Kpene Godsway Edem, Lokpo Sylvester Yao, Darfour-Oduro Sandra A
Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied Sciences, Ho, Ghana.
Department of Public Health Studies, Elon University, Elon, NC, USA.
BMC Endocr Disord. 2025 Jan 10;25(1):9. doi: 10.1186/s12902-025-01831-5.
The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients.
The study was retrospective involving medical records of T2DM patients. Data extracted included mortality outcome (dead or alive), sociodemographic characteristics (age, sex, marital status, educational level, occupation and location), family history of diseases (diabetes, cardiovascular disease (CVD), or asthma), lifestyle (smoking and alcohol intake), comorbidities (such as skin infections, sickle cell disease, urinary tract infections, and pneumonia) and complications of diabetes (CVD, nephropathy, neuropathy, foot ulcers, and diabetic ketoacidosis) were analyzed using Stata version 16.0 and Python 3.6.1 programming language. Both descriptive and inferential statistics were done to describe and build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN), eXtreme Gradient Boosting (XGBoost) and logistic regression were evaluated using the best-fitting predictive model for T2DM mortality.
Of the 328 participants, 183 (55.79%) were female, and the percentage of mortality was 11.28%. A 100% mortality was recorded among the T2DM patients with sepsis (p-value = 0.012). T2DM in-patients were 3.83 times as likely to die [AOR = 3.83; 95% CI: (1.53-9.61)] if they had nephropathy compared to T2DM in-patients without nephropathy (p-value = 0.004). The full model which included sociodemographic characteristics, family history, lifestyle variables and complications of T2DM had the best prediction of T2DM mortality outcome (ROC = 72.97%). The accuracy for (test and train datasets) were as follows: (90% and 90%), (100% and 100%), (90% and 90%), (90% and 88%) and (88% and 90%) respectively for the various ML classification techniques: logistic regression, Decision tree classifier, kNN classifier, SVM and XGBoost.
This study found that all in-patients with sepsis died. Nephropathy was the identified significant predictor of T2DM mortality. Decision tree classifier provided the best classifying potential.
中低收入国家2型糖尿病（T2DM）患病率不断上升，需要采取预防性公共卫生干预措施。包括来自加纳的研究在内的非洲研究一致显示，T2DM相关死亡率很高。虽然此前在霍市的研究主要调查了T2DM患者的危险因素、合并症和生活质量，但本研究专门调查了这些患者的死亡预测因素。
本研究为回顾性研究，涉及T2DM患者的病历。提取的数据包括死亡结局（死亡或存活）、社会人口学特征（年龄、性别、婚姻状况、教育程度、职业和地点）、家族病史（糖尿病、心血管疾病（CVD）或哮喘）、生活方式（吸烟和饮酒）、合并症（如皮肤感染、镰状细胞病、尿路感染和肺炎）以及糖尿病并发症（CVD、肾病、神经病变、足部溃疡和糖尿病酮症酸中毒），并使用Stata 16.0版和Python 3.6.1编程语言进行分析。分别进行描述性统计和推断性统计以描述和建立预测模型。使用针对T2DM死亡率的最佳拟合预测模型评估支持向量机（SVM）、决策树、k近邻（kNN）、极端梯度提升（XGBoost）和逻辑回归等机器学习（ML）技术的性能。
在328名参与者中，183名（55.79%）为女性，死亡率为11.28%。患有败血症的T2DM患者死亡率为100%（p值 = 0.012）。与无肾病的T2DM住院患者相比，患有肾病的T2DM住院患者死亡可能性高3.83倍[AOR = 3.83；95%置信区间：（1.53 - 9.61）]（p值 = 0.004）。包含社会人口学特征、家族病史、生活方式变量和T2DM并发症的完整模型对T2DM死亡结局的预测效果最佳（ROC = 72.97%）。各种ML分类技术（测试和训练数据集）的准确率分别如下：逻辑回归为（90%和90%）、决策树分类器为（100%和100%）、kNN分类器为（90%和90%）、SVM为（90%和88%）、XGBoost为（88%和90%）。
本研究发现所有患有败血症的住院患者均死亡。肾病是确定的T2DM死亡率的重要预测因素。决策树分类器具有最佳的分类潜力。
