Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach
Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach
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DOI：
10.1186/s12984-020-00704-3
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发表时间：
2020-06-10
影响因子：
5.1
通讯作者：
Jayaraman, Arun
中科院分区：
文献类型：
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作者：
Harari, Yaar;O'Brien, Megan K.;Jayaraman, Arun
背景在临床实践中，治疗师经常依靠临床结果测量来量化患者的损伤和功能。使用基线临床信息预测患者的出院结果可以帮助临床医生设计更有针对性的治疗策略，并更好地预测患者的辅助需求和出院护理计划。本研究的目的是建立住院康复期间四种标准化临床结局指标（功能独立性测量、十米步行试验、六分钟步行试验、贝格平衡量表）的预测模型。方法50名在美国住院康复医院接受治疗的脑卒中幸存者参加了这项研究。临床出院评分的预测因素包括人口统计学、卒中特征和入院时的临床检查评分。我们使用Pearson积差和斯皮尔曼秩相关系数计算临床结局指标和预测因子之间的相关性，交叉验证Lasso回归为每个临床结局指标的出院评分建立预测方程，并使用基于随机森林的排列分析比较预测因子的相对重要性。结果预测方程可解释出院评分变异的70-77%，预测新患者预后的标准化误差为13-15%。最重要的预测因素是入院时的临床测试分数。影响至少一种临床结局的出院评分的其他变量包括从卒中发作到康复入院的时间、年龄、性别、体重指数、种族和言语障碍或语言障碍的诊断。结论本研究中提出的模型可以帮助临床医生和研究人员预测参加符合美国医疗保险标准的住院卒中康复计划的个人的临床结局的出院评分。
Background In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation. Methods Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. We used the Pearson product-moment and Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and predictors, a cross-validated Lasso regression to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest based permutation analysis to compare the relative importance of the predictors. Results The predictive equations explained 70-77% of the variance in discharge scores and resulted in a normalized error of 13-15% for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment. Conclusions The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to U.S. Medicare standards.