Research Article
Austin Med Sci. 2022; 7(1): 1064.
COVID-19 Associated Mucormycosis (CAM) - A Case Series from a Tertiary Care Hospital in India
Sharma P¹, Singh MK¹, Malhotra N¹, Sharma DK¹, Beniwal D¹, Kataria S², Arora S³, Jariwala S³, Singh P¹* and Trehan N²
¹Medanta Institute of Education and Research, Gurugram, Haryana, India
²Medanta, The Medicity, Gurugram, Haryana, India
³Albert Einstein College of Medicine, Bronx, NY, USA
*Corresponding author: Padam Singh, Chief Statistical Adviser, Medanta Institute of Education and Research Sector-38, Gurgaon-122001, Haryana, India
Received: March 24, 2022; Accepted: April 14, 2022; Published: April 21, 2022
Abstract
Purpose: Coronavirus disease (COVID-19) remains a health concern with new emerging challenges as rise of opportunistic mucormycosis infections in COVID-19 patients has been found to increase morbidity and mortality. Even though rare, COVID-19 associated mucormycosis has been reported from across the world. We present a retrospective cases series of 132 patients diagnosed as COVID-19 associated mucormycosis in a tertiary care hospital in North India during May 9th to 30th 2021.
Materials and Methods: Our 1500 bed tertiary care hospital is one of the partners in the Mycotic Infections in COVID-19 (MUNCO) registry, Albert Einstein College of Medicine, Bronx, New York. Data were collected in the REDCap database. Profile of the patients has been presented according to demographic profile, comorbidities, laboratory parameters, ICU admission, oxygen support and treatment outcomes. Analysis of risk factors for mortality has been undertaken along with a prediction model for mortality.
Result: The proportion of males and females was 72.7% and 27.3% respectively. The mean age of the study subjects was 53.1 years. The major comorbidity was Diabetes (57.6%) followed by Hypertension (40.9%) and Coronary artery disease (11.4%). About 13% patients used long term corticosteroid. Further, 70.5% patients required an ICU admission and 47.0% patients required oxygen support whereas ventilator requirement was for 13.6% patients. Overall death rate of mucormycosis patients was 16.7%. The risk factors identified for death were raised levels of CRP >73, Ferritin >358, SGOT >39 and Creatinine >1.15 as well as oxygen requirement.
Conclusion: Clinicians must examine the possibility of concomitant fungal infection in COVID-19 patients especially who are geriatric or comorbid with Diabetes Mellitus. Those with raised levels of CRP, Ferritin, SGOT and Creatinine levels as well as those needing oxygen need to be vigilantly monitored to minimize the risk of mortality.
Keywords: Coronavirus disease; Mucormycosis; CAM; Fungal infections
Introduction
Mucormycosis has been reported in patients suffering from covid-19 which is described as COVID-19 Associated Mucormycosis (CAM) [1]. According to the World Health Organization (WHO), mucormycosis is a rare but serious angio-invasive infection caused by a group of fungi called mucormycetes [2]. CAM has been detected mainly in patients with pre-existing conditions, such as diabetes mellitus, diabetic ketoacidosis, hematological malignancies, transplantation, and prolonged neutropenia or on corticosteroids [3].
Across the globe, the highest number of mucormycosis cases has been observed in India with more than 4,000 cases with COVID-19 associated mucormycosis [4]. Herein, we describe a case series of 132 patients with mucormycosis associated with COVID-19 infection.
Materials and Methods
This was a retrospective study of all hospitalized cases of mucormycosis associated with COVID-19 infection admitted to a tertiary, 1500-bed hospital, NCR, India during the month of May 2021, immediately following the second peak of COVID (March- April 2021). The patients diagnosed with mucormycosis were identified with a positive KOH mount and associated clinical features suggestive of fungal infection. Our tertiary care hospital is one of the partners in the Mycotic Infections in COVID-19 (MUNCO) registry, Albert Einstein College of Medicine, Bronx, New York wherein data were collected in the REDCap database. Study was approved by the Institutional Ethics Committee (MICR No: 1315/2021 dated June 17, 2021) and registered with the Clinical Trial Registry of India (NCT04935463).
The statistical analysis included profiling of patients on different demographic, preexisting comorbidities, laboratory parameters, ICU procedure, oxygen support requirement and treatment outcomes. Quantitative parameters were expressed as mean and standard deviation and categorical as absolute number and percentage. Student t-test and One Way ANOVA was used for testing of difference in mean between groups. Chi square test was used for testing of associations. The diagnostic accuracy of risk factors in predicting unfavorable outcome were assessed by receiver operating characteristic (ROC) curve analysis and obtained the optimal cut-off value with the best combination of sensitivity and specificity. Univariate logistic regression analysis was used to explore the association between explanatory variables and unfavorable outcomes. The variables showing significant association on univariate logistic regression analysis were considered in step wise multivariate logistic regression analysis. Importantly, a predictive model for risk of death has been developed based on significant risk factors as revealed by step wise multivariate logistic regression. The predictive power of the model has also been assessed. P-value <0.05 was considered statistically significant. All analysis was done using SPSS software, version 24.0.
Results
A total of 132 patients diagnosed with COVID-19 associated mucormycosis admitted in our hospital between May 9th to May 30th 2021. Out of 132 patients, 40 (30.3%) were diagnosed with Sino-orbital mucormycosis (SOM), 75 (56.8%) with rhino-cerebral mucormycosis (RCM) and 17 (12.8%) had pulmonary (PM) involvement (Table 1).
Total (n=132)
Types of Mucormycosis
Survival Status
Pulmonary Involvement (n=17)
Rhino-cerebral (n=77)
Sino-orbital (n=38)
p-value
Death (n=22)
Alive (n=110)
p-value
Gender
Male
96 (72.70%)
14 (82.40%)
54 (70.1%)
28 (73.7%)
0.585
14 (63.6%)
82 (74.5%)
0.294
Female
36 (27.30%)
3 (17.60%)
23 (29.9%)
10 (26.3%)
8 (36.4%)
28 (25.5%)
Age (Years)
< 31
4 (3%)
0 (0%)
4 (5.20%)
0 (0%)
0.246
0 (0%)
4 (3.60%)
0.010*
31 - 40
23 (17.40%)
2 (11.80%)
16 (20.8%)
5 (13.2%)
0 (0%)
23 (20.9%)
41 - 50
27 (20.50%)
4 (23.50%)
17 (22.1%)
6 (15.8%)
5 (22.7%)
22 (20%)
51 - 60
37 (28%)
7 (41.20%)
16 (20.8%)
14 (36.8%)
5 (22.7%)
32 (29.1%)
61 - 70
34 (25.80%)
2 (11.80%)
22 (28.6%)
10 (26.3%)
8 (36.4%)
26 (23.6%)
> 70
7 (5.30%)
2 (11.80%)
2 (2.60%)
3 (7.90%)
4 (18.2%)
3 (2.70%)
Mean ± SD
53.1±12.7
55.2±1.7
51.1±13.3
56.2±11.7
0.099
60.8±11.2
51.5±12.4
0.001*
Comorbidities
Diabetes
76 (57.60%)
7 (41.20%)
40 (51.9%)
29 (76.3%)
0.016*
15 (68.2%)
61 (55.5%)
0.270
Hypertension
54 (40.90%)
9 (52.90%)
31 (40.3%)
14 (36.8%)
0.524
12 (54.5%)
42 (38.2%)
0.154
Long term corticosteroid use
17 (12.90%)
3 (17.60%)
9 (11.7%)
5 (13.2%)
0.81
3 (13.6%)
14 (12.7%)
0.97
Coronary artery disease
15 (11.40%)
2 (11.80%)
8 (10.4%)
5 (13.2%)
0.96
5 (22.7%)
10 (9.10%)
0.066
Number of Comorbidities
No Comorbidities
21 (15.90%)
4 (23.50%)
14 (18.2%)
3 (7.90%)
0.176
1 (4.50%)
20 (18.2%)
0.110
At least 1 Comorbidities
66 (50%)
5 (29.40%)
42 (54.5%)
19 (50%)
9 (40.9%)
57 (51.8%)
At least 2 Comorbidities
39 (29.50%)
8 (47.10%)
17 (22.1%)
14 (36.8%)
10 (45.5%)
29 (26.4%)
At least 3 Comorbidities
6 (4.50%)
0 (0%)
4 (5.20%)
2 (5.30%)
2 (9.10%)
4 (3.60%)
ICU Procedure & Oxygen Support
ICU Admission
93 (70.50%)
11 (64.70%)
57 (74%)
25 (65.8%)
0.566
20 (90.9%)
73 (66.4%)
0.021*
Oxygen Required
62 (47%)
10 (58.80%)
31 (40.3%)
21 (55.3%)
0.183
15 (68.2%)
47 (42.7%)
0.029*
Ventilator
18 (13.60%)
2 (11.80%)
9 (11.7%)
7 (18.4%)
0.595
5 (22.7%)
13 (11.8%)
0.173
HFNC
33 (25%)
6 (35.30%)
15 (19.5%)
12 (31.6%)
0.213
10 (45.5%)
23 (20.9%)
0.015*
ECMO
7 (5.30%)
2 (11.80%)
3 (3.90%)
2 (5.30%)
0.424
1 (4.50%)
6 (5.50%)
0.862
Nasal Prongs
7 (5.30%)
1 (5.90%)
3 (3.90%)
3 (7.90%)
0.663
1 (4.50%)
6 (5.50%)
0.862
Laboratory parameters
Hospital stay (days)
10 (6 - 15)
11 (4 - 15)
10 (7 - 15)
9 (6 - 15)
0.926
13 (9 - 17)
10 (6 - 15)
0.098
Hb (g/dl)
10.4±2.1
11±2.1
10.7±2.1
9.6±1.8
0.022*
9.8±2
10.5±2.1
0.129
TLC (103/ul)
10.7±5.5
8.6±4.1
11.3±6.1
10.4±4.3
0.168
12.8±7.8
10.2±4.8
0.043*
CRP (mg/L)
71.8±69.2
76.6±5.2
71.5±74.4
70.1±67.1
0.95
131.4±70.1
60.2±63.1
0.001*
Ferritin (ng/ml)
389.1±473.1
371.8±261.8
365.6±352.1
444.4±709.8
0.697
757.7±919.3
315.3±270.7
0.001*
Bilirubin (mg/dl)
0.7±0.5
0.7±0.4
0.6±0.3
0.7±0.8
0.53
1±1
0.6±0.3
0.001*
Albumin (gm/dl)
4.5±20.3
2.9±0.6
5.8±26.6
2.7±0.7
0.75
13.2±49.8
2.8±0.7
0.028*
SGOT (U/L)
46.3±380.4
61.3±57.3
47.1±4.4
37.9±16.4
0.18
57±40.3.
44.1±37.8
0.153
SGPT (U/L)
45.6±325
61.4±54.3
44.4±25.6
41±31.1
0.87
46.2±27.2
45.5±33.5
0.923
Creatinine (mg/dl)
1.1±0.8
1.08±0.61
1.13±0.8
1.13±0.89
0.973
1.6±1.1
1±0.7
0.002*
Na (mmol/l)
151.4±121.8
136.8±10.4
148.6±113.7
163.3±160.2
0.726
139.7±7.3
153.7±133.4
0.626
K (mmol/l)
4.2±4.1
3.4±0.9
4.6±5.3
3.6±0.7
0.34
3.7±0.7
4.3±4.5
0.550
RBS (blood glucose) (mg/dl)
155.3±58.4
155.7±57.3
148.1±49.8
169.8±72.5
0.172
171±86.9
152.2±50.9
0.167
Outcome
Death
22 (16.70%)
1 (5.9%)
13 (16.9%)
8 (21.1%)
0.377
-
-
-
Table 1: Association of baseline characteristics with types of Mucormycosi and Survival status of the patients.
Demographics
The proportion of male and female was 72.7% and 27.3% respectively. The mean age of the study subjects was 53.1 (±12.7) years. Though there were differences in the gender and age profile across three mucormycosis groups of patients, these differences were not statistically significant (p>0.05) (Table 1).
Preexisting comorbidities
Diabetes was the major comorbidity reported by 76 (57.6%) followed by Hypertension - 54 (40.9%). Coronary artery disease was reported by 15 (11.4%) patients. The use of long-term corticosteroid was by 17 (12.9%) patients. Importantly the proportion of diabetics was significantly higher among Sino-Orbital Mucormycosis (76.3%) followed by Rhino-Cerebral Mucormycosis (51.9%) and least among Pulmonary Involvement (41.2%) (p=0.016) (Table 1).
ICU procedure & oxygen support
Out of 132 patients, 93 (70.5%) patients required ICU admission and 62 (47.0%) patients required oxygen support during their hospital stay whereas ventilator was required for 18 (13.6%) patients. 33 (25.0%) and 7 (5.3%) patients required High flow Nasal Cannula (FHNC) and Extracorporeal Membrane Oxygenation (ECMO) procedures respectively. Nasal prongs required among 7 (5.3%) patients. The differences for these parameters among three types of mucormycosis patients were not statistically significant (Table 1).
Haemoglobin level was significantly lower among Sino-Orbital Mucormycosis (9.6±1.8) followed by Rhino-Cerebral Mucormycosis (10.7±2.1) and Pulmonary Involvement (11±2.1) (p=0.022). No statistically significant differences were observed in all other laboratory parameters among three types of mucormycosis patients (Table 1).
Outcome at discharge
Median length of hospital stay (days) was 10 (IQR: 6–15) days which was not statistically different among the three types of mucormycosis (p=0.926). The overall death rate of mucormycosis patients was 16.7% (n=22). Further, death rates among Sino-orbital mucormycosis, rhino-cerebral mucormycosis and pulmonary involvement were 1/17 (5.9%), 13/77 (16.9%) & 8/38 (21.1%) respectively. However the difference was not statistically significant (p=0.377) (Table 1).
Risk factors and prediction of death
The significant risk factors associated with death are higher age, requirement of ICU admission and need for oxygen support during the hospital stay as well as HFNC procedure (p<0.05) (Table 1).
Based on the laboratory parameters, the risk factors identified for Death were raised levels of TLC, CRP, Ferritin, Bilirubin, Albumin (serum) and Creatinine (p<0.05) (Table 1).
Area under the curve (AUC) and optimal cut-off points of laboratory parameters
ROC curve analysis revealed that the predicting ability of risk factors for Death among mucormycosis patients were Age (AUC=0.683; p-value=0.008), CRP (AUC=0.784; p- value=0.0001), Ferritin (AUC=0.674; p-value=0.012), SGOT (AUC=0.646; p-value=0.034) and Creatinine (AUC=0.677; p-value=0.011). ROC analysis further revealed that the optimal cut-off value of age was 57 years, CRP >73, Ferritin >358, SGOT >39 and Creatinine >1.15 (Table 2).
Test Result Variable(s)
AUC (95% CI)
Optimal Cut-off Point
Sensitivity (Sn)
Specificity (Sp)
Positive Predicative Value (PPV)
P-value
Age (Years)
0.683 (0.565 - 0.801)
57
59.10%
63.60%
24.50%
CRP (mg/L)
0.784 (0.667 - 0.901)
73
81.00%
75.00%
38.60%
Ferritin (ng/ml)
0.674 (0.528 - 0.821)
358
59.10%
76.40%
33.30%
SGOT (U/L)
0.646 (0.527 - 0.765)
39
68.20%
55.50%
23.40%
Creatinine (mg/dl)
0.677 (0.539 - 0.815)
1.15
68.20%
70.00%
31.30%
*p-value <0.05, statistically significant.
Table 2: Area under the Curve (AUC) with optimal cut-off values of the risk factors for the prediction of Death.
Univariate logistic regression for prediction of risk
On univariate logistic regression analysis, ICU Admission, Oxygen Required, HFNC, TLC, CRP >73, Ferritin >358, Bilirubin >0.621, SGOT >39 and Creatinine >1.15 were observed as independent predictor of Death. The CRP >73 (OR=12.75, p=0.0001) had a higher power for predicting Death followed by Bilirubin (OR=5.35, p=0.016), ICU Admission (OR=5.07, p=0.035), Creatinine >1.15 (OR=5.00, p=0.001), Ferritin >358 (OR=4.67, p=0.002), HFNC (OR=3.15, p=0.019), Oxygen Required (OR=2.87, p=0.034), SGOT >39 (OR=2.67, p=0.048) and TLC (OR=1.08, p=0.049) (Table 3).
Beta Coefficient
Std. Error
Odds Ratio (OR)
95% CI for OR
P-value
Lower
Upper
Age > 57 Years
0.93
0.48
2.53
0.99
6.44
0.052
Female
0.5
0.49
1.65
0.63
4.36
0.309
ICU Admission
1.62
0.77
5.07
1.12
22.86
0.035*
Oxygen Required
1.06
0.5
2.87
1.09
7.6
0.034*
Ventilator
0.79
0.59
2.19
0.69
6.95
0.182
HFNC
1.15
0.49
3.15
1.21
8.21
0.019*
ECMO
0.19
1.11
1.21
0.14
10.59
0.862
Nasal Prongs
0.19
1.11
1.21
0.14
10.59
0.862
Hospital stay (Days)
0.02
0.02
1.02
0.97
1.06
0.41
Hb (g/dl)
-0.18
0.12
0.83
0.66
1.06
0.131
TLC (10^3/ul)
0.08
0.04
1.08
1
1.17
0.049*
CRP (mg/L)
2.55
0.6
12.75
3.95
41.21
0.0001*
Ferritin (ng/ml)
1.54
0.49
4.67
1.79
12.15
0.002*
Bilirubin (mg/dl)
1.68
0.7
5.35
1.37
20.91
0.016*
Albumin (gm/dl)
0.02
0.03
1.02
0.96
1.09
0.436
SGOT (U/L)
0.98
0.5
2.67
1.01
7.06
0.048*
SGPT (U/L)
0
0.01
1
0.99
1.01
0.922
Creatinine (mg/dl)
1.61
0.5
5
1.87
13.4
0.001*
Na (mmol/l)
0
0
1
0.99
1.01
0.654
K (mmol/l)
-0.07
0.14
0.93
0.7
1.23
0.609
RBS (blood glucose) (mg/dl)
0
0
1
1
1.01
0.178
Long term corticosteroid use
0.08
0.68
1.08
0.28
4.14
0.908
Coronary artery disease
1.08
0.61
2.94
0.89
9.67
0.076
Diabetes
0.54
0.5
1.72
0.65
4.55
0.274
Hypertension
0.66
0.47
1.94
0.77
4.89
0.159
No Comorbidities
1
-
-
-
-
-
At least 1 Comorbidities
1.15
1.09
3.16
0.38
26.52
0.29
At least 2 Comorbidities
1.93
1.09
6.9
0.82
58.21
0.076
At least 3 Comorbidities
2.3
1.34
10
0.72
138.68
0.086
*p-value <0.05, statistically significant.
Table 3: Univariate Logistic Regression analysis for prediction of Death.
Multivariate logistic regression for prediction of risk
A Step Wise Forward Multivariate Logistic Regression was attempted. The variables entered in the model at different steps along with the risk of death in the presence of risk factors are presented in Table 4.
The above analysis indicates that the risk of death with CRP (mg/L) >73 alone as 38.7%; CRP (mg/L) >73 and Ferritin (ng/ml) >358 as 59.4%; CRP (mg/L) >73, Ferritin (ng/ml) >358 and Oxygen Required as 75.0%; CRP (mg/L) >73, Ferritin (ng/ml) >358, Oxygen Required and SGOT (U/L) >39 as 86.2% & CRP >73 and Ferritin (ng/ ml) >358, Oxygen Required, SGOT (U/L) >39 and Creatinine (mg/dl) >1.15 as 92.3% (Table 4).
Beta Coefficient
Std. Error
Odds Ratio (95% C.I. for OR)
p - value
Risk of Death (%)
Step 1
CRP (mg/L) > 73
2.55
0.6
12.75 (3.95 - 41.21)
0.0001*
38.70%
Constant
-3.01
Step 2
CRP (mg/L) > 73
2.47
0.62
11.82 (3.52 - 39.70)
0.0001*
59.40%
Ferritin (ng/ml) > 358
1.52
0.56
4.56 (1.52 - 13.67)
0.007
Constant
-3.61
Step 3
Oxygen Required
1.49
0.62
4.44(1.32 - 14.95)
0.016
75.00%
CRP (mg/L) > 73
2.64
0.65
13.99 (3.92 - 49.98)
0.0001*
Ferritin (ng/ml) > 358
1.52
0.59
4.59 (1.45 - 14.56)
0.01
Constant
-4.55
Step 4
Oxygen Required
1.52
0.63
4.56 (1.32 - 15.75)
0.017
86.20%
CRP (mg/L) > 73
2.67
0.67
14.50 (3.89 - 54.02)
0.0001*
Ferritin (ng/ml) > 358
1.76
0.63
5.81 (1.68 - 20.04)
0.005
SGOT (U/L) > 39
1.31
0.64
3.71 (1.05 - 13.09)
0.042
Constant
-5.43
Step 5
Oxygen Required
1.7
0.68
5.50 (1.46 - 20.71)
0.012
92.30%
CRP (mg/L) > 73
2.76
0.72
15.78 (3.86 - 64.54)
0.0001*
Ferritin (ng/ml) > 358
1.49
0.67
4.43 (1.20 - 16.31)
0.025
SGOT (U/L) > 39
1.41
0.68
4.10 (1.07 - 15.67)
0.039
Creatinine (mg/dl) > 1.15
1.31
0.66
3.72 (1.01 - 13.61)
0.048
Constant
-6.19
*p-value <0.05, statistically significant.
Table 4: Multivariate Logistic Regression (Step Wise Forward LR Method) analysis for prediction of Death.
Model development
Odds are the ratio of two probabilities where the numerator is the probability p of an event and the denominator is the complementary probability of that event not occurring. That is:
Odds of the Event (Eq.1)
The logistic regression model, with usual notations is given by:
Logit (p) = β0+β1 X1+β2 X2+β3 X3+…+βnXn (Eq.2)
Probability of occurrence (p) is given by:
The predictors that entered into the final prediction model are CRP >73 (OR=15.78, p=0.0001), Oxygen Required (OR=5.50, p=0.012), Ferritin >358 (OR=4.43, p=0.025), SGOT >39 (OR=4.10, p=0.039) and Creatinine >1.15 (OR=3.72, p=0.048). Thus, that higher CRP, Ferritin, SGOT, Creatinine and the need of oxygen requirement increased the risk of death independently of the effect of possible confounders (Table 4).
From the equation (2), the logit of the model is given by:
Logit (Death) = -6.19 + (2.76*CRP>73) + (1.70*Oxygen Required) + (1.49*Ferritin >358) + (1.41*SGOT >39) + (1.31*Creatinine >1.15)
Thus, the Probability of Death is given by:
The prediction model is robust with prediction power of 90.7%. The false positive (a case in that the patient would likely to die when, in fact it did not) and false negative (a case in that patients would not die, when in fact it did) rates are 9.1% and 9.3% respectively (Table 5).
Observed
Predicted
Outcome Classification
Death
Alive
Death
10
11
Alive
1
107
False Positives (FP) and False Negative (FN)
FP = 1/11 = 9.1%
FN = 11/118 = 9.3%
Overall Right Predication (ORP)
ORP = (10 + 107)/(21+108) = 117/129 = 90.7%
Table 5: Predictive power of the model.
Discussion
Although, few case reports and case series have been published, the association between COVID-19 and mucormycosis yet needs to be established. Hardeva Ram Nehara et al. [5] published a case series of five patients with rhinocerebral mucormycosis. Similarly, Amanda Werthman et al. [6] presented a case of rhino-orbital-cerebral mucormycosis. In our study, case series of one hundred and thirty two patients with mucormycosis associated with COVID-19 infection and showed that 71.9% had lymphocytopenia during hospital admission which confirmed lymphocytopenia as a risk factor for invasive fungal infection. A study conducted by Nikolay N. Klimko et al. [7] reported that 86% had lymphocytopenia and was consistent with our result. Our case series reported a large number of patients with a history of diabetes mellitus and corticosteroids used to treat COVID-19 infection. It was observed that proportion of diabetics were higher in Sino-orbital mucormycosis. Similarly, a study by Dora E Corzo-Leon et al. [8] showed diabetes as a major risk factor for mucormycosis. Another study conducted by Prashant Sirohiya et al. [9] reported similar comorbidities when compared with our study. A case series by Yudhyavir Singh et al. [10] analyzed the same laboratory parameters as our study and the results were found to be broadly similar.
A large number of rhino-cerebral mucormycosis patients were diagnosed in our case series which is considered as one of the most common type of mucormycosis.
Covid-19 associated mucormycosis is associated with high mortality which is due to severity of COVID-19. In our case series, the overall mortality rate is 16.7%. A case series by Garg et al. [11] reported 87.5% of mortality in patients with severe COVID-19. Another case series by Sharma et al. [12] reported no deaths, which was quite surprising. Our study observed that higher age, requirement of ICU admission and need for oxygen support during the hospital stay were significant risk factors associated with death. Based on laboratory parameters the risk factors identified for Death were raised levels of TLC, CRP, Ferritin, Bilirubin, Albumin (serum) and Creatinine. Globally, case fatality rate of mucormycosis is found to be 46% [13]. Diagnosis of mucormycosis is difficult. Early diagnosis and treatment is indispensable because a delay of even 6 days is associated with a doubling of 30-day mortality from 35% to 66%. In spite of early diagnosis and treatment, the recovery from mucormycosis is poor9. Mucormycosis is mainly spread through inhaled fungal spores [2]. Therefore, wearing of face masks to prevent COVID-19 should also be encouraged to prevent the spread of mucormycosis.
Conclusion
In our case series of 132 COVID-19 associated mucormycosis patients, Diabetes was the major comorbidity reported followed by Hypertension. Importantly proportion of diabetics was significantly higher among Sino-Orbital Mucormycosis followed by Rhino- Cerebral Mucormycosis and least among Pulmonary Involvement. In addition to this, long-term steroid use was more vulnerable to COVID-19 associated Mucormycosis. As to the risk factors for mortality, the significant ones were the raised levels of CRP >73, Ferritin >358, SGOT >39 and Creatinine >1.15 as well as oxygen requirement. These risk factors contribute to over 90% of mortality. Clinicians should therefore monitor these risk factors in the management of COVID-19 associated mucormycosis patients. Early diagnosis and on-time treatment is pivotal in the management of mucormycosis.
Limitations
We acknowledge few limitations, as this case series has covered patients’ data from a single tertiary care center where most of the patients are referrals. So Individual medication history i.e. duration of steroid therapy etc. of each patient could not be extensively traced.
Acknowledgement
The investigators appreciate the support of Mr. Pankaj Sahni (CEO), Mr. Rajiv Sikka and the e-HIS team at Medanta. Special thanks to Mr. Kuldeep, Ms. Anjusha, Hrishikesh Sarma and Radhesh Notiyal for their support in enrolment of patients in the MUNCO registry. Thanks are also to all the participants of the research project.
Medanta Institute of Education and Research acknowledge the role of COVID SURAKSHA grant from BIRAC for human resources support towards data collection.
References
- Mekonnen ZK, Ashraf DC, Jankowski T, Grob SR, Vagefi MR, Kersten RC, et al. Acute invasive rhino-orbital mucormycosis in a patient with COVID- 19-associated acute respiratory distress syndrome. Ophthalmic plastic and reconstructive surgery. 2021; 37: e40.
- WHO emergency on COVID-19.
- Petrikkos G, Skiada A, Lortholary O, Roilides E, Walsh TJ, Kontoyiannis DP. Epidemiology and clinical manifestations of mucormycosis. Clinical Infectious Diseases. 2012; 54: S23-34.
- Doctors around the world call for rapid response to deadly mucormycosis (the so-called “black fungus”) found in COVID patients in India.
- Nehara HR, Puri I, Singhal V, Sunil IH, Bishnoi BR, Sirohi P. Rhinocerebral mucormycosis in COVID-19 patient with diabetes a deadly trio: Case series from the north-western part of India. Indian Journal of medical microbiology. 2021; 39: 380-383.
- Werthman-Ehrenreich A. Mucormycosis with orbital compartment syndrome in a patient with COVID-19. The American journal of emergency medicine. 2021; 42: 264-e5-264-e8.
- Klimko NN, Khostelidi SN, Volkova AG, Popova MO, Bogomolova TS, Zuborovskaya LS, et al. Mucormycosis in haematological patients: case report and results of prospective study in Saint Petersburg, Russia. Mycoses. 2014; 57: 91-96.
- Corzo-León DE, Chora-Hernández LD, Rodríguez-Zulueta AP, Walsh TJ. Diabetes mellitus as the major risk factor for mucormycosis in Mexico: epidemiology, diagnosis, and outcomes of reported cases. Medical mycology. 2018; 56: 29-43.
- Sirohiya P, Vig S, Mathur T, Meena JK, Panda S, Goswami G, et al. Coronavirus disease (COVID-19) associated Mucormycosis: An Anaesthesiologist’s Perspective. medRxiv. 2021.
- Singh Y, Ganesh V, Kumar S, Patel N, Aggarwala R, Soni KD, et al. Coronavirus disease-associated mucormycosis from a tertiary care hospital in India: a case series. Cureus. 2021; 13.
- Garg D, Muthu V, Sehgal IS, Ramachandran R, Kaur H, Bhalla A, et al. Coronavirus disease (Covid-19) associated mucormycosis (CAM): case report and systematic review of literature. Mycopathologia. 2021; 186: 289- 298.
- Sharma S, Grover M, Bhargava S, Samdani S, Kataria T. Post coronavirus disease mucormycosis: a deadly addition to the pandemic spectrum. The Journal of Laryngology & Otology. 2021; 135: 442-447.
- Chamilos G, Lewis RE, Kontoyiannis DP. Delaying amphotericin B-based frontline therapy significantly increases mortality among patients with hematologic malignancy who have zygomycosis. Clinical Infectious Diseases. 2008; 47: 503-509.
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