Yang Zhenkun, Li Yuanjie, Liu Yang, Zhong Ziyi, Ditchfield Coleen, Guo Taipu, Yang Mingjuan, Chen Yang
Department of Cardiology, Tianjin Medical University General Hospital, Tianjin, China.
Tianjin Research Institute of Anesthesiology, Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China.
Diabetol Metab Syndr. 2024 Nov 23;16(1):280. doi: 10.1186/s13098-024-01534-2.
Diastolic heart failure (DHF) and type 2 diabetes mellitus (T2DM) often coexist, causing increased mortality rates. Glycaemic variability (GV) exacerbates cardiovascular complications, but its impact on outcomes in patients with DHF and T2DM remains unclear. This study examined the relationships between GV with mortality outcomes, and developed a machine learning (ML) model for long-term mortality in these patients.
Patients with DHF and T2DM were included from the Medical Information Mart for Intensive Care IV, with admissions (2008-2019) as primary analysis cohort and admissions (2020-2022) as external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the associations of GV with 90-day, 1-year, and 3-year all-cause mortality. The primary analysis cohort was split into training and internal validation cohorts, then developing ML models for predicting 1-year all-cause mortality in training cohort, which were validated using the internal and external validation cohorts.
2,128 patients with DHF and T2DM were included in primary analysis cohort (meidian age 71.0years [IQR: 62.0-79.0]; 46.9% male), 498 patients with DHF and T2DM were included in the external validation cohort (meidian age 75.0years [IQR: 67.0-81.0]; 54.0% male). Multivariate Cox proportional hazards models showed that high GV tertiles were associated with higher risk of 90-day (T2: HR 1.45, 95%CI 1.09-1.93; T3: HR 1.96, 95%CI 1.48-2.60), 1-year (T2: HR 1.25, 95%CI 1.02-1.53; T3: HR 1.54, 95%CI 1.26-1.89), and 3-year (T2: HR 1.31, 95%CI: 1.10-1.56; T3: HR 1.48, 95%CI 1.23-1.77) all-cause mortality, compared with lowest GV tertile. Chronic kidney disease, creatinine, potassium, haemoglobin, and white blood cell were identified as mediators of GV and 1-year all-cause mortality. Additionally, GV and other clinical features were pre-selected to construct ML models. The random forest model performed best, with AUC (0.770) and G-mean (0.591) in internal validation, with AUC (0.753) and G-mean (0.599) in external validation.
GV was determined as an independent risk factor for short-term and long-term all-cause mortality in patients with DHF and T2DM, with a potential intervention threshold around 25.0%. The ML model incorporating GV demonstrated strong predictive performance for 1-year all-cause mortality, highlighting its importance in early risk stratification management of these patients.
舒张性心力衰竭（DHF）与2型糖尿病（T2DM）常并存，导致死亡率增加。血糖变异性（GV）会加剧心血管并发症，但其对DHF和T2DM患者预后的影响仍不明确。本研究探讨了GV与死亡率结局之间的关系，并开发了一种用于预测这些患者长期死亡率的机器学习（ML）模型。
从重症监护医学信息数据库IV中纳入DHF和T2DM患者，将2008 - 2019年的入院患者作为主要分析队列，2020 - 2022年的入院患者作为外部验证队列。采用多变量Cox比例风险模型和受限立方样条分析来评估GV与90天、1年和3年全因死亡率的关联。将主要分析队列分为训练队列和内部验证队列，然后在训练队列中开发用于预测1年全因死亡率的ML模型，并使用内部和外部验证队列进行验证。
主要分析队列纳入了2128例DHF和T2DM患者（中位年龄71.0岁[四分位间距：62.0 - 79.0]；46.9%为男性），外部验证队列纳入了498例DHF和T2DM患者（中位年龄75.0岁[四分位间距：67.0 - 81.0]；54.0%为男性）。多变量Cox比例风险模型显示，与最低GV三分位数相比，高GV三分位数与90天（T2：风险比[HR] 1.45，95%置信区间[CI] 1.09 - 1.93；T3：HR 1.96，95%CI 1.48 - 2.60）、1年（T2：HR 1.25，95%CI 1.02 - 1.53；T3：HR 1.54，95%CI 1.26 - 1.89）和3年（T2：HR 1.31，95%CI：1.10 - 1.56；T3：HR 1.48，95%CI 1.23 - 1.77）全因死亡率的较高风险相关。慢性肾脏病、肌酐、钾、血红蛋白和白细胞被确定为GV与1年全因死亡率之间的中介因素。此外，预先选择GV和其他临床特征来构建ML模型。随机森林模型表现最佳，内部验证中的曲线下面积（AUC）为0.770，几何均值（G - mean）为0.591，外部验证中的AUC为0.753，G - mean为0.599。
GV被确定为DHF和T2DM患者短期和长期全因死亡率的独立危险因素，潜在干预阈值约为25.0%。纳入GV的ML模型对1年全因死亡率具有较强的预测性能，突出了其在这些患者早期风险分层管理中的重要性。
