OBJECTIVES: This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. METHODS: The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. RESULTS: A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. CONCLUSIONS: The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
OBJECTIVES: This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. METHODS: The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. RESULTS: A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. CONCLUSIONS: The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
Authors: Krish C Dewan; Karan S Dewan; Jay J Idrees; Suparna M Navale; Brad F Rosinski; Lars G Svensson; A Marc Gillinov; Douglas R Johnston; Faisal Bakaeen; Edward G Soltesz Journal: JAMA Surg Date: 2019-03-01 Impact factor: 14.766
Authors: Amy S Kelley; Evan Bollens-Lund; Kenneth E Covinsky; Jonathan S Skinner; R Sean Morrison Journal: J Palliat Med Date: 2017-08-03 Impact factor: 2.947
Authors: Eliza W Beal; Fabio Bagante; Anghela Paredes; Qinyu Chen; Ozgur Akgul; Katiuscha Merath; Mary E Dillhoff; Jordan M Cloyd; Timothy M Pawlik Journal: J Gastrointest Surg Date: 2018-07-23 Impact factor: 3.452
Authors: V J Sabesan; J D Whaley; M LaVelle; G Petersen-Fitts; D Lombardo; D Yong; D Malone; J Khan; D J L Lima Journal: Musculoskelet Surg Date: 2019-01-01