Preexposure Prophylaxis to Prevent Acquisition of HIV: US Preventive Services Task Force Recommendation Statement.

Importance: An estimated 1.2 million persons in the US currently have HIV, and more than 760 000 persons have died of complications related to HIV since the first cases were reported in 1981. Although treatable, HIV is not curable and has significant health consequences. Therefore, effective strategies to prevent HIV are an important public health and clinical priority. Objective: The US Preventive Services Task Force (USPSTF) commissioned a systematic review to evaluate the benefits and harms of preexposure prophylaxis with antiretroviral therapy for the prevention of HIV acquisition, and the diagnostic accuracy of risk assessment tools to identify persons at increased risk of HIV acquisition. Population: Adolescents and adults who do not have HIV and are at increased risk of HIV. Evidence Assessment: The USPSTF concludes with high certainty that there is a substantial net benefit from the use of effective antiretroviral therapy to reduce the risk of acquisition of HIV in persons at increased risk of acquiring HIV. Recommendation: The USPSTF recommends that clinicians prescribe preexposure prophylaxis using effective antiretroviral therapy to persons at increased risk of HIV acquisition to decrease the risk of acquiring HIV. (A recommendation).

[Research on applying genetic and environmental risk score in risk-adapted colorectal cancer screening].

Objective: To evaluate the effectiveness of a risk-adapted colorectal cancer screening strategy constructed utilizing genetic and environmental risk score (ERS). Methods: A polygenic risk score (PRS) was constructed based on 20 previously published single nucleotide polymorphisms for colorectal cancer in East Asian populations, using 2 160 samples with MassARRAY test results from a multicenter randomized controlled trial of colorectal cancer screening in China. The ERS was calculated using the Asia-Pacific Colorectal Screening Score system. Logistic regression was used to analyze the association between PRS alone and PRS combined with ERS and colorectal neoplasms risk, respectively. We also designed a risk-adapted screening strategy based on PRS and ERS (high-risk participants undergo a single colonoscopy, low-risk participants undergo an annual fecal immunochemical test, and those with positive results undergo further diagnostic colonoscopy) and compared its effectiveness with the all-acceptance colonoscopy strategy. Results: The high PRS group had a 26% increased risk of colorectal neoplasms compared with the low PRS group (OR=1.26, 95%CI: 1.03-1.54, P=0.026). Participants with the highest PRS and ERS were 3.03 times more likely to develop advanced colorectal neoplasms than those with the lowest score (95%CI: 1.87-4.90, P<0.001). As the risk-adapted screening simulation reached the third round, the detection rate of the PRS combined with ERS strategy was not statistically different from the all-acceptance colonoscopy strategy (8.79% vs. 10.46%, P=0.075) and had a higher positive predictive value (14.11% vs. 10.46%, P<0.001) and lower number of colonoscopies per advanced neoplasms detected (7.1 vs. 9.6, P<0.001). Conclusion: The risk-adapted screening strategy combining PRS and ERS helps achieve population risk stratification and better effectiveness than the traditional colonoscopy-based screening strategy.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Predictive Ability of Visit-to-Visit Variability of HbA1c Measurements for the Development of Diabetic Kidney Disease: A Retrospective Longitudinal Observational Study.

AIMS: This study is aimed at clarifying the relationship between visit-to-visit variability of glycated hemoglobin (HbA1c) and the risk of diabetic kidney disease (DKD) and to identifying the most useful index of visit-to-visit variability of HbA1c. METHODS: This clinic-based retrospective longitudinal study included 699 Japanese type 2 diabetes mellitus patients. Visit-to-visit variability of HbA1c was calculated as the internal standard deviation of HbA1c (HbA1c-SD), the coefficient of variation of HbA1c (HbA1c-CV), the HbA1c change score (HbA1c-HVS), and the area under the HbA1c curve (HbA1c-AUC) with 3-year serial HbA1c measurement data, and the associations between these indices and the development/progression of DKD were examined. RESULTS: Cox proportional hazards models showed that the HbA1c-SD and HbA1c-AUC were associated with the incidence of microalbuminuria, independently of the HbA1c level. These results were verified and replicated in propensity score (PS) matching and bootstrap analyses. Moreover, the HbA1c-SD and HbA1c-AUC were also associated with oxidized human serum albumin (HSA), an oxidative stress marker. CONCLUSIONS: Visit-to-visit variability of HbA1c was an independent risk factor of microalbuminuria in association with oxidative stress among type 2 diabetes mellitus patients. HbA1c-AUC, a novel index of HbA1c variability, may be a potent prognostic indicator in predicting the risk of microalbuminuria.

Half of germline pathogenic and likely pathogenic variants found on panel tests do not fulfil NHS testing criteria.

Genetic testing for cancer predisposition has been curtailed by the cost of sequencing, and testing has been restricted by eligibility criteria. As the cost of sequencing decreases, the question of expanding multi-gene cancer panels to a broader population arises. We evaluated how many additional actionable genetic variants are returned by unrestricted panel testing in the private sector compared to those which would be returned by adhering to current NHS eligibility criteria. We reviewed 152 patients referred for multi-gene cancer panels in the private sector between 2014 and 2016. Genetic counselling and disclosure of all results was standard of care provided by the Consultant. Every panel conducted was compared to current eligibility criteria. A germline pathogenic / likely pathogenic variant (P/LP), in a gene relevant to the personal or family history of cancer, was detected in 15 patients (detection rate of 10%). 46.7% of those found to have the P/LP variants (7 of 15), or 4.6% of the entire set (7 of 152), did not fulfil NHS eligibility criteria. 46.7% of P/LP variants in this study would have been missed by national testing guidelines, all of which were actionable. However, patients who do not fulfil eligibility criteria have a higher Variant of Uncertain Significance (VUS) burden. We demonstrated that the current England NHS threshold for genetic testing is missing pathogenic variants which would alter management in 4.6%, nearly 1 in 20 individuals. However, the clinical service burden that would ensue is a detection of VUS of 34%.

Construction and validation of the prognostic model for patients with neuroendocrine cervical carcinoma: a competing risk nomogram analysis.

PURPOSE: Neuroendocrine cervical carcinoma (NECC) is an uncommon malignancy of the female reproductive system. This study aimed to evaluate cancer-specific mortality and to construct prognostic nomograms for predicting the survival of patients with NECC. METHODS: we assembled the patients with NECC diagnosed between 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Meanwhile, we identified other patients with NECC from the Wenling Maternal and Child Health Care Hospital between 2002 to 2017. Fine and Gray's test and Kaplan-Meier methods were used to evaluate cancer-specific mortality and overall survival (OS) rates, respectively. Nomograms were constructed for predicting cancer-specific survival (CSS) and OS for patients with NECC. The developed nomograms were validated both internally and externally. RESULTS: a total of 894 patients with NECC were extracted from the SEER database, then classified into the training cohort (n = 628) and the internal validation cohort (n = 266). Besides, 106 patients from the Wenling Maternal and Child Health Care Hospital served as an external validation cohort. Nomograms for predicting CSS and OS were constructed on clinical predictors. The validation of nomograms was calculated by calibration curves and concordance indexes (C-indexes). Furthermore, the developed nomograms presented higher areas under the receiver operating characteristic (ROC) curves when compared to the FIGO staging system. CONCLUSIONS: we established the first competing risk nomograms to predict the survival of patients with NECC. Such a model with high predictive accuracy could be a practical tool for clinicians.

Revisiting the Implications of Positive Germline Testing Results Using Multi-gene Panels in Breast Cancer Patients.

BACKGROUND/AIM: The use of multi-gene panels for germline testing in breast cancer enables the estimation of cancer risk and guides risk-reducing management options. The aim of this study was to present data that demonstrate the different levels of actionability for multi-gene panels used in genetic testing of breast cancer patients and their family members. MATERIALS AND METHODS: We performed an analysis in our clinical database to identify breast cancer patients undergoing genetic testing. We reviewed positive results in respect of risk estimation and management, cascade family testing, secondary findings and information for treatment decision-making. RESULTS: A total of 415 positive test reports were identified with 57.1%, 18.1%, 10.8% and 13.5% of individuals having pathogenic/likely pathogenic variants in high, moderate, low and with insufficient evidence for breast cancer risk genes, respectively. Six point seven percent of individuals were double heterozygotes. CONCLUSION: Germline findings in 92% of individuals are linked to evidence-based treatment information and risk estimates for predisposition to breast and/or other cancer types. The use of germline findings for treatment decision making expands the indication of genetic testing to include individuals that could benefit from targeted treatments.

Derivation of the no expected sensitization induction level for dermal quantitative risk assessment of fragrance ingredients using a weight of evidence approach.

Some fragrance ingredients may have the potential to induce skin sensitization in humans but can still be safely formulated into consumer products. Quantitative Risk Assessment (QRA) for dermal sensitization is required to determine safe levels at which potential skin sensitizers can be incorporated into consumer products. The no expected sensitization induction level or NESIL is the point of departure for the dermal QRA. Sensitization assessment factors are applied to the NESIL to determine acceptable exposure levels at which no skin sensitization induction would be expected in the general population. This paper details the key steps involved in deriving a weight of evidence (WoE) NESIL for a given fragrance ingredient using all existing data, including in vivo, in vitro, and in silico. Read-across can be used to derive a NESIL for a group of structurally similar materials when data are insufficient. When sufficient target and read-across data are lacking, exposure waiving threshold (the DST) may be used. We outline the process as it currently stands at the Research Institute for Fragrance Materials Inc. (RIFM) and provide examples, but it is dynamic and is bound to change with evolving science as new approach methodologies (NAMs) are actively incorporated.

Diagnostic Performance of the Caprini Risk Assessment Model Combined With D-Dimer for Preoperative Deep Vein Thrombosis in Patients With Thoracolumbar Fractures Caused by High-Energy Injuries.

OBJECTIVE: To assess the validity of the Caprini risk assessment model (RAM) in risk stratification for deep vein thrombosis (DVT) and to investigate the diagnostic value of Caprini score combined with D-dimer in predicting DVT. METHODS: This study involved 429 patients with thoracolumbar fractures caused by high-energy injuries between October 2016 and November 2019. All patients were treated surgically and had a mean age of 45.3 ± 11.4 years. Patients were risk-stratified using the 2013 Caprini RAM. Mechanical and chemical prophylaxis were used for DVT. Duplex ultrasound of both lower extremities was performed before surgery. RESULTS: Of the 429 patients, 62 (14.45%) developed DVT. The incidence of preoperative DVT was correlated with Caprini score according to risk stratification(χ2 = 117.4, P < 0.001). Based on the original Caprini RAM, all the patients scored in the highest risk category (score ≥5). Further substratification showed that the majority (277 of 429, 64.57%) of the patients were in the Caprini score range 7-8 and the risk of preoperative DVT was significantly higher among patients with Caprini score >10. The area under the receiver operating characteristic curve of Caprini score and D-dimer was 0.816 and 0.769 when Caprini score >8 or D-dimer >1.81mg/L was considered the criterion of predicting the risk of DVT. When combining the 2 variables, the area under the ROC curve can increase to 0.846. CONCLUSIONS: The Caprini RAM is an effective and reliable DVT risk stratification tool in patients with thoracolumbar fractures caused by high-energy injuries. Caprini score >8 or D-dimer >1.81 mg/L may predict the occurrence of preoperative DVT and the Caprini score combined with D-dimer exhibit better diagnostic performance.

A Simple Risk Scoring System for Predicting the Occurrence of Aspiration Pneumonia After Gastric Endoscopic Submucosal Dissection.

BACKGROUND: Aspiration pneumonia after endoscopic submucosal dissection (ESD) is rare, but can be fatal. We aimed to investigate risk factors and develop a simple risk scoring system for aspiration pneumonia. METHODS: We retrospectively reviewed medical records of 7833 patients who underwent gastric ESD for gastric neoplasm under anesthesiologist-directed sedation. Candidate risk factors were screened and assessed for significance using a least absolute shrinkage and selection operator (LASSO)-based method. Top significant factors were incorporated into a multivariable logistic regression model, whose prediction performance was compared with those of other machine learning models. The final risk scoring system was created based on the estimated odds ratios of the logistic regression model. RESULTS: The incidence of aspiration pneumonia was 1.5%. The logistic regression model showed comparable performance to the best predictive model, extreme gradient boost (area under receiver operating characteristic curve [AUROC], 0.731 vs 0.740). The estimated odds ratios were subsequently used for the development of the clinical scoring system. The final scoring system exhibited an AUROC of 0.730 in the test dataset with risk factors: age (≥70 years, 4 points), male sex (8 points), body mass index (≥27 kg/m2, 4 points), procedure time (≥80 minutes, 5 points), lesion in the lower third of the stomach (5 points), tumor size (≥10 mm, 3 points), recovery time (≥35 minutes, 4 points), and desaturation during ESD (9 points). For patients with total scores ranging between 0 and 33 points, aspiration pneumonia probabilities spanned between 0.1% and 17.9%. External validation using an additional cohort of 827 patients yielded AUROCs of 0.698 for the logistic regression model and 0.680 for the scoring system. CONCLUSIONS: Our simple risk scoring system has 8 predictors incorporating patient-, procedure-, and sedation-related factors. This system may help clinicians to stratify patients at risk of aspiration pneumonia after ESD.

Body composition and bone mineral density by Dual Energy X-ray Absorptiometry: Reference values for young children.

BACKGROUND & AIMS: Childhood obesity is a global public health threat, with an alarming rise in incidence. Obesity at young age has short-term and long-term morbidity. It is, therefore, important to accurately assess body composition throughout infancy and childhood to identify excess adiposity. However, reference values for age 2-5 years, needed to interpret measurements and identify young children at risk, are lacking. Our primary objective was to fill the current gap in reference values by constructing sex-specific body composition reference values and charts for fat mass (FM), fat mass percentage (FM%), fat mass index (FMI), lean body mass (LBM), lean body mass index (LBMI) and total body less head bone mineral density (BMDTBLH) for children aged 2-5 years using Dual-Energy X-ray Absorptiometry (DXA). METHODS: We performed 599 accurate DXA-measurements in 340 term-born children aged 2-5 years, using Lunar Prodigy with Encore software (V14.1). Using GAMLSS, sex-specific reference values and charts were created for FM, FM%, FMI, LBM, LBMI and BMDTBLH. RESULTS: Sex-specific body composition reference values and charts for age 2-5 years were constructed. In boys and girls, FM and LBM increased from age 2-5 years (all p ≤ 0.001), but body size-corrected FM% and FMI decreased (all p ≤ 0.023). LBMI remained similar between 2 and 5 years of age. Girls had higher FM, FM% and FMI and lower LBM and LBMI compared to boys. BMC and BMDTBLH increased with age between 2 and 5 years of age (all p < 0.001) and were similar for boys and girls. CONCLUSIONS: We present sex-specific reference values and charts for body composition and total body bone mineral density measured by DXA, based on a large cohort of healthy children aged 2-5 years. These longitudinal references can be used for clinical practice and research purposes to monitor body composition and bone mineral density development and identify children at risk for excess adiposity.

Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients.

BACKGROUND: Due to the low cancer-detection rate in patients with PIRADS category 3 lesions, we created machine learning (ML) models to facilitate decision-making about whether to perform prostate biopsies or monitor clinical information without biopsy results. METHODS: In our retrospective, single-center study, 101 eligible patients with at least one PIRADS category 3 lesion but no higher PIRADS lesions underwent MRI/US fusion biopsies between September 2017 and June 2020. Thirty additional patients were included as the validation cohort from the next chronological period from June 2020 to October 2020. Our ML research was a supervised classification problem, with a binary output based on pathological reports of cancerous or benign tissue. The clinical inputs were age, prostate-specific antigen (PSA), prostate volume, prostate-specific antigen density (PSAD), and the number of previous biopsies. The radiology-report inputs were the number of lesions, maximum lesion diameter, lesion location, and lesion zone. We subsequently removed the inputs with low importance. Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting Tree (XGBoost) were employed. From receiver operating characteristic (ROC) curves, we determined Area Under the ROC Curve (AUC), the cut-off point, and sensitivity score (recall score) to evaluate the ML-model performance. RESULTS: Twenty-four adenocarcinoma patients had a mean age of 70 ± 5.79 years, a mean PSA of 12.42 ± 6.67 ng/ml, a mean prostate volume of 46.49 ± 23.13 ml, and a mean PSAD of 0.31 ± 0.22 ng/ml2 . Seventy-seven patients with benign tissue reports had a mean age of 66.39 ± 6.66 years, a mean PSA of 11.31 ± 7.50 ng/ml, a mean prostate volume of 65.25 ± 35.88 ml, and a mean PSAD of 0.19 ± 0.13 ng/ml2 . On the validation cohort, XGBoost had the best AUC of 0.76, which considered 80% sensitivity and 72% specificity at a probability cutoff of 57%. The remaining possible ML models performed worse with lesser AUC. The worst was Naïve Bayes, with AUC of 0.50. CONCLUSIONS: ML models facilitate PIRADS 3 patient selection for MRI/US fusion biopsies. ML could optimize how we use previously known clinical risk factors to their full potential.

Failure of the Ottawa Score to Predict the Risk of Recurrent Venous Thromboembolism in Cancer Patients: The Prospective PREDICARE Cohort Study.

INTRODUCTION: Recurrent venous thromboembolism (VTE) despite curative anticoagulation is frequent in patients with cancer. Identifying patients with a high risk of recurrence could have therapeutic implications. A prospective study was designed to validate the Ottawa risk score of recurrent VTE in cancer patients. METHODS: In a prospective multicenter observational cohort, adult cancer patients with a recent diagnosis of symptomatic or incidental lower limb deep vein thrombosis or pulmonary embolism (PE) were treated with tinzaparin for 6 months. The primary endpoint was the recurrence of symptomatic or asymptomatic VTE within the first 6 months of treatment. All clinical events were centrally reviewed and adjudicated. Time-to-event outcomes were estimated by the Kalbfleisch and Prentice method to take into account the competing risk of death. A C-statistic value of > 0.70 was needed to validate the Ottawa score. RESULTS: A total of 409 patients were included and analyzed on an intention-to-treat basis. Median age was 68 years, 60.4% of patients had PE, and VTE was symptomatic in 271 patients (66.3%). The main primary sites were lung (31.3%), lower digestive tract (14.4%), and breast (13.9%) cancers. The Ottawa score was high (≥ 1) in 58% of patients. The 6-month cumulative incidence of recurrent VTE was 7.3% (95% confidence interval [CI]: 4.9-11.1) overall, and 5.0% (95% CI: 2.3-10.8) versus 9.1% (95%CI: 6.1-13.6) in the Ottawa low versus high risk groups, respectively. The C-statistic value was 0.60 (95% CI: 0.55-0.65). CONCLUSION: In this prospective cohort of patients with cancer receiving tinzaparin for VTE, the Ottawa score failed to accurately predict recurrent VTE.

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach, accounting for the complex relationships among variables, including the dynamic nature of changing risk factors. METHODS: We studied a prospective U.S. cohort of 3,435,224 patients from medical databases in a 2-year investigation. Stroke outcomes were examined in relationship to diverse multimorbid conditions, demographic variables, and other inputs, with ML accounting for the dynamic nature of changing multimorbidity risk factors, two clinical risk scores, and a clinical multimorbid index. RESULTS: Common clinical risk scores had moderate and comparable c indices with stroke outcomes in the training and external validation samples (validation-CHADS2: c index 0.812, 95% confidence interval [CI] 0.808-0.815; CHA2DS2-VASc: c index 0.809, 95% CI 0.805-0.812). A clinical multimorbid index had higher discriminant validity values for both the training/external validation samples (validation: c index 0.850, 95% CI 0.847-0.853). The ML-based algorithms yielded the highest discriminant validity values for the gradient boosting/neural network logistic regression formulations with no significant differences among the ML approaches (validation for logistic regression: c index 0.866, 95% CI 0.856-0.876). Calibration of the ML-based formulation was satisfactory across a wide range of predicted probabilities. Decision curve analysis demonstrated that clinical utility for the ML-based formulation was better than that for the two current clinical rules and the newly developed multimorbid tool. Also, ML models and clinical stroke risk scores were more clinically useful than the "treat all" strategy. CONCLUSION: Complex relationships of various comorbidities uncovered using a ML approach for diverse (and dynamic) multimorbidity changes have major consequences for stroke risk prediction. This approach may facilitate automated approaches for dynamic risk stratification in the significant presence of multimorbidity, helping in the decision-making process for risk assessment and integrated/holistic management.

Effect of routine first-trimester combined screening for pre-eclampsia on small-for-gestational-age birth: secondary interrupted time series analysis.

OBJECTIVE: To evaluate the impact of a first-trimester combined screening program for pre-eclampsia, based on the Fetal Medicine Foundation (FMF) algorithm, on the rate of small-for-gestational age (SGA) at birth and adverse pregnancy outcome. METHODS: This was a retrospective cohort study of data obtained from a London tertiary hospital between January 2017 and March 2019. The data were derived from a secondary analysis of the cohort evaluated in a clinical-effectiveness study on the implementation of a first-trimester screening program for pre-eclampsia. The cohort included 7720 women screened according to the UK National Institute for Health and Care Excellence (NICE) risk-based approach and 4841 women screened by the FMF multimodal approach, which combines maternal risk factors, blood pressure, pregnancy-associated plasma protein-A and uterine artery Doppler indices. The care package for the FMF-screened group included 150-mg aspirin prophylaxis, ultrasound scans at 28 and 36 weeks' gestation and scheduled delivery at 40 weeks. Outcome measures included the rates of SGA neonates at birth, admission to the neonatal unit, intrauterine demise, neonatal death and hypoxic-ischemic encephalopathy assessed by interrupted time series analysis (ITSA). RESULTS: There was no significant difference in the rates of intrauterine demise, neonatal death and hypoxic-ischemic encephalopathy between the FMF-screened and NICE-screened cohorts. ITSA showed a significant reduction in the rate of term SGA birth < 10th percentile at 21 months following implementation of the FMF screening program, with a relative effect reduction of 45.1% (P = 0.004). However, there was no significant relative effect reduction in term SGA birth < 5th or < 3rd percentile. CONCLUSIONS: First-trimester combined screening for pre-eclampsia based on the FMF algorithm accompanied by a care package including serial ultrasound scans for growth evaluation and elective birth from 40 weeks' gestation resulted in a significant 45% relative effect reduction in term SGA birth < 10th percentile but did not affect term SGA birth < 5th or < 3rd percentile. Further screening strategies to detect and improve the outcome of cases with SGA birth < 5th percentile need to be considered. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy.

BACKGROUND AND PURPOSE: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States. METHODS: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011-2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. RESULTS: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. CONCLUSIONS: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

Comparative performance of risk prediction models for hepatitis B-related hepatocellular carcinoma in the United States.

BACKGROUND & AIMS: Guidelines recommend hepatocellular carcinoma (HCC) surveillance in patients with chronic HBV infection. Several HCC risk prediction models are available to guide surveillance decisions, but their comparative performance remains unclear. METHODS: Using a retrospective cohort of patients with HBV treated with nucleos(t)ide analogues at 130 Veterans Administration facilities between 9/1/2008 and 12/31/2018, we calculated risk scores from 10 HCC risk prediction models (REACH-B, PAGE-B, m-PAGE-B, CU-HCC, HCC-RESCUE, CAMD, APA-B, REAL-B, AASL-HCC, RWS-HCC). We estimated the models' discrimination and calibration. We calculated HCC incidence in risk categories defined by the reported cut-offs for all models. RESULTS: Of 3,101 patients with HBV (32.2% with cirrhosis), 47.0% were treated with entecavir, 40.6% tenofovir, and 12.4% received both. During a median follow-up of 4.5 years, 113 patients developed HCC at an incidence of 0.75/100 person-years. AUC values for 3-year HCC risk were the highest for RWS-HCC, APA-B, REAL-B, and AASL-HCC (all >0.80). Of these, 3 (APA-B, RWS-HCC, REAL-B) incorporated alpha-fetoprotein. AUC values for the other models ranged from 0.73 for PAGE-B to 0.79 for CAMD and HCC-RESCUE. Of the 7 models with AUC >0.75, only APA-B was poorly calibrated. In total, 10-20% of the cohort was deemed low-risk based on the published cut-offs. None of the patients in the low-risk groups defined by PAGE-B, m-PAGE-B, AASL-HCC, and REAL-B developed HCC during the study timeframe. CONCLUSION: In this national cohort of US-based patients with HBV on antiviral treatment, most models performed well in predicting HCC risk. A low-risk group, in which no cases of HCC occurred within a 3-year timeframe, was identified by several models (PAGE-B, m-PAGE-B, CAMD, AASL-HCC, REAL-B). Further studies are warranted to examine whether these patients could be excluded from HCC surveillance. LAY SUMMARY: Risk prediction models for hepatocellular carcinoma (HCC) in patients infected with hepatitis B virus (HBV) could guide HCC surveillance decisions. In this large cohort of US-based patients receiving treatment for HBV, most published models discriminated between those who did or did not develop HCC, although the RWS-HCC, REAL-B, and AASL-HCC performed the best. If confirmed in future studies, these models could help identify a low-risk subset of patients on antiviral treatment who could be excluded from HCC surveillance.