Predictors of Early Hospice or Death in Patients With Inoperable Lung Cancer Treated With Curative Intent.

INTRODUCTION: Treatment for inoperable stage II to III non-small cell lung cancer (NSCLC) involves chemo-radiotherapy (CRT). However, some patients transition to hospice or die early during their treatment course. We present a model to prognosticate early poor outcomes in NSCLC patients treated with curative-intent CRT. METHODS AND MATERIALS: Across a statewide consortium, data was prospectively collected on stage II to III NSCLC patients who received CRT between 2012 and 2019. Early poor outcomes included hospice enrollment or death within 3 months of completing CRT. Logistic regression models were used to assess predictors in prognostic models. LASSO regression with multiple imputation were used to build a final multivariate model, accounting for missing covariates. RESULTS: Of the 2267 included patients, 128 experienced early poor outcomes. Mean age was 71 years and 59% received concurrent chemotherapy. The best predictive model, created parsimoniously from statistically significant univariate predictors, included age, ECOG, planning target volume (PTV), mean heart dose, pretreatment lack of energy, and cough. The estimated area under the ROC curve for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%. CONCLUSIONS: This multivariate model identified a combination of clinical variables and patient reported factors that may identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at higher risk for early poor outcomes.

Racial Differences in Treatments and Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Thoracic Radiation Therapy.

PURPOSE: Historical racial disparities in lung cancer surgery rates resulted in lower survival in Black patients. Our objective was to examine racial differences in thoracic radiation treatments and toxicities in patients with non-small-cell lung cancer. METHODS AND MATERIALS: A large institutional review board-approved statewide patient-level database of patients with stage II-III non-small-cell lung cancer who received definitive thoracic radiation from March 2012 to November 2019 was analyzed to assess associations between race and other variables. Race (White or Black) was defined by patient self-report. Provider-reported toxicity was defined by Common Terminology Criteria for Adverse Events version 4.0. Patient-reported toxicity was determined by the Functional Assessment of Cancer Therapy-Lung quality-of-life instrument. Univariable and multivariable regression models were fitted to assess relationships between race and variables of interest. Spearman rank-correlation coefficients were calculated between provider-reported toxicity and similar patient-reported outcomes. RESULTS: One thousand four hundred forty-one patients from 24 institutions with mean age 68 years (range, 38-94 years) were evaluated. Race was not significantly associated with radiation or chemotherapy approach. There was significantly increased patient-reported general pain in Black patients at the preradiation and end-of-radiation time points. Black patients were significantly less likely to have provider-reported grade 2+ pneumonitis (odds ratio 0.36, P = .03), even after controlling for known patient and treatment factors. Correlation coefficients between provider- and patient-reported toxicities were generally similar across race groups except for a stronger correlation between patient- and provider-reported esophagitis in White patients. CONCLUSION: In this large multi-institutional study, we found no evidence of racial differences in radiation treatment or chemotherapy approaches. We did, however, unexpectedly find that Black race was associated with lower odds of provider-reported grade 2+ radiation pneumonitis. The stronger correlation between patient- and provider-reported esophagitis and swallowing symptoms for White patients also suggests possible under-recognition of symptoms in Black patients. Further research is needed to study the implications for Black patients.

Association Between Physician- and Patient-Reported Symptoms in Patients Treated With Definitive Radiation Therapy for Locally Advanced Lung Cancer in a Statewide Consortium.

PURPOSE: Little data have been reported about the patient experience during curative radiation therapy (RT) for lung cancer in routine clinical practice or how this relates to treatment toxicity as reported by clinicians. The purpose of this study was to compare clinician-reported adverse events (AEs) with patient-reported outcomes (PROs), including both specific symptoms/side effects, as well as overall quality of life (QoL) during and after definitive RT for locally advanced lung cancer (LALC) in a large statewide cohort. METHODS AND MATERIALS: PROs were prospectively collected from patients treated with definitive RT for LALC at 24 institutions within the Michigan Radiation Oncology Quality Consortium between 2012 and 2018 using the Functional Assessment of Cancer Therapy trial outcome index. Physicians prospectively recorded AEs using the Common Terminology Criteria for Adverse Events, version 4.0. Patient-reported QoL changes from baseline were assessed during and after RT using the Functional Assessment of Cancer Therapy trial outcome index. Spearman correlation coefficients were calculated for AEs and similar PROs, and a multivariable analysis was used to assess associations with QoL. RESULTS: A total 1361 patients were included in the study, and 53% of respondents reported clinically meaningful declines in QoL at the end of RT. The correlation between clinician-reported esophagitis and patient-reported trouble swallowing was moderate (R = .67), but correlations between clinician-reported pneumonitis and patient-reported shortness of breath (R = .13) and cough (R = .09) were weak. Clinician-reported AEs were significantly associated with clinically meaningful declines in patient-reported QoL (R = - .46 for summary AE score). QoL was more strongly associated with fatigue (R = - .41) than lung-specific AEs. CONCLUSIONS: AEs are associated with clinically meaningful declines in QoL during and after RT for LALC, but associations between AEs and QoL are only modest. This highlights the importance of PRO data, and future research should assess whether earlier detection of PRO changes could allow for interventions that reduce the frequency of treatment-related clinically meaningful declines in QoL.

Predictors of Pneumonitis After Conventionally Fractionated Radiotherapy for Locally Advanced Lung Cancer.

PURPOSE: Multiple factors influence the risk of developing pneumonitis after radiation therapy (RT) for lung cancer, but few resources exist to guide clinicians in predicting risk in an individual patient treated with modern techniques. We analyzed toxicity data from a state-wide consortium to develop an integrated pneumonitis risk model. METHODS AND MATERIALS: All patients (N = 1302) received conventionally fractionated RT for stage II-III non-small cell lung cancer between April 2012 and July 2019. Pneumonitis occurring within 6 months of treatment was graded by local practitioners and collected prospectively from 27 academic and community clinics participating in a state-wide quality consortium. Pneumonitis was modeled as either grade ≥2 (G2+) or grade ≥3 (G3+). Logistic regression models were fit to quantify univariable associations with dose and clinical factors, and stepwise Akaike information criterion-based modeling was used to build multivariable prediction models. RESULTS: The overall rate of pneumonitis of any grade in the 6 months following RT was 16% (208 cases). Seven percent of cases (n = 94) were G2+ and <1% (n = 11) were G3+. Adjusting for incomplete follow-up, estimated rates for G2+ and G3+ were 14% and 2%, respectively. In univariate analyses, gEUD, V5, V10, V20, V30, and mean lung dose (MLD) were positively associated with G2+ pneumonitis risk, whereas current smoking status was associated with lower odds of pneumonitis. G2+ pneumonitis risk of ≥22% was independently predicted by MLD of ≥20 Gy, V20 of ≥35%, and V5 of ≥75%. In multivariate analyses, the lung V5 metric remained a significant predictor of G2+ pneumonitis, even when controlling for MLD, despite their close correlation. For G3+ pneumonitis, MLD and V20 were statistically significant predictors. Number of patient comorbidities was an independent predictor of G3+, but not G2+ pneumonitis. CONCLUSIONS: We present an analysis of pneumonitis risk after definitive RT for lung cancer using a large, prospective dataset. We incorporate comorbidity burden, smoking status, and dosimetric parameters in an integrated risk model. These data may guide clinicians in assessing pneumonitis risk in individual patients.

Gestational Age at Birth and Risk of Developmental Delay: The Upstate KIDS Study.

OBJECTIVE: The aim of this study is to model the association between gestational age at birth and early child development through 3 years of age. STUDY DESIGN: Development of 5,868 children in Upstate KIDS (New York State; 2008-2014) was assessed at 7 time points using the Ages and Stages Questionnaire (ASQ). The ASQ was implemented using gestational age corrected dates of birth at 4, 8, 12, 18, 24, 30, and 36 months. Whether children were eligible for developmental services from the Early Intervention Program was determined through linkage. Gestational age was based on vital records. Statistical models adjusted for covariates including sociodemographic factors, maternal smoking, and plurality. RESULTS: Compared with gestational age of 39 weeks, adjusted odds ratios (aOR) and 95% confidence intervals of failing the ASQ for children delivered at <32, 32-34, 35-36, 37, 38, and 40 weeks of gestational age were 5.32 (3.42-8.28), 2.43 (1.60-3.69), 1.38 (1.00-1.90), 1.37 (0.98-1.90), 1.29 (0.99-1.67), 0.73 (0.55-0.96), and 0.51 (0.32-0.82). Similar risks of being eligible for Early Intervention Program services were observed (aOR: 4.19, 2.10, 1.29, 1.20, 1.01, 1.00 [ref], 0.92, and 0.78 respectively for <32, 32-34, 37, 38, 39 [ref], 40, and 41 weeks). CONCLUSION: Gestational age was inversely associated with developmental delays for all gestational ages. Evidence from our study is potentially informative for low-risk deliveries at 39 weeks, but it is notable that deliveries at 40 weeks exhibited further lower risk.

Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial.

Clinical trials studying treatments for rare diseases are challenging to design and conduct due to the limited number of patients eligible for the trial. One design used to address this challenge is the small n, sequential, multiple assignment, randomized trial (snSMART). We propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo. Patients are randomized to an initial treatment (stage 1). In stage 2, patients are rerandomized, depending on their initial treatment and their response to that treatment in stage 1, to either the same or a different dose of treatment. Data from both stages are used to determine the efficacy of the active treatment. We present a Bayesian approach where information is borrowed between stage 1 and stage 2. We compare our approach to standard methods using only stage 1 data and a log-linear Poisson model that uses data from both stages where parameters are estimated using generalized estimating equations. We observe that the Bayesian method has smaller root-mean-square-error and 95% credible interval widths than standard methods in the tested scenarios. We conclude that it is advantageous to utilize data from both stages for a primary efficacy analysis and that the specific snSMART design shown here can be used in the registration of a drug for the treatment of rare diseases.

Integrated Survival Estimates for Cancer Treatment Delay Among Adults With Cancer During the COVID-19 Pandemic.

Importance: Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective: To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants: In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures: COVID-19 and cancer. Main Outcomes and Measures: Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results: At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance: This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.