Contribution of a Measure of Disease Complexity (COMPLEX) to Prediction of Outcome and Charges Among Hospitalized Patients

Naessens, James M.; Leibson, Cynthia L.; I, Krishan; Ballard, David J.

doi:10.1016/s0025-6196(12)61143-4

Cited by 33 publications

(20 citation statements)

References 27 publications

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“…In general, comorbid conditions have been handled analytically by: (1) stratifying patients into groups-those with a comorbidity and those without; (2) using separate binary indicators for discrete conditions; or (3) summarizing comorbidity information into an index or score that provides a single parameter for measuring multiple comorbidities. 1,[8][9][10][11][12][13][14][15] One of the most commonly used indexes was developed by Charlson et al 1 Although it was developed for the express purpose of prospectively predicting 1 year mortality among patients being considered for breast cancer clinical trials, it has been applied to discharge abstract and claims data to predict short-term outcomes such as inhospital mortality, blood transfusions, hospitalization charges, and length of stay. [16][17][18][19][20] Romano et al 21,22 explored the Charlson Index and pointed out a number of precautions when using indexes to control for comorbidities.…”

Section: Introductionmentioning

confidence: 99%

Comorbidity Measures for Use with Administrative Data

et al. 1998

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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.

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Section: Introductionmentioning

confidence: 99%

Comorbidity Measures for Use with Administrative Data

et al. 1998

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“…Various methods used to control for comorbidity have been adapted to adjust for the impact of comorbid conditions on costs of care in multivariate analyses [51][52][53][54]. It is important that the method accounts both for the mix of conditions and the severity of each condition as they impact cost.…”

Section: Adjusting For Comorbiditymentioning

confidence: 99%

The burden of illness and economic evaluation for COPD

Ramsey¹,

Sullivan²

2003

Eur Respir J

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In today9s cost-conscious environment, understanding the economic implications associated with disease processes and their treatments is as important as understanding their clinical impact.Medical expenditures for treating chronic obstructive pulmonary disease (COPD) and the indirect costs of morbidity can represent a substantial economic and social burden. Very little economic information concerning COPD is available, however, particularly outside of a few very well-developed Western nations.This article will provide an approach for conducting high-quality studies aimed at estimating the economic burden of chronic obstructive pulmonary disease and for making economic evaluations of healthcare interventions aimed at preventing and treating this disease.

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“…Comorbidity, which can be defined as the coexistence of multiple conditions in a single individual, has been shown to modify the prognosis of the disease state (Chen et al 2001;Kazmierski 2006;Piccirillo 2000). Conditions that contribute to patient and/or disease complexity are often high resource users; therefore, they present opportunities for continuously improving efficiency and optimising outcome (Lee, Soffel & Luf 1992;Naessens et al 1992;Norcini 2005;Taheri, Butz & Greenfield 1999).…”

Section: Introductionmentioning

confidence: 99%

Recorded Categories of Non-Principal Diagnoses in Victorian Public Hospital Transient Ischaemic Attack and Stroke Admissions

Nadathur

2008

HIM J

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Information about the number and types of non-principal diagnoses (NPDs) would make an important contribution to prediction of outcome and hence patient management. The study reported here is based on analysis of three fiscal years of the Victorian public hospital transient ischaemic attack (TIA) and stroke admissions. The incidence of NPDs and co-occurrence of NPD-associated prefix categories (that identify the onset or relevance of each condition to the episode) are described in each of the broad stroke subtypes. The distributions of length of stay and in-hospital deaths in the cohorts without and with NPDs and in the various prefix categories are determined. The study also compares the age and gender distribution in the various subpopulations of interest. The importance of collecting complete and accurate data on nature of NPDs and its potential in describing the complexity of presentation are discussed.

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Contribution of a Measure of Disease Complexity (COMPLEX) to Prediction of Outcome and Charges Among Hospitalized Patients

Cited by 33 publications

References 27 publications

Comorbidity Measures for Use with Administrative Data

Comorbidity Measures for Use with Administrative Data

The burden of illness and economic evaluation for COPD

Recorded Categories of Non-Principal Diagnoses in Victorian Public Hospital Transient Ischaemic Attack and Stroke Admissions

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