Timing of Acute Kidney Injury in Infarction-Related Cardiogenic Shock: Early Onset Signals a High-Risk Phenotype

Timing of Acute Kidney Injury in Infarction-Related Cardiogenic Shock: Early Onset Signals a High-Risk Phenotype | 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 Timing of Acute Kidney Injury in Infarction-Related Cardiogenic Shock: Early Onset Signals a High-Risk Phenotype Priyanka Boettger, Henriette Preusse-Sondermann, Jamschid Sedighi, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7511711/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in BMC Nephrology → Version 1 posted 12 You are reading this latest preprint version Abstract Background Acute kidney injury (AKI) is common in cardiogenic shock (CS) and increases mortality, but the prognostic impact of onset timing in infarct-related CS is unclear. We examined whether early versus late AKI onset is associated with differences in patient characteristics and outcomes. Methods In this retrospective cohort study, 369 patients with infarct-related CS were classified by AKI timing within the first 96 h of admission: early (≤ 48 h) or late (> 48 h), according to KDIGO criteria. Clinical, hemodynamic, and inflammatory parameters and outcomes were compared. Multivariable logistic regression identified independent predictors of early AKI and in-hospital mortality. Results AKI occurred in 143 patients (42.8%), with 56.6% early-onset. In-hospital mortality was higher with early AKI than late AKI (71.6% vs. 54.8%; absolute difference 16.8%, 95% CI 3.1–30.5; p = 0.018). Early AKI patients had higher lactate at admission (median 4.3 vs. 3.1 mmol/L; p = 0.028), greater norepinephrine requirements (0.34 vs. 0.21 µg/kg/min; p = 0.044), and more frequent mechanical ventilation (81.5% vs. 61.3%; p = 0.011). In multivariable analysis, early AKI independently predicted in-hospital mortality (adjusted OR 2.12, 95% CI 1.16–3.87; p = 0.015), and was associated with baseline creatinine (OR 5.68 per 1 mg/dL, p = 0.008) and 24-h lactate (OR 2.67 per mmol/L, p < 0.001). Conclusions In infarct-related CS, AKI within 48 h marks a high-risk hemodynamic phenotype with markedly increased mortality, driven by renal vulnerability and early hypoperfusion. Incorporating AKI timing into risk stratification may help target early renoprotective interventions. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cardiogenic shock (CS) secondary to acute myocardial infarction remains a clinical emergency with persistently high mortality, despite advances in early revascularization, mechanical circulatory support, and critical care protocols [ 1 , 2 ]. Defined by inadequate tissue perfusion due to reduced cardiac output, CS initiates a cascade of vasoconstriction, neurohormonal activation, systemic inflammation, and ultimately multi-organ failure [ 1 ]. Among extracardiac complications, the kidney is particularly vulnerable. Acute kidney injury (AKI) occurs frequently in CS and is associated with longer ICU stay, increased need for renal replacement therapy (RRT), and excess mortality [ 3 ]. The pathophysiology is multifactorial, involving renal hypoperfusion, venous congestion, systemic inflammation, nephrotoxins, and metabolic stress [ 4 , 5 ]. Recent studies have highlighted the clinical and biological heterogeneity of AKI in critical illness, particularly with respect to timing of onset. Early AKI—typically defined as occurring within the first 48 hours—often reflects primary hemodynamic insult, while late AKI, developing beyond 48 hours, is more frequently associated with secondary injury mechanisms such as sepsis, cumulative vasopressor exposure, nephrotoxins, and multiorgan dysfunction [ 6 ] [ 7 ]. Patients with early AKI often have pre-existing renal impairment and elevated lactate levels, indicating systemic hypoperfusion [ 8 ], whereas late AKI is more common in older, multimorbid patients and is frequently irreversible [ 9 ]. Importantly, both early and late AKI are associated with increased mortality, but late AKI confers particularly poor outcomes, including higher risk of death, arrhythmias, bleeding, and longer ICU stay [ 10 ] [ 11 ] [ 12 ]. Late AKI also contributes to persistent cardiovascular dysfunction through maladaptive remodeling and sympathetic overstimulation, and predisposes survivors to chronic kidney disease and recurrent heart failure [ 13 ] [ 8 ]. Despite these insights, the prognostic relevance of AKI timing has not been systematically examined in infarct-related cardiogenic shock [ 14 ]. Existing AKI classifications such as KDIGO do not distinguish between early and late phenotypes, and few studies have explored whether early-onset AKI predicts mortality independently of renal function or shock severity at admission. The aim of this study was therefore to investigate the timing of AKI onset in infarct-related cardiogenic shock and to examine whether the time of development is associated with differences in patient characteristics, treatment intensity, and clinical outcomes, including in-hospital mortality. To address this gap, we focused exclusively on patients with infarct-related cardiogenic shock, thereby providing a homogeneous population to evaluate the prognostic implications of AKI timing within the acute shock phase. Methods Study design and population We conducted a retrospective, single-center observational study at an academic hospital in Germany, including consecutive patients admitted with infarct-related cardiogenic shock (CS) [ 15 ]. CS was diagnosed based on persistent hypotension (systolic blood pressure < 90 mmHg for ≥ 30 minutes or need for vasopressor support), clinical signs of end-organ hypoperfusion (e.g., oliguria, altered consciousness) [ 16 ], and evidence of acute myocardial infarction as defined by current AHA and ESC guidelines [ 17 , 18 ]. Data collection and variables This retrospective study included all consecutive patients with infarct-related cardiogenic shock admitted between January 2010 and July 2015 to the academic hospital, an academic hospital in Germany. Patients were excluded if timing of AKI could not be determined (n = 8), if they had pre-existing dialysis-dependent end-stage renal disease, or if cardiogenic shock was not infarct-related. Baseline demographics, comorbidities, laboratory parameters, hemodynamic data, and therapeutic interventions were recorded prospectively using an electronic case report form. Particular attention was paid to renal function dynamics, vasopressor use, and respiratory support. Serum creatinine was measured at least daily throughout the ICU stay. Baseline creatinine was defined as the most recent outpatient value within the preceding 12 months; if unavailable, the admission creatinine was used. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria[ 19 ]. To investigate the impact of timing on outcomes, patients who developed acute kidney injury (AKI) during the first 96 hours of ICU admission were stratified based on onset: early AKI was defined as occurring within 48 hours, and late AKI as developing between 48 and 96 hours. The 96-hour cut-off was chosen to capture the acute shock phase, where renal dysfunction is most likely attributable to hemodynamic instability rather than later ICU complications. Patients without AKI during this period served as an additional reference group to contextualize outcomes across the full spectrum of renal trajectories. Baseline characteristics, hemodynamic profiles, inflammatory markers (e.g., CRP, lactate), and outcome variables were compared across these groups. Outcomes The primary outcome was in-hospital mortality. Secondary outcomes included AKI severity (KDIGO stages 1–3), requirement for renal replacement therapy (RRT), mechanical ventilation duration, and norepinephrine dosing over time. Statistical analysis Data were analyzed using SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarized as means with standard deviations or medians with interquartile ranges, as appropriate, and categorical variables as counts and percentages. Group comparisons were performed using chi-square or Fisher’s exact test for categorical variables and Student’s t-test or Mann–Whitney U test for continuous variables. Temporal changes in creatinine, lactate, and norepinephrine requirements were assessed using repeated-measures ANOVA. Survival was analyzed with Kaplan–Meier curves and log-rank testing. Cox proportional hazards regression was applied to examine the association between AKI timing and in-hospital mortality. To strengthen causal inference, we performed a 1:1 propensity score–matched analysis comparing patients with and without early AKI, matched on age, sex, left ventricular ejection fraction, baseline creatinine, lactate, and vasopressor dose. Multivariable logistic regression was performed to identify predictors of early AKI and independent correlates of in-hospital mortality. To account for the modest sample size, parsimonious models were prespecified (1 predictor per 10 outcome events). Candidate covariates were selected a priori based on clinical relevance in cardiogenic shock and established associations with AKI and mortality in prior literature, and further supplemented by variables with p < 0.10 in univariate analyses. The final adjustment set included age, sex, diabetes mellitus, chronic kidney disease, baseline eGFR, prior myocardial infarction, left ventricular ejection fraction, lactate at admission, use of mechanical circulatory support, vasopressor dose, and need for mechanical ventilation. To minimize small-sample bias, Firth’s penalized likelihood method was applied as a sensitivity analysis, and internal validity was evaluated by nonparametric bootstrap resampling with 1,000 iterations. Model discrimination for in-hospital mortality was quantified by the area under the receiver operating characteristic curve (AUC). Missing data were handled by complete case analysis. All statistical tests were two-sided, with a p-value < 0.05 considered statistically significant. Results Baseline characteristics by timing of AKI onset Among the 143 patients with acute kidney injury (AKI), 81 (56.6%) developed early-onset AKI (≤ 48 hours after ICU admission), while 62 (43.4%) developed late-onset AKI (> 48 hours). (Fig. 1). Baseline demographics, comorbidities, and laboratory values stratified by timing of AKI are summarized in Table 1 and were largely comparable between patients with early- and late-onset AKI (Table 1). No significant differences were observed in age, sex, BMI, or the prevalence of hypertension and diabetes. Baseline characteristics stratified by AKI timing are shown in Table 1. Demographics and major comorbidities were largely comparable between groups. Age (69.9 ± 11.4 vs. 70.8 ± 9.2 years, p = 0.56), sex distribution (female: 36.6% vs. 37.1%, p = 0.95), and the prevalence of hypertension (76.5% vs. 75.8%, p = 0.91), diabetes mellitus (50.6% vs. 54.8%, p = 0.63), and obesity (67.9% vs. 66.1%, p = 0.82) did not differ significantly. Hyperlipoproteinemia was more frequent in late-onset AKI (64.5% vs. 51.2%, p = 0.034). Patients with early-onset AKI presented with higher baseline creatinine (1.34 ± 0.38 vs. 1.21 ± 0.41 mg/dL, p = 0.048), higher admission lactate (3.7 [IQR 2.8–5.3] vs. 2.9 [2.1–4.6] mmol/L, p = 0.032), and higher CRP levels (14.2 ± 7.5 vs. 11.3 ± 6.2 mg/dL, p = 0.038). Mechanical ventilation was required more often in early-onset AKI (81.5% vs. 61.3%, p = 0.011). Outcome parameters also differed: in-hospital mortality was higher in the early-onset group (71.6% vs. 54.8%, p = 0.018), as was the need for renal replacement therapy (29.6% vs. 16.1%, p = 0.037). Norepinephrine requirements at 24 hours were modestly higher in early AKI (0.34 ± 0.14 vs. 0.27 ± 0.11 µg/kg/min, p = 0.044). No other significant differences were observed. Figure 2 illustrates the temporal distribution of AKI onset, showing that the majority of cases clustered within the first 48 hours after ICU admission. Consistently, the cumulative incidence curve in Figure 3 demonstrates a steep rise in AKI occurrence during this early phase followed by a plateau, supporting the distinction between early-onset (≤48 h) and late-onset (>48 h) subtypes. Baseline lactate was also higher in the early-onset group (median: 3.7 mmol/L [IQR 2.8–5.3] vs. 2.9 mmol/L [IQR 2.1–4.6], p = 0.032), reflecting more pronounced initial circulatory impairment. Other pre-existing cardiac conditions, including prior myocardial infarction (24.7% vs. 22.6%, p = 0.77) and chronic heart failure (34.6% vs. 30.6%, p = 0.63), were similarly distributed. Rates of known coronary artery disease and prior CABG were balanced across groups. Collectively, these findings suggest that early-onset AKI is not driven by differences in baseline comorbidity but may instead reflect a greater degree of initial hemodynamic compromise and impaired renal perfusion. Furthermore, C-reactive protein (CRP) levels at admission were significantly higher in early-onset AKI (14.2 ± 7.5 mg/dL vs. 11.3 ± 6.2 mg/dL, p = 0.038), and mechanical ventilation was more frequently required at presentation (81.5% vs. 61.3%, p = 0.011), suggesting an elevated baseline inflammatory and respiratory burden in this group. Table 1 Baseline characteristics of the study population (N = 369) Values are presented as mean ± standard deviation, median [interquartile range], or percentage (absolute number). Early AKI was defined as onset within ≤ 48 hours of ICU admission; late AKI as onset > 48–96 hours. P-values refer to comparisons between early and late AKI groups using chi-square or Fisher’s exact test for categorical variables, and Student’s t-test or Mann–Whitney U test for continuous variables, as appropriate. Bold p-values indicate statistical significance (p < 0.05). Variable Total cohort (N = 369) Early AKI (n = 81) Late AKI (n = 62) p-value Age, years (mean ± SD) 69.2 ± 12.2 69.9 ± 11.4 70.8 ± 9.2 0.56 Age group 75 years 36.0% (132) 37.0% (30) 40.3% (25) 0.71 Sex: Male 66.9% (247) 63.4% (51) 62.9% (39) 0.95 Sex: Female 33.1% (122) 36.6% (30) 37.1% (23) 0.95 Height, cm (mean ± SD) 171 ± 12.2 170 ± 11.8 171 ± 12.4 0.74 Weight, kg (mean ± SD) 80.6 ± 13.8 79.8 ± 13.1 81.0 ± 13.6 0.68 Body mass index, kg/m² (mean ± SD) 27.3 ± 4.4 27.4 ± 4.5 27.2 ± 4.3 0.81 Smoking history 45.7% (163) 44.4% (36) 46.8% (29) 0.79 Known coronary artery disease 31.4% (101) 24.7% (20) 22.6% (14) 0.77 Prior myocardial infarction 23.3% (74) 24.7% (20) 22.6% (14) 0.77 Previous CABG surgery 15.3% (49) 14.8% (12) 16.1% (10) 0.84 Chronic heart failure 33.6% (124) 34.6% (28) 30.6% (19) 0.63 Hypertension 75.4% (260) 76.5% (62) 75.8% (47) 0.91 Diabetes mellitus 52.4% (182) 50.6% (41) 54.8% (34) 0.63 Hyperlipoproteinemia 55.8% (193) 51.2% (41) 64.5% (40) 0.034 Obesity 63.8% (217) 67.9% (55) 66.1% (41) 0.82 Family history of CAD 33.7% (116) 32.1% (26) 33.9% (21) 0.81 Chronic kidney disease 84.8% (313) 87.7% (71) 82.3% (51) 0.45 KDIGO stage 3 35.6% (131) 37.0% (30) 34.0% (21) 0.72 KDIGO stage 5 1.8% (7) 2.5% (2) 1.6% (1) 0.62 Baseline creatinine (mg/dL, mean ± SD) 1.28 ± 0.40 1.34 ± 0.38 1.21 ± 0.41 0.048 Lactate at admission (mmol/L, median [IQR]) 3.3 [2.3–5.0] 3.7 [2.8–5.3] 2.9 [2.1–4.6] 0.032 C-reactive protein (mg/dL, mean ± SD) 12.9 ± 6.9 14.2 ± 7.5 11.3 ± 6.2 0.038 Mechanical ventilation 71.0% (262) 81.5% (66) 61.3% (38) 0.011 In-hospital mortality 57.7% (213) 71.6% (58) 54.8% (34) 0.018 30-day post-discharge mortality 1.1% (4) 1.2% (1) 0.9% (1) 0.81 Renal replacement therapy 13.6% (50) 29.6% (24) 16.1% (10) 0.037 Norepinephrine dose at 24h (µg/kg/min, mean ± SD) 0.31 ± 0.13 0.34 ± 0.14 0.27 ± 0.11 0.044 Timing of AKI onset and associated outcomes Among the 143 patients who developed acute kidney injury (AKI), 81 (56.6%) experienced early-onset AKI—defined as occurring within 48 hours of ICU admission—while 62 (43.4%) had late-onset AKI (> 48 h). The median time to AKI onset was 38 hours (IQR 24–62), with no significant sex differences (p = 0.41). This distribution highlights a temporal vulnerability window within the first two days of cardiogenic shock (CS). Early-onset AKI was associated with significantly worse outcomes. In-hospital mortality was notably higher in the early-onset group compared to late-onset AKI (71.6% vs. 54.8%; absolute difference 16.8%, 95% CI 3.1–30.5; p = 0.018) ( Fig. 4 ). Similarly, renal replacement therapy (RRT) was required in 29.6% of early-onset patients versus 16.1% in the late-onset group (p = 0.037). Within the early-onset subgroup, patients receiving RRT had the highest mortality (83.3%), significantly exceeding that of early-onset AKI patients without RRT (64.7%, p = 0.041) ( Fig. 4 ). Circulatory and metabolic parameters Markers of circulatory and metabolic stress were more severely impaired in patients with early-onset AKI. These patients had significantly higher lactate levels both at ICU admission (median 4.3 mmol/L vs. 3.1 mmol/L; p = 0.028) and during the first 24 hours (mean 3.7 ± 1.6 vs. 2.9 ± 1.3 mmol/L; p = 0.032), indicating sustained metabolic imbalance ( Fig. 4 ). In parallel, early-onset AKI was associated with greater vasopressor requirements (median norepinephrine dose 0.34 µg/kg/min vs. 0.21 µg/kg/min; p = 0.044), reflecting more pronounced circulatory dysfunction in the initial phase of cardiogenic shock ( Fig. 4 ). Mechanical ventilation at admission was more common among patients with early AKI (81.5% vs. 61.3%, p = 0.011), suggesting more severe respiratory or hemodynamic compromise. Predictors of early-onset AKI In a multivariable logistic regression model evaluating independent predictors of early-onset acute kidney injury (≤ 48 hours after cardiogenic shock onset), two variables remained significantly associated. Higher baseline serum creatinine was the strongest predictor of early AKI (adjusted odds ratio [aOR], 5.68; 95% CI, 1.46–20.48; p = 0.008). Elevated serum lactate at 24 hours was also independently associated (aOR per mmol/L increase, 2.67; 95% CI, 1.54–4.63; p < 0.001). A trend toward significance was observed for age (aOR per year, 1.05; 95% CI, 0.99–1.11; p = 0.057). Table 2 Independent Predictors of Early-Onset Acute Kidney Injury in Cardiogenic Shock. Multivariable logistic regression analysis identifying variables associated with AKI onset within 48 hours. Only baseline creatinine and lactate at 24 hours were independently associated with early AKI. Abbreviations : AKI,acute kidney injury; OR, odds ratio; CI, confidence interval; CKD, chronic kidney disease. Variable Odds Ratio (95% CI) P Value Baseline serum creatinine (mg/dL) 5.68 (1.46–20.48) 0.008 Serum lactate at 24 h (mmol/L) 2.67 (1.54–4.63) < 0.001 Age (per year) 1.05 (0.99–1.11) 0.057 Female sex 0.88 (0.34–2.28) 0.79 Norepinephrine dose at 24 h (µg/kg/min) 1.12 (0.91–1.37) 0.28 Mechanical ventilation 1.34 (0.53–3.36) 0.52 Body mass index (per kg/m²) 0.97 (0.90–1.05) 0.45 Chronic kidney disease 1.78 (0.77–4.10) 0.18 Hyperlipoproteinemia 1.42 (0.62–3.22) 0.40 GFR < 30 mL/min/1.73m² 1.21 (0.47–3.15) 0.69 Diabetes mellitus 0.92 (0.38–2.25) 0.86 Hypertension 1.14 (0.48–2.69) 0.77 No significant associations were found for female sex, body mass index, norepinephrine dose, mechanical ventilation, or pre-existing comorbidities. Specifically, chronic kidney disease, diabetes mellitus, hypertension, hyperlipoproteinemia, and baseline GFR < 30 mL/min/1.73 m² were not predictive of early AKI. Complete model estimates are summarized in Table 2. To facilitate visual interpretation, a graphical summary of odds ratios and confidence intervals is additionally provided in Fig. 5. The adjusted model included 10 clinically relevant covariates, prespecified to balance confounding control with the limited number of outcome events. Early-onset AKI and in-hospital mortality To evaluate the prognostic significance of early-onset AKI, a second multivariable logistic regression model was constructed with in-hospital mortality as the outcome. After adjusting for age, sex, baseline creatinine, serum lactate at 24 hours, and norepinephrine dose, early AKI was independently associated with a significantly increased risk of in-hospital death (aOR, 2.12; 95% CI, 1.16–3.87; p = 0.015). The predictive model showed moderate discriminative performance (area under the curve [AUC], 0.72; 95% CI, 0.66–0.78) and good calibration (Hosmer–Lemeshow p = 0.45). A steep rise in cumulative AKI incidence occurred within the first 48 hours (Fig. 2), underscoring the temporal clustering of early events. Although only 6.3% of AKI cases developed within 24 hours, these patients had the highest in-hospital mortality (77.8%), identifying a particularly high-risk subgroup (Fig. 1). Kaplan–Meier analysis showed significantly lower survival among patients with early-onset AKI (log-rank p < 0.01; Fig. 6). In a Cox regression model adjusted for age, baseline creatinine, and serum lactate at 24 hours, early AKI was independently associated with increased in-hospital mortality (hazard ratio, 2.41; 95% CI, 1.51–3.85; p < 0.001). Timing-based Classification and Exploratory Analyses In an exploratory secondary analysis, we applied a refined classification of AKI onset to better characterize risk trajectories. Patients were categorized into four groups based on AKI timing: no AKI, early-onset ( 48 hours). Early-onset AKI was associated with the highest in-hospital mortality (42.8%), followed by intermediate-onset AKI (33.3%). In contrast, patients with late-onset AKI (> 48h) had comparable outcomes to those without AKI (mortality 18.7% vs. 15.3%, p = 0.42). In a multivariable Cox regression model, early-onset AKI remained an independent predictor of mortality (HR 2.87, 95% CI 1.73–4.76), while intermediate-onset AKI showed a weaker association (HR 1.95, 95% CI 1.14–3.33). Late-onset AKI was not independently associated with mortality (Fig. 7). To assess robustness, we performed a 1:1 propensity score–matched analysis comparing patients with and without early-onset AKI, matched for age, sex, LVEF, baseline creatinine, lactate, and vasopressor use. The association between early-onset AKI and mortality persisted in the matched cohort (HR 2.11, 95% CI 1.33–3.32; p < 0.001). These findings suggest that AKI timing may represent distinct pathophysiological phenotypes and support the clinical relevance of temporal subclassification in cardiogenic shock. Sensitivity and robustness analyses To account for the modest sample size, multivariable models were restricted to a parsimonious set of prespecified covariates (age, sex, baseline creatinine, lactate, and vasopressor dose). Effect estimates for the association between early AKI and in-hospital mortality remained directionally consistent with the primary analysis (adjusted OR 2.45, 95% CI 1.21–4.96; p = 0.013), indicating robustness of the findings despite model simplification. To further address small-sample bias, Firth’s penalized likelihood logistic regression was applied and yielded similar results (adjusted OR 2.39, 95% CI 1.14–4.85; p = 0.015). Internal validity was additionally assessed using nonparametric bootstrap resampling with 1,000 iterations, which produced bias-corrected confidence intervals consistent with the main model (adjusted OR 2.41, 95% CI 1.17–4.89). The logistic regression model including AKI timing demonstrated good discriminative ability for in-hospital mortality, with an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.70–0.84). When late AKI was alternatively defined as occurring within 48–168 hours after admission, no independent association with mortality was observed (adjusted OR 1.12, 95% CI 0.54–2.35; p = 0.75), consistent with the main analysis. Discussion this prospective cohort of patients with infarct-related cardiogenic shock (CS) [ 20 ], the timing of acute kidney injury (AKI) onset emerged as a clinically relevant prognostic factor [ 12 ]. Patients who developed AKI within the first 48 hours—especially within < 24 hours—had significantly higher in-hospital mortality compared to those with later AKI onset, despite similar baseline characteristics. Early AKI was also associated with increased lactate levels, higher vasopressor requirements, and more frequent use of renal replacement therapy (RRT), pointing to a phenotype of more profound circulatory derangement in the early shock phase. These findings reinforce the notion that early AKI reflects ischemic–hemodynamic insult, while late AKI may arise from multifactorial contributors such as inflammation, nephrotoxicity, and fluid overload [ 5 ] [ 21 ]. Prior studies in CS have consistently shown that early AKI portends worse outcomes and aligns closely with markers of systemic hypoperfusion [ 12 , 22 ] [ 11 ]. Our data extend this knowledge by demonstrating that the AKI time course is not merely a chronological phenomenon, but a surrogate for distinct underlying pathophysiology. Beyond confirming previous observations, our study adds nuance by focusing on a homogeneous cohort of infarct-related cardiogenic shock and by differentiating very early AKI (≤ 24 hours) from later occurrences. This temporal refinement highlights early AKI not only as a complication but also as a dynamic marker of circulatory collapse, thereby extending existing evidence. From a mechanistic perspective, early AKI in CS likely represents acute ischemic tubular injury due to renal hypoperfusion, loss of autoregulation, and microvascular dysfunction [ 14 ] [ 23 ]. Our findings of higher serum lactate and norepinephrine dose at 24 hours in the early AKI group support this hypothesis. Serum lactate is a robust biomarker of global tissue hypoperfusion and has been shown to correlate with both AKI incidence and mortality in critically ill patients (Zhou et al., 2015; Garcia-Alvarez et al., 2014). Similarly, high vasopressor demand indicates persistent circulatory failure and may amplify renal ischemia through vasoconstrictive effects [ 1 ]. Our multivariable analysis identified baseline creatinine and serum lactate at 24 hours as independent predictors of early AKI. The association with baseline creatinine suggests that patients with subclinical or overt chronic kidney dysfunction may have reduced renal reserve and are thus more susceptible to acute insults [ 11 ] [ 24 ]. This vulnerability may also reflect maladaptive repair responses and heightened sensitivity to systemic inflammation. Interestingly, while norepinephrine dose at 24h was higher in early AKI patients in univariate analysis, it did not independently predict early AKI in the adjusted model. This may reflect the complex interplay between vasopressor dose, timing, and underlying shock severity, as well as possible confounding by indication [ 21 ]. Likewise, mechanical ventilation, diabetes, hypertension, and sex did not independently associate with early AKI. Although sex differences in AKI susceptibility and outcomes have been reported [ 25 ] [ 3 ], their relevance may depend on contextual factors such as hormonal status, vascular reactivity, and timing of injury. The clustering of AKI onset within the first 48 hours supports the clinical distinction between early and late AKI phenotypes in CS. Early AKI appears to track with primary hemodynamic collapse and ischemia–reperfusion injury, whereas late AKI may reflect prolonged critical illness, secondary inflammation, or medication-induced nephrotoxicity [ 26 , 27 ]. Recognizing these distinct trajectories could enable clinicians to tailor monitoring intensity, fluid resuscitation strategies, and timing of RRT initiation [ 5 ]. Beyond individual predictors, our data support efforts to redefine AKI phenotypes beyond static staging systems such as KDIGO, incorporating dynamic parameters such as timing, lactate clearance, and perfusion indices [ 22 ] [ 28 ]. For instance, lactate-guided resuscitation may help identify high-risk phenotypes and inform timely interventions in patients at risk of early AKI [ 16 ]. In infarct-related cardiogenic shock, the timing of AKI onset carries independent prognostic value. Early-onset AKI within 48 hours identifies a hemodynamically compromised phenotype with excess mortality, underscoring the need to incorporate temporal patterns into future risk stratification and management strategies. Limitations This study has limitations. It was conducted at a single academic center, which may limit generalizability. The retrospective observational design precludes causal inference, and residual confounding cannot be excluded despite adjustment. Given the modest sample size, statistical power was limited, particularly for late-onset AKI, and the absence of an independent association with mortality should therefore be interpreted as hypothesis-generating rather than definitive. To mitigate overfitting, we applied parsimonious models and internal validation, but small-sample bias remains possible. Timing of AKI onset may have been misclassified because serum creatinine was measured daily rather than continuously. Important contributors to renal injury, including fluid balance, nephrotoxin exposure, and urine output, were not systematically recorded. Sex-specific hormonal influences were not assessed, and long-term renal outcomes were beyond the scope of this analysis. Conclusion In infarct-related cardiogenic shock, the timing of acute kidney injury (AKI) onset holds critical prognostic value. Early-onset AKI—particularly within 48 hours—defines a high-risk phenotype marked by severe hemodynamic instability, metabolic stress, and substantially increased in-hospital mortality. Elevated lactate levels and reduced baseline renal function independently predicted early AKI, reflecting the interplay of global hypoperfusion and renal susceptibility. These findings support incorporating AKI timing into clinical risk models and underscore the need for early recognition and tailored renoprotective strategies to improve outcomes in this vulnerable population. Declarations Ethics approval and consent to participate This retrospective observational study was approved by the institutional ethics committee (Approval Number FF55/2018). The requirement for informed consent was waived due to the use of anonymized data and the non-interventional retrospective study design. All procedures were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study received no external funding. Authors’ contributions PB, JS and HPS conceived and designed the study, performed data analysis, and drafted the manuscript. HPS, JJ, HH, KP and MJ contributed to patient data acquisition and analysis. BA, UB, HL and SS provided clinical expertise and critical revisions of the manuscript. BA, MB and HL supervised the study and approved the final version. All authors read and approved the final manuscript. Acknowledgements The authors thank the intensive care and cardiology teams for their clinical dedication and support in data collection. References Buerke, M., et al., Pathophysiology, diagnosis, and treatment of infarction-related cardiogenic shock. Herz, 2011. 36 (2): p. 73–83. Thiele, H., et al., Intraaortic balloon support for myocardial infarction with cardiogenic shock. N Engl J Med, 2012. 367 (14): p. 1287–96. Joannidis, M., et al., Acute kidney injury in critically ill patients classified by AKIN versus RIFLE using the SAPS 3 database. Intensive Care Med, 2009. 35 (10): p. 1692–702. Hultström, M., Neurohormonal interactions on the renal oxygen delivery and consumption in haemorrhagic shock-induced acute kidney injury. Acta Physiol (Oxf), 2013. 209 (1): p. 11–25. Pannu, N. and R.N. Gibney, Renal replacement therapy in the intensive care unit. Ther Clin Risk Manag, 2005. 1 (2): p. 141–50. Patsalis, N., et al., Early risk predictors of acute kidney injury and short-term survival during Impella support in cardiogenic shock. Sci Rep, 2024. 14 (1): p. 17484. Li, S., et al., Associated factors and short-term mortality of early versus late acute kidney injury following on-pump cardiac surgery. Interact Cardiovasc Thorac Surg, 2022. 35 (3). Tang, W.H.W., et al., Evaluation and Management of Kidney Dysfunction in Advanced Heart Failure: A Scientific Statement From the American Heart Association. Circulation, 2024. 150 (16): p. e280–e295. Sundermeyer, J., et al., Kidney injury in patients with heart failure-related cardiogenic shock: Results from an international, multicentre cohort study. Eur J Heart Fail, 2025. Adegbala, O., et al., Characteristics and Outcomes of Patients With Cardiogenic Shock Utilizing Hemodialysis for Acute Kidney Injury. Am J Cardiol, 2019. 123 (11): p. 1816–1821. Li, S., et al., Acute Kidney Injury in Critically Ill Patients After Noncardiac Major Surgery: Early Versus Late Onset. Crit Care Med, 2019. 47 (6): p. e437–e444. Vallabhajosyula, S., et al., Temporal trends, predictors, and outcomes of acute kidney injury and hemodialysis use in acute myocardial infarction-related cardiogenic shock. PLoS One, 2019. 14 (9): p. e0222894. Noels, H., et al., Renal-Cardiac Crosstalk in the Pathogenesis and Progression of Heart Failure. Circ Res, 2025. 136 (11): p. 1306–1334. Ghionzoli, N., et al., Cardiogenic shock and acute kidney injury: the rule rather than the exception. Heart Fail Rev, 2021. 26 (3): p. 487–496. Blumer, V., et al., Cardiogenic Shock in Older Adults: A Focus on Age-Associated Risks and Approach to Management: A Scientific Statement From the American Heart Association. Circulation, 2024. 149 (14): p. e1051–e1065. van Diepen, S., et al., Contemporary Management of Cardiogenic Shock: A Scientific Statement From the American Heart Association. Circulation, 2017. 136 (16): p. e232–e268. Rao, S.V., et al., 2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients With Acute Coronary Syndromes: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 2025. 151 (13): p. e771–e862. Byrne, R.A., et al., 2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC). European Heart Journal, 2023. 44 (38): p. 3720–3826. Khwaja, A., KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract, 2012. 120 (4): p. c179–84. Sinha, S.S., et al., 2025 Concise Clinical Guidance: An ACC Expert Consensus Statement on the Evaluation and Management of Cardiogenic Shock. JACC, 2025. 85 (16): p. 1618–1641. Ostermann, M., et al., Recommendations on Acute Kidney Injury Biomarkers From the Acute Disease Quality Initiative Consensus Conference: A Consensus Statement. JAMA Netw Open, 2020. 3 (10): p. e2019209. Marbach, J.A., et al., Lactate Clearance Is Associated With Improved Survival in Cardiogenic Shock: A Systematic Review and Meta-Analysis of Prognostic Factor Studies. J Card Fail, 2021. 27 (10): p. 1082–1089. Singh, S., et al., Acute Kidney Injury in Cardiogenic Shock: An Updated Narrative Review. J Cardiovasc Dev Dis, 2021. 8 (8). Neugarten, J., L. Golestaneh, and N.V. Kolhe, Sex differences in acute kidney injury requiring dialysis. BMC Nephrol, 2018. 19 (1): p. 131. Ronco, C., R. Bellomo, and J.A. Kellum, Acute kidney injury. Lancet, 2019. 394 (10212): p. 1949–1964. Ostermann, M. and M. Joannidis, Acute kidney injury 2016: diagnosis and diagnostic workup. Crit Care, 2016. 20 (1): p. 299. Hoste, E.A., et al., Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med, 2015. 41 (8): p. 1411–23. Nguyen, H.B., et al., Early lactate clearance is associated with improved outcome in severe sepsis and septic shock. Crit Care Med, 2004. 32 (8): p. 1637–42. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 06 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor invited by journal 04 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 01 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7511711","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512922180,"identity":"9b509d55-fca6-433d-b6ea-fd042d943741","order_by":0,"name":"Priyanka Boettger","email":"data:image/png;base64,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","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":true,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Boettger","suffix":""},{"id":512922181,"identity":"cd1785f4-daaf-4807-a01c-32b01da914cc","order_by":1,"name":"Henriette Preusse-Sondermann","email":"","orcid":"","institution":"St. Marien Hospital Siegen","correspondingAuthor":false,"prefix":"","firstName":"Henriette","middleName":"","lastName":"Preusse-Sondermann","suffix":""},{"id":512922182,"identity":"5d311bc5-fcf3-4d26-9de3-197ad3c9b753","order_by":2,"name":"Jamschid Sedighi","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Jamschid","middleName":"","lastName":"Sedighi","suffix":""},{"id":512922184,"identity":"85a44de6-1af7-4692-b554-7d24cbec0067","order_by":3,"name":"Jannik Jobst","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Jannik","middleName":"","lastName":"Jobst","suffix":""},{"id":512922186,"identity":"bf0e7b0d-cbf1-4a15-ba13-16cb7e066808","order_by":4,"name":"Hassan Hassan","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Hassan","suffix":""},{"id":512922190,"identity":"3ca83131-1660-430d-badf-5163cc0c5243","order_by":5,"name":"Utku Bayram","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Utku","middleName":"","lastName":"Bayram","suffix":""},{"id":512922192,"identity":"50f6f9f4-1ff9-4dbd-8184-0618911cdc72","order_by":6,"name":"Kerstin Piayda","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Kerstin","middleName":"","lastName":"Piayda","suffix":""},{"id":512922193,"identity":"f32ebeca-9600-4998-aa67-697dc9bd691b","order_by":7,"name":"Matthias Janusch","email":"","orcid":"","institution":"St. Marien Hospital Siegen","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Janusch","suffix":""},{"id":512922195,"identity":"3a42e57f-e82d-4f69-8f33-d62c367dd99e","order_by":8,"name":"Birgit Assmus","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Birgit","middleName":"","lastName":"Assmus","suffix":""},{"id":512922196,"identity":"195bf1f3-98ec-4e98-8575-cba91f606087","order_by":9,"name":"Bernhard Unsoeld","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Unsoeld","suffix":""},{"id":512922197,"identity":"2e42dcb8-0e88-46e2-b101-c889ac147676","order_by":10,"name":"Henning Lemm","email":"","orcid":"","institution":"St. Marien Hospital Siegen","correspondingAuthor":false,"prefix":"","firstName":"Henning","middleName":"","lastName":"Lemm","suffix":""},{"id":512922198,"identity":"75c7ffc5-d353-4991-a71f-014af6947a81","order_by":11,"name":"Samuel Sossalla","email":"","orcid":"","institution":"Justus-Liebig University","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Sossalla","suffix":""},{"id":512922199,"identity":"a465fe0e-231e-4aee-a9a0-ea0aa4ba97fc","order_by":12,"name":"Michael Buerke","email":"","orcid":"","institution":"St. Marien Hospital Siegen","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Buerke","suffix":""}],"badges":[],"createdAt":"2025-09-01 22:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7511711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7511711/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-025-04730-y","type":"published","date":"2026-01-06T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91306292,"identity":"697f945a-1eb8-43fd-a482-a2172dc4d772","added_by":"auto","created_at":"2025-09-15 06:31:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":195343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow diagram \u003c/strong\u003eOf 369 patients with infarct-related cardiogenic shock included in the analysis, 143 (42.8%) developed acute kidney injury (AKI) within the first 96 hours of admission, while 191 (57.2%) did not. Among patients with AKI, 81 (56.6%) experienced early-onset AKI (≤48 hours) and 62 (43.4%) developed late-onset AKI (\u0026gt;48 hours). Renal replacement therapy (RRT) was required more frequently in patients with early-onset AKI (29.6%) compared to late-onset AKI (16.1%).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/a2b32bbe5d1d8420a13bdb2d.png"},{"id":91306294,"identity":"3977fcda-1e7d-4a2b-8380-17e7c4a8867a","added_by":"auto","created_at":"2025-09-15 06:31:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTiming of Acute Kidney Injury (AKI) Onset in Cardiogenic Shock. \u003c/strong\u003eTemporal distribution of first AKI onset among patients with infarct-related cardiogenic shock. The majority of AKI cases occurred within the first 48 hours following ICU admission, indicating a critical period of renal vulnerability likely driven by early hemodynamic instability. The clustering of early AKI onset supports the concept of a hemodynamic AKI phenotype, predominantly ischemic in origin. Data on AKI timing were unavailable for 8 patients and thus excluded from the analysis (n = 134).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/383cacf16ec45d410edbafda.png"},{"id":91306293,"identity":"48159a79-5afb-4881-ba72-71d24b98dd46","added_by":"auto","created_at":"2025-09-15 06:31:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32763,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative Incidence of AKI by Onset Timing \u003c/strong\u003eThe cumulative incidence of acute kidney injury (AKI) is plotted relative to time since shock onset. A steep rise in AKI cases is observed during the first 48 hours, followed by a plateauing of the curve, indicating a temporal clustering of early AKI events. This inflection point supports the classification of AKI into early-onset (≤48 h) and late-onset (\u0026gt;48 h) subtypes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/4f9f7d6abbfe1153fa32434e.png"},{"id":91308140,"identity":"665379db-62ef-40d3-af7f-4477a74de657","added_by":"auto","created_at":"2025-09-15 06:47:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOutcomes stratified by timing of acute kidney injury (AKI) in infarct-related cardiogenic shock.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) ICU mortality was significantly higher in patients with early-onset AKI (≤48 h) compared with late-onset AKI (\u0026gt;48–96 h) (71.6% vs. 54.8%; \u003cem\u003ep\u003c/em\u003e= 0.018).\u003c/p\u003e\n\u003cp\u003e(B) Requirement for renal replacement therapy (RRT) was more frequent in early- versus late-onset AKI (29.6% vs. 16.1%; \u003cem\u003ep\u003c/em\u003e= 0.037).\u003c/p\u003e\n\u003cp\u003e(C) Serum lactate concentrations at admission were elevated in early-onset AKI (median 3.7 [IQR 2.8–5.3] mmol/L) compared with late-onset AKI (2.9 [2.1–4.6] mmol/L; \u003cem\u003ep\u003c/em\u003e= 0.032).\u003c/p\u003e\n\u003cp\u003e(D) Mean norepinephrine dose at 24 h after ICU admission was higher in early-onset AKI (0.34 ± 0.14 µg/kg/min) than in late-onset AKI (0.27 ± 0.11 µg/kg/min; \u003cem\u003ep\u003c/em\u003e= 0.044).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/d93d62d7e4842066bc72fd03.png"},{"id":91308141,"identity":"33a6c649-eea1-4bad-aa58-38b5fc68057b","added_by":"auto","created_at":"2025-09-15 06:47:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":198718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted Odds Ratios for Early-Onset Acute Kidney Injury. \u003c/strong\u003eMultivariable logistic regression showing associations between clinical variables and early-onset acute kidney injury (AKI) in infarct-related cardiogenic shock. Odds ratios are adjusted; bars indicate 95% confidence intervals. Baseline creatinine and 24-hour lactate were independent predictors. Chronic kidney disease, diabetes, hypertension, and low GFR were not associated with early AKI.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/4be341da8d7f79afd1093a60.png"},{"id":91306300,"identity":"5c67e26c-60d5-4fac-b33f-09ace5283db7","added_by":"auto","created_at":"2025-09-15 06:31:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":217479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival analysis according to timing of acute kidney injury (AKI).\u003c/strong\u003e Shown are Kaplan–Meier estimates of 30-day survival among patients with infarct-related cardiogenic shock, stratified by acute kidney injury (AKI) status. Survival was lowest in patients with early AKI (≤48 hours, blue), intermediate in those with late AKI (\u0026gt;48 hours, orange), and highest in patients without AKI (green) (log-rank p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/9da3ef9a1c704257dcb7420a.png"},{"id":91307716,"identity":"8215040a-c579-4b1e-8c47-b428dc335c2c","added_by":"auto","created_at":"2025-09-15 06:39:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":262291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative mortality over time after cardiogenic shock onset, stratified by acute kidney injury (AKI) timing\u003c/strong\u003e. Early AKI (blue circles) denotes onset within 24 hours of shock; Late AKI (orange squares) denotes onset after 24 hours; No AKI (green triangles) represents patients without AKI. The vertical black dotted line indicates shock onset (0 h), and the vertical red dashed line marks the cut-off for late AKI onset (\u0026gt; 24 h).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/18b2fe0a1f31ddf3b7ca61cd.png"},{"id":100068608,"identity":"781580a1-9cd2-49c0-8368-460c4d889509","added_by":"auto","created_at":"2026-01-12 16:00:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2382549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7511711/v1/ce815bf7-a71d-476d-9d7f-2c6688a792de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Timing of Acute Kidney Injury in Infarction-Related Cardiogenic Shock: Early Onset Signals a High-Risk Phenotype","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiogenic shock (CS) secondary to acute myocardial infarction remains a clinical emergency with persistently high mortality, despite advances in early revascularization, mechanical circulatory support, and critical care protocols [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Defined by inadequate tissue perfusion due to reduced cardiac output, CS initiates a cascade of vasoconstriction, neurohormonal activation, systemic inflammation, and ultimately multi-organ failure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong extracardiac complications, the kidney is particularly vulnerable. Acute kidney injury (AKI) occurs frequently in CS and is associated with longer ICU stay, increased need for renal replacement therapy (RRT), and excess mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pathophysiology is multifactorial, involving renal hypoperfusion, venous congestion, systemic inflammation, nephrotoxins, and metabolic stress [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent studies have highlighted the clinical and biological heterogeneity of AKI in critical illness, particularly with respect to timing of onset. Early AKI\u0026mdash;typically defined as occurring within the first 48 hours\u0026mdash;often reflects primary hemodynamic insult, while late AKI, developing beyond 48 hours, is more frequently associated with secondary injury mechanisms such as sepsis, cumulative vasopressor exposure, nephrotoxins, and multiorgan dysfunction [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with early AKI often have pre-existing renal impairment and elevated lactate levels, indicating systemic hypoperfusion [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], whereas late AKI is more common in older, multimorbid patients and is frequently irreversible [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImportantly, both early and late AKI are associated with increased mortality, but late AKI confers particularly poor outcomes, including higher risk of death, arrhythmias, bleeding, and longer ICU stay [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Late AKI also contributes to persistent cardiovascular dysfunction through maladaptive remodeling and sympathetic overstimulation, and predisposes survivors to chronic kidney disease and recurrent heart failure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these insights, the prognostic relevance of AKI timing has not been systematically examined in infarct-related cardiogenic shock [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Existing AKI classifications such as KDIGO do not distinguish between early and late phenotypes, and few studies have explored whether early-onset AKI predicts mortality independently of renal function or shock severity at admission. The aim of this study was therefore to investigate the timing of AKI onset in infarct-related cardiogenic shock and to examine whether the time of development is associated with differences in patient characteristics, treatment intensity, and clinical outcomes, including in-hospital mortality. To address this gap, we focused exclusively on patients with infarct-related cardiogenic shock, thereby providing a homogeneous population to evaluate the prognostic implications of AKI timing within the acute shock phase.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective, single-center observational study at an academic hospital in Germany, including consecutive patients admitted with infarct-related cardiogenic shock (CS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. CS was diagnosed based on persistent hypotension (systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg for \u0026ge;\u0026thinsp;30 minutes or need for vasopressor support), clinical signs of end-organ hypoperfusion (e.g., oliguria, altered consciousness) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and evidence of acute myocardial infarction as defined by current AHA and ESC guidelines [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection and variables\u003c/h3\u003e\n\u003cp\u003eThis retrospective study included all consecutive patients with infarct-related cardiogenic shock admitted between January 2010 and July 2015 to the academic hospital, an academic hospital in Germany. Patients were excluded if timing of AKI could not be determined (n\u0026thinsp;=\u0026thinsp;8), if they had pre-existing dialysis-dependent end-stage renal disease, or if cardiogenic shock was not infarct-related.\u003c/p\u003e\u003cp\u003eBaseline demographics, comorbidities, laboratory parameters, hemodynamic data, and therapeutic interventions were recorded prospectively using an electronic case report form. Particular attention was paid to renal function dynamics, vasopressor use, and respiratory support. Serum creatinine was measured at least daily throughout the ICU stay. Baseline creatinine was defined as the most recent outpatient value within the preceding 12 months; if unavailable, the admission creatinine was used.\u003c/p\u003e\u003cp\u003eAKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To investigate the impact of timing on outcomes, patients who developed acute kidney injury (AKI) during the first 96 hours of ICU admission were stratified based on onset: early AKI was defined as occurring within 48 hours, and late AKI as developing between 48 and 96 hours. The 96-hour cut-off was chosen to capture the acute shock phase, where renal dysfunction is most likely attributable to hemodynamic instability rather than later ICU complications. Patients without AKI during this period served as an additional reference group to contextualize outcomes across the full spectrum of renal trajectories. Baseline characteristics, hemodynamic profiles, inflammatory markers (e.g., CRP, lactate), and outcome variables were compared across these groups.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was in-hospital mortality. Secondary outcomes included AKI severity (KDIGO stages 1\u0026ndash;3), requirement for renal replacement therapy (RRT), mechanical ventilation duration, and norepinephrine dosing over time.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarized as means with standard deviations or medians with interquartile ranges, as appropriate, and categorical variables as counts and percentages. Group comparisons were performed using chi-square or Fisher\u0026rsquo;s exact test for categorical variables and Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test for continuous variables. Temporal changes in creatinine, lactate, and norepinephrine requirements were assessed using repeated-measures ANOVA. Survival was analyzed with Kaplan\u0026ndash;Meier curves and log-rank testing. Cox proportional hazards regression was applied to examine the association between AKI timing and in-hospital mortality. To strengthen causal inference, we performed a 1:1 propensity score\u0026ndash;matched analysis comparing patients with and without early AKI, matched on age, sex, left ventricular ejection fraction, baseline creatinine, lactate, and vasopressor dose.\u003c/p\u003e\u003cp\u003eMultivariable logistic regression was performed to identify predictors of early AKI and independent correlates of in-hospital mortality. To account for the modest sample size, parsimonious models were prespecified (1 predictor per 10 outcome events). Candidate covariates were selected a priori based on clinical relevance in cardiogenic shock and established associations with AKI and mortality in prior literature, and further supplemented by variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analyses. The final adjustment set included age, sex, diabetes mellitus, chronic kidney disease, baseline eGFR, prior myocardial infarction, left ventricular ejection fraction, lactate at admission, use of mechanical circulatory support, vasopressor dose, and need for mechanical ventilation. To minimize small-sample bias, Firth\u0026rsquo;s penalized likelihood method was applied as a sensitivity analysis, and internal validity was evaluated by nonparametric bootstrap resampling with 1,000 iterations. Model discrimination for in-hospital mortality was quantified by the area under the receiver operating characteristic curve (AUC). Missing data were handled by complete case analysis. All statistical tests were two-sided, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eBaseline characteristics by timing of AKI onset\u003c/h2\u003e\n \u003cp\u003eAmong the 143 patients with acute kidney injury (AKI), 81 (56.6%) developed early-onset AKI (≤ 48 hours after ICU admission), while 62 (43.4%) developed late-onset AKI (\u0026gt; 48 hours). (Fig. 1). Baseline demographics, comorbidities, and laboratory values stratified by timing of AKI are summarized in Table 1 and were largely comparable between patients with early- and late-onset AKI (Table 1). No significant differences were observed in age, sex, BMI, or the prevalence of hypertension and diabetes.\u003c/p\u003e\n \u003cp\u003eBaseline characteristics stratified by AKI timing are shown in Table 1. Demographics and major comorbidities were largely comparable between groups. Age (69.9 ± 11.4 vs. 70.8 ± 9.2 years, p = 0.56), sex distribution (female: 36.6% vs. 37.1%, p = 0.95), and the prevalence of hypertension (76.5% vs. 75.8%, p = 0.91), diabetes mellitus (50.6% vs. 54.8%, p = 0.63), and obesity (67.9% vs. 66.1%, p = 0.82) did not differ significantly. Hyperlipoproteinemia was more frequent in late-onset AKI (64.5% vs. 51.2%, p = 0.034). Patients with early-onset AKI presented with higher baseline creatinine (1.34 ± 0.38 vs. 1.21 ± 0.41 mg/dL, p = 0.048), higher admission lactate (3.7 [IQR 2.8–5.3] vs. 2.9 [2.1–4.6] mmol/L, p = 0.032), and higher CRP levels (14.2 ± 7.5 vs. 11.3 ± 6.2 mg/dL, p = 0.038). Mechanical ventilation was required more often in early-onset AKI (81.5% vs. 61.3%, p = 0.011). Outcome parameters also differed: in-hospital mortality was higher in the early-onset group (71.6% vs. 54.8%, p = 0.018), as was the need for renal replacement therapy (29.6% vs. 16.1%, p = 0.037). Norepinephrine requirements at 24 hours were modestly higher in early AKI (0.34 ± 0.14 vs. 0.27 ± 0.11 µg/kg/min, p = 0.044). No other significant differences were observed.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e illustrates the temporal distribution of AKI onset, showing that the majority of cases clustered within the first 48 hours after ICU admission. Consistently, the cumulative incidence curve in \u003cstrong\u003eFigure 3\u003c/strong\u003e demonstrates a steep rise in AKI occurrence during this early phase followed by a plateau, supporting the distinction between early-onset (≤48 h) and late-onset (\u0026gt;48 h) subtypes.\u003c/p\u003e\n \u003cp\u003eBaseline lactate was also higher in the early-onset group (median: 3.7 mmol/L [IQR 2.8–5.3] vs. 2.9 mmol/L [IQR 2.1–4.6], p = 0.032), reflecting more pronounced initial circulatory impairment. Other pre-existing cardiac conditions, including prior myocardial infarction (24.7% vs. 22.6%, p = 0.77) and chronic heart failure (34.6% vs. 30.6%, p = 0.63), were similarly distributed. Rates of known coronary artery disease and prior CABG were balanced across groups. Collectively, these findings suggest that early-onset AKI is not driven by differences in baseline comorbidity but may instead reflect a greater degree of initial hemodynamic compromise and impaired renal perfusion. Furthermore, C-reactive protein (CRP) levels at admission were significantly higher in early-onset AKI (14.2 ± 7.5 mg/dL vs. 11.3 ± 6.2 mg/dL, p = 0.038), and mechanical ventilation was more frequently required at presentation (81.5% vs. 61.3%, p = 0.011), suggesting an elevated baseline inflammatory and respiratory burden in this group.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics of the study population (N = 369)\u003c/strong\u003e Values are presented as mean ± standard deviation, median [interquartile range], or percentage (absolute number). Early AKI was defined as onset within ≤ 48 hours of ICU admission; late AKI as onset \u0026gt; 48–96 hours. P-values refer to comparisons between early and late AKI groups using chi-square or Fisher’s exact test for categorical variables, and Student’s t-test or Mann–Whitney U test for continuous variables, as appropriate. \u003cstrong\u003eBold p-values indicate statistical significance (p \u0026lt; 0.05).\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal cohort (N = 369)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEarly AKI (n = 81)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLate AKI (n = 62)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years (mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.2 ± 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.9 ± 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.8 ± 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group \u0026lt; 60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3% (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.0% (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.4% (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group 60–75 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.7% (153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.0% (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.3% (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group \u0026gt; 75 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.0% (132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.0% (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.3% (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex: Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.9% (247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.4% (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.9% (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex: Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.1% (122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.6% (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.1% (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight, cm (mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 ± 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 ± 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 ± 12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight, kg (mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.6 ± 13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.8 ± 13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.0 ± 13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index, kg/m² (mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3 ± 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.4 ± 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.2 ± 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.7% (163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4% (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.8% (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnown coronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.4% (101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7% (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6% (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrior myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.3% (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7% (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6% (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious CABG surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3% (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.8% (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.1% (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.6% (124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.6% (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.6% (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.4% (260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.5% (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.8% (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4% (182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.6% (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.8% (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipoproteinemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.8% (193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.2% (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.5% (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8% (217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.9% (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.1% (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of CAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.7% (116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.1% (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.9% (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.8% (313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.7% (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.3% (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDIGO stage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.6% (131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.0% (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.0% (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDIGO stage 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8% (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline creatinine (mg/dL, mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 ± 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 ± 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 ± 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactate at admission (mmol/L, median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3 [2.3–5.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7 [2.8–5.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9 [2.1–4.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-reactive protein (mg/dL, mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.9 ± 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2 ± 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3 ± 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.0% (262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.5% (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.3% (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn-hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.7% (213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.6% (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.8% (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30-day post-discharge mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1% (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal replacement therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6% (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.6% (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.1% (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorepinephrine dose at 24h (µg/kg/min, mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31 ± 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34 ± 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 ± 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eTiming of AKI onset and associated outcomes\u003c/h3\u003e\n\u003cp\u003eAmong the 143 patients who developed acute kidney injury (AKI), 81 (56.6%) experienced early-onset AKI—defined as occurring within 48 hours of ICU admission—while 62 (43.4%) had late-onset AKI (\u0026gt; 48 h). The median time to AKI onset was 38 hours (IQR 24–62), with no significant sex differences (p = 0.41). This distribution highlights a temporal vulnerability window within the first two days of cardiogenic shock (CS). Early-onset AKI was associated with significantly worse outcomes. In-hospital mortality was notably higher in the early-onset group compared to late-onset AKI (71.6% vs. 54.8%; absolute difference 16.8%, 95% CI 3.1–30.5; p = 0.018) \u003cstrong\u003e(\u003c/strong\u003eFig. 4\u003cstrong\u003e).\u003c/strong\u003e Similarly, renal replacement therapy (RRT) was required in 29.6% of early-onset patients versus 16.1% in the late-onset group (p = 0.037). Within the early-onset subgroup, patients receiving RRT had the highest mortality (83.3%), significantly exceeding that of early-onset AKI patients without RRT (64.7%, p = 0.041) \u003cstrong\u003e(\u003c/strong\u003eFig. 4\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eCirculatory and metabolic parameters\u003c/h3\u003e\n\u003cp\u003eMarkers of circulatory and metabolic stress were more severely impaired in patients with early-onset AKI. These patients had significantly higher lactate levels both at ICU admission (median 4.3 mmol/L vs. 3.1 mmol/L; p = 0.028) and during the first 24 hours (mean 3.7 ± 1.6 vs. 2.9 ± 1.3 mmol/L; p = 0.032), indicating sustained metabolic imbalance \u003cstrong\u003e(\u003c/strong\u003eFig. 4\u003cstrong\u003e).\u003c/strong\u003e In parallel, early-onset AKI was associated with greater vasopressor requirements (median norepinephrine dose 0.34 µg/kg/min vs. 0.21 µg/kg/min; p = 0.044), reflecting more pronounced circulatory dysfunction in the initial phase of cardiogenic shock \u003cstrong\u003e(\u003c/strong\u003eFig. 4\u003cstrong\u003e).\u003c/strong\u003e Mechanical ventilation at admission was more common among patients with early AKI (81.5% vs. 61.3%, p = 0.011), suggesting more severe respiratory or hemodynamic compromise.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003ePredictors of early-onset AKI\u003c/h2\u003e\n \u003cp\u003eIn a multivariable logistic regression model evaluating independent predictors of early-onset acute kidney injury (≤ 48 hours after cardiogenic shock onset), two variables remained significantly associated. Higher baseline serum creatinine was the strongest predictor of early AKI (adjusted odds ratio [aOR], 5.68; 95% CI, 1.46–20.48; p = 0.008). Elevated serum lactate at 24 hours was also independently associated (aOR per mmol/L increase, 2.67; 95% CI, 1.54–4.63; p \u0026lt; 0.001). A trend toward significance was observed for age (aOR per year, 1.05; 95% CI, 0.99–1.11; p = 0.057).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent Predictors of Early-Onset Acute Kidney Injury in Cardiogenic Shock.\u003c/strong\u003e Multivariable logistic regression analysis identifying variables associated with AKI onset within 48 hours. Only baseline creatinine and lactate at 24 hours were independently associated with early AKI. \u003cem\u003eAbbreviations\u003c/em\u003e: AKI,acute kidney injury; OR, odds ratio; CI, confidence interval; CKD, chronic kidney disease.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline serum creatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.68 (1.46–20.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum lactate at 24 h (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.67 (1.54–4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05 (0.99–1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88 (0.34–2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorepinephrine dose at 24 h (µg/kg/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12 (0.91–1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34 (0.53–3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index (per kg/m²)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97 (0.90–1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78 (0.77–4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipoproteinemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42 (0.62–3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFR \u0026lt; 30 mL/min/1.73m²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21 (0.47–3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92 (0.38–2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14 (0.48–2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNo significant associations were found for female sex, body mass index, norepinephrine dose, mechanical ventilation, or pre-existing comorbidities. Specifically, chronic kidney disease, diabetes mellitus, hypertension, hyperlipoproteinemia, and baseline GFR \u0026lt; 30 mL/min/1.73 m² were not predictive of early AKI. Complete model estimates are summarized in Table 2. To facilitate visual interpretation, a graphical summary of odds ratios and confidence intervals is additionally provided in Fig. 5. The adjusted model included 10 clinically relevant covariates, prespecified to balance confounding control with the limited number of outcome events.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eEarly-onset AKI and in-hospital mortality\u003c/h2\u003e\n \u003cp\u003eTo evaluate the prognostic significance of early-onset AKI, a second multivariable logistic regression model was constructed with in-hospital mortality as the outcome. After adjusting for age, sex, baseline creatinine, serum lactate at 24 hours, and norepinephrine dose, early AKI was independently associated with a significantly increased risk of in-hospital death (aOR, 2.12; 95% CI, 1.16–3.87; p = 0.015). The predictive model showed moderate discriminative performance (area under the curve [AUC], 0.72; 95% CI, 0.66–0.78) and good calibration (Hosmer–Lemeshow p = 0.45). A steep rise in cumulative AKI incidence occurred within the first 48 hours (Fig. 2), underscoring the temporal clustering of early events. Although only 6.3% of AKI cases developed within 24 hours, these patients had the highest in-hospital mortality (77.8%), identifying a particularly high-risk subgroup (Fig. 1). Kaplan–Meier analysis showed significantly lower survival among patients with early-onset AKI (log-rank p \u0026lt; 0.01; Fig. 6). In a Cox regression model adjusted for age, baseline creatinine, and serum lactate at 24 hours, early AKI was independently associated with increased in-hospital mortality (hazard ratio, 2.41; 95% CI, 1.51–3.85; p \u0026lt; 0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eTiming-based Classification and Exploratory Analyses\u003c/h2\u003e\n \u003cp\u003eIn an exploratory secondary analysis, we applied a refined classification of AKI onset to better characterize risk trajectories. Patients were categorized into four groups based on AKI timing: no AKI, early-onset (\u0026lt; 24 hours), intermediate-onset (24–48 hours), and late-onset (\u0026gt; 48 hours).\u003c/p\u003e\n \u003cp\u003eEarly-onset AKI was associated with the highest in-hospital mortality (42.8%), followed by intermediate-onset AKI (33.3%). In contrast, patients with late-onset AKI (\u0026gt; 48h) had comparable outcomes to those without AKI (mortality 18.7% vs. 15.3%, p = 0.42). In a multivariable Cox regression model, early-onset AKI remained an independent predictor of mortality (HR 2.87, 95% CI 1.73–4.76), while intermediate-onset AKI showed a weaker association (HR 1.95, 95% CI 1.14–3.33). Late-onset AKI was not independently associated with mortality (Fig. 7). To assess robustness, we performed a 1:1 propensity score–matched analysis comparing patients with and without early-onset AKI, matched for age, sex, LVEF, baseline creatinine, lactate, and vasopressor use. The association between early-onset AKI and mortality persisted in the matched cohort (HR 2.11, 95% CI 1.33–3.32; p \u0026lt; 0.001). These findings suggest that AKI timing may represent distinct pathophysiological phenotypes and support the clinical relevance of temporal subclassification in cardiogenic shock.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eSensitivity and robustness analyses\u003c/h2\u003e\n \u003cp\u003eTo account for the modest sample size, multivariable models were restricted to a parsimonious set of prespecified covariates (age, sex, baseline creatinine, lactate, and vasopressor dose). Effect estimates for the association between early AKI and in-hospital mortality remained directionally consistent with the primary analysis (adjusted OR 2.45, 95% CI 1.21–4.96; p = 0.013), indicating robustness of the findings despite model simplification.\u003c/p\u003e\n \u003cp\u003eTo further address small-sample bias, Firth’s penalized likelihood logistic regression was applied and yielded similar results (adjusted OR 2.39, 95% CI 1.14–4.85; p = 0.015). Internal validity was additionally assessed using nonparametric bootstrap resampling with 1,000 iterations, which produced bias-corrected confidence intervals consistent with the main model (adjusted OR 2.41, 95% CI 1.17–4.89).\u003c/p\u003e\n \u003cp\u003eThe logistic regression model including AKI timing demonstrated good discriminative ability for in-hospital mortality, with an area under the receiver operating characteristic curve (AUC) of 0.77 (95% CI 0.70–0.84). When late AKI was alternatively defined as occurring within 48–168 hours after admission, no independent association with mortality was observed (adjusted OR 1.12, 95% CI 0.54–2.35; p = 0.75), consistent with the main analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ethis prospective cohort of patients with infarct-related cardiogenic shock (CS) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the timing of acute kidney injury (AKI) onset emerged as a clinically relevant prognostic factor [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Patients who developed AKI within the first 48 hours\u0026mdash;especially within \u0026lt;\u0026thinsp;24 hours\u0026mdash;had significantly higher in-hospital mortality compared to those with later AKI onset, despite similar baseline characteristics. Early AKI was also associated with increased lactate levels, higher vasopressor requirements, and more frequent use of renal replacement therapy (RRT), pointing to a phenotype of more profound circulatory derangement in the early shock phase.\u003c/p\u003e\u003cp\u003eThese findings reinforce the notion that early AKI reflects ischemic\u0026ndash;hemodynamic insult, while late AKI may arise from multifactorial contributors such as inflammation, nephrotoxicity, and fluid overload [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Prior studies in CS have consistently shown that early AKI portends worse outcomes and aligns closely with markers of systemic hypoperfusion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our data extend this knowledge by demonstrating that the AKI time course is not merely a chronological phenomenon, but a surrogate for distinct underlying pathophysiology. Beyond confirming previous observations, our study adds nuance by focusing on a homogeneous cohort of infarct-related cardiogenic shock and by differentiating very early AKI (\u0026le;\u0026thinsp;24 hours) from later occurrences. This temporal refinement highlights early AKI not only as a complication but also as a dynamic marker of circulatory collapse, thereby extending existing evidence.\u003c/p\u003e\u003cp\u003eFrom a mechanistic perspective, early AKI in CS likely represents acute ischemic tubular injury due to renal hypoperfusion, loss of autoregulation, and microvascular dysfunction [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our findings of higher serum lactate and norepinephrine dose at 24 hours in the early AKI group support this hypothesis. Serum lactate is a robust biomarker of global tissue hypoperfusion and has been shown to correlate with both AKI incidence and mortality in critically ill patients (Zhou et al., 2015; Garcia-Alvarez et al., 2014). Similarly, high vasopressor demand indicates persistent circulatory failure and may amplify renal ischemia through vasoconstrictive effects [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Our multivariable analysis identified baseline creatinine and serum lactate at 24 hours as independent predictors of early AKI. The association with baseline creatinine suggests that patients with subclinical or overt chronic kidney dysfunction may have reduced renal reserve and are thus more susceptible to acute insults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This vulnerability may also reflect maladaptive repair responses and heightened sensitivity to systemic inflammation.\u003c/p\u003e\u003cp\u003eInterestingly, while norepinephrine dose at 24h was higher in early AKI patients in univariate analysis, it did not independently predict early AKI in the adjusted model. This may reflect the complex interplay between vasopressor dose, timing, and underlying shock severity, as well as possible confounding by indication [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Likewise, mechanical ventilation, diabetes, hypertension, and sex did not independently associate with early AKI. Although sex differences in AKI susceptibility and outcomes have been reported [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], their relevance may depend on contextual factors such as hormonal status, vascular reactivity, and timing of injury.\u003c/p\u003e\u003cp\u003eThe clustering of AKI onset within the first 48 hours supports the clinical distinction between early and late AKI phenotypes in CS. Early AKI appears to track with primary hemodynamic collapse and ischemia\u0026ndash;reperfusion injury, whereas late AKI may reflect prolonged critical illness, secondary inflammation, or medication-induced nephrotoxicity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recognizing these distinct trajectories could enable clinicians to tailor monitoring intensity, fluid resuscitation strategies, and timing of RRT initiation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Beyond individual predictors, our data support efforts to redefine AKI phenotypes beyond static staging systems such as KDIGO, incorporating dynamic parameters such as timing, lactate clearance, and perfusion indices [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For instance, lactate-guided resuscitation may help identify high-risk phenotypes and inform timely interventions in patients at risk of early AKI [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn infarct-related cardiogenic shock, the timing of AKI onset carries independent prognostic value. Early-onset AKI within 48 hours identifies a hemodynamically compromised phenotype with excess mortality, underscoring the need to incorporate temporal patterns into future risk stratification and management strategies.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has limitations. It was conducted at a single academic center, which may limit generalizability. The retrospective observational design precludes causal inference, and residual confounding cannot be excluded despite adjustment. Given the modest sample size, statistical power was limited, particularly for late-onset AKI, and the absence of an independent association with mortality should therefore be interpreted as hypothesis-generating rather than definitive. To mitigate overfitting, we applied parsimonious models and internal validation, but small-sample bias remains possible. Timing of AKI onset may have been misclassified because serum creatinine was measured daily rather than continuously. Important contributors to renal injury, including fluid balance, nephrotoxin exposure, and urine output, were not systematically recorded. Sex-specific hormonal influences were not assessed, and long-term renal outcomes were beyond the scope of this analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn infarct-related cardiogenic shock, the timing of acute kidney injury (AKI) onset holds critical prognostic value. Early-onset AKI\u0026mdash;particularly within 48 hours\u0026mdash;defines a high-risk phenotype marked by severe hemodynamic instability, metabolic stress, and substantially increased in-hospital mortality. Elevated lactate levels and reduced baseline renal function independently predicted early AKI, reflecting the interplay of global hypoperfusion and renal susceptibility. These findings support incorporating AKI timing into clinical risk models and underscore the need for early recognition and tailored renoprotective strategies to improve outcomes in this vulnerable population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eThis retrospective observational study was approved by the institutional ethics committee (Approval Number FF55/2018). The requirement for informed consent was waived due to the use of anonymized data and the non-interventional retrospective study design. All procedures were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e PB, JS and HPS conceived and designed the study, performed data analysis, and drafted the manuscript. HPS, JJ, HH, KP and MJ contributed to patient data acquisition and analysis. BA, UB, HL and SS provided clinical expertise and critical revisions of the manuscript. BA, MB and HL supervised the study and approved the final version. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The authors thank the intensive care and cardiology teams for their clinical dedication and support in data collection.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBuerke, M., et al., \u003cem\u003ePathophysiology, diagnosis, and treatment of infarction-related cardiogenic shock.\u003c/em\u003e Herz, 2011. \u003cstrong\u003e36\u003c/strong\u003e(2): p. 73\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eThiele, H., et al., \u003cem\u003eIntraaortic balloon support for myocardial infarction with cardiogenic shock.\u003c/em\u003e N Engl J Med, 2012. \u003cstrong\u003e367\u003c/strong\u003e(14): p. 1287\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eJoannidis, M., et al., \u003cem\u003eAcute kidney injury in critically ill patients classified by AKIN versus RIFLE using the SAPS 3 database.\u003c/em\u003e Intensive Care Med, 2009. \u003cstrong\u003e35\u003c/strong\u003e(10): p. 1692\u0026ndash;702.\u003c/li\u003e\n\u003cli\u003eHultstr\u0026ouml;m, M., \u003cem\u003eNeurohormonal interactions on the renal oxygen delivery and consumption in haemorrhagic shock-induced acute kidney injury.\u003c/em\u003e Acta Physiol (Oxf), 2013. \u003cstrong\u003e209\u003c/strong\u003e(1): p. 11\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003ePannu, N. and R.N. Gibney, \u003cem\u003eRenal replacement therapy in the intensive care unit.\u003c/em\u003e Ther Clin Risk Manag, 2005. \u003cstrong\u003e1\u003c/strong\u003e(2): p. 141\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003ePatsalis, N., et al., \u003cem\u003eEarly risk predictors of acute kidney injury and short-term survival during Impella support in cardiogenic shock.\u003c/em\u003e Sci Rep, 2024. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 17484.\u003c/li\u003e\n\u003cli\u003eLi, S., et al., \u003cem\u003eAssociated factors and short-term mortality of early versus late acute kidney injury following on-pump cardiac surgery.\u003c/em\u003e Interact Cardiovasc Thorac Surg, 2022. \u003cstrong\u003e35\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eTang, W.H.W., et al., \u003cem\u003eEvaluation and Management of Kidney Dysfunction in Advanced Heart Failure: A Scientific Statement From the American Heart Association.\u003c/em\u003e Circulation, 2024. \u003cstrong\u003e150\u003c/strong\u003e(16): p. e280\u0026ndash;e295.\u003c/li\u003e\n\u003cli\u003eSundermeyer, J., et al., \u003cem\u003eKidney injury in patients with heart failure-related cardiogenic shock: Results from an international, multicentre cohort study.\u003c/em\u003e Eur J Heart Fail, 2025.\u003c/li\u003e\n\u003cli\u003eAdegbala, O., et al., \u003cem\u003eCharacteristics and Outcomes of Patients With Cardiogenic Shock Utilizing Hemodialysis for Acute Kidney Injury.\u003c/em\u003e Am J Cardiol, 2019. \u003cstrong\u003e123\u003c/strong\u003e(11): p. 1816\u0026ndash;1821.\u003c/li\u003e\n\u003cli\u003eLi, S., et al., \u003cem\u003eAcute Kidney Injury in Critically Ill Patients After Noncardiac Major Surgery: Early Versus Late Onset.\u003c/em\u003e Crit Care Med, 2019. \u003cstrong\u003e47\u003c/strong\u003e(6): p. e437\u0026ndash;e444.\u003c/li\u003e\n\u003cli\u003eVallabhajosyula, S., et al., \u003cem\u003eTemporal trends, predictors, and outcomes of acute kidney injury and hemodialysis use in acute myocardial infarction-related cardiogenic shock.\u003c/em\u003e PLoS One, 2019. \u003cstrong\u003e14\u003c/strong\u003e(9): p. e0222894.\u003c/li\u003e\n\u003cli\u003eNoels, H., et al., \u003cem\u003eRenal-Cardiac Crosstalk in the Pathogenesis and Progression of Heart Failure.\u003c/em\u003e Circ Res, 2025. \u003cstrong\u003e136\u003c/strong\u003e(11): p. 1306\u0026ndash;1334.\u003c/li\u003e\n\u003cli\u003eGhionzoli, N., et al., \u003cem\u003eCardiogenic shock and acute kidney injury: the rule rather than the exception.\u003c/em\u003e Heart Fail Rev, 2021. \u003cstrong\u003e26\u003c/strong\u003e(3): p. 487\u0026ndash;496.\u003c/li\u003e\n\u003cli\u003eBlumer, V., et al., \u003cem\u003eCardiogenic Shock in Older Adults: A Focus on Age-Associated Risks and Approach to Management: A Scientific Statement From the American Heart Association.\u003c/em\u003e Circulation, 2024. \u003cstrong\u003e149\u003c/strong\u003e(14): p. e1051\u0026ndash;e1065.\u003c/li\u003e\n\u003cli\u003evan Diepen, S., et al., \u003cem\u003eContemporary Management of Cardiogenic Shock: A Scientific Statement From the American Heart Association.\u003c/em\u003e Circulation, 2017. \u003cstrong\u003e136\u003c/strong\u003e(16): p. e232\u0026ndash;e268.\u003c/li\u003e\n\u003cli\u003eRao, S.V., et al., \u003cem\u003e2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the Management of Patients With Acute Coronary Syndromes: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.\u003c/em\u003e Circulation, 2025. \u003cstrong\u003e151\u003c/strong\u003e(13): p. e771\u0026ndash;e862.\u003c/li\u003e\n\u003cli\u003eByrne, R.A., et al., \u003cem\u003e2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC).\u003c/em\u003e European Heart Journal, 2023. \u003cstrong\u003e44\u003c/strong\u003e(38): p. 3720\u0026ndash;3826.\u003c/li\u003e\n\u003cli\u003eKhwaja, A., \u003cem\u003eKDIGO clinical practice guidelines for acute kidney injury.\u003c/em\u003e Nephron Clin Pract, 2012. \u003cstrong\u003e120\u003c/strong\u003e(4): p. c179\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eSinha, S.S., et al., \u003cem\u003e2025 Concise Clinical Guidance: An ACC Expert Consensus Statement on the Evaluation and Management of Cardiogenic Shock.\u003c/em\u003e JACC, 2025. \u003cstrong\u003e85\u003c/strong\u003e(16): p. 1618\u0026ndash;1641.\u003c/li\u003e\n\u003cli\u003eOstermann, M., et al., \u003cem\u003eRecommendations on Acute Kidney Injury Biomarkers From the Acute Disease Quality Initiative Consensus Conference: A Consensus Statement.\u003c/em\u003e JAMA Netw Open, 2020. \u003cstrong\u003e3\u003c/strong\u003e(10): p. e2019209.\u003c/li\u003e\n\u003cli\u003eMarbach, J.A., et al., \u003cem\u003eLactate Clearance Is Associated With Improved Survival in Cardiogenic Shock: A Systematic Review and Meta-Analysis of Prognostic Factor Studies.\u003c/em\u003e J Card Fail, 2021. \u003cstrong\u003e27\u003c/strong\u003e(10): p. 1082\u0026ndash;1089.\u003c/li\u003e\n\u003cli\u003eSingh, S., et al., \u003cem\u003eAcute Kidney Injury in Cardiogenic Shock: An Updated Narrative Review.\u003c/em\u003e J Cardiovasc Dev Dis, 2021. \u003cstrong\u003e8\u003c/strong\u003e(8).\u003c/li\u003e\n\u003cli\u003eNeugarten, J., L. Golestaneh, and N.V. Kolhe, \u003cem\u003eSex differences in acute kidney injury requiring dialysis.\u003c/em\u003e BMC Nephrol, 2018. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 131.\u003c/li\u003e\n\u003cli\u003eRonco, C., R. Bellomo, and J.A. Kellum, \u003cem\u003eAcute kidney injury.\u003c/em\u003e Lancet, 2019. \u003cstrong\u003e394\u003c/strong\u003e(10212): p. 1949\u0026ndash;1964.\u003c/li\u003e\n\u003cli\u003eOstermann, M. and M. Joannidis, \u003cem\u003eAcute kidney injury 2016: diagnosis and diagnostic workup.\u003c/em\u003e Crit Care, 2016. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 299.\u003c/li\u003e\n\u003cli\u003eHoste, E.A., et al., \u003cem\u003eEpidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study.\u003c/em\u003e Intensive Care Med, 2015. \u003cstrong\u003e41\u003c/strong\u003e(8): p. 1411\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eNguyen, H.B., et al., \u003cem\u003eEarly lactate clearance is associated with improved outcome in severe sepsis and septic shock.\u003c/em\u003e Crit Care Med, 2004. \u003cstrong\u003e32\u003c/strong\u003e(8): p. 1637\u0026ndash;42.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7511711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7511711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAcute kidney injury (AKI) is common in cardiogenic shock (CS) and increases mortality, but the prognostic impact of onset timing in infarct-related CS is unclear. We examined whether early versus late AKI onset is associated with differences in patient characteristics and outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective cohort study, 369 patients with infarct-related CS were classified by AKI timing within the first 96 h of admission: early (\u0026le;\u0026thinsp;48 h) or late (\u0026gt;\u0026thinsp;48 h), according to KDIGO criteria. Clinical, hemodynamic, and inflammatory parameters and outcomes were compared. Multivariable logistic regression identified independent predictors of early AKI and in-hospital mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAKI occurred in 143 patients (42.8%), with 56.6% early-onset. In-hospital mortality was higher with early AKI than late AKI (71.6% vs. 54.8%; absolute difference 16.8%, 95% CI 3.1\u0026ndash;30.5; p\u0026thinsp;=\u0026thinsp;0.018). Early AKI patients had higher lactate at admission (median 4.3 vs. 3.1 mmol/L; p\u0026thinsp;=\u0026thinsp;0.028), greater norepinephrine requirements (0.34 vs. 0.21 \u0026micro;g/kg/min; p\u0026thinsp;=\u0026thinsp;0.044), and more frequent mechanical ventilation (81.5% vs. 61.3%; p\u0026thinsp;=\u0026thinsp;0.011). In multivariable analysis, early AKI independently predicted in-hospital mortality (adjusted OR 2.12, 95% CI 1.16\u0026ndash;3.87; p\u0026thinsp;=\u0026thinsp;0.015), and was associated with baseline creatinine (OR 5.68 per 1 mg/dL, p\u0026thinsp;=\u0026thinsp;0.008) and 24-h lactate (OR 2.67 per mmol/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIn infarct-related CS, AKI within 48 h marks a high-risk hemodynamic phenotype with markedly increased mortality, driven by renal vulnerability and early hypoperfusion. Incorporating AKI timing into risk stratification may help target early renoprotective interventions.\u003c/p\u003e","manuscriptTitle":"Timing of Acute Kidney Injury in Infarction-Related Cardiogenic Shock: Early Onset Signals a High-Risk Phenotype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 06:31:36","doi":"10.21203/rs.3.rs-7511711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-06T07:51:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T09:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169134891336049327202275573577964271121","date":"2025-09-27T18:25:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124420639594125648414115927963986072000","date":"2025-09-25T07:52:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-21T09:54:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172751077037425145947477512335744462237","date":"2025-09-14T21:07:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258036158095001532275648033428482297858","date":"2025-09-10T07:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T05:30:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T09:29:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T11:20:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T11:19:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-09-01T22:09:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"783e68ce-e500-4ce0-bd8b-44fded455dd2","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T15:59:40+00:00","versionOfRecord":{"articleIdentity":"rs-7511711","link":"https://doi.org/10.1186/s12882-025-04730-y","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2026-01-06 15:56:57","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-09-15 06:31:36","video":"","vorDoi":"10.1186/s12882-025-04730-y","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04730-y","workflowStages":[]},"version":"v1","identity":"rs-7511711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7511711","identity":"rs-7511711","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}