Prediction Score Research Articles

Risk factors and development of a predictive score model for tumor lysis syndrome in childhood leukemia: a 10-year experience from a single tertiary hospital in Thailand

Tumor lysis syndrome (TLS) in childhood leukemia was assessed retrospectively in 252 patients in a single tertiary center in Thailand during 2009–2019. Fifty-one (20.2%) developed TLS during their induction chemotherapy; 60.7% (31/51) were spontaneous TLS and 47% (24/51) developed clinical TLS. The predictive score model consisted of white blood cell (WBC) count more than 50,000 cells/mm3, glomerular filtration rate less than 90, and aspartate transaminase more than 44 units/L. The TLS development rates were 11.1%, 46.2%, and 78.5% in the low, intermediate, and high-risk groups, respectively. Death during the first induction phase in patients with TLS was significantly higher than in the patients without TLS. However, the 5-year overall survival rates for the children with and without TLS were not significantly different.

Novel “Ex-PRO” score for the cardiovascular complications prediction in extrasystolic arrhythmia

Novel “Ex-PRO” score for the cardiovascular complications prediction in extrasystolic arrhythmia

Hanc score is a replicable clinical risk score for outcome prediction of Taiwanese clopidogrel non-responders after percutaneous coronary intervention

Hanc score is a replicable clinical risk score for outcome prediction of Taiwanese clopidogrel non-responders after percutaneous coronary intervention

A predictive score for atrial fibrillation in poststroke patients.

Atrial fibrillation (AF) is a risk factor for cerebral ischemia. Identifying the presence of AF, especially in paroxysmal cases, may take time and lacks clear support in the literature regarding the optimal investigative approach; in resource-limited settings, identifying a higher-risk group for AF can assist in planning further investigation. To develop a scoring tool to predict the risk of incident AF in the poststroke follow-up. A retrospective longitudinal study with data collected from electronic medical records of patients hospitalized and followed up for cerebral ischemia from 2014 to 2021 at a tertiary stroke center. Demographic, clinical, laboratory, electrocardiogram, and echocardiogram data, as well as neuroimaging data, were collected. Stepwise logistic regression was employed to identify associated variables. A score with integer numbers was created based on beta coefficients. Calibration and validation were performed to evaluate accuracy. We included 872 patients in the final analysis. The score was created with left atrial diameter ≥ 42 mm (2 points), age ≥ 70 years (1 point), presence of septal aneurysm (2 points), and score ≥ 6 points at admission on the National Institutes of Health Stroke Scale (NIHSS; 1 point). The score ranges from 0 to 6. Patients with a score ≥ 2 points had a fivefold increased risk of having AF detected in the follow-up. The area under the curve (AUC) was of 0.77 (0.72-0.85). We were able structure an accurate risk score tool for incident AF, which could be validated in multicenter samples in future studies.

Escalating Surgical Treatment for Left Ventricular Assist Device Infection and Expected Mortality: Clinical Risk Prediction Score.

Left ventricular assist devices (LVAD) improve survival for patients with cardiac failure, but LVAD-specific infection (VSI) remains a challenge with poorly understood predictive risk factors. The indications and use of escalating medical treatment to surgical debridement and potential flap reconstruction are not well characterized. A retrospective review of consecutive patients undergoing primary LVAD implantation at a tertiary academic center was performed. The primary outcome measures were 90-day and overall mortality after VSI. Cox proportional hazards regression was used to generate a risk prediction score for mortality. Of the 760 patients undergoing primary LVAD implantation, 255 (34%) developed VSI; of these patients, 91 (36%) were managed medically, 134 (52%) with surgical debridement, and 30 (12%) with surgical debridement and flap reconstruction. One-year survival after infection was 85% with median survival of 2.40 years. Factors independently associated with increased mortality were diabetes (hazard ratio [HR] 1.44, p = 0.04), MRSA infection (HR 1.64, p = 0.03), deep space (pump pocket or outflow cannula) involvement (HR 2.26, p < 0.001), and extracorporeal membrane oxygenation after LVAD (HR 2.52, p < 0.01). Factors independently associated with decreased mortality were flap reconstruction (HR 0.49, p = 0.02) and methicillin-sensitive Staphylococcus aureus infection (HR 0.63, p = 0.03). A clinical risk prediction score was developed using these factors and showed significant differences in median survival, which was 5.67 years for low-risk (score 0 to 1), 3.62 years for intermediate-risk (score 2), and 1.48 years for high-risk (score ≥3; p < 0.001) patients. We developed a clinical risk prediction score to stratify patients with VSI. In selected cases, escalating surgical treatment was associated with increased survival. Future work is needed to determine whether early surgical debridement and flap reconstruction can alter outcomes in select cases of VSI.

CAPS: a simple clinical tool for β-amyloid positivity prediction in clinical Alzheimer syndrome.

With the advent of anti-β-amyloid therapies, clinical distinction between Aβ + and Aβ- in cognitive impairment is becoming increasingly important for stratifying referral and better utilization of biomarker assays. Cognitive profile, rate of decline, neuropsychiatric inventory questionnaire (NPI-Q), and imaging characteristics were collected from 52 subjects with possible/probable AD. Participants with Aβ+ status had lower baseline MMSE scores (24.50 vs. 26.85, p = 0.009) and higher total NPI-Q scores (2.73 vs. 1.18, p < 0.001). NPI-Q score was found to be the only independent predictor for β-amyloid positivity (p = 0.008). A simple scoring system, namely Clinical β-Amyloid Positivity Prediction Score (CAPS), was developed by using the following parameters: NPI-Q, rapidity of cognitive decline, and white matter microangiopathy. Data from 48 participants were included in the analysis of accuracy of CAPS. CAP Score of 3 or 4 successfully classified Aβ + individuals in 86.7% cases. Clinical β-Amyloid Positivity Prediction Score is a simple clinical tool for use in primary care and memory clinic settings to predict β-amyloid positivity in individuals with clinical Alzheimer Syndrome can potentially facilitate referral for Anti Aβ therapies.

Prognostic Clinical and Biological Markers for Amyotrophic Lateral Sclerosis Disease Progression: Validation and Implications for Clinical Trial Design and Analysis.

With increasing recognition of the value of incorporating prognostic markers into amyotrophic lateral sclerosis (ALS) trial design and analysis plans, there is a pressing need to understand which among the prevailing clinical and biochemical markers have real value, and how they can be optimally used. A subset of patients with ALS recruited through the multi-center Phenotype-Genotype-Biomarker study (clinicaltrials.gov: NCT02327845) was identified as "trial-like" based on meeting common trial eligibility criteria. Clinical phenotyping was performed by evaluators trained in relevant assessments. Serum neurofilament light (NfL) and phosphorylated neurofilament heavy (pNfH), urinary p75ECD, plasma microRNA-181, and an array of biochemical and clinical measures were evaluated for their prognostic value. Associations with functional progression were estimated by random-slopes mixed models of ALS functional rating scale-revised (ALSFRS-R) score. Associations with survival were estimated by log-rank test and Cox proportional hazards regression. Potential sample size savings from adjusting for given biomarkers in a hypothetical trial were estimated. Baseline serum NfL is a powerful prognostic biomarker, predicting survival and ALSFRS-R rate of decline. Serum NfL <40pg/ml and >100pg/ml correspond to future ALSFRS-R slopes of ~0.5 and 1.5 points/month, respectively. Serum NfL also adds value to the best available clinical predictors, encapsulated by the European Network to Cure ALS (ENCALS) predictor score. In models of functional decline, the addition of NfL yields ~25% sample size saving above those achieved by inclusion of either clinical predictors or ENCALS score alone. The prognostic value of serum pNfH, urinary p75ECD, and plasma miR-181ab is more limited. Among the multitude of biomarkers considered, only blood NfL adds value to the ENCALS prediction model and should be incorporated into analysis plans for all ongoing and future ALS trials. Defined thresholds of NfL might also be used in trial design, for enrichment or stratified randomisation, to improve trial efficiency. NIH (U01-NS107027, U54-NS092091). ALSA (16-TACL-242).

Evaluation of risk prediction scores for adults hospitalized with COVID-19 in a highly-vaccinated population, Aotearoa New Zealand 2022

ObjectivesCOVID-19 severity prediction scores need further validation due to evolving COVID-19 illness. We evaluated existing COVID-19 risk prediction scores in Aotearoa New Zealand, including for Māori and Pacific peoples who have been inequitably affected by COVID-19. MethodsWe conducted a multicenter retrospective cohort study in adults hospitalized with COVID-19 from January to May 2022, including all Māori and Pacific patients, and every second non-Māori, non-Pacific (NMNP) patient to achieve equal analytic power by ethnic grouping. We assessed the accuracy of existing severity scores (4C Mortality, CURB-65, PRIEST, and VACO) to predict death in the hospital or within 28 days. ResultsOf 2319 patients, 582 (25.1%) identified as Māori, 914 (39.4%) as Pacific, and 862 (37.2%) as NMNP. There were 146 (6.3%, 95% confidence interval 5.4-7.4%) deaths, with a predicted probability of death higher than observed mortality for VACO (10.4%), modified PRIEST (15.1%) and 4C mortality (15.5%) scores, but lower for CURB-65 (4.5%). C-statistics (95% CI) of severity scores were: 4C mortality: Māori 0.82 (0.75, 0.88), Pacific 0.87 (0.83, 0.90), NMNP 0.90 (0.86, 0.93); CURB-65: Māori 0.83 (0.69, 0.92), Pacific 0.87 (0.82, 0.91), NMNP 0.86 (0.80, 0.91); modified PRIEST: Māori 0.85 (0.79, 0.90), Pacific 0.81 (0.76, 0.86), NMNP 0.83 (0.78, 0.87); and VACO: Māori 0.79 (0.75, 0.83), Pacific 0.71 (0.58, 0.82), NMNP 0.78 (0.73, 0.83). ConclusionsFollowing re-calibration, existing risk prediction scores accurately predicted mortality.

Simultaneous prognostic score validation in patients with out-of-hospital cardiac arrest by a post-hoc analysis based on national multicenter registry

Using a nationwide multicenter prospective registry in Japan’s data, we calculated prognostic and predictive scores, including the Out-of-Hospital Cardiac Arrest (OHCA); Cardiac Arrest Hospital Prognosis (CAHP); Nonshockable rhythm, Unwitnessed arrest, Long no-flow or Long low-flow period, blood PH < 7.2, Lactate > 7.0 mmol/L, End-stage chronic kidney disease on dialysis, Age ≥ 85 years, Still resuscitation, and Extracardiac cause (NULL-PLEASE); revised post-Cardiac Arrest Syndrome for Therapeutic hypothermia (rCAST); and MIRACLE2 scores, for adult patients with cardiac arrest. The MIRACLE2 score was validated with the modified MIRACLE2 score, which excludes information of pupillary reflexes. Each score was calculated only for the cases with no missing data for the variables used. These scores’ accuracies were compared using neurological outcomes 30 days after out-of-hospital cardiac arrest (OOHCA). Patients with a cerebral performance category scale of 1 or 2 were designated as having favorable neurological outcomes. Each score’s discrimination ability was evaluated by the receiver operating characteristic curve’s area under the curve (AUC). To assess in detail in areas of high specificity and high sensitivity, which are areas of interest to clinicians, partial AUCs were also used. The analysis included 11,924 hospitalized adult patients. The AUCs of the OHCA, MIRACLE2, CAHP, rCAST, and NULL-PLEASE scores for favorable neurological outcomes were 0.713, 0.727, 0.785, 0.761, and 0.831, respectively. The CAHP and NULL-PLEASE scores were significantly more accurate than the rest. Accuracies did not differ significantly between the CAHP and NULL-PLEASE scores. The NULL-PLEASE score was significantly better at discriminating favorable neurological prognoses at 30 days in patients with OOHCA compared to other scoring systems.

The Predictive Value of Amplitude-Integrated Electroencephalography for the Neurodevelopmental Outcomes of Preterm Newborns at 12 Months Corrected Age.

In clinical practice, it is crucial to identify diagnostic methods that can forecast the neurodevelopmental outcomes of very preterm neonates. Our study aimed to assess the predictive significance of amplitude-integrated electroencephalography (aEEG) for the neurodevelopmental outcomes of preterm infants at 12 months corrected age and to establish the cut-off score that could indicate potential neurodevelopmental impairments. Preterm neonates born before 32 weeks of gestational age between June 2020 and July 2022 were included in a prospective manner. Amplitude-integrated electroencephalography recordings were conducted at five age intervals (days 1-3; first, second, third and fourth weeks). Recordings were analyzed using the Burdjalov scoring system. The neurodevelopment assessment with Bayley Scales of Infant Development-Second Edition was carried out at 12 months corrected age. A total of 140 newborns were included in the study. Neurodevelopment was assessed in 108 infants at 12 months corrected age. Higher total aEEG Burdjalov scores were observed in groups with normal cognitive and motor development. The most sensitive and specific score for prediction of cognitive impairment in 12 months corrected age was an aEEG evaluation of 5.5 according to Burdjalov score within the first three days. The most sensitive and specific score for prediction of motor impairment was 8.5 within the first week. According to our research there is currently not enough data to accurately foresee the development of newborns at 12 months corrected age according to early aEEG test results. However, conducting a research with bigger sample size and repeated evaluations at a later age might increase the prognostic value of aEEG. In this study cut-off scores of aEEG performed early in life to predict later neurodevelopment outcomes were determined.

Exploring the prognostic landscape of oral squamous cell carcinoma through mitochondrial damage-related genes

Oral squamous cell carcinoma (OSCC), the most prevalent form of oral cancer, poses significant challenges to the medical community due to its high recurrence rate and low survival rate. Mitochondrial Damage-Related Genes (MDGs) have been closely associated with the occurrence, metastasis, and progression of OSCC. Consequently, we constructed a prognostic model for OSCC based on MDGs and identified potential mitochondrial damage-related biomarkers. Gene expression profiles and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Differential analysis was conducted to identify MDGs associated with OSCC. COX analysis was employed to screen seven prognosis-related MDGs and build a prognostic prediction model for OSCC. Cases were categorized into low-risk or high-risk groups based on the optimal risk score threshold. Kaplan-Meier (KM) analysis revealed significant survival differences (P < 0.05). Additionally, the area under the ROC curve (AUC) for patient survival at 1 year, 3 years, and 5 years were 0.687, 0.704, and 0.70, respectively, indicating a high long-term predictive accuracy of the prognostic model. To enhance predictive accuracy, age, gender, risk score, and TN staging were incorporated into a nomogram and verified using calibration curves. Risk scoring based on MDGs was identified as a potential independent prognostic biomarker. Furthermore, BID and SLC25A20 were identified as two potential independent mitochondrial damage-related prognostic biomarkers, offering new therapeutic targets for OSCC.

Developing a risk score to inform the use of rhBMP-2 in adult spinal deformity surgery.

Recombinant human bone morphogenetic protein-2 (rhBMP-2) has not shown superior benefit overall in cost-effectiveness during adult spinal deformity (ASD) surgery. Retrospective PURPOSE: Generate a risk score for pseudarthrosis to inform the utilization of rhBMP-2, balancing costs against quality of life and complications. ASD patients with 3-year data were included. Quality of life gained was calculated from ODI to SF-6D and translated to quality-adjusted life years (QALYs). Cost was calculated using the PearlDiver database and CMS definitions for complications and comorbidities. Established weights were generated for predictive variables via logistic regression to yield a predictive risk score for pseudarthrosis that accounted for frailty, diabetes, depression, ASA grade, thoracolumbar kyphosis and three-column osteotomy use. Risk score categories, established via conditional inference tree (CIT)-derived thresholds were tested for cost-utility of rhBMP-2 usage, controlling for age, prior fusion, and baseline deformity and disability. 64% of ASD patients received rhBMP-2 (308/481). There were 17 (3.5%) patients that developed pseudarthrosis. rhBMP-2 use overall did not lower pseudarthrosis rates (OR: 0.5, [0.2-1.3]). Pseudarthrosis rates for each risk category were: No Risk (NoR) 0%; Low-Risk (LowR) 1.6%; Moderate Risk (ModR) 9.3%; High-Risk (HighR) 24.3%. Patients receiving rhBMP-2 had similar QALYs overall to those that did not (0.163 vs. 0.171, p = .65). rhBMP-2 usage had worse cost-utility in the LowR cohort (p < .001). In ModR patients, rhBMP-2 usage had equivocal cost-utility ($53,398 vs. $61,581, p = .232). In the HighR cohort, the cost-utility was reduced via rhBMP-2 usage ($98,328 vs. $211,091, p < .001). Our study shows rhBMP-2 demonstrates effective cost-utility for individuals at high risk for developing pseudarthrosis. The generated score can aid spine surgeons in the assessment of risk and enhance justification for the strategic use of rhBMP-2 in the appropriate clinical contexts. III.

Acute phase optimization in burn care: Online tools and comprehensive predictive models for adult and pediatric patients

BackgroundSevere burn injuries significantly challenge acute medical care, particularly in resource-limited environments. Current predictive scoring systems, often impractical and adult-focused, neglect crucial aspects like mechanical ventilation and length of hospital stay (LOS). MethodsThis study analyzed 2,618 severe burn patients, developing new predictive models for survival, mechanical ventilation, and LOS, based on promptly accessible factors applicable in any setting. ResultsWe observed significant seasonality and clear age- and gender-specific patterns, highlighting the necessity for targeted interventions. We developed and publicly released new predictive models for mortality, mechanical ventilation, and LOS for both adult and pediatric populations. DiscussionTargeting deficiencies in existing scoring systems, this study potentially advances acute burn management, with a particular focus on resource-limited settings. It provides crucial insights into the epidemiology, etiology, and prognostic factors of severe burn injuries, encapsulated in 10 actionable points. We also present an innovative freely accessible online assessment tool: https://burn-scores.com. ConclusionBy bridging gaps in current scoring methodologies and improving acute phase management, our research offers insights to improve clinical outcomes for severe burn patients globally. The integration of tailored predictive models and technology-driven solutions, especially relevant in resource-constrained settings, represents a major stride in enhancing the quality of burn care.

Analysis by TeloView® Technology Predicts the Response of Hodgkin's Lymphoma to First-Line ABVD Therapy.

Classic Hodgkin's lymphoma (cHL) is a curable cancer with a disease-free survival rate of over 10 years. Over 80% of diagnosed patients respond favorably to first-line chemotherapy, but few biomarkers exist that can predict the 15-20% of patients who experience refractory or early relapsed disease. To date, the identification of patients who will not respond to first-line therapy based on disease staging and traditional clinical risk factor analysis is still not possible. Three-dimensional (3D) telomere analysis using the TeloView® software platform has been shown to be a reliable tool to quantify genomic instability and to inform on disease progression and patients' response to therapy in several cancers. It also demonstrated telomere dysfunction in cHL elucidating biological mechanisms related to disease progression. Here, we report 3D telomere analysis on a multicenter cohort of 156 cHL patients. We used the cohort data as a training data set and identified significant 3D telomere parameters suitable to predict individual patient outcomes at the point of diagnosis. Multivariate analysis using logistic regression procedures allowed for developing a predictive scoring model using four 3D telomere parameters as predictors, including the proportion of t-stumps (very short telomeres), which has been a prominent predictor for cHL patient outcome in a previously published study using TeloView® analysis. The percentage of t-stumps was by far the most prominent predictor to identify refractory/relapsing (RR) cHL prior to initiation of adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD) therapy. The model characteristics include an AUC of 0.83 in ROC analysis and a sensitivity and specificity of 0.82 and 0.78 respectively.

A prediction model for hemolysis, elevated liver enzymes and low platelets syndrome in pre-eclampsia with severe features.

The aim of the present study was to determine the risk factors for patients with pre-eclampsia (PE) with severe features to develop hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome and to design a prediction score model that incorporates these risk factors. A retrospective cohort study was conducted at a tertiary university-affiliated medical center between 2011 and 2019. The study population comprised patients diagnosed with PE with severe features, divided into two groups: those with HELLP syndrome (study group) and those without (control group). A logistic regression was employed to identify independent predictors of HELLP syndrome. A predictive model for the occurrence of HELLP syndrome in the context of PE with severe features was developed using a receiver operating characteristic curve analysis. Overall, 445 patients were included, of whom 69 patients were in the study group and 376 in the control group. A multivariate logistic analysis regression showed that maternal age <40 (OR = 2.28, 95% CI: 1.13-5.33, P = 0.045), nulliparity (OR = 2.22, 95% CI: 1.14-4.88, P = 0.042), mild hypertension (OR = 2.31, 95% CI: 1.54-4.82, P = 0.019), epigastric pain (OR = 3.41, 95% CI: 1.92-7.23, P < 0.001) and placental abruption (OR = 6.38, 95% CI: 1.29-35.61, P < 0.001) were independent risk factors for HELLP syndrome. A prediction score model reached a predictive performance with an area under the curve of 0.765 (95% CI: 0.709-0.821). This study identified several key risk factors for developing HELLP syndrome among patients with PE with severe features and determined that a prediction score model has the potential to aid clinicians in identifying high risk patients.

SURFASA Score for prediction of steroid response in children and adolescents with severe autoimmune hepatitis

SURFASA Score for prediction of steroid response in children and adolescents with severe autoimmune hepatitis

2264 A novel frailty specific same day emergency care (SDEC) score—an initial retrospective validation cohort

Abstract Aim Several scores have been developed to identify SDEC patients from Emergency Department (ED) triage and acute medical intakes. Scores are designed to improve system efficiency, overcrowding and patient experience but none have been developed for older adults. Previous work has shown that existing scores e.g. Glasgow Admission Prediction Score, Sydney Triage to Admission Risk Tool and the Ambulatory Score were not able to predict admission in our population (1). We have developed a novel, frailty-focused score. Methods The Older Person’s Assessment service (OPAS) is ED based, accepting patients with frailty syndromes aged &amp;gt;70 years to provide a comprehensive geriatric assessment (CGA) and is extended into medical SDEC. The databases were retrospectively analysed and interactions with age, Charlson Co-morbidity index (CCI) and Clinical Frailty Score (CFS) were evaluated alongside NEWS, 4AT, including who with and where the patient resides. Results 1011 attendances, 414 (40.9%) Male, mean age 82.3(±8.4) years, CFS 5.3(±1.2) and CCI 8.0(±1.8), 701 (69.3%) discharged same-day and 629 (62.2%) fallers. OPAS: 776 attendances, 306 (39.4%) Male, age 82.4(±8.7) years, CFS 5.3(±1.1) and CCI 7.9(±1.9), 540 (69.5%) discharged same-day, 557 (71.8%) fallers. SDEC: 234 attendances, 108 (46.2%) Male, age 81.8(±8.0) years, CFS 5.2(±1.3) and CCI 8.2(±1.7),162 (69.2%) discharged same-day, 72 (30.1%) fallers. There was significant difference between groups with NEWS (p &amp;lt; 0.02), mortality (P &amp;lt; 0.001) and presenting complaint (p &amp;lt; 0.001). We used a cut-off Score &amp;gt; 6.5 indicating admission (p &amp;lt; 0.0001). Each variable’s weighing was determined using T-tests and Chi-squared analysis. Overall score Sensitivity 0.75, Specificity 0.63, Positive Predictive Value 0.65, Negative Predictive value 0.57, Area under Curve 0.65. Conclusion Frailty is an important determinant in identifying whether ambulatory care is appropriate. The efficacy of the score is comparable to the results derived in validation cohorts of existing and recommended scores. We are currently prospectively testing the score but clinical judgement, alongside a MDT providing a CGA is gold standard care.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction.

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants

BackgroundGenome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.ResultsWe benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER—Functional SNV IdeNtification using DNase footprints and eRNA.ConclusionsWe demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

Using Pre-operative Insulin Dose to Predict Diabetes Remission After Roux-En-Y Gastric Bypass and Sleeve Gastrectomy.

Obesity is intricately associated with type-2 diabetes (T2D) and other cardiovascular conditions, increasing morbidity, mortality, and health care costs. Metabolic and bariatric surgeries (MBS) have shown promising results in significant weight loss and T2D remission, but existing predictive scores for post-MBS diabetes remission do not consider insulin dosage, potentially overlooking a critical factor. A retrospective analysis of patients with T2D who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG). The study focused on insulin dosage impact, divided into quartiles, on remission rates post-MBS. The effectiveness of RYGB vs SG was compared within insulin dose quartiles with up to 5 years of follow up. A total of 508 patients (64% female, 94.9% White, mean age 53.5 ± 10.5years, BMI (46.0 ± 8.3kg/m2) were included in the analysis. This study demonstrates a profound association between insulin dosage quartiles and T2D remission after MBS. Patients with lower insulin requirements showed superior remission rates; those in the lowest quartile had remission rates of 73%, 70%, and 62% at 1, 3, and 5 years, respectively, compared to 34%, 37%, and 36% in the highest quartile (P < 0.001 across all intervals). RYGB surgery showed a significantly better remission in the second and third insulin quartiles, suggesting its effectiveness over SG for patients with mid-range insulin requirements. This study underscores the importance of considering insulin dosage when predicting T2D remission post-MBS. The findings advocate for a more nuanced selection of MBS procedures based on individual insulin profiles, potentially enhancing diabetes remission outcomes.