Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.

Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.

Delayed return of bowel function after general surgery in South Australia.

Introduction: Reference ranges for determining pathological versus normal postoperative return of bowel function are not well characterised for general surgery patients. This study aimed to characterise time to first postoperative passage of stool after general surgery; determine associations between clinical factors and delayed time to first postoperative stool; and evaluate the association between delay to first postoperative stool and prolonged length of hospital stay.

Methods: This study included consecutive admissions at two tertiary hospitals across a two-year period whom underwent a range of general surgery operations.

Emergency laparotomy preoperative risk assessment tool performance: A systematic review.

Background: Preoperative assessment of risk for emergency laparotomy may enhance decision making with regards to urgency or perioperative critical care admission and promote a more informed consent process for patients. Accordingly, we aimed to assess the performance of risk assessment tools in predicting mortality after emergency laparotomy.

Methods: PubMed, Embase, the Cochrane Library and CINAHL were searched to 12 February 2022 for observational studies reporting expected mortality based on a preoperative risk assessment and actual mortality after emergency laparotomy.

Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis.

Background: Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.

Proactive deep learning-facilitated inpatient penicillin allergy delabelling: An implementation study.

Introduction: Erroneous penicillin allergy labels are associated with significant health and economic costs. This study aimed to determine whether deep learning-facilitated proactive consultation to facilitate delabelling may further enhance inpatient penicillin allergy delabelling.

Methods: This prospective implementation study utilised a deep learning-guided proactive consultation service, which utilized an inpatient penicillin allergy delabelling protocol.

Management of epilepsia partialis continua: A systematic review.

Purpose: Epilepsia partialis continua (EPC) is form of focal motor status epilepticus, with limited guidelines regarding effective pharmacological management. This systematic review aimed to describe previously utilized pharmacological management strategies for EPC, with a focus on patient outcomes.

Methods: A systematic review of the databases PubMed, EMBASE, and SCOPUS was performed from inception to May 2024.

The Adelaide Score: prospective implementation of an artificial intelligence system to improve hospital and cost efficiency.

Background: The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge.

Methods: A prospective implementation trial was conducted at the Lyell McEwin Hospital in South Australia. The Adelaide Score was added to existing human, artificial intelligence, and other technological infrastructure for the first 28 days of April 2024 (intervention), and outcomes were compared using parametric, non-parametric and health economic analyses, to those in the first 28 days of April 2023 (control).

Charting the course: a scoping review on telehealth research trends in Australia.

This review of telehealth research describes the landscape of Australian digital health and telehealth research from 1999 to 2022, focusing on outlining past, present and future trends. A scoping review was conducted using the Joanna Briggs Institute methodology and PRISMA extension for scoping reviews framework, which identified 495 primary research studies of digital or telehealth interventions aimed at improving health outcomes. Data were charted according to technological modality, health focus, professional representation, participant location, year and size.

Perioperative aspirin and coronary artery bypass graft surgery: An updated meta-analysis of randomized controlled trials.

Background: The decision to continue aspirin before elective coronary artery bypass graft surgery remains contentious because of competing thrombotic and bleeding risks. We performed a contemporary systematic review and meta-analysis to compare outcomes between patients undergoing coronary artery bypass grafting who stopped and continued aspirin before surgery.

Methods: PubMed, MEDLINE, and CENTRAL databases were searched from inception to 4 October 2023 for randomized controlled trials comparing patients undergoing coronary artery bypass grafting who continued preoperative aspirin with those who discontinued before surgery.

Impact of frailty, malnutrition and socioeconomic status on perioperative outcomes.

Background: Frailty, malnutrition and low socioeconomic status may mutually perpetuate each other in a self-reinforcing and interdependent manner. The intertwined nature of these factors may be overlooked when investigating impacts on perioperative outcomes. This study aimed to investigate the impact of frailty, malnutrition and socioeconomic status on perioperative outcomes.

Copying in medical documentation: developing an evidence-based approach.

Background: Electronic medical records (EMRs) provide multiple efficiencies in communication to clinicians. The ability to copy and paste text in an EMR can be useful; however, it also conveys a risk of inaccurate documentation. Studies in international settings have described such overuse of copying to result in 'note bloat', with the dilution of relevant clinical information and potential clinical detriment.

Direct oral anticoagulant assay utilization and associated bleeding events: a multi-center cohort study.

Background: There is a lack of evidence regarding direct oral anticoagulant (DOAC) assay plasma concentrations and their association with bleeding events or transfusion requirements. This multicenter study aimed to characterize the use and plasma levels of DOAC assays of anticoagulated patients who present to emergency with a bleeding event and their association with bleeding severity.

Methods: A multicenter retrospective cohort study of consecutive emergency bleeding presentations with a DOAC assay over a five-year period was conducted.

A Delayed Wake-Up Call: Is Perioperative Sleep Contributing to Delayed Graft Function?

Renal transplantation offers dialysis-dependent patients a new lease on life. Compared with chronic dialysis, kidney transplant recipients benefit from a substantially improved quality and quantity of life. Despite increases in age and comorbidity among contemporary transplant recipients, recipients can expect substantial decreases in all-cause mortality, hospitalizations from infections (despite the required immunosuppression), and cardiovascular events.

Environmental impact of large language models in medicine.

The environmental impact of large language models (LLMs) in medicine spans carbon emission, water consumption and rare mineral usage. Prior-generation LLMs, such as GPT-3, already have concerning environmental impacts. Next-generation LLMs, such as GPT-4, are more energy intensive and used frequently, posing potentially significant environmental harms.

Medication shortage behaviour change with multidisciplinary clinician-designed digital notification intervention.

Objectives: To evaluate the effect of a clinician-designed digital notification system on the use of intravenous paracetamol during a medication shortage.

Methods: An in-house digital notification platform was designed through multidisciplinary collaboration. A 4-week pre- and post-implementation methodology was employed to evaluate the effect of the intervention.

Iso-lating optimal automated external defibrillator signage: An international survey.

Introduction: This study investigated the public's preference to a recognisable and meaningful signage for Automated External Defibrillators (AEDs) in alignment with ISO 7010 standards, aiming to identify improvements for better public awareness and response during out-of-hospital cardiac arrests (OHCA).

Methods: A survey was administered via SurveyMonkey® and Heart of the Nation's social media. The survey evaluated recognition of ISO signage colors and AED symbols, and preferences for alternative AED signs.

Evidence-Based Crossword Puzzles for Health Professions Education: A Systematic Review.

Unlabelled: Crossword puzzles have been utilised as a means of health professions education (HPE) gamification. A systematic review conducted in accordance with PRISMA guidelines was performed to evaluate the educational impact and describe the characteristics of crosswords in HPE contexts. Twenty-nine studies fulfilled inclusion criteria.