Anne P. Ehlers

Engaging Stakeholders in Surgical Research: The Design of a Pragmatic Clinical Trial to Study Management of Acute Appendicitis

Discussion | The participation of a multidisciplinary stakeholder team provided unique perspectives that helped improve recruitment and retention rates in the RCT. Implementation of stakeholder recommendations on how to explain the purpose of the trial to eligible participants in the urgent emergency care setting significantly improved enrollment. The implementation of stakeholder recommendations for maximizing patient follow-up also significantly improved retention rates. We believe that our success in achieving these goals stems in part from involving stakeholders throughout the entirety of the project, building strong ongoing relationships, fostering open communication, and appreciating all opinions. This study demonstrates the potential value and effect of involving patients, families, and other health care stakeholders in the design and performance of surgical trials.

Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey

BackgroundProspect theory suggests that when faced with an uncertain outcome, people display loss aversion by preferring to risk a greater loss rather than incurring certain, lesser cost. Providing probability information improves decision making towards the economically optimal choice in these situations. Clinicians frequently make decisions when the outcome is uncertain, and loss aversion may influence choices. This study explores the extent to which prospect theory, loss aversion, and probability information in a non-clinical domain explains clinical decision making under uncertainty.MethodsFour hundred sixty two participants (n = 117 non-medical undergraduates, n = 113 medical students, n = 117 resident trainees, and n = 115 medical/surgical faculty) completed a three-part online task. First, participants completed an iced-road salting task using temperature forecasts with or without explicit probability information. Second, participants chose between less or more risk-averse (“defensive medicine”) decisions in standardized scenarios. Last, participants chose between recommending therapy with certain outcomes or risking additional years gained or lost.ResultsIn the road salting task, the mean expected value for decisions made by clinicians was better than for non-clinicians(−

Predicting Adverse Events After Surgery

1,022 vs -

Methods for Incorporating Stakeholder Engagement into Clinical Trial Design

1,061; <0.001). Probability information improved decision making for all participants, but non-clinicians improved more (mean improvement of

Improved Risk Prediction Following Surgery Using Machine Learning Algorithms

64 versus

Alvimopan Use, Outcomes, and Costs: In reply to Fujita

33; p = 0.027). Mean defensive decisions decreased across training level (medical students 2.1 ± 0.9, residents 1.6 ± 0.8, faculty1.6 ± 1.1; p-trend < 0.001) and prospect-theory-concordant decisions increased (25.4%, 33.9%, and 40.7%;p-trend = 0.016). There was no relationship identified between road salting choices with defensive medicine and prospect-theory-concordant decisions.ConclusionsAll participants made more economically-rational decisions when provided explicit probability information in a non-clinical domain. However, choices in the non-clinical domain were not related to prospect-theory concordant decision making and risk aversion tendencies in the clinical domain. Recognizing this discordance may be important when applying prospect theory to interventions aimed at improving clinical care.

Evidence for an Antibiotics-First Strategy for Uncomplicated Appendicitis in Adults: A Systematic Review and Gap Analysis

Abstract Predicting risk of adverse events (AEs) following surgical procedure is of significant interest, as that may guide in better resource utilization and an improved quality of care. Currently available comorbidity indices are largely inaccurate to predict adverse events other than death, as well as off-the-shelf machine learning models do not typically account for the temporal sequence of events to enable predictive analytics. We propose a study to improve the current techniques for assessing and predicting the risk of adverse events (AEs) associated with multiple chronic conditions by designing machine learning models that account for and incorporate the temporal sequence and timing of conditions. We formalize the task as a binary classification problem. Our technical contributions include devising novel sequence based feature discovery techniques to augment existing supervised classification algorithms, as well as formalizing the classification task as a Markov Chain Model (MCM) that captures the temporal sequence of prior chronic conditions/events. Finally, we design a hybrid or multi-classifier that combines prediction from the aforementioned classification models to finally predict AE. Our experimental results, conducted using the Truven Health MarketScan Research Databases with more than 27 million of claim records on two different surgery types, discover interesting insights that can guide patient-centered decision-making and can direct healthcare teams to adjust techniques and interventions. We also extensively compare the performance of our solutions to appropriate baselines.

Alvimopan Use, Outcomes, and Costs: A Report from the Surgical Care and Outcomes Assessment Program Comparative Effectiveness Research Translation Network Collaborative

Context: Lack of engagement with healthcare stakeholders results in missed opportunities to understand translation of evidence into practice. Case: Stakeholder engagement is a key component of the Comparing Outcomes of Drugs and Appendectomy (CODA) Study, a pragmatic clinical trial funded by PCORI to evaluate the effectiveness of antibiotics versus urgent appendectomy for acute uncomplicated appendicitis. We provide a framework for developing a stakeholder coordinating center (SCC) and describe two examples of how stakeholder engagement can inform study development. Findings: Coordinating engagement activities through the SCC established a commitment to the important partnership with stakeholders. It also facilitated communication and provided a central mechanism for obtaining input on key decisions such as development of patient-centered consent documents and appropriate stopping rules for a specific sub-population of patients with appendicitis. Major themes: Translatable lessons include thoughtful planning for engagement, identifying stakeholders with a direct interest in the study conduct and findings, and integration of input received into the decisions that drive the conduct of the study. Conclusions: Standards for conducting patient-centered research should address the ability to successfully engage patients by demonstrating the capacity to recruit study participants, engage them over the duration of the study, and disseminate findings that are congruent with stakeholder needs. The process of sharing important clinical research findings has improved patient care, and we believe that dissemination of novel engagement strategies can lead to increased success in study design and execution.

Achalasia Treatment, Outcomes, Utilization, and Costs: A Population-Based Study from the United States

Background: Machine learning is used to analyze big data, often for the purposes of prediction. Analyzing a patient’s healthcare utilization pattern may provide more precise estimates of risk for adverse events (AE) or death. We sought to characterize healthcare utilization prior to surgery using machine learning for the purposes of risk prediction. Methods: Patients from MarketScan Commercial Claims and Encounters Database undergoing elective surgery from 2007–2012 with ≥1 comorbidity were included. All available healthcare claims occurring within six months prior to surgery were assessed. More than 300 predictors were defined by considering all combinations of conditions, encounter types, and timing along with sociodemographic factors. We used a supervised Naive Bayes algorithm to predict risk of AE or death within 90 days of surgery. We compared the model’s performance to the Charlson’s comorbidity index, a commonly used risk prediction tool. Results: Among 410,521 patients (mean age 52, 52 ± 9.4, 56% female), 4.7% had an AE and 0.01% died. The Charlson’s comorbidity index predicted 57% of AE’s and 59% of deaths. The Naive Bayes algorithm predicted 79% of AE’s and 78% of deaths. Claims for cancer, kidney disease, and peripheral vascular disease were the primary drivers of AE or death following surgery. Conclusions: The use of machine learning algorithms improves upon one commonly used risk estimator. Precisely quantifying the risk of an AE following surgery may better inform patient-centered decision-making and direct targeted quality improvement interventions while supporting activities of accountable care organizations that rely on accurate estimates of population risk.

Factors influencing delayed hospital presentation in patients with appendicitis: the APPE survey

of alvimopan on major postoperative complications and mortality have been poorly studied. Head-to-head studies with less expensive pharmacologic and nonpharmacologic interventions in an attempt to increase gastrointestinal motility are scarce. Opioid use for postsurgical acute pain with concurrent alvimopan may decrease the length of hospital stay, but it may increase the risk of opioid addiction and opioid-related adverse events after hospital discharge. Opioid saving with epidural analgesics and NSAID use is a safe alternative for bowel recovery, even if the hospital stay is prolonged by 1 day.