The Relative Ability of Comorbidity Ascertainment Methodologies to Predict In-Hospital Mortality Among Hospitalized Community-acquired Pneumonia Patients
Bibliographic Data
| ID | 9097115 |
|---|---|
| Authors | Ronald E Weir (Pritzker School of Medicine, corresponding author), Christopher S Lyttle (0000-0002-5202-5838, Center for Health and the Social Sciences), David O Meltzer (0000-0003-2790-7393, Center for Health and the Social Sciences), Tien S Dong (0000-0003-0105-8063, University of California, Los Angeles), Gregory W Ruhnke (0000-0002-6509-8243, Department of Internal Medicine, Section of Hospital Medicine) |
| Year | 2018 |
| Volume | 56 |
| Issue | 11 |
| Pages | 950-955 |
| Publication date | 2018-11-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000000989 |
| PMID | 30234766 |
| OpenAlex | W2892818219 |
| Language | EN |
| References cited | 37 |
BACKGROUND: Despite widespread use of comorbidities for population health descriptions and risk adjustment, the ideal method for ascertaining comorbidities is not known. We sought to compare the relative value of several methodologies by which comorbidities may be ascertained. METHODS: This is an observational study of 1596 patients admitted to the University of Chicago for community-acquired pneumonia from 1998 to 2012. We collected data via chart abstraction, administrative data, and patient report, then performed logistic regression analyses, specifying comorbidities as independent variables and in-hospital mortality as the dependent variable. Finally, we compared area under the curve (AUC) statistics to determine the relative ability of each method of comorbidity ascertainment to predict in-hospital mortality. RESULTS: Chart review (AUC, 0.72) and administrative data (Charlson AUC, 0.83; Elixhauser AUC, 0.84) predicted in-hospital mortality with greater fidelity than patient report (AUC, 0.61). However, multivariate logistic regression analyses demonstrated that individual comorbidity derivation via chart review had the strongest relationship with in-hospital mortality. This is consistent with prior literature suggesting that administrative data have inherent, paradoxical biases with important implications for risk adjustment based solely on administrative data. CONCLUSIONS: Although comorbidities derived through administrative data did produce an AUC greater than chart review, our analyses suggest a coding bias in several comorbidities with a paradoxically protective effect. Therefore, chart review, while labor and resource intensive, may be the ideal method for ascertainment of clinically relevant comorbidities
Comorbidity · Environmental health · Intensive care medicine · Logistic regression · Pneumonia · Population · Chronic Disease Management Strategies · Emergency Medicine · Internal Medicine · Medicine · Primary Care and Health Outcomes · Sepsis Diagnosis and Treatment
Risk Prediction Models for Hospital Readmission
Defining Comorbidity
Presentation adapting a clinical comorbidity index for use with ICD-9-CM administrative data
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
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Can Comorbidity Be Measured By Questionnaire Rather than Medical Record Review
Developing a Claim-based Version of the ACE-27 Comorbidity Index
Comorbidity Measures for Use with Administrative Data
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Trends in Mortality and Medical Spending in Patients Hospitalized for Community-Acquired Pneumonia
Using Medicare Claims for Outcomes Research
| Highly cited | No |
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