Literature DB >> 19800674

The impact of hospital type on the efficacy of chemotherapy treatment in ovarian cancer patients.

Flora Vernooij1, Petronella O Witteveen, Eva Verweij, Yolanda van der Graaf, A Peter M Heintz.   

Abstract

OBJECTIVE: The hospital type affects the surgical outcomes of ovarian cancer patients. In the present study, we wanted to investigate the effect of hospital type on chemotherapy efficacy.
METHODS: Data were collected from 1077 ovarian cancer patients treated from 1996 to 2003 in a random sample of 18 Dutch hospitals. Hospitals were categorized by the number of medical oncologists working in a hospital and additionally by chemotherapy volume (< or =100, 101-200, or >200 patients yearly) and ovarian cancer patient-volume (< or =6, 7-12, >12 yearly). The outcomes were the proportions of patients achieving complete remission, recurrence rates, and disease-free and overall survival. Data were analyzed using multivariable logistic regression (complete remission and recurrence) and Cox regression (survival).
RESULTS: Data of 761 of the 777 patients who received chemotherapy could be analyzed. Hospital type did not affect the complete remission rates, recurrence rates, or the disease-free survival. Overall survival was better in hospitals with 2 or more medical oncologists and in hospitals with a high ovarian cancer patient-volume (hazard ratios both 0.8 (95% confidence interval=0.7-1.0)).
CONCLUSIONS: Thus, hospital type did not influence the outcomes of first-line chemotherapy in ovarian cancer patients. However, overall survival was better in hospitals with 2 or more medical oncologists and in hospitals with a high ovarian cancer patient-volume, suggesting differences in second-line chemotherapy.

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Year:  2009        PMID: 19800674     DOI: 10.1016/j.ygyno.2009.08.018

Source DB:  PubMed          Journal:  Gynecol Oncol        ISSN: 0090-8258            Impact factor:   5.482


  2 in total

Review 1.  The optimal organization of gynecologic oncology services: a systematic review.

Authors:  M Fung-Kee-Fung; E B Kennedy; J Biagi; T Colgan; D D'Souza; L M Elit; A Hunter; J Irish; R McLeod; B Rosen
Journal:  Curr Oncol       Date:  2015-08       Impact factor: 3.677

2.  Malignancy Assessment Using Gene Identification in Captured Cells Algorithm for the Prediction of Malignancy in Women With a Pelvic Mass.

Authors:  Richard George Moore; Negar Khazan; Madeline Ann Coulter; Rakesh Singh; Michael Craig Miller; Umayal Sivagnanalingam; Brent DuBeshter; Cynthia Angel; Cici Liu; Kelly Seto; David Englert; Philip Meachem; Kyu Kwang Kim
Journal:  Obstet Gynecol       Date:  2022-09-08       Impact factor: 7.623

  2 in total

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