The Impact of Hemoglobin Trajectory on Clinical Outcomes in Severe Cardiogenic Shock: Insights from a Cohort Study

The Impact of Hemoglobin Trajectory on Clinical Outcomes in Severe Cardiogenic Shock: Insights from a Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Hemoglobin Trajectory on Clinical Outcomes in Severe Cardiogenic Shock: Insights from a Cohort Study Jing Tian, Yi Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6516923/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The aim of this study was to investigate the relationship between haemoglobin (Hb) trajectory and 28-day mortality in patients with critical CS. Methods We reviewed 1352 patients with critical CS in the Critical Care IV (MIMIC-IV) database, using latent class growth mixture model (LCGMM) to classify patients into 4 categories based on Hb trajectory (Class 1: “high-value-slow-decrease” class; Class 2: “consistent-low” class; Class 3: “high-value-fast-decrease” class; Class 4: “low-value-fast-increase” class). Prognostic analyses of the four groups of patients were performed using Kaplan-Meier curves, and the effect of Hb on 28-day mortality was explored using univariate and multivariate Cox regression models. Results We found that compared to the other three Classes, patients in Class 2 had the highest 28-day mortality [196 (34.8%) vs. 146 (26.5%), 50 (27.2%),14 (25.9%), P=0.016] and also had the highest in-hospital mortality, 90-day mortality, and 180-day mortality. After multifactorial Cox regression modelling, Hb levels were found to severely affect the patient's 28-day prognosis (HR 0.98, 95%CI 0.88, 1.08, P=0.035). Conclusions The 28-day mortality rate in patients with severe CS varies according to the trajectory of Hb levels (<9g/dL). Patients had the highest mortality when Hb levels were persistently low. Cardiogenic shock Haemoglobin Mortality Anaemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Cardiogenic shock (CS) is a clinical syndrome of impaired tissue perfusion caused by primary cardiac insufficiency and insufficient cardiac output (CO) [ 1 ] . CS most often begins with a significant reduction in ventricular contractility, usually affecting the left ventricle but also occurring in isolated right ventricular or biventricular failure, in which CO is the most important variable in left ventricular failure and a key determinant of systemic oxygen delivery [ 2 ] . Thus, overall oxygen delivery in patients with CS fails to meet oxygen consumption and, in addition, CO decreases with successive episodes of inadequate peripheral perfusion. Inadequate perfusion affects all organs, triggers tissue hypoxia and altered cellular metabolism, and causes acidosis, pressure receptor and chemoreceptor activation, and ultimately a decrease in coronary perfusion pressure, which can further deteriorate cardiac function [ 3 ] . Therefore, cardiogenic shock is one of the deadliest clinical conditions in critical care medicine with a mortality rate > 40% [ 4 ] . Maintaining systemic delivery in patients with CS is critical and may have a favourable prognosis for survival. Haemoglobin (Hb) is the main carrier of oxygen in the cardiovascular system [ 5 ] . Low Hb levels lead to reduced tissue oxygenation, impaired oxygen utilisation and chronic tissue ischaemia in patients with CS [ 6 ] . Previous studies have shown that chronic anaemia can lead to increased cardiac output in patients, secondary to reduced afterload, increased preload and increased chronotropic and chronoforce effects [ 7 , 8 ] . Over time, this may lead to ventricular dilatation and left ventricular hypertrophy [ 9 , 10 ] . It has been previously demonstrated that low Hb is associated with adverse cardiovascular outcomes, however, the disease of CS has not been adequately studied. Low Hb levels in CS patients may be associated with an additional risk of deterioration of tissue oxygen metabolism, which in turn increases the poor prognosis of the patient. Therefore, in order to improve the prognosis of patients with CS, it is necessary to explore the dynamics of Hb levels in patients during treatment. MATERIALS AND METHODS Study design Conducted as a retrospective, observational cohort analysis, all pertinent information was sourced from the Medical Information Marketplace for Critical Care IV (MIMIC-IV), a database open to the public, assembled from Beth Israel Deaconess Medical Center's (BIDMC) electronic health records. The author (Jing Tian) obtained the necessary authorization to access the database. It is important to emphasize that our study focused on an analysis of a third-party open-access database that had been approved by the Institutional Review Board (IRB). Therefore, our own institution's IRB review process was determined to be exempt [ 11 ] . Study population The database's disease diagnoses mainly relied on codes from the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10), as documented by hospital personnel. Our research pinpointed 1827 critically ill adults diagnosed with CS (identified by codes 78551, R570, T8111XA). From this group, 475 patients were omitted due to incomplete Hb data, culminating in the inclusion of 1352 patients. Variables and outcomes measures Information regarding the initial traits of patients who were admitted to the intensive care unit within a day was gathered from the MIMIC-IV database. The dataset encompassed demographic details like gender and age, along with fundamental clinical indicators. Signs of the severity of the illness encompassed the Sequential Organ Failure Assessment (Sofa), Simplified Acute Physiology Score II (SAPS II), Score Acute physiology score III (APS III), and Systemic Inflammatory Response Syndrome (Sirs). Furthermore, comprehensive lab examinations (including white blood cell count [WBC], platelets [PLT], neutrophils [NE], lymphocytes [LYM], Hb levels (spanning from day one to day five) and creatinine levels), arterial blood gas readings, and medical treatments (epinephrine, norepinephrine, dobutamine, and furosemide) were documented. Furthermore, calculations were made for the patient's mechanical ventilation (MV), duration in the intensive care unit, and the overall length of their stay. The identification of comorbidities relied on documented ICD-9 codes, encompassing conditions like hypertension, diabetes, ARF, CKD, and stroke. Additional results included the status of MV, duration of hospitalization, ICU stay length, mortality in the ICU, and mortality within the hospital. The main result measured was the 28-day outlook for patients critically ill with CS. Statistical analysis Continuous variables are expressed as mean ± standard deviation (SD) or median with interquartile range (IQR), while categorical variables are expressed as numbers with proportions. Differences between groups were compared by one-way analysis of variance (ANOVA) or Wilcoxon's rank sum test for continuous variables and for categorical variables. Kaplan-Meier curves and log-rank tests were used to compare differences in 28-day mortality between different groups of patients. The effect of Hb levels on 28-day mortality in each group of patients was explored using univariate and multivariate Cox regression models. We used a latent class growth mixture model (LCGMM) to explore heterogeneity in the course of Hb dynamics to distinguish subgroups with similar underlying Hb development trajectories over time [ 12 ] . The model was fitted using the R package ‘lcmm’ in order to group patients with similar trajectories of Hb development from the first day of ICU admission to the fifth day. Three possible polynomial specifications were available to describe the longitudinal Hb response as a function of time: linear, quadratic and cubic specifications, with each polynomial model (orders 1 to 3) modelled as a level 1 to 4 solution, respectively. The selection of the best model was evaluated by a combination of the following criteria: (1) observation of Bayesian Information Criterion (BIC) improvements; (2) entropy > 0.7; (3) at least 10% of participants in each trajectory category; (4) average posterior category membership probability values; (5) confirmation of visually distinct trajectories [ 13 , 14 ] . A double-sided P < 0.05 was regarded as statistically significant. All statistical analysis was performed by the R software (version 4.1.3). RESULTS Demonstration of the trajectory model A total of 1352 patients with critical CS were included in the trajectory modelling analysis. Four classes were identified using the LCGMM model. The four categories were Class 1, Class 2, Class 3 and Class 4. As demonstrated in Fig. 1 , Class 1 was the high-value-slow-decrease class, which included 550 patients (40.7%), and it was characterized by the Hb level remaining essentially stable for the first three days and then gradually decreasing. Class 2 was the consistent-low class, which included 564 patients (41.6%), and it was characterized by the Hb level remaining stable for the first five days and then gradually decreasing. Class 3 is the high-value-fast-decrease class, including 184 patients (13.6%), which is characterised by an initial high Hb level that decreases rapidly over time. class 4 is the low-value-fast-increase class, including 54 patients (4%), which is characterised by a low initial Hb level that increases rapidly over time. Basic data analysis Based on the trajectory model, a total of 1352 patients were divided into 4 groups, Class 1, Class 2, Class 3 and Class 4. As suggested in Table 1 , the median age of patients in Class 1 was 70 years, of which 371 (67.5%) were males, the median age of patients in Class 2 was 72 years, of which 309 (54.8%) were males, Class 3 was 67 years, of which 123 (66.8%) were males, and Class 4 was 71 years, of which 33 (61.8%) were males. 54.8%), median age of patients in Class 3 was 67 years, of which 123 (66.8%) were males, and median age of patients in Class 4 was 71 years, of which 33 (61.1%) were males. There were significant differences in co-morbidities, laboratory tests and clinical medications among the four classes, with patients in Class 2 having the highest prevalence of diabetes mellitus [237 (42.0%) vs. 221 (40.2%), 53 (28.8%), 17 (31.5%), P = 0.008], the lowest level of WBC [12 (8, 16) K/µL vs. 13 (9, 18) K/µL, 12 (9, 17) K/µL, 14 (11, 20) K/µL, P = 0.003], LYM [1.25 (0.89, 1.60) K/µL vs.1.34 (1.07, 1.63) K/µL, 1.46 (1.22, 1.83) K/µL, 1.35 (1.11, 1.56) K/µL, P < 0.001 ] and PLT[183 (128, 253) K/µL vs. 204 (153, 267) K/µL, 196 (141, 256) K/µL, 213 (168, 261) K/µL, P < 0.001], and higher rates of epinephrine use [162 (28.7%) vs. 117 (21.3%), 45 (24.5%), 17 (31.5%), P = 0.024], compared to patients in the other three classes. All four classes were essentially similar in terms of disease severity scores and arterial blood gas analyses and did not demonstrate statistical differences (P > 0.05). Table 1 Baseline data for patients in four classes Class 1 N = 550 Class 2 N = 564 Class 3 N = 184 Class 4 N = 54 P-value Age, years 70 (62, 80) 72 (63, 80) 67 (57, 76) 71 (55, 80) 0.001 Gender, % < 0.001 Male 371 (67.5%) 309 (54.8%) 123 (66.8%) 33 (61.1%) Female 179 (32.5%) 255 (45.2%) 61 (33.2%) 21 (38.9%) BMI, kg/m 2 28.6 (25.7, 32.0) 28.6 (25.3, 31.1) 28.1 (25.4, 30.7) 28.8 (25.1, 30.8) 0.328 Comorbidities, % Hypertension 0.159 No 394 (71.6%) 437 (77.5%) 136 (73.9%) 39 (72.2%) Yes 156 (28.4%) 127 (22.5%) 48 (26.1%) 15 (27.8%) Diabetes 0.008 No 329 (59.8%) 327 (58.0%) 131 (71.2%) 37 (68.5%) Yes 221 (40.2%) 237 (42.0%) 53 (28.8%) 17 (31.5%) CKD 0.148 No 373 (67.8%) 348 (61.7%) 118 (64.1%) 38 (70.4%) Yes 177 (32.2%) 216 (38.3%) 66 (35.9%) 16 (29.6%) Stroke 0.786 No 495 (90.0%) 513 (91.0%) 165 (89.7%) 47 (87.0%) Yes 55 (10.0%) 51 (9.0%) 19 (10.3%) 7 (13.0%) ARF 0.777 No 182 (33.1%) 184 (32.6%) 54 (29.3%) 19 (35.2%) Yes 368 (66.9%) 380 (67.4%) 130 (70.7%) 35 (64.8%) Severity index sofa 7.0 (5.0, 10.0) 7.5 (5.0, 10.0) 7.0 (5.0, 10.0) 9.0 (4.3, 10.0) 0.633 sapsii 43 (34, 54) 44 (35, 53) 43 (35, 53) 44 (38, 52) 0.841 apsiii 54 (41, 69) 54 (43, 67) 54 (42, 67) 49 (39, 66) 0.590 sirs 3.00 (2.00, 3.00) 3.00 (2.00, 3.00) 3.00 (2.00, 3.00) 3.00 (3.00, 3.75) 0.210 Laboratory WBC, K/µL 13 (9, 18) 12 (8, 16) 12 (9, 17) 14 (11, 20) 0.003 NE, K/µL 11.2 (8.8, 14.8) 10.8 (8.1, 14.2) 10.7 (8.4, 14.1) 13.2 (10.7, 15.5) < 0.001 LYM, K/µL 1.34 (1.07, 1.63) 1.25 (0.89, 1.60) 1.46 (1.22, 1.83) 1.35 (1.11, 1.56) < 0.001 PLT, K/µL 204 (153, 267) 183 (128, 253) 196 (141, 256) 213 (168, 261) < 0.001 Creatinine, mg/dL 1.50 (1.10, 2.28) 1.50 (1.00, 2.30) 1.45 (0.90, 2.10) 1.50 (1.10, 2.00) 0.427 Arterial Blood Gas PH 7.37 (7.28, 7.42) 7.37 (7.29, 7.43) 7.37 (7.30, 7.43) 7.35 (7.23, 7.40) 0.222 PaCO2, mmHg 41 (35, 48) 40 (35, 46) 40 (34, 48) 43 (36, 50) 0.384 PaO2, mmHg 82 (46, 161) 93 (44, 201) 89 (48, 170) 94 (47, 178) 0.413 Lablactate, mmol/L 2.10 (1.43, 3.40) 2.10 (1.50, 3.40) 2.10 (1.50, 2.93) 2.30 (1.75, 3.68) 0.428 Medicine use, % Eepinephrine 0.024 No 433 (78.7%) 402 (71.3%) 139 (75.5%) 37 (68.5%) Yes 117 (21.3%) 162 (28.7%) 45 (24.5%) 17 (31.5%) Norepinephrine 0.188 No 214 (38.9%) 188 (33.3%) 68 (37.0%) 16 (29.6%) Yes 336 (61.1%) 376 (66.7%) 116 (63.0%) 38 (70.4%) Dopamine 0.119 No 418 (76.0%) 459 (81.4%) 148 (80.4%) 40 (74.1%) Yes 132 (24.0%) 105 (18.6%) 36 (19.6%) 14 (25.9%) Furosemide 0.217 No 100 (18.2%) 116 (20.6%) 45 (24.5%) 8 (14.8%) Yes 450 (81.8%) 448 (79.4%) 139 (75.5%) 46 (85.2%) BMI Body mass index, CKD Chronic kidney disease, ARF Acute renal failure, SOFA score Sepsis-related organ failure score, APS III score Acute physiology score III, Sirs score Systemic inflammatory response syndrome score, Saps ii score Simplified acute physiology score II, WBC White blood cell, NE Neutrophil, LYM Lymphocyte, PLT Blood platelet Clinically relevant outcomes The secondary clinical outcomes of the four classes of patients are demonstrated in Table 2 . The patients in Class 2 were essentially similar to the remaining three classes in terms of MV utilisation, duration of MV, length of hospital stay and length of ICU stay, with no significant differences (p > 0.05). In Figs. 2 and 3 the in-hospital mortality and ICU mortality rates of the four classes of patients are presented. Among them, in Fig. 2 , it was found that patients in class 2 had the highest in-hospital mortality rate compared to the other three classes [182 (32.3%) vs. 133 (24.2%), 45 (24.5%), 13 (24.1%), P = 0.014]. In Fig. 3 , it was found that although class 2 patients had the highest ICU mortality rate [120 (21.3%) vs. 91 (16.5%), 26 (14.1%), 9 (16.7%), P = 0.081], no significant difference was found when compared between the other three classes. Table 2 Clinical secondary outcomes of patients in the four classes Class 1 N = 550 Class 2 N = 564 Class 3 N = 184 Class 4 N = 54 P-value MV use, % 0.322 No 239 (43.5%) 243 (43.1%) 84 (45.7%) 17 (31.5%) Yes 311 (56.5%) 321 (56.9%) 100 (54.3%) 37 (68.5%) MV time, hours 15 (0, 104) 16 (0, 107) 14 (0, 93) 28 (0, 148) 0.366 Length of hospital stay, days 13 (9, 20) 13 (8, 22) 14 (9, 22) 13 (8, 24) 0.991 Length of ICU stay, days 6 (4, 11) 6 (4, 10) 6 (3, 9) 6 (4, 12) 0.297 MV Mechanical ventilation, ICU Intensive Care Unit In terms of the primary outcome, which is the 28-day prognosis of the four classes of patients. According to Fig. 4 , patients in class 2 had the highest 28-day mortality rate compared to the other three classes of patients [196 (34.8%) vs. 146 (26.5%), 50 (27.2%),14 (25.9%), P = 0.016]. In addition, according to the analysis of Figs. 5 and 6 , it was found that class 2 had the worst prognosis in terms of prognosis at 90 and 180 days, where deaths at 90 days amounted to 43.6% (246 cases) and at 180 days to 47.7%% (269 cases), and there was a significant difference (P < 0.05). Factors influencing 28-day mortality In Table 3 , in order to investigate the role of Hb level on the occurrence of 28-day death in critically ill CS patients, univariate and multivariate Cox regression models were developed. In univariate regression analyses, age, morbidities (Diabetes, CKD, ARF), disease severity scores (sofa, sapsii, apsiii, sirs), arterial blood gas analyses (PH, lactate, PaO2), and Hb levels affected the 28-day mortality rate of patients (P < 0.05); in subsequent multivariate analyses, Diabetes, ARF, sofa scores, the sapsii score, apsiii score, PaO2, lactate level, and Hb level threaten patients' 28-day prognosis (P < 0.05). Table 3 Exploring univariate and multivariate analyses of 28-day mortality Univariable Multivariable HR 95% CI P-value HR 95% CI P-value Age, years 1.01 1.00, 1.02 0.016 1.01 1.00, 1.01 0.069 Gender, % Male — — — — — — Female 1.17 0.96, 1.42 0.125 — — — BMI 1.01 0.99, 1.02 0.773 — — — Comorbidities, % Hypertension No — — — — — — Yes 1.08 0.79, 1.23 0.890 — — — Diabetes No — — — — — — Yes 1.22 1.00, 1.49 0.045 1.23 0.98,1.22 0.032 CKD No — — — — — — Yes 1.46 1.20, 1.78 < 0.001 1.70 0.80, 1.22 0.211 stroke No — — — — — — Yes 1.01 0.73, 1.39 0.975 — — — ARF No — — — — — — Yes 1.94 1.53, 2.46 < 0.001 1.61 1.24, 2.08 < 0.001 Severity index sofa 1.06 1.03, 1.09 < 0.001 1.90 0.86, 0.94 < 0.001 sapsii 1.03 1.02, 1.04 < 0.001 1.03 1.02, 1.05 < 0.001 apsiii 1.02 1.01, 1.02 < 0.001 1.01 1.00, 1.12 0.018 sirs 1.22 1.08, 1.36 < 0.001 1.08 0.94, 1.23 0.264 Laboratory WBC, K/µL 1.00 1.00, 1.01 0.395 — — — NE, K/µL 1.01 1.00, 1.03 0.108 — — — LYC, K/µL 0.98 0.91, 1.05 0.547 — — — PLT, K/µL 1.00 1.00, 1.00 0.528 — — — Creatinine, mg/dL 1.10 1.05, 1.15 < 0.001 1.05 0.99, 1.12 0.121 Hb, g/dL 0.91 0.87, 0.96 < 0.001 0.98 0.88, 1.08 0.035 Arterial Blood Gas PH 0.36 0.14, 0.88 0.026 3.49 0.98, 12.45 0.054 PaCO2, mmHg 1.00 0.99, 1.01 0.426 — — — PaO2, mmHg 1.00 1.00, 1.00 0.005 0.89 1.00, 1.01 0.003 Lablactate 1.09 1.06, 1.13 < 0.001 1.08 1.03, 1.12 < 0.001 BMI Body mass index, CKD Chronic kidney disease, ARF Acute renal failure, SOFA score Sepsis-related organ failure score, APS III score Acute physiology score III, Sirs score Systemic inflammatory response syndrome score, Saps ii score Simplified acute physiology score II, WBC White blood cell, NE Neutrophil, LYM Lymphocyte, PLT Blood platelet, Hb Hemoglobin DISCUSSION Our findings suggest that mortality in patients with critical CS varies according to the patient's Hb trajectory from day one to day five. When the patient's Hb trajectory showed a consistent-low level (< 9g/dL), the in-hospital mortality rate of this patient was significantly increased, and in addition, the 28-day mortality rate showed a high level. Notably, Hb levels in patients with severe CS could influence 28-day prognosis (HR 0.98, 95%CI 0.88, 1.08, P = 0.035), as analysed by a multifactorial Cox regression model. Hb is the main carrier of oxygen in the cardiovascular system. In general, Hb levels are influenced by many factors, including genetics, gender, age, etc [ 15 ] . Very high and very low Hb levels, as well as high Hb levels within the normal range of variability, have been reported to be predictors of all-cause mortality and cardiovascular disease-related mortality [ 8 , 16 , 17 ] . When the Hb concentration falls below 10 g/dL, the body relieves tissue hypoxia by haemodynamic mechanisms such as increased cardiac output [ 18 ] . The hemodynamic compensatory mechanisms for changes in cardiac function are relatively complex, the main ones being a decrease in afterload due to a decrease in systemic vascular resistance, an increase in preload due to an increase in blood oxygenation, and a compensatory increase in left ventricular function due to an increase in venous return, as well as an increase in sympathetic activity and inotropy [ 19 , 20 ] . In a retrospective study, patients with ST-segment elevation myocardial infarction were found to have a significantly increased risk of heart failure at Hb < 14 g/dL, with an adjusted OR (Odds ratio) of 1.21 (95% CI 1.12, 1.30, P < 0.001) for each 1 g/dL reduction in Hb [ 21 ] . This suggests that low Hb has a significant impact on the prognosis of patients with cardiovascular disease. We also obtained similar conclusions. While CS poses a significant risk to cardiovascular conditions and is intimately linked to systemic inflammation, it's an acute condition characterized by pump malfunction resulting in both myocardial and systemic perfusion inadequacy, and due to compensatory physiological processes, CS spirals into a detrimental loop culminating in multiorgan dysfunction [ 22 , 23 ] . Van et al. suggested that hypoxia is critical for CS, describing a disconnect between oxygen delivery and inadequate oxygen metabolism, and is associated with tissue microcirculatory function throughout the body [ 24 ] .When CS patients develop infections secondary to pneumonia or bacterial translocation, impaired perfusion due to low output and increased systemic vascular resistance may be exacerbated by the use of vasopressors, which may result in hypoxic epithelial injury and a continuous inflammatory response and may lead to bacterial migration into the circulation, further exacerbating shock [ 25 ] . Therefore, when the condition of CS patients is further aggravated, on the one hand, lower Hb levels may further compromise myocardial function in CS patients through damaged tissue and cellular oxidative metabolism, enhanced inflammatory response and reduced oxygen supply, which may directly lead to ventricular pump failure [ 26 , 27 ] , and on the other hand inflammation may affect erythropoiesis, with a reduction in Hb, ultimately allowing anaemia to develop. In addition, in anaemic patients with reduced oxygen carrying capacity, CS patients are unable to increase cardiac output to maintain adequate organ perfusion and avoid hypoxia [ 28 ] . This predicts that when patients with CS develop anaemia, their prognosis may be difficult to reach expectations. In a retrospective study of patients with ST-segment myocardial infarction with CS, an increase in 1-year mortality in patients with lower baseline Hb levels was demonstrated after multivariate logistic regression analysis (OR 1.17; P = 0.042), proving that admission Hb concentration was an independent predictor of 1-year mortality in patients with STEMI who underwent direct PCI (Percutaneous coronary intervention) [ 29 ] . The early decline in Hb levels from day 1 to day 3 found in the study by Jonas et al. suggests an impaired short-term prognosis in CS patients [ 30 ] . We also came to a similar conclusion that patients had higher 28-day, 90-day, and 180-day mortality rates when Hb levels were persistently low (< 9 g/dL), and after multifactorial Cox regression analyses. Limitations This study has several limitations that warrant in-depth discussion. Firstly, the research design is a single-center retrospective cohort study based on the MIMIC-IV database, which primarily comprises clinical data from Western populations. This may, to some extent, limit the external validity of the study findings, particularly when generalizing the results to different racial and geographic populations, potentially introducing population representation bias. Secondly, due to the observational nature of the database analysis, we are unable to establish a causal relationship between inflammatory markers and Hb levels through experimental design or longitudinal tracking, which restricts a deeper exploration of the underlying pathophysiological mechanisms. Thirdly, the limitations of the research data prevent us from obtaining baseline hemoglobin levels prior to patients' admission to the ICU, nor can we accurately assess whether erythropoietin (EPO) or other hemoglobin-modulating medications were used. These factors may introduce confounding effects on the study results. Fourthly, due to the lack of detailed clinical records and laboratory data, we are unable to systematically investigate the specific etiological mechanisms of hemoglobin loss in patients, such as the presence of chronic blood loss, hemolysis, or other hematological disorders. Lastly, given that the majority of patients in this cohort did not receive blood transfusions, we did not include transfusion-related data in our analysis, which may overlook the significant impact of transfusions on the dynamic changes in hemoglobin levels. These limitations highlight the need for future research to adopt multicenter, prospective cohort designs, combined with more comprehensive clinical data collection, to further validate and deepen the findings of this study. CONCLUSION We identified four distinct trajectories of Hb levels in patients with severe CS. We found that patients with consistently low Hb levels (< 9 g/dL) had a worse 28-day prognosis and that Hb level was an independent risk factor for patients' 28-day prognosis. This may inform future precision medicine. Declarations Sources of Funding None. Authors’ contributions Jing Tian designed the study. Jing Tian extracted, collected and analyzed data and prepared tables and figures. Jing Tian and Yi Han reviewed the results, interpreted data, and wrote the manuscript. All authors have made an intellectual contribution to the manuscript and approved the submission. Ethics approval and consent to participate The Institutional Review Boards of both the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC) have approved this study and the sharing of the research resource and waived the requirement of informed consent due to retrospective design. Data Availability The datasets generated and/or analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request. Conflict of interests The authors declare that they have no competing interests. Competing interests The authors declare no competing interests. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Clinical trial number Not applicable. References BOHULA E A, KATZ J N, van DIEPEN S, et al. Demographics, Care Patterns, and Outcomes of Patients Admitted to Cardiac Intensive Care Units[J]. JAMA Cardiology, 2019,4(9): 928. LIM H S. Cardiogenic Shock: Failure of Oxygen Delivery and Oxygen Utilization[J]. Clinical Cardiology, 2016,39(8): 477-483. C O, W B, M A, et al. Cardiogenic Shock, Reflections at the Crossroad between Perfusion, Tissue Hypoxia and Mitochondrial function[J]. Can J Cardiol, 2020,2(36): 184-196. 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Scientific Reports, 2021,11(1): 19936. MCMAHON L P, MASON K, SKINNER S L, et al. Effects of haemoglobin normalization on quality of life and cardiovascular parameters in end-stage renal failure[J]. Nephrol Dial Transplant, 2000,15(9): 1425-1430. NUHU F, BHANDARI S. Oxidative Stress and Cardiovascular Complications in Chronic Kidney Disease, the Impact of Anaemia[J]. Pharmaceuticals, 2018,11(4): 103. JL B, BW T, SM S. Effects of Iron Repletion and Correction of Anemia on Norepinephrine Turnover and Thyroid Metabolism in Iron Deficiency (43040[J]. Proc Soc Exp Biol Med, 1990,4(193): 306-312. STEINVIL A, BANAI S, LESHEM-RUBINOW E, et al. The development of anemia of inflammation during acute myocardial infarction[J]. International Journal of Cardiology, 2012,156(2): 160-164. PENG Y, WANG J, XIANG H, et al. Prognostic Value of Neutrophil-Lymphocyte Ratio in Cardiogenic Shock: A Cohort Study[J]. Medical Science Monitor, 2020,26: e922167. SWAMINATHAN P D, STANCU M, VENKATESH P, et al. Obesity is associated with higher mortality in patients with cardiogenic shock[J]. International Journal of Cardiology, 2007,117(2): 278-279. van DIEPEN S, KATZ J N, ALBERT N M, et al. Contemporary Management of Cardiogenic Shock: A Scientific Statement From the American Heart Association[J]. Circulation, 2017,136(16): e232-e268. BOURCIER S, OUDJIT A, GOUDARD G, et al. Diagnosis of non-occlusive acute mesenteric ischemia in the intensive care unit[J]. Annals of Intensive Care, 2016,6(1): 112. SHACHAM Y, LESHEM RUBINOW E, BEN ASSA E, et al. Lower Admission Hemoglobin Levels Are Associated With Longer Symptom Duration in AcuteST ‐Elevation Myocardial Infarction[J]. Clinical Cardiology, 2014,37(2): 73-77. SOLOMON A, BLUM A, PELEG A, et al. Endothelial Progenitor Cells Are Suppressed in Anemic Patients with Acute Coronary Syndrome[J]. The American Journal of Medicine, 2012,125(6): 604-611. SHUM H P, YAN W W, CHAN T M. Recent knowledge on the pathophysiology of septic acute kidney injury: A narrative review[J]. J Crit Care, 2016,31(1): 82-89. VIS M M, SJAUW K D, van der SCHAAF R J, et al. Prognostic Value of Admission Hemoglobin Levels in ST-Segment Elevation Myocardial Infarction Patients Presenting With Cardiogenic Shock[J]. The American Journal of Cardiology, 2007,99(9): 1201-1202. DUDDA J, WEIDNER K, BEHNES M, et al. Effect of Hemoglobin Levels in Patients with Cardiogenic Shock of Any Cause: Insights from a Single-Centre, Prospective Registry[J]. Clin Lab, 2023,69(8): 10-7754. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Editor invited by journal 16 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 15 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6516923","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469601372,"identity":"4f4200c4-2cc2-438e-a2ab-6b45eab74f02","order_by":0,"name":"Jing Tian","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tian","suffix":""},{"id":469601373,"identity":"ecf2a4e7-8846-46b3-bf8d-d85cd3e346a3","order_by":1,"name":"Yi Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACZh4GBsYGBgYDZgbGBwkVNaRpYTZ4cOYYMdbAtDAwsEk+bGEmrMHgOO/Bz4U77PLN2XmPVSQ2sDHwt3cn4NdymC9ZeuaZZMudzXxpNxJ3yDBInDm7gYAWHgNp3jZmAyDD7EbiGTYGA4lcglqMf/O21YO1FCS2MROlxQxoy2GwFgaitEgCVVrzth03sGzmMZZIOHOMh6Bf+M6fMb7N21ZtYM5/xvDjj4oaOf72XvxaFA6gCfDgVQ4C8g0ElYyCUTAKRsGIBwA9QEMauaCbfAAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2025-04-24 04:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6516923/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6516923/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84667670,"identity":"f90e744b-92a1-4b39-97ed-c9c4c35d4daa","added_by":"auto","created_at":"2025-06-16 05:58:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":220492,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectory of haemoglobin levels from day 1 to day 5\u003c/p\u003e\n\u003cp\u003eClass 1: “high-value-slow-decrease” class;\u003c/p\u003e\n\u003cp\u003eClass 2: “consistent-low” class;\u003c/p\u003e\n\u003cp\u003eClass 3: “high-value-fast-decrease” class;\u003c/p\u003e\n\u003cp\u003eClass 4: “low-value-fast-increase” class\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/13ef963a4dc1709f72ff95bd.png"},{"id":84668544,"identity":"518b45ca-776b-4671-9822-856f2b1b0c36","added_by":"auto","created_at":"2025-06-16 06:14:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63054,"visible":true,"origin":"","legend":"\u003cp\u003eIn-hospital mortality in four classes of patients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/817cfda8466ed4c08f8725f9.png"},{"id":84667668,"identity":"81143b3b-2d0d-4322-8062-d8d1b3269b53","added_by":"auto","created_at":"2025-06-16 05:58:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68724,"visible":true,"origin":"","legend":"\u003cp\u003eICU mortality in four classes of patients\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/1a16c7e27cdf597663583623.png"},{"id":84667672,"identity":"c6790dfb-d455-4505-8ec6-22e79917e455","added_by":"auto","created_at":"2025-06-16 05:58:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89015,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of 28-day outcomes in four classes of patients\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/59c6798f0aedeff8ef29c492.png"},{"id":84667988,"identity":"63929637-1b65-47c7-9e1b-936a96287337","added_by":"auto","created_at":"2025-06-16 06:06:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99347,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of 90-day outcomes in four classes of patient\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/bf7d5b58f4634bbaffdcc3d9.png"},{"id":84667678,"identity":"1fcf60cb-d20f-4856-892d-b7b53dca5335","added_by":"auto","created_at":"2025-06-16 05:58:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":104587,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of 90-day outcomes in four classes of patient\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/a4b4f7e7752e2f622e1b213a.png"},{"id":84668751,"identity":"39214f36-7d33-4723-b068-07ae26f0ab93","added_by":"auto","created_at":"2025-06-16 06:22:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1367667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6516923/v1/ab109fb2-a61f-4605-a44a-ba8da7d8c4ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Hemoglobin Trajectory on Clinical Outcomes in Severe Cardiogenic Shock: Insights from a Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiogenic shock (CS) is a clinical syndrome of impaired tissue perfusion caused by primary cardiac insufficiency and insufficient cardiac output (CO)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. CS most often begins with a significant reduction in ventricular contractility, usually affecting the left ventricle but also occurring in isolated right ventricular or biventricular failure, in which CO is the most important variable in left ventricular failure and a key determinant of systemic oxygen delivery\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Thus, overall oxygen delivery in patients with CS fails to meet oxygen consumption and, in addition, CO decreases with successive episodes of inadequate peripheral perfusion. Inadequate perfusion affects all organs, triggers tissue hypoxia and altered cellular metabolism, and causes acidosis, pressure receptor and chemoreceptor activation, and ultimately a decrease in coronary perfusion pressure, which can further deteriorate cardiac function\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, cardiogenic shock is one of the deadliest clinical conditions in critical care medicine with a mortality rate\u0026thinsp;\u0026gt;\u0026thinsp;40%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Maintaining systemic delivery in patients with CS is critical and may have a favourable prognosis for survival.\u003c/p\u003e \u003cp\u003eHaemoglobin (Hb) is the main carrier of oxygen in the cardiovascular system\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Low Hb levels lead to reduced tissue oxygenation, impaired oxygen utilisation and chronic tissue ischaemia in patients with CS\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that chronic anaemia can lead to increased cardiac output in patients, secondary to reduced afterload, increased preload and increased chronotropic and chronoforce effects\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Over time, this may lead to ventricular dilatation and left ventricular hypertrophy\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. It has been previously demonstrated that low Hb is associated with adverse cardiovascular outcomes, however, the disease of CS has not been adequately studied.\u003c/p\u003e \u003cp\u003eLow Hb levels in CS patients may be associated with an additional risk of deterioration of tissue oxygen metabolism, which in turn increases the poor prognosis of the patient. Therefore, in order to improve the prognosis of patients with CS, it is necessary to explore the dynamics of Hb levels in patients during treatment.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eConducted as a retrospective, observational cohort analysis, all pertinent information was sourced from the Medical Information Marketplace for Critical Care IV (MIMIC-IV), a database open to the public, assembled from Beth Israel Deaconess Medical Center's (BIDMC) electronic health records. The author (Jing Tian) obtained the necessary authorization to access the database. It is important to emphasize that our study focused on an analysis of a third-party open-access database that had been approved by the Institutional Review Board (IRB). Therefore, our own institution's IRB review process was determined to be exempt\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe database's disease diagnoses mainly relied on codes from the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10), as documented by hospital personnel. Our research pinpointed 1827 critically ill adults diagnosed with CS (identified by codes 78551, R570, T8111XA). From this group, 475 patients were omitted due to incomplete Hb data, culminating in the inclusion of 1352 patients.\u003c/p\u003e \u003cp\u003eVariables and outcomes measures\u003c/p\u003e \u003cp\u003eInformation regarding the initial traits of patients who were admitted to the intensive care unit within a day was gathered from the MIMIC-IV database. The dataset encompassed demographic details like gender and age, along with fundamental clinical indicators. Signs of the severity of the illness encompassed the Sequential Organ Failure Assessment (Sofa), Simplified Acute Physiology Score II (SAPS II), Score Acute physiology score III (APS III), and Systemic Inflammatory Response Syndrome (Sirs). Furthermore, comprehensive lab examinations (including white blood cell count [WBC], platelets [PLT], neutrophils [NE], lymphocytes [LYM], Hb levels (spanning from day one to day five) and creatinine levels), arterial blood gas readings, and medical treatments (epinephrine, norepinephrine, dobutamine, and furosemide) were documented. Furthermore, calculations were made for the patient's mechanical ventilation (MV), duration in the intensive care unit, and the overall length of their stay. The identification of comorbidities relied on documented ICD-9 codes, encompassing conditions like hypertension, diabetes, ARF, CKD, and stroke.\u003c/p\u003e \u003cp\u003eAdditional results included the status of MV, duration of hospitalization, ICU stay length, mortality in the ICU, and mortality within the hospital. The main result measured was the 28-day outlook for patients critically ill with CS.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range (IQR), while categorical variables are expressed as numbers with proportions. Differences between groups were compared by one-way analysis of variance (ANOVA) or Wilcoxon's rank sum test for continuous variables and for categorical variables. Kaplan-Meier curves and log-rank tests were used to compare differences in 28-day mortality between different groups of patients. The effect of Hb levels on 28-day mortality in each group of patients was explored using univariate and multivariate Cox regression models.\u003c/p\u003e \u003cp\u003eWe used a latent class growth mixture model (LCGMM) to explore heterogeneity in the course of Hb dynamics to distinguish subgroups with similar underlying Hb development trajectories over time\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The model was fitted using the R package \u0026lsquo;lcmm\u0026rsquo; in order to group patients with similar trajectories of Hb development from the first day of ICU admission to the fifth day. Three possible polynomial specifications were available to describe the longitudinal Hb response as a function of time: linear, quadratic and cubic specifications, with each polynomial model (orders 1 to 3) modelled as a level 1 to 4 solution, respectively. The selection of the best model was evaluated by a combination of the following criteria: (1) observation of Bayesian Information Criterion (BIC) improvements; (2) entropy\u0026thinsp;\u0026gt;\u0026thinsp;0.7; (3) at least 10% of participants in each trajectory category; (4) average posterior category membership probability values; (5) confirmation of visually distinct trajectories\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA double-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant. All statistical analysis was performed by the R software (version 4.1.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDemonstration of the trajectory model\u003c/p\u003e\n\u003cp\u003eA total of 1352 patients with critical CS were included in the trajectory modelling analysis. Four classes were identified using the LCGMM model. The four categories were Class 1, Class 2, Class 3 and Class 4. As demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Class 1 was the high-value-slow-decrease class, which included 550 patients (40.7%), and it was characterized by the Hb level remaining essentially stable for the first three days and then gradually decreasing. Class 2 was the consistent-low class, which included 564 patients (41.6%), and it was characterized by the Hb level remaining stable for the first five days and then gradually decreasing. Class 3 is the high-value-fast-decrease class, including 184 patients (13.6%), which is characterised by an initial high Hb level that decreases rapidly over time. class 4 is the low-value-fast-increase class, including 54 patients (4%), which is characterised by a low initial Hb level that increases rapidly over time.\u003c/p\u003e\n\u003cp\u003eBasic data analysis\u003c/p\u003e\n\u003cp\u003eBased on the trajectory model, a total of 1352 patients were divided into 4 groups, Class 1, Class 2, Class 3 and Class 4. As suggested in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the median age of patients in Class 1 was 70 years, of which 371 (67.5%) were males, the median age of patients in Class 2 was 72 years, of which 309 (54.8%) were males, Class 3 was 67 years, of which 123 (66.8%) were males, and Class 4 was 71 years, of which 33 (61.8%) were males. 54.8%), median age of patients in Class 3 was 67 years, of which 123 (66.8%) were males, and median age of patients in Class 4 was 71 years, of which 33 (61.1%) were males. There were significant differences in co-morbidities, laboratory tests and clinical medications among the four classes, with patients in Class 2 having the highest prevalence of diabetes mellitus [237 (42.0%) vs. 221 (40.2%), 53 (28.8%), 17 (31.5%), P\u0026thinsp;=\u0026thinsp;0.008], the lowest level of WBC [12 (8, 16) K/\u0026micro;L vs. 13 (9, 18) K/\u0026micro;L, 12 (9, 17) K/\u0026micro;L, 14 (11, 20) K/\u0026micro;L, P\u0026thinsp;=\u0026thinsp;0.003], LYM [1.25 (0.89, 1.60) K/\u0026micro;L vs.1.34 (1.07, 1.63) K/\u0026micro;L, 1.46 (1.22, 1.83) K/\u0026micro;L, 1.35 (1.11, 1.56) K/\u0026micro;L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ] and PLT[183 (128, 253) K/\u0026micro;L vs. 204 (153, 267) K/\u0026micro;L, 196 (141, 256) K/\u0026micro;L, 213 (168, 261) K/\u0026micro;L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and higher rates of epinephrine use [162 (28.7%) vs. 117 (21.3%), 45 (24.5%), 17 (31.5%), P\u0026thinsp;=\u0026thinsp;0.024], compared to patients in the other three classes. All four classes were essentially similar in terms of disease severity scores and arterial blood gas analyses and did not demonstrate statistical differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline data for patients in four classes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 1\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;550\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 2\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;564\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 3\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;184\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 4\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;54\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (62, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (63, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (57, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (55, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371 (67.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6 (25.7, 32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6 (25.3, 31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1 (25.4, 30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8 (25.1, 30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidities, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e394 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437 (77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (71.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373 (67.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (70.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e495 (90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e513 (91.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165 (89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182 (33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368 (66.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (70.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (64.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeverity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esofa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (5.0, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (5.0, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (5.0, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0 (4.3, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esapsii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (34, 54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (35, 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (35, 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (38, 52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eapsiii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (41, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (43, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (42, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (39, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esirs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 (3.00, 3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (9, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (9, 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (11, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2 (8.8, 14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8 (8.1, 14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.7 (8.4, 14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2 (10.7, 15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLYM, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (1.07, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (0.89, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46 (1.22, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.11, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (153, 267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183 (128, 253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196 (141, 256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213 (168, 261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.10, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.00, 2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (0.90, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.10, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial Blood Gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37 (7.28, 7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37 (7.29, 7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37 (7.30, 7.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.35 (7.23, 7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaCO2, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (35, 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (35, 46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (34, 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (36, 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaO2, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (46, 161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (44, 201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (48, 170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (47, 178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLablactate, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10 (1.43, 3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10 (1.50, 3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10 (1.50, 2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30 (1.75, 3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine use, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEepinephrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e433 (78.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorepinephrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (37.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (63.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (70.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDopamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e418 (76.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e459 (81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (80.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (74.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFurosemide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450 (81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448 (79.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (85.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eBMI Body mass index, CKD Chronic kidney disease, ARF Acute renal failure, SOFA score Sepsis-related organ failure score, APS III score Acute physiology score III, Sirs score Systemic inflammatory response syndrome score, Saps ii score Simplified acute physiology score II, WBC White blood cell, NE Neutrophil, LYM Lymphocyte, PLT Blood platelet\u003c/p\u003e\n\u003cp\u003eClinically relevant outcomes\u003c/p\u003e\n\u003cp\u003eThe secondary clinical outcomes of the four classes of patients are demonstrated in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The patients in Class 2 were essentially similar to the remaining three classes in terms of MV utilisation, duration of MV, length of hospital stay and length of ICU stay, with no significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eIn Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e the in-hospital mortality and ICU mortality rates of the four classes of patients are presented. Among them, in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, it was found that patients in class 2 had the highest in-hospital mortality rate compared to the other three classes [182 (32.3%) vs. 133 (24.2%), 45 (24.5%), 13 (24.1%), P\u0026thinsp;=\u0026thinsp;0.014]. In Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, it was found that although class 2 patients had the highest ICU mortality rate [120 (21.3%) vs. 91 (16.5%), 26 (14.1%), 9 (16.7%), P\u0026thinsp;=\u0026thinsp;0.081], no significant difference was found when compared between the other three classes.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical secondary outcomes of patients in the four classes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 1\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;550\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 2\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;564\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 3\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;184\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass 4\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;54\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV use, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243 (43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV time, hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (0, 104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (0, 107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (0, 93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0, 148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of hospital stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (9, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (8, 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (9, 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (8, 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of ICU stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4, 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (3, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eMV Mechanical ventilation, ICU Intensive Care Unit\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003eIn terms of the primary outcome, which is the 28-day prognosis of the four classes of patients. According to Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, patients in class 2 had the highest 28-day mortality rate compared to the other three classes of patients [196 (34.8%) vs. 146 (26.5%), 50 (27.2%),14 (25.9%), P\u0026thinsp;=\u0026thinsp;0.016]. In addition, according to the analysis of Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, it was found that class 2 had the worst prognosis in terms of prognosis at 90 and 180 days, where deaths at 90 days amounted to 43.6% (246 cases) and at 180 days to 47.7%% (269 cases), and there was a significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eFactors influencing 28-day mortality\u003c/p\u003e\n\u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in order to investigate the role of Hb level on the occurrence of 28-day death in critically ill CS patients, univariate and multivariate Cox regression models were developed. In univariate regression analyses, age, morbidities (Diabetes, CKD, ARF), disease severity scores (sofa, sapsii, apsiii, sirs), arterial blood gas analyses (PH, lactate, PaO2), and Hb levels affected the 28-day mortality rate of patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); in subsequent multivariate analyses, Diabetes, ARF, sofa scores, the sapsii score, apsiii score, PaO2, lactate level, and Hb level threaten patients\u0026apos; 28-day prognosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eExploring univariate and multivariate analyses of 28-day mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96, 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99, 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidities, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79, 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98,1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20, 1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80, 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73, 1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53, 2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24, 2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeverity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esofa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03, 1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86, 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esapsii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02, 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eapsiii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01, 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esirs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08, 1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94, 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLYC, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91, 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT, K/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05, 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99, 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87, 0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88, 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial Blood Gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14, 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98, 12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaCO2, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99, 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaO2, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00, 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLablactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06, 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03, 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI Body mass index, CKD Chronic kidney disease, ARF Acute renal failure, SOFA score Sepsis-related organ failure score, APS III score Acute physiology score III, Sirs score Systemic inflammatory response syndrome score, Saps ii score Simplified acute physiology score II, WBC White blood cell, NE Neutrophil, LYM Lymphocyte, PLT Blood platelet, Hb Hemoglobin\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings suggest that mortality in patients with critical CS varies according to the patient's Hb trajectory from day one to day five. When the patient's Hb trajectory showed a consistent-low level (\u0026lt;\u0026thinsp;9g/dL), the in-hospital mortality rate of this patient was significantly increased, and in addition, the 28-day mortality rate showed a high level. Notably, Hb levels in patients with severe CS could influence 28-day prognosis (HR 0.98, 95%CI 0.88, 1.08, P\u0026thinsp;=\u0026thinsp;0.035), as analysed by a multifactorial Cox regression model.\u003c/p\u003e \u003cp\u003eHb is the main carrier of oxygen in the cardiovascular system. In general, Hb levels are influenced by many factors, including genetics, gender, age, etc\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Very high and very low Hb levels, as well as high Hb levels within the normal range of variability, have been reported to be predictors of all-cause mortality and cardiovascular disease-related mortality\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. When the Hb concentration falls below 10 g/dL, the body relieves tissue hypoxia by haemodynamic mechanisms such as increased cardiac output\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The hemodynamic compensatory mechanisms for changes in cardiac function are relatively complex, the main ones being a decrease in afterload due to a decrease in systemic vascular resistance, an increase in preload due to an increase in blood oxygenation, and a compensatory increase in left ventricular function due to an increase in venous return, as well as an increase in sympathetic activity and inotropy\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In a retrospective study, patients with ST-segment elevation myocardial infarction were found to have a significantly increased risk of heart failure at Hb\u0026thinsp;\u0026lt;\u0026thinsp;14 g/dL, with an adjusted OR (Odds ratio) of 1.21 (95% CI 1.12, 1.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for each 1 g/dL reduction in Hb\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. This suggests that low Hb has a significant impact on the prognosis of patients with cardiovascular disease. We also obtained similar conclusions.\u003c/p\u003e \u003cp\u003eWhile CS poses a significant risk to cardiovascular conditions and is intimately linked to systemic inflammation, it's an acute condition characterized by pump malfunction resulting in both myocardial and systemic perfusion inadequacy, and due to compensatory physiological processes, CS spirals into a detrimental loop culminating in multiorgan dysfunction\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Van et al. suggested that hypoxia is critical for CS, describing a disconnect between oxygen delivery and inadequate oxygen metabolism, and is associated with tissue microcirculatory function throughout the body\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.When CS patients develop infections secondary to pneumonia or bacterial translocation, impaired perfusion due to low output and increased systemic vascular resistance may be exacerbated by the use of vasopressors, which may result in hypoxic epithelial injury and a continuous inflammatory response and may lead to bacterial migration into the circulation, further exacerbating shock\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Therefore, when the condition of CS patients is further aggravated, on the one hand, lower Hb levels may further compromise myocardial function in CS patients through damaged tissue and cellular oxidative metabolism, enhanced inflammatory response and reduced oxygen supply, which may directly lead to ventricular pump failure\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, and on the other hand inflammation may affect erythropoiesis, with a reduction in Hb, ultimately allowing anaemia to develop. In addition, in anaemic patients with reduced oxygen carrying capacity, CS patients are unable to increase cardiac output to maintain adequate organ perfusion and avoid hypoxia\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This predicts that when patients with CS develop anaemia, their prognosis may be difficult to reach expectations. In a retrospective study of patients with ST-segment myocardial infarction with CS, an increase in 1-year mortality in patients with lower baseline Hb levels was demonstrated after multivariate logistic regression analysis (OR 1.17; P\u0026thinsp;=\u0026thinsp;0.042), proving that admission Hb concentration was an independent predictor of 1-year mortality in patients with STEMI who underwent direct PCI (Percutaneous coronary intervention)\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The early decline in Hb levels from day 1 to day 3 found in the study by Jonas et al. suggests an impaired short-term prognosis in CS patients\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. We also came to a similar conclusion that patients had higher 28-day, 90-day, and 180-day mortality rates when Hb levels were persistently low (\u0026lt;\u0026thinsp;9 g/dL), and after multifactorial Cox regression analyses.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis study has several limitations that warrant in-depth discussion. Firstly, the research design is a single-center retrospective cohort study based on the MIMIC-IV database, which primarily comprises clinical data from Western populations. This may, to some extent, limit the external validity of the study findings, particularly when generalizing the results to different racial and geographic populations, potentially introducing population representation bias. Secondly, due to the observational nature of the database analysis, we are unable to establish a causal relationship between inflammatory markers and Hb levels through experimental design or longitudinal tracking, which restricts a deeper exploration of the underlying pathophysiological mechanisms. Thirdly, the limitations of the research data prevent us from obtaining baseline hemoglobin levels prior to patients' admission to the ICU, nor can we accurately assess whether erythropoietin (EPO) or other hemoglobin-modulating medications were used. These factors may introduce confounding effects on the study results. Fourthly, due to the lack of detailed clinical records and laboratory data, we are unable to systematically investigate the specific etiological mechanisms of hemoglobin loss in patients, such as the presence of chronic blood loss, hemolysis, or other hematological disorders. Lastly, given that the majority of patients in this cohort did not receive blood transfusions, we did not include transfusion-related data in our analysis, which may overlook the significant impact of transfusions on the dynamic changes in hemoglobin levels. These limitations highlight the need for future research to adopt multicenter, prospective cohort designs, combined with more comprehensive clinical data collection, to further validate and deepen the findings of this study.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe identified four distinct trajectories of Hb levels in patients with severe CS. We found that patients with consistently low Hb levels (\u0026lt;\u0026thinsp;9 g/dL) had a worse 28-day prognosis and that Hb level was an independent risk factor for patients' 28-day prognosis. This may inform future precision medicine.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eSources of Funding\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eJing Tian designed the study. Jing Tian extracted, collected and analyzed data and prepared tables and figures. Jing Tian and Yi Han reviewed the results, interpreted data, and wrote the manuscript. All authors have made an intellectual contribution to the manuscript and approved the submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Boards of both the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC) have approved this study and the sharing of the research resource and waived the requirement of informed consent due to retrospective design.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBOHULA E A, KATZ J N, van DIEPEN S, et al. Demographics, Care Patterns, and Outcomes of Patients Admitted to Cardiac Intensive Care Units[J]. JAMA Cardiology, 2019,4(9): 928.\u003c/li\u003e\n\u003cli\u003eLIM H S. Cardiogenic Shock: Failure of Oxygen Delivery and Oxygen Utilization[J]. Clinical Cardiology, 2016,39(8): 477-483.\u003c/li\u003e\n\u003cli\u003eC O, W B, M A, et al. Cardiogenic Shock, Reflections at the Crossroad between Perfusion, Tissue Hypoxia and Mitochondrial function[J]. Can J Cardiol, 2020,2(36): 184-196.\u003c/li\u003e\n\u003cli\u003eBRUNO R R, WOLFF G, KELM M, et al. Pharmacological treatment of cardiogenic shock \u0026ndash; A state of the art review[J]. Pharmacology \u0026amp; Therapeutics, 2022,240: 108230.\u003c/li\u003e\n\u003cli\u003eCIACCIO C, COLETTA A, COLETTA M. Role of hemoglobin structural-functional relationships in oxygen transport[J]. Molecular Aspects of Medicine, 2022,84: 101022.\u003c/li\u003e\n\u003cli\u003eMELENOVSKY V, PETRAK J, MRACEK T, et al. Myocardial iron content and mitochondrial function in human heart failure: a direct tissue analysis[J]. European Journal of Heart Failure, 2017,19(4): 522-530.\u003c/li\u003e\n\u003cli\u003eANAND I S. Anemia and Chronic Heart Failure[J]. Journal of the American College of Cardiology, 2008,52(7): 501-511.\u003c/li\u003e\n\u003cli\u003eSARNAK M J, TIGHIOUART H, MANJUNATH G, et al. Anemia as a risk factor for cardiovascular disease in the atherosclerosis risk in communities (aric) study[J]. Journal of the American College of Cardiology, 2002,40(1): 27-33.\u003c/li\u003e\n\u003cli\u003eMAMAS M A, KWOK C S, KONTOPANTELIS E, et al. Relationship Between Anemia and Mortality Outcomes in a National Acute Coronary Syndrome Cohort: Insights From the UK Myocardial Ischemia National Audit Project Registry[J]. Journal of the American Heart Association, 2016,5(11): e003348.\u003c/li\u003e\n\u003cli\u003ePALAZZUOLI A, SILVERBERG D S, IOVINE F, et al. Effects of \u0026beta;-erythropoietin treatment on left ventricular remodeling, systolic function, and B-type natriuretic peptide levels in patients with the cardiorenal anemia syndrome[J]. American Heart Journal, 2007,154(4): 645-649.\u003c/li\u003e\n\u003cli\u003eTIAN J, JIN K, QIAN H, et al. Impact of the obesity paradox on 28-day mortality in elderly patients critically ill with cardiogenic shock: a retrospective cohort study[J]. Diabetology \u0026amp; Metabolic Syndrome, 2024,16(1).\u003c/li\u003e\n\u003cli\u003eJIANG X, ZHANG C, PAN Y, et al. Effects of C-reactive protein trajectories of critically ill patients with sepsis on in-hospital mortality rate[J]. Scientific Reports, 2023,13(1): 15223.\u003c/li\u003e\n\u003cli\u003eCHEN X, DING J, SHI Z, et al. Association of longitudinal trajectories of fasting plasma glucose with all-cause and cardiovascular mortality among a Chinese older population: a retrospective cohort study[J]. BMC Public Health, 2024,24(1): 1335.\u003c/li\u003e\n\u003cli\u003eMARIONI R E, PROUST-LIMA C, AMIEVA H, et al. Cognitive lifestyle jointly predicts longitudinal cognitive decline and mortality risk[J]. European Journal of Epidemiology, 2014,29(3): 211-219.\u003c/li\u003e\n\u003cli\u003ePATEL K V. Variability and heritability of hemoglobin concentration: an opportunity to improve understanding of anemia in older adults[J]. Haematologica, 2008,93(9): 1281-1283.\u003c/li\u003e\n\u003cli\u003eKABAT G C, KIM M Y, VERMA A K, et al. Association of Hemoglobin Concentration With Total and Cause-Specific Mortality in a Cohort of Postmenopausal Women[J]. American Journal of Epidemiology, 2016,183(10): 911-919.\u003c/li\u003e\n\u003cli\u003eTAPIO J, V\u0026Auml;H\u0026Auml;NIKKIL\u0026Auml; H, KES\u0026Auml;NIEMI Y A, et al. Higher hemoglobin levels are an independent risk factor for adverse metabolism and higher mortality in a 20-year follow-up[J]. Scientific Reports, 2021,11(1): 19936.\u003c/li\u003e\n\u003cli\u003eMCMAHON L P, MASON K, SKINNER S L, et al. Effects of haemoglobin normalization on quality of life and cardiovascular parameters in end-stage renal failure[J]. Nephrol Dial Transplant, 2000,15(9): 1425-1430.\u003c/li\u003e\n\u003cli\u003eNUHU F, BHANDARI S. Oxidative Stress and Cardiovascular Complications in Chronic Kidney Disease, the Impact of Anaemia[J]. Pharmaceuticals, 2018,11(4): 103.\u003c/li\u003e\n\u003cli\u003eJL B, BW T, SM S. Effects of Iron Repletion and Correction of Anemia on Norepinephrine Turnover and Thyroid Metabolism in Iron Deficiency (43040[J]. Proc Soc Exp Biol Med, 1990,4(193): 306-312.\u003c/li\u003e\n\u003cli\u003eSTEINVIL A, BANAI S, LESHEM-RUBINOW E, et al. The development of anemia of inflammation during acute myocardial infarction[J]. International Journal of Cardiology, 2012,156(2): 160-164.\u003c/li\u003e\n\u003cli\u003ePENG Y, WANG J, XIANG H, et al. Prognostic Value of Neutrophil-Lymphocyte Ratio in Cardiogenic Shock: A Cohort Study[J]. Medical Science Monitor, 2020,26: e922167.\u003c/li\u003e\n\u003cli\u003eSWAMINATHAN P D, STANCU M, VENKATESH P, et al. Obesity is associated with higher mortality in patients with cardiogenic shock[J]. International Journal of Cardiology, 2007,117(2): 278-279.\u003c/li\u003e\n\u003cli\u003evan DIEPEN S, KATZ J N, ALBERT N M, et al. Contemporary Management of Cardiogenic Shock: A Scientific Statement From the American Heart Association[J]. Circulation, 2017,136(16): e232-e268.\u003c/li\u003e\n\u003cli\u003eBOURCIER S, OUDJIT A, GOUDARD G, et al. Diagnosis of non-occlusive acute mesenteric ischemia in the intensive care unit[J]. Annals of Intensive Care, 2016,6(1): 112.\u003c/li\u003e\n\u003cli\u003eSHACHAM Y, LESHEM RUBINOW E, BEN ASSA E, et al. Lower Admission Hemoglobin Levels Are Associated With Longer Symptom Duration in AcuteST ‐Elevation Myocardial Infarction[J]. Clinical Cardiology, 2014,37(2): 73-77.\u003c/li\u003e\n\u003cli\u003eSOLOMON A, BLUM A, PELEG A, et al. Endothelial Progenitor Cells Are Suppressed in Anemic Patients with Acute Coronary Syndrome[J]. The American Journal of Medicine, 2012,125(6): 604-611.\u003c/li\u003e\n\u003cli\u003eSHUM H P, YAN W W, CHAN T M. Recent knowledge on the pathophysiology of septic acute kidney injury: A narrative review[J]. J Crit Care, 2016,31(1): 82-89.\u003c/li\u003e\n\u003cli\u003eVIS M M, SJAUW K D, van der SCHAAF R J, et al. Prognostic Value of Admission Hemoglobin Levels in ST-Segment Elevation Myocardial Infarction Patients Presenting With Cardiogenic Shock[J]. The American Journal of Cardiology, 2007,99(9): 1201-1202.\u003c/li\u003e\n\u003cli\u003eDUDDA J, WEIDNER K, BEHNES M, et al. Effect of Hemoglobin Levels in Patients with Cardiogenic Shock of Any Cause: Insights from a Single-Centre, Prospective Registry[J]. Clin Lab, 2023,69(8): 10-7754.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiogenic shock, Haemoglobin, Mortality, Anaemia","lastPublishedDoi":"10.21203/rs.3.rs-6516923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6516923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003eThe aim of this study was to investigate the relationship between haemoglobin (Hb) trajectory and 28-day mortality in patients with critical CS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We reviewed 1352 patients with critical CS in the Critical Care IV (MIMIC-IV) database, using latent class growth mixture model (LCGMM) to classify patients into 4 categories based on Hb trajectory (Class 1: “high-value-slow-decrease” class; Class 2: “consistent-low” class; Class 3: “high-value-fast-decrease” class; Class 4: “low-value-fast-increase” class). Prognostic analyses of the four groups of patients were performed using Kaplan-Meier curves, and the effect of Hb on 28-day mortality was explored using univariate and multivariate Cox regression models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eWe found that compared to the other three Classes, patients in Class 2 had the highest 28-day mortality [196 (34.8%) vs. 146 (26.5%), 50 (27.2%),14 (25.9%), P=0.016] and also had the highest in-hospital mortality, 90-day mortality, and 180-day mortality. After multifactorial Cox regression modelling, Hb levels were found to severely affect the patient's 28-day prognosis (HR 0.98, 95%CI 0.88, 1.08, P=0.035).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThe 28-day mortality rate in patients with severe CS varies according to the trajectory of Hb levels (\u0026lt;9g/dL). Patients had the highest mortality when Hb levels were persistently low.\u003c/p\u003e","manuscriptTitle":"The Impact of Hemoglobin Trajectory on Clinical Outcomes in Severe Cardiogenic Shock: Insights from a Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 05:58:48","doi":"10.21203/rs.3.rs-6516923/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-10T11:01:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T06:02:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-16T08:06:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T03:16:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-05-16T03:14:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ae355bc-d9bd-4d39-9b80-7ba35b8f234d","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T05:58:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 05:58:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6516923","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6516923","identity":"rs-6516923","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}