Growth hormone (GH) exerts complex effects on metabolism, cognition, and overall physiological well-being, and its secretion is highly regulated by the circadian clock. The Growth Hormone Receptor Knockout (GHR-KO) mouse, a model of GH insensitivity, characterized by chronically elevated circulating GH, provides a unique opportunity to examine the consequences of disrupted GH signaling during aging. Given the close relationship between circadian rhythmicity and GH secretion, we investigated sex- and genotype-specific differences in circadian activity, neuromuscular, and neurocognitive performance in aged GHR-KO mice under varying light-dark (LD), constant dark (DD) conditions, and subsequently examined the impact of circadian disruption (CD) on these parameters. Female GHR-wildtype (fGHR-WT) mice showed greater overall total activity, enhanced circadian activity amplitude, and less fragmented rhythms compared to female GHR-knockout (fGHR-KO) mice. In contrast, fGHR-KO mice demonstrated more rapid re-entrainment following phase shifts, indicating differences in circadian adaptation. Both fGHR-WT and fGHR-KO mice exhibited superior working memory compared to male littermates, while GHR-KO mice displayed enhanced neuromuscular performance compared with GHR-WT controls. CD reduced rhythm stability and was associated with declines in neuromuscular and cognitive performance, particularly in females, as well as increased open field (OF) immobility in fGHR-KO. Collectively, these findings indicate that aged GHR-WT and GHR-KO mice exhibit distinct circadian and behavioral phenotypes, and that disruption of circadian organization exacerbates cognitive and neuromuscular vulnerability.

Lower extremity deep vein thrombosis (DVT) is a serious complication in elderly patients with hip fracture, contributing to increased morbidity and mortality. Diabetes mellitus, with its prothrombotic state, may further elevate this risk. Early identification of high-risk patients is important for targeted thromboprophylaxis. The objective of the present study was to develop and validate machine learning (ML) models for predicting DVT using clinical variables available at admission in elderly patients with hip fracture, with a specific focus on diabetes as a key predictor. A retrospective cohort study of elderly patients (≥65 years) with hip fracture who were admitted to a tertiary academic medical center (Tianjin Hospital, China) between January 2020 and December 2025, was conducted. A total of seven ML algorithms were developed and validated using a 70-30 split with 10-fold cross-validation. Model interpretability was enhanced using Shapley Additive exPlanations (SHAP) analysis. Subgroup analyses were performed to evaluate model performance across diabetic and non-diabetic populations. DVT occurred in 16.5% (66/400) of patients during hospitalization. Diabetes was present in 32.5% of the cohort and was significantly associated with DVT (odds ratio=2.64; 95% CI: 1.56-4.48). The random forest model demonstrated an improved performance [area under the curve (AUC)=0.92; accuracy=0.87; sensitivity=0.85; specificity=0.88]. SHAP analysis identified diabetes-associated variables (hemoglobin A1c, diabetes duration and fasting glucose) among the top predictors, along with age, albumin, D-dimer and immobility on admission. Lipid parameters, including low-density lipoprotein cholesterol and triglycerides, also contributed to prediction. Model performance remained robust in diabetic (AUC=0.94) and non-diabetic (AUC=0.90) subgroups. Calibration was good (slope=1.02; intercept=-0.02; Brier score=0.09; Hosmer-Lemeshow P=0.45). Decision curve analysis determined clinical net benefit across thresholds of 15-40%. ML models, particularly random forest, accurately predicted DVT in patients with elderly hip fracture using routinely available admission data. Diabetes therefore emerged as a pivotal risk factor and its inclusion enhanced predictive accuracy. The present model holds promise for early risk stratification and individualized thromboprophylaxis, although rigorous external validation is warranted before any clinical application.

Catatonia is a neuropsychiatric syndrome with diverse etiologies and substantial morbidity in older adults, particularly when thromboembolic complications occur. While affective disorders are frequent precipitants, catatonia following cerebrovascular events remains under-recognized. A 71-year-old woman with recent multifocal ischemic stroke, vascular neurocognitive disorder and prior depression presented with a subacute syndrome of immobility and mutism. At initial outpatient evaluation, she exhibited marked catatonic signs (Bush-Francis Catatonia Rating Scale [BFCRS] 31/69). Lorazepam produced only partial improvement and was limited by sedation and dysarthria. Given pharmacological resistance and tolerability issues, electroconvulsive therapy (ECT) was initiated, with clinical improvement after 2 sessions. ECT was interrupted due to COVID-19 infection, accompanied by proximal deep vein thrombosis and pulmonary embolism managed with apixaban. Within 1 month of ECT interruption, catatonia symptoms reemerged. After multidisciplinary consultations, ECT was cautiously restarted. Eleven consecutive ECT sessions were then performed, with varying frequency due to confusional states emerging at 3 sessions per week. A subsequent mechanical fall, along with neuroimaging findings consistent with a subacute intracerebral hemorrhage, prompted early discontinuation of ECT. Catatonia relapsed 2 months after discontinuation. Repetitive transcranial magnetic stimulation (rTMS) was tried without effect, and ECT was restarted with success and maintained. Adjunctive memantine and mirtazapine were trialed without clear benefit, while suspected underlying post-stroke depression was treated with high-dose venlafaxine, which was continued throughout the entire clinical follow-up. This case highlights the challenges of managing severe, post-stroke catatonia in the context of venous thromboembolism, intracerebral hematoma as well as the benefits of a multidisciplinary approach.

Transurethral ultrasound ablation (TULSA) of the prostate is an innovative, minimally invasive, incision- and radiation-free procedure guided by MRI. Early clinical data suggest TULSA is associated with lower rates of common postoperative complications including urinary incontinence and erectile dysfunction. Its minimally invasive profile makes it especially suitable for elderly patients and those with elevated medico-surgical risks. We report the anaesthetic management of the first MRI-guided TULSA procedure performed in Southeast Asia on an elderly male patient in his 80s with prostatic adenocarcinoma. General anaesthesia was administered using a balanced technique combining low-dose volatile agent with continuous infusions of remifentanil and rocuronium. This approach ensured stable haemodynamics, adequate sedation and complete immobility throughout the procedure. To date, there are no established anaesthetic protocols for TULSA. Our experience demonstrates that the essential goals of immobility, sedation and rapid recovery can be effectively achieved.

Falls prevention is a cornerstone of quality improvement in the care of older adults, yet the metrics through which it is operationalised warrant scrutiny. Current quality frameworks in acute and post-acute settings overwhelmingly treat falls as discrete sentinel events while treating functional decline as a background condition unworthy of equivalent measurement. This commentary identifies this asymmetry as a distinct governance problem, which we term event-based safety governance: the systematic privileging of countable adverse events over longitudinal harms in quality oversight. In this context, governance refers to the regulatory, reporting and performance measurement systems that shape organisational accountability and clinical priorities. Previous authors have described the clinical tension between promoting mobility and preventing falls. We advance that discussion by arguing that the problem is not merely clinical but structural: the measurement architecture itself generates defensive practice by making falls visible to institutional accountability while rendering immobility-driven decline invisible. We propose that functional trajectory monitoring, embedded alongside fall event reporting, would correct this imbalance by making the harms of inactivity organisationally legible. Until quality measurement in the care of older adults accounts for what patients lose through enforced stillness, often an unintended consequence of system-level incentives, as rigorously as it accounts for what happens when they fall, clinical judgement will remain subordinate to institutional risk avoidance. We propose three practical reforms: routine functional trajectory monitoring, integration of mobility outcomes into organisational quality dashboards and explicit recognition of mobilisation as a safety-aligned clinical activity.