Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

Hospital-wide natural language processing summarising the health data of 1 million patients

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task.

Diagnostic signature for heart failure with preserved ejection fraction (HFpEF): a machine learning approach using multi-modality electronic health record data

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is thought to be highly prevalent yet remains underdiagnosed. Evidence-based treatments are available that increase quality of life and decrease hospitalization. We sought to develop a data-driven diagnostic model to predict from electronic health records (EHR) the likelihood of HFpEF among patients with unexplained dyspnea and preserved left ventricular EF.

METHODS AND RESULTS

The derivation cohort comprised patients with dyspnea and echocardiography results. Structured and unstructured data were extracted using an automated informatics pipeline. Patients were retrospectively diagnosed as HFpEF (cases), non-HF (control cohort I), or HF with reduced EF (HFrEF; control cohort II). The ability of clinical parameters and investigations to discriminate cases from controls was evaluated by extreme gradient boosting. A likelihood scoring system was developed and validated in a separate test cohort. The derivation cohort included 1585 consecutive patients: 133 cases of HFpEF (9%), 194 non-HF cases (Control cohort I) and 1258 HFrEF cases (Control cohort II). Two HFpEF diagnostic signatures were derived, comprising symptoms, diagnoses and investigation results. A final prediction model was generated based on the averaged likelihood scores from these two models. In a validation cohort consisting of 269 consecutive patients [with 66 HFpEF cases (24.5%)], the diagnostic power of detecting HFpEF had an AUROC of 90% (P < 0.001) and average precision of 74%.

CONCLUSION

This diagnostic signature enables discrimination of HFpEF from non-cardiac dyspnea or HFrEF from EHR and can assist in the diagnostic evaluation in patients with unexplained dyspnea. This approach will enable identification of HFpEF patients who may then benefit from new evidence-based therapies.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Novel methodology for the evaluation of symptoms reported by patients with newly diagnosed atrial fibrillation: Application of natural language processing to electronic medical records data

INTRODUCTION

Understanding symptom patterns in atrial fibrillation (AF) can help in disease management. We report on the application of natural language processing (NLP) to electronic medical records (EMRs) to capture symptom reports in patients with newly diagnosed (incident) AF.

METHODS AND RESULTS

This observational retrospective study included adult patients with an index diagnosis of incident AF during January 1, 2016 through June 30, 2018, in the Optum datasets. The baseline and follow-up periods were 1 year before/after the index date, respectively. The primary objective was identification of the following predefined symptom reports: dyspnea or shortness of breath; syncope, presyncope, lightheadedness, or dizziness; chest pain; fatigue; and palpitations. In an exploratory analysis, the incidence rates of symptom reports and cardiovascular hospitalization were assessed in propensity-matched patient cohorts with incident AF receiving first-line dronedarone or sotalol. Among 30 447 patients with an index AF diagnosis, the NLP algorithm identified at least 1 predefined symptom in 9734 (31.9%) patients. The incidence rate of symptom reports was highest at 0-3 months post-diagnosis and lower at >3-6 and >6-12 months (pre-defined timepoints). Across all time periods, the most common symptoms were dyspnea or shortness of breath, followed by syncope, presyncope, lightheadedness, or dizziness. Similar temporal patterns of symptom reports were observed among patients with prescriptions for dronedarone or sotalol as first-line treatment.

CONCLUSION

This study illustrates that NLP can be applied to EMR data to characterize symptom reports in patients with incident AF, and the potential for these methods to inform comparative effectiveness.

Prognostic relevance of demographic factors in cardiac magnetic resonance-proven acute myocarditis: A cohort study

Aim

Acute myocarditis (AM) is a heterogeneous condition with variable estimates of survival. Contemporary criteria for the diagnosis of clinically suspected AM enable non-invasive assessment, resulting in greater sensitivity and more representative cohorts. We aimed to describe the demographic characteristics and long-term outcomes of patients with AM diagnosed using non-invasive criteria.

Methods and results

A total of 199 patients with cardiac magnetic resonance (CMR)-confirmed AM were included. The majority (n = 130, 65%) were male, and the average age was 39 ± 16 years. Half of the patients were White (n = 99, 52%), with the remainder from Black and Minority Ethnic (BAME) groups. The most common clinical presentation was chest pain (n = 156, 78%), with smaller numbers presenting with breathlessness (n = 25, 13%) and arrhythmias (n = 18, 9%). Patients admitted with breathlessness were sicker and more often required inotropes, steroids, and renal replacement therapy (p < 0.001, p < 0.001, and p = 0.01, respectively). Over a median follow-up of 53 (IQR 34-76) months, 11 patients (6%) experienced an adverse outcome, defined as a composite of all-cause mortality, resuscitated cardiac arrest, and appropriate implantable cardioverter defibrillator (ICD) therapy. Patients in the arrhythmia group had a worse prognosis, with a nearly sevenfold risk of adverse events [hazard ratio (HR) 6.97; 95% confidence interval (CI) 1.87-26.00, p = 0.004]. Sex and ethnicity were not significantly associated with the outcome.

Conclusion

AM is highly heterogeneous with an overall favourable prognosis. Three-quarters of patients with AM present with chest pain, which is associated with a benign prognosis. AM presenting with life-threatening arrhythmias is associated with a higher risk of adverse events.

Anticoagulation for atrial fibrillation in people with serious mental illness in the general hospital setting

OBJECTIVE

People with serious mental illnesses (SMI) have an increased risk of stroke compared to the general population. This study aims to evaluate oral anticoagulation prescription trends in atrial fibrillation (AF) patients with and without a comorbid SMI.

METHODS

An open-source retrieval system for clinical data (CogStack) was used to identify a cohort of AF patients with SMI who ever had an inpatient admission to King's College Hospital from 2011 to 2020. A Natural Language Processing pipeline was used to calculate CHA2DS2-VASc and HASBLED risk scores from Electronic Health Records free text. Antithrombotic prescriptions of warfarin and Direct acting oral anti-coagulants (DOACs) (apixaban, rivaroxaban, dabigatran, edoxaban) were extracted from discharge summaries.

RESULTS

Among patients included in the study (n = 16 916), 2.7% had a recorded co-morbid SMI diagnosis. Compared to non-SMI patients, those with SMI had significantly higher CHA2DS2-VASc (mean (SD): 5.3 (1.96) vs 4.7 (2.08), p < 0.001) and HASBLED scores (mean (SD): 3.2 (1.27) vs 2.5 (1.29), p < 0.001). Among AF patients having a CHA2DS2-VASc ≥2, those with co-morbid SMI were less likely than non-SMI patients to be prescribed an OAC (44% vs 54%, p < 0.001). However, there was no evidence of a significant difference between the two groups since 2019.

CONCLUSION

Over recent years, DOAC prescription rates have increased among AF patients with SMI in acute hospitals. More research is needed to confirm whether the introduction of DOACs has reduced OAC treatment gaps in people with serious mental illness and to assess whether the use of DOACs has improved health outcomes in this population.

Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users’ specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient’s electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine.

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.

Systematic review of current natural language processing methods and applications in cardiology

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015-2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life

OBJECTIVES

To clarify real-world linguistic nuances around dying in hospital as well as inaccuracy in individual-level prognostication to support advance care planning and personalised discussions on limitation of life sustaining treatment (LST).

DESIGN

Retrospective cross-sectional study of real-world clinical data.

SETTING

Secondary care, urban and suburban teaching hospitals.

PARTICIPANTS

All inpatients in 12-month period from 1 October 2018 to 30 September 2019.

METHODS

Using unsupervised natural language processing, word embedding in latent space was used to generate phrase clusters with most similar semantic embeddings to 'Ceiling of Treatment' and their prognostication value.

RESULTS

Word embeddings with most similarity to 'Ceiling of Treatment' clustered around phrases describing end-of-life care, ceiling of care and LST discussions. The phrases have differing prognostic profile with the highest 7-day mortality in the phrases most explicitly referring to end of life-'Withdrawal of care' (56.7%), 'terminal care/end of life care' (57.5%) and 'un-survivable' (57.6%).

CONCLUSION

Vocabulary used at end-of-life discussions are diverse and has a range of associations to 7-day mortality. This highlights the importance of correct application of terminology during LST and end-of-life discussions.

Pre-existing cardiovascular disease rather than cardiovascular risk factors drives mortality in COVID-19

BACKGROUND

The relative association between cardiovascular (CV) risk factors, such as diabetes and hypertension, established CV disease (CVD), and susceptibility to CV complications or mortality in COVID-19 remains unclear.

METHODS

We conducted a cohort study of consecutive adults hospitalised for severe COVID-19 between 1st March and 30th June 2020. Pre-existing CVD, CV risk factors and associations with mortality and CV complications were ascertained.

RESULTS

Among 1721 patients (median age 71 years, 57% male), 349 (20.3%) had pre-existing CVD (CVD), 888 (51.6%) had CV risk factors without CVD (RF-CVD), 484 (28.1%) had neither. Patients with CVD were older with a higher burden of non-CV comorbidities. During follow-up, 438 (25.5%) patients died: 37% with CVD, 25.7% with RF-CVD and 16.5% with neither. CVD was independently associated with in-hospital mortality among patients < 70 years of age (adjusted HR 2.43 [95% CI 1.16-5.07]), but not in those ≥ 70 years (aHR 1.14 [95% CI 0.77-1.69]). RF-CVD were not independently associated with mortality in either age group (< 70 y aHR 1.21 [95% CI 0.72-2.01], ≥ 70 y aHR 1.07 [95% CI 0.76-1.52]). Most CV complications occurred in patients with CVD (66%) versus RF-CVD (17%) or neither (11%; p < 0.001). 213 [12.4%] patients developed venous thromboembolism (VTE). CVD was not an independent predictor of VTE.

CONCLUSIONS

In patients hospitalised with COVID-19, pre-existing established CVD appears to be a more important contributor to mortality than CV risk factors in the absence of CVD. CVD-related hazard may be mediated, in part, by new CV complications. Optimal care and vigilance for destabilised CVD are essential in this patient group. Trial registration n/a.

A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19

BACKGROUND

People of minority ethnic backgrounds may be disproportionately affected by severe COVID-19. Whether this relates to increased infection risk, more severe disease progression, or worse in-hospital survival is unknown. The contribution of comorbidities or socioeconomic deprivation to ethnic patterning of outcomes is also unclear.

METHODS

We conducted a case-control and a cohort study in an inner city primary and secondary care setting to examine whether ethnic background affects the risk of hospital admission with severe COVID-19 and/or in-hospital mortality. Inner city adult residents admitted to hospital with confirmed COVID-19 (n = 872 cases) were compared with 3,488 matched controls randomly sampled from a primary healthcare database comprising 344,083 people residing in the same region. For the cohort study, we studied 1827 adults consecutively admitted with COVID-19. The primary exposure variable was self-defined ethnicity. Analyses were adjusted for socio-demographic and clinical variables.

FINDINGS

The 872 cases comprised 48.1% Black, 33.7% White, 12.6% Mixed/Other and 5.6% Asian patients. In conditional logistic regression analyses, Black and Mixed/Other ethnicity were associated with higher admission risk than white (OR 3.12 [95% CI 2.63-3.71] and 2.97 [2.30-3.85] respectively). Adjustment for comorbidities and deprivation modestly attenuated the association (OR 2.24 [1.83-2.74] for Black, 2.70 [2.03-3.59] for Mixed/Other). Asian ethnicity was not associated with higher admission risk (adjusted OR 1.01 [0.70-1.46]). In the cohort study of 1827 patients, 455 (28.9%) died over a median (IQR) of 8 (4-16) days. Age and male sex, but not Black (adjusted HR 1.06 [0.82-1.37]) or Mixed/Other ethnicity (adjusted HR 0.72 [0.47-1.10]), were associated with in-hospital mortality. Asian ethnicity was associated with higher in-hospital mortality but with a large confidence interval (adjusted HR 1.71 [1.15-2.56]).

INTERPRETATION

Black and Mixed ethnicity are independently associated with greater admission risk with COVID-19 and may be risk factors for development of severe disease, but do not affect in-hospital mortality risk. Comorbidities and socioeconomic factors only partly account for this and additional ethnicity-related factors may play a large role. The impact of COVID-19 may be different in Asians.

FUNDING

British Heart Foundation; the National Institute for Health Research; Health Data Research UK.

Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID-19 infection in a multi-site UK acute hospital trust

AIMS

The SARS-CoV-2 virus binds to the angiotensin-converting enzyme 2 (ACE2) receptor for cell entry. It has been suggested that angiotensin-converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARB), which are commonly used in patients with hypertension or diabetes and may raise tissue ACE2 levels, could increase the risk of severe COVID-19 infection.

METHODS AND RESULTS

We evaluated this hypothesis in a consecutive cohort of 1200 acute inpatients with COVID-19 at two hospitals with a multi-ethnic catchment population in London (UK). The mean age was 68 ± 17 years (57% male) and 74% of patients had at least one comorbidity. Overall, 415 patients (34.6%) reached the primary endpoint of death or transfer to a critical care unit for organ support within 21 days of symptom onset. A total of 399 patients (33.3%) were taking ACEi or ARB. Patients on ACEi/ARB were significantly older and had more comorbidities. The odds ratio for the primary endpoint in patients on ACEi and ARB, after adjustment for age, sex and co-morbidities, was 0.63 (95% confidence interval 0.47-0.84, P < 0.01).

CONCLUSIONS

There was no evidence for increased severity of COVID-19 in hospitalised patients on chronic treatment with ACEi or ARB. A trend towards a beneficial effect of ACEi/ARB requires further evaluation in larger meta-analyses and randomised clinical trials.

The relationship between frequency and severity of vaso-occlusive crises and health-related quality of life and work productivity in adults with sickle cell disease

Purpose

Patients with sickle cell disease (SCD) may experience sickle cell-related pain crises, also referred to as vaso-occlusive crises (VOCs), which are a substantial cause of morbidity and mortality. The study explored how VOC frequency and severity impacts health-related quality of life (HRQoL) and work productivity.

Methods

Three hundred and three adults with SCD who completed an online survey were included in the analysis. Patients answered questions regarding their experience with SCD and VOCs, and completed the Adult Sickle Cell Quality of Life Measurement Information System (ASCQ-Me) and the Workplace Productivity and Activity Impairment: Specific Health Problem (WPAI:SHP). Differences in ASCQ-Me and WPAI:SHP domains were assessed according to VOC frequency and severity.

Results

Nearly half of the patient sample (47.2%) experienced ≥ 4 VOCs in the past 12 months. The most commonly reported barriers to receiving care for SCD included discrimination by or trouble trusting healthcare professionals (39.6%, 33.3%, respectively), limited access to treatment centers (38.9%), and difficulty affording services (29.4%). Patients with more frequent VOCs reported greater impacts on emotion, social functioning, stiffness, sleep and pain, and greater absenteeism, overall productivity loss, and activity impairment than patients with less frequent VOCs (P < 0.05). Significant impacts on HRQoL and work productivity were also observed when stratifying by VOC severity (P < 0.05 for all ASCQ-Me and WPAI domains, except for presenteeism).

Conclusions

Results from the survey indicated that patients with SCD who had more frequent or severe VOCs experienced deficits in multiple domains of HRQoL and work productivity. Future research should examine the longitudinal relationship between these outcomes.