Department of Public Health, Debre Tabor University, Debre Tabor, Ethiopia
Public Health, Woldia University, Woldia, Ethiopia.
BMJ Open. 2023 Mar 28;13(3):e063170. doi: 10.1136/bmjopen-2022-063170.
To develop and validate a clinical risk score for in-hospital stroke mortality.
The study used a retrospective cohort study design.
The study was carried out in a tertiary hospital in the Northwest Ethiopian region.
The study included 912 patients who had a stroke admitted to a tertiary hospital between 11 September 2018 and 7 March 2021.
Clinical risk score for in-hospital stroke mortality.
We used EpiData V.3.1 and R V.4.0.4 for data entry and analysis, respectively. Predictors of mortality were identified by multivariable logistic regression. A bootstrapping technique was performed to internally validate the model. Simplified risk scores were established from the beta coefficients of predictors of the final reduced model. Model performance was evaluated using the area under the receiver operating characteristic curve and calibration plot.
From the total stroke cases, 132 (14.5%) patients died during the hospital stay. We developed a risk prediction model from eight prognostic determinants (age, sex, type of stroke, diabetes mellitus, temperature, Glasgow Coma Scale, pneumonia and creatinine). The area under the curve (AUC) of the model was 0.895 (95% CI: 0.859-0.932) for the original model and was the same for the bootstrapped model. The AUC of the simplified risk score model was 0.893 (95% CI: 0.856-0.929) with a calibration test p value of 0.225.
The prediction model was developed from eight easy-to-collect predictors. The model has excellent discrimination and calibration performance, similar to that of the risk score model. It is simple, easily remembered, and helps clinicians identify the risk of patients and manage it properly. Prospective studies in different healthcare settings are required to externally validate our risk score.
开发和验证住院卒中死亡率的临床风险评分。
本研究采用回顾性队列研究设计。
研究在埃塞俄比亚西北部的一家三级医院进行。
本研究纳入了 2018 年 9 月 11 日至 2021 年 3 月 7 日期间在一家三级医院因卒中住院的 912 名患者。
住院卒中死亡率的临床风险评分。
我们分别使用 EpiData V.3.1 和 R V.4.0.4 进行数据录入和分析。使用多变量逻辑回归确定死亡率的预测因素。采用自举技术对模型进行内部验证。从最终简化模型的预测因素的β系数中建立简化风险评分。使用受试者工作特征曲线和校准图评估模型性能。
在总卒中病例中，有 132（14.5%）例患者在住院期间死亡。我们从 8 个预后决定因素（年龄、性别、卒中类型、糖尿病、体温、格拉斯哥昏迷量表、肺炎和肌酐）中开发了风险预测模型。模型的曲线下面积（AUC）为原始模型的 0.895（95%置信区间：0.859-0.932），自举模型的 AUC 相同。简化风险评分模型的 AUC 为 0.893（95%置信区间：0.856-0.929），校准检验的 p 值为 0.225。
该预测模型是从 8 个易于收集的预测因素中开发出来的。该模型具有出色的区分度和校准性能，与风险评分模型相似。它简单、易于记忆，可以帮助临床医生识别患者的风险并进行适当的管理。需要在不同的医疗保健环境中进行前瞻性研究来外部验证我们的风险评分。
