Performance of simplified acute physiology score 3 admission score as a predictor of ICU mortality in a tertiary care hospital of rural Telangana, India
DOI:
https://doi.org/10.18203/2349-3933.ijam20162523Keywords:
SAPS3, Intensive care unit, Mortality, Predicted mortality, Discrimination, CalibrationAbstract
Background: This study was aimed to assess the performance of Simplified Acute Physiology Score 3 (SAPS3) as a predictor of Intensive Care Unit (ICU) mortality in critically ill patients of different case mixes admitted to an intensive care unit.
Methods: This study was performed from 1st August 2014 to 31st July 2015, in ICU of Govt. tertiary hospital in Rural Telangana. Predicted ICU mortality was calculated using SAPS3 global model. Observed versus predicted mortality rates were compared. The discrimination and calibration characteristics of the SAPS3 system to predict ICU mortality were assessed.
Results: A total of 491 patients were included. The majority (370, 75.3%) of the cases included in study were medical cases, with Cerebro-vascular accidents (150, 33.4%) and Shock, all types (96, 19.5%) as the most frequent primary diagnoses. Mean age of patients was 57.2 years with Males (296, 60.3%) predominance. Observed ICU mortality is 140 (28.5%) and SAPS 3 predicted mortality [(%), mean±SD - 41.4±14.80]. The discriminative power of the SAPS 3 model was good for the whole population (AUROC = 0.81, 0.77-0.83. Calibration was seen with Hosmer-Lemeshow goodness of fit.
Conclusions: The global SAPS 3 prediction model showed Good discrimination and Fair or satisfactory calibration in predicting mortality in our intensive care unit. Different levels of discrimination and calibration across the different subgroups analyzed suggest that overall ICU performance is affected by case mix variations. It is recommended that this model be tested in other centers and that a consolidated database be formed.
References
Sakr Y, Krauss C, Amaral ACKB, Réa-Neto A, Specht M, Reinhart K, et al. Comparison of the performance of SAPS II, SAPS 3, APACHE II, and their customized prognostic models in a surgical intensive care unit. Br J Anaesth. 2008,101:793- 803.
Khwannimit B, Bhurayanontachai R. The performance and customization of SAPS 3 admission score in a Thai medical intensive care unit. Intensive Care Med 2010,36:342-6.
Ledoux D, Canivet JL, Preiser JC, Lefranq J, Damas P. SAPS 3 admission score: an external validation in a general intensive care population. Intensive Care Med. 2008,34:1873-7.
Finazzi S, Poole D, Luciani D, Cogo PE, Bertolini G. Calibration belt for quality-of-care assessment based ondichotomous outcomes. PLoS One. 2011;6(2). doi.org/10.1371/journal.pone.0016110.
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21:128-38.
Afessa B, Tefferi A, Dunn WF, Litzow MR, Peters SG. Intensive care unit support and acute physiology and chronic health evaluation III performance in haematopoietic stem cell transplant recipients. Crit Care Med. 2003;31:1715-21.
Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, et al. SAPS 3 from evaluation of the patient to evaluation of the intensive care unit. Part 2: development of a prognostic model for hospital mortality at ICU admission.Intensive Care Med. 2005;31:1345 -55.
Hanley JA, McNeil BJ: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29-36.
Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;115(1):92-106.