Predictive Value of Serum Procalcitonin and APACHE-II Score for Acute Kidney Injury in Critically Ill Patients: A Prospective Cohort Study

Predictive Value of Serum Procalcitonin and APACHE-II Score for Acute Kidney Injury in Critically Ill Patients: A Prospective Cohort Study | 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 Predictive Value of Serum Procalcitonin and APACHE-II Score for Acute Kidney Injury in Critically Ill Patients: A Prospective Cohort Study Yakup Özgüngör, Hicret Yeniay, Emre Karagöz, Mensure Çakırgöz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7848267/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Acute kidney injury (AKI) is a common and highly fatal condition among patients treated in intensive care units (ICUs). This study aimed to investigate whether procalcitonin (PCT) levels and APACHE-II scores can predict the development of AKI in critically ill patients. Methods: This prospective cohort study was conducted in the General Intensive Care Unit of İzmir City Hospital. A total of 327 patients were included. Patients were divided into two groups according to the presence or absence of AKI. Continuous variables (age, PCT, albumin, CRP, and APACHE-II) were analyzed using either the Student’s t-test or the Mann–Whitney U test based on the Shapiro–Wilk normality test. Categorical variables were compared using the chi-square test. Variables found to be significant were entered into a forward binary logistic regression model. For subgroup analyses, patients were reclassified according to sepsis status. Results: The mean age of the study population was 68.09 ± 14.72 years, and 45.3% were female. In the entire cohort, age (p = 0.004), albumin (p = 0.005), CRP (p = 0.001), APACHE-II (p < 0.001), and PCT (p < 0.001) were significantly associated with AKI development. In logistic regression analysis, APACHE-II (p < 0.001; OR = 1.050) and PCT (p = 0.038; OR = 1.013) were identified as independent predictors of AKI. In patients without sepsis, age (p < 0.001), CRP (p = 0.001), PCT (p < 0.001), and APACHE-II (p = 0.003) remained significant, while albumin was not (p = 0.120). Among those with sepsis, only the APACHE-II score remained significant (p < 0.001). The overall model accuracy was 71.3%. Optimal cut-offs were ~0.29 ng/mL for PCT in all patients (sensitivity 78.4%, specificity 55.5%) and ~0.26 ng/mL in non-septic patients (61.5%, 64.3%), and 25 points for APACHE-II overall (58.6%, 75.1%). Conclusion: Serum PCT level and APACHE-II score are independent predictors of AKI development in ICU patients. The predictive power of PCT is particularly evident in non-septic patients. Evaluating PCT in conjunction with the APACHE-II score may provide clinical utility in the early identification of patients at high risk for kidney injury. Procalcitonin Acute Kidney Injury APACHE-II Intensive Care Unit Sepsis Biomarker Figures Figure 1 Figure 2 Background Procalcitonin (PCT) is a biomarker that is released into the bloodstream primarily in response to bacterial infections. [ 1 ] It demonstrates a sensitivity of 77% and a specificity of 79% for detecting bacterial infections. [ 2 ] Therefore, it is frequently used in intensive care units (ICUs) to identify newly developing infections. Since PCT is predominantly eliminated by the kidneys, its serum concentration may also be elevated in both acute and chronic renal failure, even in the absence of bacterial infection.[ 3 ] Although acute kidney injury (AKI) is a common condition, it remains a major cause of morbidity and mortality in modern ICUs due to its complex and heterogeneous pathophysiology.[ 4 ] Despite advances in treatment strategies, therapeutic options for AKI are still limited. Consequently, identifying biomarkers and developing predictive models for early detection of AKI before its onset have become increasingly important. [ 5 ] As early as 1975, elevated plasma immunoreactive calcitonin levels were reported in patients with AKI, particularly during the anuric or oliguric phases of renal failure. [ 6 ] Subsequently, the relationship between PCT and AKI has been investigated, especially among patients with cardiovascular diseases. In studies focusing on contrast-induced nephropathy, it was found that PCT levels measured at the time of contrast administration could predict the development of AKI within 48–72 hours thereafter.[ 7 ] The predictive value of PCT for AKI has also been explored in patients with cerebrovascular disease.[ 8 ] The authors proposed that a model incorporating PCT, age, serum chloride level, and C-reactive protein (CRP) could successfully identify patients with AKI. Similar studies have been conducted in critically ill populations. In one study involving 577 ICU patients, PCT > 0.5 µg/L at admission, age > 65 years, and pre-existing chronic kidney disease (CKD) were identified as independent risk factors for AKI.[ 9 ] The predictive value of PCT for AKI in the presence of infection is, however, more complex. In a cohort of 201 patients, PCT predicted AKI only when infection was present but sepsis was absent.[ 10 ] Another study demonstrated that in septic patients, PCT levels could not reliably predict AKI development. [ 11 ] In light of these findings, the aim of our study was to investigate whether PCT levels during the first seven days after ICU admission could serve as a determinant for the development of AKI. Methods The study was approved by the İzmir City Hospital Non-Interventional Research Ethics Committee (approval date: March 13, 2024; approval number: 2024/30). It was designed as a prospective cohort study and conducted in the General Intensive Care Unit of İzmir City Hospital, a mixed medical–surgical tertiary care center, over a six-month period. Data were collected between June 1, 2024, and December 31, 2024 . Written informed consent was obtained from all participating patients whenever possible. For patients who were unable to provide consent due to critical illness or intubation, written consent was obtained from their legally authorized representatives or next of kin. Study population: All adult patients ( ≥ 18 years ) admitted to the ICU during the study period were screened. Patients meeting the eligibility criteria were enrolled consecutively to minimize selection bias. Inclusion criteria included; PCT measurement within 24 hours of ICU admission ICU stay longer than 24 hours Complete clinical and biochemical data required for AKI and sepsis assessment. Exclusion criteria were defined as follows: AKI present at admission to ICU Known chronic kidney disease (CKD) Shorter than 24 hours ICU stay Missing baseline creatinine data Sepsis was defined based on the Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021 publication. [ 12 ] AKI and its stages were defined according to the KDIGO criteria, based on changes in serum creatinine or urine output , whichever criterion was met first. [ 13 ] At baseline, demographic data including age, sex, comorbid chronic diseases, and causes of ICU admission were recorded. Daily biochemical parameters were monitored for seven consecutive days during the ICU stay. Within the first 24 hours of admission, the following parameters were recorded: APACHE-II score, albumin, procalcitonin, creatinine, and C-reactive protein (CRP) levels. In addition, the use of contrast agents, diuretics, and nephrotoxic drugs such as aminoglycosides, vancomycin, and colistin was noted. After transferring the data to electronic format, statistical analyses were performed using SPSS version 26 . Categorical variables were expressed as counts and percentages. The distribution of continuous variables was evaluated with the Shapiro–Wilk test . Normally distributed variables were presented as mean ± standard deviation , and non-normally distributed variables as median and interquartile range (IQR) . For group comparisons, chi-square tests were used for categorical variables. Among continuous variables, the Student’s t-test was applied to those with normal distribution, and the Mann–Whitney U test to those without normal distribution. Variables that were statistically significant in univariate analyses were included in a forward binary logistic regression model to identify independent predictors. Receiver operating characteristic (ROC) analysis was used to evaluate discriminative performance. Cut-off values for PCT and APACHE-II were determined using the Youden index , and corresponding sensitivity, specificity, and area under the curve (AUC, 95% CI) were calculated. A two-tailed p < 0.05 was considered statistically significant in all analyses. A post-hoc power analysis was performed. According to the post-hoc power analysis conducted using G Power 3.1*, with α = 0.05 and Cohen’s d = 0.45 , a total sample size of n = 327 yielded a statistical power of 95% , indicating that the study had sufficient power to detect the observed effect size. Results Table 1 Baseline Characteristics of the Study Population Variable n % Gender Female 148 45.3 Male 179 54.7 Diabetes Mellitus (DM) 145 44.3 Hypertension (HT) 168 51.4 Chronic Obstructive Pulmonary Disease (COPD) 77 23.5 Coronary Artery Disease (CAD) 108 33 Reason for ICU Admission Pulmonary 181 55.4 Oncologic 14 4.3 Cardiologic 18 5.5 Neurologic 61 18.7 Nephrologic 7 2.1 Gastrointestinal 7 2.1 Trauma 39 11.9 Contrast Agent Use 55 16.8 Furosemide Use 67 20.5 Aminoglycoside Use 29 8.9 Vancomycin Use 26 8 Colistin Use 29 8.9 28-day mortality (present) 156 47.7 A total of 327 patients were included in the analysis, with a mean age of 68.1 ± 14.7 years; 45.3% (n = 148) were female. The most common comorbidities were hypertension (51.4%) and diabetes mellitus (44.3%). The primary reasons for ICU admission were pulmonary (55.4%), neurologic (18.7%), and trauma-related (11.9%) causes. The 28-day mortality rate was 47.7% (n = 156). The overall incidence of AKI during ICU stay was 35.5% (n = 116) according to KDIGO criteria. The mean procalcitonin (PCT) level was 7.74 ± 21.99 ng/mL, with a median of 4.10 and an IQR of 2.19. The mean creatinine level was 1.35 ± 2.03 mg/dL, with a median of 0.89 mg/dL and an IQR of 0.69 mg/dL. The mean albumin concentration was 30.37 ± 6.54 g/L, with a median of 31.00 g/L and an IQR of 9 g/L. The mean C-reactive protein (CRP) level was 128.37 ± 113.68 mg/L, with a median of 85.20 mg/L and an IQR of 162.4 mg/L, indicating a wide distribution. Finally, the mean APACHE-II score was 24.58 ± 12.57, with a median of 22.00 and an IQR of 18. Among these parameters, only the albumin level showed a normal distribution according to the Shapiro–Wilk test (p > 0.05). Additional information is provided in Table 1 . Patients who developed AKI were significantly older (71.2 ± 12.9 vs. 66.5 ± 15.4 years, p = 0.004) and had higher CRP, PCT, and APACHE-II values, along with lower albumin levels. Sepsis was present in 56.0% of AKI cases, compared to 31.1% among non-AKI patients (p < 0.001). Other comorbidities and nephrotoxic drug exposures were not statistically different (Table 2 ). Table 2 Comparison of Demographic and Clinical Variables Between Patients With and Without Acute Kidney Injury (AKI). Variable AKI Present n (%) AKI Absent n (%) p value Gender Female 48 (32.4) 100 (67.6) 0.296 Male 68 (38.0) 111 (62.0) Diabetes Mellitus (DM) 48 (33.1) 97 (66.9) 0.424 Hypertension (HT) 61 (36.3) 107 (63.7) 0.745 Chronic Obstructive Pulmonary Disease (COPD) 25 (32.5) 52 (67.5) 0.528 Coronary Artery Disease (CAD) 41 (38.0) 67 (62.0) 0.509 Reason for ICU Admission Pulmonary 61 (33.7) 120 (66.3) 0.285 Oncologic 9 (64.3) 5 (35.7) Cardiologic 7 (38.9) 11 (61.1) Neurologic 21 (34.4) 40 (65.6) Nephrologic 4 (57.1) 3 (42.9) Gastrointestinal 2 (28.6) 5 (71.4) Trauma / Toxicity 12 (30.8) 27 (69.2) Contrast Use 19 (34.5) 36 (65.5) 0.875 Furosemide Use 26 (38.8) 41 (61.2) 0.523 Aminoglycoside Use 11 (37.9) 18 (62.1) 0.772 Vancomycin Use 8 (30.8) 18 (69.2) 0.772 Colistin Use 10 (34.5) 19 (65.5) 0.907 Sepsis Sepsis absent 65 (31.1) 171 (68.9) < 0.001 Sepsis present 51 (56.0) 40 (44.0) Only chi-square test used All continuous variables obtained from the entire study population—including age, PCT, APACHE-II score, albumin, and CRP levels—were analyzed using either the Mann–Whitney U test or the Student’s t-test. As presented in this table, all these variables were found to be statistically significant in relation to AKI development. Table 3 Comparison of Continuous Variables Between AKI and Non-AKI Groups ( all the patients included) Variable AKI Absent (n = 211) AKI Present (n = 116) Median (IQR) p value Age (years) 66.48 ± 15.44 71.22 ± 12.87 69 (20) vs 73 (16) 0.004 ᵃ Albumin (g/L) 31.14 ± 6.46 29.02 ± 6.42 31 (9) vs 29 (8) 0.005 ᵇ CRP (mg/L) 113.19 ± 109.45 154.38 ± 116.51 74.5 (140.9) vs 140.1 (192.5) 0.001 ᵃ APACHE-II Score 21.47 ± 11.31 30.12 ± 12.79 19 (13) vs 30 (22) < 0.001 ᵃ PCT (ng/mL) 4.32 ± 15.88 13.84 ± 29.04 0.22 (0.93) vs 1.39 vs (0.98) < 0.001 ᵃ Mann–Whitney U test used (nonparametric). ᵇ Independent Samples t-test used (parametric). The comparison of continuous variables between AKI and non-AKI groups is summarized in Table 3 A binary logistic regression analysis was performed including all patients, regardless of their sepsis status. Among the variables entered into the model (APACHE-II score, CRP, PCT, and albumin), APACHE-II (p < 0.001, OR = 1.050) and PCT (p = 0.038, OR = 1.013) were identified as independent predictors of AKI development. CRP (p = 0.478) and albumin (p = 0.257) were not statistically significant. The overall model correctly classified 71.3% of cases. Table 4 includes only patients without sepsis, who were subjected to separate statistical analysis. In this subgroup, age (p < 0.001), CRP (p = 0.001), PCT (p < 0.001), and APACHE-II score (p = 0.003) were found to be significantly associated with the development of AKI, whereas albumin (p = 0.120) and CRP (p = 0.147) were not statistically significant. Table 4 Biochemical and Clinical Parameters According to AKI Status ( only non septic patients included) Variable AKI (-) Mean ± SD / Median (IQR) AKI (+) Mean ± SD / Median (IQR) p-value Age (years) 66.34 ± 15.08 / 69 (IQR: 19) 73.35 ± 11.40 / 75 (IQR: 16) < 0.001ᵃ Albumin (g/L) 31.81 ± 6.31 / 32 (IQR: 8) 30.31 ± 7.28 / 30 (IQR: 10) 0.120 ᵇ CRP (mg/L) 89.66 ± 93.56 / 61.9 (IQR: 90.8) 104.33 ± 91.45 / 81 (IQR: 115.5) 0.147ᵃ PCT (ng/mL) 0.33 ± 0.42 / 0.14 (IQR: 0.32) 0.57 ± 0.50 / 0.42 (IQR: 0.68) < 0.001ᵃ APACHE II score 20.62 ± 10.87 / 18 (IQR: 12) 26.22 ± 13.07 / 22 (IQR: 20) 0.003ᵃ ᵃ Mann–Whitney U test used (nonparametric). ᵇ Independent Samples t-test used (parametric). As shown in Table 5 , a binary logistic regression model was constructed using APACHE-II and PCT in patients without sepsis. Table 5 Binary Logistic Regression Analysis Identifying Independent Predictors of AKI Variable p-value OR (Exp B) 95% CI for OR (Lower – Upper) APACHE II 0.015 1.031 1.006–1.056 PCT (ng/mL) 0.004 2.507 1.340–4.691 In the subgroup of patients with sepsis , the Mann–Whitney U test was applied to compare continuous variables between those who developed AKI and those who did not. The results showed that only the APACHE-II score was significantly associated with AKI development ( p < 0.001 ). Other variables, including PCT (p = 0.858) , age (p = 0.752) , albumin (p = 0.342) , and CRP (p = 0.870) , were not statistically significant. In the next stage, all patients and those without sepsis were separately subjected to ROC analysis for both the APACHE-II score and PCT values, and the optimal cut-off points were determined using the Youden index, with corresponding sensitivity and specificity values calculated. Accordingly, in the analysis performed for all patients, the optimal cut-off value based on the Youden index was approximately 0.29 ng/mL for PCT, with a sensitivity of 78.4% and a specificity of 55.5%. In the subgroup of patients without sepsis, the optimal PCT cut-off was approximately 0.26 ng/mL, corresponding to a sensitivity of 61.5% and a specificity of 64.3%. For the APACHE-II score, the optimal cut-off value identified in the analysis of all patients was 25 points, yielding a sensitivity of 58.6% and a specificity of 75.1%. In the subgroup of patients without sepsis, the optimal APACHE-II cut-off value was approximately 22 points, with a sensitivity of 53.8% and a specificity of 63.2%. ROC analysis results are presented in Table 6 , and the ROC curves are shown in Fig. 1 (all patients) and Fig. 2 (non-septic patients). Table 6 Summary of ROC analysis ( APACHE-II and PCT) Variable Subgroup AUC (95% CI) Optimal Cut-off (Youden) Sensitivity (%) Specificity (%) PCT (ng/mL) All patients 0.71 (0.65–0.77) 0.29 78.4 55.5 PCT (ng/mL) Non-septic 0.69 (0.61–0.77) 0.26 61.5 64.3 APACHE-II All patients 0.75 (0.69–0.81) 25 58.6 75.1 APACHE-II Non-septic 0.72 (0.64–0.80) 22 53.8 63.2 Patients who developed AKI were further classified into three groups according to their stages. Stage 1 was observed in 21 patients (18.1%); stage 2, in 38 patients (32.8%); and stage 3, in 57 patients (49.1%). The number of days to AKI onset was 3.48 ± 1.75 days (IQR = 3) for stage 1, 3.05 ± 1.66 days (IQR = 3) for stage 2, and 2.61 ± 1.69 days (IQR = 3) for stage 3. 28-day mortality was also evaluated in terms of AKI. Accordingly, mortality within 28 days was observed in 75 of 116 patients (64.7%) who developed AKI, whereas 81 of 211 patients (38.4%) without AKI experienced mortality during the same period. Discussion This study investigated the role of serum PCT levels and APACHE-II scores in predicting the development of AKI within a general ICU population encompassing both surgical and medical patients. The main findings demonstrated that while both PCT and APACHE-II could predict AKI in the overall ICU cohort, PCT predicted AKI only among non-septic patients. This finding aligns with the established understanding that APACHE-II continues to serve as a general indicator of disease severity across all patient groups, whereas the predictive ability of PCT for AKI likely stems from its reflection of underlying non-infectious inflammatory processes that also contribute to renal injury. In the univariate analysis, age, albumin, and CRP were also statistically associated with AKI. Binary logistic regression was performed. However, since age is an integral component of the APACHE-II score, it was not re-entered into the model to avoid redundancy and potential multicollinearity. Similarly, albumin and CRP lost their significance after multivariate adjustment, a finding that can be explained by the APACHE-II score’s ability to capture overall disease severity. Comorbidities such as hypertension, diabetes, COPD, and coronary artery disease were not significantly associated with AKI in this study. Although these conditions are known risk factors in chronic populations, their limited discriminative power in this acute ICU cohort may be due to the overwhelming influence of critical illness variables. Another factor likely contributing to this non-significance is that comorbidity data were collected primarily from relatives or prior medical notes, which might have introduced reporting bias or missing information. We acknowledge this as a limitation that could lead to an underestimation of comorbidity-related effects. Similarly, the use of nephrotoxic agents (aminoglycosides, vancomycin, colistin, contrast) was not significantly associated with AKI. This finding likely reflects their relatively infrequent use in our unit and the controlled antimicrobial stewardship policy that restricts such prescriptions. While exposure to nephrotoxins is a well-established AKI risk factor, its statistical impact may be attenuated in our sample due to both low prevalence and dose variability. Nonetheless, documenting this explicitly strengthens the internal validity of the study by showing awareness of potential confounding influences. Although PCT is primarily regarded as a sepsis-related biomarker, recent evidence suggests that it may also reflect renal hypoperfusion and non-infectious inflammatory stress. [ 14 ] In a study including 1,361 individuals, higher PCT levels were associated with subsequent AKI development. [ 15 ] In ICU settings, where sepsis is prevalent, PCT trajectories often diverge between septic and non-septic patients. Moreover, because PCT is partly eliminated via the kidneys and because sepsis and septic shock themselves impair renal function, jointly evaluating the relationship between PCT and AKI in septic populations becomes inherently challenging. Consistent with this complexity, analyses that include both septic and non-septic patients frequently show that the predictive value of PCT for AKI is confined to the non-septic subgroup. [ 10 , 16 , 17 ] Consequently, alternative indices such as the PCT/albumin ratio have been proposed for septic patients. [ 18 ] In our study, aligned with the literature, PCT was not useful for predicting AKI among patients with sepsis, whereas it predicted AKI among those without sepsis. A plausible explanation is the intricate pathophysiological interplay between sepsis and AKI: in sepsis, PCT rises in parallel with IL-6, potentially masking other mechanisms that would otherwise signal impending renal injury. In addition, a single, markedly elevated PCT value at presentation may decline rapidly with appropriate antimicrobial therapy, allowing patients to recover before AKI ensues; thus, serial rather than single measurements may be more informative for AKI risk assessment in sepsis. [ 19 ] In our cohort, however, only the admission PCT value was available, and AKI occurrence was tracked over seven days. By contrast, the relationship between PCT and AKI in non-septic patients is more consistent in the literature. Studies have shown that admission PCT can predict 7-day AKI in non-septic ICU populations. [ 10 ] Meta-analytic data indicate that PCT predicts AKI among non-septic patients but not reliably in sepsis.[ 20 ] Similar findings have been discussed in non-septic cardiovascular surgical cohorts, where PCT (and IL-6) showed predictive utility for postoperative AKI. [ 21 , 22 ] Even so, in broad, heterogeneous general ICU populations—such as ours—the stand-alone predictive power of PCT for AKI remains debated. [ 23 ] In summary, the PCT level reflects not only bacterial infection but also cellular injury; therefore, its concentration in the blood may increase in various non-infectious conditions as well. [ 24 ] However, in the presence of sepsis, the primary cause of elevation is bacterial proliferation rather than cellular damage. Consequently, the rapidly rising and treatment-responsive PCT levels observed during sepsis may provide limited information regarding AKI when based on a single measurement. On the other hand, the prognostic performance of the APACHE-II score for AKI risk is well established across patient groups. [ 25 ] Prior studies have suggested that APACHE-II values above ~ 19 may denote increased AKI risk, and in our analysis the APACHE-II score successfully predicted AKI in the overall cohort as well as in both the septic and non-septic subgroups. Strengths Our study’s key strength is that it represents a general intensive care population. We enrolled 327 patients from a mixed medical–surgical ICU and subsequently stratified them into subgroups according to sepsis probability. In addition, we reported time to AKI onset, showing that KDIGO stage 3 occurred as early as 2.6 days after ICU admission. Detecting such an early-onset AKI with our model in both the overall and the non-septic populations underscores its clear contribution to early AKI risk prediction. To our knowledge, this is also the first study of this scope in a Turkish ICU population, which is important for the generalizability of similar future research. Limitations This single-center design may limit external validity due to local case mix, treatment practices, and antimicrobial/nephrotoxic exposure patterns; therefore, broad generalizations should be avoided until multi-center external validation is conducted. Only a single admission PCT measurement was available, which may have missed additional prognostic value from serial (trend) measurements, particularly in sepsis where appropriate therapy can rapidly decrease PCT. Although major covariates were included, some residual confounding may persist. Examples include dosage and duration of nephrotoxins, fluid balance, and hemodynamic targets. Finally, we did not include head-to-head comparisons with newer kidney stress/injury biomarkers (e.g., NGAL, TIMP-2·IGFBP7, cystatin C); whether these add incremental discrimination remains to be determined. Conclusion In summary, PCT predicted AKI particularly in non-septic patients, whereas it was not informative among those with a high likelihood of sepsis; in contrast, APACHE-II provided consistent predictive value across both subgroups. In clinical practice, we recommend integrating cohort-calibrated thresholds (e.g., APACHE-II ≈ 22–25, PCT ≈ 0.26–0.29 ng/mL) into a multivariable triage framework, and prioritizing nephroprotective strategies during the first 72 hours (avoidance of nephrotoxins, hemodynamic optimization, minimization of contrast-related risk). Abbreviations AKI Acute Kidney Injury APACHE-II Acute Physiology and Chronic Health Evaluation II AUC Area Under the Curve CKD Chronic Kidney Disease CRP C-Reactive Protein ICU Intensive Care Unit KDIGO Kidney Disease: Improving Global Outcomes OR Odds Ratio PCT Procalcitonin ROC Receiver Operating Characteristic SD Standard Deviation IQR Interquartile Range Declarations Ethics approval and consent to participate This study was approved by the Non-Interventional Research Ethics Committee of İzmir City Hospital (Approval No: 2024/30, dated March 13, 2024). All procedures were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all participating patients whenever possible. For patients who were unable to provide consent due to critical illness or intubation, written consent was obtained from their legally authorized representatives or next of kin. Consent for publication Not applicable. The manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Due to institutional data protection policies, individual-level patient data cannot be publicly shared. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. All study-related costs were covered by the authors. Authors’ contributions Yakup Özgüngör conceived and designed the study, performed the statistical analyses, interpreted the results, and drafted the manuscript. Hicret Yeniay contributed to patient recruitment, data collection, and validation of clinical information. Emre Karagöz participated in data analysis, literature review, and critical revision of the manuscript for important intellectual content. Mensure Çakırgöz supervised the study, contributed to the interpretation of results, and provided final approval of the version to be published. All authors read and approved the final manuscript. Acknowledgements The authors thank the intensive care nursing staff and data management unit of İzmir City Hospital for their technical and clinical support during data collection. References Wacker C, Prkno A, Brunkhorst FM, Schlattmann P. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis. 2013;13:426–35. https://doi.org/10.1016/S1473-3099(12)70323-7 . Christ-Crain M, Müller B. Biomarkers in respiratory tract infections: diagnostic guides to antibiotic prescription, prognostic markers and mediators. Eur Respir J. 2007;30:556–73. https://doi.org/10.1183/09031936.00166106 . Park JH, Kim DH, Jang HR, et al. Clinical relevance of procalcitonin and C-reactive protein as infection markers in renal impairment: a cross-sectional study. Crit Care. 2014;18:640. https://doi.org/10.1186/s13054-014-0640-8 . Wu V-C, Huang T-M, Lai C-F, et al. Acute-on-chronic kidney injury at hospital discharge is associated with long-term dialysis and mortality. Kidney Int. 2011;80:1222–30. https://doi.org/10.1038/ki.2011.259 . Malhotra R, Siew ED. Biomarkers for the Early Detection and Prognosis of Acute Kidney Injury. CJASN. 2017;12:149–73. https://doi.org/10.2215/CJN.01300216 . Ardaillou R, Beaufils M, Nivez M-P, et al. Increased Plasma Calcitonin in Early Acute Renal Failure. Clin Sci. 1975;49:301–4. https://doi.org/10.1042/cs0490301 . Kurtul A, Murat SN, Yarlioglues M, et al. Procalcitonin as an Early Predictor of Contrast-Induced Acute Kidney Injury in Patients With Acute Coronary Syndromes Who Underwent Percutaneous Coronary Intervention. Angiology. 2015;66:957–63. https://doi.org/10.1177/0003319715572218 . Wang R, He M, Ou XF, et al. Serum Procalcitonin Level Predicts Acute Kidney Injury After Traumatic Brain Injury. World Neurosurg. 2020;141:e112–7. https://doi.org/10.1016/j.wneu.2020.04.245 . Jia L, Sheng X, Zamperetti A, et al. Combination of biomarker with clinical risk factors for prediction of severe acute kidney injury in critically ill patients. BMC Nephrol. 2020;21:540. https://doi.org/10.1186/s12882-020-02202-z . Jeeha R, Skinner DL, De Vasconcellos K, Magula NP. Serum procalcitonin levels predict acute kidney injury in critically ill patients. Nephrology. 2018;23:1090–5. https://doi.org/10.1111/nep.13174 . Rodríguez A, Reyes LF, Monclou J, et al. Relationship between acute kidney injury and serum procalcitonin (PCT) concentration in critically ill patients with influenza infection. Med Intensiva. 2018;42:399–408. https://doi.org/10.1016/j.medin.2017.12.004 . Evans L, Rhodes A, Alhazzani W, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Crit Care Med. 2021;49:e1063–143. https://doi.org/10.1097/CCM.0000000000005337 . (2012) KDIGO Board Members. Kidney Int Supplements 2:3. https://doi.org/10.1038/kisup.2012.3 Abdulsada HA, Taha EM. Nitric Oxide, Procalcitonin and Oxidative Stress Index Levels in Acute Bronchitis Patients. J Fac Med Baghdad. 2024;66:129–34. https://doi.org/10.32007/jfacmedbagdad.6622257 . Nie X, Wu B, He Y, et al. Serum procalcitonin predicts development of acute kidney injury in patients with suspected infection. Clin Chem Lab Med (CCLM). 2013;51:1655–61. https://doi.org/10.1515/cclm-2012-0822 . Foulon N, Haeger SM, Okamura K, et al. Procalcitonin levels in septic and nonseptic subjects with AKI and ESKD prior to and during continuous kidney replacement therapy (CKRT). Crit Care. 2025;29:171. https://doi.org/10.1186/s13054-025-05414-7 . Ma Z, Liu W, Deng F, et al. An early warning model to predict acute kidney injury in sepsis patients with prior hypertension. Heliyon. 2024;10:e24227. https://doi.org/10.1016/j.heliyon.2024.e24227 . Chen L, Wu X, Qin H, Zhu H. The PCT to Albumin Ratio Predicts Mortality in Patients With Acute Kidney Injury Caused by Abdominal Infection-Evoked Sepsis. Front Nutr. 2021;8:584461. https://doi.org/10.3389/fnut.2021.584461 . Feng Y, He H, Jia C, et al. Meta-analysis of procalcitonin as a predictor for acute kidney injury. Med (Baltim). 2021;100:e24999. https://doi.org/10.1097/MD.0000000000024999 . Kan W-C, Huang Y-T, Wu V-C, Shiao C-C. Predictive Ability of Procalcitonin for Acute Kidney Injury: A Narrative Review Focusing on the Interference of Infection. IJMS. 2021;22:6903. https://doi.org/10.3390/ijms22136903 . Chen X, Zhou J, Fang M, et al. Procalcitonin, Interleukin-6 and C-reactive Protein Levels Predict Renal Adverse Outcomes and Mortality in Patients with Acute Type A Aortic Dissection. Front Surg. 2022;9:902108. https://doi.org/10.3389/fsurg.2022.902108 . Lin X, Xiao L, Lin W, et al. A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function. BMC Surg. 2024;24:383. https://doi.org/10.1186/s12893-024-02683-x . Chun K, Chung W, Kim AJ, et al. Association between acute kidney injury and serum procalcitonin levels and their diagnostic usefulness in critically ill patients. Sci Rep. 2019;9:4777. https://doi.org/10.1038/s41598-019-41291-1 . Rule JA, Hynan LS, Attar N, et al. Procalcitonin Identifies Cell Injury, Not Bacterial Infection, in Acute Liver Failure. PLoS ONE. 2015;10:e0138566. https://doi.org/10.1371/journal.pone.0138566 . Inci K, Aygencel G, Dundar NB, et al. Factors and outcomes related to new-onset acute kidney injury in septic medical intensive care unit patients. North Clin Istanb. 2024;11:414–21. https://doi.org/10.14744/nci.2024.30040 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor invited by journal 15 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 13 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7848267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546702586,"identity":"589d291f-2b75-4de3-ad14-f64088c2c7a0","order_by":0,"name":"Yakup Özgüngör","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACxgYGBmYEtwKImZkbSNFyBqSFEb8WEEBoYWyDGYNPefvhh48LKursNtxuv/i4cl5tNH87UMuPim24HdaTZmw84wxb8oY7Z4oNz247njvjMGMDY8+Z23j8ksMmzdvGk2xwIydNsnHbsdwGoBZmxjY8WvrfgLRIQLXMOZY7n6CWGWBbDOwMbqQfk2xsqMndQFjLM5BfEhIkb+QwGzYcO5C7EajlID6/GPYng0PMnu9G+sOHDTV1ufPOHz744EcFHi0NEDqxgYHHAEgfBvMO4FQPBPJQ2p6Bgf0BkK7Dp3gUjIJRMApGKAAAkr9eHq81qngAAAAASUVORK5CYII=","orcid":"","institution":"Izmir City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yakup","middleName":"","lastName":"Özgüngör","suffix":""},{"id":546702588,"identity":"cc837b3b-c1e0-4cd2-967e-1298876f699a","order_by":1,"name":"Hicret Yeniay","email":"","orcid":"","institution":"Balikesir City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hicret","middleName":"","lastName":"Yeniay","suffix":""},{"id":546702589,"identity":"b9a3c43e-65e5-4461-880b-0ad627aa59b0","order_by":2,"name":"Emre Karagöz","email":"","orcid":"","institution":"Izmir City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Emre","middleName":"","lastName":"Karagöz","suffix":""},{"id":546702590,"identity":"a361a235-d008-4f15-92ce-26e5580c21ed","order_by":3,"name":"Mensure Çakırgöz","email":"","orcid":"","institution":"Izmir City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mensure","middleName":"","lastName":"Çakırgöz","suffix":""}],"badges":[],"createdAt":"2025-10-13 11:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7848267/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7848267/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96555637,"identity":"d5e9f939-5236-4d2e-9298-d05892f584d9","added_by":"auto","created_at":"2025-11-23 11:39:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156350,"visible":true,"origin":"","legend":"","description":"","filename":"Procalcitonin11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/485bb38da1f52b81607367f1.docx"},{"id":96555634,"identity":"59e91156-10d9-434b-aafa-98771166eb3f","added_by":"auto","created_at":"2025-11-23 11:39:32","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6997,"visible":true,"origin":"","legend":"","description":"","filename":"b9363c8ed4f94d27b937e4c054ff02c8.json","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/673a2115c772e3bcb3c8d6c2.json"},{"id":96555633,"identity":"9e97360d-c898-408a-ad34-ad1a646acd2e","added_by":"auto","created_at":"2025-11-23 11:39:32","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103160,"visible":true,"origin":"","legend":"","description":"","filename":"b9363c8ed4f94d27b937e4c054ff02c81enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/3c713feb79e587993b9a522b.xml"},{"id":96604823,"identity":"47654516-a603-416f-bf25-b8e00a8a9a6e","added_by":"auto","created_at":"2025-11-24 09:15:10","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10990,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/975689b32da70ef84120c18f.png"},{"id":96555631,"identity":"ade0dfc2-a6e7-4e1f-980a-e1bf99a6e7c9","added_by":"auto","created_at":"2025-11-23 11:39:32","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11345,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/692f12e61664ceb28bd5918a.png"},{"id":96555638,"identity":"b6abbe77-e23c-4b8d-aa1b-a79ebcdf7253","added_by":"auto","created_at":"2025-11-23 11:39:33","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98610,"visible":true,"origin":"","legend":"","description":"","filename":"b9363c8ed4f94d27b937e4c054ff02c81structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/57ba75ed1523e56e8f733564.xml"},{"id":96555639,"identity":"24b0c9d8-78f6-4019-a523-972828984aa7","added_by":"auto","created_at":"2025-11-23 11:39:33","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110570,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/c6f35675107db96312e5b545.html"},{"id":96555632,"identity":"67e82f3f-1423-4267-9130-fd6c1ef0dedf","added_by":"auto","created_at":"2025-11-23 11:39:32","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44303,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of all patients\u003c/p\u003e\n\u003cp\u003eRed line: APACHE-II score, Blue line PCT value\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/3eae73c3897fe45abb4e6e7d.jpeg"},{"id":96555636,"identity":"f143f708-25bc-4881-82c7-f825bb1aa1ce","added_by":"auto","created_at":"2025-11-23 11:39:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47557,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of non-septic patients\u003c/p\u003e\n\u003cp\u003eRed line: APACHE-II score, Blue line PCT value\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/ecee28f899c4c8ca2726c9b4.jpeg"},{"id":96607825,"identity":"69ed274b-7e92-448c-a4c9-6dfa78315466","added_by":"auto","created_at":"2025-11-24 09:27:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1336023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7848267/v1/bab169b1-732b-4d29-9583-c674be98e413.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Serum Procalcitonin and APACHE-II Score for Acute Kidney Injury in Critically Ill Patients: A Prospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eProcalcitonin (PCT) is a biomarker that is released into the bloodstream primarily in response to bacterial infections. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] It demonstrates a sensitivity of 77% and a specificity of 79% for detecting bacterial infections. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Therefore, it is frequently used in intensive care units (ICUs) to identify newly developing infections. Since PCT is predominantly eliminated by the kidneys, its serum concentration may also be elevated in both acute and chronic renal failure, even in the absence of bacterial infection.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAlthough acute kidney injury (AKI) is a common condition, it remains a major cause of morbidity and mortality in modern ICUs due to its complex and heterogeneous pathophysiology.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Despite advances in treatment strategies, therapeutic options for AKI are still limited. Consequently, identifying biomarkers and developing predictive models for early detection of AKI before its onset have become increasingly important. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAs early as 1975, elevated plasma immunoreactive calcitonin levels were reported in patients with AKI, particularly during the anuric or oliguric phases of renal failure. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Subsequently, the relationship between PCT and AKI has been investigated, especially among patients with cardiovascular diseases. In studies focusing on contrast-induced nephropathy, it was found that PCT levels measured at the time of contrast administration could predict the development of AKI within 48\u0026ndash;72 hours thereafter.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The predictive value of PCT for AKI has also been explored in patients with cerebrovascular disease.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] The authors proposed that a model incorporating PCT, age, serum chloride level, and C-reactive protein (CRP) could successfully identify patients with AKI.\u003c/p\u003e\u003cp\u003eSimilar studies have been conducted in critically ill populations. In one study involving 577 ICU patients, PCT\u0026thinsp;\u0026gt;\u0026thinsp;0.5 \u0026micro;g/L at admission, age\u0026thinsp;\u0026gt;\u0026thinsp;65 years, and pre-existing chronic kidney disease (CKD) were identified as independent risk factors for AKI.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The predictive value of PCT for AKI in the presence of infection is, however, more complex. In a cohort of 201 patients, PCT predicted AKI only when infection was present but sepsis was absent.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Another study demonstrated that in septic patients, PCT levels could not reliably predict AKI development. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn light of these findings, the aim of our study was to investigate whether PCT levels during the first seven days after ICU admission could serve as a determinant for the development of AKI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e The study was approved by the İzmir City Hospital Non-Interventional Research Ethics Committee (approval date: March 13, 2024; approval number: 2024/30). It was designed as a \u003cb\u003eprospective cohort study\u003c/b\u003e and conducted in the General Intensive Care Unit of İzmir City Hospital, a mixed medical\u0026ndash;surgical tertiary care center, over a six-month period. Data were collected between \u003cb\u003eJune 1, 2024, and December 31, 2024\u003c/b\u003e. Written informed consent was obtained from all participating patients whenever possible. For patients who were unable to provide consent due to critical illness or intubation, written consent was obtained from their legally authorized representatives or next of kin.\u003c/p\u003e\u003cp\u003eStudy population:\u003c/p\u003e\u003cp\u003eAll adult patients (\u003cb\u003e\u0026ge;\u0026thinsp;18 years\u003c/b\u003e) admitted to the ICU during the study period were screened. Patients meeting the eligibility criteria were enrolled consecutively to minimize selection bias.\u003c/p\u003e\u003cp\u003eInclusion criteria included;\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePCT measurement within 24 hours of ICU admission\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eICU stay longer than 24 hours\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eComplete clinical and biochemical data required for AKI and sepsis assessment.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eExclusion criteria were defined as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAKI present at admission to ICU\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eKnown chronic kidney disease (CKD)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eShorter than 24 hours ICU stay\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMissing baseline creatinine data\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSepsis was defined based on the \u003cem\u003eSurviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021\u003c/em\u003e publication. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAKI and its stages were defined according to the KDIGO criteria, based on changes in \u003cb\u003eserum creatinine\u003c/b\u003e or \u003cb\u003eurine output\u003c/b\u003e, whichever criterion was met first. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAt baseline, demographic data including age, sex, comorbid chronic diseases, and causes of ICU admission were recorded. Daily biochemical parameters were monitored for seven consecutive days during the ICU stay.\u003c/p\u003e\u003cp\u003eWithin the first 24 hours of admission, the following parameters were recorded: \u003cb\u003eAPACHE-II score, albumin, procalcitonin, creatinine, and C-reactive protein (CRP)\u003c/b\u003e levels. In addition, the use of \u003cb\u003econtrast agents, diuretics, and nephrotoxic drugs\u003c/b\u003e such as aminoglycosides, vancomycin, and colistin was noted.\u003c/p\u003e\u003cp\u003eAfter transferring the data to electronic format, statistical analyses were performed using \u003cb\u003eSPSS version 26\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eCategorical variables were expressed as counts and percentages. The distribution of continuous variables was evaluated with the \u003cb\u003eShapiro\u0026ndash;Wilk test\u003c/b\u003e. Normally distributed variables were presented as \u003cb\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation\u003c/b\u003e, and non-normally distributed variables as \u003cb\u003emedian and interquartile range (IQR)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFor group comparisons, \u003cb\u003echi-square tests\u003c/b\u003e were used for categorical variables. Among continuous variables, the \u003cb\u003eStudent\u0026rsquo;s t-test\u003c/b\u003e was applied to those with normal distribution, and the \u003cb\u003eMann\u0026ndash;Whitney U test\u003c/b\u003e to those without normal distribution. Variables that were statistically significant in univariate analyses were included in a \u003cb\u003eforward binary logistic regression model\u003c/b\u003e to identify independent predictors. \u003cb\u003eReceiver operating characteristic (ROC)\u003c/b\u003e analysis was used to evaluate discriminative performance. \u003cb\u003eCut-off values\u003c/b\u003e for PCT and APACHE-II were determined using the \u003cb\u003eYouden index\u003c/b\u003e, and corresponding \u003cb\u003esensitivity, specificity, and area under the curve (AUC, 95% CI)\u003c/b\u003e were calculated.\u003c/p\u003e\u003cp\u003eA \u003cb\u003etwo-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e was considered statistically significant in all analyses.\u003c/p\u003e\u003cp\u003eA post-hoc power analysis was performed. According to the \u003cb\u003epost-hoc power analysis\u003c/b\u003e conducted using \u003cem\u003eG\u003c/em\u003ePower 3.1*, with \u003cb\u003eα\u0026thinsp;=\u0026thinsp;0.05\u003c/b\u003e and \u003cb\u003eCohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.45\u003c/b\u003e, a total sample size of \u003cb\u003en\u0026thinsp;=\u0026thinsp;327\u003c/b\u003e yielded a \u003cb\u003estatistical power of 95%\u003c/b\u003e, indicating that the study had sufficient power to detect the observed effect size.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of the Study Population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Mellitus (DM)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension (HT)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic Obstructive Pulmonary Disease (COPD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoronary Artery Disease (CAD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReason for ICU Admission\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOncologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNephrologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1\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\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eContrast Agent Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFurosemide Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAminoglycoside Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVancomycin Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eColistin Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality (present)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA total of 327 patients were included in the analysis, with a mean age of 68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7 years; 45.3% (n\u0026thinsp;=\u0026thinsp;148) were female. The most common comorbidities were hypertension (51.4%) and diabetes mellitus (44.3%). The primary reasons for ICU admission were pulmonary (55.4%), neurologic (18.7%), and trauma-related (11.9%) causes. The 28-day mortality rate was 47.7% (n\u0026thinsp;=\u0026thinsp;156). The overall incidence of AKI during ICU stay was 35.5% (n\u0026thinsp;=\u0026thinsp;116) according to KDIGO criteria. The mean procalcitonin (PCT) level was 7.74\u0026thinsp;\u0026plusmn;\u0026thinsp;21.99 ng/mL, with a median of 4.10 and an IQR of 2.19. The mean creatinine level was 1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03 mg/dL, with a median of 0.89 mg/dL and an IQR of 0.69 mg/dL. The mean albumin concentration was 30.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54 g/L, with a median of 31.00 g/L and an IQR of 9 g/L. The mean C-reactive protein (CRP) level was 128.37\u0026thinsp;\u0026plusmn;\u0026thinsp;113.68 mg/L, with a median of 85.20 mg/L and an IQR of 162.4 mg/L, indicating a wide distribution. Finally, the mean APACHE-II score was 24.58\u0026thinsp;\u0026plusmn;\u0026thinsp;12.57, with a median of 22.00 and an IQR of 18. Among these parameters, only the albumin level showed a normal distribution according to the Shapiro\u0026ndash;Wilk test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additional information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003ePatients who developed AKI were significantly older (71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 vs. 66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4 years, p\u0026thinsp;=\u0026thinsp;0.004) and had higher CRP, PCT, and APACHE-II values, along with lower albumin levels. Sepsis was present in 56.0% of AKI cases, compared to 31.1% among non-AKI patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other comorbidities and nephrotoxic drug exposures were not statistically different (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Demographic and Clinical Variables Between Patients With and Without Acute Kidney Injury (AKI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003eAKI Present n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAKI Absent n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68 (38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111 (62.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Mellitus (DM)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48 (33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97 (66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension (HT)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107 (63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic Obstructive Pulmonary Disease (COPD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoronary Artery Disease (CAD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41 (38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67 (62.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReason for ICU Admission\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120 (66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOncologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (35.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (61.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40 (65.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNephrologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (71.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrauma / Toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (69.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eContrast Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFurosemide Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAminoglycoside Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVancomycin Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eColistin Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSepsis\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis absent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e171 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40 (44.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eOnly chi-square test used\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll continuous variables obtained from the entire study population\u0026mdash;including age, PCT, APACHE-II score, albumin, and CRP levels\u0026mdash;were analyzed using either the Mann\u0026ndash;Whitney U test or the Student\u0026rsquo;s t-test. As presented in this table, all these variables were found to be statistically significant in relation to AKI development.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Continuous Variables Between AKI and Non-AKI Groups ( all the patients included)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAKI Absent (n\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAKI Present (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.48\u0026thinsp;\u0026plusmn;\u0026thinsp;15.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.22\u0026thinsp;\u0026plusmn;\u0026thinsp;12.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (20) vs 73 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlbumin (g/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (9) vs 29 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e ᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.19\u0026thinsp;\u0026plusmn;\u0026thinsp;109.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154.38\u0026thinsp;\u0026plusmn;\u0026thinsp;116.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.5 (140.9) vs 140.1 (192.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPACHE-II Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.47\u0026thinsp;\u0026plusmn;\u0026thinsp;11.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (13) vs 30 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCT (ng/mL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;15.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.84\u0026thinsp;\u0026plusmn;\u0026thinsp;29.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22 (0.93) vs 1.39 vs (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eᵃ\u003cem\u003eMann\u0026ndash;Whitney U test used (nonparametric).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eᵇ \u003cem\u003eIndependent Samples t-test used (parametric).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe comparison of continuous variables between AKI and non-AKI groups is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA binary logistic regression analysis was performed including all patients, regardless of their sepsis status. Among the variables entered into the model (APACHE-II score, CRP, PCT, and albumin), APACHE-II (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.050) and PCT (p\u0026thinsp;=\u0026thinsp;0.038, OR\u0026thinsp;=\u0026thinsp;1.013) were identified as independent predictors of AKI development. CRP (p\u0026thinsp;=\u0026thinsp;0.478) and albumin (p\u0026thinsp;=\u0026thinsp;0.257) were not statistically significant. The overall model correctly classified 71.3% of cases.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eincludes only patients without sepsis, who were subjected to separate statistical analysis. In this subgroup, age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CRP (p\u0026thinsp;=\u0026thinsp;0.001), PCT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and APACHE-II score (p\u0026thinsp;=\u0026thinsp;0.003) were found to be significantly associated with the development of AKI, whereas albumin (p\u0026thinsp;=\u0026thinsp;0.120) and CRP (p\u0026thinsp;=\u0026thinsp;0.147) were not statistically significant.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBiochemical and Clinical Parameters According to AKI Status ( only non septic patients included)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eAKI (-) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / Median (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAKI (+) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / Median (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.34\u0026thinsp;\u0026plusmn;\u0026thinsp;15.08 / 69 (IQR: 19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.35\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40 / 75 (IQR: 16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlbumin (g/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31 / 32 (IQR: 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28 / 30 (IQR: 10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.120 ᵇ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.66\u0026thinsp;\u0026plusmn;\u0026thinsp;93.56 / 61.9 (IQR: 90.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.33\u0026thinsp;\u0026plusmn;\u0026thinsp;91.45 / 81 (IQR: 115.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.147ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCT (ng/mL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 / 0.14 (IQR: 0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 / 0.42 (IQR: 0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPACHE II score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87 / 18 (IQR: 12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.22\u0026thinsp;\u0026plusmn;\u0026thinsp;13.07 / 22 (IQR: 20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003ᵃ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eᵃ\u003cem\u003eMann\u0026ndash;Whitney U test used (nonparametric).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eᵇ \u003cem\u003eIndependent Samples t-test used (parametric).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, a binary logistic regression model was constructed using APACHE-II and PCT in patients without sepsis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary Logistic Regression Analysis Identifying Independent Predictors of AKI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (Exp B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI for OR (Lower \u0026ndash; Upper)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPACHE II\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.006\u0026ndash;1.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCT (ng/mL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.507\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.340\u0026ndash;4.691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the subgroup of patients with \u003cb\u003esepsis\u003c/b\u003e, the Mann\u0026ndash;Whitney U test was applied to compare continuous variables between those who developed AKI and those who did not. The results showed that \u003cb\u003eonly the APACHE-II score\u003c/b\u003e was significantly associated with AKI development (\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e). Other variables, including \u003cb\u003ePCT (p\u0026thinsp;=\u0026thinsp;0.858)\u003c/b\u003e, \u003cb\u003eage (p\u0026thinsp;=\u0026thinsp;0.752)\u003c/b\u003e, \u003cb\u003ealbumin (p\u0026thinsp;=\u0026thinsp;0.342)\u003c/b\u003e, and \u003cb\u003eCRP (p\u0026thinsp;=\u0026thinsp;0.870)\u003c/b\u003e, were not statistically significant.\u003c/p\u003e\u003cp\u003eIn the next stage, all patients and those without sepsis were separately subjected to ROC analysis for both the APACHE-II score and PCT values, and the optimal cut-off points were determined using the Youden index, with corresponding sensitivity and specificity values calculated. Accordingly, in the analysis performed for all patients, the optimal cut-off value based on the Youden index was approximately 0.29 ng/mL for PCT, with a sensitivity of 78.4% and a specificity of 55.5%. In the subgroup of patients without sepsis, the optimal PCT cut-off was approximately 0.26 ng/mL, corresponding to a sensitivity of 61.5% and a specificity of 64.3%. For the APACHE-II score, the optimal cut-off value identified in the analysis of all patients was 25 points, yielding a sensitivity of 58.6% and a specificity of 75.1%. In the subgroup of patients without sepsis, the optimal APACHE-II cut-off value was approximately 22 points, with a sensitivity of 53.8% and a specificity of 63.2%. ROC analysis results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and the ROC curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (all patients) and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (non-septic patients).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of ROC analysis ( APACHE-II and PCT)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOptimal Cut-off (Youden)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCT (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71 (0.65\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCT (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-septic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.69 (0.61\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPACHE-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75 (0.69\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPACHE-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-septic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.64\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients who developed AKI were further classified into three groups according to their stages. Stage 1 was observed in 21 patients (18.1%); stage 2, in 38 patients (32.8%); and stage 3, in 57 patients (49.1%). The number of days to AKI onset was 3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75 days (IQR\u0026thinsp;=\u0026thinsp;3) for stage 1, 3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66 days (IQR\u0026thinsp;=\u0026thinsp;3) for stage 2, and 2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69 days (IQR\u0026thinsp;=\u0026thinsp;3) for stage 3.\u003c/p\u003e\u003cp\u003e28-day mortality was also evaluated in terms of AKI. Accordingly, mortality within 28 days was observed in 75 of 116 patients (64.7%) who developed AKI, whereas 81 of 211 patients (38.4%) without AKI experienced mortality during the same period.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the role of serum PCT levels and APACHE-II scores in predicting the development of AKI within a general ICU population encompassing both surgical and medical patients. The main findings demonstrated that while both PCT and APACHE-II could predict AKI in the overall ICU cohort, PCT predicted AKI only among non-septic patients. This finding aligns with the established understanding that APACHE-II continues to serve as a general indicator of disease severity across all patient groups, whereas the predictive ability of PCT for AKI likely stems from its reflection of underlying non-infectious inflammatory processes that also contribute to renal injury.\u003c/p\u003e\u003cp\u003eIn the univariate analysis, age, albumin, and CRP were also statistically associated with AKI. Binary logistic regression was performed. However, since age is an integral component of the APACHE-II score, it was not re-entered into the model to avoid redundancy and potential multicollinearity. Similarly, albumin and CRP lost their significance after multivariate adjustment, a finding that can be explained by the APACHE-II score\u0026rsquo;s ability to capture overall disease severity.\u003c/p\u003e\u003cp\u003eComorbidities such as hypertension, diabetes, COPD, and coronary artery disease were not significantly associated with AKI in this study. Although these conditions are known risk factors in chronic populations, their limited discriminative power in this acute ICU cohort may be due to the overwhelming influence of critical illness variables. Another factor likely contributing to this non-significance is that comorbidity data were collected primarily from relatives or prior medical notes, which might have introduced reporting bias or missing information. We acknowledge this as a limitation that could lead to an underestimation of comorbidity-related effects.\u003c/p\u003e\u003cp\u003eSimilarly, the use of nephrotoxic agents (aminoglycosides, vancomycin, colistin, contrast) was not significantly associated with AKI. This finding likely reflects their relatively infrequent use in our unit and the controlled antimicrobial stewardship policy that restricts such prescriptions. While exposure to nephrotoxins is a well-established AKI risk factor, its statistical impact may be attenuated in our sample due to both low prevalence and dose variability. Nonetheless, documenting this explicitly strengthens the internal validity of the study by showing awareness of potential confounding influences.\u003c/p\u003e\u003cp\u003eAlthough PCT is primarily regarded as a sepsis-related biomarker, recent evidence suggests that it may also reflect renal hypoperfusion and non-infectious inflammatory stress. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] In a study including 1,361 individuals, higher PCT levels were associated with subsequent AKI development. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] In ICU settings, where sepsis is prevalent, PCT trajectories often diverge between septic and non-septic patients. Moreover, because PCT is partly eliminated via the kidneys and because sepsis and septic shock themselves impair renal function, jointly evaluating the relationship between PCT and AKI in septic populations becomes inherently challenging. Consistent with this complexity, analyses that include both septic and non-septic patients frequently show that the predictive value of PCT for AKI is confined to the non-septic subgroup. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Consequently, alternative indices such as the PCT/albumin ratio have been proposed for septic patients. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn our study, aligned with the literature, PCT was not useful for predicting AKI among patients with sepsis, whereas it predicted AKI among those without sepsis. A plausible explanation is the intricate pathophysiological interplay between sepsis and AKI: in sepsis, PCT rises in parallel with IL-6, potentially masking other mechanisms that would otherwise signal impending renal injury. In addition, a single, markedly elevated PCT value at presentation may decline rapidly with appropriate antimicrobial therapy, allowing patients to recover before AKI ensues; thus, \u003cb\u003eserial\u003c/b\u003e rather than single measurements may be more informative for AKI risk assessment in sepsis. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] In our cohort, however, only the admission PCT value was available, and AKI occurrence was tracked over seven days.\u003c/p\u003e\u003cp\u003eBy contrast, the relationship between PCT and AKI in \u003cb\u003enon-septic\u003c/b\u003e patients is more consistent in the literature. Studies have shown that admission PCT can predict 7-day AKI in non-septic ICU populations. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Meta-analytic data indicate that PCT predicts AKI among non-septic patients but not reliably in sepsis.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Similar findings have been discussed in non-septic cardiovascular surgical cohorts, where PCT (and IL-6) showed predictive utility for postoperative AKI. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Even so, in broad, heterogeneous general ICU populations\u0026mdash;such as ours\u0026mdash;the stand-alone predictive power of PCT for AKI remains debated. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn summary, the PCT level reflects not only bacterial infection but also cellular injury; therefore, its concentration in the blood may increase in various non-infectious conditions as well. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] However, in the presence of sepsis, the primary cause of elevation is bacterial proliferation rather than cellular damage. Consequently, the rapidly rising and treatment-responsive PCT levels observed during sepsis may provide limited information regarding AKI when based on a single measurement.\u003c/p\u003e\u003cp\u003eOn the other hand, the prognostic performance of the \u003cb\u003eAPACHE-II score\u003c/b\u003e for AKI risk is well established across patient groups. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Prior studies have suggested that APACHE-II values above ~\u0026thinsp;19 may denote increased AKI risk, and in our analysis the APACHE-II score successfully predicted AKI in the overall cohort as well as in both the septic and non-septic subgroups.\u003c/p\u003e\u003cp\u003eStrengths\u003c/p\u003e\u003cp\u003eOur study\u0026rsquo;s key strength is that it represents a general intensive care population. We enrolled 327 patients from a mixed medical\u0026ndash;surgical ICU and subsequently stratified them into subgroups according to sepsis probability. In addition, we reported time to AKI onset, showing that KDIGO stage 3 occurred as early as 2.6 days after ICU admission. Detecting such an early-onset AKI with our model in both the overall and the non-septic populations underscores its clear contribution to early AKI risk prediction. To our knowledge, this is also the first study of this scope in a Turkish ICU population, which is important for the generalizability of similar future research.\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThis single-center design may limit external validity due to local case mix, treatment practices, and antimicrobial/nephrotoxic exposure patterns; therefore, broad generalizations should be avoided until multi-center external validation is conducted. Only a single admission PCT measurement was available, which may have missed additional prognostic value from serial (trend) measurements, particularly in sepsis where appropriate therapy can rapidly decrease PCT. Although major covariates were included, some residual confounding may persist. Examples include dosage and duration of nephrotoxins, fluid balance, and hemodynamic targets. Finally, we did not include head-to-head comparisons with newer kidney stress/injury biomarkers (e.g., NGAL, TIMP-2\u0026middot;IGFBP7, cystatin C); whether these add incremental discrimination remains to be determined.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, PCT predicted AKI particularly in non-septic patients, whereas it was not informative among those with a high likelihood of sepsis; in contrast, APACHE-II provided consistent predictive value across both subgroups. In clinical practice, we recommend integrating cohort-calibrated thresholds (e.g., APACHE-II\u0026thinsp;\u0026asymp;\u0026thinsp;22\u0026ndash;25, PCT\u0026thinsp;\u0026asymp;\u0026thinsp;0.26\u0026ndash;0.29 ng/mL) into a multivariable triage framework, and prioritizing nephroprotective strategies during the first 72 hours (avoidance of nephrotoxins, hemodynamic optimization, minimization of contrast-related risk).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute Kidney Injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAPACHE-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC-Reactive Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKDIGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKidney Disease: Improving Global Outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProcalcitonin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Non-Interventional Research Ethics Committee of İzmir City Hospital (Approval No: 2024/30, dated March 13, 2024). All procedures were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participating patients whenever possible. For patients who were unable to provide consent due to critical illness or intubation, written consent was obtained from their legally authorized representatives or next of kin.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eDue to institutional data protection policies, individual-level patient data cannot be publicly shared.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAll study-related costs were covered by the authors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eYakup \u0026Ouml;zg\u0026uuml;ng\u0026ouml;r conceived and designed the study, performed the statistical analyses, interpreted the results, and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eHicret Yeniay contributed to patient recruitment, data collection, and validation of clinical information.\u003c/p\u003e\n\u003cp\u003eEmre Karag\u0026ouml;z participated in data analysis, literature review, and critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eMensure \u0026Ccedil;akırg\u0026ouml;z supervised the study, contributed to the interpretation of results, and provided final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the intensive care nursing staff and data management unit of İzmir City Hospital for their technical and clinical support during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWacker C, Prkno A, Brunkhorst FM, Schlattmann P. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis. 2013;13:426\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1473-3099(12)70323-7\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(12)70323-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChrist-Crain M, M\u0026uuml;ller B. Biomarkers in respiratory tract infections: diagnostic guides to antibiotic prescription, prognostic markers and mediators. Eur Respir J. 2007;30:556\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/09031936.00166106\u003c/span\u003e\u003cspan address=\"10.1183/09031936.00166106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark JH, Kim DH, Jang HR, et al. Clinical relevance of procalcitonin and C-reactive protein as infection markers in renal impairment: a cross-sectional study. Crit Care. 2014;18:640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-014-0640-8\u003c/span\u003e\u003cspan address=\"10.1186/s13054-014-0640-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu V-C, Huang T-M, Lai C-F, et al. Acute-on-chronic kidney injury at hospital discharge is associated with long-term dialysis and mortality. Kidney Int. 2011;80:1222\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ki.2011.259\u003c/span\u003e\u003cspan address=\"10.1038/ki.2011.259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalhotra R, Siew ED. Biomarkers for the Early Detection and Prognosis of Acute Kidney Injury. CJASN. 2017;12:149\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2215/CJN.01300216\u003c/span\u003e\u003cspan address=\"10.2215/CJN.01300216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArdaillou R, Beaufils M, Nivez M-P, et al. Increased Plasma Calcitonin in Early Acute Renal Failure. Clin Sci. 1975;49:301\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1042/cs0490301\u003c/span\u003e\u003cspan address=\"10.1042/cs0490301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurtul A, Murat SN, Yarlioglues M, et al. Procalcitonin as an Early Predictor of Contrast-Induced Acute Kidney Injury in Patients With Acute Coronary Syndromes Who Underwent Percutaneous Coronary Intervention. Angiology. 2015;66:957\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0003319715572218\u003c/span\u003e\u003cspan address=\"10.1177/0003319715572218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang R, He M, Ou XF, et al. Serum Procalcitonin Level Predicts Acute Kidney Injury After Traumatic Brain Injury. World Neurosurg. 2020;141:e112\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wneu.2020.04.245\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2020.04.245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia L, Sheng X, Zamperetti A, et al. Combination of biomarker with clinical risk factors for prediction of severe acute kidney injury in critically ill patients. BMC Nephrol. 2020;21:540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12882-020-02202-z\u003c/span\u003e\u003cspan address=\"10.1186/s12882-020-02202-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeeha R, Skinner DL, De Vasconcellos K, Magula NP. Serum procalcitonin levels predict acute kidney injury in critically ill patients. Nephrology. 2018;23:1090\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nep.13174\u003c/span\u003e\u003cspan address=\"10.1111/nep.13174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez A, Reyes LF, Monclou J, et al. Relationship between acute kidney injury and serum procalcitonin (PCT) concentration in critically ill patients with influenza infection. Med Intensiva. 2018;42:399\u0026ndash;408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.medin.2017.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.medin.2017.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans L, Rhodes A, Alhazzani W, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Crit Care Med. 2021;49:e1063\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/CCM.0000000000005337\u003c/span\u003e\u003cspan address=\"10.1097/CCM.0000000000005337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e(2012) KDIGO Board Members. Kidney Int Supplements 2:3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/kisup.2012.3\u003c/span\u003e\u003cspan address=\"10.1038/kisup.2012.3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdulsada HA, Taha EM. Nitric Oxide, Procalcitonin and Oxidative Stress Index Levels in Acute Bronchitis Patients. J Fac Med Baghdad. 2024;66:129\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32007/jfacmedbagdad.6622257\u003c/span\u003e\u003cspan address=\"10.32007/jfacmedbagdad.6622257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNie X, Wu B, He Y, et al. Serum procalcitonin predicts development of acute kidney injury in patients with suspected infection. Clin Chem Lab Med (CCLM). 2013;51:1655\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/cclm-2012-0822\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2012-0822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoulon N, Haeger SM, Okamura K, et al. Procalcitonin levels in septic and nonseptic subjects with AKI and ESKD prior to and during continuous kidney replacement therapy (CKRT). Crit Care. 2025;29:171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-025-05414-7\u003c/span\u003e\u003cspan address=\"10.1186/s13054-025-05414-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa Z, Liu W, Deng F, et al. An early warning model to predict acute kidney injury in sepsis patients with prior hypertension. Heliyon. 2024;10:e24227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e24227\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e24227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Wu X, Qin H, Zhu H. The PCT to Albumin Ratio Predicts Mortality in Patients With Acute Kidney Injury Caused by Abdominal Infection-Evoked Sepsis. Front Nutr. 2021;8:584461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2021.584461\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.584461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng Y, He H, Jia C, et al. Meta-analysis of procalcitonin as a predictor for acute kidney injury. Med (Baltim). 2021;100:e24999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MD.0000000000024999\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000024999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKan W-C, Huang Y-T, Wu V-C, Shiao C-C. Predictive Ability of Procalcitonin for Acute Kidney Injury: A Narrative Review Focusing on the Interference of Infection. IJMS. 2021;22:6903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms22136903\u003c/span\u003e\u003cspan address=\"10.3390/ijms22136903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Zhou J, Fang M, et al. Procalcitonin, Interleukin-6 and C-reactive Protein Levels Predict Renal Adverse Outcomes and Mortality in Patients with Acute Type A Aortic Dissection. Front Surg. 2022;9:902108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fsurg.2022.902108\u003c/span\u003e\u003cspan address=\"10.3389/fsurg.2022.902108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin X, Xiao L, Lin W, et al. A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function. BMC Surg. 2024;24:383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12893-024-02683-x\u003c/span\u003e\u003cspan address=\"10.1186/s12893-024-02683-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChun K, Chung W, Kim AJ, et al. Association between acute kidney injury and serum procalcitonin levels and their diagnostic usefulness in critically ill patients. Sci Rep. 2019;9:4777. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-41291-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-41291-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRule JA, Hynan LS, Attar N, et al. Procalcitonin Identifies Cell Injury, Not Bacterial Infection, in Acute Liver Failure. PLoS ONE. 2015;10:e0138566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0138566\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0138566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInci K, Aygencel G, Dundar NB, et al. Factors and outcomes related to new-onset acute kidney injury in septic medical intensive care unit patients. North Clin Istanb. 2024;11:414\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14744/nci.2024.30040\u003c/span\u003e\u003cspan address=\"10.14744/nci.2024.30040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Procalcitonin, Acute Kidney Injury, APACHE-II, Intensive Care Unit, Sepsis, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7848267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7848267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute kidney injury (AKI) is a common and highly fatal condition among patients treated in intensive care units (ICUs). This study aimed to investigate whether procalcitonin (PCT) levels and APACHE-II scores can predict the development of AKI in critically ill patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective cohort study was conducted in the General Intensive Care Unit of İzmir City Hospital. A total of 327 patients were included. Patients were divided into two groups according to the presence or absence of AKI. Continuous variables (age, PCT, albumin, CRP, and APACHE-II) were analyzed using either the Student’s t-test or the Mann–Whitney U test based on the Shapiro–Wilk normality test. Categorical variables were compared using the chi-square test. Variables found to be significant were entered into a forward binary logistic regression model. For subgroup analyses, patients were reclassified according to sepsis status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age of the study population was 68.09 ± 14.72 years, and 45.3% were female. In the entire cohort, age (p = 0.004), albumin (p = 0.005), CRP (p = 0.001), APACHE-II (p \u0026lt; 0.001), and PCT (p \u0026lt; 0.001) were significantly associated with AKI development. In logistic regression analysis, APACHE-II (p \u0026lt; 0.001; OR = 1.050) and PCT (p = 0.038; OR = 1.013) were identified as independent predictors of AKI.\u003c/p\u003e\n\u003cp\u003eIn patients without sepsis, age (p \u0026lt; 0.001), CRP (p = 0.001), PCT (p \u0026lt; 0.001), and APACHE-II (p = 0.003) remained significant, while albumin was not (p = 0.120). Among those with sepsis, only the APACHE-II score remained significant (p \u0026lt; 0.001). The overall model accuracy was 71.3%. Optimal cut-offs were ~0.29 ng/mL for PCT in all patients (sensitivity 78.4%, specificity 55.5%) and ~0.26 ng/mL in non-septic patients (61.5%, 64.3%), and 25 points for APACHE-II overall (58.6%, 75.1%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum PCT level and APACHE-II score are independent predictors of AKI development in ICU patients. The predictive power of PCT is particularly evident in non-septic patients. Evaluating PCT in conjunction with the APACHE-II score may provide clinical utility in the early identification of patients at high risk for kidney injury.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Serum Procalcitonin and APACHE-II Score for Acute Kidney Injury in Critically Ill Patients: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:39:28","doi":"10.21203/rs.3.rs-7848267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"111887935067765282160378331395658773342","date":"2025-11-24T07:32:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T05:57:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94588433249985552909981529936929444126","date":"2025-11-15T02:27:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T18:38:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-15T08:37:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T12:44:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T12:41:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-10-13T11:02:20+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":"c5c59584-6ab7-4c13-98e2-b05b97c4c3ba","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-23T11:39:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 11:39:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7848267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7848267","identity":"rs-7848267","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}