Articles: intensive-care-units.
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Critical care medicine · Mar 2023
Randomized Controlled Trial Multicenter StudyImproving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias.
Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. ⋯ These mortality risk models demonstrated excellent performance compared with APACHE while appearing to mitigate bias introduced through major shifts in GCS documentation at two large health systems. This provides evidence to support using automated capture rather than trained personnel for capture of GCS data used in benchmarking ICUs on mortality performance.
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Acta Anaesthesiol Scand · Mar 2023
Factors associated with treatment limitations in two Swedish ICUs - prevalence and patient involvement.
The aim was to study the prevalence, documentation, and patient involvement in treatment limitations (TLs) in two Swedish intensive care units (ICUs). All patients admitted to the ICUs of two Swedish regional hospitals in 2019 were screened for inclusion. Exclusion criteria included postanesthesia care <24 h. ⋯ When the intensivist documented why a TL was established, patient autonomy was four times more commonly stated as the motivation for the TL among women compared to men (15.5% vs. 3.8% p < .05). TLs were common in two Swedish ICUs but a substantial number of patients and next-of-kin were not involved in the decision-making process or informed of the decision. Women were more often than men engaged in the decision to establish a TL.
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Patients with severe mental illness (SMI) (i.e., schizophrenia, bipolar disorder, or major depressive disorder) have been reported to have excess mortality rates from infection compared to patients without SMI, but whether SMI is associated with higher or lower case fatality rates (CFRs) among infected patients remains unclear. The primary objective was to compare the 90-day CFR in septic shock patients with and without SMI admitted to the intensive care unit (ICU), after adjusting for social disadvantage and physical health comorbidity. ⋯ Our findings suggest that, after adjusting for social disadvantage and physical health comorbidity, there are improved septic shock outcome in patients with SMI compared to patients without. This finding may be the result of different immunological profiles and exposures to psychotropic medications, which should be further explored.
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Randomized Controlled Trial
Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases.
Ischemic heart disease (IHD) is the leading cause of death and emergency department (ED) admission. We aimed to develop more accurate and straightforward scoring models to optimize the triaging of IHD patients in ED. This was a retrospective study based on the MIMIC-IV database. ⋯ In total cohort, there were 2551 (30%) patients transferred into ICU; the mortality rates were 1% at 3 days, 3% at 7 days, and 7% at 30 days. In the testing cohort, the areas under the curve of scoring models for shorter and longer term outcomes prediction were 0.7551 (95% CI 0.7297-0.7805) for ICU stay, 0.7856 (95% CI 0.7166-0.8545) for 3d-death, 0.7371 (95% CI 0.6665-0.8077) for 7d-death, and 0.7407 (95% CI 0.6972-0.7842) for 30d-death. This newly accurate and parsimonious scoring models present good discriminative performance for predicting the possibility of transferring to ICU, 3d-death, 7d-death, and 30d-death in IHD patients visiting ED.
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There is a lack of published research on the impact of the first wave of the COVID-19 pandemic in Taiwan. We investigated the mortality risk factors among critically ill patients with COVID-19 in Taiwan during the initial wave. Furthermore, we aim to develop a novel AI mortality prediction model using chest X-ray (CXR) alone. ⋯ The mortality rate of COVID-19 patients who receive invasive mechanical ventilation was high. Septic shock and high BRIXIA score were clinical predictors of mortality. The novel AI mortality prediction model using CXR alone exhibited a high performance.