Development Of Risk Prediction Model Research Articles

Biomarker identification and risk prediction model development for differentiated thyroid carcinoma lung metastasis based on primary lesion proteomics

Abstract Purpose: The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis of DTC (LMDTC). This population poses a significant challenge in clinical practice, necessitating the urgent development of effective risk stratification methods and predictive tools for lung metastasis. Experimental Design: Through proteomic analysis of large samples of primary lesion and dual validation employing parallel reaction monitoring and immunohistochemistry, we identified eight hub proteins as potential biomarkers. By expanding the sample size and conducting statistical analysis on clinical features and hub protein expression, we constructed three risk prediction models. Results: This study identified eight hub proteins-SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS and VDAC2- as potential biomarkers for predicting DTC lung metastasis risk. We developed and internally validated three risk prediction models incorporating both clinical characteristics and hub protein expression. Our findings demonstrated that the combined prediction model exhibited optimal predictive performance, with the highest discrimination (AUC: 0.986) and calibration (Brier score: 0.043). Application of the combined prediction model within a specific risk threshold (0-0.97) yielded maximal clinical benefit. Finally, we constructed a nomogram based on the combined prediction model. Conclusions: As a large sample size study in lung metastatic DTC research, the identification of biomarkers through primary lesion proteomics and the development of risk prediction models integrating clinical features and hub protein biomarkers offer valuable insights for predicting DTC lung metastasis and establishing personalised treatment strategies.

Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk.

Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy.

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study's integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Estimating individualized treatment effects using an individual participant data meta-analysis

BackgroundOne key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach.MethodsWe compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo (N=40237\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N = 40\\,237$$\\end{document}, 836 events).ResultsSimulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances.ConclusionsFor the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.

Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management.

This study investigates the feasibility of accurately predicting adverse health events without relying on costly data acquisition methods, such as laboratory tests, in the era of shifting healthcare paradigms towards community-based health promotion and personalized preventive healthcare through individual health risk assessments (HRAs). We assessed the incremental predictive value of four categories of predictor variables-demographic, lifestyle and family history, personal health device, and laboratory data-organized by data acquisition costs in the prediction of the risks of mortality and five chronic diseases. Machine learning methodologies were employed to develop risk prediction models, assess their predictive performance, and determine feature importance. Using data from the National Sample Cohort of the Korean National Health Insurance Service (NHIS), which includes eligibility, medical check-up, healthcare utilization, and mortality data from 2002 to 2019, our study involved 425,148 NHIS members who underwent medical check-ups between 2009 and 2012. Models using demographic, lifestyle, family history, and personal health device data, with or without laboratory data, showed comparable performance. A feature importance analysis in models excluding laboratory data highlighted modifiable lifestyle factors, which are a superior set of variables for developing health guidelines. Our findings support the practicality of precise HRAs using demographic, lifestyle, family history, and personal health device data. This approach addresses HRA barriers, particularly for healthy individuals, by eliminating the need for costly and inconvenient laboratory data collection, advancing accessible preventive health management strategies.

Hospital readmissions for fluid overload among individuals with diabetes and diabetic kidney disease: risk factors and multivariable prediction models.

Hospital readmissions due to recurrent fluid overload in diabetes and diabetic kidney disease can be avoided with evidence-based interventions. We aimed to identify at-risk patients who can benefit by developing risk prediction models for readmissions for fluid overload in people living with diabetes. Single-center retrospective cohort study of 1531 adults with diabetes and hospitalized for fluid overload, including congestive heart failure, pulmonary edema and generalized edema, between 2015 and 2017. The multivariable regression models for 30-day and 90-day readmission for fluid overload were compared with the LACE score for discrimination, calibration, sensitivity, specificity and net reclassification index (NRI). Readmissions for fluid overload within 30 days and 90 days occurred in 8.6% and 17.2% of patients with diabetes, and 8.2% and 18.3% of patients with diabetic kidney disease, respectively. After adjusting for demographics, comorbidities, clinical parameters, and medications, a history of alcoholism (HR 3.85, 95% CI 1.41-10.55) and prior hospitalization for fluid overload (HR 2.50, 95% CI 1.26- 4.96) were independently associated with 30-day readmission in patients with diabetic kidney disease, as well as in individuals with diabetes. Additionally, current smoking, absence of hypertension and high-dose intravenous furosemide were also associated with 30-day readmission in individuals with diabetes. Prior hospitalization for fluid overload (HR 2.43, 95% CI 1.50-3.94), cardiovascular disease (HR 1.44, 95% CI 1.03-2.02), eGFR ≤45 ml/min/1.73 m2 (HR 1.39, 95% CI 1.003-1.93) were independently associated with 90-day readmissions in individuals with diabetic kidney disease. Additionally, thiazide at discharge reduced 90-day readmission in diabetic kidney disease while high-dose intravenous furosemide predicted 90-day readmission in diabetes. The clinical and clinico-psychological models for 90-day readmission in individuals with diabetes and diabetic kidney disease had better discrimination and calibration than the LACE score. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetes were 22.4% and 28.9%, respectively. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetic kidney disease were 5.6% and 38.9%, respectively. The risk models can potentially be used to identify patients at-risk of readmission for fluid overload for evidence-based interventions such as patient education or transitional care programs to reduce preventable hospitalizations.  .

Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement

A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts’ reasoning abilities remain critical in identifying potential improvements and ensuring safety. We propose a thick data analytics (TDA) framework for eliciting and combining expert-human insight into the evaluation of models. The insight is threefold: (1) statistical methods are limited to using joint distributions of observable quantities for predictions but often there is more information available in a real-world than what is usable for algorithms, (2) domain experts can access more information (e.g., patient files) than an algorithm and bring additional knowledge into their assessments through leveraging insights and experiences, and (3) experts can re-frame and re-evaluate prediction problems to suit real-world situations. Here, we revisit an example of predicting temporal risk for intensive care admission within 24 hours of hospitalization. We propose a sampling procedure for identifying informative cases for deeper inspection. Expert feedback is used to understand sources of information to improve model development and deployment. We recommend model assessment based on objective evaluation metrics derived from subjective evaluations of the problem formulation. TDA insights facilitate iterative model development towards safer, actionable, and acceptable risk predictions.

Nomogram to predict risk of neonatal mortality among preterm neonates admitted with sepsis at University of Gondar Comprehensive Specialized Hospital: risk prediction model development and validation

BackgroundMortality in premature neonates is a global public health problem. In developing countries, nearly 50% of preterm births ends with death. Sepsis is one of the major causes of death in preterm neonates. Risk prediction model for mortality in preterm septic neonates helps for directing the decision making process made by clinicians.ObjectiveWe aimed to develop and validate nomogram for the prediction of neonatal mortality. Nomograms are tools which assist the clinical decision making process through early estimation of risks prompting early interventions.MethodsA three year retrospective follow up study was conducted at University of Gondar Comprehensive Specialized Hospital and a total of 603 preterm neonates with sepsis were included. Data was collected using KoboCollect and analyzed using STATA version 16 and R version 4.2.1. Lasso regression was used to select the most potent predictors and to minimize the problem of overfitting. Nomogram was developed using multivariable binary logistic regression analysis. Model performance was evaluated using discrimination and calibration. Internal model validation was done using bootstrapping. Net benefit of the nomogram was assessed through decision curve analysis (DCA) to assess the clinical relevance of the model.ResultThe nomogram was developed using nine predictors: gestational age, maternal history of premature rupture of membrane, hypoglycemia, respiratory distress syndrome, perinatal asphyxia, necrotizing enterocolitis, total bilirubin, platelet count and kangaroo-mother care. The model had discriminatory power of 96.7% (95% CI: 95.6, 97.9) and P-value of 0.165 in the calibration test before and after internal validation with brier score of 0.07. Based on the net benefit analysis the nomogram was found better than treat all and treat none conditions.ConclusionThe developed nomogram can be used for individualized mortality risk prediction with excellent performance, better net benefit and have been found to be useful in clinical practice with contribution in preterm neonatal mortality reduction by giving better emphasis for those at high risk.

The impact of changing the cut-off threshold of the interferon-gamma (IFN-γ) assay for diagnosing bovine tuberculosis in Ireland

In Ireland, the interferon-gamma (IFN-γ) assay is routinely used as an ancillary test interpreted in parallel with the single intradermal comparative tuberculin test (SICTT) to maximize the detection of bovine tuberculosis (bTB) infected animals. Up until 2018, a positive test result was recorded in the IFN-γ ELISA assay following whole blood stimulation with purified protein derivative (PPD)-bovine (B), PPD-avian (A) and nil sample (N), using the interpretation criteria, B-N > 50 optical density units (OD), B > 100 and B-A > 0. Following a review of available data, the threshold of the B-A component changed to B-A > 80. As predicting the impact of changing the cut-off thresholds for the IFN-γ test de novo is challenging, the aims of this study were to follow animals that initially tested negative using the new IFN-γ assay interpretation criteria and investigate their future risk of disclosure with bTB, with a focus on animals that otherwise would have been removed when using the older interpretation criteria (0 < B-A ≤ 80). Enrolled animals (n = 28,669 cattle from 527 herds) were followed up for two years (2019–2021), or to point of bTB detection or death. At the end of follow-up, 1151 (4.0%) of enrolled animals were bTB cases. The majority of these cases were diagnosed using SICTT (80.5%). The cumulative number of positive animals that would have been removed if the old cut-off (0 < B-A ≤ 80) was used amounted to 1680 cattle (5.9% of the enrolled cohort). Of these, 127 (7.5%) were diagnosed with bTB during follow-up. In contrast, 1024 of the 1151 cattle which subsequently tested positive during the study period following a negative IFN-γ test would not have been identified with the old or new IFN-γ cut-off criteria. Survival analysis showed that animals that would have been removed under the old interpretation criteria were at increased risk of a positive diagnosis with bTB during follow-up compared to other test negative animals. A newly developed risk prediction model (using a Cox proportional hazard model) showed that age, animal number of SICTT tests, number of inconclusive SICTT tests, B-A (IFN-γ assay), B-N (IFN-γ assay), animals from store herds and the percentage of the rest of the herd that were positive during the breakdown were statistically significantly associated with bTB detection. However, inclusion of the IFN-γ OD variables did not show added value in terms of prediction performance of the model.

Contesting the conventional wisdom of periodontal risk assessment.

Over the years, several reviews of periodontal risk assessment tools have been published. However, major misunderstandings still prevail in repeated attempts to use these tools for prognostic risk prediction. Here we review the principles of risk prediction and discuss the value and the challenges of using prediction models in periodontology. Most periodontal risk prediction models have not been properly developed according to guidance given for the risk prediction model development. This shortcoming has led to several problems, including the creation of arbitrary risk scores. These scores are often labelled as 'high risk' without explicit boundaries or thresholds for the underlying continuous risk estimates of patient-important outcomes. Moreover, it is apparent that prediction models are often misinterpreted as causal models by clinicians and researchers although they cannot be used as such. Additional challenges like the critical assessment of transportability and applicability of these prediction models, as well as their impact on clinical practice and patient outcomes, are not considered in the literature. Nevertheless, these instruments are promoted with claims regarding their ability to deliver more individualized and precise periodontitis treatment and prevention, purportedly resulting in improved patient outcomes. However, people with or without periodontitis deserve proper information about their risk of developing patient-important outcomes such as tooth loss or pain. The primary objective of disseminating such information should not be to emphasize assumed treatment efficacy, hype individualization of care, or promote business interests. Instead, the focus should be on providing individuals with locally validated and regularly updated predictions of specific risks based on readily accessible and valid key predictors (e.g. age and smoking).

The spatiotemporal distribution and prognostic factors of Japanese encephalitis in Shanxi Province, China, 2005-2022.

Japanese encephalitis (JE) is a naturally occurring localized disease caused by the Japanese encephalitis virus, which is spread by the Culex tritaeniorhynchus. China has a high rate of JE. Shanxi, located in North China, has a high prevalence of adult JE. Adult JE has more severe complications, mortality, and a higher disease burden, making it a public health issue. This retrospective study examined the dynamic epidemic changes, high-risk areas of JE, and clinical characteristics and prognostic factors of adult JE in Shanxi Province. The findings revealed that July to September was the primary epidemic season of JE and that JE cases were mainly in individuals over the age of 40. The incidence of JE from 2005 to 2022 demonstrated a positive spatial correlation with significant clustering characteristics, with high-incidence clusters in the south and southeast. Multivariate logistic regression analysis revealed that higher cerebrospinal fluid pressure, higher white blood cell counts, higher neutrophil percentage, deep coma, and lower albumin were independent factors for poor prognosis of adult JE. The developed risk prediction model holds great promise in early prognosis assessment of patients, providing a basis for clinical decision-making and early clinical intervention.

Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia.

: Although numerous risk prediction models have been proposed, few such models have been developed using neural network-based survival analysis. We developed risk prediction models for three cardiovascular disease risk factors (diabetes mellitus, hypertension, and dyslipidemia) among a working-age population in Japan using DeepSurv, a deep feed-forward neural network. : Data were obtained from the Japan Epidemiology Collaboration on Occupational Health Study. A total of 51 258, 44 197, and 31 452 individuals were included in the development of risk models for diabetes mellitus, hypertension, and dyslipidemia, respectively; two-thirds of whom were used to develop prediction models, and the rest were used to validate the models. We compared the performances of DeepSurv-based models with those of prediction models based on the Cox proportional hazards model. : The area under the receiver-operating characteristic curve was 0.878 [95% confidence interval (CI) = 0.864-0.892] for diabetes mellitus, 0.835 (95% CI = 0.826-0.845) for hypertension, and 0.826 (95% CI = 0.817-0.835) for dyslipidemia. Compared with the Cox proportional hazards-based models, the DeepSurv-based models had better reclassification performance [diabetes mellitus: net reclassification improvement (NRI) = 0.474, P  ≤ 0.001; hypertension: NRI = 0.194, P  ≤ 0.001; dyslipidemia: NRI = 0.397, P  ≤ 0.001] and discrimination performance [diabetes mellitus: integrated discrimination improvement (IDI) = 0.013, P  ≤ 0.001; hypertension: IDI = 0.007, P  ≤ 0.001; and dyslipidemia: IDI = 0.043, P  ≤ 0.001]. : This study suggests that DeepSurv has the potential to improve the performance of risk prediction models for cardiovascular disease risk factors.

Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches.

Severe periodontitis affects 26% of Thai adults and 11.2% of adults globally and is characterized by the loss of alveolar bone height. Full-mouth examination by periodontal probing is the gold standard for diagnosis but is time- and resource-intensive. A screening model to identify those at high risk of severe periodontitis would offer a targeted approach and aid in reducing the workload for dentists. While statistical modelling by a logistic regression is commonly applied, optimal performance depends on feature selections and engineering. Machine learning has been recently gaining favor given its potential discriminatory power and ability to deal with multiway interactions without the requirements of linear assumptions. We aim to compare the performance of screening models developed using statistical and machine learning approaches for the risk prediction of severe periodontitis. This study used data from the prospective Electricity Generating Authority of Thailand cohort. Dental examinations were performed for the 2008 and 2013 surveys. Oral examinations (ie, number of teeth and oral hygiene index and plaque scores), periodontal pocket depth, and gingival recession were performed by dentists. The outcome of interest was severe periodontitis diagnosed by the Centre for Disease Control-American Academy of Periodontology, defined as 2 or more interproximal sites with a clinical attachment level ≥6 mm (on different teeth) and 1 or more interproximal sites with a periodontal pocket depth ≥5 mm. Risk prediction models were developed using mixed-effects logistic regression (MELR), recurrent neural network, mixed-effects support vector machine, and mixed-effects decision tree models. A total of 21 features were considered as predictive features, including 4 demographic characteristics, 2 physical examinations, 4 underlying diseases, 1 medication, 2 risk behaviors, 2 oral features, and 6 laboratory features. A total of 3883 observations from 2086 participants were split into development (n=3112, 80.1%) and validation (n=771, 19.9%) sets with prevalences of periodontitis of 34.4% (n=1070) and 34.1% (n=263), respectively. The final MELR model contained 6 features (gender, education, smoking, diabetes mellitus, number of teeth, and plaque score) with an area under the curve (AUC) of 0.983 (95% CI 0.977-0.989) and positive likelihood ratio (LR+) of 11.9 (95% CI 8.8-16.3). Machine learning yielded lower performance than the MELR model, with AUC (95% CI) and LR+ (95% CI) values of 0.712 (0.669-0.754) and 2.1 (1.8-2.6), respectively, for the recurrent neural network model; 0.698 (0.681-0.734) and 2.1 (1.7-2.6), respectively, for the mixed-effects support vector machine model; and 0.662 (0.621-0.702) and 2.4 (1.9-3.0), respectively, for the mixed-effects decision tree model. The MELR model might be more useful than machine learning for large-scale screening to identify those at high risk of severe periodontitis for periodontal evaluation. External validation using data from other centers is required to evaluate the generalizability of the model.

Environmental, climatic, anthropogenic features of Filoviruses Disease Outbreak: a systematic review

Abstract Background Filoviruses, including Ebola and Marburg viruses, are a threat to global public health due to their transmission and mortality rates. Despite recent advances in forecasting infectious disease limited knowledge of the ecological niche of Filoviruses still hinders research progress. Studying the ecological context of Filoviruses can provide important information for the development of international preparedness and surveillance systems. Methods We conducted a systematic literature review according to the PRISMA 2020 checklist. A search of relevant articles was performed in PubMed, Scopus, and Web of Science databases. Eligible articles were any type of publication, published in English between 2013 and 2023, discussing environmental, climatic and anthropogenic risk factors of Filovirus disease outbreaks. Results A total of 5818 records were screened, and 41 articles were included. Of the 41 studies analyzed, 21 reported an analysis of risk factors related to Ebola Viruses, 8 of Marburg Virus, and 12 analyzed multiple species of Filoviruses. Of the 41 studies analyzed, 16 also developed risk prediction models aimed at stratifying geographic areas. We collected a total of 19 risk factors divided in 4 categories: environmental risk factors (e.g., forest coverage, cropland coverage), climate risk factors (e.g., annual temperature range, seasonal temporal frequency) population risk factors (e.g., human population density, urban population increase), cultural and behavioral risk factors (e.g., bushmeat trade, use of traditional burial practice). The analyzed models primarily use climate and environmental data, with population and cultural factors being considered to a lesser extent. Conclusions The identification of potential predictors of Filovirus disease outbreak is crucial for geographic mapping of high-risk areas. A multidisciplinary Planetary Health approach is needed to advance research and to develop targeted surveillance systems to prevent pandemic outbreaks. Key messages • Environmental and climate factors play a crucial role in filovirus spillover events, but population and cultural factors should also be considered in future research and risk prediction models. • To improve prediction of pandemic outbreaks more research on disease emergence and transmission dynamics, better surveillance of disease in humans and animals, and better access to data are required.

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction.

Background There is a need to develop electronic health record-based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Results We studied adults with heart failure (HF) from 2011 to 2019 within an integrated health care delivery system. WHF encounters were ascertained using natural language processing and structured data. We conducted boosted decision tree ensemble models to predict 1-year hospitalizations, emergency department visits/observation stays, and outpatient encounters for WHF and all-cause death within each LVEF category: HF with reduced ejection fraction (EF) (LVEF <40%), HF with mildly reduced EF (LVEF 40%-49%), and HF with preserved EF (LVEF ≥50%). Model discrimination was evaluated using area under the curve and calibration using mean squared error. We identified 338 426 adults with HF: 61 045 (18.0%) had HF with reduced EF, 49 618 (14.7%) had HF with mildly reduced EF, and 227 763 (67.3%) had HF with preserved EF. The 1-year risks of any WHF event and death were, respectively, 22.3% and 13.0% for HF with reduced EF, 17.0% and 10.1% for HF with mildly reduced EF, and 16.3% and 10.3% for HF with preserved EF. The WHF model displayed an area under the curve of 0.76 and mean squared error of 0.13, whereas the model for death displayed an area under the curve of 0.83 and mean squared error of 0.076. Performance and predictors were similar across WHF encounter types and LVEF categories. Conclusions We developed risk prediction models for 1-year WHF events and death across the LVEF spectrum using structured and unstructured electronic health record data and observed no substantial differences in model performance or predictors except for death, despite differences in underlying HF cause.

Common methodological pitfalls in ICI pneumonitis risk prediction studies.

Pneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews. The PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools. There were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets. Studies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.

Pyroptosis and inflammasomes in cancer and inflammation.

Nonprogrammed cell death (NPCD) and programmed cell death (PCD) are two types of cell death. Cell death is significantly linked to tumor development, medication resistance, cancer recurrence, and metastatic dissemination. Therefore, a comprehensive understanding of cell death is essential for the treatment of cancer. Pyroptosis is a kind of PCD distinct from autophagy and apoptosis in terms of the structure and function of cells. The defining features of pyroptosis include the release of an inflammatory cascade reaction and the expulsion of lysosomes, inflammatory mediators, and other cellular substances from within the cell. Additionally, it displays variations in osmotic pressure both within and outside the cell. Pyroptosis, as evidenced by a growing body of research, is critical for controlling the development of inflammatory diseases and cancer. In this paper, we reviewed the current level of knowledge on the mechanism of pyroptosis and inflammasomes and their connection to cancer and inflammatory diseases. This article presents a theoretical framework for investigating the potential of therapeutic targets in cancer and inflammatory diseases, overcoming medication resistance, establishing nanomedicines associated with pyroptosis, and developing risk prediction models in refractory cancer. Given the link between pyroptosis and the emergence of cancer and inflammatory diseases, pyroptosis-targeted treatments may be a cutting-edge treatment strategy.

Differences in predictors of incident heart failure according to atherosclerotic cardiovascular disease status.

Heart failure (HF) is a common cause of morbidity and mortality, related to a broad range of sociodemographic, lifestyle, cardiometabolic, and comorbidity risk factors, which may differ according to the presence of atherosclerotic cardiovascular disease (ASCVD). We assessed the association between incident HF with baseline status across these domains, overall and separated according to ASCVD status. We included 5758 participants from the Baker Biobank cohort without HF at baseline enrolled between January 2000 and December 2011. The primary endpoint was incident HF, defined as hospital admission or HF-related death, determined through linkage with state-wide administrative databases (median follow-up 12.2years). Regression models were fitted adjusted for sociodemographic variables, alcohol intake, smoking status, measures of adiposity, cardiometabolic profile measures, and individual comorbidities. During 65987 person-years (median age 59years, 38% women), incident HF occurred among 784 participants (13.6%) overall. Rates of incident HF were higher among patients with ASCVD (624/1929, 32.4%) compared with those without ASCVD (160/3829, 4.2%). Incident HF was associated with age, socio-economic status, alcohol intake, smoking status, body mass index (BMI), waist circumference, waist-hip ratio, systolic blood pressure (SBP), and low- and high-density lipoprotein cholesterol (LDL-C and HDL-C), with non-linear relationships observed for age, alcohol intake, BMI, waist circumference, waist-hip ratio, SBP, LDL-C, and HDL-C. Risk factors for incident HF were largely consistent regardless of ASCVD status, although diabetes status had a greater association with incident HF among patients without ASCVD. Incident HF is associated with a broad range of baseline sociodemographic, lifestyle, cardiometabolic, and comorbidity factors, which are mostly consistent regardless of ASCVD status. These data could be useful in efforts towards developing risk prediction models that can be used in patients with ASCVD.

Prediction of Preterm Birth among Infants with Orofacial Cleft Defects.

To develop risk prediction models for preterm birth among infants with orofacial clefts. Data from the Texas Birth Defects Registry for infants with orofacial clefts born between 1999-2014 were used to develop preterm birth predictive models. Logistic regression was used to consider maternal and infant characteristics, and internal validation of the final model was performed using bootstrapping methods. The area under the curve (AUC) statistic was generated to assess model performance, and separate predictive models were built and validated for infants with cleft lip and cleft palate alone. Several secondary analyses were conducted among subgroups of interest. State-wide, population-based Registry data. 6774 infants with orofacial clefts born in Texas between 1999-2014. Preterm birth among infants with orofacial clefts. The final predictive model performed modestly, with an optimism-corrected AUC of 0.67 among all infants with orofacial clefts. The optimism-corrected models for cleft lip (with or without cleft palate) and cleft palate alone had similar predictive capability, with AUCs of 0.66 and 0.67, respectively. Secondary analyses had similar results, but the model among infants with delivery prior to 32 weeks demonstrated higher optimism-corrected predictive capability (AUC = 0.74). This study provides a first step towards predicting preterm birth risk among infants with orofacial clefts. Identifying pregnancies affected by orofacial clefts at the highest risk for preterm birth may lead to new avenues for improving outcomes among these infants.

Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data.

Myxomatous mitral valve disease (MMVD) is the most common cause of heart failure in dogs, and assessing the risk of heart failure in dogs with MMVD is often challenging. Machine learning applied to electronic health records (EHRs) is an effective tool for predicting prognosis in the medical field. This study aimed to develop machine learning-based heart failure risk prediction models for dogs with MMVD using a dataset of EHRs. A total of 143 dogs with MMVD between May 2018 and May 2022. Complete medical records were reviewed for all patients. Demographic data, radiographic measurements, echocardiographic values, and laboratory results were obtained from the clinical database. Four machine-learning algorithms (random forest, K-nearest neighbors, naïve Bayes, support vector machine) were used to develop risk prediction models. Model performance was represented by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The best-performing model was chosen for the feature-ranking process. The random forest model showed superior performance to the other models (AUC = 0.88), while the performance of the K-nearest neighbors model showed the lowest performance (AUC = 0.69). The top three models showed excellent performance (AUC ≥ 0.8). According to the random forest algorithm's feature ranking, echocardiographic and radiographic variables had the highest predictive values for heart failure, followed by packed cell volume (PCV) and respiratory rates. Among the electrolyte variables, chloride had the highest predictive value for heart failure. These machine-learning models will enable clinicians to support decision-making in estimating the prognosis of patients with MMVD.