Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Histological characterization of liver involvement in systemic mastocytosis.

BACKGROUND AND AIMS: Systemic mastocytosis (SM) is characterized by the accumulation of atypical mast cells (MCs) in organs. Liver histology of SM has been marginally described and accurate histological classification is critical, given the consequences of aggressive SM diagnosis. We aimed to describe the histological features associated with liver SM using updated tools. METHODS: Using the database of the French Reference Centre for Mastocytosis, we retrospectively identified patients with a liver biopsy (LB) and a diagnosis of SM. All LB procedures were performed according to the local physician in charge and centrally reviewed by an expert pathologist. RESULTS: A total of 28 patients were included: 6 had indolent SM, 9 had aggressive SM, and 13 had SM with an associated hematologic neoplasm. Twenty-five (89%) patients presented hepatomegaly, and 19 (68%) had portal hypertension. The LB frequently showed slight sinusoid dilatation (82%). Fibrosis was observed in 3/6 indolent SM and in almost all advanced SM cases (21/22), but none of them showed cirrhosis. A high MC burden (>50 MCs/high-power field) was correlated with elevated blood alkaline phosphatase levels (p = .030). The presence of portal hypertension was associated with a higher mean fibrosis grade (1.6 vs. 0.8 in its absence; p = .026). In advanced SM, the presence of nodular regenerative hyperplasia (NRH) was associated with decreased overall survival (9.5 vs. 46.3 months, p = .002). CONCLUSIONS: MC infiltration induced polymorphic hepatic lesions and the degree of fibrosis is associated with portal hypertension. NRH identifies a poor prognosis subgroup of patients with advanced SM. Assessing liver histology can aid in SM prognostic evaluation.

TAXN: Translate Align Extract Normalize, a Multilingual Extraction Tool for Clinical Texts.

Several studies have shown that about 80% of the medical information in an electronic health record is only available through unstructured data. Resources such as medical terminologies in languages other than English are limited and restrain the NLP tools. We propose here to leverage English based resources in other languages using a combination of translation, word alignment, entity extraction and term normalization (TAXN). We implement this extraction pipeline in an open-source library called "medkit". We demonstrate the interest of this approach through a specific use-case: enriching a phenotypic dictionary for post-acute sequelae in COVID-19 (PASC). TAXN proved to be efficient to propose new synonyms of UMLS terms using a corpus of 70 articles in French with 356 terms enriched with at least one validated new synonym. This study was based on freely available deep-learning models.

Pathophysiologic implications of elevated prevalence of hereditary alpha-tryptasemia in all mastocytosis subtypes.

BACKGROUND: Mastocytosis and monoclonal mast cell (MC) activation syndrome (MMAS) are heterogeneous conditions characterized by the accumulation of atypical MCs. Despite the recurrent involvement of KIT mutations, the pathophysiologic origin of mastocytosis and MMAS is unclear. Although hereditary α-tryptasemia (HαT, related to TPSAB1 gene duplication) is abnormally frequent in these diseases, it is not known whether the association is coincidental or causal. OBJECTIVE: We evaluated the prevalence of HαT in all mastocytosis subtypes and MMAS and assessed the pathophysiologic association with HαT. METHODS: Clinical data, laboratory data, KIT mutations, TPSAB1 duplication (assessed by droplet digital PCR), and HαT prevalence were retrospectively recorded for all patients with mastocytosis and MMAS registered in the French national referral center database and compared to a control cohort. To increase the power of our analysis for advanced systemic mastocytosis (advSM), we pooled our cohort with literature cases. RESULTS: We included 583 patients (27 with MMAS and 556 with mastocytosis). The prevalence of HαT in mastocytosis was 12.6%, significantly higher than in the general population (5.7%, P = .002) and lower than in MMAS (33.3%, P = .02). HαT+ patients were more likely to have anaphylactic reactions and less likely to have cutaneous lesions than HαT- patients (43.0% vs 24.4%, P = .006; 57.7% vs 75.6%, respectively, P = .006). In the pooled analysis, the prevalence of HαT was higher in advSM (11.5%) than in control cohorts (5.2%, P = .01). CONCLUSION: Here we confirm the increase incidence of anaphylaxis in HαT+ mastocytosis patients. The increased prevalence of HαT in all subtypes of systemic mastocytosis (including advSM) is suggestive of pathophysiologic involvement.

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome.

OBJECTIVE: The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years. METHODS: We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance. RESULTS: A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS. SIGNIFICANCE: The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.

The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials.

In medical research, the traditional way to collect data, i.e. browsing patient files, has been proven to induce bias, errors, human labor and costs. We propose a semi-automated system able to extract every type of data, including notes. The Smart Data Extractor pre-populates clinic research forms by following rules. We performed a cross-testing experiment to compare semi-automated to manual data collection. 20 target items had to be collected for 79 patients. The average time to complete one form was 6'81" for manual data collection and 3'22" with the Smart Data Extractor. There were also more mistakes during manual data collection (163 for the whole cohort) than with the Smart Data Extractor (46 for the whole cohort). We present an easy to use, understandable and agile solution to fill out clinical research forms. It reduces human effort and provides higher quality data, avoiding data re-entry and fatigue induced errors.

Hyperventilation syndrome in children with asthma.

BACKGROUND: Hyperventilation syndrome (HVS) may be associated with asthma. In the absence of a gold standard diagnosis for children, its impact on asthma has been rarely assessed. OBJECTIVE: To assess the impact of HVS on the symptoms and lung function of children with asthma and determine the diagnostic value of the Nijmegen questionnaire in comparison to a hyperventilation test (HVT). METHODS: Data from asthmatic children followed in the department of Pediatric Pulmonology of Necker Hospital and explored for HVS were retrospectively analyzed. HVS was diagnosed by a positive HVT. Asthma exacerbations, control and lung function were assessed in children with or without a positive HVT. The sensitivity and specificity of the Nijmegen questionnaire were determined relative to the positivity of a HVT. The Nijmegen questionnaire threshold was ≥23. RESULTS: Data from 112 asthmatic children, median age 13.9 years [11.6-16], were analyzed. Twenty-eight children (25%) had mild or moderate asthma and 84 (75%) severe asthma. The HVT was performed on 108 children and was negative for 34 (31.5%) and positive for 74 (68.5%). The number of asthma exacerbations in the past 12 months, Asthma Control Test (ACT) score, and lung function did not differ between children with a positive HVT and a negative HVT. The Nijmegen questionnaire was administered to 103 children. Its sensitivity was 56.3% and specificity 56.3%. CONCLUSION: The symptoms and lung function of adolescents with asthma are not affected by the presence of HVS. The sensitivity and specificity of the Nijmegen questionnaire are low.

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.

Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France.

Background: Prior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19 or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders has yet to be evaluated. Methods: We performed an observational, multicenter, retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals. Results: Of 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up period of 14.6 days (SD = 17.9), 326 of 857 (38.0%) patients with a diagnosis of psychiatric disorder died compared with 1276 of 14,311 (8.9%) patients without such a diagnosis (odds ratio 6.27, 95% CI 5.40-7.28, p < .01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (adjusted odds ratio 3.27, 95% CI 2.78-3.85, p < .01). However, additional adjustments for obesity and number of medical conditions resulted in a nonsignificant association (adjusted odds ratio 1.02, 95% CI 0.84-1.23, p = .86). Exploratory analyses after the same adjustments suggested that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants. Conclusions: These findings suggest that the increased risk of COVID-19-related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population and not by the underlying psychiatric disease.

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study.

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section.

Adalimumab Therapy in Pediatric Crohn Disease: A 2-Year Follow-Up Comparing "Top-Down" and "Step-Up" Strategies.

OBJECTIVES: European Crohn's Colitis Organization (ECCO) and the European Society of Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) guidelines recommend the early use of anti-tumor necrosis factor (TNF) biologicals in pediatric Crohn disease (CD) patients with positive predictors for poor outcome. The objective of the present study was to compare early "Top-Down" use of adalimumab (ADA) immunomodulator/biologics-naive patients to conventional "Step-Up" management. METHODS: One hundred and twenty consecutive patients with a confirmed diagnosis of CD and treated with ADA between 2008 and 2019 were included and allocated to the ADA-Top Down (n = 59) or ADA-Step Up group (n = 61). The primary endpoint was prolonged steroid-/enteral nutrition-free clinical remission at 24 months, defined by a weighted Pediatric Crohn's Disease Activity Index (wPCDAI) < 12.5. Clinical and biological data were collected at 12 and 24 months. RESULTS: At start of ADA, disease activity was comparable between the ADA-Top Down group and the ADA-Step Up group (wPCDAI = 31 ± 16 vs 31.3 ± 15.2, respectively, P = 0.84). At 24 months, the remission rate was significantly higher in the ADA-Top Down group (73% vs 51%, P < 0.01). After propensity score, the Top-Down strategy is still more effective than the Step-Up strategy in maintaining remission at 24 months [hazard ratio (HR) = 0.36, 95% CI (0.15-0.87), P = 0.02]. Patients in the ADA-Top Down group were mainly on monotherapy compared to patients in the ADA-Step Up group (53/55 vs 28/55 respectively, P < 0.001). Serum levels of ADA were higher in the ADA-Top Down group than in the ADA-Step Up group (12.8 ± 4.3 vs 10.4 ± 3.9 µg/mL, respectively, P < 0.01). There were no serious adverse events. CONCLUSIONS: Early use of ADA appears to be more effective in maintaining relapse-free remission at 2 years, while using it as monotherapy. These findings further favor the recommendation of early anti-TNF use in high-risk CD patients.

Allograft function and muscle mass evolution after kidney transplantation.

BACKGROUND: Advanced chronic kidney disease is associated with muscle wasting, but how glomerular filtration rate (GFR) recovery after kidney transplantation is associated with muscle mass is unknown. METHODS: We took advantage of the simultaneous measurement of GFR (using iohexol plasma clearance; ioGFR) and creatinine excretion rate (a surrogate marker of muscle mass; CER) performed 3 months after transplantation and at a later time point at our institution to investigate the interplay between allograft function, muscle mass, and outcome in kidney transplant recipients. RESULTS: Between June 2005 and October 2019, 1319 successive kidney transplant recipients (mean age 50.4 ± 14.6; 38.7% female) underwent GFR measurement at our institution 3 months after kidney transplantation. CER (CER3 ) and ioGFR (ioGFR3 ) were 7.7 ± 2.6 µmol/min and 53 ± 17.1 mL/min/1.73 m2 , respectively. Multivariable analysis identified female gender, older donor and recipient age, reduced body mass index, coronary disease, dialysis history, proteinuria, and reduced ioGFR3 as independent predictors of low CER3 (ioGFR3 : ß coefficient 0.19 [95% confidence interval 0.14 to 0.24]). A total of 1165 patients had a subsequent CER measurement after a median follow-up of 9.5 months. Of them, 373 (32%) experienced an increase in CER > 10%, while 222 (19%) showed a CER decrease of more than 10%. Multivariable analysis adjusted for CER3 and other confounders identified ioGFR3 as an independent predictor of CER at follow-up (ß coefficient 0.11 [95% confidence interval 0.07 to 0.16]). In multivariable Cox analysis, reduced CER at 3 months or at follow-up were consistently associated with mortality (hazard ratio [95% confidence interval] at 3 months: 0.82 [0.74 to 0.91]; at follow-up: 0.79 [0.69 to 0.99]) but not with graft loss. CONCLUSIONS: Glomerular filtration rate recovery is a determinant of muscle mass variation after kidney transplantation. Early interventions targeting muscle mass gain may be beneficial for kidney transplant recipients.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse.

Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Using Deep Learning to Improve Phenotyping from Clinical Reports.

With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children's hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients.

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09-1.55), heart failure (RR 1.22, 95% CI 1.10-1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07-1.31), and fatigue (RR 1.18, 95% CI 1.07-1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58-2.76), venous embolism (RR 1.34, 95% CI 1.17-1.54), atrial fibrillation (RR 1.30, 95% CI 1.13-1.50), type 2 diabetes (RR 1.26, 95% CI 1.16-1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09-1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90-3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21-2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04-1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2.

OBJECTIVE: To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS: This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. RESULTS: Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). CONCLUSIONS: Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

[This corrects the article DOI: 10.2196/35190.].

Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

BACKGROUND: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. OBJECTIVE: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. METHODS: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. RESULTS: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. CONCLUSIONS: Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.