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Developing and validating a machine learning model to predict multidrug-resistant -related septic shock.
Front Immunol
January 2025
Department of Medical Laboratory, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Background: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.
Methods: A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024.
Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery.
BMC Anesthesiol
January 2025
Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Background: Clinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.
Methods: Patients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test).
Outcome Prediction in Infectious Disease.
Acta Med Indones
October 2024
Division of Tropical and Infectious Diseases, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia.
Sepsis is a critical, life-threatening condition that demands precise prediction to mitigate adverse outcomes. The heterogeneity of sepsis leads to variable prognoses, making early and accurate identification increasingly difficult. Despite ongoing advancements, no single gold standard has emerged for sepsis prediction.
View Article and Find Full Text PDFRecent Advances in the Mechanisms of Postoperative Neurocognitive Dysfunction: A Narrative Review.
Biomedicines
January 2025
Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Postoperative neurocognitive dysfunction (PND) is a prevalent and debilitating complication in elderly surgical patients, characterized by persistent cognitive decline that negatively affects recovery and quality of life. As the aging population grows, the rising number of elderly surgical patients has made PND an urgent clinical challenge. Despite increasing research efforts, the pathophysiological mechanisms underlying PND remain inadequately characterized, underscoring the need for a more integrated framework to guide targeted interventions.
View Article and Find Full Text PDFInforming ICU Digital Twins: Dynamic Assessment of Cardiorespiratory Failure Trajectories in Patients with Sepsis.
Shock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
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