A combined rapid clinical and lung ultrasound score for predicting bronchiolitis severity

doi:10.21203/rs.3.rs-2733746/v1

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A combined rapid clinical and lung ultrasound score for predicting bronchiolitis severity

https://doi.org/10.21203/rs.3.rs-2733746/v1

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Introduction

Lung Ultrasound (LUS) has demonstrated good correlation with clinical severity scores in bronchiolitis but should be combined with clinical data to achieve best results. Aim of the study is to create a quick and reliable clinical-ultrasound score to predict the risk of Pediatric Intensive Care (Picu) admission as soon as the patient enters the Emergency Department (ED).

Methods

Retrospective study conducted at two pediatric EDs. Lung was divided into six zones and scanned with ultrasound; every zone received a score. Clinical data were obtained. For the outcomes “Picu admission”and “Cpap ventilation”, a multivariate analysis was conducted and the significant factors resulting were used to create a 3- items score to predict Picu admission. Area under the Receiver-Operating Curve (AUC) for specificity and sensibility of the score was obtained.

Results

74 patients were enrolled; 34% were admitted to PICU. 31% were treated with CPAP. For the outcome “PICU admission”, multivariate analysis demonstrated presence of wheezing and reduced oral intake to be significant together with ultrasound involvement of the Right Posterior Upper Zone and Left Posterior Basal Zone. For the outcome “CPAP ventilation”, same clinical factors plus involvement of Right Posterior Upper Zone were significant. A 3-items score (1: presence of wheezing; 2: reduced oral intake; 3: LUS involvement of Right Posterior Upper Zone) for prediction of PICU admission was created which presents an AUC of 0.8249.

Conclusions

We were able to create a simple and quick score to predict need for PICU admission in bronchiolitis.

The role of Lung Ultrasound (LUS) has significantly increased during the last decade in adult, pediatric and neonatal practice, with a growing number of centres using it as a first tool to diagnose pneumonia, pneumothorax, pleural effusions, neonatal respiratory diseases and, more recently, bronchiolitis and COVID-19 [1].

Several bronchiolitis’ studies have been performed by independent researchers, widely demonstrating that LUS is able to detect different lung manifestations of the diseases, from isolated vertical artifacts, to confluent ones to subpleural consolidations of different size, representing a probable continuum of severity of lung involvement [2–14]. A number of these studies have also attempted in comparing different LUS scores in children needing or not Pediatric Intensive Care Unit (PICU) admission or any type of oxygen support, finding that children with more severe bronchiolitis usually have higher LUS scores but with overall large overlaps between the two groups [16]. Most of these studies, however, were performed in single centres and involved relatively small numbers of children, or included little or no children that required higher steps of ventilation in PICU. More recently, the first national multicentric study has been performed in Italy, providing an extensive LUS assessment of all thoracic areas in more than 200 children, showing a good concordance of a score of the Italian Academy of Thoracic Ecography (ADET) with clinical severity scores, however despite large numbers the study failed to include enough children needing PICU admission or a higher level of respiratory support such as continuous positive airway pressure, (CPAP) [¹5].¹⁵. Also, an extensive approach that require a time-consuming LUS protocol may be difficult to be adapted in real-life contexts, and more specifically in busy emergency departments or with more severe children. Last, but not least, available literature mostly focused in finding LUS parameters that predict the most severe forms of bronchiolitis. However, the most modern view of LUS see it as a clinical tool that should add information to clinical findings, and as such the best of LUS can be obtained when LUS and clinical information are used together in clinical practice [17–19].

For these reasons, we performed the present study with the aim to create a quick-to-use clinical-ultrasound score able to accurately predict which children are at highest risk of CPAP or invasive ventilation and PICU admission.

Population

This is a retrospective study, conducted between November 2018 and April 2020 in the Pediatric Emergency Departments of the “Gemelli University Hospital” in Rome and of the “Vittore Buzzi Children’s Hospital” in Milano (Italy).

Primary aim of the present study is to describe and compare clinical and LUS characteristics of infants with bronchiolitis needing admission to PICU to those who do not, in order to create a ready-to-use mixed clinical-ecographic score for the prediction of need of PICU admission. Secondary aim of the study is to describe and compare clinical and LUS characteristics of the patients needing CPAP ventilation.

All consecutive children aged 0–12 months with a clinical diagnosis of bronchiolitis and admitted to the two centres were enrolled; for each child, demographic and clinical information were collected and saved on a standardized form before the execution of the ultrasound exam.

Patients have been excluded from the study if they presented immunodepression, heart diseases, neuromuscular diseases, cystic fibrosis, bronchopulmonary dysplasia (defined according to the perinatal history, dependence of oxygen), positive history of foreign body inhalation, unstable critical conditions that required immediate life-saving procedures, and lack of parental consent.

An individual data sheet for the collection of demographic, medical and clinical data, according to the clinical classification of bronchiolitis, was used.

The study was approved by the Ethics Committees of each participating centre.

Lung Ultrasound

Lung ultrasound examinations were performed. in the Emergency Department, with a high frequency (12 − 3 MHz) linear probe (Affiniti 70, Philips, Amsterdam, Netherlands), at the patient’s bedside, using a modified three-zone per hemithorax Bedside Lung Ultrasound in Emergency (BLUE) protocol (described by Lichtenstein) [20].

Each hemithorax was divided into anterior, lateral, and posterior zones, and upper and lower zones (divided by the internipple line); anterior aspect of the chest was identified by the anterior axillary line, lateral aspect of the chest by the anterior and posterior axillary lines, and posterior aspect of the chest by the posterior axillary line and the spine, not including the scapular area. Children were scanned in a recumbent or semirecumbent position and rolled onto their side to optimize posterior scanning.

In order to characterize the Lung Ultrasound patterns the recently published definitions of the Italian Academy of Thoracic Ultrasound (Accademia di Ecografia Toracica, ADET) [21]were employed:

0: normal A lines

1: short vertical artifacts and isolated B lines

2: multiple B lines (B lines with a distance of less than half centimeter to the confluence, remaining identifiable from each other)

3: white lung (subpleural field with various shades of grey/white without distinguishing B lines) and subpleural consolidations smaller than 1 cm

4: subpleural consolidations bigger than 1 cm

Anterior zones were grouped and defined as the sum of Right Anterior Upper Lobe, Right Anterior Lower Lobe, Left Anterior Upper Lobe and Left Anterior Lower Lobe. Lateral zones were defined as the sum of Right Lateral Upper Lobe, Right Lateral Lower Lobe, Left Lateral Upper Lobe and Left Lateral Lower Lobe. Posterior Zones were defined as the sum of Right Posterior Upper Lobe, Right Posterior Lower Lobe, Left Posterior Upper Lobe and Left Posterior Lower Lobe.

Variables to be included in the score were chosen from the previously cited clinical and ultrasound variables, according to their Odds Ratios (OR) for the given outcomes. We decided to consider one single significant lung area in order to produce a rapid tool that does not imply sums of different scores but only some simple data. Relative importance of each variable was assigned based upon their Odds Ratios in the multivariate analysis with the given outcomes.

Lung ultrasound examinations were either directly performed or reviewed by D.B. and A.C.in order to reduce operator-dependency of the technique.

PICU admission was deemed necessary if the patient presented with high supplemental oxygen requirement (FiO2 ≥ 0.5 to maintain SaO2 ≥ 92%), need for non invasive or invasive ventilation, rapidly progressive upper or lower airway disease, apnea observed by a physician or nurse or described as cyanosis and/or loss of consciousness and/or decreased muscle tone [22].

Outcomes

Statistical analysis

For the outcomes “PICU admission”and “CPAP ventilation”, a logistic regression was conducted after selecting potential variables of interest that presented a Likelihood Ratio of < 0.25 in the univariate analysis. The potential candidate predictors were age, sex, ex-premature status (defined as birth before 36 + 6 weeks of gestational age), previous therapy at home; under a clinical aspect: symptom duration before medical consult expressed in hours, presence of rhinorrea, fever, retractions, crackles, wheezing, reduced food intake, peripheral saturation of Oxygen; under a microbiological aspect, detection of RSV or multiple viruses; under the ecographic aspect, the single zones’ score and the macro-areas’ score according to the previous definition, and the involvement of a zone by the pathologic process as defined by a score equal to or bigger than 2.

Wilcoxon rank-sum test was conducted to compare the ultrasound scores of patients admitted to PICU respectively in the posterior, lateral and anterior areas and in every single lung zone scanned.

The presence of a score > = 2 in at least one lung zone was tested for correlation with PICU admission and CPAP ventilation with Fisher’s exact test.

Quantitative variables were described by mean and standard deviation (SD) or median and interquartile range (IQR), depending on the variable distribution. Frequencies and percentages were used for qualitative variables.

Data were analysed with Stata 17 BE (Statacorp LLC, USA). P < 0.05 were considered significant.

74 patients were enrolled in the study, 25 (34%) of which were admitted to PICU. 23 (31%) were treated with Continuous Positive Airway Pressure (CPAP) and 38 (51%) with High Flow Nasal Cannula (HFNC). All patients treated with CPAP were admitted to PICU and of the HFNC group, 14/38 (36.8%) were admitted to PICU and 24/38 (63.2%) were not.

23 (31%) patients received steroids, 22 (30%) bronchodilators and 4 (5%) nebulized epinephrine. No patient required invasive ventilation. Respiratory Syncitial Virus was identified in 85% of patients.

Demographic and clinical data of the patients are presented in Tables 1 and 2 with the relative outcomes of “PICU admission” and “CPAP ventilation”, together with number of patients presenting involvement of single lung areas (defined as a single.area score equal or bigger than 2).

Table 1

demographic data and clinical symptoms upon presentation in patients admitted and not admitted to Picu. Data are presented as median (IQR) for continuous measures, and n(%) for categorical measures.
	All patients (n = 74)	No Picu Admission (n = 49)	Picu Admission (n = 25)	p-value	OR (95% CI)
Clinical Data
Age, months	2.2 [1.0-6.4]	2.2 [1.0-5.4]	2.0 [1.0–8.0]	0.54	1.07 (0.97–1.18)
Male sex	38 (51%)	27 (55%)	11 (44%)	0.37	0.64 (0.24–1.68)
Symptom duration, h	72 [24–96]	72 [48–96]	48 [24–96]	0.21	0.99 (0.98–1.00)
Rhynorrea	66 (89%)	49 (100%)	17 (68%)	< 0.001	-
Reduced Oral Intake	53 (72%)	31 (63%)	22 (88%)	0.026	4.25 (1.11–16.24)
Ex premie status	12 (16%)	7 (14%)	5 (20%)	0.53	1.50 (0.42–5.31)
Crackles	70 (95%)	49 (100%)	21 (84%)	0.004	-
Wheezing	16 (22%)	3 (6%)	13 (52%)	< 0.001	16.61 (4.06–67.83)
Retractions	73 (99%)	48 (98%)	25 (100%)	0.47	-
Fever	33 (45%)	22 (45%)	11 (44%)	0.94	0.96 (0.36–2.54)
LUS Score
Right Lung Areas
Anterior Basal	21 (28%)	14 (29%)	7 (28%)	0.96	0.97 (0.33–2.83)
Anterior Upper	35 (47%)	19 (39%)	16 (64%)	0.04	2.8 (1.03–7.61)
Lateral Basal	21 (28%)	13 (27%)	8 (32%)	0.62	1.30 (0.45–3.73)
Lateral Upper	27 (36%)	12 (24%)	15 (60%)	0.003	4.62 (0.45–3.73)
Posterior Basal	42 (57%)	26 (53%)	16 (64%)	0.37	1.57 (0.58–4.23)
Posterior Upper	54 (73%)	32 (65%)	22 (88%)	0.038	3.89 (1.01–14.90)
Left Lung Areas
Anterior Basal	33 (45%)	19 (39%)	14 (56%)	0.16	2.00 (0.75–5.33)
Anterior Upper	21 (28%)	9 (18%)	12 (48%)	0.007	4.10 (1.41–11.92)
Lateral Basal	36 (49%)	22 (45%)	14 (56%)	0.37	1.56 (0.59–4.11)
Lateral Upper	25 (34%)	9 (18%)	16 (64%)	< 0.001	7.9 (2.65–23.51)
Posterior Basal	37 (50%)	19 (39%)	18 (72%)	0.007	4.06 (1.42–11.54)
Posterior Upper	51 (69%)	31 (63%)	20 (80%)	0.14	2.32 (0.74–7.25)

Table 2

Population’s demographic, clinical and ultrasound characteristics upon presentation divided by CPAP ventilation. Data are presented as median(IQR) for continuous measures, and n(%) for categorical measures.
	All patients (n = 74)	No CPAP Vnetilation (n = 51)	CPAP Ventilation (n = 23)	p-value	OR (95% CI)
Clincal Data
Age, months	2.2 [1.0-6.4]	2.3 ([.0-5.5]	2.0 [1.0–7.0]	0.98	1.04 (0.94–1.15)
Male sex	38 (51%)	28 (55%)	10 (43%)	0.36	0.63 (0.23–1.70)
Symptom duration, h	72 [24–96]	72 [48–96]	48 [24.0–96]	0.18	0.99 (0.98–1.00)
Rhynorrea	66 (89%)	50 (98%)	16 (70%)	< 0.001	0.04 (0.005–0.40)
Reduced Oral Intake	53 (72%)	33 (65%)	20 (87%)	0.049	3.63 (.094–13.92)
Ex premie status	12 (16%)	8 (16%)	4 (17%)	0.850	1.13 (0.30–4.21)
Crackles	70 (95%)	51 (100%)	19 (83%)	0.002	-
Wheezing	16 (22%)	4 (8%)	12 (52%)	< 0.001	12.81 (3.46–47.42)
Retractions	73 (99%)	50 (98%)	23 (100%)	0.500	-
Fever	33 (45%)	23 (45%)	10 (43%)	0.900	0.93 (0.34–2.52)
LUS Score
Right Lung Areas
Anterior Basal	21 (28%)	14 (27%)	7 (30%)	0.790	1.15 (0.39–3.40)
Anterior Upper	35 (47%)	20 (39%)	15 (65%)	0.038	2.90 (1.04–8.10)
Lateral Basal	21 (28%)	13 (25%)	8 (35%)	0.410	1.55 (0.53–4.51)
Lateral Upper	27 (36%)	13 (25%)	14 (61%)	0.003	4.54 (1.59–12.96)
Posterior Basal	42 (57%)	26 (51%)	16 (70%)	0.140	2.19 (0.77–6.24)
Posterior Upper	54 (73%)	32 (63%)	22 (96%)	0.003	13.06 (1.62–104.85)
Left Lung Areas
Anterior Basal	33 (45%)	21 (41%)	12 (52%)	0.38	1.55 (0.57–4.19)
Anterior Upper	21 (28%)	11 (22%)	10 (43%)	0.053	2.79 (0.96–8.07)
Lateral Basal	36 (49%)	24 (47%)	12 (52%)	0.680	1.22 (0.45–3.28)
Lateral Upper	25 (34%)	11 (22%)	14 (61%)	< 0.001	5.65 (1.93–16.50)
Posterior Basal	37 (50%)	21 (41%)	16 (70%)	0.024	3.26 (1.14–9.31)
Posterior Upper	51 (69%)	32 (63%)	19 (83%)	0.088	2.82 (0.83–9.53)

Table 1

Table 2

When analysed singularly, the presence of a score higher or equal to 2 in the following lung areas was associated with PICU Admission: Right Anterior Upper, Left Anterior Upper, Left Lateral Upper, Right Posterior Upper, Left Posterior Basal Area. Same results were obtained for “CPAP ventilation”.

When considering macro-areas (namely anterior, lateral, and posterior), ultrasound scores in the lateral areas (p = 0.014) but not in the posterior (p = 0.058) and anterior ones (p = 0.06) differed significantly between patients admitted to PICU and patients not admitted to PICU. Similarly, ultrasound scores in the lateral areas (p = 0.04) and in the posterior ones (p = 0.024) were associated with CPAP ventilation.

Scores of single lung areas differed significantly between those admitted to PICU and those not in two areas, namely the Left Lateral Upper and the Right Lateral Upper one (Supplementary file).

For the outcome “PICU admission”, the univariate analysis selected the following variables among the clinical ones: presence of wheezing and reduced oral intake. These were then tested in the multivariate logistic model with the score of every single lung area, the score of every macro-area (anterior, lateral, or posterior), and the variable defining the zone involvement in a dichotomic way, if the score is equal to or bigger than 2.

Results of the multivariate analysis are presented in Table 3. Hosmer-Lemeshow test was used to assess the goodness of fit of the model.

Table 3

Multivariate analysis for prediction of Picu admission in infants presenting to the ED.
	p-value	OR (95% CI)
Model 1
Wheezing	< 0.001	34.84 (5.16–235.22)
Reduced Oral Intake	0.031	7.01 (1.19–41.24)
Right Posterior Upper Zone Involvement	0.038	6.60 (1.10–39.45)
Model 2
Wheezing	< 0.001	22.55 (4.08–124.55)
Reduced Oral Intake	0.023	8.72 (1.34–56.74)
Lateral Posterior Basal Zone Involvement	0.031	4.10 (1.13–14.84)

Table 3

For the occurrence of “CPAP ventilation”, results are presented in Table 4.

Table 4

Multivariate analysis for prediction of Cpap ventilation in infants presenting to the ED.
	p-value	OR (95% CI)
Wheezing	0.001	41.38 (4.43–386.08)
Right Posterior Upper Zone Involvement	0.010	41.50 (2.46–699.25)

Table 4

Combined clinical-ultrasound score

Given these preliminary results, a combined clinical-ultrasound score was created for determination of risk of PICU admission and CPAP ventilation, with the aim to be a quick tool when the patient enters the ED.

As mentioned, variables to compose the score were chosen from the previously cited clinical and ultrasound variables: for the clinical side, wheezing and reduced oral intake, and under the ultrasound aspect, involvement of the Right Upper Posterior Lung Zone, which was chosen because highly predictive for both outcomes. Their relative importance was assigned according to their ORs of the previous analysis.

To calculate the score, we therefore considered presence of Reduced Oral Intake *7.01 + Presence of Wheezing * 34.84 + Involvement of Right Posterior Upper Zone * 6.60.

Area Under the Curve for this model was 0.8249 (Fig. 1), thus showing excellent discrimination for the outcome.

For “CPAP” ventilation, the same score predicted the outcome with very excellent AUC as well (0.8291).

In this study, we found that a rapid combined clinical and ultrasound score was accurate for discriminating during the first clinical evaluation the risk for PICU admission and CPAP ventilation in children with bronchiolitis. To our knowledge, this is the first study including evaluating a relatively large number of children requiring PICU and using a score that combines both clinical and ultrasound information.

The use of lung ultrasound is rapidly growing worldwide and scientists are expanding its clinical use and physical knowledge in both adult and pediatric practice, including neonatology [1].Specifically, to the initial use of LUS in pleural effusions and pneumonia, several other applications including interstitial lung disease, traumas, pneumothorax, acute respiratory distress syndrome, neonatal RDS, TB and bronchiolitis have been added and further investigated [1].

In paediatrics, a large number of studies have focused on bronchiolitis [2–15]. It is now well established that LUS can detect different patterns of lung involvement in children with bronchiolitis, including vertical artifacts and different-size subpleural consolidations, which, according to a recent study with a standardized follow-up, seem to be mostly peripheral areas of disventilation/atelectasis given their rapid resolution [15]. Moreover, previous studies using different ultrasound approaches showed that children with bronchiolitis have frequent posterior paravertebral small consolidations or that patients requiring oxygen or ventilation usually have higher LUS scores, however with large overlaps of LUS scores between the different groups and mostly including small or no patients needing PICU admission [2–15]. This overlaps on one hand may imply a low accuracy of LUS in predicting bronchiolitis severity, on the other may highlight that our use of this tool can still be improved.

On this regard, experience can be gained by adult and neonatal practice, as well as knowledge of the physical bases of lung ultrasound. Experts from adult and neonatal practice are suggesting that the semi-quantification of LUS scores may be used as a tool to quantify the aeration of the peripheral lung, and this is particularly evident from several studies from De Luca’s team on preclinical and clinical data on neonatal respiratory conditions[23–27]. The rationale for such an approach is the relatively obvious hypothesis that the less the peripheral lung is expanded, the less lung areas are available for gas exchanges and, therefore, the clinical manifestation of that specific disease is expected to be more severe. These hypothesis has, importantly, fundaments on the growing knowledge of the bases of LUS and specifically from several studies of the Italian Academy of Thoracic Ultrasound, coordinated by Soldati et al, which demonstrated that vertical artifacts, white lung and consolidations are expression of a spectrum of increasing loss of the peripheral alveolar structure up to the total hepatisation of the lung, which does not contribute anymore to gas exchange[28–32]. While this is all true, clinical observations from several studies, from COVID-19 to bronchiolitis, show that although most severe patients have usually higher LUS scores, use of LUS scores may be limited in predicting disease severity, suggesting that other parameters beside the status of peripheral lung (which is the lung area that can be explored with LUS), contribute to pathophysiology.

For example, bronchiolitis is frequently characterized by wheezing[33].Interestingly, in fact, in our cohort the patients admitted to PICU had significantly higher rates of wheezing, while those not admitted mostly have upper respiratory involvement with rhinitis. However, wheezing has not a direct consistent pattern/expression on LUS, and therefore excluding such an important parameter from a severity-predicting score would make no sense. Indirectly, the bronchial obstruction could cause a peripheral disventilatory picture downstream of the obstruction which can leads to an indirect visibility subpleural consolidation, as previously described also by our team in a case series [34]. However, it is currently unknown if every functional wheezing leads to peripheral dysventilation and, therefore, we cannot speculate that every wheezing has a concomitant ultrasound-visible atelectasis, and more importantly there is no grading in the current literature linking severity of wheezing with dimension of peripheral atelectasis. Therefore, although there is space for future research to better understand how wheezing can be seen and quantified through indirect LUS features, at the moment we believe that the clinical finding of wheezing (the sound) is easier to be documented compared with the LUS pattern, and therefore we suggest to include wheezing in the clinical prediction rules of bronchiolitis-severity, in adjunction of the LUS features.

Wheezing is expression of bronchial obstruction, and since bronchi do not touch the pleura they cannot be explored by ultrasounds. Also, reduced oral intake is one of the most important complications in more severe disease and, obviously, no LUS patterns can detect this. This observation induced us to unify clinical and LUS parameters. Importantly, the concept that LUS is an adjunctive clinical tool that should add to the clinical examination has already been established, and previous studies have already demonstrated that the use of both clinical and LUS parameters significantly improve the accuracy of patient assessment compared with clinical or LUS used independently [18]. Interestingly, in our score including the most important clinical and LUS on the univariate models, we found that including both wheezing and LUS parameters gave a very accurate model for predicting PICU admission and CPAP need. To our knowledge, such a combination of easy and relevant clinical and LUS parameters has never been studied before and, importantly, its high accuracy is very promising.

In our combined LUS-clinical score, the most relevant areas of the lung for prediction of need of high support were the posterior ones. Several studies have documented that common lung diseases, including bronchiolitis, neonatal RDS, and recently COVID-19, are commonly characterized by involvement of the posterior lung areas. This finding may be also due to the frequent supine positions of young infants and the consequent gravitational deposits of airways secretions or a less ventilation of these areas [35]. Regional atelectasis is potentially easier to be found in infants and children, as the relative and absolute airway diameter is smaller compared to adults, thus, smaller volumes of secretions are needed to occlude them [36]. The involvement of the right upper zone and the left basal one (both posteriorly) has been already described [37]. Potential explanations for this preferential atelectasis can be looked for in the anatomy - the right upper lobe bronchus arises at a 90-degree angle from the right mainstem bronchus [38]- or in its the position in space - the right upper lobe is the most dependent bronchus in the supine position, making it a steeper way to clear secretions [39]; the left posterior basal area, also, could be involved because compressed by the heart [36].

Also, in our population, lateral areas scores were significantly different in those admitted to PICU and in those ventilated in CPAP compared to those who were not. Infants with bronchiolitis, in particular the most severe ones, are usually still breastfed/formula fed and the usual positions for milk feeding requires the infant being in a supine but partially lateral position. This may somehow predispose to microaspirations that gravitationally deposit on lateral area, which in turn appear less ventilated on LUS. Also, the tridimensional dinamics of thorax motion during breathing and growth is still unclear, although very recent advances suggest that different part of the thorax may contribute differently during respiration and that this may change with development [40–42]. This may lead to the speculation that lateral sections of the ribs are less expanded during pathological breathing in more severe bronchiolitis, since children mostly use the diaphragm and other accessory muscles like sternocleidomastoideum muscles, and therefore those areas are more easily atelectatic. These hypothesis requires, obviously, further confirmations but, in our opinion, were strong and interesting enough to require validation and also new pathophysiology studies.

A strength of our model is its simplicity. As clinicians involved in routine clinical practice, we focused our efforts under a pediatric emergency medicine/intensive care perspective. In these settings, it is pivotal to have tools that are quickly and easily feasible at patient bed-side. Although several associations, including the ADET and also our previous studies, have highlighted the importance of a comprehensive assessment of all lung areas while performing LUS, in very urgent settings scanning all lung areas is not feasible. Therefore, a quick tool like ours can serve as a useful first assessment that might serve in better triaging children with bronchiolitis assessed in the paediatric emergency department, and leaving a more comprehensive LUS assessment during follow-up visits in children hospitalized. For example, predicting which infants with bronchiolitis will develop severe disease later during disease course is still a challenge, as re-visits in the PED of previously discharged children coming back with worsening bronchiolitis are still a problem with organizational and legal consequences [43]. In this setting, our approach, if confirmed by other studies, can have a positive impact on the management of bronchiolitis.

Our study has limitations, mainly the relatively small number of children included, particularly for the PICU group, and the absence of patients requiring invasive ventilation. However, this is still the largest cohort including PICU patients.

In conclusion, we developed a combined clinical-ultrasound score able to accurately predict need of PICU admission and CPAP ventilation in children with bronchiolitis. Hopefully, given the very promising findings of our study, we hope that this model will be tested in further multicentric studies including larger cohorts of patients, since such a rapid tool may significantly improve the initial assessment of children with bronchiolitis.

ADET: Accademia di Ecografia Toracica (Italian Academy of Thoracic Ecography)

CPAP: Continuous Positive Airway Pressure

ED: Emergency Department

FiO2: Inspired Fraction of Oxygen

LUS: Lung Ultrasound

PICU: Pediatric Intensive Care Unit

SaO2: Saturation of Oxygen

Conflict of Interest/ Funding

The Authors have no conflict of interest to disclose. No funding was received for this work.

Data Availability Statement

Data available on request due to privacy.

Author Contribution

Conceptualization: A.C. and D.B.

Data Curation: A.C., D.B., C.D.R.

Formal analysis: A.C.

Investigation: all authors

Writing, original draft: A.C, D.B.

Writing, review and editing: all authors

Consent to participate

Informed consent was obtained from all participants’ legal guardians.

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A combined rapid clinical and lung ultrasound score for predicting bronchiolitis severity

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Lung Ultrasound

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Combined clinical-ultrasound score

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