Lasso Regression for the Prediction of Intermediate Outcomes Related to Cardiovascular Disease Prevention Using the TRANSIT Quality Indicators
Bibliographic Data
| ID | 9099743 |
|---|---|
| Authors | Cynthia Khanji (Université de Montréal, corresponding author), Lyne Lalonde (Université de Montréal, corresponding author), Céline Bareil (HEC Montréal, University of Montreal), Marie-Therese Lussier (0000-0002-8242-9868, Université de Montréal), Sylvie Perreault (0000-0002-0066-0127, Université de Montréal, corresponding author), Mireille E Schnitzer (0000-0001-8049-9646, Université de Montréal, corresponding author) |
| Year | 2019 |
| Volume | 57 |
| Issue | 1 |
| Pages | 63-72 |
| Publication date | 2019-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000001014 |
| PMID | 30439793 |
| OpenAlex | W2900937465 |
| Language | EN |
| References cited | 40 |
BACKGROUND: Cardiovascular disease morbidity and mortality are largely influenced by poor control of hypertension, dyslipidemia, and diabetes. Process indicators are essential to monitor the effectiveness of quality improvement strategies. However, process indicators should be validated by demonstrating their ability to predict desirable outcomes. The objective of this study is to identify an effective method for building prediction models and to assess the predictive validity of the TRANSIT indicators. METHODS: On the basis of blood pressure readings and laboratory test results at baseline, the TRANSIT study population was divided into 3 overlapping subpopulations: uncontrolled hypertension, uncontrolled dyslipidemia, and uncontrolled diabetes. A classic statistical method, a sparse machine learning technique, and a hybrid method combining both were used to build prediction models for whether a patient reached therapeutic targets for hypertension, dyslipidemia, and diabetes. The final models' performance for predicting these intermediate outcomes was established using cross-validated area under the curves (cvAUC). RESULTS: At baseline, 320, 247, and 303 patients were uncontrolled for hypertension, dyslipidemia, and diabetes, respectively. Among the 3 techniques used to predict reaching therapeutic targets, the hybrid method had a better discriminative capacity (cvAUCs=0.73 for hypertension, 0.64 for dyslipidemia, and 0.79 for diabetes) and succeeded in identifying indicators with a better capacity for predicting intermediate outcomes related to cardiovascular disease prevention. CONCLUSIONS: Even though this study was conducted in a complex population of patients, a set of 5 process indicators were found to have good predictive validity based on the hybrid method
Diabetes mellitus · Disease · Dyslipidemia · Environmental health · Physical therapy · Population · Advanced Causal Inference Techniques · Artificial Intelligence in Healthcare · Internal Medicine · Medicine · Statistical Methods and Inference
Model Selection and Estimation in Regression with Grouped Variables
Measuring the Accuracy of Diagnostic Systems
The Quality of Care
Evaluating the Quality of Medical Care
Regression Shrinkage and Selection Via the Lasso
Association Between Performance Measures and Glycemic Control Among Patients With Diabetes in a Community-wide Primary Care Cohort
Individual and Institutional Variables Which May Serve as Indicators of Quality of Medical Care
| Citation velocity | historical |
|---|---|
| Highly cited | No |