Dataset Information

Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT).

ABSTRACT:

Background

Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours.

Methods

Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82,259 Ontarians who were followed for a median of 8.6 years (688,000 person-years follow-up) starting in 2001. Predictive algorithms for 5-year incident stroke resulting in hospitalization were created and then validated in a similar 2007 survey of 28,605 respondents (median 4.2 years follow-up).

Results

We observed 3236 incident stroke events (1551 resulting in hospitalization; 1685 in the community setting without hospital admission). The final algorithms were discriminating (C-stat: 0.85, men; 0.87, women) and well-calibrated (in 65 of 67 subgroups for men; 61 of 65 for women). An index was developed to summarize cumulative relative risk of incident stroke from health behaviours and stress. For men, each point on the index corresponded to a 12% relative risk increase (180% risk difference, lowest (0) to highest (9) scores). For women, each point corresponded to a 14% relative risk increase (340% difference, lowest (0) to highest (11) scores). Algorithms for secondary stroke outcomes (stroke resulting in death; classified as ischemic; excluding transient ischemic attack; and in the community setting) had similar health behaviour risk hazards.

Conclusion

Incident stroke can be accurately predicted using self-reported information focused on health behaviours. Risk assessment can be performed with population health surveys to support population health planning or outside of clinical settings to support patient-focused prevention.

SUBMITTER: Manuel DG

PROVIDER: S-EPMC4670216 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT).

Manuel Douglas G DG Tuna Meltem M Perez Richard R Tanuseputro Peter P Hennessy Deirdre D Bennett Carol C Rosella Laura L Sanmartin Claudia C van Walraven Carl C Tu Jack V JV

PloS one 20151204 12

<h4>Background</h4>Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours.<h4>Methods</h4>Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82,259 Ontarians who were followed for a median of 8.6 years (688,000 person-years follow-up) starting in 2001. Predictive algorithms for 5-y ...[more]

PMID: 26637172

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Project description:BACKGROUND:Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke predictive model using a big electronic health record database. METHODS AND RESULTS:The Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within 3 years after study enrollment. The DNN was evaluated using the area under the receiver operating characteristic curve (AUC or c-statistic). The development dataset included 672,214 patients (a total of 8,952,000 records) of whom 2,060 patients had stroke events. The mean age of the population was 35.5±20.2 years, with 48.5% men. The model achieved AUC values of 0.920 (95% confidence interval [CI], 0.908-0.932) in testing dataset 1 and 0.925 (95% CI, 0.914-0.937) in testing dataset 2. Under a high sensitivity operating point, the sensitivity and specificity were 92.5% and 79.8% for testing dataset 1; 91.8% and 79.9% for testing dataset 2. Under a high specificity operating point, the sensitivity and specificity were 80.3% and 87.5% for testing dataset 1; 83.7% and 87.5% for testing dataset 2. The DNN model maintained high predictability 5 years after being developed. The model achieved similar performance to other clinical risk assessment scores. CONCLUSIONS:Using a DNN algorithm on this large electronic health record database is capable of obtaining a high performing model for assessment of ischemic stroke risk. Further research is needed to determine whether such a DNN-based IDSS could lead to an improvement in clinical practice.

Project description:BACKGROUND:Inequality of opportunity, defined as differences in the prospects for upward social mobility, might have important consequences for health. Diminished opportunity can lower the motivation to invest in future health by reducing economic returns to health investments and undermining hope. We estimated the association between county-level economic opportunity and individual-level health in young adults in the general US population. METHODS:In this population-based cross-sectional study, we used individual-level data from the 2009-12 United States Behavioral Risk Factor Surveillance Surveys. Our primary outcomes were current self-reported overall health and the number of days of poor physical and mental health in the last month. Economic opportunity was measured by the county-averaged national income rank attained by individuals born to families in the lowest income quartile. We restricted our sample to adults aged 25-35 years old to match the data used to assign exposure. Multivariable ordinary least squares and probit models were used to estimate the association between the outcomes and economic opportunity. We adjusted for a range of demographic and socioeconomic characteristics, including age, sex, race, education, income, access to health care, area income inequality, segregation, and social capital. FINDINGS:We assessed nearly 147?000 individuals between the ages of 25 years and 35 years surveyed from 2009 to 2012. In models adjusting for individual-level demographics and county-level socioeconomic characteristics, increases in county-level economic opportunity were associated with greater self-reported overall health. An interdecile increase in economic opportunity was associated with 0·76 fewer days of poor mental health (95% CI -1·26 to -0·25) and 0·53 fewer days of poor physical health (-0·96 to -0·09) in the last month. The results were robust to sensitivity analyses. INTERPRETATION:Economic opportunity is independently associated with self-reported health and health behaviours. Policies seeking to expand economic opportunities might have important spillover effects on health. FUNDING:Robert Wood Johnson Foundation Health and Society Scholars Program.

Dataset Information

Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT).

Background

Methods

Results

Conclusion

Publications

Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT).

Similar Datasets

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