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Immunotherapy is an effective and generally well-tolerated treatment strategy for older adult patients (aged ≥70 years) with advanced non-small cell lung cancer (NSCLC). Unfortunately, most patients who receive immunotherapy eventually exhibit disease progression during treatment. The present study reports on a subset of older adult patients with advanced NSCLC who could effectively continue immunotherapy beyond radiographic disease progression due to perceived clinical benefit. Local consolidative radiotherapy may be used in select older adult patients to prolong the duration of immunotherapy they receive, with a particular consideration of their preexisting co-morbidities, performance status and tolerance of potential toxicities associated with combined modality therapy. However, prospective research is needed to determine which patients benefit most from the addition of local consolidative radiotherapy, including whether type of disease progression (i.e., sites of progression, pattern of progression) and/or extent of consolidation offered (i.e., complete or incomplete) impact clinical outcomes. Further research is also warranted to determine which patients would most benefit from the continuation of immunotherapy beyond documented radiographic disease progression.

Patients with thoracic malignancies are at increased risk for mortality from coronavirus disease 2019 (COVID-19), and a large number of intertwined prognostic variables have been identified so far.Capitalizing data from the Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure, and a tree-based model to screen and optimize a broad panel of demographics and clinical COVID-19 and cancer characteristics.As of April 15, 2021, a total of 1491 consecutive eligible patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection procedure then identified the following seven major determinants of death: Eastern Cooperative Oncology Group-performance status (ECOG-PS) (OR = 2.47, 1.87-3.26), neutrophil count (OR = 2.46, 1.76-3.44), serum procalcitonin (OR = 2.37, 1.64-3.43), development of pneumonia (OR = 1.95, 1.48-2.58), C-reactive protein (OR = 1.90, 1.43-2.51), tumor stage at COVID-19 diagnosis (OR = 1.97, 1.46-2.66), and age (OR = 1.71, 1.29-2.26). The receiver operating characteristic analysis for death of the selected model confirmed its diagnostic ability (area under the receiver operating curve = 0.78, 95% confidence interval: 0.75-0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90%, and the tree-based model recognized ECOG-PS, neutrophil count, and c-reactive protein as the major determinants of prognosis.From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS was found to have the strongest association with poor outcome from COVID-19. With our analysis, we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19.

Objective Increased treatment options and longer survival for lung cancer have generated increased interest in patient preferences. Previous studies of patient preferences in lung cancer have not fully explored preference heterogeneity. We demonstrate a method to explore preference heterogeneity in the willingness of patients with lung cancer and caregivers to trade progression-free survival (PFS) with side effects. Patients and Methods Patients and caregivers attending a national lung cancer meeting completed a discrete-choice experiment (DCE) designed through a collaboration with patients. Participants answered 13 choice tasks described across PFS, short-term side effects, and four long-term side effects. Side effects were coded as a one-level change in severity (none-mild, mild-moderate, or moderate-severe). A mixed logit model in willingness-to-pay space estimated preference heterogeneity in acceptable tradeoffs (time equivalents) between PFS and side effects. The study was reported following quality indicators from the United States Food and Drug Administration’s patient preference guidance. Results A total of 87 patients and 24 caregivers participated in the DCE. Participants would trade 3.7 month PFS (95% CI (CI): 3.3–4.1) for less severe functional long-term treatment side effects, 2.3 months for less severe physical long-term effects (CI: 1.9–2.8) and cognitive long-term effects (CI: 1.8–2.8), 0.9 months (CI: 0.4–1.4) for less severe emotional long-term effects, and 1.8 months (CI: 1.4–2.3) for less severe short-term side effects. Most participants (90%) would accept treatment with more severe functional long-term effects for 8.4 additional month PFS. Conclusion Participants would trade PFS for changes in short-term side effects and long-term side effects, although preference heterogeneity existed. Lung cancer treatments that offer less PFS but also less severe side effects might be acceptable to some patients.

Ellen M Janssen,1 Sydney M Dy,2 Alexa S Meara,3 Peter J Kneuertz,4 Carolyn J Presley,5 John FP Bridges6,7 1Center for Medical Technology Policy, Baltimore, MD, USA; 2Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 3Department of Internal Medicine Division Of Rheumatology, The Ohio State University, College of Medicine, Columbus, OH, USA; 4Thoracic Surgery Division, Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH, USA; 5Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA; 6Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA; 7Department of Surgery, The Ohio State University College of Medicine, Columbus, OH, USACorrespondence: Ellen M JanssenResearch Director, Center for Medical Technology Policy, 401 East Pratt Street, Suite 631, Baltimore, MD 21202, USATel +1 443-222-8775Email ellen.janssen@cmtpnet.orgObjective: Increased treatment options and longer survival for lung cancer have generated increased interest in patient preferences. Previous studies of patient preferences in lung cancer have not fully explored preference heterogeneity. We demonstrate a method to explore preference heterogeneity in the willingness of patients with lung cancer and caregivers to trade progression-free survival (PFS) with side effects.Patients and Methods: Patients and caregivers attending a national lung cancer meeting completed a discrete-choice experiment (DCE) designed through a collaboration with patients. Participants answered 13 choice tasks described across PFS, short-term side effects, and four long-term side effects. Side effects were coded as a one-level change in severity (none-mild, mild-moderate, or moderate-severe). A mixed logit model in willingness-to-pay space estimated preference heterogeneity in acceptable tradeoffs (time equivalents) between PFS and side effects. The study was reported following quality indicators from the United States Food and Drug Administration’s patient preference guidance.Results: A total of 87 patients and 24 caregivers participated in the DCE. Participants would trade 3.7 month PFS (95% CI (CI): 3.3– 4.1) for less severe functional long-term treatment side effects, 2.3 months for less severe physical long-term effects (CI: 1.9– 2.8) and cognitive long-term effects (CI: 1.8– 2.8), 0.9 months (CI: 0.4– 1.4) for less severe emotional long-term effects, and 1.8 months (CI: 1.4– 2.3) for less severe short-term side effects. Most participants (90%) would accept treatment with more severe functional long-term effects for 8.4 additional month PFS.Conclusion: Participants would trade PFS for changes in short-term side effects and long-term side effects, although preference heterogeneity existed. Lung cancer treatments that offer less PFS but also less severe side effects might be acceptable to some patients.Keywords: non-small cell lung cancer, patient preferences, discrete choice experiment, heterogeneity, long-term side effects

The development of mobile health technology has the potential to contribute greatly to personalized medicine. Wearable sensors can assist with determining the proper treatment plans for individuals, provide quantitative information to physicians, or give individuals an objective measurement of their health. However, though treatments and interventions have become more targeted and specific, measuring the causal impact of these actions require more careful considerations of complex covariate structures as well as temporal and spatial properties of the data. Thus, emerging data from sensors and wearables in the near future will make use of and require complex models. Here, we describe a general statistical framework for sensor and wearable data that applies a Bayesian structural time series model to analyze and understand various behavior and health data collected in different environments. We show the wide applicability of this modelling framework, and how it corrects for covariates and biases to provide accurate assessments of intervention. Furthermore, it allows for a time dependent confidence interval of impact through its use of Bayesian estimation. We give three main examples, physical sensor data, environmental air sensors and longitudinal behavioral data to show the effect of various interventions through parameter estimation and comparison in pre- and post-intervention periods. The Bayesian structural time series model shows robust performance in a wide variety of tasks, further supporting its applicability to current and future mobile health and sensor data types.

Additional file 1: Figure S1. Causal diagram with references. Figure S2. Number of medications prescribed across all cancers and the frequency of multiple medications. Figure S3. Number of antibiotics, separated by class, prescribed across all cancers and the frequency of multiple antibiotics. Figure S4. Number of corticosteroids, separated by class, prescribed across all cancers and the frequency of multiple antibiotics.

Additional file 2: Table S1. References for the antibiotic susceptibilities of bacterial taxa found to be enriched in responders or non-responders to ICI therapy