Critical Care

Background: Many patients in the ICU receive oxygen to secure blood and tissue oxygenation. Increasing evidence shows exposure to high fractions of inhaled oxygen (FiO2) being associated with adverse effects. In patients with severe ARDS, veno-venous Extracorporeal Membrane Oxygenation (VV-ECMO) can be implemented as a rescue therapy and PaO2 levels can be controlled by the blood flow of the VV-ECMO. Yet, optimal oxygenation targets in ARDS patients treated with VV-ECMO are unknown. Methods: Retrospective analysis of 443 patients with severe ARDS treated with VV-ECMO. Regression analyses were performed for mortality and time-weighted averages of PaO2 and FiO2. Furthermore, considering a possible non-linear relationship, a restricted cubic spline (RCS) model was performed for PaO2. Results: A simple logistic regression for mean PaO2 and ICU mortality showed a significant positive association (per mmHg OR 0.99 [95%CI 0.98-1.00], p=0.002). RCS analysis showed a U-shaped association of mortality and mean paO2 (paO2 69.70-90.24mmHg: OR 0.92 [95%CI 0.89-0.94], p<0.001; paO2 90.24-123.40mmHg: OR 1.09 [95%CI 1.06-1.13], p<0.001). A model including PaO2 as RCS variable and FiO2 showed significant associations of mortality with both variables (PaO2 69.70-90.24mmHg: OR 0.94 [95%CI 0.91-0.97], p<0.001; paO2 90.24-123.40 mmHg: OR 1.07 [95%CI 1.04-1.11], p<0.001; FiO2: OR 35.98 [95%CI 8.67-158.60], p<0.001, VIF<1.11). Conclusions: PaO2-levels in patients with ARDS and VV-ECMO have a U-shaped association with mortality. Optimal outcomes are observed in the 90-123 mmHg range, which is higher compared to non-ECMO settings. Whether this is explainable by increased tissue oxygenation with concurrent avoidance of pulmonary hypoxia should be subject of future research.

Rationale: Sepsis is a life-threatening syndrome causing significant morbidity and mortality especially in the aging population. Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related condition of clonal expansion of hematopoietic stem cells harboring somatic mutations associated with increased incidence of chronic illness and all-cause mortality. Objective: Evaluate the association of pre-illness CHIP with mortality and morbidity in patients admitted to the ICU with sepsis. Methods: We performed a retrospective study using a de-identified electronic health record linked with a DNA biorepository. We identified adult patients with sepsis who had DNA collected prior to ICU admission. We tested the association between CHIP status, determined from whole-genome sequencing, and ICU mortality, organ support-free days, and long-term survival adjusting for age, sex, race and Sequential Organ Failure Assessment (SOFA) score on ICU admission. Measurements and Main Results: Pre-illness CHIP was associated with increased sepsis mortality (OR = 1.54, 95% CI 1.13 to 2.07, P = 0.005) and fewer days alive and free of organ support (-1.7 days, 95% CI -3.2 to -0.2, P = 0.028) after adjusting for age, sex, race, and SOFA score. In sepsis survivors, CHIP was also associated with increased long-term mortality after discharge (HR 1.40, 95% CI 1.01 to 1.93, P = 0.041). Conclusions: Pre-illness CHIP was independently associated with increased mortality and morbidity in critically-ill adults with sepsis. These findings suggest that CHIP is a risk factor for sepsis severity. Elucidating the mechanism underlying this association could uncover new therapeutic interventions for sepsis.

Background Acute respiratory distress syndrome (ARDS) is characterised by substantial physiological heterogeneity, which contribute to a very variable clinical outcomes and therefore inconsistent responses to ventilatory strategies. We aimed to externally validate physiological ARDS subphenotypes previously identified using routine ventilatory and gas-exchange variables, assess their prognostic relevance across independent cohorts, and examine heterogeneity of treatment effect according to PEEP strategy. Methods Unsupervised Gaussian Mixture Modelling was used to identify physiological subphenotypes based on ventilatory mechanics and gas-exchange parameters. Labels were subsequently used to train and validate supervised classifiers using XGBoost. Prognostic relevance was assessed across three independent cohorts, including two randomised controlled trials (ALVEOLI and LOVS). Predictive enrichment for PEEP strategy was evaluated using individual patient data from ALVEOLI and LOVS (n = 1,532) using intention-to-treat analyses, applying both one-stage and two-stage fixed-effects IPD meta-analytic approaches to test for interaction between physiological subphenotype and PEEP strategy. Results Two distinct physiological subphenotypes, termed Efficient and Restrictive, were replicated across independent cohorts. Across each cohort, patients classified as Restrictive consistently exhibited higher all-cause 28-day mortality compared to Efficient patients. When pooled across studies, the Restrictive subphenotype was associated with a significantly increased risk of death (pooled odds ratio 1.75, 95% CI 1.36-2.24), with no evidence of between-study heterogeneity. Predictive analyses showed a statistically significant interaction between physiological subphenotype and PEEP strategy in the one-stage IPD model (p for interaction = 0.037), with concordant findings in the two-stage fixed-effects IPD meta-analysis (interaction OR 1.91, 95% CI 1.00-3.66; I2 = 0%). Higher PEEP was associated with increased mortality in Efficient patients and reduced mortality in Restrictive patients, indicating effect modification by physiological subphenotype. Interpretation Physiological ARDS subphenotypes derived from routinely collected bedside data provide robust and externally validated prognostic stratification across observational and randomised trial cohorts. The observed interaction with PEEP strategy suggests that underlying physiological profiles may influence treatment response, supporting the concept that physiology-based be a starting point for personalized medicine and therefore better ventilatory strategies in future clinical trials.

BackgroundPatent ductus arteriosus (PDA) is a common and potentially serious cardiovascular condition in preterm infants, particularly those with low gestational age and birth weight. Its management remains controversial due to variability in screening, diagnostic criteria, and treatment strategies. This study aimed to evaluate risk factors, outcomes, and management strategies for PDA in preterm infants, and to identify predictors of clinical and echocardiographic response to therapy. MethodsWe conducted a retrospective cohort study over a 4-year period (2016-2019) in the neonatal intensive care unit (NICU) of a tertiary care center. All consecutive preterm infants admitted during the study period were eligible. Infants with echocardiographically confirmed PDA who received pharmacological treatment with intravenous paracetamol or ibuprofen were included in the analysis. Missing data were minimal and handled using available-case analysis. Statistical analyses included descriptive statistics, Pearsons chi-square test, and multivariable logistic regression. ResultsAmong 2154 preterm infants admitted to the NICU, 60 were diagnosed with PDA (incidence : 2.8%). The mean gestational age was 29 {+/-} 2.6 weeks, and the median birth weight was 1200 g. Respiratory distress occurred in 95% of cases, mainly due to hyaline membrane disease (86.7%). PDA was symptomatic in 80% of infants. First-line treatment resulted in clinical improvement in 77% and ductal closure in 83.3% of cases, most within 3 days. Predictors of successful closure included gestational age [&ge;] 28 weeks (OR = 5.9; 95% CI : 1.7-20.2) and antenatal corticosteroid exposure (OR = 1.2; 95% CI : 1.0-1.6). Overall mortality was 35% and was significantly higher in infants < 28 weeks (OR = 5.0; 95% CI : 2.4-10.3). Clinical improvement (OR = 3.7) and echocardiographic closure (OR = 4.5) after first-line treatment were associated with reduced mortality. ConclusionsPDA in preterm infants is associated with substantial morbidity and mortality, particularly in those born before 28 weeks of gestation. Early diagnosis, antenatal corticosteroid exposure, and timely pharmacological treatment may improve outcomes. Systematic echocardiographic screening in high-risk neonates should be considered.

Premature very low birth weight (VLBW) infants have high rates of mortality and morbidity from sepsis, necrotizing enterocolitis, and respiratory failure requiring intubation and mechanical ventilation. Earlier detection of cardiorespiratory deterioration using vital signs from continuous physiological monitoring may lead to more timely interventions and improved outcomes. To further this research area, we present PreMo, a publicly available dataset of continuous heart rate and oxygen saturation, demographics, clinical events, and outcomes for 3,829 VLBW patients from four Neonatal Intensive Care Units (NICUs) in the United States. The PreMo dataset consists of a collection of parquet files, RO-Crate metadata, and sample usage code scripts hosted on the University of Virginia LibraData Dataverse website.

IntroductionThere is increasing interest in the peripheral administration of vasopressors for two main reasons: (1) to expedite vasopressor initiation in patients with refractory shock and (2) to avoid the potential complications associated with central venous catheter placement. The current evidence on the use of peripheral vasopressor administration is primarily based on single-center observational studies. There are inconsistencies in the administration of peripheral vasopressors, including catheter gauge and location, monitoring practices, vasopressor concentrations, and duration of use. This has made it difficult for institutions to develop best practice guidelines. A randomized controlled trial is needed to address this knowledge gap. Methods and analysisThe Peripheral Use of Low-dose Vasopressors for Safety and Efficacy (PULSE) in the intensive care unit is a prospective, unblinded feasibility study. Eligible patients will be 18 years or older, have no existing central venous catheter or peripherally inserted central catheter and have the presence of shock requiring a minimum vasopressor dose of any of the following: norepinephrine 0.0625 mcg/kg/min, phenylephrine 0.625 mcg/kg/min, and epinephrine 0.0625 mcg/kg/min. Fifty patients will be randomized 1:1 into either the peripheral venous catheter or central venous catheter group. The primary outcome is feasibility, defined as (1) a recruitment rate of 4 participants per month, (2) a data capture rate of [&ge;]90%, and (3) a <50% conversion rate from peripheral to central access. The secondary outcomes include the safety of peripheral vasopressor use, alive and central-line-free days, the number of attempts needed to place a catheter, volume status, in-hospital mortality rate, ICU and hospital length of stay, and patient-centred important outcomes. ImplicationsThe data collected from this study will inform the design of a definitive randomized controlled trial to assess the safety and efficacy of protocol-driven peripheral vasopressor administration. Ethics and disseminationThis study received approval (6042888) from the Queens University Health Sciences/Affiliated Teaching Hospitals Research Ethics Boards. Results of this study will be presented at critical care conferences and submitted for publication. Trial registration numberNCT06920173 (https://clinicaltrials.gov/study/NCT06920173).

Sepsis can lead to acute respiratory distress syndrome (ARDS) and is associated with a high mortality rate. This study investigated cellular senescence-related genes in sepsis and sepsis-induced ARDS to identify novel biomarkers. Using bioinformatics analyses including WGCNA and machine learning on public datasets, six hub genes (NFIL3, GARS, PIGM, DHRS4L2, CLIP4, LY86) were identified. These genes showed strong diagnostic value and were associated with immune cell infiltration and key pathways. Validation in lipopolysaccharide (LPS)-stimulated neutrophils showed significant upregulation of NFIL3. The findings highlight the role of cellular senescence in pathogenesis and identify promising therapeutic targets for sepsis-induced ARDS.

Background Venous thromboembolism (VTE) remains a major cause of perioperative morbidity in cranial neurosurgery, yet clinical practice varies widely, and formal guidelines are inconsistent. Understanding internationally sampled neurosurgical practice is essential for informing consensus and future trials. Methods An international, 2-stage cross-sectional, internet-based survey was conducted. Practising neurosurgeons performing elective adult cranial surgery were eligible. Descriptive statistics were used to summarise practice. Responses covered patterns of pre-operative haemostasis decision making, use and timing of mechanical and/or chemical prophylaxis, use of perioperative imaging prior to anticoagulation, and frequency of clinical assessment for VTE. Associations with geographical income status, subspecialty, and years post-certification were statistically tested. Practice heterogeneity was quantified and contextual influence was summarised using mean effect sizes across stratifying variables in order to determine domains of true equipoise. Results Of 585 responses, 456 (78%) met criteria for inclusion: representing 322 units across 78 countries (71% high-income). Thirteen per cent reported no departmental VTE plan; 23% followed no guidelines and 12% used multiple. Routine pre-operative testing almost universally included haemoglobin/platelets/haematocrit, with fibrinogen more common in high-income settings. Compared with high-income country respondents, low- and middle-income respondents reported higher haemoglobin transfusion thresholds (>90 g/dL; p<0.001) and shorter antiplatelet interruption (p[&le;]0.03), and less frequent outpatient VTE assessment (p<0.001). Mechanical prophylaxis was common (TEDs 81%, IPC 62%), typically started pre- or intra-operatively. Among those completing the chemoprophylaxis section (n=310), 57% required a CT or MRI scan before LMWH which was then initiated on average 31.4 hours after surgery. 1% of respondents did not routinely use LMWH. Many clinical decisions demonstrated statistical equipoise ie. high heterogeneity with low contextual influence. Conclusion Peri-operative haemostasis and VTE prophylaxis practices in adult elective cranial neurosurgery vary substantially worldwide, with some decisions reflecting geographical or socioeconomic differences and many others reflecting true clinical equipoise rather than contextual determinants. By mapping contemporary real-world practice across diverse health-system contexts, this study provides a necessary empirical foundation for rational trial design and future guideline development.

Background: Patient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. Methods: We developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. Results: The LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). Conclusions: Overall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registration: Approval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

BackgroundIn patients requiring respiratory support, clinicians rely on physical exam, radiologic, laboratory, and ventilator-derived measures for the provision of sufficient support while minimizing ventilator and "work of breathing" induced lung injury. Point of care lung ultrasound (LUS) is a widely available tool in hospital and clinic environments. To date, LUS has not been used to evaluate lung strain. MethodsWe collected LUS images in four anesthetized, neuromuscularly blocked, and mechanically ventilated pigs being used for another experiment. A feature tracking tool was developed which tracked echo-bright lung structures in ten second clips obtained in triplicate of the right and left, upper and lower lung fields using tidal volumes of 4, 6, 8, 10, and 12 mL/kg. Pleural lines were manually drawn and a program for quantifying lung strain developed with assistance from Anthropic Claude Artificial Intelligence tool. Structures were identified in inspiratory and expiratory frames and tracked bidirectionally with median strain per frame used for calculations. ResultsTriplicate measures of lung ultrasound images in four pigs had a median coefficients of variation of 35% (23-47% IQR) and linear modeling of strain with tidal volumes of 4-12 mL/kg showed positive correlation with R2 value ranging from 0.89 to 0.97. Strain measurements were similar after bronchial administration of 1.5M hydrochloric acid. ConclusionsRegional lung strain quantification using LUS is a viable and potentially useful tool for respiratory support management.

Background: The Therapeutic Distance framework (Paper 1) achieved AUC 0.61 for orbit-based mortality prediction in 11,627 sepsis patients. We hypothesised that incorporating state-dependent parameter relevance would substantially improve prediction. Methods: We extended the framework to 84,176 ICU patients from MIMIC-IV v3.1 across 16 clinical syndromes. Validation included full-population leave-one-out (n=59,362), head-to-head comparison against SAPS-II and logistic regression on 34,467 matched patients with bootstrap confidence intervals, temporal validation, outcome permutation, sensitivity analysis, and calibration assessment. Results: Full-population leave-one-out achieved AUC 0.832 (n=59,362). On 34,467 matched patients, Therapeutic Distance (AUC 0.841) significantly outperformed both SAPS-II (0.786; delta=+0.055, 95% CI +0.048 to +0.061, p<0.001) and logistic regression (0.788). Temporal validation showed stable performance (delta=-0.006). Outcome permutation confirmed genuine signal (AUC 0.859 to 0.498 with shuffled mortality). Sensitivity analysis demonstrated near-zero variation (delta 0.0006-0.003). The framework performed well for 8 of 16 syndromes (AUC >0.70) and failed for DKA and post-cardiac surgery (AUC <0.40). Conclusions: Therapeutic Distance provides therapy-specific risk stratification that exceeds both established severity scores and standard machine learning while remaining robust to hyperparameter choices, temporal drift, and outcome permutation.

Background: Patient matching in intensive care databases yields sample sizes too small for individualised outcome analysis. Current AI systems provide population-level guideline summaries but omit stratification variables that may invert therapy signals at the individual level. Methods: We developed the Therapeutic Distance framework, which computes the z-standardised distance between a patient's clinical parameters and the centroid of MIMIC-IV patients who received each therapy: d(P,T) = sum of wi(T) x |(Li - mui(T)) / sigmai|. We hypothesise that patients at the same distance to a therapy (same orbit) have comparable outcomes. Six validation experiments were performed on 11,627 sepsis patients (SAPS-II 30-80) from MIMIC-IV v3.1. Results: Echo-stratified vasopressin recipients showed mortality of 30.1% (n=146, 95% CI 22.6-37.7%) versus 53.9% without echo (n=2,426, 95% CI 51.9-55.9%). Confidence intervals did not overlap (bootstrap, 1,000 resamples). However, echo-stratified patients had lower general severity (SAPS-II 49.2 vs 53.9) but higher cardiac biomarkers (troponin 1.0 vs 0.51 ng/mL), indicating that the observed difference is compatible with both severity confounding and a possible cardiac-specific vasopressin effect. Leave-one-out prediction with uniform weights achieved AUC 0.61 as a structural baseline. Conclusions: Therapeutic Distance replaces patient matching with orbit matching, substantially increasing usable sample sizes. The echo-vasopressin finding is hypothesis-generating and mechanistically plausible but not causally proven. The framework is intended as a clinical decision support signal under uncertainty, not as a causal inference method.

Background: How post-COVID-19 condition (PCC) differs from post-acute infection syndromes (PAIS) caused by other respiratory viruses remains uncertain. Comparing these conditions may clarify whether post-acute symptoms reflect specific consequences of SARS-CoV-2 infection or broader post-viral mechanisms. Methods: We conducted a systematic review and meta-analysis of cohort studies comparing persistent symptoms or conditions in adults after SARS-CoV-2 infection with those following other acute respiratory viral infections. PubMed, Embase, and Scopus were searched. Random-effects models were used to estimate pooled risks. Results: Among 9,371 records screened, 22 studies were included and 14 contributed to the meta-analysis. Increased risk after SARS-CoV-2 infection was observed for pulmonary embolism, abnormal breathing, fatigue, hemorrhagic stroke, memory loss/brain fog, and palpitations; heart rate abnormalities showed borderline significance. For most other outcomes pooled estimates were inconclusive. Conclusions: Only a subset of outcomes appears more frequent after SARS-CoV-2 infection, suggesting many symptoms attributed to PCC may reflect broader post-viral syndromes.

Background: Elevated lipoprotein(a) [Lp(a)] is a known risk factor for several cardiovascular-related diseases established from multiple genetic and observational studies. However, the underlying mechanisms mediating the effects of Lp(a) levels on cardiovascular disease risk and major adverse cardiovascular events (MACE) are unclear. The aim of this study was to identify proteins downstream of Lp(a) using mendelian randomization (MR) - a genetic causal inference approach. Methods: A two-sample MR was performed by initially identifying Lp(a) genetic instruments based on data from genome wide association studies (GWAS) of Lp(a) blood concentrations. These instruments were then tested for association with proteins from proteomic pQTL data (Olink from UK Biobank, 2940 proteins and SomaScan from deCODE, 4907 proteins). Results: A total of 521 proteins associated with Lp(a) were identified. Using pathway enrichment analysis, the following MACE-relevant pathways were identified comprising a total of 91 Lp(a) downstream proteins: oxidized phospholipid-related, chemotaxis of immune cells and endothelial cell activation, pro-inflammatory monocyte activation, neutrophil activity, coagulation, and lipid metabolism. Conclusion: The results suggest that the influence of Lp(a) treatments is primarily through modifying inflammation rather than lipid-lowering, thus providing insight into the mechanistic framework which mediates the effects of elevated Lp(a) on atherosclerotic cardiovascular disease.

Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very severe sleep apnea defined as an apnea-hypopnea index (AHI)[&ge;] 50 events/h, and to assess how training-set composition affects performance in this subgroup. MethodsWe analyzed 552 overnight recordings, 318 from the Sleep Lab Dataset and 234 from the Hospital Dataset. In the Hospital Dataset, 26.5% had very severe sleep apnea. We developed a deep learning model for sleep staging using RR intervals from wrist-worn photoplethysmography and three-axis accelerometry. Baseline performance was assessed by cross-validation under 5-stage and 4-stage staging. We examined night-level associations with AHI severity. We also compared the baseline model with an ablation model trained on the same number of recordings but with more Sleep Lab Dataset and lower-AHI Hospital Dataset recordings, evaluating both models in the very severe subgroup. ResultsIn 5-stage classification, Cohens kappa was 0.586 in the Sleep Lab Dataset and 0.446 in the Hospital Dataset. Under 4-stage staging, the gap narrowed, with kappa values of 0.632 and 0.525, respectively. In the Hospital Dataset, performance declined with increasing AHI severity. Among 62 recordings with very severe sleep apnea, reducing high-AHI representation in training lowered kappa from 0.365 to 0.303. ConclusionsWearable sleep staging performance declined across greater sleep apnea severity in this clinical cohort. Clinical utility may benefit from training data that better represent the target severity spectrum and from selecting staging granularity to match the intended use case. Statement of SignificanceRepeated laboratory polysomnography is impractical for long-term sleep apnea management. Wearable sleep staging could support scalable monitoring, yet its reliability in clinically severe sleep apnea has remained unclear. This study developed and evaluated a wearable sleep staging approach in both sleep-laboratory and hospital cohorts. The hospital cohort included many severe and very severe cases. Performance was lower in the hospital cohort and declined with greater sleep apnea severity. A coarser staging scheme reduced the gap between cohorts, and models trained without representative very severe cases performed worse in this target population. These findings highlight the value of severity-aware model development and motivate future multi-night home validation with reliability cues.

Background: Heart failure (HF) is a major and growing public health problem, and prior studies support a meaningful genetic contribution to HF susceptibility. Clinically, HF is commonly categorized into the major clinical sub-types of HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), which differ in pathophysiology and clinical profiles. However, previous genome-wide association studies have focused on autosomal variation and have routinely excluded the X chromosome, leaving X-linked genetic contributions to HF and its subtypes under-characterized. Methods: We performed X-chromosome wide association study (XWAS) utilizing directly genotyped data from 590,568 Million Veteran Program participants, including 90,694 HF cases across European, African, Hispanic, and Asian Americans. Sex- and ancestry-stratified logistic regression was used with XWAS quality control measures, adjusting for age and population structure, followed by fixed-effects multi-ancestry meta-analysis. Functional annotation, gene-based testing, fine-mapping, and colocalization were performed. We replicated genetic associations with all-cause HF in the UK Biobank. Results: In the multi-ancestry meta-analysis, we identified five X-chromosome-wide significant loci for all-cause HF, five for HFrEF, and one locus for HFpEF in males. No loci reached significance in female-specific analyses. In sex-combined analyses, we identified six loci for all-cause HF and four for HFrEF. The strongest and most emphasized signals mapped to genes were BRWD3, FHL1, and CHRDL1. Ancestry-specific analyses revealed additional loci, including NDP and WDR44 in African ancestry and PHF8 in Hispanic ancestry. One locus, BRWD3, was replicated in UK Biobank HF cohort. Integrated post-GWAS analyses (fine-mapping, colocalization and pleiotropy trait association studies) reinforced the biological plausibility of the X-linked signals. Conclusions: This multi-ancestry, sex-stratified XWAS identifies X-linked genetic contributions to HF and its subtypes and highlights the role of X-chromosome in heart failure pathogenesis.

Cardiac transthyretin amyloidosis (ATTR-CA) is caused by myocardial deposition of misfolded transthyretin, leading to progressive heart failure. Disease pathology, however, extends beyond passive amyloid deposition and also involves active processes such as extracellular matrix (ECM) remodeling and immune activation. Mass spectrometry (MS) is the gold standard for amyloid typing in diagnostics. Here, we applied quantitative MS-driven proteomics on formalin-fixed paraffin-embedded whole cardiac tissue sections from six ATTR-CA cases, ten unaffected controls and four AL-CA controls to investigate protein expression changes. In addition to transthyretin, over 500 proteins were upregulated in ATTR-CA biopsies, including complement and coagulation factors as well as extracellular matrix (ECM) remodeling proteins. Among these, members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family, metalloproteinases (MMPs), and Tissue Inhibitor of Metalloproteinases (TIMP3) showed significant upregulation. These proteins are key regulators of ECM turnover and structural integrity. Immunohistochemistry confirmed ADAMTS4 enrichment in amyloid deposits, while TIMP3 showed strong expression in cardiomyocytes and weaker staining within amyloid deposits. Together, these findings indicate that ECM remodeling, alongside complement and coagulation activation, represents a reproducible feature of cardiac ATTR amyloidosis. Whole-tissue proteomics provides biological insights that extend beyond amyloid typing, with potential implications for biomarker discovery and therapeutic targeting in ATTR-CA.

Background: The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a validated patient-reported outcome measure for heart failure. However, its clinical utility is limited by incomplete and inconsistent data collection. We aimed to develop and validate machine learning models to estimate KCCQ overall summary scores from electronic health record (EHR) data. Methods: We assembled a retrospective cohort of 10,889 heart failure patients with recorded KCCQ scores from the Truveta database. Predictor features were derived from structured EHR variables across 13 historical time windows (15-360 days). Multiple regression algorithms were evaluated, followed by SHapley Additive exPlanations (SHAP)-based feature reduction and nested cross-validation for hyperparameter optimization. Model performance was assessed using the coefficient of determination (R2), mean absolute error (MAE), and ordinal discrimination and calibration for categorical severity classification. Results: Histogram-based gradient boosting (HGB) with HGB-SHAP feature selection achieved the strongest performance, reducing feature dimensionality by more than 94\% while maintaining estimation accuracy. The 240-day window performed best (R2=0.522, MAE=12.485). For categorical severity classification, the model demonstrated strong ordinal discrimination (mean ordinal AUROC=0.850). Quantile-based calibration improved classification balance, increasing the F1-score for the most severe category (KCCQ<25) from 0.180 to 0.428 and the quadratic weighted kappa from 0.601 to 0.640. Longer EHR observation windows were associated with improved prediction performance. Conclusion: Machine learning models can estimate KCCQ scores from routine EHR data with clinically meaningful accuracy and strong discriminatory performance. This approach may help extend assessment of patient-reported health status to populations in which survey-based data are incompletely captured, supporting population-level cardiovascular outcomes assessment and risk stratification in heart failure care.

Objective: To describe the clinical characteristics of term neonates with neonatal bacterial meningitis (NBM) and explore the association between different pathogens and imaging complications, providing clinical evidence for early identification and individualized management. Methods: A retrospective study was conducted on 531 term neonates diagnosed with NBM admitted to the Capital Institute of Pediatrics from 2013 to 2025. Demographics, clinical manifestations, laboratory parameters, etiological results, imaging complications and treatment measures were collected. Patients were divided into favorable/adverse discharge outcome groups and pathogen-positive/negative groups. Statistical analyses were performed using appropriate tests, and Cramers V coefficient was used to analyze the association between pathogens and imaging complications. Results: (1) The most common clinical manifestations were abnormal body temperature (79.85%), altered consciousness (55.18%) and jaundice (46.52%). CSF/blood culture was positive in 133 cases (25.05%), with Escherichia coli (27.07%), group B streptococcus (17.29%) and Staphylococcus species (16.54%) as predominant pathogens. The overall incidence of imaging complications was 22.22%, mainly hydrocephalus (5.84%), subdural effusion (4.90%) and encephalomalacia (2.64%). (2) Adverse discharge outcomes occurred in 107 cases (20.15%). Compared with the favorable group, the adverse group had higher incidences of convulsions, altered consciousness, anterior fontanelle bulging, abnormal muscle tone and primitive reflexes (all P<0.001), more obvious laboratory abnormalities (higher CRP, CSF leukocytes and protein, lower CSF glucose, all P<0.05), higher culture positive rates and greater need for adjuvant therapy (all P<0.001). (3) Pathogen-positive patients had higher imaging complication rates. Gram-negative infections were associated with higher hydrocephalus and subdural effusion rates, while Gram-positive infections had higher brain abscess risk. Specifically, Escherichia coli correlated with hydrocephalus and subdural effusion; group B streptococcus with cerebral infarction and encephalomalacia; LM with intracranial hemorrhage and brain abscess; negative cultures correlated with no imaging complications (all P<0.05). Conclusion: Term NBM neonates have non-specific manifestations, mainly abnormal body temperature and altered consciousness. Predominant pathogens are Escherichia coli, group B streptococcus and Staphylococcus species, with hydrocephalus and subdural effusion as common imaging complications. Adverse outcomes are associated with severe symptoms, obvious laboratory abnormalities and higher pathogen positivity. Specific pathogens correlate with distinct imaging complications.

COVID-19 has spread rapidly and caused a global pandemic making it one of the deadliest in history. Early identification of patients with coronavirus disease 2019 who may develop critical illness is of immense importance. Therefore, novel biomarkers were needed to identify patients who will suffer rapid disease progression to severe complications and death. Many treatments were adopted including the antiviral Remdesivir, the antiretroviral Lopinavir /Ritonavir and Tocilizumab. Our study aimed not only to specify high-risk factors and biomarkers of fatal outcome in hospitalized subjects with coronavirus but also to compare the efficacy of the three considered treatments to help clinicians better choose a therapeutic strategy and reduce mortality. We divided the population (n=711) into four main groups based according to the WHO ordinal severity scale. The percentage of mortality, in and out the hospital, the length of stay in the hospital, the pulmonary inflammatory lesion and its distribution, the SARS-CoV-2 IgM and IgG variations at admission, the inflammatory markers, the complete blood count, the coagulation factors and enzymes, proteins and electrolytes profile, glucose and lipid profile, and other relevant markers were measured. The significance of the observed variation was assessed by multivariate and ANOVA analyses. We succeeded to establish a novel predictive scoring model of disease progression based on a cohort of Lebanese hospitalized patients relying on the pulmonary inflammatory lesions, inflammation biomarkers such as LDH, D-Dimer, CRP, IL-6 and the lymphocyte count, the number of comorbidities and the age of the patient which all were significantly correlated with the illness severity showing best outcomes with immunomodulatory and anticoagulant treatments by the results. As top tier, Tocilizumab was more efficient than the two other treatments in non-severe cases but none of the used treatments was insanely effective alone to reduce mortality in severe cases.