suppressive properties was used.

An antibiotic was considered to have toxin-suppressive properties if an inhibition of PVL production in vitro, even at sub-inhibitory concentration, was demonstrated, as previously described [3]. Hence, linezolid, clindamycin and rifampicin were considered as toxin-suppressive treatment, irrespectively of the potentially associated molecule.

Statistical analysis of age-dependent distribution of PVL-positivity and mortality

Evolution of pneumonia occurrence and mortality with age was investigated using bootstrapped density estimation [4]. Univariate Gaussian Kernel Density Estimation (KDE) was used to estimate the number of patients with pneumonia per year of age, as well as the number of patients who died. The mortality rate at each age was derived from these estimates. For each estimate, 95% confidence intervals (95% CIs) were obtained from 10,000 bootstrap replicates and represented as confidence bands. The analysis was conducted with a KDE bandwidth of 8 years. An additional analysis was conducted for toddlers (<3 years of age) with PVL-positive S. aureus, using a KDE bandwidth of 3 months.

Genotyping of Staphylococcus aureus strains

Bacterial DNA was extracted using commercial extraction kits (Qiagen, Courtaboeuf, France) according to the manufacturer's recommended protocol. The S. aureus microarray genotyping kit StaphyType DNA array (Alere Technologies GmbH, Jena, Germany) used, as well as related procedures and protocols, have been previously described in detail [5]. Briefly, the method uses multiplex PCR covering 332 alleles corresponding to 180 genes. The assignation of isolates to MLST CCs was determined by comparing whole-array hybridization profiles to previously MLST-typed reference strains in a dedicated database as described elsewhere [5]. The genetic diversity of S. aureus i la e a he cl al c le (CC) le el a e i a ed i g Si di e i i de , e e e i g the probability that two random isolates in a collection belong to different groups. To account for sample size variation between groups, the asymptotic Simpson index was used and 95% CIs were computed using a bootstrap procedure as described elsewhere [6].

Additional results and comments ). Among PVL-positive isolates, there was no significant difference in population structure between toddlers and older patients (p=0.41).

Interactions between predictors of mortality in multivariable Cox regression

Multicollinearity and interactions between clinical predictors can harm the robustness and bias the interpretation of multivariable models. To investigate the patterns of interactions between potential predictors of mortality, we constructed 13 additional multivariable models by removing, in each model, exactly one predictor from the full multivariable model. We examined how removing one predictor impacted the coefficients of other predictors by recording the differences between the coefficient Z-score in the full model and in the reduced model (figure S4). This revealed potential interactions of interest based on their magnitude and/or biological relevance. Interactions with a large magnitude included:

(1) the SOFA score and blood lactate level, where the SOFA score strongly predicted death only when ignoring blood lactate;

(2) methicillin resistance and the appropriateness of the antibiotics received, where an appropriate therapy moderately predicted survival only when ignoring methicillin resistance.

Interactions with a smaller amplitude bearing a potential biological relevance included:

(1) a flu-like illness and leukopenia, considering that the leukopenia is more likely to represent a severity factor if it results from the inflammatory response to S. aureus than from a viral infection.

(2) PVL and a toxin-suppressive chemotherapy, considering that the expected protective effect of toxin-suppressive antibiotics might be maximal in PVL-positive CAP patients.

We examined the impact of these 4 potential interactions by augmenting the full multivariable model with the appropriate interaction terms, performing the stepwise selection procedure (table S3). The model with interactions revealed several features compared to the full model without interactions shown in table 4. First, the SOFA score was retained only when taking the interaction with blood lactate into account. Second, the interaction between flu-like illness and a leukopenia was retained in the final model. Finally, an antitoxin therapy, independent of whether the CAP was PVL-positive or PVL-negative, was retained as a predictor of mortality, although with a wide uncertainty margin.

These data suggest that a flu-like illness appears as a protective factor in patients with leukopenia.

Apart from PVL-induced leukopenia, other causes (i.e. viral-induced) may be associated with staphylococcal pneumonia and we show herein that preceding influenza-like illness appeared to be protective of severity but only if leukopenia is added as a covariate. One possible explanation may be that, in case of preceding viral-induced leukopenia, the local PMN recruitment may be limited, thus restricting the necrotic effects of PVL. Conversely, in the absence of pre-existing leukopenia, PMN recruitment and degranulation should lead to maximal necrotic effects. This model is supported by experimental data in the rabbit pneumonia model: neutropenic rabbits exposed to a high concentration of PVL developed less intense pulmonary oedema or necrotic lesions as compared to non-neutropenic rabbits [7].

The possible association of an antitoxin therapy with mortality was intriguing at first. However, this association exhibited positive interactions with several other severity factors (figure S4), namely, haemoptysis, leukopenia, and elevated blood lactate. This suggests that antitoxin therapy was preferentially prescribed in the more severe cases and that its role in predicting mortality was more indirect, by capturing otherwise unmeasured factors of severity at admission, than causal.

Specificities of staphylococcal pneumonia in patient aged <3 years (toddlers)

Comparison between toddlers and older patients are summarised in table S1. Features significantly less frequently observed in toddlers were underlying condition (p<0.001), documented Influenza infection (p=0.026), haemoptysis (p=0.002), and bilateral involvement (p<0.001). Conversely, pneumonia was more frequently associated with a pleural effusion needing drainage (p<0.001) and pneumothorax (p<0.001). A familial history of furuncles was more frequent in toddlers (p=0.002 [7]. This lung inflammation is notably a consequence of PVL-mediated inflammasome activation, leading to polymorphonuclear (PMN) cell recruitment and activation [11]. Recruited cells are killed by PVL, resulting in uncontrolled protease release leading to necrosis [11,12]. In toddlers, it is possible that these effectors are less potent due to immunological immaturity in accordance with the lower PCT level observed in PVL-positive S. aureus toddlers than in PVL-positive S. aureus adults herein.

Another hypothesis involves passive protection by maternal antibodies. Since a familial history of furuncles was reported in nearly a quarter of toddler cases, their mothers are likely to have developed neutralising antibodies, the transmission of which may attenuate the systemic effects of PVL in the infant. After the clearance of maternal antibodies, repeated encounters of PVL-positive S. aureus by the toddler in the household might help elicit a protective immune response against PVL before pneumonia onset. Further analyses of PVL-positive S. aureus prevalence and serological tests in the mothers of infected toddlers could help test this hypothesis.

The route of infection may also be involved as PVL-positive S. aureus pneumonia of the young adult is characterised by extensive necrotic lesions of respiratory epithelium [13], indicating inhalation as the route of infection. Conversely, the bullous lesions in toddlers point towards the involvement of the supporting tissue, suggesting a haematogenous route of infection. The observed specific symptoms and lower severity in toddlers might reflect a combination of one or more of the above described factors but also other yet to be identified features.

  polymorphonuclear (PMN) cells in the lung is required for PVL-induced necrotic lesions of the epithelium [25]. PMN cell influx to the lung may be generated by two non-exclusive mechanisms: a preceding viral infection producing chemoattractant for neutrophils by epithelial cells [25], and PVL-mediated inflammasome activation (releasing IL-1 ), leading to PMN cell recruitment and activation [26]. Interestingly, this inflammatory pathway is amplified by the release of pathogen-and damage associated molecular patterns from dying neutrophils, possibly explaining the fulminant nature of PVL-associated CAP [27]. Of note, the PSMalpha3, beta-, gamma-, delta hemolysins, and LukDE have been shown to synergise with PVL to amplify IL-]. However, among the factors listed above, LukDE is the only variable genotypic determinant and there was no direct association with haemoptysis in the LASSO model. Conversely, the LASSO model revealed that beside PVL, edinB was the second genotypic marker associated with haemoptysis, a feature not described previously. Edin-B elicits large transcellular tunnels in endothelial cells (macroapertures) inducing a loss of barrier function and providing direct access of the endothelium basement membrane to S. aureus. In mouse models Edin-B promotes the translocation of S. aureus to the bloodstream during the course of pneumonia [28]. How these properties translate into haemoptysis remains to be explored.We show that leukopenia is another major feature associated with PVL and death. The relationship between leukopenia and PVL has nowadays a mechanistic explanation after the identification of C5aR on myeloid cells as the receptor for lukS-PV[29]. Beside PVL, other determinants contributed to leukopenia such as CC152 and CC398 lineage, Edin-B and SEA.

Figures legend figure 1 :

 1 Figures legend

figure 2 :

 2 figure 2: Survival of 143 patients 3 years of age with S. aureus pneumonia. Survival after ICU admission according to whether the causative strain was methicillin-resistant (MRSA) or -susceptible (MSSA) and whether it harboured PVL a . + marks denote censored (discharged) patients. Log-rank test, p=0.0007.

Results Clustering analysis of patient age identifies specific patterns in PVL-positive and PVL- negative S. aureus pneumonia

  . A total of 228 patients were eligible. After removal of those who did not comply with the inclusion criteria, those who did not provide consent, and those with missing CRF, 163 patients were included (supplementary figureS1). The included patients were hospitalised in 77 ICUs and aged from one month to 87 years. Eighty-five patients (52.1%) were infected by a PVL-positive S. aureus. Clustering by age of patients with PVL-positive CAP identified 2 non-overlapping clusters below and above 3 years of age Since only 1 toddler had PVL-negative CAP, we could not compare the outcomes of PVLpositive and -negative CAP in this group. Further comparisons according to PVL positivity and analysis of severity factors were restricted to patients aged 3 years to avoid the interpretation bias that would have resulted from the pooling of such markedly different patients (namely, toddlers and older patients) in a single analysis.

Team [2019]. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/). Anonymised data and software code required to reproduce the results are available at github.com/rasigadelab/severecap. (supplementary figure S2). In patients with PVL-negative CAP, we identified 2 overlapping clusters centred at 28 and 60 years of age. Almost all cases in patients aged <3 years ( a ) PVL-positive while PVL positivity in older patients was evenly distributed according to age (supplementary figure S2). In contrast, PVL-negative CAP was exceptional in younger patients (only one case <15 years of age) and its prevalence increased with patient age, peaking at 60 years. Hence, toddlers accounted for 12.3% of all severe staphylococcal CAP in our cohort, and 22.3% of PVL-positive cases. The microbiological and clinical features of CAP differed markedly between toddlers and older patients (supplementary table S1). PVL positivity was more frequent in toddlers, more strongly so than methicillin resistance (n=7/20 vs. 23/143, odds ratio 2.8, 95% CI 0.85 to 8.6). Additional details on the specificities observed in toddlers are given in the supplementary results. Mortality according to age. Mortality increased with age, reaching a plateau at ~30 years in PVL-positive cases. Age-dependent increase of mortality was more linear in PVL-negative cases (supplementary figure 3). Mortality was lower in toddlers than older patients (n=3/20 vs. 52/143, odds ratio 0.31, 95% CI 0.06 to 1.15). In toddlers with PVL-positive CAP (supplementary figure 3 c-d), most cases occurred before 12 months of age and were nonlethal, while mortality increased after 1 year of age.

Contribution of the strain genetic background and other virulence factors to severity.

	positive lung infection, namely haemoptysis and leukopenia [20], but not PVL itself. The 2.25, 95% CI, 1.18 to 4.27) and pvl (hazard ratio 1.98, 95% CI, 1.08 to 3.62) were
	exclusion of PVL, but not its clinical consequences, from the final model is suggestive of a independent predictors of death. Collectively, these results indicate that the S. aureus lineage
	chain of consequence between PVL, severity factors and death, where the severity factors per se does not predict death independently of mecA and pvl, which favours a direct role of
	mediate the effect of PVL on death [21]. To verify this interpretation, we constructed an these determinants rather than an indirect role as lineage markers. However, other clinical
	); median additional multivariable Cox regression model including only microbiological and baseline a a a a a a a , a
	survival time was 1 day (interquartile range, IQR 0 to 7 days) for PVL-positive cases and 7 predictors, but not the potential consequences of PVL such as treatment or severity factors. In potential role of other virulence factors.
	days (IQR 3 to 14 days; p = 0.02, Mann-Whitney U-test) for PVL-negative cases. Among the this model, PVL positivity predicted death (adjusted hazard ratio 1.81, 95% CI, 1.04 to 3.17)
	91 survivors, length of ICU stay was longer in those with PVL-positive CAP (median time independent of methicillin resistance, sex, Charlson score and a flu-like illness. Thus, PVL
	until discharge, 39 days, IQR 26 to 68 days) than in those with PVL-negative CAP (29 days, positivity was a risk factor of mortality independent of other baseline factors. See the
	IQR 20 to 42 days; p = 0.01, Mann-Whitney U-test). supplementary results and figure S4 for additional data on severity-associated features and on
	interactions between predictors of mortality.
	We then used bivariate and multivariable Cox proportional hazards models to identify
	potential independent risk factors for mortality (table 4). Predictors were included in the
	models based on their expected relevance to disease outcome. These included, in addition to Although PVL and methicillin resistance have been repeatedly associated with poor outcome
	PVL and methicillin resistance: baseline patient characteristics, namely sex and the Charlson in previous studies, it is still debated whether they exert a direct effect on outcome or rather
	comorbidity score; characteristics reflecting: (1) severity upon admission, namely the SOFA act as surrogate markers for more virulent lineages. To examine this issue, we tested whether
	score, haemoptysis, leukopenia and blood lactates, (2) inflammatory reaction, namely rash mortality and major severity factors at admission, namely, rash, haemoptysis and leukopenia,
	and blood procalcitonin, (3) a risk factor for staphylococcal superinfection of the lung, a a (supplementary table S2) and virulence gene
	namely a flu-like illness; and the appropriateness and expected toxin-suppressing activity of content, independently of the Charlson comorbidity score.
	the antibiotics received (see the supplementary results for details). In the best-fitting In models using lineage as a predictor (table 5), the Charlson score predicted leukopenia, but
	multivariable model, the independent predictors of death were methicillin resistance, not death, rash or ha , a a a ,
	haemoptysis, rash, leukopenia, elevated blood lactates, and the absence of a flu-like illness leukopenia (p = 0.06, analysis of deviance Chi² test), but not death or haemoptysis.
	(supplementary table S3). Substantial associations between lineage and outcome were found, namely between CC121
	Interestingly, PVL positivity significantly predicted death in bivariate analysis (table 4) while and a rash, and between CC152, CC398 and leukopenia (table 5). In an additional, mixed-
	the best-fitting multivariable model included previously recognised severity factors in PVL-effect Cox regression including one random intercept per lineage, both mecA (hazard ratio

Table 1 : Clinical and laboratory characteristics at admission according to PVL status in patients 3 years of age

 1 . (percentage are calculated among those with data)

			PVL-positive S.aureus	PVL-negative S.aureus
			(n=66)	missing	(n=77)	missing p value
	Mean delay in days between onset	3.39 (3.74)	0	4.64 (4.80)		0.092
	and ICU admission (SD)
	Sex (F/M)		31/35	0	27/50		0.173
	Median age in years (IQR)	47 (28.7-60.2)	0	58 (48-67)		<0.001
	Children aged < 18 years	6 (9.1%)	0	1 (1%)		0.049
	MRSA		14 (21.2%)	0	9 (11.7%)		0.170
	Absence of underlying condition°	39 (59.1%)	0	22 (28.6%)		<0.001
	M a C a	(SD)	1.18 (0.18)	0	2.14 (0.22)		0.001
	Temperature > 39°C		35 (53%)	0	29 (38.2%)		0.092
	Airway hemorrhage		29 (43.9%)	0	18 (23.4%)		0.012
	Toxin-mediated rash		14 (21.2%)	0	5 (6.6%)		0.013
	Bilateral pneumonia		46 (70.8%)	1	52 (68.4%)		0.855
	Signif. pleural effusion°°	3 (4.5%)	0	1 (1.4%)		0.343
	Leucocytes (G/l), med (IQR)	4.60 (1.4-15)	1	9.34 (5-15.8)		0.011
	Leukopenia < 3G/L		28 (43.1%)	1	14 (18.9%)		0.003
	Median blood lactate in mg/l (IQR)	3.95 (2.3-5.5)	8	2.4 (1.7-4.6)		0.015
	Median CRP in mg/l (IQR)	327.5 (191.2-396.7)	26°°°	251.6 (127-351)	18°°°	0.041
	Median procalcitonin in µg/l) (IQR) 137.5 (51.7-488.5)	40°°°	57 (8-200)	58°°°	0.032
	Appropriate antibiotics		47 (75.8%)	4	62 (80.5%)		0.539
	MRSA: Methicillin resistant Staphylococcus aureus; CRP: C-Reactive Protein
	°underlying conditions include: Malignancies, Tobacco smoking,Alcohol abuse, Diabetes and BMI >30
	°°needing drainage
	°°°Inflammatory markers (CRP or Procalcitonin) are missing for 14 patients (7 in each group)

Table 2 : Clinical and laboratory characteristics during stay according to PVL status in patients 3 years of

 2 age. (percentage are calculated among those with data)

	PVL-positive S.aureus	PVL-negative S.aureus
	(n=66)	missing	(n=77)	missing p value

Table 3 : Severity scores and markers at admission and during evolution according to PVL status in patients 3 years of age

 3 . (percentage are calculated among those with data)

		PVL-positive S.aureus	PVL-negative S.aureus
		n=66	missing	n=77	missing p value
	Admission
	Mean SOFA score (SD)	8.9 (5.68)	2	6.9 (5.01)		0.004
	Septic shock	31 (48.4%)	4	22 (30.1)		0.035
	ARDS	20 (32.8%)	7	16 (22.9)		0.352
	Severe ARDS	12 (19.7%)	7	10 (14.3)		0.485
	Invasive ventilation	32 (48.5%)	0	31 (42.5)		0.499
	ECMO	11 (16.9%)	1	3 (4.2)		0.022
	Day 1
	Mean SOFA score (SD)	10.9 (5.96)	14	8.8 (5.25)		0.040
	Septic shock	31 (54.4%)	9	28 (40.6%)		0.152
	ARDS, n (%)	27 (50%)	12	32 (50%)	13	1
	Severe ARDS, n (%)	21 (38.9%)	12	19 (29.7%)	13	0.332
	Death, n (%)	7 (10.6%)	0	4 (5.2%)		<0.001
	Mean lactate variation°(SD)	0.62 (4.38)	19	-0.69 (2.10)	14	0.063
	Day 3
	Mean SOFA score (SD)	10.6 (6.51)	24	8.1 (5.34)		0.045
	Cumulative death°°(%)	17 (25.8%)	0	7 (9.1%)		<0.001
	Day 7
	Mean SOFA score (SD)	8 (6.84)	29	6.8 (5.70)	16	0.347
	Cumulative death°°	23 (34.8%)	0	10 (13%)		<0.001
	During whole ICU stay
	Invasive ventilation	56 (84.8%)	0	60 (81.1%)		0.655
	Inhaled NO	21 (33.3%)	3	11 (15.3%)		0.016
	ECMO	20 (32.3%)	4	8 (11%)		0.003
	Death	31 (47.0%)	0	21 (27.3%)		0.023
	NOTE : SOFA

: Sequential Organ Failure Assesment, ARDS : Acute Respiratory Distres Syndrome, ECMO : Extra Corporal Membrane Oxygenation ° lactate variation between Day 0 and Day one, °° From Day 0 to day 3 or day 7,Table 4 : Cox regression analysis of predictors of death in patients 3 years with S. aureus pneumonia, including clinical predictors at admission and microbiological predictors. Hazard ratio (95% confidence interval)

 4 Bivariate models were independent Cox proportional hazards models, one per predictor. The full multivariable model included all predictors (likelihood ratio test, P < 0.001; Akaike information criterion [AIC), 420 with 13 degrees of freedom). The best-fitting model was obtained using a stepwise procedure, starting from the full model, and minimizing the AIC (409 with 6 degrees of freedom). Confidence interval widths were not corrected for test multiplicity.

	Predictor	Bivariate models	Full model	Best-fitting model
	Charlson comorbidity score (per point)	1.05 (0.91 to 1.21) 1.03 (0.85 to 1.26)	-
	Male sex (95%CI)	0.68 (0.39 to 1.17) 0.95 (0.51 to 1.78)	-
	PVL (95%CI)	1.95 (1.12 to 3.41) 0.87 (0.42 to 1.83)	-
	Methicillin resistance (95%CI)	2.57 (1.41 to 4.69) 3.86 (1.34 to 11.16)	2.87 (1.53 to 5.40)
	SOFA score (95%CI)	1.11 (1.05 to 1.16) 1.03 (0.96 to 1.11)	-
	Flu-like illness (95%CI)	0.71 (0.41 to 1.23) 0.32 (0.15 to 0.68)	0.32 (0.17 to 0.60)
	Hemoptysis (95%CI)	2.62 (1.52 to 4.52) 1.87 (0.98 to 3.54)	2.15 (1.17 to 3.93)
	Rash (95%CI)	2.17 (1.12 to 4.23) 2.59 (1.22 to 5.48)	2.52 (1.24 to 5.12)
	Leukopenia < 3G/L (95%CI)	3.45 (2.00 to 5.97) 2.38 (1.19 to 4.76)	2.38 (1.23 to 4.58)
	Procalcitonin (per 2-fold increase) (95%CI) 1.40 (1.21 to 1.63) 1.05 (0.88 to 1.24)	-
	Lactates (per 2-fold increase) (95%CI)	2.40 (1.85 to 3.12) 2.23 (1.52 to 3.28)	2.64 (1.95 to 3.57)
	Adapted antimicrobial therapy (95%CI)	0.82 (0.44 to 1.53) 1.54 (0.52 to 4.59)	-
	Antitoxin therapy (95%CI)	2.67 (1.48 to 4.81) 1.29 (0.63 to 2.65)	-
	NOTE.

Table 5 . Association of S. aureus lineage with death, rash, hemoptysis and leukopenia in 143 adult patients with severe CAP.

 5 CCs with sample size <10 were pooled and used as the reference category. Death prediction model used multivariable Cox regression, with coefficients reported as hazard ratios (HRs). Other models used multivariable logistic regression, with coefficients reported as odds ratios (ORs). '-', unresolved coefficient due to small sample size.

		Regression coefficient (95% CI)
	Predictor	Death (HR)	Rash (OR)	Haemoptysis (OR)	Leukopenia (OR)
	Charlson score	1.11 (0.95 to 1.29)	0.90 (0.61 to 1.32)	0.97 (0.78 to 1.21)	0.80 (0.62 to 1.04)
	CC121	1.71 (0.66 to 4.42)	24.19 (4.85 to 120.48) 0.68 (0.16 to 2.90)	0.90 (0.16 to 4.97)
	CC152	2.55 (1.07 to 6.09)	0.47 (0.05 to 4.61)	3.30 (0.96 to 11.29)	7.84 (2.06 to 29.81)
	CC30	0.69 (0.25 to 1.89)	0.87 (0.15 to 4.95)	0.68 (0.21 to 2.23)	1.17 (0.31 to 4.40)
	CC398	1.07 (0.39 to 2.94)	0.62 (0.07 to 5.62)	1.04 (0.30 to 3.58)	4.47 (1.22 to 16.34)
	CC5	0.38 (0.09 to 1.66)	-	0.56 (0.14 to 2.33)	0.79 (0.15 to 4.31)
	CC8	1.03 (0.34 to 3.11)	-	0.53 (0.10 to 2.88)	2.62 (0.52 to 13.15)
	CC80	1.59 (0.58 to 4.35)	0.94 (0.10 to 9.13)	1.39 (0.34 to 5.68)	3.47 (0.77 to 15.58)
	Predictor	Analysis of deviance, Chi² test
	Charlson score	p = 0.54	p = 0.45	p = 0.32	p = 0.02
	Lineage (CC)	p = 0.15	p < 0.001	p = 0.35	p = 0.01
	NOTE.

TABLE 6 . Association of S. aureus virulence determinants with death, rash, haemoptysis and leukopenia in 143 adult patients with severe CAP. Coefficient in best-fitting LASSO model

 6 The best-fitting model minimizes prediction error in leave-one-out cross-validation. In all models, 49 non-constant virulence factors as well as the Charlson comorbidity score were considered as potential predictors. Note that LASSO models do not define confidence intervals. '-', predictor not included in model. HR, hazard ratio;

	Predictor	Death (HR)	Rash (OR)	Haemoptysis (OR) Leukopenia (OR)
	Charlson score (per point)	-	-	-	0.87
	mecA	1.89	-	-	-
	agrI	-	-	-	1.52
	agrIII	-	-	-	0.79
	agrIV	-	13.99	-	-
	lukS-PV	1.23	-	1.15	2.52
	edinB	-	-	2.27	1.60
	cap5	-	0.63	-	1.78
	etD	-	-	-	2.32
	sea	-	-	-	2.05
	egc	-	-	-	0.92
	lukD	-	-	-	0.53
	icaC	0.81	-	-	-
	cna	-	1.31	-	-
	eap	0.86	-	-	-
	sdrD	0.99	-	-	-
	NOTE. Models with LASSO (L1) penalty based on Cox (death) or logistic (rash, haemoptysis, leukopenia)
	regression.

  ). No significant difference regarding the values of inflammatory markers (i.e. leukocyte count, C reactive protein,) neither at admission nor during hospitalisation, was observed but among PVL-positive patients, toddlers had significantly lower procalcitonin levels at admission (p=0.048). Respiratory failure (p=0.043) and invasive ventilation (p<0.001) were less frequently reported in toddlers. The mean number of organ dysfunctions assessed by age-adapted severity scores was lower in toddlers from admission to day 3, and the mortality rate in toddlers was also significantly lower (p=0.004).

	he e, e ec i el , 13/13 a d 22/25 ai i la ed f	child e	e	ia e e PVL-positive
	S. aureus. Patients were characterised by young age, high frequency of pleural effusion, and low
	mortality [9, 10].
	The factors determining the difference between staphylococcal pneumonia in toddlers and older
	patients is a matter of speculation. Bacterial factors are unlikely as lineage distribution of PVL-
	positive S. aureus was similar between the 2 age groups. Host response may be involved considering
	that local inflammation plays a central role in PVL-induced lesions, as demonstrated by Diep et al. in
	a rabbit model
	Clinical presentation in toddlers is remarkably different from the symptoms observed in older patients:
	in toddlers, PVL-positive S. aureus pneumonia presented as a pleuropneumonia with pleural effusion
	or pneumothorax needing drainage in most cases. Progression towards ARDS and features associated
	with PVL-positive S. aureus pneumonia in older patients, such as airway haemorrhage and leukopenia,
	were only exceptionally observed in toddlers and, despite the very young age, severity was lower. The
	mean number of organ dysfunctions was lower in toddlers and the mortality in this population was by
	far lower than in older patients with PVL-positive S. aureus CAP. This clinical presentation is
	consistent with the published descriptions of staphylococcal pneumonia (SPP) in young children
	which were described as round-shaped multifocal bilateral pneumonia, often associated with pleural
	effusion. For instance, Carrillo-Marquez et al. found a median age of 0.9 months among 117 children
	aged <18 years and hospitalised for staphylococcal pneumonia (74% community-acquired) during an
	8-year period in Texas [8]; 95.5% of cases were due to PPSA and clinical features were very close to
	those described herein: pleural effusion was common (89.5% of patients), needing drainage in most
	cases, severity was mild, and mortality <1%. Similar results were observed in France and in Greece

  Supplementary table S1. Characteristics of the patients according to age (percentages are calculated among those with data.) Supplementary TableS5. Ranking of 28 S.aureus virulence determinants by inclusion order in LASSO logistic regression of rash at admission in 143 adult patients with severe CAP. Supplementary TableS6. Ranking of 28 S.aureus virulence determinants by inclusion order in LASSO logistic regression of hemoptysis at admission in 143 adult patients with severe CAP. Model size is the number of predictors in the most parsimonious model including the predictor. Coefficient shown is from the most parsimonious model, which is not necessarily the best-fitting one. AICc, Akaike information criterion with small sample correction. BIC, Bayes information criterion. In both criteria, lower values correspond to better fit. BIC is more conservative (favors more parsimonious models) compared to AICc. '-', predictor not included in model.

	Age <3 years n=20 missing 19 (95%) 0 7 (35%) 0 20 (100%) 0 4 (23%) 3 3.15 days (3.31) 0 2 (10%) 0 7 (37%) 1 13 (68%) 1 8 (42%) 1 2 (10%) 1 16.3 G/L (12.9) 1 216.1 (132.5) 1 1.4 (1.8) 0 1.4 (1.8) 0 0.7 (0.2) 1°° 9 (45%) 0 8 (42%) 1 3(15%) 0 3 (16%) 0 PVL, Panton Valentine leucocidin; MRSA, methicillin resistant Staphylococcus aureus; ICU, intensive care Age 3 ears p n=143 missing PVL +, 58 (41%) 0 <0.001 MRSA, 23 (16%) 0 0.048 Absence of underlying disease* 65 (46%) 0 <0.001 Familial history of furuncles, 1/93 (1%) 47 0.002 Mean delay before ICU admission(SD) 4.05 days (4.36) 4 0.377 Documented Influenza infection 47 (33%) 1 0.026 Bilateral pneumonia, 106 (82%) 13 <0.001 Significant pleural effusion^ 20 (15%) 9 <0.001 Pneumothorax 9 (7%) 12 <0.001 Haemoptysis 63 (47%) 9 0.002 Mean leucocytes at admission(SD) 12.4 G/L (18.1) 4 0.359 Mean CRP at admission in mg/L(SD) 280.1 (148.9) 44 0.083 Mean N° of organ dysfunctions at Day 0(SD) 2.9 (1.7) 0 0.001 Mean N° of organ dysfunctions at Day 1(SD) 3.4 (1.8) 2° <0.001 Mean N° of organ dysfunction at Day 3 (SD) 3 (0.3) 24°° <0.001 Respiratory failure at admission 99 (69%) 0 0.043 Invasive ventilation 116 (83%) 3 <0.001 Mortality, n (%) 52 (36%) 0 0.045 Mortality among PVL+ 31 (47%) 0 0.017 unit; SD, standard deviation; CRP, C reactive protein. N°: Number * Underlying diseases include Malignancies, Tobacco smoking, Alcohol abuse, Diabetes and BMI >30 ^ needing drainage ° missing due to death before the end of Day 0 °° missing due to death before Day 2 Supplementary table S2. Clonal complex and resistance according to PVL status and patient age Clonal complex PVL-positive S.aureus (n=85) PVL-negative S.aureus (n=81) TOTAL n=166 a MSSA <3 / 3 ears MRSA <3 / 3 ears MSSA <3 / 3 ears MRSA <3 / 3 ears CC 30 5/9 1/12 27 CC 152 1/16 17 CC 8 2/3b 0/5 0/6 16 CC 398 0/2 0/14 16 CC 5 1/2 1/0 0/11 0/1 16 CC 80 0/2 3/8c 13 CC 121 1/10 0/1 0/1 13 CC 15 2/4 0/5 11 CC 1 2/4 0/2 0/1 CC45 0/8 CC 88 1/2 CC 188 0/3 CC 97 0/1 0/1 CC 7 0/2 CC 12 0/2 CC 22 0/1 0/1 CC 6 0/1 CC 20 0/1 CC 25 0/1 CC 93 0/1 CC 96 0/1 CC 1153 0/1 CC, clonal complex, PVL, Panton Valentine leucocidin ; MSSA, methicillin susceptible Staphylococcus aureus; MRSA, methicillin resistant Staphylococcus aureus; CA, community-acquired a 163 patients but 166 isolates (2 different isolates for 3 patients). b all 5 belong to CA-MRSA USA300lineage. c all 11 belong to CA-MRSA European ST80 lineage . Predictor Model size AICc BIC Coefficient mecA 1 0.446 3.380 0.107 lukS-PV 2 0.213 6.053 0.007 edinB 4 1.495 13.056 0.003 icaC 4 1.495 13.056 -0.041 eap 4 -0.068 11.494 -0.021 sdrD 5 -1.903 12.473 -0.015 sasG 6 -1.124 16.035 -0.031 agrIV 7 -0.455 19.455 0.045 charlson 8 -0.667 21.961 0.003 sea 10 2.632 30.594 0.022 egc 10 2.632 30.594 -0.022 agrIII 12 4.682 37.836 -0.042 fnbB 12 4.682 37.836 -0.044 chp 13 3.209 38.904 0.060 sdrC 14 4.405 42.603 0.000 splA 17 9.864 55.336 0.006 cna 17 9.864 55.336 0.038 agrI 18 8.753 56.568 -0.018 hlgC 18 6.756 54.571 0.027 sak 19 8.531 58.647 0.026 bbp 20 10.436 62.808 0.018 lukD 21 12.435 67.018 -0.022 aur 22 14.545 71.294 -0.004 lukE 23 14.112 72.980 -0.014 cap5 25 19.651 82.611 -0.004 cap8 25 19.651 82.611 0.000 agrII 26 22.465 87.395 0.000 etD ----fib ----NOTE. Model size is the number of predictors in the most Predictor Model size AICc BIC Coefficient agrIV 1 -4.379 -1.452 0.453 cap5 3 -19.500 -10.806 -0.023 cap8 3 -19.500 -10.806 0.000 cna 4 -19.022 -7.490 0.003 lukS-PV 5 -20.959 -6.621 0.043 agrI 6 -20.904 -3.791 -0.012 bbp 6 -19.472 -2.360 -0.071 chp 9 -15.793 9.446 -0.035 eap 9 -15.793 9.446 0.011 fnbB 10 -13.989 13.890 0.006 egc 11 -12.224 18.259 -0.056 charlson 12 -11.945 21.107 -0.004 agrII 12 -10.612 22.439 0.071 lukE 13 -10.691 24.891 -0.073 sak 15 -6.964 33.563 0.104 splA 15 -6.964 33.563 -0.088 mecA 17 -4.329 40.984 0.033 sea 18 -7.033 40.611 -0.005 lukD 19 -5.920 44.011 -0.045 aur 20 -3.682 48.492 0.046 etD 21 -1.720 52.653 0.011 sasG 21 -2.351 52.021 0.014 hlgC 23 0.873 59.502 0.059 edinB 23 1.107 59.735 0.004 sdrD 23 2.955 61.583 -0.081 fib 25 6.484 69.172 0.039 agrIII ----icaC ----sdrC ----NOTE. Model size is the number of predictors in the most Predictor Model size AICc BIC Coefficient edinB 1 -0.095 2.839 0.136 lukS-PV 2 -4.840 1.000 0.018 egc 3 -5.325 3.391 -0.031 agrII 4 -4.932 6.630 0.016 sea 5 -3.589 10.788 0.058 mecA 9 4.222 29.534 -0.058 lukE 9 4.222 29.534 0.033 sak 9 4.222 29.534 0.006 cap5 9 4.222 29.534 0.029 bbp 10 3.137 31.099 -0.028 chp 11 4.810 35.386 0.014 eap 12 5.781 38.935 0.052 cap8 14 8.704 46.903 -0.008 sdrC 14 8.704 46.903 -0.008 icaC 16 13.273 56.361 -0.002 sasG 16 13.273 56.361 -0.006 fib 17 15.312 60.785 -0.022 sdrD 18 17.304 65.119 0.013 fnbB 19 19.390 69.505 0.014 agrI 20 20.455 72.827 -0.018 aur 20 19.639 72.010 -0.028 charlson 21 22.153 76.736 0.000 agrIII 22 24.565 81.315 -0.007 splA 23 27.249 86.117 0.005 hlgC 24 29.642 90.581 0.023 lukD 25 32.491 95.451 0.006 cna 26 34.984 99.914 -0.002 agrIV 27 37.813 104.662 -0.001 etD ----mecR xylR ccrA-3 ccrB-3 merA merB ccrAA (MRSAZH47)_probe 1 ccrAA (MRSAZH47)_probe 2 ccrC (85-2082) ccrA-4 ccrB-4 mecA-M10 = mecC blaZ-SCCmec XI = (blaZ-M10) blaZ blaI blaR erm(A) erm(B) erm(C) lnu(A) mef(A) mph(C) vat(A) vat(B) vga(A) vga(A) (BM 3327) vgB(A) aacA-aphD aadD aphA3 sat dfrS1 far1 fusC (Q6GD50) fexA fosB fosB (plasmid) qacA qacC (total) qacC (consensus) qacC (equine) qacC (SA5) qacC (Ssap) qacC (ST94) sdrM vanA vanB vanZ tst1 (consensus) + tst1 ("human" allele) tst1 ("bovine" allele) sea + + sea (320E) sea (N315) / sep + seb + sed + see + seg seh + sei sej sek + sel selm seln (consensus) seln (other than RF122) selo egc (total) + + seq lukS-PV + + lukF-PV (P83) lukM lukD + + lukE + + lukX lukY lukY (ST30+ST45) hl hla + hlIII (consensus) hlIII (other than RF122) hlb_probe 1 hlb_probe 2 hlb_probe 3 un-disrupted hlb + sak + + chp + + scn + etA + etB + edinA + edinB + + edinC + arcA-SCC arcB-SCC arcC-SCC arcD-SCC aur (consensus) + + aur (other than MRSA252) aur (MRSA252) splA + + splB splE sspA + ssl01/set6_probeRF122 ssl01/set6 (COL) ssl01/set6 (Mu50+N315) ssl01/set6 (MW2+MSSA476) ssl01/set6 (MRSA252) ssl01/set6 (RF122) ssl01/set6 (other alleles) ssl02/set7 ssl02/set7 (MRSA252) ssl03/set8_probe 1 ssl03/set8_probe 2 ssl03/set8 (MRSA252, SAR0424) ssl04/set9 ssl04/set9 (MRSA252, SAR0425) ssl05/set3_probe 1 ssl05/set3 (RF122, probe-611) ssl05/set3_probe 2 (612) ssl05/set3 (MRSA252) ssl06/set21 ssl06 (NCTC8325+MW2) ssl07/set1 ssl07/set1 (AF188836) ssl08/set12_probe 1 ssl08/set12_probe 2 ssl09/set5_probe 1 ssl09/set5_probe 2 ssl09/set5 (MRSA252) ssl10/set4 ssl10 (RF122) ssl10/set4 (MRSA252) ssl11/set2 (COL) ssl11+set2(Mu50+N315) ssl11+set2(MW2+MSSA476) ssl11/set2 (MRSA252) setB3 capH5 capJ5 capK5 cap 8 (total) + + capH8 capI8 capJ8 capK8 icaA + icaC + + icaD + bap + bbp (total) + + bbp (consensus) bbp (COL+MW2) bbp (MRSA252) bbp (Mu50) bbp (RF122) bbp (ST45) clfA (total) + clfA (consensus) clfA (MRSA252) clfA (Mu50+MW2) clfB (total) + clfB (consensus) clfB (COL+Mu50) clfB (MW2) clfB (RF122) cna + + ebh (consensus) + ebpS (total) + ebpS_probe 612 ebpS_probe 614 ebpS (01-1111) ebpS (COL) fnbB (total) + + fnbB (COL) fnbB (COL+Mu50+MW2) fnbB (Mu50) fnbB (MW2) fnbB (ST15) fnbB (ST45-2) map (total) (aka eap) + + map (COL) map (MRSA252) map (Mu50+MW2) sasG (total) + + sasG (COL+Mu50) sasG (MW2) sasG (other than MRSA252+RF122) sdrC (total) + + sdrC (consensus) sdrC (B1) sdrC (COL) sdrC (Mu50) sdrC (MW2+MRSA252+RF122) sdrD (total) + + sdrD (consensus) sdrD (COL+MW2) sdrD (Mu50) sdrD (other) vwb (total) + vwb (consensus) vwb (COL+MW2) vwb (MRSA252) vwb (Mu50) vwb (RF122) isaB isaB (MRSA252) mprF (COL+MW2) hsdS2 (Mu50+N315+COL+USA300+NCTC8325) hsdS2 (MW2+MSSA476) hsdS2 (RF122) hsdS2 (MRSA252) hsdS3 (all other than RF122+ MRSA252) hsdS3 (COL+USA300+NCTC8325+MW2+MSSA476+RF122) hsdS3 (Mu50+N315) hsdS3 (CC51+ MRSA252) hsdS3 (MRSA252) hsdSx (CC25) hsdSx (CC15) hsdSx (etd) Q2FXC0 Q2YUB3 Q7A4X2 hysA1 (MRSA252) hysA1 (MRSA252+RF122) and/or hysA2 (consensus) hysA1 (MRSA252+RF122) and/or hysA2 (COL+USA300) hysA2 (all other than MRSA252) hysA2 (COL+USA300+NCTC8325) NOTE. mecI msr(A) sec + etD + + ssl07/set1 (MRSA252) clfA (COL+RF122) sdrC (other than MRSA252+RF122) hysA2 (MRSA252)
	parsimonious model including the predictor. Coefficient shown is parsimonious model including the predictor. Coefficient shown is mupA ser sspB + setB3 (MRSA252) eno + mprF (Mu50+MRSA252)
	from the most parsimonious model, which is not necessarily the best-fitting one. AICc, Akaike information criterion with small sample correction. BIC, Bayes information criterion. In both criteria, lower values correspond to better fit. BIC is more from the most parsimonious model, which is not necessarily the tet(K) selu sspP (consensus) + setB2 fib + isdA (consensus) + best-fitting one. AICc, Akaike information criterion with small tet(M) ORF CM14_ probe1 sspP (other than ST93) setB2 (MRSA252) fib (MRSA252) isdA (MRSA252) sample correction. BIC, Bayes information criterion. In both criteria, lower values correspond to better fit. BIC is more cat (total) ORF CM14_ probe2 setC setB1 fnbA (total) + isdA (other than MRSA252 )	+
	conservative (favors more parsimonious models) compared to conservative (favors more parsimonious models) compared to cat (pC221) lukF ssl01/set6_probe1_11 cap 1 (total) fnbA (consensus) lmrP (other than RF122)_probe1	+
	AICc. '-', predictor not included in model. AICc. '-', predictor not included in model. cat (pc223) lukS ssl01/set6_probe2_11 capH1 fnbA (COL) lmrP (other than RF122)_probe2	+
	cat (pMC524/pC194 ) lukS (ST22+ST45) ssl01/set6_probe1_12 capJ1 fnbA (MRSA252) lmrP (RF122)_probe1
	cat (pSBK203R) hlgA ssl01/set6_probe2_12 capK1 fnbA (Mu50+MW2) lmrP (RF122)_probe2	+	+
	cfr lukF-PV ssl01/set6_probe4_11 cap 5 (total) fnbA (RF122) hsdS1 (RF122)	+	+

Acknowledgments

We thank the technicians of the French Reference Centre for Staphylococci for their contribution in characterizing S. aureus isolates. We thank Vincent Cottin, Etienne Javouhey and Thomas Henry for fruitful discussions. Finally, we thank Philip Robinson (DRCI, Hospices Civils de Lyon) for copy-editing the manuscript.

aureus containing Panton-Valentine leukocidin. Clin Infect Dis 2007; 45: 315 321. 21. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol 2013; 42: 1511 1519. 22. Patterson CM, Loebinger MR. Community acquired pneumonia: assessment and treatment. Clin Med (Lond) 2012; 12: 283 286. 23. Diep BA, Chan L, Tattevin P, Kajikawa O, Martin TR, Basuino L, Mai TT, Marbach H, Braughton KR, Whitney AR, Gardner DJ, Fan X, Tseng CW, Liu GY, Badiou C, Etienne J, Lina G, Matthay MA, DeLeo FR, Chambers HF. Polymorphonuclear leukocytes mediate Staphylococcus aureus Panton-Valentine leukocidin-induced lung inflammation and injury. Proc Natl Acad Sci USA 2010; 107: 5587 5592. 24. Diep BA, Palazzolo-Ballance AM, Tattevin P, Basuino L, Braughton KR, Whitney AR, Chen L, Kreiswirth BN, Otto M, DeLeo FR, Chambers HF. Contribution of Panton-Valentine leukocidin in community-associated methicillin-resistant Staphylococcus aureus pathogenesis. PLoS One 2008; 3: e3198. 25. Niemann S, Ehrhardt C, Medina E, Warnking K, Tuchscherr L, Heitmann V, Ludwig S, Peters G, Löffler B. Combined action of influenza virus and Staphylococcus aureus pantonvalentine leukocidin provokes severe lung epithelium damage. J Infect Dis 2012; 206: 1138 1148. 26. Perret M, Badiou C, Lina G, Burbaud S, Benito Y, Bes M, Cottin V, Couzon F, Juruj C, Dauwalder O, Goutagny N, Diep BA, Vandenesch F, Henry T. Cross-talk between Staphylococcus aureus leukocidins-intoxicated macrophages and lung epithelial cells triggers chemokine secretion in an inflammasome-dependent manner: Macrophage-epithelia crosstalk upon PVL treatment. Cell Microbiol 2012; 14: 1019 1036. 27. Holzinger D, Gieldon L, Mysore V, Nippe N, Taxman DJ, Duncan JA, Broglie PM, Marketon K, Austermann J, Vogl T, Foell D, Niemann S, Peters G, Roth J, Löffler B. Staphylococcus aureus Panton-Valentine leukocidin induces an inflammatory response in human phagocytes via the NLRP3 inflammasome. J Leukoc Biol 2012; 92: 1069 1081. 28. Courjon J, Munro P, Benito Y, Visvikis O, Bouchiat C, Boyer L, Doye A, Lepidi H, Ghigo E, Lavigne J-P, Vandenesch F, Lemichez E. EDIN-B Promotes the Translocation of Staphylococcus aureus to the Bloodstream in the Course of Pneumonia. Toxins (Basel) 2015; 7: 4131 4142. 29. Spaan AN, Henry T, van Rooijen WJM, Perret M, Badiou C, Aerts PC, Kemmink J, de Haas CJC, van Kessel KPM, Vandenesch F, Lina G, van Strijp JAG. The staphylococcal toxin Panton-Valentine Leukocidin targets human C5a receptors. Cell Host Microbe 2013; 13: 584 594. 30. Waclavicek M, Stich N, Rappan I, Bergmeister H, Eibl MM. Analysis of the early response to TSST-1 reveals Vbeta-unrestricted extravasation, compartmentalization of the response, and unresponsiveness but not anergy to TSST-1. J Leukoc Biol 2009; 85: 44 54. 31. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche J-D, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent J-L, Angus DC. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016; 315:

Author contributions.

Concept and design: Gillet, Argaud, Vandenesch; Acquisition, analysis or interpretation of the data: Gillet,Tristan, Saadatian-Elahi, Bouchiat, Dumitrescu, Leloire, Dupieux, Laurent, Lina, Etienne, Vanhems, Argaud, Vandenesch; Statistical analysis: Rasigade, Vanhems; Drafting of the manuscript: Gillet, Tristan, Rasigade, Vandenesch; Critical revision of the manuscript for important intellectual content: all authors; Obtained funding: Vandenesch; Administrative, technical of material support: Bes, Dupieux; Study Supervision: Saadatian-Elahi, Tristan

Competing interests.

The authors declare that they have no competing interests Support statement.

The study was funded by French Ministry of Health under the Programme Hospitalier de Recherche Clinique 2011 (Grant number 2010-A01132-37). This report presents independent research funded by the Ministry of Health; the views and opinions expressed in this publication are those of the authors and do not necessarily reflect those of the Ministry of Health.

Supplementary Material Prognostic Factors of Severe Staphylococcal Pneumonia in France

Yves Gillet

References Supplementary Methods

Case definition and criteria for inclusion and exclusion

Staphylococcal pneumonia

Patients who fulfilled the following criteria were included: 1) clinical signs of lung infection (fever and cough or expectoration or abnormal auscultation) associated with radiological signs (new pulmonary infiltrate or pleural effusion), 2) >1 positive microbiological assessment of S. aureus involvement (blood culture or pleural fluid positive for S. aureus, >10 3 CFU/mL S. aureus in protected distal bronchial brush, >10 4 CFU/mL S. aureus in bronchio-al e la la age, 10 6 CFU/mL S. aureus in pure culture in sputum or tracheal aspirate), 3) admission to an intensive care unit (ICU).

Sepsis, septic shock, and Acute Respiratory Distress Syndrome (ARDS)

Since all data required were collected, sepsis and septic shock could be classified according to the third international consensus definition on sepsis and septic shock [1] despite the publication of these definitions after the onset of the study. Likewise, ARDS was classified as light, moderate, or severe acc di g he Be li defi i i f ARDS [2].

Age-adapted severity scores

Organ dysfunction was graded at admission, and at day 1, 3, and 7 according to the Pediatric Logistic Organ Dysfunction (PELOD) score for children <18 years and the Sequential Organ Failure Assessment (SOFA) score for adults. To compare the number of organ dysfunctions between paediatric and adult patients, organ dysfunction was considered as present when the corresponding score was above 0.

Study size determination

The target for study size determination was the detection of a 2-fold relative risk of death in PVLpositive severe CAP compared with PVL-negative in a 2-tailed Chi-squared test with 80% power and Supplementary table S3. Cox regression analysis of predictors of death in patients older than 3 years with S. aureus pneumonia, including interaction terms between predictors as suspected in pairwise interaction analysis. Age clustering was assessed as described in supplementary Methods. Of note, among the 41 excluded patients for which strain characteristics were available, there were two toddlers bearing a PVL-positive strains and 39 older patients of which 51% were PVL-positive. Overall, this distribution reflects that of the population retained for analysis Supplementary Figure S6. Leave-one-out cross-validation profiles of LASSO regression models of death (Cox regression), rash, hemoptysis and leukopenia (logistic regression).

Each panel shows the leave-one-out deviance and standard deviation (error bar) as a function of the LASSO penalty parameter , where larger penalty favors more parsimonious models. In each panel, vertical dashed lines identify the penalty with minimal leave-one-out deviance (left line), corresponding to the best-fitting models shown in Table 6; and the penalty with deviance equal to the minimal deviance plus one standard deviation (right line), corresponding to more parsimonious but weakly informative models that were not considered further.