Is a randomised controlled trial of take home naloxone distributed in emergency settings likely to be feasible and acceptable? Findings from a UK qualitative study exploring perspectives of people who use opioids and emergency services staff.

OBJECTIVE: Distribution of take-home naloxone (THN) by emergency services may increase access to THN and reduce deaths and morbidity from opioid overdose. As part of a feasibility study for a randomised controlled trial (RCT) of distribution of THN kits and education within ambulance services and Emergency Departments (EDs), we used qualitative methods to explore key stakeholders' perceptions of feasibility and acceptability of delivering the trial. METHODS: We undertook semi-structured interviews and focus groups with 26 people who use opioids and with 20 paramedics and ED staff from two intervention sites between 2019 and 2021. Interviews and focus groups were recorded, transcribed verbatim and analysed using Framework Analysis. RESULTS: People using opioids reported high awareness of overdose management, including personal experience of THN use. Staff perceived emergency service provision of THN as a low-cost, low-risk intervention with potential to reduce mortality, morbidity and health service use. Staff understood the trial aims and considered it compatible with their work. All participants supported widening access to THN but reported limited trial recruitment opportunities partly due to difficulties in consenting patients during overdose. Procedural problems, restrictive recruitment protocols, limited staff buy-in and patients already owning THN limited trial recruitment. Determining trial effectiveness was challenging due to high levels of alternative community provision of THN. CONCLUSIONS: Distribution of THN in emergency settings was considered feasible and acceptable for stakeholders but an RCT to establish the effectiveness of THN delivery is unlikely to generate further useful evidence due to difficulties in recruiting patients and assessing benefits.

The association between frailty and survival in patients with pleural disease: a retrospective cohort study.

BACKGROUND: There are currently no data on the relationship between frailty and mortality in pleural disease. Understanding the relationship between frailty and outcomes is increasingly important for clinicians to guide decisions regarding investigation and management. This study aims to explore the relationship between all-cause mortality and frailty status in patients with pleural disease. METHODS: In this retrospective analysis of a prospectively collected observational cohort study, outpatients presenting to the pleural service at a tertiary centre in Bristol, UK with a radiologically confirmed, undiagnosed pleural effusion underwent comprehensive assessment and were assigned a final diagnosis at 12 months. The modified frailty index (mFI) was calculated and participants classified as frail (mFI ≥ 0.4) or not frail (mFI ≤ 0.2). RESULTS: 676 participants were included from 3rd March 2008 to 29th December 2020. The median time to mortality was 490 days (IQR 161-1595). A positive association was found between 12-month mortality and frailty (aHR = 1.72, 95% CI 1.02-2.76, p = 0.025) and age ≥ 80 (aHR = 1.80, 95% CI 1.24-2.62, p = 0.002). Subgroup analyses found a stronger association between 12-month mortality and frailty in benign disease (aHR = 4.36, 95% CI 2.17-8.77, p < 0.0001) than in all pleural disease. Malignancy irrespective of frailty status was associated with an increase in all-cause mortality (aHR = 10.40, 95% CI 6.01-18.01, p < 0.0001). CONCLUSION: This is the first study evaluating the relationship between frailty and outcomes in pleural disease. Our data demonstrates a strong association between frailty and 12-month mortality in this cohort. A malignant diagnosis is an independent predictor of 12-month mortality, irrespective of frailty status. Frailty was also strongly associated with 12-month mortality in patients with a benign underlying cause for their pleural disease. This has clinical relevance for pleural physicians; evaluating patients' frailty status and its impact on mortality can guide clinicians in assessing suitability for invasive investigation and management. TRIAL REGISTRATION: This study is registered with the Health Research Authority (REC reference 08/H0102/11) and the NIHR Portfolio (Study ID 8960).

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

Genetic architecture of cardiac dynamic flow volumes.

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

Suspected tick-borne flavivirus meningoencephalomyelitis in dogs from the UK: six cases (2021).

OBJECTIVES: Tick-borne encephalitis virus and louping ill virus are neurotropic flaviviruses transmitted by ticks. Epidemiologically, tick-borne encephalitis is endemic in Europe whereas louping ill's predominant geographical distribution is the UK. Rarely, these flaviviruses affect dogs causing neurological signs. This case series aimed to describe the clinical, clinicopathological, and imaging findings, as well as the outcomes in six dogs with meningoencephalitis and/or meningomyelitis caused by a flavivirus in the UK in 2021. MATERIALS AND METHODS: Observational retrospective case-series study. Clinical data were retrieved from medical records of dogs with positive serological or immunohistochemical results from three different institutions from spring to winter 2021. RESULTS: Six dogs were included in the study. All dogs presented an initial phase of pyrexia and/or lethargy followed by progressive signs of spinal cord and/or intracranial disease. Magnetic resonance imaging showed bilateral and symmetrical lesions affecting the grey matter of the thalamus, pons, medulla oblongata, and thoracic or lumbar intumescences with none or mild parenchymal and meningeal contrast enhancement. Serology for tick-borne encephalitis virus was positive in five dogs with the presence of seroconversion in two dogs. The viral distinction between flaviviruses was not achieved. One dog with negative serology presented positive immunohistochemistry at post-mortem examination. Three dogs survived but presented neurological sequelae. Three dogs were euthanased due to the rapid progression of the clinical signs or static neurological signs. CLINICAL SIGNIFICANCE: These cases raise awareness of the presence of tick-borne encephalitis as an emergent disease or the increased prevalence of louping ill virus affecting dogs in the UK.

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.

Epistasis regulates genetic control of cardiac hypertrophy.

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141, IGF1R, TTN, and TNKS. Several loci not prioritized by univariate genome-wide association analysis are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we show that these interactions are preserved at the level of the cardiac transcriptome. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R. Our results expand the scope of genetic regulation of cardiac structure to epistasis.

Epistasis regulates genetic control of cardiac hypertrophy.

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141, IGF1R, TTN, and TNKS. Several loci not prioritized by univariate genome-wide association analysis are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we show that these interactions are preserved at the level of the cardiac transcriptome. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R. Our results expand the scope of genetic regulation of cardiac structure to epistasis.

Parental technoference and adolescents' mental health and violent behaviour: a scoping review.

PURPOSE: The term 'technoference' refers to habitual interferences and disruptions within interpersonal relationships or time spent together due to use of electronic devices. Emerging evidence suggests associations between parental technoference and young people's mental health and violent behaviours. This scoping review sought to summarise the existing literature. METHODS: A scoping review was undertaken across six databases (APA PsycINFO, MEDLINE, ASSIA, ERIC, Social Sciences Premium Collection, SciTech Premium). Searches included articles examining the association between parental technoference and adolescent mental health and violent behaviours. All included studies provided empirical findings. RESULTS: Searches retrieved 382 articles, of which 13 articles met the eligibility criteria. A narrative approach was applied to synthesise the eligible findings. Across all studies, adolescent perceptions of parental technoference were negatively associated to adolescent mental health and positively related to adolescent violent behaviours. Parental cohesion and mental health were identified as significant mediating factors. CONCLUSION: Findings suggest that parents should be aware of the environment in which they use electronic devices as their use can potentially, directly and indirectly, influence adolescent mental health and violent behaviours. Further research into the potential caveats of parental technoference could support the development of evidence-informed guidelines for parental management of electronic devices.

Development and validation of a rapid visual technique for left ventricular hypertrophy detection from the electrocardiogram.

Introduction: Left ventricular hypertrophy (LVH) detection techniques on by electrocardiogram (ECG) are cumbersome to remember with modest performance. This study validated a rapid technique for LVH detection and measured its performance against other techniques. Methods: This was a retrospective cohort study of patients at Stanford Health Care who received ECGs and resting transthoracic echocardiograms (TTE) from 2006 through 2018. The novel technique, Witteles-Somani (WS), assesses for S- and R-wave overlap on adjacent precordial leads. The WS, Sokolow-Lyon, Cornell, and Peguero-Lo Presti techniques were algorithmically implemented on ECGs. Classification metrics, receiver-operator curves, and Pearson correlations measured performance. Age- and sex-adjusted Cox proportional hazard models evaluated associations between incident cardiovascular outcomes and each technique. Results: A total of 53,333 ECG-TTE pairs from 18,873 patients were identified. Of all ECG-TTE pairs, 21,638 (40.6%) had TTE-diagnosed LVH. The WS technique had a sensitivity of 0.46, specificity of 0.66, and AUROC of 0.56, compared to Sokolow-Lyon (AUROC 0.55), Cornell (AUROC 0.63), and Peguero-Lo Presti (AUROC 0.63). Patients meeting LVH by WS technique had a higher risk of cardiovascular mortality [HR 1.18, 95% CI (1.12, 1.24), P < 0.001] and a higher risk of developing any cardiovascular disease [HR 1.29, 95% CI (1.22, 1.36), P < 0.001], myocardial infarction [HR 1.60, 95% CI (1.44, 1.78), P < 0.005], and heart failure [HR 1.24, 95% CI (1.17, 1.32), P < 0.001]. Conclusions: The WS criteria is a rapid visual technique for LVH detection with performance like other LVH detection techniques and is associated with incident cardiovascular outcomes.

A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease.

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

High-resolution characterization of short-term temporal variability in the taxonomic and resistome composition of wastewater influent.

Wastewater-based epidemiology (WBE) for population-level surveillance of antimicrobial resistance (AMR) is gaining significant traction, but the impact of wastewater sampling methods on results is unclear. In this study, we characterized taxonomic and resistome differences between single-timepoint-grab and 24 h composites of wastewater influent from a large UK-based wastewater treatment work [WWTW (population equivalent: 223 435)]. We autosampled hourly influent grab samples (n=72) over three consecutive weekdays, and prepared additional 24 h composites (n=3) from respective grabs. For taxonomic profiling, metagenomic DNA was extracted from all samples and 16S rRNA gene sequencing was performed. One composite and six grabs from day 1 underwent metagenomic sequencing for metagenomic dissimilarity estimation and resistome profiling. Taxonomic abundances of phyla varied significantly across hourly grab samples but followed a repeating diurnal pattern for all 3 days. Hierarchical clustering grouped grab samples into four time periods dissimilar in both 16S rRNA gene-based profiles and metagenomic distances. 24H-composites resembled mean daily phyla abundances and showed low variability of taxonomic profiles. Of the 122 AMR gene families (AGFs) identified across all day 1 samples, single grab samples identified a median of six (IQR: 5-8) AGFs not seen in the composite. However, 36/36 of these hits were at lateral coverage <0.5 (median: 0.19; interquartile range: 0.16-0.22) and potential false positives. Conversely, the 24H-composite identified three AGFs not seen in any grab with higher lateral coverage (0.82; 0.55-0.84). Additionally, several clinically significant human AGFs (bla VIM, bla IMP, bla KPC) were intermittently or completely missed by grab sampling but captured by the 24 h composite. Wastewater influent undergoes significant taxonomic and resistome changes on short timescales potentially affecting interpretation of results based on sampling strategy. Grab samples are more convenient and potentially capture low-prevalence/transient targets but are less comprehensive and temporally variable. Therefore, we recommend 24H-composite sampling where feasible. Further validation and optimization of WBE methods is vital for its development into a robust AMR surveillance approach.

Deep learning-based electrocardiographic screening for chronic kidney disease.

BACKGROUND: Undiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs. METHODS: We collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018. RESULTS: Using 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760-0.773) in a held-out test set and an AUC of 0.709 (0.708-0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735-0.770) for mild CKD, AUC of 0.759 (0.750-0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773-0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836-0.852]) and 1-lead ECG waveform (0.824 [0.815-0.832]). CONCLUSIONS: Our deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.

Chronic kidney disease (CKD) is a common condition involving loss of kidney function over time and results in a substantial number of deaths. However, CKD often has no symptoms during its early stages. To detect CKD earlier, we developed a computational approach for CKD screening using routinely acquired electrocardiograms (ECGs), a cheap, rapid, non-invasive, and commonly obtained test of the heart's electrical activity. Our model achieved good accuracy in identifying any stage of CKD, with especially high accuracy in younger patients and more severe stages of CKD. Given the high global burden of undiagnosed CKD, novel and accessible CKD screening strategies have the potential to help prevent disease progression and reduce premature deaths related to CKD.

Best practices for the care of pregnant people living with TB.

BACKGROUND: Each year more than 200,000 pregnant people become sick with TB, but little is known about how to optimize their diagnosis and therapy. Although there is a need for further research in this population, it is important to recognize that much can be done to improve the services they currently receive.METHODS: Following a systematic review of the literature and the input of a global team of health professionals, a series of best practices for the diagnosis, prevention and treatment of TB during pregnancy were developed.RESULTS: Best practices were developed for each of the following areas: 1) screening and diagnosis; 2) reproductive health services and family planning; 3) treatment of drug-susceptible TB; 4) treatment of rifampicin-resistant/multidrug-resistant TB; 5) compassionate infection control practices; 6) feeding considerations; 7) counseling and support; 8) treatment of TB infection/TB preventive therapy; and 9) research considerations.CONCLUSION: Effective strategies for the care of pregnant people across the TB spectrum are readily achievable and will greatly improve the lives and health of this under-served population.

Rethinking the 'one-stop' neck lump clinic: a novel pathway beyond coronavirus disease 2019.

OBJECTIVES: UK guidelines advocate 'one-stop' neck lump assessment for cancer referrals. This paper reports the pilot of a novel pre-clinic ultrasound pathway, presents outcomes, and discusses strengths and limitations in the context of the coronavirus disease 2019 pandemic. METHODS: Two-week-wait cancer referral patients with a neck lump were allocated a pre-clinic ultrasound scan followed by a clinic appointment. Demographic, patient journey and outcome data were collected and analysed. RESULTS: Ninety-nine patients underwent ultrasound assessment with or without biopsy on average 8 days following referral. Patients were followed up on average 14.1 days (range, 2-26 days) after initial referral. At the first clinic appointment, 45 patients were discharged, 10 were scheduled for surgery, 12 were diagnosed with cancer, 6 were referred to another specialty and cancer was excluded in 19 patients. Retrospectively, four ultrasounds were performed unnecessarily. CONCLUSION: Pre-clinic ultrasound scanning is an alternative to the one-stop neck lump pathway. This study demonstrates fewer clinic visits, faster diagnosis and a low proportion of unnecessary scans, whilst minimising face-to-face consultations and aerosol-generating procedures.

An approach to recognising and identifying metabolic presentations in the paediatric Irish Traveller population.

The Irish Traveller population are an endogamous, traditionally nomadic, Irish population. Irish Travellers practice consanguinity in the majority of marriages, thus resulting in a higher rate of rare autosomal recessive conditions within the population due to homozygous variants. Herein, we outline the clinical phenotypes associated with metabolic conditions seen in this population presenting in the neonatal period, infancy and childhood. Although Irish Travellers are traditionally based in Ireland and the UK, there are populations also living in mainland Europe and the USA. While there is generally an understanding amongst Irish paediatricians of the recessive conditions seen with this population in Ireland, they may be less commonly encountered abroad. It is important to consider a non-genetic aetiology alongside any consideration for a metabolic disorder. CONCLUSION: This paper acts as a comprehensive review of the metabolic conditions seen and provides a guide for the investigation of an Irish Traveller child with a suspected metabolic condition. WHAT IS KNOWN: • The Irish Traveller population are an endogenous population. • There are higher rates of inherited metabolic conditions in this population compared to the general population in Ireland. WHAT IS NEW: • This paper is a comprehensive review of all known inherited metabolic conditions encountered in the Irish Traveller population.