Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention
| Autor: | Mandeep Singh, Malcolm R. Bell, Yaron Kinar, Yoav Bar-Sinai, Chad J. Zack, Conor Senecal, Ryan J. Lennon, R. Jay Widmer, Yaakov Metzger, Amir Lerman, Rajiv Gulati |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Male
Time Factors Minnesota medicine.medical_treatment Clinical Decision-Making Population Coronary Artery Disease 030204 cardiovascular system & hematology Logistic regression Machine learning computer.software_genre Patient Readmission Risk Assessment Decision Support Techniques Machine Learning 03 medical and health sciences Percutaneous Coronary Intervention 0302 clinical medicine Predictive Value of Tests Risk Factors Humans Medicine Hospital Mortality Registries 030212 general & internal medicine education Aged Heart Failure education.field_of_study business.industry Area under the curve Reproducibility of Results Percutaneous coronary intervention Middle Aged Confidence interval Regression Treatment Outcome Conventional PCI Cohort Female Artificial intelligence Cardiology and Cardiovascular Medicine business computer |
| Zdroj: | JACC: Cardiovascular Interventions. 12:1304-1311 |
| ISSN: | 1936-8798 |
| DOI: | 10.1016/j.jcin.2019.02.035 |
| Popis: | Objectives This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI). Background Contemporary risk models for event prediction after PCI have limited predictive ability. Machine learning has the potential to identify complex nonlinear patterns within datasets, improving the predictive power of models. Methods We evaluated 11,709 distinct patients who underwent 14,349 PCIs between January 2004 and December 2013 in the Mayo Clinic PCI registry. Fifty-two demographic and clinical parameters known at the time of admission were used to predict in-hospital mortality and 358 additional variables available at discharge were examined to identify patients at risk for CHF readmission. For each event, we trained a random forest regression model (i.e., machine learning) to estimate the time-to-event. Eight-fold cross-validation was used to estimate model performance. We used the predicted time-to-event as a score, generated a receiver-operating characteristic curve, and calculated the area under the curve (AUC). Model performance was then compared with a logistic regression model using pairwise comparisons of AUCs and calculation of net reclassification indices. Results The predictive algorithm identified a high-risk cohort representing 2% of all patients who had an in-hospital mortality of 45.5% (95% confidence interval: 43.5% to 47.5%) compared with a risk of 2.1% for the general population (AUC: 0.925; 95% confidence interval: 0.92 to 0.93). Advancing age, CHF, and shock on presentation were the leading predictors for the outcome. A high-risk group representing 1% of all patients was identified with 30-day CHF rehospitalization of 8.1% (95% confidence interval: 6.3% to 10.2%). Random forest regression outperformed logistic regression for predicting 30-day CHF readmission (AUC: 0.90 vs. 0.85; p = 0.003; net reclassification improvement: 5.14%) and 180-day cardiovascular death (AUC: 0.88 vs. 0.81; p = 0.02; net reclassification improvement: 0.02%). Conclusions Random forest regression models (machine learning) were more predictive and discriminative than standard regression methods at identifying patients at risk for 180-day cardiovascular mortality and 30-day CHF rehospitalization, but not in-hospital mortality. Machine learning was effective at identifying subgroups at high risk for post-procedure mortality and readmission. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|