前瞻性验证用于估算低密度脂蛋白胆固醇的机器学习模型。

Prospective Validation of a Machine Learning Model for Low-Density Lipoprotein Cholesterol Estimation.

机构信息

Department of Laboratory Medicine, Hôtel-Dieu de France University Hospital, Beirut, Lebanon.

Directorate, Department of Laboratory Medicine, Hôtel Dieu de France University Hospital, Beirut, Lebanon.

出版信息

Lab Med. 2022 Nov 3;53(6):629-635. doi: 10.1093/labmed/lmac049.

DOI:10.1093/labmed/lmac049

PMID:35762775

Abstract

OBJECTIVE

We aim to prospectively validate a previously developed machine learning algorithm for low-density lipoprotein cholesterol (LDL-C) estimation.

METHODS

We retrospectively and prospectively evaluated a machine learning algorithm based on k-nearest neighbors (KNN) according to age, sex, healthcare setting, and triglyceridemia against a direct LDL-C assay. The agreement of low-density lipoprotein-k-nearest neighbors (LDL-KNN) with the direct measurement was assessed using intraclass correlation coefficient (ICC).

RESULTS

The analysis comprised 31,853 retrospective and 6599 prospective observations, with a mean age of 54.2 ± 17.2 years. LDL-KNN exhibited an ICC greater than 0.9 independently of age, sex, and disease status. LDL-KNN was in satisfactory agreement with direct LDL-C in observations with normal triglyceridemia and mild hypertriglyceridemia but displayed an ICC slightly below 0.9 in severely hypertriglyceridemic patients and lower in very low LDL-C observations.

CONCLUSION

LDL-KNN performs robustly across ages, genders, healthcare settings, and triglyceridemia. Further algorithm development is needed for very low LDL-C observations.

摘要

目的

我们旨在前瞻性验证先前开发的用于估算低密度脂蛋白胆固醇（LDL-C）的机器学习算法。

方法

我们根据年龄、性别、医疗保健环境和三酰甘油水平，对基于 k-最近邻（KNN）的机器学习算法进行回顾性和前瞻性评估，以与直接 LDL-C 测定法进行比较。使用组内相关系数（ICC）评估 LDL-KNN 与直接测量的一致性。

结果

该分析包括 31853 项回顾性和 6599 项前瞻性观察，平均年龄为 54.2±17.2 岁。无论年龄、性别和疾病状态如何，LDL-KNN 的 ICC 均大于 0.9。在三酰甘油正常和轻度升高的观察中，LDL-KNN 与直接 LDL-C 具有良好的一致性，但在严重高甘油三酯血症患者中 ICC 略低于 0.9，在极低 LDL-C 观察中 ICC 较低。