Can Pharmacy Data Improve Prediction of Hospital Outcomes
Comparisons With a Diagnosis-Based Comorbidity Measure
Bibliographic Data
| ID | 9101386 |
|---|---|
| Authors | Joseph P Parker (0000-0002-2601-8482, Oklahoma State Department of Health, corresponding author), Jeffrey S McCombs, Elizabeth A Graddy (0000-0001-7059-8827) |
| Year | 2003 |
| Volume | 41 |
| Issue | 3 |
| Pages | 407-419 |
| Publication date | 2003-03-01 |
| 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.0000053023.49899.3e |
| PMID | 12618644 |
| OpenAlex | W1980729598 |
| Language | EN |
| Citations received | 9 |
| References cited | 26 |
OBJECTIVES: The performance of comorbidity measures derived from the hospital discharge abstract, the outpatient pharmacy record, and from both sources combined, were compared in predicting all-cause and unplanned hospital readmission and length of stay. MATERIALS AND METHODS: Automated hospital and pharmacy data came from Kaiser-Permanente and included 6721 acute hospitalizations in Southern California from April 1993 to February 1995. The Deyo adaptation of Charlson's 17 comorbidities was derived from hospital discharge data and the 29 Chronic Disease Score (CDS) comorbidity markers were derived from outpatient pharmacy claims data. Logistic and OLS regression models were used to compare the performance of each measure in baseline models and to evaluate whether the CDS contributed additional explanatory power in a combined model. RESULTS: The CDS was a significant predictor of unplanned readmission (C = 0.68) and LOS (Adjusted R(2) = 0.26) in multivariable models adjusted for baseline patient demographic and hospitalization characteristics. The Deyo measure was a significant predictor of all-cause readmission (C = 0.63), unplanned readmission (C = 0.68), and LOS (Adjusted R(2) = 0.26). When pharmacy-based disease markers were added to the Deyo baseline model, modest, statistically significant improvements in predictive power were noted in the unplanned readmission and LOS models. CONCLUSIONS: The finding that both measures of comorbid disease demonstrated similar predictive power is noteworthy, because secondary diagnosis data document relevant illness in hospital patients and pharmacy claims data were never intended for that purpose. The results suggest that small improvements in model performance may come from combining both sources of data in models to predict hospital readmission and LOS
Comorbidity · Family medicine · Logistic regression · Predictive modelling · Predictive power · Statistics · Chronic Disease Management Strategies · Emergency Medicine · Heart Failure Treatment and Management · Internal Medicine · Medicine · Pharmaceutical Practices and Patient Outcomes · Pharmacy
New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality
Systematic Review of Risk Adjustment Models of Hospital Length of Stay (LOS)
Comparative Performance of Diagnosis-based and Prescription-based Comorbidity Scores to Predict Health-related Quality of Life
In Search of the Perfect Comorbidity Measure for Use With Administrative Claims Data
Does the Addition of Functional Status Indicators to Case-Mix Adjustment Indices Improve Prediction of Hospitalization, Institutionalization, and Death in the Elderly
Fidelity of Administrative Data When Researching Down Syndrome
Hospital Length of Stay Prediction Methods
The Impact of Pharmacy-specific Predictors on the Performance of 30-Day Readmission Risk Prediction Models
The poorer cancer survival among the unmarried in Norway
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
A new method of classifying prognostic comorbidity in longitudinal studies
Development and Estimation of a Pediatric Chronic Disease Score Using Automated Pharmacy Data
Comorbid Illness is Associated with Survival and Length of Hospital Stay in Patients with Chronic Disability
The Importance of Comorbidities in Explaining Differences in Patient Costs
Severity Measurement Methods and Judging Hospital Death Rates for Pneumonia
Using Claims Data for Epidemiologic Research
A Chronic Disease Score with Empirically Derived Weights
Pharmacy Costs Groups
| Unique citing works | 9 |
|---|---|
| Citations per year | 0,41 |
| Citation span | 2004 - 2021 (18) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 9 |