以創新的機器學習導向風險分數模型預測老年族群之 慢性腎臟病風險

A Novel Machine Learning-Based Scoring System for Estimating the Risk of Chronic Kidney Disease in Community-Dwelling Elderly

目的：本研究致力於運用新型機器學習技術，開發出能準確預測老年族群慢性腎臟病(chronic kidney disease, CKD)風險、同時擁有良好風險校準能力的CKD風險分級模型，以期能於臨床情境快速辨識出需加強照護管理的CKD高風險老年族群。

方法：本研究採用回溯性研究方法，分析台灣北部某教學醫院從2014年至2018年，3,469名受檢者的臨床相關數據，篩選出與CKD具較高關聯性的17個候選變量，運用Risk-Calibrated Supersparse Linear Integer Model（簡稱RiskSLIM）的機器學習技術，構建新型CKD風險分級模型。

結果：在3,469名受試者中，共有463名(13.3%)受試者符合Kidney Disease Outcomes and Quality Initiative（簡稱KDOQI）所定義的CKD標準。經實驗發現，在區分CKD與非 CKD受檢者的任務中，具有五項變量的RiskSLIM候選模型具有最佳的表現，命名為CHUME風險分數(Creatinine-Hypertension-bUn-Male-Exercise score, CHUME score)。與傳統模型相比，RiskSLIM模型具有更佳的風險校準能力（RiskSLIM模型的平均5-CV CAL為3.6% [95% CI 3.1%-4.1%]，對比Penalized logistic regression模型為4.7% [95% CI 4.3%-5.0%]）。若將CHUME風險分數的診斷閾值設定在總分大於等於1分時判斷為陽性，會有最佳的分類預測表現，AUC為0.912 [95% CI 0.896-0.927]；敏感度為83.8% [95% CI 80.4%-87.1%]；特異度為87.7% [95% CI 86.5%-88.8%]。

結論：CHUME風險分數是使用RiskSLIM機器學習技術構建出的CKD風險分級模型，易於使用，同時具有高敏感度及高特異度，能夠快速整合入現有的CKD臨床診療流程中，是一個適合用於社區老年族群的CKD 臨床篩檢工具。

 

Purpose: In response to the increasing need of CKD (chronic kidney disease) management, we developed a novel machine learning-based screening system to better assess CKD risk among community-dwelling older adults.

Methods: Based on a retrospective research design, our study analyzed the clinical data of 3,469 senior citizens receiving health checkups during the five year from 2014 to 2018 to identify 17 candidate variables for inclusion in the CKD risk screening model, using the machine learning technique named Risk-Calibrated Supersparse Linear Integer Model (RiskSLIM).

Results: Of the total 3,469 participants, 463 (13.3%) met the KDOQI-CKD criteria. A 5-item RiskSLIM model was found to be optimal in measuring the creatinine-hypertension-bUn-male-exercise (CHUME) score for distinguishing CKD from non-CKD cases. In contrast to the baseline Penalized logistic regression (PLR) model, the RiskSLIM model was simpler and superior in terms of accuracy in risk calibration (mean 5-CV CAL of 3.6% [95% CI 3.1%-4.1%] vs 4.7% [95% CI 4.3%-5.0%] for PLR). Operating characteristics for CHUME score with 95% CIs developed from bootstrapping from all participants were remarkable at the diagnostic threshold (score of 1-or-higher) with an estimated AUC of 0.912 [95%-CI 0.896-0.927], sensitivity of 83.8% [95%-CI 80.4%-87.1%], and specificity of 87.7% [95%-CI 86.5%-88.8%].

Conclusion: The RiskLIM-based CKD screening system is easy to use, and the CHUME score measured is marked with high sensitivity and specificity. The simplicity, as well as the efficacy, of the screening system is conducive to easy integration into clinical workflow to help assess CKD risk. We accordingly conclude that it is a suitable CKD screening tool for community-dwelling elderly.