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Updated: Mar 25, 2026

Home-Based Prescribed Pulmonary Exercise in Patients with Stable Chronic Obstructive Pulmonary Disease
Published on: August 24, 2019
Use of exercise capacity to improve SCORE risk prediction model in asymptomatic adults
Ariel Israel1, Shaye Kivity2, Yechezkel Sidi3
1Leviev Heart Centre, Chaim Sheba Medical Centre, Tel-Hashomer 52621, Israel Clalit Health Services, Jerusalem, Israel.
Aims:
The SCORE risk estimation system is used for cardiovascular risk stratification in apparently healthy adults and is based on known cardiovascular risk factors. The purpose of the current study was to evaluate whether exercise capacity can improve the accuracy of the SCORE overall survival risk estimation.
Methods And Results:
We investigated 22 878 asymptomatic men and women who were annually screened in a tertiary medical centre. All subjects were free of known ischaemic heart disease, and had completed maximal exercise stress test according to the Bruce protocol. The SCORE risk estimation system was used to evaluate individual cardiovascular risk for all subjects. The primary endpoint was mortality, after exclusion of patients with metastatic cancer during follow-up. The incremental contribution of exercise capacity in predicting the risk of death was evaluated by net reclassification improvement (NRI) and area under the receiver operating curve (AUROC). Mean age of the study population was 47.4 ± 10.3, and 71.6% were men. There were 505 (2.21%) deaths during a mean follow-up of 9.2 ± 4.1 years. Kaplan-Meier survival analysis showed that both SCORE and low exercise capacity were associated with reduced survival. When added to the SCORE risk prediction, exercise capacity allowed more accurate risk stratification: NRI analysis showed an overall improvement of 56.8% in the accuracy of classification and the AUROC increased (0.782 vs. 0.766).
Conclusion:
Both SCORE and exercise capacity are strong independent predictors of all-cause mortality. The addition of exercise capacity to the SCORE risk model can improve the accuracy of the model.
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