摘要
Background： Since the 1980s, severity of illness scoring systems has gained increasing popularity in Intensive Care Units （ICUs）. Physicians used them for predicting mortality and assessing illness severity in clinical trials. The objective of this study was to assess the performance of Simplified Acute Physiology Score 3 （SAPS 3） and its customized equation for Australasia （Australasia SAPS 3, SAPS 3 [AUS]） in predicting clinical prognosis and hospital mortality in emergency ICU （EICU）. Methods： A retrospective analysis of the EICU including 463 patients was conducted between January 2013 and December 2015 in the EICU of Peking University Third Hospital. The worst physiological data of enrolled patients were collected within 24 h after admission to calculate SAPS 3 score and predicted mortality by regression equation. Discrimination between survivals and deaths was assessed by the area under the receiver operator characteristic curve （AUC）. Calibration was evaluated by Hosmer-Lemeshow goodness-of fit test through calculating the ratio of observed-to-expected numbers of deaths which is known as the standardized mortality ratio （SMR）. Results： A total of 463 patients were enrolled in the study, and the observed hospital mortality was 26.1% （121/463）. The patients enrolled were divided into survivors and nonsurvivors. Age, SAPS 3 score, Acute Physiology and Chronic Health Evaluation Score 11 （APACHE 11）, and predicted mortality were significantly higher in nonsurvivors than survivors （P 〈 0.05 or P 〈 0.01 ）. The AUC （95% confidence intervals [C/s]） for SAPS 3 score was 0.836 （0.796-0.876）. The maximum of Youden＇s index, cutoff, sensitivity, and specificity of SAPS 3 score were 0.526%, 70.5 points, 66.9%, and 85.7%, respectively. The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 demonstrated a Chi-square test score of 10.25, P = 0.33, SMR （95% CI） = 0.63 （0.52 0.76）. The Hosmer-Lemeshow goodness-of fit test tbr SAPS 3 （AUS） demonstrated a Chi-square test score of 9.55, P 0.38, SMR （95% CI） 0.68 （0.57-0.81）. Univariate and multivariate analyses were conducted for biochemical variables that were probably correlated to prognosis. Eventually, blood urea nitrogen （BUN）, albumin,lactate and free triiodothyronine （FT3） were selected as independent risk factors for predicting prognosis. Conclusions： The SAPS 3 score system exhibited satisfactory performance even superior to APACHE 11 in discrimination. In predicting hospital mortality, SAPS 3 did not exhibit good calibration and overestimated hospital mortality, which demonstrated that SAPS 3 needs improvement in the future.
Background： Since the 1980s, severity of illness scoring systems has gained increasing popularity in Intensive Care Units （ICUs）. Physicians used them for predicting mortality and assessing illness severity in clinical trials. The objective of this study was to assess the performance of Simplified Acute Physiology Score 3 （SAPS 3） and its customized equation for Australasia （Australasia SAPS 3, SAPS 3 [AUS]） in predicting clinical prognosis and hospital mortality in emergency ICU （EICU）. Methods： A retrospective analysis of the EICU including 463 patients was conducted between January 2013 and December 2015 in the EICU of Peking University Third Hospital. The worst physiological data of enrolled patients were collected within 24 h after admission to calculate SAPS 3 score and predicted mortality by regression equation. Discrimination between survivals and deaths was assessed by the area under the receiver operator characteristic curve （AUC）. Calibration was evaluated by Hosmer-Lemeshow goodness-of fit test through calculating the ratio of observed-to-expected numbers of deaths which is known as the standardized mortality ratio （SMR）. Results： A total of 463 patients were enrolled in the study, and the observed hospital mortality was 26.1% （121/463）. The patients enrolled were divided into survivors and nonsurvivors. Age, SAPS 3 score, Acute Physiology and Chronic Health Evaluation Score 11 （APACHE 11）, and predicted mortality were significantly higher in nonsurvivors than survivors （P 〈 0.05 or P 〈 0.01 ）. The AUC （95% confidence intervals [C/s]） for SAPS 3 score was 0.836 （0.796-0.876）. The maximum of Youden＇s index, cutoff, sensitivity, and specificity of SAPS 3 score were 0.526%, 70.5 points, 66.9%, and 85.7%, respectively. The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 demonstrated a Chi-square test score of 10.25, P = 0.33, SMR （95% CI） = 0.63 （0.52 0.76）. The Hosmer-Lemeshow goodness-of fit test tbr SAPS 3 （AUS） demonstrated a Chi-square test score of 9.55, P 0.38, SMR （95% CI） 0.68 （0.57-0.81）. Univariate and multivariate analyses were conducted for biochemical variables that were probably correlated to prognosis. Eventually, blood urea nitrogen （BUN）, albumin,lactate and free triiodothyronine （FT3） were selected as independent risk factors for predicting prognosis. Conclusions： The SAPS 3 score system exhibited satisfactory performance even superior to APACHE 11 in discrimination. In predicting hospital mortality, SAPS 3 did not exhibit good calibration and overestimated hospital mortality, which demonstrated that SAPS 3 needs improvement in the future.
