Kassam Nadeem, Aghan Eric, Somji Samina, Aziz Omar, Orwa James, Surani Salim R
Internal Medicine, Aga Khan University, Dar-es-Salaam, Tanzania.
Family Medicine, Aga Khan University, Dar-es-Salaam, Tanzania.
PeerJ. 2021 Nov 16;9:e12332. doi: 10.7717/peerj.12332. eCollection 2021.
Illness predictive scoring systems are significant and meaningful adjuncts of patient management in the Intensive Care Unit (ICU). They assist in predicting patient outcomes, improve clinical decision making and provide insight into the effectiveness of care and management of patients while optimizing the use of hospital resources. We evaluated mortality predictive performance of Simplified Acute Physiology Score (SAPS 3) and Mortality Probability Models (MPM-III) and compared their performance in predicting outcome as well as identifying disease pattern and factors associated with increased mortality.
This was a retrospective cohort study of adult patients admitted to the ICU of the Aga Khan Hospital, Dar- es- Salaam, Tanzania between August 2018 and April 2020. Demographics, clinical characteristics, outcomes, source of admission, primary admission category, length of stay and the support provided with the worst physiological data within the first hour of ICU admission were extracted. SAPS 3 and MPM-III scores were calculated using an online web-based calculator. The performance of each model was assessed by discrimination and calibration. Discrimination between survivors and non-survivors was assessed by the area under the receiver operator characteristic curve (ROC) and calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test.
A total of 331 patients were enrolled in the study with a median age of 58 years (IQR 43-71), most of whom were male ( = 208, 62.8%), of African origin ( = 178, 53.8%) and admitted from the emergency department ( = 306, 92.4%). In- hospital mortality of critically ill patients was 16.1%. Discrimination was very good for all models, the area under the receiver-operating characteristic (ROC) curve for SAPS 3 and MPM-III was 0.89 (95% CI [0.844-0.935]) and 0.90 (95% CI [0.864-0.944]) respectively. Calibration as calculated by Hosmer-Lemeshow goodness-of-fit test showed good calibration for SAPS 3 and MPM-III with Chi- square values of 4.61 and 5.08 respectively and -Value > 0.05.
Both SAPS 3 and MPM-III performed well in predicting mortality and outcome in our cohort of patients admitted to the intensive care unit of a private tertiary hospital. The in-hospital mortality of critically ill patients was lower compared to studies done in other intensive care units in tertiary referral hospitals within Tanzania.
疾病预测评分系统是重症监护病房(ICU)患者管理的重要且有意义的辅助工具。它们有助于预测患者的预后,改善临床决策,并在优化医院资源利用的同时,深入了解患者护理和管理的有效性。我们评估了简化急性生理学评分(SAPS 3)和死亡率概率模型(MPM-III)的死亡率预测性能,并比较了它们在预测结果以及识别与死亡率增加相关的疾病模式和因素方面的表现。
这是一项对2018年8月至2020年4月期间入住坦桑尼亚达累斯萨拉姆阿迦汗医院ICU的成年患者进行的回顾性队列研究。提取了人口统计学、临床特征、结局、入院来源、主要入院类别、住院时间以及ICU入院后第一小时内最差生理数据所提供的支持信息。使用基于网络的在线计算器计算SAPS 3和MPM-III评分。通过区分度和校准来评估每个模型的性能。通过受试者操作特征曲线(ROC)下的面积评估幸存者和非幸存者之间的区分度,并使用Hosmer-Lemeshow拟合优度检验估计校准情况。
共有331名患者纳入研究,中位年龄为58岁(四分位间距43 - 71岁),其中大多数为男性(n = 208,62.8%),非洲裔(n = 178,53.8%),且从急诊科入院(n = 306,92.4%)。重症患者的院内死亡率为16.1%。所有模型的区分度都非常好,SAPS 3和MPM-III的受试者操作特征(ROC)曲线下面积分别为0.89(95%可信区间[0.844 - 0.935])和0.90(95%可信区间[0.864 - 0.944])。通过Hosmer-Lemeshow拟合优度检验计算的校准显示,SAPS 3和MPM-III校准良好,卡方值分别为4.61和5.08,P值>0.05。
在我们这家私立三级医院重症监护病房收治的患者队列中,SAPS 3和MPM-III在预测死亡率和结局方面表现良好。与坦桑尼亚三级转诊医院其他重症监护病房的研究相比,重症患者的院内死亡率较低。