The Importance of Plasma Renin Concentration in Intensive Care Patients with Circulatory Shock

The Importance of Plasma Renin Concentration in Intensive Care Patients with Circulatory Shock | 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 Article The Importance of Plasma Renin Concentration in Intensive Care Patients with Circulatory Shock Yasemin Bozkurt Turan, Sait Karakurt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3962245/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Renin is a hypoperfusion marker and a good index of renin-angiotensin-aldosterone system (RAAS) activity. The purpose of this study was to evaluate whether the plasma renin concentration (PRC) can represent a tissue perfusion marker for predicting mortality in patients with circulatory shock in intensive care. Method: This prospective study involved patients aged 18 or over in a tertiary intensive care unit (ICU). Sixty-nine patients were included, 37 of whom constituted the circulatory shock group, and 32 a non-shock control group. Blood specimens were collected to measure PRC levels. Combined tests including PRC, mottling scores, central venous saturation of oxygen (ScvO2), C-reactive protein (CRP), procalcitonin, and lactate were constituted. Results: The patients’ mean age was 61.5 (±16.4) years, and 58.0% (n=40) were men. Mean number of days in the ICU, ICU 28-day mortality, ICU 28-day dialysis requirements, ICU 28-day mechanical ventilation requirements, and adrenalin, noradrenalin, and terlipressin use were all higher in the patients with circulatory shock (p<0.05). Three-day survival following discharge from the ICU, Glasgow Coma Scale (GCS) scores, glomerular filtration rate (GFR), and ScvO2 levels were lower in the patients with circulatory shock (p<0.05). Sequential Organ Failure Assessment (SOFA) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, lactate, creatinine, CRP, procalcitonin, PRC, and mottling score values were higher in the circulatory shock group (p<0.05). Median overall survival time (OST) was higher in the non-circulatory shock patients (17.00 days; Wilcoxon χ^2=5.016; p=0.038). The increase in mottling (HR:1.64(1.15 – 2.33); p<0.01) and PRC (HR=1.01(1.00 – 1.02); p<0.05) levels and the decrease in GFR (HR=0.98(0.96 – 0.99); p<0.05) values in the ICU patients were correlated with length of survival (-2 Log Likelihood=59.237; Chi-square=17.105; df=3; p<0.001 (p=0.0007)). Combined test 1ᵈ (PRC, mottling, ScvO2, CRP, and procalcitonin), combined test 2ᵉ (PRC, lactate, and mottling), combined test 3ᶠ (PRC, lactate, mottling, CRP, and procalcitonin), and lactate emerged as indicators of 28-day mortality in patients with circulatory shock (p0,05). Combined test 1ᵈ (PRC, ScvO2, CRP, and procalcitonin) and combined test 3ᶠ (PRC, lactate, CRP, and procalcitonin) emerged as markers of 28-day survival in patients without circulatory shock (p0.05). Conclusion: A significant association was observed between PRC levels and survival. Combining PRC levels with lactate, mottling score, CRP, and procalcitonin results in better prediction of mortality than PRC alone. PRC levels have the potential for use as a good marker for patients with circulatory shock. Health sciences/Biomarkers Health sciences/Risk factors Renin circulatory shock mortality intensive care tissue perfusion Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Sepsis is a life-threatening organ dysfunction resulting from a dysregulated response to infection in the host [ 1 ]. Sepsis and septic shock are a major health problem affecting more than 30 million individuals worldwide every year, with reported mortality rates between one in three and one in six [ 2 ]. Impaired tissue perfusion in sepsis is a cause of increased mortality and morbidity. A number of biomarkers have been identified for the early diagnosis of tissue perfusion disorder. Lactate [ 3 ], mottling scores [ 4 ], capillary filling time [ 5 , 6 ], and central venous saturation of oxygen (S cv O 2 ) [ 7 , 8 ] can be used as guides to early resuscitation in sepsis and septic shock, and several studies have shown that the use of these can predict mortality. C-reactive protein (CRP) and procalcitonin are other biomarkers that can be used to predict prognosis in septic patients [ 9 , 10 ]. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores are employed as for classifying severity in septic patients in the intensive care unit (ICU) or predicting prognosis [ 11 ]. The renin-angiotensin-aldosterone system (RAAS) plays a very important role in blood pressure regulation and in the preservation of extracellular fluid volume [ 12 ]. Renin is secreted as a response to decreased tissue perfusion, sympathetic activation, and hypoxic metabolism [ 13 ]. It is therefore a marker of hypoperfusion and is regarded as a good indicator of for RAAS activity [ 14 ]. Renin has been reported to exhibit 100% sensitivity and negative predictive values in predicting mortality in intensive care patients [ 15 ], and has been shown to be a better marker of tissue perfusion than lactate [ 16 ]. This study compared PRC, vasopressor requirements, 28-day dialysis requirements, 28-day mechanical ventilation requirements, 28-day mortality rates, lengths of ICU stay, and survival rates on the third day after discharge from the ICU in intensive care patients with and without septic shock and investigated the role of PRC in showing tissue perfusion. METHOD This prospective study was performed with patients aged 18 or over admitted to the Marmara University Hospital tertiary ICU, Türkiye, between November 2019 and November 2020. Informed written consent was received either from the patients themselves or their families. The research was conducted in compliance with the Declaration of Helsinki and Good Clinical Practice guidelines. Approval was granted by the Marmara University Medical Faculty clinical research ethical committee (no. 09.2019.883). Individuals with hemolytic samples, using converting enzyme inhibitors (CEI) and angiotensin receptor blockers (ARB) in the previous 48 hours, using diuretics in the previous six hours, pregnant women, lactating mothers, and patients with chronic stimulation of the mineralocorticoid axis (cirrhosis, advanced heart failure, or chronic kidney failure) were excluded from the study. Eighty-nine patients were hospitalized to intensive care during the study period, of whom eight were excluded since CVP catheters were not installed, four due to using converting enzyme inhibitors and angiotensin blockers in the previous 48 hours and three due to using diuretics in the previous six hours were excluded. Two patients with cirrhosis with hemorrhagic shock, one with advanced heart failure with cardiogenic shock, and two with chronic kidney failure were also excluded. The study was thus performed with 69 patients, 37 in the septic shock group and 32 in the non-shock group. The study flow chart is shown in Fig. 1 . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [ 17 ] instructions were applied when constituting the flow chart. Specimen Collection and Measurements Blood specimens collected from the radial artery catheter in patients admitted to the ICU were placed into tubes containing ethylene diamine tetra-acetic acid (EDTA). These were centrifuged within 30 min at 2000–3000 rpm at 2–8° C for 14 min and then stored at -80° C for six months. Plasma renin levels were analyzed using the enzyme-linked immunosorbent assay (ELISA) method previously employed by Le’snik et al. [ 17 ]. Since renin measurements are not affected by the diurnal rhythm [ 15 ], blood samples were collected once for each patient. The patients’ demographic data, comorbidity status, vasopressor use, Glasgow Coma Scale (GCS) scores, mottling scores, SOFA scores, APACHE II scores, and lactate, S cv O 2 , CRP, procalcitonin, and creatinine levels were recorded. The glomerular filtration rate (GFR) was calculated using the modification of diet in renal diseases (MDRD) study formula. PRC, vasopressor requirements, 28-day dialysis requirements, 28-day mechanical ventilation requirements, 28-day mortality rates, lengths of ICU stay, and survival rates on the third day after discharge from the ICU were compared between intensive care patients with and without septic shock, and the role of PRC in showing tissue perfusion was investigated. Sample size The total sample size required in order to determine the mean difference in PRC values between the shock and without shock groups was calculated as 58, with an alpha error of 5%, power of 90%, and effect size d = 0.8, and an allocation ratio of 1:1 using G*Power version 3.1 software. Statistical Analysis Statistical analysis was conducted on IBM SPSS version 25 (IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA: IBM Corp.) and STATA 15 (Stata Statistical Software: Release 15. College Station, TX, USA: Stata Corp LLC.) softwares. Patient characteristics were compared between patients with and without circulatory shock using the independent samples T-test, Mann-Whitney U test, Pearson’s chi-square test, Yates correction for continuity, and Fisher’s exact test. In the survival analysis, follow-up time was measured from the date of admission to the ICU to the date of mortality from any cause in the ICU or the date of discharge. The seven patients with survival times exceeding 40 days in the ICU were evaluated as outliers and excluded from the survival analysis. Overall survival (OS) time and survival probability were estimated using the Kaplan-Meier method. The survival curves of the patients with and without circulatory shock were compared using the Wilcoxon (Breslow) test. Inflammatory parameters predicting survival were examined using the Cox proportional hazards (CPH) model with the forward stepwise method. Schoenfeld’s residuals were investigated for proportional hazard assumptions. Test results for mottling, S cv O 2 , CRP, procalcitonin, and PRC were combined to improve the accuracy of 28-day mortality prediction in the ICU using logistic regression. Area under receiver operating characteristics (ROC) curves were used for lactate, PRC, and combined tests as predictors of ICU 28-day mortality. Statistical significance was set at 0.05. Results Patient Characteristics The characteristics of the ICU patients and comparisons between the shock and non-shock groups are shown in Tables 1 a, 1aa, and 1b. The mean age of the patients was 61.5 (± 16.4) years, and more than half were men (n = 40, 58.0%). The shock and non-shock groups were similar in terms of age, gender, BMI, and presence of comorbidity (p > 0.05). Use of 28-day dialysis and mechanical ventilation was significantly higher in the patients with circulatory shock patients than in those without (p < 0.01). Rates of adrenalin, noradrenalin, and terlipressin use were significantly higher in the patients with circulatory shock in the ICU (p < 0,05). ICU 28-day mortality was 36.2% (n = 25), and was significantly higher in patients with circulatory shock than those without (SS:21, 56.8% vs NS:4, 12.5%, respectively, p < 0.001). The three-day survival rate after discharge from the ICU was 66.7% (n = 46), and was significantly lower in the patients with circulatory shock (SS:17, 45.9% vs NS:29, 90.6%, respectively, p < 0.001). The median number of days spent in the ICU was significantly higher in the patients with circulatory shock than in those without (SS:10.0(3.0–31.5) vs NS:3.0(1.0–9.5), respectively, p = 0,006). The patients with circulatory shock had lower GCS, GFR, and S cv O 2 values than those without shock (p < 0,05). Test results for SOFA, APACHE II scores, lactate, creatinine, CRP, procalcitonin, PRC, and mottling were higher in the patients with circulatory shock (p < 0,05) (Tables 1 a, 1aa and Table 1 b). Table 1 a: ICU patient characteristics (n = 69) Parameter All patients (n = 69) Patients with septic shock (n = 37) Patients without septic shock (n = 32) p-value Age, mean ± sd 61.5 ± 16.4 64.1 ± 13.8 58.5 ± 18.8 p = 0.162* Male, n (%) 40 (58.0%) 24 (64.9%) 16 (50.0%) p = 0.212** BMI, median (IQR) 27.4 (23.5–31.2)¹ 27.2 (23.4–30.8)² 27.7 (24.0–31.3)³ p = 0.521*** 28-day dialysis use, n (%) 14 (20.3%) 13 (92.9%) 1 (7.1%) p = 0.002** 28-day mechanical ventilation use, n (%) 32 (46.4%) 28 (87.5%) 4 (12.5%) p < 0.001** Diagnosis, n (%) Surgical 16 (23.2%) 3 (18.8%) 13 (81.2%) p = 0.001** Malignancy 25 (36.2%) 12 (48.0%) 13 (52.0%) Respiratory distress 14 (20.3%) 10 (71.4%) 4 (28.6%) Other diseases 14 (20.3%) 12 (85.7%) 2 (14.3%) Comorbidity, n (%) 52 (75.4%) 29 (55.8%) 23 (44.2%) p = 0.584** Hypertension, n (%) 33 (47.8%) 18 (54.5%) 15 (45.5%) p = 0.999** Diabetes mellitus, n (%) 17 (24.6%) 10 (58.8%) 7 (41.2%) p = 0.781** Cardiovascular diseases, n (%) 21 (30.4%) 11 (52.4%) 10 (47.6%) p = 0.999** Respiratory disease, n (%) 2 (2.9%) 1 (50.0%) 1 (50.0%) p = 1.000***** Malignancy, n (%) 6 (8.7%) 3 (50.0%) 3 (50.0%) p = 1.000***** Other diseases, n (%) 6 (8.7%) 4 (66.7%) 2 (33.3%) p = 0.809**** ¹n = 58, ²n = 32, ³n = 26, ⁴n = 29, ⁵n = 61. *T-test; **Pearson Chi-square test; ***Mann-Whitney U test; ****Yates correction for continuity;*****Fisher’s Exact test. Table 1 aa: ICU patients’ characteristics and outcomes (n = 69) Parameter All patients (n = 69) Patients with septic shock (n = 37) Patients without septic shock (n = 32) p-value Vasopressor use, n(%) 38 (55.1%) 37 (97.4%) 1 (2.6%) p < 0.001**** Terlipressin use, n (%) 7 (10.1%) 7 (100.0%) 0 (0.0%) p = 0.028**** Adrenaline use, n (%) 12 (17.4%) 12 (100.0%) 0 (0.0%) p < 0.001**** Noradrenaline use, n (%) 38 (55.1%) 37 (97.4%) 1 (2.6%) p < 0.001***** 28-day mortality, n (%) 25 (36.2%) 21 (56.8%) 4 (12.5%) p < 0.001** Three-day survival after discharge from the ICU, n (%) 46 (66.7%) 17 (45.9%) 29 (90.6%) p < 0.001** Length of ICU stay in days, median (IQR) 6.0 (2.0–16.5) 10.0 (3.0–31.5) 3.0 (1.0–9.5) p = 0.006*** ¹n = 58. ²n = 32. ³n = 26. ⁴n = 29. ⁵n = 61. *T-test; **Pearson Chi-square test; ***Mann-Whitney U test; ****Yates correction for continuity;*****Fisher’s Exact test. Table 1 b: ICU patients’ laboratory parameters (n = 69) Parameter All patients (n = 69) Patients with septic shock (n = 37) Patients without septic shock (n = 32) p-value GCS, median (IQR) 13.0 (3.5–15.0) 6.0 (3.0 -14.5) 15.0 (13.3–15.0) p < 0.001*** SOFA, median (IQR) 5.0 (2.0–11.0) 11.0 (6.5–13.0) 1.5 (0.0–4.0) p < 0.001*** APACHE II, mean ± sd 16.1 ± 7.2 18.5 ± 7.1 13.4 ± 6.5 p = 0.003* GFR, median (IQR) 79.7 (36.2–119.9) 42.6 (30.9–103.3) 92.7 (68.5 -131.4) p = 0.006*** Lactate, median (IQR) 1.9 (1.3–4.7) 3.0 (1.5–5.8) 1.5 (1.0–2.6) p < 0.001*** S cv O 2 , mean ± sd 70.9 ± 11.7 68.1 ± 12.7 74.2 ± 9.7 p = 0.030* Creatinine, median (IQR) 0.9 (0.7–1.8) 1.6 (0.7–2.1) 0.8 (0.6–1.0) p = 0.003*** CRP, median (IQR) 99.6 (35.4–198.0) 153.0 (73.5–259.0) 49.8 (13.0 -106.3) p < 0.001*** Procalcitonin, median (IQR) 1.6 (0.3–9.3) 5.3 (1.8–22.9) 0.3 (0.1–0.9) p < 0.001*** PRC, median (IQR) 169.1 (151.5–196.4) 175.4 (162.6–234.8) 155.4 (147.8–175.6) p = 0.001*** Mottling, median (IQR) 0.0 (0.0–1.0) ⁵ 0.5 (0.0–2.0) ² 0.0 (0.0–0.0) ⁴ p < 0.001*** ²n = 32. ⁴n = 29. ⁵n = 61. *T-test; **Pearson Chi-square test; ***Mann-Whitney U test; ****Yates correction for continuity;*****Fisher’s Exact test. Survival Analysis Kaplan-Meier survival curves for patients with shock (n = 31) and without shock (n = 31) are presented in Fig. 2 . The median survival time was 17 (5.4–28.7) days and the survival probability among the ICU patients was 47%. The median OST was significantly higher in patients in the ICU without circulatory shock than in those with shock (17.00 days vs 16.00 days, respectively; Wilcoxon \({\chi }^{2}\) =5,016; p = 0,038) (Table 2 ). Table 2 Median survival times of the patient groups in the ICU Number of Events Median OST (95% CI) Survival probability (95% CI) pᵃ Shock 17 16.00 (11.1–20.9) 0.42 (0.21–0.63) 0.038 No shock 3 17.00ᵇ 0.48 (0.06–0.82) All patient 20 17.00 (5.4–28.7) 0.47 (0.26–0.65) ᵃWilcoxon (Breslow) test p-value comparing survival curves of patients with and without shock. ᵇStandard error not computed. The inflammatory parameters predicting the survival of the ICU patients are shown in Table 3 (n = 48, number of events = 15). The increase in mottling (HR:1.64(1.15–2.33); p < 0.01) and PRC (HR = 1.01(1.00–1.02), p < 0.05) levels and the decrease in GFR (HR = 0.98(0.96–0.99), p < 0.05) were associated with decreased survival times in the ICU patients (-2 Log Likelihood = 59,237; Chi-square = 17.105; df = 3; p < 0.001. Table 3 Cox proportional hazard model predicting survival in ICU patients (n = 48) Parameterᵃ Hazard Ratio (95% CI) p Test of Proportional-Hazards assumption chi-square df pᵇ Mottling 1.64 (1.15–2.33) 0.006 0.48 1 0.487 GFR 0.98 (0.96–0.99) 0.025 0.82 1 0.365 PRC 1.01 (1.00–1.02) 0.038 0.05 1 0.817 ᵃVariable selection with the forward (likelihood ratio) stepwise method. The variables entered the model were age, gender, BMI, mechanical ventilation use, dialysis use, GCS, SOFA, APACHE II, mottling, GFR, lactate, S cv O 2 , creatinine, CRP, procalcitonin, PRC, and presence of circulatory shock. ᵇSchoenfeld residuals test p-value. ROC curve predicting 28-day mortality in the ICU Patients with shock The sensitivity, specificity, and area under curves (AUC) values for PRC, lactate, and combined tests are shown in Table 4 a (n = 32). The lactate and combined tests emerged as indicators of ICU 28-day mortality in patients with shock (p 0.05). The ROC curves of the tests are shown in Fig. 3 a. Table 4 a: Performance of the tests predicting 28-day mortality in ICU patients with shock Test AUC SE 95% CI for AUC p-valueᵃ Sensitivity Specificity Accuracy PRCᵇ 0.435 0.098 0.225–0.607 0.503 70.00% 35.29% 54.05% Lactate ͨ 0.731 0.085 0.517–0.859 0.017 95.00% 47.06% 72.97% Combined test 1ᵈ 0.898 0.053 0.794–0.999 < 0.001 82.35% 80.00% 81.25% Combined test 2ᵉ 0.847 0.069 0.713–0.981 0.001 85.71% 70.59% 78.13% Combined test 3ᶠ 0.894 0.055 0.786–0.999 < 0.001 58.82% 93.33% 75.00% ᵃNull hypothesis: true area = 0,5. ᵇcut-off = 169,34. ͨ cut-off = 1,50. ᵈ PRC, Mottling, S cv O 2 , CRP, and Procalcitonin. ᵉ PRC, Lactate and Mottling. ᶠ PRC, Lactate, Mottling, CRP, and Procalcitonin. Figure 3 a: ROC curves for PRK, lactate, and three combined tests predicting ICU 28-day mortality in patients with shock. The areas under the ROC of the five tests differed significantly (Chi-square = 21.09; df = 4; p < 0.001). However, there was no statistically significant difference between the AUC values for lactate and the three combined tests (Chi-square = 3.77; df = 3; p = 0.287). Similarly, the difference between the AUC values of the combined tests was not statistically significant (Chi-square = 1.06; df = 2; p = 0.589) (Fig. 3 a). Patients without shock The sensitivity, specificity, and AUC values for PRC, lactate, and combined tests (S cv O 2 , CRP, procalcitonin, and PRC) are shown in Table 4 b (n = 32). Combined tests 1 and 3 emerged as indicators of ICU 28-day mortality in patients without shock (p 0.05). The tests’ ROC curves are shown in Fig. 3 b. Significant differences were observed between the AUC values for PRC, lactate, and combined tests (Chi-square = 122.63; df = 4; p < 0.001). AUC was significantly higher for lactate than for PRC (Chi-square = 4.44; df = 1; p = 0.035), while no difference was determined in the AUC values for the bivariate comparisons of the other tests (p > 0.05). Table 4 b: Performance of tests predicting 28-day mortality in ICU patients without shock Test AUC SE 95% CI for AUC p-valueᵃ Sensitivity Specificity Accuracy PRCᵇ 0.397 0.183 0.038–0.755 0.561 66.67% 44.83% 46.88% Lactate ͨ 0.540 0.221 0.106–0.974 0.821 66.67% 72.41% 71.87% Combined test 1ᵈ 0.920 0.064 0.794–0.999 0.018 33.33% 96.55% 90.63% Combined test 2ᵉ 0.678 0.153 0.378–0.979 0.316 33.33% 100.00% 93.75% Combined test 3ᶠ 0.862 0.119 0.628–0.999 0.042 66.67% 100.00% 96.88% ᵃNull hypothesis: true area = 0.5. ᵇ cut-off = 154.61. ͨ cut-off = 2.40. ᵈ PRC, S cv O 2 , CRP and Procalcitonin. ᵉ PRC and Lactate. ᶠ PRC, Lactate, CRP, Procalcitonin. DISCUSSION This study examined the relationship between mortality, survival and PRC for the circulatory shock, non-circulatory shock, and total patient groups. PRC levels were significantly higher in the patients with shock (p = 0.001), and PRC levels were significantly associated with survival at analysis of all the intensive care patients (p = 0.038). However, no difference was determined in PRC levels between the exitus and surviving patient groups (p = 0.503). A combined test including PRC, lactate, mottling score, CRP, and procalcitonin exhibited the best performance in predicting mortality in both patients with and without shock (AUC 0.894, p < 0.001). The prediction of mortality of mottling score (p = 0.006), GFR (p = 0.025) and PRC (p = 0.038) at Cox analysis in the entire patient group was similar to that of previous studies [ 4 , 15 ]. While no significant difference in mechanical ventilation use rates in hypotensive critical patients in intensive care was observed between surviving and exitus groups (p = 0.53) [ 18 ], high mechanical ventilation rates in patients with severe sepsis [ 19 ] and exitus patients with septic shock in intensive care [ 11 ] were similar to the findings for the shock group patients in the present study (p < 0.001). While continuous renal replacement therapy (CRRT) indicated 28-day mortality in patients with septic shock at univariate analysis (p 0.006), it did not indicate this at multivariate analysis (p 0.085) [ 11 ]. PRC levels were significantly elevated in patients requiring renal replacement therapy (RRT) among patients in intensive care with septic shock (p < 0.01), while a significant positive correlation was observed between PRC and outcomes on the 28th day of RRT (correlation 0.43, p < 0.01) (20). CRRT use in patients with shock was significantly high at univariate analysis in the present study (p = 0.002), but did not predict mortality at multivariate analysis. At univariate analysis, APACHE II, SOFA, and length of ICU stay, which were high in the shock patient group, were similar to those reported in septic shock patients in other studies [ 11 , 20 ]. Lactate values, which are prognostic in the diagnosis of tissue perfusion disorder [ 3 , 4 , 21 ], and procalcitonin and CRP values used in predicting prognosis in septic patients [ 10 , 22 , 23 ], were elevated in the shock group in this study. The prediction of survival by mottling score (p = 0.006), GFR (p = 0.025) and PRC (p = 0.038) at Cox analysis in the entire patient group was similar to that reported in other studies [ 4 , 15 ]. Mottling, defined as patchy skin discoloration, reflects a decreased cutaneous blood flow [ 24 ] and vasoconstriction [ 25 ] and has been shown to predict mortality in patients with septic shock [ 4 , 26 , 27 ]. Mottling scores also predicted survival in the entire intensive care patient group in the present study (p = 0.006). Similarly to Boulainve et al’s. [ 28 ] study of septic shock patients, significantly low central venous oxygen saturation (ScvO2) was observed in the patients with shock in this research. Vasopressor use was significantly high in the shock patients in this study (p < 0.001) and was in favor of tissue hypoperfusion [ 20 ]. Lactate (AUC = 0.731, P = 0.017), combined test 1ᵈ, consisting of PRC, mottling score, ScvO2, CRP, and procalcitonin (AUC = 0.898, P < 0.001), combined test 2ᵉ, consisting of PRC, lactate, and mottling score (AUC = 0.847, p = 0.001), and combined test 3ᶠ, consisting of PRC, lactate, mottling score CRP, and procalcitonin (AUC = 0.894, P < 0.001) predicted mortality in patients with shock. In terms of the non-shock patients, combined test 1ᵈ, consisting of PRC, mottling score, ScvO2, CRP, and procalcitonin (AUC = 0.920, P = 0.018) and combined test 3ᶠ, consisting of PRC, lactate, CRP, and procalcitonin (AUC 0.862, P = 0.042) predicted mortality. Although PRC predicted survival at Cox analysis performed among the entire patient group, it failed to predict mortality at analysis in the surviving and non-surviving groups, which may be attributable to the low patient numbers. Since the sample size was calculated with G power without prediction of mortality, the patient numbers may have been insufficient in that respect. The sample size was calculated for the difference between PRC levels in groups with and without septic shock. The median PRC value in this study was 175.4 ng/L, and higher PRC values were determined to be predictive of shock development (p = 0.001) and survival (p = 0.038). A median renin concentration of 172.7 pg/mL and above in patients with catecholamine-resistant septic shock has been reported to significantly reduce 28-day mortality when angiotensin II was used compared to placebo [ 29 ]. A threshold value for plasma renin activity (PRA) ≥ 2.3 ng/ml/h in patients with heart failure has been reported to be capable of predicting cardiac mortality [ 30 ], and a PRA value ≥ 3.5 ng/ml/h in patients with septic shock is both prognostic and predictive of 28-day mortality [ 11 ], while renin concentrations have been reported to predict mortality in patients with sepsis and septic shock at a cut-off value of 87 pg/mL [ 17 ]. However, the studies described above were performed with different test methods and different patient groups, and numerical comparisons are not possible since differing renin cut-off values were employed. As shown above, studies have been performed with PRA or PRC. The present study was conducted with PRC levels. The direct renin concentration represents the renin concentration in plasma, while PRA reflects the renin enzyme-like activity required to bread down angiotensin and produce angiotensin 1 (Ang1). This makes it possible to calculate the amount of Ang1 produced in a unit of time as PRA (ng/ml/h) [ 31 ]. Studies have been performed to ensure standardization in PRA tests [ 32 ]. PRA is reported to be capable of measuring a much lower renin value than the PRC test [ 33 ]. The use of the PCR test in the present study may have limited its value in terms of measuring or understanding the true value of renin. Gleeson et al. determined that direct plasma renin levels above the normal upper limit (> 40 µU/mL) predicted intensive care mortality, but that lactate levels above the upper normal limit (> 2 mM) were not predictive of mortality [ 15 ]. Indeed, each unit increase in ln-renin concentrations is reported to raise in-hospital mortality 10-fold, although lactate exhibits no such effect [ 18 ]. Similarly, renin levels exceeding 40 pg/ml were reported to be associated with in-hospital mortality in another study, while no relationship as determined between lactate levels greater than 2 mmol/L and in-hospital mortality [ 34 ]. Lactate, an indicator of tissue hypoxia [ 35 ] was both higher in the patients with shock in the present study (p < 0.001) and also predicted mortality (p = 0.017). PRC levels and 28-day mortality rates were higher in the shock group in the present study p < 0.001), while three-day survival rates after discharge were lower (p < 0.001). Similarly, Gleeson et al. [ 15 ] and Le et al. [ 17 ] also observed a powerful association between renin levels and mortality. A significant association between GFR and survival was observed in the entire patient group in this study (p = 0.025). Similarly to the current research, another study also emphasized that renin may represent an optimal parameter for monitoring tissue hypoperfusion and may also be useful in the diagnosis of septic shock in patients receiving RRT [ 17 ]. Although numerous parameters predicting mortality have been described, due to the limited predictive power of these when evaluated individually, we applied combination tests in the expectation that a combination of different parameters would predict mortality in a more powerful manner. In addition, we thought that the daily use of this combination test would be simple and practical since blood samples could be collected from peripheral vascular access without the need for a special catheter as with ScvO2. Previous studies have observed that renin levels increase in cases of sepsis [ 36 ] and that it can predict mortality in sepsis and tissue hypoperfusion [ 37 ]. Elevated PRA levels have been shown to be capable of use in predicting infection and monitoring responses to treatment [ 11 , 38 , 39 ], and to be a potential prognostic biomarker in the follow-up of septic patients [ 11 ]. In the present study, PRC predicted survival in patients with shock and also in the entire patient group, but was not predictive of mortality in the septic shock group. In conclusion, combined test 3ᶠ involving PRC, mottling scores, CRP, and procalcitonin predicted mortality with an AUC of 0.894 (P < 0.001). The 30% ICU mortality rate reported in a previous study of shock patients in intensive care [ 15 ] was lower than that (56.8%) in the shock group patients in the present study. PRC levels were significantly associated with survival in this study. However, combining PRC levels with lactate, mottling score, CRP, and procalcitonin provides a better prediction of mortality than PRC alone. PRC levels have the potential for use as a good biomarker for patients with circulatory shock. Abbreviations RAAS renin-angiotensin-aldosterone system PRC plasma renin concentration ICU intensive care unit ScvO2 central venous saturation of oxygen CRP C-reactive protein GCS Glasgow Coma Scale GFR glomerular filtration rate SOFA Sequential Organ Failure Assessment APACHE II Acute Physiology and Chronic Health Evaluation II OST overall survival time CEI converting enzyme inhibitors ARB angiotensin receptor blockers STROBE Strengthening the Reporting of Observational Studies in Epidemiology EDTA ethylene diamine tetra-acetic acid ELISA enzyme-linked immunosorbent assay MDRD modification of diet in renal diseases OS Overall survival CPH Cox proportional hazards ROC receiver operating characteristics AUC area under curves CRRT continuous renal replacement therapy RRT renal replacement therapy PRA plasma renin activity Ang1 angiotensin 1 Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and the Guidelines for Good Clinical Practice. Written informed consent was obtained from all patients or their legal surrogates prior to inclusion.The Ethics Committee of the Faculty of Medicine of Marmara University approved the study (ethics number: 09.2019.883). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions Yasemin Bozkurt Turan (Corresponding author), MD, Department of Critical Care, Marmara University Faculty of Medicine, Pendik, Istanbul, Turkey Sait Karakurt, MD, Professor of Critical Care, Marmara University Faculty of Medicine, Pendik, Istanbul, Turkey Acknowledgements Not applicable. References Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, at al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Crit Care Med. 2021;49(11):e1063-e1143. World Health Organization Sepsis. [(accessed on 8 April 2022)]. Available online: https://www.who.int/news-room/fact-sheets/detail/sepsis) Jansen TC, van Bommel J, Schoonderbeek FJ, Visser SJS, van der Klooster JM, Lima AP et al. LACTATE Study Group: Early lactate-guided therapy in intensive care unit patients: A multicenter, open-label, randomized controlled trial. Am J Respir Crit Care Med. 2010; 182:752–761 Dumas G, Lavillegrand J-R, Joffre J, Bigé N, de-Moura EB, Baudel J-L, et al. Mottling score is a strong predictor of 14-day mortality in septic patients whatever vasopressor doses and other tissue perfusion parameters. Crit Care. 2019;23(1):211. doi: 10.1186/s13054-019-2496-4 . Jacquet-Lagrèze M, Wiart C, Schweizer R, Didier L, Ruste M, Coutrot M, et al. Capillary refill time for the management of acute circulatory failure: a survey among pediatric and adult intensivists. BMC Emerg Med. 2022 18;22(1):131. doi: 10.1186/s12873-022-00681-x . Ruste M, Sghaier R, Chesnel D, Didier L, Fellahi J-L, Jacquet-Lagrèze M. Perfusion-based deresuscitation during continuous renal replacement therapy: A before-after pilot study (The early dry Cohort). J Crit Care. 10.1016/j.jcrc.2022.154169 . Fuller BM, Dellinger RP: Lactate as a hemodynamic marker in the critically ill. Curr Opin Crit Care. 2012; 18:267–272 Chertoff J, Chisum M, Garcia B, Jorge Lascano J. Lactate kinetics in sepsis and septic shock: A review of the literature and rationale for further research. J Intensive Care. 2015; 3:39. Wu J, Li L, Luo J. Diagnostic and Prognostic Value of Monocyte Distribution Width in Sepsis. J Inflamm Res. 2022;15:4107–4117. Anush MM, Ashok VK, Sarma RI, Pillai SK. Role of C-reactive protein as an indicator for determining the outcome of sepsis. Indian J Crit Care Med. 2019;23(1):11–14. Chung KS, Song JH, Jung WJ, Kim YS, Kim SK, Chang J, et al. Implications of Plasma Renin Activity and Plasma Aldosterone Concentration in Critically Ill Patients with Septic Shock. Korean J Crit Care Med. 2017;32(2):142–153. Fournier D, Luft FC, Bader M, Ganten D, Andrade-Navarro MA. Emergence and evolution of the renin-angiotensin-aldosterone system. J Mol Med (Berl) 2012; 90(5):495–508. Kurtz A. Renin release: sites, mechanisms, and control. Annu Rev Physiol. 2011; 73:377–99. Hsu W-T, Galm BP, Schrank G, Hsu T-C, Lee S-H, Park JY et al. Effect of Renin-Angiotensin-Aldosterone System Inhibitors on Short-Term Mortality After Sepsis: A Population-Based Cohort Study. Hypertension. 2020;75(2); 483–491. Gleeson PJ, Crippa IA, Mongkolpun W, Cavicchi FZ, Meerhaeghe TV, Brimioulle S, et al. Renin as a Marker of Tissue-Perfusion and Prognosis in Critically Ill Patients. Crit. Care Med. 2019; 47(2): 152–158. Hernández G, Cavalcanti AB, Ospina-Tascón G, Dubin A, Hurtado FJ, Damiani LP, et al. Statistical analysis plan for early goal-directed therapy using a physiological holistic view - the ANDROMEDA-SHOCK: A randomized controlled trial. Rev Bras Ter Intensiva 2018 Jul-Sept;30(3):253–263. Le´snik P, Łysenko L, Krzystek-Korpacka M, Woźnica-Niesobska E, Mierzchała-Pasierb M, Janc J. Renin as a Marker of Tissue Perfusion, Septic Shock and Mortality in Septic Patients: A Prospective Observational Study. Int J Mol Sci. 2022;23(16): 9133. Jeyaraju M, McCurdy MT, Levine AR, Devarajan P, Mazzeffi MA, Mullins KE, et al.Renin Kinetics Are Superior to Lactate Kinetics for Predicting In-Hospital Mortality in Hypotensive Critically Ill Patients. Crit Care Med. 2022;50(1):50–60. Doerschug KC, Delsing AS, Schmidt GA, Ashare A. Renin-angiotensin system activation correlates with microvascular dysfunction in a prospective cohort study of clinical sepsis. Crit Care. 2010;14(1):R24. Nguyen M, Denimal D, Dargent A, Guinot P-G, Duvillard L, Quenot J-P, et al. Plasma Renin Concentration is Associated With Hemodynamic Deficiency and Adverse Renal Outcome in Septic Shock. Shock. 2019;52(4):e22-e30. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. Faix JD. Biomarkers of sepsis. Crit Rev Clin Lab Sci. 2013 Jan-Feb;50(1):23–36. Hassan J, Khan S, Zahra R, Razaq A, Zain A, Razaq L, et al. Role of Procalcitonin and C-reactive Protein as Predictors of Sepsis and in Managing Sepsis in Postoperative Patients: A Systematic Review. Cureus. 2022;14(11):e31067. Ait-Oufella H, Bourcier S, Alves M, Galbois A, Baudel J-L, Margetis D, et al. Alteration of skin perfusion in mottling area during septic shock. Ann Intensive Care. 2013;3(1):31. Lima A, Bakker J. Noninvasive monitoring of peripheral perfusion. Intensive Care Med. 2005;31(10):1316–26. de Moura EB, Amorim FF, da Cruz Santana AN, Kanhouche G, de Souza Godoy LG, de Jesus Almeida L, et al. Skin mottling score as a predictor of 28-day mortality in patients with septic shock. Intensive Care Med. 2016;42(3):479–480. Coudroy R, Jamet A, Frat J-P, Veinstein A, Chatellier D, Goudet V, et al. Incidence and impact of skin mottling over the knee and its duration on outcome in critically ill patients. Intensive Care Med. 2015;41(3):452–9. Boulain T, Garot D, Vignon P, Lascarrou J-B, Desachy A, Botoc V, at al. Prevalence of low central venous oxygen saturation in the first hours of intensive care unit admission and associated mortality in septic shock patients: a prospective multicentre study. Crit Care. 2014;18(6):609. Bellomo R, Forni LG, Busse LW, McCurdy MT, Ham KR, Boldt DW et al. Renin and survival in patients given angiotensin II for catecholamine-resistant vasodilatory shock. A clinical trial. Am J Respir Crit Care Med. 2020;202(9):1253–1261. Vergaro G, Emdin M, Iervasi A, Zyw L, Gabutti A, Poletti R, et al. Prognostic value of plasma renin activity in heart failure. Am J Cardiol. 2011;108(2):246–51. Liu Z, Jin L, Zhou W, Zhang C. The spectrum of plasma renin activity and hypertension diseases: Utility, outlook, and suggestions. J Clin Lab Anal. 2022; 36(11): e24738. Thienpont LM, Van Uytfanghe K, De Leenheer AP. Reference measurement systems in clinical chemistry. Clin Chim Acta. 2002;323(1–2):73–87. de Bruin RA, Bouhuizen A, Diederich S, Perschel FH, Boomsma F, Deinum J. Validation of a new automated renin assay. Clin Chem. 2004;50(11):2111–2116. Khanna AK. Renin kinetics and mortality-same, same but different? Crit Care Med. 2022;50(1):153–157. Andersen LW, Mackenhauer J, Roberts JC, Berg KM, Cocchi MN, Donnino MW. Etiology and therapeutic approach to elevated lactate levels. Mayo Clin Proc. 2013;88(10):1127-40. Senatore F, Balakumar P, Jagadeesh G. Dysregulation of the renin-angiotensin system in septic shock: Mechanistic insights and application of angiotensin II in clinical management. Pharmacol Res. 2021;174:105916. Khanna AK. Tissue perfusion and prognosis in the critically ill-Is renin the new lactate? Crit Care Med. 2019;47(2):288–290. Póvoa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P, et al. Early identification of intensive care unit-acquired infections with daily monitoring of C-reactive protein: a prospective observational study. Crit Care. 2006;10(2):R63. Póvoa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P, et al. Pilot study evaluating C-reactive protein levels in the assessment of response to treatment of severe bloodstream infection. Clin Infect Dis. 2005;40(12):1855–7. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3962245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":276931387,"identity":"eefece62-4270-4971-843b-c56274633b04","order_by":0,"name":"Yasemin Bozkurt Turan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACxgYoIQHifWwAizUeIFYLY+PMBjDdgFcLXB9IaTNvA8Q2vFqY248/fHRzh4295Izc549td9jU6bYfBtpSYxON04KeHGPj3DNpzNIS6YbNQIaE2ZlEoJZjabkNON2Uwyad23aYTU4ijbEZyJAwOwDUwthwGLeW/ufPfwNV8oC1WIK0nH9IQMuMBDNmkOHSIC2MIC03CNky442xNNALBpI9zxhn9ralSW67AbQlAY9fDPvTH37OBYaYxPE0hg8/22z4zc6nP3zwocYGtxbsEgk4lIOAPB65UTAKRsEoGAUQAACWLmG5TeM73gAAAABJRU5ErkJggg==","orcid":"","institution":"Marmara University Faculty of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yasemin","middleName":"Bozkurt","lastName":"Turan","suffix":""},{"id":276931388,"identity":"c2bb8ba1-d1f8-44cc-808a-901604aa3980","order_by":1,"name":"Sait Karakurt","email":"","orcid":"","institution":"Marmara University Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sait","middleName":"","lastName":"Karakurt","suffix":""}],"badges":[],"createdAt":"2024-02-16 19:59:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3962245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3962245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52448801,"identity":"0645b330-41f4-45be-9b04-29764167a4ec","added_by":"auto","created_at":"2024-03-11 18:44:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404256,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE flow chart of the study participants\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3962245/v1/19f194014f8ec15b4aacbf18.jpeg"},{"id":52448804,"identity":"8ecf639f-8eac-4ae3-bdc4-0af4ad051da4","added_by":"auto","created_at":"2024-03-11 18:45:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13774,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for patients with and without shock in the ICU\u003c/p\u003e\n\u003cp\u003eThe median survival time was 17 (5.4 – 28.7) days and the survival probability among the ICU patients was 47%. The median OST was significantly higher in patients in the ICU without circulatory shock than in those with shock (17.00 days vs 16.00 days, respectively; Wilcoxon x2=5,016; p=0,038) (Table 2).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3962245/v1/badd5fc76ea8f7576f8fb9f7.png"},{"id":52448701,"identity":"4e218f4c-9294-4374-828c-ad1dc8ceecc1","added_by":"auto","created_at":"2024-03-11 18:44:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44240,"visible":true,"origin":"","legend":"\u003cp\u003ea: ROC curves for PRK, lactate, and three combined tests predicting ICU 28-day mortality in patients with shock.\u003c/p\u003e\n\u003cp\u003eb: ROC curves for PRC, lactate, and combined tests predicting ICU 28-day mortality in patients without shock\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3962245/v1/e95c8cb0ae0fb271de6d2ac4.png"},{"id":54153857,"identity":"7a884e93-22e0-4380-b618-979ad8a167b6","added_by":"auto","created_at":"2024-04-05 11:25:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":524284,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3962245/v1/3d4715a7-5c6f-4741-94cc-edd23084b0a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Importance of Plasma Renin Concentration in Intensive Care Patients with Circulatory Shock","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSepsis is a life-threatening organ dysfunction resulting from a dysregulated response to infection in the host [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Sepsis and septic shock are a major health problem affecting more than 30\u0026nbsp;million individuals worldwide every year, with reported mortality rates between one in three and one in six [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Impaired tissue perfusion in sepsis is a cause of increased mortality and morbidity. A number of biomarkers have been identified for the early diagnosis of tissue perfusion disorder. Lactate [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], mottling scores [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], capillary filling time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and central venous saturation of oxygen (S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] can be used as guides to early resuscitation in sepsis and septic shock, and several studies have shown that the use of these can predict mortality.\u003c/p\u003e \u003cp\u003eC-reactive protein (CRP) and procalcitonin are other biomarkers that can be used to predict prognosis in septic patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores are employed as for classifying severity in septic patients in the intensive care unit (ICU) or predicting prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe renin-angiotensin-aldosterone system (RAAS) plays a very important role in blood pressure regulation and in the preservation of extracellular fluid volume [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Renin is secreted as a response to decreased tissue perfusion, sympathetic activation, and hypoxic metabolism [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is therefore a marker of hypoperfusion and is regarded as a good indicator of for RAAS activity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Renin has been reported to exhibit 100% sensitivity and negative predictive values in predicting mortality in intensive care patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and has been shown to be a better marker of tissue perfusion than lactate [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e This study compared PRC, vasopressor requirements, 28-day dialysis requirements, 28-day mechanical ventilation requirements, 28-day mortality rates, lengths of ICU stay, and survival rates on the third day after discharge from the ICU in intensive care patients with and without septic shock and investigated the role of PRC in showing tissue perfusion.\u003c/p\u003e "},{"header":"METHOD","content":" \u003cp\u003eThis prospective study was performed with patients aged 18 or over admitted to the Marmara University Hospital tertiary ICU, T\u0026uuml;rkiye, between November 2019 and November 2020. Informed written consent was received either from the patients themselves or their families. The research was conducted in compliance with the Declaration of Helsinki and Good Clinical Practice guidelines. Approval was granted by the Marmara University Medical Faculty clinical research ethical committee (no. 09.2019.883).\u003c/p\u003e \u003cp\u003eIndividuals with hemolytic samples, using converting enzyme inhibitors (CEI) and angiotensin receptor blockers (ARB) in the previous 48 hours, using diuretics in the previous six hours, pregnant women, lactating mothers, and patients with chronic stimulation of the mineralocorticoid axis (cirrhosis, advanced heart failure, or chronic kidney failure) were excluded from the study. Eighty-nine patients were hospitalized to intensive care during the study period, of whom eight were excluded since CVP catheters were not installed, four due to using converting enzyme inhibitors and angiotensin blockers in the previous 48 hours and three due to using diuretics in the previous six hours were excluded. Two patients with cirrhosis with hemorrhagic shock, one with advanced heart failure with cardiogenic shock, and two with chronic kidney failure were also excluded. The study was thus performed with 69 patients, 37 in the septic shock group and 32 in the non-shock group. The study flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology (STROBE) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] instructions were applied when constituting the flow chart.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecimen Collection and Measurements\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBlood specimens collected from the radial artery catheter in patients admitted to the ICU were placed into tubes containing ethylene diamine tetra-acetic acid (EDTA). These were centrifuged within 30 min at 2000\u0026ndash;3000 rpm at 2\u0026ndash;8\u0026deg; C for 14 min and then stored at -80\u0026deg; C for six months. Plasma renin levels were analyzed using the enzyme-linked immunosorbent assay (ELISA) method previously employed by Le\u0026rsquo;snik et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Since renin measurements are not affected by the diurnal rhythm [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], blood samples were collected once for each patient.\u003c/p\u003e \u003cp\u003eThe patients\u0026rsquo; demographic data, comorbidity status, vasopressor use, Glasgow Coma Scale (GCS) scores, mottling scores, SOFA scores, APACHE II scores, and lactate, S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, CRP, procalcitonin, and creatinine levels were recorded. The glomerular filtration rate (GFR) was calculated using the modification of diet in renal diseases (MDRD) study formula.\u003c/p\u003e \u003cp\u003ePRC, vasopressor requirements, 28-day dialysis requirements, 28-day mechanical ventilation requirements, 28-day mortality rates, lengths of ICU stay, and survival rates on the third day after discharge from the ICU were compared between intensive care patients with and without septic shock, and the role of PRC in showing tissue perfusion was investigated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample size\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe total sample size required in order to determine the mean difference in PRC values between the shock and without shock groups was calculated as 58, with an alpha error of 5%, power of 90%, and effect size d\u0026thinsp;=\u0026thinsp;0.8, and an allocation ratio of 1:1 using G*Power version 3.1 software.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted on IBM SPSS version 25 (IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA: IBM Corp.) and STATA 15 (Stata Statistical Software: Release 15. College Station, TX, USA: Stata Corp LLC.) softwares. Patient characteristics were compared between patients with and without circulatory shock using the independent samples T-test, Mann-Whitney U test, Pearson\u0026rsquo;s chi-square test, Yates correction for continuity, and Fisher\u0026rsquo;s exact test. In the survival analysis, follow-up time was measured from the date of admission to the ICU to the date of mortality from any cause in the ICU or the date of discharge. The seven patients with survival times exceeding 40 days in the ICU were evaluated as outliers and excluded from the survival analysis. Overall survival (OS) time and survival probability were estimated using the Kaplan-Meier method. The survival curves of the patients with and without circulatory shock were compared using the Wilcoxon (Breslow) test. Inflammatory parameters predicting survival were examined using the Cox proportional hazards (CPH) model with the forward stepwise method. Schoenfeld\u0026rsquo;s residuals were investigated for proportional hazard assumptions. Test results for mottling, S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, CRP, procalcitonin, and PRC were combined to improve the accuracy of 28-day mortality prediction in the ICU using logistic regression. Area under receiver operating characteristics (ROC) curves were used for lactate, PRC, and combined tests as predictors of ICU 28-day mortality. Statistical significance was set at 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ePatient Characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe characteristics of the ICU patients and comparisons between the shock and non-shock groups are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, 1aa, and 1b. The mean age of the patients was 61.5 (\u0026plusmn;\u0026thinsp;16.4) years, and more than half were men (n\u0026thinsp;=\u0026thinsp;40, 58.0%). The shock and non-shock groups were similar in terms of age, gender, BMI, and presence of comorbidity (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Use of 28-day dialysis and mechanical ventilation was significantly higher in the patients with circulatory shock patients than in those without (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Rates of adrenalin, noradrenalin, and terlipressin use were significantly higher in the patients with circulatory shock in the ICU (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05). ICU 28-day mortality was 36.2% (n\u0026thinsp;=\u0026thinsp;25), and was significantly higher in patients with circulatory shock than those without (SS:21, 56.8% vs NS:4, 12.5%, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The three-day survival rate after discharge from the ICU was 66.7% (n\u0026thinsp;=\u0026thinsp;46), and was significantly lower in the patients with circulatory shock (SS:17, 45.9% vs NS:29, 90.6%, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median number of days spent in the ICU was significantly higher in the patients with circulatory shock than in those without (SS:10.0(3.0\u0026ndash;31.5) vs NS:3.0(1.0\u0026ndash;9.5), respectively, p\u0026thinsp;=\u0026thinsp;0,006). The patients with circulatory shock had lower GCS, GFR, and S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e values than those without shock (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05). Test results for SOFA, APACHE II scores, lactate, creatinine, CRP, procalcitonin, PRC, and mottling were higher in the patients with circulatory shock (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05) (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, 1aa and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea: ICU patient characteristics (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients with septic shock (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatients without septic shock (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.162*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.212**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4 (23.5\u0026ndash;31.2)\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2 (23.4\u0026ndash;30.8)\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.7 (24.0\u0026ndash;31.3)\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.521***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e28-day dialysis use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e28-day mechanical ventilation use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRespiratory distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eComorbidity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (75.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.584**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.999**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.781**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCardiovascular diseases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.999**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRespiratory disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;1.000*****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMalignancy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;1.000*****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther diseases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.809****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026sup1;n\u0026thinsp;=\u0026thinsp;58, \u0026sup2;n\u0026thinsp;=\u0026thinsp;32, \u0026sup3;n\u0026thinsp;=\u0026thinsp;26, ⁴n\u0026thinsp;=\u0026thinsp;29, ⁵n\u0026thinsp;=\u0026thinsp;61. *T-test; **Pearson Chi-square test; ***Mann-Whitney U test; ****Yates correction for continuity;*****Fisher\u0026rsquo;s Exact test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eaa: ICU patients\u0026rsquo; characteristics and outcomes (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients with septic shock (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatients without septic shock (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVasopressor use, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (97.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTerlipressin use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.028****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAdrenaline use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNoradrenaline use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (97.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e28-day mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThree-day survival after discharge from the ICU, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (90.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLength of ICU stay in days, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (2.0\u0026ndash;16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 (3.0\u0026ndash;31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0 (1.0\u0026ndash;9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.006***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026sup1;n\u0026thinsp;=\u0026thinsp;58. \u0026sup2;n\u0026thinsp;=\u0026thinsp;32. \u0026sup3;n\u0026thinsp;=\u0026thinsp;26. ⁴n\u0026thinsp;=\u0026thinsp;29. ⁵n\u0026thinsp;=\u0026thinsp;61. *T-test; **Pearson Chi-square test; ***Mann-Whitney U test; ****Yates correction for continuity;*****Fisher\u0026rsquo;s Exact test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: ICU patients\u0026rsquo; laboratory parameters (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients with septic shock (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients without septic shock (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0 (3.5\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0 -14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0 (13.3\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (2.0\u0026ndash;11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (6.5\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (0.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.7 (36.2\u0026ndash;119.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.6 (30.9\u0026ndash;103.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.7 (68.5 -131.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.006***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (1.3\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (1.5\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.0\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.7\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (0.7\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.6\u0026ndash;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.6 (35.4\u0026ndash;198.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.0 (73.5\u0026ndash;259.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.8 (13.0 -106.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (0.3\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3 (1.8\u0026ndash;22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (0.1\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRC, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.1 (151.5\u0026ndash;196.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175.4 (162.6\u0026ndash;234.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155.4 (147.8\u0026ndash;175.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMottling, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;1.0) ⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.0\u0026ndash;2.0) \u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.0) ⁴\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026sup2;n\u0026thinsp;=\u0026thinsp;32. ⁴n\u0026thinsp;=\u0026thinsp;29. ⁵n\u0026thinsp;=\u0026thinsp;61. *T-test; **Pearson Chi-square test; ***Mann-Whitney U test; ****Yates correction for continuity;*****Fisher\u0026rsquo;s Exact test.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSurvival Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier survival curves for patients with shock (n\u0026thinsp;=\u0026thinsp;31) and without shock (n\u0026thinsp;=\u0026thinsp;31) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe median survival time was 17 (5.4\u0026ndash;28.7) days and the survival probability among the ICU patients was 47%. The median OST was significantly higher in patients in the ICU without circulatory shock than in those with shock (17.00 days vs 16.00 days, respectively; Wilcoxon \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e=5,016; p\u0026thinsp;=\u0026thinsp;0,038) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMedian survival times of the patient groups in the ICU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Events\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian OST (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival probability (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epᵃ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00 (11.1\u0026ndash;20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.21\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.06\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00 (5.4\u0026ndash;28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.26\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eᵃWilcoxon (Breslow) test p-value comparing survival curves of patients with and without shock. ᵇStandard error not computed.\u003c/p\u003e \u003cp\u003eThe inflammatory parameters predicting the survival of the ICU patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e (n\u0026thinsp;=\u0026thinsp;48, number of events\u0026thinsp;=\u0026thinsp;15). The increase in mottling (HR:1.64(1.15\u0026ndash;2.33); p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and PRC (HR\u0026thinsp;=\u0026thinsp;1.01(1.00\u0026ndash;1.02), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) levels and the decrease in GFR (HR\u0026thinsp;=\u0026thinsp;0.98(0.96\u0026ndash;0.99), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were associated with decreased survival times in the ICU patients (-2 Log Likelihood\u0026thinsp;=\u0026thinsp;59,237; Chi-square\u0026thinsp;=\u0026thinsp;17.105; df\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazard model predicting survival in ICU patients (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameterᵃ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eTest of Proportional-Hazards assumption\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003echi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epᵇ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMottling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (1.15\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eᵃVariable selection with the forward (likelihood ratio) stepwise method. The variables entered the model were age, gender, BMI, mechanical ventilation use, dialysis use, GCS, SOFA, APACHE II, mottling, GFR, lactate, S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, creatinine, CRP, procalcitonin, PRC, and presence of circulatory shock. ᵇSchoenfeld residuals test p-value.\u003c/p\u003e \u003cp\u003e \u003cem\u003eROC curve predicting 28-day mortality in the ICU\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePatients with shock\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity, specificity, and area under curves (AUC) values for PRC, lactate, and combined tests are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003ea (n\u0026thinsp;=\u0026thinsp;32). The lactate and combined tests emerged as indicators of ICU 28-day mortality in patients with shock (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while PRC was not such an indicator (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The ROC curves of the tests are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea: Performance of the tests predicting 28-day mortality in ICU patients with shock\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI for AUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-valueᵃ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.225\u0026ndash;0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate ͨ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.517\u0026ndash;0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined test 1ᵈ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined test 2ᵉ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.713\u0026ndash;0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined test 3ᶠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eᵃNull hypothesis: true area\u0026thinsp;=\u0026thinsp;0,5. ᵇcut-off =\u0026thinsp;169,34. ͨ cut-off =\u0026thinsp;1,50. ᵈ PRC, Mottling, S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, CRP, and Procalcitonin. ᵉ PRC, Lactate and Mottling. ᶠ PRC, Lactate, Mottling, CRP, and Procalcitonin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea: ROC curves for PRK, lactate, and three combined tests predicting ICU 28-day mortality in patients with shock.\u003c/p\u003e \u003cp\u003eThe areas under the ROC of the five tests differed significantly (Chi-square\u0026thinsp;=\u0026thinsp;21.09; df\u0026thinsp;=\u0026thinsp;4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was no statistically significant difference between the AUC values for lactate and the three combined tests (Chi-square\u0026thinsp;=\u0026thinsp;3.77; df\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;=\u0026thinsp;0.287). Similarly, the difference between the AUC values of the combined tests was not statistically significant (Chi-square\u0026thinsp;=\u0026thinsp;1.06; df\u0026thinsp;=\u0026thinsp;2; p\u0026thinsp;=\u0026thinsp;0.589) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePatients without shock\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity, specificity, and AUC values for PRC, lactate, and combined tests (S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, CRP, procalcitonin, and PRC) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb (n\u0026thinsp;=\u0026thinsp;32). Combined tests 1 and 3 emerged as indicators of ICU 28-day mortality in patients without shock (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not PRC, lactate, or combined test 2 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The tests\u0026rsquo; ROC curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. Significant differences were observed between the AUC values for PRC, lactate, and combined tests (Chi-square\u0026thinsp;=\u0026thinsp;122.63; df\u0026thinsp;=\u0026thinsp;4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). AUC was significantly higher for lactate than for PRC (Chi-square\u0026thinsp;=\u0026thinsp;4.44; df\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;=\u0026thinsp;0.035), while no difference was determined in the AUC values for the bivariate comparisons of the other tests (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Performance of tests predicting 28-day mortality in ICU patients without shock\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI for AUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-valueᵃ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u0026ndash;0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate ͨ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u0026ndash;0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined test 1ᵈ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined test 2ᵉ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378\u0026ndash;0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined test 3ᶠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.628\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eᵃNull hypothesis: true area\u0026thinsp;=\u0026thinsp;0.5. ᵇ cut-off =\u0026thinsp;154.61. ͨ cut-off =\u0026thinsp;2.40. ᵈ PRC, S\u003csub\u003ecv\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, CRP and Procalcitonin. ᵉ PRC and Lactate. ᶠ PRC, Lactate, CRP, Procalcitonin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the relationship between mortality, survival and PRC for the circulatory shock, non-circulatory shock, and total patient groups. PRC levels were significantly higher in the patients with shock (p\u0026thinsp;=\u0026thinsp;0.001), and PRC levels were significantly associated with survival at analysis of all the intensive care patients (p\u0026thinsp;=\u0026thinsp;0.038). However, no difference was determined in PRC levels between the exitus and surviving patient groups (p\u0026thinsp;=\u0026thinsp;0.503).\u003c/p\u003e \u003cp\u003eA combined test including PRC, lactate, mottling score, CRP, and procalcitonin exhibited the best performance in predicting mortality in both patients with and without shock (AUC 0.894, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe prediction of mortality of mottling score (p\u0026thinsp;=\u0026thinsp;0.006), GFR (p\u0026thinsp;=\u0026thinsp;0.025) and PRC (p\u0026thinsp;=\u0026thinsp;0.038) at Cox analysis in the entire patient group was similar to that of previous studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile no significant difference in mechanical ventilation use rates in hypotensive critical patients in intensive care was observed between surviving and exitus groups (p\u0026thinsp;=\u0026thinsp;0.53) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], high mechanical ventilation rates in patients with severe sepsis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and exitus patients with septic shock in intensive care [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] were similar to the findings for the shock group patients in the present study (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While continuous renal replacement therapy (CRRT) indicated 28-day mortality in patients with septic shock at univariate analysis (p 0.006), it did not indicate this at multivariate analysis (p 0.085) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. PRC levels were significantly elevated in patients requiring renal replacement therapy (RRT) among patients in intensive care with septic shock (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while a significant positive correlation was observed between PRC and outcomes on the 28th day of RRT (correlation 0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (20). CRRT use in patients with shock was significantly high at univariate analysis in the present study (p\u0026thinsp;=\u0026thinsp;0.002), but did not predict mortality at multivariate analysis.\u003c/p\u003e \u003cp\u003eAt univariate analysis, APACHE II, SOFA, and length of ICU stay, which were high in the shock patient group, were similar to those reported in septic shock patients in other studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Lactate values, which are prognostic in the diagnosis of tissue perfusion disorder [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and procalcitonin and CRP values used in predicting prognosis in septic patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], were elevated in the shock group in this study. The prediction of survival by mottling score (p\u0026thinsp;=\u0026thinsp;0.006), GFR (p\u0026thinsp;=\u0026thinsp;0.025) and PRC (p\u0026thinsp;=\u0026thinsp;0.038) at Cox analysis in the entire patient group was similar to that reported in other studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMottling, defined as patchy skin discoloration, reflects a decreased cutaneous blood flow [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and vasoconstriction [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and has been shown to predict mortality in patients with septic shock [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mottling scores also predicted survival in the entire intensive care patient group in the present study (p\u0026thinsp;=\u0026thinsp;0.006). Similarly to Boulainve et al\u0026rsquo;s. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] study of septic shock patients, significantly low central venous oxygen saturation (ScvO2) was observed in the patients with shock in this research. Vasopressor use was significantly high in the shock patients in this study (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and was in favor of tissue hypoperfusion [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLactate (AUC\u0026thinsp;=\u0026thinsp;0.731, P\u0026thinsp;=\u0026thinsp;0.017), combined test 1ᵈ, consisting of PRC, mottling score, ScvO2, CRP, and procalcitonin (AUC\u0026thinsp;=\u0026thinsp;0.898, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), combined test 2ᵉ, consisting of PRC, lactate, and mottling score (AUC\u0026thinsp;=\u0026thinsp;0.847, p\u0026thinsp;=\u0026thinsp;0.001), and combined test 3ᶠ, consisting of PRC, lactate, mottling score CRP, and procalcitonin (AUC\u0026thinsp;=\u0026thinsp;0.894, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) predicted mortality in patients with shock.\u003c/p\u003e \u003cp\u003eIn terms of the non-shock patients, combined test 1ᵈ, consisting of PRC, mottling score, ScvO2, CRP, and procalcitonin (AUC\u0026thinsp;=\u0026thinsp;0.920, P\u0026thinsp;=\u0026thinsp;0.018) and combined test 3ᶠ, consisting of PRC, lactate, CRP, and procalcitonin (AUC 0.862, P\u0026thinsp;=\u0026thinsp;0.042) predicted mortality.\u003c/p\u003e \u003cp\u003eAlthough PRC predicted survival at Cox analysis performed among the entire patient group, it failed to predict mortality at analysis in the surviving and non-surviving groups, which may be attributable to the low patient numbers. Since the sample size was calculated with G power without prediction of mortality, the patient numbers may have been insufficient in that respect. The sample size was calculated for the difference between PRC levels in groups with and without septic shock.\u003c/p\u003e \u003cp\u003eThe median PRC value in this study was 175.4 ng/L, and higher PRC values were determined to be predictive of shock development (p\u0026thinsp;=\u0026thinsp;0.001) and survival (p\u0026thinsp;=\u0026thinsp;0.038). A median renin concentration of 172.7 pg/mL and above in patients with catecholamine-resistant septic shock has been reported to significantly reduce 28-day mortality when angiotensin II was used compared to placebo [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A threshold value for plasma renin activity (PRA)\u0026thinsp;\u0026ge;\u0026thinsp;2.3 ng/ml/h in patients with heart failure has been reported to be capable of predicting cardiac mortality [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and a PRA value\u0026thinsp;\u0026ge;\u0026thinsp;3.5 ng/ml/h in patients with septic shock is both prognostic and predictive of 28-day mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while renin concentrations have been reported to predict mortality in patients with sepsis and septic shock at a cut-off value of 87 pg/mL [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the studies described above were performed with different test methods and different patient groups, and numerical comparisons are not possible since differing renin cut-off values were employed.\u003c/p\u003e \u003cp\u003eAs shown above, studies have been performed with PRA or PRC. The present study was conducted with PRC levels. The direct renin concentration represents the renin concentration in plasma, while PRA reflects the renin enzyme-like activity required to bread down angiotensin and produce angiotensin 1 (Ang1). This makes it possible to calculate the amount of Ang1 produced in a unit of time as PRA (ng/ml/h) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have been performed to ensure standardization in PRA tests [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. PRA is reported to be capable of measuring a much lower renin value than the PRC test [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The use of the PCR test in the present study may have limited its value in terms of measuring or understanding the true value of renin.\u003c/p\u003e \u003cp\u003eGleeson et al. determined that direct plasma renin levels above the normal upper limit (\u0026gt;\u0026thinsp;40 \u0026micro;U/mL) predicted intensive care mortality, but that lactate levels above the upper normal limit (\u0026gt;\u0026thinsp;2 mM) were not predictive of mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Indeed, each unit increase in ln-renin concentrations is reported to raise in-hospital mortality 10-fold, although lactate exhibits no such effect [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, renin levels exceeding 40 pg/ml were reported to be associated with in-hospital mortality in another study, while no relationship as determined between lactate levels greater than 2 mmol/L and in-hospital mortality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Lactate, an indicator of tissue hypoxia [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was both higher in the patients with shock in the present study (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and also predicted mortality (p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003cp\u003ePRC levels and 28-day mortality rates were higher in the shock group in the present study p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while three-day survival rates after discharge were lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, Gleeson et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Le et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] also observed a powerful association between renin levels and mortality.\u003c/p\u003e \u003cp\u003eA significant association between GFR and survival was observed in the entire patient group in this study (p\u0026thinsp;=\u0026thinsp;0.025). Similarly to the current research, another study also emphasized that renin may represent an optimal parameter for monitoring tissue hypoperfusion and may also be useful in the diagnosis of septic shock in patients receiving RRT [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough numerous parameters predicting mortality have been described, due to the limited predictive power of these when evaluated individually, we applied combination tests in the expectation that a combination of different parameters would predict mortality in a more powerful manner. In addition, we thought that the daily use of this combination test would be simple and practical since blood samples could be collected from peripheral vascular access without the need for a special catheter as with ScvO2.\u003c/p\u003e \u003cp\u003ePrevious studies have observed that renin levels increase in cases of sepsis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and that it can predict mortality in sepsis and tissue hypoperfusion [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Elevated PRA levels have been shown to be capable of use in predicting infection and monitoring responses to treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and to be a potential prognostic biomarker in the follow-up of septic patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the present study, PRC predicted survival in patients with shock and also in the entire patient group, but was not predictive of mortality in the septic shock group.\u003c/p\u003e \u003cp\u003eIn conclusion, combined test 3ᶠ involving PRC, mottling scores, CRP, and procalcitonin predicted mortality with an AUC of 0.894 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe 30% ICU mortality rate reported in a previous study of shock patients in intensive care [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was lower than that (56.8%) in the shock group patients in the present study.\u003c/p\u003e \u003cp\u003ePRC levels were significantly associated with survival in this study. However, combining PRC levels with lactate, mottling score, CRP, and procalcitonin provides a better prediction of mortality than PRC alone. PRC levels have the potential for use as a good biomarker for patients with circulatory shock.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRAAS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;renin-angiotensin-aldosterone system\u003c/p\u003e\n\u003cp\u003ePRC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; plasma renin concentration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; intensive care unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScvO2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;central venous saturation of oxygen \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Glasgow Coma Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGFR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; glomerular filtration rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sequential Organ Failure Assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPACHE II \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute Physiology and Chronic Health Evaluation II\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; overall survival time\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCEI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;converting enzyme inhibitors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eARB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;angiotensin receptor blockers\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSTROBE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003eEDTA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ethylene diamine tetra-acetic acid\u003c/p\u003e\n\u003cp\u003eELISA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; enzyme-linked immunosorbent assay\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDRD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;modification of diet in renal diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Overall survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCPH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cox proportional hazards\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; receiver operating characteristics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; area under curves\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRRT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; continuous renal replacement therapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRRT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; renal replacement therapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; plasma renin activity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAng1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; angiotensin 1\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and the Guidelines for Good Clinical Practice. Written informed consent was obtained from all patients or their legal surrogates prior to inclusion.The Ethics Committee of the Faculty of Medicine of Marmara University approved the study (ethics number:\u0026nbsp;09.2019.883).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYasemin Bozkurt Turan (Corresponding author),\u0026nbsp;MD, Department of Critical Care, Marmara University Faculty of Medicine, Pendik, Istanbul, Turkey\u003c/p\u003e\n\u003cp\u003eSait Karakurt, MD, Professor of\u0026nbsp;Critical Care, Marmara University Faculty of Medicine, Pendik, Istanbul, Turkey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEvans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, at al. 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J Clin Lab Anal. 2022; 36(11): e24738.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThienpont LM, Van Uytfanghe K, De Leenheer AP. Reference measurement systems in clinical chemistry. Clin Chim Acta. 2002;323(1\u0026ndash;2):73\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bruin RA, Bouhuizen A, Diederich S, Perschel FH, Boomsma F, Deinum J. Validation of a new automated renin assay. Clin Chem. 2004;50(11):2111\u0026ndash;2116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanna AK. Renin kinetics and mortality-same, same but different? Crit Care Med. 2022;50(1):153\u0026ndash;157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen LW, Mackenhauer J, Roberts JC, Berg KM, Cocchi MN, Donnino MW. Etiology and therapeutic approach to elevated lactate levels. Mayo Clin Proc. 2013;88(10):1127-40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenatore F, Balakumar P, Jagadeesh G. Dysregulation of the renin-angiotensin system in septic shock: Mechanistic insights and application of angiotensin II in clinical management. Pharmacol Res. 2021;174:105916.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanna AK. Tissue perfusion and prognosis in the critically ill-Is renin the new lactate? Crit Care Med. 2019;47(2):288\u0026ndash;290.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026oacute;voa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P, et al. Early identification of intensive care unit-acquired infections with daily monitoring of C-reactive protein: a prospective observational study. Crit Care. 2006;10(2):R63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026oacute;voa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P, et al. Pilot study evaluating C-reactive protein levels in the assessment of response to treatment of severe bloodstream infection. Clin Infect Dis. 2005;40(12):1855\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renin, circulatory shock, mortality, intensive care, tissue perfusion","lastPublishedDoi":"10.21203/rs.3.rs-3962245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3962245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction:\u003c/p\u003e\n\u003cp\u003eRenin is a hypoperfusion marker and a good index of renin-angiotensin-aldosterone system (RAAS) activity. The purpose of this study was to evaluate whether the plasma renin concentration (PRC) can represent a tissue perfusion marker for predicting mortality in patients with circulatory shock in intensive care.\u003c/p\u003e\n\u003cp\u003eMethod:\u003c/p\u003e\n\u003cp\u003eThis prospective study involved patients aged 18 or over in a tertiary intensive care unit (ICU). Sixty-nine patients were included, 37 of whom constituted the circulatory shock group, and 32 a non-shock control group. Blood specimens were collected to measure PRC levels. Combined tests including PRC, mottling scores, central venous saturation of oxygen (ScvO2), C-reactive protein (CRP), procalcitonin, and lactate were constituted.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eThe patients’ mean age was 61.5 (±16.4) years, and 58.0% (n=40) were men. Mean number of days in the ICU, ICU 28-day mortality, ICU 28-day dialysis requirements, ICU 28-day mechanical ventilation requirements, and adrenalin, noradrenalin, and terlipressin use were all higher in the patients with circulatory shock (p\u0026lt;0.05). Three-day survival following discharge from the ICU, Glasgow Coma Scale (GCS) scores, glomerular filtration rate (GFR), and ScvO2 levels were lower in the patients with circulatory shock (p\u0026lt;0.05). Sequential Organ Failure Assessment (SOFA) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, lactate, creatinine, CRP, procalcitonin, PRC, and mottling score values were higher in the circulatory shock group (p\u0026lt;0.05). Median overall survival time (OST) was higher in the non-circulatory shock patients (17.00 days; Wilcoxon χ^2=5.016; p=0.038). The increase in mottling (HR:1.64(1.15 – 2.33); p\u0026lt;0.01) and PRC (HR=1.01(1.00 – 1.02); p\u0026lt;0.05) levels and the decrease in GFR (HR=0.98(0.96 – 0.99); p\u0026lt;0.05) values in the ICU patients were correlated with length of survival (-2 Log Likelihood=59.237; Chi-square=17.105; df=3; p\u0026lt;0.001 (p=0.0007)).\u003c/p\u003e\n\u003cp\u003eCombined test 1ᵈ (PRC, mottling, ScvO2, CRP, and procalcitonin), combined test 2ᵉ (PRC, lactate, and mottling), combined test 3ᶠ (PRC, lactate, mottling, CRP, and procalcitonin), and lactate emerged as indicators of 28-day mortality in patients with circulatory shock (p\u0026lt;0.05), although PRC did not represent such an indicator (p\u0026gt;0,05).\u003c/p\u003e\n\u003cp\u003eCombined test 1ᵈ (PRC, ScvO2, CRP, and procalcitonin) and combined test 3ᶠ (PRC, lactate, CRP, and procalcitonin) emerged as markers of 28-day survival in patients without circulatory shock (p\u0026lt;0.05), but not combined test 2ᵉ (PRC and lactate), PRC, or lactate (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eConclusion:\u003c/p\u003e\n\u003cp\u003eA significant association was observed between PRC levels and survival. Combining PRC levels with lactate, mottling score, CRP, and procalcitonin results in better prediction of mortality than PRC alone. PRC levels have the potential for use as a good marker for patients with circulatory shock.\u003c/p\u003e","manuscriptTitle":"The Importance of Plasma Renin Concentration in Intensive Care Patients with Circulatory Shock","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 18:44:28","doi":"10.21203/rs.3.rs-3962245/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16525212-fe22-4c36-bfb7-82ff0bcd516f","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29186931,"name":"Health sciences/Biomarkers"},{"id":29186932,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-04-05T11:17:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 18:44:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3962245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3962245","identity":"rs-3962245","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}