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. 2019 Oct;63(9):1200-1209.
doi: 10.1111/aas.13415. Epub 2019 Jun 14.

Performance of SAPS II according to ICU length of stay: A Danish nationwide cohort study

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Performance of SAPS II according to ICU length of stay: A Danish nationwide cohort study

Anders Granholm et al. Acta Anaesthesiol Scand. 2019 Oct.

Abstract

Background: Intensive care unit (ICU) severity scores use data available at admission or shortly thereafter. There are limited contemporary data on how the prognostic performance of these scores is affected by ICU length of stay (LOS).

Methods: We conducted a nationwide cohort study using routinely collected health data from the Danish Intensive Care Database. We included adults with ICU admissions ≥24 hours between 1 January 2012 and 30 June 2016, who survived to ICU discharge and had valid ICU LOS and vital status data registered. We assessed discrimination of the Simplified Acute Physiology Score (SAPS) II for predicting mortality 90 days after ICU discharge, followed by recalibration of the model and assessment of standardized mortality ratios (SMRs) and calibration. Performance was assessed in the entire cohort and stratified by ICU LOS quartiles.

Results: We included 44 523 patients. Increasing SAPS II was associated with increasing ICU LOS. Overall discrimination (area under the receiver-operating characteristics curve) of SAPS II was 0.70 (95% CI: 0.70-0.71), with decreasing discrimination from the first (0.75, 95% CI: 0.73-0.76) to the last (0.64, 95% CI: 0.63-0.65) ICU LOS quartile. SMRs were lower (less deaths) than expected in the first ICU LOS quartile and higher (more deaths) than expected in the last two ICU LOS quartiles. Calibration decreased with increasing ICU LOS.

Conclusions: We observed that discrimination and calibration of SAPS II decreased with increasing ICU LOS, and that this affected SMRs. These findings should be acknowledged when using SAPS II for clinical, research and administrative purposes.

Keywords: SAPS II; intensive care; length of stay; mortality; prediction; prediction models.

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