Discussion
The main results of this study
indicate that advanced age and multiple comorbidities (hypertension,
diabetes, cardiovascular disease, respiratory system disease, and past
surgical history) are risk factors for severe patients with COVID-19. To
enhance the clinical use of this risk model, we developed a risk
prediction model for COVID-19 patients. To the best of our knowledge,
this is the first study to attempt to establish a risk prediction model
of severe patients with COVID-19.
Age, as a risk factor, for critically ill patients, has been reported in
several studies (Jordan et al., 2020; Leung, 2020; Li et al., 2020; Liu
et al., 2020; Wang et al., 2020). Older patients have reduced body
immunity, reduced multi-organ function, and the risk of developing
severe illness increases with age. This study also performed a ROC
analysis on the risk factor of age (Figure S1), and its AUC reached
0.922 (95% CI = 0.852-0.991). When age is 55 years old, the Youden
index reaches a maximum value of 0.711. When the patient is older than
55 years old, the risk of converting to severe illness increases
significantly. In the process of modeling, we also tried to use age as a
categorical variable and divide it into two groups, older age, and
younger age. Although it is more convenient for clinical application
when converting a continuous variable into a categorical variable, it
will lose the prediction accuracy of the model. Therefore, continuous
variables were still used for logistic regression analysis.
When performing the single factor analysis, there are significant
differences between severe and mild patients with regards to
hypertension, diabetes, cardiovascular disease, respiratory system
disease, and past surgical history (P <0.1). The above
comorbidities have been reported in the literature as risk factors
(Aghagoli et al., 2020; Cook, 2020; Jordan et al., 2020; Klonoff et al.,
2020; Leung, 2020; Li et al., 2020; Li et al., 2020; Li et al., 2020;
Liu et al., 2020; Liu et al., 2020; Long et al., 2020; Wei et al., 2020;
Yang et al., 2020; Zhang et al., 2020; Zheng et al., 2020; Zhou et al.,
2020; Zhou et al., 2020). However, when performing multivariate logistic
regression, these factors were not included in the final model, which
may have a great relationship with the small sample size of this study.
Therefore, we combined these comorbidities to reduce the dimensions and
analyzed the number of comorbidities as risk factors. The results showed
that the number of comorbidities had a significant impact on the risk of
serious illness (OR = 6.067,95%CI = 1.078-34.143). The AUC obtained by
the ROC analysis reached 0.871 (95% CI = 0.772-0.970) (Figure S2).
Because our patients had a maximum of three comorbidities, the risk
prediction model was given a maximum of 6 points (2 points for each
comorbidity). It should be noted that two of the included patients had a
stroke, two had chronic gastritis, one had hepatitis B, and one had
gout. Because the sample size of these complications was too small, they
were not considered when performing data analysis due to concern about
introducing these into the model would lead to model deviation.
Several studies have reported that biochemical indicators are risk
factors for severe patients (Ji et al., 2020; Lin et al., 2020; Lippi et
al., 2020; Liu et al., 2020; Long et al., 2020; Mehra et al., 2020; Yao
et al., 2020; Zhu et al., 2020). When extracting the data, we also
obtained some laboratory indicators of the patient at the time of
admission, but we did not obtain the full data of these indicators.
Because some indicators might change during hospitalization, these
factors were not included in the model when the model was built. From
the data of mild and severe patients, leukocytes, calcitonin, and
C-reactive protein all have significant differences (P <0.05),
suggesting that clinical attention should be paid to these values. When
the above indicators increase, it may indicate that the patient’s risk
of becoming severe is increased or it has developed into a severe case.
In addition, we believe that the changes in laboratory indicators may be
due to changes in the patient’s condition; they may not be the cause of
leading to severe coronavirus disease. That is, the changes in the index
of the laboratory examination are the result of the difference in the
disease condition. Due to our limited data, our view needs to be
supported by more data.
It has been reported in the literature that damage to liver function
(Boettler et al., 2020; Lei et al., 2020) and kidney function (Cheng et
al., 2020) is a serious risk factor. However, in our study, there is
only one patient with impaired renal function and three patients with
mildly impaired liver function in the severe-group. The sample size is
too small to obtain positive results. Univariate analysis found that the
patients’ creatinine levels were significantly different between mild
and severe patients, and the median levels were within the normal range
in these two groups.
This study has the following limitations 1) our hospital might have
given priority to treat more severe patients, which might lead to a
lower proportion of mild cases in our study, 2) the sample size is
limited, and our results need clinical validation, and 3) some
continuous variables do not conform to the normal distribution, which
will affect the power of statistical results.
In summary, advanced age and
multiple comorbidities are risk factors for severe patients with
COVID-19. This study provides a
relatively practical tool for clinicians to identify severe high-risk
cases. More attention should be paid to high-risk patients before they
develop into severe cases.