Dataset Information

An examination of cardiovascular intensive care unit mortality based on admission day and time.

ABSTRACT:

Background

Recent interest in the 'weekend effect' has been expanded to cardiovascular intensive care units, yet the impact of off-hours admission on mortality and cardiovascular ICU (CICU) length of stay remains uncertain.

Objectives

We examine the association between CICU admission day and time with mortality. Additionally, length-of-stay was also evaluated in relation to admission time.

Methods

A single-center, retrospective cohort study was conducted including 10,638 adult patients admitted to a CICU in a tertiary-care academic medical center from July 1, 2012 to June 30, 2019. ICU mortality and length-of-stay were assessed by admission day and time adjusting for comorbid conditions and other clinical variables. We used logistic regression models to evaluate the factors associated with mortality and a generalized linear model (GLM) with log link function and gamma distribution was used to evaluate the factors associated with ICU length of stay.

Results

Compared to weekday-day admissions, we observed an increased mortality for weekend-day for all admissions (6.5 vs 9.6%, Adjusted OR: 1.32 (1.03-1.72)), and for medical CICU admissions (7.6 vs 9.9%, Adjusted OR: 1.35 (1.02-1.79)). Additionally, compared to weekday-day, weekday-night admission was associated with 7% longer ICU length of stay in surgical ICU patients, 7% shorter length of stay in medical ICU patients.

Conclusion

Admission to this open-model CICU during weekend hours (Saturday 08:00-Sunday 17:59) versus nights or weekdays is associated with increased mortality. ICU staffing care models should not significantly change based on the day of the week.

SUBMITTER: Langston MC

PROVIDER: S-EPMC8919342 | biostudies-literature | 2021 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

An examination of cardiovascular intensive care unit mortality based on admission day and time.

Langston Matthew C MC Subedi Keshab K Fabrizio Carly C Wimmer Neil J NJ Choudhry Usman I UI Urrutia Luis E LE

Heart & lung : the journal of critical care 20210530 5

<h4>Background</h4>Recent interest in the 'weekend effect' has been expanded to cardiovascular intensive care units, yet the impact of off-hours admission on mortality and cardiovascular ICU (CICU) length of stay remains uncertain.<h4>Objectives</h4>We examine the association between CICU admission day and time with mortality. Additionally, length-of-stay was also evaluated in relation to admission time.<h4>Methods</h4>A single-center, retrospective cohort study was conducted including 10,638 ad ...[more]

PMID: 34077826

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Project description:ObjectiveTo compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours.BackgroundPrediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models.MethodsAll patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).ResultsA total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively.ConclusionsMachine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.

Dataset Information

An examination of cardiovascular intensive care unit mortality based on admission day and time.

Background

Objectives

Methods

Results

Conclusion

Publications

An examination of cardiovascular intensive care unit mortality based on admission day and time.

Similar Datasets

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