Machine learning in cardiac surgery: a narrative review.

Background and Objective: Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support. Methods: We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery". Key Content and Findings: ML methods have been widely used to generate pre-operative risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited. Conclusions: Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.

The utility of machine learning for predicting donor discard in abdominal transplantation.

BACKGROUND: Increasing access and better allocation of organs in the field of transplantation is a critical problem in clinical care. Limitations exist in accurately predicting allograft discard. Potential exists for machine learning to provide a balanced assessment of the potential for an organ to be used in a transplantation procedure. METHODS: We accessed and utilized all available deceased donor United Network for Organ Sharing data from 1987 to 2020. With these data, we evaluated the performance of multiple machine learning methods for predicting organ use. The machine learning methods trialed included XGBoost, random forest, Naïve Bayes (NB), logistic regression, and fully connected feedforward neural network classifier methods. The top two methods, XGBoost and random forest, were fully developed using 10-fold cross-validation and Bayesian optimization of hyperparameters. RESULTS: The top performing model at predicting liver organ use was an XGBoost model which achieved an AUC-ROC of .925, an AUC-PR of .868, and an F1 statistic of .756. The top performing model for predicting kidney organ use classification was an XGBoost model which achieved an AUC-ROC of .952, and AUC-PR of .883, and an F1 statistic of .786. CONCLUSIONS: The XGBoost method demonstrated a significant improvement in predicting donor allograft discard for both kidney and livers in solid organ transplantation procedures. Machine learning methods are well suited to be incorporated into the clinical workflow; they can provide robust quantitative predictions and meaningful data insights for clinician consideration and transplantation decision-making.

A call for standardization: Practice patterns and management of critical illness-related corticosteroid insufficiency in surgical intensive care units.

INTRODUCTION: Critical illness-rlated corticosteroid insufficiency (CIRCI) is a known sequela of severe injury and illness, yet its diagnosis and management are challenging. We hypothesized that CIRCI has significant variability in its diagnosis and management within surgical intensive care units (SICUs). Our study aimed to assess the state of practice of CIRCI in the American College of Surgery Committee on Trauma (ACS COT) certified level 1 trauma centers. METHODS: An 11-item questionnaire was developed based on a CIRCI literature search with expert input from medical endocrinology, acute care surgeons, and surgical intensivists to assess practice patterns of CIRCI.  Prior to distribution, it was validated across 2 separate institutions by board-certified critical care surgeons.  The questionnaire was distributed to trauma intensivists within level 1 trauma centers in Southeast United States and was open from April 2019 to January 2020. RESULTS: A total of 56 responses were collected with a response rate of 70%. 72% of respondents indicated they evaluate or manage CIRCI on a weekly basis.  In regards to the diagnosis of CIRCI, only 5% of respondents use a formal protocol and 32% do not use laboratory testing. While a majority of respondents (94%) use corticosteroids in septic shock, 67% of those surveyed have not implemented mineralocorticoids as part of the management.  83% of respondents indicated a knowledge gap exists in the therapeutic value of corticosteroids for hemorrhagic shock. CONCLUSIONS: This study demonstrates extreme variability in the diagnosis and management of CIRCI. In particular most providers acknowledge a knowledge gap in the diagnosis of CIRCI and the role of corticosteroids in hemorrhagic shock. Few providers are using adjunctive mineralocorticoids in septic shock, although recent level 1 evidence have shown a survival benefit. These responses reflect an opportunity for national improvement in the management of CIRCI.

Short-Term Outcomes of Concussions in Major League Baseball: A Historical Cohort Study of Return to Play, Performance, Longevity, and Financial Impact.

BACKGROUND: The short-term outcomes of concussions within Major League Baseball (MLB) warrant further consideration beyond a medical standpoint given that performance, career, and financial data remain unknown. The perception of this injury directly affects decision making from the perspective of both player and franchise. PURPOSE: To evaluate the effect of concussion on MLB players by (1) establishing return-to-play (RTP) time after concussion; (2) comparing the career length and performance of players with concussion versus those who took nonmedical leave; and (3) analyzing player financial impact after concussion. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Contracts, transactions, injury reports, and performance statistics from 2005 to 2017 were analyzed by comparing matched players who sustained a concussion versus those who took nonmedical leave. Of the 4186 eligible MLB players, 145 sustained concussions resulting in the activation of concussion protocol and 538 took nonmedical leave. RTP time was recorded. Career length was analyzed in reference to an experience-based stratification of full seasons remaining after the concussion. Changes in player performance and salary before and after concussion were compared with the same parameters for players who took nonmedical leave. RESULTS: The mean RTP time was 26 days (95% CI, 20-32 days) for athletes with concussion and 8 days (95% CI, 6-10 days) for those who took nonmedical leave. Athletes with concussion had a mean of 2.8 full seasons remaining, whereas athletes who took nonmedical leave had 3.1 seasons remaining (P = .493). The probability of playing in the MLB after concussion compared with the nonmedical leave pool was not significantly lower (P = .534, log-rank test; hazard ratio, 1.108). Postconcussion performance decreased significantly in position players, including a lower batting average and decreased on-base percentage in the players with concussion compared with those returning from nonmedical leave. Players who sustained a concussion lost a mean of US$654,990 annually compared with players who took nonmedical leave. CONCLUSION: This study of the short-term outcomes after concussion in limited-contact MLB athletes demonstrates that concussions may not decrease career spans but may result in decreased performance in addition to financial loss when compared with matched controls who took nonmedical leave. In sports such as baseball that are not subject to repetitive head trauma, career spans may not decrease after a single concussive event. However, sentinel concussions have deleterious short-term effects on performance and compensation among MLB players.

Rapid shelf-wide cooling response of a stratified coastal ocean to hurricanes.

Large uncertainty in the predicted intensity of tropical cyclones (TCs) persists compared to the steadily improving skill in the predicted TC tracks. This intensity uncertainty has its most significant implications in the coastal zone, where TC impacts to populated shorelines are greatest. Recent studies have demonstrated that rapid ahead-of-eye-center cooling of a stratified coastal ocean can have a significant impact on hurricane intensity forecasts. Using observation-validated, high-resolution ocean modeling, the stratified coastal ocean cooling processes observed in two U.S. Mid-Atlantic hurricanes were investigated: Hurricane Irene (2011)-with an inshore Mid-Atlantic Bight (MAB) track during the late summer stratified coastal ocean season-and Tropical Storm Barry (2007)-with an offshore track during early summer. For both storms, the critical ahead-of-eye-center depth-averaged force balance across the entire MAB shelf included an onshore wind stress balanced by an offshore pressure gradient. This resulted in onshore surface currents opposing offshore bottom currents that enhanced surface to bottom current shear and turbulent mixing across the thermocline, resulting in the rapid cooling of the surface layer ahead-of-eye-center. Because the same baroclinic and mixing processes occurred for two storms on opposite ends of the track and seasonal stratification envelope, the response appears robust. It will be critical to forecast these processes and their implications for a wide range of future storms using realistic 3-D coupled atmosphere-ocean models to lower the uncertainty in predictions of TC intensities and impacts and enable coastal populations to better respond to increasing rapid intensification threats in an era of rising sea levels.

Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula.

Understanding the mechanisms by which climate variability affects multiple trophic levels in food webs is essential for determining ecosystem responses to climate change. Here we use over two decades of data collected by the Palmer Long Term Ecological Research program (PAL-LTER) to determine how large-scale climate and local physical forcing affect phytoplankton, zooplankton and an apex predator along the West Antarctic Peninsula (WAP). We show that positive anomalies in chlorophyll-a (chl-a) at Palmer Station, occurring every 4-6 years, are constrained by physical processes in the preceding winter/spring and a negative phase of the Southern Annular Mode (SAM). Favorable conditions for phytoplankton included increased winter ice extent and duration, reduced spring/summer winds, and increased water column stability via enhanced salinity-driven density gradients. Years of positive chl-a anomalies are associated with the initiation of a robust krill cohort the following summer, which is evident in Adélie penguin diets, thus demonstrating tight trophic coupling. Projected climate change in this region may have a significant, negative impact on phytoplankton biomass, krill recruitment and upper trophic level predators in this coastal Antarctic ecosystem.