Background Immune checkpoint inhibitors (ICIs) have improved survival and are increasingly used for non-small cell lung cancer. However, use may be limited by immune-related adverse events such as checkpoint-inhibitor pneumonitis (CIP). Literature estimates for CIP incidence are inconsistent. Real-world adherence to guidelines, clinical course, and healthcare utilization in the treatment of CIP has not been described in large cohorts. Methods A combined claims and electronic health record database (TriNetX) was used to identify 13,113 patients with lung cancer treated with programmed cell death receptor/ligand 1 (PD-1/PD-L1) inhibitors, and a propensity score-matched control cohort treated with chemotherapy or targeted therapies. The attributable risk of CIP was calculated in the first 12 months after therapy by comparing the incidence of diagnosis codes for pneumonitis/pneumonia between cohorts. Cases of CIP, identified by the most specific code for drug-induced respiratory conditions, were further analyzed for medication usage, rates of diagnostic bronchoscopy, ICI discontinuation rates, and usage of hospital services compared with patients receiving PD-1/PD-L1 inhibitors who did not develop CIP. Results The attributable risk of pneumonitis to PD-1/PD-L1 inhibitors was 2.49% (95% CI, 1.50% to 3.47%). Median time to onset in the CIP subcohort was 3.9 months (IQR, 2.1–7.3 months). Steroid and antibiotic use increased dramatically after a pneumonitis diagnosis, and 70.2% of patients permanently discontinued ICI therapy. Compared with controls, patients with CIP had more than a threefold increased risk of needing critical care (relative risk 3.59, 95% CI, 2.31 to 5.57) and an increased risk of mortality (HR 2.34, 95% CI, 1.47 to 3.71). Conclusions In a large claims-based analysis, PD-1/PD-L1 inhibitors increase the risk of pneumonitis in patients with lung cancer by 2.49%. Cases of CIP are associated with high healthcare utilization, discontinuation of ICIs, and mortality.

Objective: The aim of this study was to determine the rate of coronavirus disease-19 (COVID-19) among patients with cancer treated with immune checkpoint inhibitors (ICIs). Materials and Methods: This was a retrospective study of 1,545 patients with cancer treated with ICIs between July 1, 2019, and February 29, 2020, and 20,418 age-, sex-, and cancer category-matched controls in a large referral hospital system. Confirmed COVID-19 case and mortality data were obtained with Massachusetts Department of Public Health from March 1 through June 19, 2020. Results: The mean age was 66.6 years, and 41.9% were female. There were 22 (1.4%) and 213 (1.0%) COVID-19 cases in the ICI and control groups, respectively. When adjusting for demographics, medical comorbidities, and local infection rates, ICIs did not increase COVID-19 susceptibility. Conclusion: ICIs did not increase the rate of COVID-19. This information may assist patients and their oncologists in decision-making surrounding cancer treatment during this pandemic.

Background/Objectives: Demand for dermatologic services exceeds specialist capacity, with average wait times of 26–50 days in the United States. Current triage methods rely on subjective judgment or disease-specific indices that do not generalize across diagnoses or translate to operational decisions. We developed and validated the Dermatologic Complexity Score (DCS), a standardized instrument to guide case prioritization across dermatology care settings and evaluate DCS as a workload-reduction filter, enabling safe delegation of approximately half of routine teledermatology cases (DCS ≤ 40) away from specialist review. Methods: We conducted a prospective validation study of the DCS using 100 consecutive teledermatology cases spanning 30 common conditions. The DCS decomposes complexity into five domains (Diagnostic, Treatment, Risk, Patient Complexity, Monitoring) summed to a 0–100 total with prespecified bands: ≤40 (low) (41–70), (moderate) (71–89), (high), ≥90 (extreme). Five board-certified dermatologists and an automated module independently scored all cases. Two primary care physicians completed all ≤40 cases to assess feasibility. Primary outcomes were interrater reliability using ICC (2,1) and agreement with automation. Secondary outcomes included time-to-decision, referral rates, and primary care feasibility. Results: Mean patient age was 46.2 years; 47% of cases scored ≤40, 33% scored 41–70, 18% scored 71–89, and 2% scored ≥90. Interrater reliability was excellent (ICC (1,2)) = 0.979; 95% CI 0.974–0.983), with near-perfect agreement between automated and mean dermatologist scores (r = 0.998). Time-to-decision increased monotonically across DCS bands from 2.11 min (≤40) to 5 (90) min (≥90) (p = 1.36 × 10⁻¹⁴). Referral rates were 0% for ≤40, 3% for 41–70, 27.8% for 71–89, and 100% for ≥90 cases. DCS strongly predicted referral decisions (AUC = 0.919). Primary care physicians successfully managed all ≤40 cases but required 6–8 additional minutes per case compared to dermatologists. Conclusions: The DCS demonstrates excellent reliability and strong construct validity, mapping systematically to clinically relevant outcomes, including decision time and referral patterns. The instrument enables standardized, reproducible triage decisions that can optimize resource allocation across teledermatology, clinic, procedural, and inpatient settings. Implementation could improve access to dermatologic care by supporting appropriate delegation of low-complexity cases to primary care while ensuring timely specialist evaluation for high-complexity conditions.