Steno Diabetes Center North Jutland (SDCN), Aalborg, Denmark.
Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark.
Osteoporos Int. 2018 Jun;29(6):1437-1445. doi: 10.1007/s00198-018-4467-z. Epub 2018 Mar 22.
There is an increasing awareness of sarcopenia in older people. We applied machine learning principles to predict mortality and incident immobility in older Belgian men through sarcopenia and frailty characteristics. Mortality could be predicted with good accuracy. Serum 25-hydroxyvitamin D and bone mineral density scores were the most important predictors.
Machine learning principles were used to predict 5-year mortality and 3-year incident severe immobility in a population of older men by frailty and sarcopenia characteristics.
Using prospective data from 1997 on 264 older Belgian men (n = 152 predictors), 29 statistical models were developed and tuned on 75% of data points then validated on the remaining 25%. The model with the highest test area under the curve (AUC) was chosen as the best. From these, ranked predictor importance was extracted.
Five-year mortality could be predicted with good accuracy (test AUC of .85 [.73; .97], sensitivity 78%, specificity 89% at a probability cut-off of 22.3%) using a Bayesian generalized linear model. Three-year incident severe immobility could be predicted with fair accuracy (test AUC .74 [.57; .91], sensitivity 67%, specificity 78% at a probability cut-off of 14.2%) using a multivariate adaptive regression splines model. Serum 25-hydroxyvitamin D levels and hip bone mineral density scores were the most important predictors of mortality, while biochemical androgen markers and Short-Form 36 Physical Domain questions were the most important predictors of immobility. Sarcopenia assessed by lean mass estimates was relevant to mortality prediction but not immobility prediction.
Using advanced statistical models and a machine learning approach 5-year mortality can be predicted with good accuracy using a Bayesian generalized linear model and 3-year incident severe immobility with fair accuracy using a multivariate adaptive regression splines model.
人们对老年人肌肉减少症的认识日益提高。我们应用机器学习原理，通过肌肉减少症和虚弱特征预测比利时老年男性的死亡率和偶发性活动能力丧失。
使用前瞻性数据，对 1997 年以后的 264 名比利时老年男性（n=152 个预测因子）进行分析，对 75%的数据点开发和调整了 29 个统计模型，然后对其余 25%的数据点进行验证。选择测试曲线下面积（AUC）最高的模型作为最佳模型。从这些模型中提取出按重要性排列的预测因子。
使用贝叶斯广义线性模型可以很好地预测 5 年死亡率（测试 AUC 为 0.85 [0.73；0.97]，灵敏度为 78%，特异性为 89%，概率截断值为 22.3%）。使用多变量自适应回归样条模型可以较好地预测 3 年偶发性严重活动能力丧失（测试 AUC 为 0.74 [0.57；0.91]，灵敏度为 67%，特异性为 78%，概率截断值为 14.2%）。血清 25-羟维生素 D 水平和髋部骨密度评分是死亡率的最重要预测因子，而生化雄激素标志物和简明健康调查问卷 36 躯体健康维度问题是活动能力丧失的最重要预测因子。通过瘦体重估计评估的肌肉减少症与死亡率预测相关，但与活动能力丧失预测无关。
使用先进的统计模型和机器学习方法，贝叶斯广义线性模型可很好地预测 5 年死亡率，多变量自适应回归样条模型可较好地预测 3 年偶发性严重活动能力丧失。
