Background/Aim: A wealth of immunological evidence points out that hyperglycemic status (regardless of diabetes) makes individuals more susceptible to infection as well as higher in-hospital complications. In this study, we primarily aimed to identify the clinical and biochemical negative impacts of hyperglycemia risk factor across the two comparative cohorts. Secondly, we also explored the prognosticating utilities of three proposed BG related prognosticators in moderate-severe admitted SARS-CoV-2 infected patients. Methods: This study was retrospectively between Mar 2020 and Sep 2021. All retrievable and calculated variables were thereafter divided into two studied cohorts, Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II). Independent and One-Sample T-Tests and Chi-Square Test were used for comparative analysis and relative risk estimation. The Receiver operating characteristic analysis was used to explore the area under the curve and the sensitivity analysis was also performed to investigate the optimal cutoff values for the BG related prognosticators. Results: The mean age of the whole study cohort was 59.40±10.60 years and the Non-Survivors Cohort were insignificantly younger than the Survivors Cohort. Survivors Cohort had insignificantly higher average blood glucose level than Non-Survivors Cohort. Oppositely, Survivors Cohort had significantly lower average total daily insulin dosing compared to Non-Survivors Cohort. The overall hospital Length of Stay (LOS) which it was significantly lower in Non-Survivors Cohort compared to Survivors Cohort. Conclusion: As there were many cconcerns for the effect of hyperglycemia on immune cells and subsequently the overall clinical impacts, there is an urgent necessity to track the daily blood glucose levels, the changes in blood glucose from baseline, or alternatively the insulin infusion rate to keep their averages around 149.9 mg/dL, -38% and 1.35 IU/hr.
In December 2019, an outbreak of novel Coronavirus related severe Acute Respiratory Syndrome (SARS-CoV-2)-associated infection, later identified as Coronavirus infection-2019 (COVID-19, was firstly reported in Wuhan city, China [1]. Indeed, the SARS-CoV-2, a single-stranded highly contagious RNA virus, have been significantly burden the whole medical systems in the world [2].
Pathophysiologically, SARS-CoV-2 associated infections may be directly related to an interrelated mortality associated storms, particularly hyperinflammatory associated cytokine storm, hypercoagulability associated thrombotic storm and hyperoxidative associated radical storm [3,4]. Their insidious progression after infection, inclusion of non-respiratory targeted organs and modernization capability, making COVID-19 disease as a global crisis that requires combined efforts to combat it [5,6]. So, Identifying the risk factors of SARS-CoV-2 associated infection progression and mortality provides a crucial step in growing evidences for appropriate clinical management and optimizing medical facilities allocation in COVID-19 affected patients [7-9].
Many risk factors for COVID-19 disease progression have been proposed, either had been internally and externally validated or need to be validated. Of important, number of co-morbidities as quantified by Charlson Index, aging, lower negative phase-reactants, higher positive acute phase-reactants, elevated Lactate Dehydrogenase (LDH) levels, abnormal liver and kidney indices, higher neutrophils and monocytes counts, especially when accompanied with lower lymphocytes counts, higher red blood cells distribution widths, thrombocytopenia, elevated d-dimer levels and uncontrolled hyperglycemia [10-12].
Regardless of diabetes or uncontrolled blood glucose statuses, stress associated hyperglycemia has direct and indirect negative impacts on immunity as pointed by a wealth of immunological evidences. Affected COVID-19 patients are likely to have a dysfunctional immunity on white blood cells, particularly lymphocytes and macrophages, that may be linked to significantly higher incidences of in-hospital complications and multiple organ failures in moderate-severe admitted SARS-CoV-2 infected patients with uncontrolled hyperglycemia [13-15].
In this study, we aimed to identify the clinical and biochemical negative impacts of hyperglycemia risk factor across the two comparative cohorts, Survivors Cohort and Non-Survivors Cohort. Secondly, we also explored the prognosticating utilities of three proposed BG related prognosticators in moderate-severe admitted SARS-CoV-2 infected patients.
This study was retrospectively conducted on admitted affected COVID-19 patients on isolation departments at Queen Alia Military Hospital, Royal Medical Services, Amman, Jordan. The study was reviewed and approved by the standing committee for coordination of health and medical research at the Royal Medical Services (Ref # 29_12/2021). Patient’s data, including demographical, anthropometrical, biochemical and nutritional data, were retrospectively retrieved from our electronic medical record system (Hakeem) over 19 months between Mar 2020 and Sep 2021. Studied patients who were below 18 years, whose hospital Length of Stay (LOS) didn’t exceed 7 days and whose studied variables were totally or partially missed were excluded from our study.
Owing to our study’s retrospective design, a signed consent form was waived. All eligible studied patients had variable diseases severities, ranges from moderate to severe, including severe ARDS affected mechanically ventilated critically ill patients. Based on PCR positivity, patients who had a negative PCR test but clinically, biochemically and radiologically go with COVID-19 disease were considered as suspected SARS-CoV-2 infected patients while patients who had a positive PCR test were considered as confirmed SARS-CoV-2 infected patients. Both suspected and confirmed SARS-CoV-2 infected patients were included in our study.
Hemodynamics variables of Shock Index (SI) and modified Shock Index (mSI) were mathematically retrieved after dividing heart rate to Systolic Blood Pressure (SBP) and mean arterial pressure, retrospectively. Prognosticators’ ratios, including C-Reactive Protein (CRP) to Albumin Ratio (CRP: ALB), Ferritin to Albumin Ratio (FER: ALB), Neutrophils to Lymphocytes Ratio (NLR) and Monocytes to Lymphocytes Ratio (MLR) were also calculated from the primary retrievable data.
Table 1: Comparatively Studied Variables between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) Among Admitted SARS-CoV-2 Infected Patients at Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied Comparative Variables | Overall Cohorts (N = 781) Mean±SD | Cohort I (Survivors) (N = 626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N = 155, 19.85%) Mean±SD | Mean Differences ±SEM | p-Value |
Age (Yrs) | 59.40±10.60 | 59.66±10.69 | 58.35±10.20 | 1.31±0.95 | 0.17 |
BW (Kg) | 73.73±10.02 | 73.59±9.96 | 74.28±10.28 | -0.69±0.90 | 0.44 |
BMI (Kg/m2) | 25.95±3.89 | 25.92±3.90 | 26.07±3.86 | -0.16±0.35 | 0.65 |
ALB0 (g/dL) | 2.83±0.27 | 2.79±0.27 | 3.00±0.19 | -0.21±0.02 | 0.00 |
ALB1 (g/dL) | 2.21±0.38 | 2.09±0.31 | 2.65±0.27 | -0.56±0.03 | 0.00* |
ALB2 (g/dL) | 3.19±0.64 | 3.04±0.60 | 3.78±0.45 | -0.74±0.05 | 0.00* |
%∆ALB12 | 44.7%±19.3% | 44.8%±12.9% | 44.5%±34.8% | 0.3%±1.7% | 0.843 |
HALB (g/day) | 12.97±4.90 | 12±4 | 18±4 | -7±0 | 0.00* |
Tavg1 | 37.95±0.69 | 38.16±0.55 | 37.07±0.48 | 1.09±0.05 | 0.00* |
Tavg2 | 37.47±0.47 | 37.46±0.45 | 37.49±0.51 | -0.02±0.04 | 0.58 |
PARA dose (g/day) | 1.90±0.94 | 1.55±0.65 | 3.32±0.46 | -1.77±0.06 | 0.00* |
SCr1 (mg/dL) | 1.12±0.18 | 1.14±0.18 | 1.01±0.14 | 0.13±0.02 | 0.00* |
BUN1 (mg/dL) | 13.16±2.20 | 13.55±2.13 | 11.58±1.71 | 1.97±0.18 | 0.00* |
BUN: SCr1 | 11.76±0.40 | 11.85±0.29 | 11.41±0.57 | 0.43±0.03 | 0.00* |
SCr2 (mg/dL) | 1.70±0.67 | 1.58±0.49 | 2.41±1.05 | -0.83±0.06 | 0.00* |
BUN2 (mg/dL) | 14.91±4.38 | 13.37±2.08 | 11.02±1.74 | 2.35±0.18 | 0.00* |
BUN: SCr2 | 8.93±0.60 | 8.69±1.21 | 4.84±0.85 | 3.85±0.10 | 0.00* |
CrClJelliffe eq (mL/min) | 45.02±17.20 | 43.68±11.88 | 22.91±7.51 | 20.78±1.00 | 0.00* |
BG1 (mg/dL) | 283.1±78.0 | 300.54±75.75 | 212.68±36.41 | 87.85±6.26 | 0.00* |
cNa1 (mEq/l) | 126.8±2.8 | 126.06±2.55 | 129.67±2.03 | -3.61±0.22 | 0.00* |
BG2 (mg/dL) | 152.0±36.3 | 153.04±39.18 | 147.74±20.81 | 5.30±3.26 | 0.10 |
cNa2 (mEq/l) | 137.8±4.4 | 137.86±3.16 | 128.77±3.84 | 9.09±0.30 | 0.00* |
Insulin dose (IU/day) | 32.10±1.96 | 31.74±1.80 | 33.55±1.90 | -1.82±0.16 | 0.00* |
HLOS | 11.23±2.85 | 11.42±2.98 | 10.45±2.08 | 0.97±0.25 | 0.00* |
Data results of the comparative variables between the Group I and Group II were statistically analyzed by independent T and One-Sample T-Test (at p-value<0.05) and expressed as Mean±SD and Mean difference±SEM, Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, Baseline, BW: Body Weight, BMI: Body Mass Index, ALB: Albumin Level, HALB: Human Albumin, SCr: Serum Creatinine, BUN: Blood Urea Nitrogen, ALB RSI: Relative Strength INDEX of Albumin levels, CrCl: Creatinine Clearance, Temp: Temperature, PARA: Paracetamol, BG: Blood Glucose level, cNa: Corrected Sodium level, HLOS: Hospital Length Of Stay
Table 2: Comparatively Studied Variables between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) Among Admitted SARS-CoV-2 Infected Patients At Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied Comparative Variables | Overall Cohorts (N=781) Mean±SD | Cohort I (Survivors) (N=626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N=155, 19.85%) Mean±SD | Mean Differences ±SEM | p-value |
DBPavg1 (mmHg) | 54.70±3.83 | 53.72±3.48 | 58.65±2.34 | -4.93±0.29 | 0.00* |
SIavg1 (bpm/mmHg) | 1.08±0.09 | 1.10±0.09 | 0.99±0.05 | 0.11±0.01 | 0.00* |
mSIavg1 (bpm/mmHg) | 1.49±0.15 | 1.52±0.14 | 1.35±0.07 | 0.17±0.01 | 0.00* |
DBPavg2 (mmHg) | 80.86±2.02 | 81.45±1.74 | 78.46±1.06 | 2.99±0.15 | 0.00* |
SIavg2 (bpm/mmHg) | 0.85±0.11 | 0.82±0.10 | 0.98±0.05 | -0.16±0.01 | 0.00* |
mSI avg2 (bpm/mmHg) | 1.08±0.15 | 1.04±0.13 | 1.25±0.07 | -0.21±0.01 | 0.00* |
%∆ DBPavg12 | 48.8%±14.6% | 52.4%±13.8% | 34.0%±5.1% | 18.5%±1.1% | 0.00* |
%∆ SIavg12 | -20.5%±14.6% | -25.3%±11.8% | -0.9%±6.0% | -24.4%±1.0% | 0.00* |
%∆ mSI avg12 | -26.5%±14.1% | -31.2%±11.3% | -7.5%±6.0% | -23.7%±0.9% | 0.00* |
BIL1 (mg/dL) | 1.57±0.05 | 1.56±0.05 | 1.61±0.04 | -0.05±0.00 | 0.00* |
BIL2 (mg/dL) | 2.57±0.19 | 2.53±0.18 | 2.71±0.16 | -0.18±0.02 | 0.00* |
INR1 | 1.53±0.05 | 1.52±0.04 | 1.57±0.04 | -0.04±0.00 | 0.00* |
INR2 | 2.59±0.12 | 2.57±0.11 | 2.68±0.11 | -0.10±0.01 | 0.00* |
FLUD Input (mL/day) | 2823±40 | 2815±29 | 2858±57 | -43±3 | 0.00* |
ENF Input (mL/day) | 291±171 | 243.0±140.9 | 482.8±146.7 | -239.8±12.7 | 0.00* |
TCI (Cal/day) | 572±219 | 523.2±192.7 | 766.9±208.0 | -243.7±17.6 | 0.00* |
PD (g/100 Cal) | 2.05±1.01 | 1.92±1.03 | 2.56±0.73 | -0.64±0.09 | 0.00* |
CarbD (g/100 Cal) | 20.04±4.62 | 20.62±4.70 | 17.71±3.39 | 2.91±0.40 | 0.00* |
pH1 | 7.34±0.12 | 7.32±0.12 | 7.44±0.03 | -0.12±0.01 | 0.00* |
cK1 (mEq/l) | 2.93±0.86 | 2.74±0.85 | 3.69±0.32 | -0.95±0.07 | 0.00* |
pH2 | 7.34±0.12 | 7.32±0.12 | 7.44±0.03 | -0.13±0.01 | 0.00* |
cK2 (mEq/l) | 3.06±1.07 | 3.11±1.17 | 2.85±0.42 | 0.26±0.10 | 0.01* |
Data results of the comparative variables between the Group I and Group II were statistically analyzed by independent T and One-Sample T-Test (at p-value<0.05) and expressed as Mean±SD and Mean difference±SEM, Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, DBP: Diastolic blood pressure, SI: Shock Index, mSI: Modified Shock Index, INR: International Normalized Ratio, BIL: Bilirubin level, FLUD: Total fluid (PO/IV) inputs, ENF: Enteral Nutritional Feeding, TCI: Total Calories Inputs, PD: Protein Densities in g per 100 Cal, CarbD: Carb Density in g/100 Cal, cK: Corrected potassium levels
Table 3: Comparatively Studied Variables between SURVIVORS Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) Among Admitted SARS-CoV-2 Infected Patients at Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied Comparative Variables | Overall Cohorts (N = 781) Mean±SD | Cohort I (Survivors) (N = 626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N = 155, 19.85%) Mean±SD | Mean Differences ±SEM | p-value |
WBC1 (Cells/µL) | 14036±3099 | 14749±2818 | 11156±2448 | 3593±247 | 0.00* |
TLC1 (Cells/µL) | 1635±798 | 1754±779 | 1156±691 | 598±68 | 0.00* |
ANC1 (Cells/µL) | 10902±2051 | 11424±1808 | 8796±1574 | 2628±158 | 0.00* |
MC1 (Cells/µL) | 1090±205 | 1142±181 | 880±157 | 263±16 | 0.00* |
NLR1 | 7.90±3.15 | 7.44±2.78 | 9.76±3.84 | -2.32±0.27 | 0.00* |
MLR1 | 0.79±0.32 | 0.74±0.28 | 0.98±0.38 | -0.23±0.03 | 0.00* |
WBC2 (Cells/µl) | 15487±2851 | 15490±2844 | 15472±2890 | 19±256 | 1 |
TLC2 (Cells/µl) | 2733±1298 | 3071±1169 | 1371±816 | 1699±99 | 0.00* |
ANC2 (Cells/µl) | 10342±1973 | 10238±1948 | 10762±2023 | -524±176 | 0.00* |
MC2 (Cells/µl) | 819±156 | 818±159 | 824±140 | -6±14 | 1 |
NLR2 | 5.6±10.7 | 3.82±1.93 | 12.99±22.34 | -9.17±0.90 | 0.00* |
MLR2 | 0.4±0.8 | 0.30±0.15 | 0.97±1.59 | -0.67±0.06 | 0.00* |
Data results of the comparative variables between the Group I and Group II were statistically analyzed by independent T and One-Sample T-Test (at p-value<0.05) and expressed as Mean±SD and Mean difference±SEM, Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, WBCs: White blood cells, TLC: Total Lymphocytes Counts, ANC: Absolute Neutrophils Count, MC: Monocytes Count, NLR: Neutrophils to Lymphocytes Ratio, MLR: Monocytes to Lymphocytes Ratio
Table 4: Comparatively Studied Variables between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) among Admitted SARS-CoV-2 Infected Patients at Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied Comparative Variables | Overall Cohorts (N = 781) Mean±SD | Cohort I (Survivors) (N = 626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N = 155, 19.85%) Mean±SD | Mean Differences ±SEM | p-value |
Prescribed PIP/TAZ (mg/day) | 11261±2375 | 11710±2361 | 8409±1465 | 3300±276 | 0.00* |
Optimal** PIP/TAZ (mg/day) | 15677±2565 | 16105±2615 | 12150±2417 | 3955±322 | 0.00* |
Deficit*** PIP/TAZ (mg/day) | -4416±426 | -4396±474 | -3741±1071 | -655±80 | 0.00* |
% Deficit PIP/TAZ | -28.7%±4.1% | -27.73%±3.92% | -30.21%±4.06% | 2.47%±0.49% | 0.00* |
Prescribed MER (mg/day) | 2286±595 | 2262±552 | 1385±531 | 878±98 | 0.00* |
Optimal** MER (mg/day) | 4572±1190 | 4525±1104 | 2769±1063 | 1755±196 | 0.00* |
Deficit*** MER (mg/day) | -2286±595 | -2262±552 | -1385±531 | -878±98 | 0.00* |
% Deficit MER | -50.0%±0.0% | -50.0%±0.0% | -50.0%±0.0% | NA | NA |
Prescribed IMI/CIL (mg/day) | 1308±353 | 1312±302 | 833±239 | 479±54 | 0.00* |
Optimal** IMI/CIL (mg/day) | 1850±451 | 1823±333 | 1333±239 | 489±59 | 0.00* |
Deficit*** IMI/CIL (mg/day) | -542±140 | -511±72 | -500±0 | -11±12 | 0.00* |
% Deficit IMI/CIL | -29.8%±5.9% | -28.73%±5.85% | -38.67%±8.13% | 9.94%±1.19% | 0.00* |
%∆ WBC12 | -14.4%±37% | -26.9%±28.7% | 36.0%±15.8% | -62.9%±2.4% | 0.00* |
%∆ TLC12 | 149%±70.2% | 141.3%±63.0% | 176.9%±88.2% | -35.6%±6.2% | 0.00* |
%∆ ANC12 | -28.6%±38% | -41.4%±30.6% | 22.9%±14.7% | -64.3%±2.5% | 0.00* |
%∆ MC12 | -36.1%±47% | -51.7%±37.3% | 27.1%±18.0% | -78.8%±3.1% | 0.00* |
%∆ NLR12 | -33.7%±72% | -48.0%±15.2% | 23.7%±146.0% | -71.7%±6.0% | 0.00* |
%∆ MLR12 | -48.1%±52% | -58.3%±12.8% | -6.9%±103.3% | -51.4%±4.2% | 0.00* |
Data results of the comparative variables between the Group I and Group II were statistically analyzed by independent T and One-Sample T-Test (at p-value<0.05) and expressed as Mean±SD and Mean difference±SEM, Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, Optimal**: Optimal dosing of the selected antibiotics based on the calculated CrCl, Deficit***: Deficit dosing of the corresponding antibiotics was calculated by subtracting the optimal dosing from the prescribed dosing and consequently the %Deficit was obtained by dividing the deficit dosing over the optimal dosing, NA: Not-Applicable and statistically can’t be computed, WBCs: White blood cells, TLC: Total Lymphocytes Counts, ANC: Absolute Neutrophils Count, MC: Monocytes Count, NLR: Neutrophils to Lymphocytes ratio, MLR: Monocytes to Lymphocytes Ratio, PIP/TAZ: Piperacillin/Tazobactam (Tazocin®), MER: Meropenem (Meronem®), IMI/CIL: Imipenem/Cilastatin (Tienam®)
Table 5: Comparatively Studied Variables between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) among Admitted SARS-CoV-2 Infected Patients at Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied comparative variables | Overall Cohorts (N = 781) Mean±SD | Cohort I (Survivors) (N = 626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N = 155, 19.85%) Mean±SD | Mean Differences ±SEM | p-value |
FER1 (ng/mL) | 746.5±310.7 | 815.13±302.54 | 469.36±144.76 | 345.78±24.99 | 0.00* |
FER: ALB1 | 362.1±206.2 | 406.31±204.92 | 183.62±69.84 | 222.68±16.70 | 0.00* |
FER2 (ng/mL) | 433.9±294.0 | 439.13±317.71 | 412.80±166.42 | 26.33±26.38 | 0.32 |
FER: ALB2 | 145.5±145.4 | 153.79±159.71 | 112.04±46.19 | 41.74±12.97 | 0.00* |
RSI_FER: ALB | 61.45±14.10 | 57.93±13.00 | 75.68±8.23 | -17.75±1.09 | 0.00* |
%∆FER12 | -39.6%±24.6% | -46.5%±17.6% | -12.0%±29.2% | -34.5%±1.8% | 0.00* |
%∆FER: ALB12 | -58.2%±15.5% | -62.9%±13.0% | -39.1%±8.1% | -23.9%±1.1% | 0.00* |
CRP1 (mg/dL) | 73.24±31.21 | 80.21±30.30 | 45.07±14.56 | 35.14±2.50 | 0.00* |
CRP: ALB1 | 35.57±20.60 | 40.01±20.45 | 17.65±6.92 | 22.36±1.67 | 0.00* |
CRP2 (mg/dL) | 41.59±29.06 | 42.43±31.34 | 38.19±16.65 | 4.25±2.60 | 0.10 |
CRP: ALB2 | 14.01±14.39 | 14.90±15.79 | 10.38±4.59 | 4.52±1.28 | 0.00* |
%∆ CRP12 | -41.3%±24.2% | -47.6%±17.0% | -15.7%±31.0% | -31.9%±1.8% | 0.00* |
%∆ CRP: ALB12 | -59.3%±14.8% | -63.7%±12.7% | -41.8%±8.3% | -21.9%±1.1% | 0.00* |
Data results of the comparative variables between the Group I and Group II were statistically analyzed by independent T and One-Sample T-Test (at p-value< 0.05) and expressed as Mean±SD and Mean difference±SEM, Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, Ferritin to Albumin levels Ratios (FER: ALB) were used to calculate the Relative Strength Index (RSI), FER: Ferritin level, ALB: Albumin level, CRP: C-Reactive Protein level, CRP: ALB: C-Reactive Protein to Albumin levels Ratio, FER: ALB: Ferritin to Albumin levels Ratio, LNR: Lymphocytes to Neutrophils Ratio, LMR: Lymphocytes to Monocytes Ratio
Table 6: Comparatively Studied Variables between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) Among Admitted SARS-CoV-2 Infected Patients at Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied comparative variables | Overall Cohorts (N = 781) Mean±SD | Cohort I (Survivors) (N = 626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N = 155, 19.85%) Mean±SD | OD | p-value | |
| Gender | F | 236 (30.2%) | 186 (29.7%) | 50 (32.3%) | 0.89 (95% CI; 0.61-1.29) | 0.537 |
| M | 545 (69.8%) | 440 (70.3%) | 105 (67.7%) | |||
| M: F ratio | 2.31: 1 | 2.37: 1 | 2.1: 1 | |||
| O2 Supply | None | 76 (9.7%) | 76 (12.1%) | 0 (0.0%) | NA | 0.00* |
| NC (3-6 L/min) | 332 (42.5%) | 292 (46.6%) | 40 (25.8%) | |||
| NIMV | 357 (45.7%) | 258 (41.2%) | 99 (63.9%) | |||
| IMV | 16 (2.0%) | 0 (0.0%) | 16 (10.3%) | |||
| PARA | Oral | 498 (63.8%) | 494 (78.9%) | 4 (2.6%) | 141.3 (95% CI; 51.4-388.4) | 0.00* |
| IV | 283 (36.2%) | 132 (21.1%) | 151 (97.4%) | |||
| cNa | <140 | 626 (80.2%) | 471 (75.2%) | 155 (100.0%) | 0.75 (95% CI; 0.72-0.79) | 0.00* |
| ≥140 | 155 (19.8%) | 155 (24.8%) | 0 (0.0%) | |||
| RSI_FER: ALB | ≤60 | 343 (43.9%) | 340 (54.3%) | 3 (1.9%) | 60.23 (95% CI; 19.01-190.9) | 0.00* |
| >60 | 438 (56.1%) | 286 (45.7%) | 152 (98.1%) | |||
Data results of the comparative variables between the 2 tested cohorts were statistically analyzed by Chi Square Test (at p-value< 0.05) and expressed as Number (Percentage), Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, Ferritin to Albumin levels Ratios (FER: ALB) were used to calculate the Relative Strength Index (RSI), *: Significant (p-value <0.05), N: Number of tested COVID-19 infected patients. NC: Nasal Canula on Oxygen flow rate of 3-6 L/min, NIMV: Non-Invasive Mechanical Ventilation, IMV: Invasive Mechanical Ventilation, ALB RSI: Relative Strength Index of Albumin, cNa: Sodium level after correction with BG, F: Female, M: Male, M: F: Male to Female ratio, 02: Oxygen, PARA: Paracetamol, NA: Not statistically applicable and can’t be computed
Table 7: Comparatively Studied Variables between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II) among Admitted SARS-CoV-2 Infected Patients at Queen Alia Military Hospital, Jordan between Mar 2020 and Sep 2021
| Studied Comparative Variables | Overall Cohorts N = 781) Mean±SD | Cohort I (Survivors) (N = 626, 80.15%) Mean±SD | Cohort II (Non-Survivors) (N = 155, 19.85%) Mean±SD | OD | p-value | |
COVID-19 | Suspected | 247 (31.6%) | 203 (32.4%) | 44 (28.4%) | 1.21 (95% CI; 0.82--1.78) | 0.33 |
Confirmed | 534 (68.4%) | 423 (67.6% | 111 (71.6%) | |||
DEX | None | 376 (48.1%) | 241 (38.5%) | 135(87.1%) | 0.09 (95% CI; 0.056-0.152) | 0.00* |
6 mg/day | 405 (51.9%) | 385(61.5%) | 20(12.9%) | |||
ABs
| Tazocin | 378 (48.4%) | 303 (48.4%) | 75 (48.4%) | 1.00 (95% CI; 0.70-1.42) | 0.997 |
Non_Tazocin | 403 (51.6%) | 323 (51.6%) | 80 (51.6%) | |||
PIP/TAZ | 403 (51.6%) | 323 (51.6%) | 80 (51.6%) | NA | 0.974 | |
MER | 201 (25.7%) | 162 (25.9%) | 39 (25.2%) | |||
IMP/CIL | 177 (22.7%) | 141 (22.5%) | 36 (23.2%) | |||
Data results of the comparative variables between the 2 tested cohorts were statistically analyzed by Chi Square Test (at p-value<0.05) and expressed as Number (Percentage), Cohort I: SARS CoV-2 infected patients who survived the until one of the end points that were pre-defined in our study, either survived the 28 hospital admission days or discharged before, Cohort II: SARS-CoV-2 infected patients who died during the 28 hospital admission days, PIP/TAZ: Piperacillin/Tazobactam (Tazocin®), IMI/CIL: Imipenem/Cilastatin (Tienam®), NA: Not statistically applicable and can’t be computed, Dex: Dexamethasone, *: Significant (p-value <0.05), N: Number of tested COVID-19 patients, ABs: Selected Antibiotics, MER: Meropenem (Meronem®)
Table 8: The optimal cut-off points, sensitivities, specificities, positive and negative predictive values, Youden and accuracy indices and the negative likelihood ratios for the 3-Blood Glucose (BG) related prognosticators for the overall affected COVID-19 patients’ mortality
Prognostic Indicator | Cut-off | TPR | FPR | YI | TNR | PPV | NPV | NLR | AI |
BG 2 (mg/dL) | 149.90 | 47.74% | 22.04% | 25.70% | 77.96% | 34.91% | 85.76% | 67.04% | 71.96% |
% ∆BG | - 38% | 98.71% | 11.82% | 86.89% | 88.18% | 67.40% | 99.64% | 1.46% | 90.27% |
Insulin rate (IU/hr) | 1.35 | 60.00% | 28.12% | 31.88% | 71.88% | 34.57% | 87.89% | 55.64% | 69.53% |
TPR: True Positive Rate (sensitivity), FPR: False Positive Rate, YI: Youden Index, TNR: True Negative Ratio (specificity), PPV: Positive Predictive Value, NPV: Negative Predictive Value, NLR: Negative Likelihood Ratio, AI: Accuracy Index
Figure 1: The area under the Receiver Operating Characteristic (ROC) curves for the 3 tested BG-Related Prognosticators (AUROC). The %D BG12 had a significantly higher AUROC compared to insulin ate with Area±SEM (95% Cl; Range) of 0.960±0.006 (95% CI; 0.948-0.972) vs 0.703±0.023 (95% CI; 0.657-0.749). BG2 had insignificant AUROC in our study 0.521±0.031 (95% CI; 0.461-0.581).
*BG2 (1st tested prognosticator): Average sodium concentation after correction with blood glucose during admission. *%DBG12 (2nd tested prognosticator): Changes in corrected sodium concentration from baseline. *Insulin rate (3rd tested prognosticator): Average insulin rate per day in IU per hour
The prescribing doses of the 3 used broad-spectrum antibiotics, Piperacillin/Tazobactam (Tazocin®), Imipenem/Cilastatin (Tienam®) and Meropenem (Meronem®), were recorded and based on the studied patients’ creatinine clearance (CrCl), we assessed the optimal dosing of each prescribed antibiotic. Deficits in antibiotics dosing were calculated after subtracting the optimal dosing from the retrievable prescribed dosing. Sodium and potassium levels were corrected according to blood glucose and pH values, respectively.
All retrievable biochemical data were averaged using at least 2 measurements. All retrievable and calculated variables were thereafter divided into parametric data and non-parametric for which the comparative parametric data were analyzed across the two studied groups, Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II), by Independent and One Sample T Tests to express the analysis results as either Mean±SD or Mean difference±SEM as fully described in Table 1-5. In other side of datqa, the non-parametric variables were analyzed using the Chi Square Test and the outcomes results were expressed as Number (Percentage) and the relative risk estimates were expressed as Odd Ratio (OD) as thoroughly summarized in Table 6-7.
The area under the ROC curves (AUROC) of the three tested prognosticators, the average blood glucose levels during admission days (BG2), the changes in blood glucose levels from baseline (%∆BG12) and the average insulin infusion rates during admission days, was statistically compared using the proposed Delong method. The optimal cutoff operating point on the constructed ROC curve was picked by investigating the highest youden’s index of the tested prognosticator. Sensitivity analysis was also pursued to yield the sensitivity (TPR), specificity (TNR), accuracy (AI), positive and negative predictive value (NPV and PPV) and Negative Likelihood Ratio (NLR) for our tested prognosticator. All results were analyzed using SPSS version 20 (Statistical Package for the Social Sciences, Chicago, IL, U.S.A.) with a p-value <0.05 as a level of significance.
From total admitted COVID-19 infected patients in our isolation departments at Queen Alia Military Hospital, Royal Medical Services, Amman, Jordan between Mar 2020 and Sep 2021, 718 eligible studied patients were finally included in this study (718/4183, 18.67%) in which 247 COVID-19 infected patients (31.6%) had suspected COVID-19 infection and 534 COVID-19 infected patients (68.4%) had confirmed COVID-19 infection. The mean age of the whole study cohort was 59.40±10.60 years and the Non-Survivors Cohort were insignificantly younger than the Survivors Cohort (58.35±10.20 years versus 59.66±10.69 years, respectively, p-value = 0.17). Insignificantly, males were distributed in the study in approximately 2.31:1 ratio compared to females [545 (69.8%) versus 236 (30.2%), respectively, p-vale = 0.829] in which 67.7% (105 affected COVID-19 men) and 32.3% (50 affected COVID-19 women) were belonged to the Non-Survivors Cohort compared to 70.3% (440 affected COVID-19 men) and 29.7% (186 affected COVID-19 women) were belonged to the Survivors Cohort.
Oxygen supply strategies for the whole studied cohort were significantly distributed between Survivors Cohort (Cohort I) and Non-Survivors Cohort (Cohort II), in which 76 (9.7%), 332 (42.5%), 357 (45.7%) and 16 (2.0%) versus 76 (20.2%), 205 (54.5%), 95 (25.3%) and 0 (0.0%) were on non-O2 supply, nasal canula at flow rate of 3-6 L/min, non-invasive mechanical ventilation and invasive mechanical ventilation, retrospectively.
Although the Survivors Cohort had significantly lower averaged human albumin intake than Non-Survivors Cohort [12±4 g/day vs 18±4 g/day, -7±0 g/day, p-value=0.00], the changes in serum albumin levels (%∆ALB12) were insignificant [44.8%±12.9% vs 44.5%±34.8%, 0.3%±1.7%, p-value=0.843]. Average Paracetamol dose was significantly higher in Non-Survivors Cohort compared to Survivors Cohort [3.32±0.46 g/day vs 1.55±0.65 g/day, +1.77±0.06, p-value = 0.00] in which the percentages distribution of Paracetamol IV compared to Paracetamol P.O in Non-Survivors Cohort [151 (97.4%) vs 132 (21.1%)] was significantly higher than in Survivors Cohort [4 (2.6%) vs 494 (78.9%)].
Prescribing antibiotics were allocated insignificantly between the two tested cohorts in which Survivors Cohort had non-Tazocin® and Tazocin® antibiotics of 323 (51.6%) and 303 (48.4%) compared 80 (51.6%) and 75 (48.4%) in Non-Survivors Cohort. According to estimated CrCl based on Jelliffe equation, Survivors Cohort had significantly lower %Deficit dosing in prescribing PIP/TAZ (Tazocin®) and IMI/CIL (Imipenem®) compared to Non-Survivors Cohort [-27.73%±3.92% and -28.73%±5.85% vs --30.21%±4.06% and -38.67%±8.13%, respectively, p-value = 0.00]. Average corrected sodium level (cNa2) was significantly higher in Survivors Cohort compared to Non-Survivors Cohort [137.86±3.16 mEq/l vs 128.77±3.84 mEq/l, +9.09±0.30mEq/l, p-value=0.00] and the incidence of hyponatremia was significantly higher in Non-Survivors Cohort compared to Survivors Cohort [155 (100.0%) vs 471 (75.2%), retrospectively, p-value = 0.00]. Survivors Cohort had insignificantly higher average Blood Glucose level (BG2) than Non-Survivors Cohort [153.04±39.18 mg/dl vs 147.74±20.81 mg/dL; +5.30±3.26 mg/dL, p-Value=0.10]. Oppositely, Survivors Cohort had significantly lower average total daily insulin dosing compared to Non-Survivors Cohort [31.74±1.80 IU/day vs 33.55±1.90 IU/day; -1.82±0.16 IU/day, p-value = 0.00].
Hemodynamically, Survivors Cohort had significantly higher reduction rate in SI and mSI (%∆SI and %∆mSI, respectively) than Non-Survivors Cohort [-25.3%±11.8% and -31.2%±11.3% vs -0.9%±6.0% and -7.5%±6.0%, respectively, p-value=0.00]. SARS-CoV-2 infected patients in Non-Survivors Cohort had significantly higher bilirubin levels and INR than in Survivors Cohort [2.71±0.16 mg/dL and 2.68±0.11 vs 2.53±0.18 mg/dL and 2.57±0.11, respectively, p-value=0.00]. Unexpectedly, the nutritional indices inputs of Total Calories (TCI) and Protein Densities (PD) were significantly lower in Survivors Cohort compared to Non-Survivors Cohort [523.2±192.7 Cal/day and 1.92±1.03 g/100 Cal vs 766.9±208.0 Cal/day and 2.56±0.73 Cal, respectively, p-value=0.00] and carbohydrate densities were significantly higher in Survivors Cohort compared to Non-Survivors Cohort [20.62±4.70 g/100 Cal vs 17.71±3.39 g/100 Cal; 2.91±0.40 g/100 Cal, p-value = 0.00].
Haematologically, the reduction percentages in white blood cells counts, absolute neutrophils counts, monocytes counts, neutrophils to lymphocytes ratios and monocytes to lymphocytes ratios (%∆WBCs12, %∆ANC12, %∆MC12, %∆NLR12and %∆MLR12) were significantly higher in Survivors Cohort compared to Non-Survivors Cohort [-26.9%±28.7%, -41.4%±30.6%, -51.7%±37.3%, -48.0%±15.2% and -58.3%±12.8% vs +36.0%±15.8%, +22.9%±14.7%, +27.1%±18.0%, +23.7%±146.0% and -6.9%±103.3%, respectively, p-Value=0.000]. Regarding prognosticator biomarkers and their ratios, the reduction percentages in FER: ALB and CRP: ALB (%∆FER: ALB12 and %∆CRP: ALB12, respectively) were also significantly higher in Survivors Cohort compared to Non-Survivors Cohort [-62.9%±13.0% and -63.7%±12.7% vs -39.1%±8.1% and -41.8%±8.3%, respectively, p-value = 0.00].
In addition to the investigated overall mortality and overall survival rates in our study [(N = 155, 19.85%) and (N = 626, 80.15%)], we also investigated the overall hospital Length of Stay (LOS) which it was significantly lower in Non-Survivors Cohort compared to Survivors Cohort [10.45±2.08 days vs 11.42±2.98 days, respectively, p-value = 0.00]. There were insignificant differences between the two studied cohorts regarding their baseline anthropometrics.
The Area Under the ROC Curves (AUROC) of our tested prognosticator is fully illustrated in Figure 1. The %∆ BG12 had a significantly higher AUROC compared to Insulin rate with Area±SEM (95% CI; Range) of 0.960±0.006 (95% CI; 0.948-0.972) vs 0.703±0.023 (95% CI; 0.657-0.749). BG2 had insignificant AUROC in our study 0.521±0.031 (95% CI; 0.461-0.581). Table 8 shows the optimal cut-off points, sensitivities, specificities, positive and negative predictive values, Youden and accuracy indices and the negative likelihood ratios for the 3-Blood Glucose (BG) related prognosticators for the overall affected COVID-19 patients’ mortality. According to our study, the best cut-off value for the BG2, %∆BG12 and Insulin rate were 149.90 mg/dL, -38% and 1.35 IU/hr, respectively.
The present study includes two studied cohorts, the Survivors Cohort (Cohort I) and the Non-Survivors Cohort (Cohort II), of admitted SARS-CoV-2 infected patients with moderate-severe diseases statuses on specialized isolation center at Queen Alia Military Hospital of the Royal Medical Services (RMS) in Jordan, between Mar 2020 and Sep 2021. To the best of our knowledge, the uniqueness of our study is primarily involved in its multi-faceted comparative variables, including but not excluded to, anthropometrical, biochemical, hemodynamical, hematological and prognostical tested variables in addition to our tested prognosticator of hyperglycemia among the Survivors and Non-Survivors Cohorts.
Several systematic reviews and meta-analysis studies examine risk factors associated with overall negative clinical impacts in affected COVID-19 patients. However, most of these studied patients in these studies had not progressed to the study endpoints by the time the study was conducted, in addition to the their relatively small sample sizes which leading to bias and unreliable prediction for COVID-19 disease progression and overall SARS-CoV-2 infected patients’ mortalities [16-19].
As well known, T cells were found to be mandatory for virological clearance of SARS-CoV in already infected cells, including MERS-CoV, SARS-CoV 1, SARS-CoV 2. And observational studies have also shown a strong negative correlation between Total Lymphocytes Counts (TLC) and overall clinical outcomes [20,21]. A study of 138 hospitalized SARS-CoV-2 infected patients in Wuhan, China, found that prolonged and severe lymphopenia accompanied with leukocytosis was associated with higher mortality [22]. As previously mentioned in this study, affected COVID-19 patients are likely to have a dysfunctional immunity on white blood cells, particularly lymphocytes and macrophages, that may be linked to significantly higher incidences of in-hospital complications and multiple organ failures in moderate-severe admitted SARS-CoV-2 infected patients with uncontrolled hyperglycemia. This is consistent with previous reports that a high glucose level have been linked to significantly higher COVID-19 associated ARDS complications in severe SARS-CoV-2 infected patients. Also, relatively prolonged uncontrolled in blood glucose levels are substantially contributes to other comorbidities, including but not excluded to, peripheral arteriosclerosis, ketoacidosis and progression of diabetic nephropathy, which all are also related to overall affected COVID-19 patients’ mortalities [23-24]. Although in our study, the average blood glucose levels were higher in Survivors Cohort compared to Non-Survivors Cohort by +5.30±3.26 mg/dL, this mean difference±SEM was insignificant and alternatively we investigated that the average total insulin dosing in Survivors Cohort was significantly lower than in Non-Survivors Cohort by -1.82±0.16 IU/day.
Though, the underlying mechanisms of hyperglycemia related COVID-19 disease morbidity and mortality are currently unclear, many emerging proposed evidences link between hyperglycemia and higher propensity for Angiotensin-Converting Enzyme-2 (ACE-2) receptors expression in SARS-CoV-2 infected patients, making them vulnerable for the virus binding and entrance.Effective glycemic monitor and management may be beneficial to reduce the progression and fatality of COVID-19 patient [25-27]. In this study, we explored that the 3 tested BG related prognosticators for SARS-CoV-2 infected patients mortality, may be used for early prediction of COVID-19 disease progression with high sensitivity, specificity and accuracy values, especially for changes in BG from baseline (%∆BG12).
As there were many cconcerns for the effect of hyperglycemia on immune cells and subsequently the overall clinical impacts, there is an urgent necessity to track the daily blood glucose levels, the changes in blood glucose from baseline, or alternatively the insulin infusion rate to keep their averages around 149.9 mg/dL, -38% and 1.35 IU/hr. This study is limited by its retrospective design. A larger, multisite and prospective study is needed to control for multiple confounders. Despite these limitations, our conclusions may have an added value to the current excessively evolving controversial pieces of evidence.
World Health Organization. Coronavirus Disease (COVID-2019) Situation Reports. https://www.who.int/emer gencies/diseas es/novel-coronavirus-2019/situation-reports/. Accessed 3 Aug.
Restellini, S. et al. "Management of gastrointestinal and hepatic diseases during the COVID-19 outbreak." Revue Médicale Suisse, vol. 16, no. 691–2, 2020, pp. 845–848.
Moore, B.J.B. and June, C.H. "Cytokine release syndrome in severe COVID-19." Science, vol. 368, no. 6490, 2020, pp. 473–474.
Thiel, V. et al. "Mechanisms and enzymes involved in SARS coronavirus genome expression." Journal of General Virology, vol. 84, 2003, pp. 2305–2315.
Horby, P. et al. "Dexamethasone in hospitalized patients with Covid-19-preliminary report." New England Journal of Medicine, vol. 17, 2020.
Cheng, K. et al. "Clinical characteristics of 463 patients with common and severe type coronavirus disease 2019." Shanghai Medical Journal, vol. 43, no. 4, 2020, pp. 1–15.
Sun, Y. et al. "Characteristics and prognostic factors of disease severity in patients with COVID-19: the Beijing experience." Journal of Autoimmunity, vol. 112, 2020, Article ID 102473.
Wynants, L et al. "Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal." BMJ, vol. 369, 2020, m1328.
Piccininni, M. et al. "Use of all-cause mortality to quantify the consequences of COVID-19 in Nembro, Lombardy: descriptive study." BMJ, vol. 369, 2020, m1835.
Mao, B. et al. "Assessing risk factors for SARS-CoV-2 infection in patients presenting with symptoms in Shanghai, China: A multicentre, observational cohort study." Lancet Digital Health, vol. 2, 2020, e323–30.
Ji, D. et al. "Prediction for progression risk in patients with COVID-19 pneumonia: The CALL Score." Clinical Infectious Diseases, vol. 71, 2020, pp. 1393–1399.
Sahu, K.K. and Siddiqui, A.D. "From hematologist’s desk: The effect of COVID-19 on the blood system." American Journal of Hematology, vol. 95, no. 8, 2020, pp. E213–E215.
Wu, C. et al. "Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China." JAMA Internal Medicine, vol. 180, no. 7, 2020, pp. 1–11.
Bode, B.G.V. et al. "Glycemic characteristics and clinical outcomes of COVID-19 patients hospitalized in the United States." Journal of Diabetes Science and Technology, vol. 14, no. 4, 2020, pp. 813–821.
Benfield, T. et al. "Influence of diabetes and hyperglycaemia on infectious disease hospitalisation and outcome." Diabetologia, vol. 50, 2007, pp. 549–554.
Suaya, J.A. et al. "Skin and soft tissue infections and associated complications among commercially insured patients aged 0–64 years with and without diabetes in the U.S." PLoS One, vol. 8, 2013, e60057.
Bellou, V.T.I. et al. "Risk factors for adverse clinical outcomes in patients with COVID-19: A systematic review and meta-analysis." medRxiv, preprint, 2020.
Cummings, M.J.B.M. and Abrams, D. "Epidemiology, clinical course and outcomes of critically ill adults with COVID-19 in New York City: A prospective cohort study." The Lancet, vol. 395, no. 10239, 2020, pp. 1763–1770.
Grasselli, G. and Zanella, A. "Critically ill patients with COVID-19 in New York City." The Lancet, vol. 395, 2020, pp. 1740–1741.
Paranjpe, I. et al. "Association of treatment-dose anticoagulation with in-hospital survival among hospitalized patients with COVID-19." Journal of the American College of Cardiology, vol. 76, no. 1, 2020, pp. 122–124.
Wang, D. et al. "Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China." JAMA, vol. 323, 2020, pp. 1061–1069.
Wang, S. et al. "Fasting blood glucose at admission is an independent predictor for 28-day mortality in patients with COVID-19 without previous diagnosis of diabetes: a multicentre retrospective study." Diabetologia, vol. 63, 2020, pp. 2102–2111.
Kondapally Seshasai, S.R. et al. "Diabetes mellitus, fasting glucose and risk of cause-specific death." New England Journal of Medicine, vol. 364, 2011, pp. 829–841.
Sinclair, A.J. and Abdelhafiz, A.H. "Age, frailty and diabetes-triple jeopardy for vulnerability to COVID-19 infection." EClinicalMedicine, vol. 22, 2020, 100343.
Ceriello, A. "Hyperglycemia and the worse prognosis of COVID-19: Why a fast blood glucose control should be mandatory." Diabetes Research and Clinical Practice, vol. 163, 2020, 108186.
Denman, A.M. "Lymphocyte function and virus infections." Journal of Clinical Pathology, vol. 32, n.d., pp. 39–47.
Chen, X.Q. et al. "Lymphocyte-to-monocyte ratio effectively predicts survival outcome of patients with obstructive colorectal cancer." World Journal of Gastroenterology, vol. 25, 2019, pp. 4970–4984.
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