"You have to be sure that the patient has the full picture": Adaptation of the Best Case/Worst Case communication tool for geriatric oncology.

BACKGROUND: Shared decision making (SDM) is especially important for older adults with cancer given the risks of over- and undertreatment, uncertainty regarding benefits/harms worsened by research underrepresentation, and individual preferences. We aimed to adapt the Best Case/Worst Case (BC/WC) communication tool, which improves SDM in geriatric surgery, to geriatric oncology. METHODS: We conducted focus groups with 40 stakeholders (fourteen older adults with lung cancer, twelve caregivers, fourteen medical oncologists) to elicit perspectives on using the BC/WC tool for geriatric oncology and to identify components needing refinement. During each focus group, participants viewed a BC/WC demonstration video and answered questions modified from the Decision Aid Acceptability Scale. We analyzed transcripts using deductive and inductive thematic analyses. DISCUSSION: Participants believed that the BC/WC tool could help patients understand their cancer care choices, explore tradeoffs and picture potential outcomes, and deliberate about decisions based on their goals, preferences, and values. Oncologists also reported the tool could guide conversations to address points that may frequently be skipped (e.g., alternative options, treatment goals). Participant preferences varied widely regarding discussion of the worst-case scenario and desire for statistical information. CONCLUSION: The BC/WC tool is a promising strategy that may improve SDM in geriatric oncology and patient understanding of alternative options and treatment goals. Based on participant input, adaptations will include framing cancer care as a series of decisions, eliciting patient preferences and asking permission before offering the worst-case scenario, and selection of the two most relevant options to present if multiple exist.

Changes in older adults' life space during lung cancer treatment: A mixed methods cohort study.

BACKGROUND: Maintenance of function during cancer treatment is important to older adults. Characteristics associated with pretreatment life-space mobility and changes during non-small cell lung cancer (NSCLC) treatment remain unknown. METHODS: This mixed methods cohort study recruited adults age ≥65 with advanced NSCLC starting palliative chemotherapy, immunotherapy, and/or targeted therapy from a Comprehensive Cancer Center, Veterans Affairs, and safety-net clinic. Patients completed geriatric assessments including Life-Space Assessment (LSA) pretreatment and at 1, 2, 4, and 6 months after treatment initiation. LSA scores range from 0 to 120 (greater mobility); LSA <60 is considered restricted. We used mixed-effects models to examine pretreatment LSA, change from 0 to 1 month, and change from 1 to 6 months. A subgroup participated in semistructured interviews pretreatment and at 2 and 6 months to understand the patient experience of life-space change. For each interview participant, we created joint displays of longitudinal LSA scores juxtaposed with illustrative quotes. RESULTS: Among 93 patients, median age was 73 (range 65-94). Mean pretreatment LSA score was 67.1. On average, LSA declined 10.1 points from pretreatment to 1 month and remained stable at 6 months. Pretreatment LSA score was associated with several demographic, clinical, geriatric assessment, and symptom characteristics. LSA decline at 1 month was greater among patients with high anxiety (slope = -12.6 vs. -2.3, p = 0.048). Pretreatment body mass index <21 kg/m2 was associated with LSA improvement from 1 to 6 months (slope = 4.1 vs. -0.04, p = 0.003). Joint displays illustrated the impact of different life-space trajectories on patients' lives in their words. CONCLUSION: Older adults with NSCLC have low pretreatment life space with many developing restricted life space during treatment. Incorporating life-space assessments into clinical cancer care may help older adults concretely visualize how treatment might impact their daily function to allow for informed decision making and identify early changes in mobility to implement supportive interventions.

Adherence to adjuvant endocrine therapy for breast cancer: importance in women with low income.

There are wide disparities in breast cancer-specific survival by patient sociodemographic characteristics. Women of lower income, for instance, have higher relapse and death rates from breast cancer. One possible contributing factor for this disparity is low use of adjuvant endocrine therapy-an extremely efficacious therapy in women with early stage, hormone receptor positive breast cancer, the most common subtype of breast cancer. Alone, adjuvant endocrine therapy decreases breast cancer recurrence by 50% and death by 30%. Data suggest that low use of adjuvant endocrine therapy is a potentially important and modifiable risk factor for poor outcome in low-income breast cancer patients.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities.

The Informatics and Computational Safety Analysis Staff at the US FDA's Center for Drug Evaluation and Research has created a database of pharmaceutical adverse effects (AEs) linked to pharmaceutical chemical structures and estimated population exposures. The database is being used to develop quantitative structure-activity relationship (QSAR) models for the prediction of drug-induced liver and renal injury, as well as to identify relationships among AEs. The post-market observations contained in the database were obtained from FDA's Spontaneous Reporting System (SRS) and the Adverse Event Reporting System (AERS) accessed through Elsevier PharmaPendium software. The database contains approximately 3100 unique pharmaceutical compounds and 9685 AE endpoints. To account for variations in AE reports due to different patient populations and exposures for each drug, a proportional reporting ratio (PRR) was used. The PRR was applied to all AEs to identify chemicals that could be scored as positive in the training datasets of QSAR models. Additionally, toxicologically similar AEs were grouped into clusters based upon both biological effects and statistical correlation. This clustering created a weight of evidence paradigm for the identification of compounds most likely to cause human harm based upon findings in multiple related AE endpoints.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities.

This report describes the development of quantitative structure-activity relationship (QSAR) models for predicting rare drug-induced liver and urinary tract injury in humans based upon a database of post-marketing adverse effects (AEs) linked to approximately 1600 chemical structures. The models are based upon estimated population exposure using AE proportional reporting ratios. Models were constructed for 5 types of liver injury (liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, gall bladder disorders) and 6 types of urinary tract injury (acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, urolithiases). Identical training data sets were configured for 4 QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Predictive Data Miner). Model performance was optimized and was shown to be affected by the AE scoring method and the ratio of the number of active to inactive drugs. The best QSAR models exhibited an overall average 92.4% coverage, 86.5% specificity and 39.3% sensitivity. The 4 QSAR programs were demonstrated to be complementary and enhanced performance was obtained by combining predictions from 2 programs (average 78.4% specificity, 56.2% sensitivity). Consensus predictions resulted in better performance as judged by both internal and external validation experiments.