Dataset Information

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT).

ABSTRACT: Background: Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches.

Methods: PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007-2008, 2009-2010, and 2011-2012) from the development cohort (CCHS cycles 2000-2001, 2003-2004, and 2005-2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke's R²), calibration (e.g., calibration plots), and discrimination (e.g., Harrell's concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers.

Discussion: Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.

SUBMITTER: Rosella LC

PROVIDER: S-EPMC7640636 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT).

Rosella Laura C LC O'Neill Meghan M Fisher Stacey S Hurst Mackenzie M Diemert Lori L Kornas Kathy K Hong Andy A Manuel Douglas G DG

Diagnostic and prognostic research 20201104 1

<h4>Background</h4>Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that wi ...[more]

PMID: 33292834

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Project description:BackgroundChronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population.Patients and methodsThe current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors.ResultsCox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155-1.348]); albumin (HR 0.952 [95% CI 0.931-0.974); age (HR 1.033 [95% CI 1.022-1.044]); globulin (HR 1.057 [95% CI 1.038-1.077]); smoking years (HR 1.011 [95% CI 1.006-1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007-1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010-1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017-1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the "Survival Curve Prediction System for COPD patients", was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time.ConclusionThe current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).

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Project description:BackgroundStudies from high-income countries reported reduced life expectancy in children with cerebral palsy (CP), while no population-based study has evaluated mortality of children with CP in sub-Saharan Africa. This study aimed to estimate the mortality rate (MR) of children with CP in a rural region of Uganda and identify risk factors and causes of death (CODs).Methods and findingsThis population-based, longitudinal cohort study was based on data from Iganga-Mayuge Health and Demographic Surveillance System in eastern Uganda. We identified 97 children (aged 2-17 years) with CP in 2015, whom we followed to 2019. They were compared with an age-matched cohort from the general population (n = 41 319). MRs, MR ratios (MRRs), hazard ratios (HRs), and immediate CODs were determined. MR was 3952 per 100 000 person years (95% CI 2212-6519) in children with CP and 137 per 100 000 person years (95% CI 117-159) in the general population. Standardized MRR was 25·3 in the CP cohort, compared with the general population. In children with CP, risk of death was higher in those with severe gross motor impairments than in those with milder impairments (HR 6·8; p = 0·007) and in those with severe malnutrition than in those less malnourished (HR = 3·7; p = 0·052). MR was higher in females in the CP cohort, with a higher MRR in females (53·0; 95% CI 26·4-106·3) than in males (16·3; 95% CI 7·2-37·2). Age had no significant effect on MR in the CP cohort, but MRR was higher at 10-18 years (39·6; 95% CI 14·2-110·0) than at 2-6 years (21·0; 95% CI 10·2-43·2). Anaemia, malaria, and other infections were the most common CODs in the CP cohort.ConclusionsRisk of premature death was excessively high in children with CP in rural sub-Saharan Africa, especially in those with severe motor impairments or malnutrition. While global childhood mortality has significantly decreased during recent decades, this observed excessive mortality is a hidden humanitarian crisis that needs to be addressed.

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Dataset Information

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT).

Publications

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT).

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