Juan Sanchis1, Meritxell Soler2, Julio Núñez2, Vicente Ruiz3, Clara Bonanad2, Francesc Formiga4, Ernesto Valero2, Manuel Martínez-Sellés5, Francisco Marín6, Arancha Ruescas7, Sergio García-Blas2, Gema Miñana2, Emad Abu-Assi8, Héctor Bueno9, Albert Ariza-Solé10. 1. Servei de Cardiologia, Hospital Clínic Universitari de València, INCLIVA, Universitat de València, CIBERCV, València, Spain. Electronic address: sanchis_juafor@gva.es. 2. Servei de Cardiologia, Hospital Clínic Universitari de València, INCLIVA, Universitat de València, CIBERCV, València, Spain. 3. Facultat d'Infermeria, Universitat de València, València, Spain. 4. Unitat de Medicina Geriàtrica, Servei de medicina Interna, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain. 5. Servicio de Cardiología, Hospital Universitario Gregorio Marañón, CIBERCV, Universidad Complutense, Universidad Europea, Madrid, Spain. 6. Servicio de Cardiologı'a, Hospital Virgen de la Arrixaca, IMIB-Arrixaca, CIBERCV, El Palmar, Murcia, Spain. 7. Departament de Fisioteràpia, Universitat de València, València, Spain. 8. Servicio de Cardiologia, Hospital Alvaro Cunqueiro, Vigo, Pontevedra, Spain. 9. Servicio de Cardiología, Hospital 12 de Octubre, Madrid, Spain. 10. Servei de Cardiologia, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.
Abstract
BACKGROUND: The Charlson's is the most used comorbidity index. It comprises 19 comorbidities, some of which are infrequent in elderly patients with acute coronary syndrome (ACS), while some others are manifestations of cardiac disease rather than comorbidities. Our goal was to simplify comorbidity assessment in elderly non-ST-segment elevation ACS patients. METHODS: The study group consisted of 1 training (n = 920, 76 ± 7 years) and 1 testing (n = 532; 84 ± 4 years) cohorts. The end-point was all-cause mortality at 1-year follow-up. Comorbidities were assessed selecting those medical disorders other than cardiac disease that were independently associated with mortality by multivariable analysis. RESULTS: A total of 130 (14%) patients died in the training cohort. Six comorbidities were predictive: renal failure, anemia, diabetes, peripheral artery disease, cerebrovascular disease and chronic lung disease. The increase in the number of comorbidities yielded a gradient of risk on top of well-known clinical predictors: ≥3 comorbidities (27% mortality, HR = 1.90, 95% CI 1.20-3.03, p = .006); 2 comorbidities (16% mortality, HR = 1.29, 95% CI 0.81-2.04, p = .30); and 0-1 comorbidities (7.6% mortality, reference category). The discrimination accuracy (C-statistic = 0.80) and calibration (Hosmer-Lemeshow test, p = .20) of the predictive model using the 6 comorbidities was comparable to the predictive model using the Charlson index (C-statistic = 0.80; Hosmer-Lemeshow test, p = .70). Similar results were reproduced in the testing cohort (≥3 comorbidities: 24% mortality, HR = 2.37, 95% CI 1.25-4.49, p = .008; 2 comorbidities: 14% mortality, HR = 1.59, 95% CI 0.82-3.07, p = .20; 0-1 comorbidities: 7.5% reference category). CONCLUSION: A simplified comorbidity assessment comprising 6 comorbidities provides useful risk stratification in elderly patients with ACS.
BACKGROUND: The Charlson's is the most used comorbidity index. It comprises 19 comorbidities, some of which are infrequent in elderly patients with acute coronary syndrome (ACS), while some others are manifestations of cardiac disease rather than comorbidities. Our goal was to simplify comorbidity assessment in elderly non-ST-segment elevation ACS patients. METHODS: The study group consisted of 1 training (n = 920, 76 ± 7 years) and 1 testing (n = 532; 84 ± 4 years) cohorts. The end-point was all-cause mortality at 1-year follow-up. Comorbidities were assessed selecting those medical disorders other than cardiac disease that were independently associated with mortality by multivariable analysis. RESULTS: A total of 130 (14%) patients died in the training cohort. Six comorbidities were predictive: renal failure, anemia, diabetes, peripheral artery disease, cerebrovascular disease and chronic lung disease. The increase in the number of comorbidities yielded a gradient of risk on top of well-known clinical predictors: ≥3 comorbidities (27% mortality, HR = 1.90, 95% CI 1.20-3.03, p = .006); 2 comorbidities (16% mortality, HR = 1.29, 95% CI 0.81-2.04, p = .30); and 0-1 comorbidities (7.6% mortality, reference category). The discrimination accuracy (C-statistic = 0.80) and calibration (Hosmer-Lemeshow test, p = .20) of the predictive model using the 6 comorbidities was comparable to the predictive model using the Charlson index (C-statistic = 0.80; Hosmer-Lemeshow test, p = .70). Similar results were reproduced in the testing cohort (≥3 comorbidities: 24% mortality, HR = 2.37, 95% CI 1.25-4.49, p = .008; 2 comorbidities: 14% mortality, HR = 1.59, 95% CI 0.82-3.07, p = .20; 0-1 comorbidities: 7.5% reference category). CONCLUSION: A simplified comorbidity assessment comprising 6 comorbidities provides useful risk stratification in elderly patients with ACS.
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