Abstract
ObjectivePrognostic models are key for benchmarking intensive care units (ICUs). They require up–to–date predictors and should report transportability properties for reliable predictions. We developed and validated an in–hospital mortality risk prediction model to facilitate benchmarking, quality assurance, and health economics evaluation. Study Design and SettingWe retrieved data from the database of an international (Finland, Estonia, Switzerland) multicenter ICU cohort study from 2015 to 2017. We used a hierarchical logistic regression model that included age, a modified Simplified Acute Physiology Score–II, admission type, premorbid functional status, and diagnosis as grouping variable. We used pooled and meta–analytic cross–validation approaches to assess temporal and geographical transportability. ResultsWe included 61,224 patients treated in the ICU (hospital mortality 10.6%). The developed prediction model had an area under the receiver operating characteristic curve 0.886, 95% confidence interval (CI) 0.882–0.890; a calibration slope 1.01, 95% CI (0.99–1.03); a mean calibration –0.004, 95% CI (–0.035 to 0.027). Although the model showed very good internal validity and geographic discrimination transportability, we found substantial heterogeneity of performance measures between ICUs (I–squared: 53.4–84.7%). ConclusionA novel framework evaluating the performance of our prediction model provided key information to judge the validity of our model and its adaptation for future use.
Highlights
Introduction and regulatory bodiesintensive care unit (ICU) prediction models developed in study populations from the United Kingdom (ICNARC), Australia and New Zealand (ANZIC) and the Netherlands have been updated to address poor validity and were extended with new clinical predictors like functional status which were strongly associated with the study outcome [9,10,11,12] .The performance of prediction models depends on their validity and transportability, and can be classified into different frameworks [13,14,15]
We included 61,224 patients treated in the ICU
Premorbid functional status is an important predictor of hospital outcome and improved the predictive performance of our prediction model
Summary
Geographical and temporal transportability indicate performance outside the study population used for the development of the prediction model, for example, in other hospitals or time periods [14,15]. Case mix differences, changes in mortality between ICUs and over time, drive the need to recalibrate existing prediction models or to develop new ones [14,16]. Reporting their validation and transportability is important to avoid biased outcome predictions and to support the planning of ICU benchmarking programs where new ICUs might be included or a comparison with ICUs outside an existing benchmark system might evolve. The aim of this study is to develop and validate an in-hospital mortality risk prediction model by adding simple indicators of premorbid functional status to established outcome predictors (age, severity of illness, diagnosis, admission type) to quantify the validity and transportability properties of the prediction model and to interpret their impact using a proposed framework for validation [ 14 , 15 ]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
