JCO Clinical Cancer Informatics最新文献

Purpose: The GARDE platform uses family history reported in the electronic health record (EHR) to systematically identify eligible patients for genetic testing for hereditary cancer syndromes. The goal of this study was to evaluate the change in effectiveness of GARDE to identify eligible individuals when more comprehensive family history data are provided, thus quantifying the impact of underdocumentation.

Methods: A cohort of 133,764 patients at the University of Utah Health was analyzed with GARDE comparing identification rates using EHR data versus EHR plus data from a statewide population database, the Utah Population Database (UPDB).

Results: Compared with EHR alone, EHR + UPDB increased the rate of individuals eligible for genetic testing from 4.1% to 9.2%. In the 44,692 individuals with the most comprehensive family history, eligibility more than quadrupled from 4.6% (EHR alone) to 19.3% (EHR + UPDB). The increase was significant across all demographics, but disparities still remained for historically marginalized minorities (9.2%-13.9% in non-White races compared with 19.7% in White races).

Conclusion: Augmenting EHR data with family history data from the UPDB substantially improved the detection of individuals eligible for genetic testing of hereditary cancer syndromes in all subgroups. This underscores the importance of improving methods for acquiring family history, in person or in silico. However, these increases did not ameliorate disparities. Continuous disparities are unlikely to be explained by incomplete family history alone and may also be because susceptibility genes, risk variants, and screening guidelines were discovered and developed largely in White races. Addressing disparities will require intentional data collection of family history in historically marginalized minorities and the promotion of genetic and risk assessment studies in more diverse populations to ensure equity and health care.

Purpose: There is an increasing interest in studying the potential of mobile health (mHealth) technologies, such as smartphone apps and wearables, as monitoring tools for patients with cancer during or after their treatment. However, little research is dedicated to exploring the opinions and concerns of patients regarding the adoption of these technologies. This study aimed to gain insight into patients' perspectives and preferences for participating in mHealth-based monitoring in cancer care.

Methods: A qualitative study comprising semistructured interviews was conducted in the Netherlands between April and June 2023. Participants were eligible if they were 18 years or older with a current or past diagnosis of cancer. The interview guide was developed on the basis of the technology acceptance model, with main themes being use, communication, trust, privacy, and expectations.

Results: Thirteen participants with urologic primary cancer were interviewed. Most patients had already some familiarity with the use of digital monitoring devices or wearables. Main barriers included persistent reminders of the illness, receiving notifications deemed unnecessary or unwanted, and the acknowledgment that mHealth technology does not serve as a substitute for human doctors. Conversely, patients recognized the potential for time-savings through the utilization of mHealth, viewed active monitoring as nonburdensome, considered mHealth a tool for reducing the communication threshold with their doctor, and expressed willingness to adopt such a platform if they perceived personal or societal relevance.

Conclusion: This study has elucidated which factors are important for successful development of mHealth for patients with cancer. While both barriers and facilitators play a role, patients' attitudes were positive toward the implementation of remote digital monitoring, showing promising prospects for future research of mHealth in oncology.

Purpose: Therapeutic compliance, or adherence, is critical in oncology because of the complexity and duration of cancer treatment regimens. Nonadherence can lead to suboptimal therapeutic outcomes, increased disease progression, higher mortality rates, and elevated health care costs. Traditional methods to enhance compliance, such as patient education and regular follow-ups, have shown limited success.

Materials and methods: This review examines the potential of digital health technologies to improve adherence in oncology. Various studies and trials are analyzed to assess the effectiveness of these technologies in supporting patients with cancer.

Results: mHealth applications have been shown to improve medication adherence through features like medication reminders and symptom tracking. Telemedicine facilitates continuous care and reduces the need for travel, significantly improving adherence and patient satisfaction. Patient-reported outcome measures enhance clinical decision making and personalized treatment plans by incorporating patient feedback. Electronic medical records and patient portals improve compliance by providing easy access to medical information and fostering better patient-provider communication. Connected pillboxes aid in consistent medication intake and reduce dispensing errors.

Conclusion: Digital health technologies offer significant benefits in oncology by enhancing patient engagement, improving adherence to treatment protocols, and enabling comprehensive cancer care management. However, challenges such as the digital divide, data privacy concerns, and the need for tailored interventions must be addressed. Future research should focus on evaluating the effectiveness of digital interventions and developing personalized digital health tools to maximize therapeutic compliance.

Purpose: Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer.

Methods: This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO.

Results: Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications.

Conclusion: Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

Purpose: The objective was to develop and evaluate the portability of a text mining algorithm for prospectively capturing disease progression in electronic health record (EHR) data of patients with metastatic non-small cell lung cancer (mNSCLC) treated with immunochemotherapy.

Methods: This study used EHR data from patients with mNSCLC receiving immunochemotherapy (between October 1, 2018, and December 31, 2022) in four Dutch hospitals. A text mining algorithm for capturing disease progression was developed in hospitals 1 and 2 and then transferred to hospitals 3 and 4 to evaluate portability. Performance metrics were calculated by comparing its outcomes with manual chart review. In addition, data were simulated to come available over time to assess performance in real-time applications. Median progression-free survival (PFS) was calculated using the Kaplan-Meier method to compare text mining with manual chart review.

Results: During development and portability, the text mining algorithm performed well in capturing disease progression, with all performance scores >90%. When real-time performance was simulated, the performance scores in all four hospitals exceeded 90% from week 15 after the start of follow-up. Although the exact progression dates varied in 46 patients of 157 patients with progressive disease, the number of patients labeled with progression too early (n = 24) and too late (n = 22) was well balanced with discrepancies ranging from -116 to 384 days. Nevertheless, the PFS curves constructed with text mining and manual chart review were highly similar for each hospital.

Conclusion: In this study, an accurate text mining algorithm for capturing disease progression in the EHR data of patients with mNSCLC was developed. The algorithm was portable across different hospitals, and the performance over time was good, making this an interesting approach for prospective follow-up of multicenter cohorts.

Purpose: Our study explores how attitudes of patients with cancer toward smartphone-based commercial health care apps affect their use and identifies the influencing factors.

Materials and methods: Of the 960 patients with cancer who participated in a randomized controlled trial for a smartphone-based commercial health care app, only 264 participants, who completed a survey on app usage experiences conducted between May and August 2022, were included in this study. Participants were categorized into three groups: Positive Persistence (PP), Negative Nonpersistence (NN), and Neutral (NE) on the basis of their attitude and willingness to use smartphone-based commercial health care apps. The Health-Related Quality of Life (QOL) Instrument (8 Items), European QOL (5 Dimensions; 5 Levels), The Human Interaction and Motivation questionnaire, and open-ended questionnaires were used to examine factors potentially influencing extended utilization of digital interventions.

Results: Despite demographic similarities among the three groups, only the PP and NE groups showed similar app usage compared with the NN group. The combined group (positive persistence and neutral) exhibited significant improvement in depression (P = .02), anxiety (P = .03), and visual analog scale scores (P = .02) compared with the NN group. In addition, patient interaction (P < .01) and the presence of a chatbot/information feature on the app (P < .01) demonstrated a significant difference across the three groups, with the most favorable response observed among the PP group. Patients were primarily motivated to use the app owing to its health management functions, while the personal challenges they encountered during app usage acted as deterrents.

Conclusion: These findings suggest that maintaining a non-negative attitude toward smartphone-based commercial health care apps could lead to an improvement in psychological distress. In addition, the social aspect of apps could contribute to extending patient's utilization of digital interventions.

Purpose: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to identify patients at high risk of impaired CRF and poor CRF response to AT.

Methods: We evaluated heterogeneity in CRF among 64 women with metastatic breast cancer randomly assigned to 12 weeks of highly structured AT (n = 33) or control (n = 31). Unsupervised hierarchical cluster analyses were used to identify representative variables from multidimensional prerandomization (baseline) data, and to categorize patients into mutually exclusive subgroups (ie, phenogroups). Logistic and linear regression evaluated the association between phenogroups and impaired CRF (ie, ≤16 mL O₂·kg^-1·min^-1) and CRF response.

Results: Baseline CRF ranged from 10.2 to 38.8 mL O₂·kg^-1·min^-1; CRF response ranged from -15.7 to 4.1 mL O₂·kg^-1·min^-1. Of the n = 120 candidate baseline variables, n = 32 representative variables were identified. Patients were categorized into two phenogroups. Compared with phenogroup 1 (n = 27), phenogroup 2 (n = 37) contained a higher number of patients with none or >three lines of previous anticancer therapy for metastatic disease and had lower resting left ventricular systolic and diastolic function, cardiac output reserve, hematocrit, lymphocyte count, patient-reported outcomes, and CRF (P < .05) at baseline. Among patients allocated to AT (phenogroup 1, n = 12; 44%; phenogroup 2, n = 21; 57%), CRF response (-1.94 ± 3.80 mL O₂·kg^-1·min^-1 v 0.70 ± 2.22 mL O₂·kg^-1·min^-1) was blunted in phenogroup 2 compared with phenogroup 1.

Conclusion: Phenotypic clustering identified two subgroups with unique baseline characteristics and CRF outcomes. The identification of CRF phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions among patients with cancer.

Purpose: A major barrier to the incorporation of biometric data into clinical practice is the lack of device integration with electronic medical records (EMRs). We developed infrastructure to transmit biometric data from an Apple Watch into the EMR for physician review. The study objective was to test feasibility of using this infrastructure for patients undergoing radiotherapy.

Methods: The study included patients with breast or prostate cancer receiving ≥3 weeks of radiotherapy who reported owning an Apple Watch. Daily resting heart rate (HR), HR variability, step count, and exercise minutes were automatically transferred to our EMR using a custom app installed on each patient's iPhone. Biometric data were presented to the treating radiation oncologist for review on a weekly basis during creation of the on-treatment note. Feasibility was defined a priori as physician review of biometric data for at least 90% of patients. Time trends in biometric data were tested using the Jonckheere-Terpstra test. Patient satisfaction was assessed using the System Usability Scale (SUS), with scores above 80 considered above-average user experience.

Results: Of the 20 patients enrolled, biometric data were successfully transmitted to the EMR and reviewed by the radiation oncologist for 95% (n = 19) of patients, thus meeting the a priori feasibility threshold. For patients with radiation courses ≥4 weeks, exercise minutes decreased over time (P = .01) and daily mean HR variability increased over time (P = .02). The median SUS was 82.5 (IQR, 70-87.5).

Conclusion: Our study demonstrates the feasibility of real-time integration of biometric data collected from an Apple Watch into the EMR with subsequent physician review. The high rates of physician review and patient satisfaction provide support for further development of large-scale collection of wearable device data.