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Introduction: Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations., Methods: We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each cancer in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by cancer type/data source, and contrasted with an existing binary detection rule., Results: Detection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by cancer type and dataset challenged efforts to create 1 common algorithm for all scenarios., Conclusions: Valid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and colorectal cancer patients and those who develop recurrence.

Purpose: To assess automated coronary artery calcium (CAC) and quantitative emphysema (percentage of low attenuation areas [%LAA]) for predicting mortality and lung cancer (LC) incidence in LC screening. To explore correlations between %LAA, CAC, and forced expiratory value in 1 second (FEV 1 ) and the discriminative ability of %LAA for airflow obstruction., Materials and Methods: Baseline low-dose computed tomography scans of the BioMILD trial were analyzed using an artificial intelligence software. Univariate and multivariate analyses were performed to estimate the predictive value of %LAA and CAC. Harrell C -statistic and time-dependent area under the curve (AUC) were reported for 3 nested models (Model survey : age, sex, pack-years; Model survey-LDCT : Model survey plus %LAA plus CAC; Model final : Model survey-LDCT plus selected confounders). The correlations between %LAA, CAC, and FEV 1 and the discriminative ability of %LAA for airflow obstruction were tested using the Pearson correlation coefficient and AUC-receiver operating characteristic curve, respectively., Results: A total of 4098 volunteers were enrolled. %LAA and CAC independently predicted 6-year all-cause (Model final hazard ratio [HR], 1.14 per %LAA interquartile range [IQR] increase [95% CI, 1.05-1.23], 2.13 for CAC ≥400 [95% CI, 1.36-3.28]), noncancer (Model final HR, 1.25 per %LAA IQR increase [95% CI, 1.11-1.37], 3.22 for CAC ≥400 [95%CI, 1.62-6.39]), and cardiovascular (Model final HR, 1.25 per %LAA IQR increase [95% CI, 1.00-1.46], 4.66 for CAC ≥400, [95% CI, 1.80-12.58]) mortality, with an increase in concordance probability in Model survey-LDCT compared with Model survey ( P <0.05). No significant association with LC incidence was found after adjustments. Both biomarkers negatively correlated with FEV 1 ( P <0.01). %LAA identified airflow obstruction with a moderate discriminative ability (AUC, 0.738)., Conclusions: Automated CAC and %LAA added prognostic information to age, sex, and pack-years for predicting mortality but not LC incidence in an LC screening setting. Both biomarkers negatively correlated with FEV 1 , with %LAA enabling the identification of airflow obstruction with moderate discriminative ability., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Advanced lung cancer (ALC) is a symptomatic disease often diagnosed in the context of hospitalization. The index hospitalization may be a window of opportunity to improve care delivery., Objectives: We examined the patterns of care and risk factors for subsequent acute care utilization among patients with hospital-diagnosed ALC., Research Design, Subjects, and Measures: In Surveillance, Epidemiology, and End Results-Medicare, we identified patients with incident ALC (stage IIIB-IV small cell or non-small cell) from 2007 to 2013 and an index hospitalization within 7 days of diagnosis. We used a time-to-event model with multivariable regression to identify risk factors for 30-day acute care utilization (emergency department use or readmission)., Results: More than half of incident ALC patients were hospitalized around the time of diagnosis. Among 25,627 patients with hospital-diagnosed ALC who survived to discharge, only 37% ever received systemic cancer treatment. Within 6 months, 53% had been readmitted, 50% had enrolled in hospice, and 70% had died. The 30-day acute care utilization was 38%.Small cell histology, greater comorbidity, precancer acute care use, length of index stay >8 days, and prescription of a wheelchair were associated with higher risk of 30-day acute care utilization. Age >85 years, female sex, residence in South or West regions, palliative care consultation, and discharge to hospice or a facility were associated with lower risk., Conclusions: Many patients with hospital-diagnosed ALC experience an early return to the hospital and most die within 6 months. These patients may benefit from increased access to palliative and other supportive care during index hospitalization to prevent subsequent health care utilization., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose of Review: As people living with human immunodeficiency virus (HIV, PLWH) age, aging-related comorbidities have come into focus as major challenges to their overall health. In this review, an in-depth overview of the two most commonly encountered chronic lung diseases in PLWH, chronic obstructive pulmonary disease (COPD) and lung cancer, is provided., Recent Findings: The risk for both COPD and lung cancer remains significantly higher in PLWH compared to the HIV-uninfected population, although fortunately rates of lung cancer appear to be declining over the last two decades. Outcomes for PLWH with these conditions, though, continue to be poor with worse survival rates in comparison to the general population. PLWH still face major barriers in accessing care for these conditions, including a higher likelihood of being underdiagnosed with COPD and a lower likelihood of being referred for lung cancer screening or treatment. A lack of evidence for optimal treatment strategies for both COPD and lung cancer still hampers the care of PLWH with these conditions., Summary: COPD and lung cancer represent substantial burdens of disease in PLWH. Improved access to standard-of-care screening and treatment and greater investigation into therapeutic responses specifically in this population are recommended., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)