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Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

ABSTRACT: Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13-year follow up data set from MESA (the Multi-Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD-free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10-year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of "All CVD " events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short-term CVD risk prediction.

SUBMITTER: Kakadiaris IA

PROVIDER: S-EPMC6404456 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

Kakadiaris Ioannis A IA Vrigkas Michalis M Yen Albert A AA Kuznetsova Tatiana T Budoff Matthew M Naghavi Morteza M

Journal of the American Heart Association 20181101 22

Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machin ...[more]

PMID: 30571498

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Project description:BACKGROUND:The 2017 American College of Cardiology/American Heart Association (ACC/AHA) blood pressure (BP) guideline provides updated recommendations for antihypertensive medication initiation and intensification. OBJECTIVES:Determine the risk for cardiovascular disease (CVD) events among adults recommended and not recommended antihypertensive medication initiation or intensification by the 2017 ACC/AHA BP guideline. METHODS:The authors analyzed data for black and white REGARDS (REasons for Geographic And Racial Differences in Stroke) study participants (age ?45 years). Systolic BP (SBP) and diastolic BP (DBP) were measured twice at baseline (2003 to 2007) and averaged. Participants not taking (n = 14,039) and taking (n = 15,179) antihypertensive medication were categorized according to their recommendations for antihypertensive medication initiation and intensification by the 2017 ACC/AHA guideline. Overall, 4,094 CVD events (stroke, coronary heart disease, and heart failure) occurred by December 31, 2014. RESULTS:Among participants not taking antihypertensive medication, 34.4% were recommended pharmacological antihypertensive treatment initiation. The CVD event rate per 1,000 person-years among participants recommended antihypertensive medication initiation with SBP/DBP ?140/90 mm Hg was 22.7 (95% confidence interval [CI]: 20.3 to 25.0). Among participants with SBP/DBP 130 to 139/80 to 89 mm Hg, the CVD event rate was 20.5 (95% CI: 18.5 to 22.6) and 3.4 (95% CI: 2.4 to 4.4) for those recommended and not recommended antihypertensive medication initiation, respectively. Among participants taking antihypertensive medication, 62.8% were recommended treatment intensification. The CVD event rate per 1,000 person-years among participants recommended treatment intensification was 33.6 (95% CI: 31.5 to 35.6) and 22.4 (95% CI: 20.8 to 23.9) for those with SBP/DBP ?140/90 mm Hg and 130 to 139/80 to 89 mm Hg, respectively. CONCLUSIONS:Implementing the 2017 ACC/AHA guideline would direct antihypertensive medication initiation and intensification to adults with high CVD risk.

Project description:Purpose To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction. Methods A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression. Results Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98-6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25-3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58-26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01-1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD. Conclusions An internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high-risk candidates and appropriately set postoperative expectations for patients. Clinical Relevance Identifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care.

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Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

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Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

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