176 Acta Med Indones - Indones J Intern Med • Vol 54 • Number 2 • April 2022 ORIGINAL ARTICLE Evaluation of COVID-19 Patients According to the Survival Time Adem Atici1*, Ramazan Asoglu2, Hasan Ali Barman3, Mustafa Adem Tatlisu1, Gonul Aciksari1, Yusuf Yılmaz1, Fatma Betul Ozcan1, Mustafa Caliskan1 1 Cardiology Department, Istanbul Medeniyet UniversityFaculty of Medicine, Goztepe Training and Research Hospital, Dr. Erkin street, 34722, Istanbul, Turkey. 2 Cardiology Department, Adiyaman Training and Research Hospital, Yunus Emre Mahallesi, 1164 Sokak No:13, Merkez/Adıyaman, Turkey. 3 Cardiology Department, Faculty of Medicine, Istanbul University – Cerrahpasa, Institute of Cardiology, Istanbul,Turkey. *Corresponding Author: Adem Atici, MD. Cardiology Department, Istanbul Medeniyet University Faculty of Medicine Dr. Erkin street, 34722, Istanbul, Turkey. Email: adematici10@gmail.com. AbSTrACT Background: Coronavirus disease 2019 (COVID-19) was first detected as a form of atypical pneumonia. COVID-19 is a highly contagious virus, and some patients may experience acute respiratory distress syndrome (ARDS) and acute respiratory failure leading to death. We aim to evaluate the clinical, imaging, and laboratory parameters according to survival time to predict mortality in fatal COVID-19 patients. Methods: Fatal 350 and survived 150 COVID-19 patients were included in the study. Fatal patients were divided into three groups according to the median value of the survival days. Demographic characteristics and in-hospital complications were obtained from medical databases. Results: Of the non-survived patients, 30% (104) died within three days, 32% (110) died within 4-10 days, and 39% (136) died within over ten days. Pneumonia on computational tomography (CT), symptom duration before hospital admission (SDBHA), intensive care unit (ICU), hypertension (HT), C-reactive protein (CRP), D-dimer, multi-organ dysfunction syndrome (MODS), cardiac and acute kidney injury, left ventricular ejection fraction (LVEF), right ventricular fractional area change (RV-FAC), and Tocilizumab/Steroid therapy were independent predictors of mortality within three days compared to between 4-10 days and over ten days mortality. A combined diagnosis model was evaluated for the age, CT score, SDBHA, hs-TnI, and D-dimer. The combined model had a higher area under the ROC curve (0.913). Conclusion: This study showed that age, pneumonia on CT, SDBHA, ICU, HT, CRP, d-dimer, cardiac injury, MODS, acute kidney injury, LVEF, and RV-FAC were independently associated with short-term mortality in non-surviving COVID-19 patients in the Turkish population. Moreover, Tocilizumab/Steroid therapy was a protective and independent predictor of mortality within three days. Keywords: COVID-19, mortality, acute respiratory distress syndrome, echocardiography. Vol 54 • Number 2 • April 2022 Evaluation of COVID-19 Patients According to the Survival Time 177 INTrODUCTION Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or Coronavirus disease 2019 (COVID-19) was first detected as a form of atypical pneumonia in Wuhan, China, in December 2019.1 COVID-19 was an unprecedented epidemic, and the World Health Organization (WHO) declared it a pandemic.2 According to the WHO report, about 243 million people were diagnosed with COVID-19 in 219 countries by 24 October 2021. COVID-19 is a highly contagious virus and killed approximately 4.9 million people worldwide.3 Mild acute respiratory infection symptoms such as fever, dry cough, and tiredness are common in the early stages of COVID-19. Some COVID-19 patients may experience ARDS and acute respiratory failure leading to death. Although pulmonary complications were the leading cause of death, multiple organ dysfunction syndrome (MODS), myocardial, kidney, and liver injuries could lead to death in COVID-19 patients.1,4-6 About two-thirds of severe COVID-19 patients have a fatal outcome.7-9 Therefore, many clinical features and laboratory parameters were evaluated to predict mortality in COVID-19 patients. It was reported that age, gender, comorbidities, s m o k i n g h i s t o r y, a n d m a n y b i o m a r k e r s including d-dimer and troponin were a predictor of mortality.10-13 Although there is no specific treatment for COVID-19 so far, corticosteroids and some anti-inflammatory agents have been shown to be effective in treatment.14 In addition, supportive care and early detection are beneficial.15,16 Therefore, the determination of simple and reliable predictors of survival in severe COVID-19 patients is necessary. Due to the limited number of intensive care unit beds and the financial burden of the COVID-19 disease in some countries, adequate supportive therapy and correct triage are essential in the survival period. This study aimed to compare clinical, imaging, and laboratory parameters according to the day of death of patients who died from COVID-19 and determine independent predictors according to the day of death. METHODS The study was planned with a retrospective, cross-sectional, multicenter and observational design. Three hundred and fifty deceased and 150 surviving COVID-19 patients were included in the research for 28 March 2020 and 15 January 2021. The presence of SARS-CoV-2 RNA was detected by real-time reverse transcription-polymerase chain reaction (RT-PCR) in the Ministry of Health Public Health Microbiology Reference Laboratory after obtaining oropharyngeal and nasal specimens by using the same swab and placing the swab on the same transport medium. The guidelines for COVID-19, which the Ministry of Health prepared, were implemented, and the patients used the suggested medications. The anticoagulant, steroid, antibiotic therapy, antiviral therapy, invasive and non-invasive mechanical ventilation was performed according to these guidelines. COVID-19 RT-PCR (+), surviving COVID-19 patients, and deceased COVID-19 patients were included in the study. Patients with the following conditions were excluded from the study: age < 18 years, pregnancy, ST-elevation myocardial infarction, advanced malignancy, severe valvular heart disease, and negative PCR tests. Demographic characteristics and in-hospital complications were obtained from medical databases. Patient age, gender, smoking status, hypertension (HT), diabetes mellitus (DM), coronary artery disease (CAD), hyperlipidemia (HLD), malignancy, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD) history were recorded. Also, laboratory parameters such as urea, creatinine, sodium, potassium, glucose, high-sensitivity troponin I (hs-TnI), d-dimer, hemoglobin, white blood cell (WBC), procalcitonin, and C-reactive protein (CRP) were obtained from hospital admission records. In all cases, a semi- quantitative computational tomography (CT) severity scoring proposed by Pan et al. was calculated for each of the five lobes considering the extent of anatomic involvement.17 Deceased patients were divided into three groups according to the median value of the survival days. The study was conducted under the Helsinki Adem Atici Acta Med Indones-Indones J Intern Med 178 Declaration, and the study protocol was approved by the local ethics committee and the Ministry of Health (approval number: 2020/0623). Definitions Myocardial injury was defined as a troponin value exceeding the upper reference limit (URL, 99%) according to the Fourth Universal Definition of Myocardial Infarction (MI).18 Acute kidney injury (AKI) was defined based on the kidney disease: Improving Global Outcomes (KDIGO) definition.19 CAD was diagnosed in patients with a history of previous percutaneous coronary intervention (PCI) or coronary artery bypass surgery (CABG). MODS is defined as the concurrent dysfunction of two or more organs or systems, including hematological, gastrointestinal, cardiovascular, neurological, respiratory, hepatic, and renal.9 T r a n s t h o r a c i c T w o - d i m e n s i o n a l Echocardiography Two-dimensional echocardiography (2DE) studies were performed by a cardiologist using an X5 transducer (Philips Epiq7; Philips Healthcare, Inc., Andover, MA, USA) to evaluate the parasternal and apical images (2D, M-mode, Doppler echocardiography). The echocardiographic examination was performed within the first 24 hours after admission, and the data were recorded. In the echocardiographic examination, three cycles were recorded and analyzed during any phase of respiration. After the 2DE images were recorded, the analysis was performed by two independent, experienced cardiologists blinded by the clinical data of the patients. Echocardiographic images were obtained in all four standard views (long-axis parasternal, short-axis parasternal, two-chamber apical, and four-chamber apical) using the techniques recommended by the American Society of Echocardiography (ASE) guidelines.20 Electrocardiographic Evaluation 12-lead admission electrocardiography (ECG) was obtained from each patient on admission before any treatment was started. All standard 12-lead electrocardiograms were recorded on digitized 12-lead ECG recordings using the on-screen digital caliper software (Cardio Calipers version 3.3, Iconico, Inc., New York, NY). All ECGs (filter range 0.5-150 Hz, AC filter 60 Hz, 25 mm/s, 10 mm/mV) were analyzed by two independent cardiologists blinded to the clinical data of the patients according to the modified Minnesota criteria, and the findings were recorded on sheets.21 Corrected QT interval (QTc); the QT interval measured in either lead II or V5-6, QTc was calculated using Bazett’s formula (QTc = QT / (√RR).22 QRS fragmentation (fQRS) was defined as a notch in the R wave or S wave in two consecutive leads associated with the myocardial region, or multiple R’ waves and QRS<120 ms.23 Statistical Analyses All statistical tests were conducted using the Statistical Package for the Social Sciences 21.0 for Windows (SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov test was used to analyze the normality of the data. Normally distributed variables were expressed as mean ± standard deviation (SD), while non-normally distributed variables were expressed as median with interquartile range (IQR). The categorical variables are presented as percentages. A Chi- square test was used to assess differences in categorical variables between groups. The primary analysis used ANOVA to compare all reported data for parametric variables, whereas the Kruskal–Wallis test was used to compare nonparametric variables between the median value of the survival days. The univariate effects of type of age, gender, pneumonia on CT, symptom duration before hospital admission (SDBHA), intensive care unit (ICU), HT, CAD, CRP, d-dimer, cardiac injury, MODS, Acute kidney injury, LVEF, RV-FAC and Tocilizumab/ Steroid on death of patients was investigated using the log rank test. The possible factors identified with univariate analyses were further entered into the Cox regression analysis, with backward selection, to determine independent predictors of death. The proportional hazards assumption and model fit was assessed by means of residual (Schoenfeld and Martingale) analysis. Multinomial logistic regression analysis was used to identify independent predictors of mortality in three days. Receiver operating characteristic (ROC) curves were obtained, and the optimal values with the greatest total Vol 54 • Number 2 • April 2022 Evaluation of COVID-19 Patients According to the Survival Time 179 sensitivity and specificity in the prediction of mortality in three days were selected. All the parameters in the ROC curve analysis were included in the binary logistic regression analysis. Combined model was created with the obtained probability value. A combined model, which was created with mortality predictors, was analyzed by ROC curves. Finally, 20 patients were assigned randomly to test the intra-observer and interobserver variability expressed as the intra- class correlation coefficient for the CT score, echocardiographic and electrocardiographic measurements, respectively. Significance was assumed at a 2-sided p <0.05. rESULTS Three hundred and fifty non-surviving patients were divided into three groups according to the day of the death. Of the non-surviving patients, 30% (104) died within three days, 32% (110) died within 4–10 days, and 39% (136) died after ten days. The patients’ clinical and demographic characteristics are shown in Table 1. The patients who died within three days were older than the others (p<0.001). While the body mass index (BMI), gender, and smoking were similar between study groups (p>0.05), heart rate (HR), respiratory rate (RR), pneumonia on CT, CT score, and SDBHA were statistically different between the study groups (p <0.001). Moreover, systolic arterial pressure (SAP), diastolic arterial pressure (DAP), ICU admission and body temperature values were different in study groups (p<0.001). In patients’ past medical histories, DM, HLD, and malignancy were similar in the study population. Also, HT, CAD, COPD, and CKD were significantly higher in patients who died within three days (p<0.05). The hemoglobin, sodium, potassium, and glucose levels were similar among the three groups. WBC, creatinine, CRP, hs-TnI, d-dimer, procalcitonin, and oxygen saturation (sO2) levels were significantly different in patients who died three days compared to other groups (p<0.05). The previous medication was similar between the study groups (p>0.05). While the used drugs were compared between the groups during the disease, steroid and tocilizumab were significantly higher in the survival group than the non-survival group. Invasive mechanical ventilation (IMV), non-invasive mechanical ventilation (NIMV), high-flow oxygen (HFO), vasopressor, and renal replacement therapy (RTT) rates were higher in the non-surviving patients compared to surviving patients (p<0.05). MODS, cardiac, and kidney injury rates were significantly higher in patients who died three days than in other groups (p<0.05). Table 1. The Demographic and Clinical Data of COVID-19 Patients. Survivor (n=150) Non-survivor≤3 days (n=104) Non-survivor 4-10 days (n = 110) Non-survivor >10 days (n = 136) p Clinical characteristics Age (years) 54.6±8.5# & @ 67.8±9.1# * a 64.0 ± 8.1& * 63.4 ± 7.7 @ a <0.001 Male, n (%) 93(62) 69(66) 74(67) 77(56) 0.187 BMI (kg/m2) 23.9±3.3 23.4±2.3 24.2 ± 3.4 24.4 ±3.8 0.108 HR, beats/min 82.0±10.7# & 91.3±12.3# * a 86.4±14.9& * e 81.3±12.0 a e <0.001 RR, times/min 21.3±6.5# & 28.0±8.5# * a 24.0±4.8& * e 21.7±4.2 a e <0.001 SAP, mmHg 107.6±14.8# 98.514.4# * a 104.2±13.7* 106.9±15.0 a <0.001 DAP, mmHg 66.1±11.1# 60.5±11.3 # a 63.9±10.4 65.6±11.1 a <0.001 Smoker, n (%) 65(43) 49(47) 54(49) 50(36) 0.125 Pneumonia on CT, n (%) 98(65)# & 96(92)# * a 90(81) & * e 96(70) a e <0.001 CT score 2(0-4)# & 6(3-11)# * a 2(2-7)& * e 2(1-5) a e <0.001 SDBHA (days) 4.1±2.0# & @ 7.23.1# * a 5.9±2.5& * e 4.9±2.1@ a e <0.001 Hospital stay (days) 13(7-17)# & 2(2-2)# * a 5(4-8)& * e 15(12-18) a e <0.001 ICU admission, n (%) 40(27)# & 75(72)# * a 48(43)& * 43(31) a <0.001 Body Temperature (°C) 36.9±1.2# 37.71.9# * a 37.0±0.8* 36.9±0.6 a <0.001 Chronic medical illness HT, n (%) 68(45)# 71(68)# * a 57(51)* 65(47) a 0.012 DM, n (%) 36(24) 33(31) 25(22) 32(23) 0.301 Adem Atici Acta Med Indones-Indones J Intern Med 180 CAD, n (%) 30(20)# 37(35)# * a 24(21)* 29(21) a 0.034 HLD, n (%) 38(25) 28(26) 31(28) 38(27) 0.875 Malignite, n (%) 9(6) 13(12) 9(8) 7(5) 0.203 COPD, n (%) 18(12)# 26(25)# * a 14(12)* 18(12) a 0.037 CKD, n (%) 15(10)# 24(23)# * a 14(12)* 16(11) a 0.039 Laboratory findings Haemoglobin(g/dl) 11.02.3 11.2±2.4 11.7 ± 1.8 11.5 ± 2.0 0.167 WBC (103 /μl) 8.0(5.0-14.0)# 9.3(7.0-19.7)# * a 8.3(5.1-13.1)* 8.3(5.9-13.0) a 0.009 Creatinine (mg/dl) 1.2(0.9-2.0)# 1.7(1.1-2.6)# * a 1.4(0.9-2.1)* 1.3(0.9-2.2) a 0.021 Sodium (mmol/L) 140.0±6.4 141.7±9.2 139.9 ± 9.3 141.4 ± 9.7 0.346 Potassium (mmol/L) 4.3±0.6 4.5±0.8 4.3 ± 0.8 4.3 ± 0.8 0.512 Glucose (mg/dL) 135(99-199) 141(105-205) 136(102-205) 149(112-237) 0.462 CRP (mg/dL) 110(80-165)# 131(111-185)# * a 114(89-171)* 113(70-172) a <0.001 hs-TnI (NR<14pg/ml) 30(13-44)# & @ 60(32-152)# * a 47(20-93)& * e 34(14-58) @ a e <0.001 D-dimer (ng/mL) 1460(757-2920)#&@ 3490(1395-4080)#*a 2525(1120-4100)&*e 1465(925-3655) @a e <0.001 Procalcitonin (ng/mL) 0.7(0.2-1.3)# & 1.8(0.4-11.7) # a 1.7(0.4-3.2) & e 0.9(0.3-2.7) a e 0.006 sO2 95.8±5.0# & 90.5±5.3# * a 92.9±5.1& * e 94.4±3.9 a e <0.001 Treatments ACEİ/ARB, n (%) 60(40) 50(48) 60(54) 58(42) 0.238 BB, n (%) 60(40) 51(49) 51(46) 52(38) 0.221 CCB, n (%) 38(25) 32(30) 35(31) 37(27) 0.665 ASA, n (%) 45(30) 37(35) 39(35) 38(27) 0.341 Statin, n (%) 38(25) 34(32) 32(29) 34(25) 0.421 OAD, n (%) 48(32) 36(34) 38(34) 41(30) 0.688 Steroid, n(%) 109(73)#&@ 40(39)#* a 60(55)&* 78(58)@ a <0.001 Tocilizumab, n(%) 24(16)#&@ 1(1)# 6(6)& 7(5)@ 0.033 IMV, n(%) 33(22)#& 72(70)#* a 39(36)&* 38(28) a 0.004 NIMV, n(%) 21(14)#&@ 28(27)#* a 58(53)&* 66(49)@ a <0.001 HFO, n(%) 37(25)#& 3(3)# a 12(11)& e 31(23) a e 0.007 Vasopressor, n(%) 24(16)#&@ 70(68)#* a 35(32)&* 40(30)@ a <0.001 RRT, n(%) 0(0)#&@ 27(26)# 20(19)& 28(21)@ 0.031 Organ Injury Cardiac injury, n (%) 33(22)# & @ 62(59)# * a 42(38)& * 44(32) @ a <0.001 MODS, n (%) 23(15)# 37(35)# * a 25(22)* 25(18) a 0.014 Acute kidney injury, n (%) 26(17)# 38(36)# * a 25(22)* 33(24) a 0.042 # P<0.05 Between surviver and ≤3 days groups, &P<0.05 Between surviver and 4-10 days groups, @P<0.05 Between surviver and >10 days groups, *P<0.05 Between ≤3 days and 4-10 days groups, ªP<0.05 between 3 days and >10 days groups, eP<0.05 between 4-10 days and >10 days groups. Abbreviations: BMI, body mass index; HR, heart rate; RR, respiratory rate; SAP, systolic arterial pressure; DAP, diastolic arterial pressure; CT, computed tomography; SDBHA, symptom duration before hospital admission; ICU, intensive care unit; HT, hypertension; DM, diabetes mellitus; CAD, coronary artery disease; HLD, hyperlipidemia; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; WBC, white blood cell, CRP, C-reactive protein; hs-TnI, high sensitive-Troponin I; NR, normal range; CK, creatinine kinase; sO2, oxygen saturation ; ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; BB, beta blocker; CCB, calcium channel blocker; ASA, acetylsalicylic acid; OAD, oral antidiabetic; IMV, invasive mechanical ventilation; NIMV, non-invasive mechanical ventilation; HFO, high-flow oxygen; RRT, renal replacement therapy; MODS, multiple organ dysfunction syndrome. The patients’ echocardiography and ECG parameters are shown in Table 2. The LVEF and tricuspid annular plane systolic excursion (TAPSE) values were statistically different among the study groups (p<0.001). Left ventricular diastolic functions were lower in non-surviving patients than in patients who survived, and it was lowest in patients who died within the first three days. Left atrium (LA), right ventricular diameter, RV-FAC, systolic pulmonary artery pressure (sPAP), and pericardial effusion values were significantly higher in patients who died three days compared to other patients (p<0.001). While the left ventricular end-diastolic diameter (LVEDD) was similar between study groups, left ventricular end-systolic diameter (LVESD) was significantly higher in patients who died within three days. While there was no statistically significant difference between the groups in terms of the Vol 54 • Number 2 • April 2022 Evaluation of COVID-19 Patients According to the Survival Time 181 Table 2. Comparison of Conventional Echocardiographic and Electrocardiographic Parameters of COVID-19 Patients. Variables Survive (n=150) Non-survive 3 days (n=104) Non-survive 4-10 days (n = 110) Non-survive >10 days (n = 136) p Left heart findings LVEF (%) 59.9±7.1# & 53.1±9.9# * a 57.3 ± 7.4& * e 59.6 ± 5.7 a e <0.001 LVEDD (mm) 44.9±3.5 45.7±4.1 44.6±3.4 44.7±3.4 0.091 LVESD (mm) 28.8±3.7# 30.7±4.0 # a 29.9 ± 3.9 28.9 ±3.3 a 0.013 LA (mm) 36.7±4.1# 42.3±4.5# * a 36.5±3.3* 37.3±5.1 a <0.001 E/A ratio 1.2±0.4#& @ 0.7±0.2# * a 0.9±0.3& * 1.0±0.4@ a <0.001 RV diamater(mm) 33.1±4.8#& @ 39.5±4.7# * a 36.5±4.1& * 36.1±4.4@ a <0.001 RV-FAC (%) 45.5±5.5# & 39.7±6.7# * a 42.9±5.3& * 43.9±4.8 a <0.001 TAPSE (mm) 21.4±3.4# & 18.2±3.2# * a 19.9±3.1& * e 21.5±3.0 a e <0.001 sPAP, mmHg 30.1±5.1# 34.8±7.8# * a 31.6±8.0* 30.6±7.9 a <0.001 ACP, n(%) 0(0) 7(7) 3(3) 3(2) 0.129 Pericardial effusion, n(%) 8(5)#& @ 31(30)# * a 16(17)& * 21(16)@ a 0.005 Sinus Rhythm, n (%) 139(93)# 82(78)# * a 97(88)* 125(91) a 0.008 HR, beats/min 78.9±12.7# & 91.3±12.3# * a 86.4±14.9& * e 81.3±12.0 a e <0.001 RBBB, n(%) 12(8) 16(15) 10(9) 10(7) 0.182 LBBB, n(%) 9(6) 11(10) 7(6) 6(4) 0.328 ST depression,, n(%) 30(20)# 48(46)# * a 31(28)* 30(22) a <0.001 fQRS, n(%) 18(12) 15(14) 19(17) 19(14) 0.716 QTc 428.9±22.1 432.4±26.3 429.2±22.0 430.5±21.3 0.394 # P<0.05 Between surviver and ≤3 days groups, &P<0.05 Between surviver and 4-10 days groups, @P<0.05 Between surviver and >10 days groups, *P<0.05 Between 3 days and 4-10 days groups, ªP<0.05 between 3 days and >10 days groups, eP<0.05 between 4-10 days and >10 days groups. Abbreviations: LVEF, left ventricular ejection fraction; LVEDD, left ventricular end diastolic diameter; LVESV, left ventricular end systolic diameter; LA, left atrial; RV-FAC, right ventricular fractional area change; TAPSE, tricuspid annular plane systolic excursion; sPAP, systolic pulmonary artery pressure; ACP, acute cor pulmonale; HR, heart rate; RBBB, right bundle branch block; LBBB, left bundle branch block; fQRS, fragmante QRS; QTc, corrected QT. frequency of acute corrected QT values, it was highest in patients who died within the first three days. In the electrocardiographic analysis, right bundle branch block (RBBB), left bundle branch block (LBBB), fQRS, and QTc values were similar among the study groups. However, HR, ST-depression, and non-sinus rhythm ratios were higher in patients who died within three days compared to other patients. Parameters affecting mortality were evaluated by univariate and multivariate analyzes using Cox regression analysis. Age, Pneumonia on CT, SDBHA, ICU, HT, CRP, d-dimer, cardiac injury, MODS, acute kidney injury, LVEF, RV-FAC, and Tocilizumab/Steroid parameters, which were statistically significant in the univariate analysis, were included in the multivariate analysis. These parameters were determined as independent predictors of mortality (Table 3). Table 4 shows the independent predictors of mortality within three days. First, a regression model was used to elicit mortality predictors in regression analyses. Age, gender, pneumonia on CT, SDBHA, ICU, HT, CAD, CRP, d-dimer, MODS, cardiac and acute kidney injury, LVEF, RV-FAC, and Tocilizumab/Steroid were included in the regression analyses. Gender and CAD were not independent predictors of mortality within three days. However, age, pneumonia on CT, SDBHA, ICU, HT, CRP, d-dimer, MODS, cardiac and acute kidney injury, LVEF, RV-FAC, and Tocilizumab/Steroid were independent predictors of mortality within three days compared to the 4–10 days and more than ten days mortality and the surviving patients. ROC curve analysis was used to evaluate the values for age, CT score, SDBHA, hs- TnI, and d-dimer to predict mortality within three days (Figure 1). Areas under the curve (AUC) for Age, CT score, SDBHA, hs-TnI, and d-dimer were determined (0.755 / 0.734 / 0.766 / 0.639 / 0.620, respectively). Table 5 shows the sensitivity, specificity, and cut-off values of age, CT score, SDBHA, hs-TnI, and d-dimer. The age, CT score, SDBHA, hs-TnI, and d-dimer were evaluated by binary logistic regression Adem Atici Acta Med Indones-Indones J Intern Med 182 analysis to determine the combined diagnosis model. Then the combined diagnosis model was analyzed by the ROC curve. In Figure 2, the red line represents the combined diagnosis model, and the AUC was 0.913. reproducibility CT score, and echocardiography and electrocardiography values of 20 patients were randomly selected to assess intra-observer and interobserver reliability. The intra-observer and interobserver variabilities for CT score were 0.93 and 0.90, respectively. The intra-observer and interobserver variabilities for echocardiography were 0.91 and 0.88, respectively, and the intra-observer and interobserver variabilities for electrocardiography were 0.94 and 0.91, respectively. DISCUSSION This study has investigated short- and long- term mortality predictors in surviving and non- surviving COVID-19 patients. First, we showed that age, pneumonia on CT, SDBHA, ICU admission, HT, CRP, d-dimer, MODS, cardiac and acute kidney injury, LVEF, RV-FAC and Tocilizumab/Steroid therapy were independent predictors of mortality within three days. Second, the AUC values of the age, CT score, SDBHA, hs-TnI, and d-dimer were statistically significant in showing mortality within three days. Finally, the combined diagnosis model had a strong predictive value for mortality within three days in COVID-19 patients who died. The rapid spread of COVID-19 infection worldwide has put the health systems in a difficult situation that has never been experienced before. The exact cause of patient death has not been fully elucidated against the hyperinflammatory reaction and hypercoagulopathy that is the p r i m a r y p a t h o p h y s i o l o g i c a l m e c h a n i s m of COVID-19.24,25 Unlike classical ARDS, COVID-19 ARDS is characterized by early pulmonary endothelial damage using Ang 2 and ICAM-1 pathological pathways.26 It is known that ICU patients have higher mortality rates than non-ICU patients (30–70%).27 Due to the high mortality rates in severe COVID-19 patients, many previous studies tried to find the best model for predicting mortality. As in our research, the data presented in the literature indicate that age was an independent predictor of mortality.12,28,29 A recent study comparing patients according to age group showed that mortality increased with age.30 Pulmonary infiltrates Table 3. Cox Regression Analysis on the Risk Factors Associated With Mortality in PatientsWith COVID-19. Variable Univariate Multivariate Hr 95%CI p Hr 95%CI p Age 2.295 1.488-5.142 <0.001 1.110 1.033-1.254 0.001 Gender 1.601 0.771-4.976 0.450 Pneumonia on CT 5.245 2.101-10.431 <0.001 6.513 2.266-12.765 <0.001 SDBHA 1.421 1.091-2.822 0.009 1.102 1.017-1.273 0.011 ICU 3.003 1.641-8.499 <0.001 4.653 1.989-9.762 <0.001 HT 1.932 1.081-4.989 0.002 2.010 1.256-5.665 0.008 CAD 1.210 0.991-1.909 0.231 CRP 3.141 1.754-8.249 <0.001 1.975 1.168-4.052 0.005 D-dimer 1.215 1.084-1.413 <0.001 1.022 1.006-1.049 0.003 Cardiac injury 3.165 1.622-8.555 <0.001 1.952 1.075-3.405 0.010 MODS 3.972 1.255-7.973 <0.001 3.080 1.753-7.231 <0.001 Acute kidney injury 1.563 1.107-3.882 <0.001 1.217 1.029-3.918 0.014 LVEF 0.894 0.710-0.994 <0.001 0.924 0.886-0.981 <0.001 RV-FAC 0.855 0.612-0.949 <0.001 0.875 0.811-0.951 <0.001 Tocilizumab/Steroid 0.377 0.218-0.689 <0.001 0.410 0.261-0.732 0.001 Abbreviations: CT, computed tomography; SDBHA, symptom duration before hospital admission; ICU, intensive care unit; HT, hypertension; CAD, coronary artery disease; CRP, C-reactive protein; MODS, multiple organ dysfunction syndrome; LVEF, left ventricular ejection fraction; RV-FAC, right ventricular fractional area change. Vol 54 • Number 2 • April 2022 Evaluation of COVID-19 Patients According to the Survival Time 183 Ta bl e 4. M ul tin om ia l L og is tic R eg re ss io n an al ys is o n th e ris k fa ct or s as so ci at ed w ith s ho rt- te rm m or ta lit y in p at ie nt s w ith C O V ID -1 9. S ur vi ve O r 95 % C I p Va ri ab le (4 -1 0 da ys ) O r O r 95 % C I p Va ri ab le (> 10 da ys ) O r 95 % C I p A ge 2. 11 3 1. 30 1- 3. 44 3 0. 00 9 A ge 1. 65 4 1. 65 4 1. 06 4- 2. 74 1 0. 01 7 A ge 1. 86 5 1. 09 4- 3. 36 2 0. 01 1 G en de r 1. 42 1 0. 82 4- 4. 43 2 0. 32 1 G en de r 1. 32 6 1. 32 6 0. 89 9- 4. 14 1 0. 34 1 G en de r 1. 53 2 0. 87 2- 4. 17 2 0. 51 2 P ne um on ia o n C T 7. 65 3 2. 53 4- 12 .8 56 <0 .0 01 P ne um on ia o n C T 3. 03 1 3. 03 1 1. 75 4- 6. 10 0. 00 1 P ne um on ia o n C T 6. 01 2 2. 21 0- 13 .9 78 <0 .0 01 S D B H A 1. 43 2 1. 14 1- 2. 46 5 0. 01 4 S D B H A 1. 23 1 1. 23 1 1. 09 2- 2. 87 6 0. 02 2 S D B H A 1. 30 2 1. 09 9- 1. 60 0 0. 01 8 IC U 3. 44 1 1. 58 0- 8. 74 5 <0 .0 01 IC U 3. 35 2 3. 35 2 1. 24 3- 8. 68 3 <0 .0 01 IC U 3. 21 2 1. 43 1- 8. 43 5 <0 .0 01 H T 1. 87 6 1. 05 3- 4. 12 6 0. 01 3 H T 1. 14 2 1. 14 2 1. 02 0- 2. 63 7 0. 02 0 H T 1. 21 2 1. 07 8- 4. 03 1 0. 01 6 C A D 1. 15 4 0. 85 3- 2. 79 8 0. 37 2 C A D 1. 02 1 1. 02 1 0. 98 4- 1. 07 2 0. 67 2 C A D 1. 14 2 0. 83 1- 3. 57 9 0. 59 7 C R P 1. 95 7 1. 06 9- 5. 13 2 0. 00 4 C R P 1. 47 4 1. 47 4 1. 09 1- 2. 98 2 0. 00 9 C R P 1. 53 1 1. 10 3- 2. 98 5 0. 00 7 D -d im er 1. 05 3 1. 01 1- 1. 16 3 <0 .0 01 D -d im er 1. 01 2 1. 01 2 1. 00 3- 1. 02 8 0. 00 3 D -d im er 1. 02 1 1. 00 6- 1. 03 9 0. 00 1 C ar di ac in ju ry 4. 76 5 1. 94 9- 11 .4 23 <0 .0 01 C ar di ac in ju ry 4. 23 1 4. 23 1 1. 46 3- 10 .8 56 <0 .0 01 C ar di ac in ju ry 4. 97 2 1. 34 2- 9. 18 7 <0 .0 01 M O D S 3. 96 5 1. 45 1- 8. 76 3 <0 .0 01 M O D S 3. 44 2 3. 44 2 1. 47 4- 7. 34 5 <0 .0 01 M O D S 3. 90 2 1. 79 2- 8. 94 5 <0 .0 01 A cu te k id ne y in ju ry 1. 72 1 1. 06 8- 4. 17 3 0. 01 2 A cu te k id ne y in ju ry 1. 45 1 1. 45 1 1. 14 3- 3. 37 3 0. 02 6 A cu te k id ne y in ju ry 1. 60 5 1. 10 1- 3. 86 9 0. 01 6 LV E F 0. 82 1 0. 71 3- 0. 95 1 <0 .0 01 LV E F 0. 91 2 0. 91 2 0. 88 7- 0. 97 2 0. 00 5 LV E F 0. 88 9 0. 79 8- 0. 97 3 <0 .0 01 R V- FA C 0. 81 7 0. 69 9- 0. 94 8 <0 .0 01 R V- FA C 0. 90 2 0. 90 2 0. 85 9- 0. 94 9 0. 00 1 R V- FA C 0. 87 3 0. 72 7- 0. 95 6 <0 .0 01 To ci liz um ab / S te ro id 0. 31 0 0. 19 8- 0. 63 2 <0 .0 01 To ci liz um ab / S te ro id 0. 40 9 0. 40 9 0. 23 8- 0. 82 5 <0 .0 01 To ci liz um ab / S te ro id 0. 36 9 0. 22 0- 0. 70 1 <0 .0 01 A bb re vi at io ns : C T, c om pu te d to m og ra ph y; S D B H A , s ym pt om d ur at io n be fo re h os pi ta l a dm is si on ; I C U , i nt en si ve c ar e un it; H T, h yp er te ns io n; C A D , c or on ar y ar te ry d is ea se ; C R P, C -r ea ct iv e pr ot ei n; M O D S , m ul tip le o rg an d ys fu nc tio n sy nd ro m e; L V E F, le ft ve nt ric ul ar e je ct io n fra ct io n; R V- FA C , r ig ht v en tri cu la r f ra ct io na l a re a ch an ge . Adem Atici Acta Med Indones-Indones J Intern Med 184 Table 5. Parameter Values Predicting Early Mortality as a Result of ROC Analysis in Patients with Death due to COVID-19. Variable AUC p 95%CI Sensitivity Specificity Cut-off value Age 0.755 <0.001 0.701-0.810 65 66 ³ 64.5 CT score 0.734 <0.001 0.678-797 74 60 ³ 3.5 SDBHA 0.766 <0.001 0.717-0.815 79 63 ³ 5.5 hs-TnI 0.639 <0.001 0.576-0.701 59 57 40.5 D-dimer 0.620 <0.001 0.560-0.681 61 61 ³ 2705 CDM 0.913 <0.001 0.883-0.942 84 80 ³ 0.25 Abbreviation: CT, computed tomography; SDBHA, symptom duration before hospital admission hs-TnI, high sensitive- Troponin I; CDM, Combined diagnosis model Figure 1. In ROC curve analyses, areas under the curve (AUC) for Age, computed tomography (CT) score, symptom duration before hospital admission (SDBHA), high sensitive-Troponin I (hs-TnI), and D-dimer were determined (0.755 / 0.734 / 0.766 / 0.639 / 0.620 respectively). Figure 2. The combined diagnosis model of the age, computed tomography (CT) score, symptom duration before hospital admission (SDBHA), high sensitive-Troponin I (hs-TnI), and D-dimer was analyzed by the ROC curve. The red line represents the combined diagnosis model, and the area under the curve (AUC) was 0.913. Vol 54 • Number 2 • April 2022 Evaluation of COVID-19 Patients According to the Survival Time 185 on CT are also an independent predictor of mortality over time. This study presented that COVID-19 patients with pulmonary infiltration have a poor prognosis, consistent with other literature reports.30,31 Unlike previous studies,11,32 we indicated SDBHA was an independent predictor of mortality. Possible mechanisms that affect SDBHA as an independent predictor were advanced disease due to delayed diagnosis and thrombotic complications. Given the importance of the early treatment of COVID-19, it seems logical that delayed hospital admissions are related to short-term mortality. The current study presented that ICU admission, HT, CRP, and d-dimer were short-term mortality predictors, which has been proven many times in previous studies.33 COVID-19 has adverse effects on the cardiovascular system, and the myocardial injury rate was 14%–19% in these patients.1,34 High platelet activation has been shown to correlate with disease severity, myocardial damage, and mortality.35 The current study showed that COVID-19 associated myocardial injury was an independent predictor of short- term mortality, consistent with the literature report.29,36,37 Therefore, it seems logical that decreased LVEF and RV-FAC values were independent predictors of short-term mortality in COVID-19 patients with cardiac injury. Barman et al. demonstrated that decreased LVEF and RV-FAC were associated with disease severity in COVID-19 patients.38 Similar to our study results, a previous investigation showed that decreased left and right ventricular function were related to mortality in COVID-19 patients.39 It is known that myocardial injury is associated with worse prognosis in COVID-19 patients.12,40 It seems that cardiac functions are affected by many mechanisms and mortality significantly increased in these patients. The mechanisms that affect cardiac functions, such as: (I) cytokine storm and multi-organ failure due to acute systemic inflammatory response, (II) an imbalance between myocardial oxygen supply and demand which secondary to severe hypoxia due to acute respiratory failure, (III) medications related to cardiotoxicity, (IIII) increased coronary thrombosis and embolic complications due to systemic inflammation, (V) the heart inflammation caused by COVID-19 can directly cause myocarditis. Considering these mechanisms, decreased left and right ventricular functions affect early mortality in COVID-19 patients. Moreover, in the regression analyses, we determined MODS was an independent predictor of short-term mortality. Our study results showed the COVID-19 adverse effect is not limited to lung injury but also renal insufficiency and cardiac injury.41,42 Clinicians should be aware of and manage the potential systemic complications of COVID-19, such as MODS. COVID-19 associated mortality predictors provide potential clinical benefit to improve characterization and comprehensive evaluation of these patients who have an inadequate response to conventional therapy. This study also determined that age, CT score, SDBHA, hs-TnI, and d-dimer were independently associated with short-term mortality in non-survived COVID-19 patients. Moreover, these parameters’ diagnostic value was compatible with previous studies.31,43-45 To determine the best-fitting model, we analyzed various variables in binary logistic regressions. Then we used a combined model to find the best predictor of short-term mortality in COVID-19 patients who died. The current study indicated the combined diagnosis model was a strong predictor of short-term mortality (AUC value 0.91 (95% CI, 0.88–0.94)). Because of the high mortality rate in critically ill COVID-19 patients (49%), it is crucial to identify patients with a bad prognosis in the early stages.46 Therefore, we assumed the combined diagnosis model might help physicians predict the prognosis of COVID-19 patients earlier and guide their treatment methods. Thus, severe COVID-19 patients can be monitored closely for mortality and might be treated in the early stages of the disease. Even though COVID-19 patients may have a good or poor clinical prognosis, the course of the disease is not entirely predictable. The current study was designed to partially fill this critical gap. Therefore, we have evaluated the effects of various clinical factors on mortality by days. The current study is unique and has specific strengths compared to previous studies. Adem Atici Acta Med Indones-Indones J Intern Med 186 COVID-19 patients who died were categorized according to their survival time rather than other factors used in earlier reports. Another advantage of our study is that the combined diagnosis model was created by clinical, laboratory, and imaging parameters. The combined model was a predictor of short-term mortality in non-surviving COVID-19 patients, which is a strength of our study compared with literature data. Another essential difference in our study is that we tried to find a more accurate definition of patients who died within the first 72 hours. If we can identify the acute phase, and then we can raise awareness to diagnose these patients earlier. On February 24, 2022, about 25 months since the first reported case of COVID-19 and after a global estimated 426 million cases and 5.8 million deaths was reported.47 On 25 November 2021, the world health organization listed Omicron as a new variant of concern. Omicron has some deletions and more than 30 mutations.48 Moreover, Omicron has 15 mutations in the receptor-binding domain of spike. These mutations are increased transmissibility, higher viral binding affinity, and higher antibody escape.49,50 The Omicron variant is more infectious than the previous variants.51 Also, an increased risk of reinfection related to Omicron.52 Omicron variant is related to lower risk of COVID-19 hospitalization.53 Vaccinated people have a much lower risk of severe disease from omicron infection. Cough, runny/stuffy nose, fatigue/lethargy, sore throat, headache, and fever were the most prevalent symptoms.54 The current COVID-19 vaccines associated with lower immunity to the omicron variant. Moreover, a new booster dose will increase the efficacy against omicron infection.55 By March 2021, thirteen vaccines have been authorized for use in many countries. These vaccines have been demonstrated to be effective in preventing the infection of COVID-19 at varying efficacy. COVID-19 vaccines have essentially focused on prevention of infection and hospitalizations.56,57 SARS-CoV-2 infection in vaccinated persons is expected to trigger memory antibody and cellular responses owing to prior vaccination; these immune responses could mitigate disease progression, possibly preventing life-threatening organ failure and death.58,59 Tenforde et al. evaluated the association between vaccination and COVID-19 hospitalization and disease severity. They presented that COVID-19 hospitalization was strongly associated with lower likelihood of vaccination for previous variants. And vaccinated cases less commonly received invasive mechanical ventilation. Moreover, COVID-19 hospitalization was strongly related to a lower likelihood of vaccination. Among patients hospitalized with COVID-19, the outcome of death or invasive mechanical ventilation was associated with a lower likelihood of vaccination.60 We have designed our research in March 2020 and January 2021. And our patients had not got omicron variant at that time. We know that patients with omicron have lower hospitality and mortality. Also, our patients were not vaccinated, so they have higher mortality rates than vaccinated patients. This study has limitations, including the retrospective study design, and the number of patients was relatively low. Another limitation is that we did not include the complaints of the patients on admission. A subgroup analysis of MODS was not performed due to the limited number of patients. Also, the study’s design did not allow the accurate retrieval of data to include underlying diseases, potentially up or down- scoring the net effect of each comorbidity. As criteria for hospitalization of COVID–19 patients are different across different institutions, an inclusion bias cannot be excluded. Finally, as this is an observational study, residual confounding may exist. CONCLUSION In conclusion, this study discovered that age, pneumonia on CT, SDBHA, ICU, HT, CRP, d-dimer, cardiac injury, MODS, acute kidney injury, LVEF, and RV-FAC were all independently associated with short-term mortality in COVID-19 patients in the Turkish population. Moreover, Tocilizumab/Steroid therapy was a protective and independent predictor of mortality within three days. The combined diagnosis model was a strong predictor of short-term mortality in non- Vol 54 • Number 2 • April 2022 Evaluation of COVID-19 Patients According to the Survival Time 187 surviving COVID-19 patients. Because of the increased mortality risk in severe COVID-19 patients, it is essential to identify poor prognosis markers at an early stage. More prospective randomized studies are needed to confirm our findings. COMPETINg INTErESTS All authors have no declarations of interest to report. FUNDINg This study received no grant from any funding agency in the public, commercial or not-for-profit sectors. AuThorS’ ConTribuTionS Atici A, Asoglu R, Barman HA and Aciksari G contributed to the conception and design of the study; Baycan OF, Tatlisu MA and Ozcan FB collected data; Atici A, and Yilmaz Y analysed the data; Atici A, and Caliskan M wrote and revised the manuscript. 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