Predictor Of Clinical Outcome Research Articles

Evaluating self-efficacy as a treatment mechanism during an intensive treatment program for posttraumatic stress disorder.

Although traumatic exposures are common, only a small percentage of people exposed to trauma go on to develop posttraumatic stress disorder (PTSD). This phenomenon suggests that there may be psychological factors that influence posttraumatic recovery trajectories. Beliefs about one's ability to cope with traumatic events have been proposed as a mechanism of posttraumatic recovery. The present study evaluated coping self-efficacy (CSE) as a treatment mechanism. Data were collected from 423 military service members and veterans who completed a 2-week cognitive processing therapy-based intensive treatment program for PTSD. Linear mixed-effects models were used to evaluate the associations between CSE and clinical symptoms over time. CSE and clinical symptoms were assessed at baseline, every other day during treatment, and at posttreatment. In addition, general self-efficacy (GSE) was assessed at baseline and included in the analyses. Participants reported that increases in CSE began early and steadily increased across all domains during treatment. In addition, decreases in PTSD and depression severity also began early and steadily decreased during treatment. Although improvements in CSE predicted decreases in clinical symptoms, changes in CSE did not precede clinical improvement. Baseline GSE was a significant predictor of clinical outcomes, but changes in clinical symptoms during treatment did not differ based on one's baseline GSE. The present study demonstrated that although changes in CSE do not temporarily precede changes in clinical symptoms, changes in CSE predicted changes in clinical symptoms, suggesting that CSE may serve as an indicator of treatment response. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Evaluation of cardiac troponin I as a predictor of clinical outcomes in cattle treated for bovine respiratory disease (BRD) in commercial feedyards

Evaluation of cardiac troponin I as a predictor of clinical outcomes in cattle treated for bovine respiratory disease (BRD) in commercial feedyards

Crosslinked Versus Non-Crosslinked Resorbable Collagen Membranes for Periodontal Regeneration: A Multicenter, Randomized, Double-Blind, Non-Inferiority Clinical Trial.

This study aimed to evaluate and compare the results of combination therapy involving bone grafting and two different resorbable collagen membranes in 1-, 2- and 3-wall infrabony defects. A total of 174 patients with infrabony defects (≥ 7 mm periodontal probing depth) were randomized to receive deproteinized bovine bone mineral (DBBM) with either a native porcine non-crosslinked collagen membrane (N-CM, control, n = 87) or a novel porcine crosslinked collagen membrane (C-CM, test, n = 87). Clinical parameters, including periodontal probing depth (PPD), clinical attachment level (CAL), and gingival recession (GR), were recorded at baseline, 12 weeks, and 24 weeks. Radiographic evaluations measured linear bone gain (LBG) and percentage of bone fill (%BF) at baseline and 24 weeks. Generalized Estimating Equations (GEE) were used to identify predictors of clinical outcomes. The primary outcome was the total effectiveness rate based on a composite outcome score integrating clinical and radiographic parameters at 24 weeks. One hundred seventy three patients completed the study. At 24 weeks, mean improvements in PPD were 4.17 ± 1.48 mm and 4.16 ± 0.97 mm for the control and test groups, respectively, while CAL gains were 3.69 ± 1.32 mm and 3.60 ± 1.81 mm. Radiographic linear bone gain was 3.12 ± 2.19 mm in the control group and 3.00 ± 1.92 mm in the test group. Subgroup analysis showed trends favoring the test group for PPD (p = 0.046) and CAL (p = 0.042) improvements in 1-wall defects. The total effectiveness rate was 96.55% in the control group and 95.35% in the test group, with a difference of -1.2% (95% CI: -5.88% to 3.47%). Among those with effective results, the test group had a higher proportion achieving significantly effective outcomes compared to the control group (96.5% vs. 86.2%, p = 0.032). Regression analysis identified treatment group, defect morphology, and baseline defect depth as significant predictors of PPD and CAL outcomes. The novel porcine crosslinked collagen membrane demonstrated non-inferiority to the native non-crosslinked membrane in periodontal regeneration. Regression analysis highlighted defect morphology and baseline defect depth as key predictors of outcomes, while subgroup analysis suggested potential advantages of the C-CM in challenging defect morphologies, such as 1-wall defects. These findings provide valuable insights into clinical decision-making. However, the findings are limited by the short-term nature of the study (24 weeks), and further long-term investigations are needed to confirm these preliminary results and assess their clinical relevance. ClinicalTrials.gov identifier: NCT04851847.

Predictors of Cryptococcus gattii clinical presentation and outcome: An international study.

Infection by Cryptococcus gattii can lead to pulmonary or central nervous system (CNS) disease, or both. Whether site of infection and disease severity are associated with C. gattii species and lineages or with certain underlying medical conditions, or both is unclear. We conducted a retrospective cohort study to identify factors associated with site of infection and mortality among C. gattii cases. We extracted data on 258 C. gattii cases from Australia, Canada and the United States reported from 1999 to 2011. We conducted unadjusted and multivariable logistic regression analyses to evaluate factors associated with site of infection and C. gattii mortality among hospitalized cases (N=218). Hospitalized C. gattii cases with CNS and other extrapulmonary disease were younger, more likely to reside in Australia and be infected with VGI lineage but less likely to have comorbidities and die as compared to pulmonary cases. The odds of having CNS and/or other extrapulmonary disease were 9 times higher in cases with VGI infection (aOR=9.21, 95%CI=3.28-25.89). Age >70 years (aOR=6.69, 95%CI=2.44-18.30), chronic lung disease (aOR=2.62, 95%CI=1.05-6.51) and an immunocompromised status (aOR=2.08, 95%CI=1.05-6.51) were associated with higher odds of C. gattii mortality. Among hospitalized cases, C. gattii species and lineage are associated with site of infection but not with the risk of death, whereas older age and comorbidities increase the risk of death.

Tricuspid annular plane systolic excursion/pulmonary arterial systolic pressure ratio as a predictor of outcome in acute heart failure.

This study delves into the significance of TAPSE/PASP as a predictive tool, shedding light on its potential to revolutionize the landscape of AHF management. We included 152 patients with AHF. Echocardiographic evaluation for left ventricle systolic and diastolic function was performed at the time of admission. RV functions were evaluated by calculating the following (TAPSE, PASP, TAPSE/PASP ratio). Data were analyzed to find the predictors of mortality and/or rehospitalization. The TAPSE/PASP ratio emerged as a significant independent predictor of clinical outcomes in AHF patients (HR=2.6; 95%CI: 1.04-6.47; p=0.04). Furthermore, it was the sole predictor of rehospitalization for AHF (HR=3.97; 95%CI: 1.38-11.40; p=0.01). It also independently predicted all-cause mortality in AHF, with an HR of 2.73 (95% CI: 1.25-9.12; p=0.03). When evaluating its predictive accuracy, the TAPSE/PASP ratio with a cutoff value <0.35 mm/mmHg demonstrated a sensitivity of 65%, specificity of 70%, and an area under the receiver operating characteristic curve of 0.70 for forecasting adverse outcomes. The non-invasive TAPSE/PASP ratio is an independent predictor of mortality and /or rehospitalization in patients with acute heart failure.

Preoperative Neutrophil-to-Lymphocyte Ratio as a Predictor of Clinical Outcomes in Patients Undergoing Femoral Endarterectomy.

Background/Objectives: This study aimed to evaluate the prognostic value of preoperative neutrophil-to-lymphocyte ratio (NLR) in patients with peripheral arterial disease (PAD) undergoing femoral endarterectomy. Methods: We performed a retrospective analysis of our institutional data, evaluating consecutive patients with PAD who underwent femoral endarterectomy between January 2013 and March 2023. The main objective was to assess the prognostic value of preoperative NLR for 5-year mortality. Additionally, we examined its relationship with perioperative clinicopathological features and 5-year major adverse limb events (MALEs). Results: During the study period, 200 consecutive patients underwent femoral endarterectomy. Of these, 128 patients with available NLR values within 30 days prior to surgery were analyzed. According to the receiver operating characteristic curve, the cut-off value of NLR was 4.0. Eighty-seven patients (68.0%) were assigned to the low-NLR group, and 41 patients (32.0%) to the high-NLR group. The frequency of postoperative complications did not differ significantly between the two groups. Freedom from MALEs up to five years was significantly lower in the high-NLR group (66.0% vs. 46.5%, p = 0.006). The overall survival rates were significantly lower in the high-NLR group (p < 0.001). At 1, 3, and 5 years, the survival rates in the low-NLR group were 96.4%, 91.6%, and 84.5%, respectively, while those in the high-NLR group were 84.2%, 59.5%, and 42.5%. Univariate analysis showed that cerebrovascular disease, end-stage renal disease, Rutherford category ≥ 4, a low albumin concentration (<3.5 g/dL), and a high NLR were significantly associated with 5-year mortality. Multivariate analysis indicated that a high NLR was the only independent factor associated with 5-year mortality. Conclusions: Preoperative NLR > 4.0 was significantly associated with 5-year rates of MALE and mortality in patients with symptomatic CFA occlusive disease who underwent femoral endarterectomy.

DigiLoCS: A leap forward in predictive organ-on-chip simulations.

Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between in vitro findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The in vitro liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and in vitro to in vivo extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits: i) better prediction of intrinsic liver clearance of drugs compared to the conventional model and ii)explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various in vitro liver-on-chip systems. Accurate prediction of in vivo clearance from in vitro data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic in vitro systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.

Predictors of Clinical Outcome after Percutaneous Coronary Intervention in Hemodialysis Patients with Acute Myocardial Ischemia Syndrome

Introduction: Acute myocardial ischemia syndrome (AMIS), encompassing conditions such as unstable angina and acute myocardial infarction, is a major global health concern due to its significant morbidity and mortality rates. Objective: The main objective of the study is to find the predictors of clinical outcomes after percutaneous coronary intervention in hemodialysis patients with acute myocardial ischemia syndrome. Methodology: This retrospective cohort study was conducted at Army Cardiac Hospital Lahore from June 2023 to June 2024. Data were collected from 178 patients. Data were extracted from electronic health records, capturing comprehensive patient information across multiple domains. Results: The study analyzed data from 178 hemodialysis patients with mean age of 58.91 ± 8.02 years, with a gender distribution of 60% males and 40% females. Comorbid conditions were prevalent, with 65% having diabetes mellitus and 85% hypertension. Smoking was reported by 25% of individuals, and 40% had a history of myocardial infarction (MI) or revascularization. Laboratory parameters indicated anemia (mean hemoglobin: 10.2 ± 1.5 g/dL), low albumin levels (3.2 ± 0.5 g/dL), and systemic inflammation, as 70% had elevated CRP levels. Conclusion: It is concluded that hemodialysis patients undergoing PCI for acute myocardial ischemia syndrome face significant risks influenced by nutritional status, systemic inflammation, and comorbidities such as diabetes and cardiogenic shock

Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.

Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughput technology in the biomedical field has made it possible to obtain multi-omics data, whose integration can compensate for missing or unreliable information in a single data source. In this study, we integrated clinical data and two omics data, i.e., gene expression and DNA methylation data, to study the prognosis of neuroblastoma. Since the features in omics data are redundant, it is crucial to conduct feature selection on them. We proposed a two-step feature selection (TSFS) method to quickly and accurately select the optimal features, where the first step aims at selecting candidate features and the second step is to remove redundant features among them using our proposed maximal association coefficient (MAC). Our goal is to predict composite clinical outcomes for neuroblastoma patients, i.e., their survival time and vital status at the last follow-up, which was validated to be two inter-correlated tasks. We conducted a series of experiments and evaluated the experimental results using accuracy and AUC (area under the ROC curve) evaluation metrics, which indicated that by the combination of the integration of the three types of data, our proposed TSFS method and a multi-task learning method can synergistically improve the reliability and accuracy of the prediction models.

Predictors of parenchymal hematoma and clinical outcome after mechanical thrombectomy in patients with large ischemic core due to large vessel occlusion: a retrospective multicenter study

BackgroundThe aim of our study was to find predictors of parenchymal hematoma (PH) and clinical outcome after mechanical thrombectomy (MT) in patients with large vessel occlusion (LVO) and baseline large...

Evaluation of Myocardial Perfusion Imaging in Patients Undergoing Cardiac Surgery

Background: Precise preoperative risk assessment is essential in cardiac surgery to enhance patient outcomes. Myocardial perfusion imaging (MPI) plays a critical role in assessing myocardial ischemia and guiding surgical decisions, but its specific impact on surgical outcomes requires further investigation. Methods: This retrospective study included 780 patients who underwent cardiac surgery at the Interventional Centers in Xining, Qinghai, China, between March 2020 and September 2023. MPI findings were analyzed to assess their association with intraoperative and postoperative complications. Positron emission tomography (PET) imaging was evaluated for its sensitivity and specificity in detecting ischemia. Results: The cohort had a mean age of 65 years, with 66.5% being male, and a high prevalence of hypertension (69.6%) and diabetes mellitus (41.3%). The MPI results indicated that 33.2% of patients had normal perfusion, while 22.3, 16.8 and 14.9% of patients had mild, moderate and severe ischemia, respectively. Patients with severe ischemia experienced significantly higher rates of intraoperative complications (16.4%), postoperative myocardial infarction (10.3%), arrhythmias (14.7%) and mortality (5.2%) compared to patients with normal perfusion (1.9, 0.8, 3.1 and 0.4%, respectively). PET imaging demonstrated superior diagnostic performance, with the highest specificity (88.3%) and sensitivity (92.6%) for detecting ischemia and guiding surgical decisions. Conclusions: The severity of ischemia, as assessed by MPI, is a significant predictor of clinical outcomes in cardiac surgery. These findings underscored the importance of using MPI not only for preoperative risk stratification but also for guiding personalized treatment based on ischemia severity. PET imaging's diagnostic accuracy further highlights its role as a preferred modality in this context.

Intra-abdominal Hypertension Is a Strong Predictor of Mortality and Poor Clinical Outcome in Severe Acute Pancreatitis.

Acute pancreatitis is one of the leading causes of mortality and morbidity. Most acute pancreatitis scoring systems have no pathophysiologic basis when evaluating severity. Such a limitation led to an interest in measuring intra-abdominal pressure (IAP) as a method to predict outcomes in patients with acute pancreatitis. Investigate the predictive impact of intra-abdominal hypertension (IAH) on mortality and clinical outcomes in a patient hospitalized with severe acute pancreatitis. We conducted a systematic search of the PubMed, Embase, and Cochrane databases from inception through November 2021 for studies evaluating the effect of IAH on acute pancreatitis. Relevant data were extracted and analyzed using STATA 17 software. A random-effects model was used for all variables. Publication bias was assessed using Egger's test. Fourteen studies investigating 1197 patients were included. Mortality, multiorgan dysfunction syndrome, pancreatic necrosis, renal, respiratory, and cardiovascular failure were more likely in the IAH group. However, infected necrosis and surgical intervention were not statistically significant between the two groups. After excluding abdominal compartment syndrome patients, mortality and respiratory failure were the only outcomes, which remained statistically significant. Patients admitted to the hospital with severe acute pancreatitis have higher odds for mortality, multiorgan dysfunction syndrome, renal, respiratory, and cardiovascular failure if they developed IAH. IAH remained a strong predictor of mortality and respiratory failure even in the absence of abdominal compartment syndrome. Therefore, the development of IAH is a strong predictor of mortality and poor clinical outcome in such a population.

Community-onset pediatric status epilepticus: Barriers to care and outcomes in a real-world setting.

Status epilepticus (SE) is a neurological emergency in childhood, often leading to neuronal damage and long-term outcomes. The study aims to identify barriers in the pre-hospital and in-hospital management of community-onset pediatric SE and to evaluate the effectiveness of pediatric scores on outcomes prediction. This monocentric observational retrospective cohort study included patients treated for community-onset pediatric SE in a tertiary care hospital between 2010 and 2021. Data were extracted following Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Inclusion criteria were community-onset SE (according to the International League Against Epilepsy [ILAE] Task Force on SE Classification), admission to the pediatric emergency department (PED), age: 1 month to 18 years. Pre-hospital, in-hospital management and outcomes were analyzed. Pediatric scores for prediction of clinical worsening (Pediatric Early Warning Score - PEWS) and SE outcome (Status Epilepticus in Pediatric patients Severity Score - STEPSS; Pre-status Epilepticus PCPCS, background Electroencephalographic abnormalities, Drug refractoriness, Semiology and critical Sickness Score - PEDSS) were retrospectively assessed for their accuracy in predicting short-term and long-term outcomes. A total of 103 consecutive episodes of SE were included. Out-of-hospital rescue medications administration occurred in 54.4% of cases and was associated with higher SE resolution rate before PED admission (48.2% vs 27.6%, p = .033). Longer in-PED time to treatment was observed in case of delay to PED referral (r = 0.268, p = .048) or non-red triage labels (12 vs 5 min, p = 0.032), and was associated with longer in-PED duration of SE (r = 0.645, p < .001). Longer SE duration was observed in episodes leading to hospitalization compared to those discharged (50 vs 16 min, p < .001). In-PED electroencephalography (EEG) recordings were available in 39.8% of events. Predictive scores varied in accuracy, with PEWS ≥5 showing high sensitivity for intensive care unit (ICU) admission but low specificity. No patients died, 6.3% of SE was refractory. Effective pre-hospital administration of rescue medications and prompt PED management are crucial to reduce SE duration and improve outcomes. Predictive scores can aid in assessment of the severity and prognosis of SE; their utility is still not defined. Identifying and addressing actionable care barriers in SE management pathways is essential to enhance patient outcomes in pediatric SE.

Adjustment of the ACEF score by the triglyceride glucose index improves the prediction of clinical outcomes in patients undergoing percutaneous coronary intervention

BackgroundThe ACEF score (age, creatinine, and left ventricular ejection fraction) and the triglyceride-glucose (TyG) index have been identified as robust risk prediction models for adverse outcomes post-percutaneous coronary intervention (PCI) in coronary atherosclerotic heart disease (CHD) patients. This study aimed to assess whether incorporating the TyG index enhances the predictive ability of the ACEF score and enhances risk stratification for CHD patients undergoing PCI.MethodsThis observational cohort study enrolled 1248 patients diagnosed with CHD who underwent PCI at the Third People’s Hospital of Chengdu, China, between May 2018 and December 2022. Receiver operating characteristic (ROC) curves were employed to establish the optimal cutoff values for the TyG index and ACEF score. The primary endpoint event was defined as major adverse cardiac and cerebrovascular events (MACCEs). Cumulative survival curves were plotted using the Kaplan–Meier method. Univariable and multivariable Cox regression analyses were conducted to identify predictors of MACCEs. The predictive ability of the ACEF score and the TyG index was assessed using the area under the ROC curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsOver a median follow-up period of 30.60 months, 214 patients experienced MACCEs. Kaplan-Meier survival analysis demonstrated a significant association between elevated ACEF score (> 1.27) and TyG index levels (> 8.82) with a heightened incidence of MACCEs (HR = 2.018, 95%CI 1.593–2.789; HR = 2.057, 95%CI 1.572–2.691; Log-Rank test, both P < 0.001). Multivariable Cox regression analysis revealed that even after adjusting for multiple confounders, both the ACEF score and TyG index remained as predictors of MACCEs (HR = 1.702, 95%CI 1.210–2.394, P = 0.002; HR = 1.575, 95%CI 1.296–1.916, P < 0.001). Additionally, the addition of the ACEF score and TyG index into the conventional risk model significantly improved the ability to predict MACCEs, as evidenced by the increase in the C-statistic value from 0.664 to 0.703, along with notable improvements in continuous NRI (0.391), categorical NRI (0.107), and IDI (0.033) (all P < 0.001).ConclusionThe combination of the TyG index and the ACEF score enhances the predictive ability for long-term MACCEs in patients with CHD undergoing PCI.

Natural History and Predictors of Clinical Outcomes in Autoimmune Liver Diseases: A Multicenter Study.

Prognosis in autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC) has historically been poor. This multicenter retrospective cohort study investigated the natural history and assessed the predictors of outcomes in patients with AIH, PBC, and PSC. AIH, PBC, and PSC patients were identified from the state-wide Hepascore and Clinical Outcome cohort. Overall death or transplant, liver-related mortality (liver-related death or transplant), and liver-related decompensation were determined using a population-based data linkage system. Baseline Liver Outcome Score (LOS), Hepascore, and MELD were examined for predicting outcomes. Two-hundred thirty-seven AIH patients (24% male, median age 56.6 years [range, 14.3-94.0]), 157 PBC patients (8.3% male, median age 60.5 years [range, 25.6-87.1]), and 167 PSC patients (52.7% male, median age 55.6 years [range, 18.4-88.6]) were enrolled. Five-year transplant-free survival was 88% (95%CI: 81-92%) in AIH, 92% (95%CI: 85-96%) in PBC, and 61% (95%CI: 51-69%) in PSC. PSC had a significantly worse overall death or transplant, liver-related mortality, and liver-related decompensation when compared to AIH and PBC (p < 0.0001). LOS was a significant independent predictor of overall death or transplant, liver-related mortality, and liver-related decompensation among patients with AIH and PBC. LOS was a significant independent predictor of overall death or transplant in patients with PSC, and Hepascore was a significant independent predictor of liver-related mortality and liver-related decompensation. Outcomes for AIH and PBC are excellent but remain poor in PSC. LOS is a predictor of outcomes in autoimmune liver disease.

Irisin Predicts Poor Clinical Outcomes in Patients with Heart Failure with Preserved Ejection Fraction and Low Levels of N-Terminal Pro-B-Type Natriuretic Peptide.

Despite existing evidence of the high predictive value of natriuretic peptides (NPs) in patients with heart failure (HF), patients treated with guideline-directed therapy who have low or near-normal NP levels are unlikely to be correctly stratified for risk of clinical outcomes. The aim of this study is to detect plausible predictors for poor one-year clinical outcomes in patients with HFpEF and low NT-proBNP treated with in accordance with conventional guidelines. A total of 337 patients with HF with preserved ejection fraction (HFpEF) who had low levels of N-terminal natriuretic pro-peptide (NT-proBNP) at discharge due to optimal guideline-based therapy were enrolled in the study. The course of the observation was 3 years. Echocardiography and the assessment of conventional hematological and biochemical parameters, including NT-proBNP, tumor necrosis factor-alpha, high-sensitivity C-reactive protein (hs-CRP), adropin, irisin, visfatin, and fetuin-A, were performed at baseline and at the end of the study. Three-year cumulative clinical endpoints (cardiovascular death, myocardial infarction or unstable angina or acute coronary syndrome, worsening HF, sudden cardiac death, or cardiac-related surgery or all-cause death) were detected in 104 patients, whereas 233 did not meet the endpoint. After adjusting for an age ≥ 64 years and a presence of atrial fibrillation, diabetes mellitus, chronic kidney disease (CKD) stages 1-3 and dilated cardiomyopathy, the multivariable Cox regression analysis showed that an irisin level of ≤7.2 ng/mL was an independent predictor of cumulative clinical endpoint. Moreover, patients with levels of irisin > 7.2 ng/mL had a better Kaplan-Meier survival rate than those with a lower serum irisin level (≤7.2 ng/mL). Multivariable analysis showed that an age ≥ 64 years; the presence of atrial fibrillation, diabetes mellitus, CKD stages 1-3 and dilated cardiomyopathy; an LAVI ≥ 39 mL/m2; and serum levels of hs-CRP ≥ 6.10 mg/L, irisin ≤ 7.2 ng/mL, and visfatin ≤ 1.1 ng/mL were predictors of poor clinical outcomes in HFpEF with low levels of NT-proBNP. A serum level of irisin ≤ 7.2 ng/mL could emerge as valuable biomarker for predicting long-term prognosis among HFpEF patients with low or near-normal levels of NT-proBNP.

Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT).

Background: Intracerebral hemorrhages (ICH) and perihematomal edema (PHE) are respective imaging markers of primary and secondary brain injury in hemorrhagic stroke. In this study, we explored the potential added value of PHE radiomic features for prognostication in ICH patients. Methods: Using a multicentric trial cohort of acute supratentorial ICH (n = 852) patients, we extracted radiomic features from ICH and PHE lesions on admission non-contrast head CTs. We trained and tested combinations of different machine learning classifiers and feature selection methods for prediction of poor outcome-defined by 4-to-6 modified Rankin Scale scores at 3-month follow-up-using five different input strategies: (a) ICH radiomics, (b) ICH and PHE radiomics, (c) admission clinical predictors of poor outcomes, (d) ICH radiomics and clinical variables, and (e) ICH and PHE radiomics with clinical variables. Models were trained on 500 patients, tested, and compared in 352 using the receiver operating characteristics Area Under the Curve (AUC), Integrated Discrimination Index (IDI), and Net Reclassification Index (NRI). Results: Comparing the best performing models in the independent test cohort, both IDI and NRI demonstrated better individual-level risk assessment by addition of PHE radiomics as input to ICH radiomics (both p < 0.001), but with insignificant improvement in outcome prediction (AUC of 0.74 versus 0.71, p = 0.157). The addition of ICH and PHE radiomics to clinical variables also improved IDI and NRI risk-classification (both p < 0.001), but with a insignificant increase in AUC of 0.85 versus 0.83 (p = 0.118), respectively. All machine learning models had greater or equal accuracy in outcome prediction compared to the widely used ICH score. Conclusions: The addition of PHE radiomics to hemorrhage lesion radiomics, as well as radiomics to clinical risk factors, can improve individual-level risk assessment, albeit with an insignificant increase in prognostic accuracy. Machine learning models offer quantitative and immediate risk stratification-on par with or more accurate than the ICH score-which can potentially guide patients' selection for interventions such as hematoma evacuation.

Lactate Is a Strong Predictor of Poor Outcomes in Patients with Severe Traumatic Brain Injury.

Background: Lactate is a byproduct of glycolysis, often linked to oxygen deprivation. This study aimed to examine how lactate levels (LLs) affect clinical outcomes in patients with severe TBI, hypothesizing that higher LLs would correlate with worse outcomes. Methods: This is a level 1 single-center, retrospective study of patients with severe TBI between 1 January 2020 and 31 December 2023, inclusive. Results: Single-factor ANOVA indicated a significant decrease in LLs with increasing age. Linear regression models showed the same for hospital admission, Intensive Care Unit (ICU) admission LLs, and death LLs. Prognostic scores such as Injury Severity Scores (ISS) and Glasgow Coma Score (GCS) showed a strong correlation with both Hospital admission and ICU admission LLs. ANOVA indicated higher LLs with increasing ISS and increasing LLs with decreasing GCS. Linear regressions revealed a strong positive correlation between ISS and LLs. On linear regression, the LL measured at hospital admission and ICU admission was positively associated with the length of stay (LOS) in the hospital, LOS in the ICU, ventilator days, and mortality. Linear regression models showed that a decreased delta LL during ICU admission led to an increased LOS at the hospital and the ICU, as well as a higher number of days on a ventilator. Discussion: We discovered that high LLs were linked to higher AIS and GCS scores, longer stays in the hospital and ICU, more days requiring a ventilator, and higher mortality rates in patients with severe TBI. Conclusions: LLs can be considered a strong predictor of poor clinical outcomes in patients with severe TBI.

Plasma brain-derived tau correlates with cerebral infarct volume.

A blood-based biomarker that accurately reflects neuronal injury in acute ischemic stroke could be an easily accessible and cost-effective complement to clinical and radiological evaluation. Here, we investigate whether plasma levels of the novel biomarker brain-derived tau (BD-tau) reflect cerebral infarct volumes and whether BD-tau can improve clinical outcome prediction. The present study included 713 consecutive cases from two different hospital-based cohorts, the Sahlgrenska Academy Study on Ischemic Stroke (SAHLSIS) and SAHLSIS phase 2 (SAHLSIS2). Acute stroke severity was determined by the Scandinavian Stroke Scale converted to the National Institutes of Health stroke scale (NIHSS) in SAHLSIS and by the NIHSS in SAHLSIS2. All participants were assessed for functional outcome 3 months after stroke by the modified Rankin Scale, and 254 participants in SAHLSIS had quantitative neuroimaging available. Plasma BD-tau concentrations and cerebral infarct volumes were highly correlated (ρ 0.72, p<0.001). BD-tau improved the prognostic accuracy of suffering an unfavorable outcome over age and stroke severity in the whole cohort. However, the gain in predictive power was dependent on stroke severity and infarct location. The largest improvement was observed for mild ischemic strokes (NIHSS <5; area under the curve [AUC]=0.73 for age+NIHSS versus AUC=0.84 with addition of BD-tau; DeLong p 0.02), posterior circulation stroke (AUC=0.75 vs. AUC=0.84; DeLong p 0.06) and more specifically for infarcts in the brainstem/cerebellum (AUC=0.74 vs. 0.87; DeLong p 0.009). Plasma BD-tau can provide information on the extent of acute neuronal damage in ischemic stroke and adds prognostic value for outcome, especially for mild and posterior circulation strokes.

Wavelets-Based Texture Analysis of Post Neoadjuvant Chemoradiotherapy Magnetic Resonance Imaging as a Tool for Recognition of Pathological Complete Response in Rectal Cancer, a Retrospective Study.

Background: Patients with locally advanced rectal cancer (LARC) treated by neoadjuvant chemoradiotherapy (nCRT) may experience pathological complete response (pCR). Tools that can identify pCR are required to define candidates suitable for the watch and wait (WW) strategy. Automated image analysis is used for predicting clinical aspects of diseases. Texture analysis of magnetic resonance imaging (MRI) wavelets algorithms provides a novel way to identify pCR. We aimed to evaluate wavelets-based image analysis of MRI for predicting pCR. Methods: MRI images of rectal cancer from 22 patients who underwent nCRT were captured at best representative views of the tumor. The MRI images were digitized and their texture was analyzed using different mother wavelets. Each mother wavelet was used to scan the image repeatedly at different frequencies. Based on these analyses, coefficients of similarity were calculated providing a variety of textural variables that were subsequently correlated with histopathology in each case. This allowed for proper identification of the best mother wavelets able to predict pCR. The predictive formula of complete response was computed using the independent statistical variables that were singled out by the multivariate regression model. Results: The statistical model used four wavelet variables to predict pCR with an accuracy of 100%, sensitivity of 100%, specificity of 100%, and PPV and NPV of 100%. Conclusions: Wavelet-transformed texture analysis of radiomic MRI can predict pCR in patients with LARC. It may provide a potential accurate surrogate method for the prediction of clinical outcomes of nCRT, resulting in an effective selection of patients amenable to WW.