What paramedics think about when they think about fatigue : contributing factors

Objective: Paramedic fatigue is associated with burnout, attrition, sickleave, work disability, physical and mental health complaints and impaired performance. However, no studies have addressed how fatigue is understood by paramedics. The present study addresses this shortcoming by exploring factors paramedics recognise as contributors to fatigue. Methods: Forty-nine (12F; 38 years ± 9.7 years) Australian paramedics completed a survey on perceived causes of performance impairing fatigue. A total of 107 responses were systematically coded following principles common to qualitative data analysis: data immersion, coding, categorisation and theme generation. Results: Six themes emerged: working time, sleep, workload, health and wellbeing, work–life balance and environment. Consistent with a scientific understanding of fatigue, prior sleep and wake, time of day and task-related factors were often identified as contributing to fatigue. In other cases, paramedics’ attributions deviated from a scientific understanding of direct causes of fatigue. Conclusions: These findings demonstrate that paramedics have a broad understanding of fatigue. It is critical to take this into account when discussing fatigue with paramedics, particularly in the case of fatigue education or wellness programmes. These data highlight areas for intervention and education to minimise the experience of paramedic fatigue and the negative health and safety outcomes for paramedics and patients as a result

Screening for non-adherence to antihypertensive treatment as a part of the diagnostic pathway to renal denervation.

Renal denervation is a potential therapeutic option for resistant hypertension. A thorough clinical assessment to exclude reversible/spurious causes of resistance to antihypertensive therapy is required prior to this procedure. The extent to which non-adherence to antihypertensive treatment contributes to apparent resistance to antihypertensive therapy in patients considered for renal denervation is not known. Patients (n=34) referred for renal denervation entered the evaluation pathway that included screening for adherence to antihypertensive treatment by high-performance liquid chromatography-tandem mass spectrometry-based urine analysis. Biochemical non-adherence to antihypertensive treatment was the most common cause of non-eligibility for renal denervation-23.5% of patients were either partially or completely non-adherent to prescribed antihypertensive treatment. About 5.9% of those referred for renal denervation had admitted non-adherence prior to performing the screening test. Suboptimal pharmacological treatment of hypertension and 'white-coat effect' accounted for apparently resistant hypertension in a further 17.7 and 5.9% of patients, respectively. Taken together, these three causes of pseudo-resistant hypertension accounted for 52.9% of patients referred for renal denervation. Only 14.7% of referred patients were ultimately deemed eligible for renal denervation. Without biochemical screening for therapeutic non-adherence, the eligibility rate for renal denervation would have been 38.2%. Non-adherence to antihypertensive treatment and other forms of therapeutic pseudo-resistance are by far the most common reason of 'resistant hypertension' in patients referred for renal denervation. We suggest that inclusion of biochemical screening for non-adherence to antihypertensive treatment may be helpful in evaluation of patients with 'resistant hypertension' prior to consideration of renal denervation

Set a global target for ecosystems

The conservation community must be able to track countries’ progress in protecting wetlands, reefs, forests and more, argue James Watson and colleagues. [Figure not available: see fulltext.]

Deep Orientated Distance-transform Network for Geometric-aware Centerline Detection

The detection of structure centerlines from imaging data plays a crucial role in the understanding, application and further analysis of many diverse problems, such as road mapping, crack detection, medical imaging and biometric identification. In each of these cases, pixel-wise segmentation is not sufficient to understand and quantify overall graph structure and connectivity without further processing that can lead to compound error. We thus require a method for automatic extraction of graph representations of patterning. In this paper, we propose a novel Deep Orientated Distance-transform Network (DODN), which predicts the centerline map and an orientated distance map, comprising orientation and distance in relation to the centerline and allowing exploitation of its geometric properties. This is refined by jointly modeling the relationship between neighboring pixels and connectivity to further enhance the estimated centerline and produce a graph of the structure. The proposed approach is evaluated on a diverse range of problems, including crack detection, road mapping and superficial vein centerline detection from infrared/ color images, improving over the state-of-the-art by 2.1%, 10.9% and 17.3%/ 4.6% respectively in terms of quality, demonstrating its generalizability and performance in a wide range of mapping problems.</p

Developing a longitudinal database of routinely recorded primary care consultations linked to service use and outcome data

The primary care consultation provides access to the majority of health care services and is central toobtaining diagnoses, treatment and ongoing management of long-term conditions. This paper reportsthe findings of an interdisciplinary feasibility study to explore the benefits and practical, technical andethical challenges (and solutions) of creating a longitudinal database of recorded GP consultations inTayside, Scotland which could be linked to existing routine data on intermediate and long-term healthoutcomes. After consultation we attempted to recruit and audio-record the consultations of all patientsattending three general practices over a two week period. Background patient data, and patient and staffexperiences of participation were also collected. Eventually, two practices participated with 77% ofpatients approached agreeing to participate. The findings suggest that the perceived integrity of theconsultation was preserved. The overwhelming majority of patients believed that recording wasworthwhile and did not feel it impacted on communication or the treatment they received; 93% indicatedthey would be willing to have subsequent consultations recorded and 81% would recommendparticipation to a friend. Staff had similar beliefs but raised concerns about potential increases inworkload, confidentiality issues and ease of software use. We conclude that practice participation couldbe increased by providing safeguards on data use, financial reward, integrated recording software, andprocedures to lessen the impact on workload. The resulting Scottish Clinical Interactions Project (SCIP)would provide the largest and most detailed longitudinal insight into real world medical consultations inthe world, permitting the linking of consultation events and practices to subsequent outcomes andbehaviours

Resource efficient aortic distensibility calculation by end to end spatiotemporal learning of aortic lumen from multicentre multivendor multidisease CMR images

Aortic distensibility (AD) is important for the prognosis of multiple cardiovascular diseases. We propose a novel resource-efficient deep learning (DL) model, inspired by the bi-directional ConvLSTM U-Net with densely connected convolutions, to perform end-to-end hierarchical learning of the aorta from cine cardiovascular MRI towards streamlining AD quantification. Unlike current DL aortic segmentation approaches, our pipeline: (i) performs simultaneous spatio-temporal learning of the video input, (ii) combines the feature maps from the encoder and decoder using non-linear functions, and (iii) takes into account the high class imbalance. By using multi-centre multi-vendor data from a highly heterogeneous patient cohort, we demonstrate that the proposed method outperforms the state-of-the-art method in terms of accuracy and at the same time it consumes ∼ 3.9 times less fuel and generates ∼ 2.8 less carbon emissions. Our model could provide a valuable tool for exploring genome-wide associations of the AD with the cognitive performance in large-scale biomedical databases. By making energy usage and carbon emissions explicit, the presented work aligns with efforts to keep DL’s energy requirements and carbon cost in check. The improved resource efficiency of our pipeline might open up the more systematic DL-powered evaluation of the MRI-derived aortic stiffness

The Relationship Between Cardiac Troponin in People Hospitalised for Exacerbation of COPD and Major Adverse Cardiac Events (MACE) and COPD Readmissions

Background: No single biomarker currently risk stratifies chronic obstructive pulmonary disease (COPD) patients at the time of an exacerbation, though previous studies have suggested that patients with elevated troponin at exacerbation have worse outcomes. This study evaluated the relationship between peak cardiac troponin and subsequent major adverse cardiac events (MACE) including all-cause mortality and COPD hospital readmission, among patients admitted with COPD exacerbation. Methods: Data from five cross-regional hospitals in England were analysed using the National Institute of Health Research Health Informatics Collaborative (NIHR-HIC) acute coronary syndrome database (2008–2017). People hospitalised with a COPD exacerbation were included, and peak troponin levels were standardised relative to the 99th percentile (upper limit of normal). We used Cox Proportional Hazard models adjusting for age, sex, laboratory results and clinical risk factors, and implemented logarithmic transformation (base-10 logarithm). The primary outcome was risk of MACE within 90 days from peak troponin measurement. Secondary outcome was risk of COPD readmission within 90 days from peak troponin measurement. Results: There were 2487 patients included. Of these, 377 (15.2%) patients had a MACE event and 203 (8.2%) were readmitted within 90 days from peak troponin measurement. A total of 1107 (44.5%) patients had an elevated troponin level. Of 1107 patients with elevated troponin at exacerbation, 256 (22.8%) had a MACE event and 101 (9.0%) a COPD readmission within 90 days from peak troponin measurement. Patients with troponin above the upper limit of normal had a higher risk of MACE (adjusted HR 2.20, 95% CI 1.75–2.77) and COPD hospital readmission (adjusted HR 1.37, 95% CI 1.02–1.83) when compared with patients without elevated troponin. Conclusion: An elevated troponin level at the time of COPD exacerbation may be a useful tool for predicting MACE in COPD patients. The relationship between degree of troponin elevation and risk of future events is complex and requires further investigation

Cognitive and psychiatric symptom trajectories 2–3 years after hospital admission for COVID-19: a longitudinal, prospective cohort study in the UK

Background: COVID-19 is known to be associated with increased risks of cognitive and psychiatric outcomes after the acute phase of disease. We aimed to assess whether these symptoms can emerge or persist more than 1 year after hospitalisation for COVID-19, to identify which early aspects of COVID-19 illness predict longer-term symptoms, and to establish how these symptoms relate to occupational functioning. Methods: The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study of adults (aged ≥18 years) who were hospitalised with a clinical diagnosis of COVID-19 at participating National Health Service hospitals across the UK. In the C-Fog study, a subset of PHOSP-COVID participants who consented to be recontacted for other research were invited to complete a computerised cognitive assessment and clinical scales between 2 years and 3 years after hospital admission. Participants completed eight cognitive tasks, covering eight cognitive domains, from the Cognitron battery, in addition to the 9-item Patient Health Questionnaire for depression, the Generalised Anxiety Disorder 7-item scale, the Functional Assessment of Chronic Illness Therapy Fatigue Scale, and the 20-item Cognitive Change Index (CCI-20) questionnaire to assess subjective cognitive decline. We evaluated how the absolute risks of symptoms evolved between follow-ups at 6 months, 12 months, and 2–3 years, and whether symptoms at 2–3 years were predicted by earlier aspects of COVID-19 illness. Participants completed an occupation change questionnaire to establish whether their occupation or working status had changed and, if so, why. We assessed which symptoms at 2–3 years were associated with occupation change. People with lived experience were involved in the study. Findings: 2469 PHOSP-COVID participants were invited to participate in the C-Fog study, and 475 participants (191 [40·2%] females and 284 [59·8%] males; mean age 58·26 [SD 11·13] years) who were discharged from one of 83 hospitals provided data at the 2–3-year follow-up. Participants had worse cognitive scores than would be expected on the basis of their sociodemographic characteristics across all cognitive domains tested (average score 0·71 SD below the mean [IQR 0·16–1·04]; p<0·0001). Most participants reported at least mild depression (263 [74·5%] of 353), anxiety (189 [53·5%] of 353), fatigue (220 [62·3%] of 353), or subjective cognitive decline (184 [52·1%] of 353), and more than a fifth reported severe depression (79 [22·4%] of 353), fatigue (87 [24·6%] of 353), or subjective cognitive decline (88 [24·9%] of 353). Depression, anxiety, and fatigue were worse at 2–3 years than at 6 months or 12 months, with evidence of both worsening of existing symptoms and emergence of new symptoms. Symptoms at 2–3 years were not predicted by the severity of acute COVID-19 illness, but were strongly predicted by the degree of recovery at 6 months (explaining 35·0–48·8% of the variance in anxiety, depression, fatigue, and subjective cognitive decline); by a biocognitive profile linking acutely raised D-dimer relative to C-reactive protein with subjective cognitive deficits at 6 months (explaining 7·0–17·2% of the variance in anxiety, depression, fatigue, and subjective cognitive decline); and by anxiety, depression, fatigue, and subjective cognitive deficit at 6 months. Objective cognitive deficits at 2–3 years were not predicted by any of the factors tested, except for cognitive deficits at 6 months, explaining 10·6% of their variance. 95 of 353 participants (26·9% [95% CI 22·6–31·8]) reported occupational change, with poor health being the most common reason for this change. Occupation change was strongly and specifically associated with objective cognitive deficits (odds ratio [OR] 1·51 [95% CI 1·04–2·22] for every SD decrease in overall cognitive score) and subjective cognitive decline (OR 1·54 [1·21–1·98] for every point increase in CCI-20). Interpretation: Psychiatric and cognitive symptoms appear to increase over the first 2–3 years post-hospitalisation due to both worsening of symptoms already present at 6 months and emergence of new symptoms. New symptoms occur mostly in people with other symptoms already present at 6 months. Early identification and management of symptoms might therefore be an effective strategy to prevent later onset of a complex syndrome. Occupation change is common and associated mainly with objective and subjective cognitive deficits. Interventions to promote cognitive recovery or to prevent cognitive decline are therefore needed to limit the functional and economic impacts of COVID-19. Funding: National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Wolfson Foundation, MQ Mental Health Research, MRC-UK Research and Innovation, and National Institute for Health and Care Research.</p