Association of maternal lipid levels with birth weight and cord blood insulin: a Bayesian network analysis.

OBJECTIVE: To assess the independent association of maternal lipid levels with birth weight and cord blood insulin (CBI) level. SETTING: The Born in Guangzhou Cohort Study, Guangzhou, China. PARTICIPANTS: Women who delivered between January 2015 and June 2016 and with umbilical cord blood retained were eligible for this study. Those with prepregnancy health conditions, without an available fasting blood sample in the second trimester, or without demographic and glycaemic information were excluded. After random selection, data from 1522 mother-child pairs were used in this study. EXPOSURES AND OUTCOME MEASURES: Additive Bayesian network analysis was used to investigate the interdependency of lipid profiles with other metabolic risk factors (prepregnancy body mass index (BMI), fasting glucose and early gestational weight gain) in association with birth weight and CBI, along with multivariable linear regression models. RESULTS: In multivariable linear regressions, maternal triglyceride was associated with increased birth weight (adjusted ß=67.46, 95% CI 41.85 to 93.06 g per mmol/L) and CBI (adjusted ß=0.89, 95% CI 0.06 to 1.72 µU/mL per mmol/L increase), while high-density lipoprotein cholesterol was associated with decreased birth weight (adjusted ß=-45.29, 95% CI -85.49 to -5.09 g per mmol/L). After considering the interdependency of maternal metabolic risk factors in the Network analysis, none of the maternal lipid profiles was independently associated with birth weight and CBI. Instead, prepregnancy BMI was the global strongest factor for birth weight and CBI directly and indirectly. CONCLUSIONS: Gestational dyslipidaemia appears to be secondary to metabolic dysfunction with no clear association with metabolic adverse outcomes in neonates. Maternal prepregnancy overweight/obesity appears the most influential upstream metabolic risk factor for both maternal and neonatal metabolic health; these data imply weight management may need to be addressed from the preconception period and during early pregnancy.

Chromosomal polymorphisms in assisted reproduction: an analysis of 942 cycles.

The use of intracytoplasmic sperm injection (ICSI) has recently increased worldwide. The live birth rate per ICSI cycle is low, and over half of infertile couples remain childless. Chromosomal polymorphisms are up to five times more common in couples with infertility compared to the general population. We aimed to investigate the association between chromosomal polymorphisms and reproductive outcomes in couples undergoing ICSI treatment. We analysed 942 ICSI fresh and frozen embryo transfer cycles in 697 women who underwent karyotyping analysis using Giemsa-Trypsin-Leishman banding prior to assisted conception at the Fertility Centre of Lanka Hospitals, Sri Lanka, between 2016 and 2018. The primary outcomes were pregnancy, miscarriage, and live birth rates. We compared outcomes according to the presence or absence of chromosomal polymorphism in females, males and couples. There were 294 pregnancies (31.2%) recorded in the study; 130 suffered a miscarriage (13.8%), 13 were ectopic pregnancies (1.3%) and 151 resulted in a live birth (16.0%). The evidence from univariable and multivariable analyses (adjusted for age, BMI, ovarian reserve and treatment type) did not confidently identify a difference in pregnancy, miscarriage or live birth rates between couples with no chromosomal polymorphisms compared to couples where the female, male or both partners were carriers of a chromosomal polymorphism. Further, we did not identify a clear association between the presence of chromosomal polymorphisms and reproductive outcomes compared to participants without chromosomal polymorphisms. Wide CIs precluded the identification of clinically meaningful associations. Lay summary: Infertility affects approximately one in eight couples worldwide. The use of intracytoplasmic sperm injection (ICSI), where the sperm is directly injected into an egg using a micromanipulator outside the body, has become particularly popular in recent years. However, the success rate remains low. In human cells, the genetic material is arranged in structures called chromosomes. Chromosomal polymorphism is a normal variation where the genetic material is arranged differently to the average individual and is more common in infertile couples compared to the general population. We analysed data from 942 ICSI cycles in 697 couples who underwent karyotyping analysis to assess the changes in chromosomes between 2016 and 2018. The pregnancy rate was 31.2%, with 16.0% of participants experiencing a live birth, while 13.8% of pregnancies resulted in a miscarriage and 1.3% were outside the womb cavity (ectopic). The evidence did not identify a clear association between the chromosomal polymorphism and the outcome of treatment.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care.

OBJECTIVES: Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score. DESIGN: Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection. SETTING: Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. PARTICIPANTS: Patients with COVID-19 admitted to UHB January-August 2020 were included. MAIN OUTCOME MEASURES: Death and ITU admission within 28 days of admission. RESULTS: 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785). CONCLUSIONS: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

Disease burden of diabetes, diabetic retinopathy and their future projections in the UK: cross-sectional analyses of a primary care database.

OBJECTIVES: To estimate the current disease burden, trends and future projections for diabetes mellitus (DM) and diabetic retinopathy (DR) in the IQVIA Medical Research Data (IMRD). PARTICIPANTS/DESIGN/SETTING: We performed a cross-sectional study of patients aged 12 and above to determine the prevalence of DM and DR from the IMRD database (primary care database) in January 2017, involving a total population of 1 80 824 patients with DM. We also carried out a series of cross-sectional studies to investigate prevalence trends, and then applied a double exponential smoothing model to forecast the future burden of DM and DR in the UK. RESULTS: The crude DM prevalence in 2017 was 5.2%. The DR, sight-threatening retinopathy (STR) and diabetic maculopathy prevalence figures in 2017 were 33.78%, 12.28% and 7.86%, respectively, in our IMRD cross-sectional study. There were upward trends in the prevalence of DM, DR and STR, most marked and accelerating in STR in type 1 DM but slowing in type 2 DM, and in the overall prevalence of DR. CONCLUSION: Our results suggest differential rising trends in the prevalence of DM and DR. Preventive strategies, as well as treatment services planning, can be based on these projected prevalence estimates. Improvements that are necessary for the optimisation of care pathways, and preparations to meet demand and capacity challenges, can also be based on this information. The limitations of the study can be overcome by a future collaborative study linking DR screening and hospital eye services data.

Information given by websites selling home self-sampling COVID-19 tests: an analysis of accuracy and completeness.

OBJECTIVES: To assess the accuracy and completeness of information provided by websites selling home self-sampling and testing kits for COVID-19. DESIGN: Cross-sectional observational study. SETTING: All websites (n=27) selling direct to user home self-sampling and testing kits for COVID-19 (41 tests) in the UK (39 tests) and USA (two tests) identified by a website search on 23 May 2020. MAIN OUTCOME MEASURES: Thirteen predefined basic information items to communicate to a user, including who should be tested, when and how testing should be done, test accuracy, and interpretation of results. RESULTS: Many websites did not provide the name or manufacturer of the test (32/41; 78%), when to use the test (10/41; 24%), test accuracy (12/41; 29%), and how to interpret results (21/41; 51%). Sensitivity and specificity were the most commonly reported test accuracy measures (either reported for 27/41 [66%] tests): we could only link these figures to manufacturers' documents or publications for four (10%) tests. Predictive values, most relevant to users, were rarely reported (five [12%] tests reported positive predictive values). For molecular virus tests, 9/23 (39%) websites explained that test positives should self-isolate, and 8/23 (35%) explained that test negatives may still have the disease. For antibody tests, 12/18 (67%) websites explained that testing positive does not necessarily infer immunity from future infection. Seven (39%) websites selling antibody tests claimed the test had a CE mark, when they were for a different intended use (venous blood rather than finger-prick samples). CONCLUSIONS: At the point of online purchase of home self-sampling COVID-19 tests, users in the UK are provided with incomplete, and, in some cases, misleading information on test accuracy, intended use, and test interpretation. Best practice guidance for communication about tests to the public should be developed and enforced for online sales of COVID-19 tests.

Predictors for diabetic retinopathy progression-findings from nominal group technique and Evidence review.

OBJECTIVES: Risk stratification is needed for patients referred to hospital eye services by Diabetic Eye Screening Programme UK. This requires a set of candidate predictors. The literature contains a large number of predictors. The objective of this research was to arrive at a small set of clinically important predictors for the outcome of the progression of diabetic retinopathy (DR). They need to be evidence based and readily available during the clinical consultation. METHODS AND ANALYSIS: Initial list of predictors was obtained from a systematic review of prediction models. We sought the clinical expert opinion using a formal qualitative study design. A series of nominal group technique meetings to shorten the list and to rank the predictors for importance by voting were held with National Health Service hospital-based clinicians involved in caring for patients with DR in the UK. We then evaluated the evidence base for the selected predictors by critically appraising the evidence. RESULTS: The source list was presented at nominal group meetings (n=4), attended by 44 clinicians. Twenty-five predictors from the original list were ranked as important predictors and eight new predictors were proposed. Two additional predictors were retained after evidence check. Of these 35, 21 had robust supporting evidence in the literature condensed into a set of 19 predictors by categorising DR. CONCLUSION: We identified a set of 19 clinically meaningful predictors of DR progression that can help stratify higher-risk patients referred to hospital eye services and should be considered in the development of an individual risk stratification model. STUDY DESIGN: A qualitative study and evidence review. SETTING: Secondary eye care centres in North East, Midlands and South of England.

Glucose, Insulin, and Lipids in Cord Blood of Neonates and Their Association with Birthweight: Differential Metabolic Risk of Large for Gestational Age and Small for Gestational Age Babies.

OBJECTIVES: To investigate the association of birthweight percentile with cord blood glucose, lipids, and insulin levels. STUDY DESIGN: Data obtained from 1522 newborns were included in the Born in Guangzhou Cohort study. The generalized additive model and multivariable linear regression model were used to explore the nonlinear and linear relationships between birthweight and cord blood metabolic measures, and to evaluate the differences of metabolic measures Z-scores among small for gestational age, appropriate for gestational age, and large for gestational age babies. RESULTS: Birthweight Z-score was linearly associated with increased cord blood insulin Z-score (adjusted ß = 0.30; 95% CI, 0.22-0.37). Compared with appropriate for gestational age babies, neonates born small for gestational age had significantly higher cord blood triglycerides Z-score (adjusted mean difference [MDadj], 0.60; 95% CI, 0.40-0.79) and lower cord blood insulin (MDadj, -0.37; 95% CI, -0.57 to -0.16), high-density lipoprotein cholesterol (MDadj, -0.34; 95% CI, -0.55 to -0.13), total cholesterol (MDadj, -0.26; 95% CI, -0.47 to -0.05), and low-density lipoprotein (MDadj, -0.23; 95% CI, -0.43 to -0.02) Z-scores, and neonates born large for gestational age had higher cord blood insulin Z-score (MDadj, 0.31; 95% CI, 0.09 to 0.52). CONCLUSIONS: Our findings support the hypothesis that babies born small for gestational age and large for gestational age are exposed to different intrauterine environments, which may contribute to altered fat accumulation patterns with implications for the risk of metabolic dysfunction later in life. There is a need to consider the development of tailored intervention strategies to prevent metabolic dysfunction in adult life for these babies.

How do pharmacy students select their pre-registration training providers? A mixed methods evaluation of the national recruitment scheme in England and Wales.

OBJECTIVES: A national pre-registration pharmacist recruitment scheme, which replaces the local recruitment models, was introduced in England and Wales in 2017. This study aimed to explore pharmacy students' behaviour and associated factors in their selection of pre-registration training programmes. METHODS: A mixed-method study using (a) analysis of data from all applicants (n = 2694) of the national recruitment scheme, (b) an online survey and (c) a virtual focus group was undertaken. Survey and focus group questions were developed based on the Theoretical Domains Framework (TDF). Descriptive and inferential analysis of quantitative data was undertaken using Stata software. Qualitative data from focus groups and responses from the open-ended questions were analysed using framework technique. KEY FINDINGS: A vast majority of applicants (n = 2182, 83.9%) selected a hospital training programme as their first ranked preference, with the rest opting for community pharmacy. Urban areas, particularly London, were most popular geographically. A total of 307 survey responses were returned. Long-term career aspirations, followed by geographical factors, were rated most highly in applicants' decision-making. Qualitative data from survey and focus group demonstrated information about programmes/employers, perceived opportunity for skills development and aspiration towards a career path as key contributory factors in their decision-making. CONCLUSIONS: Secondary care was the most desirable destination for pharmacy students to undertake early career training. The clinical roles and career opportunities in community pharmacy needs to be promoted as there is a risk that community pharmacy training programme places may be seen as a 'left over' opportunity for less competitive candidates to uptake.

National recruitment scheme for pre-registration pharmacist training in England and Wales: a mixed method evaluation of experiences of applicant pharmacy students.

BACKGROUND: A national pre-registration pharmacist training recruitment scheme, which replaces local recruitment models, was introduced in England and Wales in 2017. The national recruitment system allows pharmacy students to apply for the 52 weeks training programmes (mandatory requirement for registration as a pharmacist), through a single application system prior to undertaking a nationally administered assessment. This study aimed to explore experiences of pharmacy students on the national recruitment scheme, particularly their views on the selection methodology, application process, and offer outcomes. METHODS: This mixed method study involved a) an online survey of all (approximate n = 2800) year 4 (final year of MPharm degree) pharmacy students in England and Wales and b) a qualitative focus group with four students. The study population was eligible to participate in the 2017/18 national recruitment scheme. Survey respondents were invited to participate in a focus group. Quantitative data were analysed using descriptive and inferential analysis. Qualitative data were analysed using the framework technique. Participation was voluntary. Ethical approval from University of Birmingham was obtained. RESULTS: A total of 307 completed surveys were returned (approximate response rate 11%). Respondents were generally satisfied with the application process and commended the fairness of the selection methodology and convenience in allowing them to apply to multiple training providers. Most survey respondents (n = 181, 72.9%) were either satisfied or highly satisfied with the training programme they were offered based on their assessment performances. Three themes and eight sub-themes obtained from the analysis of over 200 open comments data from the survey and transcript of a focus group with four participants. Results suggested the need to widen the timeframe available for applicants to shortlist their preferred employers, improve the method of programme listing in the application system, and consideration of prior achievements including academic performances and placement experiences to be included in the selection methodology. CONCLUSIONS: Experiences of pharmacy students on the national recruitment scheme suggest that respondents considered the selection methodology to be fair. Student engagement and satisfaction with the recruitment system can be maximised through improved listing of employers and widening the timescales for students to shortlist their preferred employers during application process. Inclusion of University achievements in the selection methodology will require consideration of evidence based approaches. Low response rate limits generalisation of findings.

Prognostic prediction models for diabetic retinopathy progression: a systematic review.

With the increasing incidence of diabetic retinopathy and its improved detection, there is increased demand for diabetic retinopathy treatment services. Prognostic prediction models have been used to optimise services but these were intended for early detection of sight-threatening retinopathy and are mostly used in diabetic retinopathy screening services. We wanted to look into the predictive ability and applicability of the existing models for the higher-risk patients referred into hospitals. We searched MEDLINE, EMBASE, COCHRANE CENTRAL, conference abstracts and reference lists of included publications for studies of any design using search terms related to diabetes, diabetic retinopathy and prognostic models. Search results were screened for relevance to the review question. Included studies had data extracted on model characteristics, predictive ability and validation. They were assessed for quality using criteria specified by PROBAST and CHARMS checklists, independently by two reviewers. Twenty-two articles reporting on 14 prognostic models (including four updates) met the selection criteria. Eleven models had internal validation, eight had external validation and one had neither. Discriminative ability with c-statistics ranged from 0.57 to 0.91. Studies ranged from low to high risk of bias, mostly due to the need for external validation or missing data. Participants, outcomes, predictors handling and modelling methods varied. Most models focussed on lower-risk patients, the majority had high risk of bias and doubtful applicability, but three models had some applicability for higher-risk patients. However, these models will also need updating and external validation in multiple hospital settings before being implemented into clinical practice.

A systematic review of epigenetic biomarkers in progression from non-dysplastic Barrett's oesophagus to oesophageal adenocarcinoma.

OBJECTIVES: The objective of this systematic review is to identify and summarise studies which examine epigenetic biomarkers in patients with Barrett's oesophagus (BO) and their association with progression to oesophageal adenocarcinoma (OADC). BO is a precursor lesion for OADC. There is no clinical test to predict patients who are likely to progress to OADC. An epigenetic biomarker could predict patients who are at high risk of progression from BO to OADC which could facilitate earlier diagnosis and spare those unlikely to develop cancer from regular invasive surveillance endoscopy. SETTING: A systematic search was conducted of the following databases: MEDLINE, MEDLINE in Process, EMBASE, Cochrane Central, ISI Conference Proceedings Citation Index and the British Library's ZETOC. Studies were conducted in secondary and tertiary care settings. PARTICIPANTS: All studies measuring epigenetic change in patients over 18 years old who progressed from non-dysplastic BO to OADC were included. Genetic, in vitro and studies which did not measure progression in the same patient cohort were excluded. Study inclusion and risk of bias of individual eligible studies were assessed in duplicate by two reviewers using a modified Quality in Prognostic Studies tool. RESULTS: 14 studies met the inclusion criteria. 42 epigenetic markers were identified, and 5 studies developed models aiming to predict progression to OADC. CONCLUSIONS: The evidence from this systematic review is suggestive of a role for p16 as an epigenetic biomarker for the progression of BO to OADC. PROSPERO NUMBER: CRD42016038654.