Articles: critical-illness.
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Multicenter Study
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.
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. ⋯ 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.
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So far, only a few studies have examined and confirmed the correlation between end-expiratory carbon dioxide partial pressure (PETCO2) and arterial carbon dioxide tension (PaCO2) during invasive mechanical ventilation in critically ill patients. This study aimed to observe the correlation between PaCO2 and PETCO2 in patients on invasive mechanical ventilation. This was a cross-sectional study of adult patients on invasive mechanical ventilation enrolled between June 2018 and March 2019. ⋯ For oxygenation index <200 mm Hg, correlation coefficient r = 0.69, P < .001; oxygenation index ≥200, r = 0.73, P < .001. Under different oxygenation indexes, there was no statistically significant difference between the 2 correlation coefficients. Among 116 pairs of data with oxygenation index <200 mm Hg, the difference of PaCO2-PETCO2 ≥10 mm Hg was found in 25 pairs (21.55%); in 182 pairs of data with oxygenation index ≥200 mm Hg, the difference of PaCO2-PETCO2 ≥10 mm Hg was found in 26 pairsIn patients on invasive mechanical ventilation, there was a good correlation between PETCO2 and PaCO2 in different ventilator modes, different disease types, and different oxygenation indexes, especially in synchronized intermittent mandatory ventilation mode and chronic obstructive pulmonary disease patients.
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Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. ⋯ Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.
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Am. J. Respir. Crit. Care Med. · Aug 2021
Multicenter StudyHospital-Level Variation in Death for Critically Ill Patients with COVID-19.
Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear. ⋯ There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).