Pre-Earthquake Kidney Function is a Predictor of Outcomes in Earthquake-Related Crush Syndrome

Pre-Earthquake Kidney Function is a Predictor of Outcomes in Earthquake-Related Crush Syndrome | 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 Pre-Earthquake Kidney Function is a Predictor of Outcomes in Earthquake-Related Crush Syndrome Ramazan Danıs, Savas Ozturk, Ismail Kocyigit, Jehat Kılıc, Merve Civan, and 41 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5989283/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jun, 2025 Read the published version in BMC Nephrology → Version 1 posted 15 You are reading this latest preprint version Abstract Background The devastating earthquakes in Kahramanmaraş, Türkiye, in February 2024, caused extensive trauma and loss of lives, causing unique challenges in the management of earthquake-related crush syndrome. The current study investigates the prognostic value of pre-earthquake kidney function for mortality prediction in patients diagnosed with crush syndrome. Methods A multi-center retrospective analysis was performed using data from 469 patients treated at 46 nephrology clinics. Pre-earthquake Kidney function, defined by serum creatinine and estimated glomerular filtration rate (eGFR) levels, was obtained from pre-earthquake health records. Clinical findings, laboratory parameters, complications, and survival probabilities were analyzed. Multivariate Cox regression was used to identify independent predictors of in-hospital mortality. Results The mean age of participants was 42.56 ± 16.92 years (Non-survivors: 50.46 ± 20.03 years, Survivors: 42.34 ± 16.80 years (p = 0.172)). The in-hospital mortality rate was 2.8%. Non-survivors exhibited significantly higher pre-earthquake creatinine levels than survivors (1.04 ± 0.61 mg/dL vs. 0.77 ± 0.33 mg/dL, p = 0.03), with lower eGFR (85.2 ± 34.7 mL/min/1.73 m² vs. 115.8 ± 39.4 mL/min/1.73 m², p = 0.008). Compared with survivors, non-survivors had higher incidences of AKI (92.3% vs. 61.6%, p = 0.037) and more severe metabolic disturbances, including hyperkalemia (5.41 ± 1.72 mmol/L vs. 5.13 ± 0.98 mmol/L, p = 0.008). Regression analysis revealed that pre-earthquake creatinine (HR: 9.121, 95% CI: 2.686–30.970, p < 0.001) and potassium levels at admission (HR: 3.338, 95% CI: 1.540–7.232, p = 0.002) were independent predictors of mortality. Conclusions Pre-earthquake kidney function significantly predicts mortality in crush syndrome patients, highlighting the importance of baseline kidney assessment in disaster preparedness. Crush syndrome kidney function mortality disaster nephrology acute kidney injury hyperkalemia Türkiye Figures Figure 1 BACKGROUND The Kahramanmaraş Province, Türkiye, suffered from two great earthquakes on February 6, 2023, which magnitudes reached 7.6 and 7.7 on the Richter scale. This tragedy took the lives of 53,000 and injured 120,000 people among 11 provinces with a population of approximately 14 million citizens ( 1 ). Because the size of the disaster was so large, the healthcare infrastructures were overwhelmed, and therefore the number of emergency medical patients began to build up. Crush syndrome is a severe medical condition that occurs when muscle tissue is intensely compressed or damaged, often as a result of traumatic events such as earthquakes, storms, wars, or traffic accidents( 2 ). The severity of crush syndrome among affected individuals is influenced by several factors, including the duration of compression under rubble, age, body mass index (BMI), comorbidities, the timeliness of treatment initiation, and the speed of hospital transfer ( 3 ). A very important factor in the prognosis of patients after these disasters may be their pre-earthquake kidney function status. The status of kidney health, estimated by measures such as serum creatinine and glomerular filtration rate (GFR), is important in determining the susceptibility and severity of various complications following different nephrotoxic situations ( 4 – 5 ). Poor kidney function, as evidenced by high baseline creatinine levels, may predispose them to more serious complications such as acute kidney injury (AKI), especially when combined with other aforementioned stressors related to crush injury ( 6 ). In the case of a decreased GFR the excretion of substances produced during muscle tissue injury may be impaired. Hence, understanding these pre-existing kidney conditions is crucial for managing the patient and the survival rate in disaster situations. Individuals with impaired kidney function may require specific medical interventions considering the patient's baseline kidney status, ensuring timely and appropriate treatment to avoid complications. This study explores how previous creatinine levels and GFR impact mortality rates among patients diagnosed with crush syndrome after the Kahramanmaraş earthquake at our centers. MATERIAL AND METHODS Data collection The methods of this study, data collection, clinical definitions and statistical analyses were explained in detail in our previous publications ( 1 ). Briefly, the data were obtained from 46 nephrology clinics using web-based forms prepared by Turkish Society of Nephrology Renal Disaster Working Group. These clinics were either situated in earthquake regions or received transferred patients from those affected areas. The collected information included demographic features, clinical findings, laboratory results, comorbid diseases, complications, need for dialysis, intensive care unit (ICU) admissions, and survival status. Serum creatinine values and CKD-EPI formula in pre-earthquake periods were obtained from hospital records or e-Nabız system, which is Türkiye's national online health information platform ( 7 ) were used in the calculation of estimated glomerular filtration rate (eGFR) using the latter serum creatinine levels retrospectively. The detailed definitions for clinical parameters, including the severity of AKI according to KDIGO criteria, oliguria, dialysis, and complications related to sepsis, acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), fasciotomy-related infections, compartment syndrome, and the criteria for survival outcomes, were the same as in our previous studies ( 1 ). Data regarding compartment syndrome, fasciotomy, and trauma to the head, abdomen, or thorax were collected. Complications such as sepsis, ARDS, DIC, and fasciotomy site infection were included retrospectively. Definition of Crush Syndrome Crush syndrome was diagnosed based on serum creatine kinase (CK) levels exceeding 1000 U/L at admission, in conjunction with oliguria (urinary output 40 mg/dL), serum creatinine (SCr) (> 2.0 mg/dL), uric acid (> 8 mg/dL), potassium (K) (> 6 mEq/L), and phosphorus (P) (> 8 mg/dL), alongside decreased serum calcium (Ca) (< 8 mg/dL). This biochemical profile reflects the systemic impact of severe muscle injury, emphasizing the need for early recognition and intervention to prevent complications such as acute kidney injury (AKI) and life-threatening electrolyte imbalances ( 1 ). Outcome Parameters : To ensure consistency, outcomes were defined as follows: AKI: Based on KDIGO criteria (increase in serum creatinine by ≥ 0.3 mg/dL within 48 hours or ≥ 1.5 times baseline within 7 days, or urine output < 0.5 mL/kg/hour for 6 hours). Severity was classified into Stage 1, Stage 2 and Stage 3. Oliguria: Urine output < 400 mL/day. Compartment Syndrome: Increased compartment pressure compromising circulation. Fasciotomy-related infection: Soft tissue infection at the fasciotomy site confirmed by clinical and microbiological findings. At discharge, survival outcomes were defined as: in-hospital death; partial recovery, indicating incomplete recovery of crush syndrome; and complete recovery, indicating full recovery of crush syndrome with no further intervention required. Statistical Analysis Continuous variables are presented as mean ± standard deviation (SD) or median (interquartile range), whichever distribution the data follows. Comparisons between survivors and non-survivors involved a Student's t-test, Mann-Whitney U test for continuous variables, and chi-square test/Fisher's exact test for categorical variables. Multivariate Cox regression analysis was performed to identify independent predictors of in-hospital mortality. The analysis included variables such as age, gender, trauma (head, abdomen or thorax), AKI, ICU admission, compartment syndrome, and laboratory parameters (previous creatinine, admission creatinine, potassium, albumin, hemoglobin, leukocytes, and thrombocytes). Three models were developed to assess the contribution of these parameters to mortality risk, with hazard ratios (HR), 95% confidence intervals (CI), and p-values reported for each variable in Table 3 . All the statistical analyses were conducted with the use of IBM SPSS Statistics (Version 26.0, Armonk, NY). A p-value of less than 0.05 is considered to denote statistical significance. RESULTS The study included 469 patients from a total of 1024 patients diagnosed with crush syndrome who had serum creatinine measured within the last year before the earthquake. Demographic and Clinical Characteristics The mean age of participants was 42.56 ± 16.92 years, with non-survivors having a higher mean age (50.46 ± 20.03 years) than survivors (42.34 ± 16.80 years); however, this difference did not reach statistical significance (p = 0.172) (Table 1 ). The gender distribution was similar between groups, with 48.0% males and 52.0% females in the overall population. Among non-survivors, 38.5% were male, and 61.5% were female, compared to 48.2% males and 51.8% females among survivors (p = 0.678). Comorbid conditions in the study population included chronic kidney disease (3.6%), diabetes mellitus (10.2%), hypertension (15.8%), and coronary artery disease (7.5%). Although CKD and CAD were more prevalent in non-survivors (10.0% and 20.0%, respectively) compared to survivors (3.6% and 7.4%), the differences were not statistically significant (p = 0.320 for CKD, p = 0.175 for CAD). Similarly, hypertension was more frequent in non-survivors (30.0%) than in survivors (15.9%) without statistical significance (p = 0.210). None of the non-survivors had diabetes, while 10.7% of survivors were diabetic (p = 0.607). The use of key medications included ACE inhibitors/ARB (9.4% overall), with a higher, borderline significant usage in non-survivors (30.0%) compared to survivors (9.4%, p = 0.065). Insulin and oral anti-diabetic medications were exclusively used by survivors (2.3% and 8.1%, respectively, p = 1.000 for both). Anti-aggregant medications were administered to 9.0% of participants, with a notably higher prevalence in non-survivors (27.3%) compared to survivors (8.8%, p = 0.072). Table 1 Demographic characteristics, comorbidities, medication usage, and renal functions Variable Total (n = 469) Non-survivor (n = 13) Survivor (n = 456) P-value Demographic Characteristics Age (years) 42.56 ± 16.92 50.46 ± 20.03 42.34 ± 16.80 0.172 Gender 0.678 - Male 225 (48.0%) 5 (38.5%) 220 (48.2%) - Female 244 (52.0%) 8 (61.5%) 236 (51.8%) Comorbidities - Chronic Kidney Disease (CKD) 17 (3.6%) 1 (10.0%) 16 (3.6%) 0.320 - Diabetes Mellitus (DM)** 48 (10.2%) 0 (0.0%) 48 (10.7%) 0.607 - Hypertension (HT)** 74 (15.8%) 3 (30.0%) 71 (15.9%) 0.210 - Coronary Artery Disease (CAD)** 35 (7.5%) 2 (20.0%) 33 (7.4%) 0.175 Medication Usage - Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers (ACE/ARB) 44 (9.4%) 3 (30.0%) 41 (9.4%) 0.065 - Insulin 11 (2.3%) 0 (0.0%) 11 (2.5%) 1.000 - Oral Anti-Diabetic Medications (OAD) 38 (8.1%) 0 (0.0%) 38 (8.6%) 1.000 - Anti-aggregant Medications 42 (9.0%) 3 (27.3%) 39 (8.8%) 0.072 Clinical Findings Creatinine (mg/dL) (Previous) 0.77 ± 0.35 1.04 ± 0.61 0.77 ± 0.33 0.030 Glomerular Filtration Rate (GFR) (Previous) (mL/min/1.73 m²) 115.3 ± 39.5 85.2 ± 34.7 115.8 ± 39.4 0.008 Creatinine (mg/dL) (Admission) 2.54 ± 2.20 3.54 ± 1.50 2.51 ± 2.21 0.10 GFR (Admission) (mL/min/1.73 m²) 63.11 ± 56.40 21.60 ± 17.10 63.50 ± 56.50 0.005 Creatinine (mg/dL) (Discharge) 1.05 ± 0.99 2.53 ± 1.10 0.93 ± 0.91 < 0.001 GFR (Discharge) (mL/min/1.73 m²) 123.0 ± 64.02 37.41 ± 36.58 125.3 ± 63.04 < 0.001 Abbreviations : CKD: Chronic Kidney Disease, DM: Diabetes Mellitus, HT: Hypertension, CAD: Coronary Artery Disease, OAD: Oral Antidiabetic, AKI: Acute Kidney Injury, Pre-Existing Kidney Functions Non-survivors had higher mean creatinine levels prior to admission (1.04 ± 0.61mg/dl vs. 0.77 ± 0.33mg/dl, p = 0.030) and markedly elevated admission levels (2.53 ± 1.10 vs. 0.93 ± 0.91, p < 0.001), though admission levels were not significantly different (p = 0.10) (Table 1 ). Similarly, eGFR was significantly lower in non-survivors both prior to admission (85.2 ± 34.7ml/min/1.73m2 vs. 115.8 ± 39.4ml/min/1.73m2, p = 0.008) and at admission (21.60ml/min/1.73m2 ± 17.10 vs. 63.50 ± 56.50ml/min/1.73m2, p = 0.005), with the admission GFR also showing a marked reduction in non-survivors (37.41 ± 36.58 vs. 125.3 ± 63.04, p < 0.001). In hospital Mortality Rate and Other Outcomes : In total, 13 (2.8%) patients died in the study. A significantly higher proportion of non-survivors n:12 (92.3%) had AKI compared to survivors (n: 281 61.6%, p = 0.037) (Table 2 ). According to the KDIGO criteria, most AKI cases were categorized as stage 3 (64.8%), with no significant difference in severity distribution between groups (p = 0.263). Oliguria was present in 28.4% of participants, with a higher frequency in non-survivors (88.9%) compared to survivors (60.1%, p = 0.158). Hemodialysis was required in 41.6% of cases, with non-survivors showing a notably higher need (91.7%) than survivors (65.5%, p = 0.067). Table 2 Clinical Findings and Outcomes Variable Total (n = 469) Non-survivor (n = 13) Survivor (n = 456) P AKI 293 (62.5%) 12 (92.3%) 281 (61.6%) 0.037 AKI Stage (KDIGO) Criteria (n = 293) 0.263 - Stage 1 57 (19.5%) 2 (16.7%) 55 (19.6%) - Stage 2 46 (15.7%) 0 (0.0%) 46 (16.4%) - Stage 3 190 (64.8%) 10 (8.3%) 180 (64.1%) Oliguria 133 (28.4%) 8 (88.9%) 125 (60.1%) 0.158 Hemodialysis Need 195 (41.6%) 11 (91.7%) 184 (65.5%) 0.067 ICU admission 247 (52.7%) 10 (76.9%) 237 (52.0%) 0.135 Fasciotomy 115 (24.5%) 4 (30.8%) 111 (24.3%) 0.531 Compartment Syndrome 172 (36.7%) 5 (38.5%) 167 (36.6%) 1.000 Fasciotomy Infection 47 (10.0%) 2 (15.4%) 45 (9.9%) 0.629 Sepsis 56 (11.9%) 5 (38.5%) 51 (11.2%) 0.012 ARDS 13 (2.8%) 5 (38.5%) 8 (1.8%) < 0.05 DIC 10 (2.1%) 2 (15.4%) 8 (1.8%) 0.028 Trauma in extremities 409 (87.2%) 9 (69.2%) 400 (87.7%) 0.071 Head, Abdomen, or Thorax Trauma 180 (38.4%) 3 (23.1%) 177 (38.8%) 0.387 Days of Dialysis (n = 189) 8.49 ± 7.55 10.0 ± 12.3 10.8 ± 7.10 0.078 Length of Stay in ICU (Days) 9.40 ± 9.40 10.2 ± 12.7 10.1 ± 8.47 0.330 Duration of AKI (Days) 16.40 ± 9.22 11.2 ± 12.0 18.5 ± 9.14 0.716 Duration of Oliguria (Days) 7.01 ± 5.90 4.25 ± 3.20 7.70 ± 6.48 0.920 Survival Outcomes - Exitus (Death) 13 (2.8%) 13 (100%) - - - Partial Recovery 179 (38.2%) - 179 (38.2%) - - Complete Recovery 200 (42.6%) - 200 (42.6%) - - Referral to Other Clinics 77 (16.4%) - 77 (16.4%) - Abbreviations : AKI: Acute Kidney Injury, KDIGO: Kidney Disease: Improving Global Outcomes, ICU: Intensive Care Unit, LOS: Length of Stay, ARDS: Acute Respiratory Distress Syndrome, DIC: Disseminated Intravascular Coagulation, Hb: Hemoglobin Intensive care unit (ICU) length of stay was longer for non-survivors (10.2 ± 12.7, 76.9%) compared to survivors (10.1 ± 8.47, 52.0%, p = 0.135) (Table 2 ). Fasciotomy was performed in 24.5% of participants, and compartment syndrome occurred in 36.7%, with no significant differences between groups (p = 0.531 and p = 1.000, respectively). Infection following fasciotomy was reported in 10.0% of participants (p = 0.629). Sepsis occurred more frequently in non-survivors (38.5%) compared to survivors (11.2%, p = 0.012), while ARDS was significantly higher in non-survivors (38.5% vs. 1.8%, p < 0.05). DIC was also more common in non-survivors (15.4%) than survivors (1.8%, p = 0.028). Trauma was prevalent in 87.2% of participants, with a lower frequency in non-survivors (69.2%) compared to survivors (87.7%, p = 0.071). Trauma involving the head, abdomen, or thorax was present in 38.4% of cases (p = 0.387). Regarding hospital stay, the mean duration of ICU stay was 9.40 ± 9.40 days, and dialysis was required for an average of 8.49 ± 7.55 days. AKI lasted an average of 16.40 ± 9.22 days, with no significant differences in these parameters between non-survivors and survivors. Oliguria persisted for an average of 7.01 ± 5.90 days (p = 0.920). Survival outcomes showed that 2.8% of participants died, 38.2% had partial recovery, 42.6% achieved complete recovery, and 16.4% were referred to other clinics. Laboratory Parameters According to Survival Status Non-survivors had significantly higher BUN (55.2 ± 8.84mg/dl vs. 50.3 ± 37.2 mg/dl, p = 0.010), uric acid (9.42 ± 1.67mg/dl vs. 7.23 ± 2.82mg/dl, p < 0.001), potassium (5.41 ± 1.72mmol/L vs. 5.13 ± 0.98 mmol/L, p = 0.008), and phosphorus (8.84 ± 3.73mg/dl vs. 4.85 ± 2.24 mg/dl, p < 0.001) ( Supplemental Table 1 ). Lactate levels were also significantly higher in non-survivors (7.66 ± 5.80mmol/L vs. 2.66 ± 2.58 mmol/L, p = 0.001). Liver enzymes, including AST (2545.3 ± 3733.2 U/L vs. 488.7 ± 504.9 U/L, p = 0.032) and ALT (1319.0 ± 2104.2 U/L vs. 206.6 ± 216.3 U/L, p = 0.039), were markedly elevated in non-survivors. Additionally, pH was significantly lower in non-survivors (7.16 ± 0.19 vs. 7.35 ± 0.09, p < 0.001), as were bicarbonate levels (12.8 ± 4.71 mEq/L vs. 20.2 ± 4.61 mEq/L, p < 0.001), indicating more severe metabolic acidosis. Platelet count and its logarithmic value were significantly lower in non-survivors (184.6 ± 53.1 vs. 238.5 ± 92.8 cells/mm³, p = 0.022). No significant differences were observed in sodium, chloride, calcium, hemoglobin, or leukocyte levels. The average duration of earthquake-associated AKI and oliguria did not differ significantly between groups. Multivariate Analyses : Cox regression analysis was carried out in three models to evaluate the effect of different parameters on survival. Age had a significant association with better survival in Model 1, Model 2 and Model 3 with an HR of 1.034 (95% CI: 1.004–1.065, p = 0.026), 1.037 (95% CI: 1.005–1.070, p = 0.022), and 1.050 (95% CI: 1.008–1.093, p = 0.0185) respectively. There had been no influence of gender on the survival rate according to all these models. Trauma (head, abdomen, or thorax) was insignificantly associated with mortality in Model 1, HR 2.677, 95% CI 0.698–10.261, p = 0.151, and significantly so in Model 2, HR 3.080, 95% CI 0.746–12.712, p = 0.120, and this effect was insignificantly strengthened in Model 3 HR 4.692, 95% CI 0.810–27.168, p = 0.084. AKI presence, ICU admission, and compartment syndrome were not significantly associated with survival in any model. Significant predictors in Model 3 included serum potassium at admission and previous creatinine levels, which were associated with increased mortality (HR: 3.338, 95% CI: 1.540–7.232, p = 0.002, HR: 9.121, 95% CI: 2.686–30.970, p:<0.001 respectively). Other laboratory parameters that did not significantly predict survival included creatinine admission levels, albumin, hemoglobin, leukocyte count, and platelets. Platelets had a borderline association with survival (HR: 0.016, 95% CI: 0.000–1.141, p = 0.058). Table 3 Multivariate Cox regression of parameters related in-hospital mortality Variable Model 1 HR (95% CI) p-Value Model 2 HR (95% CI) p-Value Model 3 HR (95% CI) p-Value Age (years) 1.034 (1.004–1.065) 0.026 1.037 (1.005–1.070) 0.022 1.050 (1.008–1.093) 0.0185 Gender (Female) 1.598 (0.516–4.947) 0.416 1.547 (0.495–4.862) 0.456 1.422 (0.346–5.839) 0.626 Trauma 2.677 (0.698–10.261) 0.151 3.080 (0.746–12.712) 0.120 4.692 (0.810–27.168) 0.084 Acute Kidney Injury (AKI) 4.047 (0.494–33.141) 0.193 0.745 (0.060–9.241) 0.819 ICU Admission 2.241 (0.567–8.848) 0.250 2.252 (0.229–22.114) 0.486 Compartment Syndrome 0.725 (0.214–2.464) 0.607 0.778 (0.161–3.763) 0.755 Creatinine (mg/dL) (Previous) 9.121 (2.686–30.970) 0.002 Creatinine (mg/dL) (Admission) 0.912 (0.636–1.306) 0.614 Potassium (mmol/dL) 3.338 (1.540–7.232) 0.002 Albumin (g/dL) 0.364 (0.111–1.192) 0.095 Hemoglobin (g/dL) 1.157 (0.906–1.477) 0.242 Leukocytes (/mm³) 1.000 (1.000–1.000) 0.973 *Platelet (x10³/mm³) 0.016 (0.000–1.141) 0.058 Abbreviations : AKI: Acute Kidney Injury, ICU: Intensive Care Unit, GFR: Glomerular Filtration Rate The optimal cutoff value for serum creatinine (Previous) in predicting mortality was determined as 0.7950 based on the ROC curve analysis (Figure 1). Sensitivity: 69.2% Specificity: 61.6% DISCUSSION In this study, we mainly aimed to investigate the effect of previous creatinine and GFR on mortality. When we divided the patients into two groups as survivors and non-survivors, creatinine and GFR changes were found to be significant between these two groups (p: 0.030 p: 0.008 respectively). Although this showed that the difference between the two groups was significant, univariate analysis was performed to investigate the effect of this situation on mortality. In univariate analysis, 1-year ago creatinine and GFR values were found to be significant (HR: 2.512, p: 0.05, HR: 0.973 p: 0.002 respectively). In the multivariate regression analysis, GFR was excluded due to multicollinearity. The analysis identified that an increase in creatinine levels one year prior significantly impacted mortality rates (HR:9,121, 95% CI:2.686–30.970, p < 0,001). The major finding of our study is that impaired pre-earthquake kidney function is significantly associated with increased in-hospital mortality among patients with earthquake-related crush syndrome. The baseline serum creatinine levels were significantly higher in non-survivors than in survivors: 1.04 ± 0.61 mg/dL versus 0.77 ± 0.33 mg/dL, respectively (p = 0.030). This aligns with previous literature indicating that poor kidney function at presentation is a robust predictor of adverse outcomes in trauma patients ( 8 ). The pre-admission eGFR was also lower in non-survivors than in survivors: 85.2 ± 34.7 mL/min/1.73 m² versus 115.8 ± 39.4 mL/min/1.73 m², respectively (p = 0.008). These observations are corroborated by the present study for and point toward the universal application of kidney function as a prognostic marker in trauma ( 9 ). Our study aligns with other studies that kidney dysfunction is already an excellent predictor of poor trauma patient outcomes ( 10 ). Various studies indicate that patients who suffer from CKD or with diminished baseline kidney function will then go on to suffer from complications of AKI, sepsis, and multi-organ failure after the incident ( 11 ). Crush syndrome is a serious medical condition that occurs when muscle tissue is damaged, leading to the release of myoglobin and some electrolytes into the bloodstream. This often happens after traumatic incidents such as earthquakes or armed conflicts, where victims may be trapped under debris for extended periods. Additionally, Rhabdomyolysis can develop as a result of prolonged seizures, certain drug overdoses, or specific autoimmune diseases that affect muscle tissues. The prevalence of crush syndrome can vary significantly depending on the circumstances and location. Research has shown that the incidence of Crush Syndrome can range dramatically, from as low as 5% in places like Hanshin-Awaji, Japan, to as high as 37% in Kahramanmaraş, Türkiye. Understanding the factors that contribute to these differences is crucial for improving prevention and treatment strategies for this potentially life-threatening condition ( 12 – 13 ). Kidney damage frequently occurs as a complication after crush syndrome, and it plays a critical role in influencing mortality rates among affected individuals. Research has shown that the prevalence of kidney damage varies widely, with reported rates ranging from 12–41% ( 15 – 16 ). These variations highlight the complex nature of crush syndrome and underscore the importance of prompt medical intervention to mitigate the risk of kidney-related complications and improve survival outcomes. The reasons for the change in this rate may be due to many factors such as the distance of the place where the incident occurred to health centers, the readiness of rescue and health teams at the time of the incident, the duration of being under the rubble, the duration of reaching the health center, early initiation of hydration therapy, and the time the incident occurred. In our study, the AKI rate was 62.4% and this may have been affected by many factors such as the earthquake occurring at night, the earthquake affecting a wide area, and the rescue and health teams being caught unprepared. AKI developing after Crush Syndrome is an important risk factor for mortality, and in our study, a statistically significant difference was found in terms of AKI rates between the 2 groups of survivors and non-survivors (n: 281 (61.6%), n: 12 (92.3%), respectively) in patients treated in the hospital (p: 0.037) ( 14 – 18 ). Sever et al., in their study investigating the effects of complications on mortality, divided the patients into two groups as survivors and non-survivors, and found that infection, sepsis, ARDS, DIC, Mechanic Ventilators (MV), CV catheter complications were statistically significantly higher in the non-survivor group. In our study, sepsis, DIC and ARDS complications were also found to be significant (p: 0.012, p: 0.028, p: <0.05 respectively). In the study conducted by Sever et al., sepsis was found to be significant as a complication, but in our study, since infection was only examined in the fasciotomy area, it was not found to be statistically significant(p:0.629) ( 19 ). Laboratory parameters of metabolic derangement were higher in non-survivors: high BUN, uric acid, potassium, phosphorus, lactate, and liver enzymes. These biochemical changes reflect a more severe systemic response to trauma and underscore the multifaceted effects of kidney impairment on patient outcomes. This can also be explained by crush syndrome, a condition precipitated by the systemic release of myoglobin and other intracellular contents from damaged muscle tissues, leading to a cascade of metabolic and physiological disturbances. Myoglobin-induced nephrotoxicity, along with systemic inflammatory responses, can result in AKI, electrolyte imbalance, and multi-organ ( 20 – 22 ). In the study conducted by Ozturk et al., increased potassium level, uric acid, lactate levels were found to be associated with increased mortality, and in our study, consistent with this study, the increase in potassium level was found to be associated with mortality (HR: 3,338, 95% CI: 1.540–7,232, p = 0.002). Although many variables were found to be significant in the univariate analysis in our study, only age, potassium and previous creatinine levels were found to be effective in the regression analysis. The highly significantly raised potassium levels among the non-survivors are a cause for concern: 5.41 ± 1.72 mmol/L versus 5.13 ± 0.98 mmol/L, p = 0.008, as hyperkalemia may lead to life-threatening cardiac arrhythmias( 23 ). In a comprehensive study conducted by Sever et al., which analyzed a total of 401 patients, hyperkalemia was identified as one of the key factors influencing mortality risk ( 24 ). This study maintains various strengths: it is a multi-center dataset comprising 469 patients from 46 diverse nephrology clinics and therefore enhances generalizability in similar disaster scenarios. The extensive data collection-from demographics to clinical presentations and laboratory parameters down to outcomes-permits an expansive review of possible mortality predictors. The use of standardized definitions further adds to data classification consistency, such as that provided by the KDIGO guidelines for AKI. The added value of incorporating pre-earthquake kidney function measurement allows a better understanding of how baseline health status influences outcomes in disasters-a gap in the existing literature. This study is subject to several key limitations that must be acknowledged. Firstly, it was designed as a retrospective analysis, which inherently restricts the ability to draw causal conclusions. The retrospective design may be subject to selection bias, as this study relied on available and accurate medical records; thus, patients without recorded pre-earthquake serum creatinine levels were excluded. Additionally, the study was primarily conducted through a web-based platform, which may have influenced participant engagement and data integrity. While we aimed to gather a comprehensive dataset simultaneously, the absence of contributions from multiple medical centers during the data collection phase resulted in a reduced patient population. This limitation is significant because it could affect the generalizability of our findings. Moreover, despite the overall large sample size, we encountered challenges accessing current measurements. Specifically, we could only retrieve the creatinine and estimated glomerular filtration rate (eGFR) results from one year prior for each patient, which may not adequately reflect their current kidney function. This gap highlights the necessity for more extensive research studies that involve a larger cohort of patients to better understand the impacts and trends associated with CKD. These factors together underscore the need for cautious interpretation of the study's results. The low mortality rate of 2.8% among 469 patients limits the statistical power to identify all relevant mortality predictors, which may affect the robustness of multivariate analyses. Moreover, unmeasured confounders included the exact time of crush injury before rescue, the quality and timing of pre-hospital care, and medical resources available at the time of disaster, all of which might have biased the results. Finally, these results may reflect specific characteristics in the healthcare structure and population demographics of Türkiye and therefore generalizability may be limited to other regions with different healthcare systems or population characteristics. The identification of pre-earthquake kidney dysfunction as a predictor of mortality has important implications for disaster preparedness and clinical management. Screening populations in earthquake-prone regions for kidney impairment could facilitate the stratification of individuals at higher risk, enabling targeted allocation of medical resources during and after disasters. Early identification of patients with raised baseline creatinine and low eGFR can allow for early intervention, including aggressive fluid management, early use of kidney replacement therapies, and close monitoring of electrolytes. Furthermore, baseline kidney assessments as part of disaster management may also enhance triage systems to ensure that those with impaired kidney function are prioritized for special care. This could reduce not only the mortality rate but also the severe complications associated with AKI, sepsis, and multi-organ failure. In conclusion this study indicated that pre-earthquake kidney status is one of the most important predictors of mortality and high levels of creatinine one year prior to the earthquake had significant associations with increased mortality in crush syndrome, emphasizing the baseline kidney status and necessitating targeted medical strategies in regard to pre-earthquake kidney status. Although creatinine levels at admission were not independently associated with mortality in the multivariate analysis, the elevated predisaster creatinine could suggest that chronic kidney impairment, rather than acute changes at presentation, may predispose patients to a worse outcome by limiting their physiological resilience during severe trauma. These data add to the body of literature advocating comprehensive preparation for disasters in general, where the assessment of kidney health has to be included to assure better outcomes. Declarations Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki. The study protocol received approval from the Clinical Research Ethics Committee of Istanbul University, Istanbul Faculty of Medicine (Decision date/no: 17.02.2023/04). Written consent was not obtained with the ethics committee's knowledge. Consent for publication: Not applicable Availability of data and materials: The data utilized and/or analyzed in this study are available from the corresponding author upon request. Competing interests: The authors state that they have no competing interests. Funding : The authors declare no funding interests. Authors' contributions : All authors were responsible for and participated in the design, data collection, statistical analysis, writing, and critical review of the study. Acknowledgements : Nothing to declare. Clinical Trial Number : Not applicable References Ozturk S, Tuglular S, Olmaz R, Kocyigit I. Patients with crush syndrome and kidney disease: lessons learned from the earthquake in Kahramanmaraş, Türkiye. Kidney Int. 2024;106(5):771–776. doi: 10.1016/j.kint.2024.08.008. PMID: 39428169. Phil, McKenna. 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Long-term prognosis after acute kidney injury (AKI): what is the role of baseline kidney function and recovery? A systematic review. BMJ Open. 2015;5. https://doi.org/10.1136/bmjopen-2014-006497 . Inker, L. A., Eneanya, N. D., Coresh, J., Tighiouart, H., Wang, D., Sang, Y., … Levey,A. S. (2021). New creatinine-and cystatin C–based equations to estimate GFR without race. New England Journal of Medicine, 385(19), 1737–1749. Hamzić-Mehmedbašić A, Rašić S, Balavac M, Rebić D, Delić-Šarac M, Durak-Nalbantić A. Prognostic indicators of adverse outcome and death in acute kidney injury hospital survivors. J Injury Prev. 2016;5(2):61. Mataloun SE, Machado FR, Senna APR, Guimarães HP, Amaral JLG. Incidence, risk factors and prognostic factors of acute failure in patients admitted to an intensive care unit. Braz J Med Biol Res. 2006;39:1339–47. Fried, L. F., Shlipak, M. G., Crump, C., Kronmal, R. A., Bleyer, A. J., Gottdiener,J. S., … Newman, A. B. (2003). insufficiency as a predictor of cardiovascular outcomes and mortality in elderly individuals. Journal of the American College of Cardiology,41(8), 1364–1372. Ou SM, Lee KH, Tsai MT, Tseng WC, Chu YC, Tarng DC. Sepsis and the risks of long-term adverse outcomes in patients with chronic kidney disease. Front Med. 2022;9:809292. Koyuncu S, Sipahioglu H, Bol O, İlik HKZ, Dilci A, Elmaağaç M, Yalçınkaya M, Gencer V, Ozan F, Günal Aİ, Kocyigit I. The Evaluation of Different Treatment Approaches in Patients With Earthquake-Related Crush Syndrome. Cureus. 2023;15(10):e47194. 10.7759/cureus.47194 . PMID: 37854473; PMCID: PMC10580897. Lovallo E, Koyfman A, Foran M. Crush syndrome, African Journal of Emergency Medicine, 2, Issue 3, 2012, Pages 117–23, ISSN 2211-419X, https://doi.org/10.1016/j.afjem.2012.05.005 Søvik, S., Isachsen, M. S., Nordhuus, K. M., Tveiten, C. K., Eken, T., Sunde, K.,… Beitland, S. (2019). Acute kidney injury in trauma patients admitted to the ICU:a systematic review and meta-analysis. Intensive Care Medicine, 45, 407–419. He Q, Wang F, Li G, et al. Crush syndrome and acute kidney injury in the Wenchuan earthquake. J Trauma. 2011;70:1213–8. 10.1097/TA.0b013e3182117b57 . Sever MS, Erek E, Vanholder R, et al. The Marmara earthquake: epidemiological analysis of the victims with nephrological problems. Kidney Int. 2001;60:1114–23. 10.1046/j.1523-1755.2001.0600031114.x . Jin H, Lin X, Liu Z, et al. Remote ischemic postconditioning protects against crush-induced acute kidney injury via down-regulation of apoptosis and senescence. Eur J Trauma Emerg Surg. 2022;48:4585–93. 10.1007/s00068-022-01910-5 . Siegelson HJ, Kaplan BH. Medical disaster management in the United States. N Engl J Med. 1989;320:941–2. Sever, M. S., Erek, E., Vanholder, R., Koc, M., Yavuz, M., Aysuna, N., … Lameire,N. (2004). Lessons learned from the catastrophic Marmara earthquake: factors influencing.Clinical nephrology, 61(6), 413–421. Plotnikov EY, Chupyrkina AA, Pevzner IB, Isaev NK, Zorov DB. Myoglobin causes oxidative stress, increase of NO production and dysfunction of kidney's mitochondria. Biochim et Biophys Acta (BBA)-Molecular Basis Disease. 2009;1792(8):796–803. Safari S, Eshaghzade M, Najafi I, Baratloo A, Hashemi B, Forouzanfar MM, Rahmati F. (2017). Trends of serum electrolyte changes in crush syndrome patients of bam earthquake; a cross sectional study. Emergency, 5(1). Usuda, D., Shimozawa, S., Takami, H., Kako, Y., Sakamoto, T., Shimazaki, J., … Oba,J. (2023). Crush syndrome: a review for prehospital providers and emergency clinicians.Journal of Translational Medicine, 21(1), 584. Hoppe LK, Muhlack DC, Koenig W, Carr PR, Brenner H, Schöttker B. Association of abnormal serum potassium levels with arrhythmias and cardiovascular mortality: a systematic review and meta-analysis of observational studies. Cardiovasc Drugs Ther. 2018;32:197–212. Sever MS, Erek E, Vanholder R, et al. Serum potassium in the crush syndrome victims of the Marmara disaster. Clin Nephrol. 2003;59:326–33. Additional Declarations No competing interests reported. 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Giresun","correspondingAuthor":false,"prefix":"","firstName":"Ozdem","middleName":"","lastName":"Kavraz","suffix":""},{"id":435937374,"identity":"14a6f5ad-f786-4fa5-8cdc-6e0781539d3f","order_by":42,"name":"Abdulkadir Unsal","email":"","orcid":"","institution":"Şişli Hamidiye Etfal Education and Research Hospital, Istanbul","correspondingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"","lastName":"Unsal","suffix":""},{"id":435937375,"identity":"dd90df22-a2e9-4875-8113-9c5942030fa9","order_by":43,"name":"Sedat Ustundag","email":"","orcid":"","institution":"Trakya University Faculty of Medicine, Division of Nephrology, Edirne","correspondingAuthor":false,"prefix":"","firstName":"Sedat","middleName":"","lastName":"Ustundag","suffix":""},{"id":435937376,"identity":"7c917ad6-9f2b-472d-8571-70b07263abc8","order_by":44,"name":"Ali Rıza Odabas","email":"","orcid":"","institution":"Sultan Abdulhamid Han Research and Training Hospital, Health Sciences University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Rıza","lastName":"Odabas","suffix":""},{"id":435937377,"identity":"47801d65-031c-4b9d-a6fc-5f0f4aef972c","order_by":45,"name":"Serhan Tuglular","email":"","orcid":"","institution":"Department of Internal Medicine, Division of Nephrology, School of Medicine, Marmara University, Istanbul","correspondingAuthor":false,"prefix":"","firstName":"Serhan","middleName":"","lastName":"Tuglular","suffix":""}],"badges":[],"createdAt":"2025-02-08 18:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5989283/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5989283/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-025-04183-3","type":"published","date":"2025-06-08T15:57:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79813850,"identity":"9c3c1ed9-20f0-4f45-a09f-826a7c49c84d","added_by":"auto","created_at":"2025-04-03 07:12:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis for previous serum creatinine in predicting mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5989283/v1/e27bfd1f98209fd149d55f26.png"},{"id":84242977,"identity":"ecf25497-7fea-45ab-8715-c05f31af3723","added_by":"auto","created_at":"2025-06-09 16:12:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1442244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5989283/v1/d31d89e4-cfb3-4220-8366-8ffb86765446.pdf"},{"id":79813852,"identity":"48c76461-1db8-429f-a4d4-c964611e234f","added_by":"auto","created_at":"2025-04-03 07:12:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18379,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5989283/v1/6153e24cef3236d0eed387a9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-Earthquake Kidney Function is a Predictor of Outcomes in Earthquake-Related Crush Syndrome","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe Kahramanmaraş Province, T\u0026uuml;rkiye, suffered from two great earthquakes on February 6, 2023, which magnitudes reached 7.6 and 7.7 on the Richter scale. This tragedy took the lives of 53,000 and injured 120,000 people among 11 provinces with a population of approximately 14\u0026nbsp;million citizens (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Because the size of the disaster was so large, the healthcare infrastructures were overwhelmed, and therefore the number of emergency medical patients began to build up. Crush syndrome is a severe medical condition that occurs when muscle tissue is intensely compressed or damaged, often as a result of traumatic events such as earthquakes, storms, wars, or traffic accidents(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The severity of crush syndrome among affected individuals is influenced by several factors, including the duration of compression under rubble, age, body mass index (BMI), comorbidities, the timeliness of treatment initiation, and the speed of hospital transfer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA very important factor in the prognosis of patients after these disasters may be their pre-earthquake kidney function status. The status of kidney health, estimated by measures such as serum creatinine and glomerular filtration rate (GFR), is important in determining the susceptibility and severity of various complications following different nephrotoxic situations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Poor kidney function, as evidenced by high baseline creatinine levels, may predispose them to more serious complications such as acute kidney injury (AKI), especially when combined with other aforementioned stressors related to crush injury (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In the case of a decreased GFR the excretion of substances produced during muscle tissue injury may be impaired. Hence, understanding these pre-existing kidney conditions is crucial for managing the patient and the survival rate in disaster situations. Individuals with impaired kidney function may require specific medical interventions considering the patient's baseline kidney status, ensuring timely and appropriate treatment to avoid complications.\u003c/p\u003e \u003cp\u003eThis study explores how previous creatinine levels and GFR impact mortality rates among patients diagnosed with crush syndrome after the Kahramanmaraş earthquake at our centers.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e \u003cstrong\u003eData collection\u003c/strong\u003e \u003cp\u003eThe methods of this study, data collection, clinical definitions and statistical analyses were explained in detail in our previous publications (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Briefly, the data were obtained from 46 nephrology clinics using web-based forms prepared by Turkish Society of Nephrology Renal Disaster Working Group. These clinics were either situated in earthquake regions or received transferred patients from those affected areas. The collected information included demographic features, clinical findings, laboratory results, comorbid diseases, complications, need for dialysis, intensive care unit (ICU) admissions, and survival status. Serum creatinine values and CKD-EPI formula in pre-earthquake periods were obtained from hospital records or e-Nabız system, which is T\u0026uuml;rkiye's national online health information platform (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) were used in the calculation of estimated glomerular filtration rate (eGFR) using the latter serum creatinine levels retrospectively. The detailed definitions for clinical parameters, including the severity of AKI according to KDIGO criteria, oliguria, dialysis, and complications related to sepsis, acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), fasciotomy-related infections, compartment syndrome, and the criteria for survival outcomes, were the same as in our previous studies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Data regarding compartment syndrome, fasciotomy, and trauma to the head, abdomen, or thorax were collected. Complications such as sepsis, ARDS, DIC, and fasciotomy site infection were included retrospectively.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinition of Crush Syndrome\u003c/strong\u003e \u003cp\u003eCrush syndrome was diagnosed based on serum creatine kinase (CK) levels exceeding 1000 U/L at admission, in conjunction with oliguria (urinary output\u0026thinsp;\u0026lt;\u0026thinsp;400 mL/day) and abnormalities in key biochemical markers. These included elevated blood urea nitrogen (BUN) (\u0026gt;\u0026thinsp;40 mg/dL), serum creatinine (SCr) (\u0026gt;\u0026thinsp;2.0 mg/dL), uric acid (\u0026gt;\u0026thinsp;8 mg/dL), potassium (K) (\u0026gt;\u0026thinsp;6 mEq/L), and phosphorus (P) (\u0026gt;\u0026thinsp;8 mg/dL), alongside decreased serum calcium (Ca) (\u0026lt;\u0026thinsp;8 mg/dL). This biochemical profile reflects the systemic impact of severe muscle injury, emphasizing the need for early recognition and intervention to prevent complications such as acute kidney injury (AKI) and life-threatening electrolyte imbalances (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcome Parameters\u003c/b\u003e: To ensure consistency, outcomes were defined as follows: AKI: Based on KDIGO criteria (increase in serum creatinine by \u0026ge;\u0026thinsp;0.3 mg/dL within 48 hours or \u0026ge;\u0026thinsp;1.5 times baseline within 7 days, or urine output\u0026thinsp;\u0026lt;\u0026thinsp;0.5 mL/kg/hour for 6 hours). Severity was classified into Stage 1, Stage 2 and Stage 3. Oliguria: Urine output\u0026thinsp;\u0026lt;\u0026thinsp;400 mL/day. Compartment Syndrome: Increased compartment pressure compromising circulation. Fasciotomy-related infection: Soft tissue infection at the fasciotomy site confirmed by clinical and microbiological findings. At discharge, survival outcomes were defined as: in-hospital death; partial recovery, indicating incomplete recovery of crush syndrome; and complete recovery, indicating full recovery of crush syndrome with no further intervention required.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range), whichever distribution the data follows. Comparisons between survivors and non-survivors involved a Student's t-test, Mann-Whitney U test for continuous variables, and chi-square test/Fisher's exact test for categorical variables. Multivariate Cox regression analysis was performed to identify independent predictors of in-hospital mortality. The analysis included variables such as age, gender, trauma (head, abdomen or thorax), AKI, ICU admission, compartment syndrome, and laboratory parameters (previous creatinine, admission creatinine, potassium, albumin, hemoglobin, leukocytes, and thrombocytes). Three models were developed to assess the contribution of these parameters to mortality risk, with hazard ratios (HR), 95% confidence intervals (CI), and p-values reported for each variable in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All the statistical analyses were conducted with the use of IBM SPSS Statistics (Version 26.0, Armonk, NY). A p-value of less than 0.05 is considered to denote statistical significance.\u003c/p\u003e "},{"header":"RESULTS","content":"\u003cp\u003eThe study included 469 patients from a total of 1024 patients diagnosed with crush syndrome who had serum creatinine measured within the last year before the earthquake.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographic and Clinical Characteristics\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe mean age of participants was 42.56 ± 16.92 years, with non-survivors having a higher mean age (50.46 ± 20.03 years) than survivors (42.34 ± 16.80 years); however, this difference did not reach statistical significance (p = 0.172) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The gender distribution was similar between groups, with 48.0% males and 52.0% females in the overall population. Among non-survivors, 38.5% were male, and 61.5% were female, compared to 48.2% males and 51.8% females among survivors (p = 0.678). Comorbid conditions in the study population included chronic kidney disease (3.6%), diabetes mellitus (10.2%), hypertension (15.8%), and coronary artery disease (7.5%). Although CKD and CAD were more prevalent in non-survivors (10.0% and 20.0%, respectively) compared to survivors (3.6% and 7.4%), the differences were not statistically significant (p = 0.320 for CKD, p = 0.175 for CAD). Similarly, hypertension was more frequent in non-survivors (30.0%) than in survivors (15.9%) without statistical significance (p = 0.210). None of the non-survivors had diabetes, while 10.7% of survivors were diabetic (p = 0.607). The use of key medications included ACE inhibitors/ARB (9.4% overall), with a higher, borderline significant usage in non-survivors (30.0%) compared to survivors (9.4%, p = 0.065). Insulin and oral anti-diabetic medications were exclusively used by survivors (2.3% and 8.1%, respectively, p = 1.000 for both). Anti-aggregant medications were administered to 9.0% of participants, with a notably higher prevalence in non-survivors (27.3%) compared to survivors (8.8%, p = 0.072).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics, comorbidities, medication usage, and renal functions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\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\u003eTotal (n = 469)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivor (n = 13)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivor (n = 456)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Characteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.56 ± 16.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.46 ± 20.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.34 ± 16.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.172\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.678\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (48.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (38.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220 (48.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (52.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (61.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 (51.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Chronic Kidney Disease (CKD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (3.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (10.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (3.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.320\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Diabetes Mellitus (DM)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (10.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (10.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.607\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Hypertension (HT)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (15.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (30.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (15.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.210\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Coronary Artery Disease (CAD)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (7.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (20.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (7.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.175\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication Usage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers (ACE/ARB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (9.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (30.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (9.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.065\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Insulin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (2.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (2.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Oral Anti-Diabetic Medications (OAD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (8.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (8.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e- Anti-aggregant Medications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (9.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (27.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (8.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.072\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL) (Previous)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 ± 0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 ± 0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 ± 0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlomerular Filtration Rate (GFR) (Previous) (mL/min/1.73 m²)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.3 ± 39.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.2 ± 34.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.8 ± 39.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL) (Admission)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54 ± 2.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54 ± 1.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51 ± 2.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFR (Admission) (mL/min/1.73 m²)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.11 ± 56.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.60 ± 17.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.50 ± 56.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL) (Discharge)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 ± 0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53 ± 1.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 ± 0.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFR (Discharge) (mL/min/1.73 m²)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.0 ± 64.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.41 ± 36.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125.3 ± 63.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: CKD: Chronic Kidney Disease, DM: Diabetes Mellitus, HT: Hypertension, CAD: Coronary Artery Disease, OAD: Oral Antidiabetic, AKI: Acute Kidney Injury,\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePre-Existing Kidney Functions\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNon-survivors had higher mean creatinine levels prior to admission (1.04 ± 0.61mg/dl vs. 0.77 ± 0.33mg/dl, p = 0.030) and markedly elevated admission levels (2.53 ± 1.10 vs. 0.93 ± 0.91, p \u0026lt; 0.001), though admission levels were not significantly different (p = 0.10) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, eGFR was significantly lower in non-survivors both prior to admission (85.2 ± 34.7ml/min/1.73m2 vs. 115.8 ± 39.4ml/min/1.73m2, p = 0.008) and at admission (21.60ml/min/1.73m2 ± 17.10 vs. 63.50 ± 56.50ml/min/1.73m2, p = 0.005), with the admission GFR also showing a marked reduction in non-survivors (37.41 ± 36.58 vs. 125.3 ± 63.04, p \u0026lt; 0.001).\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn hospital Mortality Rate and Other Outcomes\u003c/b\u003e: In total, 13 (2.8%) patients died in the study. A significantly higher proportion of non-survivors n:12 (92.3%) had AKI compared to survivors (n: 281 61.6%, p = 0.037) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the KDIGO criteria, most AKI cases were categorized as stage 3 (64.8%), with no significant difference in severity distribution between groups (p = 0.263). Oliguria was present in 28.4% of participants, with a higher frequency in non-survivors (88.9%) compared to survivors (60.1%, p = 0.158). Hemodialysis was required in 41.6% of cases, with non-survivors showing a notably higher need (91.7%) than survivors (65.5%, p = 0.067).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Findings and Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\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\u003eTotal (n = 469)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivor (n = 13)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivor (n = 456)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e293 (62.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (92.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e281 (61.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI Stage (KDIGO) Criteria (n = 293)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.263\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Stage 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (19.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (19.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Stage 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (15.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (16.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Stage 3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190 (64.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (64.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOliguria\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133 (28.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (88.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (60.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.158\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemodialysis Need\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195 (41.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (91.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (65.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.067\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247 (52.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (76.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237 (52.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasciotomy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (24.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (30.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (24.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.531\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompartment Syndrome\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172 (36.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (38.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (36.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasciotomy Infection\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (10.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (15.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (9.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.629\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (11.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (38.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (11.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (2.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (38.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (2.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (15.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma in extremities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409 (87.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (69.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (87.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.071\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead, Abdomen, or Thorax Trauma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180 (38.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (23.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 (38.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.387\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays of Dialysis (n = 189)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.49 ± 7.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 ± 12.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8 ± 7.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.078\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of Stay in ICU (Days)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.40 ± 9.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2 ± 12.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1 ± 8.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.330\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of AKI (Days)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.40 ± 9.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2 ± 12.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5 ± 9.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.716\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of Oliguria (Days)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.01 ± 5.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25 ± 3.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.70 ± 6.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.920\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival Outcomes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Exitus (Death)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (2.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (100%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Partial Recovery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179 (38.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (38.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Complete Recovery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200 (42.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200 (42.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Referral to Other Clinics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (16.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (16.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: AKI: Acute Kidney Injury, KDIGO: Kidney Disease: Improving Global Outcomes, ICU: Intensive Care Unit, LOS: Length of Stay, ARDS: Acute Respiratory Distress Syndrome, DIC: Disseminated Intravascular Coagulation, Hb: Hemoglobin\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIntensive care unit (ICU) length of stay was longer for non-survivors (10.2 ± 12.7, 76.9%) compared to survivors (10.1 ± 8.47, 52.0%, p = 0.135) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Fasciotomy was performed in 24.5% of participants, and compartment syndrome occurred in 36.7%, with no significant differences between groups (p = 0.531 and p = 1.000, respectively). Infection following fasciotomy was reported in 10.0% of participants (p = 0.629). Sepsis occurred more frequently in non-survivors (38.5%) compared to survivors (11.2%, p = 0.012), while ARDS was significantly higher in non-survivors (38.5% vs. 1.8%, p \u0026lt; 0.05). DIC was also more common in non-survivors (15.4%) than survivors (1.8%, p = 0.028). Trauma was prevalent in 87.2% of participants, with a lower frequency in non-survivors (69.2%) compared to survivors (87.7%, p = 0.071). Trauma involving the head, abdomen, or thorax was present in 38.4% of cases (p = 0.387).\u003c/p\u003e \u003cp\u003eRegarding hospital stay, the mean duration of ICU stay was 9.40 ± 9.40 days, and dialysis was required for an average of 8.49 ± 7.55 days. AKI lasted an average of 16.40 ± 9.22 days, with no significant differences in these parameters between non-survivors and survivors. Oliguria persisted for an average of 7.01 ± 5.90 days (p = 0.920). Survival outcomes showed that 2.8% of participants died, 38.2% had partial recovery, 42.6% achieved complete recovery, and 16.4% were referred to other clinics.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLaboratory Parameters According to Survival Status\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNon-survivors had significantly higher BUN (55.2 ± 8.84mg/dl vs. 50.3 ± 37.2 mg/dl, p = 0.010), uric acid (9.42 ± 1.67mg/dl vs. 7.23 ± 2.82mg/dl, p \u0026lt; 0.001), potassium (5.41 ± 1.72mmol/L vs. 5.13 ± 0.98 mmol/L, p = 0.008), and phosphorus (8.84 ± 3.73mg/dl vs. 4.85 ± 2.24 mg/dl, p \u0026lt; 0.001) (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e). Lactate levels were also significantly higher in non-survivors (7.66 ± 5.80mmol/L vs. 2.66 ± 2.58 mmol/L, p = 0.001). Liver enzymes, including AST (2545.3 ± 3733.2 U/L vs. 488.7 ± 504.9 U/L, p = 0.032) and ALT (1319.0 ± 2104.2 U/L vs. 206.6 ± 216.3 U/L, p = 0.039), were markedly elevated in non-survivors. Additionally, pH was significantly lower in non-survivors (7.16 ± 0.19 vs. 7.35 ± 0.09, p \u0026lt; 0.001), as were bicarbonate levels (12.8 ± 4.71 mEq/L vs. 20.2 ± 4.61 mEq/L, p \u0026lt; 0.001), indicating more severe metabolic acidosis. Platelet count and its logarithmic value were significantly lower in non-survivors (184.6 ± 53.1 vs. 238.5 ± 92.8 cells/mm³, p = 0.022). No significant differences were observed in sodium, chloride, calcium, hemoglobin, or leukocyte levels. The average duration of earthquake-associated AKI and oliguria did not differ significantly between groups.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultivariate Analyses\u003c/b\u003e: Cox regression analysis was carried out in three models to evaluate the effect of different parameters on survival. Age had a significant association with better survival in Model 1, Model 2 and Model 3 with an HR of 1.034 (95% CI: 1.004–1.065, p = 0.026), 1.037 (95% CI: 1.005–1.070, p = 0.022), and 1.050 (95% CI: 1.008–1.093, p = 0.0185) respectively. There had been no influence of gender on the survival rate according to all these models. Trauma (head, abdomen, or thorax) was insignificantly associated with mortality in Model 1, HR 2.677, 95% CI 0.698–10.261, p = 0.151, and significantly so in Model 2, HR 3.080, 95% CI 0.746–12.712, p = 0.120, and this effect was insignificantly strengthened in Model 3 HR 4.692, 95% CI 0.810–27.168, p = 0.084. AKI presence, ICU admission, and compartment syndrome were not significantly associated with survival in any model.\u003c/p\u003e \u003cp\u003eSignificant predictors in Model 3 included serum potassium at admission and previous creatinine levels, which were associated with increased mortality (HR: 3.338, 95% CI: 1.540–7.232, p = 0.002, HR: 9.121, 95% CI: 2.686–30.970, p:\u0026lt;0.001 respectively). Other laboratory parameters that did not significantly predict survival included creatinine admission levels, albumin, hemoglobin, leukocyte count, and platelets. Platelets had a borderline association with survival (HR: 0.016, 95% CI: 0.000–1.141, p = 0.058).\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression of parameters related in-hospital mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eModel 1 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-Value\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.034 (1.004\u0026ndash;1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.037 (1.005\u0026ndash;1.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.050 (1.008\u0026ndash;1.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.598 (0.516\u0026ndash;4.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.547 (0.495\u0026ndash;4.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.422 (0.346\u0026ndash;5.839)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.677 (0.698\u0026ndash;10.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.080 (0.746\u0026ndash;12.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.692 (0.810\u0026ndash;27.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Kidney Injury (AKI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.047 (0.494\u0026ndash;33.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745 (0.060\u0026ndash;9.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.241 (0.567\u0026ndash;8.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.252 (0.229\u0026ndash;22.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompartment Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.725 (0.214\u0026ndash;2.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.778 (0.161\u0026ndash;3.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL) (Previous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.121 (2.686\u0026ndash;30.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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) (Admission)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.912 (0.636\u0026ndash;1.306)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.338 (1.540\u0026ndash;7.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.364 (0.111\u0026ndash;1.192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.157 (0.906\u0026ndash;1.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocytes (/mm\u0026sup3;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Platelet (x10\u0026sup3;/mm\u0026sup3;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016 (0.000\u0026ndash;1.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: AKI: Acute Kidney Injury, ICU: Intensive Care Unit, GFR: Glomerular Filtration Rate\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003eThe optimal cutoff value for serum creatinine (Previous) in predicting mortality was determined as 0.7950 based on the ROC curve analysis (Figure 1).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eSensitivity: 69.2%\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSpecificity: 61.6%\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"DISCUSSION","content":" \u003cp\u003eIn this study, we mainly aimed to investigate the effect of previous creatinine and GFR on mortality. When we divided the patients into two groups as survivors and non-survivors, creatinine and GFR changes were found to be significant between these two groups (p: 0.030 p: 0.008 respectively). Although this showed that the difference between the two groups was significant, univariate analysis was performed to investigate the effect of this situation on mortality. In univariate analysis, 1-year ago creatinine and GFR values were found to be significant (HR: 2.512, p: 0.05, HR: 0.973 p: 0.002 respectively). In the multivariate regression analysis, GFR was excluded due to multicollinearity. The analysis identified that an increase in creatinine levels one year prior significantly impacted mortality rates (HR:9,121, 95% CI:2.686–30.970, p \u0026lt; 0,001). The major finding of our study is that impaired pre-earthquake kidney function is significantly associated with increased in-hospital mortality among patients with earthquake-related crush syndrome. The baseline serum creatinine levels were significantly higher in non-survivors than in survivors: 1.04 ± 0.61 mg/dL versus 0.77 ± 0.33 mg/dL, respectively (p = 0.030). This aligns with previous literature indicating that poor kidney function at presentation is a robust predictor of adverse outcomes in trauma patients (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The pre-admission eGFR was also lower in non-survivors than in survivors: 85.2 ± 34.7 mL/min/1.73 m² versus 115.8 ± 39.4 mL/min/1.73 m², respectively (p = 0.008). These observations are corroborated by the present study for and point toward the universal application of kidney function as a prognostic marker in trauma (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Our study aligns with other studies that kidney dysfunction is already an excellent predictor of poor trauma patient outcomes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Various studies indicate that patients who suffer from CKD or with diminished baseline kidney function will then go on to suffer from complications of AKI, sepsis, and multi-organ failure after the incident (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrush syndrome is a serious medical condition that occurs when muscle tissue is damaged, leading to the release of myoglobin and some electrolytes into the bloodstream. This often happens after traumatic incidents such as earthquakes or armed conflicts, where victims may be trapped under debris for extended periods. Additionally, Rhabdomyolysis can develop as a result of prolonged seizures, certain drug overdoses, or specific autoimmune diseases that affect muscle tissues. The prevalence of crush syndrome can vary significantly depending on the circumstances and location. Research has shown that the incidence of Crush Syndrome can range dramatically, from as low as 5% in places like Hanshin-Awaji, Japan, to as high as 37% in Kahramanmaraş, Türkiye. Understanding the factors that contribute to these differences is crucial for improving prevention and treatment strategies for this potentially life-threatening condition (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKidney damage frequently occurs as a complication after crush syndrome, and it plays a critical role in influencing mortality rates among affected individuals. Research has shown that the prevalence of kidney damage varies widely, with reported rates ranging from 12–41% (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These variations highlight the complex nature of crush syndrome and underscore the importance of prompt medical intervention to mitigate the risk of kidney-related complications and improve survival outcomes. The reasons for the change in this rate may be due to many factors such as the distance of the place where the incident occurred to health centers, the readiness of rescue and health teams at the time of the incident, the duration of being under the rubble, the duration of reaching the health center, early initiation of hydration therapy, and the time the incident occurred. In our study, the AKI rate was 62.4% and this may have been affected by many factors such as the earthquake occurring at night, the earthquake affecting a wide area, and the rescue and health teams being caught unprepared. AKI developing after Crush Syndrome is an important risk factor for mortality, and in our study, a statistically significant difference was found in terms of AKI rates between the 2 groups of survivors and non-survivors (n: 281 (61.6%), n: 12 (92.3%), respectively) in patients treated in the hospital (p: 0.037) (\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSever et al., in their study investigating the effects of complications on mortality, divided the patients into two groups as survivors and non-survivors, and found that infection, sepsis, ARDS, DIC, Mechanic Ventilators (MV), CV catheter complications were statistically significantly higher in the non-survivor group. In our study, sepsis, DIC and ARDS complications were also found to be significant (p: 0.012, p: 0.028, p: \u0026lt;0.05 respectively). In the study conducted by Sever et al., sepsis was found to be significant as a complication, but in our study, since infection was only examined in the fasciotomy area, it was not found to be statistically significant(p:0.629) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLaboratory parameters of metabolic derangement were higher in non-survivors: high BUN, uric acid, potassium, phosphorus, lactate, and liver enzymes. These biochemical changes reflect a more severe systemic response to trauma and underscore the multifaceted effects of kidney impairment on patient outcomes. This can also be explained by crush syndrome, a condition precipitated by the systemic release of myoglobin and other intracellular contents from damaged muscle tissues, leading to a cascade of metabolic and physiological disturbances. Myoglobin-induced nephrotoxicity, along with systemic inflammatory responses, can result in AKI, electrolyte imbalance, and multi-organ (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In the study conducted by Ozturk et al., increased potassium level, uric acid, lactate levels were found to be associated with increased mortality, and in our study, consistent with this study, the increase in potassium level was found to be associated with mortality (HR: 3,338, 95% CI: 1.540–7,232, p = 0.002). Although many variables were found to be significant in the univariate analysis in our study, only age, potassium and previous creatinine levels were found to be effective in the regression analysis. The highly significantly raised potassium levels among the non-survivors are a cause for concern: 5.41 ± 1.72 mmol/L versus 5.13 ± 0.98 mmol/L, p = 0.008, as hyperkalemia may lead to life-threatening cardiac arrhythmias(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In a comprehensive study conducted by Sever et al., which analyzed a total of 401 patients, hyperkalemia was identified as one of the key factors influencing mortality risk (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study maintains various strengths: it is a multi-center dataset comprising 469 patients from 46 diverse nephrology clinics and therefore enhances generalizability in similar disaster scenarios. The extensive data collection-from demographics to clinical presentations and laboratory parameters down to outcomes-permits an expansive review of possible mortality predictors. The use of standardized definitions further adds to data classification consistency, such as that provided by the KDIGO guidelines for AKI. The added value of incorporating pre-earthquake kidney function measurement allows a better understanding of how baseline health status influences outcomes in disasters-a gap in the existing literature.\u003c/p\u003e \u003cp\u003eThis study is subject to several key limitations that must be acknowledged. Firstly, it was designed as a retrospective analysis, which inherently restricts the ability to draw causal conclusions. The retrospective design may be subject to selection bias, as this study relied on available and accurate medical records; thus, patients without recorded pre-earthquake serum creatinine levels were excluded. Additionally, the study was primarily conducted through a web-based platform, which may have influenced participant engagement and data integrity. While we aimed to gather a comprehensive dataset simultaneously, the absence of contributions from multiple medical centers during the data collection phase resulted in a reduced patient population. This limitation is significant because it could affect the generalizability of our findings. Moreover, despite the overall large sample size, we encountered challenges accessing current measurements. Specifically, we could only retrieve the creatinine and estimated glomerular filtration rate (eGFR) results from one year prior for each patient, which may not adequately reflect their current kidney function. This gap highlights the necessity for more extensive research studies that involve a larger cohort of patients to better understand the impacts and trends associated with CKD. These factors together underscore the need for cautious interpretation of the study's results. The low mortality rate of 2.8% among 469 patients limits the statistical power to identify all relevant mortality predictors, which may affect the robustness of multivariate analyses. Moreover, unmeasured confounders included the exact time of crush injury before rescue, the quality and timing of pre-hospital care, and medical resources available at the time of disaster, all of which might have biased the results. Finally, these results may reflect specific characteristics in the healthcare structure and population demographics of Türkiye and therefore generalizability may be limited to other regions with different healthcare systems or population characteristics.\u003c/p\u003e \u003cp\u003eThe identification of pre-earthquake kidney dysfunction as a predictor of mortality has important implications for disaster preparedness and clinical management. Screening populations in earthquake-prone regions for kidney impairment could facilitate the stratification of individuals at higher risk, enabling targeted allocation of medical resources during and after disasters. Early identification of patients with raised baseline creatinine and low eGFR can allow for early intervention, including aggressive fluid management, early use of kidney replacement therapies, and close monitoring of electrolytes. Furthermore, baseline kidney assessments as part of disaster management may also enhance triage systems to ensure that those with impaired kidney function are prioritized for special care. This could reduce not only the mortality rate but also the severe complications associated with AKI, sepsis, and multi-organ failure.\u003c/p\u003e \u003cp\u003eIn conclusion this study indicated that pre-earthquake kidney status is one of the most important predictors of mortality and high levels of creatinine one year prior to the earthquake had significant associations with increased mortality in crush syndrome, emphasizing the baseline kidney status and necessitating targeted medical strategies in regard to pre-earthquake kidney status. Although creatinine levels at admission were not independently associated with mortality in the multivariate analysis, the elevated predisaster creatinine could suggest that chronic kidney impairment, rather than acute changes at presentation, may predispose patients to a worse outcome by limiting their physiological resilience during severe trauma. These data add to the body of literature advocating comprehensive preparation for disasters in general, where the assessment of kidney health has to be included to assure better outcomes.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003ch2\u003e \u003c/h2\u003e \u003ch2\u003e \u003c/h2\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e The study was conducted in accordance with the Declaration of Helsinki. The study protocol received approval from the Clinical Research Ethics Committee of Istanbul University, Istanbul Faculty of Medicine (Decision date/no: 17.02.2023/04). Written consent was not obtained with the ethics committee\u0026apos;s knowledge.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e The data utilized and/or analyzed in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u0026nbsp;\u003c/em\u003eThe authors state that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e: The authors declare no funding interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e: All authors were responsible for and participated in the design, data collection, statistical analysis, writing, and critical review of the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e: Nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical Trial Number\u003c/em\u003e: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOzturk S, Tuglular S, Olmaz R, Kocyigit I. 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Crush syndrome, African Journal of Emergency Medicine, 2, Issue 3, 2012, Pages 117\u0026ndash;23, ISSN 2211-419X, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.afjem.2012.05.005\u003c/span\u003e\u003cspan address=\"10.1016/j.afjem.2012.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;vik, S., Isachsen, M. S., Nordhuus, K. M., Tveiten, C. K., Eken, T., Sunde, K.,\u0026hellip; Beitland, S. (2019). Acute kidney injury in trauma patients admitted to the ICU:a systematic review and meta-analysis. Intensive Care Medicine, 45, 407\u0026ndash;419.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Q, Wang F, Li G, et al. Crush syndrome and acute kidney injury in the Wenchuan earthquake. 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Crush syndrome: a review for prehospital providers and emergency clinicians.Journal of Translational Medicine, 21(1), 584.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoppe LK, Muhlack DC, Koenig W, Carr PR, Brenner H, Sch\u0026ouml;ttker B. Association of abnormal serum potassium levels with arrhythmias and cardiovascular mortality: a systematic review and meta-analysis of observational studies. Cardiovasc Drugs Ther. 2018;32:197\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSever MS, Erek E, Vanholder R, et al. Serum potassium in the crush syndrome victims of the Marmara disaster. Clin Nephrol. 2003;59:326\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crush syndrome, kidney function, mortality, disaster nephrology, acute kidney injury, hyperkalemia, Türkiye","lastPublishedDoi":"10.21203/rs.3.rs-5989283/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5989283/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe devastating earthquakes in Kahramanmaraş, T\u0026uuml;rkiye, in February 2024, caused extensive trauma and loss of lives, causing unique challenges in the management of earthquake-related crush syndrome. The current study investigates the prognostic value of pre-earthquake kidney function for mortality prediction in patients diagnosed with crush syndrome.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multi-center retrospective analysis was performed using data from 469 patients treated at 46 nephrology clinics. Pre-earthquake Kidney function, defined by serum creatinine and estimated glomerular filtration rate (eGFR) levels, was obtained from pre-earthquake health records. Clinical findings, laboratory parameters, complications, and survival probabilities were analyzed. Multivariate Cox regression was used to identify independent predictors of in-hospital mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of participants was 42.56\u0026thinsp;\u0026plusmn;\u0026thinsp;16.92 years (Non-survivors: 50.46\u0026thinsp;\u0026plusmn;\u0026thinsp;20.03 years, Survivors: 42.34\u0026thinsp;\u0026plusmn;\u0026thinsp;16.80 years (p\u0026thinsp;=\u0026thinsp;0.172)). The in-hospital mortality rate was 2.8%. Non-survivors exhibited significantly higher pre-earthquake creatinine levels than survivors (1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 mg/dL vs. 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mg/dL, p\u0026thinsp;=\u0026thinsp;0.03), with lower eGFR (85.2\u0026thinsp;\u0026plusmn;\u0026thinsp;34.7 mL/min/1.73 m\u0026sup2; vs. 115.8\u0026thinsp;\u0026plusmn;\u0026thinsp;39.4 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.008). Compared with survivors, non-survivors had higher incidences of AKI (92.3% vs. 61.6%, p\u0026thinsp;=\u0026thinsp;0.037) and more severe metabolic disturbances, including hyperkalemia (5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72 mmol/L vs. 5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98 mmol/L, p\u0026thinsp;=\u0026thinsp;0.008). Regression analysis revealed that pre-earthquake creatinine (HR: 9.121, 95% CI: 2.686\u0026ndash;30.970, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and potassium levels at admission (HR: 3.338, 95% CI: 1.540\u0026ndash;7.232, p\u0026thinsp;=\u0026thinsp;0.002) were independent predictors of mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePre-earthquake kidney function significantly predicts mortality in crush syndrome patients, highlighting the importance of baseline kidney assessment in disaster preparedness.\u003c/p\u003e","manuscriptTitle":"Pre-Earthquake Kidney Function is a Predictor of Outcomes in Earthquake-Related Crush Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 07:12:20","doi":"10.21203/rs.3.rs-5989283/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-05T18:32:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T00:01:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243403375527630837138266526544605079819","date":"2025-05-04T07:19:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-04T04:52:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94470508802768407432870584663362534261","date":"2025-05-04T03:11:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86638910500398393555865659958500903738","date":"2025-04-30T16:23:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45750429116598793164860823230969791795","date":"2025-04-28T17:25:03+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T15:09:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T20:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242770990725880765187556744841012755549","date":"2025-04-02T19:55:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T19:35:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249244372164163591202711311400408610051","date":"2025-04-01T19:29:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-30T14:08:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T10:52:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-03-17T19:20:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b728456-fc0f-4e0b-b07d-6a5c8139972a","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:08:18+00:00","versionOfRecord":{"articleIdentity":"rs-5989283","link":"https://doi.org/10.1186/s12882-025-04183-3","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-06-08 15:57:57","publishedOnDateReadable":"June 8th, 2025"},"versionCreatedAt":"2025-04-03 07:12:20","video":"","vorDoi":"10.1186/s12882-025-04183-3","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04183-3","workflowStages":[]},"version":"v1","identity":"rs-5989283","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5989283","identity":"rs-5989283","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}