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\begin{document}

\title{Pulmonary function in children post -SARS-CoV-2 infection: a systematic
review and meta-analysis}



\author[1]{Nasrin Moazzen}%
\author[2]{Elham  Bakhtiari }%
\author[3]{Sara Ghahremani}%
\affil[1]{Mashhad University of Medical Sciences Ghaem Hospital}%
\affil[2]{Mashhad University of Medical Sciences Faculty of Medicine}%
\affil[3]{Tehran University of Medical Sciences}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
\textbf{Objective:} There are some concerns regarding long-term
complications of COVID-19 in children. A systematic review and
meta-analysis was performed evaluating the respiratory symptoms and
pulmonary function, post-SARS-CoV-2 infection. \textbf{Methods:} A
systematic search was performed in databases up to 30 December 2022
\textbf{.} Studies evaluating respiratory symptoms and pulmonary
function after COVID-19 infection in children were selected. The major
outcomes were frequency of respiratory symptoms and mean of spirometry
parameters. Pooled mean with 95\% confidence intervals (CIs) was
calculated. \textbf{Results:} A total of 6 articles with 272 patients
were included in meta-analysis. Dyspnea and cough were the most common
symptoms. The meta-mean of forced expiratory volume (FEV1) and forced
vital capacity (FVC) was 101.72\%, 95\% CI= (98.72, 104.73) and 101.31
\%, 95\% CI= (95.44, 107.18) respectively. The meta-mean of FEV1/FVC and
Forced~expiratory flow~at 25 and 75\% was 96.16 \%, 95\% CI= (90.47,
101.85) and 105.05 \%, 95\% CI= (101.74, 108.36) respectively. The
meta-mean of diffusing capacity for carbon monoxide was 105.30\%,
95\%CI= (88.12, 122.49). There was no significant difference in
spirometry parameters before and after bronchodilator inhalation.
\textbf{Conclusions:} Despite of some clinical respiratory symptoms,
meta-results showed no abnormality in pulmonary function in follow-up of
children with SARS-CoV-2 infection. Disease severity and asthma
background had not confounded this outcome.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The coronavirus disease 19 (COVID 19) which spread worldwide during few
years ago, caused great challenge in health, economy, social and
environment (1). This mysterious virus represents with very
heterogeneous organ involvements. The most prevalent presentations are
fever, cough and anosmia (2). In spite of most early reports considered
mild infection in children, gradually increasing concerns was developed
about long-term complications of disease (3, 4). Early in 2020, there
were several reports of a disease mimicking Kawasaki in children, which
present with fever and muco-cutaneous as well as multi-organ
involvement. Majority of patients with this new emerging syndrome,
represent with cardiac involvement and require intensive care unit (ICU)
admission (5). After that, many data were released about severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children
with more morbidity and mortality.

Autopsy examination in patients who dead due to COVID 19 has shown
different grades of fibro proliferative procedures and diffuse alveolar
injury. Therefore, it seems that COVID 19 survivors might be at risk for
respiratory sequel and persistent impaired pulmonary function tests.
According to expected pathophysiology, restrictive pattern is more
probable. Available data has revealed that abnormal diffusing capacity
for carbon monoxide (DLCO), which is relevant with severity of acute
illness, is the most prevalent result in pulmonary function~test (PFT)
of post-acute patients. While ground grass opacities is most common in
high-resolution computed tomography (HRCT) of them (6, 7). A recent
meta-analysis has shown that 77\% of infected patients with SARS-Cov2
had abnormal lung computed tomography (CT) in acute phase (2). Several
studies reported persistent post-COVID 19 respiratory symptoms. In one
study, 25-42\% of patients reported moderate to severe dyspnea, 4-8
weeks after hospital discharge (8).

Because of continues growth and development of respiratory system in
pediatrics especially during infancy and early childhood, they are more
vulnerable to pulmonary sequel (9).

Pulmonary function test is one of methods, which can evaluate long-term
pulmonary sequel in survived COVID 19 patients. This is safe, objective
and more favorable than imaging. This tool is useful in assessment of
degree of airway restriction and obstruction (9).

To the best of our knowledge, there is not any systematic review and
meta-analysis regarding long-term respiratory outcome in pediatric
population post-SARS-CoV-2 infection. Since future of pediatric
survivors of COVID 19 is uncertain, we have conducted present systematic
review and meta-analysis to summarize available evidence and define the
gapes, which may initiate future studies.

\textbf{Methods} :

Literature Search strategy and Study selection

Relevant databases including Medline, Web of sciences, Embase and Scopus
were searched comprehensively to assess literature up to 30 December
2022 in English language. The search terms were including ``COVID-19 or
corona virus 2019'' or ``SARS-CoV-2'' AND (''pulmonary function'' OR
''pulmonary diseases'' OR ``lung problem'') AND ''children'' and
''pediatrics''. They were used separately or/and in combinations to
obtain the eligible documents. The references of eligible articles were
searched manually to find additional relevant papers. This study was
conducted in adherence to the Preferred Reporting Items for Systematic
Reviews and Meta-Analysis (PRISMA) statement (10). The current study was
based on published articles. Therefore, consent form was not needed. Two
researchers (EB and NM) independently reviewed titles and abstracts of
all studies to identify relevant articles. Articles were including
according to following criteria: (1) English language, longitudinal or
cross-sectional studies evaluating the pulmonary function and clinical
symptoms of children after COVID 19 infection (2) spirometry parameters
have been measured (3) the study population were pediatrics. Case
reports, case series, letter to editors, unpublished reports,
duplications and laboratory studies were excluded. In duplicate
articles, the recent and more informative one was included. Included
articles were assessed using Newcastle-Ottawa Quality Assessment Scale
(NOS) for cohort and cross-sectional studies (11) if a study obtained
five stars it was considered as good quality.

Data extraction

Two independent researchers (EB and NM) extracted data from eligible
studies. A data collection sheet was used for data extraction. A third
author (SGh) judged disagreement between EB and NM. Data from each study
were including: author's name, year of publication, county of study,
study design, number of studied patients, age range of children,
interval between COVID 19 and pulmonary function assessment, mean\selectlanguage{ngerman}±
standard deviation (SD) of spirometry parameters and frequency of
respiratory symptoms.

Statistical analysis

The meta-mean with 95\% confidence interval (CI) was calculated based on
the mean and SD of spirometry parameters. If a study only reported the
median, range and/or inter-quartile range (IQR); mean and SD were
estimated, according to Hozo et al (12). The Cochran Q statistic and
inconsistency index (I\textsuperscript{2}) were used to assess the
heterogeneity among studies. If I\textsuperscript{2} was more than 50\%,
and p value was lesser than 0.05, heterogeneity was considered
signi\selectlanguage{english}ficant. The random effect model was used in significant
heterogeneity, whereas the fixed effect model was applied for
non-significant heterogeneity. To assess the stability of the results,
sequential omitting of individual studies in the meta-analysis was
performed using sensitivity analysis. Subgroups were analyzed based on
disease severity. Probable confounders were verified using meta
regression. The standardized mean difference (SMD) was calculated in
studies, which measured spirometry parameters twice. Publication bias
was assessed using Egger's linear regression test. Agreement between
authors in data selection and extraction was assessed using Cohen's
kappa statistic. Statistical analysis was performed using Comprehensive
Meta-Analysis (CMA) computer program (Biostat, Englewood, NJ). A p value
less than 0.05 was considered as statistically significant.

\textbf{Results:}

\textbf{Literature search}

A diagram of study selection is presented in figure 1. In primary
search, 5834 papers were obtained evaluating the respiratory function
and symptoms subsequent to COVID-19 in children. During screening
process, some studies were excluded because they were review article or
case series. Some studies were excluded because they studied adults, or
evaluated respiratory function and symptoms during COVID-19 infection.
Some studies were excluded because of duplication. Finally, seven
articles including 345 patients were enrolled in the present review
(13-18). Eligible studies were including 5 cross sectionals (13, 14, 16,
17, 19) and 2 longitudinal studies (15, 18). In one study the z score of
spirometry parameters were reported. It only included in systematic
review (16). Details of eligible studies are presented in table 1.
Cohen's kappa statistic for interrater agreement in data selection and
extraction was 0.98, p value\textless{}0.0001.

\textbf{Systematic review of respiratory symptoms and spirometry
parameters}

Seven studies from Italy, Turkey, Germany and USA evaluated the clinical
symptoms and respiratory functions in children post COVID-19 infection.
Sample size ranged from 16 to 82 participants. The age of patients was
varying from 7 to 15 years. Respiratory function was evaluated at least
6 weeks after infection. The most common clinical symptoms were cough,
dyspnea, exercise intolerance and fatigue. In two studies no respiratory
symptoms were reported (14, 19). Regarding spirometry parameters, four
studies reported that COVID-19 did not affect respiratory functions
(13-16) and three studies reported that it could affect pulmonary
function (17-19). In four studies patients with a history of asthma were
excluded (13, 14, 17, 19). In two studies spirometry parameters were
measured before and after bronchodilator inhalation (13, 18). In five
studies all types of COVID-19 were included (15, 17-19) and in two
studies only mild or asymptomatic patients were enrolled (13, 14). Among
7 eligible studies, one study reported the z score of spirometry
parameters (16). So it not included to meta-analysis.

Meta-analysis of spirometry parameters

The spirometry parameters were reported in seven articles (13-18). In
Knoke study the z score of parameters were reported (16). So, it does
not include to meta-analysis. In two studies, spirometry parameters were
reported pre- and post-bronchodilator inhalation (13, 18). In five
studies, spirometry parameters were reported without bronchodilator
inhalation (14-17, 19). The FEV1, FVC and FEV1/FVC were reported in 345
patients. According to random effect modeling in meta-analysis, the mean
of FEV1 was 101.72\%, 95\% CI= (98.72, 104.73),
I\textsuperscript{2}=81.7. Forest plot is shown in figure2.

The pooled mean of FVC was 101.31 \%, 95\% CI= (95.44, 107.18),
I\textsuperscript{2}=93.38. Forest plot is shown in figure3. The pooled
mean of FEV1/FVC was 96.16 \%, 95\% CI= (90.47, 101.85),
I\textsuperscript{2}=96.86. Forest plot is shown in figure 4. The pooled
mean of FEF25-75 was reported in four studies (14, 15, 18, 19). The
pooled mean of FEF25-75 was 105.05 \%, 95\% CI= (101.74, 108.36),
I\textsuperscript{2}=26.93. Forest plot is shown in figure5. The mean of
DLCO was reported in four studies (13, 15, 17, 18). The pooled mean of
DLCO was 105.30 \%, 95\% CI= (88.12, 122.49),
I\textsuperscript{2}=98.10. Forest plot is shown in figure 6. In two
studies, FEV1, FVC and FEV1/FVC were reported before and after
bronchodilator inhalation (13, 18). Meta- analysis confirmed that there
was no significant difference in spirometry parameters before and after
bronchodilator inhalation. The SMD for FEV1 was -0.21, 95\% CI= (-0.65,
0.23), p value= 0.35, I\textsuperscript{2}=38.3. The SMD for FVC1 was
-0.07, 95\% CI= (-0.35, 0.21), p value= 0.14, I\textsuperscript{2}=zero.
The SMD for FEV1/FVC was -0.29, 95\% CI= (-0.58, 0.01), p value= 0.07,
I\textsuperscript{2}= zero.

\textbf{Heterogeneity analysis}

Subgroup analysis according to severity of disease and sensitivity
analysis were carried out evaluating the possible source of
heterogeneity. In two studies (13, 14) patients with asymptomatic
COVID-19 were studied and in four studies all types of disease
(asymptomatic and symptomatic) were studied (15, 17-19). According to
meta-analysis the pooled mean of FEV1 in asymptomatic subgroup was
98.59\%, 95\%CI= (96.96, 100.23), I\textsuperscript{2}=zero. The pooled
mean of FEV1 in symptomatic subgroup was 103.91\%, 95\%CI= (101.08,
106.74), I\textsuperscript{2}=53.27. The pooled mean of FVC in
asymptomatic subgroup was 95.17\%, 95\%CI= (92.80, 97.54),
I\textsuperscript{2}=zero. The pooled mean of FVC in symptomatic
subgroup was 104.62\%, 95\%CI= (98.00, 111.24),
I\textsuperscript{2}=91.17. The pooled mean of FEV1/FVC in asymptomatic
subgroup was 98.28, 95\%CI= (86.25, 110.31), I\textsuperscript{2}=zero.
The pooled mean of FEV1/FVC in symptomatic subgroup was 94.99\%, CI=
(88.19, 101.79), I\textsuperscript{2}=95.65.

Meta regression showed neither disease severity nor asthma comorbidity
had a significant effect on pooled mean of FEV1 (p value= 0.35 and 0.21
respectively) and FVC (p value= 0.80 and 0.51 respectively). In
sensitivity analysis, the effect of each study on the pooled mean was
assessed. There was no major deviation from pooled mean by omitting
studies in FEV1, FVC FEV1/FVC, FEF25-75 and DLCO outcomes, indicating
the stability and robustness of the results (Data not shown).

\textbf{Publication bias}

Egger's regression asymmetry test was used to explore the probable
publication bias for FEV1, FVC and DLCO parameters. The Egger's test
result provided no significant bias across the included studies (p
value= 0.39, 0.69 and 0.53 respectively).

\textbf{Discussion}

After widespread distribution of COVID 19 in pediatric patients, one of
the most important issues was long-lasting complications in our next
generation. Current evidences have mentioned increased risk of diabetes
mellitus type I and severe diabetes ketoacidosis in children infected by
SARS-CoV-2 (20). Autoimmune disorders might be more expected in coming
years due to impact of COVID 19 on immune system (21). One of the most
prevalent symptoms in children infected by SARS-CoV-2 is respiratory
manifestation (22). There are increasing evidences of pulmonary sequel
especially in adult population after infection (23). To the best of our
knowledge present study is the first systematic review and meta-analysis
evaluating the impact of COVID 19 on respiratory system of younger
generation in long-term. In six eligible studies, 272 pediatric patients
(143 females and 129 males) were evaluated with the mean age of 12.68
years. The mean of recovery time after SARS-CoV-2 infection was 3.94
months.

Infection generally was mild and most of patients had no or only mild
symptoms. All spirometry parameters were in normal range. Two studies
were evaluated post bronchodilator parameters. Their results showed no
reversible obstructive changes in airways of children with history of
COVID 19. Fortunately, all studies reported FEF 25-75\%. It is one of
the most sensitive measures of obstructive diseases in peripheral
airways (24-27). The meta-mean of FEF25-75\% was 105.05\%, which was in
normal range. According to our meta-results, no obstructive disease in
studied population was detected.

One of the most expected involved areas in respiratory system during
COVID 19 is alveolar epithelial cells (28, 29). It seems that peripheral
airways with an internal diameter less than 2 millimeters are more prone
to impair after SARS-CoV-2 infection. While these parts of respiratory
system represent 90\% of total lung capacity but only have role in less
than 20\% of airflow (30, 31). So, simple spirometry which measuring
FEV1 and FVC hardly might detect early stages of pulmonary involvement
after COVID 19. Measuring diffusion capacity is more sensitive in
detection of pulmonary diseases especially in early stages (32).
Unfortunately, only 177 out of 238 participants had DLCO values.
However, according to meta-results the mean of DLCO was within normal
range (108.97 \%, 95\%CI: (86.15, 131.79)). A meta-analysis in adults
was evaluated pulmonary function post-COVID 19 infection. Results showed
decreased DLCO in nearly 40\% of survivors (33, 34). Decreased DLCO
might be an early indicator of interstitial lung diseases even before
change in lung volumes (35, 36). Chronic interstitial pneumonia and
diffuse alveolar hemorrhage are demonstrated in few studies, which have
reported histological finding in autopsy (37-39). Patients with
SARS-CoV-2 may had pulmonary fibrosis, which is considered as defined
sequel of barotrauma. All of these pathologies can impair carbon
monoxide diffusion capacity (40). In present study, one explanation for
normal DLCO may be none-severe infection in most of studied children. We
have tried to evaluate impact of disease severity in spirometry
parameters. However, there were not significant difference between the
result in symptomatic and asymptomatic patients. Future studies with
longer period of follow-up and evaluating patients with more severe
respiratory presentation are needed. We had also heterogeneity in atopy
and asthma background of our included studies. However, according to
meta-regression chronic pulmonary disease (such as asthma) had not a
significant effect on pooled mean of major outcomes.

Less severity of respiratory system involvement in children infected by
SARS-CoV-2 comparing with adult, might be possible explanation for
different outcome between them (3). In addition, preexisting diseases in
adults like chronic respiratory diseases, cardiac diseases and diabetes
mellitus may induce impairment in pulmonary function. On the other hand,
children during infancy and preschool age usually have more severe
course during infection (41, 42). Because majority of our included
participants were teenage, more studies which can evaluate pulmonary
sequel in infants and young toddlers, should be designed. In addition,
different variants of SARS-CoV-2 like Delta or Omicron had resulted to
different presentation and probably different outcomes. Therefore,
studies, which determine type of variants, may be useful. It is possible
that pulmonary sequel of survived children is so tiny and routine
pulmonary function test cannot detect abnormalities. It is useful to
design exercise-challenging studies in survived children after COVID 19
to detect subtle or mild changes in pulmonary function.

\textbf{Conclusion:}

Although more evidences are needed, our review showed no abnormality in
pulmonary function test in follow-up of children with a SARS-CoV-2
infection history. Disease severity and asthma background had not
confounded this outcome.

Limitation:

There are some limitations regarding present study: A) in spite of an
attempt for a comprehensive search, it maybe that some eligible articles
were missed. B) Eligible studies were observational and they were
threatened with bias in different levels. It may affect the
meta-results.

\textbf{Conflict of interest}

There is no conflict of interest.

\textbf{Data availability statement}

The data that support the findings of this study are openly available at
the web space (as original articles)

\textbf{Contributors' Statement}

Dr Bakhtiari and Dr Moazzen designed the data collection instruments,
collected data, carried out the initial analyses, and reviewed and
revised the manuscript.

Dr Ghahremani and Dr Moazzen conceptualized and designed the study,
coordinated and supervised data, drafted the initial manuscript, and
reviewed and revised the manuscript.

All authors approved the final manuscript as submitted and agree to be
accountable for all aspects of the work.

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Figure 1. Diagram of study selection

Figure 2. Pooled mean of FEV1 in included studies

Figure 3. Pooled mean of FVC in included studies

Figure 4. Pooled mean of FEV1/FVC in included studies

Figure5. Pooled mean of FEF25-75 in included studies

Figure6. Pooled mean of DLCO in included studies

Table1. Characteristics of eligible studies included in meta-analysis.\selectlanguage{english}
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\textbf{Hosted file}

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