Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
International ophthalmology
Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy
PURPOSE: To explore the relationship between thyroid-associated ophthalmopathy (TAO) and liver injury and establish a model for identifying liver injury by using machine learning so as to provide an effective diagnostic tool for liver injury in patients with TAO. METHOD: A single-center retrospective study was conducted to collect the clinical data of 318 patients with TAO admitted to a hospital from 2016 to 2022. The patients were divided into a TAO liver injury group (104 cases) and TAO normal liver function group (214 cases) according to whether the patients had normal liver function. The multivariate binomial logistic regression model was used to analyse the risk factors for liver injury in patients with TAO. Feature selection was performed using the random forest algorithm. The research data were divided into a training set and a test set at a ratio of 6:4. Taking whether accompanied by liver injury (0 = no, 1 = yes) as the outcome variable, models were established based on logistic regression, random forest, support vector machine, and decision tree. The performance of the models was evaluated using metrics, including sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and accuracy. RESULT: The random forest method was used for feature screening, and variables ranked among the top 10 according to either mean decrease accuracy or mean decrease Gini were selected for model construction. In five-fold cross-validation, the RF model showed the highest accuracy of 0.937 and AUC of 0.977. In the test set, the RF model also showed good discrimination, with an AUC of 0.973, and the SVM model showed the highest AUC of 0.986, while SVM and LR achieved the highest accuracy of 0.914. CONCLUSION: This study shows that a classification model based on machine learning can effectively identify the risk of liver damage in patients with TAO.
18 July 2026
Read appraisal →Renal failure
Novel biomarkers for acute kidney injury in high-risk pediatric populations: a systematic review
Acute Kidney Injury (AKI) is currently diagnosed in pediatric patients by measuring rises in serum creatinine. The objective of this systematic review is to determine the diagnostic accuracy of novel biomarkers for early detection or prediction of pediatric AKI across a variety of high-risk pediatric populations. Relevant studies were searched in PubMed, Cochrane Library, and Web of Science databases. Observational studies and randomized trials assessing diagnostic biomarkers for AKI in high-risk pediatric populations were included, while studies limited to traditional biomarkers, adult populations, or non-original research were excluded. Risk of bias was assessed using the QUADAS-2 tool. The review was prospectively registered in PROSPERO (CRD420261293432). Diagnostic accuracy outcomes included AUC, sensitivity, specificity, and other performance measures. 53 total studies were included and grouped into 1 of 5 categories based on the specific population of pediatric patients studied and the general etiology of their AKI. Of note, neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and cystatin-C were among the most studied biomarkers and showed significant promise as reliable indicators of kidney injury. The product of tissue inhibitor of metalloproteinase-2 and insulin-like growth factor binding protein-7, interleukin-18, and others showed some evidence of being predictive, early markers of AKI and warrant additional investigation. The review further discusses the integration of novel biomarkers within existing clinical detection frameworks, including validated risk stratification tools and emerging FDA-approved biomarker assays, to contextualize their translational potential in high-risk pediatric populations. Future advances in precision medicine may lead to earlier diagnosis and better prognosis for pediatric patients suffering from AKI. What is known: Acute kidney injury in children is associated with significant morbidity, and serum creatinine often detects kidney injury only after substantial damage has occurred.This study adds: This systematic review summarizes current evidence on emerging pediatric acute kidney injury biomarkers and highlights those with the greatest potential for earlier diagnosis across multiple high-risk populations.Potential impact: Earlier identification of acute kidney injury using novel biomarkers may enable more timely intervention and improve outcomes, although further validation is needed before widespread clinical implementation.
17 July 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers
Chronic kidney disease (CKD) is a systemic condition that leads to progressive renal failure and metabolic imbalances that may be detected in the keratinized bio-tissues of the body such as fingernails. Nevertheless, still its early detection is difficult due to the invasive nature of current clinical screening approaches. This research paper provides a precise, non-invasive, and AI-enhanced diagnostic model of CKD screening through the combination of Laser-Induced Breakdown Spectroscopy (LIBS) and the cutting-edge ensemble machine learning to determine the elemental patterns of fingernail biomarkers. In this study, 55 participants with chronic kidney disease and 45 healthy controls were taken as the subjects and their emission spectra were measured with a nanosecond LIBS spectrometer using a laser of a fundamental wavelength @ 1064 nm. To handle the high-dimensional spectral data, Principal Component Analysis (PCA) was used as a feature extraction approach, forming the basis for novel machine-learning models. A sophisticated ensemble learning algorithm specifically Extreme Gradient Boosting (XGBoost) was applied to examine the biochemical change between CKD and control samples and the predictive quality was tested by a relative comparison with a Support Vector Machine (SVM) algorithm. Using 10-fold cross-validation, the XGBoost model outperformed SVM, attaining 97% accuracy, 98% sensitivity, 96% precision and specificity, and a 97% F1-Score. Moreover, external validation on a separate dataset showed that the model is robust and generalizable with 95% accuracy, 97% sensitivity, and an F1-score of 95%. The results conclude that fingernail-based LIBS integrated with ensemble machine learning is a potential and non-invasive instrument for CKD classification.
6 July 2026
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