Publications
31 results found
Kelshiker MA, Chhatwal K, Bachtiger P, et al., 2024, From ether to ethernet: ensuring ethical policy in digital transformation of waitlist triage for cardiovascular procedures., NPJ Digit Med, Vol: 7
Davies HJ, Hammour G, Xiao H, et al., 2024, Physically Meaningful Surrogate Data for COPD., IEEE Open J Eng Med Biol, Vol: 5, Pages: 148-156
The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.
Painter A, van Dael J, Neves A, et al., 2023, Identifying benefits and concerns with using digital health services during COVID-19: evidence from a hospital-based patient survey, Health Informatics Journal, Vol: 29, ISSN: 0965-8335
Despite large-scale adoption during COVID-19, patient perceptions on the benefits and potential risks with receiving care through digital technologies have remained largely unexplored. A quantitative content analysis of responses to a questionnaire (N = 6766) conducted at a multi-site acute trust in London (UK), was adopted to identify commonly reported benefits and concerns. Patients reported a range of promising benefits beyond immediate usage during COVID-19, including ease of access; support for disease and care management; improved timeliness of access and treatment; and better prioritisation of healthcare resources. However, in addition to known risks such as data security and inequity in access, our findings also illuminate some less studied concerns, including perceptions of compromised safety; negative impacts on patient-clinician relationships; and difficulties in interpreting health information provided through electronic health records and mHealth apps. Implications for future research and practice are discussed.
Zaman S, Padayachee Y, Shah M, et al., 2023, Smartphone-based remote monitoring in heart failure with reduced ejection fraction: retrospective cohort study of secondary care use and costs, JMIR Cardio, Vol: 7, ISSN: 2561-1011
BACKGROUND: Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown. OBJECTIVE: The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM. METHODS: We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling. RESULTS: A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustai
Bachtiger P, Kelshiker MA, Petri CF, et al., 2023, Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis, BMJ Health & Care Informatics, Vol: 30, ISSN: 2632-1009
BACKGROUND AND AIMS: Most patients with heart failure (HF) are diagnosed following a hospital admission. The clinical and health economic impacts of index HF diagnosis made on admission to hospital versus community settings are not known. METHODS: We used the North West London Discover database to examine 34 208 patients receiving an index diagnosis of HF between January 2015 and December 2020. A propensity score-matched (PSM) cohort was identified to adjust for differences in socioeconomic status, cardiovascular risk and pre-diagnosis health resource utilisation cost. Outcomes were stratified by two pathways to index HF diagnosis: a 'hospital pathway' was defined by diagnosis following hospital admission; and a 'community pathway' by diagnosis via a general practitioner or outpatient services. The primary clinical and health economic endpoints were all-cause mortality and cost-consequence differential, respectively. RESULTS: The diagnosis of HF was via hospital pathway in 68% (23 273) of patients. The PSM cohort included 17 174 patients (8582 per group) and was matched across all selected confounders (p>0.05). The ratio of deaths per person-months at 24 months comparing community versus hospital diagnosis was 0.780 (95% CI 0.722 to 0.841, p<0.0001). By 72 months, the ratio of deaths was 0.960 (0.905 to 1.020, p=0.18). Diagnosis via hospital pathway incurred an overall extra longitudinal cost of £2485 per patient. CONCLUSIONS: Index diagnosis of HF through hospital admission continues to dominate and is associated with a significantly greater short-term risk of mortality and substantially increased long-term costs than if first diagnosed in the community. This study highlights the potential for community diagnosis-early, before symptoms necessitate hospitalisation-to improve both clinical and health economic outcomes.
Mashar M, Chawla S, Chen F, et al., 2023, Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?, JMIR AI, Vol: 2, Pages: e42940-e42940
<jats:p>Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms “Artificial intelligence,” “Machine learning,” and “regulation” from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper.</jats:p>
Wickramathilaka A, Kelshiker MA, Griffin J, et al., 2022, Reproducibility of use by physicians and patients of a smart stethoscope for artificial-intelligence enhanced diagnosis of heart failure, Scientific Sessions of the American-Heart-Association / Resuscitation Science Symposium, Publisher: Lippincott, Williams & Wilkins, Pages: 1-3, ISSN: 0009-7322
Padayachee Y, Shah M, Auton A, et al., 2022, Smartphone-based remote monitoring (RM) in chronic heart failure reduces emergency hospital attendances, unplanned admissions and secondary care costs: a retrospective cohort study, ESC Congress, Publisher: Oxford University Press, Pages: 2816-2816, ISSN: 0195-668X
Auton A, Padayachee Y, Samways J, et al., 2022, Smartphone-based remote monitoring in chronic heart failure: patient & clinician user experience, impact on patient engagement and quality of life, European Heart Journal, Vol: 43, Pages: 2808-2808, ISSN: 0195-668X
BackgroundHeart failure with reduced ejection fraction (HFrEF) lowers patients' quality of life (QoL) [1]. Digital interventions such as ESC's “Heart Failure Matters” website aim to encourage patient-engagement & self-management [2], which remain major challenges in HFrEF care. Although remote monitoring (RM) has been tested in HFrEF with inconclusive impact on prognosis [3], its impact on patients' experience and engagement is unclear [4]. Furthermore, the perspective of clinicians using RM technologies remains unknown. We present users' experience of Luscii, a novel smartphone-based RM platform enabling HFrEF patients to submit clinical measurements, symptoms, complete educational modules, & communicate with HF specialist nurses (HFSNs).Purpose(I) To evaluate the usage-type & user experience of patients and HFSNs.(II) To assess the impact of using the RM platform on self-reported QoLMethodsA two-part retrospective analysis of HFrEF patients from our regional service using the RM platform: Part A: Thematic analysis of patient feedback provided via the platform and a focus group of six HFSNs. Part B: Scores for a locally-devised HF questionnaire (HFQ), depression (PHQ-9) & anxiety (GAD-7) questionnaires were extracted from the RM platform at two timepoints: at on-boarding and 3 months after. Paired non-parametric tests were used to evaluate difference between median scores across the two time points.Results83 patients (mean age 62 years; 27% female) used the RM platform between April and November 2021. 2 dropped out & 2 died before 3 months. Part A: Patients and HFSNs exchanged information on many topics via the platform, including patient educational modules (Figure 1). Thematic analysis revealed positive and negative impacts with many overlapping subthemes between the two user groups (Figure 2). Part B: At 3 months there was no difference in HFQ score (19 vs. 18, p=0.57, maximum possible score = 50). PHQ-9 (3 vs. 3, p=0.48, maximum
Davies HJ, Bachtiger P, Williams I, et al., 2022, Wearable in-ear PPG: detailed respiratory variations enable classification of COPD, IEEE Transactions on Biomedical Engineering, Vol: 69, ISSN: 0018-9294
An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.
Bachtiger P, Adamson A, Maclean W, et al., 2022, Increasing but inadequate intention to receive Covid-19 vaccination over the first 50 days of impact of the more infectious variant and roll-out of vaccination in UK: indicators for public health messaging, Publisher: MedRxiv
Objectives To inform critical public health messaging by determining how changes in Covid-19 vaccine hesitancy, attitudes to the priorities for administration, the emergence of new variants and availability of vaccines may affect the trajectory and achievement of herd immunity.Methods >9,000 respondents in an ongoing cross-sectional participatory longitudinal epidemiology study (LoC-19, n=18,581) completed a questionnaire within their personal electronic health record in the week reporting first effective Covid-19 vaccines, and then again after widespread publicity of the increased transmissibility of a new variant (November 13th and December 31st 2020 respectively). Questions covered willingness to receive Covid-19 vaccination and attitudes to prioritisation. Descriptive statistics, unadjusted and adjusted odds ratios (ORs) and natural language processing of free-text responses are reported, and how changes over the first 50 days of both vaccination roll-out and new-variant impact modelling of anticipated transmission rates and the likelihood and time to herd immunity.Findings Compared with the week reporting the first efficacious vaccine there was a 15% increase in acceptance of Covid-19 vaccination, attributable in one third to the impact of the new variant, with 75% of respondents “shielding” – staying at home and not leaving unless essential – regardless of health status or tier rules. 12.5% of respondents plan to change their behaviour two weeks after completing vaccination compared with 45% intending to do so only when cases have reduced to a low level. Despite the increase from 71% to 86% over this critical 50-day period, modelling of planned uptake of vaccination remains below that required for rapid effective herd immunity – now estimated to be 90 percent in the presence of a new variant escalating R0 to levels requiring further lockdowns. To inform the public messaging essential therefore to improve uptake, age and female
Bachtiger P, Petri CF, Scott FE, et al., 2022, Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study, The Lancet Digital Health, Vol: 4, ISSN: 2589-7500
BACKGROUND: Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. METHODS: We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0-1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415. FINDINGS: Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the p
Bachtiger P, Petri CF, Scott FE, et al., 2021, Artificial Intelligence For Point-of-Care Heart Failure Screening During ECG-Enabled Stethoscope Examination: Independent Real-World Prospective Multicenter External Validation Study, Scientific Sessions of the American-Heart-Association / Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: E586-E587, ISSN: 0009-7322
Bachtiger P, Petri CF, Scott FE, et al., 2021, Artificial Intelligence For Point-of-Care Heart Failure Screening During ECG-Enabled Stethoscope Examination: Independent Real-World Prospective Multicenter External Validation Study, Scientific Sessions of the American-Heart-Association / Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: E586-E587, ISSN: 0009-7322
Bachtiger P, Scott F, Park S, et al., 2021, Multicentre validation of point-of-care screening tool for heart failure: single-lead ECG recorded by smart stethoscope predicts low ejection fraction using artificial intelligence, ESC Congress 2021, Publisher: European Society of Cardiology, Pages: 3071-3071, ISSN: 0195-668X
Background/IntroductionArtificial intelligence (AI) applied to 12-lead ECG can identify left ventricular ejection fraction (EF) ≤35% with a sensitivity and specificity of 86.3% and 85.7%, respectively. Whether AI algorithms trained on 12-lead can accurately predict EF from single-lead ECGs (recorded by a smart stethoscope) remains unknown. This could facilitate point-of-care screening for low EF during routine clinical examination.PurposeFirst independent multicentre real-world UK National Health Service (NHS) prospective validation of 12-lead-ECG-trained AI algorithm applied to single-lead ECG recorded by a smart stethoscope, with AI algorithm tuned to detect EF ≤40%.MethodsProspective recruitment of unselected patients attending for echocardiography across six urban NHS hospital sites (UK). In addition to transthoracic echocardiogram (routine care), all participants had 15 seconds of supine, single-lead ECG recorded at six different positions (figure), encompassing standard anatomical positions for cardiac auscultation. A convolutional neural network (CNN) previously trained on 35,970 independent pairings of 12-lead-ECG and echocardiograms was retrained to use the single-lead ECG as input. Accuracy of CNN detection of low EF (binary ≤40%) is reported at a threshold of 0.5 against gold-standard; echo-determined percentage EF.ResultsAmong 353 patients recruited (mean age 63±17; 58% male, 43.1% non-white), 309 (87.5%) had an EF >40%, and 44 (12.5%) had EF ≤40%. The best single recording position in isolation was position 3 (sensitivity 57.9% [42.2–73.6], specificity 86.3% [82.2–90.3]). Taking any of the six positions performed during the examination as predicting EF ≤40%, this achieved a sensitivity of 81.2% and specificity of 61.5%.Conclusion(s)In this first prospective multicentre validation study the retrained AI algorithm reliably detected low EF from single-lead ECGs acquired using a novel ECG-enabled stethoscope in standard
Bachtiger P, Park S, Letchford E, et al., 2021, Triage-HF plus: 12-month study of remote monitoring pathway for triage of heart failure risk initiated during the Covid-19 pandemic, Publisher: OXFORD UNIV PRESS, Pages: 3082-3082, ISSN: 0195-668X
Bachtiger P, Adamson A, Maclean WA, et al., 2021, Determinants of shielding behaviour during the COVID-19 pandemic and associations with wellbeing in >7,000 NHS patients: 17-week longitudinal observational study., JMIR Public Health and Surveillance, Vol: 7, Pages: 1-14, ISSN: 2369-2960
BACKGROUND: The UK National Health Service (NHS) classified 2.2 million people as clinically extremely vulnerable (CEV) during the first wave of the 2020 COVID-19 pandemic, advising them to 'shield' - to not leave home for any reason. OBJECTIVE: The aim of this study was to measure the determinants of shielding behaviour and associations with wellbeing in a large NHS patient population, towards informing future health policy. METHODS: Patients contributing to an ongoing longitudinal participatory epidemiology study (LoC-19, n = 42,924) received weekly email invitations to complete questionnaires (17-week shielding period starting 9th April 2020) within their NHS personal electronic health record. Question items focused on wellbeing. Participants were stratified into four groups by self-reported CEV status (qualifying condition) and adoption of shielding behaviour (baselined at week 1 or 2). Distribution of CEV criteria is reported alongside situational variables and uni- and multivariable logistic regression. Longitudinal trends in physical and mental wellbeing were displayed graphically. Free-text responses reporting variables impacting wellbeing were semi-quantified using natural language processing. In the lead up to a second national lockdown (October 23rd, 2020), a follow-up questionnaire evaluated subjective concern if further shielding were advised. RESULTS: 7,240 participants were included. Among the CEV (2,391), 1,133 (47.3%) assumed shielding behaviour at baseline, compared with 633 (15.0%) in the non-CEV group. Those CEV who shielded were more likely to be Asian (Odds Ratio OR 2.02 [1.49-2.76]), female (OR 1.24 [1.05-1.45]), older (OR per year increase 1.01 [1.00-1.02]) and live in a home with outdoor space (OR 1.34 [1.06-1.70]) or 3-4 other inhabitants (3 = OR 1.49 [1.15-1.94], 4 = OR 1.49 [1.10-2.01]); and be solid organ transplant recipients (2.85 [2.18-3.77]) or have severe chronic lung disease (OR 1.63 [1.30-2.04]). Receipt of a government letter adv
Bachtiger P, Adamson A, Maclean WA, et al., 2021, Determinants of Shielding Behavior During the COVID-19 Pandemic and Associations With Well-being Among National Health Service Patients: Longitudinal Observational Study (Preprint)
<sec> <title>BACKGROUND</title> <p>The UK National Health Service (NHS) classified 2.2 million people as clinically extremely vulnerable (CEV) during the first wave of the 2020 COVID-19 pandemic, advising them to “shield” (to not leave home for any reason).</p> </sec> <sec> <title>OBJECTIVE</title> <p>The aim of this study was to measure the determinants of shielding behavior and associations with well-being in a large NHS patient population for informing future health policy.</p> </sec> <sec> <title>METHODS</title> <p>Patients contributing to an ongoing longitudinal participatory epidemiology study (Longitudinal Effects on Wellbeing of the COVID-19 Pandemic [LoC-19], n=42,924) received weekly email invitations to complete questionnaires (17-week shielding period starting April 9, 2020) within their NHS personal electronic health record. Question items focused on well-being. Participants were stratified into four groups by self-reported CEV status (qualifying condition) and adoption of shielding behavior (baselined at week 1 or 2). The distribution of CEV criteria was reported alongside situational variables and univariable and multivariable logistic regression. Longitudinal trends in physical and mental well-being were displayed graphically. Free-text responses reporting variables impacting well-being were semiquantified using natural language processing. In the lead up to a second national lockdown (October 23, 2020), a follow-up questionnaire evaluated subjective concern if further shielding was advised.</p> </sec> <sec> <title>RESULTS</title>
Bachtiger P, Adamson A, Chow J-J, et al., 2021, The Impact of the COVID-19 Pandemic on the Uptake of Influenza Vaccine: UK-Wide Observational Study, Publisher: JMIR PUBLICATIONS, INC
Bachtiger P, Adamson A, Chow J-J, et al., 2021, The impact of the Covid-19 pandemic on uptake of influenza vaccine: a UK-wide observational study., JMIR Public Health and Surveillance, Vol: 7, Pages: 1-14, ISSN: 2369-2960
BACKGROUND: In the face of the Covid-19 pandemic, the UK National Health Service (NHS) flu vaccination eligibility is extended this season to ~32.4 million (48.8%) of the population. Knowing intended uptake will inform supply and public health messaging to maximise vaccination. OBJECTIVE: The objective of this study was to measure the impact of the Covid-19 pandemic on acceptance of flu vaccination in the 2020-21 season, specifically focusing on those previously eligible who routinely decline vaccination and the newly eligible. METHODS: Intention to receive influenza vaccine in 2020-21 was asked of all registrants of the NHS's largest electronic personal health record by online questionnaire on 31st July 2020. Of those who were either newly or previously eligible but had not previously received influenza vaccination, multivariable logistic regression and network diagrams were used to examine reasons to have or decline vaccination. RESULTS: Among 6,641 respondents, 945 (14.2%) were previously eligible but not vaccinated, of whom 536 (56.7%) intended to receive flu vaccination in 2020/21, as did 466 (68.6%) of the newly eligible. Intention to receive the flu vaccine was associated with increased age, index of multiple deprivation (IMD) quintile, and considering oneself at high risk from Covid-19. Among those eligible but intending not to be vaccinated in 2020/21, 164 (30.2%) gave misinformed reasons. 47 (49.9%) of previously unvaccinated healthcare workers would decline vaccination in 2020/21. CONCLUSIONS: In this sample, Covid-19 has increased acceptance of flu vaccination in those previously eligible but unvaccinated and motivates substantial uptake in the newly eligible. This study is essential for informing resource planning and the need for effective messaging campaigns to address negative misconceptions, also necessary for Covid-19 vaccination programmes. CLINICALTRIAL: Not applicable.
Bachtiger P, Adamson A, Chow J-J, et al., 2020, The Impact of the COVID-19 Pandemic on the Uptake of Influenza Vaccine: UK-Wide Observational Study (Preprint)
<sec> <title>BACKGROUND</title> <p>In the face of the COVID-19 pandemic, the UK National Health Service (NHS) extended eligibility for influenza vaccination this season to approximately 32.4 million people (48.8% of the population). Knowing the intended uptake of the vaccine will inform supply and public health messaging to maximize vaccination.</p> </sec> <sec> <title>OBJECTIVE</title> <p>The objective of this study was to measure the impact of the COVID-19 pandemic on the acceptance of influenza vaccination in the 2020-2021 season, specifically focusing on people who were previously eligible but routinely declined vaccination and newly eligible people.</p> </sec> <sec> <title>METHODS</title> <p>Intention to receive the influenza vaccine in 2020-2021 was asked of all registrants of the largest electronic personal health record in the NHS by a web-based questionnaire on July 31, 2020. Of those who were either newly or previously eligible but had not previously received an influenza vaccination, multivariable logistic regression and network diagrams were used to examine their reasons to undergo or decline vaccination.</p> </sec> <sec> <title>RESULTS</title> <p>Among 6641 respondents, 945 (14.2%) were previously eligible but were not vaccinated; of these, 536 (56.7%) intended to receive an influenza vaccination in 2020-2021, as did 466 (68.6%) of the newly eligible respondents. Intention to receive the influenza vaccine was associated with increased age, index of multiple deprivation quintile, and considering oneself to be at high risk fr
Bachtiger P, Adamson A, Maclean W, et al., 2020, Inadequate intention to receive Covid-19 vaccination: indicators for public health messaging needed to improve uptake in UK, Publisher: MedRxiv
Data promising effective Covid-19 vaccines have accelerated the UK’s mass vaccination programme. The UK public’s attitudes to the government’s prioritisation list are unknown, and achieving critical population immunity will require the remaining majority to accept both vaccination and the delay in access of up to a year or more. This cross-sectional observational study sent an online questionnaire to registrants of the UK National Health Service’s largest personal health record. Question items covered willingness for Covid-19 vaccine uptake and attitudes to prioritisation. Among 9,122 responses, 71.5% indicated wanting a vaccine, below what previous modelling indicated as critical levels for progressing towards herd immunity. 22.7% disagreed with the prioritisation list, though 70.3% were against being able to expedite vaccination through payment. Age and female gender were, respectively, strongly positively and negatively associated with wanting a vaccine. Teachers and Black, Asian and Minority Ethnic (BAME) groups were most cited by respondents for prioritisation. This study identifies factors to inform the public health messaging critical to improving uptake.
Bachtiger P, Adamson A, Quint JK, et al., 2020, Belief of having had unconfirmed Covid-19 infection reduces willingness to participate in app-based contact tracing, npj Digital Medicine, Vol: 3, Pages: 1-7, ISSN: 2398-6352
<jats:title>Abstract</jats:title> <jats:p>Contact tracing and lockdown are health policies being used worldwide to combat the coronavirus (COVID-19). The UK National Health Service (NHS) Track and Trace Service has plans for a nationwide app that notifies the need for self-isolation to those in contact with a person testing positive for COVID-19. To be successful, such an app will require high uptake, the determinants and willingness for which are unclear but essential to understand for effective public health benefit. The objective of this study was to measure the determinants of willingness to participate in an NHS app-based contact-tracing programme using a questionnaire within the Care Information Exchange (CIE)—the largest patient-facing electronic health record in the NHS. Among 47,708 registered NHS users of the CIE, 27% completed a questionnaire asking about willingness to participate in app-based contact tracing, understanding of government advice, mental and physical wellbeing and their healthcare utilisation—related or not to COVID-19. Descriptive statistics are reported alongside univariate and multivariable logistic regression models, with positive or negative responses to a question on app-based contact tracing as the dependent variable. 26.1% of all CIE participants were included in the analysis (<jats:italic>N</jats:italic> = 12,434, 43.0% male, mean age 55.2). 60.3% of respondents were willing to participate in app-based contact tracing. Out of those who responded ‘no’, 67.2% stated that this was due to privacy concerns. In univariate analysis, worsening mood, fear and anxiety in relation to changes in government rules around lockdown were associated with lower willingness to participate. Multivariable analysis showed that difficulty understanding government rules was associated with a decreased inclination to download the app, with those scoring 1–2 and 3–4
Bachtiger P, Peters NS, Walsh SLF, 2020, Machine learning for COVID-19-asking the right questions, The Lancet Digital Health, Vol: 2, Pages: E391-E392, ISSN: 2589-7500
Mattie H, Reidy P, Bachtiger P, et al., 2020, A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods, POPULATION HEALTH MANAGEMENT, Vol: 23, Pages: 319-325, ISSN: 1942-7891
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Bachtiger P, Adamson A, Quint JK, et al., 2020, Belief of Previous COVID-19 Infection and Unclear Government Policy are Associated with Reduced Willingness to Participate in App-Based Contact Tracing: A UK-Wide Observational Study of 13,000 Patients
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Contact tracing and lockdown are health policies being used worldwide to combat the coronavirus (COVID-19). While easing lockdown, the UK National Health Service (NHS) launched its Track and Trace Service at the end of May 2020, and aims by end of June 2020 also to include app-based notification and advice to self-isolate for those who have been in contact with a person known to have COVID-19. To be successful, such an app will require high uptake, the determinants and willingness for which are unclear but essential to understand for effective public health benefit.</jats:p></jats:sec><jats:sec><jats:title>Objectives</jats:title><jats:p>To measure the determinants of willingness to participate in an NHS app-based contact tracing programme using a questionnaire within the Care Information Exchange (CIE) - the largest patient-facing electronic health record in the NHS.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Observational study of 47,708 registered NHS users of the CIE, 27% of whom completed a novel questionnaire asking about willingness to participate in app-based contact tracing, understanding of government advice, mental and physical wellbeing and their healthcare utilisation -- related or not to COVID-19. Descriptive statistics are reported alongside univariate and multivariable logistic regression models, with positive or negative responses to a question on app-based contact tracing as the dependent variable.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>26.1% of all CIE participants were included in the analysis (N = 12,434, 43.0% male, mean age 55.2). 60.3% of respondents were willing to participate in app-based contact tracing. Out of those who responded ‘no’, 67.2% state
Bachtiger P, Plymen CM, Pabari PA, et al., 2020, Artificial intelligence, data sensors and interconnectivity: future Opportunities for heart failure, Cardiac Failure Review, Vol: 6, Pages: e11-e11, ISSN: 2057-7540
A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors - so-called interconnectivity - and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.
Dauvin A, Donado C, Bachtiger P, et al., 2019, Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients, NPJ DIGITAL MEDICINE, Vol: 2, ISSN: 2398-6352
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