Development Of Risk Model Research Articles

Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus.

To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus. A retrospective study. We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test. A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day. No Patient or Public Contribution.

Evaluating Health Canada’s Proposed Front-of-Pack Labelling Policy: Burden of Healthcare Use Attributed to Health Canada’s Proposed Front-of-Pack Labelling Policy

The economic burden of disease due to consumption of poor diet costs billions of dollars annually to the Canadian healthcare system. This project is in response to the growing interest in the use of nutrient profiling systems on front-of-package (FOP) nutrition labels in Canada. There is a high demand for simplified healthy eating messaging. Health Canada announced improving food environments is a priority in the Healthy Eating Strategy and will implement the mandatory FOP label policy in 2026. Two Ontario-based studies examined the impacts of health behaviors (smoking, alcohol consumption, poor diet and physical inactivity) attributed to hospital bed-days and costs. They found 36% of hospital use was attributable to all health behaviours resulting in 900,000 bed-days annually. Between 2004-2013, 22% of healthcare costs totalling $89.4 billion were attributable to the four health behaviours. These are likely underestimates as diet was measured based on fruit/vegetables frequency. The objective is to evaluate Health Canada’s Proposed FOP policy on hospital bed-days. Detailed dietary data from the nationally representative cross-sectional national survey data Canadian Community Heath Survey-Nutrition 2004 linked to Discharge Abstract Database (2004-14) for hospital use will be used for analysis. Data-driven methodology will be employed and development of multivariable risk models using zero-inflated negative binomial regression with multiple exposure groups to test a dose-response. Results are undergoing, will be ready September 2024. This is the first study to analyze Health Canada’s proposed FOP policy related to economic outcomes. These findings will provide evidence-based recommendations to policymakers on nutrition policy.

A Rank Graduation Box for SAFE AI

The growth of Artificial Intelligence applications requires to develop risk management models that can balance opportunities with risks.We contribute to the development of Artificial Intelligence risk models proposing a Rank Graduation Box (RGB), a set of integrated statistical metrics that can measure the “Sustainability”, “Accuracy”, “Fairness” and “Explainability” of any Artificial Intelligence application.Our metrics are consistent with each other, as they are all derived from a common underlying statistical methodology: the Lorenz curve.The validity of the metrics is assessed by means of their practical application to both simulated and real data. The results from the comparison of alternative machine learning models to simulated data are aligned with the generating models, in general indicating linear regression models as the most accurate, regression tree models as the most fair, Random Forest models as the most robust; and leading to model explanations similar to the true ones from the generating model.The outcomes from the application of Random Forest models to real data show that the proposed RGB metrics are more interpretable and more consistent with the expectations, with respect to standard metrics such as AUC, RMSE and Shapley values. The evidence also shows that the RGB metrics are very general and can be applied to any machine learning method, regardless of the underlying data and model.

Development and application of a serious adverse events risk model for concurrent chemoradiotherapy in patients with nasopharyngeal carcinoma.

The objective of this study was to construct a concise prediction model for serious adverse events (SAEs) in order to assess the likelihood of SAE occurrence among hospitalized patients undergoing concurrent chemoradiotherapy. An electronic database of a Cancer Centre was utilized to conduct a cross-sectional review survey. Our research involved the recruitment of 239 patients who were undergoing concurrent chemoradiotherapy in the Department of Nasopharynx and Radiotherapy. The clinical prediction rule was derived using logistic regression analysis, with SAE serving as the primary outcome. Internal verification was conducted. The occurrence rate of SAE in the derivation cohort was 59.4%. The ultimate model used had 3 variables, namely cystatin C, C-reactive protein, and serum amyloid A. The model exhibited an area under the curve of 0.626 (95% CI: 0.555-0.696; P < .001). The model accurately predicts the occurrence of SAE, and the variable data can be easily obtained, and the assessment technique is straightforward.

Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta-analysis.

The main aim of this study is to synthesize the prevalent predictive models for pressure injuries in hospitalized patients, with the goal of identifying common predictive factors linked to pressure injuries in hospitalized patients. This endeavour holds the potential to provide clinical nurses with a valuable reference for providing targeted care to high-risk patients. Pressure injuries (PIs) are a frequently occurring health problem throughout the world. There are mounting studies about risk prediction model of PIs reported and published. However, the prediction performance of the models is still unclear. Systematic review and meta-analysis: The Cochrane Library, PubMed, Embase, CINAHL, Web of Science and Chinese databases including CNKI (China National Knowledge Infrastructure), Wanfang Database, Weipu Database and CBM (China Biology Medicine). This systematic review was conducted following PRISMA recommendations. The databases of Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and CNKI, Weipu Database, Wanfang Database and CBM were searched for all studies published before September 2023. We included studies with cohort, case-control designs, reporting the development of risk model and have been validated externally and internally among the hospitalized patients. Two researchers selected the retrieved studies according to the inclusion and exclusion criteria, and critically evaluated the quality of studies based on the CHARMS checklist. The PRISMA guideline was used to report the systematic review and meta-analysis. Sixty-two studies were included, which contained 99 pressure injuries risk prediction models. The AUC (area under ROC curve) of modelling in 32 prediction models were reported ranged from .70 to .99, while the AUC of verification in 38 models were reported ranged from .70 to .98. Gender (OR = 1.41, CI: .99 ~ 1.31), age (WMD = 8.81, CI: 8.11 ~ 9.57), diabetes mellitus (OR = 1.64, CI: 1.36 ~ 1.99), mechanical ventilation (OR = 2.71, CI: 2.05 ~ 3.57), length of hospital stay (WMD = 7.65, CI: 7.24 ~ 8.05) were the most common predictors of pressure injuries. Studies of PIs risk prediction model in hospitalized patients had high research quality, and the risk prediction models also had good predictive performance. However, some of the included studies lacked of internal or external validation in modelling, which affected the stability and extendibility. The aged, male patient in ICU, albumin, haematocrit, low haemoglobin level, diabetes, mechanical ventilation and length of stay in hospital were high-risk factors for pressure injuries in hospitalized patients. In the future, it is recommended that clinical nurses, in practice, select predictive models with better performance to identify high-risk patients based on the actual situation and provide care targeting the high-risk factors to prevent the occurrence of diseases. The risk prediction model is an effective tool for identifying patients at the risk of developing PIs. With the help of risk prediction tool, nurses can identify the high-risk patients and common predictive factors, predict the probability of developing PIs, then provide specific preventive measures to improve the outcomes of these patients. CRD42023445258.

Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods

BackgroundIn randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual’s risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis.MethodsThree approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States’ participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran’s Q test was used to detect if ARR varied significantly by subgroup.ResultsThe proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0–26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1–24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4–39.4%). Cochran’s Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants.ConclusionsThe choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses.

Personalized risk prediction for prolonged ileus after minimally invasive colorectal cancer surgery: in-depth risk factor analysis and model development

PurposeDespite the increasing preference for minimally invasive surgery for colorectal cancer (CRC), the incidence of prolonged postoperative ileus (PPOI) remains high. Thus, this study aimed to identify risk factors for PPOI in patients with CRC who underwent minimally invasive surgery (MICRS) and to develop a practical nomogram for predicting individual PPOI risk.MethodsA consecutive series of 2368 patients who underwent MICRS between 2013 and 2023 at two tertiary academic centers were retrospectively studied. Using the data from 1895 patients in the training cohort, a multivariable logistic regression model was employed to select significant variables for the construction of a best-fit nomogram. The nomogram was internally and externally validated.ResultsPPOI occurred in 9.5% of patients. Six independent risk factors were identified to construct a nomogram: advanced age (OR 1.055, P = 0.002), male sex (OR 2.914, P = 0.011), age-adjusted Charlson comorbidity index ≥ 6 (OR 2.643, P = 0.025), preoperative sarcopenia (OR 0.857, P = 0.02), preoperative prognostic nutritional index (OR 2.206, P = 0.047), and intraoperative fluid overload (OR 2.227, P = 0.045). The AUCs of the model for predicting PPOI in the training and external validation cohorts were 0.887 and 0.838, respectively. The calibration curves demonstrated excellent consistency between the nomogram-predicted and observed probabilities in both cohorts. Individuals with a total nomogram score of < 197 or ≥ 197 were considered to be at low or high risk for PPOI, respectively.ConclusionsThe integrated nomogram we developed could provide personalized risk prediction of PPOI after MICRS. This quantification enables surgeons to implement personalized prevention strategies, thereby improving patient outcomes.

Development of a prognostic risk model of uveal melanoma based on N7-methylguanosine-related regulators

BackgroundUveal melanoma (UVM) stands as the predominant type of primary intraocular malignancy among adults. The clinical significance of N7-methylguanosine (m7G), a prevalent RNA modifications, in UVM remains unclear.MethodsPrimary information from 80 UVM patients were analyzed as the training set, incorporating clinical information, mutation annotations and mRNA expression obtained from The Cancer Genome Atlas (TCGA) website. The validation set was carried out using Gene Expression Omnibus (GEO) database GSE22138 and GSE84976. Kaplan–Meier and Cox regression of univariate analyses were subjected to identify m7G-related regulators as prognostic genes.ResultA prognostic risk model comprising EIF4E2, NUDT16, SNUPN and WDR4 was established through Cox regression of LASSO. Evaluation of the model’s predictability for UVM patients’ prognosis by Receiver Operating Characteristic (ROC) curves in the training set, demonstrated excellent performance Area Under the Curve (AUC) > 0.75. The high-risk prognosis within the TCGA cohort exhibit a notable worse outcome. Additionally, an independent correlation between the risk score and overall survival (OS) among UVM patients were identified. External validation of this model was carried out using the validation sets (GSE22138 and GSE84976). Immune-related analysis revealed that patients with high score of m7G-related risk model exhibited elevated level of immune infiltration and immune checkpoint gene expression.ConclusionWe have developed a risk prediction model based on four m7G-related regulators, facilitating effective estimate UVM patients’ survival by clinicians. Our findings shed novel light on essential role of m7G-related regulators in UVM and suggest potential novel targets for the diagnosis, prognosis and therapy of UVM.

An evaluation of sample size requirements for developing risk prediction models with binary outcomes

BackgroundRisk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions.MethodsRecently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae.ResultsWe found that both formulae work reasonably well when the anticipated model strength is not too high (c-statistic < 0.8), regardless of the outcome prevalence. However, for higher model strengths the CS formula underestimates the sample size substantially. For example, for c-statistic = 0.85 and 0.9, the sample size needed to be increased by at least 50% and 100%, respectively, to meet the target expected CS. On the other hand, the MAPE formula tends to overestimate the sample size for high model strengths. These conclusions were more pronounced for higher prevalence than for lower prevalence. Similar results were drawn when the outcome was time to event with censoring. Given these findings, we propose a simulation-based approach, implemented in the new R package ‘samplesizedev’, to correctly estimate the sample size even for high model strengths. The software can also calculate the variability in CS and MAPE, thus allowing for assessment of model stability.ConclusionsThe calibration and MAPE formulae suggest sample sizes that are generally appropriate for use when the model strength is not too high. However, they tend to be biased for higher model strengths, which are not uncommon in clinical risk prediction studies. On those occasions, our proposed adjustments to the sample size calculations will be relevant.

Association of Dynamic Trajectories of Time-Series Data and Life-Threatening Mass Effect in Large Middle Cerebral Artery Stroke.

Life-threatening, space-occupying mass effect due to cerebral edema and/or hemorrhagic transformation is an early complication of patients with middle cerebral artery stroke. Little is known about longitudinal trajectories of laboratory and vital signs leading up to radiographic and clinical deterioration related to this mass effect. We curated a retrospective data set of 635 patients with large middle cerebral artery stroke totaling 95,463 data points for 10 longitudinal covariates and 40 time-independent covariates. We assessed trajectories of the 10 longitudinal variables during the 72h preceding three outcomes representative of life-threatening mass effect: midline shift ≥ 5mm, pineal gland shift (PGS) > 4mm, and decompressive hemicraniectomy (DHC). We used a "backward-looking" trajectory approach. Patients were aligned based on outcome occurrence time and the trajectory of each variable was assessed before that outcome by accounting for cases and noncases, adjusting for confounders. We evaluated longitudinal trajectories with Cox proportional time-dependent regression. Of 635 patients, 49.0% were female, and the mean age was 69years. Thirty five percent of patients had midline shift ≥ 5mm, 24.3% of patients had PGS > 4mm, and 10.7% of patients underwent DHC. Backward-looking trajectories showed mild increases in white blood cell count (10-11K/UL within 72h), temperature (up to half a degree within 24h), and sodium levels (1-3mEq/L within 24h) before the three outcomes of interest. We also observed a decrease in heart rate (75-65 beats per minute) 24h before DHC. We found a significant association between increased white blood cell count with PGS > 4mm (hazard ratio 1.05, p value 0.007). Longitudinal profiling adjusted for confounders demonstrated that white blood cell count, temperature, and sodium levels appear to increase before radiographic and clinical indicators of space-occupying mass effect. These findings will inform the development of multivariable dynamic risk models to aid prediction of life-threatening, space-occupying mass effect.

The role of cuproptosis-related genes in pan-cancer and the development of cuproptosis-related risk model in colon adenocarcinoma

Cancer is widely regarded as a leading cause of death in humans, with colon adenocarcinoma (COAD) ranking among the most prevalent types. Cuproptosis is a novel form of cell death mediated by protein lipoylation. Cuproptosis-related genes (CRGs) participate in tumourigenesis and development. Their role in pan-cancer and COAD require further investigation. This study comprehensively evaluated the relationship among CRGs, pan-cancer, and COAD. Our research revealed the differential expression of CRGs and the cuproptosis potential index (CPI) between normal and tumour tissues, and further explored the correlation of CRGs or CPI with prognosis, immune infiltration, tumor mutant burden(TMB), microsatellite instability (MSI), and drug sensitivity in pan-cancer. Gene set enrichment analysis (GSEA) revealed that oxidative phosphorylation and fatty acid metabolism pathways were significantly enriched in the high CPI group of most tumours. FDX1 and CDKN2A were chosen for further exploration, and we found an independent association between FDX1 and CDKN2A and prognosis, immune infiltration, TMB, and MSI in pan-cancer. Furthermore, a prognostic risk model based on the association between CRGs and COAD was built, and the correlations between the risk score and prognosis, immune-related characteristics, and drug sensitivity were analysed. COAD was then divided into three subtypes using cluster analysis, and the differences among the subtypes in prognosis, CPI, immune-related characteristics, and drug sensitivity were determined. Due to the level of LIPT1 was notably positive related with the risk score, the cytological identification was carried out to identify the association of LIPT1 with proliferation and migration of colon cancer cells. In summary, CRGs can be used as potential prognostic biomarkers to predict immune infiltration levels in patients with pan-cancer. In addition, the risk model could more accurately predict the prognosis and immune infiltration levels of COAD and better guide the direction of clinical medication. Thus, FDX1, CDKN2A, and LIPT1 may serve as prospective new targets for cancer therapy.

Mendelian randomization based on genome-wide association studies and expression quantitative trait loci, predicting gene targets for the complexity of osteoarthritis as well as the clinical prognosis of the condition.

Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints. The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways. Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways. Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-β1-activated Smad3/4 signaling pathway. The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-β1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

Identification of subgroups and development of prognostic risk models along the glycolysis–cholesterol synthesis axis in lung adenocarcinoma

Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.

Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.

To develop a comorbidity risk score specifically for lung resection surgeries. We reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient's overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI). The rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset. The new machine learning model could predict postoperative complications with acceptable accuracy. 2020-0375.

Development and Validation of an Ensemble Learning Risk Model for Sepsis After Abdominal Surgery

Development and Validation of an Ensemble Learning Risk Model for Sepsis After Abdominal Surgery

Risk Model Development for ANZSCTS Database Reporting

Risk Model Development for ANZSCTS Database Reporting

Development of a cancer risk model and mobile health application to inform the public about cancer risks and risk factors

ObjectiveTo develop and evaluate a mobile health application, the Cancer Risk Calculator (CRC), aimed at improving public health literacy by providing personalized information on cancer risks and preventive measures. Materials and MethodsThe CRC was developed through a comprehensive process involving the identification of necessary content, integration of average cancer risks using data from reliable sources, creation of a novel risk model emphasizing modifiable factors, and the application’s development for easy access. The application covers 38 cancer types, 18 subtypes, and approximately 790 risk factors, utilizing data from the Surveillance, Epidemiology, and End Results Program and scientific literature. ResultsCRC offers users personalized risk assessments across a broad range of cancers, emphasizing modifiable risk factors to encourage preventive behaviors. It distinguishes itself by covering more cancer types and risk factors than existing tools, with preliminary user feedback indicating its utility in promoting health literacy and lifestyle changes. DiscussionThe CRC application stands out as an innovative tool in health informatics, significantly enhancing public understanding of cancer risks. Its development underscores the potential of digital health technologies to bolster preventive healthcare strategies through improved health literacy. ConclusionThe Cancer Risk Calculator is a pivotal development in mobile health technology, offering comprehensive and personalized insights into cancer risks and prevention. It serves as a valuable resource for public health education, facilitating informed decisions and lifestyle modifications for cancer prevention.

Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios.

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;20:1715-1735. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Development and validation of an early mortality risk model for pediatric hemophagocytic lymphohistiocytosis: a comparison with HScore, PELOD-2, P-MODS, and pSOFA.

There has been no severity evaluation model for pediatric patients with hemophagocytic lymphohistiocytosis (HLH) that uses readily available parameters. This study aimed to develop a novel model for predicting the early mortality risk in pediatric patients with HLH using easily obtained parameters whatever etiologic subtype. Patients from one center were divided into training and validation sets for model derivation. The developed model was validated using an independent validation cohort from the second center. The prediction model with nomogram was developed based on logistic regression. The model performance underwent internal and external evaluation and validation using the area under the receiver operating characteristic curve (AUC), calibration curve with 1000 bootstrap resampling, and decision curve analysis (DCA). Model performance was compared with the most prevalent severity evaluation scores, including the PELOD-2, P-MODS, and pSOFA scores. The prediction model included nine variables: glutamic-pyruvic transaminase, albumin, globulin, myohemoglobin, creatine kinase, serum potassium, procalcitonin, serum ferritin, and interval between onset and diagnosis. The AUC of the model for predicting the 28-day mortality was 0.933 and 0.932 in the training and validation sets, respectively. The AUC values of the HScore, PELOD-2, P-MODS and pSOFA were 0.815, 0.745, 0.659 and 0.788, respectively. The DCA of the 28-day mortality prediction exhibited a greater net benefit than the HScore, PELOD-2, P-MODS and pSOFA. Subgroup analyses demonstrated good model performance across HLH subtypes. The novel mortality prediction model in this study can contribute to the rapid assessment of early mortality risk after diagnosis with readily available parameters.

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients.

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.