Objective And Background: The Risk Analysis Index (RAI) predicts 30-, 180-, and 365-day mortality based on variables constitutive of frailty. Initially validated, in a single-center Veteran hospital, we sought to improve model performance by recalibrating the RAI in a large, veteran surgical registry, and to externally validate it in both a national surgical registry and a cohort of surgical patients for whom RAI was measured prospectively before surgery.
Methods: The RAI was recalibrated among development and confirmation samples within the Veterans Affairs Surgical Quality Improvement Program (VASQIP; 2010-2014; N = 480,731) including major, elective noncardiac surgery patients to create the revised RAI (RAI-rev), comparing discrimination and calibration. The model was tested externally in the American College of Surgeons National Surgical Quality Improvement Program dataset (NSQIP; 2005-2014; N = 1,391,785), and in a prospectively collected cohort from the Nebraska Western Iowa Health Care System VA (NWIHCS; N = 6,856).
Results: Recalibrating the RAI significantly improved discrimination for 30-day [c = 0.84-0.86], 180-day [c = 0.81-0.84], and 365-day mortality [c = 0.78-0.82] (P < 0.001 for all) in VASQIP. The RAI-rev also had markedly better calibration (median absolute difference between observed and predicted 180-day mortality: decreased from 8.45% to 1.23%). RAI-rev was highly predictive of 30-day mortality (c = 0.87) in external validation with excellent calibration (median absolute difference between observed and predicted 30-day mortality: 0.6%). The discrimination was highly robust in men (c = 0.85) and women (c = 0.89). Discrimination also improved in the prospectively measured cohort from NWIHCS for 180-day mortality [c = 0.77 to 0.80] (P < 0.001).
Conclusions: The RAI-rev has improved discrimination and calibration as a frailty-screening tool in surgical patients. It has robust external validity in men and women across a wide range of surgical settings and available for immediate implementation for risk assessment and counseling in preoperative patients.
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http://dx.doi.org/10.1097/SLA.0000000000003276 | DOI Listing |
Gastro Hep Adv
October 2024
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California.
Background And Aims: Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFNeurol Clin Pract
April 2025
Department of Neurology, Emory University School of Medicine, Atlanta, GA.
Background And Objectives: Telemedicine has become a mainstay of ALS clinical care, but there is currently no standardized approach for assessing and tracking changes to the neurologic examination in this format. The goal of this study was to create a standardized telemedicine-based motor examination scale to objectively and reliably track ALS progression and use Rasch methodology to validate the scale and improve its psychometric properties.
Methods: A draft telemedicine examination scale with 25 items assessing movement in the bulbar muscles, neck, trunk, and extremities was created by an ALS expert panel, incorporating input from patient advisors.
Front Nutr
January 2025
Department of Epidemiology and Health Statistics, Tianjin Medical University, Tianjin, China.
Background: Although more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well these models will work in clinical settings. Future research faces challenges in validating model accuracy across populations, enhancing interpretability for clinical use, and overcoming dataset limitations.
Objective: To thoroughly examine studies that have been published on feeding intolerance risk prediction models for enteral nutrition patients.
Front Surg
January 2025
Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: To accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and interpretable biomarker combining clinical and radiomics features using preoperative CT.
Methods: The study included a cohort of 1,325 lung adenocarcinoma patients from three centers, which was divided into four groups: a training cohort ( = 930), a testing cohort ( = 238), an external validation 1 cohort ( = 93), and 2 cohort ( = 64). We collected clinical characteristics and semantic features, and extracted radiomics features.
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