Analysis of risk factors and construction of a prediction model for posttraumatic stress disorder among Chinese college students during the COVID-19 pandemic.

BACKGROUND: To explore the risk factors of post-traumatic stress disorder (PTSD) among Chinese college students during the COVID-19 pandemic and the construction and validation of risk prediction models. METHODS: A total of 10,705 university students were selected for the study. The questionnaire included the Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire 9 (PHQ-9), PTSD Checklist for DSM-5 (PCL-5), and self-designed questionnaire. These assessments were conducted to facilitate the survey, construct the predictive model and validate the model's validity. RESULTS: Sex, left-behind experience, poverty status, anxiety score, and depression score were identified as independent risk factors influencing psychological trauma among Chinese college students during the COVID-19 pandemic, while COVID-19 infection emerged as a protective factor against psychological trauma. A column chart was constructed to visualize the six independent risk factors derived from logistic regression analysis. The Hosmer-Lemeshow test results (χ2 = 13.021, P = 0.111) indicated that the risk prediction model fitted well. The receiver operating characteristic (ROC) curve showed an area under the curve (AUC) of 0.864 in the model group and 0.855 in the validation group. The calibration curves of the model closely resembled the ideal curve. Decision curve analysis (DCA) revealed that the model provided net benefit and demonstrated good clinical utility. LIMITATIONS: The validation of the model is currently restricted to internal assessments. However, further confirmation through larger sample sizes, multicenter investigations, and prospective studies is necessary. CONCLUSIONS: The model effectively predicted PTSD risk among Chinese college students during the COVID-19 pandemic, indicating strong clinical applicability.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Imaging of Solid Pulmonary Nodules.

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation.

Background: The use of AI-driven technologies in probing big data to generate better risk prediction models has been an ongoing and expanding area of investigation. The AI-driven models may perform better as compared to linear models; however, more investigations are needed in this area to refine their predictability and applicability to the field of durable MCS and cardiac transplantation. Methods: A literature review was carried out using Google Scholar/PubMed from 2000 to 2023. Results: This review defines the knowledge gaps and describes different AI-driven approaches that may be used to further our understanding. Conclusions: The limitations of current models are due to missing data, data imbalances, and the uneven distribution of variables in the datasets from which the models are derived. There is an urgent need for predictive models that can integrate a large number of clinical variables from multicenter data to account for the variability in patient characteristics that influence patient selection, outcomes, and survival for both durable MCS and HT; this may be fulfilled by AI-driven risk prediction models.

A predictive model of 30-day mortality in patients with acute type A aortic dissection.

OBJECTIVE: Acute type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease that requires an effective predictive model to predict and assess a patient's risk of death. Our study aimed to construct a model for predicting the risk of 30-day death in patients with ATAAD and the prediction accuracy of the German Registry of Acute Aortic Dissection Type A (GERAADA) Score and the European System for Cardiac Operative Risk Evaluation (EuroSCORE II) was verified. MATERIALS AND METHODS: Between June 2019 and June 2023, 109 patients with ATAAD underwent surgical treatment at our hospital (35 in the death group and 74 in the survival group). The differences in image parameters between the two groups were compared. Search for independent predictors and develop models that predict 30-day mortality in patients with ATAAD. GERAADA Score and EuroSCORE II were retrospectively calculated and indicated mortality was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Logistic regression analysis showed that ascending aortic length and pericardial effusion were independent predictors of death within 30 days in patients with ATAAD. We constructed four models, GERAADA Score (Model 1), EuroSCORE II (Model 2), Model 1, ascending aorta length, and pericardial effusion (Model 3), and Model 2, ascending aorta length, and pericardial effusion (Model 4). The area under the curve (AUC = 0.832) of Model 3 was significantly different from those of Models 1 (AUC = 0.683) and 2 (AUC = 0.599), respectively (p < 0.05, DeLong test). CONCLUSIONS: Adding ascending aorta length and pericardial effusion to the GERAADA Score can improve the predictive power of 30-day mortality in patients with ATAAD.

Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study.

Background: Improving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here, we compared the performance of the Helseundersøkelsen i Nord-Trøndelag (HUNT) Lung Cancer Model (HUNT LCM) versus the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON)) and 2021 United States Preventive Services Task Force (USPSTF) criteria regarding LC risk prediction and efficiency. Methods: We used linked data from 10 Norwegian prospective population-based cohorts, Cohort of Norway. The study included 44,831 ever-smokers, of which 686 (1.5%) patients developed LC; the median follow-up time was 11.6 years (0.01-20.8 years). Results: Within 6 years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased the LC prediction rate by 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity (p < 0.001 and p = 0.028), and reduced the number needed to predict one LC case (29 versus 40, p < 0.001 and 36 versus 40, p = 0.02), respectively. Applying the HUNT LCM 6-year 0.98% risk score as a cutoff (14.0% of ever-smokers) predicted 70.7% of all LC, increasing LC prediction rate with 89.2% and 18.9% versus the NELSON and 2021 USPSTF, respectively (both p < 0.001). Conclusions: The HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving the prediction of LC diagnosis, and may be used as a validated clinical tool for screening selection.

Navigating the Landscape of Cardiovascular Risk Scores: A Comparative Analysis of Eight Risk Prediction Models in a High-Risk Cohort in Lithuania.

Background: Numerous cardiovascular risk prediction models (RPM) have been developed, however, agreement studies between these models are scarce. We aimed to assess the inter-model agreement between eight RPMs: assessing cardiovascular risk using SIGN, the Australian CVD risk score (AusCVDRisk), the Framingham Risk Score for Hard Coronary Heart Disease, the Multi-Ethnic Study of Atherosclerosis risk score, the Pooled Cohort Equation (PCE), the QRISK3 cardiovascular risk calculator, the Reynolds Risk Score, and Systematic Coronary Risk Evaluation-2 (SCORE2). Methods: A cross-sectional study was conducted on 11,174 40-65-year-old individuals with diagnosed metabolic syndrome from a single tertiary university hospital in Lithuania. Cardiovascular risk was calculated using the eight RPMs, and the results were categorized into high, intermediate, and low-risk groups. Inter-model agreement was quantified using Cohen's Kappa coefficients. Results: The study revealed significant heterogeneity in risk categorizations with only 1.49% of cases where all models agree on the risk category. SCORE2 predominantly categorized participants as high-risk (67.39%), while the PCE identified the majority as low-risk (62.03%). Cohen's Kappa coefficients ranged from -0.09 to 0.64, indicating varying degrees of inter-model agreement. Conclusions: The choice of RPM can substantially influence clinical decision-making and patient management. The PCE and AusCVDRisk models exhibited the highest degree of agreement while the SCORE2 model consistently exhibited low agreement with other models.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.

BACKGROUND: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS: PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS: In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION: AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma.

BACKGROUND: Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis. AIM: To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC. METHODS: The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice. RESULTS: Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value. CONCLUSION: The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Impact of Social Support on Cardiovascular Risk Prediction Models: A Systematic Review.

Cardiovascular diseases (CVD) stand as the primary causes of both mortality and morbidity on a global scale. Social factors such as low social support can increase the risk of developing heart diseases and have shown poor prognosis in cardiac patients. Resources such as PubMed and Google Scholar were searched using a boolean algorithm for articles published between 2003 and 2023. Eligible articles showed an association between social support and cardiovascular risks. A systematic review was conducted using the guidance published in the Cochrane Prognosis Method Group and the PRISMA checklist, for reviews of selected articles. A total of five studies were included in our final analysis. Overall, we found that participants with low social support developed cardiovascular events, and providing a good support system can decrease the risk of readmission in patients with a history of CVD. We also found that integrating social determinants in the cardiovascular risk prediction model showed improvement in accessing the risk. Population with good social support showed low mortality and decreased rate of readmission. There are various prediction models, but the social determinants are not primarily included while calculating the algorithms. Although it has been proven in multiple studies that including the social determinants of health (SDOH) improves the accuracy of cardiovascular risk prediction models. Hence, the inclusion of SDOH should be highly encouraged.

The performance of FIT-based and other risk prediction models for colorectal neoplasia in symptomatic patients: a systematic review.

Background: Colorectal cancer (CRC) incidence and mortality are increasing internationally. Endoscopy services are under significant pressure with many overwhelmed. Faecal immunochemical testing (FIT) has been advocated to identify a high-risk population of symptomatic patients requiring definitive investigation by colonoscopy. Combining FIT with other factors in a risk prediction model could further improve performance in identifying those requiring investigation most urgently. We systematically reviewed performance of models predicting risk of CRC and/or advanced colorectal polyps (ACP) in symptomatic patients, with a particular focus on those models including FIT. Methods: The review protocol was published on PROSPERO (CRD42022314710). Searches were conducted from database inception to April 2023 in MEDLINE, EMBASE, Cochrane libraries, SCOPUS and CINAHL. Risk of bias of each study was assessed using The Prediction study Risk Of Bias Assessment Tool. A narrative synthesis based on the guidelines for Synthesis Without Meta-Analysis was performed due to study heterogeneity. Findings: We included 62 studies; 23 included FIT (n = 22) or guaiac Faecal Occult Blood Testing (n = 1) combined with one or more other variables. Twenty-one studies were conducted solely in primary care. Generally, prediction models including FIT consistently had good discriminatory ability for CRC/ACP (i.e. AUC >0.8) and performed better than models without FIT although some models without FIT also performed well. However, many studies did not present calibration and internal and external validation were limited. Two studies were rated as low risk of bias; neither model included FIT. Interpretation: Risk prediction models, including and not including FIT, show promise for identifying those most at risk of colorectal neoplasia. Substantial limitations in evidence remain, including heterogeneity, high risk of bias, and lack of external validation. Further evaluation in studies adhering to gold standard methodology, in appropriate populations, is required before widespread adoption in clinical practice. Funding: National Institute for Health and Care Research (NIHR) [Health Technology Assessment Programme (HTA) Programme (Project number 133852).

Comparison of various prediction models in the effect of Roux-en-Y gastric bypass on type 2 diabetes in the Chinese population 5 years after surgery.

BACKGROUND: Various prediction models of type 2 diabetes (T2D) remission have been externally verified internationally. However, long-term validated results after Roux-en-Y gastric bypass (RYGB) surgery are lacking. The best model for the Chinese population is also unknown. OBJECTIVES: To evaluate the prediction effect of various prediction models on the long-term diabetes remission after RYGB in the Chinese population and to provide reference for clinical use. SETTING: A retrospective clinical study at a university hospital. METHODS: We retrospectively analyzed Chinese population data 5 years after RYGB and externally validated 11 predictive models to evaluate the predictive effect of each model on long-term T2D remission after RYGB. RESULTS: We enrolled 84 patients. The mean body mass index was 41 kg/m2, and the percentage of excess weight loss (%EWL) was 72.3%. The mean glycated hemoglobin level was 8.4% preoperatively and decreased to 5.9% after 5 years. The 5-year postoperative complete and partial remission rates of T2D were 31% and 70.2%, respectively. The ABCD scoring model (sensitivity, 84%; specificity, 76%; area under the curve [AUC], .866) and the Panuzi et al. [34] study (sensitivity, 84%; specificity, 81%; AUC, .842) showed excellent results. In the Hosmer-Lemeshow goodness-of-fit test, calibration values for ABCD and Panuzi et al. [34] were .14 and .21, respectively. The predicted-to-observed ratios of ABCD and Panuzi et al. [34] were .83 and .88, respectively. CONCLUSIONS: T2D was relieved to varying degrees 5 years after RYGB in patients with obesity. The prediction models in ABCD and the Panuzi et al. [34] studies showed the best prediction effects. ABCD was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts.

AIMS: To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c. METHODS: We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720). RESULTS: The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012). CONCLUSIONS: Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.

Evaluation of Two Online Risk Prediction Models for the Mortality Rate of Acute Type A Aortic Dissection Surgery: The German Registry of Acute Aortic Dissection Type A Score and the European System for Cardiac Operative Risk Evaluation II.

EuroSCORE II is one of the most widely utilized cardiovascular surgery risk scoring systems. Recently, a new online score calculator, namely the German Registry of Acute Aortic Dissection Type A (GERAADA), was launched to predict 30-day surgical mortality for acute type A aortic dissection (ATAAD) patients. The aim of this study is to evaluate the predictive performance of these two scores. We calculated the two scores for 1346 ATAAD patients from January 2012 to December 2021. The overall performance was evaluated using Brier scores and Hosmer-Lemeshow statistics. Receiver Operating Characteristic (ROC) curves were employed to assess diagnostic ability, and the standardized mortality ratio (SMR) was utilized to evaluate calibration. The GERAADA score and EuroSCORE II predicted 30-day mortality rates of 14.7% and 3.1%, respectively, while the observed rate was 12.5%. The predictive ability of EuroSCORE II (AUC 0.708, 95% CI: 0.664-0.792) was superior to that of the GERAADA score (0.648, 95% CI: 0.605-0.692). The GERAADA score had higher sensitivity but lower specificity than EuroSCORE II. And the GERAADA score may overestimate mortality (0.76, 95% CI: 0.65-0.89), while EuroSCORE II may underestimate the mortality rate (3.17, 95% CI: 2.92-3.44). The EuroSCORE II was superior in predicting surgical mortality among ATAAD patients. But the observed 30-day mortality rate certified a good calibration for the GERAADA score.

Development and Validation of a Predictive Risk Algorithm for Bleeding in Individuals on Long-term Hemodialysis: An International Prospective Cohort Study (BLEED-HD).

Background: Individuals with kidney disease are at a high risk of bleeding and as such tools that identify those at highest risk may aid mitigation strategies. Objective: We set out to develop and validate a prediction equation (BLEED-HD) to identify patients on maintenance hemodialysis at high risk of bleeding. Design: International prospective cohort study (development); retrospective cohort study (validation). Settings: Development: 15 countries (Dialysis Outcomes and Practice Patterns Study [DOPPS] phase 2-6 from 2002 to 2018); Validation: Ontario, Canada. Patients: Development: 53 147 patients; Validation: 19 318 patients. Measurements: Hospitalization for a bleeding event. Methods: Cox proportional hazards models. Results: Among the DOPPS cohort (mean age, 63.7 years; female, 39.7%), a bleeding event occurred in 2773 patients (5.2%, event rate 32 per 1000 person-years), with a median follow-up of 1.6 (interquartile range [IQR], 0.9-2.1) years. BLEED-HD included 6 variables: age, sex, country, previous gastrointestinal bleeding, prosthetic heart valve, and vitamin K antagonist use. The observed 3-year probability of bleeding by deciles of risk ranged from 2.2% to 10.8%. Model discrimination was low to moderate (c-statistic = 0.65) with excellent calibration (Brier score range = 0.036-0.095). Discrimination and calibration of BLEED-HD were similar in an external validation of 19 318 patients from Ontario, Canada. Compared to existing bleeding scores, BLEED-HD demonstrated better discrimination and calibration (c-statistic: HEMORRHAGE = 0.59, HAS-BLED = 0.59, and ATRIA = 0.57, c-stat difference, net reclassification index [NRI], and integrated discrimination index [IDI] all P value <.0001). Limitations: Dialysis procedure anticoagulation was not available; validation cohort was considerably older than the development cohort. Conclusion: In patients on maintenance hemodialysis, BLEED-HD is a simple risk equation that may be more applicable than existing risk tools in predicting the risk of bleeding in this high-risk population.

Contexte: Les personnes atteintes d'insuffisance rénale présentent un risque élevé d'hémorragie. Des outils permettant de déceler les personnes les plus exposées au risque pourrait aider à mettre en œuvre des stratégies d'atténuation. Objectifs: Nous avons mis au point et validé une équation prédictive (BLEED-HD) afin d'identifier les patients sous hémodialyse d'entretien qui présentent un risque élevé d'hémorragie. Type d'étude: Étude de cohorte prospective internationale (développement); étude de cohorte rétrospective (validation). Cadre: Développement: dans 15 pays (étude DOPPS phases 2 à 6 entre 2002 et 2018); validation: en Ontario (Canada). Sujets: Développement: 53 147 patients; validation: 19 318 patients. Mesures: Hospitalisation pour un événement hémorragique. Méthodologie: Modèles à risques proportionnels de Cox. Résultats: Dans la cohorte DOPPS (âge moyen: 63,7 ans; 39,7 % de femmes), 2 773 patients avaient subi un événement hémorragique (5,2 %; taux d'événements: 32 pour 1 000 années-personnes) avec un suivi médian de 1,6 an (ÉIQ: 0,9 à 2,1). BLEED-HD prend six variables en compte: âge, sexe, pays d'origine, saignement gastro-intestinal antérieur, présence d'une valve cardiaque prothétique et utilisation d'un antagoniste de la vitamine K. La probabilité observée de saignements dans les 3 ans par déciles de risque allait de 2,2 à 10,8 %. La discrimination du modèle variait de faible à modérée (statistique c: 0,65) avec un excellent étalonnage (plage de score de Brier: 0,036-0,095). La discrimination et l'étalonnage de se sont avérés semblables lors de la validation externe auprès de 19 318 patients de l'Ontario (Canada). Par rapport aux scores d'hémorragie existants, l'équation BLEED-HD a démontré une meilleure discrimination et un meilleur étalonnage (statistique c: HEMORRHAGE 0,59; HAS-BLED 0,59 et ATRIA 0,57; différence dans les c-stat, indices NRI et IDI toutes valeurs de p < 0,0001). Limites: L'information sur l'anticoagulant utilisé dans la procédure de dialyse n'était pas disponible; la cohorte de validation était beaucoup plus âgée que la cohorte de développement. Conclusion: Pour les patients sous hémodialyse d'entretien, BLEED-HD est une équation simple de calcul du risque qui peut être plus facilement applicable que les outils existants pour prédire le risque d'hémorragie dans cette population à haut risque.

Saturated fatty acid intake, genetic risk and colorectal cancer incidence: A large-scale prospective cohort study.

Previous investigations mainly focused on the associations of dietary fatty acids with colorectal cancer (CRC) risk, which ignored gene-environment interaction and mechanisms interpretation. We conducted a case-control study (751 cases and 3058 controls) and a prospective cohort study (125 021 participants) to explore the associations between dietary fatty acids, genetic risks, and CRC. Results showed that high intake of saturated fatty acid (SFA) was associated with a higher risk of CRC than low SFA intake (HR =1.22, 95% CI:1.02-1.46). Participants at high genetic risk had a greater risk of CRC with the HR of 2.48 (2.11-2.91) than those at low genetic risk. A multiplicative interaction of genetic risk and SFA intake with incident CRC risk was found (PInteraction = 7.59 × 10-20 ), demonstrating that participants with high genetic risk and high SFA intake had a 3.75-fold greater risk of CRC than those with low genetic risk and low SFA intake. Furthermore, incorporating PRS and SFA into traditional clinical risk factors improved the discriminatory accuracy for CRC risk stratification (AUC from 0.706 to 0.731). Multi-omics data showed that exposure to SFA-rich high-fat dietary (HFD) can responsively induce epigenome reprogramming of some oncogenes and pathological activation of fatty acid metabolism pathway, which may contribute to CRC development through changes in gut microbiomes, metabolites, and tumor-infiltrating immune cells. These findings suggest that individuals with high genetic risk of CRC may benefit from reducing SFA intake. The incorporation of SFA intake and PRS into traditional clinical risk factors will help improve high-risk sub-populations in individualized CRC prevention.

Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review.

BACKGROUND: The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. METHODS: Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008-20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. RESULTS: Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. CONCLUSION: Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.

There is no such thing as a validated prediction model.

BACKGROUND: Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? MAIN BODY: We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. CONCLUSION: Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making.

Colorectal Cancer Risk Assessment and Precision Approaches to Screening: Brave New World or Worlds Apart?

Current colorectal cancer (CRC) screening recommendations take a "one-size-fits-all" approach using age as the major criterion to initiate screening. Precision screening that incorporates factors beyond age to risk stratify individuals could improve on current approaches and optimally use available resources with benefits for patients, providers, and health care systems. Prediction models could identify high-risk groups who would benefit from more intensive screening, while low-risk groups could be recommended less intensive screening incorporating noninvasive screening modalities. In addition to age, prediction models incorporate well-established risk factors such as genetics (eg, family CRC history, germline, and polygenic risk scores), lifestyle (eg, smoking, alcohol, diet, and physical inactivity), sex, and race and ethnicity among others. Although several risk prediction models have been validated, few have been systematically studied for risk-adapted population CRC screening. In order to envisage clinical implementation of precision screening in the future, it will be critical to develop reliable and accurate prediction models that apply to all individuals in a population; prospectively study risk-adapted CRC screening on the population level; garner acceptance from patients and providers; and assess feasibility, resources, cost, and cost-effectiveness of these new paradigms. This review evaluates the current state of risk prediction modeling and provides a roadmap for future implementation of precision CRC screening.