Severe bronchiolitis before and after the COVID-19 pandemic: A retrospective database analysis by the Italian Network of PICU Study group (TIPNet)

doi:10.21203/rs.3.rs-5027164/v1

Background: The first post-COVID-19 pandemic year demonstrated an unusual bronchiolitis epidemic in both hemispheres and has been attributed to the removal of barriers implemented during SARS-CoV-2 infection. Several countries reported an increase in respiratory syncytial virus (RSV) bronchiolitis, with more hospitalizations and greater need for respiratory support. We aimed to evaluate the consequences of the COVID-19 pandemic on the epidemiology and management of severe bronchiolitis in Pediatric Intensive Care Units (PICUs) in Italy.

Methods: multicenter, retrospective, cohort database analysis. All children younger than 24 months admitted to 7 PICUs from October 2017 to April 2023 diagnosed with bronchiolitis were included. We compared patients from Pre-COVID and Post-COVID eras, excluding patients from the 2020-2021 season due to low numbers. Logistic regression models were used to assess the impact of Pre/Post-COVID period on the need for invasive ventilation.

Results: 715 patients were admitted to PICU during the study period, 451 patients Pre-COVID and 251 patients Post-COVID. Patients in the Post-COVID group were older, had more comorbidities and higher Pediatric Index of Mortality score at admission but the need for respiratory support was not significantly different. There was high variability in bronchiolitis management across Centers. Presenting Pre-COVID was protective against the risk of mechanical ventilation, adjusted for age and disease severity at admission (OR 0.38, 95% CI 0.16-0.89), while RSV infection increased the risk of intubation (OR 2.49, 95% CI 1.1-5.63).

Conclusions: PICUs have faced an unexpected peak of significantly more severe cases of bronchiolitis after the COVID-19 pandemic, but this didn’t require increased respiratory support.

Bronchiolitis

Non-Invasive Ventilation

intubation

Pediatric Intensive Care Unit

Respiratory Syncytial Virus (RSV)

severity

Bronchiolitis is a common viral lower respiratory tract infection characterised by acute small airway inflammation and represents the leading cause of hospital admission in infants and young children^1,2. In temperate regions, bronchiolitis shows a seasonal pattern, characterized by an increase in the number of cases in late October, a peak in January/February, and an end in April³. The most severe cases are usually secondary to respiratory syncytial virus (RSV), which infects a large proportion of children less than 2 years of age^4,5. Severe bronchiolitis is the main cause of admission to Pediatric Intensive Care Unit (PICU) and is characterized by airway obstruction, hypoxemia, increased work of breathing and respiratory distress, which may require advanced supportive management, including hydration, oxygen, or mechanical ventilation^6-8. During the first year of the COVID-19 pandemic (2020), several authors reported a significant reduction in hospital admissions for bronchiolitis compared to previous years, likely due to the extraordinary reduction in interpersonal contacts resulting from SARS-CoV-2 safety measures^9-11. During the following year, countries gradually reduced the distancing rules and restored a normal social and working life, aided by the initial Coronavirus vaccination campaign. The exponential increase in human contacts led to a subsequent rise in common viral infections, including those caused by RSV¹². This phenomenon was first described in Australia in 2020, when an unseasonal bronchiolitis epidemic was observed just after the relaxation of social distancing measures¹³.

Our study aimed to compare the epidemiology, disease course and management of severe bronchiolitis before and after the COVID-19 pandemic in seven Italian PICUs and to identify possible risk factors for mechanical ventilation.

Study Design and Setting

This is a multicenter, retrospective, database analysis based on a prospectively compiled electronic web-based national registry of the Italian Network of PICU Study Group (TIPNet).

TIPNet is a national research network established in 2010, which includes 17 Italian PICUs. The registry’s use for nonprofit research purposes has been approved by the Ethics Committee of the Buzzi Hospital, the coordinating registry Center (approval number 269-052014 of 23/05/2014 and number 0047897 of 16/11/2020). Data were collected and managed using the REDCap (Research Electronic Data Capture) platform hosted at the Unit of Biostatistics, Epidemiology and Public Health of the University of Padua^14,15. The guidelines for reporting observational studies according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement were followed¹⁶.

Inclusion criteria and outcome

We considered the completeness of the data entered relative to the admissions conducted by each Center as an inclusion criterion for the study and we established a cut-off of 90% complete data for each patient. Seven out of 17 PICUs met this criterion.
All patients younger than 24 months of age admitted to these PICUs from 2017 to 2023 with a diagnosis of bronchiolitis were included. The period spanned from October 1st to April 30th, which is the typical bronchiolitis season in Italy.

The cohort was subsequently divided into a Pre-COVID period (patients admitted in 2017-2018, 2018-2019 and 2019-2020 seasons) and a Post-COVID period (those admitted during 2021-2022 and 2022-2023 seasons). The 2020-2021 season was intentionally excluded due to the low number of patients admitted, to reduce confounding and potential biases.

The primary outcome was defined as the proportion of infants requiring invasive mechanical ventilation (IMV) in the Pre-COVID and the Post-COVID groups. The secondary outcomes were the assessment of disease severity at admission, type and duration of invasive and non-invasive ventilation (NIV) and PICU length of stay (LOS) in the two study periods. NIV was defined as positive airway pressure delivered through a nasal mask, nasal prongs, a facial mask or a helmet. NIV ventilation modes were classified as follows: continuous positive airway pressure (CPAP), pressure support ventilation (PSV), bilevel positive airway pressure (BiPAP) and assisted pressure control ventilation (A-PCV). Mechanical ventilation was defined as invasive mechanical ventilation (IMV). High flow nasal cannula (HFNC) was defined as 2 L·kg−1·min−1 flow of a gas oxygen mixture through nasal cannula and coded separately from non-invasive support. Each PICU was classified as “small PICU” if it had fewer than six beds and as “large PICU” if it had six or more beds.

Data Collection

The following data were collected for each patient: age, gender, weight (kg), comorbidities, history of prematurity (<36 weeks of gestational age), admission to a small/large PICU, Pediatric Index of Mortality-3 (PIM-3) score¹⁷ at admission, inspired oxygen fraction and lactates at admission, use of HFNC, use of respiratory support (IMV only, NIV only or both) and its duration (in days), type of NIV interface used, use of high-frequency oscillatory ventilation (HFOV) and surfactant or neuromuscular blocking agents (NMB), type of microorganism isolated, presence of co-infection (two microorganisms isolated) or bacterial infection, PICU LOS in days, and in-hospital mortality.

Statistical Analysis

Descriptive data are presented as absolute numbers and percentages for categorical variables and as median and interquartile ranges (IQRs) for continuous ones. Chi-square and Kruskal-Wallis tests, as appropriate, were used to evaluate significant differences in sample characteristics in the two periods considered (Pre-COVID and Post-COVID). Univariable logistic regression models were used to assess the association between IMV and the main patient characteristics, including the admission period. Multilevel logistic regression models were then used to assess the potential association between IMV and the admission period, considering the hierarchical structure of the data and including a random effect to account for the variability between Centers. Results were reported as odds ratios (ORs) and 95% confidence intervals (CIs). Models’ goodness-of-fit was assessed with the Nagelkerke R2¹⁸ and the model performance was evaluated using the area under the receiver operating characteristics curve (AUROC). All statistical tests were two-sided and the level of statistical significance was set at 0.05. All analyses were conducted using R software (version 4.2.2)¹⁹.

In total, 715 patients with a diagnosis of bronchiolitis were admitted to the seven PICUs during the six seasons considered. The seasonal admission numbers and percentages were relatively consistent across all seasons except for the 2020-2021 COVID-19 season, during which there was a 10-fold reduction (Figure 1, panel A). Excluding 13 patients from the 2020-2021 season, the remaining 702 patients were grouped according to the period of admission, with 451 patients admitted to PICU in the Pre-COVID period and 251 in the Post-COVID period. The temporal distribution of admissions is depicted in Figure 1 (Figure 1, panel B).

Epidemiology and severity at admission

In the Post-COVID period, children admitted to the PICU were generally older (73 days [IQR 40-178] vs 55 days [IQR 33-125], p=0.039) and presented with a significantly worse PIM-3 score at admission (median 0.52% [IQR 0.38-0.67] vs 0.46% [IQR 0.37-0.62], p = 0.046). They also had more comorbidities (21% vs 13%, p = 0.039): chronic respiratory conditions (4.4%), congenital cardiac disease (3.7%), neurologic (3.1%) and syndromic disorders (3.1%) were the most common underlying conditions in both periods (Table 1). RSV cases made up 82% of the sample and did not change over time. Bacterial infection and coinfection with two microorganisms occurred in 7% and 15% of the overall sample, respectively. The PICU LOS was not significantly different in the two periods considered (median 4.0 days [IQR 3.0-7.0] vs 5 days [IQR 3.0-8.0], p = 0.373). Sepsis occurred in 1% of cases and overall mortality was 0.3%.

Table 1 Characteristics of the sample by period.

Characteristic	N	Pre COVID, N = 451	Post COVID, N = 251	Overall, N = 702	p-value
Large center		246 (55%)	127 (51%)	373 (53%)	0.300
Gender (M)	702	234 (52%)	147 (59%)	381 (54%)	0.144
Age (d), median(IQR)	702	55 (33, 125)	73 (40, 178)	62 (34, 142)	0.039
Ethnicity	702				0.236
Caucasian		343 (76%)	180 (72%)	523 (75%)
Arabic		55 (12%)	29 (12%)	84 (12%)
African		20 (4.4%)	17 (6.8%)	37 (5.3%)
Asiatic		22 (4.9%)	12 (4.8%)	34 (4.8%)
Hispanic		8 (1.8%)	6 (2.4%)	14 (2.0%)
Mixed ethnicity		3 (0.7%)	6 (2.4%)	9 (1.3%)
Afro-american		0 (0%)	1 (0.4%)	1 (0.1%)
Weight (kg), median(IQR)	700	4.60 (3.70, 6.30)	5.00 (4.00, 6.95)	4.80 (3.80, 6.50)	0.056
Comorbidities	702	59 (13%)	52 (21%)	111 (16%)	0.039
Ex-premature Respiratory Cardiac Neurologic Syndromic	702 702 702 702 702	61 (14%) 18 (4%) 17 (3.8%) 17 (3.8%) 13 (2.9%)	20 (8.0%) 13 (5.2%) 9 (3.6%) 5 (2.0%) 9 (3.6%)	81 (12%) 31 (4.4%) 26 (3.7%) 22 (3.1%) 22 (3.1%)	0.056 0.5 >0.9 0.2 0.6
PIM 3 score (%), median (IQR)	673	0.46 (0.37, 0.62)	0.52 (0.38, 0.67)	0.48 (0.37, 0.63)	0.046
FiO₂ at admission, median(IQR)	394	0.35 (0.30, 0.40)	0.40 (0.30, 0.45)	0.40 (0.30, 0.40)	0.052
Lactates at admission (mmol/L) median(IQR)	297	1.40 (1.00, 2.12)	1.38 (0.90, 2.20)	1.40 (1.00, 2.20)	0.325
RSV cases	538	287 (84%)	155 (79%)	442 (82%)	0.465
Sepsis	538	1 (0.2%)	3 (1%)	4 (1%)	0.187
Bacterial infection	538	16 (5%)	21 (11%)	37 (7%)	0.800
Co-infection (2 microorganisms isolated)	538	33 (10%)	48 (25%)	81 (15%)	0.600

Abbreviations: PIM, Pediatric Index of Mortality; FiO2, Fraction of Inspired Oxygen; RSV, respiratory syncytial virus. Column N reports the number of records available for each variable

Respiratory support and adjunctive therapies

Data on respiratory support are reported in Table 2. NIV was used in 70% of the patients and the overall rate of IMV (alone or before and/or after NIV) was 17%.

The use of HFNC and the need for NIV and its duration did not show significant changes between the two periods. Nasal prongs (44%) and nasal masks (22%) were the two interfaces more commonly used. During the Pre-COVID period, nasal prongs were more frequently used (50% vs 33%, p < 0.001), whereas nasal masks were more commonly used during the Post-COVID period (34% vs 16%, p < 0.001).

NIV modes also changed with time, with PSV or BiPAP being used more frequently in the Post-COVID era (55% vs 26%, p < 0.001), while CPAP and A-PCV were mainly used in the Pre-COVID period (p <0.05 in both cases). The rate of NIV failure and complications remained consistently lower than 10% and 1%, respectively.

The rate of IMV use was comparable between the two periods (15% vs 17%) and the extubation failure was very low overall (5%). The seasonal comparison of respiratory support (Figure 2) confirmed the same trends, with substantial stability in the need for NIV and IMV over time.

The use of HFOV, NMB and surfactant in intubated patients did not change in the two periods considered, nor did the rate of chest tube placement, which was always very low (0.9%).

Table 2 Comparison between respiratory support and adjunctive therapies in pre-COVID and post-COVID groups

Characteristic	N	Pre COVID, N = 451	Post COVID, N = 251	Overall, N = 702	p-value
HFNC	702	222 (49%)	134 (53%)	356 (51%)	0.360
NIV	702	320 (71%)	174 (69%)	494 (70%)	0.672
IMV	702	78 (17%)	38 (15%)	116 (17%)	0.626
NIV ventilation modes:
PSV/BiPAP	469	73 (26%)	101 (55%)	174 (37%)	<0.001
CPAP	469	126 (44%)	56 (31%)	182 (39%)	0.012
A-PCV	469	85 (30%)	26 (14%)	111 (24%)	<0.001
NIV interfaces:
Nasal mask	702	70 (16%)	86 (34%)	156 (22%)	<0.001
Facial mask	702	18 (4.0%)	3 (1.2%)	21 (3.0%)	0.086
Nasal prongs	702	224 (50%)	84 (33%)	308 (44%)	<0.001
Helmet	702	47 (10%)	33 (13%)	80 (11%)	0.360
Full face	702	33 (7.3%)	7 (2.8%)	40 (5.7%)	0.031
NIV complications	297	1 (0.9%)	1 (0.5%)	2 (0.7%)	0.999
NIV failure	534	31 (8.8%)	11 (6%)	42 (7.9%)	0.400
Chest tube positioning	702	4 (0.9%)	2 (0.8%)	6 (0.9%)	0.999
HFOV	116	8 (10%)	1 (2.6%)	9 (7.8%)	0.360
NMB	115	24 (31%)	13 (35%)	37 (32%)	0.672
Surfactant	117	10 (13%)	2 (5.1%)	12 (10%)	0.400
Extubation failure	116	4 (3%)	2 (2%)	6 (5%)	0.999
NIV length (d), median(IQR)	567	3.00 (2.00, 5.00)	3.00 (2.00, 4.00)	3.00 (2.00, 5.00)	0.360
IMV length (d), median(IQR)	115	11 (7, 14)	8 (5, 13)	10 (6, 14)	0.125
Antibiotic therapy	648	246 (56%)	138 (65%)	384 (59%)	0.086
PICU LOS (d), median(IQR)	702	5.0 (3.0, 8.0)	4.0 (3.0, 7.0)	4.0 (3.0, 7.0)	0.373
Mortality	702	0 (0%)	2 (0.8%)	2 (0.3%)	0.260

Abbreviations: HFNC, High Flow Nasal Cannula; NIV, Non-Invasive Ventilation; IMV, Invasive Mechanical Ventilation; PICU, Pediatric Intensive Care Unit; PSV, Pressure Support Ventilation; BiPAP, Bilevel Positive Airway Pressure; CPAP, Continuous Positive Airway Pressure; A-PCV, Assisted Pressure Controlled Ventilation; HFOV, High-Frequency Oscillatory Ventilation; NMB, Neuromuscular Blockade; LOS, Length of Stay

Comparison between Centers

The clinical characteristics of patients admitted for bronchiolitis according to the Center are reported in Table I-supplementary. There was huge heterogeneity across Centers in terms of both case mix and management practices. Some PICUs admitted predominantly younger children (median 50 days, IQR 34-104) compared to others (median 125 days, IQR 35-347). The PIM-3 ranged from 0.16% (IQR 0.12-0.51) to 0.53% (IQR 0.45-0.68). Using the average risk profile for intubation, the probability of a child with bronchiolitis being intubated varied from 4.7% to 50% across Units. Even NIV rates, modalities and interfaces used varied widely.

Risk factors for intubation

Univariable logistic regression models assessed the association between mechanical ventilation and patient characteristics (Table II - Supplementary). These results showed that patients’ age, ex-prematurity and the use of facial masks were associated with a lower risk of intubation outcome. Conversely, the use of nasal masks, nasal prongs and helmets were risk factors for a subsequent IMV.

Considering the hierarchical structure of the data, the best multilevel regression model for intubation outcome (Figure 3) included study period, patient’s age and severity (PIM-3), PSV/BiPAP/A-PCV ventilation modes and the size of PICU (large centers vs small ones). In this model, the Pre-COVID period represents a protective factor towards the risk of IMV (OR 0.38, 95% CI 0.16-0.89). Furthermore, RSV infection was associated with a higher risk of mechanical ventilation (OR 2.49, 95% CI 1.10-5.63). PIM3 score, age, the use of A-PCV/BiPAP/PSV modes and the size of the Center were not associated with the risk of intubation. For this model, the Intraclass Correlation Coefficient (ICC) of 0.22 indicates that approximately 22% of the total variance is attributable to differences between Centers and the variables entered into the model explained 65% of the variance of the outcome (Nagelkerke R2 = 0.645). Moreover, the model showed good accuracy, with an AUC of 0.76 (95% IC 0.70-0.81).

Our study shows the possible impact of the COVID-19 pandemic on the epidemiology of severe bronchiolitis in Italian PICUs, revealing a shift towards older children and an association with a higher risk of mortality. Nonetheless, the need for NIV and IMV remained unchanged over the last six years, although the Post-COVID period and RSV infection have been identified as risk factors for intubation.

The 2020-2021 winter season showed a tenfold reduction in PICU admissions due to bronchiolitis in Italy, consistent with findings from other countries around the world^9-11. Traditionally, RSV is thought to be spread by older siblings, typically aged 1-5 years²⁰. During the winter of 2020-2021 in Italy, as in other countries, this age group largely stayed at home due to social restrictions, further supporting their role in spreading the disease.

The following year saw a resurgence of PICU admissions for bronchiolitis in both hemispheres, with anticipated or even unusual rise^21-23 in non-usual months. It has been hypothesized that the lack of exposure to RSV during the first pandemic year may have led to an “immunity debt”, resulting in a more severe course of the disease²⁴ or affecting an older population^13,25 that had previously lacked exposure. In our cohort, we observed a peak in admissions earlier than the typical Italian bronchiolitis season during both the 2021/2022 and 2022/2023 seasons. This unexpected wave of hospitalizations forced hospitals to reorganize departments and allocate personnel to meet the increased demand²⁶. Although age and severity at admission were significantly higher in the Post-COVID period, this did not translate into longer hospitalizations or increased use of invasive or non-invasive respiratory support.

Recent studies have described a higher incidence of bronchiolitis in the post‐pandemic period, but findings on disease severity are conflicting. Some studies align with our results^13,27, while others, such as Brisca et al.²⁸, reported a substantial increase in the need for mechanical ventilation in previously healthy infants admitted for bronchiolitis during the 2022-2023 season. This discrepancy might be due to the setting of the studies; we included seven small/large PICUs, whereas Brisca’s single-center study was conducted in a large PICU that treats intubated patients from all over the region.

Our study confirmed that RSV infection is a significant risk factor for intubation, as reported by other authors²⁹, while coinfection and bacterial infection were not associated with the need for IMV. The number of patients with comorbidities has also significantly increased, reflecting a trend that has been observed for years in PICU admissions³⁰. In our sample, one-fifth of the patients had at least one comorbidity, with the respiratory system being the most affected. This result could be underestimated, as bronchiolitis may unmask underlying cardiac, pulmonary, or neurological condition, even in children previously considered healthy³¹.

An interesting finding from our study is that the COVID-19 pandemic appeared to influence the risk of intubation in the PICUs included. Although the percentage of intubated children remained stable across all study years, multivariable analysis revealed a strong effect of the time period on intubation. Specifically, the pre-pandemic period acted as a protective factor, even after adjusting for age and disease severity at admission. Additionally, there is a “Center effect” of about 22% on the variability of the observed outcome, indicating that some PICUs intubate more frequently in the Post-COVID era, despite similar clinical conditions. The reasons for this tendency are difficult to ascertain. As hospitals were reorganized to manage COVID-19 pandemic, changes in staffing, such as lower nurses-to-patient ratios or other organizational modifications could have contributed^32,33. A multicenter Italian study reported a dramatic increase in the use of HFNC in pediatric wards during the first Post-COVID season³⁴, which may have delayed PICU admissions. We attempted to understand the causes of this "Center effect" by categorizing PICUs into large and small Centers, hypothesizing that Centers of similar size might have similar organizational and staffing challenges, but no association between Center size and the risk of intubation has been found.

Significant differences between Centers were observed in ventilation practices, regardless of the pandemic. We observed a tenfold variation in the prevalence of intubation rate between PICUs. Schlapbach et al. reported similar results³⁵, noting that intubation rates were neither explained by the case mix nor by time trends and likely reflected underlying differences in unit-to-unit practice. These results highlighted a gap between scientific evidence and care practice, despite the continuous publication of guidelines on bronchiolitis management³⁶. Factors such as nurse availability and training, already described as crucial for successful NIV management, may play a role in these differences³⁷.

Another interesting aspect, probably unrelated to the pandemic, is the change in interface and NIV mode usage over time. We observed a shift from nasal prongs to nasal masks as the preferred interface for NIV. A 2015 study conducted in Italy³⁸ on the use of NIV in Acute Respiratory Failure (ARF) showed that most children – around 50% - received NIV through nasal prongs. Although the two scenarios are not directly comparable – we are considering only bronchiolitis cases, while Wolfler et al.³⁸ referred to any kind of ARF – our data suggest a change in interface preference over the years. A possible explanation is that concerns have been raised about asynchrony with nasal prongs³⁹ and associations between asynchrony and worse clinical outcomes have been described⁴⁰.

CPAP usage decreased from from 44% to 31% between the two study periods, while PSV/BiPAP usage doubled. Compared to the 2015 study³⁸, where CPAP was the most commonly used NIV strategy in infants, in our cohort PSV/BiPAP represents 55% of total non-invasive strategies in the Post-COVID period. This pattern is consistent with a European survey on NIV practices in children⁴¹, which showed that PSV was the most commonly used bilevel ventilation mode, as observed in our cohort. Interestingly, only a few studies have evaluated the performance of BiPAP in bronchiolitis management⁴².

This study has several limitations. It is based on a database analysis from a prospectively compiled national registry and missing data from available records may have influenced our analyses, although this bias has been controlled for by the study design. Furthermore, not all Italian PICUs could participate in the study, therefore, the potentially partial sample may limit the generalizability of the results obtained. Moreover, we didn’t collect the timing for intubation (eg before arriving in PICU) but we only knew whether the patient was intubated and the length of IMV. Finally, we have no data on blood gas analysis (apart from data present in the PIM-3 score), blood chemistry, or chest-X-ray results because they were not collected in the TIPNet database.

Despite these limitations, we can conclude that our hospitals had to cope with an unexpected peak of significantly more severe cases of bronchiolitis, which, however, did not require greater respiratory support. We can speculate that the higher risk of intubation in the Post-Covid period could be due to factors not only related to the clinical conditions of the patient, but also to the specific Center admitting the patient, with associated organizational, management and staffing challenges. In light of these results, it seems necessary to strengthen the network at a national level to standardize the levels of care in each PICU and to outline uniform intubation criteria in cases of severe bronchiolitis.

AUTHOR CONTRIBUTIONS

FI: conceptualization; investigation; writing – original draft; writing ‐ review and editing; validation; data curation. RC: investigation; statistical analysis; methodology; writing ‐ review and editing formal analysis; data curation. MD: writing – review and editing; formal analysis; data curation; EZ: investigation; data curation; formal analysis. VD: investigation; data curation; formal analysis; FS: investigation; data curation; formal analysis; CN: investigation; data curation; formal analysis. AT: investigation; data curation; formal analysis. AA: investigation; writing – review and editing; formal analysis; validation. EP: writing – review and editing; data curation; formal analysis. MCM: validation; data curation; writing – review and editing. GMB: investigation; data curation; formal analysis. AW: writing – review and editing; formal analysis; validation. DG: methodology; formal analysis; validation. AC: investigation; writing – original draft; writing ‐ review and editing; validation. All authors read and approved the final manuscript.

ACKNOWLEDGMENTS

There are not acknowledgments in our paper.

CONSENT TO PARTICIPATE AND ETHICS APPROVAL DECLARATIONS

The study used the electronic web-based national registry of the Italian Network of PICU Study Group (TIPNet) and all participants signed an informed consent to allow data to be inserted into the database. The registry’s use for nonprofit research purposes has been approved by the Ethics Committee of the Buzzi Hospital, the coordinating registry Center (approval number 269-052014 of 23/05/2014 and number 0047897 of 16/11/2020).

CONFLICT OF INTEREST

The authors declare that they have no competing interests

FUNDING

The research received no external funding.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CLINICAL TRIAL NUMBER

Not applicable

Meissner HC. Viral bronchiolitis in children. N Engl J Med. (2016) 374:62–72. doi: 10.1056/NEJMra1413456
Ralston SL, Lieberthal AS, Meissner HC, Alverson BK, Baley JE, Gadomski AM, et al. Clinical practice guideline: the diagnosis, management, and prevention of bronchiolitis. Pediatrics. (2014) 134:e1474–502.
Obando‐Pacheco P, Justicia‐Grande AJ, Rivero‐Calle I, et al. Respiratory syncytial virus seasonality: a global overview. J Infect Dis. (2018);217(9):1356‐1364. doi:10.1093/infdis/jiy056
Fleming DM, Pannell RS, Cross KW. Mortality in children from influenza and respiratory syncytial virus. J Epidemiol Commun Health. (2005) 59:586–90. doi: 10.1136/jech.2004.026450
Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. (2003) 289:179–86. doi: 10.1001/jama.289.2.179
Oymar K, Skjerven HO, Mikalsen IB. Acute bronchiolitis in infants, a review. Scand J Trauma Resusc Emerg Med. (2014) 22:23. doi: 10.1186/1757-7241-22-23
Oakley E, Borland M, Neutze J, Acworth J, Krieser D, Dalziel S, et al. Nasogastric hydration versus intravenous hydration for infants with bronchiolitis: a randomised trial. Lancet Respir Med. (2013) 1:113–20. doi: 10.1016/S2213-2600(12)70053-X
Lazner MR, Basu AP, Klonin H. Non-invasive ventilation for severe bronchiolitis: analysis and evidence. Pediatr Pulmonol. (2012) 47:909–16. doi: 10.1002/ppul.22513
Binns E, Koenraads M, Hristeva L, et al. Influenza and respiratory syncytial virus during the COVID‐19 pandemic: Time for a new paradigm? Ped Pulmonol. (2022) 57(1):38-42. doi:10.1002/ppul.25719
Angoulvant F, Ouldali N, Yang DD, Filser M, Gajdos V, Rybak A et al. COVID-19 pandemic: impact caused by school closure and national lockdown on pediatric visits and admissions for viral and non-viral infections, a time series analysis. Clin Infect Dis Jan (2020) 27;72(2):319-322. doi: 10.1093/cid/ciaa710.
Sperotto F, Wolfler A, Biban P, Montagnini L, Ocagli H, Comoretto R, et al. Italian Network of Pediatric Intensive Care Unit Research Group (TIPNet). Unplanned and medical admissions to pediatric intensive care units significantly decreased during COVID-19 outbreak in Northern Italy. Eur J Pediatr. (2021) Feb;180(2):643-648. doi: 10.1007/s00431-020-03832-z.
Remien KA, Amarin JZ, Horvat CM et al. Admissions for Bronchiolitis at Children’s Hospitals Before and During the COVID-19 Pandemic JAMA Network Open (2023) 6(10):e2339884. doi:10.1001/jamanetworkopen.2023.39884
Saravanos GL, Hu N, Homaira N, et al. RSV Epidemiology in Australia Before and During COVID-19. Pediatrics. (2022) 149(2):e2021053537. doi:10.1542/peds.2021-053537
PA Harris, R Taylor, R Thielke, J Payne, N Gonzalez, JG. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. (2009) Apr;42(2):377-81.
PA Harris, R Taylor, BL Minor, V Elliott, M Fernandez, L O’Neal, L McLeod, G Delacqua, F Delacqua, J Kirby, SN Duda, REDCap Consortium, The REDCap consortium: Building an international community of software partners, J Biomed Inform. (2019) May 9 doi: 10.1016/j.jbi.2019.103208
Von Elm E, Altman DG, Egger M et al The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet (2007) Oct 20; 370(9596):1453-7
Straney L, Clements A, Parslow RC, et al; ANZICS Paediatric Study Group and the Paediatric Intensive Care Audit Network: Paediatric index of mortality 3: An updated model for predict- ing mortality in pediatric intensive care*. Pediatr Crit Care Med (2013) 14:673–681

Nagelkerke NJD, A note on a general definition of the coefficient of determination, Biometrika, Volume 78, Issue 3, September 1991, Pages 691–692, doi.org/10.1093/biomet/78.3.691
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2022). https://www.R-project.org/

Hardelid Pia, Verfuerden Maximiliane, McMenamin Jim, Smyth Rosalind L, Gilbert Ruth. The contribution of child, family and health service factors to respiratory syncytial virus (RSV) hospital admissions in the first 3 years of life: birth cohort study in Scotland, 2009 to 2015. Euro Surveill. (2019) 24(1):pii=1800046. doi:10.2807/1560-7917.ES.2019.24.1.1800046
Yeoh DK, Foley DA, Minney-Smith CA, et al. Impact of Coronavirus Disease 2019 Public Health Measures on Detections of Influenza and Respiratory Syncytial Virus in Children During the 2020 Australian Winter. Clinical Infectious Diseases. (2021) 72(12):2199-2202. doi:10.1093/cid/ciaa1475
Camporesi A, Morello R, Ferro V, et al. Epidemiology, Microbiology and Severity of Bronchiolitis in the First Post-Lockdown Cold Season in Three Different Geographical Areas in Italy: A Prospective, Observational Study. Children. (2022) 9(4):491. doi:10.3390/children9040491
Fourgeaud J, Toubiana J, Chappuy H, et al. Impact of public health measures on the post-COVID-19 respiratory syncytial virus epidemics in France. Eur J Clin Microbiol Infect Dis. (2021) 40(11):2389-2395. doi:10.1007/s10096-021-04323-1
Hatter L, Eathorne A, Hills T, Bruce P, Beasley R. Respiratory syncytial virus: paying the immunity debt with interest. The Lancet Child & Adolescent Health. (2021) 5(12):e44-e45. doi:10.1016/S2352-4642(21)00333-3
Nygaard U, Hartling UB, Nielsen J, et al. Hospital admissions and need for mechanical ventilation in children with respiratory syncytial virus before and during the COVID-19 pandemic: a Danish nationwide cohort study. The Lancet Child & Adolescent Health. (2023) 7(3):171-179. doi:10.1016/S2352-4642(22)00371-6
Curatola A, Lazzareschi I, Bersani G, Covino M, Gatto A, Chiaretti A. Impact of COVID‐19 outbreak in acute bronchiolitis: lesson from a tertiary Italian emergency department. Pediatr Pulmonol. (2021) 56(8):2484‐2488. doi:10.1002/ppul.25442
Castagno E, Raffaldi I, Del Monte F, Garazzino S, Bondone C New epidemiological trends of respiratory syncytial virus bronchiolitis during COVID‑19 pandemic. World Journal of Pediatrics (2023) 19(5):502-504. doi: 10.1007/s12519-022-00623-4.
Brisca G, Strati MD, Buratti S et al The increase of bronchiolitis severity in the 2022–2023 season in an Italian tertiary children's hospital: An isolated phenomenon or a warning sign? Pediatric Pulmonology (2024) 59:1236–1245
Boccard V, Prevost B, Denamur S, Peulier-Maitre E, NathanN, Corvol H. Bronchiolitis: Increased severity in the post-COVID-19 era. Pediatr Pulmon (2024) doi: 10.1002/ppul.27172
O'BrienS, Nadel S, Almossawi O, Inwald DP. The Impact of Chronic Health Conditions on Length of Stay and Mortality in a General PICU Pediatr Crit Care Med (2017) Jan;18(1):1-7. doi:10.1097/PCC.0000000000000976
Ghazaly M and Nadel S Characteristics of children admitted to intensive care with acute bronchiolitis Eur J Pediatr. (2018) 177(6): 913–920 doi: 10.1007/s00431-018-3138-6
Kursumovic E, Cook TM, Vindrola-Padros C, et al. The impact of COVID‐19 on anaesthesia and critical care services in the UK: a serial service evaluation. Anaesthesia. (2021) Sep; 76(9): 1167–1175. doi: 10.1111/anae.15512
Hill K, McCabe C, Brenner M. Impact of adapting paediatric intensive care units for adult care during the COVID-19 pandemic: a scoping review. BMJ Open (2023) 28;13(8):e068174. doi: 10.1136/bmjopen-2022-068174
Ghirardo S, Cozzi G, Tonin G, et al. Increased use of high-flow nasal cannulas after the pandemic in bronchiolitis: a more severe disease or a changed physician’s attitude? Eur J Pediatr. (2022) 181(11):3931-3936. doi:10.1007/s00431-022-04601-w
Schlapbach LJ, Straney L, Gelbart B et al. Burden of disease and change in practice in critically ill infants with bronchiolitis. Eur Respir J (2017) 49:1601648 DOI:10.1183/13993003.01648-2016
Milesi C, Baudin F, Durand P, Emeriaud G, Essouri S, Pouyau R et al. Clinical practice guidelines: management of severe bronchiolitis in infants under 12 months old admitted to a pediatric critical care unit. Intensive Care Med (2023) 49:5–25. https://doi.org/10.1007/s00134-022-06918-4
Cassibba J, Fumagalli A, Alexandre A, Chauveau A, Milési C, Mortamet G. How crucial is the role of pediatric critical care nurses in the management of patients with noninvasive ventilatory support. Ped Pulmonol (2023) 58(9):2678-2680. doi:10.1002/ppul.26549
Wolfler A, Calderini E, Iannella E, et al. Evolution of Noninvasive Mechanical Ventilation Use: A Cohort Study Among Italian PICUs*. Pediatric Critical Care Medicine (2015) 16(5):418-427. doi:10.1097/PCC.0000000000000387
Studeny SR, Chatburn RL, Liu W, Hanna WJ. Asynchrony During Pediatric Noninvasive Ventilation With a Nasal Cannula Interface: A Lung Model Study. Respir Care (2021) 66(7):1087-1095. doi:10.4187/respcare.08130
Christophe M, Julien B, Gilles C. Improving synchrony in young infants supported by noninvasive ventilation for severe bronchiolitis: Yes, we can… so we should! Pediatric Pulmonol (2021) 56(2):319-322. doi:10.1002/ppul.25184
Mayordomo-Colunga J, Pons-Odena M, Medina A, Rey C, Milesi C, Kallio M et al. Non-invasive ventilation practices in children across Europe. Pediatric Pulmonol (2018) 1–8. DOI:10.1002/ppul.23988
Delacroix E, Millet A, Pin I, Mortamet G. Use of bilevel positive pressure ventilation in patients with bronchiolitis. Pediatric Pulmonol (2020) 55(11):3134-3138. doi:10.1002/ppul.25033

No competing interests reported.

Severe bronchiolitis before and after the COVID-19 pandemic: A retrospective database analysis by the Italian Network of PICU Study group (TIPNet)

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Abstract

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INTRODUCTION

METHODS

RESULTS

DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

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