Adapting the Rx-Risk-V for Mortality Prediction in Outpatient Populations
Bibliographic Data
| ID | 9102503 |
|---|---|
| Authors | Michael L Johnson (0000-0002-4018-4647, Michael E. DeBakey VA Medical Center, corresponding author), Hashem B El-Serag, Hashem B El‐Serag (0000-0001-5964-7579, Michael E. DeBakey VA Medical Center, corresponding author), Tung Thomas Tran (Baylor College of Medicine), Christine Hartman (0000-0003-1301-7189, Michael E. DeBakey VA Medical Center, corresponding author), Peter Richardson (0000-0002-3349-345X, Michael E. DeBakey VA Medical Center, corresponding author), Neena S Abraham (0000-0002-8532-6354, Michael E. DeBakey VA Medical Center, corresponding author) |
| Year | 2006 |
| Volume | 44 |
| Issue | 8 |
| Pages | 793-797 |
| Publication date | 2006-08-01 |
| Peer Reviewed | Yes |
| 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 | 10.1097/01.mlr.0000218804.41758.ef |
| PMID | 16862043 |
| OpenAlex | W2076590137 |
| Language | EN |
| Citations received | 4 |
| References cited | 21 |
OBJECTIVES: We sought to operationalize, test, and validate an outpatient pharmacy-based case-mix adjuster. METHODS: Outpatients from the Department of Veterans Affairs (VA) prescribed a nonsteroidal anti-inflammatory drug (NSAID) or cyclooxygenase-2 selective drug during 2002 were identified. We updated and extended the Rx-Risk-V by adding 26 additional disease categories and mapping them to VA drug-class codes; derived empirical weights for each from a logistic model of 1-year mortality; adjusted for age, race and sex; and scored the weights into 1 measure of comorbidity. We compared the weighted score to the Deyo diagnosis-based comorbidity index and validated it in a national cohort of 260,321 outpatients with chronic heart failure (CHF). RESULTS: One-year mortality among the 724,270-outpatient NSAID cohort was 1.6% (n = 11,766). Using a baseline model of age, race, and gender (c-index = 0.716), we found that the Deyo measure improved the prediction of mortality (c-index = 0.765), and the pharmacy comorbidity score further improved the prediction (c-index = 0.782), an increase of 25.8%. Using both, we found further improvement (c-index = 0.792). Among the CHF cohort, 9.7% (n = 25,251) died within 1 year. Performance of the baseline model controlling for age, race, and gender (c index = 0.620) improved with addition of the pharmacy comorbidity score (c index = 0.689), compared with the addition of the Deyo measure (c index = 0.651), an increase of 55.1%. Together, they slightly improved prediction in CHF patients (c index = 0.695). CONCLUSIONS: The updated and extended Rx-Risk-V is useful for case-mix adjustment of mortality in an outpatient population
Cohort · Cohort study · Comorbidity · Family medicine · Physical therapy · Veterans Affairs · Chronic Disease Management Strategies · Internal Medicine · Machine Learning in Healthcare · Medicine · Pharmaceutical Practices and Patient Outcomes · Pharmacy
Do short birth intervals have long-term implications for parental health? Results from analyses of complete cohort Norwegian register data
Comparative Performance of Diagnosis-based and Prescription-based Comorbidity Scores to Predict Health-related Quality of Life
Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients
The poorer cancer survival among the unmarried in Norway
Development of a comorbidity index using physician claims data
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
A new method of classifying prognostic comorbidity in longitudinal studies
Construction and Characteristics of the RxRisk-V
Administrative Data
The Performance of Different Lookback Periods and Sources of Information for Charlson Comorbidity Adjustment in Medicare Claims
Risk Adjustment for Measuring Health Care Outcomes
Risk Adjustment Using Automated Ambulatory Pharmacy Data
A Chronic Disease Score with Empirically Derived Weights
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,31 |
| Citation span | 2013 - 2017 (5) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 4 |