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.

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.

Delays in the diagnosis of ischaemic stroke presenting with persistent reduced level of consciousness: A systematic review.

INTRODUCTION: Stroke presenting with a reduced level of consciousness (RLOC) may result in diagnostic error and/or delay. Missed or delayed diagnosis of acute ischaemic stroke may preclude otherwise applicable hyperacute stroke interventions. The frequency, reasons for, and consequences of diagnostic error and delay due to RLOC are uncertain. METHOD: The databases PubMed, EMBASE, and Cochrane library were searched in adherence with the PRISMA guidelines. The systematic review was prospectively registered on PROSPERO. RESULTS: Initial searches returned 1162 results, of which 6 fulfilled inclusion criteria. The majority of identified studies show that ischaemic stroke presenting with RLOC is at increased risk of missed or delayed diagnosis. Hyperacute stroke interventions may also be delayed. There is limited evidence regarding the reason for these delays; however, the delays may result from neuroimaging delay associated with diagnostic uncertainty. There is also limited evidence regarding the outcomes of patients with stroke and RLOC who experience diagnostic delay; however, the available literature suggests that outcomes may be poor, including motor and cognitive impairment, as well as long-term impaired consciousness. The included studies did not evaluate, but have suggested urgent MRI access, educational interventions, and protocolisation of the evaluation of RLOC as means to reduce poor outcomes. CONCLUSIONS: Ischaemic stroke patients with RLOC are at risk of diagnostic delay and error. These patients may have poor outcomes. Additional research is required to identify the contributing factors more clearly and to provide amelioration strategies.

Implementation of the South Australian Regional Telestroke service is associated with improved care quality and lower stroke mortality: A retrospective cohort study.

INTRODUCTION: Stroke in Regional Australia may have worse outcomes due to difficulties accessing optimal care. The South Australian Regional Telestroke service aimed to improve telestroke neurologist access, supported by improved ambulance triage. OBJECTIVE: To assess stroke care quality and patient mortality pre- and postimplementation of a vascular neurologist-led Telestroke service. DESIGN: Historically controlled mixed methods cohort study comparing key quality indicators and patient mortality (6 months pre- vs. 18 months postimplementation date [4 June 2018]) at the three major South Australian regional stroke centres. The primary outcome was 13 care quality indicators as a combined composite risk-adjusted score, and the secondary outcome was risk-adjusted mortality at 12-month postadmission. FINDINGS: On an annualised basis, of 189 patients with stroke, more were admitted postintervention to the regional stroke centres than in the control period (158 [annualised rate 105.3, 95% CI 86.2-127.4] vs. 31 [annualised rate 62.0, 95% CI 47.5-79.5]) Baseline patient characteristics were similar in both periods. Post-implementation, median last-known-well time to presentation (3.5 h [IQR 1.6-17] vs. 2.0 [IQR 1-14]; p = 0.46) and door to needle times (121 min [IQR 97-144] vs. 90 [IQR 75-138]; p = 0.65) were not significantly lower but an improvement in the combined composite quality score was observed (0.069 [95% CI 0.004-0.134; p = 0.04]), reflecting individual improvements in some quality indicators. Mortality at 12-month postimplementation was substantially lower postimplementation (prechange 23% vs. postchange 13% [hazard ratio 0.58 (95% CI 0.44-0.76; p < 0.001)]). CONCLUSION: Implementation of a South Australian Regional Telestroke service was associated with improved care metrics and lower mortality.

Rational clinical approach to transient ischaemic attack, stroke and associated mimics: A retrospective cohort study.

OBJECTIVE: The present study was performed to identify the individual clinical features and risk factors most strongly associated with the diagnosis of transient neurological symptoms with a cerebrovascular cause (transient ischaemic attack (TIA) or stroke), as compared to common TIA mimics (including retinal ischaemia, migraine and seizure). METHODS: In a retrospective cohort study, consecutive patients presenting with transient neurological symptoms to TIA clinic in Royal Adelaide Hospital were included. Clinical features and risk factors were recorded in a standardised form, categorised into subgroups, and analysed using descriptive statistics and diagnostic performance indicators, such as sensitivity, specificity and likelihood ratios. RESULTS: For 218/1273 individuals diagnosed with stroke, the three features with the highest positive likelihood ratio were the presence of diffusion weighted imaging positive lesion on magnetic resonance imaging (23.66, 95% confidence interval (CI) 14.35-51.08), extracranial carotid atherosclerosis (3.98, 95% CI 1.19-6.87) and a history of peripheral vascular disease (3.33, 95% CI 1.64-6.27). For TIA, the three features with the highest positive likelihood ratio were extracranial carotid atherosclerosis (3.98, 95% CI 1.19-8.27), presence of atrial fibrillation (2.43, 95% CI 1.54-3.46) and pre-existing anticoagulant therapy (2.39, 95% CI 1.61-3.29). For stroke and TIA, the respective features with the lowest negative likelihood ratios were limb weakness (0.71, 95% CI 0.65-0.77) and hypertension (0.66, 95% CI 0.49-0.84). CONCLUSIONS: The present study demonstrated that specific clinical features and risk factors were associated with the final diagnosis at TIA clinic. These clinical features may assist with diagnosis of TIA in centres without access to a vascular neurologist.

Ultra-Long Transfers for Endovascular Thrombectomy-Mission Impossible?: The Australia-New Zealand Experience.

BACKGROUND: Endovascular thrombectomy (EVT) access in remote areas is limited. Preliminary data suggest that long distance transfers for EVT may be beneficial; however, the magnitude and best imaging strategy at the referring center remains uncertain. We hypothesized that patients transferred >300 miles would benefit from EVT, achieving rates of functional independence (modified Rankin Scale [mRS] score of 0-2) at 3 months similar to those patients treated at the comprehensive stroke center in the randomized EVT extended window trials and that the selection of patients with computed tomography perfusion (CTP) at the referring site would be associated with ordinal shift toward better outcomes on the mRS. METHODS: This is a retrospective analysis of patients transferred from 31 referring hospitals >300 miles (measured by the most direct road distance) to 9 comprehensive stroke centers in Australia and New Zealand for EVT consideration (April 2016 through May 2021). RESULTS: There were 131 patients; the median age was 64 [53-74] years and the median baseline National Institutes of Health Stroke Scale score was 16 [12-22]. At baseline, 79 patients (60.3%) had noncontrast CT+CT angiography, 52 (39.7%) also had CTP. At the comprehensive stroke center, 114 (87%) patients underwent cerebral angiography, and 96 (73.3%) proceeded to EVT. At 3 months, 62 patients (48.4%) had an mRS score of 0 to 2 and 81 (63.3%) mRS score of 0 to 3. CTP selection at the referring site was not associated with better ordinal scores on the mRS at 3 months (mRS median of 2 [1-3] versus 3 [1-6] in the patients selected with noncontrast CT+CT angiography, P=0.1). Nevertheless, patients selected with CTP were less likely to have an mRS score of 5 to 6 (odds ratio 0.03 [0.01-0.19]; P<0.01). CONCLUSIONS: In selected patients transferred >300 miles, there was a benefit for EVT, with outcomes similar to those treated in the comprehensive stroke center in the EVT extended window trials. Remote hospital CTP selection was not associated with ordinal mRS improvement, but was associated with fewer very poor 3-month outcomes.

Factors associated with delay to carotid endarterectomy for acute ischaemic stroke in South Australia: A multicentre retrospective cohort study.

BACKGROUND: The greatest benefits of carotid endarterectomy (CEA) accrue when performed within two weeks of acute ischaemic stroke (AIS) due to symptomatic carotid stenosis. Previous studies have identified multiple factors contributing to CEA delay. AIMS: To determine factors associated with delayed CEA in patients admitted to tertiary stroke centres within a major metropolitan region with AIS METHODS: In a retrospective cohort study, consecutive patients admitted to the tertiary hospitals with stroke units within South Australia (Lyell McEwin Hospital, Royal Adelaide Hospital and Flinders Medical Centre) between 2016 to 2020 were included. Univariable and multivariable logistic regression were used to identify individual factors associated with time from symptom onset to CEA of over two weeks. RESULTS: A total of 174 patients were included. The median time to CEA was 5 days (IQR 3-9.75). Delayed CEA beyond 14 days occurred in 28/174 (16%). Factors most associated with delayed CEA included presentation to a tertiary hospital without onsite Vascular Surgical Unit (OR 3.71, 95%CI 1.31-10.58), history of previous stroke (OR 3.38, 95% CI 1.11-9.84) and presenting NIHSS above 6 (OR 5.16, 95% CI 1.60-16.39). CONCLUSION: This study identified that presentation to a tertiary hospital without a Vascular Surgery Unit, history of previous stroke and presenting NIHSS above 6 were associated with delay to CEA in AIS patients in South Australia. Interventional studies aiming to improve the proportion of patients that receive CEA within 14 days are required.

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.

Introduction: Machine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps. Methods: Literature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance. Results: Sixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77-0.85, AUC range: 0.68-0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70-0.81, AUC range: 0.71-0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56-0.88, AUC range: 0.55-0.88) and one DL model (AUC=0.65, 95% CI: 0.62-0.68). Conclusions: Conventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524.

Cost-Effectiveness of Monitoring Patients Post-Stroke With Mobile ECG During the Hospital Stay.

Background The effectiveness of a nurse-led in-hospital monitoring protocol with mobile ECG (iECG) was investigated for detecting atrial fibrillation in patients post-ischemic stroke or post-transient ischemic attack. The study aimed to assess the cost-effectiveness of using iECG during the initial hospital stay compared with standard 24-hour Holter monitoring. Methods and Results A Markov microsimulation model was constructed to simulate the lifetime health outcomes and costs. The rate of atrial fibrillation detection in iECG and Holter monitoring during the in-hospital phase and characteristics of modeled population (ie, age, sex, CHA2DS2-VASc) were informed by patient-level data. Costs related to recurrent stroke, stroke management, medications (new oral anticoagulants), and rehabilitation were included. The cost-effectiveness analysis outcome was calculated as an incremental cost per quality-adjusted life-year gained. As results, monitoring patients with iECG post-stroke during the index hospitalization was associated with marginally higher costs (A$31 196) and greater benefits (6.70 quality-adjusted life-years) compared with 24-hour Holter surveillance (A$31 095 and 6.66 quality-adjusted life-years) over a 20-year time horizon, with an incremental cost-effectiveness ratio of $3013/ quality-adjusted life-years. Monitoring patients with iECG also contributed to lower recurrence of stroke and stroke-related deaths (140 recurrent strokes and 20 deaths avoided per 10 000 patients). The probabilistic sensitivity analyses suggested iECG is highly likely to be a cost-effective intervention (100% probability). Conclusions A nurse-led iECG monitoring protocol during the acute hospital stay was found to improve the rate of atrial fibrillation detection and contributed to slightly increased costs and improved health outcomes. Using iECG to monitor patients post-stroke during initial hospitalization is recommended to complement routine care.

Automated information extraction from free-text medical documents for stroke key performance indicators: a pilot study.

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification-based KPI and datetime field extraction was assessed. Using free-text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95-1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated.

Prospective and external validation of stroke discharge planning machine learning models.

Machine learning may be able to help with predicting factors that aid in discharge planning for stroke patients. This study aims to validate previously derived models, on external and prospective datasets, for the prediction of discharge modified Rankin scale (mRS), discharge destination, survival to discharge and length of stay. Data were collected from consecutive patients admitted with ischaemic or haemorrhagic stroke at the Royal Adelaide Hospital from September 2019 to January 2020, and at the Lyell McEwin Hospital from January 2017 to January 2020. The previously derived models were then applied to these datasets with three pre-defined cut-off scores (high-sensitivity, Youden's index, and high-specificity) to return indicators of performance including area under the receiver operator curve (AUC), sensitivity and specificity. The number of individuals included in the prospective and external datasets were 334 and 824 respectively. The models performed well on both the prospective and external datasets in the prediction of discharge mRS ≤ 2 (AUC 0.85 and 0.87), discharge destination to home (AUC 0.76 and 0.78) and survival to discharge (AUC 0.91 and 0.92). Accurate prediction of length of stay with only admission data remains difficult (AUC 0.62 and 0.66). This study demonstrates successful prospective and external validation of machine learning models using six variables to predict information relevant to discharge planning for stroke patients. Further research is required to demonstrate patient or system benefits following implementation of these models.

Machine learning in the prediction of medical inpatient length of stay.

Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previous studies on the application of machine learning to the prediction of total hospital inpatient LOS for medical patients. A review of machine learning in the prediction of total hospital LOS for medical inpatients was conducted using the databases PubMed, EMBASE and Web of Science. Of the 673 publications returned by the initial search, 21 articles met inclusion criteria. Of these articles the most commonly represented medical specialty was cardiology. Studies were also identified that had specifically evaluated machine learning LOS prediction in patients with diabetes and tuberculosis. The performance of the machine learning models in the identified studies varied significantly depending on factors including differing input datasets and different LOS thresholds and outcome metrics. Common methodological shortcomings included a lack of reporting of patient demographics and lack of reporting of clinical details of included patients. The variable performance reported by the studies identified in this review supports the need for further research of the utility of machine learning in the prediction of total inpatient LOS in medical patients. Future studies should follow and report a more standardised methodology to better assess performance and to allow replication and validation. In particular, prospective validation studies and studies assessing the clinical impact of such machine learning models would be beneficial.

Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.

Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. In the prediction of discharge within the next 2 days, when the algorithm was applied to prospective and external datasets, the area under the receiver operator curve for this task were 0.78 and 0.74, respectively. The performance in the prediction of discharge within the next 7 days was more limited (area under the receiver operator curve 0.68 and 0.67). This study has shown that in prospective and external validation datasets the previously derived deep learning algorithms have demonstrated moderate performance in the prediction of which patients will be discharged within the next 2 days. Future studies may seek to further refine or evaluate the effect of the implementation of such algorithms.

Correction: Two-year efficacy of varenicline tartrate and counselling for inpatient smoking cessation (STOP study): A randomized controlled clinical trial.

[This corrects the article DOI: 10.1371/journal.pone.0231095.].

Improving the accuracy of stroke clinical coding with open-source software and natural language processing.

Clinical coding is an important task, which is required for accurate activity-based funding. Natural language processing may be able to assist with improving the efficiency and accuracy of clinical coding. The aims of this study were to explore the feasibility of using natural language processing for stroke hospital admissions, employed with open-source software libraries, to aid in the identification of potentially misclassified (1) category of Adjacent Diagnosis Related Groups (ADRG), (2) the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) diagnoses, and (3) Diagnosis Related Groups (DRG). Data was collected for consecutive individuals admitted to the Royal Adelaide Hospital Stroke Unit over a five-month period for misclassification identification analysis. 152 admissions were included in the study. Using free-text discharge summaries, a random forest classifier correctly identified two cases classified as B70 ("Stroke and Other Cerebrovascular Disorders") that should be classified as B02 (having received endovascular thrombectomy). A regular expression-based analysis correctly identified 33 cases in which ataxia was present but was not coded. Two cases were identified that should have been classified as B70D, rather than B70A/B/C, based on transfer to another centre within five days of admission. A variety of techniques may be useful to help identify misclassifications in ADRG, ICD-10-AM and DRG codes. Such techniques can be implemented with open-source software libraries, and may have significant financial implications. Future studies may seek to apply open-source software libraries to the identification of misclassifications of all ICD-10-AM diagnoses in stroke patients.

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.

Nurse Led Smartphone Electrographic Monitoring for Atrial Fibrillation after Ischemic Stroke: SPOT-AF.

BACKGROUND AND PURPOSE: Paroxysmal atrial fibrillation (PAF) underlying acute stroke frequently evades detection by standard practice, considered to be a combination of routine electrocardiogram (ECG) monitoring, and 24-hour Holter recordings. We hypothesized that nurse-led in-hospital intermittent monitoring approach would increase PAF detection rate. METHODS: We recruited patients hospitalised for stroke/transient ischemic attack, without history of atrial fibrillation (AF), in a prospective multi-centre observational study. Patients were monitored using a smartphone-enabled handheld ECG (iECG) during routine nursing observations, and underwent 24-hour Holter monitoring according to local practice. The primary outcome was comparison of AF detection by nurse-led iECG versus Holter monitoring in patients who received both tests: secondary outcome was oral anticoagulant commencement at 3-month following PAF detection. RESULTS: One thousand and seventy-nine patients underwent iECG monitoring: 294 had iECG and Holter monitoring. AF was detected in 25/294 (8.5%) by iECG, and 8/294 (2.8%) by 24-hour Holter recordings (P<0.001). Median duration from stroke onset to AF detection for iECG was 3 days (interquartile range [IQR], 2 to 6) compared with 7 days (IQR, 6 to 10) for Holter recordings (P=0.02). Of 25 patients with AF detected by iECG, 11 were commenced on oral anticoagulant, compared to 5/8 for Holter. AF was detected in 8.8% (69/785 patients) who underwent iECG recordings only (P=0.8 vs. those who had both iECG and 24-hour Holter). CONCLUSIONS: Nurse-led in-hospital iECG surveillance after stroke is feasible and effective and detects more PAF earlier and more frequently than routine 24-hour Holter recordings. Screening with iECG could be incorporated into routine post-stroke nursing observations to increase diagnosis of PAF, and facilitate institution of guideline-recommended anticoagulation.

Stroke prognostication for discharge planning with machine learning: A derivation study.

Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data to predict the likely length of stay (LOS) for patients admitted with stroke. Secondary aims included the prediction of discharge modified Rankin Scale (mRS), in-hospital mortality, and discharge destination. In this study a retrospective dataset was used to develop and test a variety of machine learning models. The patients included in the study were all stroke admissions (both ischaemic stroke and intracerebral haemorrhage) at a single tertiary hospital between December 2016 and September 2019. The machine learning models developed and tested (75%/25% train/test split) included logistic regression, random forests, decision trees and artificial neural networks. The study included 2840 patients. In LOS prediction the highest area under the receiver operator curve (AUC) was achieved on the unseen test dataset by an artificial neural network at 0.67. Higher AUC were achieved using logistic regression models in the prediction of discharge functional independence (mRS ≤2) (AUC 0.90) and in the prediction of in-hospital mortality (AUC 0.90). Logistic regression was also the best performing model for predicting home vs non-home discharge destination (AUC 0.81). This study indicates that machine learning may aid in the prognostication of factors relevant to post-stroke discharge planning. Further prospective and external validation is required, as well as assessment of the impact of subsequent implementation.