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PubMed2026

Diagnostic accuracy of nuclear STAT6 immunohistochemistry for solitary fibrous tumour: a systematic review and meta-analysis.

Nuclear STAT6 immunohistochemistry is the diagnostic surrogate for the NAB2::STAT6 fusion of solitary fibrous tumour (SFT); its sensitivity is established, but specificity varies for unexamined reasons. This review quantified pooled accuracy and tested whether antibody clone and nuclear threshold govern specificity. PubMed, Scopus and Web of Science were searched to 29 June 2026 for studies reporting nuclear STAT6 immunohistochemistry against a reference standard (NAB2::STAT6 confirmation and/or expert consensus) in SFT and comparators, with extractable two-by-two data. Two reviewers screened, extracted data and applied QUADAS-2. A bivariate generalised linear mixed model gave summary sensitivity and specificity, and exploratory subgroup analysis and meta-regression tested antibody clone, anatomical site and reference-standard type. Twenty-three studies (1216 SFT and 4715 comparators) were included. Summary sensitivity was 98.7% (95% confidence interval 96.7-99.5) and specificity 99.1% (97.8-99.6); the diagnostic odds ratio was approximately 8656. The monoclonal YE361 subgroup (8 studies) reached specificity 99.9% (99.3-100), with one false positive among 861 comparators, versus 98.1% (96.0-99.1) for polyclonal and other antibodies. False positives concentrated in dedifferentiated liposarcoma and prostatic stromal tumours. Estimates were stable after removing studies at higher risk of bias (98.9%/99.1%) and on leave-one-out analysis; the Deeks test was non-significant (p = 0.08). Nuclear STAT6 immunohistochemistry is therefore highly sensitive and specific for SFT, and the residual specificity loss is structured and largely avoidable: the monoclonal YE361 read at a strict nuclear threshold is preferred, with MDM2 and CDK4 applied to exclude dedifferentiated liposarcoma when nuclear STAT6 is unexpectedly positive.

Humans↗
PubMed2026

Prognostic significance of NLRP-3 expression in solid cancers: a systematic review and meta-analysis.

BACKGROUND: The inflammasome is a critical immunological sensor comprised of NLRP-3, ASC, and CASPASE-1. Mutations in NLRP-3 are prevalent in inflammatory diseases. However, the role of NLRP-3 in cancer is controversial. This study investigates whether NLRP-3 expression is associated with clinical outcomes in patients with solid cancers. METHODS: PubMed (MEDLINE), Embase, Cochrane, and Google Scholar were searched for articles reporting NLRP-3 expression and disease outcome data in cancer patients. RevMan Review Manager was used to calculate pooled hazard ratios and Mantel-Haenszel pooled odds ratios. RNA sequencing datasets from the TCGA Pan-Cancer (PANCAN) were used for external validation. RESULTS: Patients with higher NLRP-3 expression showed a significant association with larger tumor size, advanced tumor grade, TNM stage, and presence of metastasis. High NLRP-3 expression has a significant association with poor OS (HR:2.12, 95% CI = 1.49-3.03), p&#x2009;<&#x2009;0.0001) and DFS (HR:1.86, 95% CI = 1.30- 2.65, p&#x2009;=&#x2009;0.0007). Subgroup analysis showed that higher NLRP-3 expression is associated with worse OS in head and neck cancer (HR: 2.77, 95% CI = 1.88-4.09, p&#x2009;<&#x2009;0.00001), colorectal cancers (HR:2.14, 95% CI= 1.59- 2.87, p&#x2009;<&#x2009;0.00001), and pancreatic cancer patients (HR: 3.19, 95% CI = 1.73-5.91, p&#x2009;=&#x2009;0.0002). CONCLUSION: High NLRP-3 expression is associated with advanced disease and poor outcomes in many solid tumours.

Humans↗
PubMed2026

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗
PubMed2026

Effect of SGLT2 inhibitor drugs on triglyceride-glucose (TyG) index in adults: A systematic review and meta-analysis.

BACKGROUND: Insulin resistance is a serious public health concern. The triglyceride-glucose (TyG) index is a simple, cheap, and reproducible surrogate of insulin resistance, and sodium-glucose cotransporter-2 (SGLT2) inhibitors have reshaped cardio-metabolic care beyond glycemic control. METHODS: We followed PRISMA and registered the protocol in PROSPERO (CRD420251056341). We searched PubMed, Scopus, Web of Science, EMBASE, and Cochrane from inception to May 31st, 2026, including observational studies and clinical trials reporting baseline and follow-up TyG in adults. Two reviewers screened records, a third resolved disagreements, data were extracted with a standardized form, and quality was assessed with Cochrane RoB 2.0, the Newcastle-Ottawa Scale, and the JBI checklists. The primary outcome was within-group change in TyG pooled with random-effects (Hartung-Knapp); tests were two-sided with a significance threshold of 0.05. RESULTS: Twelve studies comprising thirteen study arms were included, with a total of 1845 participants. Follow-up ranged from 12 weeks to 5 years. Across studies, SGLT2 inhibitor therapy was associated with a significant reduction in TyG index (mean difference = -0.28, 95% confidence interval [-0.41; -0.14], I2 = 99.5%). Egger's test suggested possible small-study effects, whereas Begg's test and trim-and-fill analysis did not show clear evidence of publication bias; leave-one-out analyses showed that no single study materially influenced the pooled estimate. CONCLUSION: Despite heterogeneity in populations, drug choice, and follow-up duration, SGLT2 inhibitors were associated with a significant decrease in TyG, although small-study effects cannot be excluded.

Humans↗
PubMed2026

Assessing comorbidities and predicting risk: A primer for APRNs.

Today's clinical environments are rife with tools designed to comprehensively account for medical complexity and comorbidities while predicting risk for a host of adverse health-related outcomes. Therefore, it is imperative that advanced practice registered nurses (APRNs) understand the structure and function of these tools, their similarities and differences, their limitations, and strategies for appropriate incorporation into practice. This article offers a practical overview for APRNs, emphasizing clinical implications and guidance for aligning assessment tools with the clinical population of interest to improve care delivery, quality, and patient outcomes.

Humans↗
PubMed2026

Phase IIB, Randomized, Double-Blind, Placebo-Controlled Clinical Trial of Intravenous Defibrotide for the Prevention and Treatment of Respiratory Distress and Cytokine Release Syndrome in COVID-19.

INTRODUCTION: Endothelial dysfunction is key in COVID-19 pathogenesis. This randomized, double-blind phase IIb trial investigated continuous intravenous infusion of defibrotide in patients hospitalized with SARS-CoV-2 infection and respiratory failure. METHODS: One-hundred and fifty patients were randomized (2:1) to defibrotide or placebo, stratified by disease severity (WHO COVID-19 severity scale 4/5 vs. 6). The primary endpoint was clinical improvement time (days from first improvement through Day 30). RESULTS: Median clinical improvement time was not significantly different with defibrotide versus placebo (15.0 [IQR: 0-24] vs. 20.0 [IQR: 9-25] days; p&#x2009;=&#x2009;0.10). Day-30 (23.0% vs. 22.0%) and Day-60 (26.0% vs. 22.0%) mortality, reduction in mean fraction of inspired oxygen during treatment, and median duration of hospitalization did not differ with defibrotide versus placebo. Defibrotide demonstrated favorable safety, with no differences versus placebo in serious adverse events (34.0% vs. 36.0%), hypotension (16.0% vs. 12.0%), or hemorrhage (13.0% vs. 8.0%). Exploratory pre-specified biomarker analyses showed greater early d-dimer reduction and lymphocyte recovery with defibrotide, although these results require validation. CONCLUSION: Continuous intravenous infusion of defibrotide was safe but did not improve clinical outcomes in severe COVID-19. Further analyses will explore mechanistic actions and pharmacokinetics of defibrotide and the pathophysiology of endothelial dysfunction in COVID-19. TRIAL REGISTRATION: EudraCT identifier: 2020-001409-21. CLINICALTRIALS: gov identifier: NCT04348383.

Adult↗
PubMed2026

Recurrent myocardial infarction identified by centralized troponin review: Insights from the MINT trial.

BACKGROUND: The utility of routine troponin testing to identify recurrent myocardial infarction (MI) after an incident MI is unclear. We assessed the incidence and prognosis of recurrent MIs identified from centralized troponin review in patients from the Myocardial Ischemia and Transfusion (MINT) trial. METHODS: The MINT trial randomized patients with acute MI and anemia to a liberal vs restrictive red blood cell transfusion strategy. Suspected recurrent MIs were identified through both site-report and centralized review of troponin levels collected for 3 days following randomization. Differences in cardiac, noncardiac, and all-cause death at 30 and 180 days were compared across patients with any site-reported MI, only centrally identified MI, and no recurrent MI. RESULTS: Among 3,504 patients, 275 (7.8%) had a recurrent MI within 30 days; 119 (43.3%) by site-report, and 156 (56.7%) by central troponin review only. Rates of cardiac and all-cause death at 30 and 180 days were highest for patients with site-reported MI, intermediate for centrally identified MI, and lowest for no recurrent MI; rates of noncardiac death did not vary. Patients with only centrally identified recurrent MI had an increased risk of cardiac death at 30 days (RR 1.9, 95% CI 1.0-3.4) and 180 days (RR 1.7, 95% CI 1.1-2.7) compared to those without recurrent MI. CONCLUSIONS: In patients with acute MI and anemia, centralized troponin review identified more than half of all recurrent MI events. Patients with centrally identified MI had a higher risk of cardiac death than those with no recurrent MI. TRIAL REGISTRATION: ClinicalTrials.gov NCT02981407 https://clinicaltrials.gov/study/NCT02619136.

Humans↗
PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c.&#xa0;20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗
PubMed2025

Predictive Validity of Violence Screening Tools in Emergency and Psychiatric Services: A Systematic Review.

Violence against healthcare staff, including a threat or an act of violence toward people during their work, poses a physical and psychological risk to workers internationally. Screening is an important strategy in preventing violence against healthcare professionals. The aim of this systematic review was to synthesize evidence on the predictive validity of risk assessment tools used to screen for violence and aggression risk toward healthcare workers in emergency and psychiatric departments (PD). Primary studies that examined the predictive validity of risk assessment tools for workplace violence were identified via a systematic search of Medline, PsycINFO, Embase, and the Cochrane databases. There were 62 eligible studies, ten of which had a lower risk of bias (RoB). Those studies with high RoB were primarily due to a failure to present calibration measures as part of the analysis. All included studies adopted a longitudinal design and were conducted in PDs. The ten highest-quality studies reported on eight different instruments, four of which showed acceptable to outstanding predictive performance. The Dynamic Appraisal of Situational Aggression and the Br&#xf8;set Violence Checklist showed the best predictive performance; they were also validated in emergency departments and are best suited for short-term risk prediction. We recommend that the selection of a risk assessment tool should consider the following: (a) the target population, (b) the violence operationalization, and (c) the purpose of the monitoring. We note that the use of a screening tool should be a part of a multicomponent strategy to ensure staff safety.

Humans↗
PubMed2026

Comparative safety of lipid-lowering drugs alone or in combination: insights from a systematic review and network meta-analysis.

BACKGROUND AND AIMS: Although the safety profile of lipid-lowering therapies (LLTs) is known, there are no comprehensive comparative assessments. We aimed to compare the risk of muscle-related events, diabetes, liver dysfunction, and cognitive disorders among LLTs through a network meta-analysis. METHODS AND RESULTS: Databases were searched from inception to May 2025. Eligible studies included adult patients, using statins, ezetimibe, PCSK9 monoclonal antibodies (PCSK9mAbs), inclisiran, bempedoic acid, or their combinations as intervention, reporting the information about any of the selected adverse events, a total sample size of &#x2265;200 subjects, and had &#x2265;1 month of intervention. Pooled estimates were assessed by fixed effects model within a frequentist setting. Pooled relative risks (RR) and their 95% confidence interval were estimated. A total of 303,397 subjects from 153 RCTs were included. Bempedoic acid ranked the lowest risk of myalgia (vs PCSK9mAbs, RR 0.80 [0.69, 0.93]). PCSK9mAbs were associated with lower incidence of creatine kinase (CK) elevation, diabetes, and liver dysfunction comparing to statins (statins vs PCSK9mAbs, RR 1.44 [1.14, 1.81], RR 1.13 [1.05, 1.22], and RR 1.38 [1.17, 1.62], respectively). In terms of muscle-related events and cognitive disorders, no significant risk differences were found among treatments and their combinations. CONCLUSIONS: PCSK9mAbs appear to have a more favourable safety profile regarding the risk of CK elevation, diabetes, and liver dysfunction. Bempedoic acid seem to be a better choice for subjects with high risk of myalgia. This information can be valuable when selecting therapy for specific patient subgroups at higher risk of certain adverse events.

Humans↗
PubMed2026

Dietary approaches for glycemic management in type 1 diabetes: A systematic review of Mediterranean and low-carbohydrate diets.

BACKGROUND/OBJECTIVES: Specific dietary approaches for better management of type 1 diabetes (T1D) have not been thoroughly investigated. We conducted a systematic review to evaluate the Mediterranean and low-carbohydrate diets for glycemic management in people with T1D. METHODS: We examined longitudinal studies (cohort studies and clinical trials) including individuals with T1D who followed low-carbohydrate diets (<26% calories from carbohydrates and/or <130&#x202f;g of carbohydrates per day) and/or a Mediterranean diet, while hemoglobin A1c (HbA1c) and/or time in range (TIR) were measured. Additional eligibility criteria included publication in English and availability of a full-text primary research article. Non-longitudinal studies, abstracts, and studies published in languages other than English were excluded. Results were synthesized narratively, and the GRADEpro Guideline Development Tool was used to assess article quality. RESULTS: A total of 565 studies were identified from PubMed, the Web of Science, and citation chasing. After removal of duplicates and further evaluation, 22 studies (6 cohort studies and 16 clinical trials; total n&#x202f;=&#x202f;3284) were included in this review. The search was initially completed in May 2024, and updated February 2026. Low-carbohydrate diets were associated with better glycemic management when compared to usual diets or baseline glycemic parameters. Studies using CGM were overall underpowered. The impact of Mediterranean diets was less clear, but generally appeared to be less effective at improving glycemic management than low-carbohydrate diets. DISCUSSION: Although structured dietary interventions for T1D hold promise for improving glycemic outcomes, further research is needed to determine exactly which dietary intervention is most beneficial for this population. The evidence provided by included studies is limited by small sample sizes and short durations; better powered, longer-term studies are required to inform clinical recommendations.

Humans↗
PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗
PubMed2026

Feasibility of routine clinical liquid-based cytology for lung cancer compact panel testing.

BACKGROUND: The Lung Cancer Compact Panel (cPANEL) is a recently approved highly sensitive multiplex gene panel in Japan that supports both DNA- and RNA-based next-generation sequencing. Although cytological specimens are acceptable for cPANEL, unfixed cell pellets or dedicated preservation tubes are typically recommended. However, evidence remains limited regarding whether residual liquid-based cytology (LBC) cell suspensions prepared for routine cytological diagnosis can be used directly for cPANEL testing without dedicated molecular preservation or additional preanalytical processing. In this study, we evaluated the feasibility of applying LBC specimens that are widely used in contemporary clinical practice to cPANEL. METHODS: We analyzed DNA and RNA quality in 69 clinical LBC specimens. Among these, 51 specimens containing non-small cell lung cancer cells with previously determined driver alteration status were subjected to cPANEL testing to evaluate assay concordance with clinical companion diagnostic results. RESULTS: DNA integrity was generally well preserved (DNA Integrity Number [DIN]: 6.2&#xa0;&#xb1;&#xa0;1.5). In contrast, RNA integrity showed greater variability (DV200: 16.4&#xa0;&#xb1;&#xa0;12.1%). ThinPrep-fixed specimens demonstrated lower DIN and DV200 values compared with CytoRich Red-fixed specimens. Although all samples successfully passed the DNA-based cPANEL assay, six cases (11.8%) failed the RNA-based assay, with RNA yield being a major contributing factor. Among the 46 evaluable specimens, concordance was 95.7% and sensitivity was 92.3%, or 88.9% including RNA module failures as cPANEL-negative. CONCLUSIONS: With appropriate fixative selection and adequate cellularity, cPANEL using clinical LBC specimens may serve as a practical diagnostic platform. We demonstrated that routine LBC specimens can be directly applied to cPANEL without special preanalytical processing.

Humans↗
PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline&#xff1a;A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗
PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗