Purpose The Clinical Evaluation of Pertuzumab and Trastuzumab (CLEOPATRA) study showed a 15.7-month survival benefit with the addition of pertuzumab to docetaxel and trastuzumab (THP) as first-line treatment for patients with human epidermal growth factor receptor 2 (HER2) –overexpressing metastatic breast cancer. We performed a cost-effectiveness analysis to assess the value of adding pertuzumab. Patient and Methods We developed a decision-analytic Markov model to evaluate the cost effectiveness of docetaxel plus trastuzumab (TH) with or without pertuzumab in US patients with metastatic breast cancer. The model followed patients weekly over their remaining lifetimes. Health states included stable disease, progressing disease, hospice, and death. Transition probabilities were based on the CLEOPATRA study. Costs reflected the 2014 Medicare rates. Health state utilities were the same as those used in other recent cost-effectiveness studies of trastuzumab and pertuzumab. Outcomes included health benefits expressed as discounted quality-adjusted life-years (QALYs), costs in US dollars, and cost effectiveness expressed as an incremental cost-effectiveness ratio. One- and multiway deterministic and probabilistic sensitivity analyses explored the effects of specific assumptions. Results Modeled median survival was 39.4 months for TH and 56.9 months for THP. The addition of pertuzumab resulted in an additional 1.81 life-years gained, or 0.62 QALYs, at a cost of $472,668 per QALY gained. Deterministic sensitivity analysis showed that THP is unlikely to be cost effective even under the most favorable assumptions, and probabilistic sensitivity analysis predicted 0% chance of cost effectiveness at a willingness to pay of $100,000 per QALY gained. Conclusion THP in patients with metastatic HER2-positive breast cancer is unlikely to be cost effective in the United States.

The prophylactic placement of a percutaneous endoscopic gastrostomy (PEG) tube in the head and neck cancer (HNC) patient is controversial. We sought to identify factors associated with prophylactic PEG placement and actual PEG use. Since 2010, data regarding PEG placement and use were prospectively recorded in a departmental database from January 2010 to December 2012. HNC patients treated with intensity-modulated radiation therapy (IMRT) were retrospectively evaluated from 2010 to 2012. Variables potentially associated with patient post-radiation dysphagia from previous literature, and our experience was evaluated. We performed multivariate logistic regression on these variables with PEG placement and PEG use, respectively, to compare the difference of association between the two arms. We identified 192 HNC patients treated with IMRT. Prophylactic PEG placement occurred in 121 (63.0 %) patients, with PEG use in 97 (80.2 %) patients. PEG placement was associated with male gender (p < .01), N stage ≥ N2 (p < .05), pretreatment swallowing difficulties (p < .01), concurrent chemotherapy (p < .01), pretreatment KPS ≥80 (p = .01), and previous surgery (p = .02). Concurrent chemotherapy (p = .03) was positively associated with the use of PEG feeding by the patient, whereas pretreatment KPS ≥80 (p = .03) and prophylactic gabapentin use (p < .01) were negatively associated with PEG use. The analysis suggests there were discrepancies between prophylactic PEG tube placement and actual use. Favorable pretreatment KPS, no pretreatment dysphagia, no concurrent chemotherapy, and the use of gabapentin were significantly associated with reduced PEG use. This analysis may help refine the indications for prophylactic PEG placement.

Background: Oncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer. Methods: The model was trained and tested using 12 588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables was used. A landmarking approach was used due to the multiple observations per patient, with t 0 set to the time of metastatic cancer diagnosis. Performance was also evaluated using 399 palliative radiation courses in test set patients. Results: The C-index for overall survival was 0.786 in the test set (averaged across landmark times). For palliative radiation courses, the C-index was 0.745 (95% confidence interval [CI] ¼ 0.715 to 0.775) compared with 0.635 (95% CI ¼ 0.601 to 0.669) for a published model using performance status, primary tumor site, and treated site (two-sided P < .001). Our model's predictions were well-calibrated. Conclusions: The model showed high predictive performance, which will need to be validated using external data. Because it is fully automated, the model can be used to examine providers' practice patterns and could be deployed in a decision support tool to help improve quality of care.

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