Vimont Alexandre, Béliard Sophie, Valéro René, Leleu Henri, Durand-Zaleski Isabelle
Assistance Publique Hôpitaux de Paris, URC-ECO, CRESS-UMR1153, Paris, France.
Public Health Expertise (PHE), Paris, France.
Diabetol Metab Syndr. 2023 Jun 15;15(1):128. doi: 10.1186/s13098-023-01105-x.
Prognostic models in patients living with diabetes allow physicians to estimate individual risk based on medical records and biological results. Clinical risk factors are not always all available to evaluate these models so that they may be complemented with models from claims databases. The objective of this study was to develop, validate and compare models predicting the annual risk of severe complications and mortality in patients living with type 2 diabetes (T2D) from a national claims data.
Adult patients with T2D were identified in a national medical claims database through their history of treatments or hospitalizations. Prognostic models were developed using logistic regression (LR), random forest (RF) and neural network (NN) to predict annual risk of outcome: severe cardiovascular (CV) complications, other severe T2D-related complications, and all-cause mortality. Risk factors included demographics, comorbidities, the adjusted Diabetes Severity and Comorbidity Index (aDSCI) and diabetes medications. Model performance was assessed using discrimination (C-statistics), balanced accuracy, sensibility and specificity.
A total of 22,708 patients with T2D were identified, with mean age of 68 years and average duration of T2D of 9.7 years. Age, aDSCI, disease duration, diabetes medications and chronic cardiovascular disease were the most important predictors for all outcomes. Discrimination with C-statistic ranged from 0.715 to 0.786 for severe CV complications, from 0.670 to 0.847 for other severe complications and from 0.814 to 0.860 for all-cause mortality, with RF having consistently the highest discrimination.
The proposed models reliably predict severe complications and mortality in patients with T2D, without requiring medical records or biological measures. These predictions could be used by payers to alert primary care providers and high-risk patients living with T2D.
糖尿病患者的预后模型可使医生根据病历和生物学结果评估个体风险。临床风险因素并非总能全部用于评估这些模型，因此可通过理赔数据库中的模型加以补充。本研究的目的是利用全国理赔数据开发、验证并比较预测2型糖尿病（T2D）患者严重并发症和死亡年度风险的模型。
通过全国医疗理赔数据库中成年T2D患者的治疗或住院病史来识别他们。采用逻辑回归（LR）、随机森林（RF）和神经网络（NN）开发预后模型，以预测以下结局的年度风险：严重心血管（CV）并发症、其他严重的T2D相关并发症以及全因死亡率。风险因素包括人口统计学特征、合并症、调整后的糖尿病严重程度和合并症指数（aDSCI）以及糖尿病药物治疗情况。使用鉴别力（C统计量）、平衡准确性、敏感性和特异性来评估模型性能。
共识别出22,708例T2D患者，平均年龄68岁，T2D平均病程9.7年。年龄、aDSCI、病程、糖尿病药物治疗情况和慢性心血管疾病是所有结局的最重要预测因素。严重CV并发症的C统计量鉴别力范围为0.715至0.786，其他严重并发症为0.670至0.847，全因死亡率为0.814至0.860，其中RF的鉴别力始终最高。
所提出的模型能够可靠地预测T2D患者的严重并发症和死亡率，无需病历或生物学检测。这些预测结果可供医保支付方用于提醒初级保健提供者和T2D高危患者。
