Acute Hospital Encounters in Cancer Patients Treated With Definitive Radiation Therapy

Dyadic Yoga for Head and Neck Cancer Patients Undergoing Chemoradiation and their Family Caregivers

OBJECTIVES

Concurrent chemoradiation to treat head and neck cancer (HNC) may result in debilitating toxicities. Targeted exercise such as yoga therapy may buffer against treatment-related sequelae; thus, this pilot RCT examined the feasibility and preliminary efficacy of a yoga intervention. Because family caregivers report low caregiving efficacy and elevated levels of distress, we included them in this trial as active study participants.

METHODS

HNC patients and their caregivers were randomized to a 15-session dyadic yoga program or a waitlist control (WLC) group. Prior to randomization, patients completed standard symptom (MDASI-HN) and patients and caregivers completed quality of life (SF-36) assessments. The 15-session program was delivered parallel to patients' treatment schedules. Participants were re-assessed at patients' last day of chemoradiation and again 30 days later. Patients' emergency department visits, unplanned hospital admissions and gastric feeding tube placements were recorded over the treatment course and up to 30 days later.

RESULTS

With a consent rate of 76%, 37 dyads were randomized. Participants in the yoga group completed a mean of 12.5 sessions and rated the program as "beneficial." Patients in the yoga group had clinically significantly less symptom interference and HNC symptom severity and better QOL than those in the WLC group. They were also less likely to have a hospital admission (OR = 3.00), emergency department visit (OR = 2.14), and/or a feeding tube placement (OR = 1.78).

CONCLUSION

Yoga therapy appears to be a feasible, acceptable, and possibly efficacious behavioral supportive care strategy for HNC patients undergoing chemoradiation. A larger efficacy trial is warranted.

Machine Learning Model of Emergency Department Use for Patients Undergoing Treatment for Head and Neck Cancer Using Comprehensive Multifactor Electronic Health Records

PURPOSE

To use a hybrid method, combining statistical profiling, machine learning (ML), and clinical evaluation to predict emergency department (ED) visits among patients with head and neck cancer undergoing radiotherapy.

MATERIALS AND METHODS

Patients with head and neck cancer treated with radiation therapy from 2015 to 2019 were identified using electronic health record data. Records from 60 days before 90 days after treatment were analyzed. Statistical profiling and ML were used to create a predictive model for ED visits during or after radiation therapy. A comprehensive set of variables were studied. Multiple ML models were developed including extreme gradient-boosted decision tree and generalized logistic regression with comparison of multiple predictive performance metrics.

RESULTS

Of the 1,355 patients studied, 13% had an ED visit during or after treatment. Our hybrid methodology enabled evidence-based winnowing of candidate features from 141 to 11 with clinically applicable, evidence-based thresholds. Extreme gradient boosting had the highest area under the curve (0.81 ± 0.06) with a sensitivity of 0.89 ± 0.10 and exceeded generalized logistic regression (area under the curve 0.64 ± 0.02). Significant predictors of ED visits during treatment included increasingly complex opioid use, number of prior ED visits, tumor volume, rate of change of blood urea nitrogen, total bilirubin, body mass index, and distance from hospital.

CONCLUSION

Our approach combining bootstrapped statistical profiling and ML importance analysis supported integration of clinician input to identify a distilled set of phenotypical characteristics for developing ML models predicting which patients undergoing head and neck cancer radiation therapy were at risk for ED visits.

Activity Monitoring for Toxicity Detection and Management in Patients Undergoing Chemoradiation for Gastrointestinal Malignancies

PURPOSE

Physical activity is associated with decreased hospitalization during cancer treatment. We hypothesize that activity data can help identify and triage high-risk patients with GI cancer undergoing concurrent chemoradiation.

MATERIALS AND METHODS

This prospective study randomly assigned patients to activity monitoring versus observation. In the intervention arm, a 20% decrease in daily steps or 20% increase in heart rate triggered triage visits to provide supportive care, medication changes, and escalation of care. In the observation group, activity data were recorded but not monitored. The primary objective was to show a 20% increase in triage visits in the intervention group. Secondary objectives were estimating the rates of emergency department (ED) visits and hospitalizations. Crude and adjusted odds ratios were computed using logistic regression modeling.

RESULTS

There were 22 patients in the intervention and 18 in the observation group. Baseline patient and treatment characteristics were similar. The primary objective was met, with 3.4 more triage visits in the intervention group than in the observation group (95% CI, 2.10 to 5.50; P < .0001). Twenty-six (65.0%) patients required at least one triage visit, with a higher rate in the intervention arm compared with that in the observation arm (86.4% v 38.9%; odds ratio, 9.95; 95% CI, 2.13 to 46.56; P = .004). There was no statistically significant difference in ED visit (9.1% v 22.2%; P = .38) or hospitalization (4.5% v 16.7%; P = .31).

CONCLUSION

It is feasible to use activity data to trigger triage visits for symptom management. Further studies are investigating whether automated activity monitoring can assist with early outpatient management to decrease ED visits and hospitalizations.

Care Disparities Across the Health Care Continuum for Older Adults: Lessons From Multidisciplinary Perspectives

Older adults comprise a considerable proportion of patients with cancer in the world. Across multiple cancer types, cancer treatment outcomes among older age groups are often inferior to those among younger adults. Cancer care for older individuals is complicated by the need to adapt treatment to baseline health, fitness, and frailty, all of which vary widely within this age group. Rates of social deprivation and socioeconomic disparities are also higher in older adults, with many living on reduced incomes, further compounding health inequality. It is important to recognize and avoid undertreatment and overtreatment of cancer in this age group; however, simply addressing this problem by mandating standard treatment of all would lead to harms resulting from treatment toxicity and futility. However, there is little high-quality evidence on which to base these decisions, because older adults are poorly represented in clinical trials. Clinicians must recognize that simple extrapolation of outcomes from younger age cohorts may not be appropriate because of variance in disease stage and biology, variation in fitness and treatment tolerance, and reduced life expectancy. Older patients may also have different life goals and priorities, with a greater focus on quality of life and less on length of life at any cost. Health care professionals struggle with treatment of older adults with cancer, with high rates of variability in practice between and within countries. This suggests that better national and international recommendations that more fully address the needs of this special patient population are required and that primary research focused on the older age group is urgently required to inform these guidelines.

A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes

PURPOSE

Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patients are lacking. We developed a machine learning model to identify high-risk patients.

METHODS AND MATERIALS

In the study, 1341 consecutive patients undergoing GI (abdominal or pelvic) radiation treatment (RT) from March 2016 to July 2018 (derivation) and July 2018 to January 2019 (validation) were assessed for unplanned hospitalizations within 30 days of finishing RT. In the derivation cohort of 663 abdominal and 427 pelvic RT patients, a machine learning approach derived random forest, gradient boosted decision tree, and logistic regression models to predict 30-day unplanned hospitalizations. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and prospectively validated in 161 abdominal and 90 pelvic RT patients using Mann-Whitney rank-sum test. Highest quintile of risk for hospitalization was defined as "high-risk" and the remainder "low-risk." Hospitalizations for high- versus low-risk patients were compared using Pearson's χ² test and survival using Kaplan-Meier log-rank test.

RESULTS

Overall, 13% and 11% of patients receiving abdominal and pelvic RT experienced 30-day unplanned hospitalization. In the derivation phase, gradient boosted decision tree cross-validation yielded AUC = 0.823 (abdominal patients) and random forest yielded AUC = 0.776 (pelvic patients). In the validation phase, these models yielded AUC = 0.749 and 0.764, respectively (P < .001 and P = .002). Validation models discriminated high- versus low-risk patients: in abdominal RT patients, frequency of hospitalization was 39% versus 9% in high- versus low-risk groups (P < .001) and 6-month survival was 67% versus 92% (P = .001). In pelvic RT patients, frequency of hospitalization was 33% versus 8% (P = .002) and survival was 86% versus 92% (P = .15) in high- versus low-risk patients.

CONCLUSIONS

In patients with GI cancer undergoing RT as part of multimodality treatment, machine learning models for 30-day unplanned hospitalization discriminated high- versus low-risk patients. Future applications will test utility of models to prompt interventions to decrease hospitalizations and adverse outcomes.

Comparative Effectiveness of Proton vs Photon Therapy as Part of Concurrent Chemoradiotherapy for Locally Advanced Cancer

Importance

Concurrent chemoradiotherapy is the standard-of-care curative treatment for many cancers but is associated with substantial morbidity. Concurrent chemoradiotherapy administered with proton therapy might reduce toxicity and achieve comparable cancer control outcomes compared with conventional photon radiotherapy by reducing the radiation dose to normal tissues.

Objective

To assess whether proton therapy in the setting of concurrent chemoradiotherapy is associated with fewer 90-day unplanned hospitalizations (Common Terminology Criteria for Adverse Events, version 4 [CTCAEv4], grade ≥3) or other adverse events and similar disease-free and overall survival compared with concurrent photon therapy and chemoradiotherapy.

Design, Setting, and Participants

This retrospective, nonrandomized comparative effectiveness study included 1483 adult patients with nonmetastatic, locally advanced cancer treated with concurrent chemoradiotherapy with curative intent from January 1, 2011, through December 31, 2016, at a large academic health system. Three hundred ninety-one patients received proton therapy and 1092, photon therapy. Data were analyzed from October 15, 2018, through February 1, 2019.

Interventions

Proton vs photon chemoradiotherapy.

Main Outcomes and Measures

The primary end point was 90-day adverse events associated with unplanned hospitalizations (CTCAEv4 grade ≥3). Secondary end points included Eastern Cooperative Oncology Group (ECOG) performance status decline during treatment, 90-day adverse events of at least CTCAEv4 grade 2 that limit instrumental activities of daily living, and disease-free and overall survival. Data on adverse events and survival were gathered prospectively. Modified Poisson regression models with inverse propensity score weighting were used to model adverse event outcomes, and Cox proportional hazards regression models with weighting were used for survival outcomes. Propensity scores were estimated using an ensemble machine-learning approach.

Results

Among the 1483 patients included in the analysis (935 men [63.0%]; median age, 62 [range, 18-93] years), those receiving proton therapy were significantly older (median age, 66 [range, 18-93] vs 61 [range, 19-91] years; P < .01), had less favorable Charlson-Deyo comorbidity scores (median, 3.0 vs 2.0; P < .01), and had lower integral radiation dose to tissues outside the target (mean [SD] volume, 14.1 [6.4] vs 19.1 [10.6] cGy/cc × 107; P < .01). Baseline grade ≥2 toxicity (22% vs 24%; P = .37) and ECOG performance status (mean [SD], 0.62 [0.74] vs 0.68 [0.80]; P = .16) were similar between the 2 cohorts. In propensity score weighted-analyses, proton chemoradiotherapy was associated with a significantly lower relative risk of 90-day adverse events of at least grade 3 (0.31; 95% CI, 0.15-0.66; P = .002), 90-day adverse events of at least grade 2 (0.78; 95% CI, 0.65-0.93; P = .006), and decline in performance status during treatment (0.51; 95% CI, 0.37-0.71; P < .001). There was no difference in disease-free or overall survival.

Conclusions and Relevance

In this analysis, proton chemoradiotherapy was associated with significantly reduced acute adverse events that caused unplanned hospitalizations, with similar disease-free and overall survival. Prospective trials are warranted to validate these results.

Guidelines to Reduce Hospitalization Rates for Patients Receiving Curative-Intent Radiation Therapy During the COVID-19 Pandemic: Report From a Multicenter New York Area Institution

As the coronavirus disease 2019 pandemic spreads around the globe, access to radiation therapy remains critical for patients with cancer. The priority for all radiation oncology departments is to protect the staff and to maintain operations in providing access to those patients requiring radiation therapy services. Patients with tumors of the aerodigestive tract and pelvis, among others, often experience toxicity during treatment, and there is a baseline risk that adverse effects may require hospital-based management. Routine care during weekly visits is important to guide patients through treatment and to mitigate against the need for hospitalization. Nevertheless, hospitalizations occur and there is a risk of nosocomial severe acute respiratory syndrome coronavirus-2 spread. During the coronavirus disease 2019 pandemic, typical resources used to help manage patients, such as dental services, interventional radiology, rehabilitation, and others are limited or not at all available. Recognizing the need to provide access to treatment and the anticipated toxicity of such treatment, we have developed and implemented guidelines for clinical care management with the hope of avoiding added risk to our patients. If successful, these concepts may be integrated into our care directives in nonpandemic times.

Factors associated with the receipt and completion of whole brain radiation therapy among older adults in the United States from 2010-2013

INTRODUCTION

Whole brain radiation therapy (WBRT) is widely used to treat patients with brain metastases. However, there is debate regarding its utility in patients with poor prognoses. In this study, we sought to characterize the use of WBRT in the United States, especially in adults aged 55 and above.

MATERIAL AND METHODS

Patients with brain metastases were identified using the National Cancer Database between 2010 and 2013. The receipt and completion of WBRT with various patient factors were correlated using multivariable logistic regression.

RESULTS

28,422 patients with brain metastases were identified, 23,362 of whom were aged 55 or above. 14,845 patients received WBRT and 12,310 patients completed treatment. Among adults aged 55 and above, 11,945 patients received WBRT, and 9812 patients completed treatment. Patients aged 60 and above were less likely to receive WBRT, while those aged 65 and above were less likely to complete WBRT.

DISCUSSION

These results suggest that WBRT may be over-utilized in the United States, especially among older adults. Better interventions to improve pre-WBRT decision-making in this population are needed to select patients who might derive benefit.

Identifying the Most Costly Patients in Radiation Oncology and Predicting the Top Spenders

PURPOSE:

Quality payment programs aim to adjust payments on the basis of quality and cost; however, few quality metrics exist in radiation oncology. This study evaluates and predicts the top spenders (TS) after radiation therapy (RT).

MATERIALS AND METHODS:

Patient characteristics, cancer details, treatments, toxicity, and survival data were collected for patients treated with RT at Mayo Clinic from 2007 to 2016. Standardized costs were obtained and adjusted for inflation. TSs were identified as those with greater than 93rd percentile costs (≥ $120,812). Prediction models were developed to predict TSs using training and validation sets using information available at consultation, after RT, and at last follow-up.

RESULTS:

A total of 15,131 patients were included and 1,065 TSs identified. Mean cost overall was $55,290 (median, $39,996) for all patients. Prediction models 1, 2, and 3 had concordance statistics of 0.83 to 0.83, 0.85 to 0.85, and 0.87 to 0.88, respectively in training and validation, indicating excellent prediction of TSs. Factors that were most predictive of TSs included stage N/A and stage 4 ( v stage 0; odds ratio [OR], 18.23 and 8.44, respectively; P < .001); hematologic, upper GI, skin and lung cancers ( v breast; OR, 11.45, 7.69, 3.81, and 2.43, respectively; P < .01); immunotherapy, surgery, and chemotherapy use (OR, 4.36, 2.51, and 1.61, respectively; P < .01); hospitalizations within 90 days of RT (OR, 2.26; P < .01); or death during the episode (OR, 1.56; P < .01).

CONCLUSION:

This is the first study of its kind to predict with high accuracy the highest spenders in radiation oncology. These patients may benefit from pre-emptive management to mitigate costs, or may require exclusion or adjustment from quality payment programs.