ACM BCB 2023: Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data

BACKGROUND

Managing acute postoperative pain and minimizing chronic opioid use is crucial for patient recovery and long-term well-being.

OBJECTIVE

This study explored using preoperative electronic health records (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.

METHODS

The study cohort consisted of ~347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use.

RESULTS

The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve (AUC) score for severe pain vs. other pain was highest (0.88) when predicting acute post-operative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes.

CONCLUSIONS

SHAP detection of individual risk factors for severe pain can help healthcare providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.

Feasibility of the Genetic Information Assistant Chatbot to Provide Genetic Education and Study Genetic Test Adoption Among Pancreatic Cancer Patients at Johns Hopkins Hospital

Genetic testing is a valuable tool to guide care of pancreatic cancer patients, yet personal and family uncertainty about the benefits of genetic testing (i.e., decisional conflict) may lead to low adoption. Enabling patients to learn more about genetic testing before their scheduled appointments may help to address this decisional conflict problem. We completed a feasibility assessment of a chatbot to provide genetic education (GEd) with 60 pancreatic cancer patients and using the chatbot to deliver surveys to assess: (a) opinions about the GEd, and (b) decisional conflict about genetic testing. Findings demonstrate intervention and study feasibility with about 80% of participants engaging with the GEd chatbot, 71% of which completed at least one survey. Overall, participants appear to have favorable opinions of the chatbot-delivered education and thought it was helpful to decide about genetic testing. Furthermore, patients who chose to get genetic testing spent more time interacting with the chatbot. Findings will be used to improve chatbot design and to facilitate a well-powered future trial.

Integrin αIIbβ3 intermediates: From molecular dynamics to adhesion assembly

The platelet integrin αIIbβ3 undergoes long-range conformational transitions associated with its functional conversion from inactive (low-affinity) to active (high-affinity) during hemostasis. Although new conformations that are intermediate between the well-characterized bent and extended states have been identified, their molecular dynamic properties and functions in the assembly of adhesions remain largely unexplored. In this study, we evaluated the properties of intermediate conformations of integrin αIIbβ3 and characterized their effects on the assembly of adhesions by combining all-atom simulations, principal component analysis, and mesoscale modeling. Our results show that in the low-affinity, bent conformation, the integrin ectodomain tends to pivot around the legs; in intermediate conformations, the headpiece becomes partially extended, away from the lower legs. In the fully open, active state, αIIbβ3 is flexible, and the motions between headpiece and lower legs are accompanied by fluctuations of the transmembrane helices. At the mesoscale, bent integrins form only unstable adhesions, but intermediate or open conformations stabilize the adhesions. These studies reveal a mechanism by which small variations in ligand binding affinity and enhancement of the ligand-bound lifetime in the presence of actin retrograde flow stabilize αIIbβ3 integrin adhesions.

Risk for Poor Post-Operative Quality of Life Among Wearable Use Subgroups in an All of Us Research Cohort

The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program. Through multivariable analysis, high frailty index (OR 1.69, 95% 1.05-7.22, p<0.006), and older age (OR 1.76, 95% 1.55-4.08, p<0.024) were found to be the driving risk factors of poor recovery post-surgery. Our logistic regression model included 15 variables, 5 of which included wearable device data. In wearable use subgroups, the model had better accuracy for high wearable users (81%). Findings demonstrate the potential for models that use wearable measures to assess frailty to inform clinicians of patients at risk for poor surgical outcomes. Our model performed with high accuracy across multiple surgery types and were robust to variable consistency in wearable use.