Interpretable Multi-Task Deep Neural Networks for Dynamic Predictions of Postoperative Complications

Accurate prediction of postoperative complications can inform shared decisions between patients and surgeons regarding the appropriateness of surgery, preoperative risk-reduction strategies, and postoperative resource use. Traditional predictive analytic tools are hindered by suboptimal performance and usability. We hypothesized that novel deep learning techniques would outperform logistic regression models in predicting postoperative complications. In a single-center longitudinal cohort of 43,943 adult patients undergoing 52,529 major inpatient surgeries, deep learning yielded greater discrimination than logistic regression for all nine complications. Predictive performance was strongest when leveraging the full spectrum of preoperative and intraoperative physiologic time-series electronic health record data. A single multi-task deep learning model yielded greater performance than separate models trained on individual complications. Integrated gradients interpretability mechanisms demonstrated the substantial importance of missing data. Interpretable, multi-task deep neural networks made accurate, patient-level predictions that harbor the potential to augment surgical decision-making

Development of Computable Phenotype to Identify and Characterize Transitions in Acuity Status in Intensive Care Unit

Background: In the United States, 5.7 million patients are admitted annually to intensive care units (ICU), with costs exceeding $82 billion. Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by the time constraints imposed on healthcare providers. Methods: Using the University of Florida Health (UFH) Integrated Data Repository as Honest Broker, we created a database with electronic health records data from a retrospective study cohort of 38,749 adult patients admitted to ICU at UF Health between 06/01/2014 and 08/22/2019. This repository includes demographic information, comorbidities, vital signs, laboratory values, medications with date and timestamps, and diagnoses and procedure codes for all index admission encounters as well as encounters within 12 months prior to index admission and 12 months follow-up. We developed algorithms to identify acuity status of the patient every four hours during each ICU stay. Results: We had 383,193 encounters (121,800 unique patients) admitted to the hospital, and 51,073 encounters (38,749 unique patients) with at least one ICU stay that lasted more than four hours. These patients requiring ICU admission had longer median hospital stay (7 days vs. 1 day) and higher in-hospital mortality (9.6% vs. 0.4%) compared with those not admitted to the ICU. Among patients who were admitted to the ICU and expired during hospital admission, more deaths occurred in the ICU than on general hospital wards (7.4% vs. 0.8%, respectively). Conclusions: We developed phenotyping algorithms that determined patient acuity status every four hours while admitted to the ICU. This approach may be useful in developing prognostic and clinical decision-support tools to aid patients, caregivers, and providers in shared decision-making processes regarding resource use and escalation of care.Comment: 21 Pages, that include 6 figures, 3 tables and 1 supplemental Tabl

Overlapping but disparate inflammatory and immunosuppressive responses to SARS-CoV-2 and bacterial sepsis: An immunological time course analysis

Both severe SARS-CoV-2 infections and bacterial sepsis exhibit an immunological dyscrasia and propensity for secondary infections. The nature of the immunological dyscrasias for these differing etiologies and their time course remain unclear. In this study, thirty hospitalized patients with SARS-CoV-2 infection were compared with ten critically ill patients with bacterial sepsis over 21 days, as well as ten healthy control subjects. Blood was sampled between days 1 and 21 after admission for targeted plasma biomarker analysis, cellular phenotyping, and leukocyte functional analysi

Surgical resident experience with common bile duct exploration and assessment of performance and autonomy with formative feedback

Background Common bile duct exploration (CBDE) is safe and effective for managing choledocholithiasis, but most US general surgeons have limited experience with CBDE and are uncomfortable performing this procedure in practice. Surgical trainee exposure to CBDE is limited, and their learning curve for achieving autonomous, practice-ready performance has not been previously described. This study tests the hypothesis that receipt of one or more prior CBDE operative performance assessments, combined with formative feedback, is associated with greater resident operative performance and autonomy. Methods Resident and attending assessments of resident operative performance and autonomy were obtained for 189 laparoscopic or open CBDEs performed at 28 institutions. Performance and autonomy were graded along validated ordinal scales. Cases in which the resident had one or more prior CBDE case evaluations (n = 48) were compared with cases in which the resident had no prior evaluations (n = 141). Results Compared with cases in which the resident had no prior CBDE case evaluations, cases with a prior evaluation had greater proportions of practice-ready or exceptional performance ratings according to both residents (27% vs. 11%, p = .009) and attendings (58% vs. 19%, p < .001) and had greater proportions of passive help or supervision only autonomy ratings according to both residents (17% vs. 4%, p = .009) and attendings (69% vs. 32%, p < .01). Conclusions Residents with at least one prior CBDE evaluation and formative feedback demonstrated better operative performance and received greater autonomy than residents without prior evaluations, underscoring the propensity of feedback to help residents achieve autonomous, practice-ready performance for rare operations

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

Background Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons’ knowledge and perception of using AI-based tools in clinical decision-making processes. Methods An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society’s website and Twitter profile. Results 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI