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Abstract Background There are persistent racial and ethnic health disparities in end-of-life health outcomes in the United States. African American patients are less likely than White patients to access palliative care, enroll in hospice care, have documented goals of care discussions with their healthcare providers, receive adequate symptom control, or die at home. We developed Community Health Worker Intervention for Disparities in Palliative Care (DeCIDE PC) to address these disparities. DeCIDE PC is an integrated community health worker (CHW) palliative care intervention that uses community health workers (CHWs) as care team members to enhance the receipt of palliative care for African Americans with advanced cancer. The overall objectives of this study are to (1) assess the effectiveness of the DeCIDE PC intervention in improving palliative care outcomes amongst African American patients with advanced solid organ malignancy and their informal caregivers, and (2) develop generalizable knowledge on how contextual factors influence implementation to facilitate dissemination, uptake, and sustainability of the intervention. Methods We will conduct a multicenter, randomized, assessor-blind, parallel-group, pragmatic, hybrid type 1 effectiveness-implementation trial at three cancer centers across the United States. The DeCIDE PC intervention will be delivered over 6 months with CHW support tailored to the individual needs of the patient and caregiver. The primary outcome will be advance care planning. The treatment effect will be modeled using logistic regression. The secondary outcomes are quality of life, quality of communication, hospice care utilization, and patient symptoms. Discussion We expect the DeCIDE PC intervention to improve integration of palliative care, reduce multilevel barriers to care, enhance clinic and patient linkage to resources, and ultimately improve palliative care outcomes for African American patients with advanced cancer. If found to be effective, the DeCIDE PC intervention may be a transformative model with the potential to guide large-scale adoption of promising strategies to improve palliative care use and decrease disparities in end-of-life care for African American patients with advanced cancer in the United States. Trial registration Registered on ClinicalTrials.gov (NCT05407844). First posted on June 7, 2022.

Abstract Background Children with medical complexity (CMC) often rely upon the use of multiple medications to sustain quality of life and control substantial symptom burden. Pediatric polypharmacy (≥ 5 concurrent medications) is prevalent and increases the risk of medication-related problems (MRPs). Although MRPs are associated with pediatric morbidity and healthcare utilization, polypharmacy is infrequently assessed during routine clinical care for CMC. The aim of this randomized controlled trial is to determine if a structured pharmacist-led Pediatric Medication Therapy Management (pMTM) intervention reduces MRP counts, as well as the secondary outcomes of symptom burden and acute healthcare utilization. Methods This is a hybrid type 2 randomized controlled trial assessing the effectiveness of pMTM compared to usual care in a large, patient-centered medical home for CMC. Eligible patients include all children ages 2–18 years old, with ≥ 1 complex chronic condition, and with ≥ 5 active medications, as well as their English-speaking primary caregivers. Child participants and their primary parental caregivers will be randomized to pMTM or usual care before a non-acute primary care visit and followed for 90 days. Using generalized linear models, the overall effectiveness of the intervention will be evaluated using total MRP counts at 90 days following pMTM intervention or usual care visit. Following attrition, a total of 296 CMC will contribute measurements at 90 days, which provides > 90% power to detect a clinically significant 1.0 reduction in total MRPs with an alpha level of 0.05. Secondary outcomes include Parent-Reported Outcomes of Symptoms (PRO-Sx) symptom burden scores and acute healthcare visit counts. Program replication costs will be assessed using time-driven activity-based scoring. Discussion This pMTM trial aims to test hypotheses that a patient-centered medication optimization intervention delivered by pediatric pharmacists will result in lower MRP counts, stable or improved symptom burdens, and fewer cumulative acute healthcare encounters at 90 days following pMTM compared to usual care. The results of this trial will be used to quantify medication-related outcomes, safety, and value for a high-utilization group of CMC, and outcomes may elucidate the role of integrated pharmacist services as a key component of outpatient complex care programs for this priority pediatric population. Trial Registration This trial was prospectively registered at clinicaltrials.gov (NCT05761847) on Feb 25, 2023.

Abstract Background Formal surgical risk assessment tools have been developed to predict risk of adverse postoperative patient outcomes. Such tools accurately predict common postoperative complications, inform patients and providers of likely perioperative outcomes, guide decision making, and improve patient care. However, these are underutilized. We studied the attitudes towards and techniques of how surgeons preoperatively assess risk. Methods Surgeons at a large academic tertiary referral hospital and affiliate community hospitals were emailed a 16-question survey via REDCap (Research Electronic Data Capture) between 8/2019-6/2020. Reminder emails were sent once weekly for three weeks. All completed surveys by surgical residents and attendings were included; incomplete surveys were excluded. Surveys were analyzed using descriptive statistics (frequency distributions and percentages for categorical variables, means, and standard deviations for continuous variables), and Fisher’s exact test and unpaired t-tests comparing responses by surgical attendings vs. residents. Results A total of 108 surgical faculty, 95 surgical residents, and 58 affiliate surgeons were emailed the survey. Overall response rates were 50.0% for faculty surgeons, 47.4% for residents, and 36.2% for affiliate surgeons. Only 20.8% of surgeons used risk calculators most or all of the time. Attending surgeons were more likely to use prior experience and current literature while residents used risk calculators more frequently. Risk assessment tools were more likely to be used when predicting major complications and death in older patients with significant risk factors. Greatest barriers for use of risk assessment tools included time, inaccessibility, and trust in accuracy. Conclusions A small percentage of surgeons use surgical risk calculators as part of their routine practice. Time, inaccessibility, and trust in accuracy were the most significant barriers to use.

Abstract Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.