Citations

Abstract Introduction Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models. Methods We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The model was evaluated for calibration and discrimination in three prospective cohorts of European-ancestry women (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in terms of risk stratification and reclassification proportions were assessed among European-ancestry women aged 50–70 years in US and Sweden. Results For women younger and older than 50 years, the model with questionnaire-based risk factors, PRS and density was generally well calibrated across risk with some evidence of miscalibration at the extremes of the risk distribution. Incorporation of density led to modest improvements risk discrimination beyond the model with questionnaire-based risk factors and PRS: the area under the curve (AUC) among younger women was 67.0% (95% CI: 63.5–70.6%) vs. 65.6% (95% CI: 61.9–69.3%) for models with and without density; and 66.1% (95% CI 64.4–67.8%) vs. 65.5% (95% CI: 63.8–67.2%) among older women. The model with density identified 18.4% of US women 50–70 years old ≥ 3% 5-year predicted risk (threshold used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. At this threshold, 7.9% of US women were reclassified by adding density to the model, resulting in the identification of 2.8% of additional future cases. The model with density identified 10.3% of Swedish women ≥ 3% 5-year predicted risk, with 29.4% of future cases expected to occur in this group. At this threshold, 5.3% of women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases. Conclusion Integrating density with questionnaire-based risk factors and PRS could potentially identify more women of European-ancestry with elevated risk of breast cancer in the United States and Sweden. Further investigations of the integrated model in non-European ancestry populations are needed prior to considering clinical applications.

Alzheimer’s disease (AD) and Alzheimer’s Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver’s biometrics, offering insights into a driver’s cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies.

Purpose: To identify areas of consensus among experts on the performance of endothelial keratoplasty by using a modified Delphi approach, to help create a framework for novice surgeons to adopt these procedures. Methods: Thirty-one international experts in endothelial keratoplasty participated. Two rounds of electronic survey were followed by a hybrid, virtual meeting. Consensus was set at 75%, and results with agreement between 70% and 75% were deemed as achieving near consensus. Results: A consensus was reached for 98 statements covering the preoperative, intraoperative, and postoperative aspects of Descemet membrane endothelial keratoplasty (DMEK) and Descemet stripping endothelial keratoplasty/Descemet stripping automated endothelial keratoplasty. Four statements achieved near consensus, and consensus could not be achieved for 11 statements. For DMEK, the panel supported a peel technique to prepare tissue for endothelium out DMEK, implanted via an injector and supported by a near full air/gas fill as a baseline procedure onto which more advanced techniques can be built. DMEK tissue should be marked to ensure correct orientation. An inferior peripheral iridotomy should be used to prevent pupil block when a near full air/gas fill in used in endothelial keratoplasty (EK). Descemet stripping automated endothelial keratoplasty was considered preferable to Descemet stripping endothelial keratoplasty where access to microkeratome preparation was available. Conclusions: The Delphi process allowed areas of consensus on the performance of EK to be established by a group of international experts. The statements generated are a helpful framework for novice surgeons learning EK. Further research is needed to help determine what specific tomographic features indicate EK, when guttae are considered visually significant and how to approach combined aphakia and endothelial dysfunction.

The estimation of surgical blood loss is routinely performed during and after surgical procedures and has morbidity and mortality implications related to the risk of under- and over-resuscitation. The strategies for estimating surgical blood loss include visual estimation, the gravimetric method, the colorimetric method, formula-based methods, and other techniques (e.g., cell salvage). Currently, visual estimation continues to be the most widely used technique. In addition, unique considerations exist when these techniques are applied to various clinical settings such as massive transfusion, cardiac surgery, and obstetrics. Ultimately, when using estimated surgical blood loss to guide perioperative fluid management and transfusion thresholds, it is also important to mitigate the risks associated with resuscitation by targeting a goal-directed fluid therapy approach by utilizing markers of fluid-responsiveness to optimize stroke volume (cardiac output) and delivery of oxygen.

Multigene panel testing has allowed for the detection of a growing number of inherited pathogenic/likely pathogenic variants in people at high risk of cancer, including endometrial cancer (EC). Hereditary syndromes associated with EC include Lynch syndrome, PTEN hamartoma tumor syndrome, and Peutz-Jeghers syndrome. This manuscript provides the latest recommendations from the NCCN Guidelines for Genetic/Familial High-Risk Assessment: Colorectal, Endometrial, and Gastric on the screening and management of EC in patients at high risk for these syndromes, as well as the advantages and limitations of multigene panel testing. This manuscript also describes recent updates to these guidelines regarding de-implementation of colon cancer screening in individuals with CHEK2 pathogenic/likely pathogenic variants, based on the most up-to-date evidence regarding the association between CHEK2 pathogenic/likely pathogenic variants and colon cancer risk.

Background: There are limited evidence-based guidelines to predict which osteochondritis dissecans (OCD) lesions will heal with nonoperative treatment. Purpose: To train a set of classification algorithms to predict nonoperative OCD healing while identifying new clinically meaningful predictors. Study Design: Case-control study; Level of evidence, 3. Methods: Patients with OCD of the knee with open physes undergoing nonoperative management were prospectively queried from the Research on OCD of the Knee (ROCK) cohort ( https://kneeocd.org ) in April 2022. Patients were included if they met the study criteria for nonoperative treatment success or failure. Nonoperative treatment success was defined as complete healing on magnetic resonance imaging (MRI) and total return to sports participation. Failure was defined as the crossover from nonoperative management to surgery at any point at or beyond the 3-month follow-up. If a patient did not meet one of these criteria, they were not included. Normalized lesion size, lesion location, patient characteristics, and symptoms were used as clinically relevant predictors. Results: A total of 64 patients were included, of whom 24 (37.5%) patients successfully healed with nonoperative management. Multivariate logistic regression revealed that a 1% increase in normalized lesion width was associated with an increase in the likelihood of nonoperative failure (odds ratio [OR], 1.41 [95% CI, 1.17-1.81]; P < .01). By contrast, lesions in the posterior sagittal zone (OR, 0.08 [95% CI, 0.009-0.43]; P < .01) or the medial-most coronal zone (for lesions of the medial femoral) and lateral-most coronal zone (for lesions of the lateral femoral condyle) on MRI (OR, 0.05 [95% CI, 0.004-0.44]; P < .01) were associated with a decrease in the likelihood of nonoperative treatment failure. Support vector machines had a cross-validated area under the receiver operating characteristic curve of 0.89 and a classification accuracy of 83.3%. Conclusion: Lesion location in the posterior aspect of the condyle on sagittal MRI and lesion location in the medial-most or lateral-most locations on coronal MRI were identified as statistically significant predictors of increased nonoperative treatment success on multivariate analysis. Machine learning models can predict which OCD lesions will heal with nonoperative management with superior accuracy compared with previously published models.

Abstract Menopausal users of hormone replacement therapy (HRT) are at increased breast cancer risk and decreased colorectal cancer (CRC) risk compared with individuals who have never used HRT, but these opposing associations may differ by familial risk of breast cancer and CRC. We harmonized data from 3 cohorts and generated separate breast cancer and CRC familial risk scores based on cancer family history. We defined moderate or strong family history as a risk score of 0.4 or higher, where 0.4 was equivalent to a 50-year-old woman with 1 parent diagnosed with either breast cancer or CRC at 55 years of age. Of 24 486 women assessed, 1243 and 405 were diagnosed with incident breast cancer and CRC, respectively. For breast cancer, menopausal HRT ever use versus never use hazard ratios were 1.27 (95% CI = 1.11 to 1.45) for a breast cancer familial risk score below 0.4 and 1.01 (95% CI = 0.82 to 1.25) for a breast cancer familial risk score of 0.4 or higher (Pdifference = .08). For CRC, menopausal HRT hazard ratios were 0.63 (95% CI = 0.50 to 0.78) for a CRC familial risk score below 0.4 and 1.21 (95% CI = 0.73 to 2.00) for a CRC familial risk score of 0.4 or higher (Pdifference = .03). Associations with menopausal HRT use that apply to the general population may not hold for women at moderate or strong familial risk of these cancers.

SummaryGlobal obesity rates have risen dramatically, now exceeding deaths from starvation. Metabolic and bariatric surgery (MBS), initially for severe obesity (BMI ≥35 kg/m2), is performed globally over 500 000 times annually, offering significant metabolic benefits beyond weight loss. However, varying eligibility criteria globally impact patient care and healthcare resources. Updated in 2022, ASMBS and IFSO guidelines aim to standardise MBS indications, reflecting current understanding and emphasising comprehensive preoperative assessments. Yet, clinical variability persists, necessitating consensus‐based recommendations. This modified Delphi study engaged 45 global experts to establish consensus on perioperative management in MBS. Experts selected from bariatric societies possessed expertise in MBS and participated in a two‐round Delphi protocol. Consensus was achieved on 90 of 169 statements (53.3%), encompassing multidisciplinary team composition, patient selection criteria, preoperative testing, and referral pathways. The agreement highlighted the critical role of comprehensive preoperative assessments and the integration of healthcare professionals in MBS. These findings offer essential insights to standardise perioperative practices and advocate for evidence‐based guidelines in MBS globally. The study underscores the need for unified protocols to optimise outcomes and guide future research in MBS.