Predictors of In-Hospital Mortality: After a Methanol Poisoning Outbreak in Istanbul

Predictors of In-Hospital Mortality: After a Methanol Poisoning Outbreak in Istanbul | 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 Research Article Predictors of In-Hospital Mortality: After a Methanol Poisoning Outbreak in Istanbul Ozgur Dikme, Ozlem Dikme, Erdem Kurt, Sila Sadillioglu, Mustafa Örfi Erdede This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8703189/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Background Methanol poisoning remains a major public health problem, particularly during outbreaks related to illicit alcohol consumption, and is associated with high mortality. Early identification of patients at high risk of death is critical to guide timely triage and aggressive management in the emergency department (ED). Objectives To identify clinical and laboratory predictors of in-hospital mortality among patients with methanol poisoning during an outbreak and to evaluate the prognostic performance of key parameters using receiver operating characteristic (ROC) curve analysis and logistic regression. Methods This retrospective observational cohort study was conducted in the ED of a tertiary-care hospital in Istanbul, Türkiye, during a methanol poisoning outbreak between December 1, 2024, and January 31, 2025. Adult patients (≥ 18 years) diagnosed with methanol poisoning were included. Demographic data, clinical findings, laboratory results, arterial blood gas parameters, and treatments were collected. The primary outcome was in-hospital mortality. ROC curve analyses and univariate and multivariable logistic regression models were performed. Results A total of 55 patients were included (92.7% male; mean age 46.0 ± 12.3 years). In-hospital mortality occurred in 25 patients (45.5%). Non-survivors had significantly lower arterial pH, bicarbonate, and base excess values and higher lactate levels and anion gap compared with survivors (all p < 0.001). Arterial pH demonstrated excellent prognostic performance (AUC 0.969), with an optimal cut-off value of ≤ 6.89 (92.0% sensitivity, 96.7% specificity). In multivariable analysis, arterial pH remained an independent predictor of mortality, with each 0.1-unit decrease associated with a 2.78-fold increase in the odds of death. In a model excluding arterial blood gas parameters, higher lactate levels and lower Glasgow Coma Scale scores were independently associated with mortality. Conclusions During methanol poisoning outbreaks, arterial pH is the strongest predictor of in-hospital mortality. Serum lactate and neurological status provide additional prognostic value when arterial blood gas analysis is unavailable. Methanol poisoning In-hospital mortality Metabolic acidosis Arterial pH Outbreak Emergency department Figures Figure 1 Introduction Methanol poisoning remains a significant public health problem worldwide, particularly in regions where illicit alcoholic beverages are readily available ( 1 , 2 ). Methanol is a toxic alcohol that is metabolized to formaldehyde and formic acid, leading to severe metabolic acidosis, visual disturbances, neurological impairment, and potentially fatal outcomes ( 3 , 4 ). The clinical presentation of methanol poisoning typically follows a biphasic pattern. An initial phase is characterized by mild and nonspecific symptoms resembling ethanol intoxication, such as dizziness, nausea, and inebriation. This phase is often followed by a latent period during which patients may appear clinically stable. Subsequently, as toxic metabolites accumulate, patients may develop severe metabolic complications, including profound metabolic acidosis, respiratory failure, visual disturbances, and neurological deterioration, which are associated with a markedly increased risk of mortality ( 5 , 6 ). Despite advances in supportive care and extracorporeal treatments, methanol poisoning continues to be associated with substantial morbidity and mortality ( 7 , 8 , 9 ) Early recognition and prompt initiation of appropriate treatment are therefore critical determinants of outcome. Timely administration of antidotal therapy with fomepizole or ethanol, aggressive correction of metabolic acidosis, and initiation of hemodialysis when indicated can substantially reduce the accumulation of toxic metabolites and prevent irreversible organ damage ( 10 , 11 ). Despite advances in the understanding of methanol poisoning and treatment protocols, reported mortality rates remain unacceptably high, ranging from 20% to 40% across case series worldwide. Moreover, survivors frequently experience significant long-term morbidity, including permanent visual impairment and neurological deficits ( 12 , 13 ). The heterogeneous clinical presentation and variable disease progression of methanol poisoning pose substantial challenges for emergency physicians in risk stratification and treatment decision-making, especially in resource-limited settings where access to specialized antidotes may be restricted ( 7 ). Outbreaks of methanol poisoning, often following the distribution of illegally produced alcoholic beverages, can result in clusters of critically ill patients presenting to emergency departments (EDs) over a short period. Such outbreaks place considerable strain on EDs and critical care services and underscore the need for rapid, reliable prognostic tools to guide triage, resource allocation, and early aggressive management. Delays in diagnosis or treatment, particularly during outbreak settings, are associated with rapid clinical deterioration and increased mortality ( 8 , 9 , 14 ). However, current literature reveals important gaps in the identification of reliable and clinically applicable prognostic factors, particularly in the ED setting, where rapid assessment is essential. Many existing studies are limited by small sample sizes, heterogeneous populations, and the absence of validated predictive models to identify high-risk patients who may benefit from intensive monitoring and early intervention. In December 2024, a large methanol poisoning outbreak occurred in Istanbul, Türkiye, following the circulation of illicit alcoholic beverages, resulting in a sudden surge of patients presenting to EDs within a short time frame. This outbreak provided a unique opportunity to evaluate prognostic factors for mortality in a real-world, high-burden clinical setting. The aim of this study was to identify clinical and laboratory predictors of in-hospital mortality among patients with methanol poisoning during this outbreak period. Specifically, we sought to evaluate the discriminative performance of key parameters using receiver operating characteristic (ROC) curve analysis and to determine independent predictors of mortality through logistic regression modeling. Methods Study Design and Setting This retrospective observational cohort study was conducted in the ED of a tertiary-care hospital in Istanbul, Türkiye. The study was performed during the methanol poisoning outbreak in Istanbul, which occurred following the circulation of illicit alcoholic beverages released to the market in December 2024. All consecutive patients presenting to our emergency department between December 1, 2024, and January 31, 2025, were screened for eligibility. Study Population All consecutive patients aged ≥ 18 years who presented to the ED during the defined outbreak period and were diagnosed with methanol poisoning related to illicit alcohol consumption were included. The diagnosis was based on a compatible history of exposure and/or clinical presentation supported by laboratory findings consistent with toxic alcohol poisoning (metabolic acidosis with increased anion gap (> 12 mEq/L) and/or supportive arterial blood gas abnormalities). Patients with missing key clinical or laboratory data required for outcome assessment were excluded. Data Collection Demographic characteristics, vital signs at presentation, clinical symptoms, neurological status assessed using the Glasgow Coma Scale (GCS), laboratory findings, arterial blood gas parameters, imaging findings, and treatment characteristics were extracted from electronic medical records. Laboratory variables included complete blood count, inflammatory markers, renal and hepatic function tests, pancreatic enzymes, cardiac biomarkers, electrolytes, and arterial blood gas parameters. Outcome Measures The primary outcome of the study was in-hospital mortality. Patients were categorized into survivor and non-survivor groups according to hospital discharge status. Statistical Analysis Continuous variables were assessed for normality using visual inspection and distribution characteristics and were expressed as median with interquartile range (IQR). Categorical variables were presented as numbers and percentages. Comparisons between survivors and non-survivors were performed using the Mann–Whitney U test for continuous variables and the χ² test or Fisher’s exact test, as appropriate, for categorical variables. ROC curve analyses were conducted to evaluate the discriminative ability of selected clinical and laboratory parameters for predicting in-hospital mortality. The area under the curve (AUC) was calculated for each variable, and optimal cut-off values were determined using the Youden index. Univariate logistic regression analyses were initially performed to identify variables associated with in-hospital mortality. Variables with clinical relevance and/or statistical significance in univariate analyses were subsequently included in multivariable logistic regression models. Because arterial pH is a narrowly distributed continuous variable, unscaled odds ratios may be misleading; therefore, arterial pH was scaled and interpreted per 0.1-unit decrease in multivariable logistic regression models. Two separate multivariable models were constructed: one including arterial blood gas parameters and another excluding arterial blood gas parameters to evaluate alternative predictors applicable in settings where arterial blood gas analysis may not be readily available. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. All statistical analyses were performed using IBM SPSS Statistics (IBM Corp., Armonk, NY, USA). A two-sided p value < 0.05 was considered statistically significant. Ethical Considerations The study was conducted in accordance with the principles of the Declaration of Helsinki. Approval for the study was obtained from the local institutional ethics committee (approval number: 95, April 18, 2025). Due to the retrospective nature of the study, informed consent was waived. Results A total of 55 patients with methanol poisoning were included in the study, predominantly male (92.7%), with a mean age of 46.0 ± 12.3 years (range, 18–74). In-hospital mortality occurred in 25 patients (45.5%). Baseline characteristics according to in-hospital mortality are presented in Table 1 . Table 1 Patients’ characteristics according to in-hospital mortality. Variables Survivors (n = 30) Non-survivors (n = 25) p value Demographic & Vital Signs Age (years) 46.0 (38.5–55.0) 45.0 (39.0–55.0) 0.899 Systolic BP (mmHg) 126.5 (123.8–145.8) 125.0 (100.0–134.0) 0.045 Diastolic BP (mmHg) 70.0 (64.0–84.0) 68.0 (54.0–75.0) 0.212 Heart rate (beats/min) 93.0 (89.3–101.0) 90.0 (78.0–100.0) 0.147 SpO₂ (%) 98.0 (97.3–99.0) 99.0 (98.0–100.0) 0.217 Length of stay (hours) 48.0 (3.0–114.0) 30.0 (4.0–120.0) 0.986 Neurological Status Glasgow Coma Scale 15.0 (15.0–15.0) 8.0 (5.0–13.0) < 0.001 Altered mental status, n (%) 7 (23.3%) 15 (60.0%) 0.012 Seizure, n (%) 0 (0.0%) 4 (16.0%) 0.037 Visual disturbance, n (%) 15 (50.0%) 22 (88.0%) 0.004 Presenting Symptoms Abdominal pain 1 (3.3%) 6 (24.0%) 0.039 Hematology & Inflammation WBC (×10³/µL) 10.96 (9.18–14.04) 14.82 (11.49–18.27) 0.003 Neutrophils (×10³/µL) 8.09 (5.77–10.58) 10.23 (8.78–13.78) 0.009 Lymphocytes (×10³/µL) 2.29 (1.15–3.00) 2.86 (1.97–3.95) 0.024 Immature granulocytes 0.05 (0.03–0.10) 0.12 (0.04–0.29) 0.022 IG percentage (%) 0.40 (0.23–0.80) 0.70 (0.40–1.20) 0.036 Biochemistry Glucose (mg/dL) 111.0 (96.0–132.0) 138.0 (118.0–176.0) 0.006 Urea (mg/dL) 30.0 (23.0–39.0) 43.0 (33.0–63.0) 0.002 Creatinine (mg/dL) 0.95 (0.80–1.20) 1.60 (1.10–2.40) < 0.001 AST (U/L) 30.0 (22.0–41.0) 44.0 (32.0–88.0) 0.001 ALT (U/L) 27.0 (20.0–39.0) 40.0 (25.0–71.0) 0.009 GGT (U/L) 36.0 (23.0–67.0) 59.0 (36.0–111.0) 0.014 Amylase (U/L) 58.0 (44.0–77.0) 83.0 (55.0–147.0) 0.008 Lipase (U/L) 33.0 (22.0–52.0) 61.0 (34.0–133.0) 0.010 Potassium (mmol/L) 4.1 (3.8–4.4) 4.6 (4.2–5.1) 0.004 eGFR (mL/min/1.73m²) 98.0 (82.0–112.0) 52.0 (31.0–74.0) < 0.001 Troponin (ng/L) 6.0 (3.0–12.0) 24.0 (9.0–88.0) < 0.001 Arterial Blood Gas Parameters Arterial pH 7.31 (7.12–7.35) 6.68 (6.66–6.81) < 0.001 PCO₂ (mmHg) 28.0 (21.0–35.0) 55.0 (41.0–72.0) < 0.001 HCO₃⁻ (mmol/L) 18.75 (7.88–25.30) 6.70 (4.90–8.60) < 0.001 Base excess (mmol/L) -6.45 (− 19.73 to 0.98) −29.50 (-33.00 to -26.70) < 0.001 Lactate (mmol/L) 1.79 (1.25–3.05) 8.75 (7.02–11.14) < 0.001 Anion gap 17.75 (11.38–23.82) 29.20 (25.10–32.00) < 0.001 Treatments Hemodialysis, n (%) 14 (46.7%) 12 (48.0%) 1.000 NaHCO 3 therapy, n (%) 9 (30.0%) 22 (88.0%) < 0.001 Ethanol therapy, n (%) 7 (23.3%) 10 (40.0%) 0.245 Intubation, n (%) 5 (16.7%) 25 (100.0%) < 0.001 Continuous variables are presented as median (interquartile range) and categorical variables as number (percentage). Mann–Whitney U test was used for continuous variables and χ² or Fisher’s exact test for categorical variables. A p value < 0.05 was considered statistically significant. There was no significant difference in age between survivors and non-survivors (median 46.0 [IQR 38.5–55.0] vs. 45.0 [IQR 39.0–55.0] years; p = 0.899). Systolic BP at presentation was slightly lower in non-survivors (125.0 vs. 126.5 mmHg; p = 0.045), while other vital signs, including diastolic BP, heart rate, and oxygen saturation, were comparable between groups. Neurological status differed markedly between groups. Non-survivors had significantly lower GCS scores compared with survivors (median 8.0 [IQR 5.0–13.0] vs. 15.0 [IQR 15.0–15.0]; p < 0.001). Altered mental status, seizures, and visual disturbances were significantly more frequent among non-survivors (all p < 0.05). Regarding laboratory findings, non-survivors demonstrated significantly higher inflammatory and hematological parameters, including WBC and neutrophil counts, as well as higher immature granulocyte counts and percentages (all p < 0.05). Markers of renal dysfunction (urea, creatinine, and decreased eGFR), hepatic injury (AST, ALT, GGT), pancreatic enzymes (amylase and lipase), serum potassium, glucose, and troponin levels were also significantly higher in non-survivors (all p < 0.05). Arterial blood gas parameters showed the most pronounced differences between groups. Non-survivors presented with significantly lower arterial pH, bicarbonate, and base excess values, along with markedly higher lactate levels and anion gap compared with survivors (all p < 0.001). In addition, arterial PCO₂ levels were significantly higher in non-survivors (p < 0.001). With respect to treatment characteristics, the rate of hemodialysis was similar between groups (p = 1.000). However, sodium bicarbonate therapy and endotracheal intubation were significantly more common among non-survivors (both p < 0.001). Ethanol therapy rates did not differ significantly between groups. ROC analyses were performed to evaluate the discriminative ability of clinical and laboratory parameters for predicting in-hospital mortality (Fig. 1 , Table 2 ). Arterial pH demonstrated excellent prognostic performance with an area under the curve (AUC) of 0.969, identifying an optimal cut-off value of ≤ 6.89, which yielded 92.0% sensitivity and 96.7% specificity. Base excess also showed excellent discrimination (AUC 0.954, cut-off ≤ − 25.4), followed by serum lactate (AUC 0.917, cut-off ≥ 5.26 mmol/L). Anion gap (AUC 0.858), GCS scores (AUC 0.827), and bicarbonate levels (AUC 0.803) demonstrated good discriminatory ability. Table 2 ROC analysis for prediction of in-hospital mortality. Variable AUC Optimal cut-off Sensitivity (%) Specificity (%) Arterial pH 0.969 ≤ 6.89 92.0 96.7 Base excess (mmol/L) 0.954 ≤ −25.4 88.0 93.3 Lactate (mmol/L) 0.917 ≥ 5.26 92.0 93.3 Anion gap 0.858 ≥ 23.0 88.0 73.3 GCS score 0.827 ≤ 11 72.0 90.0 HCO₃⁻ (mmol/L) 0.803 ≤ 7.6 68.0 80.0 Optimal cut-off values were determined using the Youden index. In univariate logistic regression analyses, lower arterial pH, lower base excess, higher lactate levels, higher anion gap, and lower GCS scores were all significantly associated with increased in-hospital mortality (all p < 0.001), whereas age was not significantly associated with mortality (Table 3 ). In the multivariable logistic regression model including arterial pH and lactate, arterial pH remained an independent predictor of in-hospital mortality, while lactate lost statistical significance. Specifically, each 0.1-unit decrease in arterial pH was associated with a 2.78-fold increase in the odds of death (OR 2.78, 95% CI 1.34–5.76; p = 0.006). In an alternative multivariable model excluding arterial blood gas parameters, both higher lactate levels and lower GCS scores remained independently associated with mortality. Each 1 mmol/L increase in lactate was associated with a 1.60-fold increase in mortality risk (OR 1.60, 95% CI 1.23–2.08; p < 0.001), and each 1-point decrease in GCS was associated with a 1.38-fold increase in mortality risk (OR 1.38, 95% CI 1.07–1.78; p = 0.015). Table 3 Logistic Regression Models for In-Hospital Mortality in Methanol Poisoning. Variable OR (95% CI) pvalue Univariate Logistic Regression Arterial pH - < 0.001 Base excess 0.77 (0.66–0.90) 0.001 Lactate 1.77 (1.35–2.32) < 0.001 Anion gap 1.25 (1.11–1.40) < 0.001 GCS score 0.65 (0.52–0.82) < 0.001 Age 1.01 (0.97–1.06) 0.59 Multivariable Logistic Regression – Model A (ABG-based) Arterial pH (per 0.1-unit decrease) 2.78 (1.34–5.76) 0.006 Lactate (per 1 mmol/L increase) 1.10 (0.82–1.47) 0.53 Multivariable Logistic Regression – Model B (Non-ABG model) Lactate (per 1 mmol/L increase) 1.60 (1.23–2.08) < 0.001 GCS score (per 1-point decrease) 1.38 (1.07–1.78) 0.015 Univariate OR for arterial pH is not presented due to scale instability; pH was therefore modeled per 0.1-unit decrease. Discussion In this retrospective cohort study conducted during a methanol poisoning outbreak in Istanbul, we identified several clinical and laboratory parameters associated with in-hospital mortality. The principal finding of this study is that the severity of metabolic acidosis, particularly arterial pH, was the strongest predictor of mortality. Arterial pH demonstrated excellent discriminative ability on ROC analysis and remained an independent predictor of death in multivariable logistic regression, even after adjustment for serum lactate. The pathophysiology of methanol poisoning is primarily driven by the accumulation of formic acid, which leads to profound metabolic acidosis, mitochondrial dysfunction, and cellular hypoxia. Therefore, arterial pH reflects the cumulative burden of toxicity and delayed metabolism rather than a single laboratory abnormality ( 6 , 15 , 16 ). Our findings support previous observations that severe acidosis is a key determinant of poor outcomes in methanol poisoning and further emphasize arterial pH as a robust and clinically meaningful prognostic marker. Serum lactate concentrations are used to evaluate the severity of physiologic stress in multiple disease states, having been validated in the setting of trauma and sepsis specifically ( 17 , 18 ). It has been hypothesized that a serum lactate concentration would be elevated in the setting of acute ethanol intoxication and thus would not be a clinically useful test to obtain. However, this relationship has not been quantified ( 19 , 20 ). In our study, serum lactate level showed strong prognostic performance in ROC analysis and was significantly associated with mortality in univariate and multivariable models excluding arterial blood gas parameters. However, when arterial pH was included in the multivariable model, lactate lost its independent association with mortality, suggesting that arterial pH may integrate both toxic metabolic burden and secondary tissue hypoxia more comprehensively than lactate alone. In addition to acid–base disturbances, neurological impairment emerged as an important predictor of mortality ( 1 , 4 ). Consistent with the literature in our study lower GCS scores were independently associated with death in the non–arterial blood gas model, highlighting the prognostic value of neurological status, particularly in settings where arterial blood gas analysis may not be immediately available. Visual disturbances, seizures, and altered mental status were also more frequent among non-survivors, all patients died who had seizure at ED. Markers of multiorgan dysfunction, including renal impairment, hepatic injury, elevated pancreatic enzymes, hyperkalemia, hypernatremia and increased cardiac biomarkers, were significantly more pronounced among non-survivors ( 21 , 22 , 23 , 24 , 25 ). These findings suggest that fatal methanol poisoning is not solely a metabolic disorder but rather a systemic toxic state involving multiple organ systems. The high prevalence of sodium bicarbonate administration and endotracheal intubation among non-survivors in the study likely reflects disease severity rather than a causal relationship with poor outcomes. The outbreak setting of this study is of particular importance. Unlike sporadic methanol poisonings, outbreak-related cases often present late, in clusters, and with severe metabolic derangements. This context underscores the need for rapid, reliable prognostic tools to support early triage decisions, including urgent hemodialysis and intensive care admission ( 7 , 8 , 9 , 14 , 26 , 27 ). Our findings suggest that simple and readily available parameters, such as arterial pH, lactate levels, and GCS, may be used to stratify in-hospital mortality risk effectively during such high-burden periods. From a clinical perspective, an arterial pH threshold of ≤ 6.89 demonstrated both high sensitivity and specificity for predicting in-hospital mortality and may serve as a critical trigger for early aggressive intervention, including prompt consideration of hemodialysis and intensive care admission. In resource-limited or surge conditions, readily available parameters such as serum lactate levels and GCS scores may provide valuable alternative prognostic information when arterial blood gas analysis is delayed or unavailable, facilitating timely risk stratification and clinical decision-making. Several limitations should be considered when interpreting these findings. The retrospective, single-center design may limit the generalizability of the results to settings with different patient populations, treatment protocols, or resource availability. The sample size, while reflective of the outbreak nature of the cohort, constrained the number of variables that could be included in multivariable models and may have limited statistical power to detect associations of smaller magnitude. Serum methanol and formate concentrations were not consistently available across patients and therefore could not be analyzed as prognostic markers, despite their potential relevance to quantifying toxic burden. Additionally, fomepizole was not available at our institution during the study period, necessitating the use of ethanol as an antidote. Given the more predictable pharmacokinetics and favorable safety profile of fomepizole, this limitation may have influenced treatment-related outcomes and complicates direct comparison with centers where fomepizole is routinely used. Treatment decisions, including initiation and duration of hemodialysis, were guided by clinical severity rather than a standardized protocol, introducing the possibility of confounding by indication. This pragmatic approach reflects real-world practice but limits causal inference. Conclusions In conclusion, arterial pH is the most powerful predictor of in-hospital mortality in patients with methanol poisoning during outbreak settings. Serum lactate and neurological status provide additional prognostic value, particularly when arterial blood gas analysis is unavailable. Early recognition of severe metabolic acidosis should prompt timely escalation of care and consideration of extracorporeal treatment during outbreak conditions. Declarations Funding This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution ODi conceived and designed the study, coordinated data collection, performed the primary statistical analyses, interpreted the results, and drafted the initial manuscript.ODk contributed to study design, data acquisition, and clinical data interpretation, and critically revised the manuscript for important intellectual content.EK contributed to methodological planning, statistical interpretation, and critical revision of the manuscript with a focus on clinical relevance and emergency medicine practice.SS contributed to data collection, data verification, and preliminary analyses, and assisted in manuscript preparation.MOE contributed to data collection and clinical interpretation and reviewed the manuscript for accuracy and consistency.All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. References Yousefinejad V, Moradi B, Mohammadi Baneh A, Sheikhesmaeili F, Babahajian A. Prognostic Factors of Outcome in Methanol Poisoning; an 8-year Retrospective Cross-sectional Study. Arch Acad Emerg Med. 2020;8(1):e69. Alrashed M, Aldeghaither NS, Almutairi SY, et al. The Perils of Methanol Exposure: Insights into Toxicity and Clinical Management. Toxics. 2024;12(12):924. 10.3390/toxics12120924 . Batur A, Yilmaz S, Celik MM, et al. A potential prognostic indicator in methanol intoxication: Body temperature. Acta Med. 2024;55(2):128–36. 10.32552/2024.ActaMedica.1012 . Mardan H, Hosseini SM, Yousefinejad V, et al. Predictors of Mortality in Methanol Poisoning: A Systematic Review and Meta-analysis. Int J Med Toxicol Forensic Med. 2024;14(1):1–14. Zakharov S, Pelclova D, Navratil T, et al. Fomepizole versus ethanol in the treatment of acute methanol poisoning: comparison of clinical effectiveness in a mass poisoning outbreak. Clin Toxicol (Phila). 2015;53(8):797–806. 10.3109/15563650.2015.1059946 . Kraut JA, Mullins ME. Toxic alcohols. N Engl J Med. 2018;378(3):270–80. 10.1056/NEJMra1615295 . Roberts DM, Yates C, Megarbane B, et al. Recommendations for the role of extracorporeal treatments in the management of acute methanol poisoning: a systematic review and consensus statement. Crit Care Med. 2015;43(2):461–72. 10.1097/CCM.0000708 . Md Noor J, Hawari R, Mokhtar MF, Yussof SJ, Chew N, Norzan NA, et al. Methanol outbreak: a Malaysian tertiary hospital experience. Int J Emerg Med. 2020;13(1):6. 10.1186/s12245-020-0264-5 . Hassanian-Moghaddam H, Zamani N, Roberts DM, Brent J, McMartin K, Aaron C, et al. Consensus statements on the approach to patients in a methanol poisoning outbreak. Clin Toxicol (Phila). 2019;57(12):1129–36. 10.1080/15563650.2019.1636992 . Barceloux DG, Bond GR, Krenzelok EP, Cooper H, Vale JA. American Academy of Clinical Toxicology practice guidelines on the treatment of methanol poisoning. J Toxicol Clin Toxicol. 2002;40(4):415–46. 10.1081/clt-120006745 . Brent J, McMartin K, Phillips S, et al. Fomepizole for the treatment of methanol poisoning. N Engl J Med. 2001;344(6):424–9. 10.1056/NEJM200102083440605 . Beatty L, Green R, Magee K, Zed P. A systematic review of ethanol and fomepizole use in toxic alcohol ingestions. Emerg Med Int. 2013; 2013:638057. 10.1155/2013/638057 Rietjens SJ, de Lange DW, Meulenbelt J. Ethylene glycol or methanol intoxication: which antidote should be used, fomepizole or ethanol? Neth J Med. 2014;72(2):73–9. Ran M, Li Y, Zhang L, Wu W, Lin J, Liu Q, et al. Clinical features, treatment, and prognosis of acute methanol poisoning: experiences in an outbreak. Int J Clin Exp Med. 2019;12(5):5938–50. Mochizuki K, Fujii T, Paul E, Anstey M, Pilcher DV, Bellomo R. Early metabolic acidosis in critically ill patients: a binational multicentre study. Crit Care Resusc. 2023;23(1):67–75. 10.51893/2021.1.OA6 . Published 2023 Oct 18. Tangri N, Reaven NL, Funk SE, Ferguson TW, Collister D, Mathur V. Metabolic acidosis is associated with increased risk of adverse kidney outcomes and mortality in patients with non-dialysis dependent chronic kidney disease: an observational cohort study. BMC Nephrol. 2021;22(1):185. Published 2021 May 19. 10.1186/s12882-021-02385-z Gustafson ML, Hollosi S, Chumbe JT. The effect of ethanol on lactate and base deficit as predictors of morbidity and mortality in trauma. Am J Emerg Med. 2015;33:607–13. Wardi G, Brice J, Correia M. Demystifying lactate in the emergency department. Ann Emerg Med. 2020;75:287–98. Sonoo T, Iwai S, Inokuchi R. Quantitative analysis of high plasma lactate concentration in ED patients after alcohol intake. Am J Emerg Med. 2016;34:825–9. MacDonald L, Kruse JA, Levy DB. Lactic acidosis and acute ethanol intoxication. Am J Emerg Med. 1994;12:32–5. Kuusela E, Järvisalo MJ, Hellman T, Uusalo P. Mortality and associated risk factors in patients with severe methanol or ethylene glycol poisoning treated with dialysis: a retrospective cohort study. J Int Med Res. 2022;50(2):3000605221081427. 10.1177/03000605221081427 . Wang C, Hiremath S, Sikora L, et al. Kidney outcomes after methanol and ethylene glycol poisoning: a systematic review and meta-analysis. Clin Toxicol (Phila). 2023;61(5):326–35. 10.1080/15563650.2023.2200547 . Gheshlaghi F, Rezaei MR, Eizadi-Mood N, Fattahi F, Nazarianpirdosti M, Oskui AG. Predictors of Mortality in Methanol Poisoning: A Systematic Review and Meta-analysis. Int J Med Toxicol Forensic Med. 2024;14. https://doi.org/10.32598/ijmtfm.v14i1.43414 . Nekoukar Z, Zakariaei Z, Taghizadeh F, Musavi F, Banimostafavi ES, Sharifpour A, Ebrahim Ghuchi N, Fakhar M, Tabaripour R, Safanavaei S. Methanol poisoning as a new world challenge: A review. Ann Med Surg (Lond). 2021;66:102445. 10.1016/j.amsu.2021.102445 . Kaewput W, Thongprayoon C, Petnak T, Chewcharat A, Boonpheng B, Bathini T, Vallabhajosyula S, Cheungpasitporn W. Inpatient Burden and Mortality of Methanol Intoxication in the United States. Am J Med Sci. 2021;361(1):69–74. 10.1016/j.amjms.2020.08.014 . Gulen M, Satar S, Avci A, Acehan S, Orhan U, Nazik H. Methanol poisoning in Turkey: two outbreaks, a single center experience. Alcohol. 2020;88:83–90. 10.1016/j.alcohol.2020.07.002 . Paasma R, Hovda KE, Tikkerberi A, Jacobsen D. Methanol mass poisoning in Estonia: outbreak in 154 patients. Clin Toxicol (Phila). 2007;45(2):152–7. 10.1080/15563650600956329 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8703189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587781796,"identity":"77787d8b-c4ed-4f14-a330-05bedf763b9f","order_by":0,"name":"Ozgur Dikme","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDCCAxCKsY0HSH5gYEggTQvjDJK0NAC1MPMQo4Xv9uHDH7+23ZPt4zmd+Ni2zS6Pn72B8cPHHNxaJM+lJRjLthUbt/H2bjbObUsuluw5wCw5cxtuLQZneAySJdsSEtv4ebdJ57YxJ264kcDGzEtAy2Golu2/LdvqidJi2PgRpIW3dxszY9thwlokz7AlMzOcSzBu4zm7WbLn3PHEmT0Hm/H6he8M8+GPP8oSZOf35G788KOsOrGfvfngh494tIAAKDoggJENTDbgVw9S8gPO/ENQ8SgYBaNgFIxAAACjP1YXM0G05gAAAABJRU5ErkJggg==","orcid":"","institution":"İstanbul Eğitim ve Araştırma Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Ozgur","middleName":"","lastName":"Dikme","suffix":""},{"id":587781797,"identity":"54f70163-c093-4610-97cd-085553dafe57","order_by":1,"name":"Ozlem Dikme","email":"","orcid":"","institution":"İstanbul Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Ozlem","middleName":"","lastName":"Dikme","suffix":""},{"id":587781798,"identity":"abdd5970-14d6-4556-9707-7b5352a4a5a6","order_by":2,"name":"Erdem Kurt","email":"","orcid":"","institution":"İstanbul Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Erdem","middleName":"","lastName":"Kurt","suffix":""},{"id":587781799,"identity":"d2056b4b-22d0-4ba9-993f-d30a92b88346","order_by":3,"name":"Sila Sadillioglu","email":"","orcid":"","institution":"İstanbul Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Sila","middleName":"","lastName":"Sadillioglu","suffix":""},{"id":587781800,"identity":"0f79e230-1ac1-4237-ab16-1c72cec7227a","order_by":4,"name":"Mustafa Örfi Erdede","email":"","orcid":"","institution":"İstanbul Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Örfi","lastName":"Erdede","suffix":""}],"badges":[],"createdAt":"2026-01-26 18:38:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8703189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8703189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102261485,"identity":"e68a0bac-5791-47e3-9999-61295be8a671","added_by":"auto","created_at":"2026-02-10 00:40:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29460,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves of acid–base parameters for predicting in-hospital mortality in patients with methanol poisoning.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8703189/v1/a39b56b749c83777b7f4c7e3.png"},{"id":102261486,"identity":"e5c98de8-8540-480a-a129-ec28f506a923","added_by":"auto","created_at":"2026-02-10 00:40:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":803004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8703189/v1/c30eebc0-6fa1-4480-bdbd-6e9b5eb6e5fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of In-Hospital Mortality: After a Methanol Poisoning Outbreak in Istanbul","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMethanol poisoning remains a significant public health problem worldwide, particularly in regions where illicit alcoholic beverages are readily available (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Methanol is a toxic alcohol that is metabolized to formaldehyde and formic acid, leading to severe metabolic acidosis, visual disturbances, neurological impairment, and potentially fatal outcomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe clinical presentation of methanol poisoning typically follows a biphasic pattern. An initial phase is characterized by mild and nonspecific symptoms resembling ethanol intoxication, such as dizziness, nausea, and inebriation. This phase is often followed by a latent period during which patients may appear clinically stable. Subsequently, as toxic metabolites accumulate, patients may develop severe metabolic complications, including profound metabolic acidosis, respiratory failure, visual disturbances, and neurological deterioration, which are associated with a markedly increased risk of mortality (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Despite advances in supportive care and extracorporeal treatments, methanol poisoning continues to be associated with substantial morbidity and mortality (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eEarly recognition and prompt initiation of appropriate treatment are therefore critical determinants of outcome. Timely administration of antidotal therapy with fomepizole or ethanol, aggressive correction of metabolic acidosis, and initiation of hemodialysis when indicated can substantially reduce the accumulation of toxic metabolites and prevent irreversible organ damage (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite advances in the understanding of methanol poisoning and treatment protocols, reported mortality rates remain unacceptably high, ranging from 20% to 40% across case series worldwide. Moreover, survivors frequently experience significant long-term morbidity, including permanent visual impairment and neurological deficits (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The heterogeneous clinical presentation and variable disease progression of methanol poisoning pose substantial challenges for emergency physicians in risk stratification and treatment decision-making, especially in resource-limited settings where access to specialized antidotes may be restricted (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOutbreaks of methanol poisoning, often following the distribution of illegally produced alcoholic beverages, can result in clusters of critically ill patients presenting to emergency departments (EDs) over a short period. Such outbreaks place considerable strain on EDs and critical care services and underscore the need for rapid, reliable prognostic tools to guide triage, resource allocation, and early aggressive management. Delays in diagnosis or treatment, particularly during outbreak settings, are associated with rapid clinical deterioration and increased mortality (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, current literature reveals important gaps in the identification of reliable and clinically applicable prognostic factors, particularly in the ED setting, where rapid assessment is essential. Many existing studies are limited by small sample sizes, heterogeneous populations, and the absence of validated predictive models to identify high-risk patients who may benefit from intensive monitoring and early intervention. In December 2024, a large methanol poisoning outbreak occurred in Istanbul, T\u0026uuml;rkiye, following the circulation of illicit alcoholic beverages, resulting in a sudden surge of patients presenting to EDs within a short time frame. This outbreak provided a unique opportunity to evaluate prognostic factors for mortality in a real-world, high-burden clinical setting. The aim of this study was to identify clinical and laboratory predictors of in-hospital mortality among patients with methanol poisoning during this outbreak period. Specifically, we sought to evaluate the discriminative performance of key parameters using receiver operating characteristic (ROC) curve analysis and to determine independent predictors of mortality through logistic regression modeling.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis retrospective observational cohort study was conducted in the ED of a tertiary-care hospital in Istanbul, T\u0026uuml;rkiye. The study was performed during the methanol poisoning outbreak in Istanbul, which occurred following the circulation of illicit alcoholic beverages released to the market in December 2024. All consecutive patients presenting to our emergency department between December 1, 2024, and January 31, 2025, were screened for eligibility.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eAll consecutive patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who presented to the ED during the defined outbreak period and were diagnosed with methanol poisoning related to illicit alcohol consumption were included. The diagnosis was based on a compatible history of exposure and/or clinical presentation supported by laboratory findings consistent with toxic alcohol poisoning (metabolic acidosis with increased anion gap (\u0026gt;\u0026thinsp;12 mEq/L) and/or supportive arterial blood gas abnormalities). Patients with missing key clinical or laboratory data required for outcome assessment were excluded.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics, vital signs at presentation, clinical symptoms, neurological status assessed using the Glasgow Coma Scale (GCS), laboratory findings, arterial blood gas parameters, imaging findings, and treatment characteristics were extracted from electronic medical records. Laboratory variables included complete blood count, inflammatory markers, renal and hepatic function tests, pancreatic enzymes, cardiac biomarkers, electrolytes, and arterial blood gas parameters.\u003c/p\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of the study was in-hospital mortality. Patients were categorized into survivor and non-survivor groups according to hospital discharge status.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were assessed for normality using visual inspection and distribution characteristics and were expressed as median with interquartile range (IQR). Categorical variables were presented as numbers and percentages. Comparisons between survivors and non-survivors were performed using the Mann\u0026ndash;Whitney U test for continuous variables and the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test, as appropriate, for categorical variables.\u003c/p\u003e \u003cp\u003eROC curve analyses were conducted to evaluate the discriminative ability of selected clinical and laboratory parameters for predicting in-hospital mortality. The area under the curve (AUC) was calculated for each variable, and optimal cut-off values were determined using the Youden index.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analyses were initially performed to identify variables associated with in-hospital mortality. Variables with clinical relevance and/or statistical significance in univariate analyses were subsequently included in multivariable logistic regression models. Because arterial pH is a narrowly distributed continuous variable, unscaled odds ratios may be misleading; therefore, arterial pH was scaled and interpreted per 0.1-unit decrease in multivariable logistic regression models. Two separate multivariable models were constructed: one including arterial blood gas parameters and another excluding arterial blood gas parameters to evaluate alternative predictors applicable in settings where arterial blood gas analysis may not be readily available. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics (IBM Corp., Armonk, NY, USA). A two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the principles of the Declaration of Helsinki. Approval for the study was obtained from the local institutional ethics committee (approval number: 95, April 18, 2025). Due to the retrospective nature of the study, informed consent was waived.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 55 patients with methanol poisoning were included in the study, predominantly male (92.7%), with a mean age of 46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3 years (range, 18\u0026ndash;74). In-hospital mortality occurred in 25 patients (45.5%). Baseline characteristics according to in-hospital mortality are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; characteristics according to in-hospital mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvivors (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDemographic \u0026amp; Vital Signs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.0 (38.5\u0026ndash;55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0 (39.0\u0026ndash;55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.5 (123.8\u0026ndash;145.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.0 (100.0\u0026ndash;134.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (64.0\u0026ndash;84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.0 (54.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.0 (89.3\u0026ndash;101.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.0 (78.0\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO₂ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.0 (97.3\u0026ndash;99.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.0 (98.0\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (3.0\u0026ndash;114.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0 (4.0\u0026ndash;120.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurological Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlasgow Coma Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 (15.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltered mental status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeizure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual disturbance, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePresenting Symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHematology \u0026amp; Inflammation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.96 (9.18\u0026ndash;14.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.82 (11.49\u0026ndash;18.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.09 (5.77\u0026ndash;10.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.23 (8.78\u0026ndash;13.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29 (1.15\u0026ndash;3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86 (1.97\u0026ndash;3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmature granulocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 (0.04\u0026ndash;0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIG percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.23\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.40\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemistry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.0 (96.0\u0026ndash;132.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.0 (118.0\u0026ndash;176.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0 (23.0\u0026ndash;39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0 (33.0\u0026ndash;63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.80\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60 (1.10\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0 (22.0\u0026ndash;41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0 (32.0\u0026ndash;88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.0 (20.0\u0026ndash;39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0 (25.0\u0026ndash;71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.0 (23.0\u0026ndash;67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0 (36.0\u0026ndash;111.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmylase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.0 (44.0\u0026ndash;77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.0 (55.0\u0026ndash;147.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0 (22.0\u0026ndash;52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0 (34.0\u0026ndash;133.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 (3.8\u0026ndash;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6 (4.2\u0026ndash;5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.0 (82.0\u0026ndash;112.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.0 (31.0\u0026ndash;74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0 (9.0\u0026ndash;88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArterial Blood Gas Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.31 (7.12\u0026ndash;7.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.68 (6.66\u0026ndash;6.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCO₂ (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0 (21.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0 (41.0\u0026ndash;72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO₃⁻ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.75 (7.88\u0026ndash;25.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.70 (4.90\u0026ndash;8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase excess (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.45 (\u0026minus;\u0026thinsp;19.73 to 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;29.50 (-33.00 to -26.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79 (1.25\u0026ndash;3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.75 (7.02\u0026ndash;11.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.75 (11.38\u0026ndash;23.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.20 (25.10\u0026ndash;32.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemodialysis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthanol therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntubation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are presented as median (interquartile range) and categorical variables as number (percentage). Mann\u0026ndash;Whitney U test was used for continuous variables and χ\u0026sup2; or Fisher\u0026rsquo;s exact test for categorical variables. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was no significant difference in age between survivors and non-survivors (median 46.0 [IQR 38.5\u0026ndash;55.0] vs. 45.0 [IQR 39.0\u0026ndash;55.0] years; p\u0026thinsp;=\u0026thinsp;0.899). Systolic BP at presentation was slightly lower in non-survivors (125.0 vs. 126.5 mmHg; p\u0026thinsp;=\u0026thinsp;0.045), while other vital signs, including diastolic BP, heart rate, and oxygen saturation, were comparable between groups. Neurological status differed markedly between groups. Non-survivors had significantly lower GCS scores compared with survivors (median 8.0 [IQR 5.0\u0026ndash;13.0] vs. 15.0 [IQR 15.0\u0026ndash;15.0]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Altered mental status, seizures, and visual disturbances were significantly more frequent among non-survivors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eRegarding laboratory findings, non-survivors demonstrated significantly higher inflammatory and hematological parameters, including WBC and neutrophil counts, as well as higher immature granulocyte counts and percentages (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Markers of renal dysfunction (urea, creatinine, and decreased eGFR), hepatic injury (AST, ALT, GGT), pancreatic enzymes (amylase and lipase), serum potassium, glucose, and troponin levels were also significantly higher in non-survivors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eArterial blood gas parameters showed the most pronounced differences between groups. Non-survivors presented with significantly lower arterial pH, bicarbonate, and base excess values, along with markedly higher lactate levels and anion gap compared with survivors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, arterial PCO₂ levels were significantly higher in non-survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWith respect to treatment characteristics, the rate of hemodialysis was similar between groups (p\u0026thinsp;=\u0026thinsp;1.000). However, sodium bicarbonate therapy and endotracheal intubation were significantly more common among non-survivors (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ethanol therapy rates did not differ significantly between groups.\u003c/p\u003e \u003cp\u003eROC analyses were performed to evaluate the discriminative ability of clinical and laboratory parameters for predicting in-hospital mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Arterial pH demonstrated excellent prognostic performance with an area under the curve (AUC) of 0.969, identifying an optimal cut-off value of \u0026le;\u0026thinsp;6.89, which yielded 92.0% sensitivity and 96.7% specificity. Base excess also showed excellent discrimination (AUC 0.954, cut-off \u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;25.4), followed by serum lactate (AUC 0.917, cut-off \u0026ge;\u0026thinsp;5.26 mmol/L). Anion gap (AUC 0.858), GCS scores (AUC 0.827), and bicarbonate levels (AUC 0.803) demonstrated good discriminatory ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC analysis for prediction of in-hospital mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal cut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase excess (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le; \u0026minus;25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO₃⁻ (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOptimal cut-off values were determined using the Youden index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn univariate logistic regression analyses, lower arterial pH, lower base excess, higher lactate levels, higher anion gap, and lower GCS scores were all significantly associated with increased in-hospital mortality (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas age was not significantly associated with mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the multivariable logistic regression model including arterial pH and lactate, arterial pH remained an independent predictor of in-hospital mortality, while lactate lost statistical significance. Specifically, each 0.1-unit decrease in arterial pH was associated with a 2.78-fold increase in the odds of death (OR 2.78, 95% CI 1.34\u0026ndash;5.76; p\u0026thinsp;=\u0026thinsp;0.006). In an alternative multivariable model excluding arterial blood gas parameters, both higher lactate levels and lower GCS scores remained independently associated with mortality. Each 1 mmol/L increase in lactate was associated with a 1.60-fold increase in mortality risk (OR 1.60, 95% CI 1.23\u0026ndash;2.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and each 1-point decrease in GCS was associated with a 1.38-fold increase in mortality risk (OR 1.38, 95% CI 1.07\u0026ndash;1.78; p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Models for In-Hospital Mortality in Methanol Poisoning.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUnivariate Logistic Regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase excess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.66\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77 (1.35\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 (1.11\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.52\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultivariable Logistic Regression \u0026ndash; Model A (ABG-based)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial pH (per 0.1-unit decrease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.78 (1.34\u0026ndash;5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (per 1 mmol/L increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.82\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultivariable\u003c/b\u003e \u003cb\u003eLogistic Regression \u0026ndash; Model B (Non-ABG model)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (per 1 mmol/L increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.23\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS score (per 1-point decrease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.07\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eUnivariate OR for arterial pH is not presented due to scale instability; pH was therefore modeled per 0.1-unit decrease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study conducted during a methanol poisoning outbreak in Istanbul, we identified several clinical and laboratory parameters associated with in-hospital mortality. The principal finding of this study is that the severity of metabolic acidosis, particularly arterial pH, was the strongest predictor of mortality. Arterial pH demonstrated excellent discriminative ability on ROC analysis and remained an independent predictor of death in multivariable logistic regression, even after adjustment for serum lactate.\u003c/p\u003e \u003cp\u003eThe pathophysiology of methanol poisoning is primarily driven by the accumulation of formic acid, which leads to profound metabolic acidosis, mitochondrial dysfunction, and cellular hypoxia. Therefore, arterial pH reflects the cumulative burden of toxicity and delayed metabolism rather than a single laboratory abnormality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our findings support previous observations that severe acidosis is a key determinant of poor outcomes in methanol poisoning and further emphasize arterial pH as a robust and clinically meaningful prognostic marker.\u003c/p\u003e \u003cp\u003eSerum lactate concentrations are used to evaluate the severity of physiologic stress in multiple disease states, having been validated in the setting of trauma and sepsis specifically (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). It has been hypothesized that a serum lactate concentration would be elevated in the setting of acute ethanol intoxication and thus would not be a clinically useful test to obtain. However, this relationship has not been quantified (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In our study, serum lactate level showed strong prognostic performance in ROC analysis and was significantly associated with mortality in univariate and multivariable models excluding arterial blood gas parameters. However, when arterial pH was included in the multivariable model, lactate lost its independent association with mortality, suggesting that arterial pH may integrate both toxic metabolic burden and secondary tissue hypoxia more comprehensively than lactate alone.\u003c/p\u003e \u003cp\u003eIn addition to acid\u0026ndash;base disturbances, neurological impairment emerged as an important predictor of mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Consistent with the literature in our study lower GCS scores were independently associated with death in the non\u0026ndash;arterial blood gas model, highlighting the prognostic value of neurological status, particularly in settings where arterial blood gas analysis may not be immediately available. Visual disturbances, seizures, and altered mental status were also more frequent among non-survivors, all patients died who had seizure at ED.\u003c/p\u003e \u003cp\u003eMarkers of multiorgan dysfunction, including renal impairment, hepatic injury, elevated pancreatic enzymes, hyperkalemia, hypernatremia and increased cardiac biomarkers, were significantly more pronounced among non-survivors (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings suggest that fatal methanol poisoning is not solely a metabolic disorder but rather a systemic toxic state involving multiple organ systems. The high prevalence of sodium bicarbonate administration and endotracheal intubation among non-survivors in the study likely reflects disease severity rather than a causal relationship with poor outcomes.\u003c/p\u003e \u003cp\u003eThe outbreak setting of this study is of particular importance. Unlike sporadic methanol poisonings, outbreak-related cases often present late, in clusters, and with severe metabolic derangements. This context underscores the need for rapid, reliable prognostic tools to support early triage decisions, including urgent hemodialysis and intensive care admission (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Our findings suggest that simple and readily available parameters, such as arterial pH, lactate levels, and GCS, may be used to stratify in-hospital mortality risk effectively during such high-burden periods.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, an arterial pH threshold of \u0026le;\u0026thinsp;6.89 demonstrated both high sensitivity and specificity for predicting in-hospital mortality and may serve as a critical trigger for early aggressive intervention, including prompt consideration of hemodialysis and intensive care admission. In resource-limited or surge conditions, readily available parameters such as serum lactate levels and GCS scores may provide valuable alternative prognostic information when arterial blood gas analysis is delayed or unavailable, facilitating timely risk stratification and clinical decision-making.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these findings. The retrospective, single-center design may limit the generalizability of the results to settings with different patient populations, treatment protocols, or resource availability. The sample size, while reflective of the outbreak nature of the cohort, constrained the number of variables that could be included in multivariable models and may have limited statistical power to detect associations of smaller magnitude.\u003c/p\u003e \u003cp\u003eSerum methanol and formate concentrations were not consistently available across patients and therefore could not be analyzed as prognostic markers, despite their potential relevance to quantifying toxic burden. Additionally, fomepizole was not available at our institution during the study period, necessitating the use of ethanol as an antidote. Given the more predictable pharmacokinetics and favorable safety profile of fomepizole, this limitation may have influenced treatment-related outcomes and complicates direct comparison with centers where fomepizole is routinely used.\u003c/p\u003e \u003cp\u003eTreatment decisions, including initiation and duration of hemodialysis, were guided by clinical severity rather than a standardized protocol, introducing the possibility of confounding by indication. This pragmatic approach reflects real-world practice but limits causal inference.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, arterial pH is the most powerful predictor of in-hospital mortality in patients with methanol poisoning during outbreak settings. Serum lactate and neurological status provide additional prognostic value, particularly when arterial blood gas analysis is unavailable. Early recognition of severe metabolic acidosis should prompt timely escalation of care and consideration of extracorporeal treatment during outbreak conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eODi conceived and designed the study, coordinated data collection, performed the primary statistical analyses, interpreted the results, and drafted the initial manuscript.ODk contributed to study design, data acquisition, and clinical data interpretation, and critically revised the manuscript for important intellectual content.EK contributed to methodological planning, statistical interpretation, and critical revision of the manuscript with a focus on clinical relevance and emergency medicine practice.SS contributed to data collection, data verification, and preliminary analyses, and assisted in manuscript preparation.MOE contributed to data collection and clinical interpretation and reviewed the manuscript for accuracy and consistency.All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYousefinejad V, Moradi B, Mohammadi Baneh A, Sheikhesmaeili F, Babahajian A. Prognostic Factors of Outcome in Methanol Poisoning; an 8-year Retrospective Cross-sectional Study. Arch Acad Emerg Med. 2020;8(1):e69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlrashed M, Aldeghaither NS, Almutairi SY, et al. The Perils of Methanol Exposure: Insights into Toxicity and Clinical Management. Toxics. 2024;12(12):924. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/toxics12120924\u003c/span\u003e\u003cspan address=\"10.3390/toxics12120924\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatur A, Yilmaz S, Celik MM, et al. A potential prognostic indicator in methanol intoxication: Body temperature. Acta Med. 2024;55(2):128\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.32552/2024.ActaMedica.1012\u003c/span\u003e\u003cspan address=\"10.32552/2024.ActaMedica.1012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMardan H, Hosseini SM, Yousefinejad V, et al. Predictors of Mortality in Methanol Poisoning: A Systematic Review and Meta-analysis. Int J Med Toxicol Forensic Med. 2024;14(1):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakharov S, Pelclova D, Navratil T, et al. Fomepizole versus ethanol in the treatment of acute methanol poisoning: comparison of clinical effectiveness in a mass poisoning outbreak. Clin Toxicol (Phila). 2015;53(8):797\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/15563650.2015.1059946\u003c/span\u003e\u003cspan address=\"10.3109/15563650.2015.1059946\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraut JA, Mullins ME. Toxic alcohols. N Engl J Med. 2018;378(3):270\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMra1615295\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra1615295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts DM, Yates C, Megarbane B, et al. Recommendations for the role of extracorporeal treatments in the management of acute methanol poisoning: a systematic review and consensus statement. Crit Care Med. 2015;43(2):461\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CCM.0000708\u003c/span\u003e\u003cspan address=\"10.1097/CCM.0000708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMd Noor J, Hawari R, Mokhtar MF, Yussof SJ, Chew N, Norzan NA, et al. Methanol outbreak: a Malaysian tertiary hospital experience. Int J Emerg Med. 2020;13(1):6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12245-020-0264-5\u003c/span\u003e\u003cspan address=\"10.1186/s12245-020-0264-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassanian-Moghaddam H, Zamani N, Roberts DM, Brent J, McMartin K, Aaron C, et al. Consensus statements on the approach to patients in a methanol poisoning outbreak. Clin Toxicol (Phila). 2019;57(12):1129\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15563650.2019.1636992\u003c/span\u003e\u003cspan address=\"10.1080/15563650.2019.1636992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarceloux DG, Bond GR, Krenzelok EP, Cooper H, Vale JA. American Academy of Clinical Toxicology practice guidelines on the treatment of methanol poisoning. J Toxicol Clin Toxicol. 2002;40(4):415\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1081/clt-120006745\u003c/span\u003e\u003cspan address=\"10.1081/clt-120006745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrent J, McMartin K, Phillips S, et al. Fomepizole for the treatment of methanol poisoning. N Engl J Med. 2001;344(6):424\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJM200102083440605\u003c/span\u003e\u003cspan address=\"10.1056/NEJM200102083440605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeatty L, Green R, Magee K, Zed P. A systematic review of ethanol and fomepizole use in toxic alcohol ingestions. Emerg Med Int. 2013; 2013:638057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2013/638057\u003c/span\u003e\u003cspan address=\"10.1155/2013/638057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRietjens SJ, de Lange DW, Meulenbelt J. Ethylene glycol or methanol intoxication: which antidote should be used, fomepizole or ethanol? Neth J Med. 2014;72(2):73\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRan M, Li Y, Zhang L, Wu W, Lin J, Liu Q, et al. Clinical features, treatment, and prognosis of acute methanol poisoning: experiences in an outbreak. Int J Clin Exp Med. 2019;12(5):5938\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMochizuki K, Fujii T, Paul E, Anstey M, Pilcher DV, Bellomo R. Early metabolic acidosis in critically ill patients: a binational multicentre study. Crit Care Resusc. 2023;23(1):67\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.51893/2021.1.OA6\u003c/span\u003e\u003cspan address=\"10.51893/2021.1.OA6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2023 Oct 18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTangri N, Reaven NL, Funk SE, Ferguson TW, Collister D, Mathur V. Metabolic acidosis is associated with increased risk of adverse kidney outcomes and mortality in patients with non-dialysis dependent chronic kidney disease: an observational cohort study. BMC Nephrol. 2021;22(1):185. Published 2021 May 19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12882-021-02385-z\u003c/span\u003e\u003cspan address=\"10.1186/s12882-021-02385-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGustafson ML, Hollosi S, Chumbe JT. The effect of ethanol on lactate and base deficit as predictors of morbidity and mortality in trauma. Am J Emerg Med. 2015;33:607\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWardi G, Brice J, Correia M. Demystifying lactate in the emergency department. Ann Emerg Med. 2020;75:287\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonoo T, Iwai S, Inokuchi R. Quantitative analysis of high plasma lactate concentration in ED patients after alcohol intake. Am J Emerg Med. 2016;34:825\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacDonald L, Kruse JA, Levy DB. Lactic acidosis and acute ethanol intoxication. Am J Emerg Med. 1994;12:32\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuusela E, J\u0026auml;rvisalo MJ, Hellman T, Uusalo P. Mortality and associated risk factors in patients with severe methanol or ethylene glycol poisoning treated with dialysis: a retrospective cohort study. J Int Med Res. 2022;50(2):3000605221081427. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/03000605221081427\u003c/span\u003e\u003cspan address=\"10.1177/03000605221081427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Hiremath S, Sikora L, et al. Kidney outcomes after methanol and ethylene glycol poisoning: a systematic review and meta-analysis. Clin Toxicol (Phila). 2023;61(5):326\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15563650.2023.2200547\u003c/span\u003e\u003cspan address=\"10.1080/15563650.2023.2200547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGheshlaghi F, Rezaei MR, Eizadi-Mood N, Fattahi F, Nazarianpirdosti M, Oskui AG. Predictors of Mortality in Methanol Poisoning: A Systematic Review and Meta-analysis. Int J Med Toxicol Forensic Med. 2024;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32598/ijmtfm.v14i1.43414\u003c/span\u003e\u003cspan address=\"10.32598/ijmtfm.v14i1.43414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNekoukar Z, Zakariaei Z, Taghizadeh F, Musavi F, Banimostafavi ES, Sharifpour A, Ebrahim Ghuchi N, Fakhar M, Tabaripour R, Safanavaei S. Methanol poisoning as a new world challenge: A review. Ann Med Surg (Lond). 2021;66:102445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amsu.2021.102445\u003c/span\u003e\u003cspan address=\"10.1016/j.amsu.2021.102445\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaewput W, Thongprayoon C, Petnak T, Chewcharat A, Boonpheng B, Bathini T, Vallabhajosyula S, Cheungpasitporn W. Inpatient Burden and Mortality of Methanol Intoxication in the United States. Am J Med Sci. 2021;361(1):69\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjms.2020.08.014\u003c/span\u003e\u003cspan address=\"10.1016/j.amjms.2020.08.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGulen M, Satar S, Avci A, Acehan S, Orhan U, Nazik H. Methanol poisoning in Turkey: two outbreaks, a single center experience. Alcohol. 2020;88:83\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.alcohol.2020.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.alcohol.2020.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaasma R, Hovda KE, Tikkerberi A, Jacobsen D. Methanol mass poisoning in Estonia: outbreak in 154 patients. Clin Toxicol (Phila). 2007;45(2):152\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15563650600956329\u003c/span\u003e\u003cspan address=\"10.1080/15563650600956329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Methanol poisoning, In-hospital mortality, Metabolic acidosis, Arterial pH, Outbreak, Emergency department","lastPublishedDoi":"10.21203/rs.3.rs-8703189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8703189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMethanol poisoning remains a major public health problem, particularly during outbreaks related to illicit alcohol consumption, and is associated with high mortality. Early identification of patients at high risk of death is critical to guide timely triage and aggressive management in the emergency department (ED).\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo identify clinical and laboratory predictors of in-hospital mortality among patients with methanol poisoning during an outbreak and to evaluate the prognostic performance of key parameters using receiver operating characteristic (ROC) curve analysis and logistic regression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective observational cohort study was conducted in the ED of a tertiary-care hospital in Istanbul, T\u0026uuml;rkiye, during a methanol poisoning outbreak between December 1, 2024, and January 31, 2025. Adult patients (\u0026ge;\u0026thinsp;18 years) diagnosed with methanol poisoning were included. Demographic data, clinical findings, laboratory results, arterial blood gas parameters, and treatments were collected. The primary outcome was in-hospital mortality. ROC curve analyses and univariate and multivariable logistic regression models were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 55 patients were included (92.7% male; mean age 46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3 years). In-hospital mortality occurred in 25 patients (45.5%). Non-survivors had significantly lower arterial pH, bicarbonate, and base excess values and higher lactate levels and anion gap compared with survivors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Arterial pH demonstrated excellent prognostic performance (AUC 0.969), with an optimal cut-off value of \u0026le;\u0026thinsp;6.89 (92.0% sensitivity, 96.7% specificity). In multivariable analysis, arterial pH remained an independent predictor of mortality, with each 0.1-unit decrease associated with a 2.78-fold increase in the odds of death. In a model excluding arterial blood gas parameters, higher lactate levels and lower Glasgow Coma Scale scores were independently associated with mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDuring methanol poisoning outbreaks, arterial pH is the strongest predictor of in-hospital mortality. Serum lactate and neurological status provide additional prognostic value when arterial blood gas analysis is unavailable.\u003c/p\u003e","manuscriptTitle":"Predictors of In-Hospital Mortality: After a Methanol Poisoning Outbreak in Istanbul","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 00:40:02","doi":"10.21203/rs.3.rs-8703189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-03T04:10:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T14:33:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T10:14:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123095367875634447731715526700306276772","date":"2026-03-26T23:43:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T09:25:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3935421912253448048044513006517691011","date":"2026-03-24T08:03:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197971450133871811203983668423377985191","date":"2026-03-23T09:56:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38501439182101547441178132804619319991","date":"2026-03-23T05:54:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T23:04:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312684133253558261544571882992361453685","date":"2026-02-19T21:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54202559705811339652899388543074256969","date":"2026-02-08T17:51:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11917448787332591601150149048572557312","date":"2026-02-07T14:28:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T11:25:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-30T09:45:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-28T07:28:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-28T07:23:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2026-01-26T18:27:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17f211a4-f619-4bd3-9aee-983f8d77c4d6","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T06:09:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 00:40:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8703189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8703189","identity":"rs-8703189","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}