Abstract
Background
This study investigates in‐hospital mortality amongst patients with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and its relation to serum levels of gamma‐glutamyl transferase (GGT).
Methods
Patients were stratified according to serum levels of gamma‐glutamyl transferase (GGT) (GGT<50 IU/L or GGT≥50 IU/L).
Results
A total of 802 participants were considered, amongst whom 486 had GGT<50 IU/L and a mean age of 48.1 (16.5) years, whilst 316 had GGT≥50 IU/L and a mean age of 53.8 (14.7) years. The chief sources of SARS‐CoV‐2 transmission were contact (366, 45.7%) and community (320, 40%). Most patients with GGT≥50 IU/L had either pneumonia (247, 78.2%) or acute respiratory distress syndrome (ARDS) (85, 26.9%), whilst those with GGT<50 IU/L had hypertension (141, 29%) or diabetes mellitus (DM) (147, 30.2%). Mortality was higher amongst patients with GGT≥50 IU/L (54, 17.1%) than amongst those with GGT<50 IU/L (29, 5.9%). More patients with GGT≥50 required high (83, 27.6%) or low (104, 34.6%) levels of oxygen, whereas most of those with GGT<50 had no requirement of oxygen (306, 71.2%). Multivariable logistic regression analysis indicated that GGT≥50 IU/L (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.20–3.45, p=0.009), age (OR: 1.05, 95% CI: 1.03–1.07, p<0.001), hypertension (OR: 2.06, 95% CI: 1.19–3.63, p=0.011), methylprednisolone (OR: 2.96, 95% CI: 1.74–5.01, p<0.001) and fever (OR: 2.03, 95% CI: 1.15–3.68, p=0.016) were significant predictors of all‐cause cumulative mortality. A Cox proportional hazards regression model (B = −0.68, SE =0.24, HR =0.51, p = 0.004) showed that patients with GGT<50 IU/L had a 0.51‐times lower risk of all‐cause cumulative mortality than patients with GGT≥50 IU/L.
Conclusion
Higher levels of serum GGT were found to be an independent predictor of in‐hospital mortality.
Keywords: COVID‐19, gamma‐glutamyl transferase, in‐hospital mortality, SARS‐CoV‐2
Kaplan–Meier survival plot of mortality according to GGT levels in patients with coronavirus disease [COVID‐19]. X‐axis: Days since admission.
1. INTRODUCTION
Amongst cases of coronavirus disease (COVID‐19), 60% of patients have deranged liver diseases. 1 Many studies have shown that 2–11% of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) have an underlying liver disease. 2 In SARS‐CoV‐2, deranged liver function is considered a marker of the severity of the disease. 3 , 4 , 5 Gamma‐glutamyl transferase (GGT) is considered a specific diagnostic biomarker of hepatic cholangiocytic activity. 6 , 7 SARS‐CoV‐2‐related liver injury in relation to cholangiocytic activity can be assessed by analysing GGT serum levels. 8 GGT levels are elevated in SARS‐CoV‐2 infection, 2 which is mostly attributed to the immune‐mediated response and cytotoxicity. 9 , 10
2. METHODS
2.1. Study design and subjects
This study examined 802 patients with confirmed SARS‐CoV‐2 infection, including both Kuwaitis and non‐Kuwaitis aged 18 years or older. Patients were enrolled in this retrospective cohort study between 26 February and 8 September 2020. [Figure 1.] All the data were obtained from electronic medical records from two tertiary care hospitals in Kuwait: Jaber Al‐Ahmed Hospital and Al Adan General Hospital. 11 , 12 , 13 An electronic case‐record form (CRF) was used for data entry.
FIGURE 1.
Study flowchart
SARS‐CoV‐2 infection was confirmed by a positive result of reverse transcription‐polymerase chain reaction (RT‐PCR) using a swab of the nasopharynx. The care of all patients was standardized according to a protocol established by the Ministry of Health in Kuwait. SARS‐CoV‐2 patients were stratified according to serum levels of GGT (GGT<50 IU/L and GGT≥50 IU/L). The Standing Committee For the Coordination of Health and Medical Research at the Ministry of Health in Kuwait waived the requirement of informed consent and approved the study (Institutional review board number 2020/1422).
GGT measurement was carried out in biochemistry laboratories in Jaber Al‐Ahmed and Al Adan General Hospitals. Patient serum and plasma samples were handled by the laboratory technicians. Quantitative measurement of GGT is reported by Beckman Coulter AU analysers, which is a kinetic colour test. Method of the machine based on the guidelines of the International Federation for Clinical Chemistry (IFCC). The lowest measurable value of the test, representing the tests’ sensitivity, was approximated at 2 U/L. Estimates of precision are according to Clinical and Laboratory Standards Institute (CLSI) guidance; the coefficient of variation was less than 5%.
According to the study hospital protocol, biochemistry laboratory results would usually be reported in the electronic medical records on the first days of admission. Blood samples were collected by a nurse on the same day of the report. We documented this one‐time point result for each study participant admitted with a confirmed COVID‐19 diagnosis. Hence, the GGT results in our study reflect the baseline laboratory profile. Our study analysed GGT on admission as a predictor for COVID‐19‐related mortality. Other GGT‐related predictors, such as those related to the treatment effect or effect of hospitalization, were beyond the scope of the study.
2.2. Definitions
The primary outcome measured was SARS‐CoV‐2‐related mortality as defined by ICD‐10 code U07.1. The secondary outcome measures were the duration of hospital stay and the need for admission to the intensive care unit (ICU). The following clinical and laboratory variables were collected: sociodemographic determinants, co‐morbidities, clinical presentations, laboratory results, medications received in hospital, oxygen requirement and durations of ICU and in‐hospital stays.
Patients with a confirmed diagnosis of restrictive or obstructive disease were considered in the chronic lung disease category. The immunosuppression category was defined as patients on immunosuppressive therapy. The requirement of oxygen was considered ‘none’, ‘low’, or ‘high'. Patients who were on oxygen via a nasal cannula or a nonrebreather mask were classified as the low requirement category. Those who required extracorporeal membrane oxygenation (ECMO), invasive ventilation, noninvasive ventilation or high‐flow oxygen were grouped in the high requirement category.
2.3. Statistical analysis
Descriptive statistics were used to summarize the data in the form of the frequency, percentage, mean ± standard deviation (SD) and median ±interquartile range (IQR). Pearson's χ2 test was performed to determine the factors associated with the GGT cohorts (GGT<50 IU/L, GGT≥50 IU/L). Multivariable logistic regression was used to check the impacts of GGT, age, hypertension, methylprednisolone and fever on mortality. The relationship between GGT (GGT<50 IU/L, GGT≥50 IU/L) and mortality was assessed using Cox regression analysis and a Kaplan–Meier survival curve. An alpha level of 5% was used to check the significance of the results. SPSS version 27 (IBM Corp., Armonk, NY, USA) and R software (R Foundation for Statistical Computing, Vienna, Austria) were used to conduct the statistical analyses of the data. 14
3. RESULTS
The baseline characteristics of the COVID‐19 patients are shown in Table 1. A total of 802 hospitalized patients were enrolled in the study and stratified based on GGT<50 IU/L and GGT≥50 IU/L. The ratio of females to males was 297:504. The average age of patients with GGT≥50 IU/L was 53.8 ± 14.7 years, opposed to that of patients with GGT <50 IU/L (48.1 ± 16.5 years).
TABLE 1.
Baseline characteristics of COVID‐19 patients stratified by serum GGT levels
| [ALL] | GGT <50 IU/L | GGT ≥50 IU/L | p value | N | |
|---|---|---|---|---|---|
| N = 802 | N = 486 | N = 316 | |||
| Age, mean ± SD, years | 50.3 (16.0) | 48.1 (16.5) | 53.8 (14.7) | <0.001 | 802 |
| BMI, mean ± SD, kg/m2 | 29.2 (6.47) | 28.8 (6.65) | 29.9 (6.09) | 0.090 | 482 |
| Sex: | |||||
| Female | 297 (37.1%) | 193 (39.8%) | 104 (32.9%) | 0.058 | 801 |
| Male | 504 (62.9%) | 292 (60.2%) | 212 (67.1%) | ||
| Smoking: | |||||
| Current smoker | 31 (19.5%) | 21 (20.2%) | 10 (18.2%) | 0.898 | 159 |
| Ex‐smoker | 16 (10.1%) | 11 (10.6%) | 5 (9.09%) | ||
| Never smoked | 112 (70.4%) | 72 (69.2%) | 40 (72.7%) | ||
| Source of transmission: | |||||
| Community | 320 (40.0%) | 160 (32.9%) | 160 (50.8%) | <0.001 | 801 |
| Contact | 366 (45.7%) | 236 (48.6%) | 130 (41.3%) | ||
| Health care worker | 22 (2.75%) | 14 (2.88%) | 8 (2.54%) | ||
| Hospital acquired | 11 (1.37%) | 5 (1.03%) | 6 (1.90%) | ||
| Imported | 82 (10.2%) | 71 (14.6%) | 11 (3.49%) | ||
| Hypertension | 274 (34.2%) | 141 (29.0%) | 133 (42.1%) | <0.001 | 802 |
| DM | 273 (34.0%) | 147 (30.2%) | 126 (39.9%) | 0.006 | 802 |
| CVD | 66 (8.23%) | 46 (9.47%) | 20 (6.33%) | 0.148 | 802 |
| Chronic lung disease | 72 (8.98%) | 38 (7.82%) | 34 (10.8%) | 0.195 | 802 |
| Chronic kidney disease | 40 (4.99%) | 21 (4.32%) | 19 (6.01%) | 0.363 | 802 |
| Immunocompromised host | 15 (1.87%) | 6 (1.23%) | 9 (2.85%) | 0.167 | 802 |
| Pneumonia | 444 (55.4%) | 197 (40.5%) | 247 (78.2%) | <0.001 | 802 |
| ARDS | 128 (16.0%) | 43 (8.85%) | 85 (26.9%) | <0.001 | 802 |
| ICU admission | 131 (16.3%) | 44 (9.05%) | 87 (27.5%) | <0.001 | 802 |
| ICU duration of stay (days) IQR | 14.0 [2.00;65.2] | 14.0 [2.00;66.2] | 13.5 [1.18;62.0] | 0.615 | 132 |
| Admission to discharge (days) IQR | 16.0 [3.00;52.2] | 16.0 [3.00;49.0] | 17.0 [3.00;60.8] | 0.028 | 793 |
| Mortality | 83 (10.3%) | 29 (5.97%) | 54 (17.1%) | <0.001 | 802 |
The values are n (%) unless specified otherwise.
Abbreviations: ARDS, acute respiratory distress syndrome; BMI, body mass index; COVID‐19, coronavirus disease; CVD, cardiovascular disease; DM, diabetes mellitus; GGT, gamma‐glutamyl transferase; ICU, intensive care unit; IQR, interquartile range; SD, standard deviation.
The key source of COVID‐19 transmission amongst patients was either the community (320, 40%) or direct contact (366, 45.7%). More patients with GGT<50 IU/L (236, 48.6%) were affected by COVID‐19 due to contact than patients with GGT ≥50 IU/L (130, 41.3%). It is worth noting that more patients with GGT ≥50 IU/L had pneumonia (247, 78.2%) and acute respiratory distress syndrome (ARDS) (85, 26.9%), whereas those with GGT<50 IU/L had higher rates of hypertension (141, 29%) and diabetes mellitus (DM) (147, 30.2%).
More patients with GGT ≥50 IU/L had to be admitted to the ICU than patients with GGT<50 IU/L. The mortality rate of patients with GGT ≥50 IU/L (54, 17.1%) was also higher than that of patients with GGT<50 IU/L (29, 5.9%). The major significant symptoms (p < 0.001) of patients with higher GGT levels were fever (227, 71.8%) and shortness of breath (132, 41.8%), whilst those of patients with lower GGT levels were no symptoms (119, 24.5%) and dry cough (203, 41.8%) (Table 2).
TABLE 2.
Signs and symptoms of COVID‐19 stratified by serum GGT levels
| [ALL] | GGT <50 IU/L | GGT ≥50 IU/L | p value | N | |
|---|---|---|---|---|---|
| N = 802 | N = 486 | N = 316 | |||
| Asymptomatic | 138 (17.2%) | 119 (24.5%) | 19 (6.01%) | <0.001 | 802 |
| Headache | 91 (11.3%) | 56 (11.5%) | 35 (11.1%) | 0.935 | 802 |
| Sore throat | 82 (10.2%) | 52 (10.7%) | 30 (9.49%) | 0.666 | 802 |
| Fever | 448 (55.9%) | 221 (45.5%) | 227 (71.8%) | <0.001 | 802 |
| Dry cough | 385 (48.0%) | 203 (41.8%) | 182 (57.6%) | <0.001 | 802 |
| Productive cough | 48 (5.99%) | 29 (5.97%) | 19 (6.01%) | >0.999 | 802 |
| SOB | 229 (28.6%) | 97 (20.0%) | 132 (41.8%) | <0.001 | 802 |
| Fatigue or myalgia | 182 (22.7%) | 107 (22.0%) | 75 (23.7%) | 0.630 | 802 |
| Diarrhoea | 106 (13.2%) | 67 (13.8%) | 39 (12.3%) | 0.629 | 802 |
| Nausea | 55 (6.86%) | 28 (5.76%) | 27 (8.54%) | 0.167 | 802 |
| Vomiting | 50 (6.23%) | 27 (5.56%) | 23 (7.28%) | 0.403 | 802 |
| Change of taste or smell | 29 (3.62%) | 22 (4.53%) | 7 (2.22%) | 0.129 | 802 |
The values are n (%) unless specified otherwise.
Abbreviations: COVID‐19, coronavirus disease; GGT, gamma‐glutamyl transferase; SOB, shortness of breath.
Compared to the patients with GGT<50 IU/L, those with GGT≥50 IU/L had significantly higher platelet, white blood cell (WBC) and neutrophil counts and creatinine, lactate dehydrogenase (LDH), C‐reactive protein (CRP), procalcitonin (PCT), D‐dimer, high‐sensitivity (HS) serum troponin, ferritin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total bilirubin and direct bilirubin levels. Moreover, patients with lower GGT (GGT<50 IU/L) had significantly higher lymphocyte counts and albumin levels (Table 3).
TABLE 3.
Laboratory findings of COVID‐19 patients stratified by serum GGT levels
| [ALL] | GGT <50 IU/L | GGT ≥50 IU/L | p value | N | |
|---|---|---|---|---|---|
| N = 802 | N = 486 | N = 316 | |||
| Haemoglobin (g/L) | 130 [128;131] | 131 [128;133] | 129 [123;131] | 0.061 | 798 |
| Platelets (10⁹/L) | 259 [250;269] | 250 [238;261] | 279 [262;289] | 0.009 | 797 |
| WBC (10⁹/L) | 6.70 [6.50;7.00] | 6.20 [5.90;6.50] | 7.65 [7.10;8.30] | <0.001 | 796 |
| Neutrophils count | 4.10 [3.90;4.40] | 3.60 [3.30;3.80] | 5.50 [4.90;6.10] | <0.001 | 795 |
| Lymphocytes count | 1.50 [1.40;1.60] | 1.70 [1.50;1.80] | 1.20 [1.00;1.40] | <0.001 | 795 |
| Creatinine (umol/L) | 76.0 [74.0;77.0] | 73.0 [71.0;75.0] | 81.0 [77.0;84.0] | <0.001 | 793 |
| LDH (IU/L) | 290 [274;306] | 246 [231;270] | 344 [322;366] | <0.001 | 511 |
| CRP (mg/L) | 40.5 [32.0;52.0] | 17.0 [13.0;21.0] | 87.0 [77.0;99.0] | <0.001 | 760 |
| Procalcitonin (ng/mL) | 0.08 [0.07;0.10] | 0.06 [0.05;0.07] | 0.24 [0.18;0.41] | <0.001 | 491 |
| D‐Dimer (ng/mL) | 354 [311;410] | 264 [250;321] | 467 [395;518] | <0.001 | 502 |
| 25 (OH) Vitamin D (nmol/L) | 40.0 [37.0;44.0] | 41.0 [37.0;45.0] | 38.0 [32.0;47.0] | 0.454 | 224 |
| Troponin I HS (ng/L) | 9.00 [8.00;12.0] | 7.00 [6.00;12.0] | 10.0 [8.00;15.0] | 0.007 | 308 |
| Ferritin (ng/mL) | 426 [387;473] | 322 [282;390] | 536 [454;659] | <0.001 | 477 |
| Creatinine kinase (IU/L) | 82.5 [59.0;125] | 81.0 [42.0;208] | 88.0 [55.0;178] | 0.765 | 28 |
| ALT (IU/L) | 32.0 [30.0;35.0] | 25.0 [23.0;26.0] | 58.0 [51.0;66.0] | <0.001 | 802 |
| AST (IU/L) | 32.0 [30.0;33.0] | 26.0 [24.0;27.0] | 49.2 [47.0;54.0] | <0.001 | 802 |
| ALP (IU/L) | 69.0 [66.0;71.0] | 61.0 [60.0;63.0] | 87.0 [83.0;92.0] | <0.001 | 801 |
| Albumin (g/L) | 34.9 [34.1;35.3] | 36.0 [35.4;36.8] | 32.7 [31.8;33.5] | <0.001 | 799 |
| T. Bilirubin (umol/L) | 11.7 [11.3;12.1] | 11.2 [10.5;11.6] | 12.9 [12.0;13.9] | <0.001 | 802 |
| D. Bilirubin (umol/L) | 2.40 [2.30;2.50] | 2.12 [2.00;2.20] | 3.00 [2.80;3.30] | <0.001 | 800 |
The values are median [IQR].
Abbreviations: ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; COVID‐19, coronavirus disease; CRP, C‐reactive protein; D. bilirubin, direct bilirubin; GGT, gamma‐glutamyl transferase; HS, high‐sensitivity; LDH, lactate dehydrogenase; T. bilirubin, total bilirubin; WBC, white blood cells.
Significantly more patients with GGT ≥50 IU/L had received antibiotics (214, 67.7%), methylprednisolone (84, 26.6%), dexamethasone (39, 12.3%), therapeutic anticoagulation (138, 43.7%), azithromycin (12, 3.8%), hydroxychloroquine (66, 20.9%), kaletra (lopinavir/ritonavir) (56, 17.7%), hydrocortisone (14, 4.4%) and current use of ACE inhibitors (39, 14.7%) than the patients with GGT<50 IU/L. Moreover, more patients with GGT≥50 IU/L required either high (83, 27.6%) or low levels of oxygen (104, 34.6%). More patients with GGT<50 IU/L had no requirement for oxygen (306, 71.2%) (Table 4).
TABLE 4.
Medications administered to COVID‐19 patients stratified by serum GGT levels
| [ALL] | GGT <50 IU/L | GGT ≥50 IU/L | p value | N | |
|---|---|---|---|---|---|
| N = 802 | N = 486 | N = 316 | |||
| Antibiotics | 365 (45.5%) | 151 (31.1%) | 214 (67.7%) | <0.001 | 802 |
| Methylprednisolone | 130 (16.2%) | 46 (9.47%) | 84 (26.6%) | <0.001 | 802 |
| Dexamethasone | 60 (7.48%) | 21 (4.32%) | 39 (12.3%) | <0.001 | 802 |
| Vitamin C effervescent tablets | 571 (71.2%) | 354 (72.8%) | 217 (68.7%) | 0.232 | 802 |
| Therapeutic anticoagulation | 233 (29.1%) | 95 (19.5%) | 138 (43.7%) | <0.001 | 802 |
| Azithromycin | 18 (2.24%) | 6 (1.23%) | 12 (3.80%) | 0.032 | 802 |
| Vitamin D: | |||||
| With Vit‐D | 299 (37.3%) | 193 (39.7%) | 106 (33.5%) | 0.091 | 802 |
| Without Vit‐D | 503 (62.7%) | 293 (60.3%) | 210 (66.5%) | ||
| Hydroxychloroquine | 111 (13.8%) | 45 (9.26%) | 66 (20.9%) | <0.001 | 802 |
| Kaletra (lopinavir/ritonavir) | 108 (13.5%) | 52 (10.7%) | 56 (17.7%) | 0.006 | 802 |
| Tocilizumab | 12 (1.50%) | 4 (0.82%) | 8 (2.53%) | 0.072 | 802 |
| Hydrocortisone | 19 (2.37%) | 5 (1.03%) | 14 (4.43%) | 0.004 | 802 |
| Receiving ACE inhibitors | 70 (10.5%) | 31 (7.71%) | 39 (14.7%) | 0.006 | 667 |
| Receiving ARBs | 100 (15.0%) | 54 (13.5%) | 46 (17.2%) | 0.234 | 668 |
| Receiving statin | 192 (27.6%) | 107 (25.5%) | 85 (30.7%) | 0.161 | 696 |
| Oxygen requirements: | |||||
| High oxygen requirement | 125 (17.1%) | 42 (9.77%) | 83 (27.6%) | <0.001 | 731 |
| Low oxygen requirements | 186 (25.4%) | 82 (19.1%) | 104 (34.6%) | ||
| None | 420 (57.5%) | 306 (71.2%) | 114 (37.9%) | ||
The values are n (%), unless specified otherwise.
Abbreviations: ACE, angiotensin‐converting enzyme; ARBs, angiotensin II receptor blockers; COVID‐19, coronavirus disease; GGT, gamma‐glutamyl transferase.
The impact of GGT, age, hypertension, methylprednisolone and fever on cumulative all‐cause mortality was assessed using a multivariable logistic regression model (Table 5). Multivariable analysis showed that GGT ≥50 IU/L (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.20–3.45, p = 0.009), age (OR: 1.05, 95% CI: 1.03–1.07, p < 0.001), hypertension (OR: 2.06, 95% CI: 1.19–3.63, p = 0.011), methylprednisolone (OR: 2.96, 95% CI: 1.74–5.01, p < 0.001) and fever (OR: 2.03, 95% CI: 1.15–3.68, p = 0.016) were significantly associated with cumulative all‐cause mortality in COVID‐19 patients (Table 5). A Cox proportional hazards model was conducted to determine whether GGT had a significant effect on the hazard of mortality (Table 6). The ‘no’ category of mortality was used to indicate survival, whilst the ‘yes’ category was used to represent a hazard event. The results of the model were significant based on an alpha value of 0.05, LL =8.70, df =1 and p = 0.003, indicating that GGT was able to adequately predict the hazard of mortality. The coefficients for GGT (B = −0.68, SE =0.24, HR =0.51, p = 0.004) indicate the patients with GGT<50 IU/L had a 0.51‐times lower risk of mortality than the patients with GGT≥50 IU/L.
TABLE 5.
Logistic regression analysis of risk factors for in‐hospital death in the overall study cohort
| Mortality | Alive | Dead | Crude OR (95% CI, p value) | Adjusted OR (95% CI, p value) | |
|---|---|---|---|---|---|
| GGT (IU/L) | GGT ≥50 | 262 (82.9) | 54 (17.1) | 3.25 (2.03–5.29, p < 0.001) | 2.02 (1.20–3.45, p = 0.009) |
| Age (n, years) | Mean (SD) | 48.8 (15.4) | 63.5 (15.1) | 1.06 (1.05–1.08, p < 0.001) | 1.05 (1.03–1.07, p < 0.001) |
| Hypertension | Yes | 217 (79.2) | 57 (20.8) | 5.07 (3.14–8.40, p < 0.001) | 2.06 (1.19–3.63, p = 0.011) |
| Methylprednisolone | Yes | 93 (71.5) | 37 (28.5) | 5.41 (3.33–8.79, p < 0.001) | 2.96 (1.74–5.01, p < 0.001) |
| Fever | Yes | 387 (86.4) | 61 (13.6) | 2.38 (1.45–4.04, p = 0.001) | 2.03 (1.15–3.68, p = 0.016) |
The percentages are raw percentages. Multivariable logistic regression analysis was conducted using the simultaneous method. The model was adjusted for GGT, age, hypertension, methylprednisolone use and fever.
Abbreviations: CI, confidence interval; GGT, gamma‐glutamyl transferase; OR, odds ratio; SD, standard deviation.
TABLE 6.
Cox Proportional Hazards Regression Coefficients for GGT
| Variable | B | SE | 95% CI | z | p | HR |
|---|---|---|---|---|---|---|
| GGT:GGT<50 | −0.68 | 0.24 | [−1.14, −0.22] | −2.89 | 0.004 | 0.51 |
Kaplan–Meier survival probability plots were obtained for GGT. Each plot represents the survival probabilities for different groups over time. The Kaplan–Meier survival analysis showed that the cumulative probability of dying in the initial period was higher for patients with GGT≥50 IU/L. [Figure 2].
FIGURE 2.
Kaplan–Meier survival plot of mortality according to GGT levels in patients with coronavirus disease [COVID‐19]. X‐axis: Days since admission
4. DISCUSSION
Our study is one of the first to concentrate on in‐hospital mortality in SARS‐CoV‐2 in specific relation to serum GGT levels. The main finding of our study is that higher levels of serum GGT (≥50 IU/L) were an independent predictor of in‐hospital mortality. Other than serum GGT levels, age, hypertension, methylprednisolone use and fever were found to be predictors of in‐hospital mortality. There were more elderly patients with GGT≥50 IU/L. ICU admissions were also higher with GGT≥50 IU/L.
Other variables of liver function tests, such as ALP and ALT, were also elevated with GGT levels. The chief source of transmission of SARS‐CoV‐2 amongst the patients was contact (366, 45.7%) or the community (320, 40%). Most patients with GGT≥50 IU/L had either pneumonia or ARDS. Those with GGT≥50 U/L more often required high or low levels of oxygen.
Abnormal GGT levels during admission predict worse outcomes in critically ill SARS‐CoV‐2 patients. 15 , 16 Higher levels of GGT were associated with elderly patients. 17 The severity of SARS‐CoV‐2 has been observed to be higher in elderly men who have elevated GGT levels. 18 Worse SARS‐CoV‐2‐related prognoses and outcomes have been reported in a male cohort with elevated GGT. 19 Elevated GGT and CRP have strong interactions with the outcomes of SARS‐CoV‐2. 20 Elevated GGT is mostly seen in association with elevated ALP and AST in SARS‐CoV‐2. 21
ICU admissions have been seen more often in SARS‐CoV‐2 patients with elevated serum GGT levels. 18 Higher mortality has been reported in patients who were previously known to have had liver disease. 22 , 23 One‐month mortality was seen to be higher in SARS‐CoV‐2 patients with cirrhosis. 24 Altered levels of GGT have been seen in SARS‐CoV‐2 patients and are associated with longer hospital stays. 25 , 26 , 27
4.1. Limitations
Our study has various limitations. Unmeasured confounding factors, such as clinical comorbidities and medications, could have affected the outcomes. This Kuwaiti study included all the SARS‐CoV‐2‐positive patients.
5. CONCLUSIONS
This study demonstrated that serum GGT≥50 IU/L is an independent predictor of in‐hospital mortality in SARS‐CoV‐2 patients. The incidence of ICU admission was higher with elevated serum GGT levels. More prospective studies are required to better understand the role of serum GGT levels in predicting in‐hospital mortality in COVID‐19.
CONFLICT OF INTEREST
No conflict of interest to disclose for any author on this manuscript.
AUTHOR CONTRIBUTIONS
MAR designed the study. MAR and RR participated in data analysis and wrote the manuscript. AAS and JP performed the statistical analysis and reviewed the manuscript. The remaining authors collected the data. All authors had access to the data and took responsibility for the integrity and accuracy of data analysis. All authors have read and approved the manuscript. The authors thank Dr Danah Alothman, Dr Mohamed Elmetwalli Ghazi, and Dr Dhari Alown for their support in manuscript review.
PATIENT CONSENT STATEMENT
The requirement for patient consent was waived because of the retrospective observational study design.
PERMISSION TO REPRODUCE MATERIAL FROM OTHER SOURCES
No material from other sources was included in this study.
ACKNOWLEDGEMENT
‘The authors would like to thank Dr Danah Alothman, Dr Mohamed Elmetwalli Ghazi and Dr Dhari Alown for their support in manuscript review'.
Alroomi M, Rajan R, Alsaber A, et al. In‐hospital mortality in SARS‐CoV‐2 stratified by gamma‐glutamyl transferase levels. J Clin Lab Anal. 2022;36:e24291. doi: 10.1002/jcla.24291
Funding information
No funding was received for this study.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available because of privacy or ethical restrictions.
REFERENCES
- 1. Phipps MM, Barraza LH, LaSota ED, et al. Acute Liver Injury in COVID‐19: Prevalence and Association with Clinical Outcomes in a Large U.S. Cohort. Hepatology. 2020;72:807‐817. 10.1002/hep.31404 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Zhang C, Shi L, Wang FS. Liver injury in COVID‐19: management and challenges. Lancet Gastroenterol Hepatol. 2020;5:428‐430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Cai Q, Huang D, Yu H, et al. COVID‐19: Abnormal liver function tests. J Hepatol. 2020;73(3):566‐574. 10.1016/j.jhep.2020.04.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Xie H, Zhao J, Lian N, et al. Clinical characteristics of non‐ICU hospitalized patients with coronavirus disease 2019 and liver injury: a retrospective study. Liver Int. 2020;40(6):1321‐1326. 10.1111/liv.14449 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Fan Z, Chen L, Li J, et al. Clinical Features of COVID‐19‐Related Liver Functional Abnormality. Clin Gastroenterol Hepatol. 2020;18(7):1561‐1566. 10.1016/j.cgh.2020.04.002. Epub 2020 Apr 10. PMID: 32283325; PMCID: PMC7194865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Hanigan MH, Frierson HF Jr. Immunohistochemical detection of gamma‐glutamyl transpeptidase in normal human tissue. J Histochem Cytochem. 1996;44:1101‐1108. [DOI] [PubMed] [Google Scholar]
- 7. Whitfield JB. Gamma glutamyl transferase. Crit Rev Clin Lab Sci. 2001;38:263‐355. [DOI] [PubMed] [Google Scholar]
- 8. Saini N, Saini RK, Kumari M, et al. Evaluation of gamma glutamyl‐transferase (GGT) levels in COVID‐19: a retrospective analysis in tertiary care centre. Indian J Biochem Biophys. 2020;57(6):681‐686. [Google Scholar]
- 9. Wang Y, Liu S, Liu H, et al. SARS‐CoV‐2 infection of the liver directly contributes to hepatic impairment in patients with COVID‐19. J Hepatol. 2020;73:807‐816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS‐CoV‐2 pneumonia in Wuhan, China: a single‐centered, retrospective, observational study. Lancet Respir Med. 2020;8:475‐481. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Al‐Jarallah M, Rajan R, Saber AAL, et al. In‐hospital mortality in SARS‐CoV‐2 stratified by hemoglobin levels: a retrospective study. eJHaem. 2021;2:335‐339. doi: 10.1002/jha2.195 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Al‐Jarallah M, Rajan R, Dashti R, et al. In‐hospital mortality in SARS‐CoV‐2 stratified by serum 25‐hydroxy‐vitamin D levels: a retrospective study. J Med Virol. 2021;93:5880‐5885. 10.1002/jmv.27133 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Alroomi M, Rajan R, Omar AA, et al. Ferritin level: A predictor of severity and mortality in hospitalized COVID‐19 patients. Immun Inflamm Dis. 2021;9:1648‐1655. 10.1002/iid3.517 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Laine T, Reyes EM. Tutorial: survival estimation for Cox regression models with time‐varying coefficients using SAS and R. J Stat Softw. 2014;61:1‐23. [Google Scholar]
- 15. Badr HS, Du H, Marshall M, Dong E, Squire MM, Gardner LM. Association between mobility patterns and COVID‐19 transmission in the USA: a mathematical modelling study. Lancet Infect Dis. 2020;20(11):1247‐1254. 10.1016/S1473-3099(20)30553-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Asghar MS, Akram M, Rasheed U, et al. Derangements of Liver enzymes in Covid‐19 positive patients of Pakistan: a retrospective comparative analysis with other populations. Archives of Microbiology & Immunology. 2020;4:110‐120. [Google Scholar]
- 17. Daeppen JB, Smith TL, Schuckit MA. Influence of age and body mass index on gamma‐glutamyltransferase activity: a 15‐year follow‐up evaluation in a community sample. Alcohol Clin Exp Res. 1998;22:941‐944. [PubMed] [Google Scholar]
- 18. Xu L, Liu J, Lu M, Yang D, Zheng X. Liver injury during highly pathogenic human coronavirus infections. Liver Int. 2020;40(5):998‐1004. 10.1111/liv.14435 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Zhang J, Zhou L, Yang Y, Peng W, Wang W, Chen X. Therapeutic and triage strategies for 2019 novel coronavirus disease in fever clinics. Lancet Respir Med. 2020;8:e11‐e12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID‐19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20(4):25‐434. 10.1016/S1473-3099(20)30086-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Shao T, Tong Y, Lu S, et al. γ‐Glutamyltransferase elevations are frequent in patients with COVID‐19: a clinical epidemiologic study. Hepatol Commun. 2020;11;4(12):1744–1750. 10.1002/hep4.1576 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Hashemi N, Viveiros K, Redd WD, et al. Impact of chronic liver disease on outcomes of hospitalized patients with COVID‐19: a multicentre United States experience. Liver Int. 2020;40(10):2515‐2521. 10.1111/liv.14583 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with covid‐19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020;22(369):m1985. 10.1136/bmj.m1985 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Iavarone M, D'Ambrosio R, Soria A, et al. High rates of 30‐day mortality in patients with cirrhosis and COVID‐19. J Hepatol. 2020;73:1063‐1071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Morgan K, Samuel K, Vandeputte M, Hayes PC, Plevris JN. SARS‐CoV‐2 Infection and the Liver. Pathogens. 2020;9(6):430. 10.3390/pathogens9060430 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Tu W‐J, Liu Q, Cao J‐L, et al. γ‐Glutamyl transferase as a risk factor for all‐cause or cardiovascular disease mortality among 5912 ischemic strokes. Stroke. 2017;48(10):2888‐2891. [DOI] [PubMed] [Google Scholar]
- 27. Cao J, Tu W‐J, Cheng W, et al. Clinical features and short‐term outcomes of 102 patients with coronavirus disease 2019 in Wuhan, China. Clin Infect Dis. 2020;71(15):748‐755. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available because of privacy or ethical restrictions.