BACKGROUND AND OBJECTIVES:

Early risk stratification of infants with bronchiolitis receiving airway support is critical for focusing appropriate therapies, yet the tools to risk categorize this subpopulation do not exist. Our objective was to identify predictors of “escalated care” in bronchiolitis. We hypothesized there would be a significant association between escalated care and predictors in the emergency department. We subsequently developed a risk score for escalated care.

METHODS:

We conducted a retrospective cohort study of previously healthy infants aged <12 months with bronchiolitis. Our primary outcome was escalated care (ie, hospitalization with high-flow nasal cannula, noninvasive or invasive ventilation, or intensive care admission). The predictors evaluated were age, prematurity, day of illness, poor feeding, dehydration, apnea, nasal flaring and/or grunting, respiratory rate, oxygen saturation, and retractions.

RESULTS:

Of 2722 patients, 261 (9.6%) received escalated care. Multivariable predictors of escalated care were oxygen saturation <90% (odds ratio [OR]: 8.9 [95% confidence interval (CI) 5.1–15.7]), nasal flaring and/or grunting (OR: 3.8 [95% CI 2.6–5.4]), apnea (OR: 3.0 [95% CI 1.9–4.8]), retractions (OR: 3.0 [95% CI 1.6–5.7]), age ≤2 months (OR: 2.1 [95% CI 1.5–3.0]), dehydration (OR 2.1 [95% CI 1.4–3.3]), and poor feeding (OR: 1.9 [95% CI 1.3–2.7]). One of 217 (0.5%) infants without predictors received escalated care. The risk score ranged from 0 to 14 points, with the estimated risk of escalated care from 0.46% (0 points) to 96.9% (14 points). The area under the curve was 85%.

CONCLUSIONS:

We identified variables measured in the emergency department predictive of escalated care in bronchiolitis and derived a risk score to stratify risk of this outcome. This score may be used to aid management and disposition decisions.

What’s Known on This Subject:

Infants with bronchiolitis receiving airway support require early recognition. Their risk stratification in the emergency department is important for timely link to required resources, yet the tools for risk categorization do not exist. Here we address this knowledge gap.

What This Study Adds:

We identified several clinical variables readily available in the emergency department strongly predictive of the receipt of escalated care in bronchiolitis and derived a risk score with high discriminatory ability and excellent model stability to stratify escalated care risk during hospital stay.

Bronchiolitis is a viral syndrome characterized by upper respiratory tract infection and respiratory distress.1 In the United States, bronchiolitis accounts for 16% of all hospitalizations in the first year of life, at an annual cost of up to 1.78 billion US dollars.2,5 Bronchiolitis also represents a major health care and financial burden in other developed nations.5,6 There is a lack of evidence of benefit of pharmacologic interventions in bronchiolitis, and management guidelines only recommend the use of hydration, oxygenation, and airway support.1,7,13 

Previous studies of severe bronchiolitis9,14,20 have been focused on hospitalization, which encompasses a broad range of disease severity and the inpatient population, rather than infants presenting to the emergency department (ED).21,23 Studies of infants managed in ICUs have not included patients treated with high-flow nasal cannula (HFNC),16,24,29 which is an increasingly used respiratory support in severe bronchiolitis.30,31 Receipt of airway support, including HFNC, is a critical outcome for bronchiolitis, offering a more objective measure of disease severity compared with hospitalization. Infants receiving airway support require additional specialized care, and their initial clinical presentation may differ from that of their counterparts with milder bronchiolitis. Timely recognition of infants who will require airway support may improve the quality and consistency of care provided, optimize resource use, and decrease the costs of care.32 Importantly, the risk stratification of infants presenting to the ED with bronchiolitis who are at risk for receiving airway support during their hospital stay has been insufficiently studied, and tools to risk stratify this subpopulation are not well developed.

Our primary objective of this retrospective multinational cohort study was to identify clinical predictors of hospitalization with airway support (“escalated care”) among infants evaluated in EDs around the globe. We hypothesized there would be significant associations between receipt of escalated care and certain demographic and clinical predictors easily obtainable in the ED. We also developed and internally validated a clinical risk score for escalated care.

This was a planned secondary analysis of a multinational retrospective cohort study conducted at 38 pediatric EDs associated with the international Pediatric Emergency Research Networks (PERN), which consist of the following 6 collaborative networks: Pediatric Emergency Research Canada (PERC), the Pediatric Emergency Medicine Collaborative Research Committee (PEM-CRC) of the American Academy of Pediatrics, the Pediatric Emergency Care Applied Research Network (PECARN) in the United States, Pediatric Research in Emergency Departments International Collaborative (PREDICT) in Australia and New Zealand, Pediatric Emergency Research United Kingdom and Ireland (PERUKI), and Research in European Pediatric Emergency Medicine (REPEM).33 The original study was approved by the PERN Executive Committee and the research ethics boards of all participating hospitals.

Included children were younger than 12 months and diagnosed in a participating ED between January and December 2013 with bronchiolitis, defined as a viral respiratory infection with respiratory distress.1,7 The first bronchiolitis was defined by no previous visits to a health care provider for bronchiolitis. Infants with comorbidities including chronic lung, cardiac, neuro-muscular disease, immune deficiencies, and renal or hepatic insufficiency were excluded.

At each hospital, we identified consecutive infants who presented to the ED within the study period and had a discharge diagnosis of bronchiolitis or respiratory syncytial virus (RSV) bronchiolitis from the International Classification of Diseases, Ninth Revision or International Classification of Diseases, 10th Revision (codes J 21.0, 21.8, 21.9/466.1). By using a random number generator, each site was used to identify a random sample of records for review. Trained abstractors identified eligible records and entered the data on standardized case report forms and into a secure web-based electronic database. Targeted information included demographics, presenting symptoms and physical examination in the ED, vital signs, transcutaneous oxygen saturation measured in triage in room air, disposition from the ED (discharge from the hospital, admission to inpatient ward or ICU), and airway support interventions constituting escalated care as defined below.

We defined all study variables a priori and described them in a manual of operations with data source hierarchy for all data points. Trained site investigators ensured data extractors reviewed the manual. The manual of operations assisted with interpretation and standardized data extraction of variables somewhat subjective in nature. Before the study initiation, all coinvestigators reviewed the case report forms to assess feasibility of collecting the required information locally and to ensure information clarity.

The primary outcome measure was escalated care during the ED or inpatient stay, defined as hospitalization plus any of the following: HFNC, noninvasive ventilation (eg, continuous or biphasic positive airway pressure), intubation and ventilation, or management in the ICU without airway support. Although the criteria for ICU admission are variable16,34 and those for HFNC have not yet been established,35 these escalated care interventions are generally limited to children with hypoxia and concern respiratory distress.30 We did not include isolated use of intravenous or nasogastric hydration or supplemental oxygen in this definition because some institutions use intravenous or nasogastric hydration routinely on admission,18 and the criteria for supplemental oxygen are disparate.36 The secondary outcome consisted of the diagnostic accuracy of the derived clinical risk score model for escalated care.

We evaluated the following potential predictors of escalated care: age in months, duration of respiratory distress in days, documented prematurity, reported poor feeding, observed dehydration, observed nasal flaring and/or grunting, reported or observed apnea, respiratory rate in triage, chest retractions, and oxygen saturation measured in triage on room air.14,16,17,19,24 To develop the risk score, the continuous variables were dichotomized according to published evidence for severe bronchiolitis and recommendation for oxygen therapy.1,16 

The study sample size was estimated to provide 80% power to answer the primary association, on the basis of the multivariable logistic regression analysis with escalated care as the binary dependent variable. On the basis of previous literature, we estimated that 5% of infants presenting for ED care would be admitted to an ICU and that ∼5% of the remaining population would be managed with HFNC16 for a total proportion receiving escalated care of ∼10%. Targeting evaluation of 10 independent variables, with the requirement of at least 10 patients with the outcome per predictor variable and allowing for colinearity, we aimed to enroll at least 200 participants receiving escalated care. We thus required at least 2000 patients presenting to the ED with bronchiolitis.

The patient characteristics were analyzed with descriptive statistics by using proportions for categorical data, means with SDs for normally distributed continuous data, and medians with ranges for continuous data lacking normal distributions. Relevant 95% confidence intervals (CIs) were calculated around targeted parameter estimates.

We used bivariable analyses to determine the association between escalated care as a binary outcome and the independent variables explored, including an age of ≤2 months,16 respiratory rate of ≥60 breaths per minute,37 and oxygen saturation of <90%.1 Multiple imputation was used for missing data.38 Selected variables associated with the outcome at a bivariable significance level <0.2 were entered into a multivariable logistic regression analysis to determine the independent association of each variable and escalated care by using a significance level <0.05. The ED was included in the analyses as a random effect. The multivariable logistic regression analysis was conducted on the full data set as well as on the imputed data set. The risk cutoff points for the continuous variables were determined from the literature and from the receiver operating characteristic curves derived for the study population yielding the highest area under the curve (AUC). The effect of individual predictors of escalated care was reported by using adjusted multivariable odds ratios (ORs) with 95% CIs.

We used the data set with complete data for all variables to develop the risk score, on the basis of the multivariable model method of Sullivan et al.39 Using this method, we assigned risk points to each predictor variable according to the magnitude of the OR in the multivariable analysis, with the total number of points corresponding to the risk estimate. We then calculated the observed and estimated percent risk of escalated care at each risk level, with relevant 95% CIs. The bootstrap method was also used to assess (1) the average concordance statistic measuring the AUC, (2) degree of association of each predictor variable, and (3) the association of the developed score with the outcome by obtaining empirical power. Moreover, the bootstrap analysis provides a conservative estimate of the relative importance of the predictor variables measured by the percentage of times these were found significant in the 1000 bootstrap samples by using significance levels of decreasing magnitude.40 We evaluated the overall predictive ability of the score using AUC.

The calibration of the risk score was assessed by using the Hosmer–Lemeshow test, and the model was internally validated by using bootstrap resampling.41 

Statistical analyses were performed by using versions 9.4 of the SAS system (SAS Institute, Inc, Cary, NC) (2002–2012) for Windows and the open source statistical software R version 3.0 (R Foundation for Statistical Computing, Vienna, Austria).

A total of 5305 potentially eligible infants were identified at the 38 sites. Of these, 1580 visits fulfilled exclusion criteria, leaving 3725 eligible participants, of which 802 were managed at 8 Canadian pediatric EDs (PERC), 978 at 10 EDs in the United States (PEM-CRC and PECARN), 805 at 8 EDs in Australia and New Zealand (PREDICT), 841 at 9 EDs in the United Kingdom and Ireland (PERUKI), and 299 infants at 3 EDs in Europe (REPEM).

The mean age of included participants was 4.5 ± 3.0 months, 2274 (61.1%) were boys, and the mean symptom duration was 2.9 ± 2.0 days.

Of the 3725 eligible patients, 2722 (73.1%) had complete data for all variables. A total of 261 of 2722 (9.6%) study infants received escalated care, of which 164 (63%) were treated with HFNC, 47 (18%) received noninvasive ventilation, 12 (5%) were mechanically ventilated, and 38 (15%) received ICU care without airway support. Of the 164 HFNC treatments, 114 (70%) were delivered on inpatient wards. The characteristics of the infants who did and did not receive escalated care appear in Table 1. The rates of escalated care ranged from 3.6% in the United Kingdom and Ireland to 15.7% in Spain and Portugal.

TABLE 1

Characteristics of Infants With and Without Escalated Care

CharacteristicEscalated CareNo Escalated CareP
n = 261n = 2461
Age, moa 2.9 ± 2.8 4.5 ± 2.9 <.0001 
Male sexb 154 (59) 1524 (62) .35 
Prematurityb 53 (21) 302 (15) .005 
Respiratory distress durationa 3.0 ± 2.3 2.8 ± 2.7 .22 
Poor feedingb 204 (78) 1341 (55) <.0001 
Apneab 70 (27) 147 (6) <.0001 
Dehydrationb 76 (29) 212 (9) <.0001 
Nasal flaring and/or gruntingb 122 (47) 364 (15) <.0001 
Chest retractionsb 246 (94) 1911 (78) <.0001 
Respiratory ratea 55.8 ± 14.7 49.3 ± 12.0 <.0001 
Oxygen saturation, %a 91.7 ± 7.3 97.2 ± 2.7 <.0001 
Temperature ≥38°C, %a 111 (43) 661 (27) <.0001 
CharacteristicEscalated CareNo Escalated CareP
n = 261n = 2461
Age, moa 2.9 ± 2.8 4.5 ± 2.9 <.0001 
Male sexb 154 (59) 1524 (62) .35 
Prematurityb 53 (21) 302 (15) .005 
Respiratory distress durationa 3.0 ± 2.3 2.8 ± 2.7 .22 
Poor feedingb 204 (78) 1341 (55) <.0001 
Apneab 70 (27) 147 (6) <.0001 
Dehydrationb 76 (29) 212 (9) <.0001 
Nasal flaring and/or gruntingb 122 (47) 364 (15) <.0001 
Chest retractionsb 246 (94) 1911 (78) <.0001 
Respiratory ratea 55.8 ± 14.7 49.3 ± 12.0 <.0001 
Oxygen saturation, %a 91.7 ± 7.3 97.2 ± 2.7 <.0001 
Temperature ≥38°C, %a 111 (43) 661 (27) <.0001 
a

Data presented as mean ± SD.

b

Data presented as n (%).

With Table 2, we summarize the bivariable associations between postulated predictors and escalated care.

TABLE 2

Association Between Patient Characteristics and Escalated Care

VariableEscalated Care (n = 261)No Escalated Care (n = 2461)Unadjusted OR95% CIP
n (%)n (%)
Age ≤2 mo 156 (60) 754 (31) 3.4 2.6–4.4 <.0001 
Prematurity 53 (21) 302 (15) 1.6 1.1–2.2 .01 
Respiratory distress duration, h 3.0 ± 2.3 2.8 ± 2.7 1.0 0.9–1.1 .5 
Respiratory rate ≥60 breaths per min 122 (47) 635 (26) 2.5 1.9–3.3 <.0001 
Apnea 70 (27) 147 (6) 5.8 4.2–8.0 <.0001 
Dehydration 76 (29) 212 (9) 4.4 3.2–5.9 <.0001 
Nasal flaring and/or grunting 122 (47) 364 (15) 5.1 3.9–6.6 <.0001 
Retractions 246 (94) 1911 (78) 4.7 2.8–8.0 <.0001 
Oxygen saturation <90% 79 (30) 40 (2) 26.3 17.5–39.6 <.0001 
Poor feeding 204 (78) 1341 (55) 3.0 2.2–4.1 <.0001 
Network      
 Canada 77 420 — — — 
 United States 103 728 — — — 
 Australia and New Zealand 36 552 — — — 
 United Kingdom and Ireland 24 648 — — — 
 Spain and/or Portugal 21 113 — — — 
VariableEscalated Care (n = 261)No Escalated Care (n = 2461)Unadjusted OR95% CIP
n (%)n (%)
Age ≤2 mo 156 (60) 754 (31) 3.4 2.6–4.4 <.0001 
Prematurity 53 (21) 302 (15) 1.6 1.1–2.2 .01 
Respiratory distress duration, h 3.0 ± 2.3 2.8 ± 2.7 1.0 0.9–1.1 .5 
Respiratory rate ≥60 breaths per min 122 (47) 635 (26) 2.5 1.9–3.3 <.0001 
Apnea 70 (27) 147 (6) 5.8 4.2–8.0 <.0001 
Dehydration 76 (29) 212 (9) 4.4 3.2–5.9 <.0001 
Nasal flaring and/or grunting 122 (47) 364 (15) 5.1 3.9–6.6 <.0001 
Retractions 246 (94) 1911 (78) 4.7 2.8–8.0 <.0001 
Oxygen saturation <90% 79 (30) 40 (2) 26.3 17.5–39.6 <.0001 
Poor feeding 204 (78) 1341 (55) 3.0 2.2–4.1 <.0001 
Network      
 Canada 77 420 — — — 
 United States 103 728 — — — 
 Australia and New Zealand 36 552 — — — 
 United Kingdom and Ireland 24 648 — — — 
 Spain and/or Portugal 21 113 — — — 

—, not applicable.

The variables included in multivariable analysis included age, poor feeding, oxygen saturation, apnea, nasal flaring and/or grunting, dehydration, retractions, and respiratory rate (Table 3). Because the respiratory rate and retractions were significantly collinear with each other,42 only the retraction variable (with a higher OR) was retained in the final model. The association between escalated care and the network was not significant (P = .095). The multivariable analysis performed on the imputed data set revealed similar results (data not shown). One of the 217 infants without any predictors (0.46%) received escalated care.

TABLE 3

Selected Predictor Variables for Multivariable Model of Escalated Care

CharacteristicsNo. Risk PointsOR (95% CI)P
Age, mo    
 >2  Reference <.0001 
 ≤2  2.10 (1.49–2.97)  
Poor feedinga    
 No  Reference .0015 
 Yes 1.85 (1.27–2.71)  
Oxygen saturation, %b    
 ≥90 Reference <.0001 
 <90 8.92 (5.08–15.66)  
Apneac    
 No  Reference <.0001 
 Yes 3.01 (1.89–4.78)  
Nasal flaring and/or gruntingd    
 No  Reference <.0001 
 Yes 3.76 (2.64–5.35) 
Dehydrationd    
 No  Reference .0007 
 Yes 2.13 (1.37–3.30)  
Retractions    
 No  Reference .0007 
 Yes 3.02 (1.59–5.73)  
CharacteristicsNo. Risk PointsOR (95% CI)P
Age, mo    
 >2  Reference <.0001 
 ≤2  2.10 (1.49–2.97)  
Poor feedinga    
 No  Reference .0015 
 Yes 1.85 (1.27–2.71)  
Oxygen saturation, %b    
 ≥90 Reference <.0001 
 <90 8.92 (5.08–15.66)  
Apneac    
 No  Reference <.0001 
 Yes 3.01 (1.89–4.78)  
Nasal flaring and/or gruntingd    
 No  Reference <.0001 
 Yes 3.76 (2.64–5.35) 
Dehydrationd    
 No  Reference .0007 
 Yes 2.13 (1.37–3.30)  
Retractions    
 No  Reference .0007 
 Yes 3.02 (1.59–5.73)  
a

Reported on history.

b

Measured in triage in room air.

c

Reported on history or observed in ED.

d

Observed in ED.

The risk points assigned to each predictor variable appear in Table 3. The overall clinical risk score for escalated care and the observed and estimated absolute percent risk of escalated care at each risk level appear in Table 4. The total risk score ranged from 0 to 14 points. The AUC for the risk score was 84.7% (95% CI 81.7%–86.8%; Fig 1).

TABLE 4

Risk Levels for Escalated Care in the Study Population

Total No. Risk PointsEstimated Risk of Escalated Care, % (95% CI)No. With Escalated Care Out of Total No. Patients in Each Risk Level%
0.9 (0.6–1.2) 1 of 217 0.46 
1.5 (1.2–2.0) 1 of 199 0.50 
2.7 (2.1–3.4) 11 of 563 1.95 
4.7 (3.9–5.6) 33 of 740 4.45 
7.9 (6.8–9.2) 34 of 423 8.03 
13.2 (11.6–15.1) 44 of 265 16.60 
21.3 (18.7–24.1) 31 of 121 25.62 
32.3 (28.2–36.8) 16 of 51 31.37 
45.8 (39.9–51.8) 18 of 44 40.90 
59.9 (52.7–66.7) 23 of 33 69.69 
10 72.6 (65.1–78.9) 9 of 17 52.94 
11 82.4 (75.7–87.5) 17 of 21 80.95 
12 89.2 (83.9–92.9) 7 of 12 58.33 
13 93.6 (89.7–96.1) 5 of 5 100.00 
14 96.3 (93.5–97.9) 11 of 11 100.00 
Total No. Risk PointsEstimated Risk of Escalated Care, % (95% CI)No. With Escalated Care Out of Total No. Patients in Each Risk Level%
0.9 (0.6–1.2) 1 of 217 0.46 
1.5 (1.2–2.0) 1 of 199 0.50 
2.7 (2.1–3.4) 11 of 563 1.95 
4.7 (3.9–5.6) 33 of 740 4.45 
7.9 (6.8–9.2) 34 of 423 8.03 
13.2 (11.6–15.1) 44 of 265 16.60 
21.3 (18.7–24.1) 31 of 121 25.62 
32.3 (28.2–36.8) 16 of 51 31.37 
45.8 (39.9–51.8) 18 of 44 40.90 
59.9 (52.7–66.7) 23 of 33 69.69 
10 72.6 (65.1–78.9) 9 of 17 52.94 
11 82.4 (75.7–87.5) 17 of 21 80.95 
12 89.2 (83.9–92.9) 7 of 12 58.33 
13 93.6 (89.7–96.1) 5 of 5 100.00 
14 96.3 (93.5–97.9) 11 of 11 100.00 
FIGURE 1

Receiver operating characteristic curve for the clinical risk score.

FIGURE 1

Receiver operating characteristic curve for the clinical risk score.

The bootstrap analysis revealed that the combination of all outcome variables used to define the risk score was significantly associated with the outcome at least 89.4% of the time (empirical power: 89.4%). During the 1000 bootstrap simulations, performed with a significance level of 0.01, the percentage of the time each individual variable was significantly associated with escalated care ranged from 83.7% (dehydration) to 100% of the time (oxygen saturation <90% and nasal flaring and/or grunting).

Bootstrapping validation revealed a corrected average AUC of 84.2% (range: 80.3%–88.2%) and a mean overoptimism value of 0%. In this validation, the risk score was significantly associated with escalated care 100% of the time. The risk score model had an excellent calibration and goodness of fit by the Hosmer–Lemeshow test (P = .34).

In this large international study of infants diagnosed with bronchiolitis in the ED, we identified several commonly used and readily available clinical variables strongly predictive of the receipt of escalated care. Infants aged >2 months with oxygen saturations ≥90% and without nasal flaring and/or grunting, retractions, or hydration issues have a low probability of this outcome. The clinical risk score derived in a large and diverse infant population is used to quantify estimated risk for escalated care in bronchiolitis during the hospital stay, with a demonstrated high stability and discrimination ability.

Most studies in which authors characterize infants with severe bronchiolitis have been focused on hospitalization,9,14,17,19,25,43 safe discharges from the ED, and ED length of stay.15,20 However, clinical severity is only 1 factor involving the decision to hospitalize a child with bronchiolitis. Other factors include social, geographic, and cultural considerations. Past studies in which authors quantify the risk of hospitalization in bronchiolitis are also limited by the need for complex calculations14 and single-center design.19 In contrast, we have focused our outcome on receipt of escalated care because this subpopulation either suffers from or is at risk for developing respiratory decompensation and needs timely identification for optimal management. Although the clinical use of the risk score will need to be prospectively validated, it has the potential to individualize bronchiolitis care by identifying infants who can be discharged from the hospital or observed in community hospitals versus those at risk for requiring care by teams skilled in airway support because of risk of escalated care.

Research of infants with bronchiolitis receiving airway support has generally been focused on the inpatients,21,23 especially infants admitted to ICUs.16,24,29 These studies had relatively small numbers of ICU patients,16,22 used single-center designs,22,26,27 were focused on RSV bronchiolitis,22,25,27 or took place before the use of RSV immunoprophylaxis.25,26 HFNC therapy has recently become a common airway support strategy in severe bronchiolitis.30,44,46 Although some physiological benefits of HFNC have been confirmed,47,50 and the increasing use of HFNC may have contributed to a corresponding drop in intubation rates,30,50,53 we currently do not know which infants with bronchiolitis are the best candidates for this intervention. Although HFNC may have a role as a rescue treatment to prevent the need for ICU care in bronchiolitis, it does not appear to modify the underlying disease course.54 Furthermore, not all infants requiring airway support are admitted to the ICU. Many patients on HFNC appear to be candidates for ward therapy,31,55,57 as was the case in this study.

The strongest predictor of escalated care was a triage oxygen saturation <90%. The definition of clinically acceptable borderline hypoxia is a much-debated topic, and previous studies of oxygen saturation as a predictor of severe bronchiolitis have yielded disparate results.16,28 Our decision to use <90% as a threshold is based on the latest American Academy of Pediatrics recommendation1 and on recent evidence revealing comparable outcomes in patients discharged with a saturation threshold of 90% vs 94%.58 Interestingly, duration of symptoms was not associated with escalated care in this study. Although previous evidence for this association has yielded disparate results,16,19,20,23 a large multisite study revealed that symptom duration <1 day predicts the need for ICU care in bronchiolitis.28 Our data on this variable were reported in days rather than hours, which may have limited our ability to fully assess this association.

The large study population drawn from many EDs on several continents with diverse practice patterns of bronchiolitis management enhances the generalizability of our study results and increases the potential use of the risk score we have derived. This tool may be used by clinicians to guide management decisions. For example, the score would support the outpatient management of infants over 2 months of age without major hydration and respiratory distress issues and with an oxygen saturation ≥90%. Authors of a recent international study found that 30% of infants with bronchiolitis hospitalized from the EDs receive no evidence-based therapies that have to be delivered in hospital,33 and the use of the risk score in select low-risk patients may result in a lower hospitalization rate and lower health care expenditures. Our risk classification may be also helpful to community EDs because they have higher rates of bronchiolitis hospitalizations compared with pediatric EDs.59 

Scoring systems in the ED have previously been criticized for their lack of relevance and applicability.60 Our risk score employs clinical items in routine use for assessment of bronchiolitis, is simple to calculate, and has good discriminatory ability comparable to other risk stratification scores in pediatric emergency medicine, such as the Pediatric Risk of Admission Score,61 the Pediatric Early Warning system score,62 and the Pediatric Respiratory Assessment Measure.63 

Important limitations include known site-to-site variability between the use of HFNC and admission criteria for ICU care. Of note, we have conducted an additional analysis of infants on HFNC treatment versus infants with no escalated care and found that the differences in the proportions of infants with poor feeding, dehydration, flaring and/or grunting, saturation <90%, respiratory rate >60 breaths per minute, and retractions between these subgroups were both clinically and statistically significant (P < .001 for all differences). The question also arises if the use of HFNC therapy was driven primarily by low oxygen saturations. The percent agreement between oxygen saturation <90% and other clinical parameters of the risk score in triage was 90.2% for apnea, 87.7% for dehydration, and 81.8% for nasal flaring and/or grunting. Although some degree of overreliance on oxygen saturations cannot be excluded, most children with saturations <90% also had other red flags for major respiratory distress. Also, we cannot be certain that the clinical variables not charted as present were truly absent. This issue may potentially limit the precision of our risk score. Despite missing values for some variables, multiple imputation correction did not change our study results, which suggests this issue did not result in major bias. For all these reasons, prospective validation of the derived risk score would represent the next step toward implementation of these results in clinical practice. Finally, because we excluded high-risk infants on the basis of comorbidities known to enhance risk of severe disease, it is important that clinicians do not use this risk score to characterize the need for escalated care in these patients.

We identified variables measured in the ED predictive of receipt of escalated care for bronchiolitis and derived a clinical risk score with high discriminatory ability and excellent model stability to stratify risk of this outcome during hospital stay. Prospective validation and determination of clinical use are now needed.

     
  • AUC

    area under the curve

  •  
  • CI

    confidence interval

  •  
  • ED

    emergency department

  •  
  • HFNC

    high-flow nasal cannula

  •  
  • OR

    odds ratio

  •  
  • PECARN

    Pediatric Emergency Care Applied Research Network

  •  
  • PEM-CRC

    Pediatric Emergency Medicine Collaborative Research Committee

  •  
  • PERC

    Pediatric Emergency Research Canada

  •  
  • PERN

    Pediatric Emergency Research Networks

  •  
  • PERUKI

    Pediatric Emergency Research United Kingdom and Ireland

  •  
  • PREDICT

    Pediatric Research in Emergency Departments International Collaborative

  •  
  • REPEM

    Research in European Pediatric Emergency Medicine

  •  
  • RSV

    respiratory syncytial virus

Dr Freire conceived the study, cowrote the study protocol, and wrote the manuscript; Dr Kuppermann designed the study and provided major input into the concept and analysis of the study and drafting and revision of the manuscript; Dr Zemek designed the study and provided major input into the concept and analysis of the study and drafting and revision of the manuscript; Drs Freedman, Plint, Babl, Dalziel, Steele, Fernandes, Florin, Kharbanda, Lyttle, Johnson, Schnadower, and Benito designed the study, drafted the manuscript, and revised it for intellectual content; Mr Atenafu conducted the statistical analysis and revised the manuscript for intellectual content; Mr Stephens conducted the analysis and revised the manuscript for intellectual content; Dr Macias designed the study, provided extensive database support, drafted the manuscript, and revised it for intellectual content; Dr Schuh conceived the study, cowrote the study protocol, wrote the manuscript, and revised it critically for intellectual content; and all authors approved the final manuscript as submitted.

FUNDING: No external funding.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2018-1982.

This study could not have happened without the invaluable help and contribution to the study data collection of the following PERN colleagues:

PECARN: Aderonke O. Adekunle-Ojo, MD (Texas Children’s Hospital, Houston, TX); Lalit Bajaj, MD, MPH (Department of Pediatrics, Children’s Hospital Colorado, Aurora, CO); Daniel Cohen, MD (Nationwide Children’s Hospital, Columbus, OH); Marisa Louie, MD (University of Michigan, Ann Arbor, MI); Elizabeth Powell, MD, MPH (Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL); Richard Ruddy, MD (Cincinnati Children’s Hospital Medical Centre, Cincinnati, OH); Ashvin Shenoy, MD (School of Medicine, University of California Davis School of Medicine, Sacramento, CA).

PERC: Samina Ali, MDCM, FRCP(C) (Stollery Children’s Hospital, Edmonton, AB, Canada); Eleanor Fitzpatrick, RN (Izaak Walton Killam Hospital, Halifax, NS, Canada); Serge Gouin, MD, FRCP(C) (Centre Hospitalier Universitaire Saint-Justine Hospital, University of Montreal, Montreal, QC, Canada); Terry P. Klassen, MD, FRCP(C), MSc (Manitoba Institute of Child Health, University of Manitoba, Winnipeg, MB, Canada); Garth Meckler, MD, MSHS, FAAP, FRCP(C) (British Columbia Children’s Hospital, Vancouver, BC, Canada); Amita Misir, MD (London Health Sciences Centre Children’s Hospital, London, ON, Canada); Judy Sweeney, RN, BScN (The Hospital for Sick Children, Toronto, ON, Canada).

PERUKI: Fawaz Arshad, BMBS (Leicester Royal Infirmary Children’s Emergency Department, Leicester, UK); Carol Blackburn, MB BCh (Our Lady’s Children’s Hospital, Dublin, Ireland); Eleftheria Boudalaki, Ptychion Iatrikes (City Hospitals Sunderland National Health Service Foundation Trust, Sunderland, UK); Sian Copley, MBBS (City Hospitals Sunderland National Health Service Foundation Trust, Sunderland, UK); Kathryn Ferris, MB BCh BAO (Royal Belfast Hospital for Sick Children, Belfast, UK); Stuart Hartshorn, MB BChir (Birmingham Children’s Hospital, Birmingham, UK); Christopher Hine, MBChB (Birmingham Children’s Hospital, Birmingham, UK); Julie-Ann Maney, MB BCh BAO (Royal Belfast Hospital for Sick Children, Belfast, UK); Fintan McErlean (Royal Belfast Hospital for Sick Children, Belfast, UK); Niall Mullen, MB BCh (City Hospitals Sunderland National Health Service Foundation Trust, Sunderland, UK); Katherine Potier de la Morandiere, MBChB (Royal Manchester Children’s Hospital, Manchester, UK); Stephen Mullen, MB BCh BAO (Royal Belfast Hospital for Sick Children, Belfast, UK); Juliette Oakley, MB BCh (The Noah’s Ark Children’s Hospital for Wales, Cardiff, UK); Nicola Oliver, MBBS (Bristol Royal Hospital for Children, Bristol, UK); Colin Powell, MD (The Noah’s Ark Children’s Hospital for Wales, Cardiff, UK); Vandana Rajagopal, MBBS (The Noah’s Ark Children’s Hospital for Wales, Cardiff, UK); Shammi Ramlakhan, MBBS (Sheffield Children’s Hospital, Sheffield, UK); John Rayner, MBChB (Sheffield Children’s Hospital, Sheffield, UK); Sarah Raywood, MB BCh (Royal Manchester Children’s Hospital, Manchester, UK); Damian Roland, MBBS, (Leicester Royal Infirmary Children’s Emergency Department, Leicester, UK); Siobhan Skirka, MBChB (Our Lady’s Children’s Hospital, Crumlin, Dublin, UK); Joanne Stone, MBChB (Sheffield Children’s Hospital, Sheffield, UK).

PREDICT: Meredith Borland, MBBS, FRACGP, FACEM (Princess Margaret Hospital for Children, Perth, WA, Australia); Simon Craig, MBBS (Monash Medical Centre, Melbourne, VIC, Australia); Amit Kochar, MD (Women’s and Children’s Hospital, Adelaide, SA, Australia); David Krieser, MBBS (Sunshine Hospital, St Albans, VIC, Australia); Cara Lacey, MBBS (Sunshine Hospital, St Albans, VIC, Australia); Jocelyn Neutze, MBChB (Middlemore Hospital, Auckland, New Zealand); Karthikeyan Velusamy, MD (Townsville Hospital, Douglas, QLD, Australia).

REPEM: Javier Benito, MD, PHD (Pediatric Emergency Department, Cruces University Hospital, Barakaldo, Bizkaia, Spain); Ana Sofia Fernandes, MD (Santa Maria Hospital, Lisbon, Portugal); Joana Gil, MD (Santa Maria Hospital, Lisbon, Portugal); Natalia Paniagua, MD (Cruces University Hospital, Barakaldo, Bizkaia, Spain); Gemma Claret Teruel, MD (Hospital Sant Joan de Déu, Barcelona, Spain); Yehezkel Waisman, MD (Schneider Children’s Medical Center of Israel, Petah Tiqva, Israel).

Research Network and Development of Pediatric Emergency Medicine in Latin America: Pedro Bonifacio Rino, MD (Hospital de Pediatria Prof. Dr Juan P. Garrahan, Buenos Aires, Argentina).

PERN Executive Committee: Nathan Kuppermann (Chair), Stuart R. Dalziel (Vice Chair), James Chamberlain (PECARN), Santiago Mintegi (REPEM), Rakesh Mistry (PEM-CRC), Lise Nigrovic (PEM-CRC), Amy C. Plint (PERC), Damien Roland (PERUKI), Patrick Van de Voorde (REPEM).

PERN Networks: Participating networks include the following: PECARN, PEM-CRC of the American Academy of Pediatrics, PERC, PERUKI, PREDICT, REPEM, and the Red de Investigacion y Desarrollo de la Emergencia Pediatrica Latinoamericana, which means Research Network and Development of Pediatric Emergency Medicine in Latin America (Argentine-Uruguayan network).

We thank Judy Sweeney and Maggie Rumantir for their generous contribution to coordinating this study and Henna Mian for her administrative assistance with the study.

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

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