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.

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-1ß, 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.

A methodological review of population-adjusted indirect comparisons reveals inconsistent reporting and suggests publication bias.

OBJECTIVES: Population-adjusted indirect comparisons (PAICs) were developed in the 2010s to allow for comparisons between two treatments evaluated in different trials while accounting for differences in patient characteristics if individual patient data (IPD) are available for only one trial. Such comparisons are increasingly used in market access applications when a pharmaceutical company compares its new treatment (with IPD available) to another treatment developed by a competitor (with only aggregated data available). This study aimed to describe the characteristics of these PAICs, assess their methodology, and describe the reported results. STUDY DESIGN AND SETTING: Original articles reporting the use of at least one PAIC were searched on PubMed between January 1, 2010 and April 2, 2022. Two reviewers independently selected articles and extracted data. RESULTS: We included 133 publications reporting the results of 288 PAICs. Half of the articles were published on or after May 7, 2020, and 71 (53%) pertained to onco-hematology. The pharmaceutical industry was involved in 130 (98%) articles. Key methodological aspects were reported inconsistently, with only three articles adequately reporting all aspects. A total of 161 (56%) articles reported a statistically significant benefit for the treatment evaluated on IPD. Conversely, only one PAIC significantly favored the treatment evaluated on aggregated data. CONCLUSION: Although the number of published PAICs is increasing, the methodology and transparency need to be improved. Moreover, our study strongly suggests a reporting bias. This situation calls for strengthening guidelines to improve trust in PAIC results and thus their reliability in market access applications.

Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Neurodevelopmental Outcome at Two Years for Preterm Infants With Intraventricular Hemorrhage: A Case-Control Study.

BACKGROUND: High-grade intraventricular hemorrhage (IVH), including grade III and grade IV IVH, is known to impact neurodevelopmental outcome of preterm infants, but prognosis remains difficult to establish due to confounding factors and significant variations in the reported outcomes. The aim of this study was to compare the neurodevelopmental outcome of preterm infants with or without severe IVH. METHODS: A retrospective case-control study was conducted including preterm infants with gestational age <32 weeks hospitalized between 2009 and 2017 in a level III neonatal intensive care unit. This study included 73 cases with high-grade IVH and 73 controls who were matched to cases, based on the same gestational age, birth weight, sex, and year of birth. The neurodevelopmental outcome was compared at two years of age corrected for prematurity between cases and controls. Neurodevelopmental impairment was defined as cerebral palsy, hearing deficiency, visual impairment, or developmental delay. Multivariate analysis was used to identify whether high-grade IVH was an independent risk factor for neurodevelopmental impairment. RESULTS: In univariate analysis, high-grade IVH was associated with death or poor neurodevelopmental outcome at two years of age corrected for prematurity (odds ratio [OR], 16.3; 95% confidence interval [CI], 5.93 to 57.8; P < 0.001), and this association remained significant after adjusting for confounding factors including neonatal infection and bronchopulmonary dysplasia in multivariate analysis (OR, 8.71; 95% CI, 2.48 to 38.09; P = 0.002). CONCLUSIONS: This study highlights the impact of high-grade IVH as an independent risk factor of poor neurodevelopmental outcomes in very preterm infants and suggests that early interventions could improve the prognosis of these infants.

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

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.

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.

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.].

Long-term Survival after Hematopoietic Cell Transplant for Sickle Cell Disease Compared to the United States Population.

Hematopoietic cell transplant for sickle cell disease is curative but is associated with life threatening complications most of which occur within the first 2 years after transplantation. In the current era with interest in gene therapy and gene editing we felt it timely to report on sickle cell disease transplant recipients who were alive for at least 2-year after transplantation, not previously reported. Our objectives were to (1) report the conditional survival rates of patients who were alive for 2 or more years after transplantation (2) identify risk factors for death beyond 2 years after transplantation and (3) compare all-cause mortality risks to those of an age-, sex- and race-matched general population in the United States. By limiting to 2-year survivors, we exclude deaths that occur as a direct consequence of the transplantation procedure. De-identified records of 1149 patients were reviewed from a publicly available data source and 950 patients were eligible (https://picsure.biodatacatalyst.nhlbi.nih.gov). All analyses were performed in this secure cloud environment using the available statistical software package(s). The validity of the public database was confirmed by reproducing results from an earlier publication. Conditional survival estimates were obtained using the Kaplan-Meier method for the sub-cohort that had survived a given length (x) of time after transplantation. Cox regression models were built to identify risk factors associated with mortality beyond 2 years after transplantation. The standardized relative mortality risk (SMR) or the ratio of observed to expected number of deaths, was used to quantify all-cause mortality risk after transplantation and compared to age, race and sex-matched general population. Person-years at risk were calculated from an anchor date (i.e., 2-, 5- and 7-years) after transplantation until date of death or last date known alive. The expected number of deaths was calculated using age, race and sex-specific US mortality rates. The median follow up was 5 years (range 2-20) and 300 (32%) patients were observed for more than 7 years. Among those who lived for at least 7 years after transplantation the 12-year probability of survival was 97% (95% CI, 92%-99%). Compared to an age-, race- and sex-matched US population, the risk for late death after transplantation was higher as late as 7 years after transplantation (hazard ratio (HR) 3.2; P= .020) but the risk receded over time. Risk factors for late death included age at transplant and donor type. For every 10-year increment in patient age, an older patient was 1.75 times more likely to die than a younger patient (P= .0004). Compared to HLA-matched siblings the use of other donors was associated with higher risk for late death (HR 3.49; P= .003). Graft failure (beyond 2-years after transplantation) was 7% (95% CI, 5%-9%) and graft failure was higher after transplantation of grafts from donors who were not HLA-matched siblings (HR 2.59, P< .0001). Long-term survival after transplantation is excellent and support this treatment as a cure for sickle cell disease. The expected risk for death recedes over time but the risk for late death is not negligible.

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.

Streamlining statistical reproducibility: NHLBI ORCHID clinical trial results reproduction.

Reproducibility in medical research has been a long-standing issue. More recently, the COVID-19 pandemic has publicly underlined this fact as the retraction of several studies reached out to general media audiences. A significant number of these retractions occurred after in-depth scrutiny of the methodology and results by the scientific community. Consequently, these retractions have undermined confidence in the peer-review process, which is not considered sufficiently reliable to generate trust in the published results. This partly stems from opacity in published results, the practical implementation of the statistical analysis often remaining undisclosed. We present a workflow that uses a combination of informatics tools to foster statistical reproducibility: an open-source programming language, Jupyter Notebook, cloud-based data repository, and an application programming interface can streamline an analysis and help to kick-start new analyses. We illustrate this principle by (1) reproducing the results of the ORCHID clinical trial, which evaluated the efficacy of hydroxychloroquine in COVID-19 patients, and (2) expanding on the analyses conducted in the original trial by investigating the association of premedication with biological laboratory results. Such workflows will be encouraged for future publications from National Heart, Lung, and Blood Institute-funded studies.

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries.

Importance: Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective: To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants: This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures: Patient characteristics, clinical features, and medication use. Results: There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. Conclusions and Relevance: This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

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.

Objectives: 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. Design: Retrospective cohort study. Setting: 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. Participants: 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. Results: 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. Conclusions: 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 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.

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

BACKGROUND: 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.