Performance of simplified acute physiology score 3 admission score as a predictor of ICU mortality in a tertiary care hospital of rural Telangana, India


  • B. Balaji Department of General Medicine, Kakatiya Medical College, Warangal, Telangana, India
  • A. Bikshapathi Rao Department of General Medicine, Kakatiya Medical College, Warangal, Telangana, India
  • V. Suresh Kumar Department of General Medicine, Kakatiya Medical College, Warangal, Telangana, India
  • . Sammaiah Department of General Medicine, Kakatiya Medical College, Warangal, Telangana, India



SAPS3, Intensive care unit, Mortality, Predicted mortality, Discrimination, Calibration


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.


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Original Research Articles