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Abstract Background This study evaluated the real-world impact of acupuncture on analgesics and healthcare resource utilization among breast cancer survivors. Methods From a United States (US) commercial claims database (25% random sample of IQVIA PharMetrics® Plus for Academics), we selected 18–63 years old malignant breast cancer survivors experiencing pain and ≥ 1 year removed from cancer diagnosis. Using the difference-in-difference technique, annualized changes in analgesics [prevalence, rates of short-term (

Abstract Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual’s likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care.

Abstract Introduction Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding. Methods The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians’ input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models. Results There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632–0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs. Conclusions We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.

Abstract Background Neuropsychiatric symptoms, comprising cognitive impairment, fatigue, insomnia, depression, and anxiety, are prevalent and may co-occur during and after chemotherapy treatment for cancer. Electroacupuncture (EA), which involves mild electrical stimulation with acupuncture, holds great potential in addressing the management of individual symptoms. However, there is a lack of studies evaluating if EA can manage concurrent neuropsychiatric symptoms in cancer (i.e., symptom cluster). Hence, we designed a trial to evaluate the efficacy, safety, and feasibility of administering EA as an intervention to mitigate neuropsychiatric symptom clusters amongst cancer patients and survivors. Methods The EAST study is a randomized, sham-controlled, patient- and assessor-blinded clinical trial. Sixty-four cancer patients and survivors with complaints of one or more neuropsychiatric symptom(s) in the seven days prior to enrollment are recruited from the University of California Irvine (UCI) and Children’s Hospital of Orange County (CHOC). Individuals with needle phobia, metastases, bleeding disorders, electronic implants, epilepsy, exposure to acupuncture in the three months prior to enrollment, and who are breastfeeding, pregnant, or planning to get pregnant during the duration of the study will be excluded. Screening for metal fragments and claustrophobia are performed prior to the optional neuroimaging procedures. Recruited patients will be randomized (1:1) in random blocks of four or six to receive either ten weekly verum EA (treatment arm, vEA) or weekly sham EA (control arm, sEA) treatment visits with a follow-up appointment four to twelve weeks after their last treatment visit. The treatment arm will receive EA at 13 acupuncture points (acupoints) chosen for their therapeutic effects, while the control arm receives minimal EA at 7 non-disease-related acupoints. Questionnaires and cognitive assessments are administered, and blood drawn to assess changes in symptom clusters and biomarkers, respectively. Conclusion The EAST study can provide insight into the efficacy of EA, an integrative medicine modality, in the management of cancer symptom clusters in routine clinical practice. Trial registration This trial is registered with clinicaltrials.gov NCT05283577.

Abstract Background The predominant oncologist-led model in many countries is unsustainable to meet the needs of a growing cohort of breast cancer survivors (BCS). Despite available alternative models, adoption rates have been poor. To help BCS navigate survivorship care, we aimed to systematically develop a decision aid (DA) to guide their choice of follow-up care model and evaluate its acceptability and usability among BCS and health care providers (HCPs). Methods We recruited BCS aged ≥ 21 years who have completed primary treatment and understand English. BCS receiving palliative care or with cognitive impairment were excluded. HCPs who routinely discussed post-treatment care with BCS were purposively sampled based on disciplines. Each participant reviewed the DA during a semi-structured interview using the ‘think aloud’ approach and completed an acceptability questionnaire. Descriptive statistics and directed content analysis were used. Results We conducted three rounds of alpha testing with 15 BCS and 8 HCPs. All BCS found the final DA prototype easy to navigate with sufficient interactivity. The information imbalance favouring the shared care option perceived by 60% of BCS in early rounds was rectified. The length of DA was optimized to be ‘just right’. Key revisions made included (1) presenting care options side-by-side to improve perceived information balance, (2) creating dedicated sections explaining HCPs’ care roles to address gaps in health system contextual knowledge, and (3) employing a multicriteria decision analysis method for preference clarification exercise to reflect the user’s openness towards shared care. Most BCS (73%) found the DA useful for decision-making, and 93% were willing to discuss the DA with their HCPs. Most HCPs (88%) agreed that the DA was a reliable tool and would be easily integrated into routine care. Conclusions Our experience highlighted the need to provide contextual information on the health care system for decisions related to care delivery. Developers should address potential variability within the care model and clarify inherent biases, such as low confidence levels in primary care. Future work could expand on the developed DA’s informational structure to apply to other care models and leverage artificial intelligence to optimize information delivery.

Abstract Purpose To identify subgroups of female breast cancer patients with distinct self-reported employment interference (EI) profiles and determine which demographic, clinical, and symptom characteristics, and quality of life outcomes were associated with subgroup membership. Methods Women with breast cancer (n = 385) were assessed for changes in EI over ten times, from prior to, through 12 months after breast cancer surgery. Latent profile analysis (LPA) was used to identify subgroups of patients with distinct EI profiles. Results Three distinct EI profiles (i.e., None – 26.2% (n = 101), Low – 42.6% (n = 164), High – 31.2% (n = 120)) were identified. Compared to the None and Low groups, patients in the High group were more likely to be younger. Higher proportions in the High group were non-White, pre-menopausal prior to surgery, had more advanced stage disease, had received an axillary lymph node dissection, had received neoadjuvant chemotherapy, had received adjuvant chemotherapy, and had a re-excision or mastectomy on the affected breast within 6 months after surgery. In addition, these patients had lower quality of life scores. Compared to the None group, the High group had higher levels of trait and state anxiety, depressive symptoms, fatigue and sleep disturbance and lower levels of cognitive function. Conclusions This study provides new knowledge regarding EI profiles among women in the year following breast cancer surgery. The non-modifiable risk factors (e.g., younger age, being non-White, having more advanced stage disease) can inform current screening procedures. The potentially modifiable risk factors can be used to develop interventions to improve employment outcomes of breast cancer patients.

Abstract Background Cancer-related fatigue (CRF) is a debilitating condition which commonly affects cancer survivors. The management of CRF remains a challenge due to the lack of effective pharmacological interventions. Traditional Chinese medicine (TCM) could be a potential therapeutic option for CRF. The modified Xiang Bei Yang Rong Tang (XBYRT) is a TCM herbal decoction, formulated to improve fatigue symptoms in cancer survivors. This clinical trial aims to evaluate the efficacy and safety of XBYRT in improving CRF and quality of life (QOL) of cancer survivors. Methods This is a single centre, randomized, double-blind, placebo-controlled, parallel trial. Eighty cancer survivors will be recruited and randomized to receive the XBYRT or placebo decoction, in a ratio of 1:1. Participants will consume the XBYRT/placebo decoction daily for 8 weeks and undergo assessments at baseline and 4, 8 and 10 weeks after baseline. The participants will be assessed for patient-reported outcomes (PRO), blood biomarkers and adverse events at each time point. The primary outcome is the overall health and QOL status, at 8 weeks follow-up. The secondary outcomes are the effects of XBYRT on fatigue levels, cancer-related cognitive impairment and QOL, as assessed by PRO. The incidence of adverse events and the effects of the XBYRT decoction on blood biomarkers associated with CRF will also be evaluated. Discussion Efficacy and safety outcomes from this trial will provide important clinical data to guide future large-scale randomized controlled trials, and the evaluation of the objective blood biomarkers can help to delineate the biological mechanisms of CRF. Trial registration number ClinicalTrials.gov NCT04104113 . Registered on 26 September 2019

Abstract Background The aim of this study was to evaluate the psychometric properties of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group—Neurotoxicity (FACT/GOG-Ntx) subscale in a longitudinal study of cancer patients treated with chemotherapy. Methods Patients were assessed with the FACT/GOG-Ntx subscale, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Chemotherapy-Induced Peripheral Neuropathy Scale 20 (EORTC QLQ-CIPN20), National Cancer Institute -Common Terminology Criteria for Adverse Events (NCI-CTCAE), and light touch test using 10 g monofilament for up to ten assessment points from baseline (prior to initiation of first chemotherapy), after the end of each cycle (up to 6 cycles, 3 weeks per cycle), and at 6, 9, and 12 months after starting chemotherapy. Psychometric analyses included internal consistency reliability, convergent validity, factorial validity, sensitivity to change and responsiveness (minimal clinically important difference, MCID). Results Cronbach’s alpha coefficients of the FACT/GOG-Ntx subscale were 0.82–0.89 across assessment points. The subscale strongly correlated with the EORTC QLQ-CIPN20 (r = 0.79–0.93) but low-to-moderately correlated with the NCI-CTCAE sensory (r s = 0.23–0.45) and motor items (r s = 0.15–0.50) as well as the monofilament test (r s = 0.23–0.47). The hypothesized 4-factor structure of the FACT/GOG-Ntx subscale was not confirmed at assessment points (χ2/df = 2.26–8.50; all P

Abstract Background There are inconsistencies in the literature regarding the prevalence and assessment of chemotherapy-induced peripheral neuropathy (CIPN). This study explored CIPN natural history and its characteristics in patients receiving taxane- and platinum-based chemotherapy. Patients and methods Multi-country multisite prospective longitudinal observational study. Patients were assessed before commencing and three weekly during chemotherapy for up to six cycles, and at 6,9, and 12 months using clinician-based scales (NCI-CTCAE; WHO-CIPN criterion), objective assessments (cotton wool test;10 g monofilament); patient-reported outcome measures (FACT/GOG-Ntx; EORTC-CIPN20), and Nerve Conduction Studies. Results In total, 343 patients were recruited in the cohort, providing 2399 observations. There was wide variation in CIPN prevalence rates using different assessments (14.2–53.4%). Prevalence of sensory neuropathy (and associated symptom profile) was also different in each type of chemotherapy, with paclitaxel (up to 63%) and oxaliplatin (up to 71.4%) showing the highest CIPN rates in most assessments and a more complex symptom profile. Peak prevalence was around the 6-month assessment (up to 71.4%). Motor neurotoxicity was common, particularly in the docetaxel subgroup (up to 22.1%; detected by NCI-CTCAE). There were relatively moderately-to-low correlations between scales (rs = 0.15,p

Abstract Background Currently, several fatigue measurement instruments are available to evaluate and measure cancer-related fatigue. Amongst them, Multidimensional Fatigue Syndrome Inventory-Short Form (MFSI-SF) is a self-reported instrument and a multidimensional scale that aims to capture the global, somatic, affective, cognitive and behavioural symptoms of fatigue. This study examines the psychometric properties and measurement equivalence of the English and Chinese versions of MFSI-SF in breast cancer and lymphoma patients in Singapore. Methods Patients were recruited from National Cancer Centre Singapore. Validity, reliability and responsiveness of MFSI-SF were evaluated in this study. Convergent validity was evaluated by correlating total and subscales of MFSI-SF to known related constructs in EORTC QLQ-C30. Known group validity was assessed based on patients’ cancer stage, pain, insomnia and depression symptoms. Reliability was evaluated by Cronbach’s α. Responsiveness analyses were performed with patients who have undergone at least one cycle of chemotherapy. Multiple regression was used to compare the total and subscale scores of MSFI-SF between the two language versions. Results Data from 246 (160 English and 86 Chinese version) breast cancer and lymphoma patients were included in the study. Moderate to high correlations were observed between correlated MFSI-SF subscales and EORTC QLQ-C30 domains (|r| = 0.524 to 0.774) except for a poor correlation (r = 0.394) observed between MFSI-SF vigour subscale and EORTC QLQ-C30 role functioning subscale. Total MFSI-SF scores could differentiate between patients with higher depression, pain and insomnia status. Internal consistency of MFSI-SF was also high (α = 0.749 to 0.944). Moderate correlation was observed between change in total MFSI-SF score and change in fatigue symptom scale score and global QoL score on EORTC QLQ-C30 (|r| = 0.478 and 0.404 respectively). Poor correlations were observed between change in scores of hypothesised subscales (|r| = 0.202 to 0.361) except for a moderate correlation between change in MFSI-SF emotional fatigue score and change in EORTC QLQ-C30 emotional functioning domain score. Measurement equivalence was established for all subscales and total MFSI-SF score except for the emotional and vigour subscales. Conclusions This study supports the use of MFSI-SF as a reasonably valid scale with good internal consistency for measuring fatigue levels in the Singapore cancer population.

Abstract Background Preliminary evidence suggests that changes in plasma brain-derived neurotrophic factor (BDNF) levels may contribute to the occurrence of chemotherapy-associated cognitive impairment (CACI), and a previous study suggested that carriers of the BDNF Met homozygous genotype are protected from CACI. Methods This multicenter, prospective cohort study involved chemotherapy-receiving early-stage breast cancer (ESBC) patients. Self-perceived cognitive function was longitudinally assessed using the validated FACT-Cog (ver. 3) across three time points: Prior to chemotherapy (T1), during chemotherapy (T2), and at the end of chemotherapy (T3). Plasma BDNF levels were quantified using enzyme-linked immunosorbent assay. Genotyping was performed using Sanger Sequencing. Results A total of 51 chemotherapy-receiving ESBC patients (mean age: 52.6 ± 9.5 years) were recruited, and 11 patients (21.6%) reported subjective cognitive impairment post-chemotherapy. Overall, there was a reduction in median plasma BDNF levels over time (T1: 5423.0 pg/ml; T2: 5313.6 pg/ml; T3: 4050.3 pg/ml; p