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Background: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) calculator, a widely used interpretable artificial intelligence risk calculator, has been validated in population-based studies and shown to predict outcomes in patients who underwent emergency general surgery better than surgeons. We sought to prospectively validate POTTER.
Study Design: Patients undergoing an emergency exploratory laparotomy for nontrauma indications at 2 academic medical centers between June 2020 and March 2022 were included. POTTER preoperative risk calculations and postoperative outcomes were systematically recorded. POTTER's performance in predicting 30-day postoperative mortality, septic shock, respiratory failure, bleeding, and pneumonia was assessed using the c-statistic methodology.
Results: A total of 361 patients were included. The median age was 63 years (interquartile range 51 to 72 years), 45.4% were women, and the overall mortality and morbidity were 24.1% and 51.4%, respectively. POTTER predicted mortality accurately with a c-statistic of 0.90. POTTER also accurately predicted the occurrence of individual postoperative complications, with c-statistics ranging between 0.80 and 0.89.
Conclusions: This is the first prospective validation of the artificial intelligence-enabled POTTER calculator. The superior accuracy, user-friendliness, and interpretability of POTTER make it a useful bedside tool for preoperative patient and family counseling.
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http://dx.doi.org/10.1097/XCS.0000000000001234 | DOI Listing |
Geroscience
September 2025
The Irish Longitudinal Study On Ageing (TILDA), Department of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland.
The complexity of epigenetic changes that accompany aging has been distilled into a number of molecular timepieces-termed epigenetic clocks-that characterize the pace of biological aging to differing degrees. Here, we develop and validate a DNA methylation-based Physiological health Age (PhysAge) score, comprised of eight DNA methylation surrogates to represent multi-system physiology and developed from commonly measured clinical biomarkers: CRP, peak flow, pulse pressure, HDL-cholesterol, Hba1c, waist-to-height ratio (WHR), cystatin C, and dehydroepianrosterone sulphate (DHEAS). We use data from the population-representative US Health and Retirement Study (HRS), split into a training (n = 1589) and test sample (n = 1588) and corroborate findings in two independent cohorts: The Irish Longitudinal Study of Aging (TILDA; n = 488) and the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA; n = 1830).
View Article and Find Full Text PDFBMC Med Res Methodol
August 2025
Early and Late Stage Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, 121 Seaport Blvr, Boston, MA, 02210, USA.
Background: Over the last decade, the pharmaceutical industry has witnessed longer, more complex, and expensive clinical trials. This complexity contributes to delays in clinical trial implementation, execution, monitoring, recruitment, data cleaning, and interpretation. Our aim was to develop a protocol complexity tool (PCT) to simplify clinical trial execution without compromising science or quality.
View Article and Find Full Text PDFBiologicals
August 2025
Department of Virology, Biomedical Primate Research Centre, Rijswijk, the Netherlands. Electronic address:
This study focuses on harmonising the competition ELISA (cELISA) assay for Plasmodium falciparum (P. falciparum), using the 1st WHO reference reagent for anti-malaria (P. falciparum) human reference serum (10/198).
View Article and Find Full Text PDFAnesthesiol Clin
September 2025
Division of Trauma, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 810, Boston, MA 02114, USA. Electronic address:
Artificial intelligence (Al) is transforming surgical care by enhancing risk prediction, preoperative planning, and surgical education. Unlike traditional statistical tools, Al-especially machine learning-can process complex, nonlinear clinical data to deliver highly accurate and personalized risk assessments. AI tools such as the POTTER calculator have demonstrated superior accuracy compared to traditional methods in predicting postoperative complications and mortality.
View Article and Find Full Text PDFAppl Spectrosc
July 2025
Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
Hydroxyl-terminated polybutadiene (HTPB) is used in a variety of formulations, particularly for military and aerospace applications as a binder for energetic materials. This work investigates details of its curing process when formulated with isophorone diisocyanate (IPDI). Raman spectroscopy was used as a fast, sensitive, non-destructive technique to monitor the curing process of HTPB-IPDI.
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