Para-clinical and Epidemiological Features of COVID-19 in Deceased Patients: A Comparison with Treated Patients

AUTHORS

Reza Khedri 1 , Ali Delirrooyfard ORCID 2 , * , Hossein Bahrami Moghadam 1 , Payam Amini 3 , Mahmood Maniati 4 , Nima Mozafari 1 , Mandana Pouladzadeh 5 , Arash Forouzan 5 , Mehran Varnaseri 6

1 Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

2 Clinical Research Development Unit, Imam Khomeini Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

3 Department of Biostatistics and Epidemiology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

4 Department of English, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

5 Department of Emergency Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

6 Department of Infectious Diseases, Razi Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

How to Cite: Khedri R, Delirrooyfard A, Bahrami Moghadam H, Amini P, Maniati M , et al. Para-clinical and Epidemiological Features of COVID-19 in Deceased Patients: A Comparison with Treated Patients, Jundishapur J Chronic Dis Care. 2021 10(2): e112390. doi: 10.5812/jjcdc.112390.

ARTICLE INFORMATION

Jundishapur Journal of Chronic Disease Care: 10 (2); e112390
Published Online: May 11, 2021
Article Type: Research Article
Received: December 22, 2020
Revised: March 1, 2021
Accepted: April 13, 2021
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Abstract

Background: Patients with COVID-19 have shown a wide variety of symptoms and mortality rates in different communities.

Objectives: This study aimed to compare the epidemiological, clinical, and paraclinical features of patients with COVID-19 who have overcome the disease with patients who died.

Methods: All hospitalized patients admitted to Special Corona Hospital who had a positive real-time PCR test for SARS-CoV-2 from January to March 2020 were included in the study. Clinical characteristics, date of disease onset, hospital admission date, and the severity of COVID-19 were obtained from each patient's medical records. Independent sample t-test was used to compare continuous variables between the groups of the discharged and expired patients. The independence between categorical variables and the outcome was assessed by Chi-square or Fisher's exact tests.

Results: The order of essential variables for admission as the starting time are pH, WBC count, loss of consciousness, neutrophil count, base excess (BE), HCO3, age, BUN, O2 saturation, and lymphocyte count.

Conclusions: In the current study, the mortality rate of COVID-19 was 30% and was significantly associated with critical disease intensity, fever, chills, loss of consciousness, ischemic heart disease (IHD) history, Parkinson's disease, invasive O2 therapy, and troponin level.

1. Background

A novel coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that has a 79.5% similarity to SARS-CoV (SARS epidemic in 2003) and spread in individuals through various routes, such as droplets, airborne particles, feces, and oral mucosa (1, 2). Patients with COVID-19 have shown a wide range of symptoms, including asymptomatic to respiratory, gastrointestinal, neurological, etc. The most common clinical symptoms were fever, cough, and fatigue. Gastrointestinal symptoms such as nausea, anorexia, diarrhea, and vomiting are common, and even some patients have experienced gastrointestinal symptoms without respiratory symptoms (3, 4). The COVID-19 mortality rate at initial studies in China has been reported to be 2.3% (4). Further, On December 04, 2020, has proceeded in more than 165,000 dying worldwide with a universal mortality rate of 6.8%, and at this time, December 04, 2020, globally mortality reported by WHO is 2.3% (5). In July 2020, an experiment in Wuhan, China, revealed that older age, hypertension, and elevated lactate dehydrogenase (LDH) need accurate detection and immediate interference to stop the possible development of rigorous COVID-19. Severe male cases with heart damage, hyperglycemia, and high-dose corticosteroid use may be in great danger of death (6). An experiment in Italy revealed that of 3,988 critically ill patients admitted from February 20 to April 22, 2020, 50.4% of patients with COVID-19 had been discharged from the intensive care unit, 48.7% had died in the intensive care unit, and 0.8% were still in intensive care units (ICU) (7).

2. Objectives

Given the fact that the symptoms of the disease, its pathogenicity, and other features can be different in various situations and places, we aimed to compare the epidemiological, clinical, and paraclinical features of patients with COVID-19 who have overcome the disease with those patients who died in Ahvaz, Southwest of Iran

3. Methods

All adult patients admitted to Special Corona Center with a diagnosis of COVID-19 over three months were included in this study. The current observational, retrospective investigation evaluated all hospitalized patients from January to March 2020 in Ahvaz city. Having clinical symptoms of COVID-19 and also positive real-time PCR for SARS-CoV-2 were the inclusion criteria. Therefore, subjects with negative laboratory results of SARS-CoV-2 were excluded from the study. All patients in this study lived in Ahvaz city during the COVID-19 outbreak. Demographic data, clinical characteristics (including medical history, history of exposure, symptoms, and laboratory findings) were extracted from each patient's medical records. The date of disease onset and hospital admission date, and the severity of COVID-19 were also noted. The onset date was defined as the day when the patients noticed any symptoms. The severity of COVID-19 was defined according to the diagnostic and treatment guideline for SARS-CoV-2 issued by the Chinese National Health Committee version 3 - 5 (8). The Ethical Approval Code is IR.AJUMS.REC.1399.088.

3.1. Statistical Analysis

Independent sample t-test was used to compare continuous variables between the groups of the discharged and expired patients. The independence between categorical variables and the outcome was assessed by chi-square or Fisher's exact tests. The Kaplan-Meyer curve was plotted to visualize the development of survival probabilities for two different starting time-points, hospital admission and clinical symptoms diagnosis. Moreover, the log-rank test was used to investigate the difference in the two starting time-point curves' survival probabilities. Survival analysis was utilized to assess the impact of various variables on time to death/discharge data. In this dataset, dying from COVID-19 was considered the event, and discharge was assumed to be the right censoring. When the number of covariates and factors exceeds the number of observations, routine and standard survival analysis approaches, such as Cox's proportional hazard regression, do not result in adequate and reliable estimations (9).

4. Results

Of all 97 cases with COVID-19, 30 (30.9%) died, and 67 (69.1%) were discharged after recovery. The distribution of variables across the two groups of cases is shown in Table 1 and 2. Death from COVID-19 was significantly associated with critical disease intensity (P < 0.001), loss of consciousness (P = 0.001), ischemic heart disease (IHD) (P = 0.005), Parkinson (p = 0.028), invasive O2 support (P < 0.001), and non-negative Troponin (P = 0.016). Dead individuals were almost 11 years older than those discharged (P = 0.001). Discharging from COVID-19 was associated with the lower mean of respiratory rate (RR), blood sugar (BS), BUN, AST, total and direct bilirubin, neutrophil count, and sodium. Moreover, discharging is affiliated with higher O2 saturation, higher lymphocyte count, and neutral pH, higher HCO3, and base excess (BE) ( Table 2).

Table 1. Distribution of Categorical Variables in Two Groups of Dead and Discharged COVID-19 Cases
VariableOutcome No. (%)P-Value
Discharge 67 (69.1%)Death 30 (30.9%)
Gender0.812
Female24 (35.80 )10 (33.30 )
Male43 (64.20 )20 (66.70 )
Tobacco and alcohol5 (7.50 )4 (13.30 )0.452
Sign and Symptoms
Cough55 (82.10 )20 (66.7 )0.118
Dyspnea26 (38.80 )15 (50.00 )0.302
Orthopnea2 (3.0 )0 (0.0 )0.999
Paroxysmal nocturnal dyspnea (PND)2 (3.0)0 (0.0)0.999
Sore throat4 (6.00)0 (0.00)0.308
Chest pain4 (6.00)1 (3.30)0.677
Fever46 (68.70 )13 (43.30 )0.018
Chills29 (43.30 )7 (23.30 )0.060
Tachypnea2 (3.00 )0 (0.00 )0.999
Loss of speech0 (0.00 )1 (3.30 )0.309
Dizzying3 (4.50)0 (0.00)0.550
Runny nose1 (1.50 )0 (0.00 )0.999
Level of consciousness (LOC)0 (0.00 )6 (20.00 )0.001
Hyperhidrosis3 (4.50 )1 (3.30 )0.793
Weakness19 (28.40 )8 (26.70 )0.864
Lethargy19 (28.40 )8 (26.70 )0.864
Sleepiness0 (0.00)1 (3.30)0.309
Hemoptysis1 (1.50)0 (0.00)0.999
Myalgia27 (40.30 )7 (23.30 )0.106
Vomiting6 (9.00 )3 (10.00 )0.870
Nausea14 (20.90 )4 (13.30 )0.376
Anorexia9 (13.40 )5 (16.70 )0.675
Constipation2 (3.00 )1 (3.30 )0.927
Diarrhea8 (11.90 )2 (6.70 )0.430
Stomachache1 (1.50 )1 (3.30 )0.550
Dry mouth1 (1.50)0 (0.0)0.999
Delusion0 (0.0)1 (3.30 )0.309
Confusion0 (0.00 )1 (3.30)0.309
Headache14 (20.90 )2 (6.70)0.137
Past medical history
Coronary artery bypass graft (CABG)5 (7.50 )4 (13.30 )0.357
Other operations3 (4.50)3 (10.0)0.297
Operation .mediastinum1 (1.50)0 (0.0)0.999
Chronic obstructive pulmonary disease (COPD)2 (3.00)1 (3.30)0.927
Diabetes mellitus (DM)15 (22.40 )11 (36.70 )0.142
Hypertension (HTN or HT)21 (31.30 )11 (36.70 )0.606
Heart failure2 (3.00 )3 (10.00 )0.149
Ischemic heart disease (IHD)6 (9.00 )9 (30.00 )0.005
Dengue hemorrhagic fever (DHF)1 (1.50 )1 (3.30 )0.525
Cerebrovascular accident (CVA)1 (1.50 )3 (10.00 )0.086
Congestive heart failure (CHF)0 (0.00 )1 (3.30 )0.309
Hyperlipidemia 3 (4.50 )2 (6.70 )0.643
Sinusitis1 (1.50 )0 (0.00 )0.999
End-stage renal disease (ESRD)2 (3.0)0 (0.0 )0.999
Chronic kidney disease (CKD)1 (1.50 )2 (6.70 )0.225
Asthma5 (7.50 )0 (0.00 )0.320
Pneumonia0 (0.00 )1 (3.30 )0.309
Allergy1 (1.50 )0 (0.00 )0.999
Tuberculosis (TB)0 (0.00 )1 (3.30 )0.309
Fatty liver1 (1.50 )1 (3.30 )0.525
Bedridden0 (0.00 )1 (3.30 )0.309
Cardiomegaly0 (0.00)1 (3.30)0.309
Hyperthyroidism2 (3.00)0 (0.00)0.999
Rheumatoid arthritis1 (1.50 )0 (0.00 )0.999
Acute kidney injury (AKI)0 (0.00 )2 (6.70 )0.093
Auto Immune hepatitis1 (1.50 )0 (0.00 )0.999
Parkinson0 (0.00 )3 (10.00 )0.028
Gout1 (1.50 )0 (0.00 )0.999
Human immunodeficiency viruses (HIV)0 (0.00 )1 (3.30 )0.309
Pacemaker1 (1.50 )0 (0.00 )0.999
Kidney transplant patients1 (1.50 )0 (0.00 )0.999
Critical criterion
Ventilator1 (1.50 )15 (50.0 )<0.001
Shock0 (0.00 )1 (3.30 )0.999
ICU/ multi organ failure2 (3.00 )5 (16.70 )0.606
Ventilator & multi organ failure64 (95.5 )9 (30.0 )0.018
O2 support<0.001
Invasive3 (4.50 )25 (83.30 )
Noninvasive17 (25.40 )5 (16.70 )
Spontaneous47 (70.10 )0 (0.00 )
Treatments
Antiviral59 (88.1)20 (66.7)0.012
Antibiotic42 (62.70)25 (83.30)0.042
Corticosteroid54 (80.60)22 (73.30)0.422
Positive troponin0 (0.00 )3 (15.80)0.016
Aware of the transmission source9 (13.40)4 (13.30)0.989
Disease intensity
Weakly 0 (0.00 )1 (5.30 )0.999
Mild50 (74.6)0 (0.00)0.001
Severe14 (20.9)1 (3.3)0.001
Critical3 (4.5)29 (96.7)0.001
Table 2. Distribution of Continuous Variables in Two Groups of Dead and Discharged COVID-19 Cases
OutcomeMean (SD)P-VALUE
Creatine kinase-MB (CK-MB)0.792
Discharge22.750 (19.441)
Death25.000 (10.412)
Respiratory rate0.043
Discharge23.552 (7.163)
Death26.767 (7.016)
Age0.001
Discharge51.930 (15.088)
Death62.830 (15.295)
O2 Sat.< 0.001
Discharge94.896 (4.537)
Death87.000 (11.117)
Blood sugar0.020
Discharge130.091 (82.084)
Death204.286 (124.959)
Creatinine0.329
Discharge1.603 (2.442)
Death2.097 (1.882)
BUN< 0.001
Discharge19.761 (14.075)
Death46.933 (41.666)
Aspartate aminotransferase (AST)0.052
Discharge50.344 (34.088)
Death155.643 (417.416)
Alanine aminotransferase (ALT)0.154
Discharge32.361 (35.944)
Death50.750 (85.117)
Total bilirubin0.016
Discharge0.995 (0.471)
Death1.311 (0.725)
Direct bilirubin0.035
Discharge0.300 (0.350)
Death0.536 (0.685)
Alkaline phosphatase0.472
Discharge195.684 (121.418)
Death173.333 (69.822)
Lactate dehydrogenase (LDH)0.216
Discharge596.072 (274.847)
Death686.134 (323.019)
WBC count0.165
Discharge8.065 (9.220)
Death10.663 (6.313)
Neutrophil count< 0.001
Discharge67.003 (12.973)
Death77.323 (10.445)
Lymphocyte count < 0.001
Discharge27.024 (13.045)
Death15.847 (8.364)
RBC count0.127
Discharge4.509 (0.566)
Death4.295 (0.741)
Hemoglobin0.071
Discharge13.021 (1.717)
Death12.260 (2.238)
Hematocrit0.139
Discharge37.975 (4.626)
Death36.197 (6.631)
Platelet count0.458
Discharge171.726 (54.449)
Death161.778 (65.304)
Prothrombin Time (PT)0.144
Discharge12.586 (2.527)
Death13.423 (2.038)
Partial Thromboplastin Time (PTT)0.452
Discharge37.426 (18.649)
Death40.423 (11.197)
International normalized ratio (INR)0.077
Discharge1.141 (0.288)
Death1.272 (0.334)
Erythrocyte sedimentation rate (ESR)0.281
Discharge42.490 (25.876)
Death51.444 (39.057)
pH< 0.001
Discharge7.404 (0.057)
Death7.288 (0.194)
PCO20.942
Discharge44.321 (8.284)
Death44.145 (13.913)
HCO30.001
Discharge26.135 (4.202)
Death21.919 (6.369)
Na0.010
Discharge135.739 (2.769)
Death138.267 (6.565)
K0.828
Discharge4.099 (0.564)
Death4.130 (0.823)
P0.441
Discharge4.267 (1.791)
Death5.290 (2.813)
Ca0.739
Discharge9.057 (1.162)
Death8.900 (0.811)
Mg0.328
Discharge2.025 (0.287)
Death2.550 (0.943)

The survival probability quartiles in two different starting times of admission and presentation of symptoms are shown in Table 3. The starting time for admission was recorded for all patients, while only 77 (79%) cases remembered the day when the first COVID-19 symptoms appeared. Based on the Kaplan-Meier (Product Limit) approach, the mean survival time with admission and beginning of symptom as the starting times was 11.92 days and 20.87 days, respectively. Moreover, 25% of the cases survived 26 days and 17 days after the beginning of symptoms and admission, respectively. The median and third quartile survival time after admission was 12 days and eight days, respectively. The median and third quartile survival time after symptoms were 22 days 16 days, respectively. In other words, 50% of the cases died between days 16 and 26 after diagnosing their clinical symptoms. Also, half of the patients died between days eight and 17 after their first admission. The log-rank test showed a significant difference in the two survival probabilities (chi-square = 17.39, DF = 1, P < 0.001).

Table 3. Means and quartiles for Survival Time in the hour
Quantity / Start FromEstimate Hour (Day)Std. Error95% Confidence Interval
Mean
Admission286.119 (11.92)25.933235.29336.949
Symptom500.899 (20.87)40.712421.103580.695
First quartile
Admission408 (17)55.118
Symptom624 (26)69.561
Median
Admission288 (12)24.79
Symptom528 (22)35.195
Third quartile
Admission192 (8)29.869
Symptom384 (16)39.123

The results of the random survival forest are shown in Figures 1 and 2. The order of essential variables for admission as the starting time is shown in Figure 1, in which pH, WBC count, loss of consciousness, neutrophil count, BE, HCO3, age, BUN, O2 saturation, and lymphocyte count were at the top list. Moreover, some critical variables for symptom recognition as the starting time were BUN, lymphocyte count, loss of consciousness, IHD, Cerebrovascular accident (CVA), CVA, age, and AST. Other variables are shown in detail in Figure 2.

Figure 1. Variable importance resulted by random survival forest for classifying cases into dead and discharged for those with admission as the starting time.
Figure 2. Variable importance resulted by random survival forest for classifying cases into dead and discharged for those with symptoms as the starting time.

5. Discussion

In the current study, the mortality rate of COVID-19 was 30% and was significantly associated with critical disease intensity, fever, chills, loss of consciousness, IHD history, Parkinson's disease, invasive O2 therapy, and troponin levels. According to several studies, coronavirus infection, similar to some viral infections, may be associated with heart damage. A study of 400 patients admitted to Wuhan, China, found that about one-fifth of patients with COVID-19 had heart disease, which increases mortality (10). Severe and sudden inflammation of the heart muscle causes arrhythmia and impairs the heart's ability to pump blood efficiently. Therefore, patients with a history of cardiovascular disease and hypertension are at higher risk of death than normal individuals (11). Moreover, fatty plaques in the arteries of the heart of people with or without cardiovascular disease symptoms may become unstable due to fever and inflammation, leading to vascular occlusion and cardiovascular problems (12).

The current study declared that increased old age correlated with death in subjects suffering from COVID-19. In most studies, older age has been stated as a related predictor of fatality in SARS-CoV-2 and COVID-19 (13, 14). Opal in 2005 revealed that T-cell and B-cell function and the overproduction of interleukins become further acting by age, leading to a lack in control of viral replication and more extensive proinflammatory responses with harmful consequences (15).

We found that patient discharging was associated with higher O2 saturation, lymphocyte count, atrial blood pH, HCO3, and BE. Moreover, the higher mean of BS, BUN, total and direct bilirubin, neutrophil count, and sodium was associated with a higher discharge rate. Other essential studies confirm the mentioned factors in our study, and the results are somehow consistent (6, 14, 16). Li et al. in Wuhan in March 2020 presented that male gender, older subject, leukocytosis, cardiac injury, high blood glucose were associated with death in patients with severe COVID-19 (11). Similarly, in February 2020, Yang found that the increased risk of death of COVID-19 patients with pneumonia is considerable with older patients, duration from the onset of symptoms to ICU admission, ratio of PaO2 to FiO2, total bilirubin concentration, and lactate concentration (17).

The mean survival time with admission and symptom starting was approximately 12 and 21 days in the current research, respectively. Another study revealed the patient information based algorithm (PIBA) considered the death rate according to data of the subjects in Wuhan and then in other cities overall China. They calculated the predicted days from hospital admission to death was 13, and the mortality rate of COVID-19 varies from 0.75% to 3% and may decrease in the future (18). the study predicted the force of continuous exposure to coronavirus on the fatality rate gain and was used in Germany, China, France, United Kingdom, Iran, Italy, and Spain, for modeling. Regarding Iran, Italy, and Spain, the fatality rate will increase to 10% with an extra 3 - 10 days of exposure (19). However, for the dead time, the results are not consistent in different studies, and some have reported death up to 57 days after symptom onset (20).

Nevertheless, we found that cases have a higher probability of discharge when the clinical symptoms are diagnosed before the admission time. Finally, our results indicated that pH, WBC count, loss of consciousness, neutrophil count, BE, HCO3, age, BUN, O2 saturation, and lymphocyte count were at the top list of factors that affect the prognosis of the disease. Moreover, some critical variables for symptom recognition at the starting time were as follows: BUN, lymphocyte count, loss of consciousness, IHD, CVA, age, and AST. It is necessary to mention that most of the mentioned factors are the same in many studies but vary in importance. Garcia et al. reported creatinine, D-dimer, lactate, potassium, arterial pO2/FIO2 (P/F ratio), and alveolar-arterial gradient at admission and IHD as prognostic factors in patients with COVID-19 (20). Another study by Cummings et al. indicated that chronic pulmonary disease, chronic cardiovascular disease, older age, and elevated interleukin-6 and D-dimer levels at admission are the most substantial prognostic factors in patients with COVID-19 (21).

5.1. Conclusions

We hypothesize that the survival probability when symptom diagnosis is considered symptom diagnosis was considered the starting time is higher than that of admission time. In other words, cases had a higher probability of discharge when the clinical signs are diagnosed before than at the time of admission. Further, genetics, immune response, health care system, and other factors may affect the prognosis and change the most critical factors affecting the COVID-19 COVID_19 prognosis in different regions.

Footnotes

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