Prediction of Long-Term Incidence of Chronic Cardiovascular and Cardiopulmonary Diseases in Primary Care Patients for Population Health Monitoring: The Intermountain Chronic Disease Model (ICHRON)

Intermittent fasting and changes in clinical risk scores: Secondary analysis of a randomized controlled trial

Background

Intermittent fasting may increase longevity and lower cardiometabolic risk. This study evaluated whether fasting modifies clinical risk scores for mortality [i.e., Intermountain Mortality Risk Score (IMRS)] or chronic diseases [e.g., Pooled Cohort Risk Equations (PCRE), Intermountain Chronic Disease score (ICHRON)].

Methods and results

Subjects (N = 71) completing the WONDERFUL trial were aged 21-70 years, had ≥1 metabolic syndrome criteria, elevated cholesterol, and no anti-diabetes medications, statins, or chronic diseases. The intermittent fasting arm underwent 24-h water-only fasting twice-per-week for 4 weeks and once-per-week for 22 weeks (26 weeks total). Analyses examined the IMRS change score at 26 weeks vs. baseline between intermittent fasting (n = 38) and ad libitum controls (n = 33), and change scores for PCRE, ICHRON, HOMA-IR, and a metabolic syndrome score (MSS). Age averaged 49 years; 65% were female. Intermittent fasting increased IMRS (0.78 ± 2.14 vs. controls: -0.61 ± 2.56; p = 0.010) but interacted with baseline IMRS (p-interaction = 0.010) to reduce HOMA-IR (but not MSS) more in subjects with higher baseline IMRS (median HOMA-IR change: fasters, -0.95; controls, +0.05) vs. lower baseline IMRS (-0.29 vs. -0.32, respectively). Intermittent fasting reduced ICHRON (-0.92 ± 2.96 vs. 0.58 ± 3.07; p = 0.035) and tended to reduce PCRE (-0.20 ± 0.22 vs. -0.14 ± 0.21; p = 0.054).

Conclusions

Intermittent fasting increased 1-year IMRS mortality risk, but decreased 10-year chronic disease risk (PCRE and ICHRON). It also reduced HOMA-IR more in subjects with higher baseline IMRS. Increased IMRS suggests fasting may elevate short-term mortality risk as a central trigger for myriad physiological responses that elicit long-term health improvements. Increased IMRS may also reveal short-term fasting-induced safety concerns.

Development of a prognostic prediction model to estimate the risk of multiple chronic diseases: constructing a copula-based model using Canadian primary care electronic medical record data

Introduction

The ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Current methods for prognostic prediction modelling are insufficient for the estimation of risk for multiple outcomes, as they do not properly capture the dependence that exists between outcomes.

Objectives

We developed a multivariate prognostic prediction model for the 5-year risk of diabetes, hypertension, and osteoarthritis that quantifies and accounts for the dependence between each disease using a copula-based model.

Methods

We used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2009 onwards, a collection of electronic medical records submitted by participating primary care practitioners across Canada. We identified patients 18 years and older without all three outcome diseases and observed any incident diabetes, osteoarthritis, or hypertension within 5-years, resulting in a large retrospective cohort for model development and internal validation (n=425,228). First, we quantified the dependence between outcomes using unadjusted and adjusted Ø coefficients. We then estimated a copula-based model to quantify the non-linear dependence between outcomes that can be used to derive risk estimates for each outcome, accounting for the observed dependence. Copula-based models are defined by univariate models for each outcome and a dependence function, specified by the parameter θ. Logistic regression was used for the univariate models and the Frank copula was selected as the dependence function.

Results

All outcome pairs demonstrated statistically significant dependence that was reduced after adjusting for covariates. The copula-based model yielded statistically significant θ parameters in agreement with the adjusted and unadjusted Ø coefficients. Our copula-based model can effectively be used to estimate trivariate probabilities.

Discussion

Quantitative estimates of multimorbidity risk inform discussions between patients and their primary care practitioners around prevention in an effort to reduce the incidence of multimorbidity.

Intermountain chronic disease risk score (ICHRON) validation for prediction of incident chronic disease diagnoses in an australian primary prevention population

BACKGROUND

The Intermountain Chronic Disease Risk Score (ICHRON) is a primary prevention risk prediction tool that uses commonly ordered blood tests and is designed to be calculated by the electronic health record. ICHRON was highly predictive of 3-year chronic disease (ChrD) diagnosis in an internal validation; however, external validation is needed.

METHODS

ICHRON was calculated among patients from a region of Australia using sex-specific weightings of age and components of the comprehensive metabolic panel and the complete blood count. Original ICHRON weightings and risk stratification thresholds from the US-based derivation at Intermountain Healthcare were used. ICHRON was evaluated as a predictor of an incident 3-year ChrD diagnosis (coronary artery disease, myocardial infarction, heart failure, atrial fibrillation, stroke, dementia, diabetes, renal failure, chronic obstructive pulmonary disease, and peripheral vascular disease).

RESULTS

A total 5,512 females (49.6 ± 19.2) and 3,461 males (53.0 ± 18.6) were studied. Among females, 50.3%, 33.7% and 16.0% were low, moderate, and high-risk, respectively; and for males, 39.6%, 35.0%, and 25.4%. Frequency of a 3-year ChrD diagnosis among females was 2.2%, 8.5%, and 15.9% for low, moderate, and high-risk groups, respectively, and 4.7%, 9.7%, and 20.0% among males. C-statistics were 0.726 (0.700, 0.752) for females and 0.694 (0.665, 0.724) for males.

CONCLUSION

ICHRON predicted ChrD diagnosis at 3-years among an external, geographically distant validation cohort. These findings show the value of ICHRON for primary care patients in distinct locales. Additionally, electronic calculation of ICHRON empowers the clinical use of this tool to identify and differentially manage and treat high-risk patients.

Predicting postdischarge hospital-associated venous thromboembolism among medical patients using a validated mortality risk score derived from common biomarkers

BACKGROUND

Discharged medical patients are at risk for venous thromboembolism (VTE). It is difficult to identify which discharged patients would benefit from extended duration thromboprophylaxis. The Intermountain Risk Score is a prediction score derived from discrete components of the complete blood cell count and basic metabolic panel and is highly predictive of 1-year mortality. We sought to ascertain if the Intermountain Risk Score might also be predictive of 90-day postdischarge hospital-associated VTE (HA-VTE).

METHODS

We applied the Intermountain Risk Score to 60 064 medical patients who survived 90 days after discharge and report predictiveness for HA-VTE. Area under the receiver operating curve analyses were performed. We then assessed whether the Intermountain Risk Score improved prediction of 2 existing VTE risk assessment models.

RESULTS

The Intermountain Risk Score poorly predicted HA-VTE (area under the curve = 0.58; 95% confidence interval [CI], 0.56-0.60). Each clinical risk assessment model was superior to the Intermountain Risk Score (UTAH area under the curve, 0.63; Kucher area under the curve, 0.62; Intermountain Risk Score area under the curve, 0.58; P < .001 for each comparison). Adding the Intermountain Risk Score to these scores did not substantially improve the performance of either risk assessment model (UTAH + Intermountain Risk Score, 0.65; Kucher + Intermountain Risk Score, 0.64).

CONCLUSION

The Intermountain Risk Score demonstrated poor predictiveness for HA-VTE when compared to existing risk assessment models. Adding the Intermountain Risk Score to existing risk assessment models did not improve upon either risk assessment model alone to justify the added complexity.

Development and Validation of the Chronic Disease Population Risk Tool (CDPoRT) to Predict Incidence of Adult Chronic Disease

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.