Chiu Sherry Yueh-Hsia, Chen Ying Isabel, Lu Juifen Rachel, Ng Soh-Ching, Chen Chih-Hung
Department of Health Care Management, College of Management, Chang Gung University, Taoyuan 33302, Taiwan.
Healthy Aging Research Center, Chang Gung University, Taoyuan 33302, Taiwan.
J Clin Med. 2021 Oct 18;10(20):4779. doi: 10.3390/jcm10204779.
Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for type 2 diabetes. A total of Taiwanese subjects aged ≥18 with outpatient data were ascertained during 2007-2013 and followed up to the end of 2016 using a hospital-based prospective cohort. Both traditional model selection with stepwise approach and LASSO method were conducted for parsimonious models' selection and comparison. Multivariable Cox regression was performed for selected variables, and a time-dependent ROC curve with an integrated AUC and cumulative mortality by risk score levels was employed to evaluate the time-related predictive performance. The prediction model, which was composed of eight influential variables (age, sex, history of cancers, history of hypertension, antihyperlipidemic drug use, HbA1c level, creatinine level, and the LDL /HDL ratio), was the same for the 7-year and 10-year models. Harrell's C-statistic was 0.7955 and 0.7775, and the integrated AUCs were 0.8136 and 0.8045 for the 7-year and 10-year models, respectively. The predictive performance of the AUCs was consistent with time. Our study developed and validated all-cause mortality prediction models with 7-year and 10-year follow-ups that were composed of the same contributing factors, though the model with 10-year follow-up had slightly greater risk coefficients. Both prediction models were consistent with time.
利用医院易于获取的数据来识别临床糖尿病护理调整的高风险死亡率，是精准医疗未来的一种便捷方法。我们旨在基于2型糖尿病的7年和10年随访结果，开发全因死亡率的风险预测模型。在2007年至2013年期间确定了共有年龄≥18岁且有门诊数据的台湾受试者，并使用基于医院的前瞻性队列随访至2016年底。采用逐步法的传统模型选择和LASSO方法进行简约模型的选择和比较。对选定变量进行多变量Cox回归，并采用具有综合AUC和按风险评分水平计算的累积死亡率的时间依赖性ROC曲线来评估时间相关的预测性能。由八个有影响的变量（年龄、性别、癌症病史、高血压病史、使用抗高脂血症药物、糖化血红蛋白水平、肌酐水平以及低密度脂蛋白/高密度脂蛋白比值）组成的预测模型，在7年和10年模型中是相同的。7年和10年模型的Harrell's C统计量分别为0.7955和0.7775，综合AUC分别为0.8136和0.8045。AUC的预测性能与时间一致。我们的研究开发并验证了具有7年和10年随访的全因死亡率预测模型，这些模型由相同的影响因素组成，尽管10年随访模型的风险系数略高。两个预测模型都与时间一致。
