eLibraryThe Prosentient digital repository system captures, stores, indexes, preserves, and distributes digital research material.http://swslhd.intersearch.com.au:80/swslhdjspui2026-05-10T17:09:47Z2026-05-10T17:09:47ZHidden in plain sight: under-recognized risks of dementia and underweight in younger acute myocardial infarction patientsZhao, E.Radhakrishnan, S.Serafimovska, A.https://swslhd.intersearch.com.au/swslhdjspui/handle/1/142512026-02-26T01:01:53Z2025-01-01T00:00:00ZTitle: Hidden in plain sight: under-recognized risks of dementia and underweight in younger acute myocardial infarction patients
Authors: Zhao, E.; Radhakrishnan, S.; Serafimovska, A.2025-01-01T00:00:00ZWill Dynamic Evaluation of Cardiogenic Shock Using Machine Learning Models Lead to Improved Survival?Goel, V.William, W.Tan, J.Lo, S.Nelson, A. J.Stub, D.Chew, D.https://swslhd.intersearch.com.au/swslhdjspui/handle/1/142502026-02-27T00:41:36Z2026-01-01T00:00:00ZTitle: Will Dynamic Evaluation of Cardiogenic Shock Using Machine Learning Models Lead to Improved Survival?
Authors: Goel, V.; William, W.; Tan, J.; Lo, S.; Nelson, A. J.; Stub, D.; Chew, D.
Abstract: Cardiogenic shock (CS) is characterised by tissue hypoxia as a result of circulatory failure arising from inadequate cardiac output and is commonly a complication of acute myocardial infarction (AMI). Despite improvement in reperfusion strategies for AMI, the survival among patients with CS remains poor. While mechanical circulatory support (MCS) technologies in AMI-CS offer promise, they have not translated to consistent improvements in patient survival, which may reflect an inability to recognise evolving CS at a reversible stage. Hence, reducing the mortality from CS requires solutions focused on timely diagnosis. CS is heterogenous, being dependent on interpreting acute haemodynamics and biomarkers, which often delays diagnosis and intervention. The continued digitisation of health information, particularly within the emergency and acute care environments has made the development of artificial intelligence (AI)-driven diagnostic decision support for the acutely deteriorating patient feasible. Such approaches have been effectively deployed in hospitals to alert frontline staff or ?shock teams? to patient deterioration, with evidence of reductions in mortality. Further, these integrated systems that can ?dynamically phenotype? patients and their clinical deterioration within the flow of data not only support clinical decision-making but also allow the establishment of virtual clinical registries assimilated within real-world practice, continuously evaluating clinical practice and outcomes. This review aims to delineate CS pathophysiology, limitations within our current diagnostic approach, understand the difficulties in conducting randomised clinical trials and explores the role of an integrated AI-based approach for early diagnosis and intervention in patients with CS. � 2025 The Author(s)2026-01-01T00:00:00ZViral infections as triggers in autoimmune blistering diseases: A systematic review of associations and mechanismsTseng, H.Frew, J. W.Murrell, D. F.https://swslhd.intersearch.com.au/swslhdjspui/handle/1/142492026-02-27T00:35:57Z2026-01-01T00:00:00ZTitle: Viral infections as triggers in autoimmune blistering diseases: A systematic review of associations and mechanisms
Authors: Tseng, H.; Frew, J. W.; Murrell, D. F.2026-01-01T00:00:00ZUtilizing risk prediction models for older patients with chronic kidney diseaseSiriwardana, A.Tangri, N.Neuen, B. L.Jardine, M. J.Foote, C.Gallagher, M.https://swslhd.intersearch.com.au/swslhdjspui/handle/1/142482026-02-27T00:32:46Z2026-01-01T00:00:00ZTitle: Utilizing risk prediction models for older patients with chronic kidney disease
Authors: Siriwardana, A.; Tangri, N.; Neuen, B. L.; Jardine, M. J.; Foote, C.; Gallagher, M.
Abstract: Older patients represent the most rapidly growing age group presenting with kidney failure. Despite this high incidence, the rate of progression to kidney failure tends to be slower in older individuals, and the competing risk of death before the development of kidney failure is a more significant consideration in older patients compared with younger counterparts. Incorporating these concepts of risk is challenging in shared decision-making discussions between clinicians, older patients, and their families. Risk prediction models are rapidly increasing in nephrology and have the potential to provide personalized absolute risk prediction for patients with advanced chronic kidney disease; however, there are several considerations of these models relevant to older patient populations. This review discusses metrics for assessing risk prediction models, examines key models for predicting kidney failure and mortality risk in older patients with advanced chronic kidney disease, and provides guidance for how best to interpret and use these models to support personalized decision-making processes with older patients. � 2025 International Society of Nephrology2026-01-01T00:00:00Z