bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated geriatric assessment and treatment (INTEGERATE) in older people with cancer planned for systemic anticancer therapy.
Authors: Soo Wee-Kheng , King Madeleine , Pope Alun , Parente Phillip , Darzins Peteris , Davis Ian D. 
Published: May 2020

12011 Background: Older people experience significant adverse effects of cancer and anti-cancer therapy due to age-related vulnerabilities, including medical, functional, cognitive, nutritional and psychosocial issues. Comprehensive geriatric assessment and management (CGAM) provides a powerful framework to assess an older person’s health status and offers a coordinated, person-centered approach to care. Despite its effectiveness, the uptake of CGAM in oncology has been limited due to a lack of randomized evidence in this setting. This study evaluated the effectiveness of CGAM in older people with cancer. Methods: INTEGERATE is a prospective, randomized, parallel group, open-label study in patients aged >70 years with cancer planned for chemotherapy, targeted therapy or immunotherapy. Patients were randomly assigned (1:1) to receive either geriatrician-led CGAM integrated with usual care (integrated oncogeriatric care) or usual care alone, using minimization to balance treatment intent, cancer type, age, sex and performance status. Health-related quality of life (HRQOL) was assessed using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 and QLQ-ELD14 at 0, 12, 18 and 24 weeks. The primary outcome was HRQOL measured by the validated Elderly Functional Index (ELFI) score. Major secondary outcomes included function, mood, nutrition, health utility, treatment delivery, healthcare utilization and survival. Results: Of the 154 patients who underwent randomization, 13 died by week 12 and 130 (92.2% of the remaining patients) completed at least two primary outcome assessments. For the primary outcome, patients in the intervention group had significantly better ELFI score than the usual care group across all followup timepoints, with a maximal difference at week 18 (estimated marginal mean ELFI score 72.0 vs 58.7, p= 0.001). In addition, significant differences favoring the intervention group over the usual care group were seen in HRQOL (domains: physical, role and social functioning; mobility, burden of illness and future worries), unplanned hospital admissions (-1.2 admissions per person-years, p< 0.001) and early treatment discontinuation (32.9% vs 53.2%, p = 0.01). Conclusions: Integrated oncogeriatric care led to improvements in HRQOL, unplanned hospital admissions and treatment discontinuation in older people receiving systemic anti-cancer therapy. Older people (>70 years) planned for anti-cancer therapy should receive CGAM to optimize their clinical care and health outcomes. Clinical trial information: ACTRN12614000399695 .
Impact of augmented intelligence (AI) on utilization of palliative care (PC) services in oncology.
Authors: Gajra Ajeet , Zettler Marjorie , Kish Jonathan , Miller Kelly , Frownfelter John , Valley Amy W. , Blau Sibel 
Published: May 2020

12015 Background: Timely integration of palliative care in the management of patients with advanced cancer is a quality benchmark in oncology. However, PC is often underutilized as evidenced by delays in identification of appropriate patients, in referrals to a PC service, and in enrollment to hospice. Jvion has developed a prescriptive analytics solution, the Machine, which combines AI algorithms with machine learning techniques and applies them to clinical and exogenous datasets to identify patients with a propensity for poor outcomes. The Machine was applied to risk for patients’ mortality within next 30 days, and recommended patient-specific, dynamic, and actionable insights. Use of the Machine requires no additional documentation within the electronic health record (EHR) and the insights generated can be integrated back in to any EHR to help inform the care plan. Herein, we report the results of a study evaluating the impact of AI-driven insights on PC utilization at a large community oncology practice. Methods: All patients were scored weekly using the Machine PC vector. The Machine risk stratified the patients and generated recommendations for the provider to consider as they developed a care plan. Patients identified as “at risk” by the Machine were assessed for a supportive care visit (PC referral) and then were referred as deemed clinically appropriate. The average monthly rates of PC consults and hospice referrals were calculated 5 months prior to and for 17 months after the launch of the Machine in the practice. Results: The oncology practice has 21 providers managing an average of 4329 unique patients per month (PPM). The mean rate of PC consults increased from 17.3 to 29.1 per 1000 PPM pre and post Machine deployment respectively (+168%). The mean monthly rate of hospice referrals increased by 8-fold from 0.2 to 1.6 per 1000 PPM pre and post deployment respectively. Eliminating the first 6 months of Machine deployment to account for user learning curve, the mean rates of monthly PC consults nearly doubled over baseline to 33.0, and hospice referrals rose 12-fold to 2.4 per 1000 patients in months 7-17 post Machine deployment. Conclusions: This oncology practice found deployment of this novel AI solution to be feasible and effective at generating actionable insights. These AI driven insights could be incorporated into workflow and improved the decision-making for whether and when a patient should be referred to PC and/or hospice services for end of life care. Further study is needed to confirm the value of AI for management of cancer patients at end of life.
Predictive model of aromatase inhibitor non-adherence using patient-reported outcomes in women with breast cancer (SWOG S1105).
Authors: Hershman Dawn L. , Moseley Anna , Arnold Kathryn B , Hillyer Grace , Gralow Julie , Henry N.Lynn , Neugut Alfred I. , Ramsey Scott David , Unger Joseph M. 
Published: May 2020

12019 Background: Non-adherence to aromatase inhibitors (AIs) for breast cancer is common and increases risk of recurrence. Few prospective studies have systematically evaluated factors associated with non-adherence. We analyzed baseline sociodemographic, prescription, and patient reported outcome (PRO) symptoms and quality-of-life to identify factors associated with non-adherence prospectively over 3-years. Methods: Patients enrolled in SWOG S1105 were required to have been on an AI for ≥30 days. Patients were assessed for non-adherence to AIs every 3 months for 36 months, with non-adherence defined as urine AI metabolite assay results satisfying any of the following: < 10 ng/mL, undetectable, specimen submitted outside of the ± 21 day follow-up appointment window, or no submitted specimen. At baseline patients were asked about insurance, pill number dispensed and medication cost, and they completed PROs focused on pain and endocrine symptoms (BPI (Brief Pain Inventory), FACT-ES (Endocrine Symptoms)), as well as their beliefs about medications (TSQM (Treatment Satisfaction Questionnaire for Medicine) and BMQ (Brief Medication Questionnaire)). PRO scales were split at the median creating high vs low binary predictors. We determined the association of baseline factors and non-adherence at 36 months. We also evaluated an adverse risk model for AI non-adherence by summing the number of statistically significant adverse factors associated with non-adherence. Logistic regression was used. Results: In total, 724 patients were registered from 40 institutions between May, 2012 and September, 2013. The median age was 60.9 years, and 64.5% were on AI < 12 months prior to registration. Overall, 35.9% were non-adherent at 36 months. Younger patients ( < 65 years) were less adherent (39% vs. 29% non-adherence, OR = 1.51, p = 0.02). Baseline scores on the BPI, FACT-ES, BMQ and TSQM were each statistically significantly associated with AI adherence. Non-adherence was significantly higher among patients scoring poorly on all 4 PRO instruments (65%) compared to those scoring poorly 0 or 1 PRO instruments (27%; OR, 4.68 [2.84-7.73], p < .0001). For each increase in the number of adverse risk PRO scores, the risk of non-adherence increased by 45% (OR = 1.45, p < .0001). Similar results were found when age was included in the score. Conclusions: Presence of multiple baseline risk factors identified through PRO instruments increases non-adherence to AI’s 4-fold. Use of PROs can identify patients for targeted interventions to improve adherence.
Impact of performance status on response and survival among patients receiving checkpoint inhibitors for advanced solid tumors.
Authors: Krishnan Mridula , Teply Benjamin A. , Yu Fang , High Robin 
Published: May 2020

12028 Background: Clinical trials leading to the approval of immune checkpoint inhibition (ICI) have almost exclusively been performed in patients with good performance status (ECOG PS of 0-1). However, ICI remains an attractive option for patients with advanced tumors and poor performance status, considering their overall tolerability. While use of ICI in patients with poor PS (ECOG PS of 2 or greater) has been rapidly adopted, whether these patients derive the same benefits as expected in the studied populations is largely unknown. We therefore performed an institutional retrospective analysis of all patients treated with palliative single agent anti-PD1 or anti-PDL1 to determine response and survival for those with poor performance status. Methods: We retrospectively identified patients with advanced solid tumor malignancies who were treated with ICI monotherapy with palliative intent at our institution between 2015-2019. The primary objective was to compare overall survival (OS) for patients with good PS (ECOG PS 0-1) with those with poor PS (ECOG PS 2 or 3-4). The log-rank test compared the survival among patients with different ECOG PS. In addition, we used a proportional hazards model to assess association between ECOG PS and the OS with adjustment for age, gender, and smoking status. A secondary objective was to compare overall response rates (ORR) of the three ECOG PS groups which were evaluated with a binary rate model. Results: We identified 266 patients treated with ICI, 87 with NSCLC, 34 with melanoma, 33 with RCC, 24 with bladder cancer, 22 with head/neck cancer, and the rest with other histologies. 187 (70%) were ECOG PS 0-1, 62 (23%) were ECOG PS 2, and 17 (7%) were ECOG PS 3-4. 89 of these patients (33%) were still alive at time of last follow-up. Across all tumor types, patients with ECOG PS 0-1 had superior survival compared to ECOG PS 2 (median survival 12.4 months vs 4.6 months, HR 0.41, p < 0.001). Median survival for ECOG PS 3-4 was lower at 2.3 months. The ORR for ECOG PS 0-1 (23%) was significantly higher to that of ECOG PS 2 (6%, p = 0.02). ORR for ECOG PS 3-4 was 12%. Conclusions: Despite the appeal of ICI for patients with advanced malignancy and poor performance status, outcomes were poor. Survival and objective response rates for patients with ECOG PS 2 and higher were significantly worse than those with ECOG PS 0-1. ICI treatment comes with cost, including potentially forgoing early hospice referral or optimal support at the end of life. Prospective trials defining the activity and role of ICI in poor PS are urgently needed.
Prospective study comparing self-administered geriatric assessment to provider’s routine clinical assessment of older patients with metastatic breast cancer treated at community oncology practices.
Authors: Seedor Rino S. , Meeker Caitlin R. , Lewis Bianca , Handorf Elizabeth A. , Filchner Kelly A. , Winn Jennifer S. , Goldstein Lori J. , Dotan Efrat 
Published: May 2020

12029 Background: Geriatric Assessment (GA) is recommended for evaluating an older cancer patient’s (pt) fitness for treatment. We conducted a prospective study evaluating the current gaps that exist in the assessment of older pts with metastatic breast cancer (MBC) in community practices (CP). Methods: Self-administered validated GA was compared to provider assessment (PA) of MBC pts ≥ 65-years-old treated at CP in the US. Providers were blinded to the GA results until their evaluation was completed. Differences in PA vs GA detected abnormalities were assessed using McNemar’s test. The effect of patient/provider factors on the rate of abnormalities not identified was assessed using regression models, clustering by provider and adjusting for the number of prior pts seen. Results: 100 pts were enrolled across 9 CP (median age 73.9, (65-90)). GA detected a total of 356 abnormalities in 96/100 (96%) pts, of which 223 required immediate interventions. African American and widowed/single pts were more likely to have abnormalities identified by GA. On average PA did not identify abnormalities detected by validated GA in 2 of 8 domains. 73% of functional status, 86% of social support, 44% of nutritional, and 96% of cognitive abnormalities detected by GA were not identified by PA (all P < 0.0001). Providers with more years of clinical experience were more likely to identify abnormalities (compared to < 5 years (y) in practice: 5-10 y in practice, p = 0.149; 11-15 y in practice, p = 0.028; > 15 y in practice, p = 0.017). GA had the most significant impact on pts with decreased ECOG PS (p = 0.045). Pts found to have an abnormal Timed Up and Go (TUG) test were more likely to have additional abnormalities in other domains (mean 4.3 vs 2.1, Wilcoxon p < 0.001), and more abnormalities not identified by the PA (p < 0.001). Providers were “surprised” by GA results in 33% of cases, mainly with cognitive or social support findings, and reported plans for management change for 40% of pts based on GA findings. Conclusions: Including a GA in the care of older pts with MBC in CP is beneficial as validated GA has a high detection rate of abnormalities not detected by PA.
Use of self-rated health to identify frailty and predict mortality in older adults with cancer. Results from the care study.
Authors: Al Obaidi Mustafa , Giri Smith , Mir Nabiel , Kenzik Kelly , McDonald Andrew Michael , Smith Crystal Young , Gbolahan Olumide B. , Paluri Ravi Kumar , Bhatia Smita , Williams Grant Richard 
Published: May 2020

12046 Background: Poor self-rated health (SRH) is a known predictor of mortality in the general adult population, but little is known about its use in older adults with cancer. The purpose of this study was to examine the association and ability of SRH to identify frail older adults and assess its ability to predict mortality in older adults with cancer. Methods: Using participants from the Cancer & Aging Resilience Evaluation (CARE) Registry who had undergone a geriatric assessment, we examined SRH using a single-item from the Patient-Reported Outcomes Measurement Information System (PROMIS) global health scale. SRH scores were dichotomized into Poor (poor and fair) and Good (good, very good, and excellent). Multivariable logistic regression analyses were used to examine associations between SRH and frailty (based on frailty index) and specific geriatric impairments adjusting for age, sex, comorbidity, cancer type and stage. Finally, the impact of SRH on all-cause mortality was assessed with a multivariable cox regression model. Results: A total of 708 participants with malignancy were included, median age was 68y, 41.5% male, and 74.6% White. Colorectal cancer was the most common cancer (27.1%) and 48.2% of the participants had Stage IV disease. Poor SRH was reported by 42% of participants and was associated with significantly higher odds of frailty (adjusted Odds Ratio [aOR] = 21.8; 95%CI 13.7-34.8). Similarly, poor SRH was independently associated with higher odds of impairments in Activities of Daily Living (ADL) (aOR = 5.6, 95%CI, 3.6-8.9), independent ADL (aOR = 8.4, 95%CI, 5.8-12.4), cognition (aOR = 4.6, 95%CI 2.3-9.3), malnutrition (aOR = 4.5, 95%CI 3.2-6.4), falls (aOR = 3.6, 95%CI 2.4-5.4), anxiety (aOR = 4.6, 95%CI 2.9-7.3), and depression (aOR = 5.4, 95%CI 3.0-9.7). The SRH demonstrated high sensitivity (84.3%) and specificity (78.4%) for identifying frailty, with a positive predictive value of 67% and negative predictive value of 90.6%. The 1y survival rate in those with Poor SRH was significantly worse (64.7% vs 84.3%, log rank p value < 0.001). In a multivariate cox regression analysis, poor SRH remained an independent predictor of worse survival (adjusted Hazard Ratio 2.29 [1.6-3.2], p< 0.01) after adjusting for age, sex, race, cancer type, stage, comorbidity, and planned treatment. Conclusions: Poor SRH is highly associated with frailty and could be a simple tool to identify frail older patients with cancer at risk for adverse events and increased mortality.
Regaining a satisfactory quality of life and predicting functional decline after major cancer surgery in older adults: The Geriatric Oncology Surgical Assessment and Functional rEcovery after Surgery (GOSAFE) study.
Authors: Montroni Isacco , Ugolini Giampaolo , Saur Nicole , Spinelli Antonino , Rostoft Siri , Ercolani Giorgio , Jaklitsh Michael T. , Van Leeuwen Barbara , de Liguori Carino Nicola , Ferrari Giovanni , Ghignone Federico , Garutti Anna , Di Candido Francesca Rita , Somasundar Ponnandai , Foca Flavia , Vertogen Bernadette , Nanni Oriana , Audisio Riccardo ,   SIOG Surgical Task Force/ESSO GOSAFE Study Group
Published: May 2020

12051 Background: Older cancer patients value quality of life (QoL) and functional outcomes as much as survival but surgical studies lack specific data. The international, multicenter GOSAFE study (ClinicalTrials.gov NCT03299270) aims to evaluate patients’ QoL and functional recovery (FR) after cancer surgery and to assess predictors of FR Methods: GOSAFE prospectively collected functional and clinical data before and after major elective cancer surgery on senior adults (≥70 years). Surgical outcomes were recorded (30, 90, and 180 days post-operatively) with QoL (EQ-5D-3L) and FR (Activities of Daily Living (ADL), Timed Up and Go (TUG) and MiniCog), 26 centers enrolled patients from February 2017 to April 2019. Results: 942 patients underwent a major cancer resection. Median age was 78 (range 70-95); 52.2% males, ASA III-IV 49%. 934 (99%) lived at home, 51% lived alone, and 87% were able to go out. Patients dependent (ADL < 5) were 8%. Frailty was detected by means of G8 ≤14 in 68.8% and fTRST ≥2 in 37% of patients. Major comorbidities (CCI > 6) were reported in 36% and 21% had cognitive impairment according to MiniCog (2.2% self-reported). 25% had > 3 kg weight loss, 27% were hospitalized in the last 90 days, 54% had ≥3 medications (6% none). Postoperative overall morbidity was 39.1% (30 day) and 22.5% (90 day), but Clavien-Dindo III-IV complications were only 13.4% and 6.9% respectively. 30/90/180-day mortality was 3.6/6/8.9% (10/30/33% in patients with severe functional disability). At 3 months after surgery, QoL was stable/improved (mean EQ-5D index 0.78 was equivalent before vs. after surgery, while the EQ-5D VAS score > 60 raised from 74.3% at baseline to 80.2%, p < 0.01). 76.6% experienced postoperative FR/stability. Logistic regression analysis showed that ASA 3-4, CCI≥7 and CD III-IV complications are significantly associated with functional decline while a G8 > 14 has a positive association with functional recovery. Age is not associated with functional outcomes. Conclusions: The largest prospective study on older patients undergoing structured frailty assessment before and after major elective cancer surgery has shown that QoL remains stable/improves after cancer surgery. The majority of patients return to independence and G8 can predict functional recovery. Older patients with multiple comorbidities, high ASA score or postoperative severe complications are likely to functionally deteriorate after oncologic surgery Clinical trial information: NCT03299270 .
The impact of weight loss on physical function in overweight or obese breast cancer survivors.
Authors: Sheng Jennifer Y. , Santa-Maria Cesar Augusto , Blackford Amanda L. , Lim David , Carpenter Ashley , Smith Karen L. , Cohen Gary Irvin , Coughlin Janelle , Appel Lawrence J , Stearns Vered , Snyder Claire Frances 
Published: May 2020

12053 Background: In the prospective POWER-remote trial, 51% and 12% of overweight/obese breast cancer survivors randomized to either a remotely delivered behavioral intervention or self-directed approach, respectively, lost ≥5% of baseline weight. We collected patient-reported outcomes (PROs) to examine the impact of >5% weight loss on symptoms, physical function (PF), and wellbeing. We hypothesized a priori that, regardless of study arm, those with ≥5% weight loss would have improved PF at 6 months v. those who did not. Methods: Women with stage 0-III breast cancer, who completed local therapy and chemotherapy, with a BMI ≥25 kg/m2 were randomized to the 12-month intervention or self-directed weight loss. POWER-remote consists of telephone coaching and online tracking of diet, activity and weight. Women in the self-directed arm received a lifestyle booklet. All women completed PROs at baseline, 6 and 12 months: PROMIS PF, pain, fatigue, anxiety, depression, sleep; FACT-endocrine symptoms; MOS-sexual function. PROs were summarized descriptively and changes within and between groups were tested with multivariable mixed effects models, adjusted for age and baseline weight. Results: From 2013-2015, 96 women enrolled; 83 were evaluable at 6 months. At 6 months, PF scores improved in those with ≥5% weight loss v. not. While endocrine symptoms, fatigue, and anxiety improved in the group who lost ≥5%, differences between groups were not statistically significant. There was no significant change in sexual function, depression, or sleep within or between groups. Similar findings were seen across domains at 12 months, except pain improved in the group losing ≥5%. Conclusions: For overweight/obese breast cancer survivors, PF and other PROs improved among patients who lost ≥5%. These results support the patient-centered benefits of weight loss in this population. Clinical trial information: NCT01871116 . [Table: see text]