Elevated serum osmolarity is associated with poor in-hospital prognosis in patients with cardiac arrest: A retrospective study based on MIMIC-IV database

Elevated serum osmolarity is associated with poor in-hospital prognosis in patients with cardiac arrest: A retrospective study based on MIMIC-IV 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 Elevated serum osmolarity is associated with poor in-hospital prognosis in patients with cardiac arrest: A retrospective study based on MIMIC-IV database Zhangping Sun, Zhihua Cheng, Ping Gong, Peijuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3365757/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 A major cause of death is cardiac arrest (CA). Serum osmolarity has been shown to be useful in predicting the prognosis of sepsis patients in earlier research. The purpose of this study is to ascertain the impact of serum osmolarity on the prognosis of cardiac arrest patients in the intensive care unit. Methods In this study, the relationship between serum osmolarity and in-hospital mortality in ICU patients experiencing cardiac arrest was investigated. The MIMIC-IV database was used to select adult patients with cardiac arrest diagnoses for this investigation. The serum concentrations of Na + , K + , glucose, and urea nitrogen were used to determine the serum osmolarity simultaneously. Results The baseline data of adult patients with CA hospitalized in the intensive care unit (ICU) from 2008 to 2019 in the American Intensive Care Database (MIMIC-IV, version v2.0) were collected. In this study, the patients were divided into survival and non-survival group, according to the 28-day prognosis. The mortality in the hyper-osmolarity group (61.96%) was significantly higher than that in the normal osmolarity group (35.51%, P < 0.001). The Kaplan-Meier survival analysis before and after matching showed that the cumulative survival rate of the hyper-osmolarity was lower (P < 0.05). The Univariate and Multivariable COX analysis of risk factors for death (After PSM) shows that hyper-osmolarity was a significant independent risk factor for 28-day mortality. It was coincident with the result of subgroup analysis. Conclusion The serum osmolarity would be a predictive biomarker that is accessible right after a cardiac arrest for CA survivors. It can be determined more quickly and at a lower cost. However, more research is required to assess serum osmolality's prognostic value in various patient populations. Health sciences/Diseases/Cardiovascular diseases Health sciences/Diseases/Metabolic disorders Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Prognostic markers cardiac arrest serum osmolarity hyper-osmolarity hypo-osmolarity ICU mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiac arrest is an urgent and harmful event with high mortality[ 1 ]. Data from China BASIC survey 2023 showed that there were nearly 1.05 million out-of-hospital CA patients in China every year, with an initial ROSC rate of only 5.98%, an overall survival rate at discharge of 1.15% and a good neurological prognosis with a discharge survival rate of only 0.83%[ 2 ]. As a result, it is crucial to determine the prognosis as soon as possible. Numerous risk scores, including the OHCA[ 3 ], Cardiac Arrest Hospital Prognosis (CAHP) scores[ 3 ], and PROLOGUE score[ 4 ] have recently been established to enhance emergency clinical decision-making for CA survivors. However, these scores necessitate thorough history-taking. Although, for CA survivors who are admitted to the emergency room (ED), cardiac arrest characteristics such the initial cardiac arrest rhythms, collapse time, and the time of initiating cardiopulmonary resuscitation (CPR) are typically accessible. Even in the well-established emergency medical service system, the accuracy of recall or recording limits the information's reliability. From this perspective, biomarkers are free from such bias and simple to interpret. Neuron-specific enolase (NSE), S-100β, and neurofilament light chain (NfL) are a few markers that can be used to predict the prognosis of CA patients[ 1 ], but some of these biomarkers are more expensive and some require a longer wait for test results. In contrast, serum osmolarity (OSM), an antiquated indicator, is a prognostic biomarker that is accessible within an hour of ROSC. Without further clinical variables, serum OSM is determined by the concentrations of sodium (Na + ), potassium (K + ), glucose, urea nitrogen, and glucose in the blood[ 5 ]. In clinical practice, the association between serum osmolarity and illness severity or hospital mortality has been investigated in relation to a variety of patient populations, including stroke[ 6 ], cerebral hemorrhage[ 7 ], acute coronary syndromes[ 8 ], pulmonary edema[ 9 ], and septic shock[ 10 ]. However, the role of serum osmolarity, which reflects the distribution of extracellular and intracellular water distribution, has not been studied in cardiac arrest patients. In the present study, we aimed to investigate the association between serum osmolarity and in-hospital mortality in ICU patients experiencing cardiac arrest given the growing understanding of the impact of elevated serum OSM on neuronal cell malfunction and brain damage. The essential information of the patients was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Materials and methods Sources of data This retrospective study collected data from the MIMIC-IV database (Version: 2.0, ( https://physionet.org/content/mimiciv/2.0/ ), which is a large, publicly accessible database that was developed and managed by the MIT Computational Physiology Laboratory [ 11 ]. The Massachusetts Institute of Technology (Cambridge, MA) and Beth Israel Deaconess Medical Center (Boston, MA) gave their approval for the establishment of this database, and permission was secured for the original data gathering. Therefore, the ethical approval statement and the need for informed consent were waived for this manuscript. The permission of access to the database is obtained by author Zhangping Sun (Record ID 55177479 and 55177484). Study population The study included ICU patients with CA diagnosed in the MIMIC database between 2008 and 2019. Only patients who were first admitted to the ICU and were over the age of 18 were included. The following criteria apply to exclusion: (1) ICU stay of less than 24 hours; (2) Absence of crucial data, such as OSM. Data extraction We included information on the patients' age, gender, comorbidities, mean arterial pressure (MAP), heart rate, temperature, respiratory rate, oxygen saturation (SpO 2 ), white blood cell (WBC) count, hemoglobin level, platelet count, sodium (Na + ), potassium (K + ), glucose, urea nitrogen, creatinine, urine output, and lactate levels that were found at the time of admission to the intensive care unit. Groups and primary endpoints In this investigation, only the values of each element that were simultaneously measured were utilized. The formula (Na + + K+) ×2 + (glucose/18) + (BUN/2.8) was used to determine the serum osmolarity (OSM)(mmol/L) based on previously published literature[ 10 ]. The normal range and reference group for this investigation were 290–309 mmol/L. When the serum osmolarity on the first day was less than 290 mmol/L, hypo-osmolarity was defined; when it was greater than 309 mmol/L, hyper-osmolarity was defined. The endpoint of our study was all-cause mortality during 28-day-hospitalization. Statistical analysis Continuous variables conformed to normal distribution, were expressed as mean ± standard deviation (x̅ ± s), with the analysis of t-test method; if variables did not conform to normal distribution, they were expressed as median (interquartile range) [M (QL, QU)], with the analysis of nonparametric (MannWhitney U) test between the two groups. Categorical data were presented as constituent ratios and analyzed by the method of chi-square test. Propensity score matching (PSM) analysis was performed to reduce bias between the survival and non-survival groups. Patients were matched in a 1:1 ratio using the nearest neighbor algorithm with a caliper of 0.2. Kaplan-Meier curves were drawn before and after matching, and the cumulative survival rate during hospitalization was compared between the normal and hyper-OSM groups by the long-rank test. Restricted cubic spline (RCS) was utilized to analyze the relevance between OSM at ICU admission and risk of all-cause mortality during hospitalization. Variables with a P value less than 0.05 in univariate analysis between survival and non-survival groups were included in multivariate Cox regression analysis, and the results were expressed as hazard ratio (HR) with 95% confidence interval (CI). All the statistical analyses mentioned above were performed using RStudio (version 4.1.2). Statistical significance was considered to be indicated by a two-sided p < 0.05. Results Baseline characteristics In the present study, the flowchart of the cohort selection is shown in Fig. 1 . Firstly, data for 1118 patients diagnosed with cardiac arrest from 2008 to 2019 were collected from MIMIC-IV database. Then, we excluded patients who were not admitted into the ICU for the first time and those younger than 18 years old. As shown in the flowchart in Fig. 1 , 798 cardiac arrest patients met the inclusion criteria and included in the final study. The average age of the participants was 67.6 years. Before PSM, compared with the survival group, the SOFA score, Apsiii score, Sapsii score, Oasis score, GCS score, OSM, WBC, Creatinine, Urine output were higher in the non-survival group. After PSM, 261 survival patients were matched with 261 non-survival patients and the baseline was better balanced between the two groups (shown in Table 1 ). Compared with the survival group, GCS and OSM in the non-survival group were still higher (P < 0.05). All-cause mortality of the groups The all-cause mortality of the included patients during 28-day was 46.37%. The mortality rate in the hyper-osmolarity group (61.96%) was significantly higher than that in the normal osmolarity group (35.51%, P < 0.001). After PSM, the mortality rate in the hyper-osmolarity group (62.67%) was also higher (P < 0.001), as shown in Table 1 . For ICU mortality, Hypo-osmolarity was independent risk factors To determine the connection between serum osmolarity and ICU mortality, both single and multivariable logistic regression analysis were utilized. But only hyper-osmolarity was substantially linked with ICU mortality, as showed in Table 2 (HR = 2.09 in univariable analysis and HR = 1.77 in multivariable analysis, p༜0.001). The results of a second logistic regression research after PSM on the risk variables for 28-day mortality showed that hyper-osmolarity was independent risk factors (HR = 1.92 in univariable analysis and HR = 2.07 in multivariable analysis, p༜0.001) (Table 3 ) as well. Kaplan-Meier survival curve analysis Before or after matching, the Kaplan-Meier survival curves in Figs. 2 and 3 showed that compared with the normal osmolarity group, the cumulative survival rate of patients with CA was significantly lower (log-rank test, P < 0.001) in the hypo-osmolarity group and the hyper-osmolarity group during hospitalization. Comparison of ROC curves We plotted ROC curves to show that the AUCs of OSM in Day1,3,7,28 of hospital admission were 79.60% (95% CI: 69.97–89.22), 75.63% (95% CI: 70.81–80.44), 71.73% (95% CI: 67.67–75.80), and 63.44% (95% CI: 57.51–69.37) respectively (before PSM). As shown in Fig. 5 , after PSM, the AUCs of OSM in Day1,3,7,28 of hospital admission were 85.49% (95% CI: 78.27–92.71), 75.58% (95% CI: 69.95–81.22), 67.23% (95% CI: 62.10-72.36), and 65.95% (95% CI: 58.78–73.12) respectively. The OSM of Day1 represented by the solid red line is always higher than others, and its clinical net benefit has a significant advantage. Subgroup analysis Figure 6 indicates as to whether the correlation between hyper-osmolality and all-cause mortality at 28-d of hospital admission in patients with CA was stable across subgroups. When the stratified analysis was performed for Age, Gender, Hypertension, Diabetes, Acute Myocardial Infarction (AMI), Chronic Heart Failure (CHF), Acute Kidney Injury (AKI), Liver Disease, the forest plot (Fig. 6 ) showed no significant interaction of hyper-osmolality with each subgroup (P for interaction: 0.3–0.8). These evidences supported that hyper-osmolality was an independent prognostic factor. Discussion Patients who are severely ill must maintain a proper water balance inside their bodies, and serum osmolarity is crucial for both extracellular and intracellular water distribution. Patients admitted to the ICU frequently have osmolarity perturbation, which is linked to intracellular dehydration or edema and may have negative effects. Hyper-osmolality might cause fluid from venous capacitance vessels to mobilize and enter the circulation, aggravating organ or tissue hypoxia[ 12 , 13 ]. For critically ill patients, maintaining a healthy balance of water in the body is essential, and the distribution of extracellular and intracellular water is significantly influenced by serum osmolality. Patients who are admitted to the ICU frequently have elevated osmolality, which is linked to intracellular dehydration or edema and can have negative effects[ 14 ]. In patients with a variety of underlying illnesses, the predictive significance of osmolality was demonstrated[ 6 , 15 ]. To our knowledge, this is the first study to identify a substantial association between serum osmolarity and mortality among acute medical admissions as cardiac arrest. First, we show that serum osmolarity on the first day of admission is a unique risk factor for 28-day death in cardiac arrest patients receiving intensive care. Lower osmolarity has been implicated in previous research as a significant risk factor for increased morbidity and death in HF patients[ 16 ]. However, our result was inconsistent with this result. According to our findings, high serum osmolarity was linked to 28-day mortality, which is consistent with the previous study[ 17 ]. The reason why there was no significant difference about the 28-day mortality between the hypo-osmolarity and normal groups, I hypothesize that it maybe the amount of hypo-osmolarity patients included in this trial was not large enough. The previous research showed that hyper-osmolality upon admission was linked to higher mortality, particularly in older patients[ 18 ]. However, in our study which included a relatively large set of observations, osmolarity's prognostic power was age-independents (Table 1 ). We included important confounding variables in the regression analysis, such as AKI and CHF, and still found that hyper-osmolarity was a significant independent risk factor for 28-day mortality. As for the risk of 28-day death, we discovered that it was roughly twice as high in the hyper-osmolarity group as it was in the hypo-osmolarity group. Hyper-osmolarity alone demonstrated a moderate predictive effect (AUC > 65%) for predicting 28-day death. In other words, the findings of this study are applicable to all cardiac arrest types. These results were agreement with the Subgroup analysis. When the stratified analysis was performed for Age, Gender, Hypertension, Diabetes, Acute Myocardial Infarction (AMI), Chronic Heart Failure (CHF), Acute Kidney Injury (AKI), Liver Disease, the forest plot (Fig. 6 ) showed no significant interaction of hyper-osmolality with each subgroup (P for interaction: 0.3–0.8). These evidences supported that hyper-OSM is an independent prognostic factor as well. Hence, we propose that it might be a preferable strategy to use hyper-osmolarity as a factor to develop a prediction model later if the role of hyper-osmolarity in cardiac arrest can be properly clarified. The results of Kaplan-Meier survival curves analysis and ROC curves of OSM in Day1,3,7,28 of hospital admission for predicting in-hospital mortality (Fig. 2 – 5 ) showed that the earlier the index of high osmolality is applied, the better the value will be, as its predictive value of short-term prognosis after cardiopulmonary resuscitation in cardiac arrest is higher (AUC 79.60% before PSM vs AUC 85.49% after PSM). Compared to other indicators, the advantage of OSM is its convenience. Clinicians place a high priority on the identification of indicators that might predict greater mortality among acute medical admissions, as indicated by the emergence of multiple clinical scoring systems. While some scoring systems claim to be simple [ 19 ], others are so complicated that they maybe need computers [ 20 ]. Without a computer, certain calculations might have a high error rate [ 21 , 22 ]. Therefore, it is emergent to identify routine parameters with prognostic relevance which can be determined in a rapid, simple and inexpensive way. The serum osmolarity is fairly simple to compute and is based on information that is typically accessible for the majority of hospital admissions. Our study's major limitation is that we only collected OSM at the time of admission. Monitoring dynamic changes in OSM may be more highly valuable in predicting in-hospital mortality in CA patients. Additionally, because this study is a retrospective analysis, there is some selection bias. As a result, we are unable to precisely measure the fluid intake indicators, such as drinking water, or to determine the precise cardiac function of the patients who were included in the study. Although the majority of the participants in our study were white Americans, it is yet unclear whether the results are applicable to other nations and ethnicities. Last but not least, there are many different main disorders that can lead to cardiac arrest, and because of database restrictions, we were unable to determine the precise etiology of the individuals included in our study. Although this study has broad implications for the volume management of CA, thorough multicenter randomized controlled clinical trials are still required to prove the true utility of OSM for CA patients. Conclusions As a result, high serum osmolarity in cardiac arrest patients is a parameter that can be easily measured, predicts a poorer prognosis, and is linked to a higher load of comorbidities, making it a possibility for a novel marker in cardiac arrest. To fully understand our findings, more researches are required. Declarations Data availability All the datasets used and analysed during the current study are available in the MIMIC-IV database (https://physionet.org/content/mimiciv/2.0/) and in this published article. Authors’ contributions Study conception and design: ZPS Acquisition, analysis, and interpretation of data: ZHC Manuscript drafting: ZPS,PJL Final approval of the manuscript: all authors All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Acknowledgements No. Declaration of interest The authors declare that they have no conflicts of interest. Funding This study was funded by Shenzhen Key Medical Discipline Construction Fund (SZXK046) and the National Nature Science Foundation of China (81571869). References Henson, T., et al., Outcome and prognostication after cardiac arrest. Ann N Y Acad Sci, 2022. 1508 (1): p. 23-34. Xie, X., et al., Efforts to Improve Survival Outcomes of Out-of-Hospital Cardiac Arrest in China: BASIC-OHCA. Circ Cardiovasc Qual Outcomes, 2023. 16 (2): p. e008856. Blatter, R., et al., Comparison of different clinical risk scores to predict long-term survival and neurological outcome in adults after cardiac arrest: results from a prospective cohort study. Ann Intensive Care, 2022. 12 (1): p. 77. Heo, W.Y., et al., External validation of cardiac arrest-specific prognostication scores developed for early prognosis estimation after out-of-hospital cardiac arrest in a Korean multicenter cohort. PLoS One, 2022. 17 (4): p. e0265275. Najem, O., M.M. Shah, and O. De Jesus, Serum Osmolality , in StatPearls . 2023: Treasure Island (FL) ineligible companies. Disclosure: Maulik Shah declares no relevant financial relationships with ineligible companies. Disclosure: Orlando De Jesus declares no relevant financial relationships with ineligible companies. Bhalla, A., et al., Influence of raised plasma osmolality on clinical outcome after acute stroke. Stroke, 2000. 31 (9): p. 2043-8. Nag, C., et al., Plasma osmolality in acute spontanious intra-cerebral hemorrhage: Does it influence hematoma volume and clinical outcome? J Res Med Sci, 2012. 17 (6): p. 548-51. Rohla, M., et al., Plasma osmolality predicts clinical outcome in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Eur Heart J Acute Cardiovasc Care, 2014. 3 (1): p. 84-92. Yagi, T., et al., Global end-diastolic volume, serum osmolarity, and albumin are risk factors for increased extravascular lung water. J Crit Care, 2011. 26 (2): p. 224 e9-13. Heng, G., et al., Increased ICU mortality in septic shock patients with hypo- or hyper- serum osmolarity: A retrospective study. Front Med (Lausanne), 2023. 10 : p. 1083769. Goldberger, A.L., et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000. 101 (23): p. E215-20. El-Sharkawy, A.M., et al., Hyperosmolar dehydration: A predictor of kidney injury and outcome in hospitalised older adults. Clin Nutr, 2020. 39 (8): p. 2593-2599. Arevalo-Lorido, J.C., et al., High serum osmolarity at admission determines a worse outcome in patients with heart failure: Is a new target emerging? Int J Cardiol, 2016. 221 : p. 238-42. Buyukkaragoz, B. and S.A. Bakkaloglu, Serum osmolality and hyperosmolar states. Pediatr Nephrol, 2023. 38 (4): p. 1013-1025. Jayashree, M. and S. Singhi, Diabetic ketoacidosis: predictors of outcome in a pediatric intensive care unit of a developing country. Pediatr Crit Care Med, 2004. 5 (5): p. 427-33. Oren, R.M., Hyponatremia in congestive heart failure. Am J Cardiol, 2005. 95 (9A): p. 2B-7B. Shen, Y., et al., Association between serum osmolarity and mortality in patients who are critically ill: a retrospective cohort study. BMJ Open, 2017. 7 (5): p. e015729. O'Neill, P.A., et al., Reduced survival with increasing plasma osmolality in elderly continuing-care patients. Age Ageing, 1990. 19 (1): p. 68-71. Fuchs, P.A., I.J. Czech, and L.J. Krzych, The Pros and Cons of the Prediction Game: The Never-ending Debate of Mortality in the Intensive Care Unit. Int J Environ Res Public Health, 2019. 16 (18). Yang, J., et al., Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit. J Crit Care, 2022. 71 : p. 154106. Prytherch, D.R., et al., Calculating early warning scores--a classroom comparison of pen and paper and hand-held computer methods. Resuscitation, 2006. 70 (2): p. 173-8. Gill, M.R., D.G. Reiley, and S.M. Green, Interrater reliability of Glasgow Coma Scale scores in the emergency department. Ann Emerg Med, 2004. 43 (2): p. 215-23. Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.pdf Table 1 Baseline characteristics of the study population between survival and non-survival groups. PSM propensity score matching. AMI, acute myocardial infarction; CHF, chronic heart failure; AKI, acute kidney injury; MAP, mean arterial pressure; SOFA, sequential organ failure assessment; GCS, Glasgow Coma Scale; WBC, white blood cell; OSM, serum osmolarity. Table2.pdf Table 2 Univariate and Multivariable COX analysis of risk factors for death (Before PSM). Table3.pdf Table 3 Univariate and Multivariable COX analysis of risk factors for death (After PSM). 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. 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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-3365757","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":235654799,"identity":"accdaa24-f503-4841-b797-d823cda47cce","order_by":0,"name":"Zhangping 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22:35:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265537,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of OSM in Day1,3,7,28 of hospital admission correlate for predicting in-hospital mortality (Before PSM).\u003c/p\u003e","description":"","filename":"fIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/d51688a684ef83c500a45073.png"},{"id":43913949,"identity":"b1f6ecb9-facd-4826-8ddc-feabb7e67b8b","added_by":"auto","created_at":"2023-09-29 22:43:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":250783,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of OSM in Day1,3,7,28 of hospital admission correlate for predicting in-hospital mortality (After PSM).\u003c/p\u003e","description":"","filename":"fIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/513b6472a65c29853710765b.png"},{"id":43912645,"identity":"b89e001c-f71e-4cf4-a41e-598078aa1806","added_by":"auto","created_at":"2023-09-29 22:35:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":165048,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for subgroup analysis of the relationship between hospital mortality and hyper-OSM (After PSM).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/a13bbd3bfb7dd6947fabbfc6.png"},{"id":56432827,"identity":"51c94160-3179-4255-96ae-c7423fa15a8a","added_by":"auto","created_at":"2024-05-14 06:38:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1485474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/968990e8-a2ec-4b5a-94d1-47c7e6229d8c.pdf"},{"id":43912637,"identity":"5b05b72d-f452-4e27-9055-c3116e3b7e36","added_by":"auto","created_at":"2023-09-29 22:35:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":253240,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1 Baseline characteristics of the study population between survival and non-survival groups. PSM propensity score matching. AMI, acute myocardial infarction; CHF, chronic heart failure; AKI, acute kidney injury; MAP, mean arterial pressure; SOFA, sequential organ failure assessment; GCS, Glasgow Coma Scale; WBC, white blood cell; OSM, serum osmolarity.\u003c/p\u003e","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/34d3109629291f911c0ba067.pdf"},{"id":43912640,"identity":"8c33d283-7bac-425f-87f0-b584bca24d8f","added_by":"auto","created_at":"2023-09-29 22:35:43","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46050,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2 Univariate and Multivariable COX analysis of risk factors for death (Before PSM).\u003c/p\u003e","description":"","filename":"Table2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/71869ca2545e5b099d95800a.pdf"},{"id":43913948,"identity":"9472418c-085a-49f2-99a9-56f349638e2f","added_by":"auto","created_at":"2023-09-29 22:43:43","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":45528,"visible":true,"origin":"","legend":"\u003cp\u003eTable 3 Univariate and Multivariable COX analysis of risk factors for death (After PSM).\u003c/p\u003e","description":"","filename":"Table3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3365757/v1/6bcbaa31948361ba3f843813.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated serum osmolarity is associated with poor in-hospital prognosis in patients with cardiac arrest: A retrospective study based on MIMIC-IV database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiac arrest is an urgent and harmful event with high mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Data from China BASIC survey 2023 showed that there were nearly 1.05\u0026nbsp;million out-of-hospital CA patients in China every year, with an initial ROSC rate of only 5.98%, an overall survival rate at discharge of 1.15% and a good neurological prognosis with a discharge survival rate of only 0.83%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As a result, it is crucial to determine the prognosis as soon as possible.\u003c/p\u003e \u003cp\u003eNumerous risk scores, including the OHCA[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], Cardiac Arrest Hospital Prognosis (CAHP) scores[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and PROLOGUE score[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] have recently been established to enhance emergency clinical decision-making for CA survivors. However, these scores necessitate thorough history-taking. Although, for CA survivors who are admitted to the emergency room (ED), cardiac arrest characteristics such the initial cardiac arrest rhythms, collapse time, and the time of initiating cardiopulmonary resuscitation (CPR) are typically accessible. Even in the well-established emergency medical service system, the accuracy of recall or recording limits the information's reliability. From this perspective, biomarkers are free from such bias and simple to interpret. Neuron-specific enolase (NSE), S-100β, and neurofilament light chain (NfL) are a few markers that can be used to predict the prognosis of CA patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], but some of these biomarkers are more expensive and some require a longer wait for test results.\u003c/p\u003e \u003cp\u003eIn contrast, serum osmolarity (OSM), an antiquated indicator, is a prognostic biomarker that is accessible within an hour of ROSC. Without further clinical variables, serum OSM is determined by the concentrations of sodium (Na\u003csup\u003e+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e), glucose, urea nitrogen, and glucose in the blood[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In clinical practice, the association between serum osmolarity and illness severity or hospital mortality has been investigated in relation to a variety of patient populations, including stroke[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], cerebral hemorrhage[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], acute coronary syndromes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], pulmonary edema[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and septic shock[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the role of serum osmolarity, which reflects the distribution of extracellular and intracellular water distribution, has not been studied in cardiac arrest patients. In the present study, we aimed to investigate the association between serum osmolarity and in-hospital mortality in ICU patients experiencing cardiac arrest given the growing understanding of the impact of elevated serum OSM on neuronal cell malfunction and brain damage. The essential information of the patients was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSources of data\u003c/h2\u003e \u003cp\u003eThis retrospective study collected data from the MIMIC-IV database (Version: 2.0, (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://physionet.org/content/mimiciv/2.0/\u003c/span\u003e\u003cspan address=\"https://physionet.org/content/mimiciv/2.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is a large, publicly accessible database that was developed and managed by the MIT Computational Physiology Laboratory [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Massachusetts Institute of Technology (Cambridge, MA) and Beth Israel Deaconess Medical Center (Boston, MA) gave their approval for the establishment of this database, and permission was secured for the original data gathering. Therefore, the ethical approval statement and the need for informed consent were waived for this manuscript. The permission of access to the database is obtained by author Zhangping Sun (Record ID 55177479 and 55177484).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe study included ICU patients with CA diagnosed in the MIMIC database between 2008 and 2019. Only patients who were first admitted to the ICU and were over the age of 18 were included. The following criteria apply to exclusion: (1) ICU stay of less than 24 hours; (2) Absence of crucial data, such as OSM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eWe included information on the patients' age, gender, comorbidities, mean arterial pressure (MAP), heart rate, temperature, respiratory rate, oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e), white blood cell (WBC) count, hemoglobin level, platelet count, sodium (Na\u003csup\u003e+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e), glucose, urea nitrogen, creatinine, urine output, and lactate levels that were found at the time of admission to the intensive care unit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGroups and primary endpoints\u003c/h2\u003e \u003cp\u003eIn this investigation, only the values of each element that were simultaneously measured were utilized. The formula (Na\u003csup\u003e+\u003c/sup\u003e + K+) \u0026times;2 + (glucose/18) + (BUN/2.8) was used to determine the serum osmolarity (OSM)(mmol/L) based on previously published literature[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The normal range and reference group for this investigation were 290\u0026ndash;309 mmol/L. When the serum osmolarity on the first day was less than 290 mmol/L, hypo-osmolarity was defined; when it was greater than 309 mmol/L, hyper-osmolarity was defined.\u003c/p\u003e \u003cp\u003eThe endpoint of our study was all-cause mortality during 28-day-hospitalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables conformed to normal distribution, were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̅ \u0026plusmn; s), with the analysis of t-test method; if variables did not conform to normal distribution, they were expressed as median (interquartile range) [M (QL, QU)], with the analysis of nonparametric (MannWhitney U) test between the two groups. Categorical data were presented as constituent ratios and analyzed by the method of chi-square test.\u003c/p\u003e \u003cp\u003ePropensity score matching (PSM) analysis was performed to reduce bias between the survival and non-survival groups. Patients were matched in a 1:1 ratio using the nearest neighbor algorithm with a caliper of 0.2. Kaplan-Meier curves were drawn before and after matching, and the cumulative survival rate during hospitalization was compared between the normal and hyper-OSM groups by the long-rank test. Restricted cubic spline (RCS) was utilized to analyze the relevance between OSM at ICU admission and risk of all-cause mortality during hospitalization. Variables with a P value less than 0.05 in univariate analysis between survival and non-survival groups were included in multivariate Cox regression analysis, and the results were expressed as hazard ratio (HR) with 95% confidence interval (CI). All the statistical analyses mentioned above were performed using RStudio (version 4.1.2). Statistical significance was considered to be indicated by a two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eIn the present study, the flowchart of the cohort selection is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Firstly, data for 1118 patients diagnosed with cardiac arrest from 2008 to 2019 were collected from MIMIC-IV database. Then, we excluded patients who were not admitted into the ICU for the first time and those younger than 18 years old. As shown in the flowchart in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, 798 cardiac arrest patients met the inclusion criteria and included in the final study. The average age of the participants was 67.6 years. Before PSM, compared with the survival group, the SOFA score, Apsiii score, Sapsii score, Oasis score, GCS score, OSM, WBC, Creatinine, Urine output were higher in the non-survival group. After PSM, 261 survival patients were matched with 261 non-survival patients and the baseline was better balanced between the two groups (shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with the survival group, GCS and OSM in the non-survival group were still higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eAll-cause mortality of the groups\u003c/h2\u003e\n \u003cp\u003eThe all-cause mortality of the included patients during 28-day was 46.37%. The mortality rate in the hyper-osmolarity group (61.96%) was significantly higher than that in the normal osmolarity group (35.51%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After PSM, the mortality rate in the hyper-osmolarity group (62.67%) was also higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eFor ICU mortality, Hypo-osmolarity was independent risk factors\u003c/h2\u003e\n \u003cp\u003eTo determine the connection between serum osmolarity and ICU mortality, both single and multivariable logistic regression analysis were utilized. But only hyper-osmolarity was substantially linked with ICU mortality, as showed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (HR\u0026thinsp;=\u0026thinsp;2.09 in univariable analysis and HR\u0026thinsp;=\u0026thinsp;1.77 in multivariable analysis, p༜0.001). The results of a second logistic regression research after PSM on the risk variables for 28-day mortality showed that hyper-osmolarity was independent risk factors (HR\u0026thinsp;=\u0026thinsp;1.92 in univariable analysis and HR\u0026thinsp;=\u0026thinsp;2.07 in multivariable analysis, p༜0.001) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) as well.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eKaplan-Meier survival curve analysis\u003c/h2\u003e\n \u003cp\u003eBefore or after matching, the Kaplan-Meier survival curves in Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e showed that compared with the normal osmolarity group, the cumulative survival rate of patients with CA was significantly lower (log-rank test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the hypo-osmolarity group and the hyper-osmolarity group during hospitalization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eComparison of ROC curves\u003c/h2\u003e\n \u003cp\u003eWe plotted ROC curves to show that the AUCs of OSM in Day1,3,7,28 of hospital admission were 79.60% (95% CI: 69.97\u0026ndash;89.22), 75.63% (95% CI: 70.81\u0026ndash;80.44), 71.73% (95% CI: 67.67\u0026ndash;75.80), and 63.44% (95% CI: 57.51\u0026ndash;69.37) respectively (before PSM). As shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, after PSM, the AUCs of OSM in Day1,3,7,28 of hospital admission were 85.49% (95% CI: 78.27\u0026ndash;92.71), 75.58% (95% CI: 69.95\u0026ndash;81.22), 67.23% (95% CI: 62.10-72.36), and 65.95% (95% CI: 58.78\u0026ndash;73.12) respectively. The OSM of Day1 represented by the solid red line is always higher than others, and its clinical net benefit has a significant advantage.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup analysis\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e indicates as to whether the correlation between hyper-osmolality and all-cause mortality at 28-d of hospital admission in patients with CA was stable across subgroups. When the stratified analysis was performed for Age, Gender, Hypertension, Diabetes, Acute Myocardial Infarction (AMI), Chronic Heart Failure (CHF), Acute Kidney Injury (AKI), Liver Disease, the forest plot (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) showed no significant interaction of hyper-osmolality with each subgroup (P for interaction: 0.3\u0026ndash;0.8). These evidences supported that hyper-osmolality was an independent prognostic factor.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePatients who are severely ill must maintain a proper water balance inside their bodies, and serum osmolarity is crucial for both extracellular and intracellular water distribution. Patients admitted to the ICU frequently have osmolarity perturbation, which is linked to intracellular dehydration or edema and may have negative effects. Hyper-osmolality might cause fluid from venous capacitance vessels to mobilize and enter the circulation, aggravating organ or tissue hypoxia[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For critically ill patients, maintaining a healthy balance of water in the body is essential, and the distribution of extracellular and intracellular water is significantly influenced by serum osmolality. Patients who are admitted to the ICU frequently have elevated osmolality, which is linked to intracellular dehydration or edema and can have negative effects[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In patients with a variety of underlying illnesses, the predictive significance of osmolality was demonstrated[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To our knowledge, this is the first study to identify a substantial association between serum osmolarity and mortality among acute medical admissions as cardiac arrest.\u003c/p\u003e \u003cp\u003eFirst, we show that serum osmolarity on the first day of admission is a unique risk factor for 28-day death in cardiac arrest patients receiving intensive care. Lower osmolarity has been implicated in previous research as a significant risk factor for increased morbidity and death in HF patients[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, our result was inconsistent with this result. According to our findings, high serum osmolarity was linked to 28-day mortality, which is consistent with the previous study[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The reason why there was no significant difference about the 28-day mortality between the hypo-osmolarity and normal groups, I hypothesize that it maybe the amount of hypo-osmolarity patients included in this trial was not large enough. The previous research showed that hyper-osmolality upon admission was linked to higher mortality, particularly in older patients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, in our study which included a relatively large set of observations, osmolarity's prognostic power was age-independents (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe included important confounding variables in the regression analysis, such as AKI and CHF, and still found that hyper-osmolarity was a significant independent risk factor for 28-day mortality. As for the risk of 28-day death, we discovered that it was roughly twice as high in the hyper-osmolarity group as it was in the hypo-osmolarity group. Hyper-osmolarity alone demonstrated a moderate predictive effect (AUC\u0026thinsp;\u0026gt;\u0026thinsp;65%) for predicting 28-day death. In other words, the findings of this study are applicable to all cardiac arrest types. These results were agreement with the Subgroup analysis. When the stratified analysis was performed for Age, Gender, Hypertension, Diabetes, Acute Myocardial Infarction (AMI), Chronic Heart Failure (CHF), Acute Kidney Injury (AKI), Liver Disease, the forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed no significant interaction of hyper-osmolality with each subgroup (P for interaction: 0.3\u0026ndash;0.8). These evidences supported that hyper-OSM is an independent prognostic factor as well. Hence, we propose that it might be a preferable strategy to use hyper-osmolarity as a factor to develop a prediction model later if the role of hyper-osmolarity in cardiac arrest can be properly clarified.\u003c/p\u003e \u003cp\u003eThe results of Kaplan-Meier survival curves analysis and ROC curves of OSM in Day1,3,7,28 of hospital admission for predicting in-hospital mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that the earlier the index of high osmolality is applied, the better the value will be, as its predictive value of short-term prognosis after cardiopulmonary resuscitation in cardiac arrest is higher (AUC 79.60% before PSM vs AUC 85.49% after PSM).\u003c/p\u003e \u003cp\u003eCompared to other indicators, the advantage of OSM is its convenience. Clinicians place a high priority on the identification of indicators that might predict greater mortality among acute medical admissions, as indicated by the emergence of multiple clinical scoring systems. While some scoring systems claim to be simple [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], others are so complicated that they maybe need computers [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Without a computer, certain calculations might have a high error rate [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, it is emergent to identify routine parameters with prognostic relevance which can be determined in a rapid, simple and inexpensive way. The serum osmolarity is fairly simple to compute and is based on information that is typically accessible for the majority of hospital admissions.\u003c/p\u003e \u003cp\u003eOur study's major limitation is that we only collected OSM at the time of admission. Monitoring dynamic changes in OSM may be more highly valuable in predicting in-hospital mortality in CA patients. Additionally, because this study is a retrospective analysis, there is some selection bias. As a result, we are unable to precisely measure the fluid intake indicators, such as drinking water, or to determine the precise cardiac function of the patients who were included in the study. Although the majority of the participants in our study were white Americans, it is yet unclear whether the results are applicable to other nations and ethnicities. Last but not least, there are many different main disorders that can lead to cardiac arrest, and because of database restrictions, we were unable to determine the precise etiology of the individuals included in our study. Although this study has broad implications for the volume management of CA, thorough multicenter randomized controlled clinical trials are still required to prove the true utility of OSM for CA patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs a result, high serum osmolarity in cardiac arrest patients is a parameter that can be easily measured, predicts a poorer prognosis, and is linked to a higher load of comorbidities, making it a possibility for a novel marker in cardiac arrest. To fully understand our findings, more researches are required.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the datasets used and analysed during the current study are available in the MIMIC-IV database (https://physionet.org/content/mimiciv/2.0/) and in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design: ZPS\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, and interpretation of data: ZHC\u003c/p\u003e\n\u003cp\u003eManuscript drafting: ZPS,PJL\u003c/p\u003e\n\u003cp\u003eFinal approval of the manuscript: all authors\u003c/p\u003e\n\u003cp\u003eAll authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Shenzhen Key Medical Discipline Construction Fund (SZXK046) and the National Nature Science Foundation of China (81571869).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHenson, T., et al., \u003cem\u003eOutcome and prognostication after cardiac arrest.\u003c/em\u003e Ann N Y Acad Sci, 2022. \u003cstrong\u003e1508\u003c/strong\u003e(1): p. 23-34.\u003c/li\u003e\n\u003cli\u003eXie, X., et al., \u003cem\u003eEfforts to Improve Survival Outcomes of Out-of-Hospital Cardiac Arrest in China: BASIC-OHCA.\u003c/em\u003e Circ Cardiovasc Qual Outcomes, 2023. \u003cstrong\u003e16\u003c/strong\u003e(2): p. e008856.\u003c/li\u003e\n\u003cli\u003eBlatter, R., et al., \u003cem\u003eComparison of different clinical risk scores to predict long-term survival and neurological outcome in adults after cardiac arrest: results from a prospective cohort study.\u003c/em\u003e Ann Intensive Care, 2022. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 77.\u003c/li\u003e\n\u003cli\u003eHeo, W.Y., et al., \u003cem\u003eExternal validation of cardiac arrest-specific prognostication scores developed for early prognosis estimation after out-of-hospital cardiac arrest in a Korean multicenter cohort.\u003c/em\u003e PLoS One, 2022. \u003cstrong\u003e17\u003c/strong\u003e(4): p. e0265275.\u003c/li\u003e\n\u003cli\u003eNajem, O., M.M. Shah, and O. De Jesus, \u003cem\u003eSerum Osmolality\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. 2023: Treasure Island (FL) ineligible companies. Disclosure: Maulik Shah declares no relevant financial relationships with ineligible companies. Disclosure: Orlando De Jesus declares no relevant financial relationships with ineligible companies.\u003c/li\u003e\n\u003cli\u003eBhalla, A., et al., \u003cem\u003eInfluence of raised plasma osmolality on clinical outcome after acute stroke.\u003c/em\u003e Stroke, 2000. \u003cstrong\u003e31\u003c/strong\u003e(9): p. 2043-8.\u003c/li\u003e\n\u003cli\u003eNag, C., et al., \u003cem\u003ePlasma osmolality in acute spontanious intra-cerebral hemorrhage: Does it influence hematoma volume and clinical outcome?\u003c/em\u003e J Res Med Sci, 2012. \u003cstrong\u003e17\u003c/strong\u003e(6): p. 548-51.\u003c/li\u003e\n\u003cli\u003eRohla, M., et al., \u003cem\u003ePlasma osmolality predicts clinical outcome in patients with acute coronary syndrome undergoing percutaneous coronary intervention.\u003c/em\u003e Eur Heart J Acute Cardiovasc Care, 2014. \u003cstrong\u003e3\u003c/strong\u003e(1): p. 84-92.\u003c/li\u003e\n\u003cli\u003eYagi, T., et al., \u003cem\u003eGlobal end-diastolic volume, serum osmolarity, and albumin are risk factors for increased extravascular lung water.\u003c/em\u003e J Crit Care, 2011. \u003cstrong\u003e26\u003c/strong\u003e(2): p. 224 e9-13.\u003c/li\u003e\n\u003cli\u003eHeng, G., et al., \u003cem\u003eIncreased ICU mortality in septic shock patients with hypo- or hyper- serum osmolarity: A retrospective study.\u003c/em\u003e Front Med (Lausanne), 2023. \u003cstrong\u003e10\u003c/strong\u003e: p. 1083769.\u003c/li\u003e\n\u003cli\u003eGoldberger, A.L., et al., \u003cem\u003ePhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.\u003c/em\u003e Circulation, 2000. \u003cstrong\u003e101\u003c/strong\u003e(23): p. E215-20.\u003c/li\u003e\n\u003cli\u003eEl-Sharkawy, A.M., et al., \u003cem\u003eHyperosmolar dehydration: A predictor of kidney injury and outcome in hospitalised older adults.\u003c/em\u003e Clin Nutr, 2020. \u003cstrong\u003e39\u003c/strong\u003e(8): p. 2593-2599.\u003c/li\u003e\n\u003cli\u003eArevalo-Lorido, J.C., et al., \u003cem\u003eHigh serum osmolarity at admission determines a worse outcome in patients with heart failure: Is a new target emerging?\u003c/em\u003e Int J Cardiol, 2016. \u003cstrong\u003e221\u003c/strong\u003e: p. 238-42.\u003c/li\u003e\n\u003cli\u003eBuyukkaragoz, B. and S.A. Bakkaloglu, \u003cem\u003eSerum osmolality and hyperosmolar states.\u003c/em\u003e Pediatr Nephrol, 2023. \u003cstrong\u003e38\u003c/strong\u003e(4): p. 1013-1025.\u003c/li\u003e\n\u003cli\u003eJayashree, M. and S. Singhi, \u003cem\u003eDiabetic ketoacidosis: predictors of outcome in a pediatric intensive care unit of a developing country.\u003c/em\u003e Pediatr Crit Care Med, 2004. \u003cstrong\u003e5\u003c/strong\u003e(5): p. 427-33.\u003c/li\u003e\n\u003cli\u003eOren, R.M., \u003cem\u003eHyponatremia in congestive heart failure.\u003c/em\u003e Am J Cardiol, 2005. \u003cstrong\u003e95\u003c/strong\u003e(9A): p. 2B-7B.\u003c/li\u003e\n\u003cli\u003eShen, Y., et al., \u003cem\u003eAssociation between serum osmolarity and mortality in patients who are critically ill: a retrospective cohort study.\u003c/em\u003e BMJ Open, 2017. \u003cstrong\u003e7\u003c/strong\u003e(5): p. e015729.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Neill, P.A., et al., \u003cem\u003eReduced survival with increasing plasma osmolality in elderly continuing-care patients.\u003c/em\u003e Age Ageing, 1990. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 68-71.\u003c/li\u003e\n\u003cli\u003eFuchs, P.A., I.J. Czech, and L.J. Krzych, \u003cem\u003eThe Pros and Cons of the Prediction Game: The Never-ending Debate of Mortality in the Intensive Care Unit.\u003c/em\u003e Int J Environ Res Public Health, 2019. \u003cstrong\u003e16\u003c/strong\u003e(18).\u003c/li\u003e\n\u003cli\u003eYang, J., et al., \u003cem\u003eDevelopment of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit.\u003c/em\u003e J Crit Care, 2022. \u003cstrong\u003e71\u003c/strong\u003e: p. 154106.\u003c/li\u003e\n\u003cli\u003ePrytherch, D.R., et al., \u003cem\u003eCalculating early warning scores--a classroom comparison of pen and paper and hand-held computer methods.\u003c/em\u003e Resuscitation, 2006. \u003cstrong\u003e70\u003c/strong\u003e(2): p. 173-8.\u003c/li\u003e\n\u003cli\u003eGill, M.R., D.G. Reiley, and S.M. Green, \u003cem\u003eInterrater reliability of Glasgow Coma Scale scores in the emergency department.\u003c/em\u003e Ann Emerg Med, 2004. \u003cstrong\u003e43\u003c/strong\u003e(2): p. 215-23.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\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":"cardiac arrest, serum osmolarity, hyper-osmolarity, hypo-osmolarity, ICU mortality","lastPublishedDoi":"10.21203/rs.3.rs-3365757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3365757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA major cause of death is cardiac arrest (CA). Serum osmolarity has been shown to be useful in predicting the prognosis of sepsis patients in earlier research. The purpose of this study is to ascertain the impact of serum osmolarity on the prognosis of cardiac arrest patients in the intensive care unit.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, the relationship between serum osmolarity and in-hospital mortality in ICU patients experiencing cardiac arrest was investigated. The MIMIC-IV database was used to select adult patients with cardiac arrest diagnoses for this investigation. The serum concentrations of Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, glucose, and urea nitrogen were used to determine the serum osmolarity simultaneously.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe baseline data of adult patients with CA hospitalized in the intensive care unit (ICU) from 2008 to 2019 in the American Intensive Care Database (MIMIC-IV, version v2.0) were collected. In this study, the patients were divided into survival and non-survival group, according to the 28-day prognosis. The mortality in the hyper-osmolarity group (61.96%) was significantly higher than that in the normal osmolarity group (35.51%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Kaplan-Meier survival analysis before and after matching showed that the cumulative survival rate of the hyper-osmolarity was lower (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Univariate and Multivariable COX analysis of risk factors for death (After PSM) shows that hyper-osmolarity was a significant independent risk factor for 28-day mortality. It was coincident with the result of subgroup analysis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe serum osmolarity would be a predictive biomarker that is accessible right after a cardiac arrest for CA survivors. It can be determined more quickly and at a lower cost. However, more research is required to assess serum osmolality's prognostic value in various patient populations.\u003c/p\u003e","manuscriptTitle":"Elevated serum osmolarity is associated with poor in-hospital prognosis in patients with cardiac arrest: A retrospective study based on MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-29 22:35:38","doi":"10.21203/rs.3.rs-3365757/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":"9e959b49-d74d-44f6-87ee-c1b6a8d2e7ad","owner":[],"postedDate":"September 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":24960923,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":24960924,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":24960925,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":24960926,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2024-05-14T06:38:02+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-29 22:35:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3365757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3365757","identity":"rs-3365757","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}