The Role of Procalcitonin in Predicting Risk of Mechanical Ventilation and Mortality among moderate to Severe COVID-19 Patients

The Role of Procalcitonin in Predicting Risk of Mechanical Ventilation and Mortality among moderate to Severe COVID-19 Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of Procalcitonin in Predicting Risk of Mechanical Ventilation and Mortality among moderate to Severe COVID-19 Patients Cher Wei Twe, Delton Khoo, Kian Boon Law, Nur Sabreena Ahmad Nordin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1026293/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2022 Read the published version in BMC Infectious Diseases → Version 1 posted 14 You are reading this latest preprint version Abstract Background Serum procalcitonin (PCT) has become an emerging prognostic biomarker of disease progression in patients with COVID-19. This study aims to determine the optimal cut-off value of PCT with regards to important clinical outcomes, especially for mechanical ventilation and all-cause mortality among moderate to severe COVID-19 patients in Malaysia. Methods A total of 319 moderate to severe COVID-19 patients hospitalized at the National Referral Hospital in December 2020 were included in the study retrospectively. Demographics, comorbidities, the severity of COVID-19 infection, laboratory and imaging findings, and treatment given were collected from the hospital information system for analysis. The optimal cut-point values for PCT were estimated in two levels. The first level involved 276 patients who had their PCT measured within 5 days following their admission. The second level involved 237 patients who had their PCT measured within 3 days following their admission. Further, a propensity score matching analysis was performed to determine the adjusted relative risk of patients with regards to various clinical outcomes according to the selected cut-point among 237 patients who had their PCT measured within 3 days. Results The results showed that a PCT level of 0.2 ng/mL was the optimal cut-point for prognosis especially for mortality outcome and the need for mechanical ventilation. Before matching, patients with PCT ≥ 0.2ng/mL were associated with significantly higher odds in all investigated outcomes. After matching, patients with PCT > 0.2 ng/mL were associated with higher odds in all-cause mortality (OR: 4.629, 95% CI: 1.387 – 15.449, p = 0.0127) and non-invasive ventilation (OR: 2.667, 95% CI: 1.039 – 6.847, p = 0.0415). Furthermore, patients with higher PCT were associated with significantly longer days of mechanical ventilation (p = 0.0213). There was however no association between higher PCT level and the need for mechanical ventilation (OR: 2.010, 95% CI: 0.828 - 4.878, p = 0.1229). Conclusion Our study indicates that a rise in PCT above 0.2ng/ml is associated with an elevated risk in all-cause mortality, the need for non-invasive ventilation, and a longer duration of mechanical ventilation. The study offers concrete evidence for PCT to be used as a prognostication marker among moderate to severe COVID-19 patients. Infectious Diseases COVID-19 SARS-CoV-2 risk factor procalcitonin mechanical ventilation mortality Figures Figure 1 Background Since the outbreak of coronavirus disease 2019 (COVID-19) in December 2019 in Wuhan, China, the COVID-19 has rapidly spread across the world and was declared as a “Public Health Emergency of International Concern” on 30th January 2020. Then, it emerged as an unanticipated threat to global health and led the World Health Organisation (WHO) to further declare COVID-19 as a pandemic on 11th March 2020 1 . As of 18th September 2021, there are more than 226 million confirmed COVID-19 cases with 4.7 million deaths reported globally. Malaysia has reported 2,049,750 cases with 22,355 deaths on the same day 2 . The clinical spectrum of COVID-19 ranges from asymptomatic to symptomatic severe disease with multiorgan involvement and failure requiring intensive care (ICU) admission and mechanical ventilation 3 . The broad clinical spectrum and highly variable clinical course among different COVID-19 patients have introduced great challenges in predicting the disease progression and outcome 4 . Laboratory biomarkers have always been very useful in day-to-day clinical practice to guide disease management and treatment decisions, especially in the management of infectious diseases like the COVID-19. Hence, ever since the start of the outbreak, the use of laboratory biomarkers including C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), and procalcitonin (PCT) to predict disease progression and severity have been explored and investigated extensively 1 , 5 . Procalcitonin (PCT) is a 116-amino-acid peptide that shares a common molecular structure with the prohormone of calcitonin. It was first discovered in humans in 1981 by J.Allison et al but its clinical use was never really explored until 1993 when M.Assicot et al first suggested a positive association between elevated serum PCT and bacterial infection and sepsis 6 , 7 . In today’s clinical practice, serum PCT has been increasingly instrumental in antibiotic stewardship and the diagnosis and management of sepsis due to bacterial infection 8 . Since the beginning of the COVID-19 pandemic, multiple observational studies and meta-analyses have been done to look into the utility of serum PCT level as a biomarker of clinical deterioration among COVID-19 patients and have shown encouraging results with various optimal PCT cut-points suggested 9 – 11 . A single-center retrospective study done in Wuhan, China showed that a serum PCT level of more than 0.2ng/ml was found in severe and critical COVID-19 patients 12 . Another retrospective study of 1099 patients reported that a PCT of more than 0.5ng/dL was associated with increased severity of COVID-19 infection 13 . Theoretically, PCT, an acute phase peptide, is released in response to proinflammatory cytokines like IL-1β, IL-6, and TNF-α typically induced by bacterial infection and are not seen in viral infections 14 . However, it was found that these pro-inflammatory cytokines are also raised in COVID-19 infection, especially among severe diseases, which makes serum PCT a promising biomarker of clinical deterioration among COVID-19 patients 15 . This study aims to investigate the optimal cut-point of PCT and its relationship with regards to various clinical outcomes, especially in all-cause mortality and the need for mechanical ventilation among moderate to severe COVID-19 patients requiring hospitalization and treatment. Methods Study design and setting A total of 319 patients who were diagnosed with moderate to severe COVID-19 pneumonia and admitted to Sungai Buloh Hospital in December 2020 were included and studied retrospectively. Demographic information, including age, gender, race, concomitant medical illnesses, the severity of COVID-19 infection, laboratory and imaging findings, and treatment given were retrieved from the hospital information system for analysis. The study included category 4 and 5 adult patients above 18 years old with a diagnosis of COVID-19 confirmed via polymerase chain reaction (PCR) test (Table 1 ). Patients were excluded if no reported PCT values. The majority of patients (87.1%) had their PCT levels measured within 5 days following their admission. As PCT levels may reduce over time after receiving treatment, we included patients with their PCT levels measured within 5 days and 3 days for the analysis of the optimal cut-point. Hence, the analysis of the optimal cut-point for PCT was performed in two levels. The first level involved 276 patients with their PCT levels measured within 5 days following admission and the second level involved 237 patients with their PCT levels measured within 3 days following admission. Besides, the study excluded patients with extremely high PCT levels (>100 ng/mL) to avoid overly skewed findings. Clinical outcomes included in the analysis for the optimal PCT cut-point were all-cause mortality, mechanical ventilation, the occurrence of thrombotic events, ICU admission, and bacterial infection. Figure 1 summarizes the patient recruitment process. In the study, COVID-19 infection was classified according to the Malaysian Clinical Practice Guideline for Management of COVID-19 3 . The guideline classifies COVID-19 severity into five clinical stages as shown in Table 1 . Stage 1 and 2 consist of patients with asymptomatic infection or symptomatic infection with no pneumonia. Patients in these two categories are considered to have mild disease. Patients with stage 3 disease have lung involvement or pneumonia, but have not experienced hypoxemia, while stage 4 and 5 consist of critically ill patients requiring oxygen support and often intensive care. Table 1 Classification of COVID-19 infection severity Clinical Stage Severity Description 1 Mild Asymptomatic - Only RT-PCR test positive 2 Symptomatic, but no pneumonia - Upper respiratory tract (URT) symptoms (e.g., pharyngeal congestion, sore throat, cough, or fever) - Other symptoms, like vomiting, diarrhea, abdominal pain, myalgia, loss of smell/taste 3 Moderate Symptomatic, pneumonia, but no hypoxemia 4 Severe Symptomatic, pneumonia, requiring supplemental oxygen OR New requirement of supplemental oxygen or increased requirement from baseline without need for non-invasive or invasive ventilation 5 Critical Critically Ill with multiorgan involvement OR New or increased need for non-invasive or invasive ventilation, including high flow nasal cannula Analysis Of Pct In Sungai Buloh Hospital, serum PCT was measured by SIEMENS ATELLICA IMMUNOASSAY (IM). Atellica IM BRAHMS PCT assay is a 2-site sandwich immunoassay using direct chemiluminescent technology that uses 3 mouse monoclonal antibodies specific for PCT. The first antibody, in the Lite Reagent, is a mouse monoclonal anti‑PCT antibody labeled with acridinium ester. The second and third antibodies, in the Ancillary Reagent, are mouse monoclonal anti‑PCT antibodies labeled with fluorescein. The immunocomplex formed with PCT is captured with mouse monoclonal anti-fluorescein antibody coupled to paramagnetic particles in the Solid Phase. This 18-minute sandwich immunoassay with a measuring range of 0.03 to 50.00 ng/mL, is aligned to the B•R•A•H•M•S PCT sensitive KRYPTOR® assay 16 . Statistical analysis Demographics and clinical characteristics were summarized in Table 2 for overall 319 patients, 276 and 237 patients who had their PCT measured within 5 days and 3 days following their admission. The optimal PCT cut-points were estimated using the receiver operative characteristic (ROC) method in two levels. In the first level, we included 276 patients who had their PCT levels measured within 5 days following their admission. In the second level, we included 237 patients who had their PCT levels measured within 3 days following their admission. The optimal cut-point was determined with priority on all-cause mortality and the need for mechanical ventilation. In further analysis, patients with their PCT levels measured within 3 days following admission were divided into two groups according to the selected cut-point. Patients with PCT levels above the cut-point were assumed to have a higher risk for inferior clinical outcomes, and vice versa. We compared and tested the differences of demographics, clinical characteristics, baseline laboratory findings, and co-morbidity profile between the two groups to identify covariates for adjustment in the propensity score matching analysis. The odds ratio (OR) was calculated and reported for various clinical outcomes with regards to the selected cut-point for PCT before and after the matching analysis. Independent t-test and Mann-Whitney U test were used to test differences of continuous variables between two groups, while Chi-squared test and Fisher’s exact test were used to assess categorical variables. All statistical tests were performed at two-sided 5% significance level. In propensity score matching analysis, patients were matched for covariates that were statistically different. Baseline laboratory parameters and vital signs were not included in the matching analysis as they were not inherited but clinical manifestations of COVID-19 infection or complications. Propensity score matching was done based on the nearest-neighbor method within a caliper width equal to 0.25 times the SD of the logit of the calculated propensity score. All analyses were performed in IBM SPSS version 26.0 for Windows and R version 4.1.0 with packages such as “cutpointr” and “MatchIt” 17 – 19 . Results Demographics, clinical characteristics, and clinical outcomes of patients were summarized in Table 2 according to the overall cohort, first and second level cohorts. Even with the exclusion of patients due to delayed measurement of PCT, first and second level cohorts were still similar to the overall cohort in terms of clinical characteristics, comorbidities profile, and clinical outcomes. Table 2 Demographics and clinical characteristics of the study cohort. Characteristics Overall n (%) First level n (%) Second level n (%) Total 319 (100.0) 276 (100.0) 237 (100.0) Age, years: Mean (SD) 56 (13.6) 54.8 (13.7) 55.0 (13.7) Gender: Female 111 (34.8) 102 (37.0) 84 (35.4) Race Malay 168 (52.7) 147 (53.3) 122 (51.5) Chinese 68 (21.3) 54 (19.6) 46 (19.4) Indian 52 (16.3) 48 (17.4) 44 (18.6) Others 31 (9.7) 27 (9.8) 25 (10.5) Comorbidities profile Hypertension: Yes 179 (56.1) 158 (57.2) 135 (57.0) Chronic Cardiac Disease: Yes 64 (20.1) 54 (19.6) 46 (19.4) Chronic Pulmonary Disease: Yes 10 (3.1) 8 (2.9) 7 (3.0) Asthma: Yes 14 (4.4) 13 (4.7) 10 (4.2) Diabetes Mellitus: Yes 147 (46.1) 128 (46.4) 110 (46.4) Pre-existing Renal Disease: Yes 73 (22.9) 56 (20.3) 46 (19.4) Chronic Liver Disease: Yes 3 (0.9) 2 (0.7) 2 (0.8) Dementia: Yes 3 (0.9) 3 (1.1) 2 (0.8) Chronic Neurological Conditions: Yes 16 (5.0) 13 (4.7) 10 (4.2) Connective Tissue Disease: Yes 7 (2.2) 7 (2.5) 5 (2.1) HIV/AIDS: Yes 2 (0.6) 1 (0.4) 1 (0.4) Malignancy: Yes 11 (3.4) 9 (3.3) 8 (3.4) Current Smoking: Yes 9 (2.8) 5 (1.8) 4 (1.7) Obesity: Yes 17 (5.3) 17 (6.2) 16 (6.8) Others: Yes 81 (25.4) 67 (24.3) 51 (21.5) COVID-19 severity when procalcitonin first sent Category 4 254 (79.6) 217 (78.6) 181 (76.4) Category 5 65 (20.4) 59 (21.4) 56 (23.6) 4C mortality score 0-3 (low-risk in-hospital mortality) 16 (5.0) 15 (5.4) 13 (5.5) 4-8 (intermediate-risk in-hospital mortality) 117 (36.7) 105 (38.0) 87 (36.7) 9-14 (high-risk in-hospital mortality) 145 (45.5) 124 (44.9) 107 (45.1) 15-21 (very high-risk in-hospital mortality) 41 (12.8) 32 (11.6) 30 (12.7) Steroid use: Yes 297 (93.1) 257 (93.1) 219 (92.4) Bloodstream infection: Yes 35 (11.0) 28 (13.0) 25 (13.4) Clinical outcomes ICU admission: Yes 143 (44.8) 129 (46.7) 120 (50.6) NIV use: Yes 65 (20.4) 60 (21.7) 57 (24.1) Duration of NIV use, days, median (IQR) 3 (1.8, 5.3) 3 (1.8, 5.3 ) 3 (1, 5) Mechanical ventilation: Yes 101 (31.7) 89 (32.2) 85 (35.9) Duration of mechanical ventilation use, days, median (IQR) 6 (4, 12) 7 (4, 12) 7 (4, 12) Organizing pneumonia Yes 135 (42.3) 118 (42.8) 102 (43.0) No 26 (8.2) 21 (7.6) 19 (8.0) CT scan not done 158 (49.5) 137 (49.6) 116 (48.9) Thrombotic event: Yes 83 (26.0) 69 (25.0) 62 (26.2) All-cause mortality: Yes 71 (22.3) 59 (21.4) 55 (23.2) Due to severe COVID-19 pneumonia 48 (67.6) 43 (72.9) 39 (70.9) Due to thrombotic event 8 (11.3) 7 (16.3) 7 (17.9) Due to comorbid 7 (9.8) 4 (9.3) 4 (10.3) Due to bacterial infections 8 (11.3) 5 (11.6) 5 (12.8) Bacteremia 3 (4.2) 1 (2.3) 1 (2.6) Intra-abdominal infection 1 (1.4) 0 (0) 0 (0) Necrotizing fasciitis 1 (1.4) 1 (2.3) 1 (2.6) Non-specified site 1 (1.4) 0 (0) 0 (0) Non-bacteremia 5 (7.0) 4 (9.3) 4 (10.3) Pulmonary infection 2 (2.8) 1 (2.3) 1 (2.6) Intra-abdominal infection 1 (1.4) 1 (2.3) 1 (2.6) Non-specified site 2 (2.8) 2 (4.7) 2 (5.1) Column percentages are reported in parenthesis for categorical variables; mean and standard deviation are reported for continuous variable age; median and interquartile range are reported for continuous variables duration of non-invasive ventilation and mechanical ventilation. Abbreviations: CT, computed tomography; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; ICU; intensive care unit; NIV, non-invasive ventilation. Determination of the optimal cut-point In the determination of optimal cut-points for PCT, we included clinical outcomes, such as all-cause mortality, mechanical ventilation, thrombotic events, ICU admissions, and bacterial infection among moderate to severe COVID-19 patients. Table 3 and Table 4 present various performance indexes and optimal cut-points for PCT with regards to the aforementioned clinical outcomes based on PCT levels measured within 5 days and 3 days following admission. For PCT levels measured within 5 days following admission, the optimal cut-points for PCT were found to be 0.20 ng/mL for all-cause mortality, 0.33 ng/mL for mechanical ventilation, 1.21 ng/mL for thrombotic events, 0.09 ng/mL for ICU admission dan 1.2 ng/mL for bacterial infection (Table 3). Highest sensitivity was found in all-cause mortality (0.8136) for PCT cut-point of 0.20 ng/mL. For PCT levels measured within 3 days following admission, the optimal cut-points for PCT were found to be 0.20 ng/mL for all-cause mortality, 0.21 ng/mL for mechanical ventilation, 1.21 ng/mL for thrombotic events, 0.07 ng/mL for ICU admission dan 1.25 ng/mL for bacterial infection (Table 4). Highest sensitivity was also found in all-cause mortality (0.8364) for PCT cut-point of 0.20 ng/mL. As 0.2 ng/mL appeared to be the optimal cut-point in all-cause mortality for PCT levels measured within 5 days and 3 days following admission, and in mechanical ventilation for PCT levels measured within 3 days following admission, therefore, 0.2 ng/mL was selected as the final PCT cut-point in the matching analysis using propensity scores. The PCT of 0.2 ng/mL represented the optimal cut-point for the two clinically important outcomes, all-cause mortality and mechanical ventilation, especially for moderate to severe COVID-19 patients with PCT measured within 3 days following admission. Table 3 Optimal cut-off PCT values (within 5 days from admission) Cut-point and Indexes All-cause mortality Mechanical ventilation Thrombotic events ICU admission Bacterial infection culture Number of patients 276 276 276 276 216 Positive cases 59 89 69 129 28 Negative cases 217 187 207 147 188 AUC 0.7741 0.7773 0.6666 0.7223 0.6444 Optimal cut-point (ng/ml) 0.20 0.33 1.21 0.09 1.2 Accuracy 0.6703 0.7391 0.7536 0.6848 0.7454 Sensitivity 0.8136 0.6742 0.4203 0.7442 0.5357 Specificity 0.6313 0.7701 0.8647 0.6327 0.7766 Precision 0.3750 0.5825 0.5088 0.6400 0.2632 TP 48 60 29 96 15 FN 11 29 40 33 13 FP 54 43 28 43 42 TN 93 144 179 144 146 TP: True positive; FN: False negative; FP: False positive; TN: True negative; AUC: Area under Curve Table 4 Optimal cut-off PCT values (within 3 days from admission) Cut-point and Indexes All-cause mortality Mechanical ventilation Thrombotic events ICU admission Bacterial infection culture Number of patients 237 237 237 237 186 Positive cases 55 85 62 120 25 Negative cases 182 152 175 117 161 AUC 0.7812 0.7676 0.6711 0.7365 0.6465 Optimal cut-point (ng/ml) 0.20 0.21 1.21 0.07 1.25 Accuracy 0.6667 0.7046 0.7468 0.7046 0.7419 Sensitivity 0.8364 0.7528 0.4355 0.8333 0.5600 Specificity 0.6154 0.6776 0.8571 0.5726 0.7702 Precision 0.3966 0.5663 0.5192 0.6667 0.2745 TP 46 64 27 100 14 FN 9 21 35 20 11 FP 70 49 25 50 37 TN 112 103 150 67 124 TP: True positive; FN: False negative; FP: False positive; TN: True negative; AUC: Area under Curve Propensity scores matching analysis To assess the prognostic value of the optimal cut-point, the analysis focused on those who had their PCT levels measured within 3 days following admission. Moreover, the selected cut-point of 0.2 ng/mL was optimal for all-cause mortality and mechanical ventilation among those with PCT levels measured within 3 days following admission. Table 5 summarizes the covariate differences according to the selected optimal cut-point for PCT among 237 patients. Significant differences were identified for age (p=0.0122), COVID-19 disease stage (p<0.0001), days of illness before admission (p=0.0036), SPO 2 under room temperature (RA) (p=0.0005), Glasgow Coma Scale (GCS) (p<0.0001), urea (p<0.0001), C-reactive protein (CRP) (p<0.0001), white blood cells count (WBC) (p=0.0003), absolute neutrophil count (ANC) (p<0.0001), number of comorbidities (p<0.0001), presence of hypertension (p=0.0007), chronic cardiac diseases (p=0.0018), diabetes mellitus (DM) (p=0.0036), pre-existing renal disease (p<0.0001) and malignancy (p=0.0327). Table 6 presents the odds ratio (OR) with regards to various clinical outcomes based on the optimal cut-point of 0.2 ng/mL for PCT. Prior to matching, our analysis found that patients with PCT level above 0.2 ng/mL were associated with significantly higher risk in all-cause mortality (OR: 8.178, 95%CI: 3.770 - 17.738, p<0.0001), mechanical ventilation (OR: 5.861, 95%CI: 3.229 – 10.637, p<0.0001), non-invasive ventilation (OR: 2.898, 95%CI: 1.541 – 5.541, p=0.0010), ICU admission (OR: 4.166, 95%CI: 2.422 – 7.166, p<0.0001) and thrombotic events (OR: 2.158, 95%CI: 1.190 – 3.915, p=0.0013). Besides, patients with PCT levels above 0.2 ng/mL also experienced significantly longer days of mechanical ventilation (p<0.0106) and length of hospital stay (p<0.0001). Table 7 shows the mortality and causes of death before propensity score matching. Table 5 Demographics, clinical characteristics & baseline laboratory findings, and comorbidity profile of COVID-19 patients according to procalcitonin level. Variables Category PCT < 0.2 (ng/ml) PCT ≥ 0.2 (ng/ml) p-value (n=121) (n=116) Demographics Gender Female 42 (34.7) 42 (36.2) 0.8097 Age (years) Mean (SD) 52.8 (12.6) 57.3 (14.4) 0.0122 Age groups (years) < 50 45 (37.2) 33 (28.4) 0.0945 50 – 59 38 (31.4) 28 (24.1) 60 – 60 23 (19.0) 34 (29.3) ≥ 70 15 (12.4) 21 (18.1) Ethnicity Malay 60 (62.3) 62 (53.4) 0.0675 Chinese 22 (18.2) 24 (20.7) Indian 20 (16.5) 24 (20.7) Others 19 (15.7) 6 (5.2) Clinical characteristics & baseline laboratory Severity of disease Category 4 111 (91.7) 70 (60.3) < 0.0001 Category 5 10 (8.3) 46 (39.7) Days of illness Mean (SD) 6.5 (3.0) 5.3 (3.1) 0.0036 SPO2 under RA (%) < 92% 98 (81.0) 111 (95.7) 0.0005 ≥ 92% 23 (19.0) 5 (4.3) RR, bpm < 20 13 (10.7) 14 (12.1) 0.2520 20 – 29 93 (76.9) 79 (68.1) ≥ 30 15 (12.4) 23 (19.8) GCS <15 11 (9.1) 50 (43.1) <0.0001 15 110 (90.9) 66 (56.9) Urea, mmol/dL < 7 87 (71.9) 31 (26.7) 14 5 (4.1) 54 (46.6) CRP, mg/dL < 5 49 (40.5) 8 (6.9) <0.0001 5 – 9.9 31 (25.6) 69 (59.5) ≥ 10 41 (33.9) 39 (33.6) WBC (x10^12/L) Mean (SD) 8.56 (3.88) 10.90 (5.80) 0.0003 ANC (x10^9/L) Mean (SD) 6.75 (3.81) 9.26 (5.53) <0.0001 Comorbidity profile No. of comorbidity 0 67 (55.4) 33 (28.4) <0.0001 1 42 (34.7) 33 (28.4) ≥ 2 12 (9.9) 50 (43.1) Hypertension Yes 56 (46.3) 79 (68.1) 0.0007 Chronic cardiac disease (excluding HPT) Yes 14 (11.6) 32 (27.6) 0.0018 Asthma Yes 6 (5.0) 4 (3.4) 0.7491 Chronic pulmonary disease (excluding asthma) Yes 3 (2.5) 4 (3.4) 0.7174 Diabetes Mellitus Yes 45 (37.2) 65 (56.0) 0.0036 Pre-existing renal disease Yes 5 (4.1) 41 (35.3) <0.0001 Stage 2 2 (40.0) 0 Stage 3 0 6(14.6) Stage 4 2 (40.0) 8 (19.5) Stage 5 1 (20.0) 27 (65.9) HIV/AIDS Yes 1 0 n.a Malignancy Yes 1 (0.8) 7 (6.0) 0.0327 Smoking Yes, current 1 (0.8) 3 (2.6) n.a Obesity (> 30kg/m 2 ) Yes 7 (5.8) 9 (7.8) 0.5449 Chronic liver disease Yes 0 2 n.a Dementia Yes 0 2 n.a Chronic neurological disease Yes 2 (1.7) 8 (6.9) 0.0555 Connective tissue disease Yes 2 (1.7) 3 (2.6) 0.6783 Abbreviations: ANC, absolute neutrophil count; CRP, C-reactive protein; GCS, Glasgow coma scale; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; PCT, procalcitonin; RR, respiratory rate; RA, room air; SD, standard deviation; WBC, white blood cell count. Table 6 Clinical outcomes before propensity score matching. Clinical outcomes Category PCT level (ng/ml) OR (95% CI) PCT < 0.2 (n=121) PCT ≥ 0.2 (n=116) p-value Procalcitonin level Mean (SD) 0.0674 (0.0331) 6.002 (11.556) n.a <0.0001 All-cause mortality Yes 9 (7.4) 46 (39.7) 8.178 (3.770 – 17.738) <0.0001 No 112 (92.6) 70 (60.3) Mechanical ventilation Yes 21 (17.4) 64 (55.2) 5.861 (3.229 – 10.637) <0.0001 No 100 (82.6) 52 (44.8) Non-invasive ventilation Yes 18 (14.9) 39 (33.6) 2.898 (1.541 – 5.541) 0.0010 No 103 (85.1) 77 (66.4) ICU admission Yes 41 (33.9) 79 (68.1) 4.166 (2.422 – 7.166) <0.0001 No 80 (66.1) 37 (31.9) Thrombotic events Yes 23 (19.0) 39 (33.6) 2.158 (1.190 – 3.915) 0.0113 No 98 (81.0) 77 (66.4) Positive culture Yes 68 (89.5) 93 (84.5) 0.643 (0.263 – 1.578) 0.3354 No 8 (10.5) 17 (15.5) Days of mechanical ventilation Median (IQR) 5 (4 – 6) 9 (5 – 12.3) n.a 0.0106 Days of non-invasive ventilation Median (IQR) 3 (2 - 4) 3 (1 - 6) 0.8071 Length of hospital stay, days Median (IQR) 11 (9 – 14) 16 (11.3 – 20.8) n.a <0.0001 Abbreviations: CI, confidence interval; ICU; intensive care unit; IQR, interquartile range; MV, mechanical ventilation; NIV, non-invasive ventilation; OR, odds ratio; PCT, procalcitonin. Table 7 Mortality and causes of death before propensity score matching Mortality outcomes Cases (%) Total number of deaths 55 Cause of death Severe COVID-19 pneumonia 39 (70.9) Bacterial infection 5 (9.1) Bacteremia 1 Non-bacteremia 4 Thrombotic event 7 (12.7) Comorbidity 4 (7.3) As the relative risk of patients categorized based on the optimal cut-point could be confounded by covariates that were significantly different, a propensity score matching was performed with the aforementioned covariates to derive two comparable arms for further analysis. The propensity score matching eventually identified 90 patients with balanced demographics, clinical characteristics, comorbidities profile, and laboratory findings except for urea (p=0.0139) and CRP (p<0.0001) as shown in Table 8 . Subsequent analyses showed that patients with PCT above 0.2 ng/mL were associated with a significantly higher risk in all-cause mortality (OR: 4.629, 95%CI: 1.387 – 15.449, p=0.0127), and marginally higher risk for non-invasive ventilation (OR: 2.667, 95% CI: 1.039 – 6.847, p=0.0415). Besides, patients with PCT above 0.2 ng/mL tended to experience longer days of mechanical ventilation (p=0.0213) as summarized in Table 9 . There was no significant association between PCT level and risk of mechanical ventilation (OR; 2.010, 95% CI; 0.828 - 4.878, p = 0.1229) after being matched for covariate differences. Table 8 Demographics, clinical characteristics & baseline laboratory findings and comorbidity profile of COVID-19 patients according to procalcitonin level after propensity score matching Variables Category PCT < 0.2 PCT ≥ 0.2 p-value (n=45) (n=45) Demographics Gender Female 17 (37.8) 17 (37.8) 1.0000 Age Mean (SD) 55.1 (11.2) 53.4 (13.1) 0.5070 Age groups < 50 13 (28.9) 16 (35.6) 0.7838 50 – 59 14 (31.1) 15 (33.3) 60 – 60 13 (28.9) 9 (20.0) ≥ 70 5 (11.1) 5 (11.1) Ethnicity Malay 22 (48.9) 27 (60.0) 0.6236 Chinese 9 (20.0) 7 (15.6) Indian 8 (17.8) 8 (17.8) Others 6 (13.3) 3 (6.7) Clinical characteristics & baseline laboratory Severity of disease Category 4 35 (77.8) 37 (82.2) 0.5982 Category 5 10 (22.2) 8 (17.8) Days of illness Mean (SD) 5.7 (2.8) 5.6 (3.0) 0.8852 SPO2 under RA (%) < 92% 38 (84.4) 42 (93.3) 0.1797 ≥ 92% 7 (15.6) 3 (6.7) RR, bpm < 20 5 (11.1) 4 (8.9) 20 – 29 33 (73.3) 31 (68.9) ≥ 30 7 (15.6) 10 (22.2) GCS <15 9 (20.0) 11 (24.4) 0.6121 15 36 (80.0) 34 (75.6) Urea, mmol/dL 14 4 (8.9) 15 (33.3) CRP, mg/dL < 5 17 (37.8) 1 (2.2) < 0.0001 5 – 9.9 13 (28.9) 15 (33.3) ≥ 10 15 (33.3) 29 (64.4) WBC (x10^12/L) Mean (SD) 9.79 (4.26) 10.69 (6.09) 0.4148 ANC (x10^9/L) Mean (SD) 7.89 (4.32) 8.74 (5.82) 0.4328 Comorbidity profile No. of comorbidity 0 15 (33.3) 15 (33.3) 0.9654 1 19 (42.2) 18 (40.0) ≥ 2 11 (24.4) 12 (26.7) Hypertension Yes 24 (53.3) 27(60.0) 0.5234 Chronic cardiac disease (excluding HPT) Yes 12 (26.7) 12 (26.7) 1.0000 Asthma Yes 1 (2.2) 0 n.a Chronic pulmonary disease (excluding asthma) Yes 1 (2.2) 2 (4.4) n.a Diabetes Mellitus Yes 24 (53.3) 21 (46.7) 0.5271 Pre-existing renal disease Yes 5 (11.1) 9 (20.0) 0.2447 Stage 2 2 0 Stage 3 0 1 Stage 4 2 0 Stage 5 1 8 HIV/AIDS Yes 0 0 n.a Malignancy Yes 1 (2.2) 0 n.a Smoking Yes, current 0 2 (4.4) n.a Obesity (> 30kg/m 2 ) Yes 2 (4.4) 4 (8.9) 0.6766 Chronic liver disease Yes 0 1 (2.2) Dementia Yes 0 0 Chronic neurological disease Yes 2 (4.4) 1 (2.2) Connective tissue disease Yes 2 (4.4) 2 (4.4) Abbreviations: ANC, absolute neutrophil count; CRP, C-reactive protein; GCS, Glasgow Coma Scale; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; PCT, procalcitonin; RR, respiratory rate; RA, room air; SD, standard deviation; WBC, white blood cell count. Table 9 Clinical outcomes after propensity score matching. Clinical outcomes Category PCT level OR (95% CI) PCT < 0.2 (n=45) PCT ≥ 0.2 (n=45) p-value Procalcitonin level Mean (SD) 0.068 (0.033) 4.849 (11.624) n.a 0.0084 All-cause mortality Yes 4 (8.9) 14 (31.1) 4.629 (1.387 – 15.449) 0.0127 No 41 (91.1) 31 (68.9) Mechanical ventilation Yes 12 (26.7) 19 (42.2) 2.010 (0.828 – 4.878) 0.1229 No 33 (73.3) 26 (57.8) Non-invasive ventilation Yes 9 (20.0) 18 (40.0) 2.667 (1.039 – 6.847) 0.0415 No 36 (80.0) 27 (60.0) ICU admission Yes 23 (51.1) 28 (62.2) 1.575 (0.681 – 3.648) 0.2886 No 22 (48.9) 17 (37.8) Thrombotic events Yes 10 (22.2) 15 (33.3) 1.750 (0.686 – 4.467) 0.2418 No 35 (77.8) 30 (66.7) Positive culture Yes 3 (10.0) 4 (9.5) 0.947 (0.196 – 4.582) 0.9464 No 27 (90.0) 38 (90.5) Days of mechanical ventilation Median (IQR) 5 (4 - 6) 8 (5 - 12) n.a 0.0213 Days of non-invasive ventilation Median (IQR) 3 (2 - 4) 3.5 (1 - 6) n.a 0.7928 Length of hospital stay, days Median (IQR) 13.5 (9.8 - 17) 15 (10 – 19.5) n.a 0.1304 Abbreviations: CI, confidence interval; ICU; intensive care unit; IQR, interquartile range; MV, mechanical ventilation; NIV, non-invasive ventilation; OR, odds ratio; PCT, procalcitonin. Abbreviations Abbreviation Meaning COVID-19 Coronavirus disease 2019 PCT Procalcitonin OR Odds Ratio CI Confidence Interval p p-value WHO World Health Organisation ICU Intensive Care Unit CRP C-Reactive Protein LDH Lactate Dehydrogenase PCR Polymerase Chain Reaction URT Upper Respiratory Tract IM Immunoassay MREC Medical Research Ethics Committee ROC Receiver Operative Characteristic IBM International Business Machines Corporation SPSS Statistical Package for the Social Sciences SD Standard Deviation HIV Human Immunodeficiency Virus AIDS Acquired Immunodeficiency Syndrome NIV Non-invasive Ventilation IQR Interquartile Range CT scan Computed Tomography AUC Area Under Curve TP True Positive FN False Negative FP False Positive TN True Negative SPO2 Oxygen saturation GCS Glasgow Coma Scale WBC White Blood Cells ANC Absolute Neutrophil Count DM Diabetes Mellitus RR Respiratory Rate MV Mechanical Ventilation HPT Hypertension RA Room Air IL Interleukin IP-10 Interferon gamma-induced protein 10 G-CSF Granulocyte colony-stimulating factor TNF-alpha Tumour Necrosis Factor alpha IFN-gamma Interferon gamma Declarations Ethics approval The study was approved by the Medical Research Ethics Committee (MREC), Ministry of Health, Malaysia, on April 6, 2021 (NMRR-21-458-59017) and (KKM/NIH/P21-564(4)). All methods were performed in accordance with the relevant guidelines and regulations. Consent to participate was waived as the study analyzed data retrospectively. Consent for publication The authors approved the final version of the manuscript and agreed to publication. Availability of data and materials The datasets generated and analyzed during the current study are available in the GitHub repository, https://github.com/william81/procalcitonin Competing interests The authors declared that no competing interest exists. Funding This study was funded by Siemens Healthcare Diagnostics for the article processing fee in an open-access journal but the funder has no role in the design, conduct, data analysis, interpretation of findings, and manuscript preparation of the study. Authors’ contributions C.W.T, D.K.Y.K, N.S.B.A.N, S.S, and K.C.L conceived and designed the study. C.W.T, D.K.Y.K, N.S.B.A.N and S.S collected the data. K.B.L and K.C.L carried out the analysis and interpretation of the results. S.K.A contributed to the funding acquisition. C.W.T, D.K.Y.K, K.B.L, N.S.B.A.N, S.S, K.C.L, S.K.A, S.N.A.B.S.B, and S.K.C contributed to writing, review, and/or revision of the manuscript. Acknowledgements The authors would like to thank the Director-General of Health for the approval to publish the research findings. References Xu, J. et al. Associations of procalcitonin, C-reaction protein and neutrophil-to-lymphocyte ratio with mortality in hospitalized COVID-19 patients in China. Scientific Reports 10 , 15058 (2020). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/. Ministry of Health Malaysia. (2020). Clinical Management of Confirmed COVID-19 Case in Adult. Retrieved from https://covid-19.moh.gov.my/garis-panduan/gp-umum-covid19 . 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 180 , 934–943 (2020). Tahtasakal, C. A. et al. Could we predict the prognosis of the COVID-19 disease? Journal of Medical Virology 93 , 2420–2430 (2021). Allison, J., Hall, L., MacIntyre, I. & Craig, R. K. The construction and partial characterization of plasmids containing complementary DNA sequences to human calcitonin precursor polyprotein. Biochem J 199 , 725–731 (1981). Assicot, M. et al. High serum procalcitonin concentrations in patients with sepsis and infection. Lancet 341 , 515–518 (1993). Rhodes, A. et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Intensive Care Medicine 43 , 304–377 (2017). Cen, Y. et al. Risk factors for disease progression in patients with mild to moderate coronavirus disease 2019-a multi-centre observational study. Clin Microbiol Infect 26 , 1242–1247 (2020). Li, J. et al. Meta-analysis investigating the relationship between clinical features, outcomes, and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Am J Infect Control 49 , 82–89 (2021). Lippi, G. & Plebani, M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clin Chim Acta 505 , 190–191 (2020). Han, J. et al. Serum Procalcitonin Levels on Admission Predict Death in Severe and Critical COVID-19 Patients in Wuhan, China. Cardiovascular Innovation and Applications 5 , 37–44 (2020). Guan, W.-J. et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med 382 , 1708–1720 (2020). Slaats, J., ten Oever, J., van de Veerdonk, F. L. & Netea, M. G. IL-1β/IL-6/CRP and IL-18/ferritin: Distinct Inflammatory Programs in Infections. PLOS Pathogens 12 , e1005973 (2016). Guo, J. et al. Cytokine Signature Associated With Disease Severity in COVID-19. Frontiers in Immunology 12 , 3276 (2021). Manufacturer Insert Procalcitonin (PCT) on Atellica IM 11202687_EN Rev.03, 2019-07. R Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2021). Ho, D., Imai, K., King, G. & Stuart, E. A. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J. Stat. Soft. 42 , 1–28 (2011). Thiele, C. & Hirschfeld, G. cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R. J. Stat. Soft. 98 , 1–27 (2021). Vanhomwegen, C. et al. Procalcitonin accurately predicts mortality but not bacterial infection in COVID-19 patients admitted to intensive care unit. Ir J Med Sci 1–4 (2021) doi:10.1007/s11845-020-02485-z. Tang, J. et al. Serum IL-6 and procalcitonin are two promising novel biomarkers for evaluating the severity of COVID-19 patients. Medicine 100 , (2021). Hu, R., Han, C., Pei, S., Yin, M. & Chen, X. Procalcitonin levels in COVID-19 patients. Int J Antimicrob Agents 56 , 106051–106051 (2020). An, P.-J., Zhu, Y. Z. & Yang, L.-P. Biochemical indicators of coronavirus disease 2019 exacerbation and the clinical implications. Pharmacol Res 159 , 104946–104946 (2020). Kamat, I. S., Ramachandran, V., Eswaran, H., Guffey, D. & Musher, D. M. Procalcitonin to Distinguish Viral From Bacterial Pneumonia: A Systematic Review and Meta-analysis. Clinical Infectious Diseases 70 , 538–542 (2020). Moore John B. & June Carl H. Cytokine release syndrome in severe COVID-19. Science 368 , 473–474 (2020). Que, Y. et al. Cytokine release syndrome in COVID-19: a major mechanism of morbidity and mortality. Int Rev Immunol 1–14 (2021) doi:10.1080/08830185.2021.1884248. Ji, P. et al. Association of elevated inflammatory markers and severe COVID-19: A meta-analysis. Medicine (Baltimore) 99 , e23315–e23315 (2020). Liu, F. et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol 127 , 104370–104370 (2020). Huang, I., Pranata, R., Lim, M. A., Oehadian, A. & Alisjahbana, B. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis. Ther Adv Respir Dis 14 , 1753466620937175–1753466620937175 (2020). Agarwal, A. et al. High-flow nasal cannula for acute hypoxemic respiratory failure in patients with COVID-19: systematic reviews of effectiveness and its risks of aerosolization, dispersion, and infection transmission. Can J Anaesth 67 , 1217–1248 (2020). Demoule, A. et al. High-Flow Nasal Cannula in Critically III Patients with Severe COVID-19. Am J Respir Crit Care Med 202 , 1039–1042 (2020). Attaway, A. H., Scheraga, R. G., Bhimraj, A., Biehl, M. & Hatipoğlu, U. Severe covid-19 pneumonia: pathogenesis and clinical management. BMJ 372 , n436 (2021). Ji, Y., Ma, Z., Peppelenbosch, M. P. & Pan, Q. Potential association between COVID-19 mortality and health-care resource availability. The Lancet Global Health 8 , e480 (2020). Emanuel, E. J. et al. Fair Allocation of Scarce Medical Resources in the Time of Covid-19. N Engl J Med 382 , 2049–2055 (2020). Pham, T., Brochard, L. J. & Slutsky, A. S. Mechanical Ventilation: State of the Art. Mayo Clinic Proceedings 92 , 1382–1400 (2017). Additional Declarations No competing interests reported. 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06:29:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1026293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1026293/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-022-07362-x","type":"published","date":"2022-04-15T11:49:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":15329757,"identity":"a348ea82-45cf-4a78-b0ca-8fef285a0061","added_by":"auto","created_at":"2021-11-08 18:53:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72162,"visible":true,"origin":"","legend":"Flowchart of patient recruitment","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1026293/v1/6ff6179e539e92f2c0732ca2.png"},{"id":20383101,"identity":"58a036ad-4ef4-405f-baf5-446923ffc933","added_by":"auto","created_at":"2022-04-15 11:49:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":668739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1026293/v1/3f590efb-07fc-4acb-b1fa-f435405c54b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Role of Procalcitonin in Predicting Risk of Mechanical Ventilation and Mortality among moderate to Severe COVID-19 Patients\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSince the outbreak of coronavirus disease 2019 (COVID-19) in December 2019 in Wuhan, China, the COVID-19 has rapidly spread across the world and was declared as a \u0026ldquo;Public Health Emergency of International Concern\u0026rdquo; on 30th January 2020. Then, it emerged as an unanticipated threat to global health and led the World Health Organisation (WHO) to further declare COVID-19 as a pandemic on 11th March 2020\u003csup\u003e1\u003c/sup\u003e. As of 18th September 2021, there are more than 226 million confirmed COVID-19 cases with 4.7 million deaths reported globally. Malaysia has reported 2,049,750 cases with 22,355 deaths on the same day\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe clinical spectrum of COVID-19 ranges from asymptomatic to symptomatic severe disease with multiorgan involvement and failure requiring intensive care (ICU) admission and mechanical ventilation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The broad clinical spectrum and highly variable clinical course among different COVID-19 patients have introduced great challenges in predicting the disease progression and outcome\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Laboratory biomarkers have always been very useful in day-to-day clinical practice to guide disease management and treatment decisions, especially in the management of infectious diseases like the COVID-19. Hence, ever since the start of the outbreak, the use of laboratory biomarkers including C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), and procalcitonin (PCT) to predict disease progression and severity have been explored and investigated extensively\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProcalcitonin (PCT) is a 116-amino-acid peptide that shares a common molecular structure with the prohormone of calcitonin. It was first discovered in humans in 1981 by J.Allison et al but its clinical use was never really explored until 1993 when M.Assicot et al first suggested a positive association between elevated serum PCT and bacterial infection and sepsis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In today\u0026rsquo;s clinical practice, serum PCT has been increasingly instrumental in antibiotic stewardship and the diagnosis and management of sepsis due to bacterial infection\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince the beginning of the COVID-19 pandemic, multiple observational studies and meta-analyses have been done to look into the utility of serum PCT level as a biomarker of clinical deterioration among COVID-19 patients and have shown encouraging results with various optimal PCT cut-points suggested\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A single-center retrospective study done in Wuhan, China showed that a serum PCT level of more than 0.2ng/ml was found in severe and critical COVID-19 patients\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Another retrospective study of 1099 patients reported that a PCT of more than 0.5ng/dL was associated with increased severity of COVID-19 infection\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Theoretically, PCT, an acute phase peptide, is released in response to proinflammatory cytokines like IL-1β, IL-6, and TNF-α typically induced by bacterial infection and are not seen in viral infections\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, it was found that these pro-inflammatory cytokines are also raised in COVID-19 infection, especially among severe diseases, which makes serum PCT a promising biomarker of clinical deterioration among COVID-19 patients\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the optimal cut-point of PCT and its relationship with regards to various clinical outcomes, especially in all-cause mortality and the need for mechanical ventilation among moderate to severe COVID-19 patients requiring hospitalization and treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy design and setting\u003c/h2\u003e\n\u003cp\u003eA total of 319 patients who were diagnosed with moderate to severe COVID-19 pneumonia and admitted to Sungai Buloh Hospital in December 2020 were included and studied retrospectively. Demographic information, including age, gender, race, concomitant medical illnesses, the severity of COVID-19 infection, laboratory and imaging findings, and treatment given were retrieved from the hospital information system for analysis. The study included category 4 and 5 adult patients above 18 years old with a diagnosis of COVID-19 confirmed via polymerase chain reaction (PCR) test (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients were excluded if no reported PCT values.\u003c/p\u003e\n\u003cp\u003eThe majority of patients (87.1%) had their PCT levels measured within 5 days following their admission. As PCT levels may reduce over time after receiving treatment, we included patients with their PCT levels measured within 5 days and 3 days for the analysis of the optimal cut-point. Hence, the analysis of the optimal cut-point for PCT was performed in two levels. The first level involved 276 patients with their PCT levels measured within 5 days following admission and the second level involved 237 patients with their PCT levels measured within 3 days following admission. Besides, the study excluded patients with extremely high PCT levels (\u0026gt;100 ng/mL) to avoid overly skewed findings. Clinical outcomes included in the analysis for the optimal PCT cut-point were all-cause mortality, mechanical ventilation, the occurrence of thrombotic events, ICU admission, and bacterial infection. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the patient recruitment process.\u003c/p\u003e\n\u003cp\u003eIn the study, COVID-19 infection was classified according to the Malaysian Clinical Practice Guideline for Management of COVID-19\u003csup\u003e3\u003c/sup\u003e. The guideline classifies COVID-19 severity into five clinical stages as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Stage 1 and 2 consist of patients with asymptomatic infection or symptomatic infection with no pneumonia. Patients in these two categories are considered to have mild disease. Patients with stage 3 disease have lung involvement or pneumonia, but have not experienced hypoxemia, while stage 4 and 5 consist of critically ill patients requiring oxygen support and often intensive care.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClassification of COVID-19 infection severity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClinical Stage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSeverity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsymptomatic\u003c/p\u003e\n\u003cp\u003e- Only RT-PCR test positive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptomatic, but no pneumonia\u003c/p\u003e\n\u003cp\u003e- Upper respiratory tract (URT) symptoms (e.g., pharyngeal congestion, sore throat, cough, or fever)\u003c/p\u003e\n\u003cp\u003e- Other symptoms, like vomiting, diarrhea, abdominal pain, myalgia, loss of smell/taste\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptomatic, pneumonia, but no hypoxemia\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptomatic, pneumonia, requiring supplemental oxygen\u003c/p\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cp\u003eNew requirement of supplemental oxygen or increased requirement from baseline without need for non-invasive or invasive ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCritically Ill with multiorgan involvement\u003c/p\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cp\u003eNew or increased need for non-invasive or invasive ventilation, including high flow nasal cannula\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch2\u003eAnalysis Of Pct\u003c/h2\u003e\n\u003cp\u003eIn Sungai Buloh Hospital, serum PCT was measured by SIEMENS ATELLICA IMMUNOASSAY (IM). Atellica IM BRAHMS PCT assay is a 2-site sandwich immunoassay using direct chemiluminescent technology that uses 3 mouse monoclonal antibodies specific for PCT. The first antibody, in the Lite Reagent, is a mouse monoclonal anti‑PCT antibody labeled with acridinium ester. The second and third antibodies, in the Ancillary Reagent, are mouse monoclonal anti‑PCT antibodies labeled with fluorescein. The immunocomplex formed with PCT is captured with mouse monoclonal anti-fluorescein antibody coupled to paramagnetic particles in the Solid Phase. This 18-minute sandwich immunoassay with a measuring range of 0.03 to 50.00 ng/mL, is aligned to the B\u0026bull;R\u0026bull;A\u0026bull;H\u0026bull;M\u0026bull;S PCT sensitive KRYPTOR\u0026reg; assay\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eDemographics and clinical characteristics were summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for overall 319 patients, 276 and 237 patients who had their PCT measured within 5 days and 3 days following their admission. The optimal PCT cut-points were estimated using the receiver operative characteristic (ROC) method in two levels. In the first level, we included 276 patients who had their PCT levels measured within 5 days following their admission. In the second level, we included 237 patients who had their PCT levels measured within 3 days following their admission. The optimal cut-point was determined with priority on all-cause mortality and the need for mechanical ventilation. In further analysis, patients with their PCT levels measured within 3 days following admission were divided into two groups according to the selected cut-point. Patients with PCT levels above the cut-point were assumed to have a higher risk for inferior clinical outcomes, and vice versa. We compared and tested the differences of demographics, clinical characteristics, baseline laboratory findings, and co-morbidity profile between the two groups to identify covariates for adjustment in the propensity score matching analysis. The odds ratio (OR) was calculated and reported for various clinical outcomes with regards to the selected cut-point for PCT before and after the matching analysis. Independent t-test and Mann-Whitney U test were used to test differences of continuous variables between two groups, while Chi-squared test and Fisher\u0026rsquo;s exact test were used to assess categorical variables. All statistical tests were performed at two-sided 5% significance level.\u003c/p\u003e\n\u003cp\u003eIn propensity score matching analysis, patients were matched for covariates that were statistically different. Baseline laboratory parameters and vital signs were not included in the matching analysis as they were not inherited but clinical manifestations of COVID-19 infection or complications. Propensity score matching was done based on the nearest-neighbor method within a caliper width equal to 0.25 times the SD of the logit of the calculated propensity score. All analyses were performed in IBM SPSS version 26.0 for Windows and R version 4.1.0 with packages such as \u0026ldquo;cutpointr\u0026rdquo; and \u0026ldquo;MatchIt\u0026rdquo;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDemographics, clinical characteristics, and clinical outcomes of patients were summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e according to the overall cohort, first and second level cohorts. Even with the exclusion of patients due to delayed measurement of PCT, first and second level cohorts were still similar to the overall cohort in terms of clinical characteristics, comorbidities profile, and clinical outcomes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographics and clinical characteristics of the study cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003cp\u003en (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFirst level\u003c/p\u003e\n\u003cp\u003en (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSecond level\u003c/p\u003e\n\u003cp\u003en (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e319 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e237 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years: Mean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.8 (13.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.0 (13.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender: Female\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111 (34.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102 (37.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84 (35.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRace\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalay\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168 (52.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (51.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChinese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (21.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (19.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (19.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (17.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (18.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (9.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (9.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (10.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidities profile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179 (56.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158 (57.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (57.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic Cardiac Disease: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (20.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (19.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (19.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic Pulmonary Disease: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (46.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128 (46.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (46.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-existing Renal Disease: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73 (22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (20.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (19.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic Liver Disease: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDementia: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic Neurological Conditions: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConnective Tissue Disease: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV/AIDS: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalignancy: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (3.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (3.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent Smoking: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObesity: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81 (25.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (24.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (21.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOVID-19 severity when procalcitonin first sent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e254 (79.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217 (78.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181 (76.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (23.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4C mortality score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0-3 (low-risk in-hospital mortality)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4-8 (intermediate-risk in-hospital mortality)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (38.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87 (36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9-14 (high-risk in-hospital mortality)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107 (45.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15-21 (very high-risk in-hospital mortality)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (12.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSteroid use: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e297 (93.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257 (93.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e219 (92.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBloodstream infection: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (13.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eICU admission: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143 (44.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (46.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120 (50.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNIV use: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (21.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of NIV use, days, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.8, 5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.8, 5.3 )\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1, 5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMechanical ventilation: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101 (31.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (32.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85 (35.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of mechanical ventilation use, days, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (4, 12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (4, 12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (4, 12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrganizing pneumonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (42.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118 (42.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102 (43.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT scan not done\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158 (49.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137 (49.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (48.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThrombotic event: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83 (26.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (26.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality: Yes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71 (22.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (23.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDue to severe COVID-19 pneumonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (67.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (72.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (70.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDue to thrombotic event\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (11.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (17.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDue to comorbid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (9.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDue to bacterial infections\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (11.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBacteremia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntra-abdominal infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNecrotizing fasciitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-specified site\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-bacteremia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePulmonary infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntra-abdominal infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-specified site\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eColumn percentages are reported in parenthesis for categorical variables; mean and standard deviation are reported for continuous variable age; median and interquartile range are reported for continuous variables duration of non-invasive ventilation and mechanical ventilation.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CT, computed tomography; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; ICU; intensive care unit; NIV, non-invasive ventilation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of the optimal cut-point\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the determination of optimal cut-points for PCT, we included clinical outcomes, such as all-cause mortality, mechanical ventilation, thrombotic events, ICU admissions, and bacterial infection among moderate to severe COVID-19 patients. Table 3 and Table 4 present various performance indexes and optimal cut-points for PCT with regards to the aforementioned clinical outcomes based on PCT levels measured within 5 days and 3 days following admission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor PCT levels measured within 5 days following admission, the optimal cut-points for PCT were found to be 0.20 ng/mL for all-cause mortality, 0.33 ng/mL for mechanical ventilation, 1.21 ng/mL for thrombotic events, 0.09 ng/mL for ICU admission dan 1.2 ng/mL for bacterial infection (Table 3). Highest sensitivity was found in all-cause mortality (0.8136) for PCT cut-point of 0.20 ng/mL. For PCT levels measured within 3 days following admission, the optimal cut-points for PCT were found to be 0.20 ng/mL for all-cause mortality, 0.21 ng/mL for mechanical ventilation, 1.21 ng/mL for thrombotic events, 0.07 ng/mL for ICU admission dan 1.25 ng/mL for bacterial infection (Table 4). Highest sensitivity was also found in all-cause mortality (0.8364) for PCT cut-point of 0.20 ng/mL.\u003c/p\u003e\n\u003cp\u003eAs 0.2 ng/mL appeared to be the optimal cut-point in all-cause mortality for PCT levels measured within 5 days and 3 days following admission, and in mechanical ventilation for PCT levels measured within 3 days following admission, therefore, 0.2 ng/mL was selected as the final PCT cut-point in the matching analysis using propensity scores. The PCT of 0.2 ng/mL represented the optimal cut-point for the two clinically important outcomes, all-cause mortality and mechanical ventilation, especially for moderate to severe COVID-19 patients with PCT measured within 3 days following admission.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOptimal cut-off PCT values (within 5 days from admission)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCut-point and Indexes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMechanical ventilation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eThrombotic\u003c/p\u003e\n\u003cp\u003eevents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eICU\u003c/p\u003e\n\u003cp\u003eadmission\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBacterial infection culture\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of patients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e216\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6666\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6444\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOptimal cut-point (ng/ml)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.20\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.33\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.09\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccuracy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7454\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5357\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecision\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3750\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5825\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2632\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eTP: True positive; FN: False negative; FP: False positive; TN: True negative; AUC: Area under Curve\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOptimal cut-off PCT values (within 3 days from admission)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003eCut-point and Indexes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003eMechanical ventilation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003eThrombotic\u003c/p\u003e\n\u003cp\u003eevents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003eICU\u003c/p\u003e\n\u003cp\u003eadmission\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003eBacterial infection culture\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNumber of patients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePositive cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNegative cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7676\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.6711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.6465\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOptimal cut-point (ng/ml)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.20\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.07\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.25\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAccuracy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.6667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7468\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7419\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.8364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.4355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.8333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.5600\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.6154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.6776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.8571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.5726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.7702\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePrecision\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.3966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.5663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.5192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.6667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.2745\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eTP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eTN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e124\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" colspan=\"6\"\u003eTP: True positive; FN: False negative; FP: False positive; TN: True negative; AUC: Area under Curve\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePropensity scores matching analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the prognostic value of the optimal cut-point, the analysis focused on those who had their PCT levels measured within 3 days following admission. Moreover, the selected cut-point of 0.2 ng/mL was optimal for all-cause mortality and mechanical ventilation among those with PCT levels measured within 3 days following admission. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the covariate differences according to the selected optimal cut-point for PCT among 237 patients. Significant differences were identified for age (p=0.0122), COVID-19 disease stage (p\u0026lt;0.0001), days of illness before admission (p=0.0036), SPO\u003csub\u003e2\u003c/sub\u003e under room temperature (RA) (p=0.0005), Glasgow Coma Scale (GCS) (p\u0026lt;0.0001), urea (p\u0026lt;0.0001), C-reactive protein (CRP) (p\u0026lt;0.0001), white blood cells count (WBC) (p=0.0003), absolute neutrophil count (ANC) (p\u0026lt;0.0001), number of comorbidities (p\u0026lt;0.0001), presence of hypertension (p=0.0007), chronic cardiac diseases (p=0.0018), diabetes mellitus (DM) (p=0.0036), pre-existing renal disease (p\u0026lt;0.0001) and malignancy (p=0.0327).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e presents the odds ratio (OR) with regards to various clinical outcomes based on the optimal cut-point of 0.2 ng/mL for PCT. Prior to matching, our analysis found that patients with PCT level above 0.2 ng/mL were associated with\u0026nbsp; significantly higher risk in all-cause mortality (OR: 8.178, 95%CI: 3.770 - 17.738, p\u0026lt;0.0001), mechanical ventilation (OR: 5.861, 95%CI: 3.229 \u0026ndash; 10.637, p\u0026lt;0.0001), non-invasive ventilation (OR: 2.898, 95%CI: 1.541 \u0026ndash; 5.541, p=0.0010), ICU admission (OR: 4.166, 95%CI: 2.422 \u0026ndash; 7.166, p\u0026lt;0.0001) and thrombotic events (OR: 2.158, 95%CI: 1.190 \u0026ndash; 3.915, p=0.0013). Besides, patients with PCT levels above 0.2 ng/mL also experienced significantly longer days of mechanical ventilation (p\u0026lt;0.0106) and length of hospital stay (p\u0026lt;0.0001). Table 7 shows the mortality and causes of death before propensity score matching.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographics, clinical characteristics \u0026amp; baseline laboratory findings, and comorbidity profile of COVID-19 patients according to procalcitonin level.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePCT \u0026lt; 0.2 (ng/ml)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePCT \u0026ge; 0.2 (ng/ml)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n=121)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n=116)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eDemographics\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (34.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.8 (12.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.3 (14.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0122\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge groups (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (37.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0945\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 \u0026ndash; 59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (31.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 \u0026ndash; 60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (19.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (29.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (12.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (18.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEthnicity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalay\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (62.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (53.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0675\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChinese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (20.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (16.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (20.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (15.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (5.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eClinical characteristics \u0026amp; baseline laboratory\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeverity of disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111 (91.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (60.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (8.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (39.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDays of illness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.5 (3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.3 (3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPO2 under RA (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 92%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (81.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111 (95.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 92%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (19.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRR, bpm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (10.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (12.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2520\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 \u0026ndash; 29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (76.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79 (68.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (12.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (19.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (43.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (90.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (56.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrea, mmol/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87 (71.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 \u0026ndash; 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (24.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt; 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (46.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRP, mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (40.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 \u0026ndash; 9.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (25.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (59.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (33.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (33.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC (x10^12/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.56 (3.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.90 (5.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANC (x10^9/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.75 (3.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.26 (5.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eComorbidity profile\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of comorbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (55.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (34.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (43.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (46.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79 (68.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic cardiac disease (excluding HPT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (27.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (3.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7491\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic pulmonary disease (excluding asthma)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (3.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7174\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (37.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (56.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-existing renal disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (35.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(14.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (19.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (65.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV/AIDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalignancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (6.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0327\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes, current\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObesity (\u0026gt; 30kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (7.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic liver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDementia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic neurological disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0555\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConnective tissue disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6783\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eAbbreviations: ANC, absolute neutrophil count; CRP, C-reactive protein; GCS, Glasgow coma scale; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; PCT, procalcitonin; RR, respiratory rate; RA, room air; SD, standard deviation; WBC, white blood cell count.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical outcomes before propensity score matching.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eClinical outcomes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePCT level (ng/ml)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePCT \u0026lt; 0.2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=121)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePCT \u0026ge; 0.2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=116)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProcalcitonin level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0674 (0.0331)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.002 (11.556)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (39.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.178 (3.770 \u0026ndash; 17.738)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (92.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (60.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMechanical ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (17.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (55.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.861 (3.229 \u0026ndash; 10.637)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (82.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (44.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-invasive ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (14.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (33.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.898 (1.541 \u0026ndash; 5.541)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103 (85.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77 (66.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eICU admission\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (33.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79 (68.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.166 (2.422 \u0026ndash; 7.166)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 (66.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (31.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThrombotic events\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (19.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (33.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.158 (1.190 \u0026ndash; 3.915)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0113\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (81.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77 (66.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive culture\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (89.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (84.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.643 (0.263 \u0026ndash; 1.578)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3354\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (10.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (15.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDays of mechanical ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (4 \u0026ndash; 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (5 \u0026ndash; 12.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDays of non-invasive ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (2 - 4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1 - 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8071\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLength of hospital stay, days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (9 \u0026ndash; 14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (11.3 \u0026ndash; 20.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eAbbreviations: CI, confidence interval; ICU; intensive care unit; IQR, interquartile range; MV, mechanical ventilation; NIV, non-invasive ventilation; OR, odds ratio; PCT, procalcitonin.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMortality and causes of death before propensity score matching\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMortality outcomes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCases (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal number of deaths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCause of death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere COVID-19 pneumonia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (70.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBacterial infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBacteremia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-bacteremia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThrombotic event\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (12.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComorbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs the relative risk of patients categorized based on the optimal cut-point could be confounded by covariates that were significantly different, a propensity score matching was performed with the aforementioned covariates to derive two comparable arms for further analysis. The propensity score matching eventually identified 90 patients with balanced demographics, clinical characteristics, comorbidities profile, and laboratory findings except for urea (p=0.0139) and CRP (p\u0026lt;0.0001) as shown in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Subsequent analyses showed that patients with PCT above 0.2 ng/mL were associated with a significantly higher risk in all-cause mortality (OR: 4.629, 95%CI: 1.387 \u0026ndash; 15.449, p=0.0127), and marginally higher risk for non-invasive ventilation (OR: 2.667, 95% CI: 1.039 \u0026ndash; 6.847, p=0.0415). Besides, patients with PCT above 0.2 ng/mL tended to experience longer days of mechanical ventilation (p=0.0213) as summarized in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. There was no significant association between PCT level and risk of mechanical ventilation (OR; 2.010, 95% CI; 0.828 - 4.878, p = 0.1229) after being matched for covariate differences.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographics, clinical characteristics \u0026amp; baseline laboratory findings and comorbidity profile of COVID-19 patients according to procalcitonin level after propensity score matching\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePCT \u0026lt; 0.2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePCT \u0026ge; 0.2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eDemographics\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (37.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (37.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.1 (11.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.4 (13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5070\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge groups\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (28.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (35.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7838\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 \u0026ndash; 59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (31.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 \u0026ndash; 60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (28.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEthnicity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalay\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (48.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6236\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChinese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (15.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (17.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (17.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (13.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eClinical characteristics \u0026amp; baseline laboratory\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeverity of disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (77.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (82.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5982\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCategory 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (17.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDays of illness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.7 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.6 (3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8852\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPO2 under RA (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 92%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (84.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (93.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1797\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 92%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (15.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRR, bpm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 \u0026ndash; 29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (73.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (68.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (15.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6121\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (80.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (75.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrea, mmol/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (44.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0139\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 \u0026ndash; 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (37.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt; 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRP, mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (37.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 \u0026ndash; 9.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (28.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (64.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC (x10^12/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.79 (4.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.69 (6.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4148\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANC (x10^9/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.89 (4.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.74 (5.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eComorbidity profile\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of comorbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9654\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (42.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge; 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5234\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic cardiac disease (excluding HPT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic pulmonary disease (excluding asthma)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (46.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5271\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-existing renal disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2447\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV/AIDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalignancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes, current\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObesity (\u0026gt; 30kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic liver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDementia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic neurological disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConnective tissue disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eAbbreviations: ANC, absolute neutrophil count; CRP, C-reactive protein; GCS, Glasgow Coma Scale; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; PCT, procalcitonin; RR, respiratory rate; RA, room air; SD, standard deviation; WBC, white blood cell count.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical outcomes after propensity score matching.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 95px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eClinical outcomes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 95px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePCT level\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 95px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePCT \u0026lt; 0.2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePCT \u0026ge; 0.2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 60px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eProcalcitonin level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.068 (0.033)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e4.849 (11.624)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0084\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e4 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e14 (31.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e4.629 (1.387 \u0026ndash; 15.449)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0127\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e41 (91.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e31 (68.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMechanical ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e12 (26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e19 (42.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2.010 (0.828 \u0026ndash; 4.878)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1229\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e33 (73.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e26 (57.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNon-invasive ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e9 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e18 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2.667 (1.039 \u0026ndash; 6.847)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0415\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e36 (80.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e27 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eICU admission\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e23 (51.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e28 (62.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1.575 (0.681 \u0026ndash; 3.648)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2886\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e22 (48.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e17 (37.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eThrombotic events\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e10 (22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e15 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1.750 (0.686 \u0026ndash; 4.467)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2418\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e35 (77.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e30 (66.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePositive culture\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e3 (10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e4 (9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.947 (0.196 \u0026ndash; 4.582)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.9464\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e27 (90.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e38 (90.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eDays of mechanical ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e5 (4 - 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e8 (5 - 12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eDays of non-invasive ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e3 (2 - 4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e3.5 (1 - 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7928\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eLength of hospital stay, days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e13.5 (9.8 - 17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e15 (10 \u0026ndash; 19.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003en.a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1304\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" colspan=\"6\"\u003eAbbreviations: CI, confidence interval; ICU; intensive care unit; IQR, interquartile range; MV, mechanical ventilation; NIV, non-invasive ventilation; OR, odds ratio; PCT, procalcitonin.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable style=\"width: 615.17px;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eAbbreviation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eMeaning\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eCoronavirus disease 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003ePCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eProcalcitonin\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eOdds Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eCI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eConfidence Interval\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eWHO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eWorld Health Organisation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eICU\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eIntensive Care Unit\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eC-Reactive Protein\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eLDH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eLactate Dehydrogenase\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003ePCR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eURT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eUpper Respiratory Tract\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eIM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eImmunoassay\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eMREC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eMedical Research Ethics Committee\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eROC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eReceiver Operative Characteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eIBM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eInternational Business Machines Corporation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eSPSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eStandard Deviation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eHIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eAIDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eAcquired Immunodeficiency Syndrome\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eNIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eNon-invasive Ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eIQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eInterquartile Range\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eCT scan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eComputed Tomography\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eArea Under Curve\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eTP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eTrue Positive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eFN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eFalse Negative\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eFalse Positive\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eTN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eTrue Negative\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eSPO2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eOxygen saturation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eGCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eGlasgow Coma Scale\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eWBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eWhite Blood Cells\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eANC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eAbsolute Neutrophil Count\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eDM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eRR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eRespiratory Rate\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eMV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eMechanical Ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eHPT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eRA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eRoom Air\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eIL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eInterleukin\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eIP-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eInterferon gamma-induced protein 10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eG-CSF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eGranulocyte colony-stimulating factor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eTNF-alpha\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eTumour Necrosis Factor alpha\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 169px;\"\u003e\n\u003cp\u003eIFN-gamma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 432.17px;\"\u003e\n\u003cp\u003eInterferon gamma\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Research Ethics Committee (MREC), Ministry of Health, Malaysia, on April 6, 2021 (NMRR-21-458-59017) and (KKM/NIH/P21-564(4)). All methods were performed in accordance with the relevant guidelines and regulations. Consent to participate was waived as the study analyzed data retrospectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors approved the final version of the manuscript and agreed to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the GitHub repository, https://github.com/william81/procalcitonin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that no competing interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Siemens Healthcare Diagnostics for the article processing fee in an open-access journal but the funder has no role in the design, conduct, data analysis, interpretation of findings, and manuscript preparation of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.W.T, D.K.Y.K, N.S.B.A.N, S.S, and K.C.L conceived and designed the study. C.W.T, D.K.Y.K, N.S.B.A.N and S.S collected the data. K.B.L and K.C.L carried out the analysis and interpretation of the results. S.K.A contributed to the funding acquisition. C.W.T, D.K.Y.K, K.B.L, N.S.B.A.N, S.S, K.C.L, S.K.A, S.N.A.B.S.B, and S.K.C contributed to writing, review, and/or revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Director-General of Health for the approval to publish the research findings.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXu, J. \u003cem\u003eet al.\u003c/em\u003e Associations of procalcitonin, C-reaction protein and neutrophil-to-lymphocyte ratio with mortality in hospitalized COVID-19 patients in China. \u003cem\u003eScientific Reports\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 15058 (2020).\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eWHO Coronavirus (COVID-19) Dashboard.\u003c/em\u003e https://covid19.who.int/.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMinistry of Health Malaysia. (2020). Clinical Management of Confirmed COVID-19 Case in Adult. 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IL-1\u0026beta;/IL-6/CRP and IL-18/ferritin: Distinct Inflammatory Programs in Infections. \u003cem\u003ePLOS Pathogens\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, e1005973 (2016).\u003c/li\u003e\n\u003cli\u003eGuo, J. \u003cem\u003eet al.\u003c/em\u003e Cytokine Signature Associated With Disease Severity in COVID-19. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 3276 (2021).\u003c/li\u003e\n\u003cli\u003eManufacturer Insert Procalcitonin (PCT) on Atellica IM 11202687_EN Rev.03, 2019-07.\u003c/li\u003e\n\u003cli\u003eR Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2021).\u003c/li\u003e\n\u003cli\u003eHo, D., Imai, K., King, G. \u0026amp; Stuart, E. A. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. \u003cem\u003eJ. Stat. Soft.\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 1\u0026ndash;28 (2011).\u003c/li\u003e\n\u003cli\u003eThiele, C. \u0026amp; Hirschfeld, G. cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R. \u003cem\u003eJ. Stat. Soft.\u003c/em\u003e\u003cstrong\u003e98\u003c/strong\u003e, 1\u0026ndash;27 (2021).\u003c/li\u003e\n\u003cli\u003eVanhomwegen, C. \u003cem\u003eet al.\u003c/em\u003e Procalcitonin accurately predicts mortality but not bacterial infection in COVID-19 patients admitted to intensive care unit. \u003cem\u003eIr J Med Sci\u003c/em\u003e 1\u0026ndash;4 (2021) doi:10.1007/s11845-020-02485-z.\u003c/li\u003e\n\u003cli\u003eTang, J. \u003cem\u003eet al.\u003c/em\u003e Serum IL-6 and procalcitonin are two promising novel biomarkers for evaluating the severity of COVID-19 patients. \u003cem\u003eMedicine\u003c/em\u003e\u003cstrong\u003e100\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eHu, R., Han, C., Pei, S., Yin, M. \u0026amp; Chen, X. Procalcitonin levels in COVID-19 patients. \u003cem\u003eInt J Antimicrob Agents\u003c/em\u003e\u003cstrong\u003e56\u003c/strong\u003e, 106051\u0026ndash;106051 (2020).\u003c/li\u003e\n\u003cli\u003eAn, P.-J., Zhu, Y. Z. \u0026amp; Yang, L.-P. Biochemical indicators of coronavirus disease 2019 exacerbation and the clinical implications. \u003cem\u003ePharmacol Res\u003c/em\u003e\u003cstrong\u003e159\u003c/strong\u003e, 104946\u0026ndash;104946 (2020).\u003c/li\u003e\n\u003cli\u003eKamat, I. S., Ramachandran, V., Eswaran, H., Guffey, D. \u0026amp; Musher, D. M. Procalcitonin to Distinguish Viral From Bacterial Pneumonia: A Systematic Review and Meta-analysis. \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e\u003cstrong\u003e70\u003c/strong\u003e, 538\u0026ndash;542 (2020).\u003c/li\u003e\n\u003cli\u003eMoore John B. \u0026amp; June Carl H. 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A., Oehadian, A. \u0026amp; Alisjahbana, B. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis. \u003cem\u003eTher Adv Respir Dis\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 1753466620937175\u0026ndash;1753466620937175 (2020).\u003c/li\u003e\n\u003cli\u003eAgarwal, A. \u003cem\u003eet al.\u003c/em\u003e High-flow nasal cannula for acute hypoxemic respiratory failure in patients with COVID-19: systematic reviews of effectiveness and its risks of aerosolization, dispersion, and infection transmission. \u003cem\u003eCan J Anaesth\u003c/em\u003e\u003cstrong\u003e67\u003c/strong\u003e, 1217\u0026ndash;1248 (2020).\u003c/li\u003e\n\u003cli\u003eDemoule, A. \u003cem\u003eet al.\u003c/em\u003e High-Flow Nasal Cannula in Critically III Patients with Severe COVID-19. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e\u003cstrong\u003e202\u003c/strong\u003e, 1039\u0026ndash;1042 (2020).\u003c/li\u003e\n\u003cli\u003eAttaway, A. H., Scheraga, R. G., Bhimraj, A., Biehl, M. \u0026amp; Hatipoğlu, U. Severe covid-19 pneumonia: pathogenesis and clinical management. \u003cem\u003eBMJ\u003c/em\u003e\u003cstrong\u003e372\u003c/strong\u003e, n436 (2021).\u003c/li\u003e\n\u003cli\u003eJi, Y., Ma, Z., Peppelenbosch, M. P. \u0026amp; Pan, Q. Potential association between COVID-19 mortality and health-care resource availability. \u003cem\u003eThe Lancet Global Health\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, e480 (2020).\u003c/li\u003e\n\u003cli\u003eEmanuel, E. J. \u003cem\u003eet al.\u003c/em\u003e Fair Allocation of Scarce Medical Resources in the Time of Covid-19. \u003cem\u003eN Engl J Med\u003c/em\u003e\u003cstrong\u003e382\u003c/strong\u003e, 2049\u0026ndash;2055 (2020).\u003c/li\u003e\n\u003cli\u003ePham, T., Brochard, L. J. \u0026amp; Slutsky, A. S. Mechanical Ventilation: State of the Art. \u003cem\u003eMayo Clinic Proceedings\u003c/em\u003e\u003cstrong\u003e92\u003c/strong\u003e, 1382\u0026ndash;1400 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, SARS-CoV-2, risk factor, procalcitonin, mechanical ventilation, mortality","lastPublishedDoi":"10.21203/rs.3.rs-1026293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1026293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSerum procalcitonin (PCT) has become an emerging prognostic biomarker of disease progression in patients with COVID-19. This study aims to determine the optimal cut-off value of PCT with regards to important clinical outcomes, especially for mechanical ventilation and all-cause mortality among moderate to severe COVID-19 patients in Malaysia.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA total of 319 moderate to severe COVID-19 patients hospitalized at the National Referral Hospital in December 2020 were included in the study retrospectively. Demographics, comorbidities, the severity of COVID-19 infection, laboratory and imaging findings, and treatment given were collected from the hospital information system for analysis. The optimal cut-point values for PCT were estimated in two levels. The first level involved 276 patients who had their PCT measured within 5 days following their admission. The second level involved 237 patients who had their PCT measured within 3 days following their admission. Further, a propensity score matching analysis was performed to determine the adjusted relative risk of patients with regards to various clinical outcomes according to the selected cut-point among 237 patients who had their PCT measured within 3 days. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe results showed that a PCT level of 0.2 ng/mL was the optimal cut-point for prognosis especially for mortality outcome and the need for mechanical ventilation. Before matching, patients with PCT ≥ 0.2ng/mL were associated with significantly higher odds in all investigated outcomes. After matching, patients with PCT \u0026gt; 0.2 ng/mL were associated with higher odds in all-cause mortality (OR: 4.629, 95% CI: 1.387 – 15.449, p = 0.0127) and non-invasive ventilation (OR: 2.667, 95% CI: 1.039 – 6.847, p = 0.0415). Furthermore, patients with higher PCT were associated with significantly longer days of mechanical ventilation (p = 0.0213). There was however no association between higher PCT level and the need for mechanical ventilation (OR: 2.010, 95% CI: 0.828 - 4.878, p = 0.1229).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOur study indicates that a rise in PCT above 0.2ng/ml is associated with an elevated risk in all-cause mortality, the need for non-invasive ventilation, and a longer duration of mechanical ventilation. The study offers concrete evidence for PCT to be used as a prognostication marker among moderate to severe COVID-19 patients.\u003c/p\u003e","manuscriptTitle":"The Role of Procalcitonin in Predicting Risk of Mechanical Ventilation and Mortality among moderate to Severe COVID-19 Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-08 18:53:06","doi":"10.21203/rs.3.rs-1026293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2022-04-07T05:46:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-03-09T17:21:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-03-03T19:18:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-02-01T02:38:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-01-27T23:24:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"09efd784-bc1d-4501-a8ad-01ebfc2547cf","date":"2022-01-21T16:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-01-21T16:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2edc5807-b696-4ec2-8307-41a9de482bcb","date":"2022-01-16T10:34:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-01-16T10:34:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-04T15:44:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-01T16:53:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-11-03T12:23:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-11-03T12:19:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2021-10-28T06:15:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56dfa491-0dea-4900-9efa-89676b811d78","owner":[],"postedDate":"November 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":8376625,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2022-04-15T11:49:08+00:00","versionOfRecord":{"articleIdentity":"rs-1026293","link":"https://doi.org/10.1186/s12879-022-07362-x","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2022-04-15 11:49:08","publishedOnDateReadable":"April 15th, 2022"},"versionCreatedAt":"2021-11-08 18:53:06","video":"","vorDoi":"10.1186/s12879-022-07362-x","vorDoiUrl":"https://doi.org/10.1186/s12879-022-07362-x","workflowStages":[]},"version":"v1","identity":"rs-1026293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1026293","identity":"rs-1026293","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}