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A new frontier for quality of care in gynecologic oncology surgery: Multi-institutional assessment of short-term outcomes for ovarian cancer using a risk-adjusted model

医学 妇科肿瘤学 肿瘤科 期限(时间) 卵巢癌 质量(理念) 癌症 内科学 妇科 量子力学 认识论 物理 哲学
作者
Giovanni Aletti,Antonio Santillan,E. A. Eisenhauer,Jian Hu,Giovanni Aletti,Karl C. Podratz,Robert G. Bristow,D. Chi,W CLIBY
出处
期刊:Gynecologic Oncology [Elsevier BV]
卷期号:107 (1): 99-106 被引量:163
标识
DOI:10.1016/j.ygyno.2007.05.032
摘要

Abstract

Objective

To test the feasibility and utility of a risk-adjusted, multicenter outcomes model for ovarian cancer surgery as a tool for quality improvement.

Methods

Patient characteristics, intra-operative findings, procedures, and outcomes were assessed in primary advanced stage ovarian cancer cases from 3 independent centers. A surgical complexity score (SCS) was developed to adjust for extent of surgery. Outcomes measures were: 30-day morbidity (sepsis, thrombo-embolic, cardiac, readmission or re-operation), 3-month mortality, length of stay (LOS), and ability to receive chemotherapy. A multivariable risk-adjusted model was developed for all the outcomes. Observed-to-expected (O/E) outcome ratios were calculated from all data.

Results

564 consecutive patients from 3 centers were analyzed. The strongest predictors of 30-day morbidity were endogenous [albumin (p<0.001) and ASA (p=0.008)] and complexity of surgery [SCS (p<0.001)]. Age (p=0.002) and ASA (p=0.001) independently predicted mortality. LOS independently correlated with age (p=0.007), albumin (p=0.004), SCS (p=0.002), and stage (p=0.024). ASA (p<0.001) and SCS (p=0.003) both impacted ability to receive chemotherapy. Observed to expected (O/E) ratios for dependent outcome variables were similar for all 3 institutions.

Conclusions

We demonstrate the benefits of a national system for studying outcomes in gynecologic surgery using a risk-adjusted model. We specifically find that endogenous patient factors and complexity of surgery are primary drivers of morbidity in ovarian cancer surgery. These data can successfully be used to formulate expected, risk-adjusted rates of complications thus providing a meaningful mechanism to identify areas ripe for quality improvement.
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