Haemmerle, Robert

Background: Patients who experience homelessness manifest poor cancer outcomes, often because of treatment delays. Few studies have used mixed methods to learn what happens at the patient and health care staff level to explain these delays. Methods: This single-institution, multi-site study interrogated the entire medical record system (thousands of patients annually) for homelessness and cancer diagnoses from January 1, 2019, through October 10, 2023. Records were abstracted for relevant quantitative and qualitative data; rigorous methods were used to analyze, integrate, and report findings. Results: Forty-three patients with a median age of 58 years (range: 32, 73 years) and 34 (79%) assigned male sex at birth are the focus of this report. The most common cancers include hematologic (n = 8), gastrointestinal (n = 8), and genitourinary (n = 8). Thirty-five patients (81%) initiated cancer therapy, and 27 (63%) manifested treatment delay (>4 weeks post-diagnosis). The median time to cancer therapy was 9 weeks (95% confidence interval: 4.6, 12 weeks). Qualitative themes that explain delays are as follows: (1) logistical challenges in communication between the patient and health care team (“lack of a cell phone”); (2) patient reluctance to receive care (“patient elected to leave Against Medical Advice”); and (3) medical challenges in providing care (“he was hospitalized after being found down and hypotensive”). Conclusions: This study confirms that initiation of cancer therapy is often delayed in patients who experience homelessness. It sheds light on reasons for delay – for example, lack of phone access, an observation which suggests that perhaps provision of a burner phone might help some patients.

e16368 Background: Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second cause of cancer death in the US by 2040. For the minority of patients who are candidates for curative-intent surgical resection, a neoadjuvant approach with chemotherapy or chemotherapy followed by chemoradiotherapy (chemoRT) has become the standard of care. The tumor microenvironment (TME) has been recognized as a key factor in therapeutic resistance and disease progression. This study aims to explore the prognostic value of human-interpretable machine learning image features (HIFs) derived from whole-slide H&E images. Methods: Consecutive PDAC patients who underwent neoadjuvant therapy followed by surgical resection were studied in this single-institution retrospective study. Clinical variables collected include tumor site (head/body/tail), surgery extent, histology, sex, age at diagnosis, grade, treatment response (4-point scale), LVI, PNI, resection margin status, pathological TNM staging, number of lymph nodes removed/positive, neoadjuvant strategy (chemotherapy vs chemoRT), and number of neoadjuvant chemo cycles. These factors were analyzed with a multivariable Cox proportional hazards model for disease-free survival (DFS) after surgery. Additionally, 420 human-interpretable image features (HIFs) derived from a commercial machine learning software were analyzed with a univariable Cox model on a binary outcome of 1-yr DFS, applying a Bonferroni correction for multiple comparisons to identify significant predictors. Results: Increased neoadjuvant chemotherapy cycles (HR = 0.62, p = 0.001, [median: 8, IQR: 6-12]) and receipt of combination neoadjuvant therapy (HR = 0.15, p = 0.020, [29% chemo, 71% +chemoRT]) exhibited a protective effect in the multivariable DFS model. 3 HIFs quantifying immune cells and macrophages within the cancerous tissue and its stroma were associated with an improved probability of 1-year DFS. Conclusions: In addition to clinical factors, HIFs may offer valuable insights into the tumor microenvironment's influence on PDAC progression, paving the way for improved prognostic models and therapeutic strategies. Future work will integrate clinical predictors with HIFs in a combined model.

This case report describes Osler nodes and Janeway lesions on a man in his 70s.

Patients with cancer or with a history of cancer often seek nutritional advice. In turn, cancer health care providers are often asked questions related to nutrition and cancer. Should I take high-dose vitamins or other high-dose supplements? Should I take a regular-dose vitamin or other regular-dose nutritional supplements? Will I experience weight loss during postoperative chemotherapy? What should be my weight goals during and after adjuvant therapy? In the setting of advanced cancer, what should I do to keep my appetite and weight up? This review attempts to provide data-driven answers to some of these commonly posed questions.

572 Background: The DEFeNDR (Decreasing the burden of Febrile Neutropenia through Dynamic RPM) program leverages in-home technology and centralized nurse monitoring to facilitate ambulatory management as an alternative to hospital-based care for cancer patients with neutropenic fever (NF). The objective of this study is to assess 30-day care utilization outcomes and patient satisfaction at 12 months after program implementation. Methods: Adult cancer patients hospitalized with NF were assessed daily for DEFeNDR program eligibility including MASCC score ≥21 (per ASCO guidelines) and no cellular therapy (CAR-T, BMT) in the prior 100 days. Patients agreeing to participate in the 30-day program received a cellular-enabled tablet with pre-connected devices for twice-daily vital signs and symptom assessments. Patient generated data were EHR-integrated in real-time. Embedded decision trees facilitated alerts for adverse trends. Virtual RPM nurses responded to alerts and utilized predefined care pathways. The primary endpoint was the mean proportion of evaluable days spent inpatient (mPEDI) within 30 days of eligibility comparing those who participated in the program (RPM) with those who declined (no RPM). Results: Of 85 eligible patients, 62 were offered DEFeNDR participation. Of those, 34 (54.8%) participated and 28 (45.2%) declined. Baseline characteristics and care utilization outcomes are summarized in Table 1. RPM patients experienced significantly lower mPEDI compared with unmonitored patients [5.1% (Standard Error [SE], 1.9%) vs 12.4% (SE, 2.1%); Wilcoxon Rank Sums p-value, 0.032]. RPM patients also experienced a trend toward fewer total hospital days and lower 30-day readmission rate, as well as shorter mean length of stay and lower rate of ICU utilization when readmitted. Non-visit care activities (EHR clinical communications, documentation, and patient messaging) were decreased from pre-hospital baseline for 58.8% of RPM patients versus 46.6% of those unmonitored. All RPM patients completing a patient satisfaction survey (n=9) indicated that they would recommend the program to patients with a similar condition. Conclusions: Cancer patients with NF participating in the DEFeNDR program experienced a significant reduction in mPEDI and care utilization compared with those who declined. Further studies are warranted to assess comparative effectiveness of this model vs usual care.[Table: see text]

Purpose of review Over the past year, loss of appetite in patients with cancer has continued to be an area of active investigation. This review provides an update of recently published findings. Recent findings Despite the emergence of new cancer therapeutic agents, this symptom of loss of appetite continues to trouble patients, and it continues to be associated with poor survival. Recent preclinical research promises to lead to newer approaches and newer, more effective palliative agents. Recent clinical research shows that agents such as olanzapine, anamorelin, and cannabis either do or might palliate this symptom. Summary Loss of appetite in patients with cancer remains an important area of clinical and research focus. Recent published data provide greater clarity with respect to how to palliate this symptom. Today, although clinicians have more options to palliate cancer-associated loss of appetite than ever before, questions remain unanswered about how to palliate this symptom optimally and how to improve the quality of life of patients who suffer from it.

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.

1535 Background: Ambulatory management of select patients with febrile neutropenia (FN) may substantially decrease patients’ burden of illness and improve their quality of life, and is recommended by American Society of Clinical Oncology (ASCO) guidelines. However, in the United States, inpatient management remains the universal standard. We hereby report the health care utilization associated with the DEFeNDR remote patient monitoring (RPM) program, which leverages technology and virtual centralized nurse monitoring to facilitate ambulatory management of FN as an alternative to hospital-based care. Methods: Adult patients with cancer hospitalized with FN were assessed daily for eligibility to participate in the DEFeNDR program. Eligibility criteria included MASCC score >21 (per ASCO guidelines), and clinical judgment of the attending provider. Patients receiving cellular therapy (CAR-T, BMT) in the prior 100 days were excluded. Eligible patients agreeing to participate in the 30-day program were provided a kit containing cellular-enabled, pre-connected devices to facilitate monitoring, including twice-daily vital signs and electronic symptom questionnaires. Data were EHR-integrated in real-time and embedded decision trees facilitated alerts for adverse trends. RPM nurses responded to alerts and escalated care as indicated by pre-specified care pathways. Program assessment was performed at 20 weeks after full implementation. The primary outcome was the mean proportion of evaluable days spent inpatient (mPEDI) within 30 days of eligibility compared between those who participated in the program vs. those who declined. Results: 35 patients were offered participation in the DEFeNDR program. Of these, 17 participated and 18 declined. Baseline characteristics are provided. Participating patients experienced mPEDI of 6.9% (Standard Error [SE], 3.7%), compared with 17.7% (SE, 3.6%) for patients who declined participation. Due to small sample size, this difference (10.8%) did not reach statistical significance (Wilcoxon Rank Sums p-value, 0.15). 30-day mortality did not differ between groups. Mean inpatient days were also lower for patients participating in the program. Additionally, when readmitted, patients participating in RPM experienced a trend toward shorter length of stay and lower rates of intensive care unit utilization. Conclusions: A trend towards decreased burden of inpatient care is evident among patients participating in the DEFeNDR program. Further studies are warranted to assess comparative effectiveness of this model vs usual care.[Table: see text]