Sethi, Sidharth Kumar

ABSTRACT Background Acute kidney injury (AKI) in hospitalised children is a major complication associated with significant morbidity and mortality. The integration of artificial intelligence (AI)/machine learning (ML) models may enable early detection and risk stratification. This systematic review evaluates the performance of AI/ML models for predicting paediatric AKI across clinical settings. Methods We systematically searched PubMed, Embase, and Web of Science for studies applying AI/ML models to predict AKI in paediatric populations. Studies reporting performance metrics such as the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were included. Results Among 470 records identified, 11 studies met the inclusion criteria, with 14 AI/ML models used. The overall sample size included 33 949 paediatric patients with an AKI proportion of 12.5%. Meta-analyses of the AUC were conducted on neural network, gradient boosting, and logistic regression. Gradient boosting had the highest pooled AUC of 0.873 (95% confidence interval 0.836–0.909). Random forest demonstrated the highest median sensitivity (0.821), specificity (0.942), PPV (0.860), NPV (0.935), and accuracy (0.821); however, these metrics could not be pooled due to inconsistent reporting and limited validation. Conclusion Gradient boosting, random forest, and logistic regression demonstrated reasonable predictive performance for paediatric AKI prediction within specific clinical contexts. However, small sample size, heterogeneity, lack of testing/validation cohorts, insufficient data, and inconsistent patient populations and AKI diagnostic criteria restrict generalisability.

AbstractBackgroundThe SARS‐CoV‐2 pandemic and corresponding acute respiratory syndrome have affected all populations and led to millions of deaths worldwide. The pandemic disproportionately affected immunocompromised and immunosuppressed adult patients who had received solid organ transplants (SOTs). With the onset of the pandemic, transplant societies across the world recommended reducing SOT activities to avoid exposing immunosuppressed recipients. Due to the risk of COVID‐19‐related outcomes, SOT providers adapted the way they deliver care to their patients, leading to a reliance on telehealth. Telehealth has helped organ transplant programs continue treatment regimens while protecting patients and physicians from COVID‐19 transmission. This review highlights the adverse effects of COVID‐19 on transplant activities and summarizes the increased role of telehealth in the management of solid organ transplant recipients (SOTRs) in both pediatric and adult populations.MethodsA comprehensive systematic review and meta‐analysis were conducted to accentuate the outcomes of COVID‐19 and analyze the efficacy of telehealth on transplant activities. This in‐depth examination summarizes extensive data on the clinical detriments of COVID‐19 in transplant recipients, advantages, disadvantages, patient/physician perspectives, and effectiveness in transplant treatment plans via telehealth.ResultsCOVID‐19 has caused an increase in mortality, morbidity, hospitalization, and ICU admission in SOTRs. Telehealth efficacy and benefits to both patients and physicians have increasingly been reported.ConclusionsDeveloping effective systems of telehealth delivery has become a top priority for healthcare providers during the COVID‐19 pandemic. Further research is necessary to validate the effectiveness of telehealth in other settings.