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

Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals

Objectives Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19. Methods This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities. Results Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%. Conclusion Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting

OTUD6B-associated intellectual disability: novel variants and genetic exclusion of retinal degeneration as part of a refined phenotype

Biallelic pathogenic variants of OTUD6B have recently been described to cause intellectual disability (ID) with seizures. Here, we report the clinical and molecular characterization of five additional patients (from two unrelated Egyptian families) with ID due to homozygous OTUD6B variants. In Family I, the two affected brothers had additional retinal degeneration, a symptom not yet reported in OTUD6B-related ID. Whole-exome sequencing (WES) identified a novel nonsense variant in OTUD6B (c.271C>T, p.(Gln91Ter)), but also a nonsense variant in RP1L1 (c.5959C>T, p.(Gln1987Ter)), all in homozygous state. Biallelic pathogenic variants in RP1L1 cause autosomal recessive retinitis pigmentosa type 88 (RP88). Thus, RP1L1 dysfunction likely accounts for the visual phenotype in this family with two simultaneous autosomal recessive disorders. In Family II, targeted sequencing revealed a novel homozygous missense variant (c.767G>T, p.(Gly256Val)), confirming the clinically suspected OTUD6B-related ID. Consistent with the clinical variability in previously reported OTUD6B patients, our patients showed inter- and intrafamilial differences with regard to the clinical and brain imaging findings. Interestingly, various orodental features were present including macrodontia, dental crowding, abnormally shaped teeth, and thick alveolar ridges. Broad distal phalanges (especially the thumbs and halluces) with prominent interphalangeal joints and fetal pads were recognized in all patients and hence considered pathognomonic. Our study extends the spectrum of the OTUD6B-associated phenotype. Retinal degeneration, albeit present in both patients from Family I, was shown to be unrelated to OTUD6B, demonstrating the need for in-depth analysis of WES data in consanguineous families to uncover simultaneous autosomal recessive disorders

Confirmation of a Phenotypic Entity for TSPEAR Variants in Egyptian Ectodermal Dysplasia Patients and Role of Ethnicity

Ectodermal dysplasia (ED) are hereditary disorders characterized by the disturbance of the ectodermal development of at least two of four ectodermal tissues: teeth, hair, nails and sweat glands. Clinical classification of ED is challenged by overlapping features, variable expressivity, and low number of patients, hindering full phenotypic spectrum identification. Disease-causing variants in elements of major developmental pathways, e.g., Ectodysplasin/NF&kappa;B, Wnt, and Tp63 pathways, have been identified in fewer than half of ED phenotypes. Whole-exome sequencing (WES) was performed for ten Egyptian ED patients presenting with tooth agenesis, normal sweating, scalp hypotrichosis, and sharing characteristic facial features. WES was followed by in silico analysis of the effects of novel detected genetic variants on mRNA and protein structure. The study identified four novel rare pathogenic and likely pathogenic TSPEAR variants, a gene which was recently found to be involved in ectodermal organogenesis. A novel in-frame deletion recurred in eight patients from six unrelated families. Comparing our cohort to previously reported TSPEAR cohorts highlighted the influence of ethnicity on TSPEAR phenotypic affection. Our study expands the clinical and mutational spectrum of the growing TSPEAR associated phenotypes, and pinpoints the influence of WES and in silico tools on identification of rare disease-causing variants

Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model

Objectives: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone triage-driven deep learning model in the prediction of positive COVID-19 patients. Setting: This is a retrospective study conducted at the family medicine department, Cairo University. Methods: The study included a dataset of 943 suspected COVID-19 patients from the phone triage during the first wave of the pandemic. The accuracy of the phone triaging system was assessed. PCR-dependent and phone triage-driven deep learning model for automated classifications of natural human responses was conducted. Results: Based on the RT-PCR results, we found that myalgia, fever, and contact with a case with respiratory symptoms had the highest sensitivity among the symptoms/ risk factors that were asked during the phone calls (86.3%, 77.5%, and 75.1%, respectively). While immunodeficiency, smoking, and loss of smell or taste had the highest specificity (96.9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%. Conclusion: Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources