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Validation of a new predictive risk model: measuring the impact of the major modifiable risks of death for patients and populations.

Lim SS, Carnahan E, Nelson EC, Gillespie CW, Mokdad AH, Murray CJ, Fisher ES - Popul Health Metr (2015)

Bottom Line: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

View Article: PubMed Central - PubMed

Affiliation: Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA 98121 USA.

ABSTRACT

Background: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.

Methods: We used data on the distribution of exposure to 12 major behavioral and biometric risk factors inthe US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult's 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.

Results: Predicted risk showed good discrimination with an area under the receiver operating characteristic (ROC) curve of 0.84 (standard error 0.01) for women and 0.84 (SE 0.01) for men. Across deciles of predicted risk, mortality was accurately predicted in men ((Χ (2) statistic = 12.3 for men, p=0.196) but slightly overpredicted in the highest decile among women (Χ (2) statistic = 22.8, p=0.002). Mortality risk was highly concentrated; for example, among those age 30-44 years, 5.1 % (95 % CI 4.1 % - 6.0 %) of the male and 5.9 % (95 % CI 4.8 % - 6.9 %) of the female population accounted for 25 % of the risk of death.

Conclusion: The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

No MeSH data available.


Related in: MedlinePlus

Receiver operator characteristic (ROC) curve for risk score (NHANES 1988–1994 and 1999–2004). Note: Males: green curve, Females: red curve
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Fig2: Receiver operator characteristic (ROC) curve for risk score (NHANES 1988–1994 and 1999–2004). Note: Males: green curve, Females: red curve

Mentions: Additional file 1 summarizes the characteristics of the 8,241 NHANES respondents included in the validation dataset. By the end of 2006, 696 deaths (419 men and 277 women) occurred in this cohort. The risk model was able to discriminate well between individuals who died and those who survived, with an area under the curve (AUC) of 0.84 (SE = 0.01) for women and 0.84 (SE = 0.01) for men (Fig. 2) for deaths from any cause. The risk model also accurately predicted the risk of death across deciles (Fig. 3) among men (Χ2 = 12.3, p = .196). Risk was slightly overestimated in the highest risk decile among women (Χ2 = 22.8, p = .002). These results indicate that the risk model is sufficiently accurate for use as a predictor of mortality risk.Fig. 2


Validation of a new predictive risk model: measuring the impact of the major modifiable risks of death for patients and populations.

Lim SS, Carnahan E, Nelson EC, Gillespie CW, Mokdad AH, Murray CJ, Fisher ES - Popul Health Metr (2015)

Receiver operator characteristic (ROC) curve for risk score (NHANES 1988–1994 and 1999–2004). Note: Males: green curve, Females: red curve
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4591717&req=5

Fig2: Receiver operator characteristic (ROC) curve for risk score (NHANES 1988–1994 and 1999–2004). Note: Males: green curve, Females: red curve
Mentions: Additional file 1 summarizes the characteristics of the 8,241 NHANES respondents included in the validation dataset. By the end of 2006, 696 deaths (419 men and 277 women) occurred in this cohort. The risk model was able to discriminate well between individuals who died and those who survived, with an area under the curve (AUC) of 0.84 (SE = 0.01) for women and 0.84 (SE = 0.01) for men (Fig. 2) for deaths from any cause. The risk model also accurately predicted the risk of death across deciles (Fig. 3) among men (Χ2 = 12.3, p = .196). Risk was slightly overestimated in the highest risk decile among women (Χ2 = 22.8, p = .002). These results indicate that the risk model is sufficiently accurate for use as a predictor of mortality risk.Fig. 2

Bottom Line: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

View Article: PubMed Central - PubMed

Affiliation: Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA 98121 USA.

ABSTRACT

Background: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.

Methods: We used data on the distribution of exposure to 12 major behavioral and biometric risk factors inthe US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult's 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.

Results: Predicted risk showed good discrimination with an area under the receiver operating characteristic (ROC) curve of 0.84 (standard error 0.01) for women and 0.84 (SE 0.01) for men. Across deciles of predicted risk, mortality was accurately predicted in men ((Χ (2) statistic = 12.3 for men, p=0.196) but slightly overpredicted in the highest decile among women (Χ (2) statistic = 22.8, p=0.002). Mortality risk was highly concentrated; for example, among those age 30-44 years, 5.1 % (95 % CI 4.1 % - 6.0 %) of the male and 5.9 % (95 % CI 4.8 % - 6.9 %) of the female population accounted for 25 % of the risk of death.

Conclusion: The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

No MeSH data available.


Related in: MedlinePlus