Letter to the Editor: Inclusive and Adequate Care Overcoming All Health Care Gaps: The Need to Specifically Look to the LGBTQI+ Population
Massimo De Martinis and Lia Ginaldi
Letter to the Editor Re: Influence of Food With Different Fat Concentrations on Alectinib Exposure: A Randomized Crossover Pharmacokinetic Trial
Andrew D. Frugé, Kristen S. Smith, Sylvia L. Crowder, and Wendy Demark-Wahnefried
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.
NCCN Guidelines® Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2024
Featured Updates to the NCCN Guidelines
Mary B. Daly, Tuya Pal, Kara N. Maxwell, Jane Churpek, Wendy Kohlmann, Zahraa AlHilli, Banu Arun, Saundra S. Buys, Heather Cheng, Susan M. Domchek, Susan Friedman, Veda Giri, Michael Goggins, Andrea Hagemann, Ashley Hendrix, Mollie L. Hutton, Beth Y. Karlan, Nawal Kassem, Seema Khan, Katia Khoury, Allison W. Kurian, Christine Laronga, Julie S. Mak, John Mansour, Kevin McDonnell, Carolyn S. Menendez, Sofia D. Merajver, Barbara S. Norquist, Kenneth Offit, Dominique Rash, Gwen Reiser, Leigha Senter-Jamieson, Kristen Mahoney Shannon, Kala Visvanathan, Jeanna Welborn, Myra J. Wick, Marie Wood, Matthew B. Yurgelun, Mary A. Dwyer, and Susan D. Darlow
The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic focus primarily on assessment of pathogenic/likely pathogenic (P/LP) variants associated with increased risk of breast, ovarian, pancreatic, and prostate cancer, including BRCA1, BRCA2, CDH1, PALB2, PTEN, and TP53, and recommended approaches to genetic counseling/testing and care strategies in individuals with these P/LP variants. These NCCN Guidelines Insights summarize important updates regarding: (1) a new section for transgender, nonbinary and gender diverse people who have a hereditary predisposition to cancer focused on risk reduction strategies for ovarian cancer, uterine cancer, prostate cancer, and breast cancer; and (2) testing criteria and management associated with TP53 P/LP variants and Li-Fraumeni syndrome.
Prostate Cancer, Version 4.2023, NCCN Clinical Practice Guidelines in Oncology
Edward M. Schaeffer, Sandy Srinivas, Nabil Adra, Yi An, Daniel Barocas, Rhonda Bitting, Alan Bryce, Brian Chapin, Heather H. Cheng, Anthony Victor D’Amico, Neil Desai, Tanya Dorff, James A. Eastham, Thomas A. Farrington, Xin Gao, Shilpa Gupta, Thomas Guzzo, Joseph E. Ippolito, Michael R. Kuettel, Joshua M. Lang, Tamara Lotan, Rana R. McKay, Todd Morgan, George Netto, Julio M. Pow-Sang, Robert Reiter, Mack Roach III, Tyler Robin, Stan Rosenfeld, Ahmad Shabsigh, Daniel Spratt, Benjamin A. Teply, Jonathan Tward, Richard Valicenti, Jessica Karen Wong, Dorothy A. Shead, Jenna Snedeker, and Deborah A. Freedman-Cass
The NCCN Guidelines for Prostate Cancer provide a framework on which to base decisions regarding the workup of patients with prostate cancer, risk stratification and management of localized disease, post-treatment monitoring, and treatment of recurrence and advanced disease. The Guidelines sections included in this article focus on the management of metastatic castration-sensitive disease, nonmetastatic castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC). Androgen deprivation therapy (ADT) with treatment intensification is strongly recommended for patients with metastatic castration-sensitive prostate cancer. For patients with nonmetastatic CRPC, ADT is continued with or without the addition of certain secondary hormone therapies depending on prostate-specific antigen doubling time. In the mCRPC setting, ADT is continued with the sequential addition of certain secondary hormone therapies, chemotherapies, immunotherapies, radiopharmaceuticals, and/or targeted therapies. The NCCN Prostate Cancer Panel emphasizes a shared decision-making approach in all disease settings based on patient preferences, prior treatment exposures, the presence or absence of visceral disease, symptoms, and potential side effects.
Reply To the Letter to the Editor by Frugé et al
Daan A.C. Lanser, Anne-Marie C. Dingemans, Ron H.J. Mathijssen, and G.D. Marijn Veerman
Validation of the PREDICT Prognostication Tool in US Patients With Breast Cancer
Nickolas Stabellini, Lifen Cao, Christopher W. Towe, Megan E. Miller, Artur H. Sousa-Santos, Amanda L. Amin, and Alberto J. Montero
Background: PREDICT is an online prognostication tool derived from breast cancer registry information on approximately 6,000 women treated in the United Kingdom that estimates the postsurgical treatment benefit of surgery alone, chemotherapy, trastuzumab, endocrine therapy, and/or adjuvant bisphosphonates in early-stage breast cancer. Our aim was to validate the PREDICT algorithm in predicting 5- and 10-year overall survival (OS) probabilities using real-world outcomes among US patients with breast cancer. Methods: A retrospective study was performed including women diagnosed with unilateral breast cancer in 2004 through 2012. Women with primary unilateral invasive breast cancer were included. Patients with bilateral or metastatic breast cancer, no breast surgery, or missing critical clinical information were excluded. Prognostic scores from PREDICT were calculated and external validity was approached by assessing statistical discrimination through area under time-dependent receiver-operator curves (AUC) and comparing the predicted survival to the observed OS in relevant subgroups. Results: We included 708,652 women, with a median age of 58 years. Most patients were White (85.4%), non-Hispanic (88.4%), and diagnosed with estrogen receptor–positive breast cancer (79.6%). Approximately 50% of patients received adjuvant chemotherapy, 67% received adjuvant endocrine therapy, 60% underwent a partial mastectomy, and 59% had 1 to 5 axillary sentinel nodes removed. Median follow-up time was 97.7 months. The population’s 5- and 10-year OS were 89.7% and 78.7%, respectively. Estimated 5- and 10-year median survival with PREDICT were 88.3% and 73.8%, and an AUC of 0.77 and 0.76, respectively. PREDICT performed most poorly in patients with high Charlson-Deyo comorbidity scores (2–3), where PREDICT overestimated OS. Sensitivity analysis by year of diagnosis and HER2 status showed similar results. Conclusions: In this prognostic study utilizing the National Cancer Database, the PREDICT tool accurately predicted 5- and 10-year OS in a contemporary and diverse population of US patients with nonmetastatic breast cancer.
Volume 21 (2023): Issue 9 (Sep 2023)
Biomarker Testing, Treatment, and Outcomes in Patients With Advanced/Metastatic Non–Small Cell Lung Cancer Using a Real-World Database
Naleen Raj Bhandari, Lisa M. Hess, Dan He, and Patrick Peterson
Background: Little is known about the impact of up-front biomarker testing on long-term outcomes in patients with advanced or metastatic non–small cell lung cancer (a/mNSCLC). This study compared overall survival (OS) by biomarker testing status and by receipt of guideline-concordant therapy in a large real-world cohort of patients with a/mNSCLC in the United States. Patients and Methods: This retrospective study used an a/mNSCLC database derived from real-world electronic healthcare records. Patients diagnosed with nonsquamous a/mNSCLC who initiated first-line therapy on or after January 1, 2015, were included. We describe the testing of patients for actionable biomarkers and whether they subsequently received guideline-recommended first-line treatment. OS was defined as the number of months from the initiation of first-line therapy to the date of death or end of follow-up, and was described using Kaplan-Meier analysis. Multivariable Cox proportional hazard modeling was conducted to compare OS between groups adjusting for baseline covariates; adjusted hazard ratios (HRs) were reported. Results: A total of 21,572 patients with a median age of 69 years (IQR, 61–76 years) and follow-up of 9.5 months (IQR, 3.5–21.5 months) were included. Among patients in the database, 88% had a record of receiving testing for at least 1 biomarker at any time, and 69% of these patients received testing before or at the start of first-line treatment. The adjusted hazard of death was 30% higher in patients who never (vs ever) received biomarker testing in the database (HR, 1.30; 95% CI, 1.24–1.37), and 12% higher in patients who did not receive (vs did receive) biomarker testing before or at the start of first-line treatment (HR, 1.12; 95% CI, 1.08–1.16). The adjusted hazard of death was 25% higher in patients who did not receive guideline-concordant first-line treatment (vs those who did) after having a biomarker-positive disease (HR, 1.25; 95% CI, 1.13–1.40). Conclusions: Findings suggest that receipt of first-line treatment that is concordant with biomarker testing results and treatment guidelines is associated with improved survival outcomes in patients with a/mNSCLC in the United States.