Citations

Agility has been adopted in the software development industry for decades and has been used in the business world for the past 10 years. In healthcare, however, agility has not been widely adopted or studied but is emerging as imperative for competitive advantage in a rapidly changing environment. Human-centred agility and change in the wake of the COVID-19 pandemic has become especially important. Change agility is composed of two aspects. While the first aspect is a mindset that embraces change, resiliency, flexibility and responsiveness to consumer needs, the second is an approach using collaborative efforts through self-organising teams. Healthcare organisations must adopt both aspects to leverage the collective value of change agility. To help staff embrace and develop a change agility mindset, healthcare organisations need to offer relevant educational courses and coaching; teach change agility principles and values by using case studies and real-life applications; and promote the right culture by building flexible systems, providing a safe learning environment and encouraging risk-taking. To grow and sustain change agility, healthcare organisations can benefit by using a continuous seven-step quest: examining the current state, defining the future state, assessing the gaps, developing strategies, executing the strategies, measuring progress and maintaining success. This paper addresses the strategy and iterative interventions developed by the Department of Management Engineering and Consulting (renamed the Strategy Department) at Mayo Clinic to prepare its professional staff to drive a business-focused, change agility mindset through benchmarking, education and experiential learning. The early experiences and lessons learned at Mayo Clinic in change agility are likely broadly transferable to other healthcare organisations.

The demand for telehealth services in the United States saw an unprecedented increase following the 2020 COVID-19 pandemic. Prior to the pandemic, intake and virtual rooming functions for scheduled, synchronous video appointments at Mayo Clinic were supported by a small centralised team of virtual agents within its Center for Connected Care (Connected Care). In response to the pandemic-generated demand, Connected Care leveraged its existing model and the support of temporary staff to rapidly scale support for the increased video visit demand across the enterprise. Once demand stabilised in the summer of 2020, Mayo Clinic’s destination medical centres in Minnesota, Florida and Arizona, and Mayo Clinic Health System’s sites in the Midwest, launched pilots to evaluate alternative telehealth delivery models. The goal of these pilots was to test the flexibility of alternative intake models for supporting long-term scalability and growth. Through these pilots, each site developed unique workflows and staffing plans with the overall intake function remaining standardised across all locations. The workflows delineated responsibilities between clinical departments and virtual agents, leveraged current infrastructure and maximised best practices from the original model. This paper outlines the models implemented, and presents lessons learned and best practices garnered from each implementation.

AbstractBackgroundThe management of early breast cancer (BC) has witnessed an uprise in the use of neoadjuvant therapy and a remarkable reshaping of the systemic therapy postneoadjuvant treatment in the last few years, with the evolution of many controversial clinical situations that require consensus.MethodsDuring the 14th Breast‐Gynecological and Immuno‐Oncology International Cancer Conference held in Egypt in 2022, a panel of 44 BC experts from 13 countries voted on statements concerning debatable challenges in the neo/adjuvant treatment setting. The recommendations were subsequently updated based on the most recent data emerging. A modified Delphi approach was used to develop this consensus. A consensus was achieved when ≥75% of voters selected an answer.Results and ConclusionsThe consensus recommendations addressed different escalation and de‐escalation strategies in the setting of neoadjuvant therapy for early BC. The recommendations recapitulate the available clinical evidence and expert opinion to individualize patient management and optimize therapy outcomes. Consensus was reached in 63% of the statements (52/83), and the rationale behind each statement was clarified.

7558 Background: Survival in patients with multiple myeloma (MM) continues to improve with the newer therapies, but there is no clear evidence of a cure and patients continue to relapse. However, most patients have the most extended period without progression with their first-line therapy, and subsequent therapies often provide a shorter duration of response due to the use of drugs with overlapping mechanisms of action and, more importantly, a shorter first remission, pointing toward more aggressive disease biology. With newer immunotherapies being used at 1st relapse, this may change. We designed this study to explore the natural history of MM after the 1st relapse and define the predictors of outcome after the initial relapse. Methods: We identified all patients in the Mayo Clinic database diagnosed between Jan 2004-June 2019 who had had initial disease progression. The study cohort included 2005 patients. We explored the overall survival (OS) from the time of documented disease progression or the start of second-line treatment in patients who had not met IMWG criteria before second-line therapy. Front-line therapy was grouped into those with either an IMiD or PI, both IMiD and PI, daratumumab with IMiD and PI, and none of these agents. The IMiD and PI were always used with dexamethasone with or without an alkylator. Results: The median age was 63 (range 24-95) years and 60% were male. The median estimated follow-up from diagnosis for all patients was 86 mos. (95% CI; 82, 88) and the median time to progression from the start of therapy was 25 mos. (95% CI; 24, 26). The median OS from the first progression was 45 mos. (95% CI; 42, 50), 36 mos. for those diagnosed before 2009, and 52-53 mos. for those diagnosed later. On univariate analysis, age <70 at 1st relapse, RISS I, novel agents in initial therapy, CR in the first line, diagnosis after 2009, initial remission >18 mos, and initiation of therapy before IMWG progression were all associated with improved OS after 1st relapse (Table). On multivariate analysis, age <70, RISS I, initial remission >18 mos, and initiation of therapy before IMWG progression were all associated with improved OS after 1st relapse. Conclusions: The study provides an estimate of the outcomes and significant predictors of outcomes after 1st relapse in patients with MM. These factors should be taken into consideration when designing clinical trials in this patient population. In particular, these factors identify a high-risk group of patients not always identified by baseline characteristics and should be the focus of future trials. [Table: see text]

7542 Background: Teclistamab, a novel bispecific monoclonal antibody targeting CD3 and BCMA, exhibits promising effects for patients with relapsed and refractory multiple myeloma (MM). However, rates of acute kidney injury (AKI) associated with this therapy remains inadequately characterized. Methods: This was a retrospective observational cohort study of patients with relapsed refractory MM who received teclistamab or Chimeric Antigen Receptor T-cell Therapy (CAR-T) therapy at Mayo Clinic between 12/1/2022-5/15/2023 and who were not in kidney failure at the time of treatment. The primary endpoint was incidence of AKI during follow-up; secondary endpoints included treatment and recovery from AKI. AKI was defined based on Kidney Disease Improving Global Outcomes criteria as an increase of serum creatinine levels > 1.5 times their baseline value after starting therapy. Cumulative incidences and Cox proportional hazard regression models were used to evaluate time to event data. Results: A total of 64 patients met inclusion criteria for the study (30 received CAR-T and 34 received teclistamab therapy). There were 14 AKI events occurring during follow-up (10 teclistamab [n = 6 stage 1, n = 3 stage 2 and n = 1 stage 3] and 4 CAR-T [n = 3 stage 1 and n = 1 stage 2]). Cumulative incidence estimates of AKI at 120 days after treatment were 32% (95% CI: 14%-47%) for teclistamab patients and 10% (95% CI: 0%-21%) for CAR-T patients. While patients receiving teclistamab were found to be at increased risk of an incident AKI event compared to patients receiving CAR-T therapy, results were not statistically significant (HR (95% CI): 3.38 (0.93-12.31), p = 0.065). Median time to development of AKI was 30 days (IQR 16, 73) in teclistamab and 59 days (IQR 9, 146) in CAR-T patients. Among the 10 patients in the teclistamab group with an AKI event, 4 (40%) had complete renal recovery and 4 (40%) had partial recovery, while among the 4 patients with CAR-T with an AKI event, 2 (50%) experienced complete renal recovery and 2 (50%) experienced partial recovery. Conclusions: Our study showed that almost one third of patients receiving teclistamab therapy for relapsed and refractory multiple myeloma experienced an incident AKI event during follow-up. This finding underscores the importance of monitoring for kidney function in these patients.

7516 Background: Immune effector cell hemophagocytic lymphohistiocytosis IEC-HS is a complication of CAR T cell therapy (CART). This study aims to look at the outcomes of CART patients who develop IEC-HS. Methods: Patients who developed IEC-HS after receiving CART and patients who developed IEC-HS without (nonCART) between December 2020 and March 2022 were included. Results: The 20 CART patients were older than the 25 nonCART patients. Peak ferritin, fibrinogen, CRP, serum creatinine and transaminases were similar between the 2 group. CART patients had lower white blood cell count and platelets and bilirubin than the nonCART patients. CART patients were more like to have markedly elevated IL-6 levels (> 315 mg/ml), higher IL-18, higher MCP-1, and suppressed IL-1β and GMCSF than nonCART patients (Table). Treatment of IEC-HS included tocilizumab (n = 19), anakinra (n = 18) siltuximab (n = 4) and basiliximab (n = 4), dexamethasone (n = 18) methylprednisolone (n = 6), etoposide (n = 1) and ruxolitinib (n = 1). A significant response defined by a 90% reduction or normalization of the cytokine was observed with IL-10 and IFN-ϒ in 92.3% and 84.6% of the CART patients respectively. An intermediate response was noted with TNF, IL-6, MCP-1 and MIP-1 in 38.4%, 30.8% and 61.6% and 38.5% of the patients respectively. No patient had a significant response in IL-2Rα or IL-1β and only 7.7% had a significant response in IL-18. Of the 5 patients who had a MIP-1 response, 4 were on basiliximab +/- high flow continuous venovenous hemofiltration and the only patient that had an IL-18 response was on both modalities. The 100-day mortality after IEC-HS in the CART patients was 40% and was associated with higher peak and lower best ferritin but not the percentage reduction (Table). Higher TNF (90.3 pg/ml vs 37.2 pg/ml, p = 0.05), markedly elevated levels of IL-6 (92% vs 50%, p = 0.03) and IL-10 (33% vs 0%) and an IL-6 response (44.4% vs 0%) were associated with survival at day 100. IL-6 responders had lower peak ferritin vs IL-6 nonresponders (14066 mcg/L vs 45051 mcg/L, p = 0.08). Enterococcus faecium bacteremia developed in 75% of the nonsurvivors vs 16.6% of the 100-day survivors (p = 0.009). Survivors cleared the enterococcus bacteremia within days while nonsurvivors had protracted bacteremia. Conclusions: IEC-HS is devastating complication of CART. Anti-cytokine therapy can reduce some cytokines and ferritin but overimmunosuppression may lead infectious complication, in particular with E. faecium. Better biomarkers are needed to finetune immunosuppressive therapy in order to avoid over-immunosuppression. [Table: see text]

Accurate assessment of GFR is crucial to guiding drug eligibility, dosing of systemic therapy, and minimizing the risks of both undertreatment and toxicity in patients with cancer. Up to 32% of patients with cancer have baseline CKD, and both malignancy and treatment may cause kidney injury and subsequent CKD. To date, there has been lack of guidance to standardize approaches to GFR estimation in the cancer population. In this two-part statement from the American Society of Onco-Nephrology, we present key messages for estimation of GFR in patients with cancer, including the choice of GFR estimating equation, use of race and body surface area adjustment, and anticancer drug dose-adjustment in the setting of CKD. These key messages are based on a systematic review of studies assessing GFR estimating equations using serum creatinine and cystatin C in patients with cancer, against a measured GFR comparator. The preponderance of current data involving validated GFR estimating equations involves the CKD Epidemiology Collaboration (CKD-EPI) equations, with 2508 patients in whom CKD-EPI using serum creatinine and cystatin C was assessed (eight studies) and 15,349 in whom CKD-EPI with serum creatinine was assessed (22 studies). The former may have improved performance metrics and be less susceptible to shortfalls of eGFR using serum creatinine alone. Since included studies were moderate quality or lower, the American Society of Onco-Nephrology Position Committee rated the certainty of evidence as low. Additional studies are needed to assess the accuracy of other validated eGFR equations in patients with cancer. Given the importance of accurate and timely eGFR assessment, we advocate for the use of validated GFR estimating equations incorporating both serum creatinine and cystatin C in patients with cancer. Measurement of GFR via exogenous filtration markers should be considered in patients with cancer for whom eGFR results in borderline eligibility for therapies or clinical trials.