Cost Analysis and Financial Implications of a Peritoneal Surface Malignancy Program in the USA.

BACKGROUND: We aimed to describe the financial implications of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) in the USA. MATERIALS AND METHODS: We conducted a retrospective cost analysis of 100 CRS/HIPEC procedures to examine the impact of patient and procedural factors on hospital costs and reimbursement. A comparison of surgeons' work relative value units (wRVUs) between CRS/HIPEC and a representative sample of complex surgical oncology procedures was made to assess the physicians' compensation rate. Univariable and multivariable backward logistic regression was used to analyze the association between perioperative variables and high direct cost (HDCs). RESULTS: The median direct cost per CRS/HIPEC procedure was US $44,770. The median hospital reimbursement was US $43,066, while professional reimbursement was US $8608, resulting in a positive contribution margin of US $7493/procedure. However, the contribution margin significantly varied with the payer mix. Privately insured patients had a positive median contribution margin of US $23,033, whereas Medicare-insured patients had a negative contribution margin of US $13,034. Length of stay (LOS) had the most significant association with HDC, and major complications had the most significant association with LOS. Finally, CRS/HIPEC procedures generated a median of 13 wRVU/h, which is significantly lower than the wRVU/h generated by open pancreatoduodenectomies, open gastrectomies, and hepatectomies. However, higher operation complexity and multiple visceral resections help compensate for the relatively low wRVU/h. CONCLUSIONS: CRS/HIPEC is an expensive operation, and prolonged LOS has the most significant impact on the total cost of the procedure. High-quality care is essential to improve patient outcomes and maintain the economic sustainability of the procedure.

Hospital Designations and Their Impact on Guideline-Concordant Care and Survival in Pancreatic Cancer. Do They Matter?

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) requires complex multidisciplinary care. European evidence suggests potential benefit from regionalization, however, data characterizing the ideal setting in the United States are sparse. Our study compares the significance of four hospital designations on guideline-concordant care (GCC) and overall survival (OS). PATIENTS AND METHODS: The Texas Cancer Registry was queried for 17,071 patients with PDAC treated between 2004 and 2015. Clinical data were correlated with hospital designations: NCI designated (NCI), high volume (HV), safety net (SNH), and American College of Surgeons Commission on Cancer accredited (ACS). Univariable (UVA) and multivariable (MVA) logistic regression were used to assess associations with GCC [on the basis of National Comprehensive Cancer Network (NCCN) recommendations]. Cox regression analysis assessed survival. RESULTS: Only 43% of patients received GCC. NCI had the largest associated risk reduction (HR 0.61, CI 0.58-0.65), followed by HV (HR 0.87, CI 0.83-0.90) and ACS (HR 0.91, CI 0.87-0.95). GCC was associated with a survival benefit in the full (HR 0.75, CI 0.69-0.81) and resected cohort (HR 0.74, CI 0.68-0.80). NCI (OR 1.52, CI 1.37-1.70), HV (OR 1.14, CI 1.05-1.23), and SNH (OR 0.78, CI 0.68-0.91) all correlated with receipt of GCC. For resected patients, ACS (OR 0.63, CI 0.50-0.79) and SNH (OR 0.50, CI 0.33-0.75) correlate with GCC. CONCLUSIONS: A total of 43% of patients received GCC. Treatment at NCI and HV correlated with improved GCC and survival. Including GCC as a metric in accreditation standards could impact survival for patients with PDAC.

Development and validation of a prognostic and predictive 32-gene signature for gastric cancer.

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.