Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.

BACKGROUND: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. METHODS: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. RESULTS: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. CONCLUSIONS: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.

Prognostic significance of troponin in patients with malignancy (NIHR Health Informatics Collaborative TROP-MALIGNANCY study).

BACKGROUND: Cardiac troponin is commonly raised in patients presenting with malignancy. The prognostic significance of raised troponin in these patients is unclear. OBJECTIVES: We sought to investigate the relation between troponin and mortality in a large, well characterised cohort of patients with a routinely measured troponin and a primary diagnosis of malignancy. METHODS: We used the National Institute for Health Research (NIHR) Health Informatics Collaborative data of 5571 patients, who had troponin levels measured at 5 UK cardiac centres between 2010 and 2017 and had a primary diagnosis of malignancy. Patients were classified into solid tumour or haematological malignancy subgroups. Peak troponin levels were standardised as a multiple of each laboratory's 99th -percentile upper limit of normal (xULN). RESULTS: 4649 patients were diagnosed with solid tumours and 922 patients with haematological malignancies. Raised troponin was an independent predictor of mortality in all patients (Troponin > 10 vs. <1 adjusted HR 2.01, 95% CI 1.73 to 2.34), in solid tumours (HR 1.84, 95% CI 1.55 to 2.19), and in haematological malignancy (HR 2.72, 95% CI 1.99 to 3.72). There was a significant trend in increasing mortality risk across troponin categories in all three subgroups (p < 0.001). CONCLUSION: Raised troponin level is associated with increased mortality in patients with a primary diagnosis of malignancy regardless of cancer subtype. Mortality risk is stable for patients with a troponin level below the ULN but increases as troponin level increases above the ULN in the absence of acute coronary syndrome.

Changes in the investigation and management of suspected myocardial infarction and injury during COVID-19: a multi-centre study using routinely collected healthcare data.

Objective: The COVID-19 pandemic was associated with a reduction in the incidence of myocardial infarction (MI) diagnosis, in part because patients were less likely to present to hospital. Whether changes in clinical decision making with respect to the investigation and management of patients with suspected MI also contributed to this phenomenon is unknown. Methods: Multicentre retrospective cohort study in three UK centres contributing data to the National Institute for Health Research Health Informatics Collaborative. Patients presenting to the Emergency Department (ED) of these centres between 1st January 2020 and 1st September 2020 were included. Three time epochs within this period were defined based on the course of the first wave of the COVID-19 pandemic: pre-pandemic (epoch 1), lockdown (epoch 2), post-lockdown (epoch 3). Results: During the study period, 10,670 unique patients attended the ED with chest pain or dyspnoea, of whom 6,928 were admitted. Despite fewer total ED attendances in epoch 2, patient presentations with dyspnoea were increased (p < 0.001), with greater likelihood of troponin testing in both chest pain (p = 0.001) and dyspnoea (p < 0.001). There was a dramatic reduction in elective and emergency cardiac procedures (both p < 0.001), and greater overall mortality of patients (p < 0.001), compared to the pre-pandemic period. Positive COVID-19 and/or troponin test results were associated with increased mortality (p < 0.001), though the temporal risk profile differed. Conclusions: The first wave of the COVID-19 pandemic was associated with significant changes not just in presentation, but also the investigation, management, and outcomes of patients presenting with suspected myocardial injury or MI.

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm.

The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the 'molecular tumour board' into 'standard of care' ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting. Generally, integrated delivery has been restricted to specific (common) cancer types either within major cancer centres or small regional networks. Here, we focus on solutions in real-world integration of genomics, pathology, surgery, oncological treatments, data from clinical source systems and analysis of whole-body imaging as digital data that can facilitate cost-effectiveness analysis, clinical trial recruitment, and outcome assessment. This urgent imperative for cancer also extends across the early diagnosis and adjuvant treatment interventions, individualised cancer vaccines, immune cell therapies, personalised synthetic lethal therapeutics and cancer screening and prevention. Oncology care systems worldwide require proactive step-changes in solutions that include inter-operative digital working that can solve patient centred challenges to ensure inclusive, quality, sustainable, fair and cost-effective adoption and efficient delivery. Here we highlight workforce, technical, clinical, regulatory and economic challenges that prevent the implementation of precision oncology at scale, and offer a systematic roadmap of integrated solutions for standard of care based on minimal essential digital tools. These include unified decision support tools, quality control, data flows within an ethical and legal data framework, training and certification, monitoring and feedback. Bridging the technical, operational, regulatory and economic gaps demands the joint actions from public and industry stakeholders across national and global boundaries.

Protocol for the Development and Analysis of the Oxford and Reading Cognitive Comorbidity, Frailty and Ageing Research Database-Electronic Patient Records (ORCHARD-EPR).

BACKGROUND: Hospital electronic patient records (EPRs) offer the opportunity to exploit large-scale routinely acquired data at relatively low cost and without selection. EPRs provide considerably richer data, and in real-time, than retrospective administrative data sets in which clinical complexity is often poorly captured. With population ageing, a wide range of hospital specialties now manage older people with multimorbidity, frailty and associated poor outcomes. We, therefore, set-up the Oxford and Reading Cognitive Comorbidity, Frailty and Ageing Research Database-Electronic Patient Records (ORCHARD-EPR) to facilitate clinically meaningful research in older hospital patients, including algorithm development, and to aid medical decision-making, implementation of guidelines, and inform policy. METHODS AND ANALYSIS: ORCHARD-EPR uses routinely acquired individual patient data on all patients aged ≥65 years with unplanned admission or Same Day Emergency Care unit attendance at four acute general hospitals serving a population of >800 000 (Oxfordshire, UK) with planned extension to the neighbouring Berkshire regional hospitals (>1 000 000). Data fields include diagnosis, comorbidities, nursing risk assessments, frailty, observations, illness acuity, laboratory tests and brain scan images. Importantly, ORCHARD-EPR contains the results from mandatory hospital-wide cognitive screening (≥70 years) comprising the 10-point Abbreviated-Mental-Test and dementia and delirium diagnosis (Confusion Assessment Method-CAM). Outcomes include length of stay, delayed transfers of care, discharge destination, readmissions and death. The rich multimodal data are further enhanced by linkage to secondary care electronic mental health records. Selection of appropriate subgroups or linkage to existing cohorts allows disease-specific studies. Over 200 000 patient episodes are included to date with data collection ongoing of which 129 248 are admissions with a length of stay ≥1 day in 64 641 unique patients. ETHICS AND DISSEMINATION: ORCHARD-EPR is approved by the South Central Oxford C Research Ethics Committee (ref: 23/SC/0258). Results will be widely disseminated through peer-reviewed publications and presentations at conferences, and regional meetings to improve hospital data quality and clinical services.

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.

Patient Biochemistry and Treatment Need in Chronic Hepatitis B Virus Infection Across Three Continents: Retrospective Cross-Sectional Cohort Studies.

INTRODUCTION: Chronic hepatitis B virus (HBV) infection is associated with significant global morbidity and mortality. Low treatment rates are observed in patients living with HBV; the reasons for this are unclear. This study sought to describe patients' demographic, clinical and biochemical characteristics across three continents and their associated treatment need. METHODS: This retrospective cross-sectional post hoc analysis of real-world data used four large electronic databases from the United States, United Kingdom and China (specifically Hong Kong and Fuzhou). Patients were identified by first evidence of chronic HBV infection in a given year (their index date) and characterized. An algorithm was designed and applied, wherein patients were categorized as treated, untreated but indicated for treatment and untreated and not indicated for treatment based on treatment status and demographic, clinical, biochemical and virological characteristics (age; evidence of fibrosis/cirrhosis; alanine aminotransferase [ALT] levels, HCV/HIV coinfection and HBV virology markers). RESULTS: In total, 12,614 US patients, 503 UK patients, 34,135 patients from Hong Kong and 21,614 from Fuzhou were included. Adults (99.4%) and males (59.0%) predominated. Overall, 34.5% of patients were treated at index (range 15.9-49.6%), with nucleos(t)ide analogue monotherapy most commonly prescribed. The proportion of untreated-but-indicated patients ranged from 12.9% in Hong Kong to 18.2% in the UK; almost two-thirds of these patients (range 61.3-66.7%) had evidence of fibrosis/cirrhosis. A quarter (25.3%) of untreated-but-indicated patients were aged ≥ 65 years. CONCLUSION: This large real-world dataset demonstrates that chronic hepatitis B infection remains a global health concern; despite the availability of effective suppressive therapy, a considerable proportion of predominantly adult patients apparently indicated for treatment are currently untreated, including many patients with fibrosis/cirrhosis. Causes of disparity in treatment status warrant further investigation.

Increase in hypoglycaemia and hyperglycaemia in people with diabetes admitted to hospital during COVID-19 pandemic.

BACKGROUND: We used detailed information on patients with diabetes admitted to hospital to determine differences in clinical outcomes before and during the COVID-19 pandemic in the UK. METHODS: The study used electronic patient record data from Imperial College Healthcare NHS Trust. Hospital admission data for patients coded for diabetes was analysed over three time periods: pre-pandemic (31st January 2019-31st January 2020), Wave 1 (1st February 2020-30th June 2020), and Wave 2 (1st September 2020-30th April 2021). We compared clinical outcomes including glycaemia and length of stay. RESULTS: We analysed data obtained from 12,878, 4008 and 7189 hospital admissions during the three pre-specified time periods. The incidence of Level 1 and Level 2 hypoglycaemia was significantly higher during Waves 1 and 2 compared to the pre-pandemic period (25 % and 25.1 % vs. 22.9 % for Level 1 and 11.7 % and 11.5 % vs. 10.3 % for Level 2). The incidence of hyperglycaemia was also significantly higher during the two waves. The median hospital length of stay increased significantly (4.1[1.6, 9.8] and 4.0[1.4, 9.4] vs. 3.5[1.2, 9.2] days). CONCLUSIONS: During the COVID-19 pandemic in the UK, hospital in-patients with diabetes had a greater number of hypoglycaemic/hyperglycaemic episodes and an increased length of stay when compared to the pre-pandemic period. This highlights the necessity for a focus on improved diabetes care during further significant disruptions to healthcare systems and ensuring minimisation of the impact on in-patient diabetes services. SUMMARY: Diabetes is associated with poorer outcomes from COVID-19. However the glycaemic control of inpatients before and during the COVID-19 pandemic is unknown. We found the incidence of hypoglycaemia and hyperglycaemia was significantly higher during the pandemic highlighting the necessity for a focus on improved diabetes care during further pandemics.

Vividness as the similarity between generated imagery and an internal model.

Vividness in visual mental imagery has been relatively under-explored compared to imagery's representational format and neural mechanisms. In this paper, we take a deeper look at vividness and suggest that in re-framing it, we can potentially reconcile disparate findings regarding visual cortex activation during imagery. Unlike traditional views of vividness that define the concept in terms of perception, we frame vividness in terms of imagery's relation to an internal model; the closer the generated imagery is to this model, the more vivid it is. This view is considered alongside existing neuroscientific, psychological, and philosophical research, as well as directions for future research.

A UK nationwide study of adults admitted to hospital with diabetic ketoacidosis or hyperosmolar hyperglycaemic state and COVID-19.

AIMS: To investigate characteristics of people hospitalized with coronavirus-disease-2019 (COVID-19) and diabetic ketoacidosis (DKA) or hyperosmolar hyperglycaemic state (HHS), and to identify risk factors for mortality and intensive care admission. MATERIALS AND METHODS: Retrospective cohort study with anonymized data from the Association of British Clinical Diabetologists nationwide audit of hospital admissions with COVID-19 and diabetes, from start of pandemic to November 2021. The primary outcome was inpatient mortality. DKA and HHS were adjudicated against national criteria. Age-adjusted odds ratios were calculated using logistic regression. RESULTS: In total, 85 confirmed DKA cases, and 20 HHS, occurred among 4073 people (211 type 1 diabetes, 3748 type 2 diabetes, 114 unknown type) hospitalized with COVID-19. Mean (SD) age was 60 (18.2) years in DKA and 74 (11.8) years in HHS (p < .001). A higher proportion of patients with HHS than with DKA were of non-White ethnicity (71.4% vs 39.0% p = .038). Mortality in DKA was 36.8% (n = 57) and 3.8% (n = 26) in type 2 and type 1 diabetes respectively. Among people with type 2 diabetes and DKA, mortality was lower in insulin users compared with non-users [21.4% vs. 52.2%; age-adjusted odds ratio 0.13 (95% CI 0.03-0.60)]. Crude mortality was lower in DKA than HHS (25.9% vs. 65.0%, p = .001) and in statin users versus non-users (36.4% vs. 100%; p = .035) but these were not statistically significant after age adjustment. CONCLUSIONS: Hospitalization with COVID-19 and adjudicated DKA is four times more common than HHS but both associate with substantial mortality. There is a strong association of previous insulin therapy with survival in type 2 diabetes-associated DKA.

Association Between SGLT2 Inhibitor Treatment and Diabetic Ketoacidosis and Mortality in People With Type 2 Diabetes Admitted to Hospital With COVID-19.

OBJECTIVE: To determine the association between prescription of SGLT2 inhibitors (SGLT2is) and diabetic ketoacidosis (DKA) incidence or mortality in people with type 2 diabetes (T2D) hospitalized with COVID-19. RESEARCH DESIGN AND METHODS: This was a retrospective cohort study based on secondary analysis of data from a large nationwide audit from a network of 40 centers in the U.K. with data collection up to December 2020. The study was originally designed to describe risk factors associated with adverse outcomes among people with diabetes who were admitted to hospital with COVID-19. The primary outcome for this analysis was DKA on or during hospital admission. The secondary outcome was mortality. Crude, age-sex adjusted, and multivariable logistic regression models were used to generate odds ratios (ORs) and 95% CIs for people prescribed SGLT2i compared with those not prescribed SGLT2i. RESULTS: The original national audit included 3,067 people with T2D who were admitted to hospital with COVID-19, of whom 230 (7.5%) were prescribed SGLT2is prior to hospital admission. The mean age of the overall cohort was 72 years, 62.3% were men, and 34.9% were prescribed insulin. Overall, 2.8% of the total population had DKA and 35.6% of people in the study died. The adjusted odds of DKA were not significantly different between those prescribed SGLT2is and those not (OR 0.56; 95% CI 0.16-1.97). The adjusted odds of mortality associated with SGLT2is were similar in the total study population (OR 1.13; 95% CI 0.78-1.63), in the subgroup prescribed insulin (OR 1.02; 95% CI 0.59-1.77), and in the subgroup that developed DKA (OR 0.21; 95% CI 0.01-8.76). CONCLUSIONS: We demonstrate a low risk of DKA and high mortality rate in people with T2D admitted to hospital with COVID-19 and limited power, but no evidence, of increased risk of DKA or in-hospital mortality associated with prescription of SGLT2is.

Can Researchers Assess the Suitability of Datasets to Answer Their Research Questions, with Access to Metadata Only?

Health research increasingly requires effective ways to identify existing datasets and assess their suitability for research. We sought to test whether researchers could use an existing metadata catalogue to assess the suitability of datasets for addressing specified research questions. Five datasets were described in the National Institute for Health Research Health Informatics Collaborative metadata catalogue, and for each dataset five associated research questions were formulated, some of which were answerable with the dataset while others were not. Thirteen researchers each assessed whether the ten questions associated with two randomly selected datasets were answerable with the described datasets. After removing instances where participants misunderstood the question or lacked subject matter knowledge to make the assessment, we found that 87 out of 109 assessments (80%) were correct. Participants particularly struggled with one dataset which consisted of EHR data. The most common reason for incorrect assessments was the inability to find the relevant information in the metadata catalogue.

Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study.

OBJECTIVE: Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source. METHODS: Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically. RESULTS: Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed. DISCUSSION: Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer. CONCLUSION: The data set has great potential to facilitate research into colorectal cancer.

Association of statin and/or renin-angiotensin-aldosterone system modulating therapy with mortality in adults with diabetes admitted to hospital with COVID-19: A retrospective multicentre European study.

BACKGROUND & AIMS: To assess the impact of pre-admission renin-angiotensin-aldosterone system inhibitor (RAASi) and statin use on mortality following COVID-19 hospitalization in adults with pre-existing diabetes. METHODS: Retrospective cohort study of adults with diabetes admitted to ninety-nine participating hospitals in the United Kingdom, France and Spain during the first wave of the COVID-19 pandemic. Logistic regression models adjusted for demographic factors and comorbidity were used to describe associations with mortality in hospital or within 28 days of admission and individual or combined RAASi and statin therapy prescription followed by a country level meta-analysis. RESULTS: Complete data were available for 3474 (42.6%) individuals. Prescribing patterns varied by country: 25-50% neither RAASi nor statin therapy, 14-36% both RAASi and statin therapy, 9-24% RAASi therapy alone, 12-36% statin alone. Overall, 20-37% of patients died within 28 days. Meta-analysis found no evidence of an association between mortality and prescription of RAASi therapy (OR 1.09, CI 0.78-1.52 (I2 22.2%)), statin (OR 0.97, CI 0.59-1.61 (I2 72.9%)) or both (OR 1.14, CI 0.67-1.92 (I2 78.3%)) compared to those prescribed neither drug class. CONCLUSIONS: This large multicentre, multinational study found no evidence of an association between mortality from COVID-19 infection in people with diabetes and use of either RAASi, statin or combination therapy. This provides reassurance that clinicians should not change their RAASi and statin therapy prescribing practice in people with diabetes during the COVID-19 pandemic.

Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies.

OBJECTIVE: (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence. METHODS: In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics. RESULTS: 13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%). CONCLUSIONS: The pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics.PROSPERO registration numberCRD42020172409.

Implications of elevated troponin on time-to-surgery in non-ST elevation myocardial infarction (NIHR Health Informatics Collaborative: TROP-CABG study).

Implications of elevated troponin on time-to-surgery in non-ST elevation myocardial infarction(NIHR Health Informatics Collaborative:TROP-CABG study). Benedetto et al. BACKGROUND: The optimal timing of coronary artery bypass grafting (CABG) in patients with non-ST elevation myocardial infarction (NSTEMI) and the utility of pre-operative troponin levels in decision-making remains unclear. We investigated (a) the association between peak pre-operative troponin and survival post-CABG in a large cohort of NSTEMI patients and (b) the interaction between troponin and time-to-surgery. METHODS AND RESULTS: Our cohort consisted of 1746 patients (1684 NSTEMI; 62 unstable angina) (mean age 69 ± 11 years,21% female) with recorded troponins that had CABG at five United Kingdom centers between 2010 and 2017. Time-segmented Cox regression was used to investigate the interaction of peak troponin and time-to-surgery on early (within 30 days) and late (beyond 30 days) survival. Average interval from peak troponin to surgery was 9 ± 15 days, with 1466 (84.0%) patients having CABG during the same admission. Sixty patients died within 30-days and another 211 died after a mean follow-up of 4 ± 2 years (30-day survival 0.97 ± 0.004 and 5-year survival 0.83 ± 0.01). Peak troponin was a strong predictor of early survival (adjusted P = 0.002) with a significant interaction with time-to-surgery (P interaction = 0.007). For peak troponin levels <100 times the upper limit of normal, there was no improvement in early survival with longer time-to-surgery. However, in patients with higher troponins, early survival increased progressively with a longer time-to-surgery, till day 10. Peak troponin did not influence survival beyond 30 days (adjusted P = 0.64). CONCLUSIONS: Peak troponin in NSTEMI patients undergoing CABG was a significant predictor of early mortality, strongly influenced the time-to-surgery and may prove to be a clinically useful biomarker in the management of these patients.

Combining faecal immunochemical testing with blood test results for colorectal cancer risk stratification: a consecutive cohort of 16,604 patients presenting to primary care.

BACKGROUND: Faecal immunochemical tests (FITs) are used to triage primary care patients with symptoms that could be caused by colorectal cancer for referral to colonoscopy. The aim of this study was to determine whether combining FIT with routine blood test results could improve the performance of FIT in the primary care setting. METHODS: Results of all consecutive FITs requested by primary care providers between March 2017 and December 2020 were retrieved from the Oxford University Hospitals NHS Foundation Trust. Demographic factors (age, sex), reason for referral, and results of blood tests within 90 days were also retrieved. Patients were followed up for incident colorectal cancer in linked hospital records. The sensitivity, specificity, positive and negative predictive values of FIT alone, FIT paired with blood test results, and several multivariable FIT models, were compared. RESULTS: One hundred thirty-nine colorectal cancers were diagnosed (0.8%). Sensitivity and specificity of FIT alone at a threshold of 10 µg Hb/g were 92.1 and 91.5% respectively. Compared to FIT alone, blood test results did not improve the performance of FIT. Pairing blood test results with FIT increased specificity but decreased sensitivity. Multivariable models including blood tests performed similarly to FIT alone. CONCLUSIONS: FIT is a highly sensitive tool for identifying higher risk individuals presenting to primary care with lower risk symptoms. Combining blood test results with FIT does not appear to lead to better discrimination for colorectal cancer than using FIT alone.

The Neural Correlates of Analogy Component Processes.

Analogical reasoning is a core facet of higher cognition in humans. Creating analogies as we navigate the environment helps us learn. Analogies involve reframing novel encounters using knowledge of familiar, relationally similar contexts stored in memory. When an analogy links a novel encounter with a familiar context, it can aid in problem solving. Reasoning by analogy is a complex process that is mediated by multiple brain regions and mechanisms. Several advanced computational architectures have been developed to simulate how these brain processes give rise to analogical reasoning, like the "learning with inferences and schema abstraction" architecture and the Companion architecture. To obtain this power to simulate human reasoning, theses architectures assume that various computational "subprocesses" comprise analogical reasoning, such as analogical access, mapping, inference, and schema induction, consistent with the structure-mapping framework proposed decades ago. However, little is known about how these subprocesses relate to actual brain processes. While some work in neuroscience has linked analogical reasoning to regions of brain prefrontal cortex, more research is needed to investigate the wide array of specific neural hypotheses generated by the computational architectures. In the current article, we review the association between historically important computational architectures of analogy and empirical studies in neuroscience. In particular, we focus on evidence for a frontoparietal brain network underlying analogical reasoning and the degree to which brain mechanisms mirror the computational subprocesses. We also offer a general vantage on the current- and future-states of neuroscience research in this domain and provide some recommendations for future neuroimaging studies.