| Autor: |
Chappidi, Meera R., Kates, Max, Stimson, C.J., Bivalacqua, Trinity J., Pierorazio, Phillip M. |
| Předmět: |
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| Zdroj: |
Journal of Urology; Jan2017, Vol. 197 Issue 1, p235-240, 6p |
| Abstrakt: |
Purpose We quantified the underestimation of hospital readmission rates that can occur with institutional databases and the incidence of care fragmentation among patients undergoing urological oncology procedures in a nationally representative database. Materials and Methods The 2013 Nationwide Readmissions Database was queried for patients undergoing prostatectomy, cystectomy, nephroureterectomy, nephrectomy, partial nephrectomy and retroperitoneal lymph node dissection for urological malignancies. Nationally representative 30 and 90-day readmission and care fragmentation rates were calculated for all procedures. Readmission rates with and without nonindex hospital readmissions were compared with Pearson’s chi-square test. Multivariable logistic regression models were used to identify predictors of care fragmentation at 90-day followup. Results For all surgical procedures readmission rates were consistently underestimated by 17% to 29% at 90-day followup. The rates of care fragmentation among readmitted patients were similar for all procedures, ranging from 24% to 34% at 90-day followup. Overall 1 in 4 readmitted patients would not be captured in institutional databases and 1 in 3 readmitted patients experienced care fragmentation. Multivariable models did not identify a predictor of care fragmentation that was consistent across all procedures. Conclusions The high rate of underestimation of readmission rates across all urological oncology procedures highlights the importance of linking institutional and payer claims databases to provide more accurate estimates of perioperative outcomes and health care utilization. The high rate of care fragmentation across all procedures emphasizes the need for future efforts to understand the clinical relevance of care fragmentation in patients with urological malignancies, and to identify patients at risk along with potentially modifiable risk factors for care fragmentation. [ABSTRACT FROM AUTHOR] |
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