Neurofibromatosis type 1 (NF1) is a multisystem genetic disorder, predisposing development of benign and malignant tumours. Given the oncogenic potential, long-term surveillance is important in patients with NF1. Proposals for NF1 care and its specific manifestations have been developed, but lack integration within routine care. This guideline aims to assimilate available information on NF1 associated tumours (based on evidence and/or expert opinion) to assist healthcare professionals in undertaking tumour surveillance of NF1 individuals. By comprehensive literature review, performed March 18th 2020, guidelines were developed by a NF1 expert group and patient representatives, conversant with clinical care of the wide NF1 disease spectrum. We used a modified Delphi procedure to overcome issues of variability in recommendations for specific (national) health care settings, and to deal with recommendations based on indirect (scarce) evidence. We defined proposals for personalised and targeted tumour management in NF1, ensuring appropriate care for those in need, whilst reducing unnecessary intervention. We also incorporated the tumour-related psychosocial and quality of life impact of NF1. The guideline reflects the current care for NF1 in Europe. They are not meant to be prescriptive and may be adjusted to local available resources at the treating centre, both within and outside EU countries. This guideline has been supported by the European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS). ERN GENTURIS is funded by the European Union. DGE is supported by the Manchester NIHRBiomedical Research Centre (IS-BRC-1215-20007).

Objective To derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department. Design Prospective observational diagnostic study. Setting Three paediatric emergency care units: two in the Netherlands and one in the United Kingdom. Participants Children with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein. Main outcome measures Pneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs. Results Oxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86). Conclusion A validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections.

Objectives To develop reference values and centile charts for respiratory rate based on age and body temperature, and to determine how well these reference values can predict the presence of lower respiratory tract infections (LRTI) in children with fever. Design Prospective observational study. Participants Febrile children aged at least 1 month to just under 16 years (derivation population, n=1555; validation population, n=671) selected from patients attending paediatric emergency departments or assessment units in hospitals. Setting One hospital in the Netherlands in 2006 and 2008 (derivation population); one hospital in the Netherlands in 2003-05 and one hospital in the United Kingdom in 2005-06 (validation population). Intervention We used the derivation population to produce respiratory rate centile charts, and calculated 50th, 75th, 90th, and 97th centiles of respiratory rate at a specific body temperature. Multivariable regression analysis explored associations between respiratory rate, age, and temperature; results were validated in the validation population by calculating diagnostic performance measures, z scores, and corresponding centiles of children with diagnoses of pneumonic LRTI (as confirmed by chest radiograph), non-pneumonic LRTI, and non-LRTI. Main outcome measure Age, respiratory rate (breaths/min) and body temperature (°C), presence of LRTI. Results Respiratory rate increased overall by 2.2 breaths/min per 1°C rise (standard error 0.2) after accounting for age and temperature in the model. We observed no interactions between age, temperature, and respiratory rates. Age and temperature dependent cut-off values at the 97th centile were more useful for ruling in LRTI (specificity 0.94 (95% confidence interval 0.92 to 0.96), positive likelihood ratio 3.66 (2.34 to 5.73)) than existing respiratory rate thresholds such as Advanced Pediatrics Life Support values (0.53 (0.48 to 0.57), 1.59 (1.41 to 1.80)). However, centile cut-offs could not discriminate between pneumonic LRTI and non-pneumonic LRTI. Conclusions Age specific and temperature dependent centile charts describe new reference values for respiratory rate in children with fever. Cut-off values at the 97th centile were more useful in detecting the presence of LRTI than existing respiratory rate thresholds.

Diagnosing serious infections in children is challenging, because of the low incidence of such infections and their non-specific presentation early in the course of illness. Prediction rules are promoted as a means to improve recognition of serious infections. A recent systematic review identified seven clinical prediction rules, of which only one had been prospectively validated, calling into question their appropriateness for clinical practice. We aimed to examine the diagnostic accuracy of these rules in multiple ambulatory care populations in Europe.