Maestría en Optimización y Cómputo Aplicado, Universidad Autónoma del Estado de Morelos, Cuernavaca, 62209, Morelos, México.
Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, 62209, Morelos, México.
J Med Syst. 2023 Aug 19;47(1):90. doi: 10.1007/s10916-023-01979-4.
Identifying risk factors associated with COVID-19 lethality is crucial in combating the ongoing pandemic. In this study, we developed lethality predictive models for each epidemiological wave and for the overall dataset using the Extreme Gradient Boosting technique and analyzed them using Shapley values to determine the contribution levels of various features, including demographics, comorbidities, medical units, and recent medical information from confirmed COVID-19 cases in Mexico between February 23, 2020, and April 15, 2022. The results showed that pneumonia and advanced age were the most important factors predicting patient death in all cohorts. Additionally, the medical unit where the patient received care acted as a risk or protective factor. IMSS medical units were identified as high-risk factors in all cohorts, except in wave four, while SSA medical units generally were moderate protective factors. We also found that intubation was a high-risk factor in the first epidemiological wave and a moderate-risk factor in the following waves. Female gender was a protective factor of moderate-high importance in all cohorts, while being between 18 and 29 years old was a moderate protective factor and being between 50 and 59 years old was a moderate risk factor. Additionally, diabetes (all cohorts), obesity (third wave), and hypertension (fourth wave) were identified as moderate risk factors. Finally, residing in municipalities with the lowest Human Development Index level represented a moderate risk factor. In conclusion, this study identified several significant risk factors associated with COVID-19 lethality in Mexico, which could aid policymakers in developing targeted interventions to reduce mortality rates.
确定与 COVID-19 致死率相关的风险因素对于抗击当前的大流行至关重要。在这项研究中，我们使用极端梯度提升技术为每个流行病学波次和整个数据集开发了致死率预测模型，并使用 Shapley 值对其进行了分析，以确定各种特征（包括人口统计学特征、合并症、医疗单位以及墨西哥确诊 COVID-19 病例的近期医疗信息）的贡献水平。结果表明，肺炎和高龄是所有队列中预测患者死亡的最重要因素。此外，患者接受治疗的医疗单位是一个风险或保护因素。除了第四波次之外，在所有队列中，IMSS 医疗单位被认为是高风险因素，而 SSA 医疗单位通常是中度保护因素。我们还发现，在第一波次的流行病学中，插管是一个高风险因素，而在后续波次中是一个中度风险因素。在所有队列中，女性是一种中度-高度重要的保护因素，而 18-29 岁是中度保护因素，50-59 岁是中度风险因素。此外，糖尿病（所有队列）、肥胖（第三波次）和高血压（第四波次）被确定为中度风险因素。最后，居住在人类发展指数水平最低的市政当局被认为是中度风险因素。总之，这项研究确定了与墨西哥 COVID-19 致死率相关的几个重要风险因素，这可以帮助决策者制定有针对性的干预措施，以降低死亡率。
