Tezcan Ozrazgat-Baslanti

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: Electronic health records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality (DQ) and performance assessment. Objective: This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field. Methods: PubMed was systematically searched for original research articles assessing EHR DQ and performance from inception until May 7, 2023. Results: Our search yielded 26 original research articles. Most articles had 1 or more significant limitations, including incomplete or inconsistent reporting (n=6, 30%), poor replicability (n=5, 25%), and limited generalizability of results (n=5, 25%). Completeness (n=21, 81%), conformance (n=18, 69%), and plausibility (n=16, 62%) were the most cited indicators of DQ, while correctness or accuracy (n=14, 54%) was most cited for data performance, with context-specific supplementation by recency (n=7, 27%), fairness (n=6, 23%), stability (n=4, 15%), and shareability (n=2, 8%) assessments. Artificial intelligence–based techniques, including natural language data extraction, data imputation, and fairness algorithms, were demonstrated to play a rising role in improving both dataset quality and performance. Conclusions: This review highlights the need for incentivizing DQ and performance assessments and their standardization. The results suggest the usefulness of artificial intelligence–based techniques for enhancing DQ and performance to unlock the full potential of EHRs to improve medical research and practice.

Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and predict disease trajectories. We conducted a systematic review of the literature from 2017 to 2022, focusing on studies that utilized machine learning techniques to identify AKI subphenotypes in adult patients. Data were extracted regarding patient demographics, clustering methodologies, discriminators, and validation efforts from selected studies. The review highlights significant variability in subphenotype identification across different populations. All studies utilized clinical data such as comorbidities and laboratory variables to group patients. Two studies incorporated biomarkers of endothelial activation and inflammation into the clinical data to identify subphenotypes. The primary discriminators were comorbidities and laboratory trajectories. The association of AKI subphenotypes with mortality, renal recovery and treatment response was heterogeneous across studies. The use of diverse clustering techniques contributed to variability, complicating the application of findings across different patient populations. Identifying AKI subphenotypes enables clinicians to better understand and manage individual patient trajectories. Future research should focus on validating these phenotypes in larger, more diverse cohorts to enhance their clinical applicability and support personalized medicine in AKI management.

Background: Machine learning–enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective: This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods: Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system’s functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results: A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions: Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.

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Acute kidney injury (AKI) is a common postoperative outcome in urology patients undergoing surgery for nephrolithiasis. The objective of this study was to determine the prevalence of postoperative AKI and its degrees of severity, identify risk factors, and understand the resultant outcomes of AKI in patients with nephrolithiasis undergoing percutaneous nephrolithotomy (PCNL). A cohort of patients admitted between 2012 and 2019 to a single tertiary-care institution who had undergone PCNL was retrospectively analyzed. Among 417 (n = 326 patients) encounters, 24.9% (n = 104) had AKI. Approximately one-quarter of AKI patients (n = 18) progressed to Stage 2 or higher AKI. Hypertension, peripheral vascular disease, chronic kidney disease, and chronic anemia were significant risk factors of post-PCNL AKI. Corticosteroids and antifungals were associated with increased odds of AKI. Cardiovascular, neurologic complications, sepsis, and prolonged intensive care unit (ICU) stay percentages were higher in AKI patients. Hospital and ICU length of stay was greater in the AKI group. Provided the limited literature regarding postoperative AKI following PCNL, and the detriment that AKI can have on clinical outcomes, it is important to continue studying this topic to better understand how to optimize patient care to address patient- and procedure-specific risk factors.