Prognostic significance of paramagnetic rim lesions in multiple sclerosis: A systematic review.

The diagnostic potential of paramagnetic rim lesions (PRLs) has been previously established; however, the prognostic significance of these lesions has not previously been consistently described. This study aimed to establish the prognostic role of PRLs in MS with respect to the Expanded Disability Status Scale (EDSS) and rates of disability progression. Databases of PubMed, EMBASE, Scopus and reference lists of selected articles were searched up to 29/04/2023. The review was conducted in accordance with PRISMA guidelines and was registered prospectively on PROSPERO (CRD42023422052). 7 studies were included in the final review. All of the eligible studies found that patients with PRLs tend to have higher baseline EDSS scores. Longitudinal assessments revealed greater EDSS progression in patients with PRLs over time in most studies. However, the effect of location of PRLs within the central nervous system were not assessed across the studies. Only one study investigated progression independent of relapse activity (PIRA) and showed that this clinical entity occurred in a greater proportion in patients with PRLs. This review supports PRLs as a predictor of EDSS progression. This measure has widespread applicability, however further multicentre studies are needed. Future research should explore the impact of PRLs on silent disability, PIRA, take into account different MS phenotypes and the topography of PRLs in prognosis.

Push or pull? Digital notification platform implementation reduces dysglycaemia.

Pushing selected information to clinicians, as opposed to the traditional method of clinicians pulling information from an electronic medical record, has the potential to improve care. A digital notification platform was designed by clinicians and implemented in a tertiary hospital to flag dysglycaemia. There were 112 patients included in the study, and the post-implementation group demonstrated lower rates of dysglycaemia (2.5% vs 1.1%, P = 0.038). These findings raise considerations for information delivery methods for multiple domains in contemporary healthcare.

Large language models can effectively extract stroke and reperfusion audit data from medical free-text discharge summaries.

INTRODUCTION: Audits are an integral part of effective modern healthcare. The collection of data for audits can be resource intensive. Large language models (LLM) may be able to assist. This pilot study aimed to assess the feasibility of using a LLM to extract stroke audit data from free-text medical documentation. METHOD: Discharge summaries from a one-month retrospective cohort of stroke admissions at a tertiary hospital were collected. A locally-deployed LLM, LLaMA3, was then used to extract a variety of routine stroke audit data from free-text discharge summaries. These data were compared to the previously collected human audit data in the statewide registry. Manual case note review was undertaken in cases of discordance. RESULTS: Overall, there was a total of 144 data points that were extracted (9 data points for each of the 16 patients). The LLM was correct in 135/144 (93.8%) of individual datapoints. This performance included binary categorical, multiple-option categorical, datetime, and free-text extraction fields. CONCLUSIONS: LLM may be able to assist with the efficient collection of stroke audit data. Such approaches may be pursued in other specialties. Future studies should seek to examine the most effective way to deploy such approaches in conjunction with human auditors and researchers.

A brief history of ramping.

'Ramping' is a commonly used term in contemporary Australian healthcare. It is also a part of the public and political zeitgeist. However, its precise definition varies among sources. In the published literature, there are distinctions between related terms, such as 'entry overload' and 'Patient Off Stretcher Time Delay'. How ramping is defined and how it came to be defined have significance for policies and procedures relating to the described phenomenon. Through examination of the history of the term, insights are obtained into the underlying issues contributing to ramping and, accordingly, associated possible solutions.

Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).

Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts. This approach was implemented in a tertiary hospital, and then compared to a historical cohort from the same time the previous year. There were 3990 patients included in the study. There was a significant increase in the proportion of inpatients who received weekend discharges in the intervention group compared to the control group (median 18%, IQR 18-20%, vs median 14%, IQR 12% to 17%, P = 0.031). There was a corresponding higher absolute number of weekend discharges during the intervention period compared to the control period (P = 0.025). The studied intervention was associated with an increase in weekend discharges and economic analyses support this approach as being cost-effective. Further studies are required to examine the generalizability of this approach to other centers.

Near-death experiences after cardiac arrest: a scoping review.

BACKGROUND: This scoping review aimed to characterise near-death experiences in the setting of cardiac arrest, a phenomenon that is poorly understood and may have clinical consequences. METHOD: PubMed/MEDLINE was searched to 23 July 2023 for prospective studies describing near-death experiences in cardiac arrest. PRISMA-ScR guidelines were adhered to. Qualitative and quantitative data were synthesised. Meta-analysis was precluded due to data heterogeneity. RESULTS: 60 records were identified, of which 11 studies involving interviews were included from various countries. Sample size ranged from 28-344, and proportion of female patients (when reported) was 0-50%, with mean age (when reported) ranging 54-64 years. Comorbidities and reasons for cardiac arrest were heterogeneously reported. Incidence of near-death experiences in the included studies varied from 6.3% to 39.3%; with variation between in-hospital (6.3-39.3%) versus out-of-hospital (18.9-21.2%) cardiac arrest. Individual variables regarding patient characteristics demonstrated statistically significant association with propensity for near-death experiences. Reported content of near-death experiences tended to reflect the language of the questionnaires used, rather than the true language used by individual study participants. Three studies conducted follow-up, and all suggested a positive life attitude change, however one found significantly higher 30-day all-cause mortality in patients with near-death experiences versus those without, in non-controlled analysis. CONCLUSIONS: From prospective studies that have investigated the phenomenon, near-death experiences may occur in as frequent as over one-third of patients with cardiac arrest. Lasting effects may follow these events, however these could also be confounded by clinical characteristics.

Subcutaneous sodium valproate in palliative care: A systematic review.

BACKGROUND: Seizures are an important palliative symptom, the management of which can be complicated by patients' capacity to swallow oral medications. In this setting, and the wish to avoid intravenous access, subcutaneous infusions may be employed. Options for antiseizure medications that can be provided subcutaneously may be limited. Subcutaneous sodium valproate may be an additional management strategy. AIM: To evaluate the published experience of subcutaneous valproate use in palliative care, namely with respect to effectiveness and tolerability. DESIGN: A systematic review was registered (PROSPERO CRD42023453427), conducted and reported according to PRISMA reporting guidelines. DATA SOURCES: The databases PubMed, EMBASE and Scopus were searched for publications until August 11, 2023. RESULTS: The searches returned 429 results, of which six fulfilled inclusion criteria. Case series were the most common study design, and most studies included <10 individuals who received subcutaneous sodium valproate. There were three studies that presented results on the utility of subcutaneous sodium valproate for seizure control, which described it to be an effective strategy. One study also described it as an effective treatment for neuropathic pain. The doses were often based on presumed 1:1 oral to subcutaneous conversion ratios. Only one study described a local site adverse reaction, which resolved with a change of administration site. CONCLUSIONS: There are limited data on the use of subcutaneous sodium valproate in palliative care. However, palliative symptoms for which subcutaneous sodium valproate have been used successfully are seizures and neuropathic pain. The available data have described few adverse effects, supporting its use with an appropriate degree of caution.

Renal disease is not associated with delays in hyperacute stroke management in South Australia.

OBJECTIVE: The aims of the present study were to determine how renal disease is associated with the time to receive hyperacute stroke care. METHODS: The present study involved a 5-year cohort of all patients admitted to stroke units in South Australia. RESULTS: In those with pre-existing renal disease there were no significant differences in the time taken to receive a scan, thrombolysis or endovascular thrombectomy. CONCLUSIONS: The present study shows that in protocolised settings there were no significant delays in hyperacute stroke management for patients with renal disease.

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.

OBJECTIVE: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation. METHODS: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria. RESULTS: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93. SIGNIFICANCE: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral. PLAIN LANGUAGE SUMMARY: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia.

Pre-stroke anticoagulation for atrial fibrillation in primary English speakers and non-primary English speakers: a multicentre retrospective cohort study.

BACKGROUND: Anticoagulation can prevent most strokes in individuals with atrial fibrillation (AF); however, many people presenting with stroke and known AF are not anticoagulated. Language barriers and poor health literacy have previously been associated with decreased patient medication adherence. The association between language barriers and initiation of anticoagulation therapy for AF is uncertain. AIMS: The aims of this study were to determine whether demographic factors, including non-English primary language, were (1) associated with not being initiated on anticoagulation for known AF prior to admission with stroke, and (2) associated with non-adherence to anticoagulation in the setting of known AF prior to admission with stroke. METHODS: A multicentre retrospective cohort study was conducted for consecutive individuals admitted to the three South Australian tertiary hospitals with stroke units over a 5-year period. RESULTS: There were 6829 individuals admitted with stroke. These cases included 5835 ischaemic stroke patients, 1333 of whom had pre-existing AF. Only 40.0% presenting with ischaemic stroke in the setting of known pre-existing AF were anticoagulated. When controlling for demographics, socioeconomic status and past medical history (including the components of the CHADS2VASC score and anticoagulation contraindications), having a primary language other than English was associated with a lower likelihood of having been commenced on anticoagulant for known pre-stroke AF (odds ratio: 0.52, 95% confidence interval: 0.36-0.77, P = 0.001), but was not associated with a differing likelihood of anticoagulation adherence. CONCLUSIONS: A significant proportion of patients with stroke have pre-existing unanticoagulated AF; these rates are substantially higher if the primary language is other than English. Targeted research and interventions to minimise evidence-treatment gaps in this cohort may significantly reduce stroke burden.

Beware of little expenses: Low-value endocrinological blood tests in geriatric medical inpatients.

OBJECTIVES: Blood tests for endocrinological derangements are frequently requested in general medical inpatients, in particular those in the older age group. Interrogation of these tests may present opportunities for healthcare savings. METHODS: This multicentre retrospective study over a 2.5-year period examined the frequency with which three common endocrinological investigations [thyroid stimulating hormone (TSH), HbA1c, 25-hydroxy Vitamin D3] were performed in this population, including the frequency of duplicate tests within a given admission, and the frequency of abnormal test results. The Medicare Benefits Schedule was used to calculate the cost associated with these tests. RESULTS: There were 28,564 individual admissions included in the study. Individuals ≥65 years old were the majority of inpatients in whom the selected tests were performed (80% of tests). TSH was performed in 6730 admissions, HbA1c was performed in 2259 admissions, and vitamin D levels were performed in 5632 admissions. There were 6114 vitamin D tests performed during the study period, of which 2911 (48%) returned outside the normal range. The cost associated with vitamin D level testing was $183,726. Over the study period, 8% of tests for TSH, HbA1c, and Vitamin D were duplicates (where a second test was performed within a single admission), which was associated with a cost of $32,134. CONCLUSIONS: Tests for common endocrinological abnormalities are associated with significant healthcare costs. Avenues by which future savings may be pursued include the investigation of strategies to reduce duplicate ordering and examining the rationale and guidelines associated with ordering tests such as vitamin D levels.

Artificial intelligence-enabled penicillin allergy delabelling: an implementation study.

Inaccurate penicillin allergy labels may be delabelled following evaluation. The intervention in this study was an email-based notification system regarding the appropriateness for penicillin allergy evaluation, with a view to delabelling, as identified by a deep learning artificial intelligence algorithm. Of the intervention group (n = 59), three (5.1%) individuals had their penicillin allergies delabelled, which was significantly more than the control group (0%, P = 0.002). Further research to optimise such approaches is required.