Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia

Hui Dai; Renjun Huang; Yalei Shang; Jian'an Huang; Nan Su; Daxiong Zeng; Hongmei Li; Yonggang Li

doi:10.4103/RID.RID_44_22

ORIGINAL ARTICLE

Year : 2022 | Volume : 9 | Issue : 4 | Page : 126-135

Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia

Hui Dai¹, Renjun Huang², Yalei Shang², Jian'an Huang³, Nan Su³, Daxiong Zeng³, Hongmei Li⁴, Yonggang Li¹
¹ Department of Radiology, First Affiliated Hospital of Soochow University; Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu Province, China
² Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
³ Department Pulmonary and Critical Care Medicine, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
⁴ Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu Province, China

Date of Submission	16-Nov-2022
Date of Decision	22-Dec-2022
Date of Acceptance	06-Feb-2023
Date of Web Publication	21-Mar-2023

Correspondence Address:
Daxiong Zeng
Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital of Soochow University, Suzhou City 215000, Jiangsu Province
China
Hongmei Li
Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou City 215000, Jiangsu Province
China
Yonggang Li
Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215000
China

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/RID.RID_44_22

Abstract

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about predicting mortality in severe COVID-19 remains unclear.
METHODS: A total of 151 COVID-19 in-patients from January 23 to March 8, 2020, were divided into severe and critically severe groups and survival and mortality groups. Differences in the clinical and imaging data between the groups were analyzed. Factors associated with COVID-19 mortality were analyzed by logistic regression, and a mortality prediction model was developed.
RESULTS: Many clinical and imaging indices were significantly different between groups, including age, epidemic history, medical history, duration of symptoms before admission, routine blood parameters, inflammatory-related factors, Na⁺, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportions of patients with imaging Stage III and a comprehensive computed tomography score were significantly increased in the mortality group. Factors in the prediction model included patient age, cardiac injury, acute kidney injury, and acute respiratory distress syndrome. The area under the receiver operating characteristic curve of the prediction model was 0.9593.
CONCLUSIONS: The clinical and imaging data reflected the severity of COVID-19 pneumonia. The mortality prediction model might be a promising method to help clinicians quickly identify COVID-19 patients who are at high risk of death.

Keywords: Coronavirus disease 2019, fatal outcome, pneumonia

How to cite this article:
Dai H, Huang R, Shang Y, Huang J, Su N, Zeng D, Li H, Li Y. Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia. Radiol Infect Dis 2022;9:126-35

How to cite this URL:
Dai H, Huang R, Shang Y, Huang J, Su N, Zeng D, Li H, Li Y. Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia. Radiol Infect Dis [serial online] 2022 [cited 2023 Jun 3];9:126-35. Available from: http://www.ridiseases.org/text.asp?2022/9/4/126/372196

Introduction

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a global pandemic. SARS-CoV-2 is a novel betacoronavirus belonging to the sarbecovirus subgenus of the Coronaviridae family and is closely related to SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). It can lead to respiratory symptoms and severe pneumonia.^[1] According to the World Health Organization, 14% of patients with SARS-CoV-2 infection are severe and require hospitalization, while 5% are critically severe and require intensive care.^[2],[3] The mortality rate of SARS-CoV-2-infected patients could be up to 4%,^[2] which is much higher than that of seasonal influenza.

A study on the epidemiological characteristics of 72,314 cases in China found that SARS-CoV-2 was highly infectious, but most patients had mild symptoms.^[4] The patients who died were often older than 60 years and suffered from common diseases such as hypertension, cardiovascular disease, or diabetes. Some severe patients rapidly developed acute respiratory distress syndrome (ARDS) and died from multiorgan failure.^[5] Recent biopsy samples from an autopsy of a patient with severe COVID-19 showed diffuse alveolar damage.^[6] However, there have been inconsistencies in the clinical and imaging results of patients with COVID-19 pneumonia, and a diversity of imaging features might exist in certain clinical stages of the disease.^[7],[8],[9] Furthermore, relatively few studies^{[10],[11],[12],[13],[14]} have summarized the clinical, laboratory and/or imaging findings of severe and critically severe COVID-19 patients. This information would benefit clinicians by aiding them in adjusting their treatment plans and would provide a better understanding of mortality prediction. Therefore, the clinical and imaging features of severe and critically severe COVID-19 patients require further exploration. It is also urgent to explore the mortality risk factors in these patients in different countries.

This study examined the clinical and imaging characteristics of patients with severe or critically severe COVID-19 pneumonia and developed a model for predicting mortality in this population.

Methods

Patient enrolment

This was a multicenter, retrospective clinical study conducted in six hospitals in Jiangsu and one hospital in Wuhan, China. A total of 151 in-patients (104 severe and 47 critically severe) with COVID-19 pneumonia were included between January 23 and March 8, 2020. All cases were confirmed by a reverse transcription-polymerase chain reaction and with the following diagnosis criteria: severe cases met any one of the following: (1) respiratory distress, respiratory rate (RR) ≥30/min, (2) resting oxygen saturation (SaO₂) ≤93%, (3) oxygenation index (partial pressure of oxygen/fraction of inspired oxygen [FiO₂]) ≤300 mmHg; critically severe cases met any one of the following: (1) respiratory failure and mechanical ventilation needed, (2) shock, and (3) concomitant failure of other organs. In total, 104 patients were diagnosed with severe and 47 with critically severe COVID-19 pneumonia. In addition, 114 patients were in the survival group and 37 were in the mortality group according to their clinical outcomes. This multicenter study was approved by the institutional review board at each center, and informed consent was obtained from all patients or their surrogates.

Clinical data

The patients' epidemic history, medical history, symptoms and signs, age and sex were recorded. Outcomes of initial laboratory examinations during the severe phase of illness were also recorded, which included routine blood tests, infection-related factors, serum ion concentrations, myocardial zymogram, liver and kidney function tests, coagulation function test, RR, blood gas analysis, and complications. The serum troponin concentration was the most important index when diagnosing cardiac injury; a value that exceeded the 95% confidence interval of the normal value indicated myocardial damage. An increase in other indexes could also indicate myocardial damage; in order of importance, these indexes were creatine kinase isoenzyme, creatine kinase, and lactate dehydrogenase (LDH).^[15] Renal injury was diagnosed as an estimated glomerular filtration rate (eGFR) <60 ml/min calculated based on serum creatinine.^[16] ARDS was diagnosed based on the Berlin Definition criteria.^[17] The medical history score was determined by summing the following items: 3 for malignant tumors; 2 for benign tumors, renal or liver malfunction; and 1 for chronic obstructive pulmonary disease, hypertension, diabetes, or others.

Imaging data

At the beginning of a severe illness, 138 patients underwent initial chest computed tomography (CT) imaging and 13 underwent chest radiography; among these, 76 underwent follow-up CT examination and 31 underwent follow-up chest radiography examination. The CT scanning parameters were: tube voltage 120 kV, tube current 110 mA, pitch 1.0, rotation time 0.5–0.75 s, slice thickness 5 mm, with 1 mm or 1.5 mm section thickness for axial, coronal, and sagittal reconstructions. For chest radiography, a flat panel detector was attached to the patient's chest, and the voltage and current were 120 kV and 200 mA, respectively. The chest imaging results were analyzed by two experienced attending radiologists who were blinded to the clinical information; they independently evaluated the images and recorded the severity. The chest CT imaging and chest radiography results were classified as mild (Stage I), progressed (Stage II), or severe (Stage III) according to the scope of the lung field involved; Stage I had <25% involvement, Stage II had 26%–50% involvement, and Stage III had more than 50% involvement. The CT score of ground-glass opacity (GGO), consolidation, and a comprehensive score of inflammatory pulmonary infiltration were quantitatively determined using a radiologic scoring system ranging from 0 to 25 points, which was an adaptation of a method previously used to describe idiopathic pulmonary fibrosis in SARS patients.^[18] Each lung lobe was evaluated from 0 to 5 points on the basis of the area involved, with a score of 0 for healthy lung, 1 for <5% of the lung lobe area involved, 2 for 6%–25%, 3 for 26%–50%, 4 for 51%–75%, and 5 for more than 75%. A total score was calculated by summing the score of each lobe.

Statistical analysis

The Mann–Whitney U test and two-sample t-test were, respectively, used for nonnormally and normally distributed data to compare the continuous variables. The Pearson Chi-square test was used to compare the categorical variables. Comparisons were made between the severe and critically severe groups and between the survival and mortality groups. Statistical analysis software was employed (SAS v. 9.4, SAS Institute Inc., Cary, NC). Univariate and multivariable logistic regression analysis was conducted, and a mortality prediction model for patients with severe COVID-19 pneumonia was developed. The model was assessed by the receiver operating characteristic (ROC) curve. P < 0.05 was considered statistically significant. The mean value of normally distributed continuous variables was recorded as the mean (Standard deviation), and the mean value of nonnormally distributed data was recorded as the median (interquartile range). Categorical variables were recorded as the count (percentage).

Results

Clinical features

The clinical data of the patients and the group comparison results are shown in [Table 1]. There were 91 (60.26%) men and 60 (39.74%) women in the study, with a mean age of 62.47 (13.39) years (range 26–92 years). The average age in the survival group was 58.52 (13.99) years and was significantly lower than that in the mortality group (68.54 [10.83] years [P < 0.001]). The epidemic history, medical history, medical history score, and duration of symptoms before admission differed between the survival and mortality groups.

Table 1: Clinical data of 151 patients with severe coronavirus disease 2019 pneumonia and group comparisons

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The white blood cell and neutrophil counts, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin, interleukin (IL)-6, IL-8, IL-10, Na⁺, myoglobin, troponin, LDH, aspartate aminotransferase, serum urea nitrogen, prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), FiO₂, and the occurrence rates of ARDS, septic shock, disseminated intravascular coagulation (DIC) and acute kidney injury (AKI) were lower, while the lymphocyte count and albumin were higher in the severe and survival groups than in the critically severe and mortality groups (P < 0.05). Compared with the critically severe group, in the severe group, the total bilirubin, fibrinogen, RR, and the percentage of patients with dyspnea were lower, while the SaO₂ was higher (P < 0.05). The serum creatinine and the occurrence rate of cardiac injury and liver injury were lower, while the proportion of patients with moderate or high fever and the eGFR were higher in the survival group than in the mortality group (P < 0.05).

Imaging findings

As shown in [Table 2], among the 151 patients, 6 (3.97%) were diagnosed as Stage I [Figure 1], 68 (45.03%) as Stage II [Figure 2], and 77 (50.99%) as Stage III [Figure 3] based on the chest CT or chest radiography images. The CT images showed that 116 (84.06%) patients had whole-lung involvement. The chest CT images showed that the lesions of 83 (60.14%) patients were mainly peripherally distributed. The proportion of Stage III patients in the mortality group was significantly higher than that in the survival group (73.0% vs. 43.9%, P < 0.05). There were significant differences in the comprehensive score between the severe and critically severe groups and between the survival and mortality groups (P < 0.05).

Table 2: Imaging data of 151 patients with severe coronavirus disease 2019 pneumonia and group comparisons

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Figure 1: A 25-year-old woman was diagnosed with severe COVID-19 on January 30 with diarrhea for 2 days. (a and b) Axial chest CT on January 30 showed mild changes (Stage I) with irregular high-density lesions in the left upper lung lobe surrounded by GGO (a and b, arrows) and patchy GGO in the right upper lung lobe (a, arrow). COVID-19 = Coronavirus disease 2019, CT = Computed tomography, GGO = Ground-glass opacity

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Figure 2: A 64-year-old woman was diagnosed with severe COVID-19 on January 31 with vomiting and anorexia. (a and c) Axial chest CT on February 1 showed lesion progression (Stage II) with multiple lesions, including GGO, consolidation, and fibrosis, mainly distributed in the lower lung lobes, (b and d) Axial chest CT on February 4 showed mild absorption of GGO and consolidation. COVID-19 = Coronavirus disease 2019, CT = Computed tomography, GGO = Ground-glass opacity

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Figure 3: A 58-year-old man diagnosed with severe COVID-19 on January 30 with asthma. Imaging showed severe lesions (Stage III). (a) Chest radiograph (a) on January 31^st showed multiple high-density lesions with peripheral distribution and blurred boundary, (b-d) Axial chest CT on February 4 showed diffusely distributed GGO in bilateral lungs involving the whole-lung lobes, with mild consolidation. COVID-19 = Coronavirus disease 2019, CT = Computed tomography, GGO = Ground-glass opacity

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In total, 94 patients had chest CT and/or chest radiography follow-up, of which 8 patients showed no obvious change, 66 patients showed absorption and 20 patients showed the progression of pulmonary lesions. The proportion of patients with absorption at imaging follow-up was higher (71.8% vs. 63.6%), while the proportion of patients with lesion progression was lower (19.7% vs. 27.3%) in the survival group than in the mortality group. However, these differences were not significant (P > 0.05).

Logistic regression analysis and prediction model

The univariate logistic regression analysis of factors associated with COVID-19 mortality is shown in [Table 3]. The estimated odds ratio for mortality was highest for DIC (59.105), followed by septic shock (37.500) and myocardial injury (34.500). The multivariate logistic regression analysis of factors associated with COVID-19 mortality is shown in [Table 3]. The mortality prediction model using risk factors in a severe patient is as follows:

Table 3: Logistic regression analysis of risk factors for mortality prediction

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The per cent concordance of the prediction model was 96.1%. The ROC curve of the prediction model is shown in [Figure 4], and the area under the ROC curve was 0.9593.

Figure 4: ROC curve of the mortality prediction model. An area under the curve of 0.9593 indicates that the multivariate logistic regression model is reliable. ROC = Receiver operating characteristic

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Discussion

COVID-19 is a novel infectious disease characterized by high transmissibility and serious illness. Some patients with severe clinical symptoms rapidly progress to ARDS and require hospitalization in an intensive care unit.^[19] Hence, it is essential to closely monitor the condition of patients by dynamically monitoring changes in their symptoms, laboratory results, and chest imaging results. This information is helpful for evaluating disease severity and quickly adjusting the treatment plan.

Some clinical features are characteristic of a severe SARS-CoV-2 infection. We found that a patient's medical history was associated with disease mortality. This association has also been found in previous studies by Sohrabi et al.,^[20] Guan et al.^[21] and Jordan et al.^[3] In the present study, the mean age of patients who died was approximately 10 years older than that of survivors, which was similar to previous results.^[22] There was a marked difference in the sex ratio of the severe group, which was almost 3: 2 male: female. This was consistent with Chen et al.'s study, which suggested that older men were more likely to be infected with SARS-CoV-2, resulting in severe and even fatal respiratory diseases such as ARDS.^[5] The duration of symptoms before admission was longer in the mortality group than in the survival group, indicating that a longer time between symptom onset and hospitalization tended to result in poorer outcomes, which was consistent with Liang et al.'s study.^[23]

In the present study, the main initial symptoms of severe patients were fever and/or cough. Dyspnea was also frequently seen in the severe patients, and was even more prevalent in the critically severe group owing to severe lung lesions. The incidence of ARDS was significantly higher in the critically severe and mortality groups than in the severe and survival groups, respectively. The RR in the critically severe patients was significantly higher than in the severe patients, as were the SaO₂ and FiO₂, which may have been due to mechanical ventilation. Regarding routine blood test parameters, increased leukocyte and neutrophil counts and a decreased lymphocyte count and INR were observed in the critically severe group and mortality group. Wang et al. found increased neutrophil counts in COVID-19 patients who died.^[24] This may be related to the cytokine storm that is sometimes induced by viral infection. The presence of lymphopenia suggests that SARS-CoV-2 might target lymphocytes and lead to disease progression.^[5]

The infection-related factors, including CRP, ESR, procalcitonin, IL-6, IL-8, and IL-10, were increased in the severe patients; these parameters were even higher in the critically severe and deceased cases. Ulhaq and Soraya suggested that frequently measuring circulating IL-6 may help to identify disease progression in COVID-19 patients.^[25] A retrospective study suggested that elevated IL-6 was related to mortality in COVID-19 patients.^[26] A significantly higher incidence of septic shock and DIC was seen in the critically severe and mortality groups. This may have been owing to the imbalance of thrombin production caused by the activation of the vascular endothelium, platelets, and white blood cells, which has been shown to occur locally and systemically in the lungs of patients with severe pneumonia, resulting in fibrin deposition, tissue damage, and microangiopathy.^[27] It could be aggravated by the occurrence of septic shock.^[28],[29] Most COVID-19 patients who died, but very few survivors, had evidence of DIC, which frequently occurs in the deterioration of COVID-19 pneumonia and has been associated with mortality.^[30] Clinicians should be vigilant in identifying the presence of DIC, especially in patients who have septic shock.

There was a significant relationship between multiple organ injury and mortality. In the critically severe and mortality groups, myoglobin, troponin, LDH, and the incidence of cardiac injury were higher than in the severe group, which was similar to the results of previous studies on the relationship between illness severity and myocardial injury in patients with COVID-19. These findings were also consistent with a study that found a correlation between heart injury and death after SARS-CoV-2 infection.^[31],[32] Recent studies on COVID-19 have shown that the incidence of liver injury ranges from 14.8% to 53%, with decreased albumin concentrations in critically ill patients; furthermore, the incidence of liver injury might be as high as 78.0% in patients who die from COVID-19.^[33] In the present study, the incidence of liver injury in the critically severe and mortality groups was significantly increased compared with that in the severe and survival groups. This indicated that liver injury was related to disease severity and mortality, and it may be exacerbated by cytokine storm or drug-induced liver damage.^[33],[34] In the present study, the eGFR, serum creatinine and serum urea nitrogen were significantly higher in the mortality group than in the survival group. There was also a significant prevalence of AKI in the critically severe and mortality groups. This finding was consistent with Cheng et al., who showed that AKI development in COVID-19 patients during hospitalization was associated with in-hospital mortality.^[35]

The coagulation function and serum Na⁺ concentration changed during the severe disease course of COVID-19 pneumonia. Recent studies have investigated coagulation and related indices in severe and nonsevere COVID-19 patients.^[19],[36] In this study, these indices were compared between severe and critically severe patients and between patients who survived and died. PT, APTT, INR, and the fibrinogen concentration were associated with disease severity, and PT, APTTT, and the INR might be associated with mortality. A previous study^[37] found that hypernatremia was associated with long-term hospitalization and death, and it was more common in critically ill patients. Abnormal changes in the central nervous system and one's mental state might result from hypernatremia, but digestive tract or urinary system disorders cannot be ruled out.^[32] In addition, hypernatremia may also be related to the administration of large amounts of intravenous sodium-containing fluid.

Regarding the imaging results, multiple lung lobes were involved in 98.6% of patients, and whole-lung lobes were involved in 84.06% of patients. The proportion of patients in Stage III was significantly increased in the mortality group, and the comprehensive CT imaging scores were significantly higher in the critically severe and mortality groups. Our results showed that the severity of CT findings was consistent with the severity of the clinical course, as has been suggested by a previous study.^[38] Li and Xia.^[39] found that the development pattern of COVID-19 on CT images was similar to that of SARS and MERS-CoV. There are some common imaging features among these viral illnesses; therefore, the final diagnosis must be made using a combination of clinical manifestation, epidemic history, and laboratory examination. Convenient and rapid CT examination is essential. A study of critically ill patients with COVID-19 pneumonia showed that early or repeated radiological examination is helpful to screen patients with COVID-19 pneumonia.^[40]

Previous studies of the mortality risk calculated overall probability based on the infected population and confirmed population.^[41],[42] However, an individual's approximate risk of death is also important, especially in severe and critically severe patients, because it might influence the treatment plan and the response of clinicians or medical institutions. In the univariate logistic regression analysis, DIC was the best mortality predictor (nearly 59 times greater risk of death than for patients without DIC), followed by septic shock and cardiac injury. The prediction model included age, cardiac injury, AKI, and ARDS, among which ARDS was the most powerful predictor. In the current COVID-19 epidemic, this prediction model might be a promising method to help clinicians quickly identify potential individuals who have a high mortality risk.

There were several limitations to this study. First, the clinical and imaging data of the patients were from multiple centers; hence, the data were heterogeneous, which might have affected the statistical results. In addition, some indices were missing too many values, and therefore their P values could not be calculated when assessing group differences. Second, the initial imaging and follow-up imaging results did not have a uniform standard. Some patients had only a chest X-ray due to their disease severity, and the follow-up intervals were not the same among all the patients. Finally, although the prediction model had a high concordance and high area under the curve, a larger cohort study is warranted to validate the accuracy and application value of this model.

Conclusions

The clinical and imaging data reflected the severity of COVID-19 pneumonia and were associated with mortality in critically severe patients. The mortality prediction model is a promising method to help clinicians quickly identify COVID-19 patients who have a high mortality risk.

Ethic statement

This study was conducted in accordance with the amended Declaration of Helsinki. Independent ethics committees approved this retrospective study, and written informed consent was obtained from all patients or their surrogates.

Consent for publication

Consent for the publication of chest CT and radiograph images in this study was obtained from the relevant patients.

Availability of data and materials

The datasets used in the study are available from the corresponding author on reasonable request.

Acknowledgements

We would like to thank Katherine Thieltges from Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.

Financial support and sponsorship

This work was mainly supported by the National Natural Science Foundation of China (grant numbers 81971573, 81671743) for the collection and analysis of data; the Clinical Key Diseases Diagnosis and Therapy Special Project of the Health and Family Planning Commission of Suzhou (LCZX201801) for the collection and interpretation of data; the Project of Invigorating Health Care through Science, Technology and Education, Jiangsu Provincial Medical Youth Talent (QNRC2016709) for the study design and data analysis, the High-level Health Personnel “six-one” Project of Jiangsu Province (LGY2016035) for the interpretation of data; the Program for Advanced Talent in Six Industries of Jiangsu Province (WSW-057); and the Novel Coronavirus Infection Emergency Control Technology of Suzhou (SYS202008) for the study design and writing the manuscript.

Conflicts of interest

There are no conflicts of interest.

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