首页|Yichang Central People’s Hospital Reports Findings in Acute Kidney Injury (Machi ne-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple W asp Stings)

Yichang Central People’s Hospital Reports Findings in Acute Kidney Injury (Machi ne-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple W asp Stings)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Kidney Diseases and Co nditions - Acute Kidney Injury is the subject of a report. According to news rep orting from Hubei, People’s Republic of China, by NewsRx journalists, research s tated, “Acute kidney injury (AKI) following multiple wasp stings is a severe com plication with potentially poor outcomes. Despite extensive research on AKI’s ri sk factors, predictive models for wasp sting-related AKI are limited.” The news correspondents obtained a quote from the research from Yichang Central People’s Hospital, “This study aims to develop and validate a machine learning-b ased clinical prediction model for AKI in individuals with wasp stings. In this retrospective cohort study, conducted at a tertiary teaching hospital in Yichang , China, from July 2013 to April 2023, 214 patients with wasp sting injuries wer e analyzed. Using least absolute shrinkage and selection operator (LASSO) regres sion and multivariate logistic regression, prognostic variables for AKI were ide ntified. A nomogram incorporating these four variables was constructed. The mode l’s performance was assessed through internal validation, leave-one-out cross-va lidation, net reclassification improvement (NRI), integrated discrimination impr ovement (IDI), and decision curve analysis (DCA). Among 214 patients affected by wasp stings, 34.6% (74/214) developed AKI. Following LASSO regres sion and multivariate logistic regression, the number of stings, presence of gro ss hematuria, systemic inflammatory response index (SIRI), and platelet count we re identified as prognostic factors. A nomogram was constructed and evaluated fo r its predictive accuracy, showing an area under the curve (AUC) of 0.757 (95% CI 0.711 to 0.804) and a concordance index (C-index) of 0.75. Validation confirm ed the model’s reliability and superior discrimination ability over existing mod els, as demonstrated by NRI, IDI, and DCA.”

HubeiPeople’s Republic of ChinaAsiaAcute Kidney InjuryCyborgsEmerging TechnologiesHealth and MedicineKidne y Diseases and ConditionsMachine LearningRisk and Prevention

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.15)