Predictions of hospital mortality rates: A comparison of data sources

Annals of Internal Medicine

Volumn 126, Issue 5, 1997, Pages 347-354

  Pine, Michael ^a   Norusis, Marija ^a   Jones, Barbara ^a   Rosenthal, Gary E ^b  

^a Michael Pine and Associates   (United States)

^b LOUIS STOKES CLEVELAND VA MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE HEART INFARCTION; AGED; ARTICLE; CEREBROVASCULAR ACCIDENT; COHORT ANALYSIS; COMORBIDITY; CONGESTIVE HEART FAILURE; DATA ANALYSIS; DISEASE CLASSIFICATION; HOSPITAL ADMISSION; HOSPITAL LABORATORY; HOSPITAL MANAGEMENT; HUMAN; MAJOR CLINICAL STUDY; MEDICAL RECORD; MORTALITY; PNEUMONIA; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL;

EID: 0031058361     PISSN: 00034819     EISSN: None     Source Type: Journal    
DOI: 10.7326/0003-4819-126-5-199703010-00002     Document Type: Article

References (27)

1. Blumberg MS. Risk-adjusting health care outcomes: a methodologic review. Medical Care Review. 1986;43:351-93.
2. Ellwood PM. Shattuck lecture - outcomes management, A technology of patient experience. N Engl J Med. 1988;318:1549-56.
3. Roper WL, Winkenwerder W, Hackbarth GM, Krakauer H. Effectiveness in health care. An Initiative to evaluate and improve medical practice. N Engl J Med. 1988;319:1197-202.
4. Hannan EL, Kilburn H Jr, Racz M, Shields E, Chassin MR. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA. 1994: 271:761-6
5. Wen SW, Hernandez R, Naylor CD. Pitfalls in nonrandomized outcomes studies. The case of incidental appendectomy with open cholecystectomy. Ann Intern Med. 1995;274:1687-91.
6. Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB, Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research. Ann Intern Med. 1993;119:844-50.
7. Dans PE. Looking for answers in all the wrong places [Editorial]. Ann Intern Med. 1993;119:855-7.
8. Sullivan LW, Toby W. Medicare Hospital Information: Technical Supplement. Washington, D.C.: U.S. Department of Health and Human Services, Health Care Financing Administration; 1992.
9. Goldfield N, Pine M, Pine J. Measuring and Managing Health Care Quality: Procedures, Techniques, and Protocols. Suppl. no 3. Gaithersburg, MD: Aspen; 1994; Appendix C:1-5.
10. Smith DW, Pine M, Bailey RC, Jones B, Brewster A, Krakauer H. Using clinical variables to estimate the risk of patient mortality. Med Care. 1991;29: 1108-29.
11. Legnini MW, Zach A, Richards T. Annual Report of the California Hospital Outcomes Project, Volume 1. Sacramento, CA: Office of Statewide Health Planning and Development; 1993:1-20.
12. Greene J. Florida signs software contracts. Modern Healthcare. 4 April 1994; 24:6.
13. Iezzoni LI, Ash AA, Shwartz M, Daley J, Hughes JS, Mackiernan YD. Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes. Ann Intern Med. 1995;123:763-70.
14. Hannan EL, Kilburn H Jr, Lindsey M, Lewis R. Clinical versus administrative data bases for CABG surgery. Does it matter? Med Care. 1992;30:892-907.
15. Rosenthal GE, Harper DL Cleveland health quality choice: a model for collaborative community-based outcomes assessment. Jt Comm J Qual Improv. 1994;20:425-42.
16. American Hospital Association. American Hospital Association Guide to the Health Care Field: Hospitals, Health Care Systems, Health Care Organizations, Agencies, and Providers, 1991 Edition. Chicago. American Hosp Assoc; 1991:A329-44.
17. SAS Institute, Inc. SAS/STAT User's Guide, Version 6, Fourth Edition. Volume 2. Cary, NC: SAS Institute; 1989;1071-126.
18. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29-36.
19. Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;115: 92-106.
20. Hanley JA, McNeil B. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983; 148:839-43.
21. Alemi F, Rice J, Hankins R. Predicting in-hospital survival of myocardial infarction. A comparative study of various severity measures. Med Care. 1990;28:762-75.
22. Steen PM, Brewster AC Bradbury RC, Estabrook E, Young JA. Predicted probabilities of hospital death as a measure of admission severity of illness. Inquiry. 1993;30:128-41.
23. Krakauer H, Bailey RC, Skellan KJ, Stewart JD, Hartz AJ, Kuhn EM, et al. Evaluation of the HCFA model for the analysis of mortality following hospitalization. Health Serv Res. 1992;27:317-35.
24. Mozes B, Easterling MJ, Sheiner LB, Melmon KL, Kline R, Goldman ES, et al. Case-mix adjustment using objective measures of severity: the case for laboratory data. Health Serv Res. 1994;28:691-712.
25. Ellis SG, Vandormael MG, Cowley MJ, DiSciascio G, Deligonul U, Topol EJ, et. al. Coronary morphologic and clinical determinants of procedural outcome with angioplasty for multivessel coronary disease. Multivessel Angioplasty Prognosis Study Group. Circulation. 1990;82:1193-202.
26. Greenland S, Finkle WD. A critical look at methods for handling missing covariates in epidemiologic regression analyses. Am J Epidemiol. 1995;142: 1255-64.
27. Romano PS, Roos LL, Luft HS, Jollis JG, Doliszny K, A comparison of administrative versus clinical data: coronary artery bypass surgery as an example. Ischemic Heart Disease Patient Outcomes Research Team. J Clin Epidemiol. 1994;47:249-60.