Unit of Endocrinology, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Diabetes Care. 2013 Sep;36(9):2830-5. doi: 10.2337/dc12-1906. Epub 2013 May 1.
To develop and validate a parsimonious model for predicting short-term all-cause mortality in patients with type 2 diabetes mellitus (T2DM).
Two cohorts of patients with T2DM were investigated. The Gargano Mortality Study (GMS, n = 679 patients) was the training set and the Foggia Mortality Study (FMS, n = 936 patients) represented the validation sample. GMS and FMS cohorts were prospectively followed up for 7.40 ± 2.15 and 4.51 ± 1.69 years, respectively, and all-cause mortality was registered. A new forward variable selection within a multivariate Cox regression was implemented. Starting from the empty model, each step selected the predictor that, once included into the multivariate Cox model, yielded the maximum continuous net reclassification improvement (cNRI). The selection procedure stopped when no further statistically significant cNRI increase was detected.
Nine variables (age, BMI, diastolic blood pressure, LDL cholesterol, triglycerides, HDL cholesterol, urine albumin-to-creatinine ratio, and antihypertensive and insulin therapy) were included in the final predictive model with a C statistic of 0.88 (95% CI 0.82-0.94) in the GMS and 0.82 (0.76-0.87) in the FMS. Finally, we used a recursive partition and amalgamation algorithm to identify patients at intermediate and high mortality risk (hazard ratio 7.0 and 24.4, respectively, as compared with those at low risk). A web-based risk calculator was also developed.
We developed and validated a parsimonious all-cause mortality equation in T2DM, providing also a user-friendly web-based risk calculator. Our model may help prioritize the use of available resources for targeting aggressive preventive and treatment strategies in a subset of very high-risk individuals.
建立并验证一个用于预测 2 型糖尿病（T2DM）患者短期全因死亡率的简约模型。
研究纳入了两批 T2DM 患者。Gargano 死亡率研究（GMS，n=679 例患者）为训练集，Foggia 死亡率研究（FMS，n=936 例患者）为验证样本。GMS 和 FMS 队列分别前瞻性随访了 7.40±2.15 年和 4.51±1.69 年，记录全因死亡率。在多变量 Cox 回归中，实施了一种新的向前变量选择方法。从空模型开始，每一步选择的预测因子是，一旦包含在多变量 Cox 模型中，就会产生最大的连续净重新分类改善（cNRI）。当没有进一步检测到统计学上显著的 cNRI 增加时，选择过程停止。
最终预测模型纳入了 9 个变量（年龄、BMI、舒张压、LDL 胆固醇、三酰甘油、HDL 胆固醇、尿白蛋白/肌酐比值、以及降压和胰岛素治疗），在 GMS 中的 C 统计量为 0.88（95%CI 0.82-0.94），在 FMS 中为 0.82（0.76-0.87）。最后，我们使用递归分区和合并算法来识别处于中危和高危死亡率的患者（与低危患者相比，风险比分别为 7.0 和 24.4）。还开发了一个基于网络的风险计算器。
我们建立并验证了一个用于预测 T2DM 患者全因死亡率的简约方程，并提供了一个易于使用的基于网络的风险计算器。我们的模型可以帮助优先使用现有资源，针对具有极高风险的个体亚组实施积极的预防和治疗策略。
