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Surgical volume threshold to improve 3-year survival in designated cancer care hospitals in 2004-2012 in Japan.

Sumiyo OkawaTakahiro TabuchiKayo NakataToshitaka MorishimaShihoko KoyamaIsrael Terungwa AgakuIsao Miyashiro
Published in: Cancer science (2022)
In Japan, cancer care hospitals designated by the national government have a surgical volume requirement of 400 annually, which is not necessarily defined based on patient outcomes. This study aimed to estimate surgical volume thresholds that ensure optimal 3-year survival for three periods. In total, 186 965 patients who had undergone surgery for solid cancers in 66 designated cancer care hospitals in Osaka between 2004 and 2012 were examined using data from a population-based cancer registry. These hospitals were categorized by the annual surgical volume of each 50 surgeries (eg, 0-49, 50-99, and so on). Using multivariable Cox proportional hazard regression, we estimated the adjusted 3-year survival probability per surgical volume category for 2004-2006, 2007-2009, and 2010-2012. Using the joinpoint regression model that computes inflection points in a linear relationship, we estimated the points at which the trend of the association between surgical volume and survival probability changes, defining them as surgical volume thresholds. The adjusted 3-year survival ranges were 71.7%-90.0%, 68.2%-90.0%, and 79.2%-90.3% in 2004-2006, 2007-2009, and 2010-2012, respectively. The surgical volume thresholds were identified at 100-149 in 2004-2006 and 2007-2009 and 200-249 in 2010-2012. The extents of change in the adjusted 3-year survival probability per increase of 50 surgical volumes were +4.00%, +6.88%, and +1.79% points until the threshold and +0.41%, +0.30%, and +0.11% points after the threshold in 2004-2006, 2007-2009, and 2010-2012, respectively. The existing surgical volume requirements met our estimated thresholds. Surgical volume thresholds based on the association with patient survival may be used as a reference to validate the surgical volume requirement.
Keyphrases
  • machine learning
  • minimally invasive
  • coronary artery disease
  • quality improvement
  • young adults
  • electronic health record