Egan, Ashley

e13806 Background: Immune checkpoint inhibitor therapy-related (ICI) pneumonitis poses a significant challenge for patients with cancer. It is associated with increased clinical morbidity and mortality, often resulting in prolonged hospital stays and delays and/or discontinuation of cancer-directed treatment. Early detection and prompt treatment of ICI pneumonitis is critical to improve patient outcomes. Therefore, our institution implemented a remote patient monitoring (RPM) program leveraging in-home, electronic health record-integrated technology and virtual centralized nursing to monitor vital signs and patient symptoms in “high-risk” patients treated with immunotherapy. Methods: Patients receiving immunotherapy deemed to be “high-risk” for ICI pneumonitis seen at Mayo Clinic Rochester, Minnesota were offered enrollment in the RPM program beginning in June 2023. High-risk patients included those with pre-existing lung disease (chronic obstructive pulmonary disease, interstitial lung disease); those with a history of resolved ICI pneumonitis being rechallenged with immunotherapy; and those with grade 1 ICI pneumonitis continuing immunotherapy. Eligible patients who agreed to participate in the program were provided a kit consisting of RPM technology and electronic symptom questionnaires. The kit included an armband for continuous vital sign monitoring, a blood pressure monitor, a scale to measure weight, and a pre-connected cellular-enabled tablet for questionnaire answering. RPM data utilized real-time electronic health system integration and predetermined parameters to alert RPM nurses. Pre-specified escalation care pathways were then used to respond and escalate care, as necessary. The primary outcome of this pilot study was to assess the feasibility of implementing an RPM program in this patient population with a secondary outcome to determine the ability to detect ICI pneumonitis before significant clinical symptoms. Results: Sixteen patients were enrolled of whom 5 declined participation (2 due to inconvenience and 3 for unclear reasons) resulting in 11 patients who underwent monitoring. Five patients graduated monitoring without detection of ICI pneumonitis within a 4-month timeframe. Six patients are currently undergoing RPM. 372 alerts occurred of which 8 (2%) were escalated to physician review. These alerts included elevated heart rate in 4, shortness of breath in 3, and elevated blood pressure in 1. One patient was triaged to an emergency department and diagnosed with grade 2 ICI pneumonitis. Conclusions: We show that RPM in patients who are at high-risk for developing ICI pneumonitis can be effectively integrated into a care model and monitor pneumonitis related symptoms. Further studies are needed to prove whether RPM is beneficial in this population including other ICI toxicities.

11145 Background: T-DXd is increasingly used in mBC. A T-DXd toxicity of special interest is interstitial lung disease/pneumonitis (ILD), occurring in 10-15% of pts in the DESTINY-Breast trials, and with potential lower incidence/severity in earlier line settings [Krop et al, ASCO 2023]. However, in routine practice, T-DXd may be used in pts with more heavily pre-treated mBC and previously exposed to other antibody-drug conjugates (ADC) [Premji et al SABCS 2023]. We evaluated the incidence/severity of T-DXd related ILD in mBC at Mayo Clinic. Methods: We retrospectively identified pts with mBC who received ≥1 dose of T-DXd at Mayo Clinic, Rochester, MN, between July 2022-December 2023. Demographic, mBC, and pulmonary clinical variables were abstracted from the clinical records. Data was summarized using descriptive statistics. Diagnosis of ILD was determined by treating clinicians, and severity to CTCAE V5 based on clinical documentation. Results: 77 pts with mBC received T-DXd during the study period. All were female, the majority (70, 91%) were Caucasian, and median age was 58. 53 (69%) had HER2-low mBC (37 HR+, 16 HR-) and 24 (31%) had HER2+ mBC (17 HR+, 7 HR-). 31 (40%) were active or former smokers. 11 (14%) had pulmonary comorbidities, including 6 (7.8%) with asthma, 2 (2.6%) with prior pneumonitis, and 3 (3.9%) with other pulmonary comorbidities. They had previously received a median of 1 line of endocrine therapy, and 4 lines of chemotherapy, with 20 (26%) having received another ADC. 17 (22%) developed any grade ILD [G1: 3 (4%), G2: 7 (9%), G3: 2 (3%), G4: 1 (1%), G5: 4 (5%)]. Among pts with ILD of any grade, 13 (76.5%) presented with at least 1 symptom (cough/SOB). The median number of T-DXd cycles prior to onset of any grade ILD was 6 (range 2-13), or 18 (range 6-39) weeks. 5 (29%) pts with any grade ILD received a prior ADC. Of pts with ≥G3 ILD (n=7), all were Caucasian, 2 (30%) were prior smokers, none had prior lung disease, and had received a median of 5 prior lines of chemo, with 4 (57%) having received a prior ADC. All grade ≥2 ILDs were treated with systemic steroids, and 1 also received IVIG. CT chest was used for diagnosis of all ILD. 7 pts (41%) required hospitalization, all of whom had bronchoscopy, with 5 (29%) requiring intubation. All pts with G1 ILD (3, 18%) restarted T-DXd without ILD recurrence. Conclusions: In this case series,22% of ps treated with T-DXd experienced ILD (5% G5), higher than the rate observed in the pivotal DESTINY-Breast trials, possibly due to real-world use of T-DXd in more heavily pre-treated pts and in pts with pulmonary comorbidities.

1552 Background: Immune checkpoint inhibitor therapy-related (ICI) pneumonitis poses a significant challenge in patients with cancer. There is a need for predictive modeling to identify those at high risk of developing serious adverse events of ICI pneumonitis. Methods: A newly created database of cancer patients diagnosed with ICI pneumonitis seen at Mayo Clinic from 2014-2022 was utilized for exploratory data analysis. Modeling was used to determine if three clinical outcomes could be predicted, namely risk of death from: pneumonitis prior to starting immunotherapy, pneumonitis at time of diagnosis of ICI pneumonitis, and any cause at time of ICI pneumonitis diagnosis. The data consisted of 170 patients with annotation for 71 clinical features. Data was divided into features available at time of cancer diagnosis and at time of ICI pneumonitis diagnosis. Exploratory modeling was performed using the gradient boosting technique Xgboost (Chen, 2015) and conducted using k-fold balanced cross validation best practices as well as a train/test/validate schema with 70%/20%/10% data proportions, respectively. Model reverse engineering was done with Shapley statistics (Lundberg, 2017) to determine which features had the largest contribution per model. Once identified, only those highly weighted features were used for logistic regression analysis providing more reproducible predictions by decreasing model variance. Results: Risk of death from pneumonitis prior to starting immunotherapy was modeled with an area under the curve of the receiver operator characteristic (AUC-ROC) of 0.79 with the most contributory features including lymphocyte count, oxygen dependence, PFT values, and PD-L1 expression. Logistic regression produced an AUC-ROC of 0.87 (95% CI 0.72-1.0, p < 0.0013). Risk of death from pneumonitis at the time of ICI pneumonitis diagnosis was modeled with an AUC-ROC of 0.85 and the most contributory features were similar to the list described in the previous endpoint. Logistic regression produced an AUC-ROC of 0.89 (95% CI 0.81-0.96, p < 0.0001). Risk of death from any cause at the time of ICI pneumonitis diagnosis until follow up concluded by 12/2022 produced a model with an AUC-ROC of 0.75 with the most contributory features including supplemental oxygen, PFT values, basic laboratory values, and PD-L1 expression, among others. Logistic regression produced an AUC-ROC of 0.85 (95% CI 0.76-0.93, p < 0.0001). Conclusions: We demonstrate that commonly available clinical data can be used to identify patients at high risk of death from ICI pneumonitis. This study identified clinical features that were predictive in each scenario from which further concerted effort could produce a new clinical model to provide clinician decision support when considering immunotherapy. Further studies should be done to further elucidate feature interdependence and generalizability.