Derivation and validation of age and temperature specific reference values and centile charts to predict lower respiratory tract infection in children with fever: prospective observational study ================================================================================================================================================================================================== * R G Nijman * M Thompson * M van Veen * R Perera * H A Moll * R Oostenbrink ## Abstract **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. ## Introduction Bacterial pneumonia is now the most common serious bacterial infection among children presenting with fever to paediatric emergency departments in industrialised countries.1 To identify children at risk of lower respiratory tract infection, several clinical signs and symptoms have been reported as potential predictors.1 2 3 4 5 Respiratory rate is the clinical feature with the most consistent and strongest evidence for predicting lower respiratory tract infection.1 2 3 4 5 6 7 8 9 10 However, other reports have failed to identify tachypnoea as a useful clinical predictor of the disease.8 Tachypnoea might not always be associated with lower respiratory tract infection, since children with the disease are also typically febrile, and the separate contributions of fever and the underlying infection to the presence of tachypnoea are unclear.11 12 Furthermore, even if the effect of fever is discounted, the increased respiratory rate might not be categorised as tachypnoea, owing to inaccurate threshold values.3 6 13 14 15 16 17 18 19 Fleming and colleagues suggested that commonly used threshold values for respiratory rate, such as those of the Advanced Pediatrics Life Support (APLS) guidelines, could cause considerable misclassification.20 The predictive value of vital signs is probably best shown by using modelling strategies that incorporate both their continuous nature and the effect of body temperature.21 To our knowledge, no such reference values are available for respiratory rate. We did a prospective observational study to determine the associations between age, body temperature, and respiratory rate, and use this information to derive reference values and centile charts for respiratory rate in children according to age and body temperature. Finally, we evaluated the predictive ability of these reference values to discriminate lower respiratory tract infections in children with fever in emergency care settings. ## Methods ### Design, setting, and participants #### Population used for deriving reference values We recruited children with fever, aged at least 1 month to just under 16 years, presenting to the paediatric emergency department of the Erasmus MC-Sophia Children’s Hospital in Rotterdam, the Netherlands, in 2006 and in 2008. This university hospital receives 9000 emergency care visits every year (of which 90% are basic paediatric emergencies).22 Clinical signs and symptoms were registered in a standardised electronic patient record.23 Temperature and respiratory rate were measured at the discretion of the attending physician or nurse. Body temperature was measured rectally. Trained emergency care nurses measured respiratory rate by using a standard approach that involved clinical counting of respiratory movements for 30 seconds. Children with measurements for both temperature and respiratory rate were eligible. Children whose respiratory rate was recorded while they were crying or distressed were excluded. We also excluded children with an acute exacerbation of a primary pulmonary condition (including lower tract respiratory infections), metabolic (including dehydration) or neurological disease potentially interfering with respiratory rate, “immediate” triage urgency according to the Manchester triage system, or a chronic disease (requiring at least two hospital visits per year). We considered outliers to be children whose respiratory rate was more than three standard deviations from the mean rate for their age group and temperature band, and excluded them from the derivation of normative reference values (fig 1⇓). ![Figure1](http://www.bmj.com/http://www.bmj.com/content/bmj/345/bmj.e4224/F1.medium.gif) [Figure1](http://www.bmj.com/content/345/bmj.e4224/F1) **Fig 1** Inclusion and exclusion of derivation population #### Populations used for validation study To validate the reference values, we selected children at risk of lower respiratory infectious disease from two populations that differed from the derivation population in both setting and time.9 10 24 The first population consisted of children, aged at least 1 month to just under 16 years, with fever (axillary temperature >38.0°C) and signs of lower respiratory tract infection (cough, difficulty breathing, or wheeze), who presented to the paediatric assessment unit of the University Hospital Coventry and Warwickshire NHS trust in Coventry, United Kingdom, between 2005 and 2006.9 The Coventry hospital is an inner city hospital delivering emergency care to about 25 000 children every year. The second validation population included children aged from 1 month up to 16 years, with fever (rectal temperature >38.0°C) and cough, who presented to the paediatric emergency department of the Erasmus MC-Sophia Children’s Hospital in 2003-05.10 24 We excluded children with an increased risk of recurrent serious infections, such as iatrogenic immunosuppression, malignancies, severe psychomotor retardation, or cystic fibrosis. We included children in whom both temperature and respiratory rate were measured. Final diagnoses of lower respiratory tract infection were divided into pneumonic lower respiratory tract infection (LRTI), non-pneumonic LRTI, and non-LRTI. Pneumonic LRTI was defined as radiological changes consistent with pneumonia—that is, the presence of micronodular or macronodular infiltrations or consolidation in chest radiographs (Coventry: single radiologist, unblinded; Erasmus MC-Sophia: two radiologists, blinded). Non-pneumonic LRTI was defined as the presence of clinical signs of lower respiratory tract infection, such as chest wall retractions, decreased oxygen saturation, crackles, or grunting, but without chest radiograph changes consistent with pneumonic LRTI. If the final diagnosis was inconclusive or the chest radiograph was absent, the investigators reached a consensus diagnosis (Coventry: MT; Erasmus MC-Sophia: HM, RO). The final consensus diagnoses were established using all available information from the medical records and additional tests. We made follow-up visits or telephone calls at one week and did a medical records check for reattendance within one week to rule out the possibility of missed diagnoses and to avoid verification bias.25 #### Ethical approval The study was approved by the local medical ethics committee of the Erasmus MC-Sophia Children’s Hospital and requirement of informed consent was waived. For the Coventry population, the Coventry local research ethics committee (04/Q2802/115) approved the study and informed consent was required and given.9 23 ### Derivation of reference values and centile charts for respiratory rate #### Univariate and multivariable linear regression analyses We calculated Spearman’s ρ correlation coefficients for respiratory rate (breaths/min), body temperature (°C), and age (years) using R statistical software, version 12.1.2 (cor.test function).26 We evaluated age as a continuous variable and in clinically relevant categories (1 month to <12 months, 12 to <24 months, 24 months to <5 years, and 5 to <16 years).14 27 To compare the correlation coefficients for temperature and respiratory rate of the different age groups, we used the Fisher r to z transformation.28 29 We used multivariable linear regression analysis (SPSS, version 17.0) to determine the relation between respiratory rate, temperature, and age. #### Age and temperature dependent centiles for respiratory rate, and minimum sample size We calculated median and upper centiles (75th, 90th, and 97th) of respiratory rate at a specific temperature for children in each age group using LMSChartMaker Pro (Medical Research Council, UK), based on the method of Cole and Green.30 We checked the final models using z score graphs, detrended Q-Q plots, and Q statistic curves for the parameters in the model (L, M, S). In each age group, we aimed to recruit 30-60 children for each relevant temperature bands (≤37.9°C, 38.0-38.9°C, 39.0-39.9°C, ≥40.0°C), according to recommendations of Virtanen and colleagues.31 #### Validation of the predictive ability of temperature dependent centiles to identify lower respiratory tract infections We calculated individual z scores for children who had pneumonic LRTI, non-pneumonic LRTI, and non-LRTI by using the following formula: ((respiratory rate/µ)λ−1) /(λ×σ) (in which µ, λ, and σ are age group and temperature specific parameters; web appendix 1).32 We compared z scores using the non-parametric test of Kruskal-Wallis. We then calculated the diagnostic performance (sensitivity, specificity, positive and negative likelihood ratios) and discriminative ability (area under receiver operating characteristics curve) of centile cut-off values (50th, 75th, 90th, and 97th). Finally, we calculated the diagnostic performance and discriminative ability of the APLS threshold values,27 and the continuous reference values of Fleming and colleagues.20 We did all calculations using the Epi, verification, and Hmisc package in R.26 ## Results Table 1⇓ describes characteristics of the derivation population (n=1555) and two validation populations (Erasmus MC-Sophia: n=311; Coventry: n=360).9 10 24 View this table: [Table 1](http://www.bmj.com/content/345/bmj.e4224/T1) Table 1  Patient characteristics of derivation and validation populations. Data are median (interquartile range) or no (%) unless stated otherwise ### Univariate and multivariable linear regression analysis Respiratory rate had a significantly negative correlation with age (r=−0.64) and positive correlation with temperature (r=0.27). The correlation between respiratory rate and temperature was significantly smaller in children aged at least 1 month to just under 12 months (r=0.13) than in those in the other age groups (12 to <24 months (r=0.34); 24 months to