Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium.

PURPOSE: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. METHODS: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. RESULTS: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). CONCLUSION: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Evaluating the Relevance of Virtual Drugs.

Information about drugs is numerous and varied, and many drugs can share the same information. Grouping drugs that have common characteristics can be useful to avoid redundancy and facilitate interoperability. Our work focused on the evaluation of the relevance of classes allowing this type of grouping: the "Virtual Drug". Thus, in this paper, we describe the process of creating this class from the data of the French Public Drug Database, which is then evaluated against the codes of the Anatomical Therapeutic Chemical classification associated with the drugs. Our evaluation showed that 99.55% of the "Virtual Drug" classes have a good intra-class consistency.

Data Element Mapping in the Data Privacy Era.

Secondary use of health data is made difficult in part because of large semantic heterogeneity. Many efforts are being made to align local terminologies with international standards. With increasing concerns about data privacy, we focused here on the use of machine learning methods to align biological data elements using aggregated features that could be shared as open data. A 3-step methodology (features engineering, blocking strategy and supervised learning) was proposed. The first results, although modest, are encouraging for the future development of this approach.

Automatic phenotyping of electronical health record: PheVis algorithm.

Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Phenorm, an automated unsupervised algorithm to identify patient medical conditions from EHR data, has been developed. PheVis extends PheNorm at the visit resolution. PheVis combines diagnosis codes together with medical concepts extracted from medical notes, incorporating past history in a machine learning approach to provide an interpretable parametric predictor of the occurrence probability for a given medical condition at each visit. PheVis is applied to two real-world use-cases using the datawarehouse of the University Hospital of Bordeaux: i) rheumatoid arthritis, a chronic condition; ii) tuberculosis, an acute condition. Cross-validated AUROC were respectively 0.943 [0.940; 0.945] and 0.987 [0.983; 0.990]. Cross-validated AUPRC were respectively 0.754 [0.744; 0.763] and 0.299 [0.198; 0.403]. PheVis performs well for chronic conditions, though absence of exclusion of past medical history by natural language processing tools limits its performance in French for acute conditions. It achieves significantly better performance than state-of-the-art unsupervised methods especially for chronic diseases.

Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning.

INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons. RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively. CONCLUSION: In this study, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science.

OBJECTIVES: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. METHODS: PubMed searches of 2019 literature concerning public health and epidemiology informatics were conducted and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the Editorial Committee a curated selection of the best papers. RESULTS: Among the 835 references retrieved from PubMed, two were finally selected as best papers. The first best paper leverages satellite images and deep learning to identify remote rural communities in low-income countries; the second paper describes the development of a worldwide human disease surveillance system based on near real-time news data from the GDELT project. Internet data and electronic health records are still widely used to detect and monitor disease activity. Identifying and targeting specific audiences for public health interventions is a growing subject of interest. CONCLUSIONS: The ever-increasing amount of data available offers endless opportunities to develop methods and tools that could assist public health surveillance and intervention belonging to the growing field of public health Data Science. The transition from proofs of concept to real world applications and adoption by health authorities remains a difficult leap to make.

Romedi: An Open Data Source About French Drugs on the Semantic Web.

The W3C project, "Linking Open Drug Data" (LODD), linked several publicly available sources of drug data together. So far, French data, like marketed drugs and their summary of product characteristics, were not integrated and remained difficult to query. In this paper, we present Romedi (Référentiel Ouvert du Médicament), an open dataset that links French data on drugs to international resources. The principles and standard recommendations created by the W3C for sharing information were adopted. Romedi was connected to the Unified Medical Language System and DrugBank, two central resources of the LODD project. A SPARQL endpoint is available to query Romedi and services are provided to annotate textual content with Romedi terms. This paper describes its content, its services, its links to external resources, and expected future developments.

SmartCRF: A Prototype to Visualize, Search and Annotate an Electronic Health Record from an i2b2 Clinical Data Warehouse.

Clinical information in electronic health records (EHRs) is mostly unstructured. With the ever-increasing amount of information in patients' EHRs, manual extraction of clinical information for data reuse can be tedious and time-consuming without dedicated tools. In this paper, we present SmartCRF, a prototype to visualize, search and ease the extraction and structuration of information from EHRs stored in an i2b2 data warehouse.

Artificial Intelligence for Surveillance in Public Health.

OBJECTIVES: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. METHODS: The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. RESULTS: Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. CONCLUSIONS: Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge.

Risk of Drug-Drug Interactions in Out-Hospital Drug Dispensings in France: Results From the DRUG-Drug Interaction Prevalence Study.

Introduction: Drug interactions could account for 1% of hospitalizations in the general population and 2-5% of hospital admissions in the elderly. However, few data are available on the drugs concerned and the potential severity of the interactions encountered. We thus first aimed to estimate the prevalence of dispensings including drugs Contraindicated or Discommended because of Interactions (CDI codispensings) and to identify the most frequently involved drug pairs. Second, we aimed to investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved. Methods: We carried out a study using a random sample of all drugs dispensings registered in a database of the French Health Insurance System between 2010 and 2015. The distribution of the drugs involved was described considering active principles, detailing the 20 most frequent ones for both contraindicated or discommended codispensings (DCs). To investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved, we developed a specific indicator, the Drug-drug interaction prevalence study-score (DIPS-score), that compares for each drug pair the observed frequency of codispensing to its expected probability. The latter is determined considering the frequencies of dispensings of the individual drugs constituting a pair of interest. Results: We analyzed 6,908,910 dispensings: 13,196 (0.2%) involved contraindicated codispensings (CCs), and 95,410 (1.4%) DCs. For CCS, the most frequently involved drug pair was "bisoprolol+flecainide" (n = 5,036); four out of five of the most represented pairs involved cardiovascular drugs. For DCS, the most frequently involved drug pair was "ramipril+spironolactone" (n = 4,741); all of the five most represented pairs involved cardiovascular drugs. The drug pair involved in the CC with the highest score value was "citalopram+hydroxyzine" (DIPS-score: 3.7; 2.9-4.6); that with the lowest score was "clarithromycin+simvastatin" (DIPS-score: 0.2; 0.2-0.3). DIPS-score median value was 0.4 for CCs and 0.6 for DCs. Conclusion: This high prevalence of CDI codispensings enforces the need for further risk-prevention actions regarding drug-drug interactions (DDIs), especially for arrhythmogenic or anti-arrhythmic drugs. In this perspective, the DIPS-score we develop could ease identifying the interactions that are poorly considered by clinicians/pharmacists and targeting interventions.

FOLFIRINOX-based neoadjuvant chemoradiotherapy for borderline and locally advanced pancreatic cancer: A pilot study from a tertiary centre.

BACKGROUND: Neoadjuvant chemoradiotherapy, potentially relevant to increase resection rate in pancreatic cancer, is still debated. AIMS: To assess tolerance, resection rate and outcomes of patients with non-metastatic pancreatic ductal adenocarcinoma treated by concomitant chemoradiotherapy. METHODS: This monocentric study included all consecutive patients treated from 2010 to 2014 for non-metastatic pancreatic adenocarcinoma. Chemotherapy was followed by chemoradiotherapy in operable patients, surgical resectability being assessed by CT-scan. RESULTS: Seventy-nine patients were included: 41 patients had borderline and 38 locally advanced tumours. All patients were treated by chemotherapy (FOLFIRINOX), followed by chemoradiotherapy (median dose: 59 Gy, range 45-66 Gy) for 94% of patients. Thirty-seven patients (47%) could subsequently benefit from surgery with a complete R0 resection in 94% of cases, with a postoperative mortality of 5%. Median overall survival was 21.5 months (median follow-up: 48.8 months). Local control, overall and disease-free survival were significantly higher for patients who underwent resection compared to others, with 89.2% vs 59.5% (p = 0.01), 49.7 vs 17.4 months (p < 0.01) and 25.5 vs 9.2 months (p < 0.01), respectively. CONCLUSION: Neoadjuvant treatment consisting of FOLFIRINOX chemotherapy followed by chemoradiotherapy is an efficient strategy for patients with borderline and locally advanced pancreatic cancer, resulting in a 43% rate of secondary complete surgical resection associated with high local control, overall and disease-free survival.

A new approach for sample size calculation in cost-effectiveness studies based on value of information.

BACKGROUND: Value of information is now recognized as a reference method in the decision process underpinning cost-effectiveness evaluation. The expected value of perfect information (EVPI) is the expected value from completely reducing the uncertainty surrounding the cost-effectiveness of an innovative intervention. Among sample size calculation methods used in cost-effectiveness studies, only one is coherent with this decision framework. It uses a Bayesian approach and requires data of a pre-existing cost-effectiveness study to derive a valid prior EVPI. When evaluating the cost-effectiveness of innovations, no observed prior EVPI is usually available to calculate the sample size. We here propose a sample size calculation method for cost-effectiveness studies, that follows the value of information theory, and, being frequentist, can be based on assumptions if no observed prior EVPI is available. METHODS: The general principle of our method is to define the sampling distribution of the incremental net monetary benefit (ΔB), or the distribution of ΔB that would be observed in a planned cost-effectiveness study of size n. Based on this sampling distribution, the EVPI that would remain at the end of the trial (EVPIn) is estimated. The optimal sample size of the planned cost-effectiveness study is the n for which the cost of including an additional participant becomes equal or higher than the value of the information gathered through this inclusion. RESULTS: Our method is illustrated through four examples. The first one is used to present the method in depth and describe how the sample size may vary according to the parameters' value. The three other examples are used to illustrate in different situations how the sample size may vary according to the ceiling cost-effectiveness ratio, and how it compares with a test statistic-based method. We developed an R package (EBASS) to run these calculations. CONCLUSIONS: Our sample size calculation method follows the value of information theory that is now recommended for analyzing and interpreting cost-effectiveness data, and sets the size of a study that balances its cost and the value of its information.

Histologic analysis has a prognostical value in colorectal biopsies assessed for suspicion of graft-versus-host disease.

Gastrointestinal (GI) graft-versus-host-disease (GVHD) is a common and severe complication of allogeneic hematopoietic stem cell transplantation, but clinical and histological features are unspecific. The aim of this study was to correlate the histological GI GVHD grade with the clinical outcomes. In a retrospective study of 112 patients with clinically suspected GI GVHD, colonic biopsies were reviewed by three pathologists without knowledge of the corresponding clinical data and classified in four scores, according to the NIH Consensus Project recommendations: no GVHD, possible, probable, and unequivocal GVHD. At the end of the study, the histological and clinical data were confronted with the following results: clinical diagnosis of GI GVHD was established for 70 patients (62.5%) and histological scores correlated well with the clinical diagnosis (p < 0.001) and particularly with the prognosis (p < 0.05).When severe lesions were observed, the 1 year overall survival declined to 9%. None of the features reported in the literature to support GVHD diagnosis, eosinophil count, endocrine cells aggregate, immunohistochemical analysis (cytomegalovirus, CD123, chromogranin), did not help us for diagnosis. So routine histopathology alone without immunohistochemistry is a strong and reproducible tool to diagnose GI GVHD with the help of clinical and biological information, and most importantly, histological grading proved to be a powerful prognostic value.

RAVEL: retrieval and visualization in ELectronic health records.

Because of the ever-increasing amount of information in patients' EHRs, healthcare professionals may face difficulties for making diagnoses and/or therapeutic decisions. Moreover, patients may misunderstand their health status. These medical practitioners need effective tools to locate in real time relevant elements within the patients' EHR and visualize them according to synthetic and intuitive presentation models. The RAVEL project aims at achieving this goal by performing a high profile industrial research and development program on the EHR considering the following areas: (i) semantic indexing, (ii) information retrieval, and (iii) data visualization. The RAVEL project is expected to implement a generic, loosely coupled to data sources prototype so that it can be transposed into different university hospitals information systems.