Cardiovasc Prev Pharmacother.  2021 Oct;3(4):115-123. 10.36011/cpp.2021.3.e15.

Modeling of Changes in Creatine Kinase after HMG-CoA Reductase Inhibitor Prescription

Affiliations
  • 1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 2Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 3Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 4Division of Endocrinology and Metabolism, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Korea

Abstract

Background
Statin-associated muscle symptoms are one of the side effects that physicians should consider when prescribing statins. In this study, creatine kinase (CK) levels were measured following statin prescription, and various factors affecting the CK levels were determined using machine learning.
Methods
Changes in the CK were observed every 3 months for a 12-month period in patients who received statins for the first time at Seoul St. Mary's Hospital. For each visit, we developed four basic models based on changes in the CK levels. Extreme gradient boosting, a scalable end-to-end tree boosting algorithm, which employs a decision-tree-based ensemble machine learning algorithm, was used for the prediction of changes in the CK.
Results
A total of 23,860 patients were included. Among them, 19 patients (0.08%) had increased CK levels of 2,000 IU·L−1 or more 3 months after statin prescription, and 65 patients (0.27%) exhibited CK levels of over 2,000 IU·L−1 at least once during the 12-month study period. The area under the receiver operator characteristic of each model for each visit was 0.709–0.769, and the accuracy was 0.700–0.803. In each of the models, the variables that had the strongest influence on changes in the CK were sex and previous CK value.
Conclusions
Through machine learning, factors influencing changes in the CK were identified. These results will provide the basis for future research, through which the optimal parameters of the CK prediction model can be found and the model can be used in clinical applications.

Keyword

Creatine kinase; Hydroxymethylglutaryl-CoA reductase inhibitors; Machine learning; Myotoxicity

Figure

  • Figure 1. AUROC curves for the prediction of a change in CK.AUROC = area under the receiver operating characteristic; CK = creatine kinase.

  • Figure 2. . Variables affecting changes in CK level. (A) Average impact on magnitude of model output based on mean SHAP value and (B) impact on model output based on SHAP value.AST = aspartate aminotransferase; CK = creatine kinase; eGFR = estimated glomerular filtration rate; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; SHAP = SHapley Additive exPlanations.


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