Jonathan Afilalo

Background: (Pre)frail older adult patients undergoing cardiac surgery experience disproportionate mortality and prolonged length of stay, underpinned by low skeletal muscle mass (SMM), which is central to the underlying frailty pathophysiology. Conventional methods for assessing SMM have well‐recognized limitations in this population. The D ₃ ‐creatine (D ₃ ‐Cr) dilution method offers a non‐invasive, direct estimate of total‐body SMM. Pre‐surgery D ₃ ‐Cr derived SMM and its relationship with strength, physical performance and multidimensional health status measures in (pre)frail older adult patients undergoing cardiac surgery have not been explored. Methods: Seventeen (pre)frail older adult patients (16 male, 1 female; median 75 [interquartile range: 67–75] years; body mass index 28.7 [24.2–29.9] kg/m ² ; Clinical Frailty Scale 3 [3, 4]) ingested a single 30‐mg oral dose of D ₃ ‐Cr 3.5 [3.0–4.3] days prior to scheduled elective or non‐emergent (inpatient) cardiac surgery. Spot urine was collected pre‐dose and 48–72 h post‐dose, and D ₃ ‐creatinine (D ₃ ‐Crn) and unlabelled creatine and creatinine were quantified by liquid chromatography–tandem mass spectrometry. Total‐body SMM was estimated using a published spillage‐correction algorithm; both a height‐adjusted and body weight–adjusted SMM index were calculated as SMM divided by height squared and SMM divided by body weight. Spearman's rank correlations (r _s ) between strength, physical performance outcomes and multidimensional health status measures and (i) absolute SMM, (ii) height‐adjusted SMM index and (iii) body weight–adjusted SMM index were calculated; bias‐corrected and accelerated bootstrap (10 000 resamples) provided 95% confidence intervals (CIs). Kendall's τ was also calculated as a sensitivity analysis. Results: Median absolute total‐body SMM derived from D ₃ ‐Cr dilution was 31.7 [27.4–40.9] kg, and the corresponding height‐adjusted and body weight–adjusted SMM index was 10.8 [9.7–13.7] and 41.7 [36.1–47.4]. Handgrip strength positively associated with absolute SMM, r _s = 0.61 (95% CI [0.12, 0.91], n = 14, p = 0.02) and height‐adjusted SMM index, r _s = 0.55 (95% CI [0.03, 0.88], n = 14, p = 0.04), but not body weight–adjusted SMM index. The 5× sit‐to‐stand, 4‐m gait speed, balance, Short Physical Performance Battery score, length of stay, SF‐36 physical functioning score and Mini Nutritional Assessment score were not significantly associated with absolute, height‐adjusted or body weight–adjusted SMM (all p ≥ 0.10). Conclusions: The D ₃ ‐Cr dilution method can be applied for the pre‐operative assessment of SMM in (pre)frail older cardiac surgery patients. Handgrip strength tracked with both absolute and height‐adjusted D ₃ ‐Cr derived SMM, providing preliminary support for an association between D ₃ ‐Cr derived muscle mass and grip strength in older cardiac surgery patients.

Background: Sarcopenia is associated with mortality following acute ischemic stroke (AIS), but diagnosis is time-consuming. Computed tomography (CT) measures of temporal muscle volume (TMV) and density (TMD) can be automatically extracted from existing scans using deep learning (DL). This study sought to demonstrate the incremental value of DL-derived TMV and TMD from CT scans on AIS mortality and length of stay (LOS). Methods: In this retrospective, single-center cohort study, consecutive AIS patients admitted from 2014 to 2023 were included. TMV and TMD were quantified on admission CT scans by a novel DL model and represented as continuous or categorical variables. Patients were classified as non-sarcopenic, pre-sarcopenic (low TMV or TMD), or sarcopenic (low TMV and TMD) using 5th-percentile thresholds derived from 50 healthy adults. The primary outcome was 30-day all-cause mortality. Secondary outcomes were 365-day all-cause mortality and LOS. Multivariable logistic and linear regression were used. Results: The cohort consisted of 2285 patients with 1151 (50%) females, and a mean (SD) age of 74.7 (13.7) years. There were 877 (38%) non-sarcopenic, 838 (37%) pre-sarcopenic, and 570 (25%) sarcopenic patients. Adjusted ORs (95% CIs) for 30-day mortality were 2.70 (1.64-4.46) and 2.91 (1.72-4.91) for pre-sarcopenia and sarcopenia. Findings were consistent across secondary outcomes. The association between sarcopenia and mortality was preserved after adding the Hospital Frailty Risk Score (HFRS) to the models. Findings were consistent in sensitivity analyses using cohort-derived threshold definitions. Conclusions: TMV and TMD extracted using a novel DL model were incrementally predictive of AIS mortality.

Background: Sarcopenia is associated with mortality and morbidity following acute ischemic stroke (AIS), but the diagnosis requires specialized equipment or time-consuming assessments. Computed tomography (CT) measures of temporal muscle volume (TMV) and density (TMD) can be opportunistically measured from existing scans and automated using deep learning (DL). This study sought to demonstrate the incremental prognostic value of DL-derived TMV and TMD from CT scans on mortality and length of stay (LOS) in AIS. Methods: In this retrospective, single-centre cohort study, consecutive AIS patients admitted from 2014 to 2023 were included. Admission CT scans were retrieved alongside clinical data from electronic health records. TMV and TMD were quantified by a novel DL model and represented as continuous or trichotomous categorical variables. TMV and TMD thresholds were derived in a cohort of 50 healthy adults and used to classify AIS patients as non-sarcopenic, pre-sarcopenic, or sarcopenic. The primary outcome was 30-day all-cause mortality. Secondary outcomes were 365-day all-cause mortality and LOS. Multivariable logistic and linear regression were used. Results: The cohort consisted of 2285 patients with 1151 (50%) females, and a mean (SD) age of 74.7 (13.7) years. Based on TMV and TMD, 877 patients (38%) were non-sarcopenic, 838 (37%) pre-sarcopenic, and 570 (25%) sarcopenic. Adjusted ORs for 30-day mortality were 2.70 (1.64 to 4.46) and 2.91 (1.72 to 4.91) for pre-sarcopenia and sarcopenia. Adjusted ORs for 365-day mortality were 2.42 (95% CI 1.74 to 3.36) and 2.96 (95% CI 2.09 to 4.17) and the additional days in hospital were 2.79 (1.69 to 3.98) and 3.26 (2.00 to 4.64) for pre-sarcopenia and sarcopenia. The association between CT-derived sarcopenia and mortality was preserved after adding HFRS to the models. Conclusions: TMV and TMD extracted using a novel DL model were incrementally predictive of AIS mortality. These metrics may be used to refine risk estimates, inform shared decision-making, and individualize treatment plans.

Medical futility in the cardiovascular care of older adults presents unique challenges that necessitate a nuanced understanding of both clinical and ethical dimensions. This state-of-the-art review explores the evolving concept of futility in the context of an ageing patient population and expanding armamentarium purported to treat even the most dire of cardiovascular diseases. The first objective is to delineate a framework for clinicians to elicit the different dimensions of futility, their relative importance to the patient, and their potential for improvement with the intervention being considered. Definitions are elaborated for quantitative futility—interventions with statistically negligible benefits—and qualitative futility—interventions misaligned with patient-specific goals and values. The second objective is to highlight the determinants of prohibitive risk, from the cardiovascular disease and procedural morbidity, to competing non-cardiovascular risks and frailty. A distinction is made between frailty and futility, and manifestations of severe frailty are reviewed based on the A-B-C-D-E mnemonic. The third and final objective is to discuss strategies and actionable approaches to care for patients once futility has been invoked. In addition to ongoing compassionate dialogue with the patient and family members, early initiation and aggressive pursuit of palliative care measures is beneficial for symptom control and quality of life. Ultimately, informed shared decision-making with a patient-centered philosophy is essential to uphold dignity and enhance the quality of life for older adults facing complex cardiovascular conditions.

Ultrasound imaging is crucial in medical diagnostics, offering real-time visualization of internal anatomical structures. However, accurate automatic segmentation of ultrasound images remains challenging, particularly in scenarios with limited labeled data. In this paper, we propose a semi-supervised learning approach for ultrasound image segmentation, leveraging the statistics of data in unlabeled images to enhance segmentation accuracy. Our method builds upon the encoder-decoder architecture and incorporates innovative semi-supervised learning techniques based on contrastive learning. We have collected ultrasound images from 80 patients and 34 healthy volunteers, focusing on applications in sarcopenia assessment and emergency response scenarios. We demonstrate the effectiveness of our approach through extensive experiments on expert segmentations in this dataset.Our results demonstrate the superior performance of the proposed method across various training data splits (i.e., 1%, 5%, 10%, 20%, 30%, and 100%). While U-NET performed the best with 100% of the training data (i.e., 154 annotated images), the proposed method achieved comparable performance with only 10% of the data (i.e., 16 annotated images). Furthermore, statistical analysis confirmed that our method significantly outperforms existing models, including U-NET, CCT, and UniMatch, in most scenarios (i.e., training set splits). These findings highlight the robustness and efficiency of the proposed method, especially in environments where labeled data is scarce

Due to the aging population, focusing on healthy aging has become a global priority. Cardiovascular diseases (CVDs) and frailty, characterized by increased vulnerability to adverse stress and health events, interact synergistically in advanced age. In older adults, hip fractures are a frequent dramatic ‘life-transition’ event. Conditions such as arrhythmias, orthostatic hypotension, heart failure, peripheral artery disease, and adverse drug reactions may facilitate falls and thus bone fractures in older adults. Cardiovascular complications or the worsening of previous CVDs may increase the degree of frailty and disability following this surgery. The close relationship between older age, CVDs, frailty, and orthopaedic surgery leads to the need to focus on the various phases of interventions in a multidisciplinary approach. This document aims to provide practical support to prevent cardiovascular complications in older and frail patients undergoing hip procedures by suggesting specific assessments and interventions. In particular, in pre-operative care, the focus should be on the assessment and management of concomitant CVD and frailty, while immediate peri- and post-operative care should highlight specific concerns for anaesthesia, prevention, and management of thrombotic complications, specific nursing needs, including the prevention of infections and delirium, and the establishment of an integrated rehabilitation program focusing on CVDs and the risk of new falls, with a positive role for care-givers. Furthermore, by optimizing the ‘hip surgery pathway’ the objective is to help avoid the deterioration of health and loss of independence that often result from this surgery through the correct management of cardiovascular patients in this peculiar context.

Sarcopenia, the age-related loss of skeletal muscle mass, is a core component of frailty that is associated with functional decline and adverse health events in older adults. Unfortunately, the available tools to diagnose sarcopenia are often inaccessible or not user-friendly for clinicians. Point-of-care ultrasound (US) is a promising tool that has been used to image the quadriceps muscle and measure its thickness (QMT) as a diagnostic criterion for sarcopenia. This measurement can be challenging for clinicians, especially when performed at the bedside using handheld systems or phased-array probes not designed for this use case. In this paper, we sought to automate this measurement using deep learning methods to improve its accuracy, reliability, and speed in the hands of untrained clinicians. In the proposed framework, which aids in better training, particularly when limited data are available, convolutional and transformer-based deep learning models with generic or data-driven pre-trained weights were compared. We evaluated regression (QMT as a continuous output in cm) and classification (QMT as an ordinal output in 0.5 cm bins) approaches, and in the latter, activation maps were generated to interpret the anatomical landmarks driving the model predictions. Finally, we evaluated a segmentation approach to derive QMT. The results showed that both transformer-based models and convolutional neural networks benefit from the proposed framework in estimating QMT. Additionally, the activation maps highlighted the interface between the femur bone and the quadriceps muscle as a key anatomical landmark for accurate predictions. The proposed framework is a pivotal step to enable the application of US-based measurement of QMT in large-scale clinical studies seeking to validate its diagnostic performance for sarcopenia, alone or with ancillary criteria assessing muscle quality or strength. We believe that implementing the proposed framework will empower clinicians to conveniently diagnose sarcopenia in clinical settings and accordingly personalize the care of older patients, leading to improved patient outcomes and a more efficient allocation of healthcare resources.