Prediction Of Outcome Research Articles

Multigene Panel Testing Revealed Novel Variants in Hereditary Spherocytosis Patients in Türkiye.

This study aims to determine the genotypic characteristics of Hereditary Spherocytosis (HS) patients in Turkiye and to examine the correlation between genotype and phenotype. Herein we had 18 patients who were admitted to pediatric hematology outpatient clinic with hemolytic anemia, jaundice, cholelithiasis, and splenomegaly. According to the Eber's classification, the patients' clinical presentations were categorized as mild, moderate, and severe. The next-generation sequencing method was used to analyze single nucleotide and copy number variations in all genes associated with HS via clinical exome sequencing (CES). The relationship between the genes with detected variants and the clinical presentation in the patients was investigated. In total, 21 variants were detected in five HS-related genes. Twelve of them were previously reported variants, and nine of them were novel variants. Seven of them were pathogenic and two of them were classified as Variant of Uncertain Significance (VUS) according to the American College of Medical Genetics and Genomics (ACMG). Herein we have discussed the phenotypic effects of novel pathogenic variants in SPTA1, SPTB, ANK1, SLC4A1, and EPB42 genes. Patients with EPB42 and SLC4A1 gene pathogenic variants had less severe clinical findings compared to other gene variants according to Eber's classification. On the other hand, patients carrying pathogenic variants of SPTA1 and SPTB genes had more severe clinical presentation. Molecular diagnosis of HS is important for treatment, prediction of the clinical outcome, and appropriate genetic counseling. As a result, our study contributes to the genotype-phenotype distribution of HS by introducing novel variants to the literature.

Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network

Background/Objectives: There is a constant need to improve the prediction of adverse neurodevelopmental outcomes in growth-restricted neonates who were born prematurely. The aim of this retrospective study was to evaluate the predictive performance of a three-layered neural network for the prediction of adverse neurodevelopmental outcomes determined at two years of age by the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) scale in prematurely born infants by affected by intrauterine growth restriction (IUGR). Methods: This observational retrospective study included premature newborns with or without IUGR admitted to a tertiary neonatal intensive care unit from Romania, between January 2018 and December 2022. The patients underwent assessment with the Amiel-Tison scale at discharge, and with the Bailey-3 scale at 3, 6, 12, 18, and 24 months of corrected age. Clinical and paraclinical data were used to construct a three-layered artificial neural network, and its predictive performance was assessed. Results: Our results indicated that this type of neural network exhibited moderate predictive performance in predicting mild forms of cognitive, motor, and language delays. However, the accuracy of predicting moderate and severe neurodevelopmental outcomes varied between moderate and low. Conclusions: Artificial neural networks can be useful tools for the prediction of several neurodevelopmental outcomes, and their predictive performance can be improved by including a large number of clinical and paraclinical parameters.

The Role of WNT5a and TGF‐β1 in Airway Remodelling and Severe Asthma

ABSTRACTBackgroundAirway remodelling is a feature of severe asthma with airway epithelial damage observed frequently. We evaluated the role of WNT5a and TGF‐β1 in asthmatic airway biopsies and in sputum and bronchial brushings assessed their role in remodelling.MethodsWNT5a and TGF‐β1 protein expression were assessed in the lamina propria epithelium of people with asthma (GINA 1–3, n‐8 and GINA 4–5, n‐14) and healthy subjects (n‐9), alongside relevant remodelling markers. The effects of WNT5a and TGF‐β1 on BEAS‐2B epithelial cell wound healing and differentiation were assessed in vitro. Replication was performed in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U‐BIOPRED) study in sputum (n = 120) and bronchial brushes (n = 147).ResultsWNT5a and TGF‐β1 protein expression were significantly increased in the airway epithelium and lamina propria in asthma patients with concurrent airflow limitation or severe disease. Furthermore, WNT5a protein expression in the lamina propria correlated with tissue eosinophils and vascular remodelling. Airway epithelial WNT5a was co‐localised predominantly to airway basal cells and correlated with Th17 gene expression (r = 0.40, p = 0.025) and both the % intact (rs = 0.54, p = 0.001) and % denuded epithelium (rs = −0.39, p = 0.003). Experiments in BEAS‐2B cells confirmed that WNT5a at maximal physiological concentrations (1 μg/mL), promoted epithelial wound healing, independently of TGF‐β1, as well as induction of EMT‐like morphology. WNT5a mRNA was associated with severe asthma, airflow limitation, sputum eosinophilia and Th2, and Th17 and neutrophil activation transcriptomes in sputum in U‐BIOPRED.ConclusionWNT5a is associated with both airway remodelling and severe asthma.Trial RegistrationClinicalTrials.gov identifier: NCT01982162

Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence

BackgroundAdolescent idiopathic scoliosis (AIS) is the most common type of scoliosis, affecting 1–4% of adolescents. The Scoliosis Research Society-22R (SRS-22R), a health-related quality-of-life instrument for AIS, has allowed orthopedists to measure subjective patient outcomes before and after corrective surgery beyond objective radiographic measurements. However, research has revealed that there is no significant correlation between the correction rate in major radiographic parameters and improvements in patient-reported outcomes (PROs), making it difficult to incorporate PROs into personalized surgical planning.MethodsThe objective of this study is to develop an artificial intelligence (AI)-enabled surgical planning and counseling support system for post-operative patient rehabilitation outcomes prediction in order to facilitate personalized AIS patient care. A unique multi-site cohort of 455 pediatric patients undergoing spinal fusion surgery at two Shriners Children’s hospitals from 2010 is investigated in our analysis. In total, 171 pre-operative clinical features are used to train six machine-learning models for post-operative outcomes prediction. We further employ explainability analysis to quantify the contribution of pre-operative radiographic and questionnaire parameters in predicting patient surgical outcomes. Moreover, we enable responsible AI by calibrating model confidence for human intervention and mitigating health disparities for algorithm fairness.ResultsThe best prediction model achieves an area under receiver operating curve (AUROC) performance of 0.86, 0.85, and 0.83 for individual SRS-22R question response prediction over three-time horizons from pre-operation to 6-month, 1-year, and 2-year post-operation, respectively. Additionally, we demonstrate the efficacy of our proposed prediction method to predict other patient rehabilitation outcomes based on minimal clinically important differences (MCID) and correction rates across all three-time horizons.ConclusionsBased on the relationship analysis, we suggest additional attention to sagittal parameters (e.g., lordosis, sagittal vertical axis) and patient self-image beyond major Cobb angles to improve surgical decision-making for AIS patients. In the age of personalized medicine, the proposed responsible AI-enabled clinical decision-support system may facilitate pre-operative counseling and shared decision-making within real-world clinical settings.

Probing the Electric Double-Layer Capacitance to Understand the Reaction Environment in Conditions of Electrochemical Amination of Acetone.

To elucidate interfacial dynamics during electrocatalytic reactions, it is crucial to understand the adsorption behavior of organic molecules on catalytic electrodes within the electric double layer (EDL). However, the EDL structure in aqueous environments remains intricate when it comes to the electrochemical amination of acetone, using methylamine as a nitrogen source. Specifically, the interactions of acetone and methylamine with the copper electrode in water remain unclear, posing challenges in the prediction and optimization of reaction outcomes. In this study, initial investigations employed impedance spectroscopy at the potential of zero charge to explore the surface preconfiguration. Here, the capacitance of the EDL was utilized as a primary descriptor to analyze the adsorption tendencies of both acetone and methylamine. Acetone shows an increase in the EDL capacitance, while methylamine shows a decrease. Experiments are interpreted using combined grand canonical density functional theory and ab initio molecular dynamics to delve into the microscopic configurations, focusing on their capacitance and polarizability. Methylamine and acetone have larger molecular polarizability than water. Acetone shows a partial hydrophobic character due to the methyl groups, forming a distinct adlayer at the interface and increasing the polarizability of the liquid interface component. In contrast, methylamine interacts more strongly with water due to its ability to both donate and accept hydrogen bonds, leading to a more significant disruption of the hydrogen bond network. This disruption of the hydrogen network decreases the local polarizability of the interface and decreases the effective capacitance. Our findings underscore the pivotal role of EDL capacitance and polarizability in determining the local reaction environment, shedding light on the fundamental processes important for electro-catalysis.

The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.

Study Objectives: Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea (OSA); however some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective was to develop predictive models for CPAP outcomes and assess whether the inclusion of physiological traits enhances prediction. Methods: We constructed predictive models from baseline information for subsequent residual apnea-hypopnea index (AHI) and optimal CPAP pressure. We compared models utilizing clinical variables with those incorporating both clinical and physiological factors. Furthermore, we assessed the performance of regression versus machine learning. All performances, including root mean square error (RSME), R-squared, accuracy, and area under the curve (AUC), were evaluated using a five-fold cross validation with ten repeats. Results: For predicting residual AHI, random forest models outperformed regression models, and models that incorporated both clinical and physiological variables also outperformed models using only clinical variables across all performance metrics. Random forest using both clinical features and physiological traits achieved the best performance. In both regression and random forest models, central apnea index is found to be the most important feature in predicting residual AHI. For predicting CPAP pressure, there was no additional predictive value of physiological traits or random forest modeling. Conclusions: Our findings demonstrated that the combined use of clinical and physiological variables yields the most robust predictive models for residual AHI, with random forest models performing best. These findings support the notion that prediction of OSA therapy outcomes may be improved by more flexible models using machine learning, potentially in combination with physiology-based models.

Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor.

Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions. We introduce SubA, an innovative substrate-aware descriptor that is inspired by the fact that specific atoms or motifs in reactants drive the reaction outcomes. By employing graph matching algorithms for molecular backbone identification and incorporating atomic and molecular properties derived from density functional theory calculations, SubA extracts essential information at both the atomic level and the molecular level. Compared to four mainstream descriptors, SubA achieves reduced dimensionality and enhanced prediction accuracy with over 2% mean absolute error reduction in both random and scaffold splitting evaluations. It also demonstrates better generalization when confronted with previously unreported substrate combinations in extended experiments. Furthermore, an interpretable analysis of SubA shows that the predictor focuses on key molecular and atomic features, offering insights into reaction mechanisms.

Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes

BackgroundPrior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.MethodsWe included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry. The cohort was divided into training (70%), validation (10%) and testing (20%) cohorts. Clinical 10-year CVD risk was calculated using the pooled cohort equation (PCE) risk score. A polygenic risk score (PRS) for coronary heart disease was also obtained. Retinal images were analysed using an EfficientNet-B2 network to predict 10-year CVD risk. The primary outcome was time to first major adverse CV event (MACE) including CV death, myocardial infarction or stroke.Results1241 individuals were included in the test cohort (mean PCE 10-year CVD risk 35%). There was a strong correlation between retinal predicted CVD risk and the PCE risk score (r = 0.66) but not the polygenic risk score (r = 0.05). There were 288 MACE events. Higher retina-predicted risk was significantly associated with increased 10-year risk of MACE (HR 1.05 per 1% increase; 95% CI 1.04–1.06, p < 0.001) and remained so after adjustment for the PCE and polygenic risk score (HR 1.03; 95% CI 1.02–1.04, p < 0.001). The retinal risk score had similar performance to the PCE (both AUC 0.697) and when combined with the PCE and polygenic risk score had significantly improved performance compared to the PCE alone (AUC 0.728). An increase in retinal-predicted risk within 3 years was associated with subsequent increased MACE likelihood.ConclusionsA deep-learning AI model could accurately predict MACE from routine retinal screening photographs with a comparable performance to traditional clinical risk assessment in a diabetic cohort. Combining the AI-derived retinal risk prediction with a coronary heart disease polygenic risk score improved risk prediction. AI retinal assessment might allow a one-stop CVD risk assessment at routine retinal screening.Graphical abstract

Circulating resistin levels and mutation burden of the RETN gene variants predict long-term mortality in a Taiwanese population

Human resistin is a proinflammatory cytokine involving the development and progression of cancer and cardiovascular diseases. However, prediction of long-term outcome using circulating resistin level and its genetic determinants in a population-based study remain to be explored. After genome-wide association study (GWAS), DNA methylation (DNAm) analysis and functional assays of a RETN rs370006313 variant, we tested whether resistin level and its genetic determinants can be used to determine the long-term outcomes of 5678 Taiwan Biobank (TWB) participants. GWAS and DNAm analysis revealed RETN variants, rs3219175, rs370006313, and rs3745368, and DNAm sites, cg21271423 and cg09909011, independently associated with circulating resistin levels. Functional assays showed rs370006313 variant played a key role in affecting RETN promoter activity, whereas genotypes of rs3219175 and rs3745368, but not rs370006313, exhibited genome-wide significant associations with RETN promoter DNAm levels. Using Kaplan-Meier survival and Cox regression analyses, participants with progressively increasing resistin levels had a higher hazard ratio for all-cause mortality and cancer mortality compared to those with lower resistin levels. Participants with all three RETN variants (high mutation burden) also exhibited significantly higher hazard ratios for all-cause mortality and cancer mortality, at 3.99 and 5.55, respectively, compared to those without a high mutation burden. In conclusion, RETN rs370006313 is a functional variant affecting RETN promoter activity. Elevated circulating resistin levels and a high RETN mutation burden predict all-cause and cancer mortality in TWB participants. Both resistin levels and RETN variants may serve as biomarkers of long-term outcomes in the general Taiwanese populations.

Two-Step Transfer Learning Improves Deep Learning–Based Drug Response Prediction in Small Datasets: A Case Study of Glioblastoma

While deep learning (DL) is used in patients’ outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures. The GBM is aggressive, and most patients do not benefit from the only approved chemotherapeutic agent TMZ. O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is the only biomarker for TMZ responsiveness but has shown limited predictive power. The 2-step TL framework was built on 3 datasets: (1) the subset of the Genomics of Drug Sensitivity in Cancer (GDSC) dataset, including miscellaneous cell cultures treated by TMZ, cyclophosphamide, bortezomib, and oxaliplatin, as the source dataset; (2) the Human Glioblastoma Cell Culture (HGCC) dataset, for fine-tuning; and (3) a small target dataset GSE232173, for validation. The latter two included specifically TMZ-treated GBM cell cultures. The DL models were pretrained on the cell cultures treated by each of the 4 drugs from GDSC, respectively. Then, the DL models were refined on HGCC, where the best source drug was identified. Finally, the DL model was validated on GSE232173. Using 2-step TL with pretraining on oxaliplatin was not only superior to those without TL and with 1-step TL but also better than 3 benchmark methods, including MGMT. The oxaliplatin-based TL improved the performance probably by increasing the weights of cell cycle-related genes, which relates to the TMZ response processes. Our findings support the potential of oxaliplatin being an alternative therapy for patients with GBM and TL facilitating drug repurposing research. We recommend that following our methodology, using mixed cancers and a related drug as the source and then fine-tuning the model with the target cancer and the target drug will enhance drug response prediction.

Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection.

Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endonasal approach (EEEA) has evolved as a more reliable surgical alternative and yields better visual outcomes than traditional craniotomy, postoperative visual deterioration remains one of the most common complications, and relevant risk factors are still poorly defined. Hence, identifying risk factors and developing a predictive model for postoperative visual deterioration is indeed necessary. However, there is still a lack of research on these topics. Therefore, the authors used the largest known case series of EEEA for craniopharyngioma to determine pertinent risk factors and develop a nomogram for the noninvasive preoperative prediction of visual outcome. A total of 483 cases of craniopharyngioma (338 in the training cohort, 145 in the validation cohort) between January 2019 and March 2023 were retrospectively reviewed, and related risk factors were identified. In total, 851 radiomic features from the MR images of each case were extracted. The least absolute shrinkage and selection operator algorithm was used to select features and construct the radiomic score (Rad-score). A support vector machine (SVM) classifier was adopted to construct a radiomic model. Moreover, a clinical-radiomic nomogram was built by multivariable logistic regression. The performance of the nomogram was assessed by its discrimination, calibration, and clinical utility. The overall incidence of postoperative visual deterioration was 9.1%. A lack of intraoperative visual evoked potential (VEP) monitoring (OR 0.221, p = 0.001), larger maximum tumor diameter (OR 1.052, p = 0.014), and tight adherence (OR 2.963, p = 0.044) were demonstrated as independent risk factors for postoperative visual deterioration. The radiomic model using the SVM based on 8 selected features exhibited good discrimination in predicting adhesion strength in the training and validation cohorts (area under the receiver operating characteristic curve [AUC] 0.85 vs 0.80). Moreover, the nomogram incorporating the Rad-score and clinical factors showed AUCs of 0.827 and 0.808 in the training and validation sets, respectively, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram. Intraoperative VEP monitoring was proven to help reduce postoperative visual deterioration, while tight adherence and larger maximum tumor diameter were confirmed as independent risk factors. The radiomic model allowed a noninvasive prediction of the adherence strength between the optic nerves and craniopharyngioma. The nomogram showed a promising performance for noninvasively predicting postoperative visual deterioration and may serve as a useful tool for clinical decision-making and patient counseling.

Deep learning-based prediction of mortality using brain midline shift and clinical information.

Brain midline shift (MLS) indicates the severity of mass effect from intracranial lesions such as traumatic brain injury, stroke, brain tumor, or hematoma. Brain MLS can be used to determine whether patients require emergency surgery and to predict patients' prognosis. Since brain MLS is usually emergent, it must be diagnosed immediately. Therefore, this study presents a computer-aided deep-learning method for detecting MLS, aiming to predict mortality in a prognosis-predicting cohort using brain MLS and clinical in-formation. The brain midline is a 3-dimensional structure, but computed tomography (CT) slices are 2-dimensional which limits brain MLS detection. Here we propose a keypoint detection method to detect brain midline on each CT slice, acquiring brain MLS distance and area in each slice. Combined with clinical information, patient mortality can be predicted using the multilayer perceptron (MLP) model. The accuracy, precision, sensitivity, specificity, and F1-score for slice selection with the proposed model are 0.966, 0.952, 0.991, 0.932, and 0.971, respectively. Both MLS distance and volume were precisely predicted at slice-level and case-level with only the slightest error. The detected midlines were clearly separated into left and right brain with a dice coefficient of 0.98. The accuracy and AUC of the MLP model were both above 0.8. The model detected large brain MLS cases well in the prediction of outcomes in the prognosis-predicting cohort. The method performs well on slice selection and brain MLS detection, and predictions of MLS distance and volume combined with clinical information predicts the patient's prognosis well.

Deep learning framework for precision grading and non-invasive Apple sweetness evaluation

Abstract This research aims to find an economical, non-invasive solution for grading apples by sweetness based on multispectral imaging. This paper examines the relationship between sugar levels and multispectral images of apple fruit taken inside a wooden multispectral imaging enclosure specially designed for taking apple pictures. In addition, using a hand-held refractometer, the sugar content of different apple samples and their corresponding multispectral images are collected. The methodology includes designing and building a prototype camera chamber, gathering pictures of various apples, and using advanced image analysis software to process the data. Prediction of outcomes: Building a practical grading system based on non-invasive multispectral imaging and finding a significant spectral feature of Apple fruit's sweetness levels in the project's range of concern. The proposed system implemented AppleNet, a convolutional neural network (CNN) within MATLAB, to process the multispectral images, and the accuracy achieved for grading the sweetness of apple fruit is 65%.

The MOST (Mortality Score for TBI): A Novel Prediction Model Beyond CRASH-Basic and IMPACT-Core for Isolated Traumatic Brain Injury

Due to significant injury heterogeneity, outcome prediction following traumatic brain injury (TBI) is challenging. This study aimed to develop a simple model for high-accuracy mortality risk prediction after TBI. Data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) from 2019 to 2021 was used to develop a summary score based on age, the Glasgow Coma Scale (GCS) component subscores, and pupillary reactivity data. We then compared the predictive accuracy to that of the Corticosteroid Randomisation After Significant Head Injury Trial (CRASH)-Basic and International Mission for Prognosis and Analysis of Clinical Trial in TBI (IMPACT)-Core models. Two separate series of sensitivity analyses were conducted to further assess our model's generalizability. We evaluated predictive performance of the models with discrimination [the area under the receiver-operating characteristic curves (AUC), sensitivity, specificity] and calibration (Brier score). Discriminative ability was compared with DeLong tests. 259,404 patients were included in the present study (mean age, 60 years; 93,495 (36 %) female). The mortality score after TBI (MOST) model (AUC = 0.875) had better discrimination (DeLong test p values < 0.00001) than CRASH-Basic (AUC = 0.837) and IMPACT-Core (AUC = 0.821) models, and superior calibration (MOST = 0.02729, CRASH-Basic = 0.02962, IMPACT-Core = 0.02962) in predicting in-hospital mortality. The MOST model similarly outperformed in predicting 3-, 7-, 14-, and 30-day mortality. The MOST model can be rapidly calculated and outperforms two widely used models for predicting mortality in TBI patients. It utilizes a larger, contemporaneous dataset that reflects modern trauma care.

Evaluation of Treatment Outcomes Using dNLR and GNRI in Combination Therapy With Atezolizumab and Bevacizumab for Hepatocellular Carcinoma.

This study aims to investigate the clinical utility of the derived neutrophil-to-lymphocyte ratio (dNLR) and the Geriatric Nutritional Risk Index (GNRI) in predicting treatment outcomes for patients with unresectable hepatocellular carcinoma (HCC) undergoing combination therapy with atezolizumab and bevacizumab (Atez/Bev). A retrospective analysis was conducted on 310 patients. The dNLR, NLR, and GNRI were calculated, and their impact on progression-free survival (PFS) and overall survival (OS) was assessed. The formula for calculating dNLR is: (neutrophil count ÷ [white blood cell count-neutrophil count]), which means it does not require lymphocyte count. Furthermore, GNRI-dNLR and GNRI-NLR scores were defined, and their prognostic values were also analyzed. The median PFS of this cohort was 7.2 months (95% CI: 5.9-8.5), and the median OS was 24.9 months (95% CI: 19.6-30.2). The dNLR, NLR, and GNRI were significant predictors of both PFS and OS. The dNLR showed a significant correlation with the NLR (Pearson correlation coefficient, p < 0.0001). Patients with high GNRI-dNLR scores demonstrated significantly worse PFS and OS compared to those with low scores (p = 0.001, p < 0.001, respectively). Compared to stratification by GNRI alone, the GNRI-dNLR or GNRI-NLR provided better stratification for both PFS and OS. The dNLR could be a valuable substitute for NLR as a prognostic marker in patients with unresectable HCC undergoing Atez/Bev therapy. It offers a feasible alternative for databases lacking lymphocyte count information, ensuring comprehensive patient stratification and outcome prediction. The GNRI-NLR or GNRI-dNLR score provided better stratification compared to GNRI alone.

The CASPAR study protocol. Can cervical stiffness predict successful vaginal delivery after induction of labour? a feasibility, cohort study.

Induction of labour (IOL) is a common obstetric intervention in the UK, affecting up to 33% of deliveries. IOL aims to achieve a vaginal delivery prior to spontaneous onset of labour to prevent harm from ongoing pregnancy complications and is known to prevent stillbirths and reduce neonatal intensive care unit admissions. However, IOL doesn't come without risk and overall, 20% of mothers having an induction will still require a caesarean section birth and in primiparous mothers this rate is even higher. There is no reliable predictive bedside tool available in clinical practice to predict which patient's undergoing the IOL process will result in a vaginal birth; the fundamental aim of the IOL process. The Bishop's Score (BS) remains in routine clinical practice as the examination tool to assess the cervix prior to IOL, despite it being proven to be ineffective as a predictive tool and largely subjective. This study will assess the use of the Pregnolia System, a new objective antenatal test of cervical stiffness. This study will explore its' potential for pre-induction cervical assessment and indication of delivery outcome following IOL. CASPAR is a feasibility study of term, primiparous women with singleton pregnancies undergoing IOL. Cervical stiffness will be assessed using the Pregnolia System; a novel, non-invasive, licensed, CE-marked, aspiration-based device proven to provide objective, quantitative cervical stiffness measurements represented as the Cervical Stiffness Index (CSI, in mbar). A measurement is obtained by applying the sterile single-use Pregnolia Probe directly to the anterior lip of the cervix, visualised via placement of a speculum. Following informed consent, CASPAR study participants will undergo the Pregnolia System cervical stiffness assessment prior to their IOL process commencing. Participant questionnaires will evaluate the acceptability of this assessment tool in this population. This study will directly compare this novel antenatal test to the current BS for both patient experience of the different cervical assessment tools and for IOL outcome prediction. This feasibility study will explore the use of this novel device in clinical practice for pre-induction cervical assessment and delivery outcome prediction. Our findings will provide novel data that could be instrumental in transforming clinical practice surrounding IOL. Determining recruitment rates and acceptability of this new assessment tool in this population will inform design of a further powered study using the Pregnolia System as the point-of-care, bedside cervical assessment tool within an IOL prediction model. This study is sponsored by The University of Liverpool and registered at ClinicalTrials.gov, identifier NCT05981469, date of registration 7th July 2023.

Integrating nutritional status and hematological biomarkers for enhanced prognosis prediction in glioma patients: A systematic review.

Multiple inflammatory and nutritional biomarkers have been established as independent prognostic factors across various solid tumors, but their role in outcomes prediction for glioma is still under investigation. Aim of the present systematic review is to report the available evidence regarding the impact of nutritional assessment and intervention for glioma prognosis and patients' quality of life (QoL). Our systematic review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The PubMed and EMBASE databases were searched to identify studies assessing the impact of nutritional status and intervention and hematological biomarkers on survival outcomes and quality of life in patients with newly diagnosed gliomas. In the search strategy Medical Subject Headings (MeSH) terms were used. Search terms included ("nutritional status" or "nutritional assessment" or "nutritional intervention") AND ("glioma" or "glioblastoma" or "high-grade glioma" or "low-grade glioma" or "anaplastic astrocytoma" or "anaplastic oligodendroglioma") AND ("prognosis" or "survival outcomes"). The quality of each study was investigated based on the Newcastle-Ottawa Scale (NOS) criteria. Selected papers were in English and included publications in humans. This study was registered on PROSPERO (Registration No. CRD42024555442). Our search retrieved 20 papers published between 2015 and 2023, all aiming at investigating correlations between hematological biomarkers (albumin, prealbumin, fibrinogen) and/or nutritional tools (Controlling Nutritional Score, CONUT; Prognostic Nutritional Index, PNI) and survival outcomes and quality of life of glioma patients. Nutritional intervention as well was evaluated for outcomes prediction. Overall, most papers contributed to the evidence of how nutritional assessment and inflammatory biomarkers could play an independent prognostic role also in the management of glioma patients. PNI, CONUT score and hematological biomarkers (e.g. albumin, globulin, neutrophils, lymphocytes) may serve as useful predictors in patients with gliomas, potentially influencing clinical decisions. Additional large-scale studies are required to validate these findings and determine the mechanisms by which nutritional status, systemic inflammation and immune status affect prognosis in glioma patients.

Insights into the use of DNA content in head and neck squamous cell carcinoma as a method for patient stratification and targeted therapy: Revisiting old concepts and exploring new possibilities.

This review aimed to emphasize the implications of DNA content in head and neck squamous cell carcinoma (HNSCC), focusing on its predictive value, role in patient stratification, and potential as a therapeutic target for this malignancy. A narrative review of the literature was conducted through electronic database searches. In conventional HNSCC, aneuploid tumors are associated with increased lymph node metastasis, locoregional recurrences, poor response to radiotherapy and chemotherapy, and worse prognosis. Few studies specifically address the role of DNA content in young HNSCC patients. These studies reveal that young patients exhibit high DNA content abnormalities, suggesting significant genomic instability and potential genetic differences compared to older patients. Regarding HPV and DNA content, no difference was found between HPV-associated and HPV-independent tumors. More research is needed to understand the role of DNA content in histological subtypes, surgical margins, and targeted therapy. This review highlights the findings related to DNA content in HNSCC, suggesting its usefulness in patient stratification and outcome prediction.

Machine Learning Analysis of Nutrient Associations with Peripheral Artery Disease: Insights from NHANES 1999-2004.

To investigate the relationship between nutrient intake and peripheral artery disease (PAD) using machine learning (ML) methods. Data from NHANES (1999-2004) were analyzed, including demographic, clinical, and dietary information. Nutrient intake was assessed through 24-hour dietary recalls using CADI and AMPM methods. PAD was defined as an Ankle-Brachial Index (ABI) <0.9. Six ML models-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Naive Bayes Classifier (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT)-were trained on a 70/30 train-test split, with missing data imputed and sample imbalance addressed via SMOTE. Model performance was evaluated using AUROC, accuracy, sensitivity, specificity, precision, recall, and F1 score. SHAP analysis was used to identify key features. In addition, to further enhance the interpretability of the model, we applied SHAP analysis to identify the features that have a significant impact on PAD prediction. This approach allowed us to determine the contribution of different variables to the model's output, providing deeper insights into how each feature influences the prediction of PAD outcomes. Of 31,126 participants, 4,520 met inclusion criteria (mean age 61.2 ± 13.5 years; 48.8% male), and 441 (9.7%) had ABI <0.9. XGBoost outperformed other models, achieving an AUROC of 0.913 (95% CI, 0.891-0.936) and F1 score of 0.932. With SMOTE, its AUROC improved to 0.926 (95% CI, 0.889-0.936) and F1 score to 0.937. SHAP analysis identified Vitamin C, saturated fatty acids, selenium, phosphorus, and protein intake as key predictors of PAD. This is the first study to apply ML algorithms to examine nutrient intake and PAD in a general population. Vitamin C and phosphorus showed negative correlations with PAD, while saturated fatty acids, protein, and selenium exhibited positive associations.

Evaluation of Modified Frailty Index-5 as a Predictor of 60-day Perioperative Morbidity and Mortality in Geriatric Patients Presenting for Orthopaedic Surgery: A Prospective Cohort Study

Introduction: Frailty indices predicting perioperative adverse outcomes have been used predominantly in retrospective studies for prediction of surgical adverse outcomes. Aim: To evaluate the modified 5-item Frailty Index (mFI-5) as a predictor of anaesthetic and surgical complications up to 60 days in geriatric patients presenting for orthopaedic surgery. Materials and Methods: The prospective cohort study was conducted at Lokmanya Tilak Municipal General Hospital, Mumbai, Maharashtra, India, from December 2019 to December 2020. Details of participants and caregivers, mFI-5 scores and surgical details of 62 patients aged &gt;65 years undergoing orthopaedic surgeries were studied. The mFI-5 score was calculated based on the presence of five co-morbidities: Congestive Heart Failure (CHF), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD) or pneumonia, functional health status and hypertension, as defined in NSQIP database. Complications of perioperative bleeding and inotropic support, along with others mentioned in the National Surgical Quality Improvement Program (NSQIP) database, were noted up to 60 days, with milestones of 48 hours, seven days and 30 days. The data were analysed for association between mFI-5 and complications by applying t-test, Chi-square test and multivariate analysis using the Statistical Package for the Social Sciences (SPSS) 20.0 version. Results: Mean age was 71.44±6.70 years, with 26 patients having an mFI-5 &gt;3 (mean 2.33±0.96). Forty-five patients had at least one complication (mean 2.39±2.59). Mortality was observed in three out of 26 patients with an mFI-5 &gt;3 (11.54%), while one in 36 patients (2.78%) died with mFI-5&lt;3. No association with mortality was observed with either unit increase in mFI-5 scores or mFI5&gt;3. Complications included use of blood and blood products, inotropes and postoperative ventilation in the first 48 hours (mean 0.73±0.75), respiratory complications and blood and blood product transfusions in the 48 hours to seven days period (mean 0.65±1.13), Surgical Site Infections (SSIs) and reoperations between 8-30 days (mean 0.74±1.41) and renal insufficiency and death in the 30-60 days period (mean 0.27±0.61). Age (p-value=0.315), gender (p-value=0.635), scheduling (p-value=0.530), site (p-value=0.077) and nature of surgery (p-value=0.172) were not statistically significant, while mFI scores ≥3 (p-value &lt;0.001), American Society of Anaesthesiologists (ASA) grades (p-value=0.016), surgical duration (p-value=0.012), CHF (p-value=0.003) and DM (p-value=0.002) were statistically significant. Conclusion: Patients aged &gt;65 years with mFI-5 scores ≥3, having CHF, DM, ASA grades &gt;2, undergoing orthopaedic surgeries of duration up to three hours, had statistically significant chance of developing postoperative complications, other than death, up to 60 days. A randomised controlled trial with mFI-5 cut-off of ≥3 and longer follow-up periods would yield better results.