Esra Adiyeke

Background: Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system that generates treatment recommendations based on patient states can be a substantial asset in perioperative decision-making for cases of intraoperative hypotension in which suboptimal management is associated with acute kidney injury (AKI), a common and morbid postoperative complication. Methods: In this retrospective cohort study, we analyzed 50,021 surgeries from 42,547 adult patients who underwent major surgery at a quaternary care hospital between 2014 and 2020. We developed a deep reinforcement learning model to recommend the optimum doses of intravenous fluids and vasopressors during surgery to avoid intraoperative hypotension and AKI defined by Kidney Disease: Improving Global Outcomes serum creatinine criteria within three days following surgery. Results: The developed model replicated 69% of physician’s decisions for the dosage of vasopressors and proposed higher or lower dosage of vasopressors than received in 10% and 21% of the treatments, respectively. In terms of intravenous fluids, the model’s recommendations were within 0.05 ml/kg/15 min of the actual dose in 41% of the cases, with higher or lower doses recommended for 27% and 32% of the treatments, respectively. The reinforcement learning policy resulted in a higher estimated policy value compared to the physicians’ actual treatments, as well as random policies and zero-drug policies. AKI incidence was lowest in patients who received medication dosages that aligned with our agent model’s decisions. Conclusions: Our findings suggest that implementation of the model’s policy has the potential to lower postoperative AKI and improve other outcomes driven by intraoperative hypotension.

Key Points: The developed dynamic model for predicting progression to stage 2 or higher AKI using multicenter data had robust performance. Clinical decision support implementation of the developed model could help prevent AKI progression. Kinetic eGFR, nephrotoxic drug burden, and BUN were the top features and remained the same across the models and sites. Background: AKI is an abrupt decline in kidney function that occurs in about 20% of hospitalized admissions and may lead to irreversible kidney damage. Methods: We developed and externally validated deep learning models to dynamically predict progression to stage 2 or higher AKI defined by Kidney Disease Improving Global Outcomes serum creatinine criteria within the next 48 hours. We used an extensive set of predictors including demographics, admission source, comorbidities, medications, laboratory, and vitals measurements. Results: Our retrospective study includes adult noncritical care patients at the University of Pittsburgh Medical Center (UPMC; 2018–2022; n =39,755) and the University of Florida Health (UFH; 2012–2019; n =122,324). In the UFH and UPMC development cohort, the mean age was 55 and 71 and 55% ( n =47,350) and 54% ( n =15,128) were female, respectively. Stage 2 or higher AKI occurred in 3% ( n =3257) and 8% ( n =2296) of UFH and UPMC patients, respectively. Area under the receiver operating characteristic curve values with 95% confidence interval (CI) ranged between 0.77 (95% CI, 0.75 to 0.78; UPMC Model—model trained on UPMC patients) and 0.81 (95% CI, 0.79 to 0.82; UFH Model—model trained on University of Florida patients) for the UFH test cohort and between 0.79 (95% CI, 0.78 to 0.8; UFH Model) and 0.83 (95% CI, 0.82 to 0.84; UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an area under the receiver operating characteristic curve of 0.81 (95% CI, 0.80 to 0.83) for UFH and 0.82 (95% CI, 0.81 to 0.84) for UPMC test cohorts. Kinetic eGFR, nephrotoxic drug burden, and BUN remained the features with the highest influence across the models and institutions. Conclusions: The model developed using multicenter data had robust performance, suggesting that implementation could help prevent AKI progression.

Background: Establishing a relationship between COVID-19 severity and olfactory dysfunction may be beneficial in patient follow-up. Thus, in this study, we aimed to evaluate the association between self-reported olfactory dysfunction and the clinical stages of COVID-19. Methods: The patients included in this study were divided into three groups according to the severity of the novel coronavirus disease as mild, severe, and critical (life-threatening) patients. Patients were then contacted by phone and asked questions with the help of structured questionnaires that evaluated their general status, sense of smell, taste and compared the data within the three groups. Results: Among the 144 subjects evaluated in the present study (mild, n = 60; moderate, n = 61, severe, n = 23), 70 of the participants were males, and 74 were females. The findings showed that olfactory loss was the most prominent feature of the COVID-19's mild clinical course and the majority of the patients with loss of smell were female and young patients. Conclusion: The findings obtained from clinically mild cases suggest that more olfactory dysfunction, indicating that the effects of viral load alone, is not decisive for olfactory dysfunction.

In this study, we analyze the price dynamics of carbon certificates that are traded under the European Union's Emissions Trading System (EU-ETS). With the aim of investigating the joint relations among carbon, electricity, and fuel prices, we model historical prices using several methods and incorporating structural changes, such as econometric time series, regime switching, and multivariate vector autoregression models. We compare the results of the structural model with the results of traditional Markov switching and autoregressive models with breaks and present performance analysis based on the mean average percentage error, root mean squared error, and coefficient of determination. According to these performance tests, models with regimes outperform the approaches where breaks are defined using ex ante dummy variables. Moreover, we conclude that among regime switching models, univariate models are better than multivariate counterparts for modeling carbon price series for the analysis of both in-sample and out-of-samples.