Development and Internal Validation of Predictive Formula for Arterial Lactate Using Peripheral Venous Sampling in Early Septic Shock

Development and Internal Validation of Predictive Formula for Arterial Lactate Using Peripheral Venous Sampling in Early Septic Shock | 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 Development and Internal Validation of Predictive Formula for Arterial Lactate Using Peripheral Venous Sampling in Early Septic Shock Chutima Cheranakhorn, Nichakan Nakwan, Suratee Chobngam, Sorawat Sangkeaw, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7906890/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: Lactate is a key biomarker for diagnosis, risk stratification, and therapeutic monitoring in septic shock. Arterial sampling is the gold standard, but it is invasive, painful, and not always feasible, especially in early resuscitation. Peripheral venous sampling is less invasive and more widely accessible. This study aimed to develop and internally validate a predictive equation to estimate arterial lactate from peripheral venous lactate in patients with septic shock. Methods: A prospective observational study was conducted in the intensive care unit of a tertiary hospital from April 2023 to April 2025. Adult patients meeting Sepsis-3 criteria for septic shock within the first 6 hours of resuscitation were enrolled. Arterial, central venous, and peripheral venous lactate were measured simultaneously or within 15 minutes. Predictive modeling was performed using multivariable regression with backward elimination. Model performance was assessed using R², mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (r). Results: Among 120 patients included, the median (IQR) arterial, central venous, and peripheral venous lactate were 2.3 (1.3–4.3), 2.7 (1.7–4.5), and 3.7 (2.1–5.9) mmol/L, respectively. The final model was: arterial lactate = 0.7414 × (peripheral venous lactate) – 0.1315 . Internal validation demonstrated strong predictive accuracy (R² = 0.8514, MAE = 0.8899, RMSE = 1.2314). The correlation coefficient showed strong agreement between arterial and central venous lactate (r = 0.97) and arterial and peripheral venous lactate (r = 0.91), across both low and high lactate ranges. Conclusions: A simple predictive equation using only peripheral venous lactate provides reliable estimation of arterial lactate in early septic shock. This approach is clinically feasible, less invasive, and may support timely decision-making in critical care, particularly in emergency departments and resource-limited settings. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Septic shock Arterial lactate Peripheral venous lactate Predictive model Intensive care Figures Figure 1 Figure 2 Figure 3 Background Lactate has become widely utilized in the management of patients with septic shock, serving as a tool for severity assessment, prognostication, and therapeutic monitoring 1,2 . The current gold standard for lactate measurement is arterial blood sampling 3 . However, in routine practice, peripheral venous blood is often more practical—it is easier, faster, and safer to obtain, especially since venous blood sampling is usually required for other laboratory tests as well. Several studies have evaluated the agreement between venous and arterial lactate levels. A recent systematic review by Tienhoven AJ et al., which included 4,090 paired samples, demonstrated that venous lactate tends to be higher than arterial values by approximately 0.18–1.06 mmol/L 4 . Importantly, the concordance between venous and arterial measurements diminishes as lactate levels increase. A clinically relevant cutoff of 2 mmol/L has been suggested: a venous lactate level of ≤ 2 mmol/L reliably excludes hyperlactatemia, whereas values greater than 2 mmol/L warrant confirmation with arterial sampling due to reduced accuracy 4 . In septic shock, lactate values are frequently > 2 mmol/L. Repeated arterial sampling can be challenging, especially outside the ICU, where arterial line monitoring is often unavailable. Moreover, arterial puncture may pose risks in patients with severe hypotension, coagulopathy, or disseminated intravascular coagulation (DIC). Beyond the practical limitations of arterial sampling, additional clinical and biochemical parameters have been considered as potential surrogates for lactate assessment and as candidate variables for model development. Previous studies suggest that lower mean arterial pressure (MAP) and higher norepinephrine requirements are associated with persistent hyperlactatemia in septic shock 5 . Abnormal glucose levels, both hypoglycemia (162 mg/dL), may alter the prognostic value of lactate through their metabolic interactions 6 . Similarly, progressive acidosis reflected by declining pH, particularly when lactate exceeds 5 mmol/L 7 , strongly predicts adverse outcomes 8 . Base excess (BE) has emerged as another practical surrogate, with a venous BE 3 mmol/L (sensitivity 91%, specificity 88.6%) 9 . Collectively, these parameters highlight the biological complexity of lactate dynamics and informed the initial selection of candidate predictors in the present model. Therefore, this study was designed to develop and validate a simple predictive equation for estimating arterial lactate using peripheral venous lactate as the primary variable, while considering clinically relevant predictors identified in prior literature. Study Design and Setting This study was a prospective observational study conducted in the intensive care unit (ICU) of Hatyai Hospital, a 1,033-bed tertiary public hospital, from April 30, 2023, to April 30, 2025. The aim was to develop and internally validate a predictive formula for estimating arterial lactate from peripheral venous lactate in patients with early septic shock. Participants Adult patients (≥18 years) who met the Sepsis-3 criteria for septic shock 10 within the first 6 hours of resuscitation and were admitted to the medical intensive care unit were eligible for inclusion. Patients were excluded if they were pregnant, in a post–cardiac arrest state, or had hyperlactatemia attributable to other causes, such as metformin-induced lactic acidosis. Sample Size Calculation Sample size estimation was based on the “events per variable” (EPV) rule, which recommends 10–25 events per independent variable to ensure stability of linear regression models and avoid overfitting 11,12 . Six predictors (peripheral venous lactate, mean arterial pressure, norepinephrine dose, blood glucose, venous pH, and base excess) were considered a priori. Thus, a minimum of approximately 20 × 6 = 120 patients was required. The final study population of 120 patients met this criterion, ensuring adequate statistical power for model development. Sampling Protocol Eligible patients underwent lactate measurement from three different sites: peripheral venous blood (with tourniquet application limited to ≤5 minutes), central venous blood, and arterial blood. Sampling was performed simultaneously or within 15 minutes of each other during the initial resuscitation period, within the first 6 hours after the diagnosis of septic shock. Each patient had lactate levels measured at the three sites once only. Subsequent lactate measurements or therapeutic interventions were performed at the discretion of the attending physician. Additional clinical and laboratory variables were recorded, including mean arterial pressure, norepinephrine dose, presence of hypo- or hyperglycemia, and venous blood gas parameters (pH and base excess). Lactate Measurement All lactate levels were analyzed using a point-of-care testing (POCT) device (StatStrip® Lactate, Nova Biomedical Corporation, Waltham, MA, USA). This handheld device requires only 0.6 μL of whole blood and provides results within 13 seconds. Previous evaluations have demonstrated excellent agreement with central laboratory analyzers (R² = 0.994) 13 . The use of POCT minimized pre-analytical delays, transportation errors, and turnaround time, thereby improving the reliability of lactate measurements during early septic shock resuscitation. Statistical Analysis Baseline characteristics were summarized using descriptive statistics, with categorical variables presented as frequencies and percentages, and continuous variables as mean ± standard deviation or median with interquartile range, as appropriate. Comparisons were performed using Chi-square or Fisher’s exact test for categorical variables, and independent t -test or Mann–Whitney U test for continuous variables. Variables with p < 0.05 were entered into a multiple linear regression model, with results reported as regression coefficients (β) with standard errors (SE) and p -values. A clinical prediction model was developed using linear regression with predictor selection based on literature, expert opinion, and backward elimination. Model performance was evaluated using R², mean absolute error (MAE), and root mean square error (RMSE). Correlation was assessed by the correlation coefficient ( r ). All analyses were performed using R software, with p <0.05 considered statistically significant. Ethics Approval and Trial Registration The study protocol was approved by the Institutional Review Board of Hatyai Hospital (Approval No. HYH EC 024-66-01). Written informed consent was obtained from all patients or their legally authorized representatives before participation. Results Between April 30, 2023, to April 30, 2025, a total of 128 patients with severe sepsis or septic shock met the eligibility criteria. Eight patients were excluded (five post–cardiac arrest, two with other causes of hyperlactatemia, and one pregnant), leaving 120 participants for analysis (Figure 1). Baseline Characteristics The mean (±SD) age of included patients was 59 ± 16.1 years, and 56.6% were male. The most common comorbidities were hypertension (37.5%), diabetes mellitus (25.0%), and dyslipidemia (19.2%). The respiratory tract was the most frequent site of infection (53.3%), followed by primary bacteremia (19.2%) and gastrointestinal infection (13.3%). The median (IQR) SOFA score was 9 (6–12). Respiratory failure (93.3%) and acute kidney injury (57.5%) were the most common organ dysfunctions. Nearly all patients (92.5%) required vasopressors, with a median dose of 0.2 mcg/kg/min (range, 0.1–0.5 mcg/kg/min). Laboratory findings showed a median arterial lactate of 2.3 (1.3–4.3) mmol/L, central venous lactate of 2.7 (1.7–4.5) mmol/L, and peripheral venous lactate of 3.7 (2.1–5.9) mmol/L. Additional laboratory values are summarized in Table 1. The most common complications were hospital-acquired infections (34.2%), renal complications (31.7%), and disseminated intravascular coagulation (20.8%). The median ICU and hospital length of stay were 7 (4.8–15.2) days and 12 (6–21.5) days, respectively. Overall hospital mortality was 52.5%. Univariate and multivariate linear regression analyses were performed to identify factors associated with arterial lactate levels (Table 2). Only peripheral venous lactate was independently associated with arterial lactate (β = 0.73, SE = 0.03, p < 0.001). Other variables, including pH, base excess, blood glucose, vasopressor dose, and mean arterial pressure, were not significantly associated after adjustment. The linear regression model for estimating arterial lactate was: Arterial lactate = 0.7414 x (Peripheral lactate)-0.1315. Internal validation demonstrated strong predictive performance, with an R² of 0.8514, a mean absolute error (MAE) of 0.8899, and a root mean square error (RMSE) of 1.2314. The correlation analysis showed a strong positive correlation between arterial and central venous lactate (r = 0.97), as well as between arterial and peripheral venous lactate (r = 0.91), across all lactate ranges, both below and above 2 mmol/L (Figures 2 and 3). Discussion This study developed a simple predictive equation to estimate arterial lactate from peripheral venous lactate in patients with septic shock. The formula, arterial lactate = 0.7414 (peripheral lactate) – 0.1315 , demonstrated good accuracy (R² = 0.8514, MAE = 0.8899, RMSE = 1.2314). The inclusion of additional covariates did not improve model performance, indicating that peripheral venous lactate alone provides sufficient predictive value. In comparison to earlier studies, these findings demonstrate a balance between accuracy and feasibility. Mikami et al. reported a higher R² (0.96) 14 , but with a more complex equation that requires venous PaO₂, which limits its applicability in routine practice. Prasad et al. described a simple model (R² = 0.92) 15 but included heterogeneous shock populations, limiting relevance to septic shock specifically. Theerawit et al. obtained a similar R² (0.873) in sepsis and septic shock 16 , but with a smaller cohort. The present analysis extends this evidence with a larger, homogenous population restricted to early septic shock, enhancing reliability and internal validity. Venous lactate values were consistently higher than arterial values, consistent with the systematic review by Tienhoven et al. A strong correlation between arterial and venous lactate persisted even at concentrations greater than 2 mmol/L 4 , which contrasts with some previous reports. This may reflect methodological rigor: synchronized arterial and venous sampling, controlled tourniquet duration, and use of validated POCT analyzers minimized pre-analytical variation. Such measures likely contributed to the strong agreement observed, which is consistent with the study of Jose et al. 17 , where near-perfect correlation was maintained even at high lactate levels. Several strengths of this study support confidence in the findings. The sample size was larger than in most prior reports and was calculated a priori, reducing the risk of overfitting. Restriction to early septic shock patients minimized confounding. Strict protocols for blood collection reduced technical error, and the use of widely available POCT lactate devices increases feasibility across both tertiary and community hospitals. The resulting formula is simple, requiring only one variable, and consistent with prior evidence. At the same time, limitations must be acknowledged: the single-center design limits generalizability, only a single lactate measurement was obtained during early resuscitation, and external validation has not yet been performed. From a clinical perspective, these findings have practical relevance. Arterial puncture is an invasive procedure and may not always be feasible in unstable patients, whereas peripheral venous sampling is less invasive and widely available. A reliable equation to estimate arterial lactate could therefore reduce patient discomfort, accelerate clinical decision-making, and support sepsis protocols in diverse settings. Although lactate kinetics were not assessed in this study, the close agreement between arterial and venous values suggests potential utility for monitoring trends and clearance, which warrants further study. In summary, this analysis supports the feasibility of using peripheral venous lactate to estimate arterial lactate in septic shock. The proposed equation is simple, accurate, and practical, with potential for broad application in both resource-rich and resource-limited environments. Future multi-center studies with serial measurements and external validation are needed to confirm generalizability and evaluate the prognostic utility of this approach in guiding resuscitation strategies. Conclusions A predictive model for arterial lactate estimation from peripheral venous sampling in early septic shock demonstrated strong accuracy and clinical feasibility. The formula is simple, requires no additional variables, and may reduce reliance on arterial puncture in both tertiary and community hospital settings. Future multicenter validation studies and assessments of its role in lactate-guided resuscitation protocols are warranted. Declarations Ethics Approval and Consent to Participate The study protocol was approved by the Ethical Committee for Research in Human Subjects, Hatyai Hospital, Thailand (Protocol No. HYH EC 024-66-01). The approval was granted on 25 April 2023 following a full board review in accordance with the International Conference on Harmonisation – Good Clinical Practice (ICH-GCP) guidelines. Written informed consent was obtained from all participants or their legally authorized representatives before enrollment. Consent for Publication Not applicable. Availability of Data and Materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions Chutima Cheranakhorn conceived and designed the study, supervised data collection, and managed the project. Sorawat Sangkeaw performed statistical analysis and provided methodological support. Nichakan Nakwan , Suratee Chobngam , Yuthaphichai Yaemchai , Chananya Changadwej , and Napa Jitjinda contributed to patient recruitment, data collection, and resource coordination. All authors reviewed, revised, and approved the final manuscript. Acknowledgements The authors would like to thank the Emergency department and the ICU nursing staff of Hatyai Hospital for their assistance in patient recruitment and sample processing. The authors also acknowledge the assistance of a large language model (ChatGPT, OpenAI, San Francisco, CA, USA) for language editing and improving manuscript clarity. All authors reviewed and approved the final content. References Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Crit Care Med. 2021;49:e1063-143. doi:10.1097/CCM.0000000000005337 Levy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Crit Care Med. 2018;46:997-1000. doi:10.1097/CCM.0000000000003119 Kruse O, Grunnet N, Barfod C. Blood lactate as a predictor for in-hospital mortality in patients admitted acutely to hospital: a systematic review. Scand J Trauma Resusc Emerg Med. 2011;19:74. doi:10.1186/1757-7241-19-74 Tienhoven AJ, Beers CAJ, Siegert CEH. Agreement between arterial and peripheral venous lactate levels in the emergency department: a systematic review. Am J Emerg Med. 2019;37:746-50. doi:10.1016/j.ajem.2019.01.034 Ferraris A, Bouisse C, Thiolliere F, Piriou V, Allaouchiche B. Mottling incidence and mottling score according to arterial lactate level in septic shock patients. Indian J Crit Care Med. 2020;24:672-6. doi:10.5005/jp-journals-10071-23531 Chen X, Bi J, Zhang J, Du Z, Ren Y, Wei S, et al. Impact of serum glucose on the predictive value of serum lactate for hospital mortality in critically ill surgical patients. Dis Markers. 2019;2019:1578502. doi:10.1155/2019/1578502 Bakker J, Nijsten MW, Jansen TC. Clinical use of lactate monitoring in critically ill patients. Ann Intensive Care. 2013;3:12. doi:10.1186/2110-5820-3-12 Gunnerson KJ, Saul M, He S, Kellum JA. Lactate versus non-lactate metabolic acidosis: a retrospective outcome evaluation of critically ill patients. Crit Care. 2006;10:R22. doi:10.1186/cc3987 Montassier E, Batard E, Segard J, Hardouin JB, Martinage A, Le Conte P, et al. Base excess is an accurate predictor of elevated lactate in ED septic patients. Am J Emerg Med. 2012;30:184-7. doi:10.1016/j.ajem.2010.09.033 Singer M, Deutschman CS, Seymour CW, Shanker-Hari M, Annan D, Bauer M et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016;315:801-10. doi:10.1001/jama.2016.0287 Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373-9. doi:10.1016/S0895-4356(96)00236-3 Riley RD, Ensor J, Snell KI, Harrell FE Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi:10.1136/bmj.m441 Ismail F, Mackay WG, Kerry A, Staines H, Rooney KD. Accuracy and timeliness of a point-of-care lactate measurement in patients with sepsis. Scand J Trauma Resusc Emerg Med. 2015;23:68. doi:10.1186/s13049-015-0151-x Mikami A, Soeno SO, Deshpande GA, Mochizuki T, Otani N, Ishimatsu S, et al. Can we predict arterial lactate from venous lactate in the ED? Am J Emerg Med. 2013;31:1118-20. doi:10.1016/j.ajem.2013.03.034 Prasad H, Vempalli N, Agrawal N, Ajun UN, Salam A, Datta SS, et al. Correlation and agreement between arterial and venous blood gas analysis in patients with hypotension: an emergency department-based cross-sectional study. Int J Emerg Med. 2023;16:18. doi:10.1186/s12245-023-00486-0 Theerawit P, Petvicharn CN, Tangsukaritvijit V, Sutherasan Y. Correlation between arterial lactate and venous lactate in patients with sepsis and septic shock. J Intensive Care Med. 2016;31(9):587-92. doi:10.1177/0885066616663169 Jose JM, Cherian A, Bidkar PU, Nohan VK. Agreement between arterial and venous lactate in patients with sepsis. Int J Clin Pract. 2021;75:e14296. doi:10.1111/ijcp.14296 Tables Table 1: Patient characteristics Characteristic All (n=120) Age-yr, mean (SD) 59 (16.1) Male, no (%) 68 (56.6) Underlying disease- n (%) Hypertension 45 (37.5) Diabetic mellitus 30 (25.0) Dyslipidemia 23 (19.2) Heart disease 21 (17.5) Chronic lung disease/Asthma 16 (13.3) HIV infection 16 (13.3) Liver disease 13 (10.8) Cancer 10 (8.3) Chronic kidney disease 9 (7.5) Connective tissue disease 4 (3.3) Other 5 (4.2) Site of infection – n (%) Respiratory 64 (53.3) Primary bacteremia 23 (19.2) Gastrointestinal tract 16 (13.3) Urinary tract 13 (10.8) Skin/Soft tissue 13 (10.8) Tropical infection 8 (6.7) Central nervous system 5 (4.2) SOFA Score, median (IQR) 9 (6,12) Organ failure – n (%) Respiratory failure 112 (93.3) Renal failure 69 (57.5) Hematologic failure 40 (33.3) Liver failure 4 (3.3) Vasopressor used – n (%) 111 (92.5) Vasopressor rate (mcg/kg/min), median (IQR) 0.2 (0.1,0.5) Vital sign, mean (SD) Mean arterial pressure (mmHg) 61.7 (8.6) Diastolic pressure (mmHg) 110.9 (21.2) Heart rate (beat/minute) 110.9 (21.2) Oxygen Saturation (%) 95.8 (4.1) Fluid resuscitate (>2Liter) – n (%) 91 (75.8) IQR; interquartile range, SOFA Sequential Organ Failure Assessment Table 1: Patient characteristics (continue) Laboratory, median (IQR) White Blood Cell (cells/mm 3 ) 14,645 (8,820- 20,015) Platelet (cell/mm 3 ) 140500 (79,000-240,000) Creatinine (mg/dL) 1.7 (1-2.6) Albumin (g/dl) 2.5 (2.1-2.9) pH from vnous blood gas 7.4 (7.3-7.4) Base excess from venous blood gas -7 (-11.6,-2.6) Blood sugar (mg%) 146.5 (115-194.2) Anion gap 15.5 (13-20) Arterial lactate (mmol/L) 2.3 (1.3,4.3) Central venous lactate (mmol/L) 2.7 (1.7,4.5) Peripheral venous lactate (mmol/L) 3.7 (2.1,5.9) Complication- n (%) Hospital acquire infection 41 (34.2) Renal failure 38 (31.7) Disseminated intravascular coagulation 25 (20.8) Renal replacement therapy 18 (15.0) Respiratory failure 15 (12.5) Cardio-Pulmonary resuscitation 9 (7.5) Heart failure 7 (5.8) Liver failure 4 (3.3) Tracheostomy 4 (3.3) Acute respiratory distress syndrome 3 (2.5) Hospital stay-day, median (IQR) 12 (6-21.5) ICU stay-day, median (IQR) 7 (4.8-15.2) Death- n (%) 63 (52.5) Table 2: Univariate and Multivariate Linear Regression Analysis of Factors Associated with Arterial Lactate Variable Univariate Multivariate Coefficient Standard Error p -value Coefficient Standard Error p -value pH -4.395 2.07 0.0358 -0.78 1.02 0.445 Base Excess -0.113 0.036 0.002 0.01 0.01 0.493 Blood sugar (mg%) 0.0034 0.0036 0.344 0.001 0.001 0.469 vasopressor rate (ml/hr) 4.4209 0.8988 <0.001 0.19 0.47 0.684 Mean arterial pressure 0.003 0.032 0.924 0.01 0.01 0.398 Peripheral venous lactate 0.741 0.03 <0.001 0.73 0.03 <0.001 Additional Declarations No competing interests reported. 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venous lactate levels in early septic shock\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7906890/v1/f2801aadfd572aa82cff5239.png"},{"id":102992714,"identity":"cd2fff35-7841-4ed0-8d27-0ac62eabe73a","added_by":"auto","created_at":"2026-02-19 11:40:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155035,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between arterial and peripheral venous lactate levels in early septic shock\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7906890/v1/4c4a29f7a5d6ff714d03a08b.png"},{"id":109296828,"identity":"ccd142c4-be3b-4cb8-a18f-6850bacf6cdf","added_by":"auto","created_at":"2026-05-15 08:51:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":450544,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7906890/v1/f75398c7-949b-4939-9a1e-eb7d0efb8f38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Internal Validation of Predictive Formula for Arterial Lactate Using Peripheral Venous Sampling in Early Septic Shock","fulltext":[{"header":"Background","content":"\u003cp\u003eLactate has become widely utilized in the management of patients with septic shock, serving as a tool for severity assessment, prognostication, and therapeutic monitoring\u003csup\u003e1,2\u003c/sup\u003e. The current gold standard for lactate measurement is arterial blood sampling\u003csup\u003e3\u003c/sup\u003e. However, in routine practice, peripheral venous blood is often more practical\u0026mdash;it is easier, faster, and safer to obtain, especially since venous blood sampling is usually required for other laboratory tests as well. Several studies have evaluated the agreement between venous and arterial lactate levels. A recent systematic review by Tienhoven AJ et al., which included 4,090 paired samples, demonstrated that venous lactate tends to be higher than arterial values by approximately 0.18\u0026ndash;1.06 mmol/L\u003csup\u003e4\u003c/sup\u003e. Importantly, the concordance between venous and arterial measurements diminishes as lactate levels increase. A clinically relevant cutoff of 2 mmol/L has been suggested: a venous lactate level of \u0026le; 2 mmol/L reliably excludes hyperlactatemia, whereas values greater than 2 mmol/L warrant confirmation with arterial sampling due to reduced accuracy\u003csup\u003e4\u003c/sup\u003e. In septic shock, lactate values are frequently \u0026gt; 2 mmol/L. Repeated arterial sampling can be challenging, especially outside the ICU, where arterial line monitoring is often unavailable. Moreover, arterial puncture may pose risks in patients with severe hypotension, coagulopathy, or disseminated intravascular coagulation (DIC). Beyond the practical limitations of arterial sampling, additional clinical and biochemical parameters have been considered as potential surrogates for lactate assessment and as candidate variables for model development. Previous studies suggest that lower mean arterial pressure (MAP) and higher norepinephrine requirements are associated with persistent hyperlactatemia in septic shock\u003csup\u003e5\u003c/sup\u003e. Abnormal glucose levels, both hypoglycemia (\u0026lt;126 mg/dL) and hyperglycemia (\u0026gt;162 mg/dL), may alter the prognostic value of lactate through their metabolic interactions\u003csup\u003e6\u003c/sup\u003e. Similarly, progressive acidosis reflected by declining pH, particularly when lactate exceeds 5 mmol/L\u003csup\u003e7\u003c/sup\u003e, strongly predicts adverse outcomes\u003csup\u003e8\u003c/sup\u003e. Base excess (BE) has emerged as another practical surrogate, with a venous BE \u0026lt; \u0026ndash;4 accurately predicting arterial lactate \u0026gt; 3 mmol/L (sensitivity 91%, specificity 88.6%)\u003csup\u003e9\u003c/sup\u003e. Collectively, these parameters highlight the biological complexity of lactate dynamics and informed the initial selection of candidate predictors in the present model. Therefore, this study was designed to develop and validate a simple predictive equation for estimating arterial lactate using peripheral venous lactate as the primary variable, while considering clinically relevant predictors identified in prior literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a prospective observational study conducted in the intensive care unit (ICU) of Hatyai Hospital, a 1,033-bed tertiary public hospital, from April 30, 2023, to April 30, 2025. The aim was to develop and internally validate a predictive formula for estimating arterial lactate from peripheral venous lactate in patients with early septic shock.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdult patients (\u0026ge;18 years) who met the Sepsis-3 criteria for septic shock\u003csup\u003e10\u003c/sup\u003e within the first 6 hours of resuscitation and were admitted to the medical intensive care unit were eligible for inclusion. Patients were excluded if they were pregnant, in a post\u0026ndash;cardiac arrest state, or had hyperlactatemia attributable to other causes, such as metformin-induced lactic acidosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size estimation was based on the \u0026ldquo;events per variable\u0026rdquo; (EPV) rule, which recommends 10\u0026ndash;25 events per independent variable to ensure stability of linear regression models and avoid overfitting\u003csup\u003e11,12\u003c/sup\u003e. Six predictors (peripheral venous lactate, mean arterial pressure, norepinephrine dose, blood glucose, venous pH, and base excess) were considered a priori. Thus, a minimum of approximately 20 \u0026times; 6 = 120 patients was required. The final study population of 120 patients met this criterion, ensuring adequate statistical power for model development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEligible patients underwent lactate measurement from three different sites: peripheral venous blood (with tourniquet application limited to \u0026le;5 minutes), central venous blood, and arterial blood. Sampling was performed simultaneously or within 15 minutes of each other during the initial resuscitation period, within the first 6 hours after the diagnosis of septic shock. Each patient had lactate levels measured at the three sites once only. Subsequent lactate measurements or therapeutic interventions were performed at the discretion of the attending physician. Additional clinical and laboratory variables were recorded, including mean arterial pressure, norepinephrine dose, presence of hypo- or hyperglycemia, and venous blood gas parameters (pH and base excess).\u003c/p\u003e\n\u003cp\u003eLactate Measurement\u003c/p\u003e\n\u003cp\u003eAll lactate levels were analyzed using a point-of-care testing (POCT) device (StatStrip\u0026reg; Lactate, Nova Biomedical Corporation, Waltham, MA, USA). This handheld device requires only 0.6 \u0026mu;L of whole blood and provides results within 13 seconds. Previous evaluations have demonstrated excellent agreement with central laboratory analyzers (R\u0026sup2; = 0.994)\u003csup\u003e13\u003c/sup\u003e. The use of POCT minimized pre-analytical delays, transportation errors, and turnaround time, thereby improving the reliability of lactate measurements during early septic shock resuscitation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were summarized using descriptive statistics, with categorical variables presented as frequencies and percentages, and continuous variables as mean \u0026plusmn; standard deviation or median with interquartile range, as appropriate. Comparisons were performed using Chi-square or Fisher\u0026rsquo;s exact test for categorical variables, and independent \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test for continuous variables. Variables with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were entered into a multiple linear regression model, with results reported as regression coefficients (\u0026beta;) with standard errors (SE) and \u003cem\u003ep\u003c/em\u003e-values. A clinical prediction model was developed using linear regression with predictor selection based on literature, expert opinion, and backward elimination. Model performance was evaluated using R\u0026sup2;, mean absolute error (MAE), and root mean square error (RMSE). Correlation was assessed by the correlation coefficient (\u003cem\u003er\u003c/em\u003e). All analyses were performed using R software, with \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05 considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Hatyai Hospital (Approval No. HYH EC 024-66-01). Written informed consent was obtained from all patients or their legally authorized representatives before participation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween April 30, 2023, to April 30, 2025, a total of 128 patients with severe sepsis or septic shock met the eligibility criteria. Eight patients were excluded (five post–cardiac arrest, two with other causes of hyperlactatemia, and one pregnant), leaving 120 participants for analysis (Figure 1). Baseline Characteristics The mean (±SD) age of included patients was 59 ± 16.1 years, and 56.6% were male. The most common comorbidities were hypertension (37.5%), diabetes mellitus (25.0%), and dyslipidemia (19.2%). The respiratory tract was the most frequent site of infection (53.3%), followed by primary bacteremia (19.2%) and gastrointestinal infection (13.3%). The median (IQR) SOFA score was 9 (6–12). Respiratory failure (93.3%) and acute kidney injury (57.5%) were the most common organ dysfunctions. Nearly all patients (92.5%) required vasopressors, with a median dose of 0.2 mcg/kg/min (range, 0.1–0.5 mcg/kg/min). Laboratory findings showed a median arterial lactate of 2.3 (1.3–4.3) mmol/L, central venous lactate of 2.7 (1.7–4.5) mmol/L, and peripheral venous lactate of 3.7 (2.1–5.9) mmol/L. Additional laboratory values are summarized in Table 1. The most common complications were hospital-acquired infections (34.2%), renal complications (31.7%), and disseminated intravascular coagulation (20.8%). The median ICU and hospital length of stay were 7 (4.8–15.2) days and 12 (6–21.5) days, respectively. Overall hospital mortality was 52.5%. Univariate and multivariate linear regression analyses were performed to identify factors associated with arterial lactate levels (Table 2). Only peripheral venous lactate was independently associated with arterial lactate (β = 0.73, SE = 0.03, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Other variables, including pH, base excess, blood glucose, vasopressor dose, and mean arterial pressure, were not significantly associated after adjustment.\u003c/p\u003e\n\u003cp\u003eThe linear regression model for estimating arterial lactate was: Arterial lactate = 0.7414 x (Peripheral lactate)-0.1315. Internal validation demonstrated strong predictive performance, with an R² of 0.8514, a mean absolute error (MAE) of 0.8899, and a root mean square error (RMSE) of 1.2314. The correlation analysis showed a strong positive correlation between arterial and central venous lactate (r = 0.97), as well as between arterial and peripheral venous lactate (r = 0.91), across all lactate ranges, both below and above 2 mmol/L (Figures 2 and 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed a simple predictive equation to estimate arterial lactate from peripheral venous lactate in patients with septic shock. The formula, \u003cem\u003earterial lactate\u0026thinsp;=\u0026thinsp;0.7414 (peripheral lactate) \u0026ndash; 0.1315\u003c/em\u003e, demonstrated good accuracy (R\u0026sup2; = 0.8514, MAE\u0026thinsp;=\u0026thinsp;0.8899, RMSE\u0026thinsp;=\u0026thinsp;1.2314). The inclusion of additional covariates did not improve model performance, indicating that peripheral venous lactate alone provides sufficient predictive value. In comparison to earlier studies, these findings demonstrate a balance between accuracy and feasibility. Mikami et al. reported a higher R\u0026sup2; (0.96)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, but with a more complex equation that requires venous PaO₂, which limits its applicability in routine practice. Prasad et al. described a simple model (R\u0026sup2; = 0.92)\u003csup\u003e15\u003c/sup\u003e but included heterogeneous shock populations, limiting relevance to septic shock specifically. Theerawit et al. obtained a similar R\u0026sup2; (0.873) in sepsis and septic shock\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, but with a smaller cohort. The present analysis extends this evidence with a larger, homogenous population restricted to early septic shock, enhancing reliability and internal validity.\u003c/p\u003e \u003cp\u003eVenous lactate values were consistently higher than arterial values, consistent with the systematic review by Tienhoven et al. A strong correlation between arterial and venous lactate persisted even at concentrations greater than 2 mmol/L\u003csup\u003e4\u003c/sup\u003e, which contrasts with some previous reports. This may reflect methodological rigor: synchronized arterial and venous sampling, controlled tourniquet duration, and use of validated POCT analyzers minimized pre-analytical variation. Such measures likely contributed to the strong agreement observed, which is consistent with the study of Jose et al.\u003csup\u003e17\u003c/sup\u003e, where near-perfect correlation was maintained even at high lactate levels.\u003c/p\u003e \u003cp\u003eSeveral strengths of this study support confidence in the findings. The sample size was larger than in most prior reports and was calculated a priori, reducing the risk of overfitting. Restriction to early septic shock patients minimized confounding. Strict protocols for blood collection reduced technical error, and the use of widely available POCT lactate devices increases feasibility across both tertiary and community hospitals. The resulting formula is simple, requiring only one variable, and consistent with prior evidence. At the same time, limitations must be acknowledged: the single-center design limits generalizability, only a single lactate measurement was obtained during early resuscitation, and external validation has not yet been performed.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, these findings have practical relevance. Arterial puncture is an invasive procedure and may not always be feasible in unstable patients, whereas peripheral venous sampling is less invasive and widely available. A reliable equation to estimate arterial lactate could therefore reduce patient discomfort, accelerate clinical decision-making, and support sepsis protocols in diverse settings. Although lactate kinetics were not assessed in this study, the close agreement between arterial and venous values suggests potential utility for monitoring trends and clearance, which warrants further study.\u003c/p\u003e \u003cp\u003eIn summary, this analysis supports the feasibility of using peripheral venous lactate to estimate arterial lactate in septic shock. The proposed equation is simple, accurate, and practical, with potential for broad application in both resource-rich and resource-limited environments. Future multi-center studies with serial measurements and external validation are needed to confirm generalizability and evaluate the prognostic utility of this approach in guiding resuscitation strategies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA predictive model for arterial lactate estimation from peripheral venous sampling in early septic shock demonstrated strong accuracy and clinical feasibility. The formula is simple, requires no additional variables, and may reduce reliance on arterial puncture in both tertiary and community hospital settings. Future multicenter validation studies and assessments of its role in lactate-guided resuscitation protocols are warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval and Consent to Participate\u003cbr\u003eThe study protocol was approved by the Ethical Committee for Research in Human Subjects, Hatyai Hospital, Thailand (Protocol No. HYH EC 024-66-01). The approval was granted on 25 April 2023 following a full board review in accordance with the International Conference on Harmonisation \u0026ndash; Good Clinical Practice (ICH-GCP) guidelines. Written informed consent was obtained from all participants or their legally authorized representatives before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eChutima Cheranakhorn\u003c/strong\u003e conceived and designed the study, supervised data collection, and managed the project. \u003cstrong\u003eSorawat Sangkeaw\u003c/strong\u003e performed statistical analysis and provided methodological support. \u003cstrong\u003eNichakan Nakwan\u003c/strong\u003e\u003cstrong\u003e, \u003cstrong\u003eSuratee Chobngam\u003c/strong\u003e, \u003cstrong\u003eYuthaphichai Yaemchai\u003c/strong\u003e, \u003cstrong\u003eChananya Changadwej\u003c/strong\u003e,\u003c/strong\u003e and \u003cstrong\u003eNapa Jitjinda\u003c/strong\u003e contributed to patient recruitment, data collection, and resource coordination. All authors reviewed, revised, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors would like to thank the Emergency department and the ICU nursing staff of Hatyai Hospital for their assistance in patient recruitment and sample processing. The authors also acknowledge the assistance of a large language model (ChatGPT, OpenAI, San Francisco, CA, USA) for language editing and improving manuscript clarity. All authors reviewed and approved the final content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eEvans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Crit Care Med. 2021;49:e1063-143. doi:10.1097/CCM.0000000000005337\u003c/li\u003e\n \u003cli\u003eLevy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Crit Care Med. 2018;46:997-1000. doi:10.1097/CCM.0000000000003119\u003c/li\u003e\n \u003cli\u003eKruse O, Grunnet N, Barfod C. Blood lactate as a predictor for in-hospital mortality in patients admitted acutely to hospital: a systematic review. Scand J Trauma Resusc Emerg Med. 2011;19:74. doi:10.1186/1757-7241-19-74\u003c/li\u003e\n \u003cli\u003eTienhoven AJ, Beers CAJ, Siegert CEH. Agreement between arterial and peripheral venous lactate levels in the emergency department: a systematic review. Am J Emerg Med. 2019;37:746-50. doi:10.1016/j.ajem.2019.01.034\u003c/li\u003e\n \u003cli\u003eFerraris A, Bouisse C, Thiolliere F, Piriou V, Allaouchiche B. Mottling incidence and mottling score according to arterial lactate level in septic shock patients. Indian J Crit Care Med. 2020;24:672-6. doi:10.5005/jp-journals-10071-23531\u003c/li\u003e\n \u003cli\u003eChen X, Bi J, Zhang J, Du Z, Ren Y, Wei S, et al. Impact of serum glucose on the predictive value of serum lactate for hospital mortality in critically ill surgical patients. Dis Markers. 2019;2019:1578502. doi:10.1155/2019/1578502\u003c/li\u003e\n \u003cli\u003eBakker J, Nijsten MW, Jansen TC. Clinical use of lactate monitoring in critically ill patients. Ann Intensive Care. 2013;3:12. doi:10.1186/2110-5820-3-12\u003c/li\u003e\n \u003cli\u003eGunnerson KJ, Saul M, He S, Kellum JA. Lactate versus non-lactate metabolic acidosis: a retrospective outcome evaluation of critically ill patients. Crit Care. 2006;10:R22. doi:10.1186/cc3987\u003c/li\u003e\n \u003cli\u003eMontassier E, Batard E, Segard J, Hardouin JB, Martinage A, Le Conte P, et al. Base excess is an accurate predictor of elevated lactate in ED septic patients. Am J Emerg Med. 2012;30:184-7. doi:10.1016/j.ajem.2010.09.033\u003c/li\u003e\n \u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shanker-Hari M, Annan D, Bauer M et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016;315:801-10. doi:10.1001/jama.2016.0287\u003c/li\u003e\n \u003cli\u003ePeduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373-9. doi:10.1016/S0895-4356(96)00236-3\u003c/li\u003e\n \u003cli\u003eRiley RD, Ensor J, Snell KI, Harrell FE Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi:10.1136/bmj.m441\u003c/li\u003e\n \u003cli\u003eIsmail F, Mackay WG, Kerry A, Staines H, Rooney KD. Accuracy and timeliness of a point-of-care lactate measurement in patients with sepsis. Scand J Trauma Resusc Emerg Med. 2015;23:68. doi:10.1186/s13049-015-0151-x\u003c/li\u003e\n \u003cli\u003eMikami A, Soeno SO, Deshpande GA, Mochizuki T, Otani N, Ishimatsu S, et al. Can we predict arterial lactate from venous lactate in the ED? Am J Emerg Med. 2013;31:1118-20. doi:10.1016/j.ajem.2013.03.034\u003c/li\u003e\n \u003cli\u003ePrasad H, Vempalli N, Agrawal N, Ajun UN, Salam A, Datta SS, et al. Correlation and agreement between arterial and venous blood gas analysis in patients with hypotension: an emergency department-based cross-sectional study. Int J Emerg Med. 2023;16:18. doi:10.1186/s12245-023-00486-0\u003c/li\u003e\n \u003cli\u003eTheerawit P, Petvicharn CN, Tangsukaritvijit V, Sutherasan Y. Correlation between arterial lactate and venous lactate in patients with sepsis and septic shock. J Intensive Care Med. 2016;31(9):587-92. doi:10.1177/0885066616663169\u003c/li\u003e\n \u003cli\u003eJose JM, Cherian A, Bidkar PU, Nohan VK. Agreement between arterial and venous lactate in patients with sepsis. Int J Clin Pract. 2021;75:e14296. doi:10.1111/ijcp.14296\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Patient characteristics\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAll (n=120)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eAge-yr, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e59 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eMale, no (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e68 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 583px;\"\u003e\n \u003cp\u003eUnderlying disease- n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e45 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDiabetic mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e30 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e23 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e21 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eChronic lung disease/Asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e16 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHIV infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e16 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e10 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConnective tissue disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 583px;\"\u003e\n \u003cp\u003eSite of infection \u0026ndash; n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRespiratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e64 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePrimary bacteremia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e23 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGastrointestinal tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e16 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eUrinary tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSkin/Soft tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTropical infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCentral nervous system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eSOFA Score, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9 (6,12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eOrgan failure \u0026ndash; n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e112 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRenal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e69 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHematologic failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e40 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLiver failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eVasopressor used \u0026ndash; n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e111 (92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eVasopressor rate (mcg/kg/min), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.2 (0.1,0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eVital sign, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean arterial pressure (mmHg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e61.7 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Diastolic pressure (mmHg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e110.9 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Heart rate (beat/minute)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e110.9 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Oxygen Saturation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e95.8 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 457px;\"\u003e\n \u003cp\u003eFluid resuscitate (\u0026gt;2Liter) \u0026ndash; n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e91 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIQR; interquartile range, SOFA Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eTable 1: Patient characteristics (continue)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 575px;\"\u003e\n \u003cp\u003eLaboratory, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; White Blood Cell (cells/mm\u003csup\u003e3\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e14,645 (8,820- 20,015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Platelet (cell/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e140500 (79,000-240,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Creatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e1.7 (1-2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Albumin (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e2.5 (2.1-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; pH from vnous blood gas\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e7.4 (7.3-7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Base excess from venous blood gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e-7 (-11.6,-2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Blood sugar (mg%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e146.5 (115-194.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Anion gap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e15.5 (13-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Arterial lactate (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e2.3 (1.3,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Central venous lactate (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e2.7 (1.7,4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Peripheral venous lactate (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e3.7 (2.1,5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003eComplication- n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Hospital acquire infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e41 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Renal failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e38\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Disseminated intravascular coagulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e25\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Renal replacement therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e18\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Respiratory failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e15\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(12.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardio-Pulmonary resuscitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e9 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e7\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Liver failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e4\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tracheostomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e4\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute respiratory distress syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e3\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003eHospital stay-day, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e12 (6-21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003eICU stay-day, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e7 (4.8-15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003eDeath- n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e63 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Univariate and Multivariate Linear Regression Analysis of Factors Associated with Arterial Lactate\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003epH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-4.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.0358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eBase Excess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eBlood sugar (mg%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003evasopressor rate (ml/hr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4.4209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.8988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eMean arterial pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003ePeripheral venous lactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Septic shock, Arterial lactate, Peripheral venous lactate, Predictive model, Intensive care","lastPublishedDoi":"10.21203/rs.3.rs-7906890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7906890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Lactate is a key biomarker for diagnosis, risk stratification, and therapeutic monitoring in septic shock. Arterial sampling is the gold standard, but it is invasive, painful, and not always feasible, especially in early resuscitation. Peripheral venous sampling is less invasive and more widely accessible. This study aimed to develop and internally validate a predictive equation to estimate arterial lactate from peripheral venous lactate in patients with septic shock.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A prospective observational study was conducted in the intensive care unit of a tertiary hospital from April 2023 to April 2025. Adult patients meeting Sepsis-3 criteria for septic shock within the first 6 hours of resuscitation were enrolled. Arterial, central venous, and peripheral venous lactate were measured simultaneously or within 15 minutes. Predictive modeling was performed using multivariable regression with backward elimination. Model performance was assessed using R², mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (r).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 120 patients included, the median (IQR) arterial, central venous, and peripheral venous lactate were 2.3 (1.3–4.3), 2.7 (1.7–4.5), and 3.7 (2.1–5.9) mmol/L, respectively. The final model was: \u003cem\u003earterial lactate = 0.7414 × (peripheral venous lactate) – 0.1315\u003c/em\u003e. Internal validation demonstrated strong predictive accuracy (R² = 0.8514, MAE = 0.8899, RMSE = 1.2314). The correlation coefficient showed strong agreement between arterial and central venous lactate (r = 0.97) and arterial and peripheral venous lactate (r = 0.91), across both low and high lactate ranges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e A simple predictive equation using only peripheral venous lactate provides reliable estimation of arterial lactate in early septic shock. This approach is clinically feasible, less invasive, and may support timely decision-making in critical care, particularly in emergency departments and resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Development and Internal Validation of Predictive Formula for Arterial Lactate Using Peripheral Venous Sampling in Early Septic Shock","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 11:39:32","doi":"10.21203/rs.3.rs-7906890/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":"11675b28-046b-4c97-8d83-78e66a5231f0","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-15T05:00:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T10:14:34+00:00","index":110,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63135321,"name":"Health sciences/Biomarkers"},{"id":63135322,"name":"Health sciences/Diseases"},{"id":63135323,"name":"Health sciences/Health care"},{"id":63135324,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-15T05:09:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 11:39:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7906890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7906890","identity":"rs-7906890","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}