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PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

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PubMed2026

Combining neuromelanin-sensitive MRI and quantitative susceptibility mapping for enhanced diagnosis and differentiation of parkinson's disease: A systematic review.

BACKGROUND: Loss of dopaminergic neurones and iron deposition in the substantia nigra pars compacta (SNpc) are two major pathological hallmarks of Parkinson's disease (PD). Such changes can be visualised by advanced techniques including neuromelanin-sensitive MRI (NM-MRI) and quantitative susceptibility mapping (QSM). This systematic review investigates the diagnostic performance and methodological development of the integrated use of NM-MRI and QSM in PD. METHODS: The systematic search was performed in four databases (Scopus, PubMed, ScienceDirect, and Web of Science) according to the PRISMA 2020 guidelines until July 2026. Bias was assessed using QUADAS-2 and certainty of evidence was assessed using GRADE. RESULTS: Seventeen studies with 2228 participants were included. Combined NM-MRI and QSM consistently showed reduced neuromelanin volume/contrast and increased iron deposition in the SNpc of PD patients compared to healthy controls. Multimodal integration yielded a significant improvement in diagnostic accuracy (AUC values 0.86-0.99), and was able to successfully differentiate PD. Recent methodological advances included simultaneous acquisition sequences (e.g. MTC-GRE, STAGE, setMag) and AI-driven automated segmentation, which led to significantly reduced scan times and improved reproducibility. CONCLUSION: The combination of NM-MRI and QSM has a synergistic effect and provides powerful complementary biomarkers for the diagnosis and differential diagnosis of PD.

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PubMed2026

Malaria rapid diagnostic tests: performance, pitfalls, and progress.

PURPOSE OF REVIEW: Malaria rapid diagnostic tests (RDTs) have revolutionized malaria diagnosis in endemic settings. RDTs are simple to use and accurate for clinical cases, although sensitivity is reduced at parasite densities below 200 parasites/μl. However, increasing prevalence of hrp2/3 gene deletions in certain areas threaten utility of histidine-rich protein 2 (HRP2)-based RDTs, and lingering HRP2 antigenemia can generate false-positive results after parasite clearance. This review summarizes current performance of malaria RDTs, threats to their validity, and recent innovations to improve their performance and continued role in malaria diagnosis. RECENT FINDINGS: Most World Health Organization (WHO) prequalified RDTs perform well for clinical diagnosis, with only occasional exceptions, including a recently reported issue affecting several countries. RDT sensitivity is generally related to malaria transmission intensity, with higher proportions of false-negative results in lower-transmission areas. Newly prequalified lactate dehydrogenase (pLDH)-based RDTs perform well for both Plasmodium falciparum in areas with >5% hrp2/3 gene deletions and for Plasmodium vivax diagnosis. Several point-of-care alternatives to RDTs, including micro-fluidic devices, hemozoin-detecting devices, and automated hematology analyzers, have shown promising results in small studies, but require larger-scale trials before widespread use. SUMMARY: RDTs remain a critical tool in clinical diagnosis of malaria, and newer pLDH-based tests perform well in areas where hrp2/3 gene deletions threaten validity of HRP2-based RDTs.

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PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

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PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

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PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

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PubMed2026

Anabolic androgen therapy in critically ill adults: A systematic review and meta-analysis.

Critical illness is characterized by a catabolic, proinflammatory state. Anabolic agents, such as testosterone, have therefore been proposed as therapeutic targets. Our objectives were to assess the effects of testosterone in critically ill populations on patient-important outcomes and identify design limitations to inform future studies. We searched for randomized control trials (RCTs) through Medline, Embase, and EBM Reviews databases from inception through February 24, 2026, including English language articles enrolling adults (≥18 years) admitted to ICU where anabolic androgen therapies (AAT) were compared with placebo or standard of care. Studies had to report at least one of: mortality, ICU and hospital lengths of stay, or duration of mechanical ventilation. We extracted data independently using a standardized data extraction tool, and feedback was received from all co-authors to ensure agreement. For each outcome, we performed meta-analyses using a random-effects model with inverse variance weighting in RevMan. We used the GRADE approach to assess certainty in pooled estimates of effect. Of 1325 screened articles, we found 4 that fit our inclusion criteria. Together, we judged risk of bias as 'some concerns' in 3 trials and 'high' in the final trial, and ultimately found that the effects of anabolic-androgen therapy on patient-important outcomes uncertain. With the uncertainty of current evidence for the effects of anabolic-androgen therapy in critically ill adults, there is insufficient support for its routine use. Future randomized evidence is needed to determine whether anabolic-androgen therapy improves clinically-important outcomes and better define its safety profile in critically ill adults.

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PubMed2026

Effect of ketofol versus Fentanyl-Midazolam sedation on neurological recovery in traumatic brain Injury: A randomised study.

Neurological recovery after traumatic brain injury (TBI) is multifactorial, and sedation is a cornerstone of neurocritical care because of its neuroprotective role. Although ketofol is widely used for anaesthesia, its effectiveness as a sedative regimen in the intensive care unit (ICU) has not been well studied. This preliminary exploratory double-blind, randomised study compared ketofol (KP) with fentanyl-midazolam (FM) sedation in adults with moderate-to-severe TBI. Sedation was administered for 72 h and titrated to a Richmond Agitation-Sedation Scale (RASS) score ≤  - 3. The primary outcome was the Extended Glasgow Outcome Scale (GOSE) at 30 days. Secondary outcomes included GOSE at 90 days, incidence of propofol infusion syndrome (PRIS), duration of mechanical ventilation, haemodynamic stability, and ICU and hospital length of stay. Of 120 enrolled patients, 111 were included in the final analysis (57 FM, 54 KP). Baseline characteristics, including injury severity and Marshall CT scores, were comparable. At 30 days, good neurological recovery (GOSE 7-8) was more frequent in the KP group than the FM group (26% vs. 10.5%, p = 0.03). At 90 days, recovery remained higher with KP (44.4% vs. 33.3%), though the difference was not statistically significant (p = 0.16). Multivariate analysis confirmed ketofol as an independent predictor of good recovery at 30 days (adjusted OR 3.63, 95% CI 1.11-11.85, p = 0.033). No PRIS occurred, and secondary outcomes were similar. Ketofol-based sedation was safe and may be associated with improved early neurological recovery compared with fentanyl-midazolam, with a favourable trend toward improved long-term neurological recovery.

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PubMed2026

Empirical Meropenem Versus Piperacillin/Tazobactam for Critically Ill Adults With Sepsis: Feasibility of a Randomised Trial.

BACKGROUND: Meropenem and piperacillin/tazobactam are commonly used empirical antibiotics in critically ill adults with sepsis, but whether one is superior to the other is uncertain. METHODS: The Empirical Meropenem versus Piperacillin/Tazobactam for Adult Patients with Sepsis (EMPRESS) trial is an ongoing investigator-initiated, randomised, open-label, adaptive clinical trial with an integrated feasibility phase comparing empirical treatment with meropenem versus piperacillin/tazobactam in critically ill adults with sepsis. The integrated feasibility phase enrolled 200 participants across 10 intensive care units (ICUs) in Denmark between 28 June and 12 December 2025. Five pre-specified feasibility criteria were evaluated; if all feasibility criteria were met, the trial would proceed unaltered, whereas failure to meet one or more criteria would require intervention and re-evaluation. RESULTS: We randomised 200 of 284 screened patients (70.4%). The median age was 70 years (interquartile range (IQR): 60-77), 65.5% were males. At randomisation, 80.0% received vasopressors or inotropes, and 43.5% were on invasive mechanical ventilation. Four of five pre-specified feasibility criteria were met: time to completion of the feasibility phase (5.5 months vs. threshold < 12.0 months), recruitment proportion (70.4% vs. threshold ≥ 50.0%), proportion of participants without consent to the continued collection of data (2.5% vs. threshold < 5.0%) and protocol adherence (81.0% vs. threshold ≥ 75.0%). The proportion of participants with timely primary outcome data availability (30-day mortality) within 45 days was 85.5% and below the pre-specified threshold of ≥ 95.0%. The proportions were low in the first 3 months (33.3%, 22.2% and 30.8%, respectively), increasing to 95.8% in the last month of the feasibility phase. All-cause mortality at 30 days was 30.5%, and specific serious adverse reactions occurred in 4.0% of participants. CONCLUSIONS: In this integrated feasibility evaluation of the EMPRESS trial comparing empirical meropenem versus piperacillin/tazobactam in critically ill adults with sepsis, four of five pre-specified feasibility criteria were met. The unmet criterion, timely primary outcome data availability, improved substantially during the feasibility phase. We consider the trial feasible and will proceed without modifications. EDITORIAL COMMENT: This feasibility study assessed recruitment, randomised allocation and data collection for the multicentre EMPRESS trial. For adaptive trials on trial platforms, careful interim checking of trial design functions is an important and necessary process. TRIAL REGISTRATION: Clinical Trials Information System EUCT number: 2023-509703-33-00; ClinicalTrials.gov identifier: NCT06184659; Universal Trial Number: U1111-1301-6379.

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