Abstract
Malignancy, bone marrow and organ transplantation are associated with deficient and defective immune systems. Immunocompromised patients are at risk for severe and chronic complication of COVID-19 infection. However, the pathogenesis, diagnosis and management of this comorbidity remain to be elucidated. The purpose of the present study was to describe key aspects of COVID-19 infection in immunocompromised patients. In this retrospective, cross-sectional study, lab findings and outcomes of 418 COVID-19 patients with secondary immunodeficiency disorders admitted to Taleghani Hospital in Tehran, from March 2020 to September 2022 were investigated. Of the 418 immunocompromised patients with COVID-19, 236 (56.5%) were male and the median age of all studied patients was 56.6 ± 16.4 with range of 14 to 92 years. Totally, 198 (47.4%) of the patients died during hospitalization. Remdesivir was used for treatment of all patients. Mortality rate among patients admitted to ICU ward (86.8%) was significantly higher than non ICU admission (p < 0.001). The death rate in patients with CKD was substantially higher than other underlying disease (p < 0.001). In terms of laboratory finding, there was a significant relationship between ICU admission and worse outcome with WBC count (HR = 1.94, 95% CI = 1. 46–2.59, p < 0.001), PMN count (HR = 1.93, 95% CI = 1.452.56, p < 0.001), Hb (HR = 1.49, 95% CI = 1.042.13, p = 0.028), AST (HR = 2.55, 95% CI = 1.913.41, p < 0.001), BUN (HR = 2.56, 95% CI = 2.063.69, p < 0.001), Cr (HR = 2.63, 95% CI = 1.89–3.64, p < 0.001), Comorbidities index (HR = 1.71, 95% CI = 1.29–2.27, p < 0.001) and aging (HR = 1.91, 95% CI = 1.4–2.54, p < 0.001). Immunocompromised status increased the risk of mortality or worse outcome in patients diagnosed with COVID-19. Our finding showed outcome predicting markers in whom the waned immune system encounter new emerging disease and improved our understanding of COVID-19 virus behavior in immunocompromised individuals.
Keywords: COVID-19, Malignancy, Bone marrow, Organ transplantation, Comorbidity
Introduction
COVID-19 as a new emerging infection was first reported in Wuhan, China. Since 2019, this virus spread dramatically in the world and become the leading cause of death worldwide. Based on the latest statistics 6,716,108 deaths have been recorded in this pandemic [29]. The clinical presentations of COVID-19 in infected individuals differ ranging from asymptomatic to mild, severe, and potentially deadly disease in which critical respiratory distress, septic shock and vital organ failures and death may happen. Mainly, a virus is transmitted through infected airborne droplets, however other routes of transition such as contaminated surfaces have been documented. Clinically, most people with COVID-19 infection show mild to moderate flu-like symptoms and only 5% develop severe and critical symptoms and require hospitalization and ICU admission. However, cumulatively, the long-term effects of the disease have not been well known.
Old age (> 60), underlying disease and male sex are the most important predisposing factor for the severe and complicated COVID-19 infection. In terms of immunity, neutralizing antibodies in combination with potent cell-mediated immunity play an important role in disease recovery and infection rescue. According to previous experiences, convalescent plasma (CP) transfusion will be helpful to decrease the fatal outcome of COVID-19 infection [6]. Immunodeficiency is a condition in which the immune system is weakened. Immunodeficiency disorders are divided into primary and secondary. Primary immunodeficiency is a genetic disorder that typically manifests during infancy and childhood. In contrast, secondary immunodeficiency occurs following the use of immunosuppressive drugs like immunosuppressant and antirejection medications, long- term hospitalization, prolonged serious illness, and systemic disorders such as HIV [16, 26]. Obviously, patients with pre-existing secondary immunodeficiency are more at risk for severe outcomes and death. In this regard identifying risk factors or predisposing agents will be helpful in the critical management of the life- threating infection such as COVID-19 among immunocompromised patients [9]. In current study the outcome of COVID-19 in a wide range of immunocompromised individuals including patients with cancer, solid tumor, hematologic malignancy, solid-organ transplant (SOT) recipients were investigated.
Materials and methods
Patients and Laboratory Findings
In a retrospective, cross-sectional study, immunocompromised patients diagnosed with COVID-19 based on PCR and chest scan tests who were admitted to the Taleghani Hospital in Tehran, Iran, March 2020–September 2022, were evaluated in respect of demographic findings, laboratory results, treatment, and outcome. Laboratory tests including white blood cells count (WBC), polymorphonuclear count (PMN), lymphocyte count (LY), hemoglobin (Hb), platelet count (Plt), lactate dehydrogenase (LDH), urea, creatinine (Cr), ferritin, C-reactive protein (CRP), alanine transaminase (ALT), and aspartate transaminase (AST) were performed at time of admission to the hospital. Remdesivir as an only available antiviral drug, was initiated at 200 mg/kg and followed by 100 mg /kg for 5 days. Dexamethasone as a steroid was administered for all patients at 8 mg/day. Also, heparin was used as an antithrombotic agent according to the official guidelines. Patients who had platelet count > 20 × 103 were underwent antithrombotic therapy. 5000 units of heparin was prescribed to patients with weight higher and lower than 70 kg, each 8 and 12 h, respectively [18, 20]. Inclusion criteria were immune- impaired patients with confirmed malignancy including different types of cancers, solid tumor, hematologic malignancy, and solid-organ transplant (SOT) recipients in whom COVID-19 infection had been confirmed according to the PCR and chest CT [30]. This study was approved by the Ethics Committee of shahid beheshti University of medical science with IR.SBMU.MSP.REC.1401.466 code. Patients who didn’t have concomitant COVID-19 and immunosuppression conditions were excluded.
Statistical analysis
Descriptive statistics including mean and standard deviation for quantitative variables and frequency and percentage for qualitative variables were reported. To assess the association between two categorical variables chi-square and Fisher’s exact tests were used. Independent sample t-test and Mann–Whitney test were used to compare normal and non-normal variables, respectively. Shapiro Wilks test was used to assess the data normality. Multiple logistic regression was used to evaluate the effect of independent variables on the severity status (ICU vs. non-ICU) of patients and the Adjusted Odds Ratio (AOR) for independent variables which were significant at bivariate analysis were reported. Based on the fitted value (Probability of ICU) of multiple logistic regression adjusted Area under Curve (AUC) with associated sensitivity and specificity determined by cut-off value obtained from the Youden index method were calculated. Bivariate and multiple Cox proportional hazard regression model were used to obtain the unadjusted and adjusted Hazard Ratio. Independent variables which were significant in the bivariate Cox proportional regression model were entered into the multiple versions of the Cox model. Unadjusted AUC was used to determine the appropriate cut-off value for continuous variables which were significantly associated by patients’ survival status. The best cut off values were determined by the Youden index and associated sensitivity and specificity were reported. The unweighted comorbidity index was used to compute the comorbidity index For each patient, The number of underlying diseases were assigned, counted and summed to produce the comorbidity score [1].
The Kaplan Meier method was used to estimate survival probability. Statistical significance set at 0.05. Statistical analysis were computed by SPSS software version 26.0.
Results
Of the 418 studied patients, 236 (56.5%) were male and 182 (43.5%) were female. A total of 198 (47.4%) individuals dead and 220 (52.6%) individuals survived. The average age of all studied patients was 56.6 ± 16.4 ranging from 14 to 92 years. The median age of patients admitted to the ICU and other wards (non-ICU) was 60.4 ± 14.5 and 54.5 ± 17.1 respectively (p < 0.001). One hundred fifty-two patients were admitted to the ICU from them 132 (86.8%) were dead. The mortality rate among patients admitted to the ICU was substantially higher than those admitted to other wards (p < 0.001). The death rate in patients with CKD was significantly higher than patients who had other underlying diseases (p < 0.001). The average day of hospitalization was 14 ± 12.9. The most prevalent underlying disease were CKD77 (18.4%, p = 0.013), HTN75 (17.9%, p = 0.029) and DM 73 (17.5%, p = 0.063). The frequency of comorbidities tabulated in Table 1.
Table 1.
The frequency of comorbidities among immunosuppressed patients
| Comorbidities | N (%) | |
|---|---|---|
| Cirrhosis | 9 (2.2) | |
| CKD | 77 (18.4) | |
| HTN | 75 (17.9) | |
| Hypothyroidism | 16 (3.8) | |
| DM | 73 (17.5) | |
| IHD | 43 (10.3) | |
| CVA | 4 (1) | |
| Asthma | 4 (1) | |
| CHF | 3 (0.7) | |
| Comorbidity index | 0.7 ± 1 | |
| Outcome |
Death Discharge |
198 (47.4) 220 (52.6) |
CKD, Chronic kidney disease; HTN, High blood pressure; DM, diabetes mellitus; IHD, Ischemic Heart Disease; CVA, cerebrovascular accident; CHF, Congestive heart failure
The WBC (p = 0.005), PMN (p = 0.003) count, AST (p = 0.001), ALT (p = 0.009), BUN (p < 0.001), Cr (p = 0.003), CRP (p < 0.001), LDH (p = 0.003), Ferritin (p = 0.001) and comorbidity index (p = 0.005) were significantly higher in ICU hospitalized patients, while the lymphocyte count showed higher values in non-ICU hospitalized patients (p = 0.006). ICU admission in solid Tumor patients (p = 0.002) with COVID-19 was significantly higher than other immunocompromised patients (HC, SOT) (Table 2).
Table 2.
Demographic and laboratory variable in respect of ICU hospitalization
| Variable | Severity | P-value | AOR (95% CI) | P-value | ||
|---|---|---|---|---|---|---|
| ICU N (%) | Non-ICU* N (%) | |||||
| Sex |
Male Female |
95 (62.5) 57 (37.5) |
141 (53) 125 (47) |
0.075 | – | – |
| Age | 60.4 ± 14.5 | 54.5 ± 17.1 | < 0.001 | 1.02 (1.00, 1.03) | 0.045 | |
| WBC (×109/L) | 16970.5 ± 30206.1 | 13407.1 ± 27645.8 | 0.005 | 1.00 (1.00, 1.00) | 0.281 | |
| LY (×109/L) | 19.7 ± 17.3 | 22.6 ± 16.4 | 0.006 | 1.02 (0.97, 1.08) | 0.439 | |
| PMN (×109/L) | 74.6 ± 18 | 71.1 ± 16.5 | 0.003 | 1.02 (0.97, 1.08) | 0.391 | |
| Hb (mg/ml) | 9.3 ± 2.4 | 9.7 ± 2.8 | 0.072 | – | – | |
| PLT (×109/L) | 169.8 ± 149.4 | 167.6 ± 154.6 | 0.804 | – | – | |
| AST (IU/L) | 82.8 ± 123.3 | 56.2 ± 70.2 | 0.001 | 1.00 (1.00, 1.00) | 0.204 | |
| ALT (IU/L) | 65.3 ± 99.1 | 51.9 ± 108.7 | 0.009 | 1.00 (1.00, 1.00) | 0.461 | |
| BUN (mg/L) | 31.6 ± 21.9 | 24.2 ± 19.1 | < 0.001 | 1.01 (0.99, 1.02) | 0.456 | |
| Cr (mg/ml) | 1.6 ± 1.2 | 1.4 ± 1.3 | 0.003 | 0.96 (0.74, 1.23) | 0.774 | |
| CRP (mg/L) | 53.6 ± 44.9 | 37.1 ± 38.8 | < 0.001 | 1.01 (1.00, 1.01) | 0.015 | |
| LDH (IU/L) | 1079.1 ± 1112 | 847.5 ± 704.1 | 0.003 | 1.00 (1.00, 1.00) | 0.252 | |
| Ferritin (µg/L) | 493.3 ± 189.2 | 432.8 ± 204.8 | 0.001 | 1.00 (1.00, 1.00) | 0.058 | |
| Cirrhosis |
Yes No |
4 (2.6) 148 (97.4) |
5 (1.9) 261 (98.1) |
0.729 | – | – |
| CKD |
Yes No |
38 (25) 114 (75) |
39 (14.7) 227 (85.3) |
0.013 |
1.42 (0.69, 2.94) Ref. |
0.343 |
| HTN |
Yes No |
36 (23.7) 116 (76.3) |
39 (14.7) 227 (85.3) |
0.029 |
1.58 (0.9, 2.77) Ref. |
0.109 |
| Hypothyroidism |
Yes No |
7 (4.6) 145 (95.4) |
9 (3.4) 257 (96.6) |
0.718 | – | – |
| DM |
Yes No |
34 (22.4) 118 (77.6) |
39 (14.7) 227 (85.3) |
0.063 | – | – |
| IHD |
Yes No |
17 (11.2) 135 (88.8) |
26 (9.8) 240 (90.2) |
0.773 | – | – |
| CVA |
Yes No |
3 (2) 149 (98) |
1 (0.4) 265 (99.6) |
0.139 | – | – |
| Asthma |
Yes No |
0 (0.0) 152 (100) |
4 (1.5) 262 (98.5) |
0.301 | – | – |
| CHF |
Yes No |
1 (0.7) 151 (99.3) |
2 (0.8) 264 (99.2) |
1.00 | – | – |
| Comorbidity index | 0.9 ± 1.1 | 0.6 ± 0.9 | 0.005 | – | ||
| Patients |
HC ST SOT |
54 (35.5) 93 (61.2) 5 (3.3) |
119 (44.7) 121 (45.5) 26 (9.8) |
0.002 |
1.63 (0.59, 5.34) 2.39 (0.87, 7.8) Ref. |
0.374 0.114 – |
CKD, Chronic kidney disease; HTN, High blood pressure; DM, diabetes mellitus; IHD, Ischemic Heart Disease; CVA, cerebrovascular accident; CHF, Congestive heart failure; ALT, alanine transaminase, AST, aspartate transaminase, Cr, creatinine; Hb, hemoglobin; Plt, platelet; CRP, C-reactive protein; LDH, l- lactate dehydrogenase; LY, ymphocyte; AST, aspartate aminotransferase; ALT, Alanine transaminase; BUN, Blood Urea Nitrogen; Cr, creatinine
HC, hematopoietic cell cancer; ST, solid Tumor; SOT :solid organ transplant
*Reference category. AUC = 70%, Sensitivity = 62.5%, Specificity = 71.4, Cut-off = 0.339
In respect of final COVID-19 outcome in immunocompromised patients, the death risk had a significant association with patient age (UHR: 1.03, 95% CI 1.02–1.04, P-value < 0.001), Lymphocyte count (UHR: 0.98, 95% CI 0.97–0.99, P-value < 0.001), PMN count (UHR: 1.02, 95% CI 1.01–1.03, P-value < 0.001), AST (UHR: 1.001, 95% CI 1.00–1.002, P-value < 0.001), ALT (UHR: 1.001, 95% CI 1.00–1.002, P-value = 0.027), BUN (UHR: 1.02, 95% CI 1.02–1.03, P-value < 0.001), Cr (UHR: 1.21, 95% CI 1.12–1.31, P-value < 0.001), CRP (UHR: 1.01, 95% CI 1.006–1.012, P-value < 0.001) and LDH (UHR: 1.002, 95% CI 1.00–1.00, P-value < 0.001), CKD (UHR: 2.42, 95% CI 1.76–3.33, P-value < 0.001), DM (UHR: 1.57, 95% CI 1.08–2.29, P-value = 0.019) and CHF (UHR: 6.27, 95% CI 1.54–25.59, P-value = 0.011) and Comorbidity index (UHR: 1.33, 95% CI 1.15–1.53, P-value < 0.001), also the hazard of death were significantly higher in patients with HC (UHR: 2.15, 95% CI 1.08–4.13, P-value = 0.03) and ST (UHR: 3.96, 95% CI 1.99–7.88, P-value < 0.001) compared to SOT patients. Regarding the multiple cox regression model which is built based on the result of univariate, Cox regression model, patients’ age (AHR:1.015, 95%CI 1.005–1.026, P-value = 0.004), BUN (AHR: 1.022, 95% CI 1.012–1.031, P-value < 0.001), CRP (AHR: 1.006, 95% CI 1.003–1.01, P-value < 0.001), and LDH (AHR: 1.0002, 95% CI 1.0001–1.0003, P-value < 0.001) had significant effects on death hazard (Table 3).
Table 3.
Demographic and laboratory variables in respect of COVID-19 outcome
| Variable | Outcome | UHR† (95% CI) |
P-value | AHR‡ (95% CI) |
P-value | |||
|---|---|---|---|---|---|---|---|---|
| Survival N (%) | Death N (%) | |||||||
| Sex |
Male N () Female |
121 (51.3) 99 (54.4) |
115 (48.7) 83 (45.6) |
Ref. 0.79 (0.59, 1.05) |
0.104 | – | – | |
| Age | 53.6 ± 16.5 | 59.9 ± 15.7 | 1.03 (1.02, 1.04) | < 0.001 | 1.015 (1.005, 1.026) | 0.004 | ||
| WBC (×109/L) | 12,835 ± 28018.6 | 16778.4 ± 29202.1 | 1.00 (1.00, 1.00) | 0.126 | – | – | ||
| LY (×109/L) | 23.7 ± 17.2 | 19.1 ± 16.1 | 0.98 (0.97, 0.99) | < 0.001 | 0.991 (0.956, 1.027) | 0.624 | ||
| PMN (×109/L) | 70.1 ± 17.2 | 74.9 ± 16.8 | 1.02 (1.01, 1.03) | < 0.001 | 0.995 (0.962, 1.029) | 0.779 | ||
| Hb (mg/ml) | 9.8 ± 2.7 | 9.3 ± 2.6 | 0.99 (0.94, 1.04) | 0.704 | – | – | ||
| PLT (×109/L) | 168.7 ± 128 | 168 ± 176.1 | 1.00 (0.99, 1.001) | 0.147 | – | – | ||
| AST (IU/L) | 50.9 ± 56.6 | 82.5 ± 120.6 | 1.001 (1.00, 1.002) | < 0.001 | 1.001 (0.999, 1.003) | 0.207 | ||
| ALT (IU/L) | 45.1 ± 45.7 | 69.8 ± 144.4 | 1.001 (1.00, 1.002) | 0.027 | 1 (0.999, 1.001) | 0.968 | ||
| BUN (mg/L) | 21.8 ± 15.8 | 32.6 ± 23.4 | 1.02 (1.02, 1.03) | < 0.001 | 1.022 (1.012, 1.031) | < 0.001 | ||
| Cr (mg/ml) | 1.3 ± 1.2 | 1.6 ± 1.3 | 1.21 (1.12, 1.31) | < 0.001 | 0.951 (0.821, 1.1) | 0.497 | ||
| CRP (mg/L) | 35.5 ± 37.7 | 51.6 ± 44.6 | 1.01 (1.006, 1.012) | < 0.001 | 1.006 (1.003, 1.01) | < 0.001 | ||
| LDH (IU/L) | 808.6 ± 685.3 | 1068.6 ± 1040.7 | 1.0002 (1.00, 1.00) | < 0.001 | 1.0002 (1.0001, 1.0003) | < 0.001 | ||
| Ferritin (µg/L) | 427.7 ± 206.5 | 484.9 ± 191.1 | 1.001 (0.99, 1.001) | 0.106 | – | – | ||
| Comorbidities | ||||||||
| Cirrhosis |
Yes No |
5 (55.6) 215 (52.6) |
4 (44.4) 194 (47.4) |
0.95 (0.35, 2.57) Ref. |
0.923 | – | – | |
| CKD |
Yes No |
24 (31.2) 196 (57.5) |
53 (68.8) 145 (42.5) |
2.42 (1.76, 3.33) Ref. |
< 0.001 |
1.027 (0.635, 1.663) Ref. |
0.913 | |
| HTN |
Yes No |
34 (45.3) 186 (54.2) |
41 (54.7) 157 (45.8) |
1.26 (0.89, 1.78) Ref. |
0.189 | – | – | |
| Hypothyroidism |
Yes No |
9 (56.2) 211 (52.5) |
7 (43.8) 191 (47.5) |
0.84 (0.39, 1.79) Ref. |
0.658 | – | – | |
| DM |
Yes No |
38 (52.1) 182 (52.8) |
35 (47.9) 163 (47.2) |
1.57 (1.08, 2.29) Ref. |
0.019 |
1.239 (0.832, 1.844) Ref. |
0.291 | |
| IHD |
Yes No |
22 (51.2) 198 (52.8) |
21 (48.8) 177 (47.2) |
1.25 (0.78,1.97) Ref. |
0.333 | – | – | |
| CVA |
Yes No |
2 (50) 218 (52.7) |
2 (50) 196 (47.3) |
1.64 (0.405, 6.64) Ref. |
0.488 | – | – | |
| Asthma |
Yes No |
4 (100) 216 (52.2) |
0 (0.0) 198 (47.8) |
Not converged | 0.125* | – | – | |
| CHF |
Yes No |
1 (33.3) 219 (52.8) |
2 (66.7) 196 (47.2) |
6.27 (1.54, 25.59) | 0.011 |
4.278 (0.981, 18.647) Ref. |
0.053 | |
| Comorbidity index | 0.6 ± 0.9 | 0.8 ± 1 | 1.33 (1.15, 1.53) | < 0.001 | – | – | ||
| Patients |
HC ST SOT |
92 (53.2) 106 (49.5) 22 (71) |
81 (46.8) 108 (50.5) 9 (29) |
2.15 (1.08, 4.13) 3.96 (1.99, 7.88) Ref. |
0.03 < 0.001 – |
1.423 (0.701, 2.89) 1.726 (0.835, 3.565) Ref. |
0.328 0.141 – |
|
CKD, Chronic kidney disease; HTN, High blood pressure; DM, diabetes mellitus; IHD, Ischemic Heart Disease; CVA, cerebrovascular accident; CHF, Congestive heart failure; ALT, alanine transaminase, AST, aspartate transaminase, Cr, creatinine; Hb, hemoglobin; Plt, platelet; CRP, C-reactive protein; LDH, l- lactate dehydrogenase; LY, ymphocyte,AST, aspartate aminotransferase; ALT, Alanine transaminase; BUN, Blood Urea Nitrogen; Cr, creatinine
HC, hematopoietic cell cancer; ST, solid Tumor; SOT, solid organ transplant
*Fisher exact test.†Unadjusted hazard ratio.‡Adjusted Hazard ratio. Death is set as the event
After setting the multiple regression model and considering the independent variables that were significant in the univariate analysis, it was observed that by using the information of these variables, the final status of the patients in terms of death or survival can be predicted with an accuracy of 85.6% (AUC = 0.856). Also, according to the area under the curve and using the Youden index method, taking into account a cut-off of 0.417, the obtained sensitivity and specificity are 90% and 71.2%, respectively (Fig. 1).
Fig. 1.
The receiver operating characteristics (ROC) analysis plotted based on the fitted values of multiple logistic regression on severity status of patients in terms of ICU versus non-ICU. AUC: 0.7, sensitivity: 62.5%, specificity: 71.5%, cut-off: 0.339
The best cut-off value of death and survive for variables age, WBC, LY, PMN, Hb, PLT, AST, ALT, BUN, Cr and comorbidities index are computed based on Receiver operating characteristics along with the Youden index and reported in Table 4.
Table 4.
Diagnostic evaluation of different variables and associated the results of Youden index
| Quantitative variables | AUC1 (95% CI) | P-value | Cut-off | Sensitivity | Specificity |
|---|---|---|---|---|---|
| Age | 61 (55.6, 66.4) | < 0.001 | 60> | 57.6 | 62.3 |
| WBC | 57.1 (51.6, 62.6) | 0.006 | 12,000> | 38.9 | 81.8 |
| LY | 60.3 (55.0, 65.7) | < 0.001 | 20< | 57.3 | 59.6 |
| PMN | 60.1 (54.6, 65.5) | < 0.001 | 81> | 42.9 | 75.5 |
| Hb | 57.3 (51.8, 62.7) | 0.005 | 11.3< | 34.5 | 80.8 |
| PLT | 53.3 (47.7, 58.8) | 0.125 | 156< | 51.8 | 59.1 |
| AST | 62.4 (57.0, 67.7) | < 0.001 | 39> | 62.1 | 64.1 |
| ALT | 57.1 (51.6, 62.6) | 0.006 | 25> | 71.2 | 40.9 |
| BUN | 66 (60.8, 71.3) | < 0.001 | 20> | 65.7 | 61.8 |
| Cr | 60.4 (55.0, 65.8) | < 0.001 | 1.7> | 29.3 | 87.7 |
| Comorbidities index | 56.3 (50.8, 61.8) | 0.007 | 1> | 52.5 | 60 |
AUC, Area under curve; WBC, white blood cells; PMN, polymorphonuclear cells; ALT, alanine transaminase, AST, aspartate transaminase; Cr, creatinine; Hb, hemoglobin; Plt, platelet; LY, lymphocyte, AST, aspartate aminotransferase; ALT, Alanine transaminase; BUN, Blood Urea Nitrogen; Cr, creatinine
The diagnostic evaluations of age, WBC, LY, PMN, Hb, PLT, AST, ALT, BUN, Cr and comorbidities index are shown in Table 4. The unadjusted ROC analysis of age, WBC, LY, PMN, Hb, AST, ALT, BUN, Cr and comorbidities index are shown in Fig. 1.
Kaplan–Meier survival curves and multivariate Cox regression models showed significantly lower survival with higher levels of WBC count (HR = 1.94, 95% CI = 1. 46–2.59, p < 0.001), PMN count (HR = 1.93, 95% CI = 1.452.56, p < 0.001), Hb (HR = 1.49, 95% CI = 1.042.13, p = 0.028), AST (HR = 2.55, 95% CI = 1.913.41, p < 0.001), BUN (HR = 2.56, 95% CI = 2.063.69, p < 0.001), Cr (HR = 2.63, 95% CI = 1.89–3.64, p < 0.001), comorbidities index (HR = 1.71, 95% CI = 1.29–2.27, p < 0.001) and aging (HR = 1.91, 95% CI = 1.4–2.54, p < 0.001) (Fig. 2) (Table 5).
Fig. 2.
Kaplan–Meier survival curves of immunocompromised patients with COVID-19 with different cut-off values with respect to different investigated variables
Table 5.
Hazard ratios of the variables under investigation obtained by Cox regression analysis in immunocompromised patients with COVID-19
| Variables | HR | 95% CI | p-value |
|---|---|---|---|
| Age | 1.91 | 1.4–2.54 | < 0.001 |
| WBC | 1.94 | 1.46–2.59 | < 0.001 |
| LY | 2.09 | 1.56–2.79 | < 0.001 |
| PMN | 1.93 | 1.45–2.56 | < 0.001 |
| ALT | 1.31 | 0.97–1.77 | 0.075 |
| AST | 2.55 | 1.91–3.41 | < 0.001 |
| Hb | 1.49 | 1.04–2.13 | 0.028 |
| Cr | 2.63 | 1.89–3.64 | < 0.001 |
| BUN | 2.56 | 2.06–3.69 | < 0.001 |
| Comorbidities index | 1.71 | 1.29–2.27 | < 0.001 |
Discussion
The coronavirus disease 2019 (COVID-19), as a new human pathogen, has become a catastrophe that caused high morbidity, mortality, and imposed high stress on healthcare systems worldwide. The key features and outcomes of COVID-19 among immunosuppressed individuals who are prone to more severe compilations compared to healthy ones, are not well characterized. Generally, COVID-19 is a biphasic disease that initially supresses the host immune system allowing viral replication and spread and then followed by cytokine stormy hyperinflammatory responses resulting in fatal complications such as organ failure, shock and death. Therefore, pre-existing immune suppression would be a challenge to combat and to eliminat the COVID-19 at early stages of infection [4]. Based on recent reports from different parts of world, it seems that outcomes of COVID-19 appeared to be worse in adult immunocompromised patients [4]. Therefore, in this study, we investigated demographic, laboratory findings and outcome of COVID-19 infection among patients with different types of immunocompromised status and underlying disease. In this study, the overall mortality rate was 47.4% which was close to previous studies in which the mortality rate reported as 36% [1] and 28% [2]. The mortality rate of other reports from Iran were 4.9% [8], 19.1% [22], and 23% [19] which were not compatible with our findings. This controversy may be related to types of cancer, transplantation and underlying disease. In line with our study, Moon Seong Baek concluded that immunosuppression is a potential risk factor for severe COVID-19 infection and adverse outcomes [3].
Male sex was reported to be more prone to death [15] but we could not find any significant relation between the mortality rate and gender.
According to literatures, some laboratory tests results become abnormal in patients admitted to hospital and ICU, so it has been suggested to use some of these laboratory biomarkers to predict the disease outcome.
It was reported that elevation of LDH, ALT, creatinine and cardiac troponin are associated with tissue injury [5]. High levels of CRP, ESR, fibrinogen, pro-calcitonin and WBC can be mediated with acute phase reaction in the course of COVID-19 infection [5]. Most of the past studies concluded that there is a substantial relationship between illness severity or ICU admission and CRP, ESR, LDH, D-dimer, creatinine, cardiac troponin I, ALT, AST, leukocytes, neutrophils, and PT elevation [17, 11, 7]. In addition to analyzing the relationship between illness severity and laboratory abnormalities, several studies have analyzed the relationship between death rate and laboratory biomarkers abnormalities. These studies have shown that the high levels of CRP, ESR, LDH, D-dimer, creatinine, cardiac troponins, leukocytes, ALT, PT, and procalcitonin biomarkers and low levels of lymphocytes, platelets, and antithrombin are related to worse outcome, ICU admission and death [17, 11, 28, 12, 25, 23, 24, 27].
In this study, the clinical effects of COVID-19 on immunocompromised patients including those who have cancer, hematopoietic cell malignancy and solid organ transplant (SOT) recipients were investigated. We found that the dead patients had high levels of WBC, PMN, BUN, Cr, AST, Hb and lymphocytopenia.
Although the rate of underlying diseases increases with age, in the general population age is an independent factor for COVID-19 severe outcome, so the likelihood of hospitalization, intubation and death elevate by age. In consistent with previous studies, our finding confirmed that age is a potential risk factor for disease severity and mortality in immunocompromised patients, so the death hazard in patients > 60 years is nearly twofold higher than patients with < 60 years [21].
In several studies, hypertension has been reported as the most prevalent underlying disease amongst general and immunocompromised adults patients [4, 15] but in our study, CKD, HTN and DM were the most common comorbidities among patients and the incidence of ICU admission and worse outcome in patients with these comorbidities was substantially higher than other underlying disease. Our study indicated that the death rate in patients who had CKD was substantially higher than those with other underlying conditions which is accordance with previous studies which indicates that CKD and COVID-19 comorbidity increase the risk of hospitalization. Overally, there is a consistence among different studies that highlight the strong relationship between CKD and COVID-19 severity and worse complication [10, 13, 14].
Understanding of the exact kind of immunodeficiency and underlying disease in patients with COVID-19 may be helpful in predicting the COVID-19 outcome and better management of the disease. However, further investigations are in urgent.
Finally, the heterogeneity of patients with impaired immune system, cancer types and their stages, different types of underlying disease and lack of generally infected people for comparison should be considered as limitations of current study.
Conclusion
The finding of our study highlighted that age, WBC, LY, PMN, Hb, AST, BUN, Cr variables and comorbidities index are valuable markers in survival predicting of immunocompromised patients infected with COVID-19 infection.
Declarations
Conflict of interest
The authors declare that they had no competing interest.
Ethics approval
Written informed consent was obtained from all patients.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Jafar Mohammadshahi, Email: j.mohammadshahi@arums.ac.ir.
Roghayeh Teimourpour, Email: r.teymourpour@gmail.com.
References
- 1.Akagun T, Yelken B, Usta M, Turkmen A, editors. Outcome of renal transplantation in patients with diabetes mellitus: a single-center experience. Transplantation Proceedings; 2022: Elsevier. [DOI] [PubMed]
- 2.Akalin E, Azzi Y, Bartash R, Seethamraju H, Parides M, Hemmige V, et al. Covid-19 and kidney transplantation. N Engl J Med. 2020;382(25):2475–7. doi: 10.1056/NEJMc2011117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Baek MS, Lee M-T, Kim W-Y, Choi JC, Jung S-Y. COVID-19-related outcomes in immunocompromised patients: a nationwide study in Korea. PLoS ONE. 2021;16(10):e0257641. doi: 10.1371/journal.pone.0257641. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Belsky JA, Tullius BP, Lamb MG, Sayegh R, Stanek JR, Auletta JJ. COVID-19 in immunocompromised patients: a systematic review of cancer, hematopoietic cell and solid organ transplant patients. J Infect. 2021;82(3):329–38. doi: 10.1016/j.jinf.2021.01.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Christensen B, Favaloro EJ, Lippi G, Van Cott EM, editors. Hematology laboratory abnormalities in patients with coronavirus disease 2019 (COVID-19). Seminars in thrombosis and hemostasis. Thieme Medical Publishers; 2020. [DOI] [PMC free article] [PubMed]
- 6.Duan K, Liu B, Li C, Zhang H, Yu T, Qu J, et al. Effectiveness of convalescent plasma therapy in severe COVID-19 patients. Proc Natl Acad Sci. 2020;117(17):9490–9496. doi: 10.1073/pnas.2004168117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Fan BE. Hematologic parameters in patients with COVID-19 infection: a reply. Am J Hematol. 2020;95(8):E215. doi: 10.1002/ajh.25847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Fazilat-Panah D, Fallah Tafti H, Rajabzadeh Y, Fatemi MA, Ahmadi N, Jahansouz D, et al. Clinical characteristics and outcomes of COVID-19 in 1294 new cancer patients: single-center, prospective cohort study from Iran. Cancer Invest. 2022;40(6):505–15. doi: 10.1080/07357907.2022.2075376. [DOI] [PubMed] [Google Scholar]
- 9.Fung M, Babik JM. COVID-19 in immunocompromised hosts: what we know so far. Clin Infect Dis. 2021;72(2):340–50. doi: 10.1093/cid/ciaa863. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Jdiaa SS, Mansour R, El Alayli A, Gautam A, Thomas P, Mustafa RA. COVID–19 and chronic kidney disease: an updated overview of reviews. J Nephrol. 2022;35:1–17. doi: 10.1007/s40620-021-01206-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lippi G, Plebani M. Laboratory abnormalities in patients with COVID-2019 infection. Clin Chem Lab Med. 2020;58(7):1131–1134. doi: 10.1515/cclm-2020-0198. [DOI] [PubMed] [Google Scholar]
- 12.Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta. 2020;506:145–8. doi: 10.1016/j.cca.2020.03.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Mahalingasivam V, Su G, Iwagami M, Davids MR, Wetmore JB, Nitsch D. COVID-19 and kidney disease: insights from epidemiology to inform clinical practice. Nat Rev Nephrol. 2022;18(8):485–98. doi: 10.1038/s41581-022-00570-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Pakhchanian H, Raiker R, Mukherjee A, Khan A, Singh S, Chatterjee A. Outcomes of COVID-19 in CKD patients: a multicenter electronic medical record cohort study. Clin J Am Soc Nephrol. 2021;16(5):785–6. doi: 10.2215/CJN.13820820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Pormohammad A, Ghorbani S, Baradaran B, Khatami A, Turner RJ, Mansournia MA, et al. Clinical characteristics, laboratory findings, radiographic signs and outcomes of 61,742 patients with confirmed COVID-19 infection: a systematic review and meta-analysis. Microb Pathog. 2020;147:104390. doi: 10.1016/j.micpath.2020.104390. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Raje N, Dinakar C. Overview of immunodeficiency disorders. Immunol Allergy Clin. 2015;35(4):599–623. doi: 10.1016/j.iac.2015.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al. Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med Infect Dis. 2020;34:101623. doi: 10.1016/j.tmaid.2020.101623. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Schulman S, Sholzberg M, Spyropoulos AC, Zarychanski R, Resnick HE, Bradbury CA, et al. ISTH guidelines for antithrombotic treatment in COVID-19. J Thromb Haemost. 2022;20(10):2214–25. doi: 10.1111/jth.15808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Sharifi M, Vaseghi G, Nasirian M, Arabzadeh S, Pourhadi M, Hajiahmadi S, et al. Clinical characteristics of COVID-19-infected cancer patients, Isfahan, Iran. J Res Med Sci. 2022;27(1):73. doi: 10.4103/jrms.jrms_541_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Spyropoulos AC, Connors JM, Douketis JD, Goldin M, Hunt BJ, Kotila TR, et al. Good practice statements for antithrombotic therapy in the management of COVID-19: guidance from the SSC of the ISTH. J Thromb Haemost. 2022;20(10):2226–36. doi: 10.1111/jth.15809. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Starke KR, Reissig D, Petereit-Haack G, Schmauder S, Nienhaus A, Seidler A. The isolated effect of age on the risk of COVID-19 severe outcomes: a systematic review with meta-analysis. BMJ global health. 2021;6(12):e006434. doi: 10.1136/bmjgh-2021-006434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Taghizadeh-Hesary F, Porouhan P, Soroosh D, PeyroShabany B, Shahidsales S, Keykhosravi B et al. COVID-19 in cancer and non-cancer patients. Int J Cancer Manage. 2021;14(4).
- 23.Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844–7. doi: 10.1111/jth.14768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Tang N, Bai H, Chen X, Gong J, Li D, Sun Z. Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy. J Thromb Haemost. 2020;18(5):1094–9. doi: 10.1111/jth.14817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Thachil J, Tang N, Gando S, Falanga A, Cattaneo M, Levi M, et al. ISTH interim guidance on recognition and management of coagulopathy in COVID-19. J Thromb Haemost. 2020;18(5):1023–6. doi: 10.1111/jth.14810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Tuano KS, Seth N, Chinen J. Secondary immunodeficiencies: an overview. Ann Allergy Asthma Immunol. 2021;127(6):617–26. doi: 10.1016/j.anai.2021.08.413. [DOI] [PubMed] [Google Scholar]
- 27.Wu C, Chen X, Cai Y, Zhou X, Xu S, Huang H, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934–43. doi: 10.1001/jamainternmed.2020.0994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62. doi: 10.1016/S0140-6736(20)30566-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.WHO. WHO Coronavirus (COVID-19) Dashboard. 2023. https://covid19.who.int/.
- 30.WHO. Diagnostic testing for SARS-CoV-2. 2020. https://www.who.int/publications/i/item/diagnostic-testing-for-sars-cov-2?fbclid=IwAR3ZkdKUg6U5neHAYIFxYtA1DQVGFw48bFie7Fq8QDz1Ab6QNwQuFZ1G678. [DOI] [PMC free article] [PubMed]