Health care utilization and mortality for people with epilepsy during COVID-19: A population study.

OBJECTIVE: This study was undertaken to characterize changes in health care utilization and mortality for people with epilepsy (PWE) during the COVID-19 pandemic. METHODS: We performed a retrospective study using linked, individual-level, population-scale anonymized health data from the Secure Anonymised Information Linkage databank. We identified PWE living in Wales during the study "pandemic period" (January 1, 2020-June 30, 2021) and during a "prepandemic" period (January 1, 2016-December 31, 2019). We compared prepandemic health care utilization, status epilepticus, and mortality rates with corresponding pandemic rates for PWE and people without epilepsy (PWOE). We performed subgroup analyses on children (<18 years old), older people (>65 years old), those with intellectual disability, and those living in the most deprived areas. We used Poisson models to calculate adjusted rate ratios (RRs). RESULTS: We identified 27 279 PWE who had significantly higher rates of hospital (50.3 visits/1000 patient months), emergency department (55.7), and outpatient attendance (172.4) when compared to PWOE (corresponding figures: 25.7, 25.2, and 87.0) in the prepandemic period. Hospital and epilepsy-related hospital admissions, and emergency department and outpatient attendances all reduced significantly for PWE (and all subgroups) during the pandemic period. RRs [95% confidence intervals (CIs)] for pandemic versus prepandemic periods were .70 [.69-.72], .77 [.73-.81], .78 [.77-.79], and .80 [.79-.81]. The corresponding rates also reduced for PWOE. New epilepsy diagnosis rates decreased during the pandemic compared with the prepandemic period (2.3/100 000/month cf. 3.1/100 000/month, RR = .73, 95% CI = .68-.78). Both all-cause deaths and deaths with epilepsy recorded on the death certificate increased for PWE during the pandemic (RR = 1.07, 95% CI = .997-1.145 and RR = 2.44, 95% CI = 2.12-2.81). When removing COVID deaths, RRs were .88 (95% CI = .81-.95) and 1.29 (95% CI = 1.08-1.53). Status epilepticus rates did not change significantly during the pandemic (RR = .95, 95% CI = .78-1.15). SIGNIFICANCE: All-cause non-COVID deaths did not increase but non-COVID deaths associated with epilepsy did increase for PWE during the COVID-19 pandemic. The longer term effects of the decrease in new epilepsy diagnoses and health care utilization and increase in deaths associated with epilepsy need further research.

Epilepsy and the risk of COVID-19-related hospitalization and death: A population study.

OBJECTIVE: People with epilepsy (PWE) may be at an increased risk of severe COVID-19. It is important to characterize this risk to inform PWE and for future health and care planning. We assessed whether PWE were at higher risk of being hospitalized with, or dying from, COVID-19. METHODS: We performed a retrospective cohort study using linked, population-scale, anonymized electronic health records from the SAIL (Secure Anonymised Information Linkage) databank. This includes hospital admission and demographic data for the complete Welsh population (3.1 million) and primary care records for 86% of the population. We identified 27 279 PWE living in Wales during the study period (March 1, 2020 to June 30, 2021). Controls were identified using exact 5:1 matching (sex, age, and socioeconomic status). We defined COVID-19 deaths as having International Classification of Diseases, 10th Revision (ICD-10) codes for COVID-19 on death certificates or occurring within 28 days of a positive SARS-CoV-2 polymerase chain reaction (PCR) test. COVID-19 hospitalizations were defined as having a COVID-19 ICD-10 code for the reason for admission or occurring within 28 days of a positive SARS-CoV-2 PCR test. We recorded COVID-19 vaccinations and comorbidities known to increase the risk of COVID-19 hospitalization and death. We used Cox proportional hazard models to calculate hazard ratios. RESULTS: There were 158 (.58%) COVID-19 deaths and 933 (3.4%) COVID-19 hospitalizations in PWE, and 370 (.27%) deaths and 1871 (1.4%) hospitalizations in controls. Hazard ratios for COVID-19 death and hospitalization in PWE compared to controls were 2.15 (95% confidence interval [CI] = 1.78-2.59) and 2.15 (95% CI = 1.94-2.37), respectively. Adjusted hazard ratios (adjusted for comorbidities) for death and hospitalization were 1.32 (95% CI = 1.08-1.62) and 1.60 (95% CI = 1.44-1.78). SIGNIFICANCE: PWE are at increased risk of being hospitalized with, and dying from, COVID-19 when compared to age-, sex-, and deprivation-matched controls, even when adjusting for comorbidities. This may have implications for prioritizing future COVID-19 treatments and vaccinations for PWE.

Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings.

BACKGROUND: Cancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case. METHODS: A novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians. RESULTS: There were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72). CONCLUSION: This study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as 'human in the loop' approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex.

Revisiting basal cell carcinoma clinical margins: Leveraging natural language processing and multivariate analysis with updated Royal College of Pathologists histological reporting standards.

INTRODUCTION: Data supporting the current British Association of Dermatologists guidelines for the management of basal cell carcinoma (BCC) are based on historic studies and do not consider the updated Royal College of Pathologists (RCPath) histological reporting standards. The aim of this study was to use natural language processing (NLP)-derived data and undertake a multivariate analysis with updated RCPath standards, providing a contemporary update on the excision margins required to achieve histological clearance in BCC. METHODS: A validated NLP information extraction model was used to perform a rapid multi-centre, pan-specialty, consecutive retrospective analysis of BCCs, managed with surgical excision using a pre-determined clinical margin, over a 17-year period (2004-2021) at Swansea Bay University Health Board. Logistic regression assessed the relationship between the peripheral and deep margins and histological clearance. RESULTS: We ran our NLP algorithm on 34,955 BCCs. Out of the 1447 BCCs that met the inclusion criteria, the peripheral margin clearance was not influenced by the BCC risk level (p = 0.670). A clinical peripheral margin of 6 mm achieved a 95% histological clearance rate (95% confidence interval [CI], 0.93-0.98). Tumour thickness inversely affected deep-margin histological clearance (OR 0.720, 95% CI, 0.525-0.991, p < 0.05). Depth level 2 had a 97% probability of achieving deep-margin histological clearance across all tumour thicknesses. CONCLUSION: Updated RCPath reporting standards minimally impact the peripheral margin histological clearance in BCC. Larger clinical peripheral margins than those indicated by current guidelines may be necessary to achieve excision rates of ≥95%. These findings emphasise the need for continuous reassessment of clinical standards to enhance patient care.

Does Health & Her app use improve menopausal symptoms? A longitudinal cohort study.

OBJECTIVES: The Health & Her app provides menopausal women with a means of monitoring their symptoms, symptom triggers and menstrual periods, and enables them to engage in a variety of digital activities designed to promote well-being. This study aimed to examine whether sustained weekly engagement with the app is associated with improvements in menopausal symptoms. DESIGN: A pre-post longitudinal cohort study. SETTING: Analysed data collected from Health & Her app users. PARTICIPANTS: 1900 women who provided symptom data via the app across a 2-month period. PRIMARY AND SECONDARY OUTCOME MEASURES: Symptom changes from baseline to 2 months was the outcome measure. A linear mixed effects model explored whether levels of weekly app engagement influenced symptom changes. Secondary analyses explored whether app-usage factors such as total number of days spent logging symptoms, reporting triggers, reporting menstrual periods and using in-app activities were independently predictive of symptom changes from baseline. Covariates included hormone replacement therapy use, hormonal contraceptive use, present comorbidities, age and dietary supplement use. RESULTS: Findings demonstrated that greater engagement with the Health & Her app for 2 months was associated with greater reductions in symptoms over time. Daily use of in-app activities and logging symptoms and menstrual periods were each independently associated with symptom reductions. CONCLUSIONS: This study demonstrated that greater weekly engagement with the app was associated with greater reductions in symptoms. It is recommended that women be made aware of menopause-specific apps, such as that provided by Health & Her, to support them to manage their symptoms.

Genetic influences on epilepsy outcomes: A whole-exome sequencing and health care records data linkage study.

OBJECTIVE: This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes. METHODS: We linked whole-exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)-derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom. RESULTS: We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty-six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure-free. There was no significant difference between the outcome groups in terms of the exome-wide and gene-based burden of rare and deleterious genetic variants. SIGNIFICANCE: We successfully uploaded, annotated, and linked genetic sequence data and NLP-derived seizure frequency data to anonymized health care records in this proof-of-concept study. We did not detect a genetic influence on real-world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing.

Introduction: Routinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data. Methods: We used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care. Results: We identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1-96.9), 84.2% (95% CI: 72.8-96.1), and 84.5% (95% CI: 73.0-95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen's Kappa score on annotated tokens was 0.85. Conclusion: Using an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.

Achievement of European Society of Cardiology/European Atherosclerosis Society lipid targets in very high-risk patients: Influence of depression and sex.

AIMS: To explore differences in the use of lipid lowering therapy and/or achievement of lipid guideline targets in patients with and without prior depression and influence of sex in very high-risk coronary patients. METHODS & FINDINGS: A retrospective observational cohort study was conducted using individual-level linked electronic health record data in patients who underwent percutaneous coronary intervention (2012-2017) in Wales. The cohort comprised of 13,781 patients (27.4% female), with 26.1% having prior depression. Lipid levels were recorded in 10,050 patients of whom 25% had depression. History of depression was independently associated with not having lipids checked (OR 0.79 95%CI 0.72-0.87 p<0.001). Patients with prior depression were less likely to achieve targets for low density lipoprotein cholesterol (LDL-C <1.8mmol/l), non-high density lipoprotein cholesterol (non-HDL-C <2.6mmol/l) and triglycerides (<2.3mmol/l) than patients without depression (OR 0.86 95%CI 0.78-0.96 p = 0.007, OR 0.80 95%CI 0.69-0.92 p = 0.003 & OR 0.69 95CI% 0.61-0.79 p<0.001 respectively). Females were less likely to achieve targets for LDL-C and non-HDL-C than males (OR 0.55 95%CI 0.50-0.61 p<0.001 & OR 0.63 95%CI 0.55-0.73 p<0.001). There was an additive effect of depression and sex; females with depression were not only least likely to be tested (OR 0.74 95%CI 0.65-0.84 p<0.001) but also (where levels were known) less likely to achieve LDL-C (OR 0.47 95%CI 0.41-0.55 p<0.001) and non-HDL-C targets (OR 0.50 95%CI 0.41-0.60 p<0.001). It was not possible to look at the influence of medication adherence on achievement of lipid targets due to limitations of the use of anonymised routinely-held clinical care data. CONCLUSION: Patients with prior depression were less likely to have their lipids monitored and achieve guideline targets within 1-year. Females with depression are the least likely to be tested and achieve lipid targets, suggesting not only a greater risk of future events, but also an opportunity to improve care.

Epilepsy mortality in Wales during COVID-19.

PURPOSE: The COVID-19 pandemic has increased mortality worldwide and those with chronic conditions may have been disproportionally affected. However, it is unknown whether the pandemic has changed mortality rates for people with epilepsy. We aimed to compare mortality rates in people with epilepsy in Wales during the pandemic with pre-pandemic rates. METHODS: We performed a retrospective study using individual-level linked population-scale anonymised electronic health records. We identified deaths in people with epilepsy (DPWE), i.e. those with a diagnosis of epilepsy, and deaths associated with epilepsy (DAE), where epilepsy was recorded as a cause of death on death certificates. We compared death rates in 2020 with average rates in 2015-2019 using Poisson models to calculate death rate ratios. RESULTS: There were 188 DAE and 628 DPWE in Wales in 2020 (death rates: 7.7/100,000/year and 25.7/100,000/year). The average rates for DAE and DPWE from 2015 to 2019 were 5.8/100,000/year and 23.8/100,000/year, respectively. Death rate ratios (2020 compared to 2015-2019) for DAE were 1.34 (95%CI 1.14-1.57, p<0.001) and for DPWE were 1.08 (0.99-1.17, p = 0.09). The death rate ratios for non-COVID deaths (deaths without COVID mentioned on death certificates) for DAE were 1.17 (0.99-1.39, p = 0.06) and for DPWE were 0.96 (0.87-1.05, p = 0.37). CONCLUSIONS: The significant increase in DAE in Wales during 2020 could be explained by the direct effect of COVID-19 infection. Non-COVID-19 deaths have not increased significantly but further work is needed to assess the longer-term impact.

Markup: A Web-Based Annotation Tool Powered by Active Learning.

Across various domains, such as health and social care, law, news, and social media, there are increasing quantities of unstructured texts being produced. These potential data sources often contain rich information that could be used for domain-specific and research purposes. However, the unstructured nature of free-text data poses a significant challenge for its utilisation due to the necessity of substantial manual intervention from domain-experts to label embedded information. Annotation tools can assist with this process by providing functionality that enables the accurate capture and transformation of unstructured texts into structured annotations, which can be used individually, or as part of larger Natural Language Processing (NLP) pipelines. We present Markup (https://www.getmarkup.com/) an open-source, web-based annotation tool that is undergoing continued development for use across all domains. Markup incorporates NLP and Active Learning (AL) technologies to enable rapid and accurate annotation using custom user configurations, predictive annotation suggestions, and automated mapping suggestions to both domain-specific ontologies, such as the Unified Medical Language System (UMLS), and custom, user-defined ontologies. We demonstrate a real-world use case of how Markup has been used in a healthcare setting to annotate structured information from unstructured clinic letters, where captured annotations were used to build and test NLP applications.

Achievement of European guideline-recommended lipid levels post-percutaneous coronary intervention: A population-level observational cohort study.

AIMS: European Society of Cardiology/European Atherosclerosis Society 2019 guidelines recommend more aggressive lipid targets in high- and very high-risk patients and the addition of adjuvant treatments to statins in uncontrolled patients. We aimed to assess (a) achievement of prior and new European Society of Cardiology/European Atherosclerosis Society lipid targets and (b) lipid-lowering therapy prescribing in a nationwide cohort of very high-risk patients. METHODS: We conducted a retrospective observational population study using linked health data in patients undergoing percutaneous coronary intervention (2012-2017). Follow-up was for one-year post-discharge. RESULTS: Altogether, 10,071 patients had a documented LDL-C level, of whom 48% had low-density lipoprotein cholesterol (LDL-C)<1.8 mmol/l (2016 target) and (23%) <1.4 mmol/l (2019 target). Five thousand three hundred and forty patients had non-high-density lipoprotein cholesterol (non-HDL-C) documented with 57% <2.6 mmol/l (2016) and 37% <2.2 mmol/l (2019). In patients with recurrent vascular events, fewer than 6% of the patients achieved the 2019 LDL-C target of <1.0 mmol/l. A total of 10,592 patients had triglyceride (TG) levels documented, of whom 14% were ≥2.3 mmol/l and 41% ≥1.5 mmol/l (2019). High-intensity statins were prescribed in 56.4% of the cohort, only 3% were prescribed ezetimibe, fibrates or prescription-grade N-3 fatty acids. Prescribing of these agents was lower amongst patients above target LDL-C, non-HDL-C and triglyceride levels. Females were more likely to have LDL-C, non-HDL-C and triglyceride levels above target. CONCLUSION: There was a low rate of achievement of the new European Society of Cardiology/European Atherosclerosis Society lipid targets in this large post-percutaneous coronary intervention population and relatively low rates of intensive lipid-lowering therapy prescribing in those with uncontrolled lipids. There is considerable potential to optimise lipid-lowering therapy further through statin intensification and appropriate use of novel lipid-lowering therapy, especially in women.

Epilepsy, antiepileptic drugs, and the risk of major cardiovascular events.

OBJECTIVE: This study was undertaken to determine whether epilepsy and antiepileptic drugs (including enzyme-inducing and non-enzyme-inducing drugs) are associated with major cardiovascular events using population-level, routinely collected data. METHODS: Using anonymized, routinely collected, health care data in Wales, UK, we performed a retrospective matched cohort study (2003-2017) of adults with epilepsy prescribed an antiepileptic drug. Controls were matched with replacement on age, gender, deprivation quintile, and year of entry into the study. Participants were followed to the end of the study for the occurrence of a major cardiovascular event, and survival models were constructed to compare the time to a major cardiovascular event (cardiac arrest, myocardial infarction, stroke, ischemic heart disease, clinically significant arrhythmia, thromboembolism, onset of heart failure, or a cardiovascular death) for individuals in the case group versus the control group. RESULTS: There were 10 241 cases (mean age = 49.6 years, 52.2% male, mean follow-up = 6.1 years) matched to 35 145 controls. A total of 3180 (31.1%) cases received enzyme-inducing antiepileptic drugs, and 7061 (68.9%) received non-enzyme-inducing antiepileptic drugs. Cases had an increased risk of experiencing a major cardiovascular event compared to controls (adjusted hazard ratio = 1.58, 95% confidence interval [CI] = 1.51-1.63, p < .001). There was no notable difference in major cardiovascular events between those treated with enzyme-inducing antiepileptic drugs and those treated with non-enzyme-inducing antiepileptic drugs (adjusted hazard ratio = .95, 95% CI = .86-1.05, p = .300). SIGNIFICANCE: Individuals with epilepsy prescribed antiepileptic drugs are at an increased risk of major cardiovascular events compared with population controls. Being prescribed an enzyme-inducing antiepileptic drug is not associated with a greater risk of a major cardiovascular event compared to treatment with other antiepileptic drugs. Our data emphasize the importance of cardiovascular risk management in the clinical care of people with epilepsy.

Factors affecting the choice of first-line therapy in Parkinson's disease patients in Wales: A population-based study.

First line treatment for Parkinson's disease (PD) is typically either L-dopa or a non-ergot dopamine agonist (DA). However, the options for the treatment of motor symptoms in PD patients have increased in the last thirty years, which have seen several new classes of PD medications introduced onto the market. The purpose of this study is to examine the changes in first line therapy of newly diagnosed Parkinson's patients between 2000 and 2016 in Wales. A population-based study evaluated data from the Secure Anonymised Information Linkage (SAIL) Databank of residents in Wales, aged 40 years or older, newly treated with PD medications between 2000 and 2016. The data was compared across three intervals: 2000-2005, 2006-2011 and 2012-2016. Patients were classified by age at diagnosis into young: 40-60 years; mid, 61-80 years; and older >80 years. Logistic regression was undertaken to determine the predictors of PD medication prescribing. For the whole study period, the profiles of 9142 newly diagnosed PD patients were analysed. L-dopa was the most common first line therapy (80.6%), followed by non-ergot DAs (12.9%) and monoamine oxidase B (MAO-B) inhibitors (7.9%). Odds of L-dopa prescribing were greater in patients >80 years (OR = 20.46 95%CI: 16.25-25.76) and in the period 2012-2016 (OR = 1.98 95% CI: 1.70-2.29). Prescribing of non-ergot DAs significantly declined in 2012-2016 (OR = 0.42 95% CI: 0.35-0.49). Additional factors influencing first line therapy were deprivation, presence of diabetes and prior use of antidepressants. For example, PD patients residing in the least deprived area were less likely to be prescribed L-dopa compared to patients residing in the most deprived area (OR = 0.77 95% CI: 0.65-0.93). First line therapy in PD in Wales has undergone a significant switch towards L-dopa over the last 16 years. The data indicates reasonable compliance with guidelines on efficacy and safety issues related to Parkinson's medications.

Incidence, Prevalence and Healthcare Outcomes in Idiopathic Intracranial Hypertension: A Population Study.

OBJECTIVE: To characterise trends in incidence, prevalence, and healthcare outcomes in the idiopathic intracranial hypertension (IIH) population in Wales using routinely collected healthcare data. METHODS: We used and validated primary and secondary care IIH diagnosis codes within the Secure Anonymised Information Linkage databank, to ascertain IIH cases and controls, in a retrospective cohort study between 2003 and 2017. We recorded body mass index (BMI), deprivation quintile, CSF diversion surgery and unscheduled hospital admissions in case and control cohorts. RESULTS: We analysed 35 million patient years of data. There were 1765 cases of IIH in 2017 (85% female). The prevalence and incidence of IIH in 2017 was 76/100,000 and 7.8/100,000/year, a significant increase from 2003 (corresponding figures=12/100,000 and 2.3/100,000/year) (p<0.001). IIH prevalence is associated with increasing BMI and increasing deprivation. The odds ratio for developing IIH in the least deprived quintile compared to the most deprived quintile, adjusted for gender and BMI, was 0.65 (95% CI 0.55 to 0.76). 9% of IIH cases had CSF shunts with less than 0.2% having bariatric surgery. Unscheduled hospital admissions were higher in the IIH cohort compared to controls (rate ratio=5.28, p<0.001) and in individuals with IIH and CSF shunts compared to those without shunts (rate ratio=2.02, p<0.01). CONCLUSIONS: IIH incidence and prevalence is increasing considerably, corresponding to population increases in BMI, and is associated with increased deprivation. This has important implications for healthcare professionals and policy makers given the comorbidities, complications and increased healthcare utilization associated with IIH.

Making the Most of Big Data in Plastic Surgery: Improving Outcomes, Protecting Patients, Informing Service Providers.

ABSTRACT: In medicine, "big data" refers to the interdisciplinary analysis of high-volume, diverse clinical and lifestyle information on large patient populations. Recent advancements in data storage and electronic record keeping have enabled the expansion of research in this field. In the United Kingdom, Big data has been highlighted as one of the government's "8 Great Technologies," and the Medical Research Council has invested more than £100 million since 2012 in developing the Health Data Research UK infrastructure. The recent Royal College of Surgeons Commission of the Future of Surgery concluded that analysis of big data is one of the 4 most likely avenues to bring some of the most innovative changes to surgical practice in the 21st century.In this article, we provide an overview of the nascent field of big data analytics in plastic and highlight how it has the potential to improve outcomes, increase safety, and aid service planning.We outline the current resources available, the emerging role of big data within the subspecialties of burns, microsurgery, skin and breast cancer, and how these data can be used. We critically review the limitations and considerations raised with big data, offer suggestions regarding database optimization, and suggest future directions for research in this exciting field.

Long-term outcomes after epilepsy surgery, a retrospective cohort study linking patient-reported outcomes and routine healthcare data.

OBJECTIVE: The objective of the study was to assess the long-term outcomes of epilepsy surgery between 1995 and 2015 in South Wales, UK, linking case note review, postal questionnaire, and routinely collected healthcare data. METHOD: We identified patients from a departmental database and collected outcome data from patient case notes, a postal questionnaire, and the QOLIE-31-P and linked with Welsh routinely collected data in the Secure Anonymised Information Linkage (SAIL) databank. RESULTS: Fifty-seven patients were included. Median age at surgery was 34 years (11-70), median: 24 years (2-56) after onset of habitual seizures. Median follow-up was 7 years (2-19). Twenty-eight (49%) patients were free from disabling seizures (Engel Class 1), 9 (16%) experienced rare disabling seizures (Class 2), 13 (23%) had worthwhile improvements (Class 3), and 7 (12%) had no improvement (Class 4). There was a 30% mean reduction in total antiepileptic drug (AED) load at five years postsurgery. Thirty-eight (66.7%) patients experienced tonic-clonic seizures presurgery verses 8 (14%) at last review. Seizure-free patients self-reported a greater overall quality of life (QOL; QOLIE-31-P) when compared with those not achieving seizure freedom. Seizure-free individuals scored a mean of 67.6/100 (100 is best), whereas those with continuing seizures scored 46.0/100 (p < 0.006). There was a significant decrease in the median rate of hospital admissions for any cause after epilepsy surgery (9.8 days per 1000 patient days before surgery compared with 3.9 after p < 0.005). SIGNIFICANCE: Epilepsy surgery was associated with significant improvements in seizures, a reduced AED load, and an improved QOL that closely correlated with seizure outcomes and reduced hospital admission rates following surgery. Despite this, there was a long delay from onset of habitual seizures to surgery. The importance of long-term follow-up is emphasized in terms of evolving medical needs and health and social care outcomes.

Toward a Risk-Utility Data Governance Framework for Research Using Genomic and Phenotypic Data in Safe Havens: Multifaceted Review.

BACKGROUND: Research using genomic data opens up new insights into health and disease. Being able to use the data in association with health and administrative record data held in safe havens can multiply the benefits. However, there is much discussion about the use of genomic data with perceptions of particular challenges in doing so safely and effectively. OBJECTIVE: This study aimed to work toward a risk-utility data governance framework for research using genomic and phenotypic data in an anonymized form for research in safe havens. METHODS: We carried out a multifaceted review drawing upon data governance arrangements in published research, case studies of organizations working with genomic and phenotypic data, public views and expectations, and example studies using genomic and phenotypic data in combination. The findings were contextualized against a backdrop of legislative and regulatory requirements and used to create recommendations. RESULTS: We proposed recommendations toward a risk-utility model with a flexible suite of controls to safeguard privacy and retain data utility for research. These were presented as overarching principles aligned to the core elements in the data sharing framework produced by the Global Alliance for Genomics and Health and as practical control measures distilled from published literature and case studies of operational safe havens to be applied as required at a project-specific level. CONCLUSIONS: The recommendations presented can be used to contribute toward a proportionate data governance framework to promote the safe, socially acceptable use of genomic and phenotypic data in safe havens. They do not purport to eradicate risk but propose case-by-case assessment with transparency and accountability. If the risks are adequately understood and mitigated, there should be no reason that linked genomic and phenotypic data should not be used in an anonymized form for research in safe havens.

Early Discontinuation of P2Y12 Antagonists and Adverse Clinical Events Post-Percutaneous Coronary Intervention: A Hospital and Primary Care Linked Cohort.

Background Early discontinuation of P2Y12 antagonists post-percutaneous coronary intervention may increase risk of stent thrombosis or nonstent recurrent myocardial infarction. Our aims were to (1) analyze the early discontinuation rate of P2Y12 antagonists post-percutaneous coronary intervention, (2) explore factors associated with early discontinuation, and (3) analyze the risk of major adverse cardiovascular events (death, acute coronary syndrome, revascularization, or stroke) associated with discontinuation from a prespecified prescribing instruction of 1 year. Method and Results We studied 2090 patients (2011-2015) who were recommended for clopidogrel for 12 months (+aspirin) post-percutaneous coronary intervention within a retrospective observational population cohort. Relationships between clopidogrel discontinuation and major adverse cardiac events were evaluated over 18-month follow-up. Discontinuation of clopidogrel in the first 4 quarters was low at 1.1%, 2.6%, 3.7%, and 6.1%, respectively. Previous revascularization, previous ischemic stroke, and age >80 years were independent predictors of early discontinuation. In a time-dependent multiple regression model, clopidogrel discontinuation and bleeding (hazard ratio=1.82 [1.01-3.30] and hazard ratio=5.30 [3.14-8.94], respectively) were independent predictors of major adverse cardiac events as were age <49 and ≥70 years (versus those aged 50-59 years), hypertension, chronic kidney disease stage 4+, previous revascularization, ischemic stroke, and thromboembolism. Furthermore, in those with both bleeding and clopidogrel discontinuation, hazard ratio for major adverse cardiac events was 9.34 (3.39-25.70). Conclusions Discontinuation of clopidogrel is low in the first year post-percutaneous coronary intervention, where a clear discharge instruction to treat for 1 year is provided. Whereas this is reassuring from the population level, at an individual level discontinuation earlier than the intended duration is associated with an increased rate of adverse events, most notably in those with both bleeding and discontinuation.

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.

OBJECTIVE: Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techniques to extract detailed clinical information from epilepsy clinic letters to enrich routinely collected data. DESIGN: We used the general architecture for text engineering (GATE) framework to build an information extraction system, ExECT (extraction of epilepsy clinical text), combining rule-based and statistical techniques. We extracted nine categories of epilepsy information in addition to clinic date and date of birth across 200 clinic letters. We compared the results of our algorithm with a manual review of the letters by an epilepsy clinician. SETTING: De-identified and pseudonymised epilepsy clinic letters from a Health Board serving half a million residents in Wales, UK. RESULTS: We identified 1925 items of information with overall precision, recall and F1 score of 91.4%, 81.4% and 86.1%, respectively. Precision and recall for epilepsy-specific categories were: epilepsy diagnosis (88.1%, 89.0%), epilepsy type (89.8%, 79.8%), focal seizures (96.2%, 69.7%), generalised seizures (88.8%, 52.3%), seizure frequency (86.3%-53.6%), medication (96.1%, 94.0%), CT (55.6%, 58.8%), MRI (82.4%, 68.8%) and electroencephalogram (81.5%, 75.3%). CONCLUSIONS: We have built an automated clinical text extraction system that can accurately extract epilepsy information from free text in clinic letters. This can enhance routinely collected data for research in the UK. The information extracted with ExECT such as epilepsy type, seizure frequency and neurological investigations are often missing from routinely collected data. We propose that our algorithm can bridge this data gap enabling further epilepsy research opportunities. While many of the rules in our pipeline were tailored to extract epilepsy specific information, our methods can be applied to other diseases and also can be used in clinical practice to record patient information in a structured manner.