Development Of Predictive Models Research Articles

Development of predictive model for post-stroke depression at discharge based on decision tree algorithm: A multi-center hospital-based cohort study

ObjectivePost-stroke depression (PSD) is one of the most common and severe neuropsychological sequelae after stroke. Using a prediction model composed of multiple predictors may be more beneficial than verifying the predictive performance of any single predictor. The primary objective of this study was to construct practical prediction tools for PSD at discharge utilizing a decision tree (DT) algorithm. MethodsA multi-center prospective cohort study was conducted from May 2018 to October 2019 and stroke patients within seven days of onset were consecutively recruited. The independent predictors of PSD at discharge were identified through multivariate logistic regression with backward elimination. Classification and regression tree (CART) algorithm was employed as the DT model's splitting method. ResultsA total of 876 stroke patients who were discharged from the neurology departments of three large general Class A tertiary hospitals in Wuhan were eligible for analysis. Firstly, we divided these 876 patients into PSD and non-PSD groups, history of coronary heart disease (OR = 1.835; 95 % CI, 1.106–3.046; P = 0.019), length of hospital stay (OR = 1.040; 95 % CI, 1.013–1.069; P = 0.001), NIHSS score (OR = 1.124; 95 % CI, 1.052–1.201; P = 0.001), and Mini mental state examination (MMSE) score (OR = 0.935; 95 % CI, 0.893–0.978; P = 0.004) were significant predictors. The subgroup analysis results have shown that hemorrhagic stroke, history of hypertension and higher modified Rankin Scale score (mRS) score were associated with PSD at discharge in the young adult stroke patients. ConclusionsSeveral predictors of PSD at discharge were identified and convenient DT models were constructed to facilitate clinical decision-making.

Integrating radiological technology in environmental health surveillance to enhance public safety

The integration of radiological technology into environmental health surveillance represents a significant advancement in enhancing public safety. Radiological technologies, including advanced imaging and monitoring systems, offer powerful tools for detecting and assessing environmental hazards that impact public health. This review explores the potential benefits, methodologies, and challenges associated with integrating radiological technology into environmental health surveillance. Recent advancements in radiological technology, such as high-resolution imaging systems and portable radiation detectors, have greatly improved the ability to monitor and analyze environmental radiation levels with precision. These technologies enable real-time detection of radiation exposure, providing critical data for assessing risks associated with radiation sources, such as industrial activities, nuclear power plants, and natural radioactive materials. By integrating these tools into environmental health surveillance frameworks, public health agencies can enhance their capacity to identify and address radiation-related hazards more effectively. The application of radiological technology in environmental health surveillance facilitates comprehensive monitoring of radiation levels across various environments, including urban, rural, and industrial areas. This data is crucial for identifying contamination hotspots, tracking changes in radiation levels over time, and evaluating the effectiveness of radiation control measures. Additionally, the integration of radiological technology supports the development of predictive models for assessing potential health impacts and guiding emergency response strategies in the event of radiation incidents. Despite its advantages, integrating radiological technology into environmental health surveillance presents several challenges. These include the high costs of advanced equipment, the need for specialized training for personnel, and the management of large volumes of data generated by monitoring systems. Addressing these challenges requires a coordinated approach involving government agencies, research institutions, and industry stakeholders to ensure effective implementation and sustainability. In conclusion, integrating radiological technology into environmental health surveillance has the potential to significantly enhance public safety by providing accurate and timely data on radiation exposure. Continued innovation and collaboration in this field will be essential for optimizing the benefits of radiological technology and addressing the challenges associated with its integration into environmental health monitoring systems. Keywords: Integrating; Radiological Technology; Environmental Health Surveillance; Enhance; Public Safety

Predicting the higher heating value of products through solid yield in torrefaction process

Torrefaction is a widely employed method to upgrade biomass for energy purpose. The higher heating value (HHV) serves as a vital indicator for assessing the energy potential of biomass. Nevertheless, HHV measurement is a time-consuming and costly process. HHV of raw biomass, torrefied biomass, and biochar has been extensively estimated using the results of elemental analysis and/or proximate analysis. However, these data must be repeatedly measured for each target object. To reduce the efforts required for the measurement of each object, this study proposes, for the first time, the use of solid yield (the most easily obtainable data) to predict the HHV of torrefied biomass.A total of 215 sets of data were retrieved from different biomass types undergoing various torrefaction technologies. Artificial neural network (ANN) and hierarchical linear regression (HLR) were employed to develop prediction models. Solid yield demonstrated a clear correlation to HHV for each individual biomass; however, diverse biomass types exhibited significant variation. Therefore, the results of elemental analysis of raw feedstocks were incorporated to account for variations in feedstock types and to iterate the models. Generally, ANN is superior to HLR in predicting the HHV of torrefied biomass with the optimized model achieving an R2 of 0.9133. Additionally, two biomasses subjected to two torrefaction technologies were used to validate the model, and an R2 of 0.9676 was achieved.As a result, in a torrefaction process, once the raw biomass is analyzed, the HHV of any torrefied biomass can be predicted simply by measuring the weight of the product.

Understanding nitrogen allocation dynamics in Indian mustard: Insights from enzyme activity and ideotype analysis

The study explores the effect of varying levels of nitrogen (N) application on N allocation in Indian mustard (Brassica juncea) genotypes from vegetative to the generative phases. The use of a heat map and PCA biplot analysis identifies two distinct sets of ideotypes. The first set is represented by genotype IC212031 having “Stay-green” phenotype with effective N regulating enzymes, high N uptake (NUpt) and better root development. The second set of ideotype is represented by HB9902 having delayed flowering phenology phases. The study highlights the relevance of Nitrate reductase (NR) in the final oil buildup in seeds (r = 0.5, p = 0.09). The considerable rise in NR activity from pre-anthesis to post-anthesis stages translates to higher seed storage compounds such as oil (r=−0.65, p = 0.03). And the developed prediction models confirmed the significance of these stages (pre-anthesis and post-anthesis) in the accumulation of seed storage components with emphasis on oil content. Similarly, this prediction model helps us to study the cause and effect relationship between various factors and oil content. The notable rise in Nitrite reductase (NiR) activity during the post-anthesis phase suggests its role in assimilating N for the storage of seed components, most likely proteins. Further, the inverse relationship between seed N (−0.53,p= 0.09) and oil content (−0.65, p= 0.03) confirmed that at the post anthesis stage the allocation of N towards precursor carbon compounds happens, which in turn forms either proteins or lipids. This becomes a critical factor in determining the final composition of the seed. Irrespective of N treatment and genotype, the N allocation follows the pattern: seed > siliquae husk > stover > root. However, higher N application rates primarily result in increased NUpt, but not necessarily improve utilisation. Overall, the study highlights the roles of root characteristics, N metabolising enzymes, and flowering phenology in enhancing N allocation and seed production in Indian mustard offering valuable insights for breeding strategies to enhance seed yield, oil content and nutrient efficiency in oilseed crops.

Abstract A143: The development of a risk prediction model to predict patients’ likelihood of completing human papillomavirus vaccination

Abstract Background: Human Papillomavirus (HPV) cancers can be preventable if HPV vaccination is received in adolescents before the age of 13, yet only 55% of Oregonian adolescents had an HPV series completion in 2020.2 The recommended vaccination age for HPV recently dropped to age 9. This new recommended age allows providers to offer HPV vaccination with routine vaccinations. To further increase HPV vaccination rates, research is critically needed on new interventions to promote vaccination among vaccine resistant groups, and populations that are most in need of targeted interventions. We assessed patterns in vaccination from 2015-2022, in preparation of the development of a prognostic risk prediction model to identify patients’ likelihood of obtaining HPV vaccination. Methods: We used a retrospective cohort from a large health system to assess vaccination patterns from 2015-2022. Eligible patients were members of Kaiser Permanente Northwest, 9-18 years old, and did not have a history of adverse vaccination outcomes. We assessed vaccination initiation, completion by age, gender and year. Results: Among 29,398 eligible patients, 44.3% had no vaccination, 14.5% had one dose, and 41.2% had completed the series. More patients were vaccinating at earlier ages from 2015-2022. Of those with one dose or who had completed the series, 84% had vaccinated by the age of 12 in 2022 compared to 74% in 2015. The change was greater in boys with 70.5% before age 12 in 2015 compared with 84% in 2022. ConclusionsWhile efforts to increase HPV vaccination and initiate at younger ages have been successful, efforts need to be targeted to specific populations. The patterns of HPV vaccination over the past 7 years highlights improvement, but system level interventions may be needed to adequately increase vaccination among resistant populations. This presentation will discuss the assessment of vaccination patterns in preparation of the development of a prognostic risk prediction model and describe how the model will be used in a system to target populations for early interventions to complete vaccination. Citation Format: Amanda A. Petrik, Matthew Slaugher, Gloria Coronado, Eric Johnson. The development of a risk prediction model to predict patients’ likelihood of completing human papillomavirus vaccination [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A143.

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

BackgroundBladder cancer (BCa), one of the most common cancers worldwide, is characterized by high rates of recurrence, progression, and mortality. Machine learning algorithms offer promising advancements in enhancing predictive models. This study aims to develop robust machine learning models for predicting BCa survival using clinical and gene expression data. MethodsClinical data from BCa patients were obtained from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression models assessed the association between clinical variables and overall survival. Machine learning algorithms, including logistic regression, random forest, XGBoost, decision tree, and LightGBM, were employed to predict survival at 1, 3, and 5 years. The TAGO database, combined with the data from The Cancer Genome Atlas and four databases from the Gene Expression Omnibus, which have available genomic data and clinical data, were selected. Gene expression data were transformed into gene sets data, and the performance of models based on clinical data and gene sets data and their combination were compared. Furthermore, the impact of model-derived scores on overall survival was evaluated. ResultsAmong 138,741 BCa patients with available clinical data, key independent predictors of survival included age, race, marital status, surgery, chemotherapy, radiation, and TNM stages. Clinical data machine learning (CML) models used these clinical predictors to achieve AUC values of 0.860, 0.821, and 0.804 in the testing sets for predicting survival at 1, 3, and 5 years, respectively. In the TAGO database, which has 863 patients with clinical and genomic data, the integrated clinical and gene expression machine learning model (IML) outperformed the CML and gene expression machine learning (GML) models in survival prediction. Patients with higher IML and GML model scores exhibited poorer survival outcomes. ConclusionsThis study successfully identifies key clinical and genomic predictors, a significant step forward in BCa research. The development of predictive models for BCa survival underscores the potential of integrated data approaches in improving BCa management and treatment strategies.

Optimized Machine Learning Models for Predicting Core Body Temperature in Dairy Cows: Enhancing Accuracy and Interpretability for Practical Livestock Management.

Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R2) value of 0.516, mean absolute error (MAE) of 0.239 °C, and root mean square error (RMSE) of 0.302 °C. Further analysis shows that the GWO-XGBoost model surpasses others in predictive accuracy with an R2 value of 0.540, RMSE as low as 0.294 °C, and MAE of just 0.232 °C, and leads in computational efficiency with an optimization time of merely 2.41 s-approximately 4500 times faster than the highest accuracy model. Through SHAP (SHapley Additive exPlanations) analysis, IRTave_TK, time zone (TZ), days in lactation (DOL), and body posture (BP) are identified as the four most critical factors in predicting CBT, and the interaction effects of IRTave_TK with other features such as body posture and time periods are unveiled. This study provides technological support for livestock management, facilitating the development and optimization of predictive models to implement timely and effective interventions, thereby maintaining the health and productivity of dairy cows.

Contingency and Synchrony: Interactional Pathways Toward Attentional Control and Intentional Communication

In this article we examine how contingency and synchrony during infant–caregiver interactions help children learn to pay attention to objects and how this, in turn, affects their ability to direct caregivers’ attention and to track communicative intentions in others. First, we present evidence that, early in life, child–caregiver interactions are asymmetric. Caregivers dynamically and contingently adapt to their child more than the other way around, providing higher-order semantic and contextual cues during attention episodes, which facilitate the development of specialized and integrated attentional brain networks in the infant brain. Then, we describe how social contingency also facilitates the child's development of predictive models and, through that, goal-directed behavior. Finally, we discuss how contingency and synchrony of brain and behavior can drive children's ability to direct their caregivers’ attention voluntarily and how this, in turn, paves the way for intentional communication.

Development of the Quantitative Property-Consequence Relationship Model for Prediction of Hydrogen Leakage and Dispersion Using Response Surface Method and Artificial Neural Network Approaches.

Hydrogen, an environmentally friendly and highly regarded future energy source, can form flammable vapor clouds upon leakage, which may transition into explosion. Predicting the dispersion behavior of hydrogen is crucial for preventing such incidents. This study aims to develop a quantitative property-consequence relationship (QPCR) model using the response surface method (RSM) and artificial neural network (ANN) to swiftly and accurately predict dispersion behavior. Initially, 8 variables were defined from source and dispersion models, constructing a data set through 6,561 PHAST simulations. Subsequently, the RSM-BBD (Box-Behnken design) and ANN-BPNN (Backpropagation neural network) models were developed, alongside a hybrid model incorporating BPNN after excluding four low-influence variables based on analysis of variance (ANOVA). All models achieved an R2 value exceeding 0.99. The hybrid model notably reduced computational costs by 97% compared to ANN-BPNN and exhibited lower mean square error (MSE). These results introduce a cost-effective approach for high-accuracy QPCR modeling and highlight the viability of diverse statistical methods.

Prediction of moisture content of cement-stabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity

This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learning models, and XGBoost emerges as the most promising model, demonstrating superior accuracy in predicting moisture content based on factors like soil properties, cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive exPlanations) to understand how these features influence the model’s predictions. UPV is the most significant factor affecting predicted moisture content, followed by cement content and soil properties like uniformity coefficient. Also, the study explores the possibility of using a reduced set of features for moisture content prediction. They demonstrate that a combination of UPV, cement content, and uniformity coefficient can achieve good accuracy, highlighting the potential for practical applications where obtaining all data points might be challenging.

Immune and inflammatory insights in atherosclerosis: development of a risk prediction model through single-cell and bulk transcriptomic analyses.

Investigation into the immune heterogeneity linked with atherosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapies aimed at combating this disease. Single-cell RNA sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to elucidate the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches, including single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. A risk score model and a corresponding nomogram were developed and validated using these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, enrichment and immune characteristic analyses were conducted based on the risk score model. The model's applicability was further corroborated by in vitro and in vivo validation of specific genes implicated in atherosclerosis. This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. The presence of diverse immune cell populations, with a particularly enriched macrophage population, was highlighted by the results. Macrophage heterogeneity was intricately characterized, revealing four distinct subtypes with varying functional attributes that underscore their complex roles in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways differing between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The use of machine learning identified potential diagnostic markers, culminating in the identification of RNASE1 and CD14. The risk score model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the risk score model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, suggesting its potential as a therapeutic target. Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies.

New intelligent models for predicting wax appearance temperature using experimental data – Flow assurance implications

Wax deposition is a major problem during petroleum production causing flow rate reduction and blockage of tubing, pipelines, and surface facilities. Wax deposition occurs when crude oil temperature falls below wax appearance temperature (WAT). WAT is an important parameter for wax deposition modeling and developing, testing, and selecting appropriate wax inhibitors. WAT can be determined in laboratory using several different techniques. Although, each method has its own disadvantages including being time-consuming, expensive, inaccurate, or posing health and safety risks. Experimental WAT data are very scarce in the literature. Reliable and accurate correlations and models for WAT prediction are rare, as well. Hence, development of fast, easy, and reliable models for prediction of WAT is inevitable. The main objective of this research work was to develop and introduce novel intelligent models for precise production of WAT. The artificial intelligent (AI) algorithms used include least-squares support-vector machines (LSSVM), recurrent neural network (RNN), and Adaptive neuro-fuzzy inference system (ANFIS). A high-quality dataset was assembled using experimental WAT data collected from the literature. The dataset consists of 81 experimental which is the largest dataset ever reported. The gathered dataset covers a wide range of density from 0.71 to 0.89 g/cm3, wax content from 0.61 to 25.78 wt%, and Pour point from −36 to 37°C. The selected input parameters include oil density, wax content, and pour point determined using a rigorous feature selection analysis. Statistical analysis showed that pour point has the highest impact on WAT prediction. The modeling results showed that the LSSVM model is the superior model with coefficient of determination (R2) of 0.9893, a root mean squared error (RMSE) of 0.1655, and an average absolute relative deviation (AARD) of 9%. The LSSVM model outperformed the only existing smart model in terms of both performance and accuracy. The proposed smart model can be used for prediction of WAT which is an essential parameter in all wax related research.

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Dynamic Response Analysis and Liquefaction Potential Evaluation of Riverbed Induced by Tidal Bore

Tidal bores, defined by sudden upstream surges of tidal water in estuaries, exert significant hydrodynamic forces on riverbeds, leading to complex sedimentary responses. This study examines the dynamic response and liquefaction potential of riverbeds subjected to tidal bores in macro-tidal estuaries. An analytical model, developed using the generalized Biot theory and integral transform methods, evaluates the dynamic behavior of riverbed sediments. Key factors such as permeability, saturation, and sediment properties are analyzed for their influence on momentary liquefaction. The results indicate that fine sand reduces liquefaction risk by facilitating pore water discharge, while silt soil increases sediment instability. Additionally, the study reveals that pressure gradients induced by tidal bores can trigger momentary liquefaction, with the maximum liquefaction depth predicted based on horizontal pressure gradients being five times that predicted based on vertical pressure gradients. This research highlights the critical role of sediment characteristics in riverbed stability, providing a comprehensive understanding of the interactions between tidal bores and riverbed dynamics. The findings contribute to the development of predictive models and guidelines for managing the risks of tidal bore-induced liquefaction in coastal and estuarine environments.

Computed tomography-based body composition indicative of diabetes after hypertriglyceridemic acute pancreatitis

BackgroundPost‑acute pancreatitis prediabetes/diabetes mellitus (PPDM‑A) is one of the common sequelae of acute pancreatitis (AP). The aim of our study was to build a machine learning (ML)-based prediction model for PPDM-A in hypertriglyceridemic acute pancreatitis (HTGP). MethodsWe retrospectively enrolled 165 patients for our study. Demographic and laboratory data and body composition were collected. Multivariate logistic regression was applied to select features for ML. Support vector machine (SVM), linear discriminant analysis (LDA), and logistic regression (LR) were used to develop prediction models for PPDM-A. Results65 patients were diagnosed with PPDM-A, and 100 patients were diagnosed with non-PPDM-A. Of the 84 body composition-related parameters, 15 were significant in discriminating between the PPDM-A and non-PPDM-A groups. Using clinical indicators and body composition parameters to develop ML models, we found that the SVM model presented the best predictive ability, obtaining the best AUC=0.796 in the training cohort, and the LDA and LR model showing an AUC of 0.783 and 0.745, respectively. ConclusionsThe association between body composition and PPDM-A provides insight into the potential pathogenesis of PPDM-A. Our model is feasible for reliably predicting PPDM-A in the early stages of AP and enables early intervention in patients with potential PPDM-A.

An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention

AbstractThe use of educational data mining and machine learning to analyse large data sets collected by educational institutions has the potential to discover valuable insights for decision-making. One such area that has gained attention is to predict student performance by analysing large educational data sets. In the relevant literature, many studies have focused on developing prediction models on student performance but comparatively less work exists on actions taken based on predicted at-risk students and evaluating their impact. Learning Analytics Intervention (LAI) studies have emerged as an approach that aims to address this gap. In LAI studies, student risk levels are predicted and disseminated to relevant stakeholders (academics, administrators and students) using learning analytics (LA) tools for targeted interventions. The interventions themselves are mainly left under the discretion of the academics and/or administrators, who are aware of the learning context and have the authority to make decisions, with LA tools facilitating this process. LAI studies have shown success in improving outcomes (e.g. improve pass rates, retention, grades), but their uptake has been slow. The main impediment to piloting LAIs by academics has been the lack of access to LAI infrastructure, which requires institutional investments to develop predictive models collecting data from diverse IT systems. Another challenge in LAIs is the development of effective interventions. This paper presents an extended LAI framework, termed Student Performance Prediction and Action (SPPA), which provides access to LAI infrastructure for academics to pilot LAIs in their courses without the need for institution-wide efforts. SPPA and its features are seamlessly accessed via a web browser and academics can develop course-specific predictive models based on historical course assessment data. Furthermore, SPPA integrates sound pedagogical approaches and provides relevant information (such as students’ knowledge gaps, personalised study plans) to assist academics in providing effective interventions. SPPA was evaluated by a number of academics piloting LAIs in their courses. Quantitative and qualitative data was collected and analysed. Academics were able to provide effective interventions using SPPA and also had a positive outlook on using SPPA and its features. SPPA is also provided as an open-source project for further development and can be a catalyst for widescale uptake in LAIs. Furthermore, a model for continuous improvement in LAIs is outlined along with a number of areas for future research and development.

The impact of radiomics libraries and gray level discretization on the discovery of immunotherapy biomarkers in NSCLC patients.

The influence of radiomics pipeline and the grey-level discretization on the discovery of immunotherapy biomarkers is still a poorly understood topic. This study is aimed at identifying robust features by comparing two radiomics libraries and their association with clinical outcomes in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). A retrospective cohort of 164 NSCLC patients administered with ICIs was used in this study. Radiomic features were extracted from the pre-treatment CT scans. Univariate models were used to assess the association of radiomics features with progression free survival (PFS), PD-L1 and CD8 cell counts. We also examined the impact of gray-level discretization on feature robustness by evaluating the association of features with clinical endpoints. We extracted 1224, 441 radiomic features using Pyradiomics and RaCat, respectively, out of which 75 were common between them. We showed that the directionality of association between features and clinical endpoints is specific to the radiomic library used. Overall, more Pyradiomics and RaCat features were statistically associated with PFS, and PD-L1, respectively. We found intensity-based features to be more agnostic to the gray-level discretization parameters. Among features that showed significant correlation with PFS with varying gray-level discretization parameters, 45% were intensity-based, compared to PD-L1, and CD8. This study highlights the heterogeneity of radiomics libraries and the gray level discretization parameters that will impact the feature selection and predictive model development. Importantly, our work highlights the significance of selecting features that are agnostic to radiomics libraries for clinical translation. Our study emphasizes the need to select stable CT-derived handcrafted features to build immunotherapy biomarkers, which is a necessary precursor for multi-institutional validation of imaging biomarkers.

Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra-high risk in the UHR 1000+ cohort.

The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data

The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and cardiometabolic risk factors from MARK-AGE data. A cross-sectional observational study was carried out on 1089 subjects (528 men and 561 women), aged 40–75 years old, randomly recruited age- and sex-stratified individuals from the general population. A correlation analysis exploring the relationships between LDLox and relevant biomarkers was undertaken, as well as the development and validation of several machine learning algorithms, for estimating the risk of the combined status of high blood pressure and obesity for the MARK-AGE subjects. The machine learning models yielded Area Under the Receiver Operating Characteristic Curve Score ranging 0.783–0.839 for the internal validation, while the external validation resulted in an Under the Receiver Operating Characteristic Curve Score between 0.648 and 0.787, with the variables based on LDLox reaching significant importance within the obtained predictions. The current study offers novel insights regarding the combined effects of LDL oxidation and other ageing markers on cardiometabolic risk. Future studies might be extended on larger patient cohorts, in order to obtain reproducible clinical assessment models.

Investigation of end-stage kidney disease risk prediction in an ethnically diverse cohort of people with type 2 diabetes: use of kidney failure risk equation

IntroductionThe four variable kidney failure (KF) risk equation (KFRE) is recommended to estimate KF risk (ie, need for dialysis or kidney transplantation). Earlier referral to clinical kidney services for people...