Viral Etiology and Risk Factors of Bronchiolitis: A Cross-Sectional Study Analysis

INTRODUCTION

Bronchiolitis refers to the viral inflammation of the lower respiratory tract and often manifests as acute airway obstruction, particularly in infants under two years old, with peak incidence occurring between two and six months of age 1. Respiratory syncytial virus (RSV) is responsible for 60-80% of bronchiolitis cases and remains the leading cause of infant hospitalizations in developed countries. Although the incidence of bronchiolitis hospitalization rates declined between 2000 and 2016 however increase in proportion of children with complex chronic condition, hospitalization to children’s hospitalizations and likewise increased in hospital cost 2,3. Co-infections with viruses such as metapneumovirus or rhinovirus can complicate bronchiolitis, often resulting in more severe illness and necessitating more intensive treatment 4. The molecular diagnostic tools available for viral infections have been very effective but carry risks such as slow processing and risk of contamination. PCR has significantly contributed to co-infection detection and how the latter plays in disease progression 1.

The risk factors in children with bronchiolitis that lead to hospitalization and admission to the Pediatric Intensive Care Unit (PICU) are prematurity, age, initial oxygen saturation, and Respiratory Syncytial Virus (RSV) 5. Advances in molecular diagnostics, such as real-time multiplex PCR, have dramatically increased the ability to detect additional viral infections, like human metapneumovirus, in pediatric patients with acute bronchiolitis. While RSV has been commonly recognized, rhinovirus, coronavirus, parainfluenza virus, adenovirus, and human metapneumovirus also are identified relatively frequently 6,7.

Simultaneous viral infections develop in 10% to 40% of hospitalized children who have bronchiolitis, according to several studies 8,9. However, there is still much uncertainty as to whether simultaneous multi-respiratory viral infections correlate with more significant bronchiolitis. Previously conducted studies included only descriptive approaches and lack confounder adjustments. 10,11. Determination of whether increased viral co-infections are related to worse outcomes is important since early identification of high-risk patients could allow timely interventions in intensive care, which would reduce the rate of emergency response within pediatric wards and decrease the severity of the adverse outcomes. To this effect, we used a cross-sectional study with the aid of multivariable regression analysis for the determination of different viral co-infections in relation to severe bronchiolitis, using PICU admissions of previously healthy infants and including confounding variables.

Pediatric respiratory conditions affect not only child health outcomes but also the larger healthcare system 12. These conditions account for about 40% of PICU admissions and include severe pneumonia, acute respiratory distress syndrome, and respiratory failure, with a mortality rate from 7% to 15% 13,14. Survivors of such life-threatening illnesses can have long-lasting effects, including psychological, physical, and neurodevelopmental sequelae. For example, more than 25% of pediatric ARDS survivors developed new functional impairments that outlast six months after discharge 13.

The cost of treatment for respiratory conditions in the PICU is heavy 15. Critical care for children is resource-intensive, involving ventilation, specialty medications, and very skilled providers. These needs put a strain on healthcare systems and may result in shortages and higher costs 16,17. More than the financial aspect, the loss of a child to a respiratory condition is emotional, and many families, caregivers, and even medical staff struggle with long-term grief and mental health issues.

An important step to mitigating the negative impacts of respiratory diseases would be in technological advances that allow early detection and efficient management of respiratory ailments. All this will go a long way in making better outcomes in children, reducing strain on health systems, as well as reducing the emotional and psychological burden related to life-threatening conditions.

These include machine learning-based predictions and EHR data analysis, developed in the last few years and now very popular owing to their ease of deployment and implementation 18,19. A novel study of the pediatric intensive care unit data develops a very simple yet robust linear machine learning model based on 11 most informative features. It yields a ROC-AUC of 0.7531 that surpasses even the conventional approaches such as PRISM III (0.6895 ROC-AUC score)20.

Biochemical markers serve a critical function in machine-learning-based systems and models intending to predict high-risk illnesses among the pediatric population. For instance, studies have reported early plasma osmolality tests correlate with in-hospital mortality when LOWESS analysis was utilized. It was also found that there was a significant relationship between in-hospital mortality rates and plasma osmolality >290 mmol/L. No such relationship was seen when plasma osmolality was below this value 21.

Equally important is the understanding of whether multiple respiratory virus infections are responsible for poorer respiratory outcomes. Early intensive care of vulnerable populations in pediatric units can prevent reactive interventions in the wards, where they often pose negative consequences. One such case-control study with a multivariable regression analysis of viral co-infections and their association with severe bronchiolitis in otherwise healthy infants admitted to the hospital with acute bronchiolitis was conducted 22. This study provided evidence for the correlation between the occurrence of co-viral infections, and PICU admission risk considering ICU admission a proxy of disease severity while adjusting covariates.

While there has been extensive research into ICU mortality in the context of COVID-19, studies utilizing machine-learning techniques for pediatric populations are scarce and limited by less access to public datasets. However, the release of the new PICU dataset enables the review of mortality prediction in various disease categories. Within this study, specific risk factors, biomarkers, and more complex modeling methods enhance the ability to predict pediatric patient mortality better when suffering from respiratory diseases 23.

Materials and methods

Study Design and Setting

This is a cross-sectional study that was done in a tertiary-care pediatric hospital. It was a time-bound study in which all pediatric patients who were admitted into the hospital within 12 months were included. Hence, formal sample size calculation was not required because entire eligible population within that specified period constituted the sample size. Medical records of all those patients were reviewed to analyze patient characteristics and clinical outcomes as factors that may influence admission to PICUs. Whole data was double checked by 2 researchers to reduce bias. The study was conducted in a hospital equipped with both a PICU and a general pediatric ward. All procedures were carried out ethically and the study protocol was approved by the hospital's IRB with the reference number RC24.09.81. The approval from the IRB ensured ethical compliance, particularly in terms of confidentiality of the patients and consent. Informed consent was obtained from all pediatric patients from their parents or guardians which ensured respect to their rights and privacy while conducting the study.

Inclusion Criteria

Inclusion criteria were devised with great care to enroll pediatric patients with confirmed viral infections for evaluation and treatment in the hospital. The participants ranged from newborns (0 months) to 5 years old (60 months). The only requirement for inclusion was that the patient should have a confirmed viral infection, which was diagnosed by viral PCR testing. This sampling approach allowed for the collection of a large pediatric caseload from neonates to older children, thus ensuring that the sample was representative and large enough.

The reliance on the viral PCR test ensured specificity and reliability in the identification of children with documented viral infections, which would thereby guarantee the results obtained in the study. Eligible patients presented with clinical signs characteristic of viral infections, such as rhinorrhea or due to pathogens like adenovirus, Boca virus, coronavirus, human metapneumovirus, influenza, parainfluenza, RSV, and many others. The other criteria were radiographic findings compatible with bronchiolitis, like bilateral hyperinflation, peribronchial cuffing, or areas of patchy consolidation. All admissions were supported by detailed hospital records, which were reviewed for data verification and evaluation. This process of rigorous inclusion ensured a robust and accurate dataset for the study.

Exclusion Criteria

Confounding variables were reduced by defining a well-structured exclusion criterion. The study’s focus was more on viral infections, and among the exclusion criteria were patients due to non-viral infections which will help to keep the focus only on the outcomes attributed to viral pathogens. Patients having incomplete medical records were also left out as their data may compromise validity and reliability if not complete.

The exclusion criteria also included children suffering from chronic diseases or immune-compromised conditions, such as congenital heart disease or cancer, which is known to strongly influence the progression of the disease and its course in managing viral infections.

Patients referred from other hospitals, who were first stabilized there before transfer, were excluded from this study, because their treatment course may have differed from the management protocol at the study hospital and thus might not be similar to the present research.

Exposure of Interest: Viral Infection

The purpose of this study was to ascertain the impact of viral infections and the isolation of viral pathogens in pediatric hospital patients. Viral PCR testing, which is highly sensitive and accurate in detecting these infections, was used to determine the primary exposure. The study took into account common respiratory viruses, including adenovirus, bocavirus, coronavirus, human metapneumovirus, influenza, parainfluenza, RSV, and rhinovirus/enterovirus. These viruses were chosen because they frequently result in respiratory infections in children that are serious enough to require critical care. The study looked into the relationship between specific viruses and the seriousness of clinical outcomes, like the need for PICU admission. The researchers' goal in examining these correlations was to ascertain whether certain viral infections are linked to more serious situations that call for critical care.

Outcome Measures

Clinical and demographic parameters and characteristics of the patients that helped to decide treatment plan were recorded. Fever, cough, rhinorrhea, respiratory distress, cyanosis, tachypnea, lethargy, and poor feeding are the most common signs of viral infections. Poor feeding was monitored closely to ascertain whether its presence was in proportion with infection severity or in relation to the need for intensive care. Other biomarkers were also recorded, such as C-reactive protein, white blood cell count, absolute neutrophil count, sodium levels, and SpO2.

The length of hospitalization was the study's secondary emphasis because extended hospital stays are frequently associated with worse sickness, increased risk of complications, or even challenging recovery circumstances. The relationship between the kind of viral infection and duration of hospital was studies in the analysis. RSV resulted in longer hospital stays due to more severe symptoms and respiratory distress, which necessitated lengthier medical interventions. The research project monitored outcomes that would best predict the need for intensive care and extended hospital stays in an attempt to identify which children could need such care.

Statistical Analysis

SPSS version 25 was used for the study's statistical analysis. Descriptive statistics data are shown as follows: continuous variables are shown as mean ± SD or median with the interquartile range, and categorical variables are shown as numbers with percentages. The Mann-Whitney U test for continuous variables and Pearson's Chi-squared test for categorical variables were used to compare the PICU and ward groups. Statistical significance was defined as a p-value of less than 0.05. To find independent predictors of PICU admission while taking any confounding factors into consideration, logistic regression analysis was used. Additional information was obtained by calculating odds ratios with 95% confidence intervals.

Results

Demographic characteristics of patients in the PICU and ward

Table 1 compares the demographics of two patient groups: PICU and ward patients, by age group, gender, and BMI category. There was no statistically significant variation in the distribution of gender between the two groups (p = 0.755). With a higher percentage of newborns in the PICU group (76% vs. 56%), the age distribution suggested a trend toward significance, although this was not statistically significant (p = 0.069). (removed obesity)

Table 1: Demographic characteristics of patients in the PICU and ward
Demographics
Characteristic	PICU (N = 42)	Ward (N = 192)	p-value
Gender			0.755
Female	17 (40%)	70 (36%)
Male	25 (60%)	122 (64%)
Age Group			0.069
Infant (0-12 months)	32 (76%)	107 (56%)
Toddler (13-36 months)	8 (19%)	50 (26%)
Preschooler (37-60 months)	2 (4.8%)	34 (18%)
BMI Category
Underweight	4 (9.5%)	3 (1.6%)
Healthy Weight	33 (79%)	153 (80%)	0.041
Overweight	2 (4.8%)	14 (7.3%)
Obesity	3 (7.1%)	22 (11%)
Severe Obesity	0 (0%)	0 (0%)

Distribution of viral PCR results in the Ward and PICU

The distribution of PCR viral findings in the PICU (N = 42) and Ward (N = 192) is displayed in Table 2, which demonstrates no statistically significant difference between the groups (p = 0.2). RSV and rhinovirus/enterovirus were more common in the PICU, while influenza was most common in the Ward (97%). The lack of significant differences was further supported by the fact that other viruses had varying distributions among the groups but failed to reach statistical significance.

Table 2: Comparison of viral PCR test results between ward and PICU admissions
Characteristic	Ward	PICU	p-value2
N = 192a	N = 42b
Viral PCR			0.2
Adenovirus	4 (67%)	2 (33%)
Boca virus	1 (100%)	0 (0%)
Coronavirus	14 (82%)	3 (18%)
Human MetapneuMovirus	6 (75%)	2 (25%)
Influenza	33 (97%)	1 (2.9%)
Parainfluenza	16 (84%)	3 (16%)
PCR Negative	31 (89%)	4 (11%)
Respiratory Syncytial Viruses (RSV)	52 (79%)	14 (21%)
Rhinovirus/enterovirus	35 (73%)	13 (27%)

Additionally, a boxplot of LOS for several viral infections is shown in Figure 1. Although variability in the median length of stay was observed but differences were not statistically significant (p = 0.21). However, it was found that coronaviruses and RSV had the widest variability with a broad interquartile range. Moreover, several high outliers in the figure showed that some patients had prolonged hospital admissions (Figure 1).

Figure 1: Median hospital stay by viral disease (PCR categories)

a n (%); b Pearson’s Chi-squared test

Biomarker levels in PICU vs. ward patient

Table 3 shows the median values and interquartile ranges (Q1, Q3) for the biomarkers of PICU and ward patients. With p-values of 0.822, 0.928, and 0.068 for CRP, ANC, and WBCs, respectively, there was no discernible difference between the groups. However, the Ward group's length of stay (LOS) was significantly shorter than that of the PICU group, and there was a demonstrated correlation between LOS and the severity of the disease (p < 0.001) which is understandable and self-explanatory.

Table 3: Comparison of biomarker levels between PICU and ward admissions
Biomarker	PICU (Median [Q1, Q3])	Ward (Median [Q1, Q3])	p-value
CRP (mg/L)	10 (4, 23)	9 (3, 25)	0.822
ANC (cells/μL)	4,045 (2,100, 7,300)	3,800 (2,120, 6,455)	0.928
WBC (cells/μL)	10,750 (8,600, 14,830)	9,610 (6,740, 12,710)	0.068
LOS (Days)	3.5 (2.0, 6.0)	2.0 (1.0, 3.0)	<0.001
Sodium (mmol/L)	139 (138, 140)	138 (137, 139.5)	0.015[AM7]
SPO2 (%)	90.0 (88.0, 95.0)	96.0 (94.0, 96.0)	<0.001

Comparison of symptom prevalence between PICU and ward patients

Table 4 shows the differences in the prevalence of symptoms between children admitted to the ward (N = 1921) and those in the PICU (N = 421), along with the corresponding p-values for statistical significance. With p-values greater than 0.05 for temperature, rhinorrhea, cough, eating, lethargy, rhonchi, and crepitus, there was no discernible difference in symptoms between the PICU and the ward. Both tachypnea and respiratory distress were slightly no significant at p = 0.07 and p = 0.06, respectively. However, with a highly significant p-value of <0.001, cyanosis is significantly more common in the PICU (40%) than in the ward (9.4%), suggesting a meaningful difference between the groups.

Table 4: Comparison of symptom prevalence in PICU and ward patients
Symptom	PICU (N = 42)	Ward (N = 192)	p-value
Fever	28 (67%)	140 (73%)	0.531
Rhinorrhea	19 (45%)	82 (43%)	0.898
Cough	38 (90%)	163 (85%)	0.486
Respiratory Distress	34 (81%)	125 (65%)	0.07
Poor Feeding	20 (48%)	83 (43%)	0.728
Lethargy	13 (31%)	45 (23%)	0.41
Tachypnea	37 (88%)	140 (73%)	0.06
Cyanosis	17 (40%)	18 (9.4%)	<0.001
Ronchi	36 (86%)	159 (83%)	0.819
Crepitus	21 (50%)	78 (41%)	0.346

Miscellaneous conditions in PICU and ward patients

In the PICU (N = 421) and the ward (N = 1921), the occurrence of various conditions is compared in Table 5. With an incidence of 4.8% compared to 2.6% in the ward (p-value of 0.026), asthma is substantially more common in the PICU. Atopy, cow milk allergy, cerebral palsy (CP), diaphragmatic hernia, Down syndrome, immunodeficiency, laryngomalacia, and SMA are among the additional conditions that are either missing or sparse in the PICU, and there is no discernible difference between the two groups. While other illnesses seem less common or clinically significant in this context, these data point to a higher burden of respiratory conditions, especially asthma, among critically ill patients in the PICU.

Table 5: Prevalence of miscellaneous conditions in PICU and ward patients
Miscellaneous Condition	PICU (N = 42)	Ward (N = 192)	p-value
Asthma	2 (4.8%)	5 (2.6%)	0.026
Atopy	1 (2.4%)	0 (0%)
Cow Milk Allergy	0 (0%)	1 (0.5%)
CP	1 (2.4%)	1 (0.5%)
Diaphragmatic Hernia	0 (0%)	1 (0.5%)
Down Syndrome	1 (2.4%)	0 (0%)
Immunodeficiency	1 (2.4%)	0 (0%)
Laryngomalacia	0 (0%)	1 (0.5%)
SMA	1 (2.4%)	0 (0%)

CP: cerebral palsy; SMA: spinal muscular atrophy

(Removed table 6 and Figure 2)

Discussion

This is a case-control study with strong epidemiological and clinical relevance. It studies the co-respiratory viral infection patterns within the same hospital population over almost a year, hence giving a much-needed insight into the prevalence and implications of these infections. Moreover, it establishes and quantifies the independent association of multiple viral infections with severe outcomes, such as PICU admission, by employing a multivariable logistic regression model that adjusts for potential confounding factors.

The findings were quite noteworthy and presented clinicians with practical means by which to identify the high-risk groups that seem more vulnerable to severe bronchiolitis with the potential risk of requiring inpatient care through PICU and general ward admittance. According to the study, high vigilance concerning these individuals should lead to an appropriate time-course intervention to improve morbidity and mortality related to viral infections.

This study compared clinical features, biomarkers, and outcomes in children admitted to the Pediatric Intensive Care Unit (PICU) with those in children admitted to general pediatric wards. Findings are reported, indicating differences in viral pathogens, demographics, symptom prevalence, and biomarkers between groups and explaining factors that lead to PICU admissions among children with viral infections.

In line with past studies, Respiratory Syncytial Virus was found to be the most frequent virus in patients in both the PICU and the general wards, accounting for most severe respiratory diseases in young children and contributing significantly to PICU admissions 24. The second most commonly isolated pathogen was Rhinovirus at 27% in PICU patients and 12% in the general ward. These findings support other studies showing rhinovirus as one of the predominant pathogens in pediatric respiratory infections 25. Conversely, the frequency of other viruses like adenovirus, Boca virus, and coronavirus were not significantly different between the groups. This might represent regional variations or seasonal patterns of viral infections reported in other cohorts26.

Demographic analysis revealed no marked difference in the gender distribution. However, in this study, there was a trend toward increased numbers of infants in the PICU, suggesting an increased risk for severe disease among young children. The observed difference in categories of BMI by site was also statistically significant because more patients being underweight comprised the PICU, and significantly fewer patients fell into the obesity category than were in the ward group. This outcome was in keeping with other research findings, indicating that malnutrition and low BMI were related to worse disease severity and prognosis for children with respiratory infections27. In contrast, the high rate of obesity in the medical ward could translate to obese children having fewer respiratory failure requiring critical care.

There were no significant differences between the two groups for CRP, ANC, or WBC counts. Sodium levels, although statistically significant, were not clinically significant. These markers may not be sensitive enough to predict the need for intensive care in viral respiratory infections. This is consistent with previous studies 28. but is inconsistent with other studies that associate low sodium levels with dehydration or more severe infections, especially in critically ill children 29.

The variation was significant with the LOS as it was longer in the case of the PICU group mainly due to the severity of their illness. The current study is in concordance with previous literature indicating that a more prolonged LOS generally represents an unfavorable course of disease rather than the type of viral infection or biochemical marker used for diagnosis 30,31,32. SpO2 was also much lower in the PICU patients; they suffer from much more serious hypoxia and respiratory distress requiring more assistance with breathing. These observations, all taken together, suggest that severity of illness, rather than a specific etiological virus or traditional biochemical marker of disease severity, is an important determinant for hospital stay durations as well as the need for type and intensity of hospital care.

More obvious is the fact that, in comparison to earlier research, the PICU group had a significantly higher incidence of asthma. Asthma is a significant risk factor for severe respiratory conditions and PICU admission during viral infection episodes, according to sufficient data 33. There were no notable differences between the groups for other disorders such Down syndrome, cerebral palsy, atopy, or cow's milk allergy. This implies that while comorbidities should be determined at the time of PICU admission, especially for respiratory disorders like asthma, other comorbidities would have less of an impact on the severity of illness in these patients. For example, in accordance with the body of existing literature 34.

The results of the current study correlate very well with the literature currently available. Among all viral pathogens identified to cause severe respiratory diseases among children, who then become a hospital and even an intensive care requirement, the most common virus identified is RSV35. Other forms of literature that have extensively documented severe outcomes as being attributed to other forms of respiratory viruses such as rhinovirus, influenza, and parainfluenza, continue to point towards these biomarkers being reliable predictors of severe outcomes 36. Low oxygen saturation, cyanosis and LOS, have repeatedly been shown to be good predictors of severe outcomes. Biomarkers are very important in the management of pediatric respiratory infections37. This study’s cross‑sectional design limits the ability to draw causal inferences between variables and merely reflects associations at a single point in time; future prospective studies are needed to better explore potential causal relationships and additional risk factors. Additionally, our exclusion of children with certain chronic conditions (e.g., congenital heart disease) may restrict the generalizability of the findings to the wider PICU population, as these conditions represent a substantial portion of admissions. Consequently, the results should be interpreted with caution, and future research including these patient subgroups is encouraged.

Recommendations:

1. Prospective multicenter studies would better establish relationships between clinical predictors and PICU admission as well as enhance the generalizability of the study findings by including diverse population from multiple centers.

1. Inclusion of chronic disease patients perhaps provides a more comprehensive understanding of variability in disease severity.

1. Triage protocols can be adapted by healthcare facilities to early recognition of prevalence of risk factors and can reduce delays in providing immediate care.

Conclusion

Comparing PICU admissions of children with those managed in general wards, this study carries immense clinical and epidemiological value based on differences in characteristics, viral causes, and outcomes. RSV and rhinovirus are common causes of severe respiratory disease in children and were especially common among cases admitted to the PICU. Among these predictor factors for PICU admission are being younger, cyanosis, and decreased oxygen saturation. These findings further emphasize the crucial role of timely identification of any risk factors and pre-existing medical conditions such as asthma in exposed children. Optimistic outcomes along with reduced hospital admissions to PICU and an avoidance of severe complications will be attained only through vigorous, focused clinical initiatives and timely intervention. Future research into larger, more diverse populations will be necessary to validate these findings, refine predictive models, and inform clinical practice in further optimizing care for pediatric viral respiratory infections.

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