By Kamel, M; El-Shazly, M;
Al-Zuabi, H; Al-Ameeri, S;
Al-Asfoor, S; Al-Majdely,
R; Almoosa, I (2023).
|
Greener Journal of Epidemiology and Public Health ISSN: 2354-2381 Vol. 11(1), pp. 12-22, 2023 Copyright ©2023, the copyright of this article is
retained by the author(s) |
|
Predictors of severe respiratory
complications among hospitalized COVID-19 patients.
Mohamed Kamel1,
Medhat El-Shazly2, Homoud Al-Zuabi3, Sarah Al-Ameeri4, Sara Al-Asfoor5, Rufaida Al-Majdely6, Ibrahim Almoosa7
1 MD, Consultant of Public Health, Department of Occupational Medicine,
Ministry of Health, Kuwait & Professor of Community Medicine, Faculty of
Medicine, Alexandria University, Egypt.
2 MD, Consultant of Public Health, Department of Planning,
Ministry of Health, Kuwait & Professor of Health Statistics, Medical
Research Institute, Alexandria University, Egypt.
3MRCGP, Consultant
Family medicine, Head of Chronic Diseases Clinic Team, Head of the
Non-communicable Disease Administration, Ministry of Health, Kuwait.
4 MRCGP, Family Medicine Specialist, Ministry of Health, Kuwait.
5 General practitioner, Ministry of Health, Kuwait.
6 MRCGP, Senior Specialist, Jaber Alahmad Quarantine, Mubarak Health Region, Ministry of Health, Kuwait.
7 MRCGP, Consultant
Family Medicine, Ministry of Health, Kuwait.
|
ARTICLE INFO |
ABSTRACT |
|
Article No.: 030823025 Type: Research |
Background: During hospitalization, 60.1% patients developed respiratory failure.
Acute Respiratory Distress Syndrome (ARDS) is a common and devastating
critical illness. It has been reported that 67% of COVID-19 patients with the
severe illness have developed ARDS, which is the main cause of death. Objectives: This study aimed at highlighting some factors that could be associated
with severe respiratory complications of COVID-19 in admitted
cases during the first wave of the disease. Methods: This study is a retrospective case-control one that was conducted by
reviewing records of all admitted COVID-19 patients in Jaber
hospital in Kuwait during the period from February till May 2019. Analysis
was initially carried on a series of univariate
comparisons, followed by multiple logistic regression analysis. Results: Male gender, older age as well as pre-existing conditions, such as hypertension,
diabetes and pulmonary diseases, predispose patients to increased risk of
severe respiratory complication. Such complications were associated with
presenting fever, cough, lower blood oxygen level as well as longer hospital
stay and ICU admission. Conclusions:. Elderly males having fever, cough and
shortness of breath and suffering hypertension, diabetes mellitus or other
associated respiratory diseases at a higher risk for developing severe
respiratory complications |
|
Accepted: 10/03/2023 Published:
21/03/2023 |
|
|
*Corresponding Author Prof. Dr. Medhat El-Shazly E-mail: medshaz@ yahoo. com Phone: +965/ 6612524 |
|
|
Keywords:
|
|
|
|
|
Introduction:
The coronavirus
disease 2019 (COVID-19) is an acute infectious pneumonia. Spreading mainly
through the droplet route and close contact, the virus causes mild symptoms in
the majority of cases, the most common being: fever, dry cough, and fatigue.
(Guan et al., 2020; Huang et al., 2020) The disease has rapidly developed into
a worldwide pandemic. At the end of April, 2020, 217,769 people died of
COVID-19 infection, (Xu W et al., 2021) and by the
day of 11 February 2021, 2,369,067 million deaths were recorded.(WHO, 2021)
Despite the public
health efforts aimed at delaying its spread; during the courses
of treatment, due to the large increase in the demand for hospital beds
and the shortage of medical equipment, coupled with the lack of specifc medicine, patients with basic diseases or old age
are more likely to progress to severe disease, leading to death. Recent reports
show that 14.1–33.0% of COVID-19 infected patients are prone to develop into
severe cases, and the mortality rate of critical cases is 61.5%, increasing
sharply with age and underlying comorbidities. (Yang X et al. 2020; Liu W et
al. 2020; Zhao X-Y et al., 2020; Li K. et al., 2020). In a previous study, the
author found that during hospitalization, 60.1% patients developed respiratory
failure. (Becerra-Munoz, 2021) Acute Respiratory Distress Syndrome (ARDS) is a
common and devastating critical illness (Bellani G.
et al.; 2016). It has been reported that 67% of COVID-19 patients with the
severe illness have developed ARDS, which is the main cause of death. However,
in the early stage of onset, quite a few patients have no obvious clinical
symptoms, so it is difcult to judge until ARDS
occurs. (Yang X et al. 2020)
In the early phase of
clinical observation, respiratory failure was attributed as a major cause of
morbidity and mortality of COVID-19 patients. (Gacche
et al., 2021) However, clinical and epidemiological data links it with patients
having pre-existing history of hypertension, chronic obstructive pulmonary disease,
diabetes, coronary heart disease, and kidney comorbidities have worse clinical
outcomes when infected with SARS-CoV-2. (Lippi et al., 2020; Lippi and Henry,
Cheng et al., 2020)
Certain demographic
factors reported in the literature are associated with a higher rate of
a respiratory failure and severe clinical course of COVID-19. Among these,
older age is a major predictor of mortality. (Cecconi,
et al., 2020) Data also suggest that male sex is a variable that is
independently associated with COVID-19 severity. (Palaiodimos
et al., 2020) Some other factors that could be associated, as
hypoxemia with which worse clinical outcomes has been reported. (Duan et al., 2020)
Predicting which
patients are more likely to develop ARDS, and thus face a greater risk of
complications including death, is particularly important in a novel and
accelerating outbreak. (Jiang X et al., 2020) The aim of the present study is
to highlight certain factors that could be associated with the development of
severe respiratory complications as ARDS or respiratory failure.
Subjects
and methods:
Setting and design:
This study is a part
of a larger one that was conducted in Jaber Al-Ahmed
hospital. The time interval of the study was set as four months from
April to July 2021. The details of the
study design, sampling and research tool were described elsewhere. (Al-Zuabi et al., 2022) Studied patients were classified
into 2 groups: cases (with respiratory complications )
and control (free from respiratory complications). Research tool included personal characteristics, associated co-morbid conditions,
presenting symptoms, investigations and vital signs on admission, COVI-19
complications, as well as outcome parameters. The
study was approved by the Ethics Committee of the Kuwaiti Ministry of
Health. The permission of the Deputy Ministry of Health in Kuwait as well as
head of Jaber hospital were
obtained.
Statistical analysis:
Analysis was initially carried out based on a series of univariate comparisons. In order to control simultaneously
for possible confounding effect of the variables, multiple logistic regression
was used for the final analysis. In the univariate
analysis Chi-square test was used to detect the association between respiratory
comlications and explanatory variables. In multiple
logistic regression analysis, the association between exposure and outcome was
expressed in terms of odds ratio (OR) together with their 95% confidence
intervals (95% CIs).
All the explanatory variables included in the logistic
model were categorized into two or more levels (R = reference category):
gender: maleR, female; age (years): <
40R, 40 – 49, 50 – 59, > 60; nationality: KuwaitiR, non-Kuwaiti; Governorate: CapitalR, Hawally, Farwaniya, Ahmadi, Jahar, Mubarak; smoking: noR,
yes; history of hypertension: noR, yes;
history of cardiovascular disease: noR,
yes; history of diabetes mellitus: noR,
yes; history of pulmonary disease: noR,
yes; history of dyslipidemia: noR, yes;
SpO2 level: ≤ 95R, > 95; lymphocytic count: normalR, low, high; FBS: normalR,
prediabetic, diabetic; creatinine
level: normalR, high; ICU admission: noR, yes; days of hospital stay: <10R,
10-14, 15-19, ≥20. All presenting symptoms were also
categorized as noR, yes. Analysis was
performed using SPSS package 22.
Results:
Reviewing the medical
records of the cases admitted to the selected hospital during the defined
period resulted in inclusion of 1482 positive cases for COVOD-19. Among them, 79 cases suffered from ARDS or respiratory failure.
Table 1 describes the
personal characteristics of the included patients according to the presence of
ARDS or respiratory failure. The proportion of males in the control group was
significantly higher than in cases group (77.0% versus 88.6%, p = 0.02). The mean
age of the control group (42.7±12.9) was insignificantly lower than that of the
case group (54.8±12.8), p < 0.001.
Table 2 shows the
frequency of co-morbid chronic diseases among patients with respiratory
complications and the control group. The proportions of hypertension,
cardiovascular diseases, diabetes mellitus, and respiratory diseases were
significantly higher in cases than controls (χ2 = 12.20,
14.94, 31.82, and 1.22, p<0.001) respectively.
As shown in table 3,
only the percentages of fever and cough were significantly higher in cases than
control (51.9% versus 25.9%, and 50.6% versus 32.4%, p<0.001)
Low blood oxygen
level on admission was significantly more encountered among cases than controls
(29.1% versus 3.8%, p < 0.001), as well as lymphocytoenia
(40.5% versus 11.9%, p < 0.001). Also, creatinine
level was higher in cases than controls signifivcantly
(Median (IQR): 86(48) versus 75(2), p <0.001. The percentage of diabetic
patients as indicated by elevated fasting blood glucose was more encountered in
cases than controls (74.7% versus 24.9%, p <0.001). Higher levels of other
laboratory paraeters were significantly more
encountered in cases than control as D Dimer, CRP, LDH, troponin and ferritin.
Table 5, showed that caes of
respiratory complication were significantly more in need of oxygen therapy than
control (72.2% versus 7.4%, p<0.001) and stayed for longer duration in
hospital (median = 23 versus 8 , p <0.01), and significantly more
proportions of cases were admitted to the ICU than control (89.9 versus 3.7, p<0.001)
After adjustment for the confounding effects between
variables, table 6 illustrated variables that retained as significant
determinants for the outcome of interest (severe respiratory complications).
Female gender was proved to be a protective factor against the out come on interest (OR = 1.4, CIs: 0.1 – 0.8). Older age
seemed to be at higher risk among admitted COVID-19 cases as patient in the age
group 40-49, and 50-59 years old were more prone to severe respiratory complications
as compared to those in the age group < 40 years (OR = 5.6, CIs: 2.8 – 9.5),
and (OR = 3.1, CIs: 1.4 – 4.9) respectively.
Regarding chronic co-morbid conditions, patients with
hypertension were 2.9 folds liable for severe respiratory complications during
hospital stay (CIs: 1.7 – 4.6), and patients with diabetes mellitus were 2.3
folds (CIs: 1.2 – 5.2). Also, pulmonar diseases patients disease were significantly more liable to severe
respiratory complications during their hospital stay (OR = 3.9, CIs: 1.9 –
8.1).
Regarding the presenting symptoms, only fever and cough
were proven to be positively associated with sever
respiratory complications (OR = 5.4, CIs: 2.9 – 11.7) and (OR = 3.0, CIs: 1.4 –
5.5) respectively. Those patients with low blood oxygen level on admission were
9.4 folds at risk of severe respiratory complications (CIs: 4.9 – 17.6). Severe
respiratory complications were significantly associated with ICU admission (OR
= 163.3, CIs: 68.9 – 389.9) and hospital stay > 20 days (OR = 5.5, CIs: 2.0
– 14.8).
Table (1):
Distribution of hospitalized COVID-19 patients according to personal
characteristics and respiratory complications.
|
Personal
characteristics |
ARDS/respiratory failure |
Test
of significance ( p ) |
||||
|
No (n=1403) |
Yes (n=79) |
|||||
|
No. |
% |
No. |
% |
|||
|
Gender |
|
|
|
|
|
|
|
Male |
1080 |
77.0 |
70 |
88.6 |
Ӽ2=6.81 |
|
|
Female |
323 |
23.0 |
9 |
11.4 |
p=0.02 |
|
|
Age (years) |
|
|
|
|
|
|
<40 |
654 |
46.6 |
9 |
11.4 |
Ӽ2=58.61 |
|
40-49 |
345 |
24.6 |
19 |
24.1 |
p<0.001 |
|
50-59 |
237 |
16.9 |
23 |
29.1 |
|
|
≥60 |
167 |
11.9 |
28 |
35.4 |
|
|
Mean ± SD |
42.7
± 12.9 |
54.8±12.8 |
t = 8.08 |
||
|
Min - Max |
19
– 94 |
22
– 93 |
p
<0.001 |
||
|
Nationality: |
|
|
|
|
|
|
Kuwaiti |
347 |
24.7 |
11 |
13.9 |
Ӽ2=4.77 |
|
Non-Kuwaiti |
1056 |
75.3 |
68 |
56.1 |
p=0.03 |
|
Governorate |
|
|
|
|
|
|
Capital |
346 |
24.7 |
17 |
21.5 |
Ӽ2=3.07 |
|
Hawalli |
283 |
20.2 |
16 |
20.3 |
p=0.69 |
|
Farwaniyah |
417 |
29.7 |
30 |
38.0 |
|
|
Ahmadi |
228 |
16.3 |
10 |
12.7 |
|
|
Jahra |
58 |
4.1 |
2 |
2.5 |
|
|
Mubarak Alkabeer |
71 |
5.1 |
4 |
5.1 |
|
|
BMI:* |
|
|
|
|
|
|
Normal/Under-weight |
178 |
34.2 |
3 |
15.0 |
Ӽ2=3.79 |
|
Over-weight |
217 |
41.7 |
10 |
10.0 |
p=0.29 |
|
Obese |
84 |
16.1 |
4 |
20.9 |
|
|
Morbid obese |
42 |
8.1 |
3 |
15.0 |
|
|
Mean ± SD |
27.41
± 5.3 |
29.11
± 4.53 |
t = 1.42 |
||
|
Min - Max |
16.18
– 59.86 |
22.87
– 38.05 |
p
=0.16 |
||
|
Smoking:** |
|
|
|
|
|
|
No |
410 |
87.2 |
21 |
84.0 |
Fisher’s
Exact |
|
Yes |
60 |
12.8 |
4 |
16.0 |
p=0.55 |
*: missing 941 cases
**: missing 987 cases
Table (2): Distribution
of hospitalized COVID-19 patients according to chronic co-morbid diseases and
respiratory complications.
|
Co-morbid diseases |
ARDS/respiratory failure |
Test of significance ( p ) |
||||||
|
No (n=1403) |
Yes (n=79) |
|||||||
|
No. |
% |
No. |
% |
|||||
|
Hypertension: |
246 |
17.5 |
29 |
36.7 |
Ӽ2=12.20
(p<0.001) |
|
|
|
Cardiovascular |
63 |
4.5 |
10 |
12.7 |
Ӽ2=10.65
(p<0.001) |
|
|
|
Diabetes mellitus |
212 |
15.1 |
34 |
43.0 |
Ӽ2=42.14
(p<0.001) |
|
|
|
Respiratory diseases |
50 |
3.6 |
10 |
12.7 |
Ӽ2=15.92
(p<0.001) |
|
|
|
Dyslipidemia |
27 |
1.9 |
5 |
6.3 |
Fisher’s Exact (p=0.25) |
|
|
|
Renal diseases |
26 |
1.9 |
6 |
7.6 |
Fisher’s Exact (p=0.01) |
|
|
|
Hypothyroidism |
24 |
1.7 |
4 |
5.1 |
Fisher’s Exact (p=0.03) |
|
|
|
Immune suppression |
3 |
0.2 |
1 |
1.3 |
Fisher’s Exact (p=0.20) |
|
|
|
Organ transplant |
3 |
0.2 |
1 |
1.3 |
Fisher’s Exact (p=0.20) |
|
|
Table (3): Distribution of hospitalized COVID-19 patients
according to symptoms on admission and respiratory complications
|
Manifestations |
ARDS/respiratory
failure |
Test of significance ( p ) |
|||
|
No (n=1403) |
Yes (n=79) |
||||
|
No. |
% |
No. |
% |
||
|
General |
|
|
|
|
|
|
Fever |
363 |
25.9 |
41 |
51.9 |
Ӽ2=25.55
(p<0.001) |
|
Headache |
57 |
4.1 |
2 |
2.5 |
Fisher’s Exact (p=0.77) |
|
Body aches |
153 |
10.9 |
7 |
8.9 |
Ӽ2=0.33
(p=0.57) |
|
Fatigue |
21 |
1.5 |
2 |
2.5 |
Fisher’s Exact (p=0.35) |
|
Respiratory |
|
|
|
|
|
|
Cough |
454 |
32.4 |
40 |
50.6 |
Ӽ2=11.24
(p<0.001) |
|
Blocked nose |
11 |
0.8 |
0 |
0.0 |
Fisher’s Exact (p=1.00) |
|
Loss of smell |
13 |
0.9 |
0 |
0.0 |
Fisher’s Exact (p=1.00) |
|
Sore throat |
173 |
12.3 |
6 |
7.6 |
Ӽ2=1.58
(p=0.21) |
|
Chest pain |
7 |
0.5 |
2 |
2.5 |
Fisher’s Exact (p=0.08) |
|
Shortness of breath |
110 |
7.8 |
44 |
55.7 |
Fisher’s Exact (p=0.32) |
|
Gastrointestinal |
|
|
|
|
|
|
Loss of taste |
16 |
1.1 |
0 |
0.0 |
Fisher’s Exact (p=1.00) |
|
Nausea |
19 |
1.4 |
1 |
1.3 |
Fisher’s Exact (p=1.00) |
|
Vomiting |
21 |
1.5 |
1 |
1.3 |
Fisher’s Exact (p=1.00) |
|
Diarrhea |
46 |
3.3 |
3 |
3.8 |
Fisher’s Exact (p=0.74) |
|
Abdominal pain |
6 |
0.4 |
1 |
1.3 |
Fisher’s Exact (p=0.32) |
Table (4): Distribution of
hospitalized COVID-19 patients according to investigations and vital signs on
admission and respiratory complications.
|
Investigations and vital signs |
ARDS/respiratory failure |
Test of significance ( p.) |
|||
|
No (n=1403) |
Yes (n=79) |
||||
|
No. |
% |
No. |
% |
||
|
Measuring
blood pressure: |
|
|
|
|
|
|
Normal |
1177 |
83.9 |
53 |
67.1 |
Ӽ2=14.96 |
|
Hypertension |
226 |
16.1 |
26 |
32.9 |
p<0.001 |
|
SBP:
(mean + SD) |
126.8±15.9 |
130.9±21.6 |
|
||
|
DBP:
(mean + SD) |
79.1±8.6 |
75.4±14.5 |
|
||
|
Heart
rate (BPM) |
|
|
|
||
|
Min
- Max |
47 – 138 |
57 – 140 |
t = 3.93 |
||
|
Mean
+ SD |
85.3±12.2 |
90.9±17.4 |
p < 0.001 |
||
|
Respiratory
rate (BPM) |
|
|
|
||
|
Min
- Max |
16 – 44 |
12 – 36 |
t = 11.07 |
||
|
Mean
+ SD |
20.9±2.0 |
23.8±4.3 |
p < 0.001 |
||
|
SpO2
(%) level: |
|
|
|
|
|
|
≥95 |
1350 |
96.2 |
56 |
70.9 |
Ӽ2=98.68 |
|
<95 |
53 |
3.8 |
23 |
29.1 |
p<0.001 |
|
Mean
+ SD |
97.6±2.0 |
95.2±4.1 |
|
||
|
D
Dimer (ng/mL) |
|
|
|
|
|
|
Median |
231 |
990 |
Mann-Whitney U |
||
|
IQR |
296 |
2179 |
p <0.001 |
||
|
CRP
(mg/L) |
|
|
|
||
|
Median |
7 |
120 |
Mann-Whitney U |
||
|
IQR |
24 |
105 |
p <0.001 |
||
|
LDH
(IU/L) |
|
|
|
||
|
Median |
219 |
535 |
Mann-Whitney U |
||
|
IQR |
131 |
321 |
p <0.001 |
||
|
Troponin
(ng/L) |
|
|
|
||
|
Median |
6.0 |
14.5 |
Mann-Whitney U |
||
|
IQR |
7.0 |
35.4 |
p <0.001 |
||
|
Ferritin
level: |
|
|
|
|
|
|
Normal |
140 |
88.1 |
34 |
100.00 |
|
|
Low |
19 |
11.9 |
0 |
0.00 |
Mann-Whitney U |
|
Median
(IQR) |
350.0 (563.6) |
11623 (1443.6) |
P < 0.001 |
||
|
Lymphocytic
levl |
|
|
|
|
|
|
Normal |
1221 |
87.0 |
46 |
58.2 |
Ӽ2=52.85 |
|
Low |
167 |
11.9 |
32 |
40.5 |
p<0.001 |
|
High |
15 |
1.1 |
1 |
1.3 |
|
|
Median
(IQR) |
1.8000 (1.20) |
1.0000 (0.40) |
|
||
|
Creatinine level: |
|
|
|
|
|
|
Normal |
1331 |
94.9 |
58 |
73.4 |
Ӽ2=58.51 |
|
High |
72 |
5.1 |
21 |
26.6 |
P<0.001 |
|
Blood
sugar level: |
|
|
|
|
|
|
Normal |
650 |
46.3 |
10 |
12.7 |
Ӽ2=93.41 |
|
Prediabetic |
404 |
28.8 |
10 |
12.7 |
p<0.001 |
|
diabetic |
349 |
24.9 |
59 |
74.6 |
|
|
Median
(IQR): |
5.7 (1.9) |
9.1 (5.5) |
|
||
|
HbA1c
(mmol/mol) |
|
|
|
||
|
Number |
84 |
8 |
Mann-Whitney U |
||
|
Median |
8.9 |
10.3 |
P = 0.27 |
||
|
IQR |
4.2 |
2.7 |
|
||
Table (5): Distribution of hospitalized COVID-19
patients according to oxygen therap, hospital stay,
ICU admission and and respiratory complications
|
|
ARDS/respiratory
failure |
Test of significance ( p ) |
|||
|
Variable |
No (n=1403) |
Yes (n=79) |
|||
|
No. |
% |
No. |
% |
||
|
Oxygen therapy |
|
|
|
|
|
|
No |
1299 |
92.6 |
22 |
27.8 |
Ӽ2 =
323.70 |
|
Yes |
104 |
7.4 |
57 |
72.2 |
p < 0.001 |
|
Type of oxygen therapy |
|
|
|
|
|
|
Mask |
39 |
37.5 |
42 |
73.7 |
Ӽ2 =
19.28 |
|
Nasal |
65 |
62.5 |
15 |
26.3 |
p <0.001 |
|
O2 (L/min) |
|
|
|
|
|
|
1 – 5 |
63 |
60.6 |
13 |
22.8 |
Ӽ2 =
39.19 |
|
6 – 10 |
32 |
30.8 |
15 |
26.3 |
P < 0.001 |
|
> 10 |
9 |
8.7 |
29 |
50.9 |
|
|
Median (IQR) |
4 (4) |
12 (10) |
|
||
|
Duration of Hospital stay (days) |
|
|
|
|
|
|
< 10 |
753 |
53.7 |
9 |
11.4 |
Ӽ2 =
91.85 |
|
10 – 14 |
194 |
13.8 |
13 |
16.5 |
P < 0.001 |
|
15 – 19 |
207 |
14.8 |
10 |
12.7 |
|
|
≥ 20 |
249 |
17.7 |
47 |
59.4 |
|
|
Median (IQR) |
8 (14) |
23 (20) |
|
||
|
ICU admission |
|
|
|
|
|
|
No |
1351 |
96.3 |
8 |
10.1 |
Ӽ2 =
729.61 |
|
Yes |
52 |
3.7 |
71 |
89.9 |
p < 0.001 |
|
Duration of ICU stay (days) |
|
|
|
|
|
|
1 – 10 |
28 |
53.8 |
27 |
38.0 |
Ӽ2 =
3.0461 |
|
> 10 |
24 |
46.2 |
44 |
62.0 |
p = 0.08 |
|
Median (IQR) |
9.5 (15) |
13 (18) |
|
||
Table (6): Factors associated with respiratory
complications among admitted COVID-19 patients.
|
Variables |
Odds Ratio |
95% CIs |
|
Age (years) |
|
|
|
< 40 R |
1 |
|
|
40 - 49 |
5.6 |
(2.8 – 9.5) |
|
50 - 59 |
3.1 |
(1.4 – 4.9) |
|
> 60 |
2.1 |
(0.9 – 3.3) |
|
Gender |
|
|
|
Male |
1 |
|
|
Female |
0.41 |
(0.022 – 0.78) |
|
Co-morbidity |
|
|
|
Hypertension |
|
|
|
No R |
1 |
|
|
Yes |
2.9 |
(1.7 – 4.6 |
|
Diabetes mellitus: |
|
|
|
No R |
1 |
|
|
Yes |
2.3 |
(1.2 – 5.2) |
|
Pulmonary disease: |
|
|
|
No R |
1 |
|
|
Yes |
3.9 |
(1.9 – 8.1) |
|
Fever |
|
|
|
≤ 39oC R |
1 |
|
|
>39oC |
5.4 |
(2.9 – 11.7) |
|
Cough: |
|
|
|
No R |
1 |
|
|
Yes |
3.0 |
(1.4 – 5.5) |
|
SpO2: |
|
|
|
Normal R |
1 |
|
|
Low |
9.4 |
(4.9 – 17.6) |
|
Duration of hospital stay (days) |
|
|
|
<10 R |
1 |
|
|
10-14 |
2.5 |
(0.8 – 8.7) |
|
15-19 |
1.4 |
(0.4 – 4.9 |
|
>20 |
5.5 |
(2.0 – 14.8) |
|
ICU admission: |
|
|
|
No R |
1 |
|
|
Yes |
163.3 |
(68.9 – 389.9) |
R = Reference category,
OR = Odds ratio, CI = Confidence interval
Discussion:
Extra-pulmonary organ systems including the cardiac,
gastrointestinal, hepatic, renal, ocular, and dermatologic are affected by
COVID-19, however the most commonly affected organ
system by COVID-19 is the pulmonary system, with the most frequent clinical
manifestations including cough, dyspnea, fever, and sore throat. (Huang et al.,
2020; Chen et al., 2020) In the severe disease state, the patient’s clinical
course is complicated by the development of pneumonia with acute respiratory
distress syndrome (ARDS), acute hypoxic respiratory failure, and/or death.
(Rodriguez-Morales et al., 2020) The current study was designed to reveal the
risk factors leading to severe forms of the respiratory tract affection of
COVID-19 (acute respiratory distress syndrome or respiratory failure) in the
main hospital established to receive all forms of COVID-19 cases requiring
hospitalization.
It is important to
reveal the extent to which COVID-19 leads to severe forms of respiratory tract
complications which are the main cause of deaths in such cases and the risk
factors, at hospital admission, that can predict such severe complications. The
current study included 1482 hospitalized COVID-19 patients to study clinical,
laboratory and outcome differences between those suffering or not from
ARDS/respiratory failure. The study revealed that 79 (5.3%) patients developed
ARDS or failure. Early studies reported an incidence rate ranging from 15.6–31%
for ARDS. (Huang et al., 2020; Chen et al., 2020; Wang et al., 2020; Guang et al., 2019; Zhou et al., 2020)
A met-analysis of
observational studies and case reports showed that nearly one third (32.8%) of
patients with COVID-19 developed ARDS during their hospital admission. (Rodriguez-Morales
et al., 2020) Similarly, in a retrospective analysis of clinical findings in 85
patients with confirmed COVID-19, 74.1%of patients developed ARDS during their
hospitalization. Lai and his colleagues. identified that about 20% developed ARDS and >25% of
patients with COVID-19 required intensive care unit (ICU) admission. (Lai et
al., 2020) The figures revealed by these studies are higher than that revealed
by the current study however, the lower frequency rate of ARDS of the current study
may be attributed to several factors mainly that any case of positive test for
COVID-19 was admitted in the hospital under study however the severity of the
disease. That is many cases were admitted inspite of
being minor symptoms. Other factors may include the small number of cases in
some studies and the different population characteristics as well as the
differences in the study approach. Also, the decreasing virulence of COVID-19
virus with progress of the epidemic combined with weaker mutant strains of the
virus may be behind this difference.
The most common
perceived manifestations on hospital admissions were those related to the
respiratory system in addition to fever (51.9% compared with 25.9%). Cough
(50.6% compared with 32.4%) and shortness of breath (55.7% compared with 7.8%)
were several folds higher among patients suffering from ARDS compared to those
non suffering from ARDS. Several previous studies revealed a similar pattern of
manifestations characterized by dry cough, fever and respiratory failure.
(Zhang et al., 2020; Chu et al., 2020; Iwasawa et
al., 2020) The study of Huang and his colleagues pointed that the most common
symptoms were fever (98%) followed by cough (76%), with over half (55%) of the
patients developing dyspnea. (Huang et al., 2020)
Although there is no
clear risk factor for COVID-19, several factors have an impact on the prognosis
of respiratory illness including sex, age, positive smoking history, and
coexisting underlying disease(s). The current study revealed that elderly males
were at a higher risk of developing ARDS, not only that but the risk of
developing ARDS gradually increases with progress of age. Those at or above 60
years of age were at around three fold likely to develop ARDS compared to those
less than 40 years. Also hypertension (36.7% compared with 17.5%), diabetes
mellitus (43.0% compared with 15.1%) and existence of other pulmonary diseases
(12.7% compared with 3.6%), the most common comorbidities on hospital admission
were significant predictors of developing ARDS. Numerous studies reported that
a significant proportion of patients had at least one underlying disease. Based
on Chih-Cheng Lai and his associate, hypertension is
the most underlying disease (14.9%) followed by diabetes mellitus (7.4%) and
cardiovascular disease. (4.2%)(Lai et al., 2020) A
literature review by Pramath Kakodkar
and his associates showed that 28% of the patients had comorbidity (s), the
most underlying disorders were hypertension (55.3%), coronary artery
disease/cerebrovascular accident (31.5%) and diabetes mellitus (30.6%)
respectively. Guan and his colleagues showed that any underlying condition was
more common among patients with severe COVID-19. (Kakodkar
et al., 2020; Guan et al., 2020)
Laboratory
investigations provide valuable information about both the clinical status and
severity of hospitalized COVID-19 patients. Consistent with other studies, the
current research revealed significant differences among multiple laboratory
investigations between those with and without ARDS.( Li
X et al., 2020; Li X et al., 2020; Yazdanpanah et
al., 2020) Also, as expected patients with ARDS were more likely to stay in the
hospital (with a median of 23 days compared with 14 days) and to be admitted to
the intensive care unit (89.9% compared with 3.7%). Also they suffered from a
higher mortality rate (51.9% compared with just only 1.1%).
The main limitations
of the current study is being hospital based and depending mainly on secondary
data (records of hospitalized patients) however, the large number of recruited
cases and a selection of the only specialized hospital to deal with COVID-19
cases from all districts of Kuwait can provide both power and advantage for
carrying out this study.
Conclusions:
Thus, this study
demonstrates a difference of COVID-19 presentation and associated comorbidities
between those suffering from ARDS/failure. Overall, this renders elderly males
having fever, cough and shortness of breath and suffering hypertension,
diabetes mellitus and to other associated respiratory diseases at a higher risk
for developing severe respiratory complications. Hospital physicians should be
aware of these differences to guide the diagnosis and subsequent management
decisions. These results would help in developing specific and effective
management strategies.
References:
Al-Zuabi, Kamel MI, El-Shazly MK et al.
(2022). Gender
difference among hospitalized COVID-19 patients. GJMS; 12(2): 161-71.
Becerra-Muñoz VM, Núñez-Gil IJ,
Eid CM, et al. (2021). Clinical profile and predictors
of in-hospital mortality among older patients hospitalized for COVID-19. Age
and Ageing; 50: 326–34.
Bellani G et al. (2016). Epidemiology, patterns of care, and
mortality for patients with acute respiratory distress syndrome in intensive
care units in 50 countries. JAMA; 315(8): 788–800.
Cecconi M, Piovani D, Brunetta E, et al. (2020). Early predictors of clinical
deterioration in a cohort of 239 patients hospitalized for Covid-19 infection
in Lombardy, Italy. J Clin Med Res; 9(5):1548.
https://doi.org/10.3390/jcm9051548.
Chen N, Zhou M, Dong
X, et al. (2020). Epidemiological and clinical characteristics of 99 cases of
2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet;
395(10223):507 –13.
Cheng Y, Luo R, Wang K, et al.
(2020). Kidney disease is associated with in-hospital death of patients with
COVID-19. Kidney Int; 97: 829-38.
Chu H, Chan JF-W,
Yuen TT-T, et al. (2020). Comparative tropism, replication kinetics, and cell
damage profiling of SARS-CoV-2 and SARS-CoV with
implications for clinical manifestations, transmissibility, and laboratory
studies of COVID-19: an observational study. Lancet Microbe 1:e14–e23.
Duan J, Wang X, Chi J, et al. (2020). Correlation between the
variables collected at admission and progression to severe cases during
hospitalization among patients with COVID-19 in Chongqing. J Med Virol; 92(11): 2616-22.
Gacche RN, Gacche RA, Chen J, Li H,
Li G. (2021). Predictors of morbidity and mortality in
COVID-19. European Review for Medical and Pharmacological Sciences; 25:
1684-707.
Goh KJ, Choong MCM, Cheong EHT, et
al. (2020). Rapid progression to acute respiratory distress syndrome: review of
current understanding of critical illness from COVID-19 infection. Ann Acad Med Singapore; 49 (3):108–18.
Guan W-j, Ni Z-y, Hu
Y, et al. (2020). Clinical
characteristics of 2019 novel coronavirus infection in China. N Engl J Med; 382(18):1708– 20.
Huang C, Wang Y, Li
X, et al. (2020). Clinical features of patients infected with 2019 novel
coronavirus in Wuhan, China. Lancet; 395:497–506.
Iwasawa T, Sato M, Yamaya T, et al.
(2020). Ultra-high-resolution computed tomography can demonstrate alveolar
collapse in novel coronavirus (COVID-19) pneumonia. Jpn
J Radiol 38:394–8.
Kakodkar P, Kaka N, Baig M. (2020). A comprehensive literature review on the clinical presentation, and management of the pandemic coronavirus disease 2019 (COVID-19). Cureus; 12(4):[e7560 p.].
Lai C-C, Liu YH, Wang
C-Y, et al. (2020). Asymptomatic carrier state, acute respiratory disease, and
pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2):
facts and myths. J Microbiol Immunol
Infect; 53(3):404-12.
Lai CC, Shih TP, Ko WC, et al. (2020). Severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019
(COVID-19): the epidemic and the challenges. Int J Antimicrob Agents; 55:105924. doi:
Li K, Wu J, Wu F, et
al. (2020).The clinical and chest CT features associated with severe and
critical COVID-19 pneumonia. Investig Radiol; 55(6), 327–31.
Li X, Ma X. (2020).
Acute respiratory failure in COVID-19: Is it "typical" ARDS? Crit Care; 24(1):198. doi:
10.1186/s13054-020-02911-9.
Lippi G, Henry BM. (2020-b). Chronic obstructive
pulmonary disease is associated with severe coronavirus disease 2019
(COVID-19). Respir Med; 167: 1-2.
Lippi G, Wong J, Henry BM. (2020-a). Hypertension in patients with coronavirus disease 2019
(COVID 19): a pooled analysis. Pol Arch Intern Med; 130: 304-09.
Liu W, Tao Z, Wang L,
et al. (2020). Analysis of factors associated with disease outcomes in
hospitalized patients with 2019 novel coronavirus disease. Chin Med J; 133(9):
1032–38.
Palaiodimos L, Kokkinidis DG, Li W, et al.
(2020). Severe obesity, increasing age and male sex are independently
associated with worse in-hospital outcomes, and higher in-hospital mortality, in
a cohort of patients with COVID-19 in the Bronx, New York. Metabolism;
108:154262.
Rodriguez-Morales AJ,
Cardona-Ospina JA, Gutierrez-Ocampo
E, et al. (2020). Clinical, laboratory
and imaging features of COVID-19: A systematic review and meta-analysis. Travel
Med Infect Dis; 34:101623. doi:
10.1016/j.tmaid.2020.101623
Wang D, Hu B, Hu C,
et al. (2020). Clinical characteristics of 138 hospitalized patients with 2019
novel coronavirus-infected pneumonia in Wuhan, China. JAMA; 323(11):1061-9.
World Health
Organization. (2021).
WHO coronavirus disease (COVID-19) pandemic. Accessed
May 10, 2021. www.who. int/emergencies/diseases/novel
coronavirus-2019.
Sun N, Gao H,
(2021). Risk factors analysis of COVID-19 patients with ARDS and prediction
based on machine learning. Sci Rep; 11(1): 2933. doi: 10.1038/s41598-021-82492-x.
Yang X, Yu Y, Xu J, et al. (2020). Clinical course and outcomes of critically ill patients
with SARS-CoV-2 pneumonia in Wuhan, China: a singlecentered,
retrospective, observational study. Lancet Respir
Med; 8(5): 475–81.
Yazdanpanah F, Garg A, Shadman
S, Asmarz HY. (2021). Literature
Review of COVID-19, Pulmonary and Extrapulmonary
Disease. Am J Med Sci; 361(5):567-74.
Zhang H, Zhou P, Wei
Y, et al. (2020). Histopathologic changes and SARS–CoV-2 immunostaining in
the lung of a patient with COVID-19. Ann Intern Med 173:185–92.
Zhao X-Y, Xu X, Yin H, et al. (2020). Clinical characteristics of
patients with 2019 coronavirus disease in a non-Wuhan area of Hubei Province,
China: a retrospective study. BMC Infect. Dis. 20(1), 311
Zhou F, Yu T, Du R,
et al. (2020).Clinical course and risk factors for mortality of adult
inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 395(10229):1054-106.
|
Cite this
Article: Kamel, M; El-Shazly,
M; Al-Zuabi, H; Al-Ameeri,
S; Al-Asfoor, S; Al-Majdely,
R; Almoosa, I (2023).
Predictors of severe respiratory complications among hospitalized COVID-19
patients. Greener Journal of Epidemiology
and Public Health, 11(1): 12-22. https://doi.org/10.5281/zenodo.7755605.
|