Development Of Risk Prediction Model Research Articles

Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review.

The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has enabled systematic collection of symptom data to manage post-treatment symptoms. The use and integration of PRO data into routine care are associated with overall treatment success, adherence, and satisfaction. Clinical trials have demonstrated the prognostic value of PROs including physical function and global health status in predicting survival. It is unknown to what extent routinely collected PRO data are used in the development of risk prediction models (RPMs) in oncology care. The objective of the scoping review is to assess how PROs are used to train risk RPMs to predict patient outcomes in oncology care. Using the scoping review methodology outlined in the Joanna Briggs Institute Manual for Evidence Synthesis, we searched four databases (MEDLINE, CINAHL, Embase, and Web of Science) to locate peer-reviewed oncology articles that used PROs as predictors to train models. Study characteristics including settings, clinical outcomes, and model training, testing, validation, and performance data were extracted for analyses. Of the 1,254 studies identified, 18 met inclusion criteria. Most studies performed retrospective analyses of prospectively collected PRO data to build prediction models. Post-treatment survival was the most common outcome predicted. Discriminative performance of models trained using PROs was better than models trained without PROs. Most studies did not report model calibration. Systematic collection of PROs in routine practice provides an opportunity to use patient-reported data to develop RPMs. Model performance improves when PROs are used in combination with other comprehensive data sources.

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.

Comparison of two modeling approaches for the identification of predictors of complications in children with cerebral palsy following spine surgery

BackgroundChildren with non-ambulatory cerebral palsy (CP) frequently develop progressive neuromuscular scoliosis and require surgical intervention. Due to their comorbidities, they are at high risk for developing peri- and post-operative complications. The objectives of this study were to compare stepwise and LASSO variable selection techniques for consistency in identifying predictors when modelling these post-operative complications and to identify potential predictors of respiratory complications and infections following spine surgery among children with CP.MethodsIn this retrospective cohort study, a large administrative claims database was queried to identify children who met the following criteria: 1) ≤ 25 years old, 2) diagnosis of CP, 3) underwent surgery during the study period, 4) had ≥ 12-months pre-operative, and 5) ≥ 3-months post-operative continuous health plan enrollment. Outcome measures included the development of a post-operative respiratory complication (e.g., pneumonia, aspiration pneumonia, atelectasis, pleural effusion, pneumothorax, pulmonary edema) or an infection (e.g., surgical site infection, urinary tract infection, meningitis, peritonitis, sepsis, or septicemia) within 3 months of surgery. Codes were used to identify CP, surgical procedures, medical comorbidities and the development of post-operative respiratory complications and infections. Two approaches to variable selection, stepwise and LASSO, were compared to determine which potential predictors of respiratory complications and infection development would be identified using each approach.ResultsThe sample included 220 children. During the 3-month follow-up, 21.8% (n = 48) developed a respiratory complication and 12.7% (n = 28) developed an infection. The prevalence of 11 variables including age, sex and 9 comorbidities were initially considered to be potential predictors based on the intended outcome of interest. Model discrimination utilizing LASSO for variable selection was slightly improved over the stepwise regression approach. LASSO resulted in retention of additional comorbidities that may have meaningful associations to consider for future studies, including gastrointestinal issues, bladder dysfunction, epilepsy, anemia and coagulation deficiency.ConclusionsPotential predictors of the development of post-operative complications were identified in this study and while identified predictors were similar using stepwise and LASSO regression approaches, model discrimination was slightly improved with LASSO. Findings will be used to inform future research processes determining which variables to consider for developing risk prediction models.

Contribution of the large-scale population cohort in disease risk prediction model study: taking United Kingdom Biobank as an example

The disease risk prediction model is the basis of precision prevention and an essential reference for clinical treatment decisions. The development of risk prediction models requires the support of a large amount of high-quality data. A large population cohort study is an important basis for this study. The United Kingdom Biobank (UKB), as a mega-population cohort and biobank, has played an essential role in the exploration of disease etiology and research related to disease prevention and control, with its rich baseline and follow-up data and concepts and mechanisms shared globally. This study followed PRISMA guidelines and included 210 articles with corresponding authors from 18 countries, of which 58 (27.62%) were from the UKB. A total of 491 disease risk prediction models were extracted for cancer, cardiovascular and cerebrovascular diseases, endocrine and metabolic diseases, respiratory diseases, and other diseases and their subgroups, of which 132 were developed by UKB without validation, 183 were developed by UKB with internal validation, 17 were developed by UKB with external validation, and 159 were developed by external development with UKB validation. A total of 188 models used only macro variables (38.29%), and 303 models combined macro and micro variables (61.71%). Model construction methods included survival outcome models, logistic regression, and machine learning. Survival outcome models were dominated by Cox proportional risk regression models and a few models considering competitive risk, accelerated failure models, or different baseline risk functions. Machine learning models included random forest, XGBoost, CatBoost, support vector machine, convolutional neural network, and other methods. The UKB is an essential resource for multiple disease risk prediction modeling studies.

Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework

Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (NP1 = 10, NP2 = 17, NP3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.

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.

43 - Development of risk prediction models to identify rapid weight gain during infancy: a machine learning approach

43 - Development of risk prediction models to identify rapid weight gain during infancy: a machine learning approach

Prediction models for postpartum stress urinary incontinence: A systematic review

BackgroundPostpartum stress urinary incontinence significantly impacts the quality of life and the physical and mental health of women. A reliable predictive model for postpartum stress urinary incontinence can serve as a preventive tool. Currently, there have been numerous studies developing predictive models to assess the risk of postpartum stress urinary incontinence, but the quality and clinical applicability of these models remain unknown. ObjectiveTo systematically review and evaluate existing models for predicting stressful postpartum risks. MethodsPubMed, EBSCO, The Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, WanFang Data, SinoMed, and VIP Data databases were systematically searched from the time of database construction to October 2023. Two researchers used Critical appraisal and data extraction for systematic reviews of prediction modeling studies: the CHARMS checklist for data extraction. Three researchers used The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist for bias and applicability assessment. ResultsEight papers including ten postpartum stress urinary incontinence prediction models were finalized. The most common predictors in the prediction models were urinary incontinence (UI) during pregnancy, followed by mode of delivery, Maternal age, parity, and UI before pregnancy. Nine of the prediction models reported discrimination with an area under the ROC curve (AUC) or C-index between 0.680 and 0.850. All included studies were at high risk of bias, mainly due to mishandling of continuous predictors, unreported or mishandled missing data, and inadequate assessment of predictive model performance. ConclusionsPostpartum stress urinary incontinence risk prediction models are in the initial development stage, and existing prediction models have a high risk of bias and poor modeling methodological quality, which may hinder their clinical application. In the future, healthcare practitioners should follow the norms of predictive model development and reporting to develop risk prediction models with superior predictive performance, low risk of bias, and easy clinical application.

Risk factors for the development of refeeding syndrome in adults: A systematic review.

Identifying patients with a particularly high risk of refeeding syndrome (RFS) is essential for taking preventive measures. To guide the development of clinical decision-making and risk prediction models or other screening tools for RFS, increased knowledge of risk factors is needed. Therefore, we conducted a systematic review to identify risk factors for the development of RFS. PubMed, EMBASE, Cochrane Library, and Web of Science were searched from January 1990 until March 2023. Studies investigating demographic, clinical, drug use, laboratory, and/or nutrition factors for RFS were considered. The Newcastle-Ottawa Scale was used to appraise the methodological quality of included studies. Of 1589 identified records, 30 studies were included. Thirty-three factors associated with increased risk of RFS after multivariable adjustments were identified. The following factors were reported by two or more studies, with 0-1 study reporting null findings: a previous history of alcohol misuse, cancer, comorbid hypertension, high Acute Physiology and Chronic Health Evaluation II score, high Sequential Organ Failure Assessment score, low Glasgow coma scale score, the use of diuretics before refeeding, low baseline serum prealbumin level, high baseline level of creatinine, and enteral nutrition. The majority of the studies (20, 66.7%) were of high methodological quality. In conclusion, this systematic review informs on several risk factors for RFS in patients. To improve risk stratification and guide development of risk prediction models or other screening tools, further confirmation is needed because there were a small number of studies and a low number of high-quality studies on each factor.

Explainable machine learning framework to predict the risk of work-related neck and shoulder musculoskeletal disorders among healthcare professionals.

This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals. A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations. The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs. This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.

The prediction model of fall risk for the elderly based on gait analysis

IntroductionEarly screening and identification are crucial for fall prevention, and developing a new method to predict fall risk in the elderly can address the current lack of objectivity in assessment tools.MethodsA total of 132 elderly individuals over 80 years old residing in some nursing homes in Shanghai were selected using a convenient sampling method. Fall history information was collected, and gait data during a 10-meter walk were recorded. Logistic regression was employed to establish the prediction model, and a nomogram was used to assess the importance of the indicators. The Bootstrap method was utilized for internal validation of the model, while the verification set was used for external validation. The predictive performance of the model was evaluated using the area under the ROC curve, calibration curve, and decision curve analysis (DCA) to assess clinical benefits.ResultsThe incidence of falls in the sample population was 36.4%. The Tinetti Gait and Balance Test (TGBT) score (OR = 0.832, 95% CI: 0.734,0.944), stride length (OR = 0.007, 95% CI: 0.000,0.104), difference in standing time (OR = 0.001, 95% CI: 0.000,0.742), and mean stride time (OR = 0.992, 95% CI:0.984,1.000) were identified as significant factors. The area under the ROC curve was 0.878 (95% CI: 0.805, 0.952), with a sensitivity of 0.935 and specificity of 0.726. The Brier score was 0.135, and the Hosmer-Lemeshow test (χ2 = 10.650, P = 0.222) indicated a good fit and calibration of the model.ConclusionThe TGBT score, stride length, difference in standing time, and stride time are all protective factors associated with fall risk among the elderly. The developed risk prediction model demonstrates good discrimination and calibration, providing valuable insights for early screening and intervention in fall risk among older adults.

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.

External Validation and Update of the Risk Prediction Model for Denosumab-Induced Hypocalcemia Developed From a Hospital-Based Administrative Database.

Denosumab is used to treat patients with bone metastasis from solid tumors, but sometimes causes severe hypocalcemia, so careful clinical management is important. This study aims to externally validate our previously developed risk prediction model for denosumab-induced hypocalcemia by using data from two facilities with different characteristics in Japan and to develop an updated model with improved performance and generalizability. In the external validation, retrospective data of Kameda General Hospital (KGH) and Miyagi Cancer Center (MCC) between June 2013 and June 2022 were used and receiver operating characteristic (ROC)-AUC was mainly evaluated. A scoring-based updated model was developed using the same data set from a hospital-based administrative database as previously employed. Selection of variables related to prediction of hypocalcemia was based on the results of external validation. For the external validation, data from 235 KGH patients and 224 MCC patients were collected. ROC-AUC values in the original model were 0.879 and 0.774, respectively. The updated model consisting of clinical laboratory tests (calcium, albumin, and alkaline phosphatase) afforded similar ROC-AUC values in the two facilities (KGH, 0.837; MCC, 0.856). We developed an updated risk prediction model for denosumab-induced hypocalcemia with small interfacility differences. Our results indicate the importance of using data from plural facilities with different characteristics in the external validation of generalized prediction models and may be generally relevant to the clinical application of risk prediction models. Our findings are expected to contribute to improved management of bone metastasis treatment.

Development and Validation of an ICU-Venous Thromboembolism Prediction Model Using Machine Learning Approaches: A Multicenter Study.

The purpose of this study was to establish and validate machine learning-based models for predicting the risk of venous thromboembolism (VTE) in intensive care unit (ICU) patients. The clinical data of 1494 ICU patients who underwent Doppler ultrasonography or venography between December 2020 and March 2023 were extracted from three tertiary hospitals. The Boruta algorithm was used to screen the essential variables associated with VTE. Five machine learning algorithms were employed: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and Logistic Regression (LR). Hyperparameter optimization was conducted on the predictive model of the training dataset. The performance in the validation dataset was measured using indicators, including the area under curve (AUC) of the receiver operating characteristic (ROC) curve, specificity, and F1 score. Finally, the optimal model was interpreted using the SHapley Additive exPlanation (SHAP) package. The incidence of VTE among the ICU patients in this study was 26.04%. We screened 19 crucial features for the risk prediction model development. Among the five models, the RF model performed best, with an AUC of 0.788 (95% CI: 0.738-0.838), an accuracy of 0.759 (95% CI: 0.709-0.809), a sensitivity of 0.633, and a Brier score of 0.166. A machine learning-based model for prediction of VTE in ICU patients were successfully developed, which could assist clinical medical staff in identifying high-risk populations for VTE in the early stages so that prevention measures can be implemented to reduce the burden on the ICU patients.

Critical appraisal and assessment of bias among studies evaluating risk prediction models for in-hospital and 30-day mortality after percutaneous coronary intervention: a systematic review

ObjectiveWe systematically assessed prediction models for the risk of in-hospital and 30-day mortality in post-percutaneous coronary intervention (PCI) patients.DesignSystematic review and narrative synthesis.Data sourcesSearched PubMed, Web of Science, Embase, Cochrane...

Risk Prediction Models for Sarcopenia in Dialysis Patients: A Systematic Review

Nowadays, numerous studies have developed risk prediction models for sarcopenia in dialysis patients.However, the quality and performance of these models have not been integrated. The purpose of our study is to provide a comprehensive overview of the current risk prediction models for sarcopenia in dialysis patients and to offer a reference for the development of high-quality prediction models. Ten electronic databases were searched from inception to March 8, 2024. Two researchers independently assessed the risk of bias and applicability of the studies, and used Revman, 5.4, software to conduct a meta-analysis of common predictors in the models. A total of 12 studies described 13 risk prediction models for dialysis patients with sarcopenia. In dialysis patients, the prevalence of sarcopenia ranged from 6.60% to 63.73%. The area under curve (AUC) of the 13 models ranged from 0.776 to 0.945. Only six models (AUC ranging from 0.73 to 0.832) were internally validated, while two were externally evaluated (AUC ranging from 0.913 to 0.955). Most studies had a high risk of bias. The most common effective predictors in the models were age, body mass index, muscle circumference, and C-reactive protein. Our study suggests that developing a prediction model for the onset of sarcopenia in dialysis patients requires a rigorous design scheme, and future verification methods will necessitate multicenter external validation.

Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.

Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.

#100 Hospital readmissions for fluid overload among individuals with diabetes mellitus

Abstract Background and Aims Hospital readmissions due to recurrent fluid overload in diabetes can be avoided with evidence-based interventions. To identify at-risk individuals, we evaluated the incidence and risk factors associated with fluid overload-related hospital readmissions and developed risk prediction models for readmissions for fluid overload in people with diabetes. Method Single-centre retrospective cohort study of 1531 adults with diabetes and hospitalized in Singapore General Hospital for fluid overload, congestive heart failure, pulmonary edema and generalized edema between 2015 and 2017. Patients were included if they had diabetes mellitus identified by the SingHealth Diabetes Registry according to diagnosis codes, laboratory results and/or the use of glucose-lowering medication and had follow up visits, but excluded if their estimated glomerular filtration rate (eGFR) &amp;lt;15 ml/min/1.73 m2. Baseline data on demographics, comorbidities, biochemistry and medication use during admission were retrieved from electronic medical records up till 30 December 2018. The outcomes were fluid overload- related readmissions within 30 days and 90 days of discharge of the index hospitalization. Multivariable Cox proportional-hazards regression analysis was performed with inclusion of covariates with p≤0.10 on univariate analysis. Multivariable regression models developed were compared with the LACE index with net reclassification index (NRI). Results Among 1531 patients, median age was 73 (IQR 64, 81) and readmissions for fluid overload within 30 days and 90 days occurred in 131 (8.6%) and 264 (17.2%) patients, respectively. Individuals with 30- or 90-day readmissions were more likely to have prior hospitalization for fluid overload and more frequent ED visits within 6 months before the index hospitalization, cardiovascular disease, hypertension, and required high-dose intravenous furosemide during the index hospitalization, or current smoker. Clinical and clinico-psychosocial models for 30- and 90-day readmissions for fluid overload were developed (Table 1). The clinical and clinico-psychosocial models showed better discrimination and calibration than LACE index for predicting 30- and 90-day readmissions for fluid overload. The NRIs for clinical models were i. 30-day readmissions: +22.1%, ii. 90-day readmissions: +27.9% respectively, while the NRIs for clinico-psychosocial models were i. 30-day readmissions: +22.4%, ii. 90-day readmissions: +28.9% respectively (Fig. 1). Conclusion Patients with diabetes who are at-risk of readmission for fluid overload can be identified for targeted interventions to reduce preventable hospitalizations.

Biomarker Identification and Risk Prediction Model Development for Differentiated Thyroid Carcinoma Lung Metastasis Based on Primary Lesion Proteomics.

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. Through proteomic analysis of large samples of primary lesion and dual validation employing parallel reaction monitoring and IHC, 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. This study identified eight hub proteins-SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS, and VDAC2-as potential biomarkers for predicting LMDTC 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. As a large sample size study in LMDTC 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 LMDTC and establishing personalized 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.