Nonlinear correlation between prognostic nutritional indices (PNI) and patients with sepsis: a retrospective study based on the MIMIC database.

Nonlinear correlation between prognostic nutritional indices (PNI) and patients with sepsis: a retrospective study based on the MIMIC database. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Nonlinear correlation between prognostic nutritional indices (PNI) and patients with sepsis: a retrospective study based on the MIMIC database. Xu Han, Baofeng Qi, Weiwei Yuan, Yue Liu, Bin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4658981/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The objective of this study was to explore the association between PNI and mortality among sepsis patients. Methods: Data in the present study were obtained from MIMIC-IV. PNI was calculated as follows: serum albumin concentration (g/L) + 0.005 × lymphocyte count. The primary outcome of this study was in-hospital mortality. COX proportional hazard regression analysis was conducted to examine the association between PNI and in-hospital mortality. A linear trend was evaluated by including the median PNI of each group as a continuous variable in the model. Restricted cubic spline (RCS) analysis was employed to explore the linear relationship between PNI and the risk of in-hospital mortality and to investigate the interaction between PNI and different factors. Results: A total of 2794 patients were included in this study and divided into four groups (Q1-Q4) according to PNI quartile values. In the fully adjusted model, in-hospital mortality of patients in the highest quartile group of PNI values was 49.4% ( HR = 0.506, 95% CI : 0.342-0.747, P = 0.001) lower than those in the lowest quartile group, respectively, with a statistically significant trend toward increased risk, P trend < 0.001. RCS analysis showed that an L-shaped association between PNI and in-hospital mortality. Subgroup analyses showed a association between PNI and in-hospital mortality in different strata of patients, with a negative correlation between PNI and in-hospital mortality in all groups ( HR <1 in each group). Conclusions: There is a strong correlation between low PNI and an increased risk of death during hospitalization in patients with sepsis. An L-shaped association was observed between PNI and in-hospital mortality in patients with sepsis, with an inflection point at 33.99. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Intensive Care Unit(ICU) Medical Information Mart For Intensive Care (MIMIC) Prognostic Nutritional Index(PNI) Sepsis. Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Sepsis is a disease caused by a dysfunctional host response to infection that results in a variety of physical abnormalities and may lead to multi-organ dysfunction and death [ 1 ] . In recent years, the global incidence of sepsis has risen as the elderly population and the number of oncology patients continue to grow [ 2 ] . Sepsis is a prevalent contributor to mortality in patients admitted to the intensive care unit (ICU), with an in-hospital death rate ranging from 20–50%. This condition imposes significant social and economic burdens [ 3 ] . Although many scholars continue to explore the mechanism and treatment of sepsis, the problem of high morbidity and mortality still cannot be completely solved [ 4 ] . Therefore, it is of great significance to accurately assess the condition of sepsis patients and correctly evaluate their prognosis, to take timely and effective therapeutic measures and to control the factors affecting the prognosis. Albumin is a protein with a high plasma content, accounting for about 60% of the total, and has a variety of effects, including maintaining acid-base balance, increasing plasma osmolality, antioxidant and anti-inflammatory effects [ 5 ] . Studies have found that low albumin levels are significantly related to the severity of the inflammatory response in patients with sepsis. In addition, it was shown that reduced albumin levels can independently predict all-cause mortality within 30 days in infected individuals [ 6 ] . When the body causes inflammation under the action of various bacteria and fungi, inflammatory factors such as TNF- α, IL-1 β, IL-6 and other inflammatory factors are constantly released, and the infection further develops into sepsis. These inflammatory factors can induce the immune suppression of body cells, and then further lead to the continuous apoptosis of lymphocytes [ 7 ] . A meta-analysis showed that absolute lymphocyte counts were significantly lower than in healthy adults and in deceased septic patients than in survivors [ 8 ] . Recent studies have shown that biomarkers associated with albumin and lymphocytes such as lactate dehydrogenase to albumin ratio (LAR), C reactive protein to albumin ratio (CAR), procalcitonin to albumin ratio (PAR), C reactive protein to lymphocyte ratio (CLR), neutrophil to lymphocyte ratio (NLR), can be used as prognostic risk factors in critically ill patients [ 9 – 12 ] . The Prognostic Nutritional Index (PNI) is determined using the measurements of serum albumin levels and peripheral blood lymphocyte counts, which provide insights into the nutritional and immune status of patients. Prior research on PNI has primarily concentrated on malignant tumors [ 13 – 15 ] . A specific study investigating the association between PNI and prognosis in sepsis-related acute renal injury patients revealed that PNI independently predicted the occurrence of acute renal injury in sepsis patients( OR = 0.841, 95% CI : 0.810–0.873) [ 16 ] . PNI is being recognized as a promising biomarker for assessing inflammation; however, there is a lack of extensive research on its potential correlation with the prognosis of sepsis patients. Hence, the objective of this study was to explore the association between PNI and mortality among sepsis patients, aiming to provide valuable insights for clinical diagnosis and treatment. 2. METHODS 2.1 Source of data Data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database (version 2.2) [ 17 ] , MIMIC-IV is a public database collected by the Beth Israel Deacon Medical Center, detareal-world clinical data on 299,712 patients between 2008 and 2019.The database has been exempted from ethical and informed consent, and the investigator has obtained access to the database (ID: 12834332). Patients or the public WERE not involved in the design, or conduct, or reporting, or dissemination plans of our research. 2.2 Criteria for inclusion Clinical data of sepsis patients admitted to ICU for the first time from 2008–2019 included in MIMIC-IV were collected. The diagnostic criteria of sepsis were referred to the international diagnostic consensus of sepsis 3.0: infection combined with sequential organ failure assessment (SOFA) ≥ 2. The inclusion criteria were as follows: (1) first admission to ICU; (2) age ≥ 18 years and diagnosed with sepsis. Exclusion criteria were (1) admission time < 24 h; (2) missing albumin and lymphocyte count data; and (3) comorbid liver diseases such as hepatitis, cirrhosis, hepatocellular carcinoma, and liver injury. The primary outcome of this study was in-hospital mortality. 2.3 Data extraction Data were acquired using PostgreSQL. Patient information included race, gender, age, heart rate, respiratory rate, temperature, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), SPO2, SOFA, simpli fied acute physiology score II (SAPSII), length of hospitalization, and a variety of comorbidities such as hypertension, diabetes mellitus, heart failure, chronic obstructive pulmonary disease (COPD), and renal failure. Laboratory parameters measured within 24 hours of admission to the ICU included blood glucose, anion gap, blood chloride, blood potassium, red blood cells, hemoglobin, platelets, white blood cells, lymphocyte count, albumin, blood calcium, and blood sodium. Variables with missing values ≥ 40% were excluded outright, and variables with missing values < 40% were filled in using multiple interpolation. PNI was calculated as follows: serum albumin concentration (g/L) + 0.005 × lymphocyte count [ 18 ] . 2.4 Statistical analysis Data were processed and analyzed using SPSS 26.0 statistical software and R 4.3.1. The study population was divided into four groups (Q1-Q4) based on the quartiles of PNI. Continuous variables that followed a normal distribution were presented as ( \(\stackrel{-}{x}\) ± s ), while continuous variables that did not follow a normal distribution were presented as median (interquartile range). To compare the differences between the groups, the Kruskal-Wallis test or ANOVA test was utilized. Categorical variables were presented as counts and percentages, and the chi-square test was used for group comparisons. COX proportional hazard regression analysis was conducted to examine the association between PNI and in-hospital mortality. Univariate analysis was initially employed to identify significant variables related to the endpoint. Subsequently, the statistically significant variables were included as covariates in the multivariate analyses. We developed COX proportional regression models to adjust for confounders and to examine the relationship between PNI and the risk of death in patients with sepsis. Furthermore, a linear trend was evaluated by including the median PNI of each group as a continuous variable in the model. Restricted cubic spline (RCS) analysis was employed to explore the linear relationship between PNI and the risk of in-hospital mortality and to investigate the interaction between PNI and different factors. Statistical significance was defined as P < 0.05 for all tests. 3. RESULTS 3.1 Patient characteristics A total of 2794 patients were included in this study and divided into four groups (Q1-Q4) according to PNI quartile values.The inclusion and exclusion process of the study population is shown in Fig. 1 , and the baseline characteristics are shown in Table 1 .The mean age of the patients was 67.29, with males accounting for 55.44% of the patients, and a total of 521 patients, or 18.65% of the total, suffered from in-hospital death. There were no statistical differences between the four groups in race, gender, MAP, body temperature, blood glucose, anion gap, blood chloride, blood potassium, combined COPD, and LOS in ICU ( P > 0.05). Patients with higher PNI values were likely to be younger, more obese, and less likely to have comorbid heart failure and renal failure than those in the lowest quartile. In addition, patients with the highest quartile values had higher red blood cells, hemoglobin, platelets, white blood cells, lymphocyte counts, albumin, blood calcium, and blood sodium than patients with the lowest quartile values, which was statistically significantly different ( P < 0.001). Blood urea nitrogen, creatinine, SOFA, SAPSII, total length of stay, in-hospital mortality were lower in patients with the highest quartile values compared to patients with the lowest quartile values, with a statistically significant difference ( P < 0.001). Table 1 Baseline characteristics of the study population Variable Total PNI P value Q1(< 30.10) Q2(30.10-35.38) Q3(35.38–41.05) Q4(≥ 41.05) N(%) 2794 694(24.84) 703(25.16) 698(24.98) 699(25.02) Race, n(%) 0.488 White 1657(59.31) 413(59.51) 437(62.16) 413(59.17) 394(56.37) Black 231(8.27) 60(8.65) 55(7.82) 58(8.31) 58(8.30) Other 906(32.42) 221(31.84) 211(30.01) 227(32.52) 247(35.33) Age, years 67.29 ± 16.41 67.47 ± 16.19 68.41 ± 15.53 67.66 ± 16.89 65.62 ± 16.89 0.014 Gender, n(%) 0.051 Male 1549(55.44) 375(54.03) 382(54.34) 373(53.44) 419(59.94) Female 1245(44.56) 319(45.97) 321(45.66) 325(46.56) 280(40.06) BMI 27.80(23.77,32.85) 27.06(22.90,31.55) 27.64(23.65,32.99) 28.06(24.03,33.30) 28.46(24.57,33.46) < 0.001 Vitals HR, beats/min 92.58 ± 21.68 97.89 ± 23.51 93.92 ± 21.87 90.43 ± 20.67 88.11 ± 19.25 < 0.001 RR, t/min 20.74 ± 6.72 21.46 ± 7.32 21.20 ± 6.53 20.20 ± 6.25 20.11 ± 6.66 < 0.001 SBP, mmHg 119.28 ± 29.28 116.36 ± 27.96 118.11 ± 29.04 120.34 ± 28.92 122.31 ± 30.85 0.001 DBP, mmHg 60.16 ± 20.13 58.43 ± 18.94 59.34 ± 19.32 60.46 ± 19.48 62.41 ± 22.41 0.003 MAP, mmHg 79.64 ± 45.63 80.52 ± 49.26 77.90 ± 43.14 79.02 ± 45.29 81.14 ± 44.68 0.538 Temperature, ℃ 36.89(36.56,37.22) 36.89(36.56,37.22) 36.89(36.56,37.22) 36.89(36.56,37.28) 36.89(36.56,37.22) 0.928 SpO2, % 98.00(95.00,100.00) 97.00(94.00,100.00) 97.00(94.00,100.00) 98.00(95.00,100.00) 98.00(95.00,100.00) 0.002 Laboratory data Red blood cell, m/uL 3.56 ± 0.84 3.33 ± 0.79 3.48 ± 0.81 3.61 ± 0.75 3.82 ± 0.91 < 0.001 Hemoglobin, g/dL 10.51 ± 2.44 9.81 ± 2.31 10.24 ± 2.33 10.69 ± 2.18 11.29 ± 2.67 < 0.001 Platelets, K/uL 184.00(129.00,250.00) 165.50(107.75,247.25) 183.00(127.00,257.00) 187.00(135.00,249.25) 193.00(140.00,247.00) < 0.001 WBC, K/uL 12.70(8.80,18.30) 12.31(7.30,19.30) 12.50(8.70,17.90) 12.60(9.00,17.10) 13.50(9.60,19.00) < 0.001 Absolute Lymphocyte Count, K/uL 1.03(0.59,1.62) 0.56(0.30,0.91) 0.85(0.50,1.23) 1.18(0.76,1.58) 1.93(1.31,2.74) < 0.001 Glucose, g/dL 135.00(108.00,177.00) 134.50(104.00,176.00) 135.00(109.00,185.00) 137.50(110.75,178.00) 131.00(106.00,171.00) 0.239 Albumin, g/dL 2.95 ± 0.61 2.26 ± 0.36 2.81 ± 0.29 3.17 ± 0.33 3.57 ± 0.48 < 0.001 Anion gap, mEq/L 16.29 ± 4.81 16.38 ± 5.04 16.32 ± 4.69 16.26 ± 4.99 16.19 ± 4.49 0.790 BUN, mg/dL 22.00(15.00,39.00) 27.00(15.00,48.00) 25.00(16.00,42.00) 22.00(14.75,36.25) 19.00(13.00,29.00) < 0.001 Calcium, mg/dL 8.21 ± 0.89 7.80 ± 0.98 8.09 ± 0.78 8.36 ± 0.82 8.58 ± 0.80 < 0.001 Chloride, mEq/L 102.93 ± 6.89 103.42 ± 7.48 102.93 ± 6.96 102.51 ± 6.75 102.86 ± 6.35 0.226 Creatinine, mg/dL 1.10(0.80,1.80) 1.30(0.80,2.20) 1.20(0.80,1.90) 1.10(0.80,1.70) 1.00(0.80,1.50) < 0.001 Lactate, mol/L 1.80(1.20,2.90) 2.00(1.30,3.40) 1.80(1.30,2.90) 1.80(1.20,2.80) 1.80(1.20,2.70) 0.002 Sodium, mEq/L 138.54 ± 5.87 137.83 ± 6.45 138.65 ± 6.38 138.60 ± 5.44 139.09 ± 5.02 < 0.001 Potassium, mEq/L 4.27 ± 0.82 4.22 ± 0.83 4.30 ± 0.83 4.32 ± 0.81 4.25 ± 0.78 0.053 Comorbidities Hypertension, n(%) 976(34.93) 232(33.43) 220(31.29) 240(34.38) 284(40.63) 0.002 Diabetes, n(%) 878(31.42) 180(25.94) 238(33.85) 235(33.67) 225(32.19) 0.004 Heart failure, n(%) 999(35.76) 200(28.82) 286(40.68) 273(39.11) 240(34.33) < 0.001 COPD n(%) 439(15.71) 103(14.84) 128(18.21) 116(16.62) 92(13.16) 0.056 Renal Failure, n(%) 1551(55.51) 458(65.99) 418(59.46) 345(49.43) 330(47.21) < 0.001 SOFA 5.00(3.00,7.00) 4.00(2.00,5.00) 3.00(2.00,5.00) 3.00(2.00,4.00) 3.00(2.00,4.00) < 0.001 SAPSII 50.00(40.00,61.00) 45.00(35.00,56.00) 42.00(34.00,51.00) 39.00(31.00,47.00) 37.00(29.00,46.00) < 0.001 LOS in hospital, days 13.96(7.94,23.72) 15.78(8.72,25.91) 14.57(8.37,24.13) 13.16(7.77,22.55) 13.04(7.01,23.01) 0.001 LOS in ICU, days 5.05(2.61,10.52) 4.99(2.68,10.85) 4.93(2.47,9.97) 5.24(2.65,10.79) 5.07(2.66,10.84) 0.331 In-hospital mortality,n(%) 521(18.65) 188(27.09) 130(18.49) 103(14.76). 100(14.31) < 0.001 Table 2 Univariate analysis for in-hospital mortality Variable HR (95% CI ) P value Race White 1.0 Black 1.328(1.110,1.589) 0.002 Other 0.682(0.471,0.989) 0.043 Age, years 1.022(1.015,1.028) < 0.001 Gender Male 1.115(0.936,1.328) 0.222 Female 1.0 BMI 0.986(0.975,0.998) 0.017 Vitals HR, beats/min 1.000(0.996,1.004) 0.929 RR, t/min 1.012(1.000,1.024) 0.060 SBP, mmHg 0.996(0.993,0.999) 0.008 DBP, mmHg 0.994(0.989,0.998)) 0.007 MAP, mmHg 1.000(0.998,1.002) 0.844 Temperature, ℃ 0.979(0.957,1.002) 0.076 SpO2, % 1.000(1.000,1.000) 0.146 Laboratory data Red blood cell, m/uL 0.962(0.870,1.062) 0.441 Hemoglobin, g/dL 0.993(0.960,1.028) 0.707 Platelets, K/uL 1.001(1.000,1.001) 0.054 WBC, K/uL 1.001(0.996,1.005) 0.783 ALC, K/uL 0.997(0.985,1.008) 0.542 Glucose, g/dL 1.001(1.000,1.002) 0.002 Albumin, g/dL 0.822(0.715,0.944) 0.006 Anion gap, mEq/L 1.053(1.037,1.069) < 0.001 BUN, mg/dL 1.007(1.004,1.010) < 0.001 Calcium, mg/dL 0.996(0.906,1.095) 0.933 Chloride, mEq/L 0.983(0.971,0.995) 0.005 Creatinine, mg/dL 1.056(1.011,1.104) 0.015 Lactate, mol/L 1.105(1.076,1.135) < 0.001 Sodium, mEq/L 1.006(0.991,1.020) 0.451 Potassium, mEq/L 1.092(0.989,1.205) 0.081 PNI sub-group Q1(< 30.10) 1.0 Q2(30.10-35.38) 0.755(0.603,0.944) 0.014 Q3(35.38–41.05) 0.621(0.488,0.790) < 0.001 Q4(≥ 41.05) 0.592(0.464,0.755) < 0.001 Comorbidities Hypertension, n(%) 0.888(0.740,1.066) 0.204 Diabetes, n(%) 1.092(0.911,1.311) 0.341 Heart failure, n(%) 1.309(1.099,1.559) 0.003 COPD n(%) 1.361(1.098,1.686) 0.005 Renal Failure, n(%) 1.743(1.442,2.107) < 0.001 SOFA 1.066(1.029,1.103) < 0.001 SAPSII 1.028(1.023,1.034) < 0.001 Abbreviations: PNI, prognostic nutritional index; BMI, body mass index; HR, heart rate; RR, respiratory rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; WBC, white blood cell;ALC, Absolute Lymphocyte Count; BUN, Blood urea nitrogen; COPD, chronic obstructive pulmonary disease; SOFA, sequential organ failure assessment; SAPSII, simpli fied acute physiology score II; Table 3 Association between PNI and In-hospital mortality in multiple regression model. Variable Model1 Model2 Model3 Model4 HR (95% CI ) P value HR (95% CI ) P value HR (95% CI ) P value HR (95% CI ) P value Q1 1.0 1.0 1.0 1.0 Q2 0.755(0.603,0.944) 0.014 0.728(0.582,0.912) 0.006 0.796(0.633,1.000) 0.050 0.671(0.515,0.874) 0.003 Q3 0.621(0.488,0.790) < 0.001 0.601(0.472,0.765) < 0.001 0.716(0.559,0.916) 0.008 0.565(0.408,0.781) 0.001 Q4 0.592(0.464,0.755) < 0.001 0.588(0.461,0.751) < 0.001 0.719(0.560,0.924) 0.010 0.506(0.342,0.747) 0.001 P trend < 0.001 < 0.001 < 0.001 < 0.001 Model 1 was not adjusted for confounders. Model 2 adjusted for race, age, and BMI. Model 3 further adjusted for comorbidities, SOFA, and SAPS II. Model 4 adjusted for systolic blood pressure, diastolic blood pressure, glucose, albumin, urea nitrogen, anion gap, blood chloride, creatinine, and lactate on the basis of Model 3. 3.3 RCS analysis Restricted cubic spline (RCS) was used to further investigate the correlation, and after correction for confounding variables, the results showed an L-shaped association between PNI and in-hospital mortality (Figure 2). We fitted the association between baseline PNI and in-hospital mortality using a standard COX proportional risk regression model and a two-segmented COX proportional risk regression model. As shown in Table 4, the inflection point was determined to be 33.99 based on the two-segmented COX proportional risk regression model ( P for Log-likelihood ratio <0.05). After adjusting for race, age, BMI, SBP, DBP, glucose, albumin, BUN, anion gap, blood chloride, creatinine, lactate, heart failure, renal failure, COPD, SOFA, and SAPS II, within the inflection point of 33.99, a significant negative correlation between PNI and in-hospital mortality was observed ( H R = 0.950, 95% CI : 0.922-0.979, P = 0.001); whereas, there was no significant difference in the in-hospital mortality rate when PNI exceeded 33.99 ( H R = 0.999, 95% CI : 0.998-1.001, P = 0.827) (Table 4). Table 4 Threshold effect analysis of the relationship between PNI and in-hospital mortality in patients with sepsis Adjusted HR (95% CI ), P value Standard COX proportional risk model fitting 0.999(0.998,1.001)0.548 Two-segment COX proportional risk model fitting Inflexion point 33.99 <33.99 0.950(0.922,0.979)0.001 ≥33.99 0.999(0.998,1.001)0.827 P for Log-likelihood ratio 0.001 The data has been adjusted for all the factors encompassed in Model 4, as presented in Table 3. HR , hazard ratio; CI , confidence interval. 3.4 Subgroup analysis Subgroup analyses showed a association between PNI and in-hospital mortality in different strata of patients, with a negative correlation between PNI and in-hospital mortality in all groups ( HR 0.05). 4. DISCUSSION To the best of our knowledge, this study is the first to demonstrate an L-shaped relationship between PNI and in-hospital mortality in patients with sepsis. We utilized threshold effect analysis to identify a pivotal point (inflection point of 33.99). The findings of this study indicate that individuals with a low PNI had higher rates of in-hospital mortality. Furthermore, COX proportional risk regression analysis revealed a strong association between a low PNI and an increased risk of in-hospital mortality in sepsis patients. Albumin is a protein synthesized by the liver that has both colloidal and non-colloidal functions in the body and is often used clinically to maintain plasma osmolality and to supplement protein [19] . Albumin can also bind multiple inflammatory mediators and modulate the immune response to systemic inflammation and sepsis [20] . Patients with sepsis are often comorbid with hypoalbuminemia under the influence of several factors affecting protein-energy metabolism, such as severe infections, stress, and electrolyte disorders. Our results show that albumin levels are significantly associated with survival status in sepsis patients( P = 0.006). Previous studies have also demonstrated a consistent correlation between low levels of albumin and unfavorable prognosis among sepsis patients. Furthermore, it has been identified as a highly reliable predictor of mortality in this population [21-23] . Although serum albumin levels can be used as a general marker of the severity of a sepsis patient's condition, albumin is an acute-phase protein that fluctuates as the patient's condition fluctuates [24] . As a result, researchers have combined albumin with other biomarkers to derive a number of prognostic indicators, such as lactate dehydrogenase-to-albumin ratio (LAR), C-reactive protein-to-albumin ratio (CAR), and procalcitoninogen-to-albumin ratio (PAR), which have been shown to correlate strongly with prognosis in septic patients [9, 11] . Lymphocytes are crucial immune cells within the body, playing a vital role in both the innate and adaptive immunity of organisms. Sepsis is characterized by a continuous cycle of pro-inflammatory and anti-inflammatory responses, which ultimately results in immune suppression and poor prognosis, and lymphocyte depletion and dysfunction are important causes of immune suppression [7] . Extensive research has consistently demonstrated that lymphopenia is a significant risk factor for poor prognosis in sepsis [25-27] . Rico-Feijoo et al. conducted a retrospective study involving 7215 sepsis patients, which revealed a positive association between lymphopenia and elevated short- and long-term mortality rates. Among the patients, 74.1% exhibited lymphopenia, and 66.3% failed to restore normal levels of lymphocytes during their stay in the ICU [28] . A prospective population-based study of 98,344 patients in Denmark showed a positive association between lymphopenia and risk of infection and infection-related deaths [29] . PNI is a multiparametric index of nutritional status based on serum albumin (ALB) levels and peripheral lymphocyte counts calculated by Onodera et al. in 1984 [30] . PNI was originally employed to evaluate the nutritional and immune status of patients prior to surgery, as well as to predict postoperative complications. However, further investigations revealed a correlation between PNI and adverse outcomes in infected patients. Xie, T et al. investigated the predictive value of PNI and Neutrophilic lymphocyte ratio (NLR) for acute kidney injury in patients with sepsis, and the results showed that the predictive value of PNI was superior to that of NLR, and the area under the curve of both were PNI (AUC = 0.760; 95% CI :0.731-0.789, P < 0.001), NLR (AUC = 0.749; 95% CI :0.722-0.777, P < 0.001) [16] .A multicenter retrospective study in Korea showed that PNI was significantly associated with poor prognosis in patients with sepsis and that mortality in septic patients receiving mechanical ventilation was linearly associated with PNI [31] . In a study of 1196 neonates with suspected sepsis, Li, T et al. found that PNI was lower in septic neonates and decreased significantly with increasing severity of sepsis, and after adjusting for confounders, PNI proved to be an independent risk factor for the presence of sepsis ( OR = 0.967, 95% CI : 0.955-0.979, P < 0.001), and the best cutoff value for PNI to predict neonatal sepsis was 50.63, with a sensitivity of 66% and a specificity of 61% (AUC = 0.66, 95% CI : 0.63-0.70, P < 0.001) [32] . A retrospective study by Wu, H et al. demonstrated that PNI ≥ 29.3 was an independent predictor of mortality within 30 days in patients with sepsis ( HR = 0.65; 95% CI : 0.56-0.76) [33] . These findings suggest that PNI is closely associated with poor prognosis in sepsis, which is also in general agreement with our findings. Patients with sepsis exhibit heightened production of inflammatory mediators and catabolic hormones, which contribute to catabolism while hindering anabolism. This results in severe malnutrition, immunosuppression, and inflammatory responses. The precise correlation between PNI and the unfavorable prognosis in sepsis patients remains not completely understood. Individuals with low PNI levels often display reduced albumin levels, indicating malnutrition and impaired protein synthesis. Studies have demonstrated a strong connection between disorders in nutritional metabolism and the clinical prognosis of patients [34, 35] . In addition, a decrease in lymphocytes can also lead to low PNI. Lymphocytes have a huge impact on the host immune response, and continuous stimulation by pathogens induces a large number of lymphocytes to increase in value and activate, and in order to prevent the immune response from going out of control, the body restricts the immune response by inducing cellular demise, which leads to a decrease in the number of lymphocytes in the body [36] .Thus, there is an association between PNI and the prognosis of patients with sepsis, and notably, our study found an L-shaped association between PNI and in-hospital mortality in patients with sepsis, with the risk of death increasing dramatically when PNI was below 33.99. Meanwhile, our study has some limitations: (1) this is only a single-center, retrospective study, and the applicability of its results may be limited, and more prospective, multicenter studies are needed to validate it; (2) some patients missing the indicators needed for the study will not be included, and the study data may be biased to a certain extent; (3) PNI is calculated at the time of admission to the ICU, and continuous monitoring of PNI may provide a more important guideline in the diagnosis, treatment, and prognostic assessment of patients with sepsis; (4) the present study could not validate the presence of a cause-and-effect relationship between PNI and the risk of death of patients with sepsis, and further studies are needed in the future. 5. CONCLUSION Our study has demonstrated a strong correlation between a low PNI and an increased risk of mortality during in-hospital for patients with sepsis. Furthermore, we have observed an L-shaped relationship between PNI and in-hospital mortality, with an inflection point at 33.99. These findings suggest that clinicians can adjust treatment plans promptly based on PNI levels, thus improving the prognosis and reducing morbidity and mortality rates in sepsis patients. Declarations DATA AVAILABILITY The clinical data for this study were collected by Monitoring in Intensive Care Database IV version 2.2 (MIMIC-IV v.2.2) ( https://physionet.org/content/mimic-iv-demo/2.2/ ). Although the database is publicly and freely available, researchers must complete the National Institutes of Health’s web-based course known as Protecting Human Research Participants to apply for permission to access the database. Data are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author. Author Contribution All authors participated in this work and agree to take responsibility for all aspects of the work and to ensure that questions relating to the accuracy or completeness of any part of the work are properly investigated and resolved.L.B. and H.X. were responsible for the design of this work.H.X. and Y.W. participated in the collection and analysis of the data.L.Y. contributed to the interpretation of the data.H.X. and Q.B. participated in the drafting and critical revision of the manuscript. The manuscript was reviewed and approved by all authors. Data Availability The clinical data for this study were collected by Monitoring in Intensive Care Database IV version 2.2 (MIMIC-IV v.2.2) (https://physionet.org/content/mimic-iv-demo/2.2/). Although the database is publicly and freely available, researchers must complete the National Institutes of Health’s web-based course known as Protecting Human Research Participants to apply for permission to access the database. Data are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author. References Evans, L., et al., Surviving sepsis campines for management of sepsis and septic shock 2021. Intensive Care Med, 2021. 47(11): p. 1181-1247. Rudd, K.E., et al., Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet, 2020. 395(10219): p. 200-211. Darba, J. and A. Marsa, Epidemiology, management and costs of sepsis in Spain (2008-2017): a retrospective multicentre study. Curr Med Res Opin, 2020. 36(7): p. 1089-1095. Marx, G., Correction to: Incidence of severe sepsis and septic shock in German intensive care units: the prospective, multicentre INSEP study. Intensive Care Med, 2018. 44(1): p. 153-156. Dominik, A. and J. Stange, Similarities, Differences, and Potential Synergies in the Mechanism of Action of Albumin Dialysis Using the MARS Albumin Dialysis Device and the CytoSorb Hemoperfusion Device in the Treatment of Liver Failure. Blood Purif, 2021. 50(1): p. 119-128. Turcato, G., et al., Prognostic role of albumin, lactate-to-albumin ratio and C-reactive protein-to-albumin ratio in infected patients. Am J Emerg Med, 2024. 78: p. 42-47. Tang, H., et al., Early immune system alterations in patients with septic shock. Front Immunol, 2023. 14: p. 1126874. Yang, J., X. Zhu and J. Feng, The Changes in the Quantity of Lymphocyte Subpopulations during the Process of Sepsis. Int J Mol Sci, 2024. 25(3). Guan, X., et al., The relationship between lactate dehydrogenase to albumin ratio and all-cause mortality during ICU stays in patients with sepsis: A retrospective cohort study with propensity score matching. Heliyon, 2024. 10(6): p. e27560. Zhang, F., et al., Prognostic value of lactic dehydrogenase-to-albumin ratio in critically ill patients with acute respiratory distress syndrome: a retrospective cohort study. J Thorac Dis, 2024. 16(1): p. 81-90. Yoo, K.H., et al., The usefulness of lactate/albumin ratio, C-reactive protein/albumin ratio, procalcitonin/albumin ratio, SOFA, and qSOFA in predicting the prognosis of patients with sepsis who presented to EDs. Am J Emerg Med, 2024. 78: p. 1-7. Shi, W., et al., C-Reactive Protein-to-Albumin Ratio (CAR) and C-Reactive Protein-to-Lymphocyte Ratio (CLR) are Valuable Inflammatory Biomarker Combination for the Accurate Prediction of Periprosthetic Joint Infection. Infect Drug Resist, 2023. 16: p. 477-486. Okadome, K., et al., Prognostic Nutritional Index, Tumor-infiltrating Lymphocytes, and Prognosis in Patients with Esophageal Cancer. Ann Surg, 2020. 271(4): p. 693-700. Dai, Y., et al., Long-term impact of prognostic nutritional index in cervical esophageal squamous cell carcinoma patients undergoing definitive radiotherapy. Ann Transl Med, 2019. 7(8): p. 175. Huang, X., et al., Prognostic value of prognostic nutritional index and systemic immune-inflammation index in patients with osteosarcoma. J Cell Physiol, 2019. 234(10): p. 18408-18414. Xie, T., et al., Clinical Value of Prognostic Nutritional Index and Neutrophil-to-Lymphocyte Ratio in Prediction of the Development of Sepsis-Induced Kidney Injury. Dis Markers, 2022. 2022: p. 1449758. Johnson, A., et al., MIMIC-IV, a freely accessible electronic health record dataset. Sci Data, 2023. 10(1): p. 1. Hirahara, N., et al., Prognostic nutritional index as a predictor of survival in resectable gastric cancer patients with normal preoperative serum carcinoembryonic antigen levels: a propensity score matching analysis. BMC Cancer, 2018. 18(1): p. 285. Wu, N., et al., Albumin, an interesting and functionally diverse protein, varies from 'native' to 'effective' (Review). Mol Med Rep, 2024. 29(2). Alcaraz-Quiles, J., et al., Oxidized Albumin Triggers a Cytokine Storm in Leukocytes Through P38 Mitogen-Activated Protein Kinase: Role in Systemic Inflammation in Decompensated Cirrhosis. Hepatology, 2018. 68(5): p. 1937-1952. Saucedo-Moreno, E.M., E. Fernandez-Rivera and J.A. Ricardez-Garcia, Hypoalbuminemia as a predictor of mortality in abdominal sepsis. Cir Cir, 2020. 88(4): p. 481-484. Fernandez-Sarmiento, J., et al., The association between hypoalbuminemia and microcirculation, endothelium, and glycocalyx disorders in children with sepsis. Microcirculation, 2023. 30(8): p. e12829. Turcato, G., et al., The role of lactate-to-albumin ratio to predict 30-day risk of death in patients with sepsis in the emergency department: a decision tree analysis. Curr Med Res Opin, 2024. 40(3): p. 345-352. Erstad, B.L., Serum Albumin Levels: Who Needs Them? Ann Pharmacother, 2021. 55(6): p. 798-804. Elcioglu, Z.C., et al., Pooled prevalence of lymphopenia in all-cause hospitalisations and association with infection: a systematic review and meta-analysis. BMC Infect Dis, 2023. 23(1): p. 848. Adigbli, D., et al., EARLY PERSISTENT LYMPHOPENIA AND RISK OF DEATH IN CRITICALLY ILL PATIENTS WITH AND WITHOUT SEPSIS. Shock, 2024. 61(2): p. 197-203. Denstaedt, S.J., B.H. Singer and T.J. Standiford, Sepsis and Nosocomial Infection: Patient Characteristics, Mechanisms, and Modulation. Front Immunol, 2018. 9: p. 2446. Rico-Feijoo, J., et al., Influence of lymphopenia on long-term mortality in septic shock, a retrospective observational study. Rev Esp Anestesiol Reanim (Engl Ed), 2024. Warny, M., et al., Lymphopenia and risk of infection and infection-related death in 98,344 individuals from a prospective Danish population-based study. PLoS Med, 2018. 15(11): p. e1002685. Onodera, T., N. Goseki and G. Kosaki, [Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients]. Nihon Geka Gakkai Zasshi, 1984. 85(9): p. 1001-5. Baek, M.S., et al., Association of malnutrition status with 30-day mortality in patients with sepsis using objective nutritional indices: a multicenter retrospective study in South Korea. Acute Crit Care, 2024. 39(1): p. 127-137. Li, T., et al., Clinical Value of Prognostic Nutritional Index in Prediction of the Presence and Severity of Neonatal Sepsis. J Inflamm Res, 2021. 14: p. 7181-7190. Wu, H., et al., Prognostic nutrition index is associated with the all-cause mortality in sepsis patients: A retrospective cohort study. J Clin Lab Anal, 2022. 36(4): p. e24297. Koekkoek, K.W. and A.R. van Zanten, Nutrition in the critically ill patient. Curr Opin Anaesthesiol, 2017. 30(2): p. 178-185. Nagai, T., et al., Nutrition status and functional prognosis among elderly patients with distal radius fracture: a retrospective cohort study. J Orthop Surg Res, 2020. 15(1): p. 133. Hamidzadeh, K., et al., Macrophages and the Recovery from Acute and Chronic Inflammation. Annu Rev Physiol, 2017. 79: p. 567-592. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4658981","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328539929,"identity":"18313cf8-b36c-4947-af07-6ca46ced1564","order_by":0,"name":"Xu Han","email":"data:image/png;base64,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","orcid":"","institution":"Bengbu Medical University Graduate School","correspondingAuthor":true,"prefix":"","firstName":"Xu","middleName":"","lastName":"Han","suffix":""},{"id":328539932,"identity":"6851fb43-c005-4daa-b164-8cab52899cf9","order_by":1,"name":"Baofeng Qi","email":"","orcid":"","institution":"Fuyang Hospital affiliated to Bengbu Medical University (Fuyang People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Baofeng","middleName":"","lastName":"Qi","suffix":""},{"id":328539933,"identity":"f61877c4-d9ae-499b-ba6f-997441c2e190","order_by":2,"name":"Weiwei Yuan","email":"","orcid":"","institution":"Anhui Medical University Clinical College","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Yuan","suffix":""},{"id":328539935,"identity":"43a2233a-0f2c-456e-b423-7d892cfd265d","order_by":3,"name":"Yue Liu","email":"","orcid":"","institution":"Anhui Medical University Clinical College","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Liu","suffix":""},{"id":328539936,"identity":"c4253f26-bba9-4078-87c5-5bd3fe56c922","order_by":4,"name":"Bin Liu","email":"","orcid":"","institution":"Bengbu Medical University Graduate School","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-29 11:00:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4658981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4658981/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61090086,"identity":"71247ad4-e2f8-4583-9612-ba96125932f3","added_by":"auto","created_at":"2024-07-25 12:55:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26412,"visible":true,"origin":"","legend":"\u003cp\u003eInclusion, exclusion flowchart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4658981/v1/2c5285323d6f177b276c947c.jpg"},{"id":61090088,"identity":"47c90ecb-7ede-44e8-9187-f28abd4bfe78","added_by":"auto","created_at":"2024-07-25 12:55:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19619,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between PNI and in-hospital mortality in patients with sepsis. The RCS was obtained by constructing a COX proportional risk model, revealing a nonlinear correlation between PNI and the risk of in-hospital mortality. The optimal cut-off value for PNI was 33.99. Data are shown as \u003cem\u003eHR\u003c/em\u003ewith 95% \u003cem\u003eCI\u003c/em\u003e. The shaded areas on each side of the regression line are the 95%CI. Abbreviations: RCS, Restricted cubic spline; PNI, prognostic nutritional index; \u003cem\u003eHR\u003c/em\u003e, hazard ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4658981/v1/03e72be0a774af0d0cc89fcf.jpg"},{"id":61090783,"identity":"603d2ecb-8b69-40ef-aced-749c89d00d9b","added_by":"auto","created_at":"2024-07-25 13:03:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40269,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of primary outcome for PNI in diferent subgroups of patients.\u003c/p\u003e\n\u003cp\u003eAbbreviations: PNI, prognostic nutritional index; HF, heart failure; COPD, chronic obstructive pulmonary disease; \u003cem\u003eHR\u003c/em\u003e, hazard ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4658981/v1/d149e0c32e9088749a3bac2e.jpg"},{"id":67862018,"identity":"210ab959-aac4-4344-8b38-59d619b3c5d8","added_by":"auto","created_at":"2024-10-30 13:01:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841257,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4658981/v1/6a1ed927-0ec0-45e1-a0cf-1d021bf01431.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nonlinear correlation between prognostic nutritional indices (PNI) and patients with sepsis: a retrospective study based on the MIMIC database.","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSepsis is a disease caused by a dysfunctional host response to infection that results in a variety of physical abnormalities and may lead to multi-organ dysfunction and death\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In recent years, the global incidence of sepsis has risen as the elderly population and the number of oncology patients continue to grow\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Sepsis is a prevalent contributor to mortality in patients admitted to the intensive care unit (ICU), with an in-hospital death rate ranging from 20\u0026ndash;50%. This condition imposes significant social and economic burdens\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Although many scholars continue to explore the mechanism and treatment of sepsis, the problem of high morbidity and mortality still cannot be completely solved\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is of great significance to accurately assess the condition of sepsis patients and correctly evaluate their prognosis, to take timely and effective therapeutic measures and to control the factors affecting the prognosis.\u003c/p\u003e \u003cp\u003eAlbumin is a protein with a high plasma content, accounting for about 60% of the total, and has a variety of effects, including maintaining acid-base balance, increasing plasma osmolality, antioxidant and anti-inflammatory effects\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Studies have found that low albumin levels are significantly related to the severity of the inflammatory response in patients with sepsis. In addition, it was shown that reduced albumin levels can independently predict all-cause mortality within 30 days in infected individuals\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. When the body causes inflammation under the action of various bacteria and fungi, inflammatory factors such as TNF- α, IL-1 β, IL-6 and other inflammatory factors are constantly released, and the infection further develops into sepsis. These inflammatory factors can induce the immune suppression of body cells, and then further lead to the continuous apoptosis of lymphocytes\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis showed that absolute lymphocyte counts were significantly lower than in healthy adults and in deceased septic patients than in survivors\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Recent studies have shown that biomarkers associated with albumin and lymphocytes such as lactate dehydrogenase to albumin ratio (LAR), C reactive protein to albumin ratio (CAR), procalcitonin to albumin ratio (PAR), C reactive protein to lymphocyte ratio (CLR), neutrophil to lymphocyte ratio (NLR), can be used as prognostic risk factors in critically ill patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The Prognostic Nutritional Index (PNI) is determined using the measurements of serum albumin levels and peripheral blood lymphocyte counts, which provide insights into the nutritional and immune status of patients. Prior research on PNI has primarily concentrated on malignant tumors\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. A specific study investigating the association between PNI and prognosis in sepsis-related acute renal injury patients revealed that PNI independently predicted the occurrence of acute renal injury in sepsis patients(\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.841, 95% \u003cem\u003eCI\u003c/em\u003e: 0.810\u0026ndash;0.873)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. PNI is being recognized as a promising biomarker for assessing inflammation; however, there is a lack of extensive research on its potential correlation with the prognosis of sepsis patients. Hence, the objective of this study was to explore the association between PNI and mortality among sepsis patients, aiming to provide valuable insights for clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Source of data\u003c/h2\u003e \u003cp\u003eData were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database (version 2.2)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, MIMIC-IV is a public database collected by the Beth Israel Deacon Medical Center, detareal-world clinical data on 299,712 patients between 2008 and 2019.The database has been exempted from ethical and informed consent, and the investigator has obtained access to the database (ID: 12834332). Patients or the public WERE not involved in the design, or conduct, or reporting, or dissemination plans of our research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Criteria for inclusion\u003c/h2\u003e \u003cp\u003eClinical data of sepsis patients admitted to ICU for the first time from 2008\u0026ndash;2019 included in MIMIC-IV were collected. The diagnostic criteria of sepsis were referred to the international diagnostic consensus of sepsis 3.0: infection combined with sequential organ failure assessment (SOFA)\u0026thinsp;\u0026ge;\u0026thinsp;2. The inclusion criteria were as follows: (1) first admission to ICU; (2) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years and diagnosed with sepsis. Exclusion criteria were (1) admission time\u0026thinsp;\u0026lt;\u0026thinsp;24 h; (2) missing albumin and lymphocyte count data; and (3) comorbid liver diseases such as hepatitis, cirrhosis, hepatocellular carcinoma, and liver injury. The primary outcome of this study was in-hospital mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data extraction\u003c/h2\u003e \u003cp\u003eData were acquired using PostgreSQL. Patient information included race, gender, age, heart rate, respiratory rate, temperature, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), SPO2, SOFA, simpli fied acute physiology score II (SAPSII), length of hospitalization, and a variety of comorbidities such as hypertension, diabetes mellitus, heart failure, chronic obstructive pulmonary disease (COPD), and renal failure. Laboratory parameters measured within 24 hours of admission to the ICU included blood glucose, anion gap, blood chloride, blood potassium, red blood cells, hemoglobin, platelets, white blood cells, lymphocyte count, albumin, blood calcium, and blood sodium. Variables with missing values\u0026thinsp;\u0026ge;\u0026thinsp;40% were excluded outright, and variables with missing values\u0026thinsp;\u0026lt;\u0026thinsp;40% were filled in using multiple interpolation. PNI was calculated as follows: serum albumin concentration (g/L)\u0026thinsp;+\u0026thinsp;0.005 \u0026times; lymphocyte count\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eData were processed and analyzed using SPSS 26.0 statistical software and R 4.3.1. The study population was divided into four groups (Q1-Q4) based on the quartiles of PNI. Continuous variables that followed a normal distribution were presented as (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e \u0026plusmn; \u003cem\u003es\u003c/em\u003e), while continuous variables that did not follow a normal distribution were presented as median (interquartile range). To compare the differences between the groups, the Kruskal-Wallis test or ANOVA test was utilized. Categorical variables were presented as counts and percentages, and the chi-square test was used for group comparisons. COX proportional hazard regression analysis was conducted to examine the association between PNI and in-hospital mortality. Univariate analysis was initially employed to identify significant variables related to the endpoint. Subsequently, the statistically significant variables were included as covariates in the multivariate analyses. We developed COX proportional regression models to adjust for confounders and to examine the relationship between PNI and the risk of death in patients with sepsis. Furthermore, a linear trend was evaluated by including the median PNI of each group as a continuous variable in the model. Restricted cubic spline (RCS) analysis was employed to explore the linear relationship between PNI and the risk of in-hospital mortality and to investigate the interaction between PNI and different factors. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all tests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 2794 patients were included in this study and divided into four groups (Q1-Q4) according to PNI quartile values.The inclusion and exclusion process of the study population is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, and the baseline characteristics are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.The mean age of the patients was 67.29, with males accounting for 55.44% of the patients, and a total of 521 patients, or 18.65% of the total, suffered from in-hospital death. There were no statistical differences between the four groups in race, gender, MAP, body temperature, blood glucose, anion gap, blood chloride, blood potassium, combined COPD, and LOS in ICU (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Patients with higher PNI values were likely to be younger, more obese, and less likely to have comorbid heart failure and renal failure than those in the lowest quartile. In addition, patients with the highest quartile values had higher red blood cells, hemoglobin, platelets, white blood cells, lymphocyte counts, albumin, blood calcium, and blood sodium than patients with the lowest quartile values, which was statistically significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Blood urea nitrogen, creatinine, SOFA, SAPSII, total length of stay, in-hospital mortality were lower in patients with the highest quartile values compared to patients with the lowest quartile values, with a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\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 characteristics of the study population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePNI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;\u0026thinsp;30.10)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2(30.10-35.38)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(35.38\u0026ndash;41.05)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4(\u0026ge;\u0026thinsp;41.05)\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\u003eN(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694(24.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e703(25.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e698(24.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e699(25.02)\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\u003eRace, n(%)\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=\"char\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1657(59.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e413(59.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437(62.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e413(59.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e394(56.37)\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\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60(8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55(7.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58(8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58(8.30)\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\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e906(32.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221(31.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211(30.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227(32.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247(35.33)\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\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.29\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.47\u0026thinsp;\u0026plusmn;\u0026thinsp;16.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.41\u0026thinsp;\u0026plusmn;\u0026thinsp;15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.66\u0026thinsp;\u0026plusmn;\u0026thinsp;16.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.62\u0026thinsp;\u0026plusmn;\u0026thinsp;16.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n(%)\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=\"char\"\u003e\n \u003cp\u003e0.051\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\u003e1549(55.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375(54.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e382(54.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373(53.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e419(59.94)\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\u003e1245(44.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319(45.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321(45.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e325(46.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280(40.06)\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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.80(23.77,32.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.06(22.90,31.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.64(23.65,32.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.06(24.03,33.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.46(24.57,33.46)\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\u003eVitals\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\u003eHR, beats/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.58\u0026thinsp;\u0026plusmn;\u0026thinsp;21.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.89\u0026thinsp;\u0026plusmn;\u0026thinsp;23.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.92\u0026thinsp;\u0026plusmn;\u0026thinsp;21.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.43\u0026thinsp;\u0026plusmn;\u0026thinsp;20.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.11\u0026thinsp;\u0026plusmn;\u0026thinsp;19.25\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\u003eRR, t/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.74\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.66\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\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.28\u0026thinsp;\u0026plusmn;\u0026thinsp;29.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116.36\u0026thinsp;\u0026plusmn;\u0026thinsp;27.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.11\u0026thinsp;\u0026plusmn;\u0026thinsp;29.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.34\u0026thinsp;\u0026plusmn;\u0026thinsp;28.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.31\u0026thinsp;\u0026plusmn;\u0026thinsp;30.85\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\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.16\u0026thinsp;\u0026plusmn;\u0026thinsp;20.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.43\u0026thinsp;\u0026plusmn;\u0026thinsp;18.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.46\u0026thinsp;\u0026plusmn;\u0026thinsp;19.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.41\u0026thinsp;\u0026plusmn;\u0026thinsp;22.41\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\u003eMAP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.64\u0026thinsp;\u0026plusmn;\u0026thinsp;45.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.52\u0026thinsp;\u0026plusmn;\u0026thinsp;49.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.90\u0026thinsp;\u0026plusmn;\u0026thinsp;43.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.02\u0026thinsp;\u0026plusmn;\u0026thinsp;45.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.14\u0026thinsp;\u0026plusmn;\u0026thinsp;44.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature, ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.89(36.56,37.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.89(36.56,37.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.89(36.56,37.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.89(36.56,37.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.89(36.56,37.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpO2, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.00(95.00,100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.00(94.00,100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.00(94.00,100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.00(95.00,100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.00(95.00,100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory data\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\u003eRed blood cell, m/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\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\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\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\u003ePlatelets, K/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.00(129.00,250.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e165.50(107.75,247.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e183.00(127.00,257.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187.00(135.00,249.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193.00(140.00,247.00)\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\u003eWBC, K/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.70(8.80,18.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.31(7.30,19.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.50(8.70,17.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.60(9.00,17.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.50(9.60,19.00)\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\u003eAbsolute Lymphocyte Count, K/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03(0.59,1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56(0.30,0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85(0.50,1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18(0.76,1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93(1.31,2.74)\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\u003eGlucose, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.00(108.00,177.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134.50(104.00,176.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135.00(109.00,185.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137.50(110.75,178.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131.00(106.00,171.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\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\u003eAnion gap, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.00(15.00,39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.00(15.00,48.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.00(16.00,42.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.00(14.75,36.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.00(13.00,29.00)\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\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\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\u003eChloride, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\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(0.80,1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30(0.80,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20(0.80,1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10(0.80,1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00(0.80,1.50)\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\u003eLactate, mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80(1.20,2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00(1.30,3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80(1.30,2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80(1.20,2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80(1.20,2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137.83\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139.09\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\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\u003ePotassium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\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, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976(34.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232(33.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220(31.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240(34.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284(40.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e878(31.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180(25.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238(33.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235(33.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225(32.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart failure, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e999(35.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200(28.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286(40.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e273(39.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240(34.33)\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\u003eCOPD n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439(15.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103(14.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128(18.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116(16.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92(13.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal Failure, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1551(55.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e458(65.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e418(59.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345(49.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330(47.21)\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\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00(3.00,7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00(2.00,5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00(2.00,5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00(2.00,4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00(2.00,4.00)\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\u003eSAPSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00(40.00,61.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.00(35.00,56.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.00(34.00,51.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.00(31.00,47.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.00(29.00,46.00)\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\u003eLOS in hospital, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.96(7.94,23.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.78(8.72,25.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.57(8.37,24.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.16(7.77,22.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.04(7.01,23.01)\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\u003eLOS in ICU, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.05(2.61,10.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.99(2.68,10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.93(2.47,9.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.24(2.65,10.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.07(2.66,10.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital mortality,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521(18.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188(27.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130(18.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103(14.76).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100(14.31)\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 \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\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\u003eUnivariate analysis for in-hospital mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e 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\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\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\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.328(1.110,1.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682(0.471,0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\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=\"char\"\u003e\n \u003cp\u003e1.022(1.015,1.028)\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\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.115(0.936,1.328)\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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\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\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.986(0.975,0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR, beats/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000(0.996,1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRR, t/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.012(1.000,1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996(0.993,0.999)\u003c/p\u003e\n \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\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994(0.989,0.998))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000(0.998,1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature, ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979(0.957,1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpO2, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000(1.000,1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed blood cell, m/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962(0.870,1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.993(0.960,1.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatelets, K/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001(1.000,1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eWBC, K/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001(0.996,1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALC, K/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997(0.985,1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001(1.000,1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822(0.715,0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnion gap, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.053(1.037,1.069)\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\u003eBUN, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.007(1.004,1.010)\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\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996(0.906,1.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.983(0.971,0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\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=\"char\"\u003e\n \u003cp\u003e1.056(1.011,1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactate, mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.105(1.076,1.135)\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\u003eSodium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006(0.991,1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.092(0.989,1.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePNI sub-group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;\u0026thinsp;30.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\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\u003eQ2(30.10-35.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.755(0.603,0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3(35.38\u0026ndash;41.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.621(0.488,0.790)\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\u003eQ4(\u0026ge;\u0026thinsp;41.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.592(0.464,0.755)\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\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888(0.740,1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.092(0.911,1.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart failure, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.309(1.099,1.559)\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\u003eCOPD n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.361(1.098,1.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal Failure, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.743(1.442,2.107)\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\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.066(1.029,1.103)\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\u003eSAPSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.028(1.023,1.034)\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 \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eAbbreviations: PNI, prognostic nutritional index; BMI, body mass index; HR, heart rate; RR, respiratory rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; WBC, white blood cell;ALC, Absolute Lymphocyte Count; BUN, Blood urea nitrogen; COPD, chronic obstructive pulmonary disease; SOFA, sequential organ failure assessment; SAPSII, simpli fied acute physiology score II;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\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\u003eAssociation between PNI and In-hospital mortality in multiple regression model.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e 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\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\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\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.755(0.603,0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.728(0.582,0.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796(0.633,1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.671(0.515,0.874)\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\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.621(0.488,0.790)\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 \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.601(0.472,0.765)\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 \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.716(0.559,0.916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.565(0.408,0.781)\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\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.592(0.464,0.755)\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 \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588(0.461,0.751)\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 \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.719(0.560,0.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.506(0.342,0.747)\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\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \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 \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 \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 \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 \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eModel 1 was not adjusted for confounders. Model 2 adjusted for race, age, and BMI. Model 3 further adjusted for comorbidities, SOFA, and SAPS II. Model 4 adjusted for systolic blood pressure, diastolic blood pressure, glucose, albumin, urea nitrogen, anion gap, blood chloride, creatinine, and lactate on the basis of Model 3.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.3 RCS analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRestricted cubic spline (RCS) was used to further investigate the correlation, and after correction for confounding variables, the results showed an L-shaped association between PNI and in-hospital mortality (Figure 2). We fitted the association between baseline PNI and in-hospital mortality using a standard COX proportional risk regression model and a two-segmented COX proportional risk regression model. As shown in Table 4, the inflection point was determined to be 33.99 based on the two-segmented COX proportional risk regression model (\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor Log-likelihood ratio \u0026lt;0.05). After adjusting for race, age, BMI, SBP, DBP, glucose, albumin, BUN, anion gap, blood chloride, creatinine, lactate, heart failure, renal failure, COPD, SOFA, and SAPS II, within the inflection point of 33.99, a significant negative correlation between PNI and in-hospital mortality was observed\u0026nbsp;(\u003cem\u003eH\u003c/em\u003e\u003cem\u003eR\u003c/em\u003e = 0.950, 95% \u003cem\u003eCI\u003c/em\u003e: 0.922-0.979, \u003cem\u003eP\u003c/em\u003e = 0.001); whereas, there was no significant difference in the in-hospital mortality rate when PNI exceeded 33.99 (\u003cem\u003eH\u003c/em\u003e\u003cem\u003eR\u003c/em\u003e = 0.999, 95% \u003cem\u003eCI\u003c/em\u003e: 0.998-1.001, \u003cem\u003eP\u003c/em\u003e = 0.827) (Table 4).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThreshold effect analysis of the relationship between PNI and in-hospital mortality in patients with sepsis\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\u003eAdjusted \u003cem\u003eHR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e), P 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\u003eStandard COX proportional risk model fitting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999(0.998,1.001)0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo-segment COX proportional risk model fitting\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\u003eInflexion point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;33.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.950(0.922,0.979)0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;33.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999(0.998,1.001)0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for Log-likelihood ratio\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 \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThe data has been adjusted for all the factors encompassed in Model 4, as presented in Table 3. \u003cem\u003eHR\u003c/em\u003e, hazard ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.4 Subgroup analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSubgroup analyses showed a association between PNI and in-hospital mortality in different strata of patients, with a negative correlation between PNI and in-hospital mortality in all groups (\u003cem\u003eHR\u003c/em\u003e \u0026lt; 1 in each group), as shown in Figure 3. There were no significant interactions between PNI and stratification variables across subgroups based on race, age, gender, comorbidities(\u003cem\u003eP\u003c/em\u003e\u003csub\u003einteraction\u003c/sub\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to demonstrate an L-shaped relationship between PNI and in-hospital mortality in patients with sepsis. We utilized threshold effect analysis to identify a pivotal point (inflection point of 33.99). The findings of this study indicate that individuals with a low PNI had higher rates of in-hospital mortality. Furthermore, COX proportional risk regression analysis revealed a strong association between a low PNI and an increased risk of in-hospital mortality in sepsis patients.\u003c/p\u003e\n\u003cp\u003eAlbumin is a protein synthesized by the liver that has both colloidal and non-colloidal functions in the body and is often used clinically to maintain plasma osmolality and to supplement protein\u003csup\u003e[19]\u003c/sup\u003e. Albumin can also bind multiple inflammatory mediators and modulate the immune response to systemic inflammation and sepsis\u003csup\u003e[20]\u003c/sup\u003e. Patients with sepsis are often comorbid with hypoalbuminemia under the influence of several factors affecting protein-energy metabolism, such as severe infections, stress, and electrolyte disorders. Our results show that albumin levels are significantly associated with survival status in sepsis patients(\u003cem\u003eP\u003c/em\u003e = 0.006). Previous studies have also demonstrated a consistent correlation between low levels of albumin and unfavorable prognosis among sepsis patients. Furthermore, it has been identified as a highly reliable predictor of mortality in this population\u003csup\u003e[21-23]\u003c/sup\u003e. Although serum albumin levels can be used as a general marker of the severity of a sepsis patient\u0026apos;s condition, albumin is an acute-phase protein that fluctuates as the patient\u0026apos;s condition fluctuates\u003csup\u003e[24]\u003c/sup\u003e. As a result, researchers have combined albumin with other biomarkers to derive a number of prognostic indicators, such as lactate dehydrogenase-to-albumin ratio (LAR), C-reactive protein-to-albumin ratio (CAR), and procalcitoninogen-to-albumin ratio (PAR), which have been shown to correlate strongly with prognosis in septic patients\u003csup\u003e[9, 11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLymphocytes are crucial immune cells within the body, playing a vital role in both the innate and adaptive immunity of organisms. Sepsis is characterized by a continuous cycle of pro-inflammatory and anti-inflammatory responses, which ultimately results in immune suppression and poor prognosis, and lymphocyte depletion and dysfunction are important causes of immune suppression\u003csup\u003e[7]\u003c/sup\u003e. Extensive research has consistently demonstrated that lymphopenia is a significant risk factor for poor prognosis in sepsis\u003csup\u003e[25-27]\u003c/sup\u003e.\u0026nbsp;Rico-Feijoo et al. conducted a retrospective study involving 7215 sepsis patients, which revealed a positive association between lymphopenia and elevated short- and long-term mortality rates. Among the patients, 74.1% exhibited lymphopenia, and 66.3% failed to restore normal levels of lymphocytes during their stay in the\u0026nbsp;ICU\u003csup\u003e[28]\u003c/sup\u003e.\u0026nbsp;A prospective population-based study of 98,344 patients in Denmark showed a positive association between lymphopenia and risk of infection and infection-related deaths\u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePNI is a multiparametric index of nutritional status based on serum albumin (ALB) levels and peripheral lymphocyte counts calculated by Onodera et al. in 1984\u003csup\u003e[30]\u003c/sup\u003e. PNI was originally employed to evaluate the nutritional and immune status of patients prior to surgery, as well as to predict postoperative complications. However, further investigations revealed a correlation between PNI and adverse outcomes in infected patients. Xie, T et al. investigated the predictive value of PNI and Neutrophilic lymphocyte ratio (NLR) for acute kidney injury in patients with sepsis, and the results showed that the predictive value of PNI was superior to that of NLR, and the area under the curve of both were PNI (AUC = 0.760; 95% \u003cem\u003eCI\u003c/em\u003e:0.731-0.789, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), NLR (AUC = 0.749; 95% \u003cem\u003eCI\u003c/em\u003e:0.722-0.777, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)\u003csup\u003e[16]\u003c/sup\u003e.A multicenter retrospective study in Korea showed that PNI was significantly associated with poor prognosis in patients with sepsis and that mortality in septic patients receiving mechanical ventilation was linearly associated with PNI\u003csup\u003e[31]\u003c/sup\u003e.\u0026nbsp;In a study of 1196 neonates with suspected sepsis, Li, T et al. found that PNI was lower in septic neonates and decreased significantly with increasing severity of sepsis, and after adjusting for confounders, PNI proved to be an independent risk factor for the presence of sepsis (\u003cem\u003eOR\u003c/em\u003e = 0.967, 95% \u003cem\u003eCI\u003c/em\u003e: 0.955-0.979,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and the best cutoff value for PNI to predict neonatal sepsis was 50.63, with a sensitivity of 66% and a specificity of 61% (AUC = 0.66, 95% \u003cem\u003eCI\u003c/em\u003e: 0.63-0.70, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)\u003csup\u003e[32]\u003c/sup\u003e.\u0026nbsp;A retrospective study by Wu, H et al. demonstrated that PNI\u0026nbsp;\u0026ge;\u0026nbsp;29.3 was an independent predictor of mortality within 30 days in patients with sepsis (\u003cem\u003eHR\u003c/em\u003e =\u0026nbsp;0.65; 95% \u003cem\u003eCI\u003c/em\u003e:\u0026nbsp;0.56-0.76)\u003csup\u003e[33]\u003c/sup\u003e. These findings suggest that PNI is closely associated with poor prognosis in sepsis, which is also in general agreement with our findings.\u003c/p\u003e\n\u003cp\u003ePatients with sepsis exhibit heightened production of inflammatory mediators and catabolic hormones, which contribute to catabolism while hindering anabolism. This results in severe malnutrition, immunosuppression, and inflammatory responses. The precise correlation between PNI and the unfavorable prognosis in sepsis patients remains not completely understood. Individuals with low PNI levels often display reduced albumin levels, indicating malnutrition and impaired protein synthesis. Studies have demonstrated a strong connection between disorders in nutritional metabolism and the clinical prognosis of patients\u003csup\u003e[34, 35]\u003c/sup\u003e. In addition, a decrease in lymphocytes can also lead to low PNI. Lymphocytes have a huge impact on the host immune response, and continuous stimulation by pathogens induces a large number of lymphocytes to increase in value and activate, and in order to prevent the immune response from going out of control, the body restricts the immune response by inducing cellular demise, which leads to a decrease in the number of lymphocytes in the body\u003csup\u003e[36]\u003c/sup\u003e.Thus, there is an association between PNI and the prognosis of patients with sepsis, and notably, our study found an L-shaped association between PNI and in-hospital mortality in patients with sepsis, with the risk of death increasing dramatically when PNI was below 33.99.\u003c/p\u003e\n\u003cp\u003eMeanwhile, our study has some limitations: (1) this is only a single-center, retrospective study, and the applicability of its results may be limited, and more prospective, multicenter studies are needed to validate it; (2) some patients missing the indicators needed for the study will not be included, and the study data may be biased to a certain extent; (3) PNI is calculated at the time of admission to the ICU, and continuous monitoring of PNI may provide a more important guideline in the diagnosis, treatment, and prognostic assessment of patients with sepsis; (4) the present study could not validate the presence of a cause-and-effect relationship between PNI and the risk of death of patients with sepsis, and further studies are needed in the future.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eOur study has demonstrated a strong correlation between a low PNI and an increased risk of mortality during in-hospital for patients with sepsis. Furthermore, we have observed an L-shaped relationship between PNI and in-hospital mortality, with an inflection point at 33.99. These findings suggest that clinicians can adjust treatment plans promptly based on PNI levels, thus improving the prognosis and reducing morbidity and mortality rates in sepsis patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e \u003cp\u003eThe clinical data for this study were collected by Monitoring in Intensive Care Database IV version 2.2 (MIMIC-IV v.2.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://physionet.org/content/mimic-iv-demo/2.2/\u003c/span\u003e\u003cspan address=\"https://physionet.org/content/mimic-iv-demo/2.2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Although the database is publicly and freely available, researchers must complete the National Institutes of Health\u0026rsquo;s web-based course known as Protecting Human Research Participants to apply for permission to access the database. Data are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors participated in this work and agree to take responsibility for all aspects of the work and to ensure that questions relating to the accuracy or completeness of any part of the work are properly investigated and resolved.L.B. and H.X. were responsible for the design of this work.H.X. and Y.W. participated in the collection and analysis of the data.L.Y. contributed to the interpretation of the data.H.X. and Q.B. participated in the drafting and critical revision of the manuscript. The manuscript was reviewed and approved by all authors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe clinical data for this study were collected by Monitoring in Intensive Care Database IV version 2.2 (MIMIC-IV v.2.2) (https://physionet.org/content/mimic-iv-demo/2.2/). Although the database is publicly and freely available, researchers must complete the National Institutes of Health\u0026rsquo;s web-based course known as Protecting Human Research Participants to apply for permission to access the database. Data are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEvans, L., et al., Surviving sepsis campines for management of sepsis and septic shock 2021. Intensive Care Med, 2021. 47(11): p. 1181-1247.\u003c/li\u003e\n\u003cli\u003eRudd, K.E., et al., Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet, 2020. 395(10219): p. 200-211.\u003c/li\u003e\n\u003cli\u003eDarba, J. and A. Marsa, Epidemiology, management and costs of sepsis in Spain (2008-2017): a retrospective multicentre study. Curr Med Res Opin, 2020. 36(7): p. 1089-1095.\u003c/li\u003e\n\u003cli\u003eMarx, G., Correction to: Incidence of severe sepsis and septic shock in German intensive care units: the prospective, multicentre INSEP study. Intensive Care Med, 2018. 44(1): p. 153-156.\u003c/li\u003e\n\u003cli\u003eDominik, A. and J. Stange, Similarities, Differences, and Potential Synergies in the Mechanism of Action of Albumin Dialysis Using the MARS Albumin Dialysis Device and the CytoSorb Hemoperfusion Device in the Treatment of Liver Failure. Blood Purif, 2021. 50(1): p. 119-128.\u003c/li\u003e\n\u003cli\u003eTurcato, G., et al., Prognostic role of albumin, lactate-to-albumin ratio and C-reactive protein-to-albumin ratio in infected patients. Am J Emerg Med, 2024. 78: p. 42-47.\u003c/li\u003e\n\u003cli\u003eTang, H., et al., Early immune system alterations in patients with septic shock. Front Immunol, 2023. 14: p. 1126874.\u003c/li\u003e\n\u003cli\u003eYang, J., X. Zhu and J. Feng, The Changes in the Quantity of Lymphocyte Subpopulations during the Process of Sepsis. Int J Mol Sci, 2024. 25(3).\u003c/li\u003e\n\u003cli\u003eGuan, X., et al., The relationship between lactate dehydrogenase to albumin ratio and all-cause mortality during ICU stays in patients with sepsis: A retrospective cohort study with propensity score matching. Heliyon, 2024. 10(6): p. e27560.\u003c/li\u003e\n\u003cli\u003eZhang, F., et al., Prognostic value of lactic dehydrogenase-to-albumin ratio in critically ill patients with acute respiratory distress syndrome: a retrospective cohort study. J Thorac Dis, 2024. 16(1): p. 81-90.\u003c/li\u003e\n\u003cli\u003eYoo, K.H., et al., The usefulness of lactate/albumin ratio, C-reactive protein/albumin ratio, procalcitonin/albumin ratio, SOFA, and qSOFA in predicting the prognosis of patients with sepsis who presented to EDs. Am J Emerg Med, 2024. 78: p. 1-7.\u003c/li\u003e\n\u003cli\u003eShi, W., et al., C-Reactive Protein-to-Albumin Ratio (CAR) and C-Reactive Protein-to-Lymphocyte Ratio (CLR) are Valuable Inflammatory Biomarker Combination for the Accurate Prediction of Periprosthetic Joint Infection. Infect Drug Resist, 2023. 16: p. 477-486.\u003c/li\u003e\n\u003cli\u003eOkadome, K., et al., Prognostic Nutritional Index, Tumor-infiltrating Lymphocytes, and Prognosis in Patients with Esophageal Cancer. Ann Surg, 2020. 271(4): p. 693-700.\u003c/li\u003e\n\u003cli\u003eDai, Y., et al., Long-term impact of prognostic nutritional index in cervical esophageal squamous cell carcinoma patients undergoing definitive radiotherapy. Ann Transl Med, 2019. 7(8): p. 175.\u003c/li\u003e\n\u003cli\u003eHuang, X., et al., Prognostic value of prognostic nutritional index and systemic immune-inflammation index in patients with osteosarcoma. J Cell Physiol, 2019. 234(10): p. 18408-18414.\u003c/li\u003e\n\u003cli\u003eXie, T., et al., Clinical Value of Prognostic Nutritional Index and Neutrophil-to-Lymphocyte Ratio in Prediction of the Development of Sepsis-Induced Kidney Injury. Dis Markers, 2022. 2022: p. 1449758.\u003c/li\u003e\n\u003cli\u003eJohnson, A., et al., MIMIC-IV, a freely accessible electronic health record dataset. Sci Data, 2023. 10(1): p. 1.\u003c/li\u003e\n\u003cli\u003eHirahara, N., et al., Prognostic nutritional index as a predictor of survival in resectable gastric cancer patients with normal preoperative serum carcinoembryonic antigen levels: a propensity score matching analysis. BMC Cancer, 2018. 18(1): p. 285.\u003c/li\u003e\n\u003cli\u003eWu, N., et al., Albumin, an interesting and functionally diverse protein, varies from \u0026apos;native\u0026apos; to \u0026apos;effective\u0026apos; (Review). Mol Med Rep, 2024. 29(2).\u003c/li\u003e\n\u003cli\u003eAlcaraz-Quiles, J., et al., Oxidized Albumin Triggers a Cytokine Storm in Leukocytes Through P38 Mitogen-Activated Protein Kinase: Role in Systemic Inflammation in Decompensated Cirrhosis. Hepatology, 2018. 68(5): p. 1937-1952.\u003c/li\u003e\n\u003cli\u003eSaucedo-Moreno, E.M., E. Fernandez-Rivera and J.A. Ricardez-Garcia, Hypoalbuminemia as a predictor of mortality in abdominal sepsis. Cir Cir, 2020. 88(4): p. 481-484.\u003c/li\u003e\n\u003cli\u003eFernandez-Sarmiento, J., et al., The association between hypoalbuminemia and microcirculation, endothelium, and glycocalyx disorders in children with sepsis. Microcirculation, 2023. 30(8): p. e12829.\u003c/li\u003e\n\u003cli\u003eTurcato, G., et al., The role of lactate-to-albumin ratio to predict 30-day risk of death in patients with sepsis in the emergency department: a decision tree analysis. Curr Med Res Opin, 2024. 40(3): p. 345-352.\u003c/li\u003e\n\u003cli\u003eErstad, B.L., Serum Albumin Levels: Who Needs Them? Ann Pharmacother, 2021. 55(6): p. 798-804.\u003c/li\u003e\n\u003cli\u003eElcioglu, Z.C., et al., Pooled prevalence of lymphopenia in all-cause hospitalisations and association with infection: a systematic review and meta-analysis. BMC Infect Dis, 2023. 23(1): p. 848.\u003c/li\u003e\n\u003cli\u003eAdigbli, D., et al., EARLY PERSISTENT LYMPHOPENIA AND RISK OF DEATH IN CRITICALLY ILL PATIENTS WITH AND WITHOUT SEPSIS. Shock, 2024. 61(2): p. 197-203.\u003c/li\u003e\n\u003cli\u003eDenstaedt, S.J., B.H. Singer and T.J. Standiford, Sepsis and Nosocomial Infection: Patient Characteristics, Mechanisms, and Modulation. Front Immunol, 2018. 9: p. 2446.\u003c/li\u003e\n\u003cli\u003eRico-Feijoo, J., et al., Influence of lymphopenia on long-term mortality in septic shock, a retrospective observational study. Rev Esp Anestesiol Reanim (Engl Ed), 2024.\u003c/li\u003e\n\u003cli\u003eWarny, M., et al., Lymphopenia and risk of infection and infection-related death in 98,344 individuals from a prospective Danish population-based study. PLoS Med, 2018. 15(11): p. e1002685.\u003c/li\u003e\n\u003cli\u003eOnodera, T., N. Goseki and G. Kosaki, [Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients]. Nihon Geka Gakkai Zasshi, 1984. 85(9): p. 1001-5.\u003c/li\u003e\n\u003cli\u003eBaek, M.S., et al., Association of malnutrition status with 30-day mortality in patients with sepsis using objective nutritional indices: a multicenter retrospective study in South Korea. Acute Crit Care, 2024. 39(1): p. 127-137.\u003c/li\u003e\n\u003cli\u003eLi, T., et al., Clinical Value of Prognostic Nutritional Index in Prediction of the Presence and Severity of Neonatal Sepsis. J Inflamm Res, 2021. 14: p. 7181-7190.\u003c/li\u003e\n\u003cli\u003eWu, H., et al., Prognostic nutrition index is associated with the all-cause mortality in sepsis patients: A retrospective cohort study. J Clin Lab Anal, 2022. 36(4): p. e24297.\u003c/li\u003e\n\u003cli\u003eKoekkoek, K.W. and A.R. van Zanten, Nutrition in the critically ill patient. Curr Opin Anaesthesiol, 2017. 30(2): p. 178-185.\u003c/li\u003e\n\u003cli\u003eNagai, T., et al., Nutrition status and functional prognosis among elderly patients with distal radius fracture: a retrospective cohort study. J Orthop Surg Res, 2020. 15(1): p. 133.\u003c/li\u003e\n\u003cli\u003eHamidzadeh, K., et al., Macrophages and the Recovery from Acute and Chronic Inflammation. Annu Rev Physiol, 2017. 79: p. 567-592.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intensive Care Unit(ICU), Medical Information Mart For Intensive Care (MIMIC), Prognostic Nutritional Index(PNI), Sepsis.","lastPublishedDoi":"10.21203/rs.3.rs-4658981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4658981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe objective of this study was to explore the association between PNI and mortality among sepsis patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData in the present study were obtained from MIMIC-IV. PNI was calculated as follows: serum albumin concentration (g/L) + 0.005 × lymphocyte count. The primary outcome of this study was in-hospital mortality. COX proportional hazard regression analysis was conducted to examine the association between PNI and in-hospital mortality. A linear trend was evaluated by including the median PNI of each group as a continuous variable in the model. Restricted cubic spline (RCS) analysis was employed to explore the linear relationship between PNI and the risk of in-hospital mortality and to investigate the interaction between PNI and different factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 2794 patients were included in this study and divided into four groups (Q1-Q4) according to PNI quartile values. In the fully adjusted model, in-hospital mortality of patients in the highest quartile group of PNI values was 49.4% (\u003cem\u003eHR\u003c/em\u003e = 0.506, 95% \u003cem\u003eCI\u003c/em\u003e: 0.342-0.747, \u003cem\u003eP\u003c/em\u003e = 0.001) lower than those in the lowest quartile group, respectively, with a statistically significant trend toward increased risk, \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend \u003c/sub\u003e\u0026lt; 0.001. RCS analysis showed that an L-shaped association between PNI and in-hospital mortality. Subgroup analyses showed a association between PNI and in-hospital mortality in different strata of patients, with a negative correlation between PNI and in-hospital mortality in all groups (\u003cem\u003eHR\u003c/em\u003e \u0026lt;1 in each group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThere is a strong correlation between low PNI and an increased risk of death during hospitalization in patients with sepsis. An L-shaped association was observed between PNI and in-hospital mortality in patients with sepsis, with an inflection point at 33.99.\u003c/p\u003e","manuscriptTitle":"Nonlinear correlation between prognostic nutritional indices (PNI) and patients with sepsis: a retrospective study based on the MIMIC database.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 12:55:48","doi":"10.21203/rs.3.rs-4658981/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4516eb6-344d-468e-b6b7-7627059af5ea","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34771867,"name":"Health sciences/Biomarkers"},{"id":34771868,"name":"Health sciences/Diseases"},{"id":34771869,"name":"Health sciences/Medical research"},{"id":34771870,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-10-30T12:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-25 12:55:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4658981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4658981","identity":"rs-4658981","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}