Independent Predictors for Hospitalization-Associated Radiation therapy Interruptions

Adam Hubler; Daniel V Wakefield; Lydia Makepeace; Matt Carnell; Ankur M Sharma; Bo Jiang; Austin P Dove; Wesley B Garner; Drucilla Edmonston; John G Little; Esra Ozdenerol; Ryan B Hanson; Michelle Y Martin; Arash Shaban-Nejad; Maria Pisu; David L Schwartz

doi:10.1016/j.adro.2022.101041

Independent Predictors for Hospitalization-Associated Radiation therapy Interruptions

Adv Radiat Oncol. 2022 Jul 30;7(6):101041. doi: 10.1016/j.adro.2022.101041. eCollection 2022 Nov-Dec.

Authors

Adam Hubler¹, Daniel V Wakefield^{1

2}, Lydia Makepeace¹, Matt Carnell³, Ankur M Sharma^{1

4}, Bo Jiang¹, Austin P Dove⁵, Wesley B Garner¹, Drucilla Edmonston¹, John G Little³, Esra Ozdenerol⁶, Ryan B Hanson⁶, Michelle Y Martin⁷, Arash Shaban-Nejad⁸, Maria Pisu⁹, David L Schwartz^{1

7}

Affiliations

¹ Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
² Tennessee Oncology, Nashville, Tennessee.
³ University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee.
⁴ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
⁵ Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennesse.
⁶ Department of Earth Sciences, University of Memphis, Memphis, Tennessee.
⁷ Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee.
⁸ UTHSC-ORNL Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennesse.
⁹ Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama.

Abstract

Purpose: Radiation treatment interruption associated with unplanned hospitalization remains understudied. The intent of this study was to benchmark the frequency of hospitalization-associated radiation therapy interruptions (HARTI), characterize disease processes causing hospitalization during radiation, identify factors predictive for HARTI, and localize neighborhood environments associated with HARTI at our academic referral center.

Methods and materials: This retrospective review of electronic health records provided descriptive statistics of HARTI event rates at our institutional practice. Uni- and multivariable logistic regression models were developed to identify significant factors predictive for HARTI. Causes of hospitalization were established from primary discharge diagnoses. HARTI rates were mapped according to patient residence addresses.

Results: Between January 1, 2015, and December 31, 2017, 197 HARTI events (5.3%) were captured across 3729 patients with 727 total missed treatments. The 3 most common causes of hospitalization were malnutrition/dehydration (n = 28; 17.7%), respiratory distress/infection (n = 24; 13.7%), and fever/sepsis (n = 17; 9.7%). Factors predictive for HARTI included African-American race (odds ratio [OR]: 1.48; 95% confidence interval [CI], 1.07-2.06; P = .018), Medicaid/uninsured status (OR: 2.05; 95% CI, 1.32-3.15; P = .0013), Medicare coverage (OR: 1.7; 95% CI, 1.21-2.39; P = .0022), lung (OR: 5.97; 95% CI, 3.22-11.44; P < .0001), and head and neck (OR: 5.6; 95% CI, 2.96-10.93; P < .0001) malignancies, and prescriptions >20 fractions (OR: 2.23; 95% CI, 1.51-3.34; P < .0001). HARTI events clustered among Medicaid/uninsured patients living in urban, low-income, majority African-American neighborhoods, and patients from middle-income suburban communities, independent of race and insurance status. Only the wealthiest residential areas demonstrated low HARTI rates.

Conclusions: HARTI disproportionately affected socioeconomically disadvantaged urban patients facing a high treatment burden in our catchment population. A complementary geospatial analysis also captured the risk experienced by middle-income suburban patients independent of race or insurance status. Confirmatory studies are warranted to provide scale and context to guide intervention strategies to equitably reduce HARTI events.