Division of Emergency Medicine, University of Cape Town, Cape Town, South Africa.
Sudan Medical Specialization Board, Khartoum, Sudan.
Ann Glob Health. 2021 Mar 26;87(1):31. doi: 10.5334/aogh.3278.
In many low- and middle-income countries, where vaccinations will be delayed and healthcare systems are underdeveloped, the COVID-19 pandemic will continue for the foreseeable future. Mortality scales can aid frontline providers in low-resource settings (LRS) in identifying those at greatest risk of death so that limited resources can be directed towards those in greatest need and unnecessary loss of life is prevented. While many prognostication tools have been developed for, or applied to, COVID-19 patients, no tools to date have been purpose-designed for, and validated in, LRS.
This study aimed to develop a pragmatic tool to assist LRS frontline providers in evaluating in-hospital mortality risk using only easy-to-obtain demographic and clinical inputs.
Machine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients at two government referral hospitals to derive contextually appropriate mortality indices for COVID-19, which were then assessed by C-indices.
Data from 467 patients were used to derive two versions of the AFEM COVID-19 Mortality Scale (AFEM-CMS), which evaluates in-hospital mortality risk using demographic and clinical inputs that are readily obtainable in hospital receiving areas. Both versions of the tool include age, sex, number of comorbidities, Glasgow Coma Scale, respiratory rate, and systolic blood pressure; in settings pulse oximetry, oxygen saturation is included and in settings access, heart rate is included. The AFEM-CMS showed good discrimination: the model including pulse oximetry had a C-statistic of 0.775 (95% CI: 0.737-0.813) and the model excluding it had a C-statistic of 0.719 (95% CI: 0.678-0.760).
In the face of an enduring pandemic in many LRS, the AFEM-CMS serves as a practical solution to aid frontline providers in effectively allocating healthcare resources. The tool's generalisability is likely narrow outside of similar extremely LRS settings, and further validation studies are essential prior to broader use.
在许多低收入和中等收入国家，疫苗接种将被推迟，医疗保健系统也不发达，因此 COVID-19 大流行在可预见的未来仍将继续。死亡率量表可以帮助资源有限的低资源环境（LRS）中的一线医护人员识别那些死亡风险最大的人，以便将有限的资源用于最需要的人，并防止不必要的生命损失。虽然已经开发了许多用于 COVID-19 患者的预测工具，或者已经将其应用于 COVID-19 患者，但迄今为止，没有专门为 LRS 设计和验证的工具。
本研究旨在开发一种实用的工具，帮助 LRS 一线医护人员仅使用易于获取的人口统计学和临床输入来评估住院死亡率风险。
使用来自苏丹两家政府转诊医院 COVID-19 患者回顾性队列的数据，使用机器学习方法推导出适合 COVID-19 的上下文相关死亡率指数，然后通过 C 指数进行评估。
使用来自 467 名患者的数据，推导出两种版本的 AFEM COVID-19 死亡率量表（AFEM-CMS），该量表使用在医院接待区易于获得的人口统计学和临床输入来评估住院死亡率风险。该工具的两个版本都包括年龄、性别、共病数量、格拉斯哥昏迷量表、呼吸频率和收缩压；在有脉搏血氧仪的环境中，包括血氧饱和度，在有脉搏血氧仪的环境中，包括心率。AFEM-CMS 表现出良好的区分度：包括脉搏血氧仪的模型具有 0.775 的 C 统计量（95%CI：0.737-0.813），不包括脉搏血氧仪的模型具有 0.719 的 C 统计量（95%CI：0.678-0.760）。
在许多 LRS 中面临持久大流行的情况下，AFEM-CMS 是一种实用的解决方案，可以帮助一线医护人员有效分配医疗资源。该工具的通用性可能在类似的极端 LRS 环境之外很窄，在更广泛使用之前，还需要进一步的验证研究。
