Related Experiment Video
Updated: Feb 10, 2026

Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
Published on: April 12, 2011
[Predictive values of different critical scoring systems for survival rate after discharge in critically ill patients
Jinsong Zhang1, Wei Li, Xufeng Chen
1Department of Emergency, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Provincial People's Hospital, Nanjing 210000, Jiangsu, China. Corresponding author: Zhang Jinsong,
Objective:
To determine the predictive values of different critical scoring systems for survival rate after discharge in critically ill patients supported by extracorporeal membrane oxygenation (ECMO).
Methods:
The clinical data of 34 critically ill patients supported by ECMO admitted to Department of Emergency of the First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital) from July 2015 to September 2017 were retrospectively analyzed. The general information and the worst values of vital signs and related pathophysiological indicators within 12 hours before ECMO treatment of patients were collected, and sequential organ failure assessment (SOFA), multiple organs dysfunction score (MODS), simplified acute physiology score II (SAPS II), and acute physiology and chronic health evaluation IV (APACHE IV) scores were calculated. The patients were divided into survival group and non-survival group according to 28-day survival after hospital discharge. General clinical characteristics and aforementioned scores were compared between the two groups. Scoring systems for predicting prognosis were assessed by using the receiver operating characteristic (ROC) curve. The Kaplan-Meier method was used to depict the surviving curve.
Results:
Thirty-four patients were finally enrolled, 13 of whom were dead at the follow-up period of 28 days after hospital discharge, and 21 survived. Duration of ECMO support in non-survival group was significantly shorter than that in survival group (hours: 101.4±7.8 vs. 134.4±12.6), SOFA, SAPS II, and APACHE IV scores were significantly higher than those of survival group (SOFA score: 10.6±3.6 vs. 8.8±3.3, SAPS II score: 38.7±14.3 vs. 31.8±12.5, APACHE IV score: 46.5±15.5 vs. 38.1±11.3, all P < 0.05). There was no significant difference in gender, age, body mass index (BMI), vital signs or related pathophysiological indicators within 12 hours before ECMO treatment, or MODS score between the two groups. ROC curve analysis showed that the area under ROC curve (AUC) of SAPS II score for predicting 28-day survival rate was the highest, which was significantly higher than that of SOFA, MODS, and APACHE IV score (0.880 vs. 0.694, 0.654, 0.682, all P < 0.05). When the best cut-off value of SAPS II score was 43, the sensitivity was 81.2%, and the specificity was 77.9%. Kaplan-Meier survival analysis showed that 28-day survival rate after hospital discharge in patients with SAPS II score < 43 (n = 18) was significantly higher than that in patients with SAPS II score ≥ 43 (n = 16; χ2 = 2.444, P = 0.018).
Conclusions:
Four critical scoring systems of SOFA, MODS, SAPS II and APACHE IV have been proved to have good prognostic ability to predict 28-day survival after hospital discharge in critically ill patients supported by ECMO. Among them, SAPS II score system has more accurate prediction value.
Related Concept Videos
Critical Values
Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
Finding Critical Values for Chi-Square
z Scores and Unusual Values
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
Critical Thinking
Colleges and universities are...
Critical Thinking I

