Histo-Molecular Factors of Response to Combined Chemotherapy and Immunotherapy in Non-Small Cell Lung Cancers.

BACKGROUND: Chemo-immunotherapy (CIT) is the standard of care for advanced non-small cell lung cancer (NSCLC), but the impact of routinely available histo-molecular biomarkers on its efficacy has not yet been fully assessed. OBJECTIVE: The purpose of this multicenter study was to evaluate the clinical activity of CIT according to oncogenic drivers, STK11 and TP53 mutations, and MET overexpression. PATIENTS AND METHODS: Patients receiving CIT for advanced NSCLC with available comprehensive molecular profile were included. The primary endpoint was progression-free survival (PFS), adjusted on main confounding factors, and secondary endpoints were overall survival (OS) and objective response rate. RESULTS: Among the 195 patients included between September 2018 and October 2021, 88 (41%) had a KRAS mutation, 16 (8.2%) an EGFR mutation or an ALK, ROS1, or RET rearrangement, 11 (5.6%) a BRAF mutation, 6 (3.1%) a MET exon 14 mutation or MET amplification, and 5 (2.6%) a HER2 mutation. Seventy-seven patients (39.5%) had none of these alterations. The median PFS was 6.4 months (95% CI 5.3-7.3). Per subgroup, the median PFS was 7.1 months (5.4-8.9) for KRAS, 5.5 months (2.5-15.3) for EGFR/ALK/ROS1/RET, 12.9 months (2.6-not reached [NR]) for BRAF, 1.5 months (0.6-NR) for MET, 3.9 months (2.6-NR) for HER2, and 5.6 months (4.7-7.8) for patients without any oncogenic alteration. No difference in PFS was observed between the KRAS, BRAF, EGFR/ALK/ROS1/RET, and no-driver subgroups. STK11 mutations were associated with poor PFS (HR 1.59 [95% CI 1.01-2.51]) whereas TP53 mutations had no impact. MET overexpression was associated with longer PFS (HR 0.59 [95% CI 0.35-0.99]). CONCLUSION: This study suggests that the efficacy of combining pembrolizumab with pemetrexed and platinum-based chemotherapy differs according to the histo-molecular biomarkers, which may help to identify patients liable to benefit from CIT.

Predictive factors of return-to-work trajectory after work-related rotator cuff syndrome: A prospective study of 96 workers.

OBJECTIVE: Sustained return to work after surgery for work-related rotator cuff syndrome (WRRCS) remains quite difficult. The main purpose of the present study was to identify predictive factors of a return-to-work (RTW) trajectory. METHODS: A total of 96 workers with WRRCS were identified by 4 surgeons. They were followed prospectively before and after the surgery, until 1 year after RTW, or for 20 months after surgery when they did not. Participants completed a series of standardized questionnaires related to working conditions, health, and beliefs, and performed functional tests at the inclusion time. During the follow-up period, they were regularly asked about their working conditions (present or not at work), activity (normal or lightened physical duties) and schedules (full- or part-time job). Statistical analysis was based on single- and multiple-factor models of prediction of the workers' trajectory. RESULTS: Three trajectories of RTW were distinguished, considering RTW and absenteeism that occurred during the follow-up: stable, unstable, and non-RTW. The median age of the sample was 49.5 [45.0-54.0], with 67.7% of workers employed in highly physically demanding jobs. In the multiple factor model, three factors were highly predictive of the trajectory: perceived health before surgery, having had a repaired ruptured-rotator-cuff tendinopathy, and the level of physical demand of the job. CONCLUSION: Three easy-to-collect predictive factors of RTW trajectory have been identified. They may be useful for healthcare professionals and care givers to identify vulnerable workers' risk of occupational dropout after arthroscopic surgery for rotator cuff tendinopathy.

Comparison of pesticide residue and specific nutrient levels in peeled and unpeeled apples.

BACKGROUND: Studies have shown that the consumption of apples has a beneficial effect on cardiovascular diseases and some cancers, largely as a result of their micronutrient and phytoconstituent contents. Apple peel not only contains more polyphenols than the flesh, but also is likely to contain pesticide residues. The present study aimed to compare the contents of certain micronutrients and residual pesticide levels in peeled and unpeeled apples. RESULTS: Peeled apples contained fewer pesticide residues at lower concentrations than unpeeled apples. However, whether samples were peeled or not, the exposure values for pesticide residues in apples never exceeded the acceptable daily intake (ADI), but ranged between 0.04% and 2.10% of the ADI in adults for food intake estimated at the 95th percentile (277 g per person per day). Determination of polyphenol, fibre, magnesium and vitamin C levels showed that the nutritional differences observed between peeled and unpeeled apples were marginal. CONCLUSION: The consumption of apples, such as the apples tested in the present study, results in an exposure to pesticides that is low for unpeeled apples, and lower for peeled apples. Moreover, there was no significant loss of nutritional value from eating peeled apples based on the nutrients investigated. © 2022 Society of Chemical Industry.

Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.

BACKGROUND: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. OBJECTIVE: The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. METHODS: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). RESULTS: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. "Track" is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). "Feature" is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables "TRACK" and "FEATURE" to store variables obtained in feature extraction and extend the OMOP CDM. CONCLUSIONS: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.

Paradoxical reactions and biologic agents: a French cohort study of 9303 patients.

BACKGROUND: Paradoxical reactions (PRs) are defined as the occurrence during biologic therapy of a pathological condition that usually responds to these drugs. OBJECTIVES: To estimate the incidence of PRs and identify risk factors. METHODS: Multicentre study of the database for the Greater Paris University Hospitals, including biologic-naive patients receiving anti-tumour necrosis factor-α, anti-interleukin-12/23, anti-interleukin-17 or anti-α4ß7-integrin agents for psoriasis, inflammatory rheumatism or inflammatory bowel disease (IBD). We used natural language processing algorithms to extract data. A cohort and a case-control study nested in the cohort with controls selected by incidence density sampling was used to identify risk factors. RESULTS: Most of the 9303 included patients (median age 43·0, 53·8% women) presented an IBD (3773, 40·6%) or a chronic inflammatory rheumatic disease (3708, 39·9%), and 8489 (91·3%) received anti-TNF-α agents. A total of 297 (3·2%) had a PR. The global incidence rate was 7·6 per 1000 person-years [95% confidence interval (CI) 6·8-8·5]. The likelihood of PR was associated with IBD [adjusted odds ratio (aOR) 1·9, 95% CI 1·1-3·2, P = 0·021] and a combination of at least two inflammatory diseases (aOR 6·1, 95% CI 3·6-10·6, P < 0·001) and was reduced with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and corticosteroids (aOR 0·6, 95% CI 0·4-0·8, P = 0·002; and OR 0·4, 95% CI 0·2-0·6, P = 0·002, respectively). CONCLUSIONS: The likelihood of PRs was associated with IBD or a combination of a least two inflammatory diseases. More studies are needed to assess the benefit of systematically adding csDMARDs for such high-risk patients. What is already known about this topic? Most published studies about paradoxical reactions concern paradoxical psoriasis in patients receiving anti-tumour necrosis factor-α agents. Few data are available for other paradoxical reactions and the most recent biologics. What does this study add? Risk of paradoxical reactions was increased with inflammatory bowel disease and a combination of at least two inflammatory diseases. Risk of paradoxical reactions was reduced with conventional synthetic disease-modifying antirheumatic drugs or corticosteroid therapy, which could be added for high-risk patients.

AP-HP Health Data Space (AHDS) to the Test of the Covid-19 Pandemic.

Sharing observational and interventional health data within a common data space enables university hospitals to leverage such data for biomedical discovery and moving towards a learning health system. OBJECTIVE: To describe the AP-HP Health Data Space (AHDS) and the IT services supporting piloting, research, innovation and patient care. METHODS: Built on three pillars - governance and ethics, technology and valorization - the AHDS and its major component, the Clinical Data Warehouse (CDW) have been developed since 2015. RESULTS: The AP-HP CDW has been made available at scale to AP-HP both healthcare professionals and public or private partners in January 2017. Supported by an institutional secured and high-performance cloud and an ecosystem of tools, mostly open source, the AHDS integrates a large amount of massive healthcare data collected during care and research activities. As of December 2021, the AHDS operates the electronic data capture for almost +840 clinical trials sponsored by AP-HP, the CDW is enabling the processing of health data from more than 11 million patients and generated +200 secondary data marts from IRB authorized research projects. During the Covid-19 pandemic, AHDS has had to evolve quickly to support administrative professionals and caregivers heavily involved in the reorganization of both patient care and biomedical research. CONCLUSION: The AP-HP Data Space is a key facilitator for data-driven evidence generation and making the health system more efficient and personalized.

Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19.

PURPOSE: The role of angiotensin receptor blockers (ARB), angiotensin-converting enzyme inhibitors (ACEi), or other antihypertensive agents in the case of Covid-19 remains controversial. We aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. METHODS: We performed a retrospective multicenter cohort study on patients hospitalized between February 1 and May 15, 2020. All patients had been followed up for at least 30 days. RESULTS: Of the 8078 hospitalized patients for Covid-19, 3686 (45.6%) had hypertension and were included in the study. In this population, the median age was 75.4 (IQR, 21.5) years and 57.1% were male. Overall in-hospital 30-day mortality was 23.1%. The main antihypertensive pharmacological classes used were calcium channel blockers (CCB) (n=1624, 44.1%), beta-blockers (n=1389, 37.7%), ARB (n=1154, 31.3%), and ACEi (n=998, 27.1%). The risk of mortality was lower in CCB (aOR, 0.83 [0.70-0.99]) and beta-blockers (aOR, 0.80 [0.67-0.95]) users and non-significant in ARB (aOR, 0.88 [0.72-1.06]) and ACEi (aOR, 0.83 [0.68-1.02]) users, compared to non-users. These results remain consistent for patients receiving CCB, beta-blocker, or ARB as monotherapies. CONCLUSION: This large multicenter retrospective of Covid-19 patients with hypertension found a reduced mortality among CCB and beta-blockers users, suggesting a putative protective effect. Our findings did not show any association between the use of renin-angiotensin-aldosterone system inhibitors and the risk of in-hospital death. Although they need to be confirmed in further studies, these results support the continuation of antihypertensive agents in patients with Covid-19, in line with the current guidelines.

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study.

BACKGROUND: In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. OBJECTIVE: The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. METHODS: We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. RESULTS: With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. CONCLUSIONS: The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.

Hydroxyzine Use and Mortality in Patients Hospitalized for COVID-19: A Multicenter Observational Study.

(1) Background: Based on its antiviral activity, anti-inflammatory properties, and functional inhibition effects on the acid sphingomyelinase/ceramide system (FIASMA), we sought to examine the potential usefulness of the H1 antihistamine hydroxyzine in patients hospitalized for COVID-19. (2) Methods: In a multicenter observational study, we included 15,103 adults hospitalized for COVID-19, of which 164 (1.1%) received hydroxyzine within the first 48 h of hospitalization, administered orally at a median daily dose of 25.0 mg (SD = 29.5). We compared mortality rates between patients who received hydroxyzine at hospital admission and those who did not, using a multivariable logistic regression model adjusting for patients' characteristics, medical conditions, and use of other medications. (3) Results: This analysis showed a significant association between hydroxyzine use and reduced mortality (AOR, 0.51; 95%CI, 0.29-0.88, p = 0.016). This association was similar in multiple sensitivity analyses. (4) Conclusions: In this retrospective observational multicenter study, the use of the FIASMA hydroxyzine was associated with reduced mortality in patients hospitalized for COVID-19. Double-blind placebo-controlled randomized clinical trials of hydroxyzine for COVID-19 are needed to confirm these results, as are studies to examine the potential usefulness of this medication for outpatients and as post-exposure prophylaxis for individuals at high risk for severe COVID-19.

Predictive factors for the duration until return to work after surgery for work-related rotator cuff syndrome: A prospective study of 92 workers.

BACKGROUND: Return to work (RTW) after surgery for a work-related rotator cuff disorder (WRRCD) is often difficult. The study's purpose was to identify preoperative factors predicting RTW. METHODS: Ninety-two workers with a WRRCD were identified by four surgeons. Before surgery, the workers completed a series of standardized questionnaires related to working conditions, health, and health beliefs. They were followed up prospectively for 20 months. Statistical analysis was based on single and multiple-factor Cox models of the duration of absence from the time of surgery until RTW. RESULTS: The median age at inclusion was 49 years (27-62), with 52 women included (57%). Sixty-one subjects (66%) were employed in highly physically demanding jobs. Forty-two (46%) stayed at work until their surgery, whereas preoperative sick leave exceeded 100 days in 20 subjects (21%). Twenty months after surgery, 14 were still not back at work. For the other participants, the mean duration until RTW was 225 days (SD 156). In the fully adjusted model, variables that were significantly predictive of the duration until RTW were: work physical demand levels, preoperative sick leave, the number of body parts causing pain or discomfort in the last 12 months, self-assessed 2-year workability, and the Readiness for RTW (RRTW) Scale. CONCLUSIONS: Several physical, psychological, and work-related factors, easily recorded, can be identified preoperatively. They may be predictive of delayed return, loss of employment, or employability as a result of shoulder surgery.

Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study.

A prior meta-analysis showed that antidepressant use in major depressive disorder was associated with reduced plasma levels of several pro-inflammatory mediators, which have been associated with severe COVID-19. Recent studies also suggest that several antidepressants may inhibit acid sphingomyelinase activity, which may prevent the infection of epithelial cells with SARS-CoV-2, and that the SSRI fluoxetine may exert in-vitro antiviral effects on SARS-CoV-2. We examined the potential usefulness of antidepressant use in patients hospitalized for COVID-19 in an observational multicenter retrospective cohort study conducted at AP-HP Greater Paris University hospitals. Of 7230 adults hospitalized for COVID-19, 345 patients (4.8%) received an antidepressant within 48 h of hospital admission. The primary endpoint was a composite of intubation or death. We compared this endpoint between patients who received antidepressants and those who did not in time-to-event analyses adjusted for patient characteristics, clinical and biological markers of disease severity, and other psychotropic medications. The primary analysis was a multivariable Cox model with inverse probability weighting. This analysis showed a significant association between antidepressant use and reduced risk of intubation or death (HR, 0.56; 95% CI, 0.43-0.73, p < 0.001). This association remained significant in multiple sensitivity analyses. Exploratory analyses suggest that this association was also significant for SSRI and non-SSRI antidepressants, and for fluoxetine, paroxetine, escitalopram, venlafaxine, and mirtazapine (all p < 0.05). These results suggest that antidepressant use could be associated with lower risk of death or intubation in patients hospitalized for COVID-19. Double-blind controlled randomized clinical trials of antidepressant medications for COVID-19 are needed.

Observational study of haloperidol in hospitalized patients with COVID-19.

BACKGROUND: Haloperidol, a widely used antipsychotic, has been suggested as potentially useful for patients with COVID-19 on the grounds of its in-vitro antiviral effects against SARS-CoV-2, possibly through sigma-1 receptor antagonist effect. METHODS: We examined the associations of haloperidol use with intubation or death and time to discharge home among adult patients hospitalized for COVID-19 at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was a composite of intubation or death and the secondary endpoint was discharge home among survivors in time-to-event analyses. In the primary analyses, we compared these two outcomes between patients receiving and not receiving haloperidol using univariate Cox regression models in matched analytic samples based on patient characteristics and other psychotropic medications. Sensitivity analyses included propensity score analyses with inverse probability weighting and multivariable Cox regression models. RESULTS: Of 15,121 adult inpatients with a positive COVID-19 PT-PCR test, 39 patients (0.03%) received haloperidol within the first 48 hours of admission. Over a mean follow-up of 13.8 days (SD = 17.9), 2,024 patients (13.4%) had a primary end-point event and 10,179 patients (77.6%) were discharged home at the time of study end on May 1st. The primary endpoint occurred in 9 patients (23.1%) who received haloperidol and 2,015 patients (13.4%) who did not. The secondary endpoint of discharge home occurred in 16 patients (61.5%) who received haloperidol and 9,907 patients (85.8%) who did not. There were no significant associations between haloperidol use and the primary (HR, 0.80; 95% CI, 0.39 to 1.62, p = 0.531) and secondary (HR, 1.30; 95% CI, 0.74 to 2.28, p = 0.355) endpoints. Results were similar in multiple sensitivity analyses. CONCLUSION: Findings from this multicenter observational study suggest that haloperidol use prescribed at a mean dose of 4.5 mg per day (SD = 5.2) for a mean duration of 8.4 days (SD = 7.2) may not be associated with risk of intubation or death, or with time to discharge home, among adult patients hospitalized for COVID-19.

Dexamethasone use and mortality in hospitalized patients with coronavirus disease 2019: A multicentre retrospective observational study.

AIMS: To examine the association between dexamethasone use and mortality among patients hospitalized for COVID-19. METHODS: We examined the association between dexamethasone use and mortality at AP-HP Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was time to death. We compared this endpoint between patients who received dexamethasone and those who did not in time-to-event analyses adjusted for patient characteristics (such as age, sex and comorbidity) and clinical and biological markers of clinical severity of COVID-19, and stratified by the need for respiratory support, i.e. mechanical ventilation or oxygen. The primary analysis was a multivariable Cox regression model. RESULTS: Of 12 217 adult patients hospitalized with a positive COVID-19 reverse transcriptase-polymerase chain reaction test, 171 (1.4%) received dexamethasone orally or by intravenous perfusion during the visit. Among patients who required respiratory support, the end-point occurred in 10/63 (15.9%) patients who received dexamethasone and 298/1129 (26.4%) patients who did not. In this group, there was a significant association between dexamethasone use and reduced mortality in the primary analysis (hazard ratio, 0.46; 95% confidence interval 0.22-0.96, P = .039). Among patients who did not require respiratory support, there was no significant association between dexamethasone use and the endpoint. CONCLUSIONS: In this multicentre observational study, dexamethasone use administered either orally or by intravenous injection at a cumulative dose between 60 mg and 150 mg was associated with reduced mortality among patients with COVID-19 requiring respiratory support.

Observational Study of Chlorpromazine in Hospitalized Patients with COVID-19.

INTRODUCTION: Chlorpromazine has been suggested as being potentially useful in patients with coronavirus disease 2019 (COVID-19) on the grounds of its potential antiviral and anti-inflammatory effects. OBJECTIVE: The aim of this study was to examine the association between chlorpromazine use and mortality among adult patients hospitalized for COVID-19. METHODS: We conducted an observational, multicenter, retrospective study at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of first prescription of chlorpromazine during hospitalization for COVID-19. The primary endpoint was death. Among patients who had not been hospitalized in intensive care units (ICUs), we compared this endpoint between those who received chlorpromazine and those who did not, in time-to-event analyses adjusted for patient characteristics, clinical markers of disease severity, and other psychotropic medications. The primary analysis used a Cox regression model with inverse probability weighting. Multiple sensitivity analyses were performed. RESULTS: Of the 14,340 adult inpatients hospitalized outside ICUs for COVID-19, 55 patients (0.4%) received chlorpromazine. Over a mean follow-up of 14.3 days (standard deviation [SD] 18.2), death occurred in 13 patients (23.6%) who received chlorpromazine and 1289 patients (9.0%) who did not. In the primary analysis, there was no significant association between chlorpromazine use and mortality (hazard ratio [HR] 2.01, 95% confidence interval [CI] 0.75-5.40; p = 0.163). Sensitivity analyses included a Cox regression in a 1:5 ratio matched analytic sample that showed a similar result (HR 1.67, 95% CI 0.91-3.06; p = 0.100) and a multivariable Cox regression that indicated a significant positive association (HR 3.10, 95% CI 1.31-7.34; p = 0.010). CONCLUSION: Our results suggest that chlorpromazine prescribed at a mean daily dose of 70.8 mg (SD 65.3) was not associated with reduced mortality.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. OBJECTIVE: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). METHODS: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. RESULTS: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. CONCLUSIONS: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

Terminologies augmented recurrent neural network model for clinical named entity recognition.

OBJECTIVE: We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. METHODS: We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and a hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (Drug names, Signs or symptoms, Diseases or disorders, Diagnostic procedures or lab tests and Therapeutic procedures). We evaluated each NER systems using exact and partial match definition of F-measure for NER. The APcNER contains 4,837 entities, which took 28 h to annotate. The inter-annotator agreement as measured by Cohen's Kappa was substantial for non-exact match (Κ = 0.61) and moderate considering exact match (Κ = 0.42). In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents, and i2b2-small a version reduced to match APcNER number of entities. RESULTS: For drug name recognition on both i2b2-2009 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75% respectively. For i2b2-small and APcNER, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 87.8% versus 85.6% and 86.4% versus 81.9% respectively. On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5% in exact match and 84.1% in non-exact match. CONCLUSION: APcNER is a French corpus for clinical-NER of five types of entities which covers a large variety of document types. The extension of the supervised model with terminology has allowed an easy increase in performance, especially for rare entities, and established near state of the art results on the i2b2-2009 corpus.

Initializing a hospital-wide data quality program. The AP-HP experience.

BACKGROUND AND OBJECTIVES: Data Quality (DQ) programs are recognized as a critical aspect of new-generation research platforms using electronic health record (EHR) data for building Learning Healthcare Systems. The AP-HP Clinical Data Repository aggregates EHR data from 37 hospitals to enable large-scale research and secondary data analysis. This paper describes the DQ program currently in place at AP-HP and the lessons learned from two DQ campaigns initiated in 2017. MATERIALS AND METHODS: As part of the AP-HP DQ program, two domains - patient identification (PI) and healthcare services (HS) - were selected for conducting DQ campaigns consisting of 5 phases: defining the scope, measuring, analyzing, improving and controlling DQ. Semi-automated DQ profiling was conducted in two data sets - the PI data set containing 8.8 M patients and the HS data set containing 13,099 consultation agendas and 2122 care units. Seventeen DQ measures were defined and DQ issues were classified using a unified DQ reporting framework. For each domain, actions plans were defined for improving and monitoring prioritized DQ issues. RESULTS: Eleven identified DQ issues (8 for the PI data set and 3 for the HS data set) were categorized into completeness (n = 6), conformance (n = 3) and plausibility (n = 2) DQ issues. DQ issues were caused by errors from data originators, ETL issues or limitations of the EHR data entry tool. The action plans included sixteen actions (9 for the PI domain and 7 for the HS domain). Though only partial implementation, the DQ campaigns already resulted in significant improvement of DQ measures. CONCLUSION: DQ assessments of hospital information systems are largely unpublished. The preliminary results of two DQ campaigns conducted at AP-HP illustrate the benefit of the engagement into a DQ program. The adoption of a unified DQ reporting framework enables the communication of DQ findings in a well-defined manner with a shared vocabulary. Dedicated tooling is needed to automate and extend the scope of the generic DQ program. Specific DQ checks will be additionally defined on a per-study basis to evaluate whether EHR data fits for specific uses.

Burden of Migraine in Europe Using Self-Reported Digital Diary Data from the Migraine Buddy© Application.

INTRODUCTION: Migraine is a neurological disease characterized by recurring attacks that can cause severe disabling pain. This study described the burden of migraine as reported by individuals with migraine in the real world using a mobile application. METHODS: A retrospective, cross-sectional analysis was conducted using data captured through the Migraine Buddy© smartphone application from adult, self-diagnosed individuals with migraine in 17 European countries. Data were analyzed descriptively for the most recent 28-day period reported by users (n = 3900) during the study period (June 2015-July 2016) who were randomly selected on the basis of data completeness (completion rates > 70%) and stratified by migraine headache days/month: 4-7 episodic migraine (EM; n = 1500), 8-14 EM (n = 1500), and chronic migraine (≥ 15; CM; n = 900). RESULTS: More than 95% of users reported that migraine negatively affected their daily activities during at least one migraine attack. Attacks affected 50.5% (184.4 days/year), 26.9% (98 days/year), and 14.5% (53 days/year) of the year among CM, 8-14 EM, and 4-7 EM groups, respectively. On average, 44.8% CM, 40.9% 8-14 EM, and 34.7% of 4-7 EM sufferers, respectively, reported anxiety and/or depression symptoms during migraine attacks. Social or home activities, productivity, and sleep were highly affected, regardless of migraine frequency. Employed respondents (n = 3106) reported an average of 2.3 workdays missed per month and that at least one in four migraines led to work absenteeism; these migraines were commonly reported to have at least moderate to severe levels of pain, corresponding to the inability of persons to perform some or even any activities. Triptans (68%), opioids (46%), and nonsteroidal anti-inflammatory drugs (45%) were self-reported as the most common medicines used. CONCLUSIONS: This study, leveraging patient-reported data collected through a mobile application, demonstrates the high burden and impact of migraine on health-related quality of life, work productivity, and overall well-being of individuals suffering from migraines. FUNDING: Novartis Pharma AG, Switzerland.