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Abstract Background Diabetes mellitus (DM) increases the risk of tuberculosis (TB) and will hamper global TB control due to the dramatic rise in type 2 DM in TB-endemic settings. In this trial, we will examine the efficacy and safety of TB preventive therapy against the development of TB disease in people with DM who have latent TB infection (LTBI), with a 12-week course of rifapentine and isoniazid (3HP). Methods The ‘Prevention of tuberculosis in diabetes mellitus’ (PROTID) consortium will randomise 3000 HIV-negative eligible adults with DM and LTBI, as evidenced by a positive tuberculin skin test or interferon gamma release assay, to 12 weeks of 3HP or placebo. Participants will be recruited through screening adult patients attending DM clinics at referral hospitals in Tanzania and Uganda. Patients with previous TB disease or treatment with a rifamycin medication or isoniazid (INH) in the previous 2 years will be excluded. The primary outcome is the occurrence of definite or probable TB disease; secondary outcome measures include adverse events, all-cause mortality and treatment completion. The primary efficacy analysis will be intention-to-treat; per-protocol analyses will also be carried out. We will estimate the ratio of TB incidence rates in intervention and control groups, adjusting for the study site using Poisson regression. Results will be reported as efficacy estimates (1-rate ratio). Cumulative incidence rates allowing for death as a competing risk will also be reported. Approximately 1000 LTBI-negative, HIV-negative participants will be enrolled consecutively into a parallel cohort study to compare the incidence of TB in people with DM who are LTBI negative vs positive. A number of sub-studies will be conducted among others to examine the prevalence of LTBI and active TB, estimate the population impact and cost-effectiveness of LTBI treatment in people living with DM in these African countries and address gaps in the prevention and therapeutic management of combined TB-DM. Discussion PROTID is anticipated to generate key evidence to guide decisions over the use of TB preventive treatment among people with DM as an important target group for better global TB control. Trial registration ClinicalTrials.gov NCT04600167 . Registered on 23 October 2020

Electronic clinical decision support algorithms (CDSAs) have been developed to address high childhood mortality and inappropriate antibiotic prescription by helping clinicians adhere to guidelines. Previously identified challenges of CDSAs include their limited scope, usability, and outdated clinical content. To address these challenges we developed ePOCT+, a CDSA for the care of pediatric outpatients in low- and middle-income settings, and the medical algorithm suite (medAL-suite), a software for the creation and execution of CDSAs. Following the principles of digital development, we aim to describe the process and lessons learnt from the development of ePOCT+ and the medAL-suite. In particular, this work outlines the systematic integrative development process in the design and implementation of these tools required to meet the needs of clinicians to improve uptake and quality of care. We considered the feasibility, acceptability and reliability of clinical signs and symptoms, as well as the diagnostic and prognostic performance of predictors. To assure clinical validity, and appropriateness for the country of implementation the algorithm underwent numerous reviews by clinical experts and health authorities from the implementing countries. The digitalization process involved the creation of medAL-creator, a digital platform which allows clinicians without IT programming skills to easily create the algorithms, and medAL-reader the mobile health (mHealth) application used by clinicians during the consultation. Extensive feasibility tests were done with feedback from end-users of multiple countries to improve the clinical algorithm and medAL-reader software. We hope that the development framework used for developing ePOCT+ will help support the development of other CDSAs, and that the open-source medAL-suite will enable others to easily and independently implement them. Further clinical validation studies are underway in Tanzania, Rwanda, Kenya, Senegal, and India. Author summary In accordance with the principles of digital development we describe the process and lessons learnt from the development of ePOCT+, a clinical decision support algorithm (CDSA), and medAL-suite, a software, to program and implement CDSAs. The clinical algorithm was adapted from previous CDSAs in order to address challenges in regards to the limited scope of illnesses and patient population addressed, the ease of use, and limited performance of specific algorithms. Clinical algorithms were adapted and improved based on considerations of what symptoms and signs would be appropriate for primary care health workers, and how well these clinical elements predic a particular disease or severe outcome. We hope that by sharing our multi-stakeholder approach to the development of ePOCT+, it can help others in the development of other CDSAs. The medAL-creator software was developed to allow clinicians without IT programming experience to program the clinical algorithm using a drag-and-drop interface, intended to allow a wider range of health authorities and implementers to develop and adapt their own CDSA. The medAL-reader application, deploys the algorithm from medAL-creator to end-users following the usual healthcare processes within a consultation.

Electronic clinical decision support algorithms (CDSAs) have been developed to address high childhood mortality and inappropriate antibiotic prescription by helping clinicians adhere to guidelines. Previously identified challenges of CDSAs include their limited scope, usability, and outdated clinical content. To address these challenges we developed ePOCT+, a CDSA for the care of pediatric outpatients in low- and middle-income settings, and the medical algorithm suite (medAL-suite), a software for the creation and execution of CDSAs. Following the principles of digital development, we aim to describe the process and lessons learnt from the development of ePOCT+ and the medAL-suite. In particular, this work outlines the systematic integrative development process in the design and implementation of these tools required to meet the needs of clinicians to improve uptake and quality of care. We considered the feasibility, acceptability and reliability of clinical signs and symptoms, as well as the diagnostic and prognostic performance of predictors. To assure clinical validity, and appropriateness for the country of implementation the algorithm underwent numerous reviews by clinical experts and health authorities from the implementing countries. The digitalization process involved the creation of medAL-creator, a digital platform which allows clinicians without IT programming skills to easily create the algorithms, and medAL-reader the mobile health (mHealth) application used by clinicians during the consultation. Extensive feasibility tests were done with feedback from end-users of multiple countries to improve the clinical algorithm and medAL-reader software. We hope that the development framework used for developing ePOCT+ will help support the development of other CDSAs, and that the open-source medAL-suite will enable others to easily and independently implement them. Further clinical validation studies are underway in Tanzania, Rwanda, Kenya, Senegal, and India.

Aim: To provide insight in the primary health care (PHC) case management of febrile children under-five in Dar es Salaam, and to identify areas for improving quality of care.Methods: We used data from the routine care arm of the ePOCT trial, including children aged 2–59 months who presented with an acute febrile illness to two health centers in Dar es Salaam (2014–2016). The presenting complaint, anthropometrics, vital signs, test results, final diagnosis, and treatment were prospectively collected in all children. We used descriptive statistics to analyze the frequencies of diagnoses, adherence to diagnostics, and prescribed treatments.Results: We included 547 children (47% male, median age 14 months). Most diagnoses were viral: upper respiratory tract infection (60%) and/or gastro-enteritis (18%). Vital signs and anthropometric measurements taken by research staff and urinary testing failed to influence treatment decisions. In total, 518/547 (95%) children received antibiotics, while 119/547 (22%) had an indication for antibiotics based on local guidelines. Antibiotic dosing was frequently out of range. Non-recommended treatments were common (29%), most often cough syrup and vitamins.Conclusion: Our study points to challenges in using diagnostic test results, concerns regarding quality of antibiotic prescriptions, and frequent use of non-evidence-based complementary medicines in PHC in Tanzania. Larger studies on diagnostic and treatments processes in PHC in Tanzania are needed to inform effective solutions to support PHC workers in case management of children.

Objective To explore how parents judge disease severity of their febrile child and to identify symptoms they associate with serious illness, minor illness or health.Design Semistructured interviews were conducted. Interviews were audio taped, transcribed verbatim and analysed thematically.Participants Parents of children aged 0–5 years with a febrile illness.Setting Participants were recruited at the paediatric ward and the emergency department.Results Twenty-six interviews were conducted, in which 37 parents participated. Parents described disease severity of their child mainly in terms of changes in their child’s normal characteristics (behaviour and physical features). They found it harder to describe specific disease symptoms such as dyspnoea or dehydration. Their child being active, eating and drinking well, and smiling were perceived as reassuring, whereas high fever, moving very little and uncertainty about the type of infections were mentioned as alarming symptoms. Previous experience with febrile illnesses in their children was of great influence on the number and accuracy of symptoms they reported.Conclusion Parents used the normal behaviour and physical features of their child as a reference frame for judging disease severity. With a larger deviation from the child’s normal characteristics, parents considered the illness more serious. They were less able to describe specific symptoms of disease such as dyspnoea or dehydration. This knowledge is important for clinicians in their communication with parents of children with febrile illness.

ObjectiveTo describe clinical characteristics, disease course and outcomes in a large and well-documented cohort of hospitalized COVID-19 patients in the Netherlands.MethodsWe conducted a multicentre retrospective cohort study in The Netherlands including 952 of 1183 consecutively hospitalized patients that were admitted to participating hospitals between March 2nd, 2020, and May 22nd, 2020. Clinical characteristics and laboratory parameters upon admission and during hospitalization were collected until July 1st.ResultsThe median age was 69 years (IQR 58-77 years) and 605 (63.6%) were male. Cardiovascular disease was present in 558 (58.6%) patients. The median time of onset of symptoms prior to hospitalization was 7 days (IQR 5-10). A non ICU admission policy was applicable in 312 (32.8%) patients and in 165 (56.3%) of the severely ill patients admitted to the ward. At admission and during hospitalization, severely ill patients had higher values of CRP, LDH, ferritin and D-dimer with higher neutrophil counts and lower lymphocyte counts. Overall in-hospital mortality was 25.1% and 183 (19.1%) patients were admitted to ICU, of whom 56 (30.6%) died. Patients aged ≥70 years had high mortality, both at the ward (52.4%) and ICU (47.4%). The median length of ICU stay was 8 days longer in patients aged ≥70 years compared to patients aged ≤60 years.ConclusionHospitalized COVID-19 patients aged ≥70 years had high mortality and longer ICU stay compared to patients aged ≤60 years. These findings in combination with the patient burden of an ICU admission and possible long term complications after discharge should encourage us to further investigate the benefit of ICU admission in elderly and fragile COVID-19-patients.

Lower respiratory tract infections (LRTIs) in children are common and, although often mild, a major cause of mortality and hospitalization. Recently, the respiratory microbiome has been associated with both susceptibility and severity of LRTI. In this current study, we combined respiratory microbiome, viral, and clinical data to find associations with the severity of LRTI. Nasopharyngeal aspirates of children aged one month to five years included in the STRAP study (Study to Reduce Antibiotic prescription in childhood Pneumonia), who presented at the emergency department (ED) with fever and cough or dyspnea, were sequenced with nanopore 16S-rRNA gene sequencing and subsequently analyzed with hierarchical clustering to identify respiratory microbiome profiles. Samples were also tested using a panel of 15 respiratory viruses and Mycoplasma pneumoniae, which were analyzed in two groups, according to their reported virulence. The primary outcome was hospitalization, as measure of disease severity. Nasopharyngeal samples were isolated from a total of 167 children. After quality filtering, microbiome results were available for 54 children and virology panels for 158 children. Six distinct genus-dominant microbiome profiles were identified, with Haemophilus-, Moraxella-, and Streptococcus-dominant profiles being the most prevalent. However, these profiles were not found to be significantly associated with hospitalization. At least one virus was detected in 139 (88%) children, of whom 32.4% had co-infections with multiple viruses. Viral co-infections were common for adenovirus, bocavirus, and enterovirus, and uncommon for human metapneumovirus (hMPV) and influenza A virus. The detection of enteroviruses was negatively associated with hospitalization. Virulence groups were not significantly associated with hospitalization. Our data underlines high detection rates and co-infection of viruses in children with respiratory symptoms and confirms the predominant presence of Haemophilus-, Streptococcus-, and Moraxella-dominant profiles in a symptomatic pediatric population at the ED. However, we could not assess significant associations between microbiome profiles and disease severity measures.

ObjectivesPneumonia is the most common bacterial infection in children at the emergency department (ED). Clinical prediction models for childhood pneumonia have been developed (using chest x-ray as their reference standard), but without implementation in clinical practice. Given current insights in the diagnostic limitations of chest x-ray, this study aims to validate these prediction models for a clinical diagnosis of pneumonia, and to explore their potential to guide decisions on antibiotic treatment at the ED.MethodsWe systematically identified clinical prediction models for childhood pneumonia and assessed their quality. We evaluated the validity of these models in two populations, using a clinical reference standard (1. definite/probable bacterial, 2. bacterial syndrome, 3. unknown bacterial/viral, 4. viral syndrome, 5. definite/probable viral), measuring performance by the ordinal c-statistic (ORC). Validation populations included prospectively collected data of children aged 1 month to 5 years attending the ED of Rotterdam (2012-2013) or Coventry (2005-2006) with fever and cough or dyspnoea.ResultsWe identified eight prediction models and could evaluate the validity of seven, with original good performance. In the Dutch population 22/248 (9%) had a bacterial infection, in Coventry 53/301 (17%), antibiotic prescription was 21% and 35% respectively. Three models predicted a higher risk in children with bacterial infections than in those with viral disease (ORC ≥0.55) and could identify children at low risk of bacterial infection.ConclusionsThree clinical prediction models for childhood pneumonia could discriminate fairly well between a clinical reference standard of bacterial versus viral infection. However, they all require the measurement of biomarkers, raising questions on the exact target population when implementing these models in clinical practice. Moreover, choosing optimal thresholds to guide antibiotic prescription is challenging and requires careful consideration of potential harms and benefits.