Prediction of 30-day morbidity after primary cytoreductive surgery for advanced stage ovarian cancer
Introduction
Currently, treatment in advanced stage epithelial ovarian cancer (EOC) is based on primary cytoreductive surgery followed by platinum-based chemotherapy.
Successful cytoreduction to minimal residual tumour burden is the most important determinant of prognosis.1, 2, 3, 4 However, extensive surgical procedures to achieve maximal debulking are inevitably associated with postoperative morbidity and mortality. Reported 30-day morbidity after primary cytoreductive surgery for advanced stage EOC ranges from 11 to 67%.5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 Postoperative mortality (POM) rates vary between 0 and 6.7%, with a mean POM rate of 2.8%.17
Predictive parameters for postoperative complications after primary cytoreduction for advanced stage EOC are age, performance status, co-morbidity and extent of surgery. Risk-adjustment models for postoperative morbidity and mortality after major surgical procedures, developed for inter- and intra-institutional audits, have shown to improve surgical outcome.18
We currently know of only one study on risk-adjustment for surgical outcome in EOC patients.13 Prediction models for 30-day morbidity could facilitate prediction of surgical outcome in daily clinical practice and provide objective parameters to identify those patients who might benefit from alternative treatment approaches.
The objective of this study was to identify predictive parameters for 30-day morbidity after primary cytoreductive surgery for advanced stage EOC and to develop a nomogram to predict 30-day morbidity.
Section snippets
Selection of patients and study design
From January, 2004 to December, 2007 all patients with primary surgery for EOC were retrieved from the Rotterdam Cancer Registry database.
The Rotterdam Cancer Registry covers the South Western part of the Netherlands. This region comprises one university hospital, four teaching hospitals and 11 non-teaching hospitals serving a population of 2.4 million inhabitants. All newly diagnosed cases of cancer are reported to the Registry by pathology laboratories and by the hospital administration for
Analysis
Data analysis, utilising the software package SPSS 14.0 (SPSS, Chicago, IL, USA), was performed on all patients fulfilling in- and exclusion criteria of the study. The Student t-test was utilised to compare patients’ age, operative time and preoperative serum concentrations of log CA125, blood platelet and haemoglobin between the group of patients with 30-day morbidity and those patients with an uncomplicated postoperative course. Chi square tests were used to compare the preoperative presence
Recruitment and demographic characteristics of the patients
Between January 2004 and December 2007, 494 patients underwent primary surgery for EOC. One hundred and eighty-eight patients with early stage EOC and 13 patients with emergency surgery were excluded. Finally, 293 patients with advanced stage EOC who underwent primary cytoreductive surgery entered the study protocol.
Median age was 64 years (range 15.0–90.5 years), with 91 patients (31%) aged ⩾70 years at time of surgery. Fourteen (4.8%) patients were diagnosed with FIGO stage IIIA, 23(7.8%)
Discussion
In the above study we identified predictors for postoperative morbidity and mortality after primary cytoreductive surgery for advanced stage EOC. Age, WHO performance status, operative time and extent of surgery were predictive for 30-day morbidity. With these parameters a nomogram was generated to predict operative risk in the individual patient.
The ability to perform cytoreduction to minimal residual disease determines disease free interval and survival in patients with advanced stage EOC.1, 2
Conflict of interest statement
None declared.
Acknowledgments
The authors would like to thank D.W. van der Spek and B.K. Gerestein-van Os for their help with data acquisition. We thank all participating departments of gynaecology and the registrars of the Rotterdam Cancer Registry for their cooperation in finding and interpreting the data.
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