Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Socio-demographic and cardiovascular disease risk factors associated with dementia: Results of a cross-sectional study from Lebanon

Little evidence from the Arab region is available on dementia and its associated risk factors. This study is the first in Lebanon to examine the association between community older adults' socio-demographics and cardiovascular disease risk factors (CVDRF) and dementia in the aim of closing the knowledge gap. A cross-sectional household survey was conducted in 2013 in Beirut and two districts of Mount Lebanon with 502 older adults (65years and above) and their informants. Data was collected on CVDRF and socio-demographics using structured questionnaires and dementia was assessed using the 10/66 Dementia Research Group validated tools. Multivariable analysis was done using a generalized estimating equation to account for cluster effect. Being older and perceiving personal income as insufficient significantly increased the odds of dementia [OR75–84years=4.00 (95%CI=1.46, 10.95); OR85+years=7.07 (1.84, 27.03); ORinsufficient income=3.90 (1.58, 9.60)]. Having uncontrolled hypertension (versus no hypertension) was the only significant CVDRF that increased the odds of dementia [OR=6.35 (1.60, 25.10)]. Interventions targeting uncontrolled hypertension that aim to increase awareness about proper management of this chronic condition would contribute to the needed preventive efforts against CVDRFs in response to dementia risk. Further research on the association between income sufficiency – one indicator of low socio-economic status – and dementia is warranted. Keywords: Dementia, CVD risk factors, Uncontrolled hypertension, Income, Lebano

Validation of the 10/66 Dementia Research Group Diagnostic Assessment for Dementia in Arabic:A Study in Lebanon

OBJECTIVES: In the North Africa and Middle East region, the illiteracy rates among older people are high, posing a great challenge to cognitive assessment. Validated diagnostic instruments for dementia in Arabic are lacking, hampering the development of dementia research in the region. The study aimed at validating the Arabic version of the 10/66 Dementia Research Group (DRG) diagnostic assessment for dementia to determine if it is suitable for case ascertainment in epidemiological research. METHODS: 244 participants older than 65 years were included, 100 with normal cognition and 144 with mild to moderate dementia. Dementia was diagnosed by clinicians according to DSM-IV criteria. Depression was diagnosed using the Geriatric Mental State. Trained interviewers blind to the cognitive status of the participants administered the 10/66 DRG diagnostic assessment to the participants and interviewed the caregivers. The discriminatory ability of the 10/66 DRG assessment and its subcomponents were evaluated against the clinical diagnoses. RESULTS: Half of the participants had no formal education and 49% of them were depressed. The 10/66 DRG diagnostic assessment showed excellent sensitivity (92.0%), specificity (95.1%), positive predictive value (PPV, 92.9%), and low false positive rates (FPR) among controls with no formal education (8.1%) and depression (5.6%). Each subcomponent of the 10/66 DRG diagnostic assessment independently predicted dementia diagnosis. The predictive ability of the 10/66 DRG assessment was superior to that of its subcomponents. CONCLUSION: 10/66 DRG diagnostic assessment for dementia is well suited for case ascertainment in epidemiological studies among Arabic speaking older population with high prevalence of illiteracy

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers