INTRODUCTION AND OBJECTIVES: The aim was to determine the usefulness of the hospital discharge Minimum Basic Data Set (MBDS) for predicting in-hospital mortality with coronary bypass surgery by using data from a prospective observational study as a reference. METHODS: The observational study involved collecting data on all patients undergoing first coronary bypass surgery at five hospitals in Catalonia, Spain between November 2001 and November 2003. In addition, data covering the same period and hospitals were obtained from the MBDS for procedure code 36.1. We investigated the concordance between the information from the two data sources and logistic regression was used to derive predictive models for in-hospital mortality. The model derived using MBDS data was validated using data from the prospective observational study and MBDS data for the years 2004-2006. Model validity was evaluated using discrimination and calibration indices. RESULTS: Some 4.1% of cases in the observational study could not be found in the MBDS. The concordance between the two data sources was highly variable and generally low (kappa values ranged from 0.16 to 0.79). The discriminative ability of the MBDS model was equivalent to that of the observational study model (c=0.80 vs. c=0.79), but when the validity of the former was tested using prospective data and MBDS data for 2004-2006, the discrimination c-index decreased to 0.76 and 0.65, respectively, and the calibration worsened significantly (P< .001). CONCLUSIONS: The risk of in-hospital mortality following coronary surgery cannot be accurately evaluated using MBDS data. However, our results indicate that their use as a predictive tool could be improved.
INTRODUCTION AND OBJECTIVES: The aim was to determine the usefulness of the hospital discharge Minimum Basic Data Set (MBDS) for predicting in-hospital mortality with coronary bypass surgery by using data from a prospective observational study as a reference. METHODS: The observational study involved collecting data on all patients undergoing first coronary bypass surgery at five hospitals in Catalonia, Spain between November 2001 and November 2003. In addition, data covering the same period and hospitals were obtained from the MBDS for procedure code 36.1. We investigated the concordance between the information from the two data sources and logistic regression was used to derive predictive models for in-hospital mortality. The model derived using MBDS data was validated using data from the prospective observational study and MBDS data for the years 2004-2006. Model validity was evaluated using discrimination and calibration indices. RESULTS: Some 4.1% of cases in the observational study could not be found in the MBDS. The concordance between the two data sources was highly variable and generally low (kappa values ranged from 0.16 to 0.79). The discriminative ability of the MBDS model was equivalent to that of the observational study model (c=0.80 vs. c=0.79), but when the validity of the former was tested using prospective data and MBDS data for 2004-2006, the discrimination c-index decreased to 0.76 and 0.65, respectively, and the calibration worsened significantly (P< .001). CONCLUSIONS: The risk of in-hospital mortality following coronary surgery cannot be accurately evaluated using MBDS data. However, our results indicate that their use as a predictive tool could be improved.
Authors: Manuel Villanueva-Martınez; Valentın Hernandez-Barrera; Francisco Chana-Rodríguez; José Rojo-Manaute; Antonio Rıos-Luna; Jesus San Roman Montero; Angel Gil-de-Miguel; Rodrigo Jimenez-Garcia Journal: BMC Musculoskelet Disord Date: 2012-03-19 Impact factor: 2.362
Authors: Ana Lopez-de-Andrés; Isabel Jiménez-Trujillo; Rodrigo Jiménez-García; Valentín Hernández-Barrera; José M de Miguel-Yanes; Manuel Méndez-Bailón; Napoleón Perez-Farinos; Miguel Ángel Salinero-Fort; Pilar Carrasco-Garrido Journal: Cardiovasc Diabetol Date: 2015-05-07 Impact factor: 9.951
Authors: Ana Lopez-de-Andres; Rodrigo Jimenez-Garcia; Valentin Hernandez-Barrera; Isabel Jimenez-Trujillo; Carmen Gallardo-Pino; Angel Gil de Miguel; Pilar Carrasco-Garrido Journal: PLoS One Date: 2014-01-15 Impact factor: 3.240
Authors: Ana Lopez-de-Andres; Rodrigo Jimenez-García; Valentin Hernandez-Barrera; Napoleon Perez-Farinos; Jose M de Miguel-Yanes; Manuel Mendez-Bailon; Isabel Jimenez-Trujillo; Angel Gil de Miguel; Carmen Gallardo Pino; Pilar Carrasco-Garrido Journal: Cardiovasc Diabetol Date: 2014-01-03 Impact factor: 9.951
Authors: Ana Lopez-de-Andrés; Ma Isabel Jiménez-Trujillo; Valentín Hernández-Barrera; José Ma de Miguel-Yanes; Manuel Méndez-Bailón; Napoleón Perez-Farinos; Carmen de Burgos Lunar; Juan Cárdenas-Valladolid; Miguel Ángel Salinero-Fort; Rodrigo Jiménez-García; Pilar Carrasco-Garrido Journal: PLoS One Date: 2015-02-23 Impact factor: 3.240
Authors: Alejandro Alvaro-Meca; Rodrigo Jiménez-Garcia; Isabel Jimenez-Trujillo; Valentin Hernandez-Barrera; Javier de Miguel-Diez; Salvador Resino; Ana Lopez-de-Andres Journal: PLoS One Date: 2016-09-02 Impact factor: 3.240
Authors: Isabel Jiménez-Trujillo; Montserrat González-Pascual; Rodrigo Jiménez-García; Valentín Hernández-Barrera; José M de Miguel-Yanes; Manuel Méndez-Bailón; Javier de Miguel-Diez; Miguel Ángel Salinero-Fort; Napoleón Perez-Farinos; Pilar Carrasco-Garrido; Ana López-de-Andrés Journal: Medicine (Baltimore) Date: 2016-05 Impact factor: 1.889
Authors: Ana Lopez-de-Andres; Rodrigo Jimenez-Garcia; Valentín Hernández-Barrera; Isabel Jiménez-Trujillo; José M de Miguel-Yanes; David Carabantes-Alarcon; Javier de Miguel-Diez; Marta Lopez-Herranz Journal: Cardiovasc Diabetol Date: 2021-07-09 Impact factor: 9.951