ArticleJAMA network open2020
Development and Validation of the Chronic Disease Population Risk Tool (CDPoRT) to Predict Incidence of Adult Chronic Disease.
Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- A dual domain systematic review and meta-analysis of risk tool accuracy to predict cardiovascular morbidity in prehypertension and diabetic morbidity in prediabetes.Frontiers in endocrinology · 2025Pooled it
- A study protocol for a predictive model to assess population‑based risk of adverse pregnancy outcomes: The Adverse Pregnancy Outcomes Population Risk Tool (PregPoRT).Diagnostic and prognostic research · 2026Review
- Telomere-driven senescence accelerates tau pathology, neuroinflammation and neurodegeneration in a tauopathy mouse model.Acta neuropathologica communications · 2025Article
- Increasing Health Behaviors and Psychological Measures with an Adapted Version of the ACCELERATION Program.International journal of behavioral medicine · 2025Article
- Development and validation of a population-based risk algorithm for premature mortality in Canada: the Premature Mortality Population Risk Tool (PreMPoRT).BMJ public health · 2024Article
- Revolutionizing Chronic Heart Disease Management: The Role of IoT-Based Ambulatory Blood Pressure Monitoring System.Diagnostics (Basel, Switzerland) · 2024Article
- Modeling chronic disease risk across equity factors using a population-based prediction model: the Chronic Disease Population Risk Tool (CDPoRT).Journal of epidemiology and community health · 2024Article
- A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT).Diagnostic and prognostic research · 2024Article
- Article
- Predicting work disability among people with chronic conditions: a prospective cohort study.Scientific reports · 2023Article
- Understanding International Variations in Kidney Failure Incidence and Initiation of Replacement Therapy.Kidney international reports · 2022Article
- The effect of video-based multimedia training on knowledge, attitude, and performance in breast self-examination.BMC women's health · 2022Article
- An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes.Journal of healthcare engineering · 2022Article
- Hepatocellular Carcinoma Risk Prediction in the NIH-AARP Diet and Health Study Cohort: A Machine Learning Approach.Journal of hepatocellular carcinoma · 2022Article
- A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT).Diagnostic and prognostic research · 2020Article
- Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Importance: Predicting chronic disease incidence for the population provides a comprehensive picture to health policy makers of their jurisdictions' overall future chronic disease burden. However, no population-based risk algorithm exists for estimating the risk of first major chronic disease. Objective: To develop and validate the Chronic Disease Population Risk Tool (CDPoRT), a population risk algorithm that predicts the 10-year incidence of the first major chronic disease in the adult population. Design, Setting, and Participants: In this cohort study, CDPoRT was developed and validated with 6 cycles of the Canadian Community Health Survey, linked to administrative data from January 2000 to December 2014. Development and internal validation (bootstrap and split sample) of CDPoRT occurred in Ontario, Canada, from June 2018 to April 2019 followed by external validation in Manitoba from May 2019 to July 2019. The study cohorts included 133 991 adults (≥20 years) representative of the Ontario and Manitoba populations who did not have a history of major chronic disease. Exposures: Predictors were routinely collected risk factors from the Canadian Community Health Survey, such as sociodemographic factors (eg, age), modifiable lifestyle risk factors (ie, alcohol consumption, cigarette smoking, unhealthy diet, and physical inactivity), and other health-related factors (eg, body mass index). Main Outcomes and Measures: Six major chronic diseases were considered, as follows: congestive heart failure, chronic obstructive pulmonary disease, diabetes, myocardial infarction, lung cancer, and stroke. Sex-specific CDPoRT algorithms were developed with a Weibull model. Model performance was evaluated with measures of overall predictive performance (eg, Brier score), discrimination (eg, Harrell C index), and calibration (eg, calibration curves). Results: The Ontario cohort (n = 118 747) was younger (mean [SD] age, 45.6 [16.1] vs 46.3 [16.4] years), had more immigrants (23 808 [20.0%] vs 1417 [10.7%]), and had a lower mean (SD) body mass index (26.9 [5.1] vs 27.7 [5.4]) than the Manitoba cohort (n = 13 244). During development, the full and parsimonious CDPoRT models had similar Brier scores (women, 0.087; men, 0.091), Harrell C index values (women, 0.779; men, 0.783), and calibration curves. A simple version consisting of cigarette smoking, age, and body mass index performed slightly worse than the other versions (eg, Brier score for women, 0.088; for men, 0.092). Internal validation showed consistent performance across models, and CDPoRT performed well during external validation. For example, the female parsimonious version had C index values for bootstrap, split sample, and external validation of 0.778, 0.776, and 0.752, respectively. Conclusions and Relevance: In this study, CDPoRT provided accurate, population-based risk estimates for the first major chronic disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.