==== Front Ther Clin Risk ManagTher Clin Risk ManagTherapeutics and Clinical Risk ManagementTherapeutics and Clinical Risk Management1176-63361178-203XDove Medical Press 10.2147/TCRM.S170647tcrm-14-1347Original ResearchSerum uric acid on admission cannot predict long-term outcome of critically ill patients: a retrospective cohort study Chen Qinchang 1*Huang Kai 2*Li Lingling 2Lin Xixia 2Ding Cong 2Zhang Junrui 3Chen Qingui 1 1 Department of Medical Intensive Care Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, 920835644@qq.com 2 Division of Vascular and Thyroid Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China 3 Guangzhou Institute of Standardization, Guangzhou, ChinaCorrespondence: Qingui Chen, Department of Medical Intensive Care Unit, The First Affiliated Hospital, Sun Yat-sen University, No 58 Zhongshan Rd 2, 510080 Guangzhou, China, Tel +86 135 8044 2523, Email 920835644@qq.com* These authors contributed equally to this work 2018 02 8 2018 14 1347 1359 © 2018 Chen et al. This work is published and licensed by Dove Medical Press Limited2018The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.Purpose We aimed to evaluate the association of serum uric acid on admission with long-term outcome of critically ill patients. Materials and methods We conducted a retrospective cohort study using data extracted from the Medical Information Mart for Intensive Care III database. The primary endpoint was 90-day mortality. Propensity score matching (PSM) was performed, and multivariate Cox regression analysis was used to adjust for potential confounders. Receiver operating characteristic (ROC) curves were also used to assess the mortality predictions. Results A total of 2,123 patients were included finally with a PSM cohort consisting of 556 90-day non-survivors matched 1:1 with 556 90-day survivors. No statistically significant difference of median admission uric acid was observed between the two groups (survivors 5.50 mg/dL vs non-survivors 5.60 mg/dL, p=0.536). ROC area under the curve was 0.511 (95% confidence interval [CI] 0.477–0.545), suggesting that uric acid had poor discriminative powers for predicting 90-day mortality. No significant association between uric acid and 90-day mortality was found (hazard ratio 1.00, 95% CI 0.98–1.03, p=0.6835). Conclusion Serum uric acid on intensive care unit admission failed to predict 90-day mortality of critically ill patients. Keywords uric acidcritical caremortalityrisk factors ==== Body Introduction Uric acid, the end product of an exogenous pool of purines, which functions as either an antioxidant or a pro-oxidant, has been reported as a predictor of outcomes in multiple diseases.1–4 Related research studies focused mainly on cardiovascular disease and found that uric acid might serve as a biomarker of severity of coronary artery disease in patients with acute coronary syndrome, cardiovascular mortality, 1-year mortality of patients with acute coronary syndromes treated with percutaneous coronary intervention, and might improve the prognostic accuracy of some clinical models.5–8 The prognostic and predictive value of uric acid was also explored in type 2 diabetic patients and patients who had open heart surgery.9,10 However, the value of initial serum uric acid on admission in critically ill patients seems limited. Akbar et al reported that elevated uric acid levels in patients with sepsis are associated with an increased risk of acute kidney injury and acute respiratory distress syndrome, but Zhu et al found that there was no correlation between the initial levels of serum uric acid and prognosis of infection in critically ill patients.11,12 Meanwhile, it has been reported that no relationship was found between serum uric acid and short-term mortality of critically ill patients.13,14 To the best of our knowledge, there is no research to evaluate the association of serum uric acid on intensive care unit (ICU) admission with long-term outcome of critically ill patients. Thus, we performed a retrospective cohort study using a modifiable data mining technique applied to the publicly available Medical Information Mart for Intensive Care III (MIMIC-III) database, aiming to clarify whether there is an association between admission serum uric acid levels and long-term outcome.15 Materials and methods Study design and data sources We conducted a retrospective cohort study using data extracted from the MIMIC-III database, which is a large publicly available database consisting of de-identified health-related data of patients who had stayed in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. Access to database has been approved by the institutional review boards of both Beth Israel Deaconess Medical Center and Massachusetts Institute of Technology Affiliates. No informed consent was required on the de-identified patients. Participants Adult patients (aged ≥18 years) of first hospital admission and first ICU admission were considered and included, but patients staying at ICU for <1 day and patients without admission serum uric acid records were excluded. In addition, patients whose death was earlier than ICU admission time and patients whose length of hospital stay was less than length of ICU stays were excluded in order to exclude potential typographical errors and records of organ donor account (Figure 1). Variables We applied Structured Query Language to extract data from the database mainly by using codes from the MIMIC Code Repository.16,24 Age, sex, ICU mortality and hospital mortality, length of ICU stay and length of hospital stay, 28-day mortality and 90-day mortality, admission serum uric acid (admission was defined as within 24 hours after ICU admission), Simplified Acute Physiology Score II (SAPS II), the Elixhauser comorbidities, and the Elixhauser Comorbidity Index (State Inpatient Database [SID]30) were extracted or calculated.17–19 Missing components for the calculation of SAPS II were treated as normal (usually 0). Because the database has had date of birth of patients who are older than 89 years shifted to exactly 300 years before to obscure their age, we corrected them (age −300+89) before analysis. Outcome measures Ninety-day mortality after ICU admission was chosen as the primary end point, and 28-day mortality, hospital mortality, and ICU mortality were secondary outcomes. ICU mortality was determined only by the first ICU stay. Propensity score matching (PSM) We grouped the study subjects as survivors and non-survivors according to their 90-day survival status after ICU admission. The propensity score for each patient was calculated to estimate their probability of death during the first 90 days after ICU admission by using multivariable logistic regression models given the following covariates: gender, age, SAPS II, Elixhauser Comorbidity Index (SID30), sepsis (based on International Classification of Diseases, Ninth Revision [ICD-9] codes), mechanical ventilation on the first day, renal replacement therapy on the first day, congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorder, peripheral vascular disorder, hypertension, paralysis, other neurological disease, chronic pulmonary disease, uncomplicated diabetes, complicated diabetes, hypothyroidism, renal failure, liver disease, peptic ulcer, acquired immune deficiency syndrome, lymphoma, metastatic cancer, solid tumor, rheumatoid arthritis, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, blood loss anemia, deficiency anemia, alcohol abuse, drug abuse, psychoses, and depression. Matching was performed with the use of a 1:1 matching protocol without replacement (greedy-matching algorithm), with a caliper width equal to 0.05 of the standard deviation of the logit of the propensity score. The overlap of the distribution of the propensity scores across survivors and non-survivors groups is shown in Figure S1. Statistical analysis For continuous variables, data were expressed as median and interquartile range (IQR) unless otherwise stated. For categorical variables, data were shown as numbers and percentages. Comparison of continuous and categorical variables was performed using Kruskal-Wallis and chi-square (or Fisher’s exact) tests, respectively. We used receiver operating characteristic (ROC) curves to evaluate the prognostic predictive value of serum uric acid for 90-day mortality and other outcomes and used the Kaplan–Meier (K-M) method and log-rank tests to compare survival differences among patients of different admission serum uric acid levels. Variables associated with 90-day mortality were evaluated by univariate Cox regression analysis, and those with a p-value <0.1 were considered in multivariable Cox regression model. Considering the expected collinearity between comorbidities and the Elixhauser Comorbidity Index (SID30), we would choose only either one of them to be enrolled into one adjusted model when variables are potentially significant (p<0.1) in univariate analysis. Age was not included in the multivariable regression analysis since it was factored into SAPS II. Multivariable Cox regression model was performed to evaluate the association of serum uric acid on 90-day mortality and 28-day mortality, and multivariable logistic regression model was used to examine the association between hospital mortality and ICU mortality. p-values of <0.05 were considered to indicate statistical significance. Empower(R) (www.empowerstats.com; X&Y solutions, Inc., Boston, MA, USA) and R software, version 3.4.3 (http://www.r-project.org; R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analyses. Results Patient characteristics A total of 2,123 patients were included (Figure 1). As shown in Table 1, the median age of the study patients was 64.09 years (IQR 51.39–75.74 years) and 1,219 of the 2,123 cases (57.42%) were male. The median admission serum uric acid was 5.40 mg/dL (IQR 3.80–7.90 mg/dL) with a median SAPS II score of 39 (IQR 30–49). Among them, 239 (11.26%) patients were diagnosed with sepsis based on ICD-9 codes and 1,032 (48.61%) patients required mechanical ventilation on admission. The five most common comorbidities were fluid and electrolyte disorders (38.48%), congestive heart failure (20.35%), deficiency anemia (20.16%), cardiac arrhythmias (20.07%), and coagulopathy (19.31%). The 90-day mortality was 27.23% with 578 non-survivors and 1,545 survivors. The length of ICU stay and hospital stay was 3.79 (IQR 2.01–9.19) and 14.69 (IQR 8.05–26.37) days, respectively. Non-survivors had significantly higher SAPS II (p<0.001). No statistically significant difference was observed in serum uric acid between survivors and non-survivors. Characteristics of the PSM cohort A total of 556 non-survivors were successfully matched with one control. Characteristics of PSM cohort are shown in Table 2. There was no statistically significant difference between survivors and non-survivors in age, gender, SAPS II on admission, Elixhauser Comorbidity Index (SID30), and comorbidities (p>0.05), and no statistically significant difference was found on serum uric acid between survivors and non-survivors. Survival status of patients with different serum uric acid levels on admission Patients were grouped according to their serum uric acid levels on admission. The K-M survival curves presented in Figure 2 showed that there was no difference in the survival rate among different serum uric acid levels on admission (log-rank test: p=0.88) after PSM. The K-M survival curves of 28-day mortality are shown in Figure S2. ROC curve analysis As shown in Figure 3, the area under the ROC curve (AUC) of admission serum uric acid for discrimination of 90-day survivors and non-survivors was 0.522 (95% confidence interval [CI] 0.494–0.550) and 0.511 (95% CI 0.477–0.545) for all patients and PSM cohort, respectively. ROC curve analysis of other outcomes also indicated a poor predictive value of serum uric acid. Association between serum uric acid levels on admission and ICU outcomes Results of univariate Cox regression analysis of all patients and PSM cohort are presented in Tables S1 and S2, respectively. As shown in Table 3, multivariable regression analysis of PSM cohort indicated that serum uric acid was not an independent risk factor of 90-day mortality (hazard ratio [HR] 1.00, 95% CI 0.98–1.03, p=0.6835), 28-day mortality (HR 1.01, 95% CI 0.98–1.04, p=0.4894), hospital mortality (odds ratio [OR] 1.01, 95% CI 0.97–1.04, p=0.6099), and ICU mortality (OR 1.01, 95% CI 0.97–1.05, p=0.6934). Results of regression analysis of all patients are also shown in Table 3. Discussion For the first time, the present study evaluated the association between serum uric acid on ICU admission and long-term outcome of critically ill patients. Results of the study indicated that serum uric acid on admission cannot predict long-term outcome of critically ill patients. It is interesting to find no correlation between serum uric acid with clinical outcomes of critically ill patients, since many studies had reported the prognostic predictive value of serum uric acid in many clinical conditions. For example, uric acid was found to be an independent predictor of cardiovascular outcomes and increase prognostic accuracy of Cox models in hypertensives with normal renal function which allowed a risk reclassification according to a recent report of Perticone et al.8 Given serum uric acid is increased in respiratory disease, especially in the presence of hypoxia and systemic inflammation, many researchers wondered whether it could serve as a biomarker of prognostic predictive value.20 Nagaya et al reported that serum uric acid levels correlate with the severity and the mortality of primary pulmonary hypertension.21 Bartziokas et al found that serum uric acid was associated with increased 30-day mortality and risk for future acute exacerbation of chronic obstructive pulmonary disease.22 Ergun et al reported that high serum uric acid levels are predictive for not only long-term mortality but also for short-term mortality.23 However, in terms of critically ill patients, only a few studies were conducted to explore the value of uric acid and most of the conclusions were negative.12–14 Considering that most of the previous studies evaluated only the short-term outcomes with limited sample sizes, we conducted this present study aiming to evaluate the predictive value of serum uric acid for long-term outcome of critically ill patients. In our study, we included over 2,000 patients which made enough adjustment for confounders available and improved statistical power. Meanwhile, we performed PSM to further minimize the potential selection bias. Results of all patients and PSM cohort were consistent and provided a solid conclusion of the association between serum uric acid and 90-day mortality for critically ill patients, although negative. We also examined some short-term outcomes in the study, and the results were consistent with previous studies. Although the findings in our study were informative, there were several limitations in the present study. First, given the observational nature of our study, it is not possible to adjust all potential confounders. Although we considered many variables known to affect the outcomes, unmeasured confounders may have affected our results. As we know, the reference value of serum uric acid is different between male and female; hence, gender must be considered in the K-M survival curves. In fact, the results were consistent even after grouped by sex (data not shown), but there were still other potential confounders such as renal replacement therapy on the first day, fluid and electrolyte disorders, which made it difficult to take all these confounders into consideration in the K-M curves. And since there were too many specific primary diagnoses for all the patients, we categorized the primary diseases as several comorbidities (Tables 1 and 2) to make it easier to adjust and analyze. However, it was indisputable that some unmeasured confounders such as gout, uremia, and other uric acid metabolic disorder might still have affected the results. In addition, as a retrospective database study, it was difficult to account for the potential effect of therapy before ICU admission on serum uric acid levels, because such information was usually not documented. Thus, further well-designed prospective study is needed to confirm our results. Second, the present study included data from only one ICU center, which might limit the external applicability of the study results. Third, we found no association between serum uric acid on admission and long-term outcomes of critically ill patients, but whether the changes of serum uric acid would be associated with the clinical outcomes of the patients remained unknown. Conclusion This large retrospective cohort study found that there was no statistically significant association of admission serum uric acid with 90-day mortality of ICU patients, providing a stronger confirmation of the controversial issue. However, further prospective basic and clinical research studies are still needed especially to reveal the underlined mechanisms and to evaluate the potential predictive value of changes of uric acid. Supplementary materials Figure S1 Distribution of propensity scores. Figure S2 Kaplan–Meier survival curve of 28-day mortality. Abbreviations: ICU, intensive care unit; PSM, propensity score matching. Table S1 Univariate Cox regression analysis of all patients on 90-day mortality Variables HR 95% CI p-value Age (years) 1.02 1.02–1.03 <0.0001 Gender  Male 1.0  Female 1.07 0.91–1.26 0.4272 SAPS II 1.04 1.03–1.04 <0.0001 Sepsis (based on ICD-9 codes)  No 1.0  Yes 2.02 1.63–2.49 <0.0001 Mechanical ventilation on first day  No 1.0  Yes 0.99 0.84–1.16 0.8727 Renal replacement therapy on first day  No 1.0  Yes 1.22 0.89–1.67 0.2207 Uric acid (mg/dL) 1.02 1.00–1.05 0.0552 Elixhauser Comorbidity Index (SID30) 1.04 1.03–1.04 <0.0001 Comorbidities  Congestive heart failure   No 1.0   Yes 1.67 1.40–2.01 <0.0001  Cardiac arrhythmias   No 1.0   Yes 1.60 1.33–1.92 <0.0001  Valvular disease   No 1.0   Yes 1.53 1.12–2.10 0.0078  Pulmonary circulation disorder   No 1.0   Yes 1.41 1.02–1.95 0.0357  Peripheral vascular disorder   No 1.0   Yes 1.10 0.83–1.45 0.5042  Hypertension   No 1.0   Yes 1.08 0.84–1.38 0.5602  Paralysis   No 1.0   Yes 1.08 0.65–1.81 0.7581  Other neurological disease   No 1.0   Yes 1.12 0.85–1.48 0.4238  Chronic pulmonary disease   No 1.0   Yes 1.01 0.81–1.27 0.9319  Uncomplicated diabetes   No 1.0   Yes 0.97 0.78–1.20 0.7806  Complicated diabetes   No 1.0   Yes 1.01 0.73–1.41 0.9442  Hypothyroidism   No 1.0   Yes 0.93 0.68–1.27 0.6455  Renal failure   No 1.0   Yes 1.11 0.89–1.39 0.3518  Liver disease   No 1.0   Yes 1.38 1.04–1.82 0.0246  Peptic ulcer   No 1.0   Yes 0.00 0.00–Inf 0.9866  AIDS   No 1.0   Yes 1.19 0.49–2.88 0.6938  Lymphoma   No 1.0   Yes 1.47 1.04–2.08 0.0294  Metastatic cancer   No 1.0   Yes 2.63 2.04–3.39 <0.0001  Solid tumor   No 1.0   Yes 0.99 0.61–1.64 0.9836  Rheumatoid arthritis   No 1.0   Yes 1.44 0.90–2.30 0.1302  Coagulopathy   No 1.0   Yes 1.71 1.43–2.06 <0.0001  Obesity   No 1.0   Yes 1.04 0.68–1.57 0.8648  Weight loss   No 1.0   Yes 1.42 1.06–1.92 0.0205  Fluid and electrolyte disorders   No 1.0   Yes 1.47 1.25–1.73 <0.0001  Blood loss anemia   No 1.0   Yes 0.31 0.15–0.65 0.0021  Deficiency anemia   No 1.0   Yes 0.87 0.70–1.07 0.1879  Alcohol abuse   No 1.0   Yes 0.76 0.51–1.13 0.1709  Drug abuse   No 1.0   Yes 0.41 0.20–0.87 0.0198  Psychoses   No 1.0   Yes 0.45 0.24–0.88 0.0188  Depression   No 1.0   Yes 0.81 0.55–1.20 0.2953 Note: Statistical significance (p<0.05) is shown in bold. Abbreviations: HR, hazard ratio; CI, confidence interval; SAPS II, Simplified Acute Physiology Score II; ICD-9, International Classification of Diseases-Ninth Revision; AIDS, acquired immune deficiency syndrome; SID, State Inpatient Database. Table S2 Univariate Cox regression analysis of PSM cohort on 90-day mortality HR 95% CI p-value Age (years) 1.00 0.99–1.00 0.5566 Gender  Male 1.0  Female 0.95 0.80–1.13 0.5646 SAPS II 1.01 1.00–1.01 0.0606 Sepsis (based on ICD-9 codes)  No 1.0  Yes 1.16 0.93–1.43 0.1866 Mechanical ventilation on first day  No 1.0  Yes 1.04 0.88–1.23 0.6044 Renal replacement therapy on first day  No 1.0  Yes 0.98 0.71–1.34 0.8889 Uric acid (mg/dL) 1.01 0.98–1.03 0.6160 Elixhauser Comorbidity Index (SID30) 1.00 0.99–1.00 0.3701 Comorbidities  Congestive heart failure   No 1.0   Yes 0.91 0.76–1.10 0.3262  Cardiac arrhythmias   No 1.0   Yes 0.99 0.82–1.19 0.9074  Valvular disease   No 1.0   Yes 1.05 0.77–1.44 0.7671  Pulmonary circulation disorder   No 1.0   Yes 0.94 0.68–1.30 0.7088  Peripheral vascular disorder   No 1.0   Yes 0.97 0.73–1.29 0.8432  Hypertension   No 1.0   Yes 0.93 0.72–1.19 0.5546  Paralysis   No 1.0   Yes 0.88 0.52–1.50 0.6503  Other neurological disease   No 1.0   Yes 0.96 0.72–1.27 0.7778  Chronic pulmonary disease   No 1.0   Yes 0.97 0.77–1.21 0.7744  Uncomplicated diabetes   No 1.0   Yes 0.98 0.79–1.22 0.8543  Complicated diabetes   No 1.0   Yes 0.85 0.60–1.21 0.3748  Hypothyroidism   No 1.0   Yes 0.83 0.60–1.13 0.2361  Renal failure   No 1.0   Yes 0.90 0.71–1.13 0.3562  Liver disease   No 1.0   Yes 0.98 0.73–1.32 0.9022  AIDS   No 1.0   Yes 0.72 0.30–1.73 0.4603  Lymphoma   No 1.0   Yes 0.94 0.67–1.33 0.7409  Metastatic cancer   No 1.0   Yes 1.14 0.86–1.50 0.3634  Solid tumor   No 1.0   Yes 0.89 0.54–1.46 0.6411  Rheumatoid arthritis   No 1.0   Yes 0.94 0.58–1.53 0.8061  Coagulopathy   No 1.0   Yes 0.99 0.82–1.20 0.9481  Obesity   No 1.0   Yes 1.17 0.76–1.79 0.4717  Weight loss   No 1.0   Yes 0.93 0.69–1.26 0.6577  Fluid and electrolyte disorders   No 1.0   Yes 0.96 0.81–1.13 0.6048  Blood loss anemia   No 1.0   Yes 1.27 0.60–2.67 0.5344  Deficiency anemia   No 1.0   Yes 0.92 0.74–1.14 0.4309  Alcohol abuse   No 1.0   Yes 0.94 0.62–1.43 0.7775  Drug abuse   No 1.0   Yes 1.21 0.57–2.55 0.6186  Psychoses   No 1.0   Yes 0.93 0.48–1.79 0.8215  Depression   No 1.0   Yes 0.94 0.63–1.41 0.7814 Abbreviations: PSM, propensity score matching; HR, hazard ratio; CI, confidence interval; SAPS II, Simplified Acute Physiology Score II; ICD-9, International Classification of Diseases-Ninth Revision; AIDS, acquired immune deficiency syndrome; SID, State Inpatient Database. Acknowledgments The study was supported by National Natural Science Foundation of China (No 81670066), Major Science and Technology Planning Project of Guangdong Province (No 2016A020216009), Natural Science Foundation of Guangdong Province China (No 2015A030310346), and Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (“Climbing Program” Special Funds). Author contributions Qingui Chen designed the study and was the primary author of the manuscript. Qinchang Chen and Kai Huang mainly performed data extraction and statistical analysis. All authors contributed toward data analysis, drafting, and critically revising the paper, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work. Disclosure The authors report no conflicts of interest in this work. Figure 1 Flow chart of the study. Abbreviations: ICU, intensive care unit; LOS, length of stay. Figure 2 Kaplan–Meier survival curve by different levels of uric acid of all patients and PSM cohort. Abbreviations: ICU, intensive care unit; PSM, propensity score matching. Figure 3 ROC curves of admission serum uric acid for prediction of clinical outcomes in all patients and the PSM cohort. Abbreviations: ROC curves, receiver operating characteristic curves; ICU, intensive care unit; AUC, area under the ROC curves; PSM, propensity score matching. Table 1 Characteristics and comparison between survivors and non-survivors of all patients Variable All patients (n=2,123) Survivors (n=1,545) Non-survivors (n=578) p-value Age (years) 64.09 (51.39–75.74) 62.11 (48.68–73.83) 68.97 (57.79–79.52) <0.001 Male 1,219 (57.42%) 895 (57.93%) 324 (56.06%) 0.437 ICU mortality 187 (8.81%) 0 (0.00%) 187 (32.35%) <0.001 Hospital mortality 379 (17.85%) 4 (0.26%) 375 (64.88%) <0.001 Length of ICU stay (days) 3.79 (2.01–9.19) 3.47 (1.94–9.14) 4.21 (2.31–9.31) 0.001 Length of hospital stay (days) 14.69 (8.05–26.37) 14.32 (7.90–26.00) 15.84 (9.03–27.50) 0.051 Uric acid on admission (mg/dL) 5.40 (3.80–7.90) 5.30 (3.80–7.80) 5.70 (3.90–8.00) 0.119 SAPS II on admission 39 (30–49) 36.00 (28.00–45.00) 47.00 (39.00–56.00) <0.001 Elixhauser Comorbidity Index (SID30) 11.00 (4.00–23.00) 11.00 (0.00–20.00) 17.00 (11.00–28.00) <0.001 Sepsis (based on ICD-9 codes) 239 (11.26%) 132 (8.54%) 107 (18.51%) <0.001 Mechanical ventilation on first day 1,032 (48.61%) 754 (48.80%) 278 (48.10%) 0.772 Renal replacement therapy on first day 135 (6.36%) 93 (6.02%) 42 (7.27%) 0.295 Comorbidities  Congestive heart failure 432 (20.35%) 268 (17.35%) 164 (28.37%) <0.001  Cardiac arrhythmias 426 (20.07%) 268 (17.35%) 158 (27.34%) <0.001  Valvular disease 109 (5.13%) 67 (4.34%) 42 (7.27%) 0.006  Pulmonary circulation disorder 110 (5.18%) 70 (4.53%) 40 (6.92%) 0.027  Peripheral vascular disorder 185 (8.71%) 130 (8.41%) 55 (9.52%) 0.423  Hypertension 239 (11.26%) 169 (10.94%) 70 (12.11%) 0.447  Paralysis 51 (2.40%) 36 (2.33%) 15 (2.60%) 0.723  Other neurological disease 182 (8.57%) 127 (8.22%) 55 (9.52%) 0.343  Chronic pulmonary disease 327 (15.40%) 238 (15.40%) 89 (15.40%) 0.997  Uncomplicated diabetes 365 (17.19%) 267 (17.28%) 98 (16.96%) 0.859  Complicated diabetes 133 (6.26%) 96 (6.21%) 37 (6.40%) 0.874  Hypothyroidism 170 (8.01%) 126 (8.16%) 44 (7.61%) 0.682  Renal failure 302 (14.23%) 212 (13.72%) 90 (15.57%) 0.278  Liver disease 152 (7.16%) 98 (6.34%) 54 (9.34%) 0.017  Peptic ulcer 1 (0.05%) 1 (0.06%) 0 (0.00%) 1.000  AIDS 15 (0.71%) 10 (0.65%) 5 (0.87%) 0.569  Lymphoma 89 (4.19%) 55 (3.56%) 34 (5.88%) 0.017  Metastatic cancer 121 (5.70%) 54 (3.50%) 67 (11.59%) <0.001  Solid tumor 59 (2.78%) 43 (2.78%) 16 (2.77%) 0.985  Rheumatoid arthritis 49 (2.31%) 31 (2.01%) 18 (3.11%) 0.130  Coagulopathy 410 (19.31%) 252 (16.31%) 158 (27.34%) <0.001  Obesity 84 (3.96%) 61 (3.95%) 23 (3.98%) 0.974  Weight loss 127 (5.98%) 80 (5.18%) 47 (8.13%) 0.011  Fluid and electrolyte disorders 817 (38.48%) 548 (35.47%) 269 (46.54%) <0.001  Blood loss anemia 72 (3.39%) 65 (4.21%) 7 (1.21%) <0.001  Deficiency anemia 428 (20.16%) 322 (20.84%) 106 (18.34%) 0.201  Alcohol abuse 116 (5.46%) 91 (5.89%) 25 (4.33%) 0.158  Drug abuse 56 (2.64%) 49 (3.17%) 7 (1.21%) 0.012  Psychoses 68 (3.20%) 59 (3.82%) 9 (1.56%) 0.008  Depression 112 (5.28%) 86 (5.57%) 26 (4.50%) 0.327 Notes: Patients were grouped as survivors and non-survivors determined by 90-day mortality status. Data are expressed as median (interquartile range) or n (%) unless otherwise stated. Kruskal–Wallis and chi-square (or Fisher’s exact) tests were used to analyse continuous and categorical variables, respectively. Statistical significance (p<0.05) is shown in bold. Abbreviations: ICU, intensive care unit; SAPS II, Simplified Acute Physiology Score II; ICD-9, International Classification of Diseases, Ninth Revision; AIDS, acquired immune deficiency syndrome. Table 2 Characteristics and comparison between survivors and non-survivors of PSM cohort Variable All patients (n=1,112) Survivors (n=556) Non-survivors (n=556) p-value Age (years) 69.00 (57.80–79.00) 69.13 (58.27–78.71) 68.97 (57.44–79.54) 0.920 Male 621 (55.85%) 306 (55.04%) 315 (56.65%) 0.587 ICU mortality 176 (15.83%) 0 (0.00%) 176 (31.65%) <0.001 Hospital mortality 358 (32.19%) 3 (0.54%) 355 (63.85%) <0.001 Length of ICU stay (days) 4.08 (2.16–9.48) 3.81 (2.08–9.42) 4.29 (2.39–9.49) 0.164 Length of hospital stay (days) 15.98 (8.82–28.13) 16.18 (8.64–29.04) 15.87 (9.09–27.52) 0.687 Uric acid on admission (mg/dL) 5.50 (3.80–8.20) 5.50 (3.70–8.30) 5.60 (3.90–7.90) 0.536 SAPS II on admission 46.00 (37.00–55.00) 45.00 (37.00–55.00) 46.00 (38.00–55.00) 0.202 Elixhauser Comorbidity Index (SID30) 18.00 (10.00–28.00) 19.00 (9.75–28.00) 17.00 (10.75–27.25) 0.428 Sepsis (based on ICD-9 codes) 184 (16.55%) 83 (14.93%) 101 (18.17%) 0.146 Mechanical ventilation on first day 531 (47.75%) 263 (47.30%) 268 (48.20%) 0.764 Renal replacement therapy on first day 86 (7.73%) 45 (8.09%) 41 (7.37%) 0.653 Comorbidities  Congestive heart failure 321 (28.87%) 167 (30.04%) 154 (27.70%) 0.390  Cardiac arrhythmias 297 (26.71%) 148 (26.62%) 149 (26.80%) 0.946  Valvular disease 80 (7.19%) 38 (6.83%) 42 (7.55%) 0.642  Pulmonary circulation disorder 80 (7.19%) 41 (7.37%) 39 (7.01%) 0.816  Peripheral vascular disorder 105 (9.44%) 52 (9.35%) 53 (9.53%) 0.918  Hypertension 139 (12.50%) 71 (12.77%) 68 (12.23%) 0.786  Paralysis 30 (2.70%) 16 (2.88%) 14 (2.52%) 0.711  Other neurological disease 106 (9.53%) 53 (9.53%) 53 (9.53%) 1.000  Chronic pulmonary disease 183 (16.46%) 95 (17.09%) 88 (15.83%) 0.571  Uncomplicated diabetes 194 (17.45%) 97 (17.45%) 97 (17.45%) 1.000  Complicated diabetes 72 (6.47%) 39 (7.01%) 33 (5.94%) 0.465  Hypothyroidism 92 (8.27%) 51 (9.17%) 41 (7.37%) 0.276  Renal failure 182 (16.37%) 95 (17.09%) 87 (15.65%) 0.517  Liver disease 97 (8.72%) 48 (8.63%) 49 (8.81%) 0.915  AIDS 12 (1.08%) 7 (1.26%) 5 (0.90%) 0.773  Lymphoma 68 (6.12%) 34 (6.12%) 34 (6.12%) 1.000  Metastatic cancer 99 (8.90%) 44 (7.91%) 55 (9.89%) 0.247  Solid tumor 35 (3.15%) 19 (3.42%) 16 (2.88%) 0.606  Rheumatoid arthritis 35 (3.15%) 18 (3.24%) 17 (3.06%) 0.864  Coagulopathy 292 (26.26%) 146 (26.26%) 146 (26.26%) 1.000  Obesity 42 (3.78%) 20 (3.60%) 22 (3.96%) 0.753  Weight loss 93 (8.36%) 47 (8.45%) 46 (8.27%) 0.914  Fluid and electrolyte disorders 509 (45.77%) 258 (46.40%) 251 (45.14%) 0.674  Blood loss anemia 11 (0.99%) 4 (0.72%) 7 (1.26%) 0.547  Deficiency anemia 218 (19.60%) 114 (20.50%) 104 (18.71%) 0.450  Alcohol abuse 48 (4.32%) 25 (4.50%) 23 (4.14%) 0.768  Drug abuse 12 (1.08%) 5 (0.90%) 7 (1.26%) 0.773  Psychoses 21 (1.89%) 12 (2.16%) 9 (1.62%) 0.509  Depression 51 (4.59%) 26 (4.68%) 25 (4.50%) 0.886 Notes: Patients were grouped as survivors and non-survivors determined by 90-day mortality status. Data are expressed as median (interquartile range) or n (%) unless otherwise stated. Kruskal–Wallis and chi-square (or Fisher’s exact) tests were used to analyze continuous and categorical variables, respectively. Statistical significance (p<0.05) is shown in bold. Abbreviations: PSM, propensity score matching; ICU, intensive care unit; SAPS II, Simplified Acute Physiology Score II; ICD-9, International Classification of Diseases, Ninth Revision; AIDS, acquired immune deficiency syndrome. Table 3 Association of uric acid with 90-day mortality, 28-day mortality, ICU mortality, and hospital mortality Subjects HR/OR 95% CI p-value All patients 90-day mortality  Non-adjusted 1.02 1.00–1.05 0.0552  Model I 1.00 0.98–1.03 0.7743  Model II 1.01 0.98–1.03 0.5735 28-day mortality  Non-adjusted 1.03 1.00–1.06 0.0571  Model I 1.01 0.98–1.04 0.6281  Model II 1.01 0.98–1.04 0.5785 ICU mortality  Non-adjusted 1.03 0.99–1.08 0.1198  Model I 1.01 0.96–1.05 0.8157  Model II 1.00 0.96–1.05 0.9105 Hospital mortality  Non-adjusted 1.04 1.00–1.07 0.0301  Model I 1.01 0.97–1.04 0.6263  Model II 1.01 0.97–1.04 0.6924 PSM cohort 90-day mortality  Non-adjusted 1.01 0.98–1.03 0.6160  Model I 1.00 0.98–1.03 0.6835 28-day mortality  Non-adjusted 1.01 0.98–1.04 0.3784  Model I 1.01 0.98–1.04 0.4894 ICU mortality  Non-adjusted 1.02 0.98–1.07 0.3709  Model I 1.01 0.97–1.05 0.6934 Hospital mortality  Non-adjusted 1.02 0.98–1.05 0.3751  Model I 1.01 0.97–1.04 0.6099 Notes: Association of uric acid with 90-day mortality and 28-day mortality was analyzed using Cox regression models, and associations of uric acid with ICU mortality and hospital mortality were analyzed using logistic regression models. For all patients, model I was adjusted for SAPS II, Elixhauser Comorbidity Index (SID30), and sepsis based on ICD-9 codes; model II was adjusted for SAPS II, sepsis based on ICD-9 codes, congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorder, liver disease, lymphoma, metastatic cancer, coagulopathy, weight loss, fluid and electrolyte disorders, blood loss anemia, drug abuse, and psychoses. For PSM cohort, model was adjusted for SAPS II. Statistical significance (p<0.05) is shown in bold. 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