Gallstone Disease and the Risk of Cardiovascular Disease

Abstract Gallstone disease (GD) is one of the most common presentations to surgical units worldwide and shares several risk factors with cardiovascular disease (CVD). CVD remains the most common cause of death worldwide and results in considerable economic burden. Recent observational studies have demonstrated an association between GD and CVD with some studies demonstrating a stronger association with cholecystectomy. We present the findings of a meta-analysis assessing the relationship between GD and CVD. A total of fourteen cohort studies with over 1.2 million participants were included. The pooled hazard ratio (HR, 95% confidence interval [CI]) for association with GD from a random-effects model is 1.23 (95%CI: 1.16–1.30) for fatal and non-fatal CVD events. The association was present in females and males. Three studies report the relationship between cholecystectomy and CVD with a pooled HR of 1.41 (95%CI: 1.21–1.64) which compares to a HR of 1.30 (95%CI: 1.07–1.58) when cholecystectomy is excluded although confounding may influence this result. Our meta-analysis demonstrates a significant relationship between GD and CVD events which is present in both sexes. Further research is needed to assess the influence of cholecystectomy on this association

Meta-analysis and Meta-regression of Survival After Liver Transplantation for Unresectable Perihilar Cholangiocarcinoma

Objective: To systematically review studies reporting survival data following neoadjuvant chemoradiation and orthotopic liver transplantation (NCR-OLT) for unresectable perihilar cholangiocarcinoma (pCC). Background: Despite survival improvements for other cancers, the prognosis of pCC remains dismal. Since publication of the Mayo protocol in 2000, increasing numbers of series globally are reporting outcomes after NCR-OLT. Methods: MEDLINE, EMBASE, Scopus, and Web of Science databases were searched from January 2000 to February 2019. A meta-analysis of proportions was conducted, pooling 1, 3-, and 5-year overall survival and recurrence rates following NCR-OLT across centers. Per protocol and intention to treat data were interrogated. Meta-regression was used to evaluate PSC as a confounder affecting survival. Results: Twenty studies comprising 428 patients were eligible for analysis. No RCTs were retrieved; the majority of studies were noncomparative cohort studies. The pooled 1, 3-, and 5-year overall survival rates following OLT without neoadjuvant therapy were 71.2% (95% CI 62.2%–79.4%), 48.0% (95% CI 35.0%–60.9%), and 31.6% (95% CI 23.1%–40.7%). These improved to 82.8% (95% CI 73.0%–90.8%), 65.5% (95% CI 48.7%–80.5%), and 65.1% (95% CI 55.1%–74.5%) if neoadjuvant chemoradiation was completed. Pooled recurrence after 3 years was 24.1% (95% CI 17.9%–30.9%) with neoadjuvant chemoradiation, 51.7% (95% CI 33.8%–69.4%) without. Conclusions: In unresectable pCC, NCR-OLT confers long-term survival in highly selected patients able to complete neoadjuvant chemoradiation followed by transplantation. PSC patients appear to have the most favorable outcomes. A high recurrence rate is of concern when considering extending national graft selection policy to pCC.publishedVersio

Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review

Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets.Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication).Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning.Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights

Can a smartphone-delivered tool facilitate the assessment of surgical site infection and result in earlier treatment? Tracking Wound Infection with Smartphone Technology (TWIST): protocol for a randomized-controlled trial in emergency surgery patients.

Introduction National data suggest that surgical site infection (SSI) complicates 2%–10% of general surgery cases, although the patient-reported incidence is much higher. SSIs cause significant patient morbidity and represent a significant burden on acute healthcare services, in a cohort predominantly suitable for outpatient management. Over three-quarters of UK adults now own smartphones, which could be harnessed to improve access to care. We aim to investigate if a smartphone-delivered wound assessment tool results in earlier treatment.Methods and analysis This is a randomised controlled trial aiming to recruit 500 patients across National Health Service (NHS) hospitals. All emergency abdominal surgery patients over the age of 16 who own smartphones will be considered eligible, with the exclusion of those with significant visual impairment. Participants will be randomised in a 1:1 ratio between standard postoperative care and the intervention – use of the smartphone tool in addition to standard postoperative care. The main outcome measure will be time-to-diagnosis of SSI with secondary outcome measures considering use of emergency department and general practitioner services and patient experience. Follow-up will be conducted by clinicians blinded to group allocation. Analysis of time-to-diagnosis will be by comparison of means using an independent two sample t-test.Ethics and dissemination This is the first randomised controlled trial on the use of a smartphone-delivered wound assessment tool to facilitate the assessment of SSI and the impact on time-to-diagnosis. The intervention is being used in addition to standard postoperative care. The study design and protocol were reviewed and approved by Southeast Scotland Research and Ethics Committee (REC Ref: 16/SS/0072 24/05/2016). Study findings will be presented at academic conferences, published in peer-reviewed journals and are expected in 2020. A written lay summary will be available to study participants on request.Trial registration number NCT02704897; Pre-results

Genome-Wide Association Study of Non-Alcoholic Fatty Liver Disease using Electronic Health Records

Genome‐wide association studies (GWAS) have identified several risk loci for nonalcoholic fatty liver disease (NAFLD). Previous studies have largely relied on small sample sizes and have assessed quantitative traits. We performed a case‐control GWAS in the UK Biobank using recorded diagnosis of NAFLD based on diagnostic codes recommended in recent consensus guidelines. We performed a GWAS of 4,761 cases of NAFLD and 373,227 healthy controls without evidence of NAFLD. Sensitivity analyses were performed excluding other co‐existing hepatic pathology, adjusting for body mass index (BMI) and adjusting for alcohol intake. A total of 9,723,654 variants were assessed by logistic regression adjusted for age, sex, genetic principal components, and genotyping batch. We performed a GWAS meta‐analysis using available summary association statistics. Six risk loci were identified (P < 5*10(−8)) (apolipoprotein E [APOE], patatin‐like phospholipase domain containing 3 [PNPLA3, transmembrane 6 superfamily member 2 [TM6SF2], glucokinase regulator [GCKR], mitochondrial amidoxime reducing component 1 [MARC1], and tribbles pseudokinase 1 [TRIB1]). All loci retained significance in sensitivity analyses without co‐existent hepatic pathology and after adjustment for BMI. PNPLA3 and TM6SF2 remained significant after adjustment for alcohol (alcohol intake was known in only 158,388 individuals), with others demonstrating consistent direction and magnitude of effect. All six loci were significant on meta‐analysis. Rs429358 (P = 2.17*10(−11)) is a missense variant within the APOE gene determining ϵ4 versus ϵ2/ϵ3 alleles. The ϵ4 allele of APOE offered protection against NAFLD (odds ratio for heterozygotes 0.84 [95% confidence interval 0.78‐0.90] and homozygotes 0.64 [0.50‐0.79]). Conclusion: This GWAS replicates six known NAFLD‐susceptibility loci and confirms that the ϵ4 allele of APOE is associated with protection against NAFLD. The results are consistent with published GWAS using histological and radiological measures of NAFLD, confirming that NAFLD identified through diagnostic codes from consensus guidelines is a valid alternative to more invasive and costly approaches

ToKSA - Tokenized Key Sentence Annotation - a novel method for rapid approximation of ground truth for natural language processing

Objective: Identifying phenotypes and pathology from free text is an essential task for clinical work and research. Natural language processing (NLP) is a key tool for processing free text at scale. Developing and validating NLP models requires labelled data. Labels are generated through time-consuming and repetitive manual annotation and are hard to obtain for sensitive clinical data. The objective of this paper is to describe a novel approach for annotating radiology reports. Materials and Methods: We implemented tokenized key sentence-specific annotation (ToKSA) for annotating clinical data. We demonstrate ToKSA using 180,050 abdominal ultrasound reports with labels generated for symptom status, gallstone status and cholecystectomy status. Firstly, individual sentences are grouped together into a term-frequency matrix. Annotation of key (i.e. the most frequently occurring) sentences is then used to generate labels for multiple reports simultaneously. We compared ToKSA-derived labels to those generated by annotating full reports. We used ToKSA-derived labels to train a document classifier using convolutional neural networks. We compared performance of the classifier to a separate classifier trained on labels based on the full reports. Results: By annotating only 2,000 frequent sentences, we were able to generate labels for symptom status for 70,000 reports (accuracy 98.4%), gallstone status for 85,177 reports (accuracy 99.2%) and cholecystectomy status for 85,177 reports (accuracy 100%). The accuracy of the document classifier trained on ToKSA labels was similar (0.1-1.1% more accurate) to the document classifier trained on full report labels. Conclusion: ToKSA offers an accurate and efficient method for annotating free text clinical data

Clinical characteristics of children and young people admitted to hospital with covid-19 in United Kingdom: prospective multicentre observational cohort study.

OBJECTIVE: To characterise the clinical features of children and young people admitted to hospital with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the UK and explore factors associated with admission to critical care, mortality, and development of multisystem inflammatory syndrome in children and adolescents temporarily related to coronavirus disease 2019 (covid-19) (MIS-C). DESIGN: Prospective observational cohort study with rapid data gathering and near real time analysis. SETTING: 260 hospitals in England, Wales, and Scotland between 17 January and 3 July 2020, with a minimum follow-up time of two weeks (to 17 July 2020). PARTICIPANTS: 651 children and young people aged less than 19 years admitted to 138 hospitals and enrolled into the International Severe Acute Respiratory and emergency Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK study with laboratory confirmed SARS-CoV-2. MAIN OUTCOME MEASURES: Admission to critical care (high dependency or intensive care), in-hospital mortality, or meeting the WHO preliminary case definition for MIS-C. RESULTS: Median age was 4.6 (interquartile range 0.3-13.7) years, 35% (225/651) were under 12 months old, and 56% (367/650) were male. 57% (330/576) were white, 12% (67/576) South Asian, and 10% (56/576) black. 42% (276/651) had at least one recorded comorbidity. A systemic mucocutaneous-enteric cluster of symptoms was identified, which encompassed the symptoms for the WHO MIS-C criteria. 18% (116/632) of children were admitted to critical care. On multivariable analysis, this was associated with age under 1 month (odds ratio 3.21, 95% confidence interval 1.36 to 7.66; P=0.008), age 10-14 years (3.23, 1.55 to 6.99; P=0.002), and black ethnicity (2.82, 1.41 to 5.57; P=0.003). Six (1%) of 627 patients died in hospital, all of whom had profound comorbidity. 11% (52/456) met the WHO MIS-C criteria, with the first patient developing symptoms in mid-March. Children meeting MIS-C criteria were older (median age 10.7 (8.3-14.1) v 1.6 (0.2-12.9) years; P<0.001) and more likely to be of non-white ethnicity (64% (29/45) v 42% (148/355); P=0.004). Children with MIS-C were five times more likely to be admitted to critical care (73% (38/52) v 15% (62/404); P<0.001). In addition to the WHO criteria, children with MIS-C were more likely to present with fatigue (51% (24/47) v 28% (86/302); P=0.004), headache (34% (16/47) v 10% (26/263); P<0.001), myalgia (34% (15/44) v 8% (21/270); P<0.001), sore throat (30% (14/47) v (12% (34/284); P=0.003), and lymphadenopathy (20% (9/46) v 3% (10/318); P<0.001) and to have a platelet count of less than 150 × 109/L (32% (16/50) v 11% (38/348); P<0.001) than children who did not have MIS-C. No deaths occurred in the MIS-C group. CONCLUSIONS: Children and young people have less severe acute covid-19 than adults. A systemic mucocutaneous-enteric symptom cluster was also identified in acute cases that shares features with MIS-C. This study provides additional evidence for refining the WHO MIS-C preliminary case definition. Children meeting the MIS-C criteria have different demographic and clinical features depending on whether they have acute SARS-CoV-2 infection (polymerase chain reaction positive) or are post-acute (antibody positive). STUDY REGISTRATION: ISRCTN66726260

Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.

OBJECTIVE: To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). DESIGN: Prospective observational cohort study. SETTING: International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium-ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020. PARTICIPANTS: Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. MAIN OUTCOME MEASURE: In-hospital mortality. RESULTS: 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). CONCLUSIONS: An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. STUDY REGISTRATION: ISRCTN66726260