JAMA network open
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Multicenter Study
Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning.
Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis. ⋯ The findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.
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Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking. ⋯ This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.
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Extracorporeal cardiopulmonary resuscitation (ECPR) is expected to improve the neurological outcomes of patients with refractory cardiac arrest; however, it is invasive, expensive, and requires substantial human resources. The ability to predict neurological outcomes would assist in patient selection for ECPR. ⋯ In this study, the scoring system had good discrimination and calibration performance to predict favorable neurological outcomes of patients with out-of-hospital cardiac arrest and shockable rhythm who were treated with ECPR.
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Multicenter Study
Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.
Acute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated. ⋯ In this study, the machine learning algorithm demonstrated excellent discrimination in both internal and external validation, supporting its generalizability and potential as a clinical decision support tool to improve AKI detection and outcomes.
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Randomized Controlled Trial Multicenter Study
Association of Atrial Fibrillation Episode Duration With Arrhythmia Recurrence Following Ablation: A Secondary Analysis of a Randomized Clinical Trial.
Contemporary guidelines recommend that atrial fibrillation (AF) be classified based on episode duration, with these categories forming the basis of therapeutic recommendations. While pragmatic, these classifications are not based on pathophysiologic processes and may not reflect clinical outcomes. ⋯ In this study, patients with AF episodes limited to less than 24 continuous hours had a significantly lower incidence of arrhythmia recurrence following AF ablation. This suggests that current guidelines for classification of AF may not reflect clinical outcomes.