Dhanorker, Atul S.

e20030 Background: Recent studies suggested associations between higher serum Magnesium (Mg) level and better survival outcome in patients receiving immunotherapy, especially in small cell lung cancer (SCLC). However, the prognostic significance of Mg in extensive-stage (ES) SCLC treated with first-line chemoimmunotherapy remains unclear. We evaluated the association between baseline serum Mg and survival outcomes in ES-SCLC receiving platinum–etoposide plus PD-(L)1 blockade. Methods: We conducted a retrospective cohort study of adults with ES-SCLC treated with first-line platinum–etoposide plus atezolizumab or durvalumab at Mayo Clinic via Mayo Clinic Platform in 2018–2025. Baseline average serum Mg level was defined as the mean of all Mg labs from day −30 to day 0 relative to chemoimmunotherapy initiation and categorized as low ( < 1.7 mg/dL), normal (1.7-2.0 mg/dL), or high ( > 2.0 mg/dL) using clinical cutoff. Low and normal groups were combined a priori due to small low-Mg counts. The primary endpoint was overall survival (OS), estimated by KM and compared by log-rank test. OS was measured from chemoimmunotherapy initiation to death and censored at last follow-up. Multivariable Cox proportional hazards models adjusted for age, gender, ECOG, liver/brain metastases status, and smoking status. Results: The cohort included 214 patients, 124 (58%) were female; median age was 68 years; 49% had liver metastases and 30% had brain metastases. Median OS was 10.4 months (95% CI 7.6-15.9) among 99 patients with high serum Mg level, compared with 7.5 months (95% CI 6.7-10.7) in 115 patients with low or normal serum Mg level (Log-rank p = 0.126). High Mg was associated with a trend toward longer OS in unadjusted analyses (Hazard ratio [HR] 0.80, 95% CI 0.59-1.07, p = 0.129); however, the association was not statistically significant after multivariable adjustment (aHR 0.81, 95% CI 0.60-1.10, p = 0.180). Conclusions: Among patients with ES-SCLC treated with first-line platinum–etoposide plus PD-(L)1 blockade, higher baseline serum Mg was associated with numerically longer OS but was not independently associated with survival after multivariable adjustment. These findings suggest baseline serum Mg level may be associated with prognosis and warrant validation in larger cohorts. Baseline characteristics by baseline serum magnesium category in ES-SCLC treated with first-line chemoimmunotherapy. Variable Overall (N=214) High Mg >2.0 (n=99) Low/Normal Mg ≤2.0 (n=115) p value* Age, median (IQR) 68 (62, 74) 68 (61, 74) 69 (62, 75) 0.201 Female, n (%) 124 (58%) 57 (58%) 67 (58%) >0.999 ECOG ≥2, n (%) 58 (27.1%) 28 (28%) 30 (26%) 0.719 Liver metastases, n (%) 105 (49%) 49 (49%) 56 (49%) 0.648 Brain metastases, n (%) 65 (30%) 33 (33%) 32 (28%) 0.469 Ever smoker, n (%) 197 (92.1%) 90 (90.9%) 107 (93.0%) 0.974 *p values: Wilcoxon (age); χ²/Fisher (categorical).

The COVID-19 pandemic has created unique logistical challenges for vaccine transportation, inventory management, allocation and distribution at multiple levels — the federal government, states and healthcare institutions. Unpredictable weekly vaccine allocation from state health departments, changing population priorities, stringent vaccine requirements for ultracold storage, transportation, reconstitution and 2-dose administration intervals have presented challenges never seen before in the history of mass vaccination programmes, including those at Mayo Clinic. To meet the challenges, an efficient system of allocation and administration for COVID-19 vaccines was developed through collaboration with process engineering. To understand the challenges, ten health systems engineers from the Department of Management Engineering & Consulting at Mayo Clinic facilitated the institution-wide COVID-19 vaccine project, collaborating closely with diverse multidisciplinary teams that included physicians, nurses, pharmacists, administrative services, information technology, human resources, scheduling operations and public affairs. The internal consultants designed tools and solutions based on systems and process engineering methodologies to solve a myriad of complex problems, including identifying priority populations, using resources efficiently and minimising vaccine waste. Tools designed included a vaccine resource playbook; dynamic staffing models based on vaccine allocation, storage, inventory and distribution processes using a hub-andspoke model; workflows and staffing models for face-to-face and drive-through vaccine administration sites; and end-of-day workflows to reduce vaccine waste. Through the collaboration, modelling and engineering, multiple sites across Mayo Clinic have implemented successful COVID-19 vaccination programmes that are efficient in resource utilisation and have minimal waste. In this paper, we share what we have learned to help other healthcare organisations prepare for future mass vaccination scenarios.