Predicting post-discharge death or readmission: Deterioration of model performance in population having multiple admissions per patient

Journal of Evaluation in Clinical Practice

Volumn 19, Issue 6, 2013, Pages 1012-1018

  Van Walraven, Carl ^a,b   Wong, Jenna ^a,b   Forster, Alan J ^a,b,c   Hawken, Stephen ^c  

^a OTTAWA HOSPITAL RESEARCH INSTITUTE   (Canada)

^b INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^c UNIVERSITY OF OTTAWA   (Canada)

Author keywords

hospital readmission; model performance; multivariate regression

Indexed keywords

ADULT; ARTICLE; DAY HOSPITAL; FEMALE; HOSPITAL DISCHARGE; HOSPITAL READMISSION; HOSPITAL UTILIZATION; HOSPITALIZATION; HUMAN; MAJOR CLINICAL STUDY; MALE; MORTALITY; OUTCOME ASSESSMENT; PRIORITY JOURNAL; RISK FACTOR;

HOSPITAL READMISSION; MODEL PERFORMANCE; MULTIVARIATE REGRESSION;

ADULT; AGED; DEATH; FEMALE; HOSPITALIZATION; HUMANS; INSURANCE CLAIM REVIEW; MALE; MIDDLE AGED; MODELS, STATISTICAL; ONTARIO; PATIENT DISCHARGE; PATIENT READMISSION; RISK ASSESSMENT;

EID: 84888351242     PISSN: 13561294     EISSN: 13652753     Source Type: Journal    
DOI: 10.1111/jep.12012     Document Type: Article

References (11)

1. van Walraven, C., Wong, J., &, Forster, A. J., (2012) LACE+-an extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine, 6 (2), 80-89.
2. Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., &, Kripalani, S., (2011) Risk prediction models for hospital readmission. JAMA: The Journal of the American Medical Association, 306, 1688-1698.
3. Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J. C., Saunders, L. D., Beck, C. A., Feasby, T. E., &, Ghali, W. A., (2005) Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care, 43, 1130-1139.
4. Charlson, M. E., Pompei, P., Ales, K. L., &, MacKenzie, C. R., (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases, 40, 373-383.
5. van Walraven, C., Wong, J., &, Forster, A. J., (2012) Derivation and validation of a case-mix group based diagnostic score to predict 30-day death or urgent hospital readmission. Open Medicine, 6 (2), 90-100.
6. Gonen, M., (2007) Single continuous predictor. In Analyzing Receiver Operating Characteristic Curves with SAS (ed., M. Gonen,), pp. 15-36. Cary, NC: SAS Institute Inc.
7. Hosmer, D. W., Hosmer, T., Le Cessie, S., &, Lemeshow, S., (1997) A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16, 965-980.
8. Kieszak, S. M., Flanders, W. D., Kosinski, A. S., Shipp, C. C., &, Karp, H., (1999) A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. Journal of Clinical Epidemiology, 52, 137-142.
9. Quan, H., Parsons, G. A., &, Ghali, W. A., (2004) Validity of procedure codes in International Classification of Diseases, 9th revision, clinical modification administrative data. Medical Care, 42, 801-809.
10. Klabunde, C. N., Harlan, L. C., &, Warren, J. L., (2006) Data sources for measuring comorbidity: a comparison of hospital records and medicare claims for cancer patients. Medical Care, 44, 921-928.
11. Preen, D. B., Holman, C. D., Lawrence, D. M., Baynham, N. J., &, Semmens, J. B., (2004) Hospital chart review provided more accurate comorbidity information than data from a general practitioner survey or an administrative database. Journal of Clinical Epidemiology, 57, 1295-1304.