Quality of surgery and treatment and its association with hospital volume: A population-based study in more than 5000 Belgian ovarian cancer patients.

peer reviewed[en] BACKGROUND: Different sets of quality indicators are used to identify areas for improvement in ovarian cancer care. This study reports transparently on how (surgical) indicators were measured and on the association between hospital volume and indicator results in Belgium, a country setting without any centralisation of ovarian cancer care. METHODS: From the population-based Belgian Cancer Registry, patients with a borderline malignant or invasive epithelial ovarian tumour diagnosed between 2014 and 2018 were selected and linked to health insurance and vital status data (n = 5119). Thirteen quality indicators on diagnosis and treatment were assessed and the association with hospital volume was analysed using logistic regression adjusted for case-mix. RESULTS: The national results for most quality indicators on diagnosis and systemic therapy were around the predefined target value. Other indicators showed results below the benchmark: genetic testing, completeness of staging surgery, lymphadenectomy with at least 20 pelvic/para-aortic lymph nodes removed, and timely start of chemotherapy after surgery (within 42 days). Ovarian cancer care in Belgium is dispersed over 100 hospitals. Lower volume hospitals showed poorer indicator results compared to higher volume hospitals for lymphadenectomy, staging, timely start of chemotherapy and genetic testing. In addition, surgery for advanced stage tumours was performed less often in lower volume hospitals. CONCLUSIONS: The indicators that showed poorer results on a national level were also those with poorer results in lower-volume hospitals compared to higher-volume hospitals, consequently supporting centralisation. International benchmarking is hampered by different (surgical) definitions between countries and studies

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data.

High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data. Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not. A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%). Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise

Association between hospital volume and outcomes in invasive ovarian cancer in Belgium: A population-based study

Objectives: To study the association between hospital volume and outcomes in patients with invasive epithelial ovarian cancer (EOC). Methods: This study included 3988 patients diagnosed with invasive EOC between 2014 and 2018, selected from the population-based database of the Belgian Cancer Registry (BCR), and coupled with health insurance and vital status data. The associations between hospital volume and observed survival since diagnosis were assessed with Cox proportional hazard models, while volume associations with 30-day post-operative mortality and complicated recovery were evaluated using logistic regression models. Results: Treatment for EOC was very dispersed with half of the 100 centres treating fewer than six patients per year. The median survival of patients treated in centres with the highest-volume quartile was 2.5 years longer than in those with the lowest-volume quartile (4.2 years versus 1.7 years). When taking the case-mix of hospitals into account, patients treated in the lowest volume centres had a 47% higher hazard to die than patients treated in the highest volume centres (HR: 1.47, 95% CI: 1.11–1.93, p = 0.006) over the first five years after incidence. A similar association was found when focussing on the surgical volume of the hospitals and considering only operated patients with invasive EOC. Lastly, the 30-day post-operative mortality decreased significantly with increasing surgical volume. Conclusions: The large dispersion of care and expertise within Belgium and the volume-outcome associations observed in this study support the implementation of the concentration of care for patients with invasive EOC in reference centres