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Objectives Comprehensive smokefree laws, as recommended by the WHO Framework Convention of Tobacco Control (WHO FCTC), are the most effective tool to protect the population from secondhand smoke (SHS) and to ensure healthy environments. Studies evaluating how laws govern SHS protection are scarce. This study assessed the level of protection from SHS of laws from countries belonging to the WHO European Region. Methods A new methodology system was developed to evaluate the smokefree legislation according to the principles provided by the WHO guidelines for the correct implementation of Article 8 of the FCTC. For each law, six main sectors and 28 facilities were evaluated. Results Overall 68 laws from 48 countries from the WHO European Region were reviewed. 'Education' and 'Public transport' were the most protected sectors from SHS. Many WHO European laws do not provide protection from SHS across all public sectors. For example, 48.5% of general health facilities and 71.2% of restaurants are unprotected from SHS. The level of protection provided in the 28 facilities studied was low; many WHO European laws still allow smoking under certain conditions, permitting smoking in designated and/or ventilated areas. Conclusions Nine years after the adoption of the WHO FCTC there are still legal formulas in which smoking is allowed in several facilities, through the inclusion of separated areas, ventilated areas and other conditions. Tobacco control efforts still face the challenge of eradicating the legal clauses that prevent 100% smokefree environments. [ABSTRACT FROM AUTHOR]

Background Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention. Methods We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43xx1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. Results ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. Conclusions ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of cardiology, its applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The placement of cervical and intracranial stents requires the administration of antiplatelet drugs to prevent thromboembolic complications. Ticagrelor has emerged as the most widely used alternative in clopidogrel non- responders owing to its potent antiplatelet effects. Because ticagrelor does not require hepatic activation, many neurointerventionalists choose to forgo laboratory testing of platelet inhibition. In rare instances, patients may not achieve adequate platelet inhibition following ticagrelor administration. In this paper we review the mechanism of action of ticagrelor and its use in cerebrovascular procedures. We present two cases of ticagrelor non- responsiveness from two high- volume cerebrovascular centers, discuss their management, and propose an algorithm for managing ticagrelor nonresponsiveness. [ABSTRACT FROM AUTHOR]

Introduction After aneurysmal subarachnoid hemorrhage (SAH), both proximal and distal cerebral vasospasm can contribute to the development of delayed cerebral ischemia. Intra-arterial (IA) vasodilators are a mainstay of treatment for distal arterial vasospasm, but no methods of assessing the efficacy of interventions in real time have been established. Objective To introduce a new method for continuous intraprocedural assessment of endovascular treatment for cerebral vasospasm. Methods The premise of our approach was that distal cerebral arterial changes induce a consistent pattern in the morphological changes of intracranial pressure (ICP) pulse. This premise was demonstrated using a published algorithm in previous papers. In this study, we applied the algorithm to calculate the likelihood of cerebral vasodilation (VDI) and cerebral vasoconstriction (VCI) from intraprocedural ICP signals that are synchronized with injection of the IA vasodilator, verapamil. Cerebral blood flow velocities (CBFVs) on bilateral cerebral arteries were studied before and after IA therapy. Results 192 recordings of patients with SAH were reviewed, and 27 recordings had high-quality ICP waveforms. The VCI was significantly lower after the first verapamil injection (0.47±0.017) than VCI at baseline (0.49±0.020, p<0.001). A larger dose of injected verapamil resulted in a larger and longer VDI increase. CBFV of the middle cerebral artery increases across the days before the injection of verapamil and decreases after IA therapy. Conclusion This study provides preliminary validation of an algorithm for continuous assessment of distal cerebral arterial changes in response to IA vasodilator infusion in patients with vasospasm and aneurysmal SAH. [ABSTRACT FROM AUTHOR]

Around 40% of all patients undergoing angiography are found to have normal coronary arteries or non-obstructive coronary artery disease (NOCAD). Despite the high prevalence, this is a group who rarely receive a definitive diagnosis, are frequently labelled and managed inappropriately and by and large, continue to remain symptomatic. Half of this group will have coronary microvascular dysfunction (CMD), associated with a higher rate of major adverse cardiovascular events; identifying CMD represents a therapeutic target of unmet need. As the pressure wire has revolutionised our ability to interrogate epicardial coronary disease during the time of angiography, measuring flow can similarly classify NOCAD during a single procedure. Assessment of flow is a function that is already integral to some pressure wires and furthermore, the familiarity and usage of the combined Doppler and pressure wire is rapidly increasing-these are techniques that readily lend themselves to the skillset of a practising interventional cardiologist. We present a structured algorithm designed for cardiologists who frequently encounter NOCAD in the catheter laboratory, identifying specific disease phenotypes within this heterogeneous population with linked therapy. This review paper clearly explains the rationale for this algorithm and outlines its applicability to routine clinical practice and also, the importance of phenotyping for future research. Ultimately, personalised therapy could improve outcomes for both patients and healthcare providers; while these approaches in turn will need robust evaluation to ensure that they improve both clinical outcomes and health economic benefits, this proposal will provide a framework for future trials and evaluations. [ABSTRACT FROM AUTHOR]

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manua coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. Conclusions The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. [ABSTRACT FROM AUTHOR]

Infectious intracranial aneurysms (IIAs) are a rare cerebrovascular complication of systemic infections induced by microbial infiltration and degradation of the arterial vessel wall. Studies on the epidemiology and management of IIAs are limited to case reports and retrospective single-center studies, and report a large variability in epidemiological features, management, and outcomes due to the limited sample size. We conducted a systematic review of all published papers on IIAs in the English literature using MEDLINE and SCOPUS database from January 1950 to June 2017. A total of 288 publications describing 1191 patients with IIA (1398 aneurysms) were included and reviewed for epidemiological features, disease features, treatment and outcome. All patients were merged into a single cohort and summary data are presented. The majority of reported IIAs are distally located, relatively small (<5 mm), involve the anterior circulation, are associated with a relatively high rate of rupture, and demonstrate a propensity to multiplicity of aneurysms. Sensitive diagnosis of IIAs requires digital subtraction angiography and not CT angiography or MR angiography. Treatment of ruptured, symptomatic, or enlarging IIAs has evolved over the last 50 years. Endovascular therapy is associated with a high success rate and low morbidity compared with microsurgical and medical management. A treatment algorithm for the management of patients with IIA in various contexts is proposed and the need for prospective multicenter studies is emphasized. [ABSTRACT FROM AUTHOR]

The absence of specific guidance on how to use ultrasound (US) to diagnose and manage patients with inflammatory arthritis, especially with rheumatoid arthritis (RA) has hindered the optimal utilisation of US in clinical practice, potentially limiting its benefits for patient outcomes. In view of this, a group of musculoskeletal US experts formed a working group to consider how this unmet need could be satisfied and to produce guidance (additional to European League against Rheumatism (EULAR) imaging recommendations) to support clinicians in their daily clinical work. This paper describes this process and its outcome, namely five novel algorithms, which identify when US could be used. They are designed to aid diagnosis, to inform assessment of treatment response/disease monitoring and to evaluate stable disease state or remission in patients with suspected or established RA, by providing a pragmatic template for using US at certain time points of the RA management. A research agenda has also been defined for answering unmet clinical needs. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) holds great promise in neurointerventional surgery by providing clinicians with powerful tools for improving surgical precision, accuracy of diagnoses, and treatment planning. However, potential perils include biases or inaccuracies in the data used to train the algorithms, over- reliance on generative AI without human oversight, patient privacy concerns, and ethical implications of using AI in medical decisionmaking. Careful regulation and oversight are needed to ensure that the promises of generative AI in neurointerventional surgery are realized while minimizing its potential perils. [ChatGPT authored summary using the prompt "In one paragraph summarize the promises and perils of generative AI in neurointerventional surgery".] [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation. [ABSTRACT FROM AUTHOR]

Background Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed. Methods MEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random- effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC). PROSPERO: CRD42021278454. Results 43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% - 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC. Conclusion AI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice. [ABSTRACT FROM AUTHOR]

Background In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance. Objective To provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches. Methods In this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. We then review the literature for relevant publications. Searches were run in November 2021 in Ovid Medline and PubMed. Inclusion criteria included studies in English reporting use of artificial intelligence (AI), machine learning, or similar techniques in the setting of, and in applications for, acute ischemic stroke or mechanical thrombectomy. Articles that included image-level data with meaningful results and sound ML approaches were included in this discussion. Results Many publications on acute stroke imaging, including detection of large vessel occlusion, detection and quantification of intracranial hemorrhage and detection of infarct core, have been published using ML methods. Imaging inputs have included non-contrast head CT, CT angiograph and MRI, with a range of performances. We discuss and review several of the most relevant publications. Conclusions ML in acute ischemic stroke imaging has already made tremendous headway. Additional applications and further integration with clinical care is inevitable. Thus, facility with these approaches is critical for the neurointerventional clinician. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting. [ABSTRACT FROM AUTHOR]

Background: Diagnosis of congenital syphilis (CS) is not straightforward and can be challenging. This study aimed to evaluate the validity of an algorithm using timing of maternal antisyphilis treatment and titres of non-treponemal antibody as predictors of CS.Methods: Confirmed CS cases and those where CS was excluded were obtained from the Guangzhou Prevention of Mother-to-Child Transmission of syphilis programme between 2011 and 2019. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using receiver operating characteristics (ROC) in two situations: (1) receiving antisyphilis treatment or no-treatment during pregnancy and (2) initiating treatment before 28 gestational weeks (GWs), initiating after 28 GWs or receiving no treatment for syphilis seropositive women.Results: Among 1558 syphilis-exposed children, 39 had confirmed CS. Area under the curve, sensitivity and specificity of maternal non-treponemal titres before treatment and treatment during pregnancy were 0.80, 76.9%, 78.7% and 0.79, 69.2%, 88.7%, respectively, for children with CS. For the algorithm, ROC results showed that PPV and NPV for predicting CS were 37.3% and 96.4% (non-treponemal titres cut-off value 1:8 and no antisyphilis treatment), 9.4% and 100% (non-treponemal titres cut-off value 1:16 and treatment after 28 GWs), 4.2% and 99.5% (non-treponemal titres cut-off value 1:32 and treatment before 28 GWs), respectively.Conclusions: An algorithm using maternal non-treponemal titres and timing of treatment during pregnancy could be an effective strategy to diagnose or rule out CS, especially when the rate of loss to follow-up is high or there are no straightforward diagnostic tools. [ABSTRACT FROM AUTHOR]

Background: Achondroplasia is associated with foramen magnum stenosis (FMS) and significant risk of morbidity and sudden death in infants. A sensitive and reliable method of detecting infants who require decompressive surgery is required. This study aims to describe the incidence and severity of FMS in an unselected, sequential series of infants using a novel MRI score and retrospectively correlate severity with clinical examination and cardiorespiratory sleep (CRS) studies.Methods: The Achondroplasia Foramen Magnum Score (AFMS) was developed and scores were retrospectively correlated with clinical and CRS data over a 3-year period.Results: Of 36 infants (M:F, 18:18), 2 (5.6%) did not have FMS (AFMS0); 13 (36.1%) had FMS with preservation of the cerebrospinal fluid (CSF) spaces (AFMS1); 3 (8.3%) had FMS with loss of the CSF space but no spinal cord distortion (AFMS2); 13 (36.1%) had FMS with flattening of the cervical cord without signal change (AFMS3); and 5 (13.9%) had FMS resulting in cervical cord signal change (AFMS4). Mean Total Apnea and Hypopnea Index (TAHI) for AFMS0-4 was 3.4, 6.41, 2.97, 10.5 and 25.8, respectively. Severe TAHI had a specificity of 89% but only a 59% sensitivity for AFMS3-4. Neurological examination was normal in 34/36 (94%) patients. Overall, 9/36 (25%) infants required neurosurgery with minimal surgical complications.Conclusions: Clinical examination and CRS have a low sensitivity for predicting the effects of foramen stenosis on the spinal cord. Routine screening with MRI using AFMS can aid in detecting early spinal cord changes and has the potential to reduce infant morbidity and mortality. [ABSTRACT FROM AUTHOR]

Although premature ventricular beats (PVBs) in young people and athletes are usually benign, they may rarely mark underlying heart disease and risk of sudden cardiac death during sport. This review addresses the prevalence, clinical meaning and diagnostic/prognostic assessment of PVBs in the athlete. The article focuses on the characteristics of PVBs, such as the morphological pattern of the ectopic QRS and the response to exercise, which accurately stratify risk. We propose an algorithm to help the sport and exercise physician manage the athlete with PVBs. We also address (1) which athletes need more indepth investigation, including cardiac MRI to exclude an underlying pathological myocardial substrate, and (2) which athletes can remain eligible to competitive sports and who needs to be excluded. [ABSTRACT FROM AUTHOR]

Background: The clinical features of Kawasaki disease (KD) overlap with those of other paediatric febrile illnesses. A missed or delayed diagnosis increases the risk of coronary artery damage. Our computer algorithm for KD and febrile illness differentiation had a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 94.8%, 70.8%, 93.7% and 98.3%, respectively, in a single-centre validation study. We sought to determine the performance of this algorithm with febrile children from multiple institutions across the USA.Methods: We used our previously published 18-variable panel that includes illness day, the five KD clinical criteria and readily available laboratory values. We applied this two-step algorithm using a linear discriminant analysis-based clinical model followed by a random forest-based algorithm to a cohort of 1059 acute KD and 282 febrile control patients from five children's hospitals across the USA.Results: The algorithm correctly classified 970 of 1059 patients with KD and 163 of 282 febrile controls resulting in a sensitivity of 91.6%, specificity of 57.8% and PPV and NPV of 95.4% and 93.1%, respectively. The algorithm also correctly identified 218 of the 232 KD patients (94.0%) with abnormal echocardiograms.Interpretation: The expectation is that the predictive accuracy of the algorithm will be reduced in a real-world setting in which patients with KD are rare and febrile controls are common. However, the results of the current analysis suggest that this algorithm warrants a prospective, multicentre study to evaluate its potential utility as a physician support tool. [ABSTRACT FROM AUTHOR]

Objective: Distinguishing early dilated cardiomyopathy (DCM) from physiological left ventricular (LV) dilatation with LV ejection fraction <55% in athletes (grey zone) is challenging. We evaluated the role of a cascade of investigations to differentiate these two entities.Methods: Thirty-five asymptomatic active males with DCM, 25 male athletes in the 'grey zone' and 24 male athletes with normal LV ejection fraction underwent N-terminal pro-brain natriuretic peptide (NT-proBNP) measurement, ECG and exercise echocardiography. Grey-zone athletes and patients with DCM underwent cardiovascular magnetic resonance (CMR) and Holter monitoring.Results: Larger LV cavity dimensions and lower LV ejection fraction were the only differences between grey-zone and control athletes. None of the grey-zone athletes had abnormal NT-proBNP, increased ectopic burden/complex arrhythmias or pathological late gadolinium enhancement on CMR. These features were also absent in 71%, 71% and 50% of patients with DCM, respectively. 95% of grey-zone athletes and 60% of patients with DCM had normal ECG. During exercise echocardiography, 96% grey-zone athletes increased LV ejection fraction by >11% from baseline to peak exercise compared with 23% of patients with DCM (p<0.0001). Peak LV ejection fraction was >63% in 92% grey-zone athletes compared with 17% patients with DCM (p<0.0001). Failure to increase LV ejection fraction >11% from baseline to peak exercise or achieve a peak LV ejection fraction >63% had sensitivity of 77% and 83%, respectively, and specificity of 96% and 92%, respectively, for predicting DCM.Conclusion: Comprehensive assessment using a cascade of routine investigations revealed that exercise stress echocardiography has the greatest discriminatory value in differentiating between grey-zone athletes and asymptomatic patients with DCM. Our findings require validation in larger studies. [ABSTRACT FROM AUTHOR]

Objective: The European Society of Cardiology (ESC) 0/1 hour algorithm has been primarily validated in Europe, America and Australasia with less knowledge of its performance outside of these settings. We aim to evaluate the performance of the ESC 0/1 hour algorithm across different contexts.Methods: We searched PubMed, Embase, Scopus, Web of Science and the Cochrane Central Register of Controlled Trials for relevant studies published between 1 January 2008 and 31 May 2019. The primary outcome was index myocardial infarction and the secondary outcome was major adverse cardiac event or mortality. A bivariate random-effects meta-analysis was used to derive the pooled estimate of each outcome.Results: A total of 11 014 patients from 10 cohorts were analysed for the primary outcome. The algorithm based on high-sensitivity cardiac troponin (hs-cTn)T (Roche), hs-cTnI (Abbott) and hs-cTnI (Siemens) had pooled sensitivity of 98.4% (95% CI=95.1% to 99.5%), 98.1% (95% CI=94.6% to 99.3%) and 98.7% (95% CI=97.3% to 99.3%), respectively. The algorithm based on hs-cTnT (Roche) and hs-cTnI (Siemens) had pooled specificity of 91.2% (95% CI=86.0% to 94.6%) and 95.9% (95% CI=94.1% to 97.2%), respectively. Among patients in the rule-out category, the pooled mortality rate at 30 days and at 1 year was 0.1% (95% CI=0.0% to 0.4%) and 0.8% (95% CI=0.5% to 1.2%), respectively. Among patients in the observation zone, the pooled mortality rate was 0.7% (95% CI=0.3% to 1.2%) at 30 days but increased to 8.1% (95% CI=6.1% to 10.4%) at 1 year, comparable to the mortality rate in the rule-in group.Conclusion: The ESC 0/1 hour algorithm has high diagnostic accuracy but may not be sufficiently safe if the 1% miss-rate for myocardial infarction is desired.Prospero Registration Number: CRD42019142280. [ABSTRACT FROM AUTHOR]

Background Over the past decade, acute kidney injury (AKI) has become a global priority for improving patient safety and health outcomes. In the UK, a confidential inquiry into AKI led to the publication of clinical guidance and a range of policy initiatives. National patient safety directives have focused on the mandatory establishment of clinical decision support systems (CDSSs) within all acute National Health Service (NHS) trusts to improve the detection, alerting and response to AKI. We studied the organisational work of implementing AKI CDSSs within routine hospital care. Methods An ethnographic study comprising nonparticipant observation and interviews was conducted in two NHS hospitals, delivering AKI quality improvement programmes, located in one region of England. Three researchers conducted a total of 49 interviews and 150 hours of observation over an 18-month period. Analysis was conducted collaboratively and iteratively around emergent themes, relating to the organisational work of technology adoption. Results The two hospitals developed and implemented AKI CDSSs using very different approaches. Nevertheless, both resulted in adaptive work and trade-offs relating to the technology, the users, the organisation and the wider system of care. A common tension was associated with attempts to maximise benefit while minimise additional burden. In both hospitals, resource pressures exacerbated the tensions of translating AKI recommendations into routine practice. Conclusions Our analysis highlights a conflicted relationship between external context (policy and resources), and organisational structure and culture (eg, digital capability, attitudes to quality improvement). Greater consideration is required to the long-term effectiveness of the approaches taken, particularly in light of the ongoing need for adaptation to incorporate new practices into routine work. [ABSTRACT FROM AUTHOR]

Background: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines.Methods: A dataset of incidentally detected pulmonary nodules measuring 5-15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. There were 1397 nodules in 1187 patients, of which 234 nodules in 229 (19.3%) patients were cancer. Model discrimination and performance statistics at predefined score thresholds were compared between the Brock model and the LCP-CNN.Results: The area under the curve for LCP-CNN was 89.6% (95% CI 87.6 to 91.5), compared with 86.8% (95% CI 84.3 to 89.1) for the Brock model (p≤0.005). Using the LCP-CNN, we found that 24.5% of nodules scored below the lowest cancer nodule score, compared with 10.9% using the Brock score. Using the predefined thresholds, we found that the LCP-CNN gave one false negative (0.4% of cancers), whereas the Brock model gave six (2.5%), while specificity statistics were similar between the two models.Conclusion: The LCP-CNN score has better discrimination and allows a larger proportion of benign nodules to be identified without missing cancers than the Brock model. This has the potential to substantially reduce the proportion of surveillance CT scans required and thus save significant resources. [ABSTRACT FROM AUTHOR]

Objectives To develop and validate a tool to predict the risk of an older adult experiencing medication- related harm (MRH) requiring healthcare use following hospital discharge. Design, setting, participants Multicentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015. Primary outcome measure Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non- adherence, harm from medication error). Three data sources provided MRH and healthcare use information: hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated. Results 1116 out of 1280 recruited participants completed follow- up (87%). Uncertain MRH cases ('possible' and 'probable') were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced 'definite' MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool's discrimination C- statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches. Conclusions The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool's evaluation in other settings. [ABSTRACT FROM AUTHOR]

Background Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have reported higher morbidity and mortality in elderly patients than in their younger counterparts. Objective To use machine learning algorithms to develop a clinical decision support tool that can be used to select elderly patients for ET. Methods We used a retrospectively identified cohort of 110 patients undergoing ET for AIS at our institution to train a regression tree model that can predict 90-day modified Rankin Scale (mRS) scores. The identified algorithm, termed SPOT, was compared with other decision trees and regression models, and then validated using a prospective cohort of 36 patients. Results When predicting rates of functional independence at 90 days, SPOT showed a sensitivity of 89.36% and a specificity of 89.66% with an area under the receiver operating characteristic curve of 0.952. Performance of SPOT was significantly better than results obtained using National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT score, or patients' baseline deficits. The negative predictive value for SPOT was >95%, and in patients who were SPOT-negative, we observed higher rates of symptomatic intracerebral hemorrhage after thrombectomy. With mRS scores prediction, the mean absolute error for SPOT was 0.82. Conclusions SPOT is designed to aid clinical decision of whether to undergo ET in elderly patients. Our data show that SPOT is a useful tool to determine which patients to exclude from ET, and has been implemented in an online calculator for public use. [ABSTRACT FROM AUTHOR]

Background and purpose Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. however, their predictive value is generally low. We hypothesize that Machine learning (MI) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions. Materials and Methods clinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, MI models were created using all clinical variables. Third, image features were extracted from the CT images using an autoencoder and combined with clinical data to create MI models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI. Results The best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the MI algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the MI models. The AUC was highest for MI models that also included image features: 0.74 (95% CI 0.72 to 0.75). Conclusion MI algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI. [ABSTRACT FROM AUTHOR]

Objective: Previous studies examine clinical outcomes of insulin therapy in neonatal intensive care units (NICUs), without first developing safe and effective control protocols. This research quantifies the safety and performance of a computerised model-based control algorithmSTAR-GRYPHON (Stochastic TARgeted Glucose Regulation sYstem to Prevent Hyper- and hypO-glycaemia in Neonates).Design: Retrospective observational study of glycaemic control in very/extremely low birthweight infants treated with insulin from Christchurch Women's Hospital NICU between January 2013 and June 2017. Blood glucose (BG) outcomes and control performance is compared with retrospective data (n=22) and literature.Interventions: Insulin infusion doses were calculated from 3 to 4 hourly BG measurements using a computerised model-based control algorithm, STAR-GRYPHON.Main Outcome Measures: Mean BG, time in targeted range and incidence of hypoglycaemia.Results: STAR-GRYPHON (n=35) had lower mean BG concentration (7.0mmol/L vs 7.9 mmol/L), higher %BG within the 4.0-8.0 mmol/L target range (71.1% vs 50.9%) and lower %BG <4.0 mmol/L (0.6% vs 2.1%). There were only 2 BG <2.6 mmol/L (over n=2, 5.5% of patients, 0.03% of all BG outcomes), one of which may be attributed to clinical error. These results show better control to target and lower incidence of hypoglycaemia than most literature results from intensive insulin therapy protocols or study groups in children and infants.Conclusions: Model-based protocols can safely and effectively control BG in very premature infants and should be used in future studies to determine the effect of insulin therapy on clinical outcomes. [ABSTRACT FROM AUTHOR]