Hrev_master Healthcare in Low-resource Settings 2023; volume 11(s2):11323 Value of biochemical markers in predicting outcome of COVID-19 infection in University Hospital, Alexandria, Egypt Neveen Rashad Mostafa,1 Abeer Ahmed Mohamed Ali,2 Rehab Moustafa Ezzat,3 Mostafa Kamel Bakry,4 Roy Rillera Marzo5,6 1Department of Internal Medicine; 2Department of Chemical pathology, Medical Research Institute, Alexandria University; 3Department of Pulmonology, Alexandria Student University Hospital, Alexandria University; 4Department of ENT, Alexandria student university hospital, Alexandria University, Egypt; 5Department of Community Medicine, International Medical School, Management and Science University, Shah Alam; 6Global Public Health, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Kuala Lumpur, Malaysia Abstract This paper aims to examine the value of different biochemical markers in predicting the outcome of COVID-19 infection. A total of 140 patients with confirmed COVID-19 infection by polymerase chain reaction (PCR), different biochemical markers were tested, their relation to the outcome of the disease was monitored, and the most reliable tests were determined. The study found a significant correlation between all evaluated biochemical markers and severity of the disease, including C-reactive protein (CRP), D-dimer, ala- nine aminotransferase (ALT), Aspartate aminotransferase (AST), prothrombin time (PT), activated partial thromboplastin time (aPTT). In addition, ferritin, lactate dehydrogenase (LDH), procal- citonin (PCT) and Pro- Brain natriuretic peptide (proBNP) demon- strated highly sensitivity and specificity as well as significant prog- nostic performance. These markers were also independently signif- icant in predicting mortality. Early assessment of biochemical markers in patients with COVID-19 can help clinicians in tailoring treatment and providing more intensive care to those with greater mortality risk. In particular, the assessment of ferritin, LDH, pro- calcitonin and proBNP can independently predict mortality. Introduction In late 2019, an outbreak of atypical pneumonia cases emerged in Wuhan, Hubei province, China, and SARS-Cov2 was identified as the causative organism. This atypical pneumonia was called Corona virus disease 2019 (COVID-19), with the primary target of the virus being the lung, although it can infect other organs that express angiotensin converting enzyme 2 receptors (ACE2).1 SARS-CoV-2 is a single stranded RNA virus, belonging to Coronaviridae family, and has a characteristic corona when viewed under and electron microscope due to its spike like surface glycoproteins, which attach to target cell receptors in the host.1 The World Health Organization (WHO) has classified COVID-19 patients into four categories: ordinary, mild, severe and critically ill types. Patient may progress from one category to another within 7-10 days, particularly if the virus replicates rapid- ly and causes a cytokine storm.2 Clinical presentation of COVID-19 includes fever, cough, muscle pain and fatigue, with the patient may also show additional symptoms such as olfactory and gustatory dysfunction. Progression to severe disease may be affected by comorbidities of the patient such as diabetes mellitus, dyslipidemia, pulmonary dis- ease, and cardiovascular disorders; therefore, good history taking Correspondence: Neveen Rashad Mostafa, Associate ProfessoDepartment of Internal Medicine, Medical Research Institute, Alexandria University, Egypt. Tel.: +20.01557709789. E-mail: doctor.aj.2000@gmail.com Key wards: biochemical markers; COVID-19; cytokines; mortality. Contributions: NRM, conceptualize the idea, writing and editing the article; AA, running laboratory blood tests; RME, data collection and recruitment of cases; MB, data collection and recruitment and follow-up of cases. Conflict of interest: the authors declare no conflict of interest. Ethics approval and consent to participate: the Ethics Committee of Medical Research Institute approved this study (E/C.S/N.R2/2023). The study is conformed with the Helsinki Declaration of 1964, as revised in 2013, concerning human and animal rights. Informed consent: All patients participating in this study signed a written informed consent form for participating in this study. Patient consent for publication: written informed consent was obtained from a legally authorized representative(s) for anonymized patient information to be published in this article. Availability of data and materials: all data generated or analyzed during this study are included in this published article. Received for publication: 18 March 2023. Accepted for publication: 2 May 2023. Conference presentation: part of this paper was presented at the 2nd International Nursing and Health Sciences Symposium that took place at the Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2023 Licensee PAGEPress, Italy Healthcare in Low-resource Settings 2023; 11(s2):11323 doi:10.4081/hls.2023.11323 Publisher's note: all claims expressed in this article are solely those of the authors and do not necessarily represent those of their affili- ated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. [Healthcare in Low-resource Settings 2023; 11(s2):11323] [page 1] No n- co mm er cia l u se on ly is mandatory in those patients.3 A hyper-inflammatory state has been identified during COVID-19 infection, and several biochem- ical markers can differentiate between severe and non-severe out- comes. These markers can also predict the likelihood of complica- tions and the course of disease, thus helping in clinical decision- making.4 To ensure early and effective management of the disease, various biochemical markers have been used to predict the course of the COVID-19, differentiate between severe and non-severe cases, and determine the need for more advanced care.5 Recent research has focused on identifying the most specific biochemical markers that are implicated in evolution of the disease. Materials and Methods This prospective cohort study was conducted on patients admitted to Alexandria student university hospital, Egypt from June 2021 to December 2021. The study was approved by the Ethics Committee of Alexandria Student University Hospital in Egypt and was carried in accordance with the Helsinki Declaration. Written informed consent was obtained from all the participants. Inclusion criteria A total of 140 adult patients with confirmed COVID-19 infec- tion by PCR throat swab testing6 were enrolled in the study. Exclusion criteria Bacterial pneumonia, bacterial sepsis, asymptomatic patients who tested positive for the infection but did not exhibit symptoms consistent with COVID-19. Clinical data included: i) Demographic data: age and gender; ii) Clinical data: time passed from the onset of symptoms till arriv- ing to the hospital, clinical symptoms including cough, fever, headache, muscle aches, vomiting or diarrhea, with or without res- piratory symptoms; iii) Past medical history including any cardiac, renal, hepatic, or thyroid diseases, and presence of diabetes melli- tus, hypertension or any autoimmune diseases. Biochemical markers:7 i) Routine blood tests were done includ- ing CBC, Renal function, Liver function tests, and electrolytes; ii) Inflammatory markers including CRP, Ferritin, LDH, and procal- citonin; iii) Coagulation markers including D-dimer, PT, aPTT; iv) Cardiovascular markers including Pro-BNP. Definition of clinical outcome In this study, clinical outcome was defined as discharge of the patient after recovery or death. Discharge was considered when the patient was free of symptoms and had no fever for at least 3 days, and had two consecutive negative PCR test results.8 Statistical analysis of the data Data were entered into IBM SPSS software package version 20.0. (Armonk, NY: IBM Corp) and analyzed using appropriate statistical tests. Categorical data were presented as numbers and percentages. Chi-square test was applied to investigate the associ- ation between the categorical variables. Alternatively, Fisher Exact correction test was applied when more than 20% of the cells have expected count less than 5. For continuous data, they were tested for normality by the Kolmogorov- Smirnov test and Shapiro-Wilk test. Quantitative data were expressed mean, standard deviation, median and range (minimum and maximum). For normally distrib- uted quantitative variables Student t-test was used to compare two groups. On the other hand for not normally distributed quantitative variables Mann Whitney test was used to compare two groups. Logistic Regression was used to detect the most independent factor for affecting deceased patients. The obtained results were judged at the 5% level of significance. Results According to the inclusion/exclusion criteria mentioned earli- er, a total of 140 patients with confirmed COVID-19 infection were involved in the study, out of these patients, 100 patients were discharged and 40 patients passed away. The mean age patients with COVID019 was 65.2±15.3 years. The mean age of the dis- charged patients was 63.5±15.3 years, whereas the mean age of the deceased patients was 69.7±14.8 years. The age of the deceased patients was significantly higher than that of the discharged patients, p=0.031(Table 1). Male patients infected with COVID-19 were 86 (61.4%) of the study sample, the discharged males were 57 (57.0%) patients, and the deceased males were 29 (72.5%) patients. Females infected with COVID-19 were 54 (38.6%) patients of the study sample, the discharged females were 43 (43.0%) patients, and the deceased females were 11 (27.5%) patients, no significant effect of gender on mortality in COVID-19 infection, p=0.089. Article Table 1. Comparison between the two studied groups according to demographic data and co-morbidity. Total n=140 (%) Discharged n=100 (%) Deceased n=40 (%) Test of Sig. p Age (years) Mean±SD. 65.2±15.3 63.5±15.3 69.7±14.8 t= 2.179* 0.031* Median (Min.–Max.) 65 (30–98) 64 (30–91) 73 (36–98) Gender Male 86 (61.4) 57 (57.0) 29 (72.5) χχ2= 2.897 0.089 Female 54 (38.6) 43 (43.0) 11 (27.5) DM 63 (45.0) 44 (44.0) 19 (47.5) χ2=0.141 0.707 Dyslipidemia 48 (34.3) 33 (33) 15 (37.5) χ2=0.257 0.612 HTN 55 (39.3) 41 (41) 14 (35) χχ2=0.431 0.511 Anemia 14 (10) 7 (7) 7 (17.5) χχ2=3.500 FEp=0.114 Ischemic heart disease 13 (9.3) 10 (10) 3 (7.5) χ2=0.212 FEp=0.758 Thyroid 13 (9.3) 9 (9) 4 (10) χ2=0.034 FEp=1.000 SD, standard deviation; t, student t-test; χ2, Chi square test; FE, Fisher Exact; p, p value for comparing between the studied groups. *Statistically significant at p≤0.05. [page 2] [Healthcare in Low-resource Settings 2023; 11(s2):11323] No n- co mm er cia l u se on ly Patients infected with COVID-19 had multiple comorbidities, including DM in 63 (45.0%) patients, 44 (44.0%) patients were discharged and 19 (47.5%) patients were deceased, p=0.707. (Table 1). Dyslipidemia in 48 (34.3%) patients, 33 (33%) patients were discharged and 15 (37.5%) patients were deceased, p=0.612. HTN in 55 (39.3%) patients, 41 (41%) patients were discharged and 14 (35%) patients were deceased, p=0.511. Anemia in 14 (10%) patients, 7 (7%) patients were discharged and 7 (17.5%) patients were deceased, p=0.114. Ischemic heart disease in 13 (9.3%) patients, 10 (10%) patients were discharged and 3 (7.5%) patients were deceased, p=0.758. Thyroid disease in 13 (9.3%) patients, 9 (9%) patients were discharged and 4 (10%) patients were deceased, p=1.000. None of them significantly affects the outcome of the disease. Many laboratory investigations were conducted and their rela- tion to the outcome of the disease were recorded. CRP showed mean value of 17.7±16.3 mg/L in all COVID-19 infected patients, with a mean of 11.4±8.3 mg/L in discharged patients, and a mean of 33.4±20.4 mg/L in deceased patients. CRP also showed high significant increase in deceased patients more than discharged patients, p<0.001 (Table 2). Ferritin showed mean value of 1308±2006 ng/mL in all COVID-19 infected patients, with a mean of 715.8±1188.6 ng/mL in discharged patients, and a mean of 2788.8±2758.5 ng/ml in deceased patients, the increase in ferritin level was highly signifi- cant in deceased patients in comparison to discharged patients, p<0.001 (Table 2, Figure 1). LDH showed mean value of 585±590 IU/L in all COVID-19 infected patients, with a mean of 380.7±246.7 IU/L in discharged patients, and a mean of 1095.9±843.5 IU/L in deceased patients, there was a high significant increase of LDH in deceased patients Article Table 2. Comparison between the two studied groups according to laboratory investigations. Total n=140 (%) Discharged n=100 (%) Deceased n=40 (%) U p CRP (mg/L) Mean±SD. 17.7±16.3 11.4±8.3 33.4±20.4 543.0* <0.001* Median (Min.–Max.) 14.5 (0.3–71) 8.6 (0.3–36) 28 (10–71) Ferritin (ng/mL) Mean±SD. 1308±2006.1 715.8±1188.6 2788.8±2758.5 774.0* <0.001* Median (Min.–Max.) 642.5 (14–10105) 354.5 (14–7006) 1588 (21–10105) LDH (IU/L) Mean±SD. 585±590 380.7±246.7 1095.9±843.5 336.50* <0.001* Median (Min.–Max.) 444 (134–3894) 290.5 (134–966) 874.5 (500–3894) D-dimer (ng/mL) Mean±SD. 4442±4943.2 2902.5±2873.8 8290.8±6697.2 677.0* <0.001* Median (Min.–Max.) 2427 (331–28809) 1581.8 (331–10177) 8524.5(1255–28809) ALT (IU/L) Mean±SD. 91.2±230.2 42.5±32.7 212.9±406 1258.50* 0.001* Median (Min.–Max.) 41 (9–1577) 35.5 (9–148) 91 (11–1577) AST (IU/L) Mean±SD. 129.5±276.2 54.2±51 318±462.9 727.0* <0.001* Median (Min.–Max.) 45.5 (15–1692) 34 (15–284) 98.5 (30–1692) PT (seconds) Mean±SD. 17.6±5.5 16.4±5.2 20.5±5.2 1042.0* <0.001* Median (Min.–Max.) 16 (1–33) 15.7 (1–33) 18.1 (15–32) aPTT Mean±SD. 41.4±13.5 39.7±13.7 45.5±12.2 1376.50* 0.004* Median (Min.–Max.) 37.4 (20–78) 35 (20–78) 43 (25–77) Procalcitonin (ng/mL) Mean±SD. 4.4±8.3 1.7±3.8 11.0±12.2 457.0* <0.001* Median (Min.–Max.) 1.2 (0.03–45.0) 0.6 (0.03–23.0) 5.9 (0.2–45.0) Pro-BNP (pg/mL) Mean±SD. 4219.3±9446 1121.8±1601.1 11963.1±15013.3 486.50* <0.001* Median (Min.–Max.) 784.5 (43–45908) 556.5 (43–8084) 6547.5 (300–45908) SD, standard deviation; U, Mann Whitney test; p, value for comparing between the studied groups. *Statistically significant at p≤0.05. Figure 1. Error Bar showing differences in three markers between discharged and deceased patients. [Healthcare in Low-resource Settings 2023; 11(s2):11323] [page 3] No n- co mm er cia l u se on ly more than discharged patients, p<0.001. D-dimer showed mean value of 4442±4943.2 ng/mL in all COVID-19 infected patients, with a mean of 2902.5±2873.8 ng/mL in discharged patients, and a mean of 8290.8±6697.2 ng/mL in deceased patients, the increase in D-dimer was highly signifi- cant in deceased patients, p<0.001. ALT showed mean value of 91.2±230.2 IU/L in all COVID-19 infected patients, with a mean of 42.5±32.7 IU/L in discharged patients, and a mean of 212.9±406 IU/L in deceased patients, there was a significant increase of ALT in deceased patients more than discharged patients, p=0.001. AST showed mean value of 129.5±276.2 IU/L in all COVID- 19 infected patients, with a mean of 54.2±51 IU/L in discharged patients, and a mean of 318±462.9 IU/L in deceased patients, there was a significant increase of AST in deceased patients more than discharged patients, p<0.001. PT showed mean value of 17.6±5.5 seconds in all COVID-19 infected patients, with a mean of 16.4±5.2 seconds in discharged patients, and a mean of 20.5±5.2 seconds in deceased patients, PT showed high significant increase in deceased patients more than discharged patients, p<0.001. aPTT showed mean value of 41.4±13.5 seconds in all COVID- 19 infected patients, with a mean of 39.7±13.7 seconds in dis- charged patients, and a mean of 45.5±12.2 seconds in deceased patients, aPTT showed significant increase in deceased patients more than discharged patients, p<0.004. PCT showed mean value of 4.4±8.3 ng/mL in all COVID-19 infected patients, with a mean of 1.7±3.8 ng/mL in discharged patients, and a mean of 11.0±12.2 ng/mL in deceased patients, PCT showed highly significant increase in deceased patients more than discharged patients, p<0.001. Pro-BNP showed mean value of 4219.3±9446 pg/mL in all COVID-19 infected patients, with a mean of 1121.8±1601.1 pg/mL in discharged patients, and a mean of 11963.1±15013.3 pg/mL in deceased patients, there was a high significant increase of Pro-BNP in deceased patients more than discharged patients, p<0.001. To detect if the previous laboratory investigations were accurate in predicting mortality, we used the area under the curve (AUC) and 95% CI of the receiver operator characteristic (ROC) curve. We found that there was significant prognostic performance of CRP, ferritin, LDH, D-dimer, PCCT and pro-BNP with mortal- ity, and the highest performance of variables were, LDH > PCT > pro-BNP > CRP, and the least was D-dimer and ferritin. The cut off value for LDH was > 500 IU/L, for PCT was >2.09 ng/mL, for pro- BNP was >1755 pg/mL, and for CRP > 15.4 mg/L. (Table 3, Figure 2). Univariate analysis showed that each one of the variables was associated with mortality after adjustment with ALT, AST, PT, Article Table 3. Prognostic performance for CRP, Ferritin, LDH, D. dimer, Procalcitonin and Pro-BNP to predict mortality (n=40) from dis- charged (n=100). AUC p 95% C.I Cut off Sensitivity Specificity PPV NPV CRP 0.864 <0.001* 0.803–0.926 >15.4 80.0 69.0 50.8 89.6 Ferritin 0.807 <0.001* 0.718–0.895 >677# 87.50 70.0 53.8 93.3 LDH 0.916 <0.001* 0.872–0.960 >500 97.50 78.0 63.9 98.7 D. dimer 0.831 <0.001* 0.761–0.900 >2188# 92.50 63.0 50.0 95.5 Procalcitonin 0.886 <0.001* 0.815–0.957 >2.09 90.0 79.0 63.2 95.2 Pro-BNP 0.878 <0.001* 0.806–0.950 >1755# 85.0 84.0 68.0 93.3 AUC, area under a curve; p value: probability value; CI, confidence intervals; NPV, negative predictive value; PPV, positive predictive value. *Statistically significant at p≤0.05. #Cut off was choose according to Youden index. Table 4. Univariate and multivariate Logistic regression analysis for the parameters affecting deceased patients (n=40 vs. 100). Univariate Model 1 Model 2 p OR (95%C.I) (LL-UL) p OR (95%C.I) (LL-UL) p OR (95%C.I) (LL-UL) CRP (>15.4) <0.001* 8.90 (3.68–21.53) <0.001* 11.41(3.11–41.86) 0.073 3.96 (0.88–17.79) Ferritin (>677) <0.001* 16.33 (5.83–45.75) <0.001* 18.40 (4.60–73.72) 0.018* 10.95 (1.51–79.25) LDH (>500) <0.001* 138.3 (17.97–106) 0.005* 608(14.3–2588727) 0.003* 34.72 (3.23–373.7) D. dimer (>2188) <0.001* 21.0 (6.05–72.91) <0.001* 15.27(3.53–66.09) 0.573 2.64 (0.09–77.02) Procalcitonin(>2.09) <0.001* 33.86 (10.83–105.8) <0.001* 27.12(6.95–105.9) 0.003* 11.82 (2.26–61.86) Pro-BNP (>1755) <0.001* 29.75 (10.74–82.45) <0.001* 28.21 (7.07–112.6) 0.014* 7.73 (1.50–39.80) OR, odd’s ratio; CI: confidence interval; LL, lower limit; UL, upper limit; Model 1, Each marker was adjusted by other significant variables (ALT, AST, PT, aPPT and age); Model 2, Multivariate Regression for the six markers. *Statistically significant at p ≤ 0.05. [page 4] [Healthcare in Low-resource Settings 2023; 11(s2):11323] Figure 2. ROC for CRP, Ferritin, LDH, D. dimer, PCT and Pro- BNP to predict mortality. No n- co mm er cia l u se on ly aPTT, and age, model I showed that they were statistically signifi- cant, and after performing multivariate analysis (model II) we found that ferritin > 677 ng/ml, LDH >500 IU/L, PCT >2.09 ng/mL, and pro-BNP > 1755 pg/mL were still independently sig- nificant for predicting mortality (Table 4, Figure 3). Discussion Numerous studies have confirmed that increasing age and presence of chronic illness are significant risk factors in COVID- 19 infection, leading to extended hospitalization periods and increase mortality. For instance, a retrospective study conducted on older patients infected with COVID-19 at Zhongnan Hospital of Wuhan University found that the mortality rate was significantly higher among patients aged over 65 years (34.5%) than in younger patients (4.7%).9 Another study conducted in Hyderabad, Telangana, India revealed the impact of preexisting comorbidities on disease outcome, including diabetes mellitus, hypertension, coronary artery disease, and chronic kidney disease, either individ- ually or in combination. The study found that preexisting comor- bidities were significant contributing factor in increasing mortality, especially when diabetes mellitus and hypertension occurred together.10 Moreover, a multivariate retrospective cohort conducted in Bangladesh assessed the effect of sociodemographic factors, comorbidities, symptoms, Charlson comorbidity index, and access to health facilities on disease outcomes. The study reported that increased age, the presence of more than 3 symptoms, and multiple comorbidities, were associated with higher morbidity and mortali- ty in COVID-19 patients.11 In our study, we also observed that the age had a significant impact on COVID-19 mortality rates, with higher mortality rates observed in patients aged over 65 years. However, we did not observe any significant effect of other comor- bidities on COVID-19 mortality rates. One of the frequently studied aspects of COVID-19 infection is its relation to patient sex, and its effect on the rate of infection and the outcome of the disease. According to data reported on 239,709 patients in Italy, mortality is 17.7% in men and 10.8% in women, with 59% of total deaths were in males. Even though the rate of infection was lower in males than in females, with 45.8% and 54.2% respectively, indicating that evolution of the disease may be affected by gender.12 In contrast, another study found high- er infection risks among females than males at working ages, but the opposite trend was observed at older age, and across all age groups, mortality rate in males was double that in females.13 However, in our study, there was no significant effect of gender on mortality in COVID-19 infection. Laboratory investigations are crucial for the detection of COVID-19 infection and monitoring evolution of the disease. CRP is an acute-phase protein synthesized by the liver, and elevated in response to bacterial infection, which is usually used in the diag- nosis of pneumonia.14 A retrospective study conducted in China involving 76 patients with confirmed COVID-19 infection, found that CRP ≥ 52.14mg/L was correlated with the severity of infec- tion, and had prognostic value for mortality.15 Additionally, another study conducted in China found that CRP was strongly correlated with Murray score which was originally used to determine the severity of lung injury in patients developing acute respiratory dis- tress syndrome.3 In our study, CRP showed high significant increase in deceased patients more than in discharged patients, and CRP >15.4 mg/L had a significant prognostic performance with mortality, with sensitivity 80% and specificity 69% after adjust- ment with ALT, AST, PT, aPTT, and age. Ferritin, although known primarily as an iron storage protein; has multiple functions, including serving as a signaling molecule and direct mediator of the immune system. Its expression can be induced by  cytokines, and it may also plays role in induction of pro- and anti-inflammatory cytokines.16 It was observed that hyperferritinemia in patients with severe COVID-19 infection was between 1.5 and 5.3 times higher in patients with severe disease usually reaching >800 μg/L and less than theses values in patients with moderate disease.17 A Study conducted in Wuhan, China on patients survived the infection and patients died during their stay in the hospital found that ferritin levels on admission was around 1400 μg/L in non-survivors, which is between 3 and 4 times higher than that observed in survivors.18 In our study, ferritin showed sig- nificant increase in deceased patients more than discharged patients. Ferritin levels above 677 ng/mL were significantly pre- dictive of mortality, with a sensitivity of 87.5% and specificity 70% after adjusting for ALT, AST, PT, aPTT, and age. Ferritin was also found to be independent significant predictor of mortality. LDH is a glycolytic enzyme that catalyzes the conversion between L-lactate and pyruvate and conversion between NADH and NAD+. It is present in the cytoplasm of all tissues especially the heart, liver and skeletal muscles. In COVID-19 infection, there is tissue injury and low oxygenation of the cells leading to up-reg- ulation of glycolytic pathway and increased LDH levels especially in severe lung injury which leads to release of large amounts of LDH isoenzyme 3.19 A Meta-analysis performed by Martha J. showed that increased levels of LDH was associated with poor prognosis and high mortality rate, with a sensitivity of 74 % and specificity of 69%. Positive likelihood ratio was 2.4, negative like- lihood ratio was 0.38 and area under curve of 0.77, independently from age, male sex, hypertension and diabetes.20 Another meta- analysis conducted including twenty eight study showed that high levels of LDH were observed in ICU patients versus non-ICU patients and in non-survivors compared with survivors and con- cluded that LDH is an important severity marker for COVID-19 infection and can be used as a predictor of survival.21 In our study, LDH showed highly significant increase in deceased patients more than discharged patients, as LDH levels > 500 IU/L had a signifi- cant prognostic performance with mortality, with a sensitivity 97.5% and specificity of 78% after adjusting for ALT, AST, PT, aPTT, and age. LDH was also found to be independent significant Article Figure 3. Graph showing odds ratio of different markers with 95% CI after a multivariate logistic regression for COVID-19 mortality. [Healthcare in Low-resource Settings 2023; 11(s2):11323] [page 5] No n- co mm er cia l u se on ly predictor of mortality. D-dimers are fragments resulted from cleav- age of fibrin to break down clots. Therefore, any increase in pro- duction or degradation of fibrin will elevate plasma D-dimer lev- els. In severe COVID-19 infection, proinflammatory and pro- thrombotic events are prominent leading to D-dimer production.22 Studies also suggest that in SARS-CoV-2 infection there is dysreg- ulation of the coagulation cascade with diffuse alveolar damage and infiltration with mononuclear inflammatory cells in the inter- stitium, This promotes prothrombotic activity. Furthermore, proin- flammatory cytokines cause endothelial injury, which enhances coagulation and inhibit fibrinolysis in those patients. These high levels of D-dimer that indicate increased hypercoagulability increase severity and contribute to mortality.23 Reports from Wuhan hospital showed that from the patients requiring ICU admission, 26% had increased D-dimer levels. The main differences between non-survivors compared to survivors is markedly elevated D-dimer which progress on day 5 of infection, also lymphopenia, and renal dysfunction.24 In a multivariable logis- tic regression model of 171 patients in another Wuhan hospital, an initial D-dimer level >1.0 μg/mL was associated with poor progno- sis and high mortality with an odds ratio of 18.42 (2.64- 128.55; p=0.003).25 In our study, there was highly significant differ- ence in D- dimer level between deceased and discharged patients, as D-dimer levels > 2188 ng/mL had a significant prognostic per- formance with mortality, with sensitivity 92.5% and specificity 63% after adjustment with ALT, AST, PT, aPTT, and age. Cytokine storm and hypoxia associated with COVID-19 infec- tion, may also contribute to liver injury in seriously ill patients, as shock and hypoxia leading to hypoperfusion of the liver, this inturn leads to hepatic dysfunction. Moreover, liver damage may occur due the use of medications especially lopinavir and ritonavir.26 Chen et al.27 evaluated 99 confirmed cases of COVID-19 patients. He found that 43 patients presented with liver dysfunc- tion, as ALT and/or AST were elevated especially in severe cases. In a multicenter retrospective cohort study conducted in Hubei province, liver injury indicators were evaluated and their relation to death risk was recorded. The study found that AST was elevated before ALT and both were highly increased in severe group of patients, and that AST is in particular was associated with mortal- ity risk.28 In our study, ALT and AST were significantly elevated in deceased patients more than discharged patients. However, we did not find any significant prognostic performance with mortality. Viral, bacterial, or fungal infection leads to activation of host defense mechanisms results in activation of coagulation pathways as a part of communication between humoral and cellular compo- nents of the immune response in what is called thromboinflamma- tion or immunothrombosis.29 Evidence of coagulopathy has been reported with appearance of COVID-19 infection in China. Reports of the first 99 patients hospitalized in Wuhan demonstrat- ed elevated aPTT in 6% and elevated PT in 5%.27 Another report from another Wuhan hospital showed mild elevation in PT but nor- mal aPTT in the first 138 patients admitted to the hospital.24 In our study, PT and aPTT were significantly elevated in deceased patients more than discharged patients. However, we did not find any significant prognostic performance with mortality. PCT is a product of calcitonin-related gene, produced by epithelial cells during bacterial infection; it is considered as bio- marker of blood infection, and usually used to monitor antibiotic therapy.30 In a recent study, elevated serum level of PCT in COVID-19 infected patients was associated with high mortality with high sensitivity, and reported that serum level of PCT (≥ 0.10 ng/mL) was independent risk factor for mortality specially in old patients (age ≥ 60 y) and severe COVID-19 infection.15 In our study, PCT showed significant increase in deceased patients more than discharged patients, as PCT levels > 2.09 ng/mL had a signif- icant prognostic performance with mortality with, sensitivity 90% and specificity 79% after adjustment with ALT, AST, PT, aPTT, and age, it was independently significant for predicting mortality. Cardiac complications during the COVID-19 infection are pre- disposed by old age, prior cardiovascular disease and severe dis- ease presentation. The pathogenic mechanisms include pro-inflam- matory cytokines (IL-6, IL-7, IL-22, CXCL10) which contribute to plaque rupture, activation of pro-coagulation factors; and hemody- namic changes leading to ischemia and thrombosis. These cardiac events lead to production of the N-terminal pro B type natriuretic peptide (NT-proBNP) in those patients.29 A meta-analysis involv- ing 4,189 patients enrolled in 28 studies reported that patients with severe COVID-19 infection had significant high level NT-proBNP, and its level rises progressively in non-survivors.5 In another study involving 138 patients, the average levels of NT-proBNP (301.2 ng/L vs 2887.5 ng/L; p<0.01) were maximum in ICU patients who stayed on mechanical ventilation, or extracorporeal membrane oxygenation or deceased patients.24 In our study, proBNP showed significant increase in deceased patients more than discharged patients, as proBNP levels > 1755 pg/mL had a significant prog- nostic performance with mortality, with sensitivity 85% and speci- ficity 84% after adjustment with ALT, AST, PT, aPTT, and age, and it was independently significant for predicting mortality. After assessment of many biochemical markers during COVID-19 infection, we found that there was significant prognos- tic performance of CRP, ferritin, LDH, D-dimer, PCT and pro-BNP with mortality, and the highest performance of variables were LDH > PCT > pro-BNP > CRP, and the least was D-dimer and ferritin. The cut off value for LDH was > 500 IU/L, for PCT was >2.09 ng/mL, for pro-BNP was >1755 pg/ml, and for CRP > 15.4 mg/L. Univariat analysis showed that each one of the variables was asso- ciated with mortality after adjustment with ALT, AST, PT, aPTT, and age, multivariate analysis showed that ferritin > 677 ng/ml, LDH >500 IU/L, PCT >2.09 ng/mL, and pro-BNP > 1755 pg/mL were still independently significant for predicting mortality. Conclusions Early assessment of biochemical markers in patients with COVID-19 infection can assist clinicians in tailoring treatment and providing more intensive care to those at a greater risk of mortality. 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Article [Healthcare in Low-resource Settings 2023; 11(s2):11323] [page 7] No n- co mm er cia l u se on ly