Nebulized Recombinant Tissue Plasminogen Activator (rt-PA) for Acute COVID-19-Induced Respiratory Failure: An Exploratory Proof-of-Concept Trial.

Acute lung injury in COVID-19 results in diffuse alveolar damage with disruption of the alveolar-capillary barrier, coagulation activation, alveolar fibrin deposition and pulmonary capillary thrombi. Nebulized recombinant tissue plasminogen activator (rt-PA) has the potential to facilitate localized thrombolysis in the alveolar compartment and improve oxygenation. In this proof-of-concept safety study, adults with COVID-19-induced respiratory failure and a <300 mmHg PaO2/FiO2 (P/F) ratio requiring invasive mechanical ventilation (IMV) or non-invasive respiratory support (NIRS) received nebulized rt-PA in two cohorts (C1 and C2), alongside standard of care, between 23 April-30 July 2020 and 21 January-19 February 2021, respectively. Matched historical controls (MHC; n = 18) were used in C1 to explore efficacy. Safety co-primary endpoints were treatment-related bleeds and <1.0-1.5 g/L fibrinogen reduction. A variable dosing strategy with clinical efficacy endpoint and minimal safety concerns was determined in C1 for use in C2; patients were stratified by ventilation type to receive 40-60 mg rt-PA daily for ≤14 days. Nine patients in C1 (IMV, 6/9; NIRS, 3/9) and 26 in C2 (IMV, 12/26; NIRS, 14/26) received nebulized rt-PA for a mean (SD) of 6.7 (4.6) and 9.1(4.6) days, respectively. Four bleeds (one severe, three mild) in three patients were considered treatment related. There were no significant fibrinogen reductions. Greater improvements in mean P/F ratio from baseline to study end were observed in C1 compared with MHC (C1; 154 to 299 vs. MHC; 154 to 212). In C2, there was no difference in the baseline P/F ratio of NIRS and IMV patients. However, a larger improvement in the P/F ratio occurred in NIRS patients (NIRS; 126 to 240 vs. IMV; 120 to 188) and fewer treatment days were required (NIRS; 7.86 vs. IMV; 10.5). Nebulized rt-PA appears to be well-tolerated, with a trend towards improved oxygenation, particularly in the NIRS group. Randomized clinical trials are required to demonstrate the clinical effect significance and magnitude.

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study.

BACKGROUND: In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. OBJECTIVE: The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. METHODS: We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. RESULTS: We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2-large and AraBERTv0.2-base. CONCLUSIONS: Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.

Covid-19 ICU remote-learning course (CIRLC): Rapid ICU remote training for frontline health professionals during the COVID-19 pandemic in the UK.

Background: The unprecedented increase in critically ill patients due to the COVID-19 pandemic mandated rapid training in critical care for redeployed staff to work safely in intensive care units (ICU). Methods: The COVID-19 ICU Remote-Learning Course (CIRLC) is a remote delivery course developed in response to the pandemic. This was a one-day course focused on the fundamentals of Intensive Care. The course used blended learning with recorded lectures and interactive tutorials delivered by shielding and frontline ICU trained professionals. The course was developed within one week and piloted at three NHS Trusts. It was then made publicly available free of charge to redeployed healthcare professionals across the UK and Ireland. An iterative cycle of improvement was used to update the course content weekly. A course confidence questionnaire with quantitative and qualitative questions was used to evaluate effectiveness. Data is reported as n (%), means (SD) and thematic analysis was used for the open questions. Results: 1,269 candidates from 171 organisations completed the course, with 99 volunteer trainers. 96% of respondents rated the course as very or extremely useful. 86% rated the online platform as excellent. Overall confidence improved from 2.7/5 to 3.9/5. Qualitative data showed that the course was pitched at the appropriate level, accessible and built clinicians confidence to work in intensive care. Conclusion: This model of educational delivery with a rapid iteration cycle was a pragmatic, effective solution to knowledge-based training under social distancing measures. Whilst full course evaluation was not possible, we believe that this work demonstrates practical guidance on educational response in a pandemic as well as highlighting the altruistic nature of the critical care community.

Public opinion of the Irish "COVID Tracker" digital contact tracing App: A national survey.

Objective: This study aims to gather public opinion on the Irish "COVID Tracker" digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App. Methods: A 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App. Results: A total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure. Conclusions: DCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.

Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework.

BACKGROUND: The silent transmission of COVID-19 has led to an exponential growth of fatal infections. With over 4 million deaths worldwide, the need to control and stem transmission has never been more critical. New COVID-19 vaccines offer hope. However, administration timelines, long-term protection, and effectiveness against potential variants are still unknown. In this context, contact tracing and digital contact tracing apps (CTAs) continue to offer a mechanism to help contain transmission, keep people safe, and help kickstart economies. However, CTAs must address a wide range of often conflicting concerns, which make their development/evolution complex. For example, the app must preserve citizens' privacy while gleaning their close contacts and as much epidemiological information as possible. OBJECTIVE: In this study, we derived a compare-and-contrast evaluative framework for CTAs that integrates and expands upon existing works in this domain, with a particular focus on citizen adoption; we call this framework the Citizen-Focused Compare-and-Contrast Evaluation Framework (C3EF) for CTAs. METHODS: The framework was derived using an iterative approach. First, we reviewed the literature on CTAs and mobile health app evaluations, from which we derived a preliminary set of attributes and organizing pillars. These attributes and the probing questions that we formulated were iteratively validated, augmented, and refined by applying the provisional framework against a selection of CTAs. Each framework pillar was then subjected to internal cross-team scrutiny, where domain experts cross-checked sufficiency, relevancy, specificity, and nonredundancy of the attributes, and their organization in pillars. The consolidated framework was further validated on the selected CTAs to create a finalized version of C3EF for CTAs, which we offer in this paper. RESULTS: The final framework presents seven pillars exploring issues related to CTA design, adoption, and use: (General) Characteristics, Usability, Data Protection, Effectiveness, Transparency, Technical Performance, and Citizen Autonomy. The pillars encompass attributes, subattributes, and a set of illustrative questions (with associated example answers) to support app design, evaluation, and evolution. An online version of the framework has been made available to developers, health authorities, and others interested in assessing CTAs. CONCLUSIONS: Our CTA framework provides a holistic compare-and-contrast tool that supports the work of decision-makers in the development and evolution of CTAs for citizens. This framework supports reflection on design decisions to better understand and optimize the design compromises in play when evolving current CTAs for increased public adoption. We intend this framework to serve as a foundation for other researchers to build on and extend as the technology matures and new CTAs become available.

Sentiment analysis of user feedback on the HSE's Covid-19 contact tracing app.

BACKGROUND: Digital Contact Tracing is seen as a key tool in reducing the propagation of Covid-19. But it requires high uptake and continued participation across the population to be effective. To achieve sufficient uptake/participation, health authorities should address, and thus be aware of, user concerns. AIM: This work manually analyzes user reviews of the Irish Heath Service Executive's (HSE) Contact Tracker app, to identify user concerns and to lay the foundations for subsequent, large-scale, automated analyses of reviews. While this might seem tightly scoped to the Irish context, the HSE app provides the basis for apps in many jurisdictions in the USA and Europe. METHODS: Manual analysis of (1287) user reviews from the Google/Apple playstores was performed, to identify the aspects of the app that users focused on, and the positive/negative sentiment expressed. RESULTS: The findings suggest a largely positive sentiment towards the app, and that users thought it handled data protection and transparency aspects well. But feedback suggests that users would appreciate more targeted feedback on the incidence of the virus, and facilities for more proactive engagement, like notifications that prompt users to submit their health status daily. Finally, the analysis suggests that the "android battery" issue and the backward-compatibility issue with iPhones seriously impacted retention/uptake of the app respectively. CONCLUSION: The HSE have responded to the public's desire for targeted feedback in newer versions, but should consider increasing the app's proactive engagement. The results suggest they should also raise the backward compatibility issue, regarding older iPhones, with Apple.

Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media.

This paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

Anakinra treats fulminant myocarditis from Neisseria meningitidis septicaemia and haemophagocytic lymphohistiocytosis: a case report.

BACKGROUND: Fulminant myocarditis is a life-threatening condition characterized by acute cardiac dysfunction requiring pharmacological or mechanical circulatory support. Haemophagocytic lymphohistiocytosis (HLH) is an uncommon state of immune dysregulation and overactivation. Inflammation mediated by interleukin-1 (IL-1) is thought to play a role in the pathogenesis of myocarditis and HLH, and there is some evidence that the IL-1 receptor antagonist Anakinra may play a role in treating both these conditions. CASE SUMMARY: A 26-year-old previously healthy male presented to the Emergency Department with a 3-day history of malaise, headache, vomiting, diarrhoea, and fever. He was profoundly hypotensive on arrival, diagnosed with septic shock, and commenced on broad-spectrum antibiotics and vasopressors. Blood tests showed lymphopenia, thrombocytopenia, low fibrinogen and elevated high sensitivity troponin T, ferritin, and C-reactive protein. Echocardiography demonstrated severely impaired biventricular systolic function and a diagnosis of fulminant myocarditis was made. His condition deteriorated and he required intubation and additional inotropic support. A diagnosis of HLH was made and he was commenced on Anakinra and Methylprednisolone. His condition improved rapidly thereafter. Polymerase chain reaction testing subsequently confirmed infection with Neisseria meningitidis. DISCUSSION: In this case, fulminant myocarditis and HLH were life-threatening manifestations of meningococcal septicaemia, and the patient's condition improved rapidly following administration of the IL-1 receptor antagonist Anakinra. These complications should be borne in mind in septic patients with marked haemodynamic instability and multiorgan dysfunction, and treatment with Anakinra should be considered in those who fail to respond to conventional therapy.

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature.

BACKGROUND: Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. OBJECTIVE: This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. METHODS: A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. RESULTS: Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. CONCLUSIONS: In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.

A national survey of attitudes to COVID-19 digital contact tracing in the Republic of Ireland.

BACKGROUND: Contact tracing remains a critical part of controlling COVID-19 spread. Many countries have developed novel software applications (Apps) in an effort to augment traditional contact tracing methods. AIM: Conduct a national survey of the Irish population to examine barriers and levers to the use of a contact tracing App. METHODS: Adult participants were invited to respond via an online survey weblink sent via e-mail and messaging Apps and posted on our university website and on popular social media platforms, prior to launch of the national App solution. RESULTS: A total of 8088 responses were received, with all 26 counties of the Republic of Ireland represented. Fifty-four percent of respondents said they would definitely download a contact-tracing App, while 30% said they would probably download a contact tracing App. Ninety-five percent of respondents identified at least one reason for them to download such an App, with the most common reasons being the potential for the App to help family members and friends and a sense of responsibility to the wider community. Fifty-nine percent identified at least one reason not to download the App, with the most common reasons being fear that technology companies or the government might use the App technology for greater surveillance after the pandemic. CONCLUSION: The Irish citizens surveyed expressed high levels of willingness to download a public health-backed App to augment contact tracing. Concerns raised regarding privacy and data security will be critical if the App is to achieve the large-scale adoption and ongoing use required for its effective operation.

Safety of intravenous anakinra in COVID-19 with evidence of hyperinflammation, a case series.

OBJECTIVES: Anakinra is a selective IL-1 inhibitor, which has been used in the context of secondary haemophagocytic lymphohistiocytosis. Although usually given in the s.c. form, previous anecdotal reports have emphasized its utility when given i.v. Our aim is to report our experience on the beneficial effects of anakinra i.v. in patients with SARS-CoV-2 and evidence of hyperinflammation. METHODS: We report four patients with severe COVID-19 infection requiring intensive care admission and ventilatory support. RESULTS: All four patients showed evidence of deterioration, with hyperferritinaemia and increasing oxygen requirements and with superadded bacterial infections. Upon commencement of anakinra i.v., there was subsequent improvement in the patients clinically, with reduction in ventilatory support and inotropic support, and biochemically, with rapid improvement in inflammatory markers. CONCLUSION: Anakinra is safe to use i.v. in patients with COVID-19 and evidence of superadded bacterial infection. Although its utility has not been confirmed in a randomized trial, current research in the COVID-19 pandemic aims to establish the utility of immunosuppression, including IL-1 blockade, on the outcomes of patients with moderate to severe disease. Our case series supports its use in patients with severe, life-threatening COVID-19 and evidence of hyperinflammation.

An empirical assessment of baseline feature location techniques.

Feature Location (FL) aims to locate observable functionalities in source code. Considering its key role in software maintenance, a vast array of automated and semi-automated Feature Location Techniques (FLTs) have been proposed. To compare FLTs, an open, standard set of non-subjective, reproducible "compare-to" FLT techniques (baseline techniques) should be used for evaluation. In order to relate the performance of FLTs compared against different baseline techniques, these compare-to techniques should be evaluated against each other. But evaluation across FLTs is confounded by empirical designs that incorporate different FL goals and evaluation criteria. This paper moves towards standardizing FLT comparability by assessing eight baseline techniques in an empirical design that addresses these confounding factors. These baseline techniques are assessed in twelve case studies to rank their performance. Results of the case studies suggest that different baseline techniques perform differently and that VSM-Lucene and LSI-Matlab performed better than other implementations. By presenting the relative performances of baseline techniques this paper facilitates empirical cross-comparison of existing and future FLTs. Finally, the results suggest that the performance of FLTs partially depends on system/benchmark characteristics, in addition to the FLTs themselves.

Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis.

BACKGROUND: Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information. OBJECTIVE: The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail. METHODS: A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets' metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed. RESULTS: A total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet's author was tagged as favorited by the community. CONCLUSIONS: The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida-a decorative element in Arabic writing used to justify the text within lines-were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.

Yersinia hibernica sp. nov., isolated from pig-production environments.

A Gram-stain-negative, rod-shaped strain isolated from pig-production environments was identified as a new species within the genus Yersinia using multifaceted genomic and biochemical approaches. The genome of this strain was closed using a hybrid assembly approach combining both high accuracy short read sequencing data with long read sequencing technology. Phylogenetic analysis of the 16S rRNA gene showed ~98 % similarity to Yersinia kristensenii and ~98 % similarity to Yersinia enterocolitica. Average nucleotide identity (OrthoANI) values were calculated as 85.79 % to Y. kristensenii ATCC 33638T and 85.73 % to Y. enterocolitica ATCC 9610T thereby providing evidence that this isolate should be considered as a novel species. The type strain is CFS1934T (=NCTC 14222T=LMG 31076T).

Tuberculosis on the intensive care unit.

Patients with tuberculosis admitted to the intensive care unit represent a small (1-3%) yet significant subset of the global tuberculosis burden. This article reviews current evidence supporting the diagnosis and management of patients with tuberculosis admitted to an intensive care unit from a combination of cohort studies and national and international tuberculosis guidelines. This review considers admission, diagnosis, mechanical ventilation, infection control, treatment and prognosis of patients with tuberculosis admitted to an intensive care unit. It highlights both diagnostic and management challenges and areas where ambiguity remains and further evidence is required.

Drug-induced liver injury from antituberculous treatment: a retrospective study from a large TB centre in the UK.

BACKGROUND: We describe drug-induced liver injury (DILI) secondary to antituberculous treatment (ATT) in a large tuberculosis (TB) centre in London; we identify the proportion who had risk factors for DILI and the timing and outcome of DILI. METHODS: We identified consecutive patients who developed DILI whilst on treatment for active TB; patients with active TB without DILI were selected as controls. Comprehensive demographic and clinical data, management and outcome were recorded. RESULTS: There were 105 (6.9%) cases of ATT-associated DILI amongst 1529 patients diagnosed with active TB between April 2010 and May 2014. Risk factors for DILI were: low patient weight, HIV-1 co-infection, higher baseline ALP, and alcohol intake. Only 25.7% of patients had British or American Thoracic Society defined criteria for liver test (LT) monitoring. Half (53%) of the cases occurred within 2 weeks of starting ATT and 87.6% occurred within 8 weeks. Five (4.8%) of seven deaths were attributable to DILI. CONCLUSIONS: Only a quarter of patients who developed DILI had British or American Thoracic Society defined criteria for pre-emptive LT monitoring, suggesting that all patients on ATT should be considered for universal liver monitoring particularly during the first 8 weeks of treatment.

Factors which influence treatment initiation for pulmonary non-tuberculous mycobacterium infection in HIV negative patients; a multicentre observational study.

BACKGROUND: Clinical, radiological and microbiological criteria inform diagnosis of pulmonary Non-Tuberculous Mycobacteria (NTM) disease and treatment decisions. This multicentre, review aims to characterise NTM disease meeting ATS/IDSA criteria and define factors associated with initiation of treatment. METHODS: Sputum samples growing NTM from 5 London hospitals between 2010 and 2014 were identified. Data for HIV-negative individuals meeting ATS/IDSA guidelines for pulmonary NTM disease were extracted. Associations between clinical variables and treatment decision were investigated using Chi-squared, Fishers-exact or Mann Whitney tests. Factors associated with treatment in univariate analysis (p < 0.150) were included in a multivariate logistic regression model. RESULTS: NTM were identified from 817 individuals' sputum samples. 108 met ATS/IDSA criteria. 42/108 (39%) were initiated on treatment. Median age was 68 (56-78) in the cohort. On multivariate analysis, factors significantly associated with treatment of pulmonary NTM infection were: Cavitation on HRCT (OR: 6.49; 95% CI: 2.36-17.81), presenting with night sweats (OR 4.18; 95% CI: 1.08-16.13), and presenting with weight loss (OR 3.02; 95% CI: 1.15-7.93). Of those treated, 18(43%) have completed treatment, 9(21%) remain on treatment, 10(24%) stopped due to side effects, 5(12%) died during treatment. Mortality was 31% (n = 13) in treated versus 21% (n = 14) in the non-treated cohort. Subgroup analysis of individual NTM species did not observe any differences in treatment initiation or outcomes between groups. DISCUSSION: Decision to treat pulmonary NTM infection requires clinical judgement when interpreting clinical guidelines. Factors independently associated with decision to treat in this HIV-negative cohort include cavitation on HRCT and presenting with night sweats or weight loss.