The electromechanical window for arrhythmia-risk assessment.

The electromechanical window (EMW) is calculated by subtracting the repolarization duration from a mechanical reference representing contraction duration in the same heartbeat (eg, aortic valve closure during echocardiography with simultaneous electrocardiography). Here, we review the current knowledge on the role of the EMW as an independent parameter for ventricular arrhythmia-risk stratification. We (1) provide a standardized approach to echocardiographic EMW assessment, (2) define relevant cutoff values for both abnormal EMW negativity and positivity, (3) discuss pathophysiological underpinnings of EMW negativity, and (4) outline the potential future role of cardiac electromechanical relations in patients with proarrhythmic conditions.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

Implementing Multifactorial Risk Assessment with Polygenic Risk Scores for Personalized Breast Cancer Screening in the Population Setting: Challenges and Opportunities.

Risk-stratified breast screening has been proposed as a strategy to overcome the limitations of age-based screening. A prospective cohort study was undertaken within the PERSPECTIVE I&I project, which will generate the first Canadian evidence on multifactorial breast cancer risk assessment in the population setting to inform the implementation of risk-stratified screening. Recruited females aged 40-69 unaffected by breast cancer, with a previous mammogram, underwent multifactorial breast cancer risk assessment. The adoption of multifactorial risk assessment, the effectiveness of methods for collecting risk factor information and the costs of risk assessment were examined. Associations between participant characteristics and study sites, as well as data collection methods, were assessed using logistic regression; all p-values are two-sided. Of the 4246 participants recruited, 88.4% completed a risk assessment, with 79.8%, 15.7% and 4.4% estimated at average, higher than average and high risk, respectively. The total per-participant cost for risk assessment was CAD 315. Participants who chose to provide risk factor information on paper/telephone (27.2%) vs. online were more likely to be older (p = 0.021), not born in Canada (p = 0.043), visible minorities (p = 0.01) and have a lower attained education (p < 0.0001) and perceived fair/poor health (p < 0.001). The 34.4% of participants requiring risk factor verification for missing/unusual values were more likely to be visible minorities (p = 0.009) and have a lower attained education (p ≤ 0.006). This study demonstrates the feasibility of risk assessment for risk-stratified screening at the population level. Implementation should incorporate an equity lens to ensure cancer-screening disparities are not widened.

[The algorithm for morphological assessment of malignant potential of adrenocortical tumors using mathematical modeling method]. / Algoritm morfologicheskoi otsenki zlokachestvennogo potentsiala adrenokortikal'nykh opukholei s ispol'zovaniem metoda matematicheskogo modelirovaniya.

OBJECTIVE: To develop the mathematical model with high sensitivity and specificity to assess the malignant potential of adrenal cortical tumors, which can be used to diagnose adrenocortical carcinoma (ACC) in adults. MATERIAL AND METHODS: Pathomorphological examination of surgical and consultative material of adrenocortical neoplasms was carried out. All cases were verified according to the WHO Classification of adrenal gland tumors (5th ed., 2022), the tumor's histogenesis was confirmed by immunohistochemical examination. Statistical analysis of the histological and immunohistochemical factors in terms of their value in relation to the diagnosis of ACC was carried out on Python 3.1 in the Google Colab environment. ROC analysis was used to identify critical values of predictors. The cut-off point was selected according to the Youden`s index. Logistic regression analysis using l1-regularisation was performed. To validate the model, the initial sample was divided into training and test groups in the ratio of 9:1, respectively. RESULTS: The study included 143 patients divided into training (128 patients) and test (15 patients) samples. A prognostic algorithm was developed, which represent a diagnostically significant set of indicators of the currently used Weiss scale. The diagnosis is carried out in 3 stages. This mathematical model showed 100% accuracy (95% CI: 96-100%) on the training and test samples. CONCLUSION: The developed algorithm could solve the problem of subjectivity and complexity in the interpretation of some of the criteria of current diagnostic algorithms. The new model is unique in that, unlike others, it allows verification of all morphological variants of ACC.

Creation of a Pediatric Sedation Risk Assessment Scoring System: A Novel Method to Stratify Risk.

This study aimed to create a pediatric sedation scoring system independent of the American Society of Anesthesiology Physical Status (ASA-PS) classification that is predictive of adverse events, facilitates objective stratification, and resource allocation. Multivariable regression and machine learning algorithm analysis of 134,973 sedation encounters logged in to the Pediatric Sedation Research Consortium (PSRC) database between July 2007 and June 2011. Patient and procedure variables were correlated with adverse events with resultant ß -regression coefficients used to assign point values to each variable. Point values were then summed to create a risk assessment score. Validation of the model was performed with the 2011 to 2013 PSRC database followed by calculation of ROC curves and positive predictive values. Factors identified and resultant point values are as follows: 1 point: age ≤ 6 months, cardiac diagnosis, asthma, weight less than 5th percentile or greater than 95 th , and computed tomography (CT) scan; 2 points: magnetic resonance cholangiopancreatography (MRCP) and weight greater than 99th percentile; 4 points: magnetic resonance imaging (MRI); 5 points: trisomy 21 and esophagogastroduodenoscopy (EGD); 7 points: cough at the time of examination; and 18 points: bronchoscopy. Sum of patient and procedural values produced total risk assessment scores. Total risk assessment score of 5 had a sensitivity of 82.69% and a specificity of 26.22%, while risk assessment score of 11 had a sensitivity of 12.70% but a specificity of 95.29%. Inclusion of ASA-PS value did not improve model sensitivity or specificity and was thus excluded. Higher risk assessment scores predicted increased likelihood of adverse events during sedation. The score can be used to triage patients independent of ASA-PS with site-specific cut-off values used to determine appropriate sedation resource allocation.

Assessment of risk factors for adverse events in analgosedation for pediatric endoscopy: A 10-year retrospective analysis.

OBJECTIVES: Data regarding the occurrence of complications specifically during pediatric anesthesia for endoscopic procedures is limited. By evaluating such data, factors could be identified to assure proper staffing and preparation to minimize adverse events and improve patient safety during flexible endoscopy. METHODS: This retrospective cohort study included children undergoing anesthesia for gastroscopy, colonoscopy, bronchoscopy, or combined endoscopic procedures over 10-year period. The primary study aim was to evaluate the incidence of complications and identify risk factors for adverse events. RESULTS: Overall, 2064 endoscopic procedures including 1356 gastroscopies (65.7%), 93 colonoscopies (4.5%), 235 bronchoscopies (11.4%), and 380 combined procedures (18.4%) were performed. Of the 1613 patients, 151 (7.3%) patients exhibited an adverse event, with respiratory complications being the most common (65 [3.1%]). Combination of gastrointestinal endoscopies did not lead to an increased adverse event rate (gastroscopy: 5.5%, colonoscopy: 3.2%). Diagnostic endoscopy as compared to interventional had a lower rate. If bronchoscopy was performed, the rate was similar to that of bronchoscopy alone (19.5% vs. 20.4%). Age < 5.8 years or body weight less than 20 kg, bronchoscopy, American Society of Anesthesiologists status ≥ 2 or pre-existing anesthesia-relevant diseases, and urgency of the procedure were independent risk factors for adverse events. For each risk factor, the risk for events increased 2.1-fold [1.8-2.4]. CONCLUSIONS: This study identifies multiple factors that increase the rate of adverse events associated anesthesia-based endoscopy. Combined gastrointestinal procedures did not increase the risk for adverse events while combination of bronchoscopy to gastrointestinal endoscopy showed a similar risk as bronchoscopy alone.

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.

Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

A preoperative risk assessment tool for predicting adverse outcomes among total shoulder arthroplasty patients.

BACKGROUND: With the increased utilization of Total Shoulder Arthroplasty (TSA) in the outpatient setting, understanding the risk factors associated with complications and hospital readmissions becomes a more significant consideration. Prior developed assessment metrics in the literature either consisted of hard-to-implement tools or relied on postoperative data to guide decision-making. This study aimed to develop a preoperative risk assessment tool to help predict the risk of hospital readmission and other postoperative adverse outcomes. METHODS: We retrospectively evaluated the 2019-2022(Q2) Medicare fee-for-service inpatient and outpatient claims data to identify primary anatomic or reserve TSAs and to predict postoperative adverse outcomes within 90 days postdischarge, including all-cause hospital readmissions, postoperative complications, emergency room visits, and mortality. We screened 108 candidate predictors, including demographics, social determinants of health, TSA indications, prior 12-month hospital, and skilled nursing home admissions, comorbidities measured by hierarchical conditional categories, and prior orthopedic device-related complications. We used two approaches to reduce the number of predictors based on 80% of the data: 1) the Least Absolute Shrinkage and Selection Operator logistic regression and 2) the machine-learning-based cross-validation approach, with the resulting predictor sets being assessed in the remaining 20% of the data. A scoring system was created based on the final regression models' coefficients, and score cutoff points were determined for low, medium, and high-risk patients. RESULTS: A total of 208,634 TSA cases were included. There was a 6.8% hospital readmission rate with 11.2% of cases having at least one postoperative adverse outcome. Fifteen covariates were identified for predicting hospital readmission with the area under the curve of 0.70, and 16 were selected to predict any adverse postoperative outcome (area under the curve = 0.75). The Least Absolute Shrinkage and Selection Operator and machine learning approaches had similar performance. Advanced age and a history of fracture due to orthopedic devices are among the top predictors of hospital readmissions and other adverse outcomes. The score range for hospital readmission and an adverse postoperative outcome was 0 to 48 and 0 to 79, respectively. The cutoff points for the low, medium, and high-risk categories are 0-9, 10-14, ≥15 for hospital readmissions, and 0-11, 12-16, ≥17 for the composite outcome. CONCLUSION: Based on Medicare fee-for-service claims data, this study presents a preoperative risk stratification tool to assess hospital readmission or adverse surgical outcomes following TSA. Further investigation is warranted to validate these tools in a variety of diverse demographic settings and improve their predictive performance.

Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: a cost-effectiveness study.

Background: Nasopharyngeal carcinoma (NPC) has an extremely high incidence rate in Southern China, resulting in a severe disease burden for the local population. Current EBV serologic screening is limited by false positives, and there is opportunity to integrate polygenic risk scores for personalized screening which may enhance cost-effectiveness and resource utilization. Methods: A Markov model was developed based on epidemiological and genetic data specific to endemic areas of China, and further compared polygenic risk-stratified screening [subjects with a 10-year absolute risk (AR) greater than a threshold risk underwent EBV serological screening] to age-based screening (EBV serological screening for all subjects). For each initial screening age (30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, and 65-69 years), a modeled cohort of 100,000 participants was screened until age 69, and then followed until age 79. Results: Among subjects aged 30 to 54 years, polygenic risk-stratified screening strategies were more cost-effective than age-based screening strategies, and almost comprised the cost-effectiveness efficiency frontier. For men, screening strategies with a 1-year frequency and a 10-year absolute risk (AR) threshold of 0.7% or higher were cost-effective, with an incremental cost-effectiveness ratio (ICER) below the willingness to pay (¥203,810, twice the local per capita GDP). Specifically, the strategies with a 10-year AR threshold of 0.7% or 0.8% are the most cost-effective strategies, with an ICER ranging from ¥159,752 to ¥201,738 compared to lower-cost non-dominated strategies on the cost-effectiveness frontiers. The optimal strategies have a higher probability (29.4-35.8%) of being cost-effective compared to other strategies on the frontier. Additionally, they reduce the need for nasopharyngoscopies by 5.1-27.7% compared to optimal age-based strategies. Likewise, for women aged 30-54 years, the optimal strategy with a 0.3% threshold showed similar results. Among subjects aged 55 to 69 years, age-based screening strategies were more cost-effective for men, while no screening may be preferred for women. Conclusion: Our economic evaluation found that the polygenic risk-stratified screening could improve the cost-effectiveness among individuals aged 30-54, providing valuable guidance for NPC prevention and control policies in endemic areas of China.

Usefulness of risk assessment tools in predicting hemodynamic outcome after balloon pulmonary angioplasty: a comparative analysis.

OBJECTIVES: Several parameters of widely used risk assessment tools for pulmonary arterial hypertension (PAH) have been linked to hemodynamic outcomes of balloon pulmonary angioplasty (BPA). Therefore, we aimed to determine whether these risk assessment tools could be used to predict hemodynamic outcomes following BPA. METHODS: In this retrospective study, we included 139 patients with chronic thromboembolic pulmonary hypertension who had undergone BPA at Center for Pulmonary Vascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College (Beijing, China). We compared the accuracies of seven well-validated risk assessment tools for predicting hemodynamic outcomes following BPA. A favorable hemodynamic outcome was defined as a mean pulmonary arterial pressure < 30 mmHg at follow-up. RESULTS: The baseline risk profiles varied significantly among the risk assessment tools. The US Registry to Evaluate Early and Long-Term PAH Disease Management risk scales and the French risk assessment tools rated most patients as high-risk, while the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) series and laboratory examination-based risk scales categorized most patients as having intermediate-risk profile. COMPERA 2.0 (4-strata) exhibited the highest predictive power among all risk stratifications. Noninvasive risk stratification (COMPERA 2.0 [3-strata]) showed a comparable predictive ability to that of invasive risk stratification (COMPERA 1.0) (area under the curve 0.649 vs. 0.648). Moreover, incorporating diffusing capacity of the lungs for carbon monoxide and tricuspid regurgitation velocity into COMPERA 2.0 (4-strata) further enhanced its predictive power (net reclassification index 0.153, 95% confidence interval 0.009-0.298, p = 0.038). Additionally, this refined COMPERA version had a high calibration accuracy (slope 0.96). CONCLUSION: Although the risk strata distribution varied among different risk assessment tools, the proportion of patients achieving favorable hemodynamics decreased with the escalation of risk stratification in most models. The well-validated risk assessment tools for PAH could also predict hemodynamic outcomes following BPA, and the refined COMPERA 2.0 model exhibited the highest predictive ability among these. Applying risk assessment tools before BPA can facilitate early identification of patients in need of closer monitoring and more intensive interventions, contributing to a better prognosis after BPA.

Signs or symptoms of suspected preeclampsia - A retrospective national database study of prevalence, costs, and outcomes.

BACKGROUND: Most patients with signs or symptoms (s/s) of suspected preeclampsia are not diagnosed with preeclampsia. We sought to determine and compare the prevalence of s/s, pregnancy outcomes, and costs between patients with and without diagnosed preeclampsia. METHODS: This retrospective cohort study analyzed a large insurance research database. Pregnancies with s/s of preeclampsia versus a confirmed preeclampsia diagnosis were identified using International Classification of Diseases codes. S/s include hypertension, proteinuria, headache, visual symptoms, edema, abdominal pain, and nausea/vomiting. Pregnancies were classed as 1) s/s of preeclampsia without a confirmed preeclampsia diagnosis (suspicion only), 2) s/s with a confirmed diagnosis (preeclampsia with suspicion), 3) diagnosed preeclampsia without s/s recorded (preeclampsia only), and 4) no s/s, nor preeclampsia diagnosis (control). RESULTS: Of 1,324,424 pregnancies, 29.2 % had ≥1 documented s/s of suspected preeclampsia, and 14.2 % received a preeclampsia diagnosis. Hypertension and headache were the most common s/s, leading 20.2 % and 9.2 % pregnancies developed to preeclampsia diagnosis, respectively. Preeclampsia, with or without suspicion, had the highest rates of hypertension-related severe maternal morbidity (HR [95 % CI]: 3.0 [2.7, 3.2] and 3.6 [3.3, 4.0], respectively) versus controls. A similar trend was seen in neonatal outcomes such as preterm delivery and low birth weight. Cases in which preeclampsia was suspected but not confirmed had the highest average total maternal care costs ($6096 [95 % CI: 602, 6170] over control). CONCLUSION: There is a high prevalence but poor selectivity of traditional s/s of preeclampsia, highlighting a clinical need for improved screening method and cost-effectiveness disease management.

The Evolution of Risk Assessment in Spine Surgery: A Narrative Review.

BACKGROUND: Risk assessment is critically important in elective and high-risk interventions, particularly spine surgery. This narrative review describes the evolution of risk assessment from the earliest instruments focused on general surgical risk stratification, to more accurate and spine-specific risk calculators that quantified risk, to the current era of big data. METHODS: The PubMed and SCOPUS databases were queried on October 11, 2023 using search terms to identify risk assessment tools (RATs) in spine surgery. A total of 108 manuscripts were included after screening with full-text review using the following inclusion criteria: 1) study population of adult spine surgical patients, 2) studies describing validation and subsequent performance of preoperative RATs, and 3) studies published in English. RESULTS: Early RATs provided stratified patients into broad categories and allowed for improved communication between physicians. Subsequent risk calculators attempted to quantify risk by estimating general outcomes such as mortality, but then evolved to estimate spine-specific surgical complications. The integration of novel concepts such as invasiveness, frailty, genetic biomarkers, and sarcopenia led to the development of more sophisticated predictive models that estimate the risk of spine-specific complications and long-term outcomes. CONCLUSIONS: RATs have undergone a transformative shift from generalized risk stratification to quantitative predictive models. The next generation of tools will likely involve integration of radiographic and genetic biomarkers, machine learning, and artificial intelligence to improve the accuracy of these models and better inform patients, surgeons, and payers.

Arrhythmic Mitral Valve Prolapse and Sports Activity: Pathophysiology, Risk Stratification, and Sports Eligibility Assessment.

Although mitral valve prolapse (MVP) is the most prevalent valvular abnormality in Western countries and generally carries a good prognosis, a small subset of patients is exposed to a significant risk of malignant ventricular arrhythmias (VAs) and sudden cardiac death (SCD), the so-called arrhythmic MVP (AMVP) syndrome. Recent work has emphasized phenotypical risk features of severe AMVP and clarified its pathophysiology. However, the appropriate assessment and risk stratification of patients with suspected AMVP remains a clinical conundrum, with the possibility of both overestimating and underestimating the risk of malignant VAs, with the inappropriate use of advanced imaging and invasive electrophysiology study on one hand, and the catastrophic occurrence of SCD on the other. Furthermore, the sports eligibility assessment of athletes with AMVP remains ill defined, especially in the grey zone of intermediate arrhythmic risk. The definition, epidemiology, pathophysiology, risk stratification, and treatment of AMVP are covered in the present review. Considering recent guidelines and expert consensus statements, we propose a comprehensive pathway to facilitate appropriate counseling concerning the practice of competitive/leisure-time sports, envisioning shared decision making and the multidisciplinary "sports heart team" evaluation of borderline cases. Our final aim is to encourage an active lifestyle without compromising patients' safety.

Dynamic assessment of CHA2DS2-VASc and HAS-BLED scores for predicting ischemic stroke and major bleeding in atrial fibrillation patients.

INTRODUCTION AND OBJECTIVES: Stroke and bleeding risks in atrial fibrillation (AF) are often assessed at baseline to predict outcomes years later. We investigated whether dynamic changes in CHA2DS2-VASc and HAS-BLED scores over time modify risk prediction. METHODS: We included patients with AF who were stable while taking vitamin K antagonists. During a 6-year follow-up, all ischemic strokes/transient ischemic attacks (TIAs) and major bleeding events were recorded. CHA2DS2-VASc and HAS-BLED were recalculated every 2-years and tested for clinical outcomes at 2-year periods. RESULTS: We included 1361 patients (mean CHA2DS2-VASc and HAS-BLED 4.0±1.7 and 2.9±1.2). During the follow-up, 156 (11.5%) patients had an ischemic stroke/TIA and 269 (19.8%) had a major bleeding event. Compared with the baseline CHA2DS2-VASc, the CHA2DS2-VASc recalculated at 2 years had higher predictive ability for ischemic stroke/TIA during the period from 2 to 4 years. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) showed improvements in sensitivity and better reclassification. The CHA2DS2-VASc recalculated at 4 years had better predictive performance than the baseline CHA2DS2-VASc during the period from 4 to 6 years, with an improvement in IDI and an enhancement of the reclassification. The recalculated HAS-BLED at 2-years had higher predictive ability than the baseline score for major bleeding during the period from 2 to 4 years, with significant improvements in sensitivity and reclassification. A slight enhancement in sensitivity was observed with the HAS-BLED score recalculated at 4 years compared with the baseline score. CONCLUSIONS: In AF patients, stroke and bleeding risks are dynamic and change over time. The CHA2DS2-VASc and HAS-BLED scores should be regularly reassessed, particularly for accurate stroke risk prediction.

Severe mental illness: cardiovascular risk assessment and management.

Patients with severe mental illness (SMI) including schizophrenia and bipolar disorder die on average 15-20 years earlier than the general population often due to sudden death that, in most cases, is caused by cardiovascular disease. This state-of-the-art review aims to address the complex association between SMI and cardiovascular risk, explore disparities in cardiovascular care pathways, describe how to adequately predict cardiovascular outcomes, and propose targeted interventions to improve cardiovascular health in patients with SMI. These patients have an adverse cardiovascular risk factor profile due to an interplay between biological factors such as chronic inflammation, patient factors such as excessive smoking, and healthcare system factors such as stigma and discrimination. Several disparities in cardiovascular care pathways have been demonstrated in patients with SMI, resulting in a 47% lower likelihood of undergoing invasive coronary procedures and substantially lower rates of prescribed standard secondary prevention medications compared with the general population. Although early cardiovascular risk prediction is important, conventional risk prediction models do not accurately predict long-term cardiovascular outcomes as cardiovascular disease and mortality are only partly driven by traditional risk factors in this patient group. As such, SMI-specific risk prediction models and clinical tools such as the electrocardiogram and echocardiogram are necessary when assessing and managing cardiovascular risk associated with SMI. In conclusion, there is a necessity for differentiated cardiovascular care in patients with SMI. By addressing factors involved in the excess cardiovascular risk, reconsidering risk stratification approaches, and implementing multidisciplinary care models, clinicians can take steps towards improving cardiovascular health and long-term outcomes in patients with SMI.

Construction of an instrument to enable the assessment of the risk of falls in older outpatients: A quantitative methodological study.

OBJECTIVES: To develop an instrument to facilitate the risk assessment of falls in older outpatients. DESIGN: A quantitative methodological study using the cross-sectional data. METHODS: This study enrolled 1988 older participants who underwent comprehensive geriatric assessment (CGA) in an outpatient clinic from May 2020 to November 2022. The history of any falls (≥1 falls in a year) and recurrent falls (≥2 falls in a year) were investigated. Potential risk factors of falls were selected by stepwise logistic regression, and a screening tool was constructed based on nomogram. The tool performance was compared with two reference tools (Fried Frailty Phenotype; CGA with 10 items, CGA-10) by using receiver operating curves, sensitivity (Sen), specificity (Spe), and area under the curve (AUC). RESULTS: Age, unintentional weight loss, depression measured by the Patient Health Questionnaire-2, muscle strength measured by the five times sit-to-stand test, and stand balance measured by semi- and full-tandem standing were the most important risk factors for falls. A fall risk screening tool was constructed with the six measurements (FRST-6). FRST-6 showed the best AUC (Sen, Spe) of 0.75 (Sen = 0.72, Spe = 0.69) for recurrent falls and 0.65 (Sen = 0.74, Spe = 0.48) for any falls. FRST-6 was comparable to CGA-10 and outperformed FFP in performance. CONCLUSIONS: Age, depression, weight loss, gait, and balance were important risk factors of falls. The FRST-6 tool based on these factors showed acceptable performance in risk stratification. IMPACT: Performing a multifactorial assessment in primary care clinics is urgent for falls prevention. The FRST-6 provides a simple and practical way for falls risk screening. With this tool, healthcare professionals can efficiently identify patients at risk of falling and make appropriate recommendations in resource-limited settings. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution was received, due to our study design.

A deep patient-similarity learning framework for the assessment of diastolic dysfunction in elderly patients.

AIMS: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS AND RESULTS: A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups. CONCLUSION: Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.

A novel risk assessment tool for postoperative delirium in vascular surgery: The stress model (Siena posTopeRative dElirium in vaScular Surgery).

OBJECTIVE: Postoperative delirium (POD) is a common complication with a high health-related impact. The creation of a model (Siena posTopeRative dElirium in vaScular Surgery) to identify high-risk patients with consecutive prompt diagnosis and correct management. METHODS: This is an observational retrospective study to evaluate POD incidence in patients who underwent elective vascular surgery procedures between 2018 and 2020. POD was detected using CAM and defined as the onset of an acute confusional state, clinically manifesting as a disturbed state of consciousness, cognitive dysfunction, or alteration in perception and behavior. The total population was divided in the development and validation subsamples. Multivariable logistic regression analysis was performed, identifying variables related to the occurrence of POD. An additive score was created and the STRESS score was internally validated using the Validation subgroup. RESULTS: A total of 1067 patients were enrolled. POD occurred in 111 cases (10.4%). Multivariable logistic regression analysis for POD occurrence revealed as significant predictors: age>75 years, CKD, dyslipidaemia, psychiatric disease, CAD, hospitalization in the previous month, preoperative NLR >3.59, preoperative Hb < 12 g/dl, preoperative Barthel score <75, major amputation, CLTI revascularization, general anesthesia, and postoperative urinary catheter. These variables were used to create the STRESS score. The model was applied to both development and validation subgroups; AUC was respectively 0.7079 (p < .0001) and 0.7270 (p < .0001). CONCLUSION: The STRESS score has a good predictive potentiality for POD occurrence in elective vascular surgery procedures. However, implementation and external validation are needed to be correctly used in everyday clinical practice.

Utilization of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for noninvasive assessment of chronic kidney disease in type 2 diabetes.

BACKGROUNDS: Accumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD). OBJECTIVES: We aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD. METHODS: Total 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance. RESULTS: All enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10-6 mm2/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (ß = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (ß = -3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10-6 mm2/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI. CONCLUSIONS: ΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10-6 mm2/s and <66 × 10-6 mm2/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.