BACKGROUND: Many authors have analysed premature mortality in cohorts of type 2 diabetic patients, but no analyses have assessed mortality in hospitalised diabetic patients. AIM: To construct predictive models to estimate the likelihood of all-cause mortality and cardiovascular mortality in type 2 diabetic inpatients. DESIGN: Cohort study with follow-up from 2010 to 2014. METHODS: We evaluated mortality in a randomly selected cohort of 112 type 2 diabetic inpatients at the Hospital of Elda (Spain) in 2010-2012. OUTCOMES: all-cause mortality and cardiovascular mortality during the follow-up. Other variables: gender, age, depression, asthma/chronic obstructive pulmonary disease, hypertension, dyslipidemia, insulin, pills, smoking, walking, baseline blood glucose and creatinine. Predictive tables with risk groups were constructed to estimate the likelihood of all-cause mortality and cardiovascular mortality. Calculations were made of the area under the ROC curve (AUC). RESULTS: During the follow-up, 52 inpatients died (46.4%, 95% CI, confidence interval: 37.2-55.7%), 22 because of cardiovascular causes (19.6%, 95% CI: 12.3-27.0%). The mean follow-up time was 2.7 ± 1.5 years. The AUC for the all-cause mortality model was 0.84 (95% CI: 0.77-0.92, p < 0.001). Associated parameters: pills, smoking, walking, gender, insulin and age. The AUC for the cardiovascular mortality model was 0.79 (95% CI: 0.67-0.91, p < 0.001). Associated parameters: age, pills, walking, smoking, depression and insulin. CONCLUSIONS: This study provides tools to predict premature mortality in type 2 diabetic inpatients. However, before their general application they require joint validation by the internal medicine unit, emergency department, primary healthcare unit and endocrinology service to enable better prediction of the prognosis and more adequate decision-taking.
BACKGROUND: Many authors have analysed premature mortality in cohorts of type 2 diabeticpatients, but no analyses have assessed mortality in hospitalised diabeticpatients. AIM: To construct predictive models to estimate the likelihood of all-cause mortality and cardiovascular mortality in type 2 diabetic inpatients. DESIGN: Cohort study with follow-up from 2010 to 2014. METHODS: We evaluated mortality in a randomly selected cohort of 112 type 2 diabetic inpatients at the Hospital of Elda (Spain) in 2010-2012. OUTCOMES: all-cause mortality and cardiovascular mortality during the follow-up. Other variables: gender, age, depression, asthma/chronic obstructive pulmonary disease, hypertension, dyslipidemia, insulin, pills, smoking, walking, baseline blood glucose and creatinine. Predictive tables with risk groups were constructed to estimate the likelihood of all-cause mortality and cardiovascular mortality. Calculations were made of the area under the ROC curve (AUC). RESULTS: During the follow-up, 52 inpatients died (46.4%, 95% CI, confidence interval: 37.2-55.7%), 22 because of cardiovascular causes (19.6%, 95% CI: 12.3-27.0%). The mean follow-up time was 2.7 ± 1.5 years. The AUC for the all-cause mortality model was 0.84 (95% CI: 0.77-0.92, p < 0.001). Associated parameters: pills, smoking, walking, gender, insulin and age. The AUC for the cardiovascular mortality model was 0.79 (95% CI: 0.67-0.91, p < 0.001). Associated parameters: age, pills, walking, smoking, depression and insulin. CONCLUSIONS: This study provides tools to predict premature mortality in type 2 diabetic inpatients. However, before their general application they require joint validation by the internal medicine unit, emergency department, primary healthcare unit and endocrinology service to enable better prediction of the prognosis and more adequate decision-taking.
Authors: Dolores Ramírez-Prado; Antonio Palazón-Bru; David Manuel Folgado-de la Rosa; María Ángeles Carbonell-Torregrosa; Ana María Martínez-Díaz; Damian Robert James Martínez-St John; Vicente Francisco Gil-Guillén Journal: PeerJ Date: 2015-06-02 Impact factor: 2.984
Authors: David López-Bru; Antonio Palazón-Bru; David Manuel Folgado-de la Rosa; Vicente Francisco Gil-Guillén Journal: PLoS One Date: 2015-06-26 Impact factor: 3.240
Authors: Emma Mares-García; Antonio Palazón-Bru; David Manuel Folgado-de la Rosa; Avelino Pereira-Expósito; Álvaro Martínez-Martín; Ernesto Cortés-Castell; Vicente Francisco Gil-Guillén Journal: PeerJ Date: 2017-06-30 Impact factor: 2.984
Authors: Cesar Azrak; Manuel Vicente Baeza-Díaz; Antonio Palazón-Bru; Carmen Hernández-Martínez; Aída Navarro-Navarro; José Juan Martínez-Toldos; Vicente Francisco Gil-Guillén Journal: Medicine (Baltimore) Date: 2015-09 Impact factor: 1.817
Authors: Luis Miguel Artigao-Ródenas; Julio Antonio Carbayo-Herencia; Antonio Palazón-Bru; Juan Antonio Divisón-Garrote; Carlos Sanchis-Domènech; Isabel Vigo-Aguiar; Vicente Francisco Gil-Guillén Journal: Medicine (Baltimore) Date: 2015-11 Impact factor: 1.817