Publications by authors named "Patrik Bachtiger"

7 Publications

  • Page 1 of 1

The Impact of the Covid-19 Pandemic on Uptake of Influenza Vaccine: A UK-Wide Observational Study.

JMIR Public Health Surveill 2021 Feb 18. Epub 2021 Feb 18.

National Heart & Lung Institute, Imperial College London, Hammersmith Hospital CampusDu Cane Road, London, GB.

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.
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http://dx.doi.org/10.2196/26734DOI Listing
February 2021

Belief of having had unconfirmed Covid-19 infection reduces willingness to participate in app-based contact tracing.

NPJ Digit Med 2020 Nov 6;3(1):146. Epub 2020 Nov 6.

National Heart and Lung Institute, Imperial College London, London, UK.

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 (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% 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 in their understanding of the new government rules being 45% and 27% less inclined to download the contact-tracing app, respectively; when compared to those who rated their understanding as 5-6/10 (OR for 1-2/10 = 0.57 [CI 0.48-0.67]; OR for 3-4/10 = 0.744 [CI 0.64-0.87]), whereas scores of 7-8 and 9-10 showed a 43% and 31% respective increase. Those reporting an unconfirmed belief of having previously had and recovered from COVID-19 were 27% less likely to be willing to download the app; belief of previous recovery from COVID-19 infection OR 0.727 [0.585-0.908]). In this large UK-wide questionnaire of wellbeing in lockdown, a willingness for app-based contact tracing over an appropriate age range is 60%-close to the estimated 56% population uptake, and substantially less than the smartphone-user uptake considered necessary for an app-based contact tracing to be an effective intervention to help suppress an epidemic. Difficulty comprehending government advice and uncertainty of diagnosis, based on a public health policy of not testing to confirm self-reported COVID-19 infection during lockdown, therefore reduce willingness to adopt a government contact-tracing app to a level below the threshold for effectiveness as a tool to suppress an epidemic.
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http://dx.doi.org/10.1038/s41746-020-00357-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7648058PMC
November 2020

Machine learning for COVID-19-asking the right questions.

Lancet Digit Health 2020 08 10;2(8):e391-e392. Epub 2020 Jul 10.

National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK. Electronic address:

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http://dx.doi.org/10.1016/S2589-7500(20)30162-XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351424PMC
August 2020

Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure.

Card Fail Rev 2020 Mar 12;6:e11. Epub 2020 May 12.

Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.

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.
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http://dx.doi.org/10.15420/cfr.2019.14DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265101PMC
March 2020

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.

NPJ Digit Med 2019 29;2:116. Epub 2019 Nov 29.

8University of Arizona College of Medicine, Tucson, AZ USA.

Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86-0.89) classify an individual patient's baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.
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http://dx.doi.org/10.1038/s41746-019-0192-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884624PMC
November 2019

A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.

Popul Health Manag 2020 08 25;23(4):319-325. Epub 2019 Nov 25.

Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Digital care management programs can reduce health care costs and improve quality of care. However, it is unclear how to target patients who are most likely to benefit from these programs ex ante, a shortcoming of current "risk score"-based approaches across many interventions. This study explores a framework to define impactability by using machine learning (ML) models to identify those patients most likely to benefit from a digital health intervention for care management. Anonymized insurance claims data were used from a commercially insured population across several US states and combined with inferred sociodemographic data. The approach involves creating 2 models and the comparative analysis of the methodologies and performances therein. The authors first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) health care expenditures for patients (N 5600). This enables classification impactability if differences in predicted versus actual costs meet a predetermined threshold. Several random forest and logistic regression machine learning models were then trained to accurately categorize new patients as impactable versus not impactable. These parameters are modified through grid search to define the parameters that deliver optimal performance, reaching an overall sensitivity of 0.77 and specificity of 0.65 among all models. This approach shows that impactability for a digital health intervention can be successfully defined using ML methods, thus enabling efficient allocation of resources. This framework is generalizable to analyzing impactability of any intervention and can contribute to realizing closed-loop feedback systems for continuous improvement in health care.
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http://dx.doi.org/10.1089/pop.2019.0132DOI Listing
August 2020

Cell death markers in patients with cirrhosis and acute decompensation.

Hepatology 2018 03 24;67(3):989-1002. Epub 2018 Jan 24.

Liver Failure Group, Institute for Liver and Digestive Health, University College London, London, United Kingdom.

The aims of this study were to determine the role of cell death in patients with cirrhosis and acute decompensation (AD) and acute on chronic liver failure (ACLF) using plasma-based biomarkers. The patients studied were part of the CANONIC (CLIF Acute-on-Chronic Liver Failure in Cirrhosis) study (N = 337; AD, 258; ACLF, 79); additional cohorts included healthy volunteers, stable patients with cirrhosis, and a group of 16 AD patients for histological studies. Caspase-cleaved keratin 18 (cK18) and keratin 18 (K18), which reflect apoptotic and total cell death, respectively, and cK18:K18 ratio (apoptotic index) were measured in plasma by enzyme-linked immunosorbent assay. The concentrations of cK18 and K18 increased and the cK18:K18 ratio decreased with increasing severity of AD and ACLF (P < 0.001, respectively). Alcohol etiology, no previous decompensation, and alcohol abuse were associated with increased cell death markers whereas underlying infection was not. Close correlation was observed between the cell death markers and, markers of systemic inflammation, hepatic failure, alanine aminotransferase, and bilirubin, but not with markers of extrahepatic organ injury. Terminal deoxynucleotidyl transferase dUTP nick-end labeling staining confirmed evidence of greater hepatic cell death in patients with ACLF as opposed to AD. Inclusion of cK18 and K18 improved the performance of the CLIF-C AD score in prediction of progression from AD to ACLF (P < 0.05).

Conclusion: Cell death, likely hepatic, is an important feature of AD and ACLF and its magnitude correlates with clinical severity. Nonapoptotic forms of cell death predominate with increasing severity of AD and ACLF. The data suggests that ACLF is a heterogeneous entity and shows that the importance of cell death in its pathophysiology is dependent on predisposing factors, precipitating illness, response to injury, and type of organ failure. (Hepatology 2018;67:989-1002).
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http://dx.doi.org/10.1002/hep.29581DOI Listing
March 2018