Abstract
Background
Identifying the predictors of COVID-19 related death in diabetes patients can assist physicians for detecting risk factors related to the worse outcome in these patients. In this study we investigated the predictors of the death in patients with diabetes compared with non-diabetic COVID-19 patients.
Methods
In the present case-control study, the case group were diabetic patients with COVID-19 and the control group included Non-diabetic COVID-19 patients. The data source regarding the demographic characteristics, clinical symptoms, laboratory, and radiological findings on admission as well as the complications, treatment, and outcomes during hospitalization were gathered from their medical record through two trained nurses. Adjusted and unadjusted odds ratios (OR) estimate were calculated using the simple and multiple logistic regression through backward model.
Results
The mean (SD) age of the case group was higher than that of the control group; [65.24 (12.40) years vs. 59.35 (17.34) years, respectively (P < 0.001)]. Results of the adjusted logistic regression model showed that, advanced age (+60 year) (OR = 5.13, P = 0.006), addiction (OR = 5.26, P = 0.033), high level of Blood urea nitrogen (OR = 5.85, P < 0.001), and high level of Alkaline Phosphatase (OR = 3.38, P = 0.012) in diabetic patients were significantly associated with increase the odds of death in COVID-19 patients.
Conclusion
We found that in COVID-19 patients with diabetes; advanced age, addiction, high level of BUN and Alp and in non-diabetic COVID-19 patients advanced age, dyspnea, high level of BUN and SGOT were associated with increase risk of death in these patients.
Keywords: COVID-19, Diabetes, Risk factor, Death
1. Introduction
Diabetes is one of the most common non-communicable diseases worldwide and there is evidence of its substantial increasing trend in recent years [1]. Several studies have revealed a higher vulnerability to some infectious diseases in diabetic people [2,3]. Decreased T cell–mediated immune response and impaired neutrophil functionin diabetics can explain this higher susceptibility [2].
Alongside severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS), coronavirus disease 2019)COVID-19(is another common type of coronavirus that infect humans [4]. The role of diabetes in increasing of mortality and morbidity in patients with SARS have been shown previously [5]. Also, noticeable proportion of identified COVID-19 patients are diabetic. In such a way that, the rate of diabetes and COVID-19 comorbidity is range from 20 to 50% in different global regions [6]. Published studies showed that some underlying diseases such as hypertension and diabetes mellitus are important risk factors for the fatality of COVID-19 patients [[7], [8], [9]].
Although, studies have been designed to identify death-related factors in COVID-19 patients. However, most of them are conducted on the total COVID-19 cases, regardless of having an underlying disease or not, or the investigated variables were rare. Identifying the predictors of death in diabetic COVID-19 patients helps in better management of them, and assist physicians for detecting risk factors which contributes to the severity and mortality of COVID-19 in these patients. This study was conducted in order to identify the predictors of the worse outcome in patients with diabetes in whom COVID-19 was confirmed compared with non-diabetic COVID-19 patients.
2. Materials and method
In the present case-control study, we identified all patients admitted to Sina Hospital and Beheshti Hospital in Hamadan province, the west of Iran, which was assigned to admit COVID-19 adult patients. Patients recruited from January 2020 to January 2021.
The Ethics Committee of the Hamadan University of Medical Sciences approved this study (IR.UMSHA.REC.1399.841). In this study, patients with positive real time reverse transcriptase polymerase chain reaction (RT-PCR) on samples from upper respiratory nasopharyngeal swabs were enrolled to the study.
The case group included diabetic patients with COVID-19 and the control group included Non-diabetic COVID-19 patients. Accordingly, all 420 diagnosed diabetic patients with confirmed COVID-19 in the above mentioned time period were included and considered as case group and for increase the statistical power of the study, compared them with 1260 non-diabetic patients with COVID-19, as controls group. Controls were selected at the same time and from the same hospital in order to overcome some potential confounders such as quality of care and type of prescription drugs.
The data source regarding the demographic characteristics, clinical symptoms, laboratory, and radiological findings on admission as well as the complications, treatment, and outcomes during hospitalization were gathered from their medical record through two trained nurses and was modified according the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) [10].
The variables registered included demographic data, epidemiological information, comorbidities (chronic cardiac disease, chronic pulmonary disease, cancer, hypertension and chronic liver disease), baseline laboratory tests result (hemoglobin lymphocyte count, platelet count), signs and symptoms at admission (fever, cough, dyspnea, myalgia and headache) and outcome including mortality in hospital.
2.1. Statistical analysis
2.1.1. Logistic regression (LR)
LR is an exceedingly popular classical statistical technique used for classifications type prediction problems, has traditionally been the choice of many studies to determine the relationship between target variable of with a set of independent variables [11]. The model can be present as follows:
In this model's are factors, α and 's are regression coefficients that state the measure of effect size. (x) indicate the probability of success and given a specific value of covariates and π/1−π indicate the odds ratio of classifying the response [12]. The result induces the Odds ratio (OR) of death in diabetic patients with COVID-19 in one group compared to non-diabetic patients with COVID-19.
We used the chi-square test to compare categorical variables and the t-test to compare the continuous variables. Adjusted and unadjusted odds ratios (OR) estimate were calculated using the simple and multiple logistic regression through backward model. All statistical analyses were performed at a significance level of 0.05, using Stata software, version 16 (StataCorp, College Station, TX, USA).
3. Results
The mean (SD) age of the case group was higher than that of the control group; [65.24 (12.40) years vs. 59.35 (17.34) years, respectively (P < 0.001)]. In addition, the proportion of females was significantly higher in case than in control (57.1% versus 43.6%; P < 0.001). The details of cases and controls, including demographic characteristics, epidemiological information, comorbidities, signs and symptoms at admission and laboratory tests results are shown in Table 1 . As shown, two groups were heterogenic in regards of gender, headache and fever sign, cardiovascular and hypertension co-morbidity, and ESR, BUN, BS, NA, K, HCT and Hb markers (P < 0.05).
Table 1.
Comparison of baseline characteristics of the patients in the case and control groups.
Variable | Case (n = 420) |
Control (n = 1260) |
P-value | ||
---|---|---|---|---|---|
No | % | No | % | ||
Demographics Characteristic | |||||
Gender | <0.001∗a | ||||
Male | 180 | 42.9 | 711 | 56.4 | |
Female | 240 | 57.1 | 549 | 43.6 | |
Habitat | 0.534 | ||||
rural | 67 | 16.0 | 201 | 16.0 | |
urban | 353 | 84.0 | 1059 | 84.0 | |
Smoking status | 0.086 | ||||
Nonsmoker | 397 | 94.5 | 1157 | 91.8 | |
Smoker | 23 | 5.5 | 103 | 8.2 | |
Injecting drug use | 0.094 | ||||
No | 397 | 94.5 | 1160 | 92.1 | |
Yes | 23 | 5.5 | 100 | 7.9 | |
Admission signs and symptoms | |||||
Headache | <0.001∗a | ||||
No | 369 | 87.9 | 1001 | 79.5 | |
Yes | 51 | 12.1 | 258 | 20.5 | |
Fatigue | 0.500 | ||||
No | 406 | 96.7 | 1225 | 97.3 | |
Yes | 14 | 3.3 | 34 | 2.7 | |
Myalgia | 0.374 | ||||
No | 232 | 55.2 | 664 | 52.7 | |
Yes | 188 | 44.8 | 595 | 47.3 | |
Diarrhea | 0.272 | ||||
No | 381 | 90.7 | 1118 | 88.8 | |
Yes | 39 | 9.3 | 141 | 11.2 | |
Nausea | 0.490 | ||||
No | 316 | 75.2 | 968 | 76.9 | |
Yes | 104 | 24.8 | 291 | 23.1 | |
Vomiting | 0.534 | ||||
No | 330 | 78.6 | 1007 | 80.0 | |
Yes | 90 | 21.4 | 252 | 20.0 | |
Abdominal Pain | 0.404 | ||||
No | 405 | 96.4 | 1203 | 95.5 | |
Yes | 15 | 3.6 | 57 | 4.5 | |
Chest pain | 0.331 | ||||
No | 390 | 92.9 | 1151 | 91.3 | |
Yes | 30 | 7.1 | 109 | 8.7 | |
Sputum | 0.563 | ||||
No | 417 | 99.3 | 1254 | 99.5 | |
Yes | 3 | 0.7 | 6 | 0.5 | |
Sweat | 0.432 | ||||
No | 411 | 97.9 | 1224 | 97.1 | |
Yes | 9 | 2.1 | 36 | 2.9 | |
Fever | 0.014∗a | ||||
No | 213 | 50.7 | 552 | 43.8 | |
Yes | 207 | 49.3 | 708 | 56.2 | |
Chills | 0.067 | ||||
No | 238 | 56.7 | 649 | 51.5 | |
Yes | 182 | 43.3 | 611 | 48.5 | |
Dry cough | 0.776 | ||||
No | 240 | 57.1 | 710 | 56.3 | |
Yes | 180 | 42.9 | 550 | 43.7 | |
Sore Throat | 0.280 | ||||
No | 409 | 97.4 | 1213 | 96.3 | |
Yes | 11 | 2.6 | 47 | 3.7 | |
Sputum Cough | 0.717 | ||||
No | 345 | 82.1 | 1025 | 81.3 | |
Yes | 75 | 17.9 | 235 | 18.7 | |
Urinary symptoms | 0.085 | ||||
No | 402 | 95.7 | 1227 | 97.4 | |
Yes | 18 | 4.3 | 33 | 2.6 | |
Vertigo | 0.818 | ||||
No | 406 | 96.7 | 1215 | 96.4 | |
Yes | 14 | 3.3 | 45 | 3.6 | |
Constipation | 0.832 | ||||
No | 412 | 98.1 | 1238 | 98.3 | |
Yes | 8 | 1.9 | 22 | 1.7 | |
Weakness | 0.349 | ||||
No | 226 | 53.8 | 711 | 56.4 | |
Yes | 194 | 46.2 | 549 | 43.6 | |
Anorexia | 0.574 | ||||
No | 269 | 64.0 | 826 | 65.6 | |
Yes | 151 | 36.0 | 434 | 34.4 | |
Awareness | 0.038∗a | ||||
No | 400 | 95.2 | 1226 | 97.3 | |
Yes | 20 | 4.8 | 34 | 2.7 | |
loss of taste and smell | 0.137 | ||||
No | 379 | 90.2 | 1106 | 87.8 | |
Yes | 41 | 9.8 | 154 | 12.2 | |
Dyspnea | 0.499 | ||||
No | 177 | 42.1 | 507 | 40.3 | |
Yes | 243 | 57.9 | 752 | 59.7 | |
Stomach ache | 0.299 | ||||
No | 413 | 98.3 | 1247 | 99.0 | |
Yes | 7 | 1.7 | 13 | 1.0 | |
Comorbidities | |||||
cardiovascular diseases | <0.001∗a | ||||
No | 298 | 71.0 | 1048 | 86.0 | |
Yes | 122 | 29.0 | 176 | 14.0 | |
Hypertension | <0.001∗a | ||||
No | 184 | 43.8 | 892 | 70.8 | |
Yes | 236 | 56.2 | 368 | 29.2 | |
cancer | 0.697 | ||||
No | 410 | 97.6 | 1234 | 97.9 | |
Yes | 10 | 2.4 | 26 | 2.1 | |
Pulmonary Disease | 0.529 | ||||
No | 364 | 86.7 | 1092 | 86.7 | |
Yes | 56 | 13.3 | 168 | 13.3 | |
Liver diseasee | 0.881 | ||||
No | 416 | 99.0 | 1249 | 99.1 | |
Yes | 4 | 1.0 | 11 | 0.9 | |
Continuous variables | Mean | SD | Mean | SD | P-value |
vital signs | |||||
Number of Breaths | 19.59 | 3.70 | 19.93 | 4.08 | 0.128 |
Body Temperature | 37.24 | 0.71 | 37.29 | 1.05 | 0.367 |
Diastolic | 78.64 | 12.46 | 75.94 | 10.70 | <0.001∗b |
Systolic | 126.44 | 20.98 | 120.46 | 17.72 | <0.001∗b |
Heart Rate | 92.80 | 15.02 | 93.04 | 36.38 | 0.892 |
Laboratory parameters | |||||
ESR | 49.69 | 29.78 | 40.92 | 28.86 | <0.001∗b |
BUN | 24.18 | 19.43 | 20.07 | 15.25 | <0.001∗b |
BS | 217.03 | 121.15 | 124.82 | 49.89 | <0.001∗b |
CR | 1.28 | 0.81 | 1.33 | 3.70 | 0.803 |
PT | 13.73 | 3.31 | 13.65 | 4.05 | 0.717 |
CPK | 191.32 | 295.26 | 248.34 | 461.13 | 0.063 |
SGPT or ALT | 37.54 | 63.43 | 43.31 | 112.04 | 0.336 |
SGOT | 44.86 | 118.85 | 53.22 | 192.29 | 0.419 |
Alp | 214.95 | 140.58 | 203.64 | 105.23 | 0.104 |
PTT | 34.25 | 12.56 | 35.57 | 12.64 | 0.086 |
K | 4.29 | 0.56 | 4.15 | 1.08 | 0.019∗b |
NA | 136.45 | 3.99 | 137.60 | 3.76 | <0.001∗b |
Plat | 196.68 | 75.44 | 198.59 | 89.93 | 0.697 |
HCT | 41.70 | 5.48 | 42.49 | 5.75 | 0.014∗b |
Hb | 13.55 | 1.97 | 13.93 | 2.10 | <0.001∗b |
LDH | 576.70 | 338.13 | 580.85 | 324.60 | 0.838 |
Lym | 23.00 | 13.17 | 23.49 | 12.26 | 0.494 |
NEUT | 72.73 | 13.85 | 72.06 | 13.29 | 0.386 |
SD: standard deviation, a Chi-Square Test, b T- Test, ∗Significant at the level of P < 0.05. ESR: Erythrocyte Sedimentation Rate, BUN: Blood Urea Nitrogen, BS: Blood Sugar, CR: Blood creatinine, PT: Prothrombin Time, CPK: Creatine Phosphokinase, ALT: Alanine Aminotransferase, SGOT: Serum Glutamic-Oxaloacetic Transaminase, Alp: Alkaline Phosphatase Level Test, PTT: Partial Thromboplastin Time, K: Potassium, NA: Sodium, PLT: Blood Platelets, HCT: Hematocrit, HB: Hemoglobin, LDH: Lactate Dehydrogenase, lym: Lymphocyte, NEUT: Neutrophils.
The effect of various potential risk factors for mortality of COVID-19 in diabetic AND non-diabetic patients is given in Table 2, Table 3 using crude (Table 2) and adjusted (Table 3) OR.
Table 2.
Association between covid-19 mortality and potential risk factors in diabetic and non-diabetic patients using unadjusted odds ratio.
Characteristic demographics | Diabetes N = 420 |
Unadjusted Odds Ratio (95% CI) |
P value |
Non- Diabetes N = 1260 |
Unadjusted Odds Ratio (95% CI) |
P value |
|||
---|---|---|---|---|---|---|---|---|---|
Alive | Dead | Alive | Dead | ||||||
Age (year) | |||||||||
<60 | 128(38.2) | 9(10.6) | – | – | 581(55.0) | 38(18.8) | – | – | |
>=60 | 207(61.8) | 76(89.4) | 18.64 (1.57, 221.71) | 0.021∗ | 475(45.0) | 164(81.2) | 13.44 (4.93, 36.64) | <0.001∗ | |
Gender | |||||||||
Male | 138(41.2) | 42(49.4) | – | – | 582(55.1) | 128(63.4) | – | – | |
Female | 197(58.8) | 43(50.6) | 0.61 (0.11, 3.16) | 0.560 | 474(44.9) | 74(36.6) | 0.62 (0.28, 1.36) | 0.231 | |
Residence | |||||||||
Rural | 53(15.8) | 14(16.5) | – | – | 171(16.2) | 29(14.4) | – | – | |
urban | 282(84.2) | 71(83.5) | 70.10 (2.19, 240.74) | 0.016∗ | 885(83.8) | 173(85.6) | 3.78 (1.29, 11.06) | 0.015∗ | |
Smoking status | |||||||||
Nonsmoker | 316(94.3) | 81(95.3) | – | – | 973(92.1) | 182(90.1) | – | – | |
Smoker | 19(5,7) | 4(4.7) | 0.07(0.01, 0.47) | 0.020∗ | 83(7.9) | 20(9.9) | 1.12 (0.28, 4.49) | 0.871 | |
Injecting drug use | |||||||||
No | 320(95.5) | 77(90.6) | – | – | 974(92.2) | 184(91.1) | – | – | |
Yes | 15(4.5) | 8(9.4) | 23.94 (1.22, 471.14) | 0.037∗ | 82(7.8) | 18(8.9) | 1.78 (0.42, 7.53) | 0.436 | |
Admission signs & symptoms | |||||||||
Headache | |||||||||
No | 291(86.9) | 78(91.8) | – | – | 828(78.4) | 172(85.6) | – | – | |
Yes | 44(13.1) | 7(8.2) | 0.59 (0.05, 6.92) | 0.675 | 228(21.6) | 29(14.4) | 2.37 (0.99, 5.64) | 0.052 | |
Fatigue | |||||||||
No | 326(97.3) | 80(94.1) | – | – | 814(77.1) | 153(76.1) | – | – | |
Yes | 9(2.7) | 5(5.9) | 31.43 (1.21, 81.91) | 0.038∗ | 48(23.9) | 290(23.1) | 0.38 (0.05, 2.77) | 0.342 | |
Myalgia | |||||||||
No | 179(53.4) | 53(62.4) | – | – | 540(51.1) | 124(61.7) | – | – | |
Yes | 156(46.6) | 32(37.6) | 0.38 (0.06, 2.34) | 0.295 | 516(48.9) | 77(38.3) | 1.89 (0.92, 3.86) | 0.0812 | |
Diarrhea | |||||||||
No | 302(90.1) | 79(92.9) | – | – | 937(88.7) | 180(89.6) | – | – | |
Yes | 33(9.9) | 6(7.1) | 1.75 (0.16, 19.67) | 0.649 | 119(11.3) | 21(10.4) | 3.23 (1.13, 9,18) | 0.028∗ | |
Nausea | |||||||||
No | 247(73.7) | 69(81.2) | – | – | 814(77.1) | 153(76.1) | - | – | |
Yes | 88(26.3) | 16(18.8) | 1.46 (0.06, 37.59) | 0.819 | 242(22.9) | 48(23.9) | 2.63 (0.84, 8.25) | 0.097 | |
Vomiting | |||||||||
No | 258(77.0) | 72(84.7) | – | – | 845(80.0) | 160(79.6) | – | – | |
Yes | 77(23.0) | 13(15.3) | 0.16 (0.01, 6.50) | 0.329 | 211(20.0) | 41(20.4) | 0.85 (0.26, 2.83) | 0.794 | |
Fever | |||||||||
No | 168(50.1) | 45(52.9) | – | – | 456(43.2) | 96(47.5) | – | – | |
Yes | 167(49.9) | 40(47.1) | 0.22 (0.02, 2.44) | 0.216 | 600(56.8) | 106(52.5) | 3.90 (0.99, 15.42) | 0.052 | |
Chills | |||||||||
No | 186(55.5) | 52(61.2) | – | – | 529(50.1) | 120(59.4) | – | – | |
Yes | 149(44.5) | 33(38.8) | 17.37 (1.08, 28.04) | 0.044∗ | 527(49.9) | 82(40.6) | 0.20 (0.05, 0.78) | 0.021∗ | |
Dry cough | |||||||||
No | 187(55.8) | 53(62.4) | – | – | 594(56.3) | 116(57.4) | – | – | |
Yes | 148(44.2) | 32(37.6) | 0.99 (0.24, 4.36) | 0.8987 | 462(43.8) | 86(42.6) | 0.81 (0.38, 1.73) | 0.578 | |
Urinary symptoms | |||||||||
No | 327(97.6) | 75(88.2) | – | – | 1032(97.7) | 193(95.5) | – | – | |
Yes | 8(2.4) | 10(11.8) | 209.32 (7.57, 578.953) | 0.002∗ | 24(2.3) | 9(4.5) | 1.04 (0.19, 5.53) | 0.969 | |
Vertigo | |||||||||
No | 325(97.0) | 81(95.3) | – | – | 1016(96.2) | 197(97.5) | – | – | |
Yes | 10(3.0) | 4(4.7) | 5.42 (0.19, 160.43) | 0.329 | 40(3.8) | 5(2.5) | 2.46 (0.40, 15.01) | 0.331 | |
Constipation | |||||||||
No | 329(98.2) | 83(97.6) | – | – | 1040(98.5) | 196(97.0) | – | – | |
Yes | 6(1.8) | 2(2.4) | 53.99 (0.72, 404.86) | 0.070 | 16(1.5) | 6(3.0) | 7.66 (0.82, 71.62) | 0.074 | |
Anorexia | |||||||||
No | 210(62.7) | 59(69.4) | – | – | 702(66.5) | 123(60.9) | – | – | |
Yes | 125(37.3) | 26(30.6) | 0.16 (0.03, 0.92) | 0.040∗ | 354(33.5) | 79(39.1) | 0.52 (0.24, 1.07) | 0.077 | |
loss of taste and smell | |||||||||
No | 298(89.0) | 81(95.3) | – | – | 921(87.2) | 185(91.6) | – | – | |
Yes | 37(11.0) | 4(4.7) | 0.09 (0.01, 1.31) | 0.079 | 135(12.8) | 17(8.4) | 1.04 (0.31, 3.49) | 0.948 | |
Dyspnea | |||||||||
No | 147(43.9) | 30(35.3) | – | – | 453(42.9) | 54(26.9) | – | – | |
Yes | 188(56.1) | 55(64.7) | 1.88 (0.40, 8.74) | 0.422 | 603(57.1) | 147(73.1) | 4.36 (1.99, 9.55) | <0.001∗ | |
Comorbidities | |||||||||
Cardiovascular diseases | |||||||||
No | 249(74.3) | 49(57.6) | – | – | 931(88.2) | 151(74.8) | – | – | |
Yes | 86(25.7) | 36(42.4) | 2.39 (0.44, 13.19) | 0.316 | 125(11.8) | 51(25.2) | 1.44(0.59, 3.53) | 0.420 | |
Hypertension | |||||||||
No | 154(46.0) | 30(35.3) | – | – | 770(72.9) | 120(59.4) | – | – | |
Yes | 181(54.0) | 55(64.7) | 0.23 (0.24, 2.12) | 0.193 | 286(27.1) | 82(40.6) | 1.61 (0.73, 3.52) | 0.231 | |
Pulmonary Disease | |||||||||
No | 294(87.8) | 70(82.4) | – | – | 924(87.5) | 166(82.2) | – | – | |
Yes | 41(12.2) | 15(17.6) | 2.19 (0.27, 17.63) | 0.460 | 132(12.5) | 36(17.8) | 0.42 (0.14, 1.30) | 0.133 | |
vital signs | |||||||||
Body Temperature | |||||||||
<37 | 167(49.9) | 42(49.4) | – | – | 500(47.3) | 98(48.8) | – | – | |
>=37 | 68(50.1) | 43(50.61) | 2.81 (0.42, 18.99) | 0.290 | 556(52.7) | 103(51.2) | 1.06 (0.53, 2.14) | 0.859 | |
Systole Bp | |||||||||
<140 | 247(73.7) | 68(80.0) | – | – | 886(83.9) | 171(84.7) | – | – | |
>=140 | 88(26.3) | 17(20.0) | 3.27 (0.10, 104.92) | 0.502 | 170(16.1) | 31(15.3) | 1.09 (0.24, 5.07) | 0.913 | |
Diastolic Bp | |||||||||
<90 | 254(75.8) | 70(82.4) | – | – | 884(83.7) | 171(84.7) | – | - | |
>=90 | 81(24.4) | 15(17.6) | 0.06 (0.01, 3.38) | 0.169 | 172(16.3) | 31(15.3) | 0.42 (0.09, 1.84) | 0.250 | |
laboratory parameters | |||||||||
ESR | |||||||||
Normal | 153(49.0) | 30(38.5) | – | – | 580(60.0) | 92(47.9) | – | – | |
Abnormal | 159(51.0) | 48(61.5) | 0.15 (0.02, 1.05) | 0.056 | 385(39.9) | 100(52.1) | 1.19 (0.56, 2.55) | 0.647 | |
BUN | |||||||||
<20 | 224(67.1) | 18(21.2) | – | – | 785(74.5) | 57(28.4) | – | – | |
>=20 | 110(32.9) | 67(78.8) | 52.03 (4.35, 62.68) | 0.002∗ | 268(25.5) | 144(71.6) | 6.57 (3.80, 14.04) | <0.001∗ | |
BS | |||||||||
70–105 | 34(12.0) | 8(10.5) | – | – | 335(44.3) | 36(22.0) | – | – | |
>=105 | 249(88.0) | 68(89.5) | 2.69 (0.05, 14.44) | 0.623 | 421(55.7) | 128(78.0) | 1.23 (0.57, 2.64) | 0.589 | |
PT | |||||||||
<13 | 98(34.0) | 20(24.7) | – | – | 272(30.3) | 53(27.6) | – | – | |
>=13 | 190(66.0) | 61(75.3) | 0.04 (0.01, 0.49) | 0.012∗ | 625(69.7) | 139(72.4) | 1.86 (0.79, 4.34) | 0.153 | |
CPK | |||||||||
Normal | 126(61.5) | 39(75.0) | – | – | 423(64.0) | 94(75.8) | – | – | |
Abnormal | 79(38.5) | 13(25.0) | 0.82 (0.14, 4.67) | 0.824 | 238(36.0) | 30(24.2) | 1.37 (0.60, 3.04) | 0.426 | |
SGPT | |||||||||
<37 | 241(77.5) | 54(64.3) | – | – | 710(72.7) | 111(56.9) | – | – | |
>=37 | 70(22.5) | 30(35.7) | 1.56 (0.21, 11.299) | 0.662 | 257(27.3) | 84(43.1) | 1.38 (0.59, 3.16) | 0.457 | |
SGOT | |||||||||
<45 | 279(85.8) | 51(61.4) | – | – | 840(81.3) | 96(48.5) | – | – | |
>=45 | 46(14.2) | 32(38.6) | 1.09 (0.12, 10.05) | 0.940 | 193(18.7) | 102(51.5) | 3.85 (1.63, 9.09) | 0.002∗ | |
Alp | |||||||||
<270 | 251(86.6) | 52(66.7) | – | – | 762(85.7) | 140(76.9) | – | – | |
>=270 | 39(13.4) | 26(33.3) | 21.65 (1.66, 28.08) | 0.019∗ | 127(14.3) | 42(23.1) | 0.45 (0.18, 1.11) | 0.085 | |
K | |||||||||
3.5–5.1 | 11(3.4) | 6(7.1) | – | – | 56(5.5) | 17(8.6) | – | – | |
=<3.5 | 300(92.3) | 69(82.1) | 0.02 (0.01, 1.16) | 0.059 | 932(91.6) | 166(83.8) | 0.13 (0.03, 0.63) | 0.011∗ | |
>=5.1 | 14(4.3) | 9(10.7) | 0.04 (0.01, 4.98) | 0.191 | 30(2.9) | 15(7.6) | 0.07 (0.01, 0.57) | 0.012∗ | |
NA | |||||||||
136–145 | 156(48.3) | 47(56.6) | – | – | 332(32.7) | 93(47.0) | – | – | |
>=136 | 167(51.7) | 36(43.4) | 0.97 (0.17, 5.50) | 0.974 | 682(67.3) | 105(53.0) | 0.95 (0.46, 1.97) | 0.897 | |
LLDH | |||||||||
<942 | 251(90.6) | 46(67.6) | – | – | 816(90.5) | 88(54.7) | – | - | |
>942 | 26(9.4) | 22(32.4) | 12.21 (0.69, 213.85) | 0.087 | 86(9.5) | 73(45.3) | 5.36 (2.19, 13.07) | <0.001∗ |
∗Significant at the level of P < 0.05.
ESR: Erythrocyte Sedimentation Rate, BUN: Blood Urea Nitrogen, BS: Blood Sugar, CR: Blood creatinine, PT: Prothrombin Time, CPK: Creatine Phosphokinase, ALT: Alanine Aminotransferase, SGOT: Serum Glutamic-Oxaloacetic Transaminase, Alp: Alkaline Phosphatase Level Test, PTT: Partial Thromboplastin Time, K: Potassium, NA: Sodium, PLT: Blood Platelets, HCT: Hematocrit, HB: Hemoglobin, LDH: Lactate Dehydrogenase, lym: Lymphocyte, NEUT: Neutrophils.
Table 3.
Association between covid-19 mortality and potential risk factors in diabetic and non-diabetic patients using adjusted odds ratio.
Characteristic demographics | Diabetes N = 420 |
Adjusted Odds Ratio (95% CI) |
P value |
Non- Diabetes N = 1260 |
Adjusted Odds Ratio (95% CI) |
P value |
||
---|---|---|---|---|---|---|---|---|
Alive | Dead | Alive | Dead | |||||
Age (year) | ||||||||
<60 | 128 | 9 | – | – | 581 | 38 | – | – |
>=60 | 207 | 76 | 5.13 (1.61, 16.39) | 0.006 | 475 | 164 | 4.69 (2.71, 8.13) | <0.001 |
Residence | ||||||||
Rural | 53 | 14 | – | – | 171 | 29 | – | – |
urban | 282 | 71 | 1.79 (0.61, 5.25) | 0.289 | 885 | 173 | 1.89 (0.99, 3.57) | 0.052 |
Smoking status | ||||||||
Nonsmoker | 316 | 81 | – | – | 973 | 182 | – | – |
Smoker | 19 | 4 | 0.29 (0.06, 1.39) | 0.121 | 83 | 20 | 2.42 (0.99, 5.89) | 0.052 |
addiction | ||||||||
No | 320 | 77 | – | – | 974 | 184 | – | – |
Yes | 15 | 8 | 5.26 (1.14, 24.19) | 0.033 | 82 | 18 | 0.63 (0.24, 1.73) | 0.342 |
Admission signs & symptoms | ||||||||
Fatigue | ||||||||
No | 326 | 80 | – | – | 814 | 153 | – | – |
Yes | 9 | 5 | 2.59 (0.52, 12.89) | 0.246 | 48 | 290 | 0.47 (0.10, 2.14) | 0.326 |
Diarrhea | ||||||||
No | 302 | 79 | – | – | 937 | 180 | – | – |
Yes | 33 | 6 | 1.75 (0.16, 19.67) | 0.649 | 119 | 21 | 1.46 (0.74, 2.90) | 0.274 |
Chills | ||||||||
No | 186 | 52 | – | – | 529 | 120 | – | – |
Yes | 149 | 33 | 1.21 (0.55, 2.69) | 0.638 | 527 | 82 | 1.03 (0.65, 1.61) | 0.912 |
Anorexia | ||||||||
No | 210 | 59 | – | – | 702 | 123 | – | – |
Yes | 125 | 26 | 0.54 (0.24, 1.24) | 0.146 | 354 | 79 | 0.88 (0.56, 1.38) | 0.579 |
Dyspnea | ||||||||
No | 147 | 30 | – | – | 453 | 54 | – | – |
Yes | 188 | 55 | 1.44(0.75, 3.19) | 0.371 | 603 | 147 | 2.46 (1.52, 3.98) | <0.001 |
laboratory parameters | ||||||||
BUN | ||||||||
<20 | 224 | 18 | – | – | 785 | 57 | – | – |
>=20 | 110 | 67 | 5.85 (2.54, 13.52) | <0.001 | 268 | 144 | 3.65 (2.28, 5.85) | <0.001 |
PT | ||||||||
<13 | 98 | 20 | – | – | 272 | 53 | – | – |
>=13 | 190 | 61 | 0.97 (0.42, 2.17) | 0.923 | 625 | 139 | 0.96 (0.59, 1.58) | 0.885 |
SGOT | ||||||||
<45 | 279 | 51 | – | – | 840 | 96 | – | – |
>=45 | 46 | 32 | 2.65 (0.99, 7.03) | 0.050 | 193 | 102 | 2.51 (1.56, 4.04) | <0.001 |
Alp | ||||||||
<270 | 251 | 52 | – | – | 762 | 140 | – | – |
>=270 | 39 | 26 | 3.38 (1.31, 8.75) | 0.012 | 127 | 42 | 1.34 (0.77, 2.32) | 0.300 |
∗Significant at the level of P < 0.05.
BUN: Blood Urea Nitrogen, PT: Prothrombin Time, SGOT: Serum Glutamic-Oxaloacetic Transaminase, Alp: Alkaline Phosphatase Level Test, K: Potassium, LDH: Lactate Dehydrogenase.
Results of the adjusted logistic regression model (Table 3) showed that, advanced age (+60 year) (OR = 5.13, P = 0.006), addiction (OR = 5.26, P = 0.033), high level of BUN (OR = 5.85, P < 0.001), and high level of Alp (OR = 3.38, P = 0.012) in diabetic patients and advanced age (+60 year) (OR = 4.69, P < 0.001), Dyspnea (OR = 2.46, P < 0.001), high level of BUN (OR = 3.65, P < 0.001) and high level of SGOT (OR = 2.51, P < 0.001) in non-diabetic patients were significantly associated with increase the odds of death in COVID-19 patients.
4. Discussion
In this study we analyzed the demographic characteristics, epidemiological information, comorbidities, signs and symptoms at admission and laboratory tests results of COVID-19 patients with diabetes compared COVID-19 patients without diabetes and identified the risk factors associated with in-hospital death of these patients. In this study we found that in COVID-19 patients with diabetes advanced age, addiction, high level of BUN and Alp and in non-diabetic COVID-19 patients advanced age, dyspnea, high level of BUN and SGOT were associated with increase risk of death in these patients.
Similar to our study, in previous studies advaned age has been considered as a risk factor of death in non-diabetic COVID-19 patients and also in COVID-19 patients with diabete type 1 and 2 diabetes [13]. In Cheng et al. study age older than 60 years was identified as a independent risk factors for serious disease in SARS-CoV-2 infection [14]. Tehrani and collagenous revealed that advanced age contribute to a fatal outcome in hospitalized COVID-19 patients [15]. It has been shown that high mortality rate of elderly patients with COVID-19 is mainly due to the frequent occurrence of multiple comorbidities including but not limited to hyperglycemia [16].
We observed that non-diabetic COVID-19 patients with dyspnea were significantly more likely to die than patients without dyspnea. Dyspnea is a sign of respiratory disease, so can be an important risk factor for progression COVID-19 to advanced stages [17]. Shi et al. reported that presence of dyspnea was a risk factors for death in SARS-CoV-2 infection [18].It has been reported in several studies that COVID-19 patients with dyspnea had a higher risk for hospitalization, ICU addmission, mechanical ventilation, severe disease, disease progression and mortality than those without dyspnea [[19], [20], [21], [22]].
Other finding of present study showed that addiction is a risk factor of death in COVID-19 patients with diabetes. It may be because patients with diabetes are at higher risk of receiving polypharmacy than patiens without diabetes. So, addicted patiens with diabetes have increased risks of adverse drug events [23]. In this regard, Baillargeon et al. indicated that COVID-19 patients with substance use are at greater risk for adverse outcomes [24]. Wang et al. found COVID-19 patients with substance use especially opioids use are at increased risk for adverse outcomes [25].
In present study, high BUN levels was another rsik factor of COVID-19 related death in diabetic and non-diabetic patients. Recently, there is a lot of evidence of increase in COVID-19 mortality rate in patients with higher serum BUN levels [26]. According to Pazoki et al. study, kidney damage indicators, including serum creatinine and blood urea nitrogen (BUN) is linked with higher mortality in COVID-19 patients [27]. A meta-analysis conducted by Shao and collagenous on 40 studies and 25,278 patients revealed a positive relationship between BUN levels and mortality rate in patients with COVID-19 [28].
In our study, high ALP and SGOT (AST) levels respectively in diabetic and non-diabetic patients were associated with increased risk of COVID-19 related death. Both of these enzymes are indicators of liver damage and dysfunction, which can be seen in more than half of patients with COVID-19 [29]. It has been shown that the SARS-CoV-2 virus may also bind to ACE2 on cholangiocytes and induce a systemic inflammatory response leading to liver injury [30]. Also, it has been suspected that some detrimental effects on liver injury is mainly due to certain medications used during COVID-19 hospitalization [31]. In agreement with our findings, Pazoki et al. showed that high serum levels of ALP and AST was a risk factor of in-hospital mortality and disease severity in diabetic patients with confirmed or clinically suspected COVID-19 [27]. Shen et al. reported a similar findings [32]. However, liver damage and increased levels of liver enzymes in serum including AST and ALP is also reported in the diabetic patients in Islam et al. study [33].
Limitations of this study include the following: first, due to the retrospective nature of the study, we could not assess all clinical and laboratory information such as d-dimer for all patients. Secondly, in this study we included only hospitalized patients with relatively severe symptoms and patients with mild or moderate symptoms were not assessed in the present study.
5. Conclusion
We found that in COVID-19 patients with diabetes; advanced age, addiction, high level of BUN and Alp and in non-diabetic COVID-19 patients advanced age, dyspnea, high level of BUN and SGOT were associated with increase risk of death in these patients.
Funding
Hamadan University of Medical Sciences financially supported this study.
Declaration of competing interest
The authors claimed no conflict of interest.
Acknowledgement
Deputy of Research and Technology of Hamadan University of Medical Sciences approved our study (Research code: 9910237323). We would like gratefully acknowledge of all hospital staffs of Sina and Beheshti hospitals engaging in the treatment and care of COVID-19 patients.
References
- 1.Knapp S. Diabetes and infection: is there a link?-A mini-review. Gerontology. 2013;59(2):99–104. doi: 10.1159/000345107. [DOI] [PubMed] [Google Scholar]
- 2.Muller L., Gorter K., Hak E., Goudzwaard W., Schellevis F., Hoepelman A., et al. Increased risk of common infections in patients with type 1 and type 2 diabetes mellitus. Clin Infect Dis. 2005;41(3):281–288. doi: 10.1086/431587. [DOI] [PubMed] [Google Scholar]
- 3.Joshi N., Caputo G.M., Weitekamp M.R., Karchmer A. Infections in patients with diabetes mellitus. N Engl J Med. 1999;341(25):1906–1912. doi: 10.1056/NEJM199912163412507. [DOI] [PubMed] [Google Scholar]
- 4.World Health Organization . 2020. Coronavirus disease 2019 (COVID-19): situation report—37. [Google Scholar]
- 5.Yang J., Feng Y., Yuan M., Yuan S., Fu H., Wu B., et al. Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet Med. 2006;23(6):623–628. doi: 10.1111/j.1464-5491.2006.01861.x. [DOI] [PubMed] [Google Scholar]
- 6.Bornstein S.R., Rubino F., Khunti K., Mingrone G., Hopkins D., Birkenfeld A.L., et al. Practical recommendations for the management of diabetes in patients with COVID-19. The lancet Diabetes & endocrinology. 2020 doi: 10.1016/S2213-8587(20)30152-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wang D., Hu B., Hu C., Zhu F., Liu X., Zhang J., et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama. 2020;323(11):1061–1069. doi: 10.1001/jama.2020.1585. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Chen N., Zhou M., Dong X., Qu J., Gong F., Han Y., et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–513. doi: 10.1016/S0140-6736(20)30211-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506. doi: 10.1016/S0140-6736(20)30183-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mohamed-Ahmed O., McDonald B., Pardinaz-Solis R., Sigfrid L., McMullen C., Moore S., et al. The international severe acute respiratory and emerging infection Consortium (ISARIC) response to the zika virus outbreak. F1000Research. 2016;5 [Google Scholar]
- 11.Keyhanian S., Zakerihamidi Maryam, Chehregosha F., Saravi M.M., Saravi S., Saravi A. Frequency of lymph node involvement in patients with gastric cancer in Ramsar Imam Sajjad hospital from 2010-2015. Pajoohande. 2016;21(5):313–319. [Google Scholar]
- 12.Pang-Ning T., Steinbach M., Kumar V. Pearson Addison Wesley Boston; 2006. Introduction to data mining. [Google Scholar]
- 13.Wang Z., Wang Z. Identification of risk factors for in-hospital death of COVID-19 pneumonia--lessions from the early outbreak. BMC Infect Dis. 2021;21(1):1–10. doi: 10.1186/s12879-021-05814-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Cheng S., Wu D., Li J., Zou Y., Wan Y., Shen L., et al. Risk factors for the critical illness in SARS-CoV-2 infection: a multicenter retrospective cohort study. Respir Res. 2020;21(1):1–12. doi: 10.1186/s12931-020-01492-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Tehrani S., Killander A., Åstrand P., Jakobsson J., Gille-Johnson P. Risk factors for death in adult COVID-19 patients: frailty predicts fatal outcome in older patients. Int J Infect Dis. 2021;102:415–421. doi: 10.1016/j.ijid.2020.10.071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.D’ascanio M., Innammorato M., Pasquariello L., Pizzirusso D., Guerrieri G., Castelli S., et al. Age is not the only risk factor in COVID-19: the role of comorbidities and of long staying in residential care homes. BMC Geriatr. 2021;21(1):1–10. doi: 10.1186/s12877-021-02013-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kim S.-R., Nam S.-H., Kim Y.-R. Risk factors on the progression to clinical outcomes of COVID-19 patients in South Korea: using national data. Int J Environ Res Publ Health. 2020;17(23):8847. doi: 10.3390/ijerph17238847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Shi L., Wang Y., Wang Y., Duan G., Yang H. Dyspnea rather than fever is a risk factor for predicting mortality in patients with COVID-19. J Infect. 2020;81(4):647–679. doi: 10.1016/j.jinf.2020.05.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ioannou G.N., Locke E., Green P., Berry K., O'Hare A.M., Shah J.A., et al. Risk factors for hospitalization, mechanical ventilation, or death among 10 131 US veterans with SARS-CoV-2 infection. JAMA network open. 2020;3(9) doi: 10.1001/jamanetworkopen.2020.22310. e2022310-e. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Guan W-j, Liang W-h, Zhao Y., Liang H-r, Chen Z-s, Li Y-m, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. 2020;55(5) doi: 10.1183/13993003.00547-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhang Jj, Cao Yy, Tan G., Dong X., Wang Bc, Lin J., et al. Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy. 2021;76(2):533–550. doi: 10.1111/all.14496. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Arentz M., Yim E., Klaff L., Lokhandwala S., Riedo F.X., Chong M., et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. Jama. 2020;323(16):1612–1614. doi: 10.1001/jama.2020.4326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Peron E.P., Ogbonna K.C., Donohoe K.L. Antidiabetic medications and polypharmacy. Clin Geriatr Med. 2015;31(1):17–vii. doi: 10.1016/j.cger.2014.08.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Baillargeon J., Polychronopoulou E., Kuo Y.-F., Raji M.A. Psychiatric Services; 2020. The impact of substance use disorder on COVID-19 outcomes. appi. ps. 202000534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Wang Q.Q., Kaelber D.C., Xu R., Volkow N.D. COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States. Mol Psychiatr. 2021;26(1):30–39. doi: 10.1038/s41380-020-00880-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Cheng A., Hu L., Wang Y., Huang L., Zhao L., Zhang C., et al. Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. Int J Antimicrob Agents. 2020;56(3):106110. doi: 10.1016/j.ijantimicag.2020.106110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Pazoki M, Keykhaei M, Kafan S, Montazeri M, Hazaveh MM, Sotoodehnia M, et al. Risk indicators associated with in-hospital mortality and severity in patients with diabetes mellitus and confirmed or clinically suspected COVID-19. J Diabetes Metab Disord.1-11. [DOI] [PMC free article] [PubMed]
- 28.Shao M., Li X., Liu F., Tian T., Luo J., Yang Y. Acute kidney injury is associated with severe infection and fatality in patients with COVID-19: a systematic review and meta-analysis of 40 studies and 25,278 patients. Pharmacol Res. 2020:105107. doi: 10.1016/j.phrs.2020.105107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Chau T.N., Lee K.C., Yao H., Tsang T.Y., Chow T.C., Yeung Y.C., et al. SARS-associated viral hepatitis caused by a novel coronavirus: report of three cases. Hepatology. 2004;39(2):302–310. doi: 10.1002/hep.20111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chai X., Hu L., Zhang Y., Han W., Lu Z., Ke A., et al. Specific ACE2 expression in cholangiocytes may cause liver damage after 2019-nCoV infection. biorxiv. 2020 [Google Scholar]
- 31.Cai Q., Huang D., Yu H., Zhu Z., Xia Z., Su Y., et al. COVID-19: abnormal liver function tests. J Hepatol. 2020;73(3):566–574. doi: 10.1016/j.jhep.2020.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Shen J.-X., Zhuang Z.-H., Zhang Q.-X., Huang J.-F., Chen G.-P., Fang Y.-Y., et al. Risk factors and prognosis in patients with COVID-19 and liver injury: a retrospective analysis. J Multidiscip Healthc. 2021;14:629. doi: 10.2147/JMDH.S293378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Islam S., Rahman S., Haque T., Sumon A.H., Ahmed A.M., Ali N. Prevalence of elevated liver enzymes and its association with type 2 diabetes: a cross-sectional study in Bangladeshi adults. Endocrinology, diabetes & metabolism. 2020;3(2) doi: 10.1002/edm2.116. [DOI] [PMC free article] [PubMed] [Google Scholar]