Critical care unit bed availability and postoperative outcomes: a multinational cohort study.

BACKGROUND: Critical care beds are a limited resource, yet research indicates that recommendations for postoperative critical care admission based on patient-level risk stratification are not followed. It is unclear how prioritisation decisions are made in real-world settings and the effect of this prioritisation on outcomes. METHODS: This was a prespecified analysis of an observational cohort study of adult patients undergoing inpatient surgery, conducted in 274 hospitals across the UK and Australasia during 2017. The primary outcome was postoperative morbidity at day 7. Logistic regression models were used to evaluate the relationship between critical care admission and patient and health system factors. The causal effect of critical care admission on outcome was estimated using variation in critical care occupancy as a natural experiment in an instrumental variable analysis. RESULTS: A total of 19,491 patients from 248 hospitals were eligible for analysis, of whom 2107 were directly admitted to critical care postoperatively. Postoperative morbidity occurred in 2829/19,491 (15%) patients. Increasing surgical risk was associated with critical care admission, as was increased availability of critical care beds (odds ratio (95%CI) 1.04 (1.01-1.06), p = 0.002) per available bed; however, the probability of admission varied significantly between hospitals (median odds ratio 3.05). There was no evidence of a difference in postoperative morbidity with critical care admission (odds ratio (95%CI) 0.91 (0.57-1.45), p = 0.710). DISCUSSION: Postoperative critical care admission is variable and related to bed availability. Statistical methods that adjust for unobserved confounding lowered the estimates of harm previously reported to have been associated with postoperative critical care admission. Our findings provide a rationale for a clinical trial which would evaluate any potential benefits for postoperative critical care admission for patients in whom there is no absolute indication for admission.

Assessment of the impact of social deprivation, distance to hospital and time to diagnosis on survival in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive condition associated with a variable prognosis. The relationship between socioeconomic status or distance travelled to respiratory clinics and prognosis is unclear. RESEARCH QUESTION: To determine whether socioeconomic status, distance to hospital and time to referral affects survival in patients with IPF. STUDY DESIGN AND METHODS: In this retrospective cohort study, we used data collected from the British Thoracic Society Interstitial Lung Diseases Registry, between 2013 and 2021 (n = 2359) and calculated the quintile of Index of Multiple Deprivation 2019 score, time from initial symptoms to hospital attendance and distance as the linear distance between hospital and home post codes. Survival was assessed using Cox proportional hazards models. RESULTS: There was a significant association between increasing quintile of deprivation and duration of symptoms prior to hospital presentation, Gender Age Physiology (GAP) index and receipt of supplemental oxygen and antifibrotic therapies at presentation. The most deprived patients had worse overall survival compared to least deprived after adjusting for smoking status, GAP index, distance to hospital and time to referral (HR = 1.39 [1.11, 1.73]; p = 0.003). Patients living furthest from a respiratory clinic also had worse survival compared to those living closest (HR = 1.29 [1.01, 1.64]; p = 0.041). INTERPRETATION: The most deprived patients with IPF have more severe disease at presentation and worse outcomes. Living far from hospital was also associated with poor outcomes. This suggests inequalities in access to healthcare and requires consideration in delivering effective and equitable care to patients with IPF.

Real-world experience of nintedanib for progressive fibrosing interstitial lung disease in the UK.

Background: Nintedanib slows progression of lung function decline in patients with progressive fibrosing (PF) interstitial lung disease (ILD) and was recommended for this indication within the United Kingdom (UK) National Health Service in Scotland in June 2021 and in England, Wales and Northern Ireland in November 2021. To date, there has been no national evaluation of the use of nintedanib for PF-ILD in a real-world setting. Methods: 26 UK centres were invited to take part in a national service evaluation between 17 November 2021 and 30 September 2022. Summary data regarding underlying diagnosis, pulmonary function tests, diagnostic criteria, radiological appearance, concurrent immunosuppressive therapy and drug tolerability were collected via electronic survey. Results: 24 UK prescribing centres responded to the service evaluation invitation. Between 17 November 2021 and 30 September 2022, 1120 patients received a multidisciplinary team recommendation to commence nintedanib for PF-ILD. The most common underlying diagnoses were hypersensitivity pneumonitis (298 out of 1120, 26.6%), connective tissue disease associated ILD (197 out of 1120, 17.6%), rheumatoid arthritis associated ILD (180 out of 1120, 16.0%), idiopathic nonspecific interstitial pneumonia (125 out of 1120, 11.1%) and unclassifiable ILD (100 out of 1120, 8.9%). Of these, 54.4% (609 out of 1120) were receiving concomitant corticosteroids, 355 (31.7%) out of 1120 were receiving concomitant mycophenolate mofetil and 340 (30.3%) out of 1120 were receiving another immunosuppressive/modulatory therapy. Radiological progression of ILD combined with worsening respiratory symptoms was the most common reason for the diagnosis of PF-ILD. Conclusion: We have demonstrated the use of nintedanib for the treatment of PF-ILD across a broad range of underlying conditions. Nintedanib is frequently co-prescribed alongside immunosuppressive and immunomodulatory therapy. The use of nintedanib for the treatment of PF-ILD has demonstrated acceptable tolerability in a real-world setting.

Examining the National Representativeness of the Axon Registry: A Neurology-Specific Patient Registry.

BACKGROUND AND OBJECTIVES: The objective of this study was to determine the external validity of the Axon Registry by comparing the 2019 calendar year data with 2 nationally representative, publicly available data sources, specifically the National Ambulatory Medical Care Survey (NAMCS) and the Medical Expenditure Panel Survey (MEPS). The Axon Registry is the American Academy of Neurology's neurology-focused qualified clinical data registry that reports and analyzes electronic health record data from participating US neurology providers. Its key function is to support quality improvement within ambulatory neurology practices while also promoting high-quality evidence-based care in clinical neurology. We compared demographics of patients who had an outpatient or office visit with a neurologist along with prevalence of selected neurologic conditions and neurologic procedures across the 3 data sets. METHODS: We performed a cross-sectional, retrospective comparison of 3 data sets: NAMCS (2012-2016), MEPS (2013-2017, 2019), and Axon Registry (2019). We obtained patient demographics (age, birth sex, race, ethnicity), patient neurologic conditions (headache, epilepsy, cerebrovascular disease, multiple sclerosis, parkinsonism, dementia, spinal pain, and polyneuropathy), provider location, and neurologic procedures (neurology visits, MR/CT neuroimaging studies and EEG/EMG neurophysiologic studies). Parameter estimates from the pooled 5-year samples of the 2 public data sets, calculated at the visit level, were compared descriptively with those of the Axon Registry. We calculated Cohen h and performed Wald tests (α = 0.05) to conduct person-level statistical comparisons between MEPS 2019 and Axon Registry 2019 data. RESULTS: The Axon Registry recorded 1.3 M annual neurology visits (NAMCS, 11 M; MEPS, 22 M) and 645 K people with neurologic conditions (MEPS, 10 M). Compared with the pooled national surveys, the Axon Registry has similar patient demographics, neurologic condition prevalence, neuroimaging and neurophysiologic utilization, and provider location. In direct comparison with MEPS 2019, the Axon Registry 2019 had fewer children (2% vs 7%), more elderly persons (21% vs 16%), fewer non-Black and non-White race persons (5% vs 8%), less number of patients with epilepsy (10% vs 13%), more patients with dementia (8% vs 6%), more patients with cerebrovascular disease (11% vs 8%), and a greater predominance of neurology providers in the Midwest (25% vs 20%). The only difference with a non-negligible effect size was the proportion of people younger than 15 years (Cohen h = 0.25). DISCUSSION: The Axon Registry demonstrates high concordance with 2 nationally representative surveys. Recruiting more and diverse neurology providers will further improve the volume, representativeness, and value of the Axon Registry.

Changes in exhaled volatile organic compounds following indirect bronchial challenge in suspected asthma.

BACKGROUND: Inhaled mannitol provokes bronchoconstriction via mediators released during osmotic degranulation of inflammatory cells, and, hence represents a useful diagnostic test for asthma and model for acute attacks. We hypothesised that the mannitol challenge would trigger changes in exhaled volatile organic compounds (VOCs), generating both candidate biomarkers and novel insights into their origin. METHODS: Participants with a clinical diagnosis of asthma, or undergoing investigation for suspected asthma, were recruited. Inhaled mannitol challenges were performed, followed by a sham challenge after 2 weeks in participants with bronchial hyper-responsiveness (BHR). VOCs were collected before and after challenges and analysed using gas chromatography-mass spectrometry. RESULTS: Forty-six patients (mean (SD) age 52 (16) years) completed a mannitol challenge, of which 16 (35%) were positive, and 15 of these completed a sham challenge. Quantities of 16 of 51 identified VOCs changed following mannitol challenge (p<0.05), of which 11 contributed to a multivariate sparse partial least square discriminative analysis model, with a classification error rate of 13.8%. Five of these 16 VOCs also changed (p<0.05) in quantity following the sham challenge, along with four further VOCs. In patients with BHR to mannitol distinct postchallenge VOC signatures were observed compared with post-sham challenge. CONCLUSION: Inhalation of mannitol was associated with changes in breath VOCs, and in people with BHR resulted in a distinct exhaled breath profile when compared with a sham challenge. These differentially expressed VOCs are likely associated with acute airway inflammation and/or bronchoconstriction and merit further investigation as potential biomarkers in asthma.

Home monitoring with connected mobile devices for asthma attack prediction with machine learning.

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Socioeconomic determinants of health, traditional risk factors and cardiovascular outcomes in Australia.

Background: Cardiovascular disease burden is decreasing, but these reductions have not been distributed equally amongst socioeconomic groups. Objectives: The aim of this study was to define the relationships between different domains of socioeconomic health, traditional cardiovascular risk factors and cardiovascular events. Methods: This was a cross-sectional study of local government areas (LGAs) in Victoria, Australia. We used data from a population health survey combined with cardiovascular event data derived from hospital and government data. Four socioeconomic domains: educational attainment, financial wellbeing, remoteness, and psychosocial health, were generated from 22 variables. The primary outcome was a composite of non-STEMI, STEMI, heart failure and cardiovascular deaths per 10,000 persons. Linear regression and cluster analysis were used to assess the relationships between risk factors and events. Results: Across 79 LGAs there were 33,654 interviews conducted. All socioeconomic domains were associated with burden of traditional risk factors, including hypertension, smoking, poor diet, diabetes, and obesity. Financial wellbeing, educational attainment and remoteness were all correlated with cardiovascular events on univariate analysis. After multivariate adjustment for age and sex, financial wellbeing, psychosocial wellbeing, and remoteness were associated with cardiovascular events, while educational attainment was not. After including traditional risk factors only financial wellbeing and remoteness remained correlated with cardiovascular events. Conclusions: Financial wellbeing and remoteness independently be associated with cardiovascular events, while educational attainment and psychosocial wellbeing are attenuated by traditional cardiovascular risk factors. Poor socioeconomic health is clustered in certain areas, which have high cardiovascular event rates.

Parkinson's disease population-wide registries in the United States: Current and future opportunities.

Parkinson's disease (PD) is a neurodegenerative disease with both genetic and environmental risk factors. Efforts to understand the growing incidence and prevalence of PD have led to several state PD registry initiatives in the United States. The California PD Registry (CPDR) is the largest state-wide PD registry and requires electronic reporting of all eligible cases by all medical providers. We borrow from our experience with the CPDR to highlight 4 gaps to population-based PD registries. Specifically we address (1) who should be included in PD registries; (2) what data should be collected in PD case reports; (3) how to ensure the validity of case reports; and (4) how can state PD registries exchange and aggregate information. We propose a set of recommendations that addresses these and other gaps toward achieving a promise of a practical, interoperable, and scalable PD registry in the U.S., which can serve as a key health information resource to support epidemiology, health equity, quality improvement, and research.

Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review.

BACKGROUND: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. OBJECTIVE: This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. METHODS: We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. RESULTS: Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. CONCLUSIONS: Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.

Urinary Leukotriene E4 as a Biomarker in NSAID-Exacerbated Respiratory Disease (N-ERD): a Systematic Review and Meta-analysis.

PURPOSE OF REVIEW: Non-steroidal exacerbated respiratory disease (N-ERD) currently requires aspirin challenge testing for diagnosis. Urinary leukotriene E4 (uLTE4) has been extensively investigated as potential biomarker in N-ERD. We aimed to assess the usefulness of uLTE4 as a biomarker in the diagnosis of N-ERD. RECENT FINDINGS: N-ERD, formerly known as aspirin-intolerant asthma (AIA), is characterised by increased leukotriene production. uLTE4 indicates cysteinyl leukotriene production, and a potential biomarker in N-ERD. Although several studies and have examined the relationship between uLTE4 and N-ERD, the usefulness of uLTE4 as a biomarker in a clinical setting remains unclear. FINDINGS: Our literature search identified 38 unique eligible studies, 35 were included in the meta-analysis. Meta-analysis was performed (i.e. pooled standardised mean difference (SMD) with 95% confidence intervals (95% CI)) and risk of bias assessed (implementing Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Cochrane DTA)). Data from 3376 subjects was analysed (1354 N-ERD, 1420 ATA, and 602 HC). uLTE4 was higher in N-ERD vs ATA (n = 35, SMD 0.80; 95% CI 0.72-0.89). uLTE4 increased following aspirin challenge in N-ERD (n = 12, SMD 0.56; 95% CI 0.26-0.85) but not ATA (n = 8, SMD 0.12; CI - 0.08-0.33). This systematic review and meta-analysis showed that uLTE4 is higher in N-ERD than ATA or HC. Likewise, people with N-ERD have greater increases in uLTE4 following aspirin challenge. However, due to the varied uLTE4 measurement and result reporting practice, clinical utility of these findings is limited. Future studies should be standardised to increase clinical significance and interpretability of the results.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

INTRODUCTION: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. METHODS AND ANALYSIS: A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. ETHICS AND DISSEMINATION: Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.

Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

The effects of social distancing and self-isolation during the COVID-19 pandemic on adults diagnosed with asthma: A qualitative study.

This study aimed to explore how social distancing and self-isolation measures, aimed at protecting vulnerable groups from COVID-19, affected the wellbeing and physical activity levels among adults diagnosed with asthma. Twenty-seven participants took part across four online focus groups. Transcripts were analysed using thematic analysis. Participants reported becoming more health conscious due to being labelled as vulnerable. Their relationship with the severity of their asthma was altered and they reported making positive changes to increase their physical activity levels. Findings suggest there is a window of opportunity to engage with people diagnosed with asthma to promote beneficial lifestyle changes and self-management.

A systematic review of the characteristics of interventions that promote physical activity in adults with asthma.

Physical activity is promoted in the asthma population through pulmonary rehabilitation, but limited funding and facilities are available. This review aimed to examine the effectiveness of interventions that promote physical activity and identify the behaviour change techniques (BCTs) and other intervention components used. Five databases were searched, and 25 studies met the inclusion criteria. Interventions had a significant positive effect on physical activity, sedentary behaviour, quality of life and asthma symptoms. BCTs used across intervention and control groups were similar in studies that showed effects and those that did not. Future interventions should employ techniques that help to maintain behaviour change.

Psychometric properties of patient reported outcome measures in idiopathic pulmonary fibrosis.

BACKGROUND: Various patient reported outcome measures (PROMs) are used in idiopathic pulmonary fibrosis (IPF). We aimed to describe their psychometric properties, assess their relationship with 1-year mortality and determine their minimal clinically important differences (MCIDs). METHODS: In a prospective multicentre study, participants with IPF completed the King's Brief Interstitial Lung Disease Questionnaire (K-BILD), the modified Medical Research Council (mMRC) dyspnoea scale, St George's Respiratory Questionnaire (SGRQ) and University of California, San Diego shortness of breath questionnaire (UCSD-SOBQ) three-monthly intervals over a 12-month period. Forced vital capacity (FVC) was matched with questionnaires and mortality was captured. Anchor- and distribution-based methods were used to derive MCID. RESULTS: Data were available from 238 participants. All PROMs had good internal consistency and high degree of correlations with other tools (except UCSD-SOBQ correlated poorly with FVC). There were significant associations with mortality for K-BILD (hazard ratio 16.67; 95% CI 2.38-100) and SGRQ (hazard ratio 4.65; 95% CI 1.32-16.62) but not with the other PROMs or FVC. The median MCID (range) for K-BILD was 6.3 (4.1-7.0), SGRQ was 7.0 (3.8-9.6), mMRC was 0.4 (0.1-0.5) and UCSD-SOBQ was 9.6 (4.1-14.2). CONCLUSIONS: The K-BILD was related to other severity measures and had the strongest relationship with mortality.

Effect of high-intensity interval training in adolescents with asthma: The eXercise for Asthma with Commando Joe's® (X4ACJ) trial.

BACKGROUND: Higher levels of cardiorespiratory fitness are associated with reduced asthma severity and increased quality of life in those with asthma. Therefore, the purpose of this study was to evaluate the effectiveness of a 6-month high-intensity interval training (HIIT) intervention in adolescents with and without asthma. METHODS: A total of 616 adolescents (334 boys; 13.0 ± 1.1 years, 1.57 ± 0.10 m, 52.6 ± 12.9 kg, mean ± SD), including 155 with asthma (78 boys), were recruited as part of a randomized controlled trial from 5 schools (4 control and 1 intervention). The 221 intervention participants (116 boys; 47 asthma) completed 6 months of school-based HIIT (30 min, 3 times per week, 10-30 s bouts at >90% age-predicted maximum heart rate with equal rest). At baseline, mid-intervention, post-intervention, and 3-month follow-up, measurements for 20-m shuttle run, body mass index (BMI), lung function, Pediatric Quality of Life Inventory, Paediatric Asthma Quality of Life Questionnaire, and Asthma Control Questionnaire were collected. Additionally, 69 adolescents (39 boys (of the 36 with asthma there were 21 boys)) also completed an incremental ramp test. For analysis, each group's data (intervention and control) were divided into those with and without asthma. RESULTS: Participants with asthma did not differ from their peers in any parameter of aerobic fitness, at any time-point, but were characterized by a greater BMI. The intervention elicited a significant improvement in maximal aerobic fitness but no change in sub-maximal parameters of aerobic fitness, lung function, or quality of life irrespective of asthma status. Those in the intervention group maintained their BMI, whereas BMI significantly increased in the control group throughout the 6-month period. CONCLUSION: HIIT represents an effective tool for improving aerobic fitness and maintaining BMI in adolescents, irrespective of asthma status. HIIT was well-tolerated by those with asthma, who evidenced a similar aerobic fitness to their healthy peers and responded equally well to a HIIT program.

Predicting asthma-related crisis events using routine electronic healthcare data: a quantitative database analysis study.

BACKGROUND: There is no published algorithm predicting asthma crisis events (accident and emergency [A&E] attendance, hospitalisation, or death) using routinely available electronic health record (EHR) data. AIM: To develop an algorithm to identify individuals at high risk of an asthma crisis event. DESIGN AND SETTING: Database analysis from primary care EHRs of people with asthma across England and Scotland. METHOD: Multivariable logistic regression was applied to a dataset of 61 861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage Databank of 174 240 patients from Wales. Outcomes were ≥1 hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance, or death (validation dataset) within a 12-month period. RESULTS: Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking, and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a receiver operating characteristic of 0.71 (95% confidence interval [CI] = 0.70 to 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI = 5.3% to 6.1%) and a negative predictive value of 98.9% (95% CI = 98.9% to 99.0%), with sensitivity of 28.5% (95% CI = 26.7% to 30.3%) and specificity of 93.3% (95% CI = 93.2% to 93.4%); those individuals had an event risk of 6.0% compared with 1.1% for the remaining population. In total, 18 people would need to be followed to identify one admission. CONCLUSION: This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding those not at high risk.

Quality of Neurologic Care in the United States: Initial Report From the Axon Registry.

OBJECTIVE: To provide the initial description of the quality of outpatient US neurologic care as collected and reported in the Axon Registry. METHODS: We describe characteristics of registry participants and the performance of neurology providers on 20 of the 2019 Axon Registry quality measures. From the distribution of providers' scores on a quality measure, we calculate the median performance for each quality measure. We test for associations between quality measure performance, provider characteristics, and intrinsic measure parameters. RESULTS: There were 948 neurology providers who contributed a total of 6,480 provider-metric observations. Overall, the average quality measure performance score at the provider level was 66 (median 77). At the measure level (n = 20), the average quality measure performance score was 53 (median 55) with a range of 2 to 100 (interquartile range 20-91). Measures with a lower-complexity category (e.g., discrete orders, singular concepts) or developed through the specialty's qualified clinical data registry pathway had higher performance distributions. There was no difference in performance between Merit-Based Incentive Payment System (MIPS) and non-MIPS providers. There was no association between quality measure performance and practice size, measure clinical topic/neurologic condition, or measure year of entry. CONCLUSIONS: This cross-sectional assessment of quality measure performance in 2019 Axon Registry data demonstrates modest performance scores and considerable variability across measures and providers. More complex measures were associated with lower performance. These findings serve as a baseline assessment of quality of ambulatory neurologic care in the United States and provide insights into future measure design.