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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

ABSTRACT: Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System in order to elucidate the determinants of in-hospital mortality in patients undergoing percutaneous coronary intervention across New York State. Methods and Results We examined 479 804 patients undergoing percutaneous coronary intervention between 2004 and 2012, utilizing traditional and advanced machine learning algorithms to determine the most significant predictors of in-hospital mortality. The entire data were randomly split into a training (80%) and a testing set (20%). Tuned hyperparameters were used to generate a trained model while the performance of the model was independently evaluated on the testing set after plotting a receiver-operator characteristic curve and using the output measure of the area under the curve ( AUC ) and the associated 95% CIs. Mean age was 65.2±11.9 years and 68.5% were women. There were 2549 in-hospital deaths within the patient population. A boosted ensemble algorithm (AdaBoost) had optimal discrimination with AUC of 0.927 (95% CI 0.923-0.929) compared with AUC of 0.913 for XGB oost (95% CI 0.906-0.919, P=0.02), AUC of 0.892 for Random Forest (95% CI 0.889-0.896, P<0.01), and AUC of 0.908 for logistic regression (95% CI 0.907-0.910, P<0.01). The 2 most significant predictors were age and ejection fraction. Conclusions A big data approach that utilizes advanced machine learning algorithms identifies new associations among risk factors and provides high accuracy for the prediction of in-hospital mortality in patients undergoing percutaneous coronary intervention.

SUBMITTER: Al'Aref SJ

PROVIDER: S-EPMC6474922 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivePrimary percutaneous coronary intervention (PPCI) for ST-elevation myocardial infarction (STEMI) is insufficiently implemented in many countries. We investigated patient and hospital characteristics associated with PPCI utilisation.MethodsWhole country registry data (MINAP, Myocardial Ischaemia National Audit Project) comprising PPCI-capable National Health Service trusts in England (84 hospital trusts; 92 350 hospitalisations; 90 489 patients), 2003-2013. Multilevel Poisson regression modelled the relationship between incidence rate ratios (IRR) of PPCI and patient and trust-level factors.ResultsOverall, standardised rates of PPCI increased from 0.01% to 86.3% (2003-2013). While, on average, there was a yearly increase in PPCI utilisation of 30% (adjusted IRR 1.30, 95% CI 1.23 to 1.36), it varied substantially between trusts. PPCI rates were lower for patients with previous myocardial infarction (0.95, 0.93 to 0.98), heart failure (0.86, 0.81 to 0.92), angina (0.96, 0.94 to 0.98), diabetes (0.97, 0.95 to 0.99), chronic renal failure (0.89, 0.85 to 0.90), cerebrovascular disease (0.96, 0.93 to 0.99), age >80 years (0.87, 0.85 to 0.90), and travel distances >30 km (0.95, 0.93 to 0.98). PPCI rates were higher for patients with previous percutaneous coronary intervention (1.09, 1.05 to 1.12) and among trusts with >5 interventional cardiologists (1.30, 1.25 to 1.34), more visiting interventional cardiologists (1-5: 1.31, 1.26 to 1.36; ≥6: 1.42, 1.35 to 1.49), and a 24 h, 7-days-a-week PPCI service (2.69, 2.58 to 2.81). Half of the unexplained variation in PPCI rates was due to between-trust differences.ConclusionsFollowing an 8 year implementation phase, PPCI utilisation rates stabilised at 85%. However, older and sicker patients were less likely to receive PPCI and there remained between-trust variation in PPCI rates not attributable to differences in staffing levels. Compliance with clinical pathways for STEMI is needed to ensure more equitable quality of care.

Project description:Importance:Multiple states publicly report a hospital's risk-adjusted mortality rate for percutaneous coronary intervention (PCI) as a quality measure. However, whether reported annual PCI mortality is associated with a hospital's future performance is unclear. Objective:To evaluate the association between reported risk-adjusted hospital PCI-related mortality and a hospital's future PCI-related mortality. Design, Setting, and Participants:This study used data from the New York Percutaneous Intervention Reporting System from January 1, 1998, to December 31, 2016, to assess hospitals that perform PCI. Exposures:Public-reported, risk-adjusted, 30-day mortality after PCI. Main Outcomes and Measures:The primary analysis evaluated the association between a hospital's reported risk-adjusted PCI-related mortality and future PCI-related mortality. The correlation between a hospital's observed to expected (O/E) PCI-related mortality rates each year and future O/E mortality ratios was assessed. Multivariable linear regression was used to examine the association between index year O/E mortality and O/E mortality in subsequent years while adjusting for PCI volume and patient severity. Results:This study included 67 New York hospitals and 960 hospital-years. Hospitals with low PCI-related mortality (O/E mortality ratio, ?1) and high mortality (O/E mortality ratio, >1) had inverse associations between their O/E mortality ratio in the index year and the subsequent change in the ratio (hospitals with low mortality, r?=?-0.45; hospitals with high mortality, r?=?-0.60). Little of the variation in risk-adjusted mortality was explained by prior performance. An increase in the O/E mortality ratio from 1.0 to 2.0 in the index year was associated with a higher O/E mortality ratio of only 0.15 (95% CI, 0.02-0.27) in the following year. Conclusions and Relevance:At hospitals with high or low PCI-related mortality rates, the rates largely regressed to the mean the following year. A hospital's risk-adjusted mortality rate was poorly associated with its future mortality. The annual hospital PCI-related mortality may not be a reliable factor associated with hospital quality to consider in a practice change or when helping patients select high-quality hospitals.

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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

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