Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study

Hautala, Arto J.; Shavazipour, Babooshka; Afşar, Bekir; Tulppo, Mikko P.; Miettinen, Kaisa

doi:10.1016/j.cvdhj.2023.05.001

Cited by 1 publication

(5 citation statements)

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“…This aids in identifying key contributors to healthcare costs. We observed that depression, expressed as the higher DEPS score, was the primary contributing factor of health care costs in a 12-month follow-up ( 7 ). Interestingly, for the ECR group in the present study, the DEPS score had the lowest explanatory power in predicting health care costs without practically no contribution (0.1%).…”

Section: Discussionmentioning

confidence: 99%

“…A darker color signifies a higher level of importance for the respective risk factor. * Usual care group ranking has been published earlier by Hautala et al ( 7 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, we utilized specific feature importance analysis to assess the predictive power of treated causal and modifiable risk factors at baseline in forecasting health care costs over a 12-month follow-up period for patients recovering from acute coronary syndrome (ACS) under usual care within the Finnish health care system. Our findings highlighted that a higher depression score emerged as the leading predictor of health care costs, succeeded by elevated levels of low-density lipoprotein (LDL) cholesterol and a reduced left ventricular ejection fraction ( 7 ).…”

Section: Introductionmentioning

confidence: 87%

“…Various methods exist, such as those based on statistical correlations and variances, but the choice of method should align with the specific variables and data types, necessitating the evaluation of multiple techniques for suitability. Based on our previous study ( 7 ), we performed the feature importance analysis including Cross Decomposition ( 23 ), Partial Least Squares Canonical Analysis (PLSC), Partial Least Squares based on Singular Value Decomposition (PLSSVD), Partial Least Square Regression (PLSRegression), Canonical Correlation Analysis (CCA), and Analysis of Variance (ANOVA) test relying on p -values for ranking.…”

Section: Methodsmentioning

confidence: 99%

“…Various methods exist, such as those based on statistical correlations and variances, but the choice of method should align with the specific variables and data types, necessitating the evaluation of multiple techniques for suitability. Based on our previous study (7), we performed the feature importance analysis including Cross Decomposition (23) Following the ranking of health care cost risk factors, we conducted a linear regression analysis to predict costs. This involved starting with the top-ranking risk marker and gradually adding the following best markers, resulting in the creation of 13 predictive models.…”

Section: Definition Of Predictive Modelsmentioning

confidence: 99%

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Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation

Hautala,

Shavazipour,

Afsar

et al. 2024

Front. Public Health

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IntroductionExercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals’ work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD.MethodsThe data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total).ResultsThe ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = − 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811).DiscussionPresence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.

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Section: Discussionmentioning

confidence: 99%

“…A darker color signifies a higher level of importance for the respective risk factor. * Usual care group ranking has been published earlier by Hautala et al ( 7 ).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

“…Various methods exist, such as those based on statistical correlations and variances, but the choice of method should align with the specific variables and data types, necessitating the evaluation of multiple techniques for suitability. Based on our previous study (7), we performed the feature importance analysis including Cross Decomposition (23) Following the ranking of health care cost risk factors, we conducted a linear regression analysis to predict costs. This involved starting with the top-ranking risk marker and gradually adding the following best markers, resulting in the creation of 13 predictive models.…”

Section: Definition Of Predictive Modelsmentioning

confidence: 99%

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