Biological Subphenotypes of Acute Respiratory Distress Syndrome Show Prognostic Enrichment in Mechanically Ventilated Patients without Acute Respiratory Distress Syndrome

Rationale: Recent studies showed that biological subphenotypes in acute respiratory distress syndrome (ARDS) provide prognostic enrichment and show potential for predictive enrichment.Objectives: To determine whether these subphenotypes and their prognostic and potential for predictive enrichment could be extended to other patients in the ICU, irrespective of fulfilling the definition of ARDS.Methods: This is a secondary analysis of a prospective observational study of adult patients admitted to the ICU. We tested the prognostic enrichment of both cluster-derived and latentclass analysis (LCA)-derived biological ARDS subphenotypes by evaluating the association with clinical outcome (ICU-day, 30-day mortality, and ventilator-free days) using logistic regression and Cox regression analysis. We performed a principal component analysis to compare blood leukocyte gene expression profiles between subphenotypes and the presence of ARDS.Measurements and Main Results: We included 2,499 mechanically ventilated patients (674 with and 1,825 without ARDS). The cluster-derived "reactive" subphenotype was, independently of ARDS, significantly associated with a higher probability of ICU mortality, higher 30-day mortality, and a lower probability of successful extubation while alive compared with the "uninflamed" subphenotype. The blood leukocyte gene expression profiles of individual subphenotypes were similar for patients with and without ARDS. LCA-derived subphenotypes also showed similar profiles.Conclusions: The prognostic and potential for predictive enrichment of biological ARDS subphenotypes may be extended to mechanically ventilated critically ill patients without ARDS. Using the concept of biological subphenotypes for splitting cohorts of critically ill patients could add to improving future precision-based trial strategies and lead to identifying treatable traits for all critically ill patients

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Background The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. Methods A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. Results Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. Conclusions In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Anticholinergic medication use and transition to delirium in critically Ill patients: A prospective cohort study.

FSW - Self-regulation models for health behavior and psychopathology - ou

Lupus anticoagulant associates with thrombosis in patients with COVID-19 admitted to intensive care units: A retrospective cohort study

Background Thrombosis is a frequent and severe complication in patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU). Lupus anticoagulant (LA) is a strong acquired risk factor for thrombosis in various diseases and is frequently observed in patients with COVID-19. Whether LA is associated with thrombosis in patients with severe COVID-19 is currently unclear. Objective To investigate if LA is associated with thrombosis in critically ill patients with COVID-19. Patients/Methods The presence of LA and other antiphospholipid antibodies was assessed in patients with COVID-19 admitted to the ICU. LA was determined with dilute Russell's viper venom time (dRVVT) and LA-sensitive activated partial thromboplastin time (aPTT) reagents. Results Of 169 patients with COVID-19, 116 (69%) tested positive for at least one antiphospholipid antibody upon admission to the ICU. Forty (24%) patients tested positive for LA; of whom 29 (17%) tested positive with a dRVVT, 19 (11%) tested positive with an LA-sensitive aPTT, and 8 (5%) tested positive on both tests. Fifty-eight (34%) patients developed thrombosis after ICU admission. The odds ratio (OR) for thrombosis in patients with LA based on a dRVVT was 2.5 (95% confidence interval [CI], 1.1-5.7), which increased to 4.5 (95% CI, 1.4-14.3) in patients at or below the median age in this study (64 years). LA positivity based on a dRVVT or LA-sensitive aPTT was only associated with thrombosis in patients aged less than 65 years (OR, 3.8; 95% CI, 1.3-11.4) and disappeared after adjustment for C-reactive protein. Conclusion Lupus anticoagulant on admission is strongly associated with thrombosis in critically ill patients with COVID-19, especially in patients aged less than 65 years.Immunogenetics and cellular immunology of bacterial infectious disease

Estimated dead space fraction and the ventilatory ratio are associated with mortality in early ARDS

Background: Indirect indices for measuring impaired ventilation, such as the estimated dead space fraction and the ventilatory ratio, have been shown to be independently associated with an increased risk of mortality. This study aimed to compare various methods for dead space estimation and the ventilatory ratio in patients with acute respiratory distress syndrome (ARDS) and to determine their independent values for predicting death at day 30. The present study is a post hoc analysis of a prospective observational cohort study of ICUs of two tertiary care hospitals in the Netherlands. Results: Individual patient data from 940 ARDS patients were analyzed. Estimated dead space fraction and the ventilatory ratio at days 1 and 2 were significantly higher among non-survivors (p < 0.01). Dead space fraction calculation using the estimate from physiological variables [V/V] and the ventilatory ratio at day 2 showed independent association with mortality at 30 days (odds ratio 1.28 [95% CI 1.02-1.61], p < 0.03 and 1.20 [95% CI, 1.01-1.40], p < 0.03, respectively); whereas, the Harris-Benedict [V/V] and Penn State [V/V] estimations were not associated with mortality. The predicted validity of the estimated dead space fraction and the ventilatory ratio improved the baseline model based on PEEP, PaO/FiO, driving pressure and compliance of the respiratory system at day 2 (AUROCC 0.72 vs. 0.69, p < 0.05). Conclusions: Estimated methods for dead space calculation and the ventilatory ratio during the early course of ARDS are associated with mortality at day 30 and add statistically significant but limited improvement in the predictive accuracy to indices of oxygenation and respiratory system mechanics at the second day of mechanical ventilation