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Objectives: The standardized mortality ratio (SMR) is a common metric to benchmark ICUs. However, SMR may be artificially distorted by the admission of potential organ donors (POD), who have nearly 100% mortality, although risk prediction models may not identify them as high-risk patients. We aimed to evaluate the impact of PODs on SMR.
Design: Retrospective registry-based multicenter study.
Setting: Twenty ICUs in Finland, Estonia, and Switzerland in 2015-2017.
Patients: Sixty thousand forty-seven ICU patients.
Interventions: None.
Measurements And Main Results: We used a previously validated mortality risk model to calculate the SMRs. We investigated the impact of PODs on the overall SMR, individual ICU SMR and ICU benchmarking. Of the 60,047 patients admitted to the ICUs, 514 (0.9%) were PODs, and 477 (93%) of them died. POD deaths accounted for 7% of the total 6738 in-hospital deaths. POD admission rates varied from 0.5 to 18.3 per 1000 admissions across ICUs. The risk prediction model predicted a 39% in-hospital mortality for PODs, but the observed mortality was 93%. The ratio of the SMR of the cohort without PODs to the SMR of the cohort with PODs was 0.96 (95% CI, 0.93-0.99). Benchmarking results changed in 70% of ICUs after excluding PODs.
Conclusions: Despite their relatively small overall number, PODs make up a large proportion of ICU patients who die. PODs cause bias in SMRs and in ICU benchmarking. We suggest excluding PODs when benchmarking ICUs with SMR.
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http://dx.doi.org/10.1097/CCM.0000000000006098 | DOI Listing |
Nurs Health Sci
December 2024
Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
The Practice Environment Scale of the Nursing Work Index has been used worldwide to measure nurse work environments. International benchmark values for this scale can assist managers in assessing their work environment. The objective was to conduct a meta-analysis of this instrument's composite and subscale values across continents, nursing unit types, and time.
View Article and Find Full Text PDFLancet Reg Health Am
December 2024
MiCare Centre, Mount Siani Hospital, Toronto, ON, Canada.
Background: Excessive antimicrobial exposure is associated with an increase in neonatal mortality, morbidities and adverse neurodevelopment. Canadian Neonatal Network has been promoting judicious antimicrobial use through the Evidence-based Practice for Improving Quality processes. Our objective was to evaluate the antimicrobial consumption among neonates in tertiary neonatal intensive care units (NICU) in Canada in the recent decade.
View Article and Find Full Text PDFJ Am Med Inform Assoc
November 2024
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.
Objective: Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk prediction by estimating time-to-event distributions. Here, we propose a novel conditional variational autoencoder-based method, DySurv, which uses a combination of static and longitudinal measurements from electronic health records to estimate the individual risk of death dynamically.
Materials And Methods: DySurv directly estimates the cumulative risk incidence function without making any parametric assumptions on the underlying stochastic process of the time-to-event.
Age Ageing
November 2024
Nursing Research, University Hospital Schleswig-Holstein, Kiel, Germany.
Background: Delirium, an acute brain dysfunction, is proposed to be highly prevalent in clinical care and shown to significantly increase the risk of mortality and dementia.
Objectives: To report on the global prevalence of clinically documented delirium and delirium-related clinical practices in wards caring for paediatric and adult patients in healthcare facilities.
Design: A prospective, cross-sectional, 39-question survey completed on World Delirium Awareness Day, 15 March 2023.
IEEE Open J Eng Med Biol
February 2024
Department of Electronics, Informatics and EngineeringPolitecnico di Milano 20133 Milano Italy.
The purpose of this study is to evaluate the importance of cardiorespiratory variables within a Reinforcement Learning (RL) recommendation system aimed at establishing optimal strategies for drug treatment of septic patients in the intensive care unit (ICU). We developed a RL model in order to establish drug administration strategies for septic patients using only a set of cardiorespiratory variables. We then compared this model with other RL models trained with a different set of features.
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