Adrien Barbaresi
email: adrien.barbaresi@oeaw.ac.at

Antonio Ruiz
email: a-ruiz@sophia.ac.jp

Using Elasticsearch for Linguistic Analysis of Tweets in Time and Space

Keywords: Social media, Twitter, NoSQL databases, Time series, Spatial data mining

The collection and analysis of microtexts is both straightforward from a computational viewpoint and complex in a scientific perspective, they often feature non-standard data and are accompanied by a profusion of metadata. We address corpus construction and visualization issues in order to study spontaneous speech and variation through short messages. To this end, we introduce an experimental setting based on a generic NoSQL database (Elasticsearch) and its front-end (Kibana). We focus on Spanish and German and present concrete examples of faceted searches on short messages coming from the Twitter platform. The results are discussed with a particular emphasis on the impact of querying and visualization techniques first for longitudinal studies in the course of time and second for results aggregated in a spatial perspective.

Introduction

Web documents in general and computer-mediated communication in particular put linguists in front of new challenges. As empirical evidence is now widely used in linguistics, the Web turns to a major source, which changes the way research is conducted. That is why [START_REF] Leech | New resources, or just better old ones? The Holy Grail of representativeness[END_REF] speaks of linguists as "inhabiting an expanding universe". The shift towards web texts took place around the year 2000, while focus on computer-mediated communication (CMC) is closely related to the emergence of social networks in the course of the 2000s. The sharp decrease in publication of work documenting web corpus construction and use after 2008 hints at a further development towards a particular focus on CMC data, which can be explained by the growing popularity of short message services (also called microblogs) in the form of social networks. The latter often provide larger number of identifiable users as well as clear data on network activity and range, for example by tracking followers. Because of the profusion of data and metadata, schemes and methods are needed to live up to the potential of these sources. It is indeed widely acknowledged that social and humanities scientists need to acquire and develop the skills to do data analysis and experiment with the visualization tools necessary to manage and interpret big data [START_REF] Manovich | Trending: The promises and the challenges of big social data[END_REF]. At the time of Web 2.0 and APIs, a URL is merely an access point, the resulting website is often tailored not only to what the user has just typed in or said, but to a whole navigation and engagement history. Furthermore, the actual text may become accessory compared to the profusion of metadata which encase it. The clearly structured machine-readable formats combine up to hundreds of different fields ranging from previously studied information such as the user name or the date of posting to new, typical information such as the number of followers or the time zone of the user. That being said, not all information is as relevant or objective as one would expect, so that it has to be refined before reaching any conclusion. Following from a first approach to download and indexing [START_REF] Barbaresi | Collection and Indexing of Tweets with a Geographical Focus[END_REF], the present article tries to document both research methodology and research objects in an articulated fashion. Beyond the selection of a medium and the collection and preprocessing of messages, our work follows from two distinct procedures for handling social media information [START_REF] Tsou | Visualization of social media: seeing a mirage or a message?[END_REF]: "transform the original information formats into analytic forms" and "explore multiple analytical methods", to try to answer the following research question: "What should a comprehensive spacetime analysis framework look like for different scenarios or questions?". Consequently, the visualizations introduced here are an attempt to find "the best combination of analytical methods to analyze social media information in depth from both temporal and spatial aspects" [START_REF] Tsou | Visualization of social media: seeing a mirage or a message?[END_REF]. We address corpus construction and visualization issues in order to study spontaneous speech and variation through short messages. We present and discuss an experimental setting to observe language through tweets, with a particular emphasis on the impact of visualization techniques on time series (messages seen in the course of time) and space (aggregated projections on maps of geolocated messages).

Experimental setting

Twitter as a source

The interest in Twitter is generally considered to reside in the immediacy of the information presented, the volume and variability of the data contained, and the presence of geolocated messages [START_REF] Krishnamurthy | A Few Chirps about Twitter[END_REF]. Other social networks do not deliver the same amount of text, especially for languages other than English, for example on Reddit [START_REF] Barbaresi | Collection, Description, and Visualization of the German Reddit Corpus. In 2nd Workshop on Natural Language Processing for Computer-Mediated Communication[END_REF]. Most importantly, they cannot be deemed as stable in time in terms of popularity and API access [START_REF] Barbaresi | Crawling microblogging services to gather language-classified URLs. Workflow and case study[END_REF]. Nevertheless, because of access restrictions -mostly mechanical constraints on the free streaming access point -it is not possible to retrieve all tweets one would need. It is indeed necessary to enter search terms or a geographic window, which may greatly affect results especially for highly frequent keywords [START_REF] Ljubešic | Tweet-CaT: a Tool for Building Twitter Corpora of Smaller Languages[END_REF]. The API is supposed to deliver a random sample representing a volume of about 1% of all tweets when used with a worldwide geographic window. Since August 2009, Twitter has allowed tweets to include geographic metadata, which are considered to be a valuable source for performing linguistic studies with a high level of granularity, although the geolocation is not always accurate. Currently, the public Twitter Application Programming Interfaces (APIs) can provide five types of geocoding sources: geo-tagged coordinates, place check-in location (by way of a bounding box), user profile location, time zones, and texts containing explicit or implicit locational information [START_REF] Tsou | Identifying Data Noises, User Biases, and System Errors in Geo-tagged Twitter Messages[END_REF]. The geo-tagged coordinates are the most frequently used type of information. The geolocalized messages can conveniently be projected on maps, which is highly relevant for various research fields, for instance variation studies in linguistics. That being said, it is important to note that geolocated tweets are a small minority, with estimates as low as 2% of all tweets [START_REF] Leetaru | Mapping the global Twitter heartbeat: The geography of Twitter[END_REF]. However, even the bounding boxes used to retrieve tweets do not always function as expected due to systematic errors [START_REF] Tsou | Identifying Data Noises, User Biases, and System Errors in Geo-tagged Twitter Messages[END_REF]. Additionally, the geolocation results of profile locations are not a useful proxy for device locations, and the success at being able to place users within a geographic region varies with the peculiarities of the region [START_REF] Graham | Where in the world are you? Geolocation and language identification in Twitter[END_REF]. From the point of view of corpus and computational linguistics, tweets are both highly relevant and difficult to process. Short messages published on social networks constitute a "frontier" area due to their dissimilarity with existing corpora [START_REF] Lui | Accurate Language Identification of Twitter Messages[END_REF], most notably with reference corpora. Some metadata are more useful than others, and some languages fit more easily into the allocated space than others (previously 140 characters, now 280 for most languages). Regarding the content itself, the quantity of information in general or the relevance for linguistic studies in particular may vary greatly. In spite of the restrictions on the API, the delivered volume may already be sufficient for diverse types of studies, and focusing on a given geographical region can be a way to provide enough relevant linguistic evidence. After appropriate filtering and selection, it is possible to draw maps to compare the geolocations of tweets with population density as a preliminary to study language variation [START_REF] Arshi Saloot | Twitter corpus creation: The case of a Malay Chat-style-text Corpus (MCC)[END_REF] or to use as input for classification tasks to determine language use in terms of variants on the social network [START_REF] Alshutayri | Exploring Twitter as a Source of an Arabic Dialect Corpus[END_REF].

Corpus building

While some studies ground on a collection process which is limited in time, the corpora described in this article are monitor corpora, as data collection goes on they grow with time. So-called "heavy tweeters" [START_REF] Krishnamurthy | A Few Chirps about Twitter[END_REF] as well as peculiarities of the API [START_REF] Morstatter | Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose[END_REF] raise the question of sampling processes. The ran-dom sampling methodology used by Twitter to generate the streams of tweets is rarely put into question. This means that steps have to be taken in order to minimize or at least to assess the impact of differences in user activity as well as potentially unknown sampling biases. Studies have shown that it is desirable to gather a set of users which is both large and diverse [START_REF] Zafar | Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream[END_REF], so that the collection process is opportunistic. Such steps are described in previous work. It is possible to take decisions based on relevant metadata such as the number of followers or retweets as well as on information contained in the tweets themselves, such as the mention "RT" for retweet [START_REF] Ruiz Tinoco | Twitter como Corpus para Estudios de Geolingüística del Español[END_REF]. Additionally, it is possible only to take tweets coming from selected sources into account, for example by review the source fields in the collected tweets and focusing on common access points and clients [START_REF] Tsou | Identifying Data Noises, User Biases, and System Errors in Geo-tagged Twitter Messages[END_REF], most notably the website itself and the official Twitter mobile apps.

A suitable database infrastructure

The volume of storage space required to record and process the tweets is on the order of magnitude of several terabytes. Web data are a typical challenge for linguists working at public research institutions who do not dispose of large amounts of computing power [START_REF] Tanguy | La ruée linguistique vers le Web[END_REF]. Additionally, Twitter data come in a form which has to be refined to suit the needs of linguists, as not all information and all metadata fields are linguistically relevant. In order to keep up with the challenges related to data structure and growing amount of tweets, we present our search engine of choice. The interest of NoSQL databases is known as regards the feature-rich content returned by the Twitter API [START_REF] Kumar | Twitter Data Analytics[END_REF], they make it possible to access the corpus and see through it in various ways by using faceted searches. Their logic also supports indexing a variable number of metadata and efficiently divide the corpus into several subcorpora. In that sense, our purpose is to be opportunistic enough during corpus creation in order to allow for subcorpora which match particular interests. Two main components of the open-source ELK stack (Elasticsearch, Logstash, Kibana) are used, namely Elasticsearch 1 to index the tweets and its front-end Kibana 2 to provide a user-friendly interface to queries, results, and visualizations. Installation and configuration are straightforward on most platforms, it is also possible to directly append servers to an existing cluster. Additionally, a sharding structure is implemented: shards (whether on the same machine or not) can be activated or deactivated to suit particular needs. Even with a single-server installation, it is convenient to process large amounts of tweets, that is on the basis of 10 Gb of data collected per day on the streaming API. The creation of subcorpora is possible through facets corresponding to a number of constraints acting on the text or the metadata (countries, precise time or date intervals, geographical window, etc.). Finally, the software is opensource and currently updated frequently, which gives access to the latest optimizations or customizations (for example through Kibana's plugins). Although it is not primarily a search engine for linguists, Elasticsearch takes advantage of the native JSON format of the tweets as well as of a number of relevant field types after a subsequent mapping, which allows for refined queries on text and metadata, which in turn ca be relevant for linguists, as we discuss in the remainder of this article. In order to give a user-friendly access to the results, dashboards can be configured out of a series of indicators. Despite its advantages for the structuration of non-standard data, the main drawback of nested JSON format resides in the lack of familiarity or compatibility with the formats commonly used in corpus linguistics, however the integration is possible through a number of conversion tools (e.g. plugins). The main drawbacks result at the time being from the built-in linguistic processing as well as a lack of integrated linguistic annotation. Considering non-standard speech, the standard lemmatization/normalization of queries and results by the search engine may be imprecise. Language-specific analysis modules can be selected, the default lemmatization is employed here due to the multilingual nature of our data, so that tokens can mainly be accessed on token or surface level.

Faceted querying and visualizations

In this section, we present three case studies focused on structured data synthesis in order to demonstrate the characteristics of language use on Twitter as well as the kind of information made available by our experimental setting.

Results presented as a dashboard

The first case deals with the amount of information available, which has to be filtered and presented it a synthetic fashion in order to allow for linguistic interpretation. We start from a classical display of results in corpus linguistics, the "word in context" (or KWIC) feature. Figure 1 shows a version of it which has been adapted to the tweets: results from several fields are aggregated into a dashboard, in that case the date, the text of the tweet (with the actual results), the name as chosen by the user, the "screen name" or account ID on Twitter, and the follower count. That way, it is straightforward to make a number of assumptions regarding the status of both user and tweet (for example concerning their influence). This example concerns colloquial German, where the contraction of denkst du in denkste has been considered to be typical for computer-based communication [START_REF] Bartz | Optimierung des Stuttgart-Tübingen-Tagset für die linguistische Annotation von Korpora zur internetbasierten Kommunikation: Phänomene, Herausforderungen, Erweiterungsvorschläge[END_REF]. The query 3 contains the search for the exact expression denkste (without normalization), it also restricts the context to tweets in German which are not explicit retweets.

Combined time series

In a second example, we use the leverage provided by the amount of data to shed light on particular phenomena and use and visualization to corroborate hypotheses on language. Since metadata include the time of posting, it is possible to split the corpus in units of time. It is also quite 3 text:"denkste" AND lang:de AND retweeted:False natural to look at the axis of time in the monitor corpus, both in a linear and in a aggregated way. We present a longitudinal study which would be costly and tedious to realize in a classical fashion but where comparable information can be derived from the tweets themselves without artefacts linked to metadata. Figures 2a and2b display the results of a time series query comparing two characterizing variants in Spanish in the course of the day: buenos días and buen día, Figure 2a focuses on tweets sent from Argentina while Figure 2b focuses on tweets sent from Spain, both are rendered using the Kibana's timelion plugin. 4 While a global figure would show patterns relative to the time zone of the users, these two uncouple the information to show the difference: a predominancy of buen día in Argentina and of buenos días in Spain, with a roughly similar pattern in the course of time highlighting that this expression is almost exclusively used in the morning. All this information is gathered in a suitable fashion for variation studies and is interpretable provided it is presented with the adequate circumspection.

Spatial analysis

Finally, our collection processes and infrastructure allow for the spatial studies on languages. A higher granularity of both queries and display is possible, as well as a direct access to the geolocalized tweets, which are then naturally interpretable in the form of a map. The tweets are automatically grouped by built-in clustering processes. The circles on the map display the number of tweets available for a particular place or region. In order to illustrate the immediacy and practicality of the information available this way, we take Spanish diminutives as example, as there is a fair proportion of geolocated tweets to be found from different countries. There are several known diminutives for the word café, Figure 3a depicts the spatial distribution of tweets for the token cafecito, mostly in Central and South America, whereas Figure 3b focuses on cafelito, nearly exclusively to be found on the Spanish Peninsula. This comparison on maps using millions of tweets collected in 2017 confirms empirically this fact known to variation studies. On this order of magnitude, map processing from already indexed data is a matter of seconds and thus suitable for exploratory research from a linguistic and from a practical point of view.

Discussion

Twitter is a particular medium from an informational as well as from a sociological point of view. Age or gender biases are difficult to assess [START_REF] Peersman | Predicting age and gender in online social networks[END_REF], although they are certainly impacting both the structuration of the networks and the linguistic analysis. Additionally, a corpus may be affected by certain trends or keywords, beyond simple retweets or more complex repetitions of certain patterns. "Early adopters" and "power users" can distort a corpus quantitatively and qualitatively on the side of the observer and at the same time influence other users in their use of the language on the productive side. Additionally, our experimental setting is not neutral and greatly contributes to shape the potential experiments. Nu- meric indicators tend to be favored as they allow for immediate realization of graphs such as bar charts. The analysis of text can get much more complicated, starting from the linguistic annotation tasks: language identification of tweets is error-prone. Catalan for example very often resorts to the "undetermined" category according to Twitter data. Furthermore, tweets are used for various purposes and may entail a diverging amount of "text" in a traditional linguistic sense (statements, replies, phatic formulas as opposed to hashtags, gifs, emoticons, and emojis).

Installations and tweet corpora are maintained at both institutions 5 , they are used by colleagues and students alike, mostly for exploratory studies on spontaneous non-standard language. In this context, despite the lack of deep linguis-tic analysis, the interface is more user-friendly, easier to explain to students for example, and thus more directly usable for linguistic studies. The aggregation of data into a dashboard provides a way to find the right balance between profusion of data and relevance. We can still highlight two main artefacts of the apparatus in this case. First, there is a strong tendency to adjust the queries to the output, that is to test for hypotheses which lead to clear-cut results. Second, the projection on maps is the most prominent feature. Geolocated tweets distinguish this platform from other social networks, and corpus users are fond of reactive, interpretable maps.

Conclusion

The actual contents of a web corpus can often only be listed with certainty once the corpus is complete. In fact, corresponding to the potential lack of information concerning the metadata of the texts is a lack of information regarding the content, which has to be recorded and evaluated a posteriori, in a post hoc evaluation [START_REF] Baroni | The WaCky Wide Web: a collection of very large linguistically processed web-crawled corpora[END_REF]. The notion of a posteriori analysis is a key concept regarding the study of tweets, whereas corpus design as considered by the tradition of corpus linguistics is systematically a priori. This bears both a risk and a chance: the linguistic relevance of the documents included is harder to assess, but it is also possible to determine new structural elements and discover relations in the data, for instance linguistic phenomena.

In this sense, we addressed corpus construction and visualization issues in order to study spontaneous speech and variation through short messages, notably metadata such as a location embedded in tweets. The technological stack based on Elasticsearch and Kibana has convinced us by its stability, scalability, and ease of use, although it ought to be complemented by further refinements and annotations in order to suit the needs of linguists. We believe that our experimental setting can bring linguistic studies closer to actual language use. To this end, the adequation of the corpus with a given research goal has to be assessed. It is perfectly possible to adapt the geometry of the corpus to target a particular user type, region, or language. Yet beyond the scope of geographic variation as traditionally seen by examining utterances in a lexical or syntactic or other linguistic aspects, the study of online social networks also opens up new possibilities regarding user involvement and activity or general characteristics of the populations, features which would have needed a particular data collection effort in the past and which were not put into focus in variation studies.

Figure 1 :

 1 Figure 1: Detail/qualitative analysis in the dashboard mode, exact search for the contraction denkste in tweets identified as being in German and excluding retweets

Figure 3 :

 3 Figure 3: Spatial distribution of two diminutives of the word café in tweets

https://www.elastic.co/ Elasticsearch seems to be among the top-10 most popular database software at the moment https://dbengines.com/en/ranking

2 https://www.elastic.co/de/products/kibana

https://www.elastic.co/guide/en/kibana/current/timelion.html

Academy Corpora, Austrian Academy of Sciences and Faculty of Foreign Studies, Sophia University.


E Giacomello

C Gray

M Legnani

P Martinuzzo

Pradella

Chantarangkul

Dlott Js

Martinuzzo Me

Variability of cut-off values for the detection of lupus anticoagulants: results of an international multicenter multiplatform study

Variability of cut-off values for the detection of lupus anticoagulants: results of an international multicenter multiplatform study

Introduction

Lupus anticoagulants (LAs) are a heterogeneous family of immunoglobulins that upon binding to complexes of proteins and negatively charged phospholipids prolong phospholipiddependent coagulation tests in vitro, but are responsible for venous/arterial thromboembolism and pregnancy complications 1. Positivity for LA is considered to be one of the laboratory criteria to define the antiphospholipid syndrome 2 and its persistent positivity in combination with previous thrombotic events qualifies the patient for long-term anticoagulation 1. Hence, the laboratory detection of LA is of paramount importance. Unfortunately, no specific test for LA exists and its detection rests on phospholipid-dependent coagulation tests combined with a set of diagnostic criteria developed and issued by the Scientific and Standardization Committee (SSC), Subcommittee for Lupus Anticoagulant/Phospholipid-Dependent Antibodies of the International Society on Thrombosis and Haemostasis (ISTH). They include the following: (a) evidence that one (or more) phospholipid-dependent coagulation test is prolonged beyond the upper limit of the normal range; (b) evidence that the prolongation does not revert to normal upon mixing equal portions of patient and normal plasma; and (c) evidence that the circulating anticoagulant is directed against negatively charged phospholipids, complexed with proteins, and not directed against specific coagulation factors. Criteria a, b and c are called screening, mixing and confirmation, respectively 3. The LA ISTH guidelines recommend two types of tests for LA detection, including an activated partial thromboplastin time (APTT)-derived and a dilute Russell viper venom (dRVV)derived test. Analyses with these two tests include screening, mixing and confirmation procedures. Results obtained with the above procedures should be interpreted by comparing the patients' values with specific cut-off values determined for each test and procedure.

There are no studies evaluating the variation in the LA cut-off values as determined in different laboratories with different platforms or the effect that the statistical analysis might have on interpretation of results. All the above issues prompted us to organize and carry out an international multicenter multiplatform study with the aim of assessing the variations in cut-off values specific for combinations of tests and coagulometers (here called platforms), run in different laboratories, when testing relatively large numbers of plasmas from healthy donors. Results have been compared in order to assess between-laboratory differences while using the same platform but different sets of plasmas from healthy donors. Other aims of the study were to assess whether results from healthy donors are 'normally' distributed and the extent of differences in the cut-off values determined by different methods of calculation. A common set of LA-positive plasmas has been used to assess the impact that different cut-off values can have on the LA detection rate.

Material and methods

Design of the study

Manufacturers of three widely used platforms for LA detection were asked to support the study. Instrumentation Laboratory, Diagnostica Stago (Asnieres, France) and Siemens Healthcare Diagnostics (Marburg, Germany) were contacted and agreed to participate. With their help, we selected five (or more) laboratories within each platform and they were asked to join the study by collecting 120 plasma samples from individual healthy donors. Participants were provided with a protocol detailing how blood donors should be selected, handling of blood, plasma preparation and storage, and testing (see below). The reagents needed to run the tests in each laboratory were provided by the relevant manufacturers. At the end of the study, the results were entered into a (provided) data collection form and sent to the organizing laboratory (Milano) for centralized statistical analysis.

Selection of healthy donors

Participants were asked to select 120 male and female (60 : 60) healthy donors who ranged in age from 18 to 70 years, were in good health and were free from diseases or drugs known to affect coagulation. ISTH guidelines for LA detection suggest selecting donors who are less than 50 years of age 3. We elected to expand the age range in order to increase the likelihood of individual centers accessing the requisite number of donors. Blood donors were acceptable provided that blood samples were collected by a separate venipuncture before or a long time after blood donation. Participants were free to collect the blood samples needed to complete the study on different days according to local availability.

Blood collection and plasma preparation

Participants were asked to collect venous blood into vacuum tubes containing 1/10 trisodium citrate 109 mm and to prepare plasma from double centrifugation as recommended by the ISTH guidelines to obtain platelet-poor plasma 3. Plasmas were to be stored frozen at -70 °C until testing. Samples collected before planning this study were permitted if their preparation fulfilled the recommendations in the ISTH guidelines 3 and they had been stored at -70 °C for no longer than 12 months.

Pooled normal plasma

Participants were advised to prepare the pooled normal plasma (PNP) locally by collecting blood samples from 30 healthy donors (15 : 15; male : female) into vacuum tubes containing 1/10 volume trisodium citrate 109 mm. Blood was centrifuged as for samples from blood donors used for the study. Equal amounts of the individual plasmas had to be pooled in a plastic container and after mixing the bulk of PNP had to be divided into small aliquots (~1.0 mL) in plastic capped tubes that had to be stored frozen at -70 °C and tested in parallel with the samples from blood donors. The same PNP had to be used to perform the mixing study. In case of unavailability of local PNP a lyophilized commercial preparation that fulfilled the SSC requirements for LA testing could be used.

Freeze-dried plasmas

Suitable numbers of vials of the 1st International Reference Panel for Lupus Anticoagulant, 13/172, consisting of freeze-dried plasma that was negative (plasma A) or positive for LA (B, weak; C, strong) were provided by NIBSC (Potters Bar, Hertfordshire, UK) and were sent to each participating laboratory as coded unknown plasmas in order to assess them for LA detection rate with different methods of calculating cut-off values. Information on how to reconstitute the plasmas was provided. Participants were asked to test plasmas A, B, C and B diluted 1 : 2 with the local PNP, on three test occasions.

Testing

Participants were asked to complete the study by testing the 120 samples from healthy donors and the NIBSC unknown freeze-dried plasmas using an APTT-based test and a dRVV-based test. Participants were free to split all the measurements on different days according to the laboratory workload. Single or duplicate measurements had to be taken according to the local practice. Participants were asked to run the following procedures.

Screening

Testing was performed for the platelet-poor plasma with the phospholipid-dependent screening (APTT-and dRVV-based) tests according to the manufacturer's specification.

Mixing

Testing was performed upon mixtures of equal portions of platelet-poor plasma from each donor and local PNP. The mixture was tested without pre-incubation.

Confirmation

Testing was performed for the (undiluted) platelet-poor plasma with the confirmation tests of the same reagent for screening tests with higher concentrations of phospholipids as specified by the manufacturer.

Results (average values if duplicates) for the screening, mixing and confirmatory procedures had to be entered into the data collection form as clotting times (seconds), together with the clotting time of the PNP run in parallel. It should be noted that the methods provided by the three manufacturers and used in this study, although sharing the same principles (APTT-based or dRVV-based), are substantially different in terms of reagent composition and hence performance.

Data analysis

Results for the APTT-derived and dRVV-derived tests obtained with screening, mixing and confirmation have been analyzed to calculate cut-off values from data obtained by each participant. Raw data used for the analysis were clotting times for screening and mixing and percentage correction or Delta, calculated from results obtained with confirmation procedures performed at low and high phospholipid concentrations. Mixing has been assessed according to the widely used method of Rosner 4 by means of the index of circulating anticoagulants (ICA) as where CT stands for the clotting time. Confirmation has been assessed as percentage correction (%correction) according to 5 or Delta according to where PL stands for the phospholipid concentrations. Cut-off values for the three procedures were also calculated by using ratios (patient-to-PNP) for clotting times.

Data were tested to assess for deviation from the normal (Gaussian) distribution by means of the Kolmogorov-Smirnov test with P < 0.05. Cut-off values have been determined by different methods of calculation for each participant, method and procedure without exclusion of outliers. Centiles were calculated from the distribution of the data according to the weighted average method. Results obtained for the freeze-dried NIBSC LA plasmas were used to calculate the LA detection rate achieved by the different methods of calculating cutoff values. For the calculations of detection rates, plasmas were considered LA positive when the result for each procedure and method was greater than the cut-off value determined in this study according to each of the investigated methods of calculation. Analyses were performed with the IBM SPSS Statistics version 23 Software (New York, NY, USA).

Results

Three groups, each composed of five participants, were identified among those laboratories that used routinely one of the three commercial platforms (here called IL, Siemens and Stago). All of them agreed to participate and received the protocol. At the end of the study, results were received from four, two and five participants in the IL, Siemens and Stago groups, respectively. Eight participants were from Europe and there was one each from India, North America and South America. The LA detection platforms used in the study are shown in Table 1. One laboratory did not provide PNP results for the confirmation procedure; two reported duplicate rather than triplicate measurements for the freeze-dried LA plasmas and one did not perform confirmatory assays.

Deviation from the normal (Gaussian) distribution

The preliminary analysis showed that in many instances (> 50%) there was a significant deviation (P < 0.05) from the normal distribution (Table 2). In particular, deviations were more often observed when results were expressed as clotting times (average 60%) than as ratios (average 49%). Finally, deviations were more often observed for the APTT-derived methods (average, 57%) than for dRVV-derived (average, 51%) methods. Because of the above deviations, it was decided to calculate cut-off values by means of non-parametric statistics and the 99th and 95th centiles were chosen for further analysis.

Cut-off values

Cut-off values calculated for the three procedures (screening, mixing and confirmation) for all the laboratories, platforms, tests (APTT-derived and dRVV-derived) and results are shown in Figs 1, 2. Table 3 shows the median and range (minimum-maximum) of cut-off values for each platform/test and method of calculation. As expected, cut-off values were shorter when calculated as the 95th vs. 99th centile. Overall, there was a relatively large between-laboratory variability (even within the same platform) with no major improvement when results were expressed as a ratio (patient-to-PNP) for the APTT-derived or dRVV-derived methods.

LA detection rate

NIBSC freeze-dried plasmas have been used to assess the LA detection rate obtained for each laboratory with each platform for screening, mixing and confirming. Results are shown in Tables 4,5.

APTT derived

Overall, detection rates for screening, mixing or confirming were excellent for plasma A (negative) regardless of the method of calculating cut-off values, good for plasma C (strongly positive), acceptable for B (weakly positive), but low for plasma B diluted 1 : 2 in PNP (Table 4). For plasma B (undiluted or diluted), the detection rate was better when cut-offs were calculated as 95th centiles rather than as 99th centiles. Overall, detection rates for confirming were similar when cut-off values were calculated as Delta or as % correction. There were no major differences between the detection rates calculated from clotting times as compared with those calculated from ratios.

dRVV derived

Overall, detection rates for screening, mixing or confirming were excellent for plasma A (negative) and plasma C (strongly positive) regardless of the method of calculating cut-off values, acceptable for plasma B (weakly positive), but very low for plasma B diluted 1 : 2 in PNP (Table 5). For plasma B (undiluted or diluted), the detection rate was better when cut-off values were calculated as 95th centiles rather than as 99th centiles. Overall, detection rates for confirming were similar when cut-off values were calculated as % correction or as Delta. There were no major differences between the detection rates calculated from clotting times as compared with those calculated from ratios.

Discussion

Surveys performed over the years 6-13 have shown the variable performance of clinical laboratories with respect to sensitivity and specificity of LA tests. The rates of false-positive and false-negative detections remain relatively high. The former are of particular concern because they qualify the patients for long-term and unnecessary oral anticoagulant treatment 1. Although some of the reasons for the poor performances have been elucidated 14, some others, such as the effect of cut-off values, although investigated in a multicenter study with a single platform 15, are still poorly understood. The ISTH guidelines for LA detection recommend testing plasmas from 40 or more healthy donors and taking the cut-off as the value corresponding to the 99th centile of their distribution for the screening and mixing procedures and as the mean percentage correction (low vs. high phospholipid concentration) for the confirmation procedure 3. However, for most laboratories it is difficult to follow these recommendations, as plasmas from healthy donors are not readily available. Commercial frozen normal plasmas, if available, are difficult to access because of their cost. Furthermore, taking the 99th centile of the distribution of results from healthy donors as the cut-off value may give rise to bias when plasmas are less than 100. Finally, most laboratory operators are much more familiar with the cut-off values determined as the interval defined by the parametric methods (i.e. mean + SD), which was, however, not recommended in the ISTH guidelines as it was felt that the data were not normally distributed. Last but not least, most manufacturers provide in the package insert for their products, indications on cut-off values to be used in each laboratory to interpret results obtained with the same combination of reagent and coagulometer (platform). The validity of this procedure has, however, not been thoroughly investigated to see whether small variations in the application of testing in different laboratories may introduce local bias in the determination of cut-off as well as patient values and hence in the interpretation of results.

The present study aimed to answer some of these questions. We identified three widely used commercial platforms for LA detection and selected five clinical laboratories across the world using each of the three platforms. Each laboratory was asked to include in the study at least 120 healthy donors and a common set of freeze-dried plasmas negative or positive for LA that were to be tested along with the donors' plasmas. The collaborative study gave the opportunity to collect and analyze huge numbers of data (i.e. > 20 000) that are not completely analyzed here and will form the basis of additional reports.

Interestingly, there were differences in dealing with the LA detection policy between different platforms. All included in their protocol APTT-derived and dRVV-derived tests as recommended by the ISTH guidelines 3. However, three platforms used integrated tests (i.e. dual tests at low and high phospholipid concentrations) for the dRVV-derived tests. For the APTT-derived test, the approach was different. Laboratories with the Siemens platform used a brand of APTT for screening/mixing and a different brand of APTT for confirmation. Laboratories with the Instrumentation Laboratory (IL) platform used an integrated test and laboratories with the Stago platform used a brand of APTT for screening/mixing and a different brand of APTT with dual tests without and with hexagonal phospholipids for confirmation. From the scrutiny of results, the following conclusions on cut-off values and LA detection rates can be drawn.

Cut-off values

As expected, there were differences in the cut-off values for the different platforms when results were expressed as clotting times and these differences were not attenuated when results were expressed as a ratio (see Figs 1 and 2, Table 3). It is of more interest to note that in general there were differences in the cut-off values determined from results stemming from participants using the same platform. This is (apparently) an unexpected finding, but the most likely explanation rests on the between-laboratory differences of the selected population of donors combined with small differences brought about by the different lots of reagents employed in different laboratories, together with small between-laboratory differences in the application of the same platform. In contrast to a previous paper that reported cut-off values determined in five laboratories and for a single platform using the same lot of reagents 15, in the present study the between-laboratory differences were only marginally attenuated when results were expressed as a ratio. The reason probably rests with the variability of PNP prepared in different laboratories. Perhaps, a common international standard PNP is needed to harmonize results between laboratories. However, this notwithstanding, clinical laboratories should be encouraged to express their results as ratios, as this minimizes the day-to-day variability. Another reason for the between-laboratory variation in cut-off observed in the study when using the same platform may be the different lots of reagents or blood collecting systems used in different laboratories with the same platform. Indeed, two laboratories that used the same platform and shared the same lot of reagents obtained similar cut-off values (not shown). Overall, the relatively high inter-laboratory variability in the cut-off values even within the same platform observed in this study should be indicative of the notion 16, until now not directly documented in a large collaborative study involving many laboratories and three widely used commercial platforms, that cut-off values should be determined locally and that the values reported in the reagent package insert must be taken only as preliminary information.

The study also investigated whether results from healthy donors are or are not normally distributed. On average, more than half of the datasets were not normally distributed and there were differences between methods and platforms. Hence, the practice of using the parametric method for calculating cut-off, based on the mean + SD, is questionable and the more robust non-parametric calculation based on centiles should be implemented. As expected, the cut-off values were smaller when calculated as 95th rather than 99th centiles. It should, however, be pointed out that the choice between the two models of calculation should be based on the LA detection rate determined with truly positive or negative plasma rather than on statistical considerations. This is especially important if one considers that persistently positive LA combined with previous thrombosis qualifies patients for long-term anticoagulation 1.

LA detection rate

Although defining truly positive or negative LA plasmas is difficult because of the lack of specific tests, we attempted to investigate the LA detection rate using a common set of previously validated LA plasmas that had been tested by the participants along with the plasmas from donors. From scrutiny of the results, the following considerations can be derived.

LA-negative plasma

It is reassuring that no false-positive LAs were found by participants when testing plasma A (LA-negative), both for the APTT-derived and dRVV-derived tests (screening, mixing and confirming), whatever the methods for calculating cut-off or expressing results, except for the screening dRVV Siemens platform when cut-off was calculated as the 95th centile with results expressed as clotting times (see Tables 4,5). The inherent limitation of the above conclusions rests on the fact that plasma A was negative for LA but had normal clotting time.

A negative-LA plasma with abnormal clotting time would have been needed to draw definite conclusions.

LA-strongly-positive plasma

Overall, the LA detection rate obtained by participants when using the APTT-derived test was high for plasma C (strongly positive) (range of values, 81-94%) (see Table 4). The detection rate was slightly better when using more stringent cut-off values (95th centiles) regardless of the expression of results. For the dRVV-derived tests, the detection rates were higher than those observed for the APTT-derived tests (range of values, 90-100%), with slightly better detection rates when using more stringent cut-off values (95th centiles) regardless of the expression of results (see Table 5).

LA-weakly-positive plasmas

For plasma B (weakly positive), detection rates for the APTT-derived methods were (as expected) smaller than those observed for the strongly positive plasma (range of values, 63-94%), with a better detection rate when using more stringent cut-off values (95th centiles) regardless of the expression of results. Finally, when plasma B (weakly positive) was diluted 1 : 2 in PNP, the LA potency for the APTT-derived test became very weak and the detection rate considerably lower (range of values, 37-81%), with a better detection rate when using more stringent cut-off values (95th centiles) regardless of the expression of results (see Table 4). For the dRVV-derived tests, detection rates for plasma B were similar to those observed for the APTT-derived tests (range of values, 53-100%). It is of interest that detection rates for the diluted plasma B were smaller for the dRVV-derived (range of value, 0-58%) than those observed for the APTT-derived tests (37-81%) (see Tables 4,5). However, no definitive conclusions can be drawn on the performance of the APTT-derived vs. the dRVV-derived tests in detecting LA, as the results pertain to the specific set of LA plasmas used in this study, which are not necessarily representative of the whole population of patients with LA.

In addition, none of the plasmas in the panel had abnormal prolonged clotting time because of the presence of coagulation factor deficiencies or presence of an inhibitor other than LA. There were differences in the LA detection rate according to the method of calculating cutoff: those based on 99th centiles were smaller than those based on 95th centiles (see Table 3). These findings may be of considerable practical interest. Owing to the lack of standardization of LA reporting 17, the potency of LA in individual patients cannot be accurately determined. Therefore, although surmised 18, it is not yet known with certainty if weak LA positivity is as clinically relevant as strong LA positivity. It can be argued that if the goal of the laboratory is to pick up not only the strongly positive but also the weakly positive patients, more stringent cut-off values based on the 95th centiles should be selected. This choice would carry the risk of increasing the numbers of false-positive patients and should therefore be carefully considered.

Although we recognize that the presence of outliers could overstate the estimated variability, in this preliminary evaluation, we elected to analyze all the results stemming from the participants without detection (and rejection) of outliers. Some guidance on the methods that are useful for outlier detection when calculating cut-off values for laboratory parameters of clinical interest has been given previously [see 19 for more details]. However, no clear recommendations on the best method to use, nor evaluation of the impact that different methods may have on interpretation of result, have been established. Furthermore, owing to the complexity of the diagnostic procedures, the detection of outliers in the LA setting might require strategies that are different from those used for other parameters. Hence, we believe that further analyses of the data collected in this collaborative study are appropriate to investigate the impact of different models of outlier detection and these will form the basis of a specific report that is in preparation.

In conclusion, the collaborative study involving 11 laboratories collecting plasmas from hundreds of donors and using the three widely used commercial platforms for LA detection leads to the following considerations. (i) Clotting times or ratios for healthy subjects were not normally distributed in the majority of cases. The take-home message is that cut-off values should be determined preferably by the non-parametric method based on centiles. (ii) There are relatively large between-laboratory cut-off variations even within the same platform and the variability is marginally attenuated when results are expressed as ratios. The take-home message is that cut-off values should be determined locally. (iii) There are major differences between cut-off values calculated as 99th or 95th centiles that translate into a different LA detection rate (the lower the centile the greater the detection rate). The take-home message is that cut-off values determined as the 95th centile allow a better LA detection rate.

Addendum

A. Tripodi conceived the study, reviewed results, and wrote the manuscript. V. Chantarangkul collected data, carried out statistical analyses, and reviewed the results and the manuscript. M. Cini, K. Devreese, J. S. Dlott, R. Giacomello, C. Legnani, M. E. Martinuzzo, P. Pradella, A. Siegemund, S. Subramanian, P. Suchon, and S. Testa selected donors, collected plasmas, supervised measurements at their own laboratories, and reviewed and accepted the manuscript. E. Gray prepared and characterized the freeze-dried LA-positive or negative plasmas, and reviewed and accepted the manuscript.

Figure 1

 1 Figure 1 APTT-derived cut-off values determined in different laboratories using three commercial platforms for LA detection for screening, mixing and confirmation procedures. Cut-off values have been determined according to clotting times (upper panels) or ratio of patient plasma to PNP (lower panels) as the 99th (closed symbols) or 95th (open symbols) centiles of distribution of results for healthy donors. APTT, activated partial thromboplastin time; ICA, index of circulating anticoagulant; LA, lupus anticoagulant; IL, Instrumentation Laboratory; PNP, pooled normal plasma; ST, Stago; SM, Siemens; ICA, index of circulating anticoagulant calculated as ICA = [(CTmixture -CTPNP)/CTpatient] × 100; % correction calculated as % correction = [(CTlowPL -CThighPL)/CTlowPL] × 100. Delta calculated as Delta = CTlowPL -CThighPL.

Figure 2

 2 Figure 2 dRVV-derived cut-off values determined in different laboratories using three commercial platforms for LA detection for screening, mixing and confirmation procedures. dRVV, dilute Russel viper venom. (see legend to Fig. 1 for other details).

Table 4 .

 4 APTT-derived LA detection rate according to different cut-off values. Numbers represent the percentage of positive detection obtained by participants [(n. of positive LA/n. of observations) × 100] while testing the common LA-negative (plasma A), LA-stronglypositive (plasma C), LA-weakly-positive (plasma B) and plasma B diluted 180 90 74 89 100 100 20 60

•

  LA, lupus anticoagulant; dRVV, dilute Russell viper venom test; ICA, index of circulating anticoagulant; IL, Instrumentation Laboratory; SM, Siemens; ST, Stago; TOT, total; CT, clotting time; PNP, pooled normal plasma; PL, phospholipids. *Calculated as ICA = [(CTmixture -CTPNP)/CTpatient] × 100. † % correction = [(CTlowPL -CThighPL)/CTlowPL] × 100. ‡ Delta = CTlowPL -CThighPL.

Table 1 .

 1 Commercial platforms used throughout the study

			APTT-based	dRVV-based
	Platform Laboratory	Screen and mix	Confirm	Screen and mix	Confirm	Coagulometer
			Low PL High PL	Low PL	High PL
		5	HemosIL SCT	HemosIL SCT	HemosIL Screen dRVVT	HemosIL Confirm dRVVT	ACL TOP 500
	IL	6 7	HemosIL SCT HemosIL SCT	HemosIL SCT HemosIL SCT	HemosIL dRVVT Screen HemosIL Screen dRVVT	HemosIL dRVVT Confirm HemosIL Confirm -IL dRVVT	ACL TOP 700 ACL TOP 700
		8	HemosIL SCT	HemosIL SCT	HemosIL Screen dRVVT	HemosIL Confirm dRVVT	ACL-TOP 500
	Siemens	4 9	ACTIN FSL ACTIN FSL	Pathromtin SL ACTIN FS	LA1 (Siemens) LA1 (Siemens)	LA2 (Siemens) LA2 (Siemens)	BCS BCS
				STACLOT	STACLOT	STACLOT	STAR
		1	PTT-LA	LA -/+ HX	DRVV	DRVV	EVOLUTION
				PL	SCREEN	CONFIRM	3002
					STACLOT	STACLOT	STAR
		2	PTT-LA Not done	DRVV	DRVV	EVOLUTION
					SCREEN	CONFIRM	3002
				STACLOT	STACLOT	STACLOT	STAR
	Stago	3	PTT-LA	LA -/+ HX	DRVV	DRVV	EVOLUTION
				PL	SCREEN	CONFIRM	3002
				STACLOT	STACLOT	STACLOT
		10	PTT-LA	LA -/+ HX	DRVV	DRVV	STA compact
				PL	SCREEN	CONFIRM
				STACLOT	STACLOT	STACLOT	STAR
		11	PTT-LA	LA -/+ HX	DRVV	DRVV	EVOLUTION
				PL	SCREEN	CONFIRM	3002

Table 2 .

 2 Details of the analysis to assess whether results from individual laboratories were 'normally' distributed. Number (%) represents the proportion of deviation from 'normality' (P < 0.05) for each method and expression of results

				Kolmogorov-Smirnov test
	Test	Clotting time (seconds)	Ratio (patient plasma to PNP)
		IL Siemens Stago	All	IL Siemens Stago	All
	APTT -screening	2/4	1/2	3/5	6/11	1/4	1/2	3/5	5/11
	APTT -mixing (ICA%)	2/4	1/2	3/5	6/11	2/4	1/2	3/5	6/11
	APTT -confirm (% correction)	2/4	1/2	4/4	7/10	1/3	0/2	3/4	4/9
	APTT -confirm (Delta)	2/4	2/2	4/4	8/10	1/3	1/2	3/4	5/9
	dRVV -screening	2/4	1/2	5/5	8/11	2/4	2/2	4/5	8/11
	dRVV -mixing (ICA%)	0/4	0/2	3/5	3/11	0/4	0/2	0/5	0/11
	dRVV -confirm (% correction)	1/4	1/2	3/4	5/10	0/3	1/2	3/4	4/9
	dRVV -confirm (Delta)	2/4	1/2	4/4	7/10	2/3	1/2	4/4	7/9
	Overall	13/32 (40)	8/16 (50)	29/36 (81)	50/84 (60)	9/28 (32)	7/16 (44)	23/36 (64)	39/80 (49)

• APTT, activated partial thromboplastin time; ICA, index of circulating anticoagulant; IL, Instrumentation Laboratory; dRVV, dilute Russell viper venom test; PNP, pooled normal plasma.

Table 3 .

 3 Median (minimum and maximum) of cut-off values obtained by participants according to different platforms and models of calculation (99th or 95th centile, seconds or ratio [patient-to-PNP]) Stago; CT, clotting time; PNP, pooled normal plasma; PL, phospholipids. *Calculated as % ICA = [(CTmixture -CTPNP)/CTpatient] × 100. † % correction = [(CTlowPL -CThighPL)/CTlowPL] × 100. ‡ Delta = CTlowPL -CThighPL.

	Clotting time (seconds)	Ratio (patient plasma to PNP)
	IL	SM	ST	IL	SM	ST
	99th 95th 99th 95th 99th 95th 99th 95th 99th 95th 99th 95th
	APTTscreen
	Median 45.9 40.4 36.8 34.0 46.6 43.3 1.38 1.24 1.30 1.21 1.30 1.23
	Min

• IL, Instrumentation Laboratory; APTT, activated partial thromboplastin time; dRVV, dilute Russel viper venom.

Acknowledgements

The authors wish to thank X. Rubiralta, Instrumentation Laboratory (Orangeburg, NY, USA), C. Wagner, Siemens Health Care (Marburg, Germany), and M. Lubineau, Stago Diagnostics (Asnieres, France) for providing the reagents needed for the study.

Disclosure of Conflict of Interests

The authors state that they have no conflict of interest.