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Clinical Science Aspects

The Prognostic Usefulness of the Lactate/Albumin Ratio for Predicting Clinical Outcomes in Out-of-Hospital Cardiac Arrest

a Prospective, Multicenter Observational Study (koCARC) Study

Kong, Taeyoung*; Chung, Sung Phil*; Lee, Hye Sun; Kim, Sinae; Lee, Jihwan*; Hwang, Sung Oh; Shin, Sang Do§; Song, Kyoung Jun§; Cha, Kyung Chul; You, Je Sung* on behalf of the Korean Cardiac Arrest Research Consortium (KoCARC) Investigators

Author Information
doi: 10.1097/SHK.0000000000001405

Abstract

INTRODUCTION

Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death worldwide (1). Emergency medical service-assessed cardiac arrest occurs in 347,322 adults in the United States annually, with an incidence of 140.7 per 100,000 population (2). Despite recent improvements in postresuscitation care, the overall survival rate is still 2.6% to 10.8% (1, 2). Favorable neurologic outcome after survival remains low globally (3). The imbalance between supply and demand of oxygen induces hyperlactatemia reflecting tissue hypoxemia (4). Higher levels of lactate are generally used as a marker for risk stratification and early predictor for diagnosis, treatment, and poor outcomes in critically ill patients (4, 5). In patients with return of spontaneous circulation (ROSC) after cardiac arrest, isolated values of lactate are not suitable for clinical application to predict unfavorable neurologic outcomes and mortality (6–8). Compared with isolated application of lactate, effective lactate clearance over the first 6 or 12 h after postresuscitation is significantly associated with favorable neurologic outcome and improved survival independently of the initial lactate level (8, 9). However, lactate value may be affected by several conditions, including renal or hepatic dysfunction, glycolysis, and medications (5). Lactate also requires additional effort and time to identify values of repeated measurements for lactate clearance levels. Initial routine laboratory tests commonly include serum albumin in hospitalized critically ill patients (10–12). Several studies proposed that human serum albumin has protective effects, and low serum albumin is associated with mortality and morbidity in critically ill patients. To maximize the benefits for the initial value of lactate on emergency department (ED) admission as a prognostic factor, we aimed to evaluate the lactate/albumin ratio (LAR) with initially obtained values of lactate and albumin to identify its significance as a prognostic marker for favorable neurologic outcome and survival in patients with ROSC after OHCA. Based on the prognostic value of the LAR, we developed new nomograms and externally validated the tools for predicting clinical outcomes after OHCA.

MATERIALS AND METHODS

Study design and population

We conducted an observational study using a prospective, multicenter registry of out-of-cardiac arrest resuscitation provided by the Korean Cardiac Arrest Research Consortium ((KoCARC) registry. This study is registered with ClinicalTrials.gov (Identifier: NCT03222999) from October 2015 to June 2017 (13). The KoCARC was organized in 2014 by recruiting hospitals willing to participate voluntarily to the consortium. It is a collaborative research network developed to comprehend various studies conducted in the field of out-of-cardiac arrest resuscitation and to strengthen the cooperative effort in conducting studies (13). KoCARC investigators have been prospectively collecting predetermined data from OHCA patients at the EDs of 32 teaching hospitals throughout South Korea since October 2015 (13). The study was approved by the institutional review boards (IRBs) of each participating institution and by the local IRB before data collection. The registry enrolled patients with OHCA who were transported to participating ED by emergency medical service with resuscitation efforts and whose arrest was identified as a presumed cardiac etiology by emergency physicians in the ED (13). This excluded OHCA patients with definite noncardiac etiologies, such as trauma, drowning, poisoning, burn, asphyxia, and hanging patients. Other exclusion criteria included patients under hospice care, pregnant, with terminal illness documented by medical record, and with predocumented “Do Not Attempt Resuscitation” card (13).

Data collection

We collected all data using standardized web-based case report form, which consisted of standard definitions of variables including clinical characteristics, laboratory values, therapeutic intervention, and clinical outcomes in OHCA patients, by research coordinators in individual institutions (13). The study was conducted simultaneously in 32 institutions with the same protocol. All biomarkers were collected immediately after ROSC. The form also consisted of seven research fields categorized by risks and prognostic factors in OHCA. Each field has core and optional variables. The total core variables were more than 100. To control data quality, the web-based data entry system could primarily filter outliers or incorrect values (13). Furthermore, the local research coordinator in each participating ED is responsible for verifying data accuracy (13). The quality management committee, consisting of emergency physicians, statistical experts, local research coordinators, and investigators in each ED, monitors and regularly reviews data quality (13). In addition, the committee gives feedback about quality management to the research coordinators and investigators through regular meetings (13). We enrolled all patients with ROSC of the KoCARC registry. However, we excluded those without lactate and albumin values immediately after ROSC because the values were used to analyze the usefulness of LAR as a prognostic marker in OHCA. This study also excluded patients aged less than 18 years and who transferred from other hospitals after ROSC. We retrieved all data including demographics, comorbidities, cardiac arrest characteristics, laboratory results, and therapeutic intervention for this study from the registry.

Clinical outcomes

The KoCARC registry primarily collected data of survival to hospital discharge and neurologic recovery at hospital discharge (13). Hospital outcome included date and time of death or discharge (13). Neurologic outcome was assessed using the cerebral performance category (CPC) scale based on hospital discharge. In this study, based on the clinical outcomes of the KoCARC registry, the primary outcome was a neurologic outcome at hospital discharge, defined as favorable (CPC 1–2) and unfavorable (CPC 3–5) outcomes. The secondary outcome was survival to discharge.

External validation cohort

External validation was conducted using the validation cohort between May 2012 and January 2016 and between July 2017 and January 2018 at Yonsei University College of Medicine, Severance Hospital, a single tertiary academic hospital that attends to 85,000 patients in the ED annually. We collected all data of the validation cohort according to inclusion and exclusion criteria of this study. A total of 213 patients were enrolled into the validation cohort during the study period (Supplement 1, Supplemental Digital Content, http://links.lww.com/SHK/A911).

Statistical analysis

All data are presented as median (interquartile range), mean ± standard deviation, and percentage or frequency, as appropriate. Categorical and continuous variables were compared using the chi-square test or Fisher exact test and a two-sample t test or Mann–Whitney U test. We conducted univariable analyses to identify relationships in clinical data. To highlight independent indicators of prognosis, we evaluated favorable neurologic outcome and survival to discharge as independent prognostic factors (odds ratios [ORs] and 95% confidence intervals [CIs]) in OHCA patients using multivariable logistic regression analysis, integrating major covariates (variables with a P <0.05) from results of our univariable analysis. To assess the ability of the LAR to predict the development of favorable neurologic outcome and survival to discharge, we determined areas under the receiver-operating characteristic (ROC) curves (AUROCs) using ROC curves and identified the optimal cutoff value of the LAR using Youden's index. We measured the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values to verify the improvement of the prediction model by adding the LAR for the prediction of clinical outcomes. To determine the statistical significance of the IDI and NRI indices, we performed resampling 1,000 times using a standard bootstrap method. We created an intuitive graph of a statistical predictive model based on logistic regression and constructed nomograms to provide the overall probability of clinical outcomes for OHCA patients. To determine the nomogram-predicted probability of favorable neurologic outcome and mortality, we applied the score to all 213 patients enrolled in the validation cohort of the other period. Accuracy of the score was then quantified using the ROC curve for external validation. Calibration curve represented the agreement between the observed and predicted probability of clinical outcomes. To evaluate suitability of the models, we used Hosmer and Lemeshow goodness-of-fit test for logistic regression model and Nam-D’Agostino goodness-of-fit test in the survival setting representing calibration of nomogram (14, 15). All statistical analyses were performed using SAS version 9.2 (SAS Institute Inc., Cary, NC), R software, version 3.2.5 for Windows (the R foundation for statistical computing, Vienna, Austria), and MedCalc, version 12.7.0 (MedCalc Software, Ostend, Belgium). A P <0.05 was considered significant in all analyses except Hosmer and Lemeshow goodness-of-fit test.

RESULTS

Of the 4,719 OHCA patients enrolled in the KoCARC registry during the study period, we excluded patients aged less than 18 years and who transferred from other hospitals. A total of 1,442 patients achieved sustained ROSC. Figure 1 shows the enrolment, exclusion criteria, and clinical outcome data for OHCA patients in this study. We excluded 4,195 patients from the analysis according to the predetermined criteria (Fig. 1). Finally, 524 patients were included in the study. We analyzed eligible patients according to neurologic outcomes and survival to discharge. Of the 524 patients, 156 (29.8%) survived to discharge, and 95 (18.1%) achieved good neurologic outcomes. Significant differences were found in LARs immediately after ED admission or ROSC between the two patient groups with respect to neurologic outcomes and survival to discharge (Table 1). The multivariate logistic model demonstrated that the LAR was also an independent prognostic indicator of neurological outcomes and survival to discharge. An increased LAR immediately after ED admission or ROSC was also significantly associated with reduced favorable neurologic outcomes (OR 0.787; 95% CI, 0.630–0.983; P = 0.035) and survival at discharge (OR 0.744; 95% CI, 0.638–0.867; P < 0.001) (Table 2 and Supplement 2, Supplemental Digital Content, http://links.lww.com/SHK/A911). The areas under the curve (AUC) for predicting neurologic outcome and survival to discharge using the LAR were 0.824 (P < 0.001) and 0.781 (P < 0.001), respectively. The optimal cutoff values for the LAR using Youden's index were 2.82 (sensitivity, 76.8 [68.4–85.3]; specificity, 78.6 [74.7–82.4]) and 3.62 (sensitivity, 80.1 [73.9–86.4]; specificity, 64.4 [59.5–69.3]) to predict favorable neurologic outcome and survival at discharge, respectively. An LAR value of more than the optimal cutoff could significantly predict decreased favorable neurologic outcome (OR 0.082, 95.0% CI, 0.048–0.140; P < 0.001) and survival to discharge (OR 0.137, 95.0% CI, 0.088–0.214; P < 0.001). Comparisons of the ROC curves showed that the area under the ROC for the LAR was significantly superior to that of lactate (Fig. 2). IDI and NRI indices are indicators used to verify the improvement in reclassification in a nested model, thus demonstrating how the predictive power is improved when the LAR is added to traditional risk factors of OHCA. The addition of the LAR yielded a significantly positive IDI for LAR values in predicting neurologic outcomes. Furthermore, the addition of the LAR resulted in significantly positive IDI and NRI for LAR values in predicting survival to discharge (Table 3). We compared the predictive power of LAR with several meaningful variables that do not require laboratory effort for survival and good neurologic outcome using the Delong method (Supplement 3 and 4, Supplemental Digital Content, http://links.lww.com/SHK/A911). In the survival discharge prediction, LAR showed better predictive power than witnessed arrest, type of first monitored rhythm, and length of basic life support. It also showed no inferior predictive power compared with the length of advanced cardiac life support. We constructed nomograms based on the multivariate logistic model (Fig. 3). The probability of favorable neurologic outcomes or survival discharge for each OHCA patient was calculated using predetermined nomograms. In the internal validation with the training set, the model for predicting favorable neurologic outcomes had an AUC of 0.927 (95% CI, 0.897–0.956, P < 0.001) and a P value for Hosmer and Lemeshow goodness-of-fit test of 0.533. The prediction of survival discharge had an AUC of 0.872 (95% CI, 0.840–0.905, P < 0.001) and a P value for Hosmer and Lemeshow goodness-of-fit test of 0.808. In Hosmer and Lemeshow goodness-of-fit test, a P >0.05 indicates that the model was suitable (14, 15). In the external validation, the discrimination was good neurologic outcome with an AUC of 0.866 (95% CI, 0.810–0.921, P < 0.001) and survival discharge with an AUC of 0.781 (95% CI, 0.720–0.841, P < 0.001) (Fig. 3). We also constructed a calibration plot of each nomogram; it showed close approximation between the observed and predicted probabilities (Fig. 4). In an example of the application of the nomogram, we demonstrated a patient who had received resuscitation (Fig. 3). The quantified results of this nomogram can provide an overview and insight to physicians and the public of the prognosis of OHCA patients.

Fig. 1
Fig. 1:
Flow diagram of patient enrolment in the present study (A) and the inclusion criteria for Korean Cardiac Arrest Resuscitation Consortium (KoCARC) registry (B).
Table 1
Table 1:
Clinical characteristics of patients stratified by neurologic outcome and survival outcome at discharge
Table 2
Table 2:
Multivariate logistic regression analyses of favorable neurological outcome (A) and survival outcome at hospital discharge (B)
Fig. 2
Fig. 2:
Receiver operating characteristic curves of the lactate/albumin ratio (LAR) for the prediction of favorable neurologic outcome (A) and survival discharge.(B) Comparison of the area under the curve (AUC) between lactate and LAR when predicting the favorable neurologic outcome (C) and survival discharge (D).
Table 3
Table 3:
Comparison of the performance of predicting favorable neurological outcome (A) and survival discharge (B) with and without the lactate/albumin ratio by area under the curve operating characteristic (AUROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI)
Fig. 3
Fig. 3:
Nomogram predicting the probability of favorable neurologic outcome (A) and survival discharge (B) after OHCA.(A) The first patient was 20 years old (39 points). The duration of arrest to ROSC was 50 min (55 points), and the hemoglobin level was 14 (29 points). The first monitored ECG rhythm was shockable (24 points), and the LAR index was 10 (26 points). According to the nomogram, the probability of favorable neurologic outcome was approximately 0.12 (12%) by total points of 173. (B) The second patient received revascularization treatment within 24 h after cardiac arrest (43 points). The patient was 20 years old (49 points). The duration of arrest to ROSC was 50 min (47 points), and the hemoglobin level was 14 (30 points). Cardiac arrest was witnessed (15 points), and the LAR index was 10 (44 points). According to the simple nomogram, his probability of survival discharge was approximately 0.6 (60%) by total points of 228. Validation of the nomogram for favorable neurologic outcome. (A-1) Internal validation using receiver-operating characteristic (ROC) curve. The area under the ROC curve (AUC) was 0.927 (95% confidence interval [CI], 0.897–0.956). (A-2) External validation using ROC. The AUC was 0.866 (95% CI, 0.810–0.921). Validation of the nomogram for survival discharge. (B-1) Internal validation using receiver-operating characteristic (ROC) curve. The area under the ROC curve (AUC) was 0.872 (95% CI, 0.840–0.905). (B-2) External validation using ROC. The AUC was 0.781 (95% CI, 0.720–0.841).
Fig. 4
Fig. 4:
Calibration plot of the nomogram for the probability of favorable neurologic outcome in the internal validation (A) and external validation (B).Calibration plot of the nomogram for the probability of survival discharge in internal validation (C) and external validation (D).

DISCUSSION

This study demonstrated that the LAR during the early resuscitation phase was an independent predictor of favorable neurologic outcome and survival to discharge in patients with ROSC after OHCA. To the best of our knowledge, this study is the first to evaluate the significant association between the LAR and the severity of patients with ROSC after OHCA in a clinical setting. To maximize the benefits for the initial value of lactate on ED admission as a prognostic factor, we applied the LAR using initially obtained values of lactate and albumin on ED admission to patients with ROSC after OHCA. We also identified that the diagnostic performance of LAR was superior to that of lactate alone for predicting the severity of patients with ROSC after OHCA. Donnino et al. revealed that lower lactate levels at 0, 12, and 24 h and greater reductions of lactate at 12 h are significantly associated with improvements in survival and neurological outcomes in patients with postcardiac arrest. However, several studies showed that lactate levels on ED admission for predicting clinical outcomes are not statistically suitable for clinical application because these AUC values are less than 0.7 (3, 6, 16). Müller et al. also reported that initial lactate level has poor prognostic value for the severity of subsequent neurological functional impairment (17). Our study showed improved statistically significant values of AUC for predicting survival to discharge (AUC = 0.781) and favorable neurologic outcomes (AUC = 0.824) using the combination of albumin and lactate level. There may be a limitation to this method, as additional time is required in the measurement of LAR compared with that of lactate alone. Most results of the metabolic panel tests could be reported within 1 h after ROSC by critical pathway. In addition, there are several attempts to measure albumin quickly and accurately, and point of care test (POCT) equipment for measuring albumin has been developed, similar to the POCT of lactate (18, 19). With advances in technology, LAR can help predict the prognosis of patients with ROSC after OHCA in the near future.

The pathophysiology of increasing the lactate level is complex and multifactorial in postcardiac arrest (16). Ischemia–reperfusion injury combined with tissue hypoperfusion can be reflected as initially increased lactate level. This tissue hypoperfusion may be exacerbated by several aggravating factors, including continuation of underlying insults, myocardial depression, systemic inflammatory response, and microcirculatory dysfunction (16). Serum albumin has been commonly used as a routine laboratory test in hospitalized patients (12). Several studies proposed that human serum albumin possesses protective effects by anti-inflammatory properties and reduction of ischemic–reperfusion injury (20). Moshage et al. proposed that both albumin synthesis in the liver and serum albumin levels are markedly decreased in inflammation (21). Low serum albumin is significantly associated with mortality and morbidity in critically ill patients (20). Despite the possibility that a single factor of lactate and albumin may be helpful in predicting severity, the prognostic value of a single measurement may be limited. This is because serum lactate levels may be higher due to impaired lactate elimination resulting from hepatic and renal dysfunction, and serum albumin may be also affected by chronic diseases, including hepatic dysfunction, nutrition support, and inflammation (5). The use of epinephrine after ROSC in OHCA patients is relatively common (22). First, the source of increased lactate after OHCA can either be shock preceding the arrest or severe tissue hypoxia during the intra-arrest no-flow and low-flow states (23). Totaro et al. suggested that the use of adrenaline may increase serum lactate levels. It is a consistent theory that epinephrine stimulates beta-2 receptors and upregulates glycolysis generating excessive pyruvate (23). This excess pyruvate is converted into lactate and increases lactate levels. Donnino et al. identified a weak correlation between the initial level of lactate and the amount of intra-arrest adrenaline (16, 24). Accurate evaluation of outcomes is difficult because the use of adrenaline during intra-arrest adrenaline and postresuscitation care can influence the initial level and clearance of lactate. LAR can provide potential benefits for predicting the prognosis of patients with ROSC after OHCA using the ratio with inverse change due to different mechanisms of two independent predictors (5). The comprehensive combination could compensate the interaction between the abnormal production and elimination of lactate in special conditions, such as hepatic or renal dysfunction, inhibition or increase of pyruvate metabolism, and upregulation in adrenaline-stimulated Na/K-adenosine triphosphatase activity (5, 25). In the early stage after OHCA, accurate predictions of clinical outcomes can provide monitoring and treatment strategies. Emergency physicians frequently depend on the characteristics of cardiac arrest and cardiopulmonary resuscitation (CPR), such as initial rhythm, bystander CPR status, and total arrest time, to estimate patient severity during the early phase after ROSC (26–30). Although these parameters of cardiac arrest and CPR are well-known predictive factors of outcomes after cardiac arrest, none of these characteristics reflect the magnitude of hypoxic injury after cardiac arrest (31). In addition, isolated parameter cannot reliably predict clinical outcomes after cardiac arrest (31). In our study, multivariate regression models included important factors that are easy to obtain in the early stage of postresuscitation, such as the first monitored rhythm, total arrest duration, witnessed cardiac arrest, hypertension, and diabetes. The addition of the LAR in multivariate logistic regression model resulted in significant improvements in predicting clinical outcomes.

Nomograms have been accepted as reliable tools to predict personalized risks by incorporating and illustrating important factors for oncologic prognosis and have been applied to facilitate management of cancer patients (14, 15). By creating an intuitive graph of a statistical predictive model based on logistic regression, nomograms could provide the overall probability of clinical outcomes for patients with a specific disease (14, 15). We newly constructed specific nomograms with clinical and laboratory variables in OHCA patients with ROSC after OHCA. To the best of our knowledge, this study is the first to evaluate the usefulness of nomograms in predicting the probability of neurologic recovery and survival discharge for each OHCA patient. The predicted probability for clinical outcomes from these nomograms may help judge the severity and further aggressive management in these patients. The nomograms of our study used multiple parameters that are easy to obtain in the early stage of postresuscitation and provided rapid prediction of probability of a specific event through more specific and measurable information. Based on internal and external validation, the proposed nomograms could accurately predict the probability of favorable neurologic outcomes and survival discharge, respectively, in patients with ROSC after OHCA. Because the nomograms consisted of the LAR and objective information that could be obtained in a general clinical setting, its application can contribute to early establishment of treatment strategy and cost-effectiveness of postcardiac arrest care.

This study has several limitations. First, despite the prospective, multicenter study design, this study did not primarily aim to identify the significance of LAR as a prognostic marker in patients with ROSC after OHCA. We could not analyze data from patients because we excluded patients with missing lactate or albumin value during ED admission. Although the KoCARC registry prospectively collected information, it is difficult to include all items that could affect values of lactate and albumin because we retrospectively analyzed the registry using only the variables determined by the committee. However, the benefits of a prospective, multicenter study design and a relatively large number of patients may overcome this limitation. Second, the KoCARC registry collected data of survival to hospital discharge and neurologic recovery at hospital discharge and 6 months later. However, we could not accurately assess clinical outcomes after survival to discharge because sufficient observation for 6-month follow-up could not be achieved in the KOCARC registry. Third, we could not obtain information on the use of lactate-elevating medications, such as metformin, in our prospective registry. Fourth, we did not include data on hemodynamic status or vasopressor therapy during the early postresuscitation phase. Both hypotension and vasopressor therapy may have affected the increasing levels of lactate by increasing ischemia in various organs and upregulation in adrenaline-stimulated Na/K-adenosine triphosphatase activity. Thus, these factors could be confounders in the analyses. Fifth, we could not perform serial measurements for biomarkers of the magnitude of ischemic insult (such as NSE, S-100) and lactate clearance because these are not mandatory in the KoCARC registry. Therefore, we were unable to directly compare LAR with indicators of brain insult or lactate clearance. Finally, despite the prospective, multicenter design of this study, the data collection period was relatively short, and the included population was not enough to generalize the results. Nevertheless, the KoCARC registry plans to collect data indefinitely to obtain accurate information about OHCA patients in Korea, and this study will be continually statistically updated via the data that will be added in the future. Further prospective multicenter studies are required to validate the usefulness of LAR as a prognostic factor and to directly compare it with factors that can be confounders in the analysis of OHCA patients.

CONCLUSIONS

The prognostic performance of the LAR was superior to a single measurement of lactate for predicting neurologic outcomes and survival to discharge in patients with ROSC after OHCA. In addition, the newly developed nomograms based on multiple parameters that are easy to obtain in the early stage of postresuscitation can provide rapid prediction of probability of favorable neurologic outcomes and survival to discharge in patients with ROSC after OHCA.

Acknowledgments

This consortium was supported by the Korean Centers for Disease Control and Prevention during the organizing stage. Currently, KoCARC is partly supported by the Korean Association of Cardiopulmonary Resuscitation. The authors would like to acknowledge and thank to members of Secretariat: Jeong Ho Park (Seoul National University hospital), Sun Young Lee (Seoul National University hospital), Jung Eun Kim (Seoul National University hospital), Na Young Kim (Seoul National University hospital), Min Ji Kwon (Seoul National University hospital); and to investigators from all participating hospitals in KoCARC: Myoung Chun Kim (Kyung Hee University Hospital at Gangdong), Sang Kuk Han (Kangbuk Samsung Medical Center), Kwang Je Baek (Konkuk University Medical Center), Han Sung Choi (Kyung Hee University Hospital), Sung Hyuk Choi (Korea University Guro Hospital), Ik Joon Jo (Samsung Medical Center), Jong Whan Shin (SMGSNU Boramae Medical Center), Sang Hyun Park (Seoul Medical Center), In Cheol Park (Yonsei University Severance Hospital), Chul Han (Ewha Womans University Mokdong Hospital), Chu Hyun Kim (Inje University Seoul Paik Hospital), Gu Hyun Kang (Hallym University Kangnam Sacred Heart Hospital), Tai Ho Im (Hanyang University Seoul Hospital), Seok Ran Yeom (Pusan National University Hospital), Jae Hoon Lee (Dong-A University Hospital), Ha Young Park (Inje University Haeundae Hospital), Jeong Bae Park (Kyungpook National University Hospital), Sung Jin Kim (Keimyung University Dongsan Medical Center), Kyung Woo Lee (Daegu Catholic University Medical Center), Woon Jeong Lee (The Catholic University of Korea Incheon ST. Mary's Hospital), Sung Hyun Yun (Catholic Kwandong University), Ah Jin Kim (Inha University Hospital), Kyung Woon Jeong (Chonnam National University Hospital), Sun Pyo Kim (Chosun University Hospital), Jin Woong Lee (Chungnam National University Hospital), Sung Soo Park (Konyang University Hospital), Ryeok Ahn (Konyang University Hospital), Kyoung Ho Choi (The Catholic University of Korea Uijeongbu ST. Mary's Hospital), Young Gi Min (Ajou University Hospital), In Byung Kim (Myongji Hospital), Ji Hoon Kim (The Catholic University of Korea Buchen ST.Mary's Hospital), Seung Chul Lee (Dongguk University Ilsan Hospital), Young Sik Kim (Bundang Jesaeng General Hospital), Hun Lim (Soonchunhyang University Bucheon Hospital), Jin Sik Park (Sejong Hospital), Jun Seok Park (Inje University Ilsan Paik Hospital), Dai Han Wi (Wonkwang University Sanbon Hospital), Ok Jun Kim (Cha University Bundang Cha Hospital), Bo Seung Kang (Hanyang University Guri Hospital), You Dong Sohn (Hallym University Pyeongchon Sacred Heart Hospital), Soon Joo Wang (Hallym University Dongtan Sacred Heart Hospital), Se Hyun Oh (GangNeung Asan Hospital), Jun Hwi Cho (Kangwon National University Hospital), Mu Eob An (Hallym University Chuncheon Sacred Heart Hospital), Ji Han Lee (Chungbuk National University Hospital), Han Joo Choi (Dankook University Hospital), Jung Won Lee (Soonchunhyang University Cheonan Hospital), Tae Oh Jung (Chonbuk National University Hospital), Dai Hai Choi (Dongguk University Gyeongju Hospital), Seong Chun Kim (Gyeongsang National University Hospital), Ji Ho Ryu (Pusan National University Yangsan Hospital), Won Kim (Cheju Halla General Hospital), Sung Wook Song (Jeju National University Hospital).

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Keywords:

Lactate/albumin ratio; neurologic outcome; out-of-hospital cardiac arrest; predictor; survival

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