Background Severity scoring systems are useful tools for measuring the severity of the disease and its outcome. This pilot study was to verify and compare the prognostic performance of the Simplified Acute Physiology ...Background Severity scoring systems are useful tools for measuring the severity of the disease and its outcome. This pilot study was to verify and compare the prognostic performance of the Simplified Acute Physiology Score II （SAPS II） and Glasgow Coma Scale （GCS） in neuro-intensive care unit （N-ICU） patients. Methods A total of 1684 patients consecutively admitted to the N-ICU at Xuanwu Hospital between January 1, 2005 and December 31, 2011 were enrolled in this study. The data-base included admission data, at 24-, 48-, and 72-hour SAPS II and GCS. Repeated measure data analysis of variance, Logistic regression analysis, the Hosmer-Lemeshow goodness-of-fit statistic, and the area under the receiver operating characteristic were used to evaluate the performance. Results There was a significant difference between the SAPS II or GCS score at four time points （F=16.110, P=0.000 or F=8.108, P=0.000）. The SAPS II scores or GCS score at four time points interacted with the outcomes with significant difference （F=116.771, P=0.000 or F=65.316, P=0.000）. Calibration of the SAPS II or GCS score at each time point on all patients was good. The percentage of a risk estimate prediction corresponding to observed mortality was also good. The 72-hour score have the greatest consistency. Discriminations of the SAPS II or GCS score at each time were all satisfactory. The 72-hour score had the greatest discriminative power. The cut-off value was 33 （sensitivity of 85.2% and specificity of 74.3%） and 6 （sensitivity of 70.6% and specificity of 65.0%）. The SAPS II at each time point on all patients showed better calibration, consistency and discrimination than GCS. The binary Logistic regression analysis identified physiological variables, GCS, age, and disease category as significant independent risk factors of death. After the two variables including underlying disease and type of admission were excluded, we built the simplified SAPS II model. A correlation was suggested between the simplified SAPS II score at each time point and outcome, regardless of the diagnosis. Conclusions The GCS scoring system tends to be a little weaker in the predictive power than the SAPS II scoring system in this Chinese cohort of N-ICU patients. The advantage of SAPS II scoring system still exists that it dose not need to take into account the diagnosis or diseases categories, even in the special N-ICU. The simplified SAPS II scoring system is considered a new idea for the estimation of effectiveness.展开更多
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 clin...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.展开更多
文摘Background Severity scoring systems are useful tools for measuring the severity of the disease and its outcome. This pilot study was to verify and compare the prognostic performance of the Simplified Acute Physiology Score II （SAPS II） and Glasgow Coma Scale （GCS） in neuro-intensive care unit （N-ICU） patients. Methods A total of 1684 patients consecutively admitted to the N-ICU at Xuanwu Hospital between January 1, 2005 and December 31, 2011 were enrolled in this study. The data-base included admission data, at 24-, 48-, and 72-hour SAPS II and GCS. Repeated measure data analysis of variance, Logistic regression analysis, the Hosmer-Lemeshow goodness-of-fit statistic, and the area under the receiver operating characteristic were used to evaluate the performance. Results There was a significant difference between the SAPS II or GCS score at four time points （F=16.110, P=0.000 or F=8.108, P=0.000）. The SAPS II scores or GCS score at four time points interacted with the outcomes with significant difference （F=116.771, P=0.000 or F=65.316, P=0.000）. Calibration of the SAPS II or GCS score at each time point on all patients was good. The percentage of a risk estimate prediction corresponding to observed mortality was also good. The 72-hour score have the greatest consistency. Discriminations of the SAPS II or GCS score at each time were all satisfactory. The 72-hour score had the greatest discriminative power. The cut-off value was 33 （sensitivity of 85.2% and specificity of 74.3%） and 6 （sensitivity of 70.6% and specificity of 65.0%）. The SAPS II at each time point on all patients showed better calibration, consistency and discrimination than GCS. The binary Logistic regression analysis identified physiological variables, GCS, age, and disease category as significant independent risk factors of death. After the two variables including underlying disease and type of admission were excluded, we built the simplified SAPS II model. A correlation was suggested between the simplified SAPS II score at each time point and outcome, regardless of the diagnosis. Conclusions The GCS scoring system tends to be a little weaker in the predictive power than the SAPS II scoring system in this Chinese cohort of N-ICU patients. The advantage of SAPS II scoring system still exists that it dose not need to take into account the diagnosis or diseases categories, even in the special N-ICU. The simplified SAPS II scoring system is considered a new idea for the estimation of effectiveness.
文摘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.
