Ansari MZ, Costello AJ, Ackland MJ, Carson N, McDonald IG. In-hospital mortality after transurethral resection of the prostate in Victorian public hospitals.
THE AUSTRALIAN AND NEW ZEALAND JOURNAL OF SURGERY 2000;
70:204-8. [PMID:
10765905 DOI:
10.1046/j.1440-1622.2000.01787.x]
[Citation(s) in RCA: 7] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND
The purpose of the present paper was (i) to identify trends in in-hospital mortality after transurethral resection of the prostate (TURP) in Victorian public hospitals; and (ii) to explore associations between in-hospital mortality after TURP and age, adverse events, type of admission (emergency/planned), location of the hospital (metropolitan/rural), teaching status of the hospital and length of stay.
METHODS
Trends in in-hospital mortality after TURP and the associations between in-hospital mortality and the aforementioned variables were studied using International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) coded Victorian hospital morbidity data from public hospitals between 1987-88 and 1994-95. Crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) were based on univariate and multivariate logistic regression, respectively.
RESULTS
After adjustment for age, comorbidity, and other confounding variables, the trend in mortality reduction over time was highly significant (P for trend < 0.0001, 95% CI for trend: 0.84-0.95). Highly significant associations with mortality were observed for emergency admissions (OR = 1.99, P < 0.0001), presence of adverse events (OR = 2.69, P < 0.0001), length of hospital stay (P for trend < 0.0001, 95% for trend: 1.88-2.15) and age (P for trend < 0.0001; 95% CI for trend: 1.26-1.48).
CONCLUSIONS
Routinely collected data from hospitals can provide tentative evidence of improved effectiveness of a surgical treatment, provided analysis takes careful account of potential sources of bias, especially those related to possible changes in case selection over time. These kinds of data should stimulate a joint effort between clinicians, quality assurance experts and epidemiologists to confirm this attribution, and to locate the causative factors.
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