Enhancement of Claims Data to Improve Risk Adjustment of Hospital Mortality

Pine, Michael; Jordan, H; Elixhauser, Anne; Fry, Donald E.; Hoaglin, David C.; Jones, Barbara; Meimban, Roger J.; Warner, David; Gonzales, Junius

doi:10.1001/jama.297.1.71

Cited by 258 publications

(176 citation statements)

References 35 publications

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“…Third, we risk-adjusted using administrative data only; inclusion of clinical variables such as the Child-Pugh score or Model for End-Stage Liver Disease score may have improved model performance, but such data are not commonly available in administrative databases, including the NIS. 41,42 Additional studies should address the incremental predictive value of enhancing administrative data with additional data sources (e.g., medical record review, laboratory databases) in patients with liver disease. Fourth, because each record in the NIS represents a hospitalization, not a patient, we cannot exclude repeated admissions of individual patients.…”

Section: Discussionmentioning

confidence: 99%

Predicting in-hospital mortality in patients with cirrhosis: Results differ across risk adjustment methods

et al. 2008

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Section: Discussionmentioning

confidence: 99%

Predicting in-hospital mortality in patients with cirrhosis: Results differ across risk adjustment methods

et al. 2008

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“…Selecting appropriate risk adjustment models can help hospitals contain costs while ensuring high levels of quality. Furthermore, inadequate comorbidity risk-adjustment might penalize practitioners and hospitals that care for the sickest patients [41].…”

Section: Introductionmentioning

confidence: 99%

The Elixhauser Comorbidity Method Outperforms the Charlson Index in Predicting Inpatient Death After Orthopaedic Surgery

Menendez

Neuhaus

Dijk

et al. 2014

Clinical Orthopaedics &Amp; Related Research

436

292

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Background Scores derived from comorbidities can help with risk adjustment of quality and safety data. The Charlson and Elixhauser comorbidity measures are wellknown risk adjustment models, yet the optimal score for orthopaedic patients remains unclear. Questions/purposes We determined whether there was a difference in the accuracy of the Charlson and Elixhauser comorbidity-based measures in predicting (1) in-hospital mortality after major orthopaedic surgery, (2) in-hospital adverse events, and (3) nonroutine discharge. Methods Among an estimated 14,007,813 patients undergoing orthopaedic surgery identified in the National Hospital Discharge Survey (1990Survey ( -2007, 0.80% died in the hospital. The association of each Charlson comorbidity measure and Elixhauser comorbidity measure with mortality was assessed in bivariate analysis. Two main multivariable logistic regression models were constructed, with in-hospital mortality as the dependent variable and one of the two comorbidity-based measures (and age, sex, and year of surgery) as independent variables. A base model that included only age, sex, and year of surgery also was evaluated. The discriminative ability of the models was quantified using the area under the receiver operating characteristic curve (AUC). The AUC quantifies the ability of our models to assign a high probability of mortality to patients who die. Values range from 0.50 to 1.0, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect discrimination. Results Elixhauser comorbidity adjustment provided a better prediction of in-hospital case mortality (AUC, 0.86; 95% CI, 0.86-0.86) compared with the Charlson model (AUC, 0.83; 95% CI, and to the base model with no comorbidities (AUC, 0.81; 95% CI, 0.81-0.81). In terms of relative improvement in predictive performance, the Elixhauser measure performed 60% better than the Charlson score in predicting mortality. The Elixhauser model discriminated inpatient morbidity better than the Charlson measure, but the discriminative ability of the model was poor and the difference in the absolute improvement in predictive power between the two models (AUC, 0.01) is of dubious clinical importance. Both comorbidity models exhibited the same degree of discrimination for estimating nonroutine discharge (AUC, 0.81; 95% CI, 0.81-0.82 for both models). Conclusions Provider-specific outcomes, particularly inpatient mortality, may be evaluated differently depending on the comorbidity risk adjustment model selected. Future research assessing and comparing the performance of the

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“…In contrast, agreement was only fair for inpatient and outpatient diagnosis codes (κ=0. 31 Overall, 6.9% of patients died during the admission; mortality was higher for pneumonia than for CHF (9.2% versus 4.3%; P<.001). Unadjusted mortality rates were lower (P<.05) for individuals with psychiatric comorbidities for each of the 3 methods, although magnitudes of the differences tended to be highest for inpatient diagnosis and mental health visits (Table 3).…”

Section: Resultsmentioning

confidence: 84%

“…Additionally, our inclusion of a laboratory-based measure addresses some of the limitations of administrative data regarding unmeasured severity of illness. 30,31 Third, it is important to acknowledge that methods we used to identify psychiatric comorbidity may identify constructs of psychiatric disease that vary with respect to disease severity or spectrum (e.g., acute, chronic, newly diagnosed illness). Such constructs may, in turn, have different associations with hospital mortality either directly or indirectly through their influence on health care delivery.…”

Section: Discussionmentioning

confidence: 99%

Variations in the Associations Between Psychiatric Comorbidity and Hospital Mortality According to the Method of Identifying Psychiatric Diagnoses

2008

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Enhancement of Claims Data to Improve Risk Adjustment of Hospital Mortality

Abstract: This study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.

Cited by 258 publications

References 35 publications

Predicting in-hospital mortality in patients with cirrhosis: Results differ across risk adjustment methods

Predicting in-hospital mortality in patients with cirrhosis: Results differ across risk adjustment methods

The Elixhauser Comorbidity Method Outperforms the Charlson Index in Predicting Inpatient Death After Orthopaedic Surgery

Variations in the Associations Between Psychiatric Comorbidity and Hospital Mortality According to the Method of Identifying Psychiatric Diagnoses

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