A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events.

PURPOSE: Error detection in radiation oncology relies heavily on voluntary reporting, and many adverse events and near misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise-unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS). METHODS AND MATERIALS: Twenty triggers were derived from an electronic radiation oncology information system. Data from the systems over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near misses. RESULTS: Of the 3159 treatment courses analyzed, 357 had a grade 3 to 4 ILS entry; 2210 courses composed the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, 9 reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near miss with an ILS entry had a direct relationship between the triggers and the near miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near miss without an ILS entry found 2 unreported near-miss events. CONCLUSIONS: The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.

Use of Radiation Therapy Within the Last Year of Life Among Cancer Patients.

PURPOSE: We examined radiation therapy (RT) use within the last year of life (LYOL). As palliative RT (PRT) has been well studied in patients with ≥6-week life expectancies, we hypothesized that PRT use would be constant over the LYOL, except for the last 30 days, when use would decline given lack of prospective data supporting it. MATERIALS AND METHODS: At a single institution, 870 cancer patients died between October 2, 2014, and September 30, 2015, and had ≥3 evaluation and management visits within the LYOL. Claims and RT data were extracted and linked. Over the LYOL, we evaluated RT use by intent (curative vs palliative) and indications. RESULTS: Within the LYOL, one-third of patients underwent RT in the last 365 days of life to 444 sites, which decreased to 24.3% and 8.5% in the last 180 and 30 days of life, respectively. Patients who received any RT in the last 365 days of life were younger at death and had a higher proportion of lung, sarcoma, and transplant disease groups. One-quarter of sites were irradiated with curative intent, which remained constant over the LYOL. In contrast, PRT was used at a supralinear rate, in which treatment of bone metastases and use of single-fraction PRT increased closer to death. CONCLUSIONS: PRT appears to be disproportionately used closer to death, with an increasing proportion of irradiated sites being bone metastases. This may be secondary to increased symptoms from advanced cancer toward the end of life. As patients with very poor prognoses (eg, within 30 days of death) are generally not included in RT clinical trials, further studies are warranted to assess whether PRT for bone metastases at the end of life is efficacious.

Influence of planning time and treatment complexity on radiation therapy errors.

PURPOSE: Radiation treatment planning is a complex process with potential for error. We hypothesized that shorter time from simulation to treatment would result in rushed work and higher incidence of errors. We examined treatment planning factors predictive for near-miss events. METHODS AND MATERIALS: Treatments delivered from March 2012 through October 2014 were analyzed. Near-miss events were prospectively recorded and coded for severity on a 0 to 4 scale; only grade 3-4 (potentially severe/critical) events were studied in this report. For 4 treatment types (3-dimensional conformal, intensity modulated radiation therapy, stereotactic body radiation therapy [SBRT], neutron), logistic regression was performed to test influence of treatment planning time and clinical variables on near-miss events. RESULTS: There were 2257 treatment courses during the study period, with 322 grade 3-4 near-miss events. SBRT treatments had more frequent events than the other 3 treatment types (18% vs 11%, P = .04). For the 3-dimensional conformal group (1354 treatments), univariate analysis showed several factors predictive of near-miss events: longer time from simulation to first treatment (P = .01), treatment of primary site versus metastasis (P < .001), longer treatment course (P < .001), and pediatric versus adult patient (P = .002). However, on multivariate regression only pediatric versus adult patient remained predictive of events (P = 0.02). For the intensity modulated radiation therapy, SBRT, and neutron groups, time between simulation and first treatment was not found to be predictive of near-miss events on univariate or multivariate regression. CONCLUSIONS: When controlling for treatment technique and other clinical factors, there was no relationship between time spent in radiation treatment planning and near-miss events. SBRT and pediatric treatments were more error-prone, indicating that clinical and technical complexity of treatments should be taken into account when targeting safety interventions.