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Frailty in Patients With Newly Diagnosed Diffuse Large B-Cell Lymphoma Receiving Curative-Intent Therapy: A Population-Based Study

Abi Vijenthira, Lee Mozessohn, Chenthila Nagamuthu, Ning Liu, Danielle Blunt, Shabbir Alibhai, Anca Prica, and Matthew C. Cheung

Background: The objectives of this study were to determine whether frailty is associated with survival in a population-based sample of patients with diffuse large B-cell lymphoma (DLBCL) and to describe the healthcare utilization patterns of frail versus nonfrail patients during treatment. Methods: A retrospective cohort study was conducted using population-based data in Ontario, Canada. Patients aged ≥66 years diagnosed between 2006 and 2017 with DLBCL or transformed follicular lymphoma who received first-line curative-intent chemoimmunotherapy were included. Frailty was defined using a modified version of a generalizable frailty index developed for use with Ontario administrative data. Cox regression was performed to examine the association between frailty and 1-year mortality. Results: A total of 5,527 patients were included (median age, 75 years [interquartile range, 70–80 years]; 48% female), of whom 2,699 (49%) were classified as frail. Within 1 year of first-line treatment, 32% (n=868) of frail patients had died compared with 20% (n=553) of nonfrail patients (unadjusted hazard ratio, 1.8; 95% CI, 1.6–2.0; P<.0001). Frail patients had higher healthcare utilization during treatment, with most hospitalizations related to infection and/or lymphoma. In multivariable modeling controlling for age, inpatient diagnosis, number of chemoimmunotherapy cycles received, comorbidity burden, and healthcare utilization, frailty remained independently associated with 1-year mortality (adjusted hazard ratio, 1.5; 95% CI, 1.3–1.7; P<.0001). Conclusions: In a population-based sample of older adult patients with DLBCL receiving front-line curative-intent therapy, half were classified as frail, and their adjusted relative rate of death in the first year after starting treatment was 50% higher than that of nonfrail patients. Frailty seems to be associated with poor treatment tolerance and a higher likelihood of requiring acute hospital-based care.

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Machine Learning–Based Early Warning Systems for Acute Care Utilization During Systemic Therapy for Cancer

Robert C. Grant, Jiang Chen He, Ferhana Khan, Ning Liu, Sho Podolsky, Yosuf Kaliwal, Melanie Powis, Faiyaz Notta, Kelvin K.W. Chan, Marzyeh Ghassemi, Steven Gallinger, and Monika K. Krzyzanowska

Background: Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. We developed and evaluated a longitudinal warning system for acute care use. Methods: Using a retrospective population-based cohort of patients who started intravenous systemic therapy for nonhematologic cancers between July 1, 2014, and June 30, 2020, we randomly separated patients into cohorts for model training, hyperparameter tuning and model selection, and system testing. Predictive features included static features, such as demographics, cancer type, and treatment regimens, and dynamic features, such as patient-reported symptoms and laboratory values. The longitudinal warning system predicted the probability of acute care utilization within 30 days after each treatment session. Machine learning systems were developed in the training and tuning cohorts and evaluated in the testing cohort. Sensitivity analyses considered feature importance, other acute care endpoints, and performance within subgroups. Results: The cohort included 105,129 patients who received 1,216,385 treatment sessions. Acute care followed 182,444 (15.0%) treatments within 30 days. The ensemble model achieved an area under the receiver operating characteristic curve of 0.742 (95% CI, 0.739–0.745) and was well calibrated in the test cohort. Important predictive features included prior acute care use, treatment regimen, and laboratory tests. If the system was set to alarm approximately once every 15 treatments, 25.5% of acute care events would be preceded by an alarm, and 47.4% of patients would experience acute care after an alarm. The system underestimated risk for some treatment regimens and potentially underserved populations such as females and non-English speakers. Conclusions: Machine learning warning systems can detect patients at risk for acute care utilization, which can aid in preventive intervention and facilitate tailored treatment. Future research should address potential biases and prospectively evaluate impact after system deployment.

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Prevalent Pseudoprogression and Pseudoresidue in Patients With Rectal Cancer Treated With Neoadjuvant Immune Checkpoint Inhibitors

Yumo Xie, Jinxin Lin, Ning Zhang, Xiaolin Wang, Puning Wang, Shaoyong Peng, Juan Li, Yuanhui Wu, Yaoyi Huang, Zhuokai Zhuang, Dingcheng Shen, Mingxuan Zhu, Xiaoxia Liu, Guangjian Liu, Xiaochun Meng, Meijin Huang, Huichuan Yu, and Yanxin Luo

Background: Immune checkpoint inhibitor (ICI) treatment in patients with microsatellite instability-high/mismatch repair deficient (MSI-H/dMMR) tumors holds promise in reshaping organ preservation in rectal cancer. However, the benefits are accompanied by distinctive patterns of response, introducing a dilemma in the response evaluation for clinical decision-making. Patients and Methods: Patients with locally advanced rectal cancer with MSI-H/dMMR tumors receiving neoadjuvant ICI (nICI) treatment (n=13) and matched patients receiving neoadjuvant chemoradiotherapy (nCRT; n=13) were included to compare clinical response and histopathologic features. Results: Among the 13 patients receiving nICI treatment, in the final radiologic evaluation prior to surgery (at a median of 103 days after initiation of therapy), progressive disease (n=3), stable disease (n=1), partial response (n=7), and complete response (n=2) were observed. However, these patients were later confirmed as having pathologic complete response, resulting in pseudoprogression and pseudoresidue with incidences of 23.1% (n=3) and 76.9% (n=10), respectively, whereas no pseudoprogression was found in the 13 patients receiving nCRT. We further revealed the histopathologic basis underlying the pseudoprogression and pseudoresidue by discovering the distinctive immune-related regression features after nICI treatment, including fibrogenesis, dense lymphocytes, and plasma cell infiltration. Conclusions: Pseudoprogression and pseudoresidue were unique and prevalent response patterns in MSI-H/dMMR rectal cancer after nICI treatment. Our findings highlight the importance of developing specific strategies for response evaluation in neoadjuvant immunotherapy to identify patients with a good response in whom sphincter/organ-preserving or watch-and-wait strategies may be considered.

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Lower Risks of New-Onset Hepatocellular Carcinoma in Patients With Type 2 Diabetes Mellitus Treated With SGLT2 Inhibitors Versus DPP4 Inhibitors

Oscar Hou In Chou, Jing Ning, Raymond Ngai Chiu Chan, Cheuk To Chung, Helen Huang, Kenrick Ng, Edward Christopher Dee, Sharen Lee, Apichat Kaewdech, Ariel K Man Chow, Nancy Kwan Man, Tong Liu, Fengshi Jing, Bernard Man Yung Cheung, Gary Tse, and Jiandong Zhou

Background: Type 2 diabetes mellitus (T2DM) may be a risk factor for development of hepatocellular carcinoma (HCC). The association between risk of developing HCC and treatment with sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP4i) is currently unknown. This study aimed to compare the risk of new-onset HCC in patients treated with SGLT2i versus DPP4i. Methods: This was a retrospective cohort study of patients with T2DM in Hong Kong receiving either SGLT2i or DPP4i between January 1, 2015, and December 31, 2020. Patients with concurrent DPP4i and SGLT2i use were excluded. Propensity score matching (1:1 ratio) was performed by using the nearest neighbor search. Multivariable Cox regression was applied to identify significant predictors. Results: A total of 62,699 patients were included (SGLT2i, n=22,154; DPP4i, n=40,545). After matching (n=44,308), 166 patients (0.37%) developed HCC: 36 in the SGLT2i group and 130 in the DPP4i group over 240,269 person-years. Overall, SGLT2i use was associated with lower risks of HCC (hazard ratio [HR], 0.42; 95% CI, 0.28–0.79) compared with DPP4i after adjustments. The association between SGLT2i and HCC development remained significant in patients with cirrhosis or advanced fibrosis (HR, 0.12; 95% CI, 0.04–0.41), hepatitis B virus (HBV) infection (HR, 0.32; 95% CI, 0.17–0.59), or hepatitis C virus (HCV) infection (HR, 0.41; 95% CI, 0.22–0.80). The results were consistent in different risk models, propensity score approaches, and sensitivity analyses. Conclusions: SGLT2i use was associated with a lower risk of HCC compared with DPP4i use after adjustments, and in the context of cirrhosis, advanced fibrosis, HBV infection, and HCV infection.