JCO oncology practice最新文献

Purpose: The classification of AML and therapeutic options are now largely driven by genetically defined subtypes. Personalization of treatment relies on timely completion and reporting of cytogenetic and molecular tests, creating challenges in clinical practice. We initiated a quality improvement study with the aim to optimize the process for ordering of genomic diagnostic tests and to reduce test turnaround times (TATs).

Methods: A multidisciplinary working group consisting of hematologists, laboratory scientists, technologists, and hematopathologists was formed and identified the following tests as necessary for expedited testing in patients with AML younger than 75 years: next-generation sequencing (NGS) myeloid panel, karyotype analysis, FLT3 PCR, NPM1 PCR, CBFB::MYH11 PCR, and RUNX1::RUNX1T1 PCR, and proposed a reflexive flow cytometry-triggered genomic diagnostic testing algorithm for newly diagnosed AML (ND-AML). We used the model of improvement and implemented three Plan-Do-Study-Act (PDSA) cycles: education and guidelines for management of ND-AML, implementation of the reflex laboratory-triggered diagnostic testing algorithm for ND-AML, and automation of NGS workflow. We assessed compliance with test ordering according to prescribed guidelines and TAT.

Results: After PDSA 2, test ordering improved significantly to more than 90% of relevant tests being initiated at AML diagnosis; and TAT was reduced by 27.6% for NGS and by 54.8% for NPM1 PCR. After PDSA 3, TAT for NGS was overall reduced by 63.3% to 11.4 days and within our 14-day target. We were able to also meet our target TAT of 5 days or less for FLT3 and NPM1 PCRs.

Discussion: A multidisciplinary approach with shared decision making between hematologists and laboratory practitioners was essential in the development of an algorithm for reflex testing in AML that resulted in improved test ordering and TAT.

Purpose: Goserelin is a gonadotropin-releasing hormone agonist for ovarian function suppression in the treatment of pre- and perimenopausal patients with breast cancer and for the preservation of ovarian function during chemotherapy. Goserelin is available in doses of 3.6 mg once every 4 weeks or 10.8 mg once every 12 weeks. This study used US real-world evidence to characterize goserelin treatment patterns.

Methods: Electronic health record data of adults with a history of breast cancer and ≥2 goserelin prescriptions between January 1, 2017, and December 31, 2022, were identified through TriNetX. Patient demographics and treatment patterns were examined.

Results: Overall, 3,620 US patients were identified: 2,870 treated with goserelin 3.6 mg once every 4 weeks, 410 treated with 10.8 mg once every 12 weeks, and 340 switched from 3.6 mg once every 4 weeks to 10.8 mg once every 12 weeks. Peak utilization of 10.8 mg once every 12 weeks (36.6%) and dose switching to 10.8 mg once every 12 weeks (26.5%) occurred in 2020. Patients who switched to 10.8 mg once every 12 weeks had the longest median treatment duration (776 days), compared with the 3.6 mg once every 4 weeks and 10.8 mg once every 12 weeks cohorts (264 and 429 days, respectively). Of patients who switched, 65% were still being treated after 2 years, compared with 30% and 40% treated with 3.6 mg once every 4 weeks only or 10.8 mg once every 12 weeks only, respectively. Patients initially treated with or who switched to 10.8 mg once every 12 weeks were more adherent (64.4%-75.0%), compared with patients treated with 3.6 mg once every 4 weeks (45.4%).

Conclusion: Treatment with goserelin 10.8 mg once every 12 weeks is associated with greater adherence and longer treatment duration, compared with 3.6 mg once every 4 weeks in patients with breast cancer in the United States.

Purpose: Little is known about guideline-concordant, early integration of palliative care (PC) in the outpatient setting among patients with advanced cancer within a safety-net system. This study examined PC delivery patterns for patients seen in a large, urban safety-net system.

Methods: Patients diagnosed with advanced-stage solid tumor and who had ≥1 outpatient oncology visit from January 2018 to July 2023 at Parkland Health were identified via electronic health record. Outcomes assessed included (1) receipt of PC referral ≤8 weeks after diagnosis, (2) receipt of any PC referral, and (3) PC visit completion. Multivariable logit models evaluated associations between key characteristics (age, race/ethnicity, gender, cancer type, preferred language, insurance, diagnosis year) and the outcomes.

Results: Among 1,296 patients (44% female; 76% non-White), 55% received a referral. Of those referred, 46% patients were referred early (≤8 weeks). Two thirds of the referred patients completed a PC visit during the study period. In adjusted regression models, patients who were Black (v White; adjusted odds ratio [aOR], 0.52 [95% CI, 0.33 to 0.82]), Hispanic (aOR, 0.33 [95% CI, 0.18 to 0.59]), or had prostate cancer (v breast cancer; aOR, 0.27 [95% CI, 0.10 to 0.69]) had lower odds of receiving early referral. Ages 40-69 (v >80 years; lowest odds for 60 to <70, aOR, 0.41 [95% CI, 0.20 to 0.85]) and patients with gynecologic cancer (aOR, 0.14 [95% CI, 0.07 to 0.28]) had lower odds of receiving any PC referral. Females had higher odds of completing a PC visit (v males; aOR, 1.45 [95% CI, 1.01 to 2.08]).

Conclusion: Many patients did not receive an outpatient referral or received it late. Observed differences by race/ethnicity, cancer type, and age suggest the need for different interventions targeting PC delivery for underserved patients with cancer.

Purpose: Adolescents and young adults (AYAs) with advanced cancer (AC) report poor quality of life (QOL), high psychological distress, and minimal engagement in health care discussions. We assessed the effect of a novel resilience coaching program (Promoting Resilience in Stress Management [PRISM]-AC) on AYA outcomes.

Methods: We conducted a multisite randomized trial of PRISM-AC versus usual care (UC) among AYAs age 12-24 years, diagnosed with AC within 2 weeks before enrollment. PRISM-AC consists of four sessions targeting AYA-endorsed resilience resources (stress management, goal-setting, cognitive reframing, and meaning-making) plus a session integrating elements of advance care planning. Participants completed surveys at baseline, and 3, 6, 9, and 12 months. The primary outcome was Pediatric QOL at 3 months; secondary/exploratory outcomes included 3-month changes in resilience (10-item Connor-Davidson Resilience Scale) and hope (Snyder Hope Scale), and trajectories of QOL, anxiety, and depression (Hospital Anxiety and Depression Scale) over 12 months. We examined associations with linear mixed effects regression models. We also explored PRISM-AC's impact on AYA participation in critical health care discussions, as documented in the electronic health record.

Results: Between April 2019 and January 2024, we enrolled 239 AYAs (56% of 426 approached) and randomly assigned 195 (82% of enrolled; 96 UC, 99 PRISM). They were of mean age 16.5 years (standard deviation, 3.9), mostly White (63%), non-Hispanic (59%), and publicly insured (53%). At 3 months, we detected no significant differences between groups with respect to QOL, anxiety, or depression; PRISM-AYAs demonstrated greater improvements in resilience (+1.3 [5.9] v -1.4 (7.5); P = .038) and hope (+2.4 [10.4] v -2.8 [11.2]; P = .001) than UC-AYAs. Over the 12-month study period, PRISM-AYAs reported more improvements in QOL and anxiety, with significant differences at later time points (PRISM-QOL improvements, 6 months: +3.4 [95% CI, 0.1 to 6.6]; P = .043; 12 months: +6.8 [95% CI, 3.3 to 10.3]; P < .001). Although participation in key health care discussions was similar between groups from baseline to 6 months, 67% (95% CI, 35 to 88) and 50% (95% CI, 22 to 78) of PRISM-AYAs participated at 9 and 12 months, respectively, compared with 39% (95% CI, 20 to 61) and 38% (95% CI, 21 to 59) of UC-AYAs.

Conclusion: Among AYAs with AC, PRISM-AC did not immediately improve QOL. Rather, it improved resilience and hope, potentially enabling longer-term improvements in QOL.

Purpose: To compare demographics, clinical characteristics, health care resource utilization (HCRU), treatment duration, and overall survival (OS) with enzalutamide (ENZA) or abiraterone acetate (AA) in patients with metastatic castration-resistant prostate cancer (mCRPC) in England.

Materials and methods: This retrospective study analyzed data from the Cancer Analysis System database on patients receiving ENZA or AA (January 2014-March 2020) for chemotherapy-naïve mCRPC (mCRPC was the only funded indication for ENZA/AA during study period). Baseline characteristics were assessed using standardized mean difference (SMD) (<0.1: balanced); differences were adjusted for using propensity score weighting (PSW). Cox proportional hazard models were used for OS and treatment duration. Number needed to treat was calculated from HCRU incidence rate ratios (IRRs).

Results: Overall, 8,485 patients were included (ENZA, 5,330; AA, 3,155). Diabetes mellitus was more prevalent in the ENZA group (SMD, 0.12) at treatment initiation. HCRU was comparable between groups before treatment initiation (SMD < 0.1), but HCRU IRR after treatment initiation favored ENZA. Compared with AA, ENZA was associated with significantly fewer inpatient stays, outpatient or accident and emergency (A&E) visits, and hospitalization days (P < .01), and significantly lower likelihood of treatment discontinuation (adjusted hazard ratio [aHR], 0.90 [95% CI, 0.86 to 0.96]; P < .01) and mortality risk (aHR, 0.92 [95% CI, 0.87 to 0.98]; P = .010). Assuming 8 months' treatment and comparable groups through PSW, 1.9 inpatient admissions, 17.3 outpatient visits, 1.4 A&E visits, and 19.5 hospitalization days could be avoided per 10 patients on ENZA versus AA.

Conclusion: Patients with mCRPC on ENZA or AA had generally similar baseline characteristics apart from diabetes prevalence. ENZA was associated with longer OS and treatment duration, and lower HCRU after treatment initiation than AA.

Purpose: Chemotherapy-induced peripheral neuropathy is a potentially debilitating adverse effect of cancer therapy that can lead to delay, reduction, or discontinuation of cancer treatment. Population-based data on peripheral neuropathy incidence among young cancer survivors are lacking.

Methods: Using a linkage between Medicaid, the California Cancer Registry, and hospitalization and emergency department data, we identified 6,028 adolescents and young adults (15-39 years) with Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), breast cancer, colorectal cancer, or testicular cancer and 418 children (<15 years) with HL or NHL during 2005-2017. We determined the cumulative incidence of peripheral neuropathy and its association with neurotoxic chemotherapy identified from Medicaid claims, using multivariable Cox proportional hazards regression models.

Results: Of 6,446 patients, 1,007 were diagnosed with peripheral neuropathy. Across each cancer type, incidence was higher among patients receiving neurotoxic drugs. For example, compared with non-neurotoxic agents, 5-year cumulative incidence was higher with oxaliplatin for colorectal cancer (24.0% v 6.2%) and paclitaxel for breast cancer (22.6% v 5.1%). In multivariable analysis, the agents most strongly associated with peripheral neuropathy were brentuximab (±other neurotoxic drugs) for HL (hazard ratio [HR], 9.53 [95% CI, 5.95 to 15.26]); brentuximab (±vinca alkaloids; HR, 7.00 [95% CI, 4.13 to 11.87]) for NHL, paclitaxel for breast cancer (HR, 4.03 [95% CI, 3.05 to 5.31]); oxaliplatin for colorectal cancer (HR, 3.46 [95% CI, 2.23 to 5.36]); and cisplatin and etoposide for testicular cancer (HR, 2.06 [95% CI, 1.37 to 3.11]).

Conclusion: The high incidence of peripheral neuropathy highlights the need for frequent monitoring, new supportive care approaches, and development of novel therapeutic agents to minimize toxicity while maintaining treatment efficacy.

Purpose: The purpose of our study was to (1) use a large language model (LLM) to identify goals-of-care (GOC) conversations in a large volume of notes, and (2) explore the potential of LLMs for a novel summarization task.

Methods: We included patients diagnosed with advanced cancer between April 1, 2024, and June 30, 2024. A validated LLM prompt for GOC was applied to electronic health records (EHRs) using a Health Insurance Portability and Accountability Act (HIPPA)-secure version of GPT-4o, a LLM developed by OpenAI. Output included (1) presence or absence of GOC documentation, (2) explanations with source text used to inform the LLM's determination, and (3) a hallucination score, indicating proportion of source text generated by the LLM that did not perfectly match text in the EHR. Two LLM prompts were designed to generate structured and unstructured GOC summaries. We randomly selected five patients and applied the summarization task to notes flagged by LLM as containing GOC. We reviewed LLM summaries to examine for relevant information.

Results: Among 326 patients associated with nearly 1,400 clinical notes, LLM flagged approximately 40% of notes for GOC documentation. Subsequent review of explanation text identified that 128 patients (nearly 40% of the total patient population) had GOC documentation. The hallucination index for explanations was low, suggesting that the LLM did not produce text that was not found in EHRs. LLM prompts produced accurate summaries in less than 2 minutes per patient.

Conclusion: LLMs can capture GOC at scale and generate clinically useful summaries. Future directions include real-time implementation in the clinical setting.

Purpose: Adults with hematologic malignancies (HMs) often experience suboptimal end-of-life (EOL) care, with patients from minoritized racial/ethnic groups at even greater risk. It is unclear whether these disparities are partly driven by modifiable factors such as insurance.

Methods: Using the Centers for Medicare and Medicaid Services database, we compared the quality of EOL care as defined by hospice use, high-intensity health care utilization, and advance care planning between Medicare advantage (MA) and Medicare fee-for service (FFS) insurance among patients with HM 66 years and older who died between 2016 and 2020. Multivariate analysis was used to compare EOL care outcomes.

Results: The study included 23,130 patients with MA and 46,145 with FFS. Compared with FFS, MA beneficiaries were more likely to be Black (11.1% v 7.8%; P < .001) or Hispanic (8.3% v 4.3%; P < .001). MA was associated with higher odds of hospice enrollment (odds ratio [OR], 1.11; 95% CI, 1.08 to 1.15) and decreased odds of hospice stays ≤7 days (OR, 0.94; 95% CI, 0.90 to 0.98). Compared with FFS, MA beneficiaries had lower odds of ≥2 emergency department visits (OR, 0.80; 95% CI, 0.76 to 0.84) or intensive care unit stays (OR, 0.83; 95% CI, 0.80 to 0.86) in the last month of life and lower odds of in-hospital death (OR, 0.74; 95% CI, 0.71 to 0.77).

Conclusion: In this large cohort of HM decedents, MA insurance was associated with greater hospice use and lower rates of high-intensity health care utilization near the EOL compared with FFS, despite being more likely to have beneficiaries of color. This suggests that insurance type may affect the quality of EOL care and partly mitigate existing disparities. Future work characterizing which elements of insurance promote high-quality EOL care may help to improve equitable access to such care.

Purpose: Ten pediatric cancer treatment sites previously implemented site-specific symptom management care pathways for 15 symptoms, which were based upon clinical practice guidelines (CPGs). The primary objective of this analysis was to describe the prevalence of care pathway- and CPG-consistent care for symptom management. The secondary objective was to identify factors associated with care pathway-consistent care.

Methods: Participants were patients age 8-18 years diagnosed with cancer within the previous 4 weeks. We identified any intervention to manage each of 15 symptoms during a 3-day period 8 weeks after enrollment. We determined whether the intervention appeared in that site's care pathway and whether it was recommended in the CPG. We determined whether type of symptom (observable v nonobservable) or patient characteristics were associated with care pathway-consistent care.

Results: Two hundred twenty participants were analyzed. The prevalence of care pathway-consistent care for each symptom ranged from 0% (problems thinking, body or face changes, and diarrhea) to 52.3% (throwing up) and was <27% for 14 of 15 symptoms. Similarly, the prevalence of CPG-consistent care was <50% across all symptoms. Participants received significantly more care pathway-consistent interventions for observable symptoms compared with nonobservable symptoms (difference 30% [95% CI, 3 to 54]). Factors associated with receipt of at least one care pathway-consistent intervention were age group, race, ethnicity, and cancer type.

Conclusion: Care pathway- and CPG-consistent care were surprisingly uncommon. Care pathway-consistent interventions were more common for observable than nonobservable symptoms and were associated with patient characteristics. Future work should identify approaches to improve care pathway-consistent care delivery.