Mining electronic health records for drugs associated with 28-days mortality in COVID-19: a pharmacopoeia wide association study (PharmWAS)

International audienceBackground 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

Initial management of diabetic ketoacidosis and prognosis according to diabetes type: a French multicentre observational retrospective study

International audienceBACKGROUND: Guidelines for the management of diabetic ketoacidosis (DKA) do not consider the type of underlying diabetes. We aimed to compare the occurrence of metabolic adverse events and the recovery time for DKA according to diabetes type.METHODS: Multicentre retrospective study conducted at five adult intermediate and intensive care units in Paris and its suburbs, France. All patients admitted for DKA between 2013 and 2014 were included. Patients were grouped and compared according to the underlying type of diabetes into three groups: type 1 diabetes, type 2 or secondary diabetes, and DKA as the first presentation of diabetes. Outcomes of interest were the rate of metabolic complications (hypoglycaemia or hypokalaemia) and the recovery time.RESULTS: Of 122 patients, 60 (49.2%) had type 1 diabetes, 28 (22.9%) had type 2 or secondary diabetes and 34 (27.9%) presented with DKA as the first presentation of diabetes (newly diagnosed diabetes). Despite having received lower insulin doses, hypoglycaemia was more frequent in patients with type 1 diabetes (76.9%) than in patients with type 2 or secondary diabetes (50.0%) and in patients with newly diagnosed diabetes (54.6%) (p = 0.026). In contrast, hypokalaemia was more frequent in the latter group (82.4%) than in patients with type 1 diabetes (57.6%) and type 2 or secondary diabetes (51.9%) (p = 0.022). The median recovery times were not significantly different between groups.CONCLUSIONS: Rates of metabolic complications associated with DKA treatment differ significantly according to underlying type of diabetes. Decreasing insulin dose may limit those complications. DKA treatment recommendations should take into account the type of diabetes

Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic

[EN] Importance The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents. Objective To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic. Design, Setting, and Participants This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children¿s hospitals in the US and France. Main Outcomes and Measures Change in the monthly proportion of mental health condition¿associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis. Results There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11¿101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health¿associated hospitalizations following onset of the pandemic compared with the prepandemic period. Conclusions and Relevance In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children¿s hospitals to care for adolescents with mental health conditions during the pandemic and beyond.Ms Hutch is supported by grant NLM 5T32LM012203-05 from the National Library of Medicine. Dr Aronow is supported by U24 HL148865 from the National Heart, Lung, and Blood Institute (NHLBI), NIH. Dr Cai is supported by R01 HL089778 from the NHLBI, NIH. Dr Hanauer is supported by UL1TR002240 from the National Center for Advancing Translational Sciences (NCATS), NIH. Dr Luo is supported by U01TR003528 from the NCATS, NIH, and 1R01LM013337 from the National Library of Medicine. Dr Sanchez-Pinto is supported by R01HD105939 from the National Institute of Child Health and Human Development, NIH. Dr South is supported by K23HL148394 and L40HL148910 from the NHLBI, NIH, and UL1TR001420 from the NCATS, NIH. Dr Visweswaran is supported by UL1TR001857 from the NCATS, NIH. Dr Xia is supported by R01NS098023 and R01NS124882 from the National Institute of Neurological Disorders and Stroke, NIH.Gutiérrez-Sacristán, A.; Serret-Larmande, A.; Hutch, MR.; Sáez Silvestre, C.; Aronow, BJ.; Bhatnagar, S.; Bonzel, C.... (2022). Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. Jama Network Open. 5(12):1-12. https://doi.org/10.1001/jamanetworkopen.2022.4654811251

Increased levels of GM-CSF and CXCL10 and low CD8+ memory stem T Cell count are markers of immunosenescence and severe COVID-19 in older people

International audienceAbstract Background Ageing leads to altered immune responses, resulting in higher susceptibility to certain infections in the elderly. Immune ageing is a heterogeneous process also associated with inflammaging, a low-grade chronic inflammation. Altered cytotoxic T cell responses and cytokine storm have previously been described in severe COVID-19 cases, however the parameters responsible for such immune response failures are not well known. The aim of our study was to characterize CD8 + T cells and cytokines associated with ageing, in a cohort of patients aged over 70 years stratified by COVID-19 severity. Results One hundred and four patients were included in the study. We found that, in older people, COVID-19 severity was associated with (i) higher level of GM-CSF, CXCL10 (IP-10), VEGF, IL-1β, CCL2 (MCP-1) and the neutrophil to lymphocyte ratio (NLR), (ii) increased terminally differentiated CD8 + T cells, and (ii) decreased early precursors CD8 + T stem cell-like memory cells (TSCM) and CD27 + CD28 + . The cytokines mentioned above were found at higher concentrations in the COVID-19 + older cohort compared to a younger cohort in which they were not associated with disease severity. Conclusions Our results highlight the particular importance of the myeloid lineage in COVID-19 severity among older people. As GM-CSF and CXCL10 were not associated with COVID-19 severity in younger patients, they may represent disease severity specific markers of ageing and should be considered in older people care

Real-life efficacy of immunotherapy for Sézary syndrome: a multicenter observational cohort study

International audienceBackground: Sézary syndrome is an extremely rare and fatal cutaneous T-cell lymphoma (CTCL). Mogamulizumab, an anti-CCR4 monoclonal antibody, has recently been associated with increased progression-free survival in a randomized clinical trial in CTCL. We aimed to evaluate OS and prognostic factors in Sézary syndrome, including treatment with mogamulizumab, in a real-life setting.Methods: Data from patients with Sézary (ISCL/EORTC stage IV) and pre-Sézary (stage IIIB) syndrome diagnosed from 2000 to 2020 were obtained from 24 centers in Europe. Age, disease stage, plasma lactate dehydrogenases levels, blood eosinophilia at diagnosis, large-cell transformation and treatment received were analyzed in a multivariable Cox proportional hazard ratio model. This study has been registered in ClinicalTrials (SURPASSe01 study: NCT05206045).Findings: Three hundred and thirty-nine patients were included (58% men, median age at diagnosis of 70 years, Q1-Q3, 61-79): 33 pre-Sézary (9.7% of 339), 296 Sézary syndrome (87.3%), of whom 10 (2.9%) had large-cell transformation. One hundred and ten patients received mogamulizumab. Median follow-up was 58 months (95% confidence interval [CI], 53-68). OS was 46.5% (95% CI, 40.6%-53.3%) at 5 years. Multivariable analysis showed that age ≥ 80 versus <50 (HR: 4.9, 95% CI, 2.1-11.2, p = 0.001), and large-cell transformation (HR: 2.8, 95% CI, 1.6-5.1, p = 0.001) were independent and significant factors associated with reduced OS. Mogamulizumab treatment was significantly associated with decreased mortality (HR: 0.34, 95% CI, 0.15-0.80, p = 0.013).Interpretation: Treatment with mogamulizumab was significantly and independently associated with decreased mortality in Sézary syndrome

Real-life efficacy of immunotherapy for Sézary syndrome: a multicenter observational cohort studyResearch in context

Summary: Background: Sézary syndrome is an extremely rare and fatal cutaneous T-cell lymphoma (CTCL). Mogamulizumab, an anti-CCR4 monoclonal antibody, has recently been associated with increased progression-free survival in a randomized clinical trial in CTCL. We aimed to evaluate OS and prognostic factors in Sézary syndrome, including treatment with mogamulizumab, in a real-life setting. Methods: Data from patients with Sézary (ISCL/EORTC stage IV) and pre-Sézary (stage IIIB) syndrome diagnosed from 2000 to 2020 were obtained from 24 centers in Europe. Age, disease stage, plasma lactate dehydrogenases levels, blood eosinophilia at diagnosis, large-cell transformation and treatment received were analyzed in a multivariable Cox proportional hazard ratio model. This study has been registered in ClinicalTrials (SURPASSe01 study: NCT05206045). Findings: Three hundred and thirty-nine patients were included (58% men, median age at diagnosis of 70 years, Q1-Q3, 61–79): 33 pre-Sézary (9.7% of 339), 296 Sézary syndrome (87.3%), of whom 10 (2.9%) had large-cell transformation. One hundred and ten patients received mogamulizumab. Median follow-up was 58 months (95% confidence interval [CI], 53–68). OS was 46.5% (95% CI, 40.6%–53.3%) at 5 years. Multivariable analysis showed that age ≥ 80 versus <50 (HR: 4.9, 95% CI, 2.1–11.2, p = 0.001), and large-cell transformation (HR: 2.8, 95% CI, 1.6–5.1, p = 0.001) were independent and significant factors associated with reduced OS. Mogamulizumab treatment was significantly associated with decreased mortality (HR: 0.34, 95% CI, 0.15–0.80, p = 0.013). Interpretation: Treatment with mogamulizumab was significantly and independently associated with decreased mortality in Sézary syndrome. Funding: French Society of Dermatology, Swiss National Science Foundation (IZLIZ3_200253/1) and SKINTEGRITY.CH collaborative research program

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

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Retrospective cohort study. The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ″ever-severe″ or ″never-severe″ using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models

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

International audienceAbstract 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