Dataset Information

Intensive care unit admission in multiple sclerosis: increased incidence and increased mortality.

ABSTRACT:

Objective

To compare the incidence of, and mortality after, intensive care unit (ICU) admission as well as the characteristics of critical illness in the multiple sclerosis (MS) population vs the general population.

Methods

We used population-based administrative data from the Canadian province of Manitoba for the period 1984 to 2010 and clinical data from 93% of admissions to provincial high-intensity adult ICUs. We identified 5,035 prevalent cases of MS and a cohort from the general population matched 5:1 on age, sex, and region of residence. We compared these populations using incidence rates and multivariable regression models adjusting for age, sex, comorbidity, and socioeconomic status.

Results

From January 2000 to October 2009, the age- and sex-standardized annual incidence of ICU admission among prevalent cohorts was 0.51% to 1.07% in the MS population and 0.34% to 0.51% in matched controls. The adjusted risk of ICU admission was higher for the MS population (hazard ratio 1.45; 95% confidence interval [CI] 1.19-1.75) than for matched controls. The MS population was more likely to be admitted for infection than the matched controls (odds ratio 1.82; 95% CI 1.10-1.32). Compared with the matched controls admitted to ICUs, 1-year mortality was higher in the MS population (relative risk 2.06; 95% CI 1.32-3.07) and was particularly elevated in patients with MS who were younger than 40 years (relative risk 3.77; 95% CI 1.45-8.11). Causes of death were MS (9.3%), infections (37.0%), and other causes (52.9%).

Conclusions

Compared with the general population, the risk of ICU admission is higher in MS, and 1-year mortality after admission is higher. Greater attention to preventing infection and managing comorbidity is needed in the MS population.

SUBMITTER: Marrie RA

PROVIDER: S-EPMC4118502 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Intensive care unit admission in multiple sclerosis: increased incidence and increased mortality.

Marrie Ruth Ann RA Bernstein Charles N CN Peschken Christine A CA Hitchon Carol A CA Chen Hui H Fransoo Randy R Garland Allan A

Neurology 20140507 23

<h4>Objective</h4>To compare the incidence of, and mortality after, intensive care unit (ICU) admission as well as the characteristics of critical illness in the multiple sclerosis (MS) population vs the general population.<h4>Methods</h4>We used population-based administrative data from the Canadian province of Manitoba for the period 1984 to 2010 and clinical data from 93% of admissions to provincial high-intensity adult ICUs. We identified 5,035 prevalent cases of MS and a cohort from the gen ...[more]

PMID: 24808019

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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

Intensive care unit admission in multiple sclerosis: increased incidence and increased mortality.

Objective

Methods

Results

Conclusions

Publications

Intensive care unit admission in multiple sclerosis: increased incidence and increased mortality.

Similar Datasets

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