*Correspondence to: Jirong LI,
E-mail:304353448@qq.com
[Abstract] Objective: To develop a pneumonia risk
prediction model for SARS-CoV-2 infected patients to reduce unnecessary
chest CT scans; Materials and Methods:Retrospective analysis was
performed on the clinical data of SARS-CoV-2-positive patients who
visited outpatient and emergency clinics and underwent chest CT scans at
the Mawangdui Branch of Hunan Provincial People’s Hospital from 20
December 2022 to 23 December 2022 and at the Tianxinge Branch of Hunan
Provincial People’s Hospital from 1 January 2023 to 4 January 2023. A
retrospective analysis of imaging and clinical data from 205 cases
(training cohort) and 94 cases (validation cohort) of
SARS-CoV-2-positive patients who visited outpatient and emergency
clinics was conducted. The predictor variables were screened using the
”univariate and then multivariate logistic regression” and ”least
absolute shrinkage and selection operator (LASSO)” approaches, and the
predictive model was constructed using multifactorial logistic
regression and represented as a nomogram. The diagnostic effectiveness
of the pneumonia risk model was evaluated using receiver operating
characteristic (ROC) curves; the Delong test and Integrated
Discrimination Improvement Index (IDI) were used to compare the AUC of
the pneumonia risk model with the AUCs for predictors incorporated in
the model alone. The calibration of the pneumonia risk model was
assessed using calibration curves; Decision curve analysis (DCA) was
used to evaluate the clinical validity of the pneumonia risk model. In
addition, a smoothed curve was fitted using a generalized additive model
(GAM) to explore the relationship between the pneumonia grade and the
model’s predicted probability of pneumonia; Results:”univariate and then multivariate logistic regression ” and Lasso
regression together show that age, natural log-transformed value
(InCRP), Monocytes percentage (%Mon) are valid predictors of pneumonia
risk; the AUC of the pneumonia risk model was 0.7820 (95% CI:
0.7254-0.8439) in the training cohort and 0.8432 (95% CI:
0.7588-0.9151) in the validation cohort; at the cut-off value of 0.5,
the sensitivity and specificity of the pneumonia risk model were
70.75%, 66.33% (training cohort), 76.09%, and 73.91% (validation
cohort), the calibration curves
showed that the pneumonia risk model has good calibration accuracy. The
decision curve analysis showed that the pneumonia risk model has high
clinical value in predicting the probability of pneumonia in SARS-CoV-2
infected patients.Conclusion: The
pneumonia risk prediction model developed in this study can be used to
predict the risk of pneumonia in SARS-CoV-2 infected patients
diagnostically.
【Key words】 SARS-CoV-2;COVID-19;prediction
model;nomogram;pneumonia
INTRODUCTION
SARS-CoV-2 infection has spread globally since 2020, leading many
countries to impose recurring quarantines, significantly impacting
public health and the global economy1-2. Globally, as
of 10 February 2023, there have been 755,385,709 confirmed cases of
COVID-19, including 6,833,388 deaths, reported to WHO. Omicron, the
mutant strain, entered the community in November 2021 and is far more
contagious and escape-resistant than the previous variants of concern
(VOC), like Delta3-8. At the beginning of 2022, the
Omicron version quickly surpasses the Delta variant as the prevalent
strain worldwide9.
During the early period of the COVID-19 pandemic, SARS-CoV-2 primarily
affected the lung and caused pneumonia10-13. As one of
the most representative and accurate diagnostic methods for
COVID-1914, chest computed tomography (CT) scans are
widely used in mainland China.
However, recent studies have demonstrated that
the most recent VOC Omicron
variant is much less likely to cause pulmonary
infections3-5,15-16, suggesting potential implications
for adapting management strategies for these infections.
In clinical practice, we found that due to the apprehension of
contracting severe pneumonia from the SARS-CoV-2, many people with mild
symptoms are choosing to receive CT scans, causing excessive CT scans
and putting a strain on the availability of healthcare resources, which
is particularly true when SRAS-CoV-2 localized epidemic outbreaks occur.
Consequently, a strategy to evaluate the risk of pneumonia among
recently infected people is essential to ensure the efficient use of
healthcare resources and decrease unnecessary exposure to
electromagnetic radiation.
With the aim of improving the classification of the risk of pneumonia in
individuals with the most recent VOC of SARS-CoV-2 infections, reducing
the overuse of CT scans, reducing non-essential ionizing radiation in
individuals, as well as reducing the associated financial burden on
patients, and optimizing the allocation of healthcare resources, we have
developed and externally validated a pneumonia risk prediction model
based on general patient data and blood routine test, to meet the needs
of the new phase of the COVID-19 epidemic control.
Material and Methods
Materials
A retrospective analysis was performed on the clinical data of
SARS-CoV-2-positive patients who visited outpatient and emergency
clinics and underwent chest CT scans at the Mawangdui Branch of Hunan
Provincial People’s Hospital from 20 December 2022 to 23 December 2022
and at the Tianxinge Branch of Hunan Provincial People’s Hospital from 1
January 2023 to 4 January 2023.Inclusion criteria: (1) Attendance as an
outpatient or emergency (not including inpatients); (2) Patients had
completed chest CT scans, and CT image quality meets diagnostic
requirements; (3) SARS-CoV-2 infection positive was diagnosed by antigen
test or nucleic acid test within 3 days before the current chest CT; (4)
Complete blood routine examination results. Exclusion criteria: (1)
Inflammation of a body part other than the lungs has been diagnosed at
the time of the current blood routine tests;(2)
the Patient was already on
antiviral medication at the time of the visit. The patient recruitment
pathway is detailed in FIGURE 1.
The study complies with the Declaration of Helsinki. It was approved by
the Medical Ethics Committee of Hunan Provincial People’s Hospital (The
First Affiliated Hospital of Hunan Normal University), exempting the
subjects from informed consent.
Methods
Device parameters and image analysis
The Mawangdui Branch (training cohort) used
CT scans with a field of view
(FOV) of 230 mm × 230 mm, a layer thickness of 5 mm, and layer spacing
of 5 mm using the United Imaging uCT 760GE 128-slice CT; the Tianxinge
Branch (validation cohort) used CT scans with a field of view (FOV) of
230 mm × 230 mm, a layer thickness of 5 mm, and layer spacing of 5 mm
using the United Imaging uCT 860
160-slice CT or United Imaging uCT 960+ 640-slice CT. Two attending
radiologists performed image analysis separately, and the final decision
in case of a dispute was determined by consultation between the two
physicians.
CT Diagnosis of COVID-19 was referred to the report published by the
RSNA17; typical findings were as follows: peripheral
distribution, ground-glass opacity, fine reticular opacity, vascular
thickening, and reverse halo sign. Patients with pneumonia were also
classified into grades 0, 1, 2, 3, and 4 according to the extent and
distribution of lung involvement (no lung involvement was categorized as
grade 0).
Statistical analysis & construction and evaluation of
predictive models
Statistical analysis was performed using Empower Stats, version 5.0
(http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA, USA), R
statistical software, version 4.2.0 (http://www.R-project.org, The R
Foundation), and the SPSS statistical software, version 27.0 (SPSS Inc.,
Chicago, IL, USA) with continuity variables expressed as medians (min,
max) and categorical variables expressed as frequencies (percentages).
Kruskal Wallis rank sum test or Fisher exact probability test was used
to compare differences between groups of
continuity variables. Chi-square
tests are used for comparisons of categorical variables. After the
natural log transformation of some continuity variables, to reduce
irrelevant and redundant information, the predictor variables of the
training cohort are filtered by
both ” univariate and then
multivariate logistic regression ” and ” least absolute shrinkage and
selection operator (LASSO)” methods, the variables selected by both
screening methods were used as the final predictor variables. The
prediction model was constructed using multivariate logistic regression
and presented in a nomogram. The ROC curves were used, and 500 in
eternal resamples were performed by Bootstrap to evaluate the
discrimination of the pneumonia risk model between the training and
validation cohorts. Delong test and Integrated Discrimination
Improvement Index (IDI) were used to compare the AUC of the pneumonia
risk model with the AUCs for predictors incorporated in the model alone.
Calibration curves were plotted to assess the calibration of the model.
The clinical validity of the model was evaluated by the net benefit of
DCA at different threshold probabilities;
in addition, a smoothed curve was
fitted using a generalized additive model (GAM) to explore the
relationship between the pneumonia grade and the model’s predicted
probability of pneumonia; a difference of p < 0.05 was
considered statistically significant.
Results
General information.
A total of 205 cases were enrolled in the training cohort, with a median
age of 47 years, the youngest being 14 years and the oldest 97 years, of
which 105 cases (51.22%) were female and 100 cases (48.78%) were male,
99 cases (48.29%) without pneumonia and 106 cases (51.71%) with
pneumonia; a total of 94 cases were enrolled in the validation cohort,
with a median age of 56 years, the youngest being 2 years and the oldest
89 years, of which 60 (63.83%) were female and 34 (36.17%) were male,
47 (50.00%) were without pneumonia, and 47 (50.00%) were with
pneumonia; the distribution of the remaining baseline indicators is
shown in TABLE 1.