Prognostic Tool Research Articles

A quantitative polymerase chain reaction (qPCR) protocol predictive of treatment outcome in cutaneous leishmaniasis caused by Leishmania braziliensis

Abstract Leishmania braziliensis is the most prevalent agent causing cutaneous leishmaniasis (CL) in Brazil. While inflammation is a hallmark of CL, few parasites are found at the lesion site, leading to challenges regarding diagnosis. Using L. braziliensis kDNA and human 18S rRNA as targets, the present study developed a qPCR assay to determine parasite load in biopsies from CL patients residing in an endemic area in northeastern Brazil. In addition, we investigated whether parasite load correlated with clinical outcome and observed that patients with higher parasite load were more likely to fail therapy. Moreover, CL patients in the early phase of infection presented higher levels of parasite transcripts than individuals in later phases. Thus, our results suggest that parasite load as determined by qPCR may constitute a valuable prognostic tool to aid in both the determination of disease severity and treatment outcome.

Anterior-temporal network hyperconnectivity is key to Alzheimer's disease: from ageing to dementia

Abstract Curing Alzheimer’s disease remains hampered by an incomplete understanding of its pathophysiology and progression. Exploring dysfunction in medial temporal lobe networks, particularly the anterior-temporal (AT) and posterior-medial (PM) systems, may provide key insights, as these networks exhibit functional connectivity alterations along the entire Alzheimer’s continuum, potentially influencing disease propagation. However, the specific changes in each network and their clinical relevance across stages are not yet fully understood. This requires considering commonly used biomarkers, clinical progression, individual variability, and age confounds. Here, we leveraged monocentric longitudinal data from 261 participants spanning the adult lifespan and the Alzheimer’s continuum. The sample included cognitively unimpaired adults aged 19 to 85 years (n = 209; eight out of 64 older adults over 60 were Aβ-positive) and Aβ-positive patients fulfilling diagnostic criteria for mild cognitive impairment (MCI, n = 26; 18 progressed to Alzheimer-dementia within seven years) or Alzheimer’s type dementia (n = 26). Participants underwent structural and resting-state functional (f) MRI, florbetapir and FDG-PET, and global cognitive assessments, with up to three visits over a maximum period of 47 months. Network connectivity was assessed using seed-based analyses with the perirhinal and parahippocampal cortices as seeds, within data-driven masks reflecting the AT and PM networks. Generalized additive and linear mixed models were run to assess age-specific effects and Alzheimer’s-related alterations. In this context, we explored various markers of pathological and clinical severity, including cerebral amyloid uptake, glucose metabolism, hippocampal volume, global cognition, diagnostic staging, and time to dementia onset. Our findings revealed distinct patterns of connectivity linked to normal aging or Alzheimer’s disease. Advancing age throughout adulthood was associated with lower PM connectivity and more subtle changes in AT connectivity, while Alzheimer’s disease was characterised by AT hyperconnectivity without global changes in PM connectivity. Specifically, AT connectivity was higher in MCI and Alzheimer-dementia patients compared to older controls and was positively associated with amyloid burden, glucose hypometabolism, hippocampal atrophy, and global cognitive deficits in older adults, ranging from unimpaired to demented. Additionally, higher AT connectivity correlated with faster progression to Alzheimer-dementia in MCI patients. This comprehensive approach allowed to reveal that excessive connectivity within the AT network is intrinsically linked to the pathological and clinical progression of Alzheimer’s disease. These insights may guide future research to better understand cascading events leading to the disease and hold promise for developing prognostic tools and therapeutic interventions targeting these specific network alterations.

Advanced Markers for Hemodynamic Monitoring in Cardiogenic Shock and End-Stage Heart Failure: A Mini Review.

Right heart catheterization (RHC) provides critical hemodynamic insights by measuring atrial, ventricular, and pulmonary artery pressures, as well as cardiac output (CO). Although the use of RHC has decreased, its application has been linked to improved outcomes. Advanced hemodynamic markers such as cardiac power output (CPO), aortic pulsatility index (API), pulmonary artery pulsatility index (PAPi), right atrial pressure to pulmonary capillary wedge pressure ratio (RAP/PCWP) and right ventricular stroke work index (RVSWI) have been introduced to enhance risk stratification in cardiogenic shock (CS) and end-stage heart failure (HF) patients. CPO has emerged as a potent prognostic tool, with values below 0.6 Watts significantly associated with mortality. Similarly, API and PAPi have demonstrated strong predictive power for adverse outcomes, including death and the need for advanced HF therapies. RAP/PCWP ratio is shown to be a valuable a prognostic tool for RV dysfunction, mortality, and adverse outcomes. Despite mixed evidence on the prognostic utility of RVSWI, its physiologic relevance in assessing right ventricular function remains important. A novel clinical observation, involving patients with an RAP numerically greater than pulmonary artery saturation, was associated with a 71% 30-day mortality rate, underscoring the potential prognostic value of this finding. This review aims to summarize key advanced hemodynamic markers and their role in improving risk stratification and guiding treatment in CS and end-stage HF. The integration of these markers into clinical practice holds the potential to enhance personalized care and improve outcomes for patients with CS and advanced HF.

Nomogram-derived immune-inflammation-nutrition score could act as a novel prognostic indicator for patients with head and neck squamous cell carcinoma

AimThis study aims to create and validate a novel systematic immune-inflammation-nutrition (SIIN) score to provide a non-invasive and accurate prognostic tool for head and neck squamous cell carcinoma (HNSCC) patients.Methods259 participants diagnosed with HNSCC from the First Affiliated Hospital of Xi’an Jiaotong University between 2008 and 2017 was included in this retrospective study. Patients were assigned to training (n=181) and validation (n=78) sets. A LASSO Cox regression model was employed to identify significant biomarkers for constructing a SIIN nomogram and to create SIIN score from this nomogram. The prognostic accuracy of the SIIN score was assessed by exploiting receiver operating characteristic (ROC) analysis, Kaplan-Meier survival analysis, Cox proportional hazard regression models, calibration and DCA curves.ResultsThe SIIN score was formulated based on six biomarkers-platelet-lymphocyte ratio (PLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), albumin-bilirubin index (ALBI), fibrinogen (FIB) and monocyte count-identified by LASSO regression analysis. (1)The SIIN score demonstrated superior predictive value, achieving area under the ROC curve (AUC) values of 0.736 and 0.700 for 3- and 5-year OS. For recurrence-free survival (RFS), the AUC values were 0.752 for 3-year and 0.701 5-year RFS, as assessed in the training set. Validated as an independent prognostic factor in both cohorts, the SIIN score showed strong correlation with adverse clinicopathological outcomes.ConclusionThe SIIN score is a promising prognostic tool that integrates immune, inflammatory, and nutritional factors for predicting clinical outcomes in HNSCC patients. It offers enhanced predictive accuracy compared to existing markers and has the potential to guide personalized treatment strategies and clinical decision-making.

Liquid biopsy: an emerging field with new opportunities for cancer diagnosis and prognosis

Cancer grades among the deadliest diseases, globally causing the death of a over million people each year. Early diagnosis has been considered ideal for efficient treatment as during later stages chances of treatment become limited. However, gold standard tissue biopsy has various limitation for instance, late-stage diagnosis and its intrusive operation making it unfit for repeated sampling. Scientists are passionately looking for new technologies and techniques for cancer diagnosis and prognosis. Liquid biopsy has emerged as new diagnostic and prognostic tool for cancer, that relies on body fluids to identify biomarkers for cancer. It offers advantages like non-invasive operation, timely detection, amenable to repeated sampling, and covers the tumor heterogeneity. Wide attention has been garnered by liquid biopsy and is undergoing rapid progress in the list of target biomarkers. The most common are circulating tumor cells, circulating tumor DNA, exosomes, tumor educated platelets, and non-coding RNAs (miRNA, lncRNA etc.). Each of these biomarkers have unique advantages, making liquid biopsy indeed a technology of future for cancer diagnosis with clinical utility. In this article, we tried to provide a thorough introduction of liquid biopsy and its markers, highlighted the common biomarkers that are deployed in liquid biopsy, briefly overview their implications as indispensable diagnostic and prognostic entities for the diverse types of cancer. Moreover, discussed future prospects of this revolutionary technology in the realm of cancer diagnosis and treatment.

Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer

BackgroundThe rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis and tailoring personalized treatments. Epigenetic changes play a critical role in breast cancer progression and therapy responses, providing a foundation for prognostic model development.MethodsWe developed the Machine Learning-derived Epigenetic Model (MLEM) to identify prognostic epigenetic gene patterns in breast cancer. Using multi-cohort transcriptomic datasets, MLEM was constructed with rigorous machine learning techniques and validated across independent datasets. The model’s performance was further corroborated through immunohistochemical validation on clinical samples.ResultsMLEM effectively stratified breast cancer patients into high- and low-risk groups. Low-MLEM patients exhibited improved prognosis, characterized by enhanced immune cell infiltration and higher responsiveness to immunotherapy. High-MLEM patients showed poorer prognosis but were more responsive to chemotherapy, with vincristine identified as a promising therapeutic option. The model demonstrated robust performance across independent validation datasets.ConclusionMLEM is a powerful prognostic tool for predicting breast cancer outcomes and tailoring personalized treatments. By integrating epigenetic insights with machine learning, this model has the potential to improve clinical decision-making and optimize therapeutic strategies for breast cancer patients.

How I Treat Higher-Risk MDS.

Myelodysplastic syndromes/neoplasms (MDS) are a widely heterogenous group of myeloid malignancies characterized by morphologic dysplasia, a defective hematopoiesis, and recurrent genetic abnormalities. The original and revised International Prognostic Scoring Systems (IPSS) have been used to risk-stratify patients with MDS to guide treatment strategies. In higher-risk MDS, the therapeutic approach is geared toward delaying leukemic transformation and prolonging survival. For over a decade, the hypomethylating agents azacitidine and decitabine have been the standard of care and, when feasible, an allogeneic hematopoietic stem cell transplantation (AHSCT) should be considered. However, the IPSS scoring systems solely rely on clinical, morphological, and cytogenetic features and do not account for somatic mutations present in over 80% of cases. These genetic abnormalities have been shown to play a crucial role in prognostication, prompting the development of molecular IPSS, and the integration of genomic features into MDS classification systems in recent years. In this review, we delineate our approach to higher-risk MDS in the context of updated classifications and the latest prognostication tools. We employ illustrative clinical cases to support our discussion and share insights from recent clinical trials, highlighting lessons learned.

Shared Decision-Making in Solid Organ Transplantation: A Review

Solid organ transplantation entails numerous complex medical and ethical decisions. Shared decision-making (SDM) has been advocated as the optimal model for navigating these decisions, providing a collaborative framework that enhances person-centered care. This approach involves patients, caregivers, and healthcare professionals in the decision-making process, ensuring that clinical decisions align with patient preferences, values, and individual circumstances alongside clinical indications. This paper reviews the implementation of SDM throughout the transplantation journey, from diagnosis and transplant referral, pre-transplant assessments, waiting lists, to the organ offer, perioperative period, and long-term follow-up. Barriers to SDM include factors at the patient, provider, and system levels, including inadequate patient–provider communication. Effective SDM requires tailored educational resources, prognostic tools, clinician training, collaborative care models, and supportive policies. Additionally, leveraging technology, such as artificial intelligence and mobile applications, can enhance patient engagement and decision quality. SDM promotes equity by involving all patients—including those from more vulnerable groups—in meaningful conversations about their treatment options, thereby mitigating disparities in access and outcomes. Future research should focus on the long-term impacts of SDM interventions, the development of comprehensive prognostic tools incorporating patient-reported outcomes, and systemic changes to integrate SDM into clinical practice, aiming to improve patient outcomes and person-centered care.

Prognostic role of midregional proadrenomedullin in predicting infection in pediatric cancer with febrile neutropenia.

Febrile neutropenia (FN) remains an important complication of cytotoxic chemotherapy for which an urgent and appropriate evaluation is imperative. To assess the diagnostic and prognostic roles of mid-regional pro-adrenomedullin (MR-ProADM) levels in predicting infection in patients with FN. This comparative cross-sectional study included 137 patients with chemotherapy-induced FN. Complete blood count, C-reactive protein (CRP), procalcitonin, and MR-ProADM were evaluated on the 1st day of FN. Chest computed tomography (CT) was performed on the 5th day. MR-ProADM levels were significantly higher in patients with FN than in controls. CRP and MR-ProADM levels were significantly higher and ANC was significantly lower in patients with versus without bacterial infections. CRP, procalcitonin, and MR-ProADM levels were significantly negatively correlated with absolute neutrophil count (ANC). CRP, procalcitonin, and MR-ProADM levels were significantly and positively correlated with FN degree, FN duration, and hospital stay length. A multivariate regression analysis showed that a longer FN duration and hospital stay length, along with elevated CRP, procalcitonin, and MR-ProADM levels, were significant risk factors for mortality. MR-ProADM is a reliable prognostic and diagnostic tool for predicting infection in patients with FN.

Spinal cord injury-specific prognostic risk assessment tool for development of type 2 diabetes.

Available diabetes risk calculators were developed for able-bodied individuals, but their metabolic profile is different from individuals with spinal cord injury. We aimed to develop a diabetes risk assessment tool specific to individuals with spinal cord injury. We used national Veterans Affairs data to identify patients with at least a 2-year history of spinal cord injury and no prior history of diabetes with a Veterans Heath Affairs visit from 2005-2007, and followed the 11,054 individuals that met inclusion criteria for up to 17 years to assess diabetes development. We used least absolute shrinkage and selection operator (LASSO) Cox regression to develop prognostic diabetes prediction models and evaluated these models on discrimination and calibration. 2937 subjects developed diabetes during follow-up; median follow-up time was 8.7 years (IQR 3.3, 15.4). The first model selected 17 predictors and demonstrated median discrimination of 0.70 (IQR 0.69, 0.72) at 15 years. The second, more parsimonious model with 4 selected predictors demonstrated median discrimination of 0.69 (IQR 0.68, 0.71) at 15 years. Both models demonstrated good calibration across predicted risk, with better calibration in the 17-predictor model. These spinal cord injury-specific risk calculators can be used by both patients and providers in assessing risk of diabetes development, and in shared decision making regarding surveillance and prevention.

Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation

BackgroundEffective management of dual antiplatelet therapy (DAPT) following drug-eluting stent (DES) implantation is crucial for preventing adverse events. Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. This study introduces a new contrastive learning-based approach to enhance prediction efficacy over multiple time intervals.MethodsWe utilized retrospective, real-world data from the OneFlorida + Clinical Research Consortium. Our study focused on two primary endpoints: ischemic and bleeding events, with prediction windows of 1, 2, 3, 6, and 12 months post-DES implantation. Our approach first utilized an auto-encoder to compress patient features into a more manageable, condensed representation. Following this, we integrated a Transformer architecture with multi-head attention mechanisms to focus on and amplify the most salient features, optimizing the representation for better predictive accuracy. Then, we applied contrastive learning to enable the model to further refine its predictive capabilities by maximizing intra-class similarities and distinguishing inter-class differences. Meanwhile, the model was holistically optimized using multiple loss functions, to ensure the predicted results closely align with the ground-truth values from various perspectives. We benchmarked model performance against three cutting-edge deep learning-based survival models, i.e., DeepSurv, DeepHit, and SurvTrace.ResultsThe final cohort comprised 19,713 adult patients who underwent DES implantation with more than 1 month of records after coronary stenting. Our approach demonstrated superior predictive performance for both ischemic and bleeding events across prediction windows of 1, 2, 3, 6, and 12 months, with time-dependent concordance (Ctd) index values ranging from 0.88 to 0.80 and 0.82 to 0.77, respectively. It consistently outperformed the baseline models, including DeepSurv, DeepHit, and SurvTrace, with statistically significant improvement in the Ctd-index values for most evaluated scenarios.ConclusionThe robust performance of our contrastive learning-based model underscores its potential to enhance DAPT management significantly. By delivering precise predictive insights at multiple time points, our method meets the current need for adaptive, personalized therapeutic strategies in cardiology, thereby offering substantial value in improving patient outcomes.

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757–0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792–0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.

Electrochemical Impedance Spectroscopy as a diagnostic and prognostic tool for EV batteries: A review

Electrochemical Impedance Spectroscopy as a diagnostic and prognostic tool for EV batteries: A review

Establishment of liquid-liquid phase separation-related prognostic model in lung adenocarcinoma and systematic analysis of its clinical significance.

To detect the prognostic importance of liquid-liquid phase separation (LLPS) in lung adenocarcinoma. The gene expression files, copy number variation data, and clinical data were downloaded from The Cancer Genome Atlas cohort. LLPS-related genes were acquired from the DrLLPS website. The prognostic model based on LLPS was constructed by the Cox regression and LASSO regression analyses after the identification of LLPS-related differentially expressed genes (DEGs). Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed. The LLPS-related prognostic risk score was validated by GSE31210 and GSE72094. The overall survival of lung adenocarcinoma patients was predicted by plotting a nomogram. The biological features of the high-risk lung adenocarcinoma were evaluated by the CIBERSORT, ESTIMATE, Gene Set Variation Analysis, and Genomics of Drug Sensitivity in Cancer. Reverse transcription-quantitative polymerase chain reaction detected hub gene expression. A total of 91 DEGs were screened out in LLPS, among which 9 genes were discovered as prognostic biomarkers of lung adenocarcinoma. GRIA1, CRTAC1, MAGEA4, and MAPK4 were identified as hub genes by the LASSO Cox regression analysis. High-risk and low-risk groups were divided according to the risk index, with the high-risk group displaying a markedly worse outcome. CRTAC1 expression was significantly decreased, MAGEA4 and MAPK4 expressions were increased, while GRIA1 expression was altered in lung adenocarcinoma cells. Tumor microenvironment, signaling pathway enrichment, and drug sensitivity significantly differed between different risk groups. This work proposed a prognostic tool based on the LLPS-related gene signature to offer prospective and effective biomarkers for lung adenocarcinoma prognosis.

Gut Microbiome Modulation in Hepatocellular Carcinoma: Preventive Role in NAFLD/NASH Progression and Potential Applications in Immunotherapy-Based Strategies

Gut Microbiome Modulation in Hepatocellular Carcinoma: Preventive Role in NAFLD/NASH Progression and Potential Applications in Immunotherapy-Based Strategies

Integrative bioinformatics and immunohistochemical analysis unravel the prognostic significance and immunological implication of LIMCH1 in breast cancer: a retrospective study

The current mortality rates for breast cancer underscore the need for better prognostic tools; moreover, LIM and calponin homology domain 1 (LIMCH1), which is a protein with dual roles in cancer, is a promising candidate for investigation. This study employed an integrative approach combining bioinformatics analysis of The Cancer Genome Atlas (TCGA) cohort and clinical immunohistochemistry (IHC) cohort data. We analysed LIMCH1 expression patterns, its associations with clinicopathological features and prognosis, and its impact on the tumour immune microenvironment (TIME). Functional annotations and single-cell RNA sequencing (scRNA-seq) data were used to explore the underlying molecular mechanisms. Our analysis revealed that high LIMCH1 expression in breast cancer patients was significantly associated with unfavourable clinical outcomes and served as an independent prognostic indicator. The functional annotations revealed pathways related to carcinogenesis and metabolic reconfiguration, thus suggesting the role of LIMCH1 in tumour progression. Additionally, LIMCH1 expression was correlated with an immunosuppressive TIME characterized by increased numbers of M2 macrophages and reduced numbers of CD8 + T cells and NK cells. Finally, we developed a nomogram incorporating LIMCH1 expression for predicting overall survival rates, thus providing a clinically applicable tool. Our research elucidates the diverse functions of LIMCH1 in the pathogenesis of breast cancer, thus highlighting its potential utility as both a prognostic indicator and a therapeutic intervention.

MSR1 in lung squamous cell carcinoma: Prognostic and immunological values in pan-cancer and single-cell analyses and a cohort study.

Lung squamous cell carcinoma (LUSC) constitutes approximately 40% of lung cancer cases and lacks effective treatments, needing new diagnostic and prognostic tools. Macrophage scavenger receptor 1 (MSR1), as a key receptor in macrophages, is essential in tumor immunity. However, its mechanisms in regulating tumor progression and immunity and its prognostic value in LUSC remain unclear. MSR1 expression in pan-cancer, particularly LUSC across distinct clinical subgroups, was identified utilizing TIMER, GEPIA, and UALCAN databases. Prognosis analysis of MSR1 in pan-cancer was conducted using SangerBox, GEPIA, PrognoScan and Kaplan-Meier plotter. Using SangerBox and TIMER, association between MSR1 expression and infiltrating immune cells was investigated. MSR1 gene co-expression network and Gene Set Enrichment Analysis (GSEA) in LUSC were constructed using LinkedOmics database. The analysis of single-cell RNA-sequencing (scRNA-seq) was conducted using the GEO database. Association between plasma MSR1 levels and LUSC risk was evaluated in a cohort study with 49,566 UK Biobank participants. MSR1 was dysregulated in various cancers and lowly expressed in LUSC tissues than in the normal. Higher MSR1 expression was substantially correlated with poor LUSC overall survival. MSR1 positively associated with tumor-associated macrophage (TAM) infiltrations and its markers (CCL2, CD68, IL10). MRS1 closely related to the immune-suppression of macrophages in LUSC. Higher plasma MSR1 levels were positively correlated with increased LUSC risk (HR = 1.33, 95% CI: 1.07-1.64; P = 0.01). MSR1 has significant prognostic and immunological values in pan-cancer and represents a possible biomarker for prognosis and diagnosis in LUSC patients.

Predictive Value of the PaO2/FIO2 Ratio for Mortality in Patients with Acute Respiratory Distress Syndrome: A Systematic Review and Meta-analysis.

Background Despite the controversy regarding its clinical utility, the PaO2/FIO2 ratio has been used to define the severity of acute respiratory distress syndrome (ARDS). This systematic review and meta-analysis (SRMA) details summary estimates of the predictive performance of PaO2/FIO2 ratio in predicting mortality in patients with ARDS. Methods To clarify the integrated diagnostic accuracy, we included studies in which the study population comprised patients with ARDS in any clinical setting, included adult patients (≥18 years old), and evaluated mortality. The MEDLINE and Cochrane Central Registry of Controlled Trials databases were searched for articles in English. We performed SRMA on the accuracy of the diagnostic prognostic tests using the Quality Assessment of Diagnostic Accuracy Studies-2 tool to evaluate the risk of bias. We obtained summary point estimates of sensitivity and specificity and calculated the area under the receiver operating characteristic (AUROC) curve of the summary receiver operating characteristic curve with 95% confidence intervals (CIs). Results Twenty-eight trials with 38270 patients were included in the quality assessment. Most of the studies were conducted in intensive-care units. Overall, the risk of bias is high. For PaO2/FIO2 of 100 and 200 the pooled sensitivity, specificity, and AUROC were 44.8% (95% CI, 38.1%-51.7%), 70.6% (95% CI, 65.9%-74.9%), 0.60 (0.58-0.64) and 83.9% (95% CI, 78.9%-87.8%), 26.1% (95% CI, 20.8%-32.1%), 0.64 (0.60-0.69), respectively. Conclusion The PaO2/FIO2 ratio alone did not have impressive prediction accuracy for mortality in patients with ARDS and might not be able to be used solely as a clinical prognostic tool.

Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.

Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user-friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients. We searched PubMed, Embase, Web of Science, and the Cochrane Database up to February 21, 2024. The risk of bias in selected studies was assessed using a bias assessment tool for predictive models. Meta-analysis synthesized results from validation sets. We included 22 studies with 25,096 stroke patients and 25 models, which were constructed using common and interpretable clinical features. In the validation cohort, the models achieved a concordance index (c-index) of 0.840 (95% CI: 0.810-0.871) for predicting poststroke cerebral edema, with a sensitivity of 0.76 (95% CI: 0.72-0.79) and a specificity of 0.87 (95% CI: 0.83-0.90). ML models are significant in predicting poststroke cerebral edema, providing clinicians with a powerful prognostic tool. However, radiomics-based research was not included. We anticipate advancements in radiomics research to enhance the predictive power of ML for poststroke cerebral edema.

Microalbuminuria: A Diagnostic and Prognostic Tool in Patients Admitted in The Hospital with Sepsis

Microalbuminuria: A Diagnostic and Prognostic Tool in Patients Admitted in The Hospital with Sepsis