Association Between Early Acetaminophen Exposure and Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Study

Association Between Early Acetaminophen Exposure and Sepsis-Associated Acute Kidney Injury: A Retrospective 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 Association Between Early Acetaminophen Exposure and Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Study Yang Yang, Meng Qi, Shengru Liang, Xing Wang, Peiwen Shi, Jiangdong Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6712781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Emerging evidence suggests that acetaminophen may confer renoprotective effects by inhibiting lipid peroxidation. This study aimed to investigate whether early acetaminophen administration is associated with a reduced risk of sepsis-associated acute kidney injury (SA-AKI). Methods We conducted a multicenter retrospective cohort study by using data from the two large clinical databases: MIMIC-IV (Medical Information Mart for Intensive Care-IV) and eICU-CRD (eICU Collaborative Research Database). Adult patients with sepsis were included, with acetaminophen exposure defined as administration within 24 hours of ICU admission. The primary outcome was severe SA-AKI (stage 2/3 AKI according to KDIGO criteria) developed within 7 days of sepsis diagnosis. Multivariable logistic regression model, adjusted for established AKI-related risk factors, were used to evaluate associations between early acetaminophen exposure and the risk of severe SA-AKI. Several sensitivity and subgroup analyses were performed to validate findings of multivariable logistic regression. Results The primary (MIMIC-IV) and validation (eICU-CRD) cohorts comprised 13,708 and 20,740 qualified patients, respectively. The incidence of severe SA-AKI was 56% (7,665/13,708) in the primary cohort and 49% (10,149/20,740) in the validation cohort. Acetaminophen was administered to 7,563 patients (55%) in the primary cohort and 10,161 patients (49%) in the validation cohort within 24 hours following ICU admission. After adjusting for AKI-related risk factors, multivariable analysis revealed that early acetaminophen use was independently associated with a reduced risk of severe SA-AKI in both the primary (odds ratio [OR], 0.70; 95% CI, 0.64–0.76; P < 0.001) and validation (OR, 0.62; 95% CI, 0.57–0.68; P < 0.001) cohorts. These associations remained consistent across sensitivity analyses and subgroup evaluations. Conclusions Early acetaminophen use was independently associated with a lower risk of severe SA-AKI in critically ill patients with sepsis. Prospective studies are warranted to confirm causality and evaluate the therapeutic potential of acetaminophen in either preventing SA-AKI or mitigating its severity. sepsis acute kidney injury acetaminophen intensive care unit cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Sepsis-associated acute kidney injury (SA-AKI), a common complication of sepsis, contributes to elevated morbidity and mortality, and is associated with increased risk of chronic comorbidities [ 1 ] . Despite substantial research efforts in this area, there is currently no specific pharmacologic prophylaxis available for this condition [ 2 – 8 ] . Emerging evidence have revealed that acetaminophen, a widely used antipyretic and analgesic agent, exhibits renoprotective properties across diverse clinical settings. Postoperative use of acetaminophen has been shown to reduce the risk of AKI in both pediatric and adult populations undergoing cardiac surgery, as reported in prior studies [ 9 , 10 ] . In patients with severe malaria, acetaminophen enhances serum creatinine (Scr) clearance by 72 hours after treatment initiation [ 11 ] . Additionally, in cases of rhabdomyolysis, acetaminophen significantly attenuated the decline in Scr clearance and reduced the requirement for renal replacement therapy (RRT), thereby improving renal functional outcomes [ 12 , 13 ] . However, the renoprotective role of acetaminophen in SA-AKI remains controversial. While a phase 2a randomized controlled trial reported marked Scr reductions by day 3 of acetaminophen treatment in patients with severe sepsis [ 14 ] , a subsequent phase 2b randomized, double-blind trial involving 447 septic patients showed no significant reduction in adverse kidney events (defined as ≥ 200% Scr elevation from baseline within 28 days of ICU admission) [ 15 ] . This discrepancy may arise from inconsistent SA-AKI diagnostic criteria across studies, which undermines both inter-study comparability and extrapolation of their findings. To address this issue, the 28th Acute Disease Quality Initiative (ADQI) consensus panel established standardized definitions for SA-AKI [ 15 ] , providing a unified framework for future research in this field. Consequently, our study aimed to evaluates acetaminophen's effect on SA-AKI using ADQI definition. We hypothesized that early acetaminophen administration (initiated within 24 hours post-ICU admission) would reduce the risk of severe SA-AKI. Additionally, we examined whether early use of acetaminophen could decrease the incidence of any-stage SA-AKI, new-onset RRT, and in-hospital mortality. Methods Data source A retrospective cohort study was conducted by utilizing two publicly accessible clinical databases: the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD) [ 16 , 17 ] . MIMIC-IV (version 2.2) comprises de-identified medical records of 50,920 patients with 73,181 ICU admissions in different ICU settings at Beth Israel Deaconess Medical Center between 2008 and 2019. eICU-CRD (version 2.0) included 139,367 patients with 200,859 ICU admissions from over 200 medical centers between 2014 and 2015. Patients from MIMIC-IV and eICU-CRD consisted of the primary and validation cohorts, respectively. One researcher (Yang Yang) obtained permission (certification number 48776647) to use the data from both databases. Given the deidentified nature of the two databases that conceal patient information, the requirement for informed consent and ethics approval was waived. This study's reporting adheres to the guidelines set forth by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement [ 18 ] . Study population Adult patients (≥ 18 years) were screened for eligibility if they satisfied the Sepsis-3 criteria on the day of ICU admission, namely, (1) suspected or documented infection, and (2) an increase in the Sequential Organ Failure Assessment (SOFA) score ≥ 2 within 24 hours post-ICU admission [ 19 ] . Exclusion criteria included: (1) ICU stay of less than 24 hours; (2) initiating RRT before acetaminophen use; (3) pre-existing AKI preceding acetaminophen administration or sepsis diagnosis; or (4) unavailable in-hospital outcome data. Moreover, for patients with multiple sepsis-related ICU admission, only the initial admission data were analyzed. Exposure and outcomes In this study, exposure was defined as receipt of at least one dose of acetaminophen within 24 hours after ICU admission. Patients who received no acetaminophen or initiated treatment ≥ 24 hours after ICU admission were designated as the unexposed group. The primary outcome, severe SA-AKI, was defined through sequential assessment: (1) AKI was diagnosed according to the Scr and urine output (UO) criteria of the Kidney Disease Improvement Global Outcome (KDIGO) guidelines [ 20 ] , and (2) severe SA-AKI was subsequently classified per ADQI consensus as stage 2 or stage 3 AKI developing within the 7-day window following sepsis diagnosis [ 15 ] . Baseline Scr levels and all Scr measurements recorded during the first 7 days of ICU admission were analyzed to determine peak AKI stage. UO data were also applied for staging when consecutive measurements were documented at intervals < 12 hours. Secondary outcomes encompassed any-stage SA-AKI (KDIGO stages 1–3), the use of RRT, and in-hospital mortality. All outcomes were analyzed as dichotomous variables. Data collection Structured Query Language (SQL) was utilized for data extraction. The extracted parameters comprised: (1) demographic characteristics including age, sex, and race; (2) comorbidities (as defined by the 9th edition of the International Classification of Diseases) and Charlson comorbidity index (CCI); (3) peak laboratory values on the day of ICU admission, including white blood cell count (WBC), lactate and red cell distribution width (RDW); (4) baseline levels of Scr and estimated glomerular filtration rate (eGFR); (5) mean values of body temperature and mean arterial pressure (MAP) recorded on the day of ICU admittance; (6) binary form of treatment variables prior to SA-AKI onset, including invasive mechanical ventilation, vasopressor use, or high-risk nephrotoxins exposure ( Additional file 1: Table S1 ); (7) highest value of disease severity scores on the day of ICU admission, including the Simplified Acute Physiology Score II (SAPS II) from MIMIC-IV, Acute Physiology and Chronic Health Evaluation IV (APACHE IV) from eICU-CRD, and SOFA score from both databases. The baseline Scr level was defined through the following criteria: (1) if the patient was diagnosed with chronic kidney disease (CKD), then use the lowest Scr value during ICU admission even if it was higher than the normal range (< 1.1 mg/dL); (2) for non-CKD patient, the lowest Scr value during admission was used if it was within the normal range (< 1.1mg/dL); (3) for all remaining cases, baseline Scr values were calculated via the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation [ 21 , 22 ] . Subsequently, the baseline eGFR was determined using the Modification of Diet in Renal Disease (MDRD) equation [ 23 ] ( Additional file 2: Table S2 ). Statistical analyses As a retrospective study, statistical power calculation was not conducted and sample size was based on the data available from the two databases. Continuous variables were presented as median (interquartile ranges [IQR]), and categorical variables were described as number (percentages). Group comparisons were conducted using the Wilcoxon rank sum test for continuous variables and the Pearson's chi-squared test for categorical variables. Multivariable logistic regression was used to assess adjusted association of acetaminophen exposure with the dichotomous primary and secondary outcomes. Covariate selection followed clinical relevance and prior evidence, incorporating demographic factors (age, sex, race), comorbidities (congestive heart failure, diabetes mellitus, chronic liver disease), baseline level of eGFR, clinical interventions (invasive ventilation, use of vasopressors or high-risk nephrotoxins), and the first day’s mean values of body temperature and MAP, as well as peak values of WBC, lactate, RDW, and disease severity scores since ICU admission [ 9 , 10 , 24 ] . Missing data pattern for each confounder was displayed in Additional file 3: Table S3 . To estimate missing values, multiple imputation using predictive mean matching was conducted on 10 completed datasets [ 25 ] . Additionally, variance inflation factors (VIFs) were computed to evaluate multicollinearity in the multivariable logistic regression models. For all analyses, p values were tested by 2-sided, with a significance threshold set at less than 0.05. All analyses were conducted using R statistical software (version 4.3.1). Several sensitivity analyses were conducted to evaluate the robustness of the results obtained from the primary analysis. Firstly, propensity score matching (PSM) was utilized to equalize confounders associated with AKI. Logistic regression model was used to calculate the propensity score (i.e. the likelihood of receiving acetaminophen) for each individual. All the confounders were incorporated into the propensity score model for matching purposes. Patients in the exposure group were matched 1:1 with those in the non-exposure group by using the nearest neighbor method with a caliper width of 0.05 without replacement. The balance of risk factors was assessed using a threshold of standardized mean difference (SMD), where an SMD below 0.1 signified balance [ 26 ] ( Additional file 4: Figure S1 ) . Multivariable logistic regression was then performed on the matched cohort. Secondly, multivariable logistic regression was performed on the first-day cohort that excluded patients receiving initial acetaminophen > 24h post-ICU admission. Thirdly, diagnostic criteria based on Scr alone were applied to assess whether changes in AKI definition could influence the results of the primary analysis. Finally, given the potential for in-hospital mortality occurring before the development of severe SA-AKI to represent a competing risk ( Additional file 5: Figure S2 ), a competing risk model adjusting for AKI-related risk factors was applied. Predefined subgroup analysis was performed by dividing the whole cohort according to several baseline factors: age (≥ 65 vs < 65 yrs), gender (male vs female), race (white vs black vs others), pre-existing chronic kidney disease (yes or no), and baseline renal function categorized as eGFR ≥ 60 vs < 60 mL/min/1.73 m 2 according to Kidney Disease Outcomes Quality Initiative (KDOQI) staging system and prior research [ 27 , 28 ] . Interaction effects between acetaminophen exposure and stratification variables were evaluated using likelihood ratio tests, with P for interaction < 0.05 indicating statistical significance of subgroup effect. Given that the SOFA score serves as a key diagnostic criterion for sepsis, we employed the sliding window Subpopulation Treatment Effect Pattern Plot (STEPP) to evaluate acetaminophen's therapeutic efficacy in severe SA-AKI across subgroups of SOFA [ 29 , 30 ] . This analytical approach enables systematic investigation of treatment-covariate interactions through continuous covariate-defined overlapping subpopulations. Specifically, patients are ranked in ascending order based on their covariate values. Then, two critical parameters must be determined: r1, which denotes the maximum number of patients who can be included in two consecutive overlapping subpopulations, and r2, which represents the number of patients in each subpopulation. The initial subpopulation consists of r2 patients with the lowest covariate values. Subsequent subpopulations are generated by replacing (r2 - r1) patients from the lower end of the current window with the next (r2 - r1) patients in the ordered list. This iterative process continues until complete population coverage is achieved, ensuring each patient participates in at least one sliding window subgroup. In this study, we set r2 to 600 as the size of each subpopulation and r1 to 200 as the number of patients included in consecutive overlapping subpopulations for the STEPP analysis. Results Patient screening The process of patient selection is illustrated in Fig. 1 . Initially, 73,181 records from MIMIC-IV and 200,859 from eICU-CRD were identified. After excluding unqualified records, the primary and validation cohorts comprised 13,708 and 20,740 participants, respectively. Among these, 7,563 (55.0%) ones in the primary cohort and 10,161 (49%) ones in the validation cohort received acetaminophen within 24 hours of ICU admission. Baseline characteristics of cohort Table 1 summarizes the baseline characteristics of patients in the primary and validation cohorts. The majority of patients in both cohorts were white, with an average age exceeding 65 years. In the primary cohort, 56% of individuals developed severe SA-AKI and 75% experienced any-stage SA-AKI. Conversely, only 7% of patients required RRT and 13% experienced in-hospital death. In the validation cohort, 49% and 62% of patients developed severe and any-stage SA-AKI, respectively; while only 9% required RRT and 14% died during hospitalization. Table 1 Characteristics of participants in the primary and validation cohorts a Variables Primary cohort (n = 13708) Validation cohort (n = 20740) Age, years 67.72 (56.2, 79.01) 66 (55, 76) Male, No. (%) 7997 (58) 10792 (52) Race, No. (%) White 9200 (67) 15949 (77) Black 1065 (8) 2212 (11) Others 3443 (25) 2579 (12) Baseline Scr, median (IQR), mg dL − 1 0.7 (0.6, 1) 0.8 (0.6, 1.03) Baseline eGFR, median (IQR), mL min − 1 1.73m − 2 94.23 (75.94, 108.11) 88.45 (73.58, 106.37) Comorbidities, No. (%) Congestive heart failure 3746 (27) 3871 (19) Diabetes 3957 (29) 6962 (34) Chronic renal disease 2685 (20) 3503 (17) COPD 3544 (26) 4553 (22) Chronic liver disease 2106 (15) 896 (4) CCI, median (IQR) 5 (3, 7) 5 (3, 7) Severity of illness, median (IQR) SAPS II 38 (30, 47) --- SOFA 5 (3, 8) 6 (4, 8) APACHE IV --- 64 (50, 82) Medication, No. (%) Use of acetaminophen 7563 (55) 10161 (49) Use of high-risk nephrotoxins 5678 (41) 7048 (34) Use of vasopressors 5171 (38) 6225 (30) Receipt of invasive ventilation 6446 (47) 8312 (40) Outcomes, No. (%) Severe SA-AKI 7665 (56) 10149 (49) Any-stage SA-AKI 10213 (75) 12764 (62) Receipt of RRT 960 (7) 1870 (9) In-hospital mortality 1711 (13) 3007 (14) a Continuous variables are presented as median (IQR) and categorical variables are presented as number (%). Abbreviations: Scr, serum creatinine; eGFR, estimated glomerular filtration rate; COPD, chronic obstructive pulmonary disease; CCI, Carlson comorbidity index; SAPS II, Simplified Acute Physiology Score II; SOFA, Sequential Organ Failure Assessment; APACHE IV, Acute Physiology and Chronic Health Evaluation IV; RRT, renal replacement treatment; SA-AKI, sepsis-associated acute kidney injury. The characteristics of patients exposed to acetaminophen, as compared to those who were not, are detailed in Table 2 . Overall, in both the primary and validation cohorts, acetaminophen-exposed patients exhibited higher proportions of White ethnicity, lower disease severity scores, reduced lactate levels, and decreased RDW values compared to non-exposed counterparts. Individuals exposed to acetaminophen also exhibited a decreased proportion of receiving high-risk nephrotoxic agents. Upon evaluating the temporal patterns of first-dose acetaminophen administration, it was observed that 66.8% of patients in the primary cohort and 64.6% in the validation cohort received their initial acetaminophen dose within the first 24 hours following ICU admission. ( Additional file 6: Figure S3 ) . Table 2 Characteristics of participants in the primary and validation cohorts according to acetaminophen exposure a Variables Primary cohort (n = 13708) Validation cohort (n = 20740) Non-ACE group (n = 6145) ACE group (n = 7563) p value Non-ACE group (n = 10579) ACE group (n = 10161) p value Age, years 67.23 (55.28, 79.14) 68.12 (56.9, 78.94) 0.055 66 (55, 76) 66 (54, 76) 0.209 Male, No. (%) 3535 (58) 4462 (59) 0.085 5511 (52) 5281 (52) 0.865 Race, No. (%) < 0.001 10792 < 0.001 White 3997 (65) 5203 (69) 7820 (74) 8129 (80) Black 518 (8) 547 (7) 991 (9) 1221(12) Others 1630 (27) 1813 (24) 1768(17) 811 (8) Baseline eGFR, median (IQR), mL min − 1 1.73m − 2 91.77 (73.03, 108.33) 95.61 (78.77, 107.96) < 0.001 88.38 (73.71, 106.46) 88.56 (73.51, 105.7) 0.670 MAP, median (IQR), mmHg b 75.43 (69.71, 82.44) 75.37 (70.48, 81.29) 0.978 75.7 (69.25, 84.26) 76.18 (69.63, 85.02) 0.023 Temperature, median (IQR), ℃ b 36.81 (36.55, 37.11) 36.89 (36.63, 37.31) < 0.001 36.84 (36.57, 37.18) 36.93 (36.63, 37.4) < 0.001 Comorbidities, No. (%) Congestive heart failure 1827 (30) 1919 (25) < 0.001 1839 (17) 2032 (20) 0.047 Diabetes mellitus 1826 (30) 2131 (28) 0.050 3302 (31) 3660 (36) < 0.001 Chronic renal disease 1402 (23) 1283 (17) < 0.001 1872 (18) 1631 (16) 0.144 COPD 1741 (28) 1803 (24) < 0.001 2311 (22) 2242 (22) 0.558 Chronic liver disease 1491 (24) 615 (8) < 0.001 691 (7) 205 (2) < 0.001 CCI, median (IQR) 5 (3, 7) 4 (3, 6) < 0.001 4 (2, 6) 4 (2, 6) 0.493 Laboratory test, median (IQR) c WBC, K uL − 1 13.8 (9.6, 19.1) 13.9 (10.3, 18.5) 0.212 14.6 (10.1, 20.5) 14.5 (10.1, 20.32) 0.601 Lactate, mmol L − 1 2.3 (1.5, 4.2) 2.0 (1.6, 3.3) 0.009 2.3 (1.4, 4.0) 2.0 (1.3, 3.4) < 0.001 RDW, % 15.2 (14, 16.9) 14.3 (13.5, 15.7) < 0.001 15.4 (14.2, 17.3) 14.1 (12.9, 16.0) < 0.001 Severity of illness, median (IQR) c SAPS II 40 (32, 50) 36 (29, 44) < 0.001 --- --- --- SOFA 6 (4, 9) 5 (3, 7) < 0.001 6 (4, 9) 5 (3, 8) < 0.001 APACHE IV --- --- --- 65 (50, 83) 63 (48, 79) < 0.001 Medication, No. (%) Use of high-risk nephrotoxins 2701 (44) 2977 (39) < 0.001 3896 (37) 3152 (31) < 0.001 Use of vasopressors 2287 (37) 2884 (38) 0.279 3276 (31) 2949 (29) < 0.001 Receipt of invasive ventilation 3081 (50) 3365 (44) < 0.001 3938 (37) 4374 (43) < 0.001 a Continuous variables are presented as median (IQR) and categorical variables are presented as number (%). P values are calculated by the univariate Wilcoxon rank sum test for continuous variables and by the univariate Pearson’s Chi-squared test for categorical variables. Severe AKI was defined as stage 2 or stage 3 AKI according to the KDIGO definition. b Mean value on the first 24 hours of ICU admission. c Maximum value on the first 24 hours of ICU admission. Abbreviations: ACE, acetaminophen exposure; eGFR, estimated glomerular filtration rate; MAP, mean arterial pressure; COPD, chronic obstructive pulmonary disease; CCI, Carlson comorbidity index; WBC, white blood cell count; RDW, red cell distribution width; SAPS II, Simplified Acute Physiology Score II; SOFA, Sequential Organ Failure Assessment; APACHE IV, Acute Physiology and Chronic Health Evaluation I Acetaminophen-related outcomes In the primary cohort, severe SA-AKI occurred in 50.2% (3,799/7,523) of participants who were exposed to acetaminophen, compared to 62.9% (3,866/6,145) in those not exposed ( P < 0.001). This pattern persisted in the validation cohort, with severe SA-AKI incidence of 41.4% (4,206/10,161) in the exposure group vs 56.2% (5,943/10,579) in the non-exposure group ( P < 0.001; Table 3 ). Additionally, acetaminophen exposure was associated with significantly reduced rates of any-stage SA-AKI, lower requirement for RRT, and decreased in-hospital mortality ( Table 3 ). Table 3 Incidence of primary and secondary outcomes based on acetaminophen exposure a Variables Primary cohort (n = 13708) Validation cohort (n = 20740) Non-ACE group (n = 6145) ACE group (n = 7563) p value Non-ACE group (n = 10579) ACE group (n = 10161) p value Primary outcome, No. (%) Severe SA-AKI 3866 (62.9) 3799 (50.2) < 0.001 5943 (56.2) 4206 (41.4) < 0.001 Secondary outcomes, No. (%) Any-stage SA-AKI 4812 (78.3) 5401 (71.4) < 0.001 7174 (67.8) 5590 (55.0) < 0.001 Use of RRT 679 (11.0) 281 (3.7) < 0.001 1229 (12.3) 571 (5.6) < 0.001 In-hospital death 814 (13.2) 903 (11.9) 0.027 1737 (16.4) 1270 (12.5) 0.002 a P values are calculated by the univariate Pearson’s Chi-squared test and all of the variables are presented as number (%). Abbreviations: ACE , acetaminophen exposure; AKI , acute kidney injury; Scr, serum creatinine; UO, urine output; RRT, renal replacement treatment. Notably, among patients developing severe SA-AKI, 75.3% of cases in the primary cohort and 67.3% in the validation cohort occurred within 48 hours post-ICU admission ( Additional file 7: Figure S4 ). Temporal pattern analysis further demonstrated consistent delays in severe SA-AKI onset among acetaminophen-exposed patients. In the primary cohort, the median onset time of severe SA-AKI was 32.67 hours (23.12–54.32) post-ICU admission for acetaminophen-exposed patients versus 26.03 hours (IQR: 16.87–41.03) for non-exposed counterparts ( P < 0.001 ). Similarly, in the validation cohort, exposed patients developed severe SA-AKI at a median of 38.98 hours (20.68–78.6), compared to 28.79 hours (13.73–59.33) in non-exposed patients ( P < 0.001 ) ( Additional file 8: Figure S5 ). Risk-adjusted analysis To address potential confounding factors, we performed multivariable logistic regression analyses adjusted for established AKI-related risk factors. The results demonstrated that early acetaminophen administration was independently associated with a reduced risk of severe SA-AKI in both the primary (odds ratio [OR], 0.70; 95%CI, 0.64–0.76; P < 0.001) and validation (OR, 0.62; 95%CI, 0.57–0.68; P < 0.001) cohorts (Fig. 2). Furthermore, early administration of acetaminophen was independently linked to decreased likelihood of any-stage SA-AKI and RRT utilization. However, no significant association was identified between acetaminophen exposure and lower in-hospital mortality in either the primary (OR, 1.06; 95%CI, 0.92–1.23; P = 0.114) or validation (OR, 1.03; 95%CI, 0.92–1.16; P = 0.591) cohort (Fig. 2). Additionally, none of the covariates demonstrated VIF values over the threshold of 4, indicating the absence of multicollinearity in all risk-adjusted logistic regression models ( Additional file 9: Figure S6 ). Sensitivity analyses Sensitivity analyses, whether employing PSM approach or focusing on the first-day cohort, and whether utilizing the Scr criteria of KDIGO guidelines or applying competing risk models, consistently revealed an independent correlation between early exposure to acetaminophen and decreased likelihood of severe SA-AKI (Fig. 3). Subgroup analysis Figure 4 displays the results of the predefined subgroup analyses. The upper 95% CI consistently remained below 1.00, and no significant interactions were detected across all subgroups in either the primary or validation cohorts, suggesting that acetaminophen's association with reduced risk of severe SA-AKI was independent of baseline characteristics of patients. The therapeutic efficacy of acetaminophen for severe SA-AKI across subgroups of SOFA score was presented in Fig. 5 . Panel a-b demonstrate a dose-response relationship between higher SOFA scores and increased risk of severe SA-AKI, a pattern most pronounced in non-exposure patients. Panels c-d reveal significant heterogeneity in renoprotective effects of acetaminophen across SOFA score strata ( p for interaction<0.005 ), with acetaminophen-treated patients in the SOFA 5–12 range showing significantly lower risk of severe SA-AKI in contrast to untreated individuals (OR with 95% CI < 1.00), suggesting a threshold-dependent therapeutic efficacy. Discussion This multicenter cohort study, integrating data from two large clinical databases, demonstrated that administering acetaminophen within 24 hours post-ICU admission was associated with a lower risk of severe SA-AKI in adult patients. The robustness of these findings was validated through multiple sensitivity analyses and remained consistent across subgroups stratified by demographic factors (age, gender, race) and renal profiles (pre-existing chronic kidney disease, baseline renal function). However, heterogeneity of treatment effect emerged in both cohorts through STEPP analysis, revealing differential risk reduction patterns of severe SA-AKI across SOFA score strata. Additionally, early acetaminophen administration was associated with lower odds of all-stage SA-AKI and RRT use, yet it did not correlate with in-hospital mortality. The accurate and timely identification of AKI serves as a foundational requirement for detecting SA-AKI in septic patients. A prior study indicates that even non-severe AKI manifesting solely through oliguria correlates with adverse long-term outcomes [ 31 ] . Crucially, omitting UO criteria specified in the KDIGO guidelines may lead to delayed AKI detection and an underestimation of its incidence [ 32 ] . Evidence from a previous analysis showed that incorporating UO criteria into KDIGO’s Scr criteria increased the reported incidence of severe AKI from under 8% to nearly 40% [ 33 ] . Consistently, our data demonstrated that when UO criteria were applied alongside Scr criteria, the prevalence of severe SA-AKI increased from 11% and 15–56% and 49% in the primary and validation cohorts, respectively (data not shown). Given these findings, our study integrated UO criteria into the AKI diagnostic framework. Importantly, whether using UO and Scr criteria in risk-adjusted analyses or Scr criteria alone in sensitivity analyses, our results consistently showed a significant association between acetaminophen administration and a reduced risk of severe SA-AKI. Recent studies using ADQI criteria have increasingly explored the association between acetaminophen exposure and clinical outcomes in SA-AKI patients [ 34 – 36 ] . Whereas, only one study has specifically examined its relationship with SA-AKI development [ 37 ] . This retrospective analysis, conducted across two U.S. medical centers, presents findings that contrast with our observations. However, their study defined acetaminophen exposure as administration within 7 days of ICU admission without clarifying the temporal relationship with AKI onset. Notably, our data showed that 75.3% of severe SA-AKI cases in the primary cohort and 67.3% in the validation cohort occurred within 48 hours post-ICU admission ( Additional file 7: Figure S4 ). Therefore, if significant AKI occurred before acetaminophen exposure in their cohort, potential protective effects of the drug might be obscured. Given the rapid progression of SA-AKI in septic patients, we strictly excluded AKI cases with onset prior to acetaminophen exposure, ensuring temporal priority of exposure relative to outcome occurrence. Additionally, compared to their limited sample size (238 septic patients, 121 SA-AKI cases), our cohort comprised 17,814 severe SA-AKI patients. The large sample size strengthens the general characteristics of the SA-AKI population and ensures sufficient statistical power for detecting clinically relevant associations. While conventional subgroup analyses serve to assess treatment effect heterogeneity, they possess inherent methodological limitations when applied to continuous covariates like SOFA scores. Traditional categorical stratification has the risk of information loss due to arbitrary cutoff points and diminishes statistical power within extreme score ranges. To address these constraints, we adopted the STEPP methodology, enabling a dynamic assessment of treatment effects across overlapping windows of SOFA scores. Our results revealed a threshold-dependent therapeutic window for acetaminophen in SA-AKI prevention. Although early administration consistently demonstrated overall risk reduction (primary cohort: OR 0.70, 95%CI 0.64–0.76; validation cohort: OR 0.62, 95%CI 0.57–0.68), significant heterogeneity emerged across SOFA strata (P for interaction < 0.005). Clinically meaningful benefits were concentrated in patients suffering from moderate organ dysfunction (SOFA 5–12), with non-significant effects observed in patients with mild (SOFA 3–4) and severe (SOFA > 12) organ dysfunction. Based on these findings, future randomized controlled trials ought to consider pre-defined subgroup analyses based on SOFA scores within statistical analysis plans, or implement stratification during randomization according to SOFA categories (3–4, 5–12, > 12) to guarantee balanced representation across subgroups. The exact mechanisms connecting early acetaminophen exposure with the reduced risk of SA-AKI are still unclear, but existing evidences support a key role of acetaminophen’s antioxidant effect in this process. Specifically, erythrocyte deformability was reduced during sepsis, which may damage the membrane of erythrocyte as it passed through microcirculatory channels [ 38 , 39 ] . Subsequently, cell-free hemoglobin (CFH) may be released into the extracellular environment and oxidize ferrous heme into ferryl heme protoporphyrin radical species, which initiate lipid peroxidation and ultimately result in AKI [ 13 , 40 ] . Acetaminophen can effectively inhibit CFH-mediated lipid peroxidation by reducing the release of ferric protoporphyrin radical species from hemoglobin into the circulation [ 13 ] . In a retrospective observational study, Janz and associates found that the plasma levels of lipid peroxidation byproduct F2-isoprostane were reduced in septic patients who received acetaminophen in contrast to those who did not [ 41 ] . They further demonstrated that receiving acetaminophen could reduce the level of F2-isoprostane and Scr among patients with severe sepsis [ 42 ] . All of these findings supported the potential protective effect of acetaminophen on renal function during sepsis by inhibiting the CFH-mediated peroxidation damage (Fig. 6 ) . Our study has certain limitations. Firstly, the retrospective design of the study hindered the determination of a causal relationship between acetaminophen exposure and the decreased occurrence of SA-AKI. Secondly, although AKI-related risk factors were meticulously screened and incorporated into the regression model, the retrospective nature determines that unmeasured confounders may bias the results of the regression analysis. Thirdly, owing to constraints in data availability, we only evaluated the effect of binary acetaminophen exposure on SA-AKI, while the impact of drug dosage and administration frequency were not evaluated from the current research. Besides, prehospital acetaminophen use was not considered in the current study; however, if a significantly greater percentage of patients with SA-AKI had prehospital exposure to acetaminophen compared to those who did not, the findings of this study may be skewed. Lastly, we did not evaluate the safety of acetaminophen, although adverse effects of acetaminophen administration in septic patients or other cases are rare [ 11 , 14 ] . Conclusions In brief, early acetaminophen use was associated with a reduced risk of severe SA-AKI. Further prospective trials are required to confirm the protective benefits of acetaminophen against SA-AK, as suggested by this retrospective research. Abbreviations ADQI, Acute Disease Quality Initiative AKI, Acute kidney injury APACHE IV, Acute Physiology and Chronic Health Evaluation IV CCI, Charlson comorbidity index CKD, Chronic kidney disease CFH, cell-free hemoglobin eICU-CRD, eICU Collaborative Research Database eGFR, Estimated glomerular filtration rate ICU, Intensive care unit KDIGO, Kidney Disease: Improving Global Outcomes MAP, Mean arterial pressure MIMIC-IV, Medical Information Mart for Intensive Care OR, odds ratio PSM, propensity score matching RDW, Red cell distribution width RRT, Renal replacement therapy SA-AKI, Sepsis-associated acute kidney injury SAPS II, Simplified Acute Physiology Score II Scr, Serum creatinine Sepsis-3, Third International Consensus Definitions for Sepsis SOFA, Sequential Organ Failure Assessment STEPP, Subpopulation Treatment Effect Pattern Plot UO, Urine output VIF, variance inflation factor WBC, White blood cell Declarations Ethics approval and consent to participate The study was conducted in compliance with the Helsinki Declaration. Since the MIMIC-IV and eICU-CRD databases are publicly accessible and all data are de-identified to remove patients’ information, obtaining informed consent from patients was not necessary. Consent for publication Not applicable Availability of data and materials Publicly available datasets were analyzed in this study. The available data for MIMIC-IV can be obtained at https://mimic.physionet.org/. The available data for eICU-CRD is available at https://eicu-crd.mit.edu/. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This research was supported by Shaanxi Province Key Research and Development Plan Project (2024SF-YBXM-210), and Innovation Science Fund of Tangdu hospital (No. 2023BTDQN001), Authors’ contributions YY extracted data. YY and MQ wrote the original draft. YY and SL conducted literature review. SL and XW assisted in statistical analysis of the data. XW and PS operated software. JY and JL organized the data and checked the integrity of the data. LL designed the study, and checked the final results. LL provided supervision for the project. All authors have made an intellectual contribution to the manuscript and approved the final submission. Acknowledgments Not applicable References Sadudee, Carlos L, Hernando, et al. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment [J]. Kidney Int, 2019, 96(5). Raghavan, Lisa, Sachin, et al. Association of statin use with risk and outcome of acute kidney injury in community-acquired pneumonia [J]. Clin J Am Soc Nephrol, 2012, 7(6). Steven D, Martin, Hani, et al. Outcomes after Angiography with Sodium Bicarbonate and Acetylcysteine [J]. N Engl J Med, 2017, 378(7). Jeffrey A, Gawain M, Dawn M, et al. 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Association Between Early Postoperative Acetaminophen Exposure and Acute Kidney Injury in Pediatric Patients Undergoing Cardiac Surgery [J]. JAMA pediatrics, 2018, 172(7): 655-63. Xiong, Jia, Wu, et al. Early Postoperative Acetaminophen Administration and Severe Acute Kidney Injury After Cardiac Surgery [J]. American journal of kidney diseases : the official journal of the National Kidney Foundation, 2023, 81(6): 675-83.e1. Plewes, Kingston, Ghose, et al. Acetaminophen as a Renoprotective Adjunctive Treatment in Patients With Severe and Moderately Severe Falciparum Malaria: A Randomized, Controlled, Open-Label Trial [J]. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2018, 67(7): 991-9. Desgrouas, Boulain. Paracetamol use and lowered risk of acute kidney injury in patients with rhabdomyolysis [J]. Journal of nephrology, 2021, 34(5): 1725-35. Boutaud, Moore, Reeder, et al. 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American journal of kidney diseases : the official journal of the National Kidney Foundation, 2002, 39: S1-266. Lazar, Cole, Bonetti, et al. Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot [J]. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2010, 28(29): 4539-44. Bonetti, Gelber. A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data [J]. Statistics in medicine, 2000, 19(19): 2595-609. Priyanka, Alexander, Junichi, et al. The impact of acute kidney injury by serum creatinine or urine output criteria on major adverse kidney events in cardiac surgery patients [J]. J Thorac Cardiovasc Surg, 2020, 162(1). Kama A, Ameen, Mathilde, et al. A comparison of RIFLE with and without urine output criteria for acute kidney injury in critically ill patients [J]. Crit Care, 2012, 16(5). Jennifer C, David S, Henry, et al. Definition of hourly urine output influences reported incidence and staging of acute kidney injury [J]. BMC Nephrol, 2020, 21(1). Shilin, Han, Qun, et al. Association between acetaminophen administration and clinical outcomes in patients with sepsis admitted to the ICU: a retrospective cohort study [J]. Front Med (Lausanne), 2024, 11(0). Hui, Ting, Dongsong. Association between acetaminophen and risk of mortality in patients with sepsis-associated acute kidney injury: A retrospective cohort study from the MIMIC-IV database [J]. J Investig Med, 2024, 73(1). Long-Zhu, Lu-Ming, Yan, et al. Association of acetaminophen use with mortality and renal recovery in patients with sepsis-associated acute kidney injury [J]. BMC Anesthesiol, 2024, 24(1). Asad E, Ohoud, Hussain, et al. Effect of Acetaminophen on the Prevention of Acute Kidney Injury in Patients With Sepsis [J]. Ann Pharmacother, 2017, 52(1). Machiedo, Powell, Rush, et al. The incidence of decreased red blood cell deformability in sepsis and the association with oxygen free radical damage and multiple-system organ failure [J]. Archives of surgery (Chicago, Ill : 1960), 1989, 124(12): 1386-9. Baskurt, Gelmont, Meiselman. Red blood cell deformability in sepsis [J]. American journal of respiratory and critical care medicine, 1998, 157(2): 421-7. N, C, V, et al. The role of lipid hydroperoxides in the myoglobin-dependent oxidation of LDL [J]. Arch Biochem Biophys, 1994, 314(1). Janz, Bastarache, Peterson, et al. Association between cell-free hemoglobin, acetaminophen, and mortality in patients with sepsis: an observational study [J]. Critical care medicine, 2013, 41(3): 784-90. Janz, Bastarache, Rice, et al. Randomized, placebo-controlled trial of acetaminophen for the reduction of oxidative injury in severe sepsis: the Acetaminophen for the Reduction of Oxidative Injury in Severe Sepsis trial [J]. Critical care medicine, 2015, 43(3): 534-41. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1TableS1.docx Additionalfile2TableS2.docx Additionalfile3TableS3.docx Additionalfile4FigureS1.pdf Additionalfile5FigureS2.pdf Additionalfile6FigureS3.pdf Additionalfile7FigureS4.pdf Additionalfile8FigureS5.pdf Additionalfile9FigureS6.pdf Additionalfiles.docx Cite Share Download PDF Status: Posted Version 1 posted 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-6712781","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461505632,"identity":"4c22cc1e-f6e1-4a40-89d1-181dba0dca4c","order_by":0,"name":"Yang Yang","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":461505633,"identity":"0f93c66a-c339-4b09-a549-afa23ba8314e","order_by":1,"name":"Meng Qi","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Qi","suffix":""},{"id":461505634,"identity":"633f8cc9-bdfc-455b-aa04-55a80e4f631c","order_by":2,"name":"Shengru Liang","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shengru","middleName":"","lastName":"Liang","suffix":""},{"id":461505635,"identity":"fd23bee0-d7a8-4068-a932-b1aa95b30261","order_by":3,"name":"Xing Wang","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Wang","suffix":""},{"id":461505636,"identity":"e6df55ab-7c2d-4925-9b7c-e18062708bde","order_by":4,"name":"Peiwen Shi","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peiwen","middleName":"","lastName":"Shi","suffix":""},{"id":461505637,"identity":"08bc98ae-8d15-4eaa-b9dd-531c1ea373b7","order_by":5,"name":"Jiangdong Liu","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiangdong","middleName":"","lastName":"Liu","suffix":""},{"id":461505638,"identity":"451a52a2-7ead-46b7-b14a-defa28db30be","order_by":6,"name":"Jiajiu Yao","email":"","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiajiu","middleName":"","lastName":"Yao","suffix":""},{"id":461505639,"identity":"3102fe05-4682-4318-afa9-626163c844ed","order_by":7,"name":"Lihong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACPmYeEGXBw94ApD4Y2NgR1MIG0SLBw3OAgYFxRkFaMmEtDBAtDCAtzDwfDjE2ENTCznvwccEvCRkesdNp0jYGB5gZ2A8f3YDfYXzJxjP7gA6Tzt0mnWNwh4+BJy3tBgG/mEnz9kjw2EO0PGMG+suMOC1gWywMDjM2EKWF5wdUCwORWoyNeRvAWjZb9hikJbMR8gs//xnDxzx/bOyBWjbe+PHHxo6f/fAxvFrAgLEN2V6CysHgD3HKRsEoGAWjYIQCAEgtOcEf2UImAAAAAElFTkSuQmCC","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-05-21 05:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6712781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6712781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83544074,"identity":"0906afdd-1a40-486f-a979-847ca8c79934","added_by":"auto","created_at":"2025-05-28 08:44:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":572944,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant screening flow chart for the primary (MIMIC-IV) and validation (eICU-CRD) cohorts.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/bbe7f9d2426b8750eca8df82.jpg"},{"id":83544079,"identity":"a6a1cf2b-ac5a-41a9-afa3-f76a272f374a","added_by":"auto","created_at":"2025-05-28 08:44:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157723,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of early acetaminophen exposure with primary and secondary outcomes. Multivariable logistic regression was used to perform the risk-adjusted analysis.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/240db0879a2bfe0523a2f7c6.jpg"},{"id":83544076,"identity":"b040deb7-e2dc-4e8d-abb8-9000459917c2","added_by":"auto","created_at":"2025-05-28 08:44:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84980,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analyses of early acetaminophen exposure and severe SA-AKI risk.\u003cbr\u003e\nUnivariable logistic regression analysis was conducted in propensity score-matched (PSM) datasets. Multivariable logistic regression analysis was applied to the creatinine-defined cohort (AKI diagnosis based solely on Scr criteria of KDIGO), and the first-day exposure cohort (excluding patients receiving initial acetaminophen \u0026gt;24 hours post-ICU admission).\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/d24909d7259c8270185bd375.jpg"},{"id":83544086,"identity":"babce09b-a0a2-4459-88dc-85ed601af57a","added_by":"auto","created_at":"2025-05-28 08:44:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":484347,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between acetaminophen exposure and the risk of severe SA-AKI. Multivariable logistic regression was used to perform subgroup analysis. \u003cem\u003ep\u003c/em\u003e for interaction was assessed by the likelihood ratio test.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/b991f351cada605de9cbdec6.jpg"},{"id":83545479,"identity":"2deac679-45fe-441c-83ba-47c51da053f5","added_by":"auto","created_at":"2025-05-28 09:00:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165031,"visible":true,"origin":"","legend":"\u003cp\u003eSubpopulation treatment effect pattern plot for the preventive effect of acetaminophen on SA-AKI. \u003cstrong\u003e(a, b) \u003c/strong\u003eThe risk of severe SA-AKI in the acetaminophen exposure and non-exposure groups. \u003cstrong\u003e(c, d)\u003c/strong\u003e Odds ratio for net adverse clinical event (severe SA-AKI).\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/936fff62ba584c7a1039e455.jpg"},{"id":83544088,"identity":"dd05f757-72f3-4be7-b3db-46b17d02eb88","added_by":"auto","created_at":"2025-05-28 08:44:34","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":234011,"visible":true,"origin":"","legend":"\u003cp\u003eThe proposed protective mechanism of acetaminophen on renal function in the early course of sepsis.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/4df229f42dcf489efdb76654.jpg"},{"id":87089068,"identity":"3c923a53-1c6c-450b-b2b6-26d4c3dfe3f5","added_by":"auto","created_at":"2025-07-19 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08:44:33","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":15154,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-6712781/v1/9d8a7714d06b039711af4b9c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Early Acetaminophen Exposure and Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis-associated acute kidney injury (SA-AKI), a common complication of sepsis,\u003c/p\u003e \u003cp\u003econtributes to elevated morbidity and mortality, and is associated with increased risk of chronic comorbidities\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite substantial research efforts in this area, there is currently no specific pharmacologic prophylaxis available for this condition\u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmerging evidence have revealed that acetaminophen, a widely used antipyretic and analgesic agent, exhibits renoprotective properties across diverse clinical settings. Postoperative use of acetaminophen has been shown to reduce the risk of AKI in both pediatric and adult populations undergoing cardiac surgery, as reported in prior studies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In patients with severe malaria, acetaminophen enhances serum creatinine (Scr) clearance by 72 hours after treatment initiation\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Additionally, in cases of rhabdomyolysis, acetaminophen significantly attenuated the decline in Scr clearance and reduced the requirement for renal replacement therapy (RRT), thereby improving renal functional outcomes\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the renoprotective role of acetaminophen in SA-AKI remains controversial. While a phase 2a randomized controlled trial reported marked Scr reductions by day 3 of acetaminophen treatment in patients with severe sepsis \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, a subsequent phase 2b randomized, double-blind trial involving 447 septic patients showed no significant reduction in adverse kidney events (defined as \u0026ge;\u0026thinsp;200% Scr elevation from baseline within 28 days of ICU admission)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This discrepancy may arise from inconsistent SA-AKI diagnostic criteria across studies, which undermines both inter-study comparability and extrapolation of their findings. To address this issue, the 28th Acute Disease Quality Initiative (ADQI) consensus panel established standardized definitions for SA-AKI \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, providing a unified framework for future research in this field.\u003c/p\u003e \u003cp\u003eConsequently, our study aimed to evaluates acetaminophen's effect on SA-AKI using ADQI definition. We hypothesized that early acetaminophen administration (initiated within 24 hours post-ICU admission) would reduce the risk of severe SA-AKI. Additionally, we examined whether early use of acetaminophen could decrease the incidence of any-stage SA-AKI, new-onset RRT, and in-hospital mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted by utilizing two publicly accessible clinical databases: the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. MIMIC-IV (version 2.2) comprises de-identified medical records of 50,920 patients with 73,181 ICU admissions in different ICU settings at Beth Israel Deaconess Medical Center between 2008 and 2019. eICU-CRD (version 2.0) included 139,367 patients with 200,859 ICU admissions from over 200 medical centers between 2014 and 2015. Patients from MIMIC-IV and eICU-CRD consisted of the primary and validation cohorts, respectively. One researcher (Yang Yang) obtained permission (certification number 48776647) to use the data from both databases. Given the deidentified nature of the two databases that conceal patient information, the requirement for informed consent and ethics approval was waived. This study's reporting adheres to the guidelines set forth by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eAdult patients (\u0026ge;\u0026thinsp;18 years) were screened for eligibility if they satisfied the Sepsis-3 criteria on the day of ICU admission, namely, (1) suspected or documented infection, and (2) an increase in the Sequential Organ Failure Assessment (SOFA) score\u0026thinsp;\u0026ge;\u0026thinsp;2 within 24 hours post-ICU admission\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Exclusion criteria included: (1) ICU stay of less than 24 hours; (2) initiating RRT before acetaminophen use; (3) pre-existing AKI preceding acetaminophen administration or sepsis diagnosis; or (4) unavailable in-hospital outcome data. Moreover, for patients with multiple sepsis-related ICU admission, only the initial admission data were analyzed.\u003c/p\u003e\n\u003ch3\u003eExposure and outcomes\u003c/h3\u003e\n\u003cp\u003eIn this study, exposure was defined as receipt of at least one dose of acetaminophen within 24 hours after ICU admission. Patients who received no acetaminophen or initiated treatment\u0026thinsp;\u0026ge;\u0026thinsp;24 hours after ICU admission were designated as the unexposed group.\u003c/p\u003e \u003cp\u003eThe primary outcome, severe SA-AKI, was defined through sequential assessment: (1) AKI was diagnosed according to the Scr and urine output (UO) criteria of the Kidney Disease Improvement Global Outcome (KDIGO) guidelines\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, and (2) severe SA-AKI was subsequently classified per ADQI consensus as stage 2 or stage 3 AKI developing within the 7-day window following sepsis diagnosis\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Baseline Scr levels and all Scr measurements recorded during the first 7 days of ICU admission were analyzed to determine peak AKI stage. UO data were also applied for staging when consecutive measurements were documented at intervals\u0026thinsp;\u0026lt;\u0026thinsp;12 hours. Secondary outcomes encompassed any-stage SA-AKI (KDIGO stages 1\u0026ndash;3), the use of RRT, and in-hospital mortality. All outcomes were analyzed as dichotomous variables.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eStructured Query Language (SQL) was utilized for data extraction. The extracted parameters comprised: (1) demographic characteristics including age, sex, and race; (2) comorbidities (as defined by the 9th edition of the International Classification of Diseases) and Charlson comorbidity index (CCI); (3) peak laboratory values on the day of ICU admission, including white blood cell count (WBC), lactate and red cell distribution width (RDW); (4) baseline levels of Scr and estimated glomerular filtration rate (eGFR); (5) mean values of body temperature and mean arterial pressure (MAP) recorded on the day of ICU admittance; (6) binary form of treatment variables prior to SA-AKI onset, including invasive mechanical ventilation, vasopressor use, or high-risk nephrotoxins exposure (\u003cb\u003eAdditional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e); (7) highest value of disease severity scores on the day of ICU admission, including the Simplified Acute Physiology Score II (SAPS II) from MIMIC-IV, Acute Physiology and Chronic Health Evaluation IV (APACHE IV) from eICU-CRD, and SOFA score from both databases.\u003c/p\u003e \u003cp\u003eThe baseline Scr level was defined through the following criteria: (1) if the patient was diagnosed with chronic kidney disease (CKD), then use the lowest Scr value during ICU admission even if it was higher than the normal range (\u0026lt;\u0026thinsp;1.1 mg/dL); (2) for non-CKD patient, the lowest Scr value during admission was used if it was within the normal range (\u0026lt;\u0026thinsp;1.1mg/dL); (3) for all remaining cases, baseline Scr values were calculated via the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Subsequently, the baseline eGFR was determined using the Modification of Diet in Renal Disease (MDRD) equation \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e (\u003cb\u003eAdditional file 2: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAs a retrospective study, statistical power calculation was not conducted and sample size was based on the data available from the two databases. Continuous variables were presented as median (interquartile ranges [IQR]), and categorical variables were described as number (percentages). Group comparisons were conducted using the Wilcoxon rank sum test for continuous variables and the Pearson's chi-squared test for categorical variables.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression was used to assess adjusted association of acetaminophen exposure with the dichotomous primary and secondary outcomes. Covariate selection followed clinical relevance and prior evidence, incorporating demographic factors (age, sex, race), comorbidities (congestive heart failure, diabetes mellitus, chronic liver disease), baseline level of eGFR, clinical interventions (invasive ventilation, use of vasopressors or high-risk nephrotoxins), and the first day\u0026rsquo;s mean values of body temperature and MAP, as well as peak values of WBC, lactate, RDW, and disease severity scores since ICU admission\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Missing data pattern for each confounder was displayed in \u003cb\u003eAdditional file 3: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/b\u003e To estimate missing values, multiple imputation using predictive mean matching was conducted on 10 completed datasets\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Additionally, variance inflation factors (VIFs) were computed to evaluate multicollinearity in the multivariable logistic regression models. For all analyses, p values were tested by 2-sided, with a significance threshold set at less than 0.05. All analyses were conducted using R statistical software (version 4.3.1).\u003c/p\u003e \u003cp\u003eSeveral sensitivity analyses were conducted to evaluate the robustness of the results obtained from the primary analysis. Firstly, propensity score matching (PSM) was utilized to equalize confounders associated with AKI. Logistic regression model was used to calculate the propensity score (i.e. the likelihood of receiving acetaminophen) for each individual. All the confounders were incorporated into the propensity score model for matching purposes. Patients in the exposure group were matched 1:1 with those in the non-exposure group by using the nearest neighbor method with a caliper width of 0.05 without replacement. The balance of risk factors was assessed using a threshold of standardized mean difference (SMD), where an SMD below 0.1 signified balance\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e(\u003cb\u003eAdditional file 4: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. Multivariable logistic regression was then performed on the matched cohort. Secondly, multivariable logistic regression was performed on the first-day cohort that excluded patients receiving initial acetaminophen\u0026thinsp;\u0026gt;\u0026thinsp;24h post-ICU admission. Thirdly, diagnostic criteria based on Scr alone were applied to assess whether changes in AKI definition could influence the results of the primary analysis. Finally, given the potential for in-hospital mortality occurring before the development of severe SA-AKI to represent a competing risk (\u003cb\u003eAdditional file 5: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e), a competing risk model adjusting for AKI-related risk factors was applied.\u003c/p\u003e \u003cp\u003ePredefined subgroup analysis was performed by dividing the whole cohort according to several baseline factors: age (\u0026ge;\u0026thinsp;65 vs\u0026thinsp;\u0026lt;\u0026thinsp;65 yrs), gender (male vs female), race (white vs black vs others), pre-existing chronic kidney disease (yes or no), and baseline renal function categorized as eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 vs\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e according to Kidney Disease Outcomes Quality Initiative (KDOQI) staging system and prior research\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Interaction effects between acetaminophen exposure and stratification variables were evaluated using likelihood ratio tests, with \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance of subgroup effect.\u003c/p\u003e \u003cp\u003eGiven that the SOFA score serves as a key diagnostic criterion for sepsis, we employed the sliding window Subpopulation Treatment Effect Pattern Plot (STEPP) to evaluate acetaminophen's therapeutic efficacy in severe SA-AKI across subgroups of SOFA\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. This analytical approach enables systematic investigation of treatment-covariate interactions through continuous covariate-defined overlapping subpopulations. Specifically, patients are ranked in ascending order based on their covariate values. Then, two critical parameters must be determined: r1, which denotes the maximum number of patients who can be included in two consecutive overlapping subpopulations, and r2, which represents the number of patients in each subpopulation. The initial subpopulation consists of r2 patients with the lowest covariate values. Subsequent subpopulations are generated by replacing (r2 - r1) patients from the lower end of the current window with the next (r2 - r1) patients in the ordered list. This iterative process continues until complete population coverage is achieved, ensuring each patient participates in at least one sliding window subgroup. In this study, we set r2 to 600 as the size of each subpopulation and r1 to 200 as the number of patients included in consecutive overlapping subpopulations for the STEPP analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient screening\u003c/h2\u003e \u003cp\u003eThe process of patient selection is illustrated in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. Initially, 73,181 records from MIMIC-IV and 200,859 from eICU-CRD were identified. After excluding unqualified records, the primary and validation cohorts comprised 13,708 and 20,740 participants, respectively. Among these, 7,563 (55.0%) ones in the primary cohort and 10,161 (49%) ones in the validation cohort received acetaminophen within 24 hours of ICU admission.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline characteristics of cohort\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of patients in the primary and validation cohorts. The majority of patients in both cohorts were white, with an average age exceeding 65 years. In the primary cohort, 56% of individuals developed severe SA-AKI and 75% experienced any-stage SA-AKI. Conversely, only 7% of patients required RRT and 13% experienced in-hospital death. In the validation cohort, 49% and 62% of patients developed severe and any-stage SA-AKI, respectively; while only 9% required RRT and 14% died during hospitalization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants in the primary and validation cohorts \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary cohort \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13708)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation cohort \u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;20740)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.72 (56.2, 79.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (55, 76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7997 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10792 (52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, No. (%)\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\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9200 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15949 (77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1065 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2212 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3443 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2579 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Scr, median (IQR), mg dL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.6, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.6, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline eGFR, median (IQR),\u003c/p\u003e \u003cp\u003emL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 1.73m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.23 (75.94, 108.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.45 (73.58, 106.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities, No. (%)\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\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3746 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3871 (19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3957 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6962 (34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2685 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3503 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3544 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4553 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2106 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e896 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity of illness, median (IQR)\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\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (30, 47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4, 8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (50, 82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication, No. (%)\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\u003eUse of acetaminophen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7563 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10161 (49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of high-risk nephrotoxins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5678 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7048 (34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of vasopressors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5171 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6225 (30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceipt of invasive ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6446 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8312 (40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes, No. (%)\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\u003eSevere SA-AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7665 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10149 (49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny-stage SA-AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10213 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12764 (62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceipt of RRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e960 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1870 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1711 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3007 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Continuous variables are presented as median (IQR) and categorical variables are presented as number (%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Scr,\u003c/em\u003e serum creatinine; \u003cem\u003eeGFR,\u003c/em\u003e estimated glomerular filtration rate; \u003cem\u003eCOPD,\u003c/em\u003e chronic obstructive pulmonary disease; \u003cem\u003eCCI,\u003c/em\u003e Carlson comorbidity index;\u0026nbsp;\u003cem\u003eSAPS II,\u003c/em\u003e Simplified Acute Physiology Score II; \u003cem\u003eSOFA,\u003c/em\u003e Sequential Organ Failure Assessment;\u0026nbsp;\u003cem\u003eAPACHE IV,\u003c/em\u003e Acute Physiology and Chronic Health Evaluation IV; \u003cem\u003eRRT,\u003c/em\u003e renal replacement treatment; \u003cem\u003eSA-AKI,\u003c/em\u003e sepsis-associated acute kidney injury.\u003c/p\u003e \u003cp\u003eThe characteristics of patients exposed to acetaminophen, as compared to those who were not, are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, in both the primary and validation cohorts, acetaminophen-exposed patients exhibited higher proportions of White ethnicity, lower disease severity scores, reduced lactate levels, and decreased RDW values compared to non-exposed counterparts. Individuals exposed to acetaminophen also exhibited a decreased proportion of receiving high-risk nephrotoxic agents. Upon evaluating the temporal patterns of first-dose acetaminophen administration, it was observed that 66.8% of patients in the primary cohort and 64.6% in the validation cohort received their initial acetaminophen dose within the first 24 hours following ICU admission. (\u003cb\u003eAdditional file 6: Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e)\u003c/b\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\u003eCharacteristics of participants in the primary and validation cohorts according to acetaminophen exposure \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrimary cohort (n\u0026thinsp;=\u0026thinsp;13708)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eValidation cohort (n\u0026thinsp;=\u0026thinsp;20740)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-ACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6145)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7563)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-ACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10579)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.23 (55.28, 79.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.12 (56.9, 78.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (55, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66 (54, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3535 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4462 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5511 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5281 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, No. (%)\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3997 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5203 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7820 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8129 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e547 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e991 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1221(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1630 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1813 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1768(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e811 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline eGFR, median (IQR),\u003c/p\u003e \u003cp\u003emL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 1.73m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.77 (73.03, 108.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.61 (78.77, 107.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.38 (73.71, 106.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.56 (73.51, 105.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP, median (IQR), mmHg \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.43 (69.71, 82.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.37 (70.48, 81.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.7 (69.25, 84.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.18 (69.63, 85.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, median (IQR), ℃ \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.81 (36.55, 37.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.89 (36.63, 37.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.84 (36.57, 37.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.93 (36.63, 37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003e\u003cb\u003eComorbidities, No. (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1827 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1919 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1839 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2032 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1826 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2131 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3302 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3660 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eChronic renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1402 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1283 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1872 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1631 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1741 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1803 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2311 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2242 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1491 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e691 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e205 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eCCI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (2, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (2, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory test, median (IQR)\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, K uL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.8 (9.6, 19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9 (10.3, 18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.6 (10.1, 20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.5 (10.1, 20.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, mmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 (1.5, 4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.6, 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3 (1.4, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0 (1.3, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eRDW, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.2 (14, 16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3 (13.5, 15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.4 (14.2, 17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.1 (12.9, 16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003e\u003cb\u003eSeverity of illness, median (IQR)\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (32, 50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (29, 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (4, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (3, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eAPACHE IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65 (50, 83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63 (48, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003e\u003cb\u003eMedication, No. (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of high-risk nephrotoxins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2701 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2977 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3896 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3152 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eUse of vasopressors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2287 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2884 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3276 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2949 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eReceipt of invasive ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3081 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3365 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3938 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4374 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Continuous variables are presented as median (IQR) and categorical variables are presented as number (%). \u003cem\u003eP\u003c/em\u003e values are calculated by the univariate Wilcoxon rank sum test for continuous variables and by the univariate Pearson\u0026rsquo;s Chi-squared test for categorical variables. Severe AKI was defined as stage 2 or stage 3 AKI according to the KDIGO definition.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Mean value on the first 24 hours of ICU admission.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e Maximum value on the first 24 hours of ICU admission.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eACE,\u003c/em\u003e acetaminophen exposure; \u003cem\u003eeGFR,\u003c/em\u003e estimated glomerular filtration rate; \u003cem\u003eMAP,\u003c/em\u003e mean arterial pressure; \u003cem\u003eCOPD,\u003c/em\u003e chronic obstructive pulmonary disease; \u003cem\u003eCCI,\u003c/em\u003e Carlson comorbidity index; \u003cem\u003eWBC,\u003c/em\u003e white blood cell count; \u003cem\u003eRDW,\u003c/em\u003e red cell distribution width; \u003cem\u003eSAPS II,\u003c/em\u003e Simplified Acute Physiology Score II; \u003cem\u003eSOFA,\u003c/em\u003e Sequential Organ Failure Assessment; \u003cem\u003eAPACHE IV,\u003c/em\u003e Acute Physiology and Chronic Health Evaluation I\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAcetaminophen-related outcomes\u003c/h2\u003e \u003cp\u003eIn the primary cohort, severe SA-AKI occurred in 50.2% (3,799/7,523) of participants who were exposed to acetaminophen, compared to 62.9% (3,866/6,145) in those not exposed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This pattern persisted in the validation cohort, with severe SA-AKI incidence of 41.4% (4,206/10,161) in the exposure group \u003cem\u003evs\u003c/em\u003e 56.2% (5,943/10,579) in the non-exposure group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, acetaminophen exposure was associated with significantly reduced rates of any-stage SA-AKI, lower requirement for RRT, and decreased in-hospital mortality \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eIncidence of primary and secondary outcomes based on acetaminophen exposure \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrimary cohort (n\u0026thinsp;=\u0026thinsp;13708)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eValidation cohort (n\u0026thinsp;=\u0026thinsp;20740)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-ACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6145)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7563)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-ACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10579)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary outcome, No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere SA-AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3866 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3799 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5943 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4206 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003e\u003cb\u003eSecondary outcomes, No. (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny-stage SA-AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4812 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5401 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7174 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5590 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eUse of RRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e679 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1229 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e571 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eIn-hospital death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e814 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e903 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1737 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1270 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e values are calculated by the univariate Pearson\u0026rsquo;s Chi-squared test and all of the variables are presented as number (%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e \u003cem\u003eACE\u003c/em\u003e, acetaminophen exposure; \u003cem\u003eAKI\u003c/em\u003e, acute kidney injury; \u003cem\u003eScr,\u003c/em\u003e serum creatinine; \u003cem\u003eUO,\u003c/em\u003e urine output; \u003cem\u003eRRT,\u003c/em\u003e renal replacement treatment.\u003c/p\u003e\u003cp\u003eNotably, among patients developing severe SA-AKI, 75.3% of cases in the primary cohort and 67.3% in the validation cohort occurred within 48 hours post-ICU admission (\u003cb\u003eAdditional file 7: Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). Temporal pattern analysis further demonstrated consistent delays in severe SA-AKI onset among acetaminophen-exposed patients. In the primary cohort, the median onset time of severe SA-AKI was 32.67 hours (23.12\u0026ndash;54.32) post-ICU admission for acetaminophen-exposed patients versus 26.03 hours (IQR: 16.87\u0026ndash;41.03) for non-exposed counterparts (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Similarly, in the validation cohort, exposed patients developed severe SA-AKI at a median of 38.98 hours (20.68\u0026ndash;78.6), compared to 28.79 hours (13.73\u0026ndash;59.33) in non-exposed patients (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) (\u003cb\u003eAdditional file 8: Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk-adjusted analysis\u003c/h2\u003e \u003cp\u003eTo address potential confounding factors, we performed multivariable logistic regression analyses adjusted for established AKI-related risk factors. The results demonstrated that early acetaminophen administration was independently associated with a reduced risk of severe SA-AKI in both the primary (odds ratio [OR], 0.70; 95%CI, 0.64\u0026ndash;0.76; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and validation (OR, 0.62; 95%CI, 0.57\u0026ndash;0.68; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) cohorts (Fig.\u0026nbsp;2). Furthermore, early administration of acetaminophen was independently linked to decreased likelihood of any-stage SA-AKI and RRT utilization. However, no significant association was identified between acetaminophen exposure and lower in-hospital mortality in either the primary (OR, 1.06; 95%CI, 0.92\u0026ndash;1.23; P\u0026thinsp;=\u0026thinsp;0.114) or validation (OR, 1.03; 95%CI, 0.92\u0026ndash;1.16; P\u0026thinsp;=\u0026thinsp;0.591) cohort (Fig.\u0026nbsp;2). Additionally, none of the covariates demonstrated VIF values over the threshold of 4, indicating the absence of multicollinearity in all risk-adjusted logistic regression models (\u003cb\u003eAdditional file 9: Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003e Sensitivity analyses, whether employing PSM approach or focusing on the first-day cohort, and whether utilizing the Scr criteria of KDIGO guidelines or applying competing risk models, consistently revealed an independent correlation between early exposure to acetaminophen and decreased likelihood of severe SA-AKI (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 4\u003c/b\u003e displays the results of the predefined subgroup analyses. The upper 95% CI consistently remained below 1.00, and no significant interactions were detected across all subgroups in either the primary or validation cohorts, suggesting that acetaminophen's association with reduced risk of severe SA-AKI was independent of baseline characteristics of patients.\u003c/p\u003e \u003cp\u003eThe therapeutic efficacy of acetaminophen for severe SA-AKI across subgroups of SOFA score was presented in \u003cb\u003eFig.\u0026nbsp;5\u003c/b\u003e. Panel a-b demonstrate a dose-response relationship between higher SOFA scores and increased risk of severe SA-AKI, a pattern most pronounced in non-exposure patients. Panels c-d reveal significant heterogeneity in renoprotective effects of acetaminophen across SOFA score strata (\u003cem\u003ep for interaction\u0026lt;0.005\u003c/em\u003e), with acetaminophen-treated patients in the SOFA 5\u0026ndash;12 range showing significantly lower risk of severe SA-AKI in contrast to untreated individuals (OR with 95% CI\u0026thinsp;\u0026lt;\u0026thinsp;1.00), suggesting a threshold-dependent therapeutic efficacy.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis multicenter cohort study, integrating data from two large clinical databases, demonstrated that administering acetaminophen within 24 hours post-ICU admission was associated with a lower risk of severe SA-AKI in adult patients. The robustness of these findings was validated through multiple sensitivity analyses and remained consistent across subgroups stratified by demographic factors (age, gender, race) and renal profiles (pre-existing chronic kidney disease, baseline renal function). However, heterogeneity of treatment effect emerged in both cohorts through STEPP analysis, revealing differential risk reduction patterns of severe SA-AKI across SOFA score strata. Additionally, early acetaminophen administration was associated with lower odds of all-stage SA-AKI and RRT use, yet it did not correlate with in-hospital mortality.\u003c/p\u003e \u003cp\u003eThe accurate and timely identification of AKI serves as a foundational requirement for detecting SA-AKI in septic patients. A prior study indicates that even non-severe AKI manifesting solely through oliguria correlates with adverse long-term outcomes\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Crucially, omitting UO criteria specified in the KDIGO guidelines may lead to delayed AKI detection and an underestimation of its incidence\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Evidence from a previous analysis showed that incorporating UO criteria into KDIGO\u0026rsquo;s Scr criteria increased the reported incidence of severe AKI from under 8% to nearly 40%\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Consistently, our data demonstrated that when UO criteria were applied alongside Scr criteria, the prevalence of severe SA-AKI increased from 11% and 15\u0026ndash;56% and 49% in the primary and validation cohorts, respectively (data not shown). Given these findings, our study integrated UO criteria into the AKI diagnostic framework. Importantly, whether using UO and Scr criteria in risk-adjusted analyses or Scr criteria alone in sensitivity analyses, our results consistently showed a significant association between acetaminophen administration and a reduced risk of severe SA-AKI.\u003c/p\u003e \u003cp\u003eRecent studies using ADQI criteria have increasingly explored the association between acetaminophen exposure and clinical outcomes in SA-AKI patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Whereas, only one study has specifically examined its relationship with SA-AKI development\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. This retrospective analysis, conducted across two U.S. medical centers, presents findings that contrast with our observations. However, their study defined acetaminophen exposure as administration within 7 days of ICU admission without clarifying the temporal relationship with AKI onset. Notably, our data showed that 75.3% of severe SA-AKI cases in the primary cohort and 67.3% in the validation cohort occurred within 48 hours post-ICU admission (\u003cb\u003eAdditional file 7: Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). Therefore, if significant AKI occurred before acetaminophen exposure in their cohort, potential protective effects of the drug might be obscured. Given the rapid progression of SA-AKI in septic patients, we strictly excluded AKI cases with onset prior to acetaminophen exposure, ensuring temporal priority of exposure relative to outcome occurrence. Additionally, compared to their limited sample size (238 septic patients, 121 SA-AKI cases), our cohort comprised 17,814 severe SA-AKI patients. The large sample size strengthens the general characteristics of the SA-AKI population and ensures sufficient statistical power for detecting clinically relevant associations.\u003c/p\u003e \u003cp\u003eWhile conventional subgroup analyses serve to assess treatment effect heterogeneity, they possess inherent methodological limitations when applied to continuous covariates like SOFA scores. Traditional categorical stratification has the risk of information loss due to arbitrary cutoff points and diminishes statistical power within extreme score ranges. To address these constraints, we adopted the STEPP methodology, enabling a dynamic assessment of treatment effects across overlapping windows of SOFA scores. Our results revealed a threshold-dependent therapeutic window for acetaminophen in SA-AKI prevention. Although early administration consistently demonstrated overall risk reduction (primary cohort: OR 0.70, 95%CI 0.64\u0026ndash;0.76; validation cohort: OR 0.62, 95%CI 0.57\u0026ndash;0.68), significant heterogeneity emerged across SOFA strata (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Clinically meaningful benefits were concentrated in patients suffering from moderate organ dysfunction (SOFA 5\u0026ndash;12), with non-significant effects observed in patients with mild (SOFA 3\u0026ndash;4) and severe (SOFA\u0026thinsp;\u0026gt;\u0026thinsp;12) organ dysfunction. Based on these findings, future randomized controlled trials ought to consider pre-defined subgroup analyses based on SOFA scores within statistical analysis plans, or implement stratification during randomization according to SOFA categories (3\u0026ndash;4, 5\u0026ndash;12, \u0026gt;\u0026thinsp;12) to guarantee balanced representation across subgroups.\u003c/p\u003e \u003cp\u003eThe exact mechanisms connecting early acetaminophen exposure with the reduced risk of SA-AKI are still unclear, but existing evidences support a key role of acetaminophen\u0026rsquo;s antioxidant effect in this process. Specifically, erythrocyte deformability was reduced during sepsis, which may damage the membrane of erythrocyte as it passed through microcirculatory channels\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Subsequently, cell-free hemoglobin (CFH) may be released into the extracellular environment and oxidize ferrous heme into ferryl heme protoporphyrin radical species, which initiate lipid peroxidation and ultimately result in AKI\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Acetaminophen can effectively inhibit CFH-mediated lipid peroxidation by reducing the release of ferric protoporphyrin radical species from hemoglobin into the circulation\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In a retrospective observational study, Janz and associates found that the plasma levels of lipid peroxidation byproduct F2-isoprostane were reduced in septic patients who received acetaminophen in contrast to those who did not\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. They further demonstrated that receiving acetaminophen could reduce the level of F2-isoprostane and Scr among patients with severe sepsis\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. All of these findings supported the potential protective effect of acetaminophen on renal function during sepsis by inhibiting the CFH-mediated peroxidation damage (Fig.\u0026nbsp;6\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eOur study has certain limitations. Firstly, the retrospective design of the study hindered the determination of a causal relationship between acetaminophen exposure and the decreased occurrence of SA-AKI. Secondly, although AKI-related risk factors were meticulously screened and incorporated into the regression model, the retrospective nature determines that unmeasured confounders may bias the results of the regression analysis. Thirdly, owing to constraints in data availability, we only evaluated the effect of binary acetaminophen exposure on SA-AKI, while the impact of drug dosage and administration frequency were not evaluated from the current research. Besides, prehospital acetaminophen use was not considered in the current study; however, if a significantly greater percentage of patients with SA-AKI had prehospital exposure to acetaminophen compared to those who did not, the findings of this study may be skewed. Lastly, we did not evaluate the safety of acetaminophen, although adverse effects of acetaminophen administration in septic patients or other cases are rare\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn brief, early acetaminophen use was associated with a reduced risk of severe SA-AKI. Further prospective trials are required to confirm the protective benefits of acetaminophen against SA-AK, as suggested by this retrospective research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADQI, Acute Disease Quality Initiative\u003c/p\u003e\n\u003cp\u003eAKI, Acute kidney injury\u003c/p\u003e\n\u003cp\u003eAPACHE IV, Acute Physiology and Chronic Health Evaluation IV\u003c/p\u003e\n\u003cp\u003eCCI, Charlson comorbidity index\u003c/p\u003e\n\u003cp\u003eCKD, Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCFH, cell-free hemoglobin\u003c/p\u003e\n\u003cp\u003eeICU-CRD, eICU Collaborative Research Database\u003c/p\u003e\n\u003cp\u003eeGFR, Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eICU, Intensive care unit\u003c/p\u003e\n\u003cp\u003eKDIGO, Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eMAP, Mean arterial pressure\u003c/p\u003e\n\u003cp\u003eMIMIC-IV, Medical Information Mart for Intensive Care\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e\n\u003cp\u003ePSM, propensity score matching\u003c/p\u003e\n\u003cp\u003eRDW, Red cell distribution width\u003c/p\u003e\n\u003cp\u003eRRT, Renal replacement therapy\u003c/p\u003e\n\u003cp\u003eSA-AKI, Sepsis-associated acute kidney injury\u003c/p\u003e\n\u003cp\u003eSAPS II, Simplified Acute Physiology Score II\u003c/p\u003e\n\u003cp\u003eScr, Serum creatinine\u003c/p\u003e\n\u003cp\u003eSepsis-3, Third International Consensus Definitions for Sepsis\u003c/p\u003e\n\u003cp\u003eSOFA, Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eSTEPP, Subpopulation Treatment Effect Pattern Plot\u003c/p\u003e\n\u003cp\u003eUO, Urine output\u003c/p\u003e\n\u003cp\u003eVIF, variance inflation factor\u003c/p\u003e\n\u003cp\u003eWBC, White blood cell\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in compliance with the Helsinki Declaration. Since the MIMIC-IV and eICU-CRD databases are publicly accessible and all data are de-identified to remove patients\u0026rsquo; information, obtaining informed consent from patients was not necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The available data for MIMIC-IV can be obtained at https://mimic.physionet.org/. The available data for eICU-CRD is available at https://eicu-crd.mit.edu/. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Shaanxi Province Key Research and Development Plan Project (2024SF-YBXM-210), and Innovation Science Fund of Tangdu hospital (No. 2023BTDQN001),\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYY extracted data. YY and MQ wrote the original draft. YY and SL conducted literature review. SL and XW assisted in statistical analysis of the data. XW and PS operated software. JY and JL organized the data and checked the integrity of the data. LL designed the study, and checked the final results. LL provided supervision for the project. All authors have made an intellectual contribution to the manuscript and approved the final submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSadudee, Carlos L, Hernando, et al. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment [J]. Kidney Int, 2019, 96(5).\u003c/li\u003e\n\u003cli\u003eRaghavan, Lisa, Sachin, et al. Association of statin use with risk and outcome of acute kidney injury in community-acquired pneumonia [J]. Clin J Am Soc Nephrol, 2012, 7(6).\u003c/li\u003e\n\u003cli\u003eSteven D, Martin, Hani, et al. Outcomes after Angiography with Sodium Bicarbonate and Acetylcysteine [J]. 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Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2018, 67(7): 991-9.\u003c/li\u003e\n\u003cli\u003eDesgrouas, Boulain. Paracetamol use and lowered risk of acute kidney injury in patients with rhabdomyolysis [J]. Journal of nephrology, 2021, 34(5): 1725-35.\u003c/li\u003e\n\u003cli\u003eBoutaud, Moore, Reeder, et al. Acetaminophen inhibits hemoprotein-catalyzed lipid peroxidation and attenuates rhabdomyolysis-induced renal failure [J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(6): 2699-704.\u003c/li\u003e\n\u003cli\u003eWare, Files, Fowler, et al. Acetaminophen for Prevention and Treatment of Organ Dysfunction in Critically Ill Patients With Sepsis: The ASTER Randomized Clinical Trial [J]. JAMA, 2024.\u003c/li\u003e\n\u003cli\u003eZarbock, Nadim, Pickkers, et al. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup [J]. Nature reviews Nephrology, 2023, 19(6): 401-17.\u003c/li\u003e\n\u003cli\u003eAlistair E W, Lucas, Lu, et al. MIMIC-IV, a freely accessible electronic health record dataset [J]. Sci Data, 2023, 10(1).\u003c/li\u003e\n\u003cli\u003eTom J, Alistair E W, Jesse D, et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research [J]. Sci Data, 2018, 5(0).\u003c/li\u003e\n\u003cli\u003eErik, Douglas G, Matthias, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies [J]. Lancet, 2007, 370(9596).\u003c/li\u003e\n\u003cli\u003eMervyn, Clifford S, Christopher Warren, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) [J]. JAMA, 2016, 315(8).\u003c/li\u003e\n\u003cli\u003eKellum, Lameire. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1) [J]. Critical care (London, England), 2013, 17(1): 204.\u003c/li\u003e\n\u003cli\u003eBellomo, Ronco, Kellum, et al. Acute renal failure - definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group [J]. Critical care (London, England), 2004, 8(4): R204-12.\u003c/li\u003e\n\u003cli\u003eDe Rosa, Samoni, Ronco. Creatinine-based definitions: from baseline creatinine to serum creatinine adjustment in intensive care [J]. Critical care (London, England), 2016, 20: 69.\u003c/li\u003e\n\u003cli\u003eLesley A, Nwamaka D, Josef, et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race [J]. N Engl J Med, 2021, 385(19).\u003c/li\u003e\n\u003cli\u003ePoston, Koyner. Sepsis associated acute kidney injury [J]. BMJ (Clinical research ed), 2019, 364: k4891.\u003c/li\u003e\n\u003cli\u003eJonathan a C, Ian R, John B, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls [J]. BMJ, 2009, 338(0).\u003c/li\u003e\n\u003cli\u003eAustin. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples [J]. Statistics in medicine, 2009, 28(25): 3083-107.\u003c/li\u003e\n\u003cli\u003ePannu, James, Hemmelgarn, et al. Modification of outcomes after acute kidney injury by the presence of CKD [J]. American journal of kidney diseases : the official journal of the National Kidney Foundation, 2011, 58(2): 206-13.\u003c/li\u003e\n\u003cli\u003eK/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification [J]. American journal of kidney diseases : the official journal of the National Kidney Foundation, 2002, 39: S1-266.\u003c/li\u003e\n\u003cli\u003eLazar, Cole, Bonetti, et al. Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot [J]. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2010, 28(29): 4539-44.\u003c/li\u003e\n\u003cli\u003eBonetti, Gelber. A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data [J]. Statistics in medicine, 2000, 19(19): 2595-609.\u003c/li\u003e\n\u003cli\u003ePriyanka, Alexander, Junichi, et al. The impact of acute kidney injury by serum creatinine or urine output criteria on major adverse kidney events in cardiac surgery patients [J]. J Thorac Cardiovasc Surg, 2020, 162(1).\u003c/li\u003e\n\u003cli\u003eKama A, Ameen, Mathilde, et al. A comparison of RIFLE with and without urine output criteria for acute kidney injury in critically ill patients [J]. Crit Care, 2012, 16(5).\u003c/li\u003e\n\u003cli\u003eJennifer C, David S, Henry, et al. Definition of hourly urine output influences reported incidence and staging of acute kidney injury [J]. BMC Nephrol, 2020, 21(1).\u003c/li\u003e\n\u003cli\u003eShilin, Han, Qun, et al. Association between acetaminophen administration and clinical outcomes in patients with sepsis admitted to the ICU: a retrospective cohort study [J]. Front Med (Lausanne), 2024, 11(0).\u003c/li\u003e\n\u003cli\u003eHui, Ting, Dongsong. Association between acetaminophen and risk of mortality in patients with sepsis-associated acute kidney injury: A retrospective cohort study from the MIMIC-IV database [J]. J Investig Med, 2024, 73(1).\u003c/li\u003e\n\u003cli\u003eLong-Zhu, Lu-Ming, Yan, et al. Association of acetaminophen use with mortality and renal recovery in patients with sepsis-associated acute kidney injury [J]. BMC Anesthesiol, 2024, 24(1).\u003c/li\u003e\n\u003cli\u003eAsad E, Ohoud, Hussain, et al. Effect of Acetaminophen on the Prevention of Acute Kidney Injury in Patients With Sepsis [J]. Ann Pharmacother, 2017, 52(1).\u003c/li\u003e\n\u003cli\u003eMachiedo, Powell, Rush, et al. The incidence of decreased red blood cell deformability in sepsis and the association with oxygen free radical damage and multiple-system organ failure [J]. Archives of surgery (Chicago, Ill : 1960), 1989, 124(12): 1386-9.\u003c/li\u003e\n\u003cli\u003eBaskurt, Gelmont, Meiselman. Red blood cell deformability in sepsis [J]. American journal of respiratory and critical care medicine, 1998, 157(2): 421-7.\u003c/li\u003e\n\u003cli\u003eN, C, V, et al. The role of lipid hydroperoxides in the myoglobin-dependent oxidation of LDL [J]. Arch Biochem Biophys, 1994, 314(1).\u003c/li\u003e\n\u003cli\u003eJanz, Bastarache, Peterson, et al. Association between cell-free hemoglobin, acetaminophen, and mortality in patients with sepsis: an observational study [J]. Critical care medicine, 2013, 41(3): 784-90.\u003c/li\u003e\n\u003cli\u003eJanz, Bastarache, Rice, et al. Randomized, placebo-controlled trial of acetaminophen for the reduction of oxidative injury in severe sepsis: the Acetaminophen for the Reduction of Oxidative Injury in Severe Sepsis trial [J]. Critical care medicine, 2015, 43(3): 534-41.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sepsis, acute kidney injury, acetaminophen, intensive care unit, cohort study","lastPublishedDoi":"10.21203/rs.3.rs-6712781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6712781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEmerging evidence suggests that acetaminophen may confer renoprotective effects by inhibiting lipid peroxidation. This study aimed to investigate whether early acetaminophen administration is associated with a reduced risk of sepsis-associated acute kidney injury (SA-AKI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a multicenter retrospective cohort study by using data from the two large clinical databases: MIMIC-IV (Medical Information Mart for Intensive Care-IV) and eICU-CRD (eICU Collaborative Research Database). Adult patients with sepsis were included, with acetaminophen exposure defined as administration within 24 hours of ICU admission. The primary outcome was severe SA-AKI (stage 2/3 AKI according to KDIGO criteria) developed within 7 days of sepsis diagnosis. Multivariable logistic regression model, adjusted for established AKI-related risk factors, were used to evaluate associations between early acetaminophen exposure and the risk of severe SA-AKI. Several sensitivity and subgroup analyses were performed to validate findings of multivariable logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe primary (MIMIC-IV) and validation (eICU-CRD) cohorts comprised 13,708 and 20,740 qualified patients, respectively. The incidence of severe SA-AKI was 56% (7,665/13,708) in the primary cohort and 49% (10,149/20,740) in the validation cohort. Acetaminophen was administered to 7,563 patients (55%) in the primary cohort and 10,161 patients (49%) in the validation cohort within 24 hours following ICU admission. After adjusting for AKI-related risk factors, multivariable analysis revealed that early acetaminophen use was independently associated with a reduced risk of severe SA-AKI in both the primary (odds ratio [OR], 0.70; 95% CI, 0.64\u0026ndash;0.76; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and validation (OR, 0.62; 95% CI, 0.57\u0026ndash;0.68; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) cohorts. These associations remained consistent across sensitivity analyses and subgroup evaluations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEarly acetaminophen use was independently associated with a lower risk of severe SA-AKI in critically ill patients with sepsis. Prospective studies are warranted to confirm causality and evaluate the therapeutic potential of acetaminophen in either preventing SA-AKI or mitigating its severity.\u003c/p\u003e","manuscriptTitle":"Association Between Early Acetaminophen Exposure and Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-28 08:44:29","doi":"10.21203/rs.3.rs-6712781/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6082ae9c-a54e-469a-9b8f-8c6e36f55382","owner":[],"postedDate":"May 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-19T07:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-28 08:44:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6712781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6712781","identity":"rs-6712781","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}