Prediction Of Acute Kidney Injury Research Articles

Prognostic impact and predictors of persistent renal dysfunction in acute kidney injury after percutaneous coronary intervention for acute myocardial infarction.

This study aimed to evaluate the prognostic impact and predictors of persistent renal dysfunction in acute kidney injury (AKI) after an emergency percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI). A total of 877 patients who underwent emergency PCI for AMI were examined. AKI was defined as serum creatinine (SCr) ≥ 0.3mg/dL or ≥ 50% from baseline within 48h after PCI. Persistent AKI was defined as residual impairment of SCr ≥ 0.3mg/dL or ≥ 50% from baseline 1month after the procedure. The primary outcome was the composite endpoints of death, myocardial infarction, hospitalization for heart failure, stroke, and dialysis. AKI and persistent AKI were observed in 82 (9.4%) and 25 (2.9%) patients, respectively. Multivariate Cox proportional hazards analysis demonstrated that persistent AKI, but not transient AKI, was an independent predictor of primary outcome (hazard ratio, 4.99; 95% confidence interval, 2.30-10.8; P < 0.001). Age > 75years, left ventricular ejection fraction < 40%, a high maximum creatinine phosphokinase MB level, and bleeding after PCI were independently associated with persistent AKI. Persistent AKI was independently associated with worse clinical outcomes in patients who underwent emergency PCI for AMI. Advanced age, poor cardiac function, large myocardial necrosis, and bleeding were predictors of persistent AKI.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Establishment and Diagnostic Value of an Early Prediction Model for Acute Pancreatitis Complicated With Acute Kidney Injury.

To establish an early prediction model for acute pancreatitis (AP) complicated with acute kidney injury (AKI) and evaluate its diagnostic value. AP patients were recruited from the Emergency Department at Peking University People's Hospital in 2021 and stratified into AKI and control (no AKI) groups. Their clinical data were analyzed. The risk for AKI development was determined using logistic analyses to establish a risk prediction model, whose diagnostic value was analyzed using a receiver operating characteristic curve. There was no significant difference in the basic renal function between the AKI (n = 79) and control (n = 179) groups. The increased triglyceride glucose index (odds ratio [OR], 2.613; 95% confidence interval [CI], 1.324-5.158; P = 0.006), age (OR, 1.076; 95% CI, 1.016-1.140; P = 0.013), and procalcitonin (OR, 1.377; 95% CI, 1.096-1.730, P = 0.006) were associated with AKI development. A model was established for prediction of AKI (sensitivity 79.75%, specificity 96.65%). The area under the receiver operating characteristic curve was 0.856 which was superior to the Ranson, Bedside Index for Severity in AP, and Acute Physiology and Chronic Health Evaluation II scores (0.856 vs 0.691 vs 0.745 vs 0.705). The prediction model based on age, triglyceride glucose, and procalcitonin is valuable for the prediction of AP-related AKI.

Systemic inflammation and acute kidney injury after colorectal surgery

BackgroundIn this retrospective review, the relative importance of systemic inflammation among other causes of acute kidney injury (AKI) was investigated in 1224 consecutive colorectal surgery patients. A potential benefit from reducing excessive postoperative inflammation on AKI might then be estimated.MethodsAKI was determined using the Kidney Disease Improving Global Outcomes (KDIGO) criteria. The entire population (mixed group), composed of patients with or without sepsis, and a subpopulation of patients without sepsis (aseptic group) were examined. Markers indicative of inflammation were procedure duration, the first postoperative white blood cell (POD # 1 WBC) for the mixed population, and the neutrophil-to-lymphocyte ratio (POD #1 NLR) for the aseptic population. Multivariable logistic regression was then performed using significant (P < 0.05) predictors. The importance of inflammation among independent predictors of AKI and AKI-related complications was then assessed.ResultsAKI occurred in 24.6% of the total population. For the mixed population, there was a link between inflammation (POD # 1 WBC) and AKI (P = 0.0001), on univariate regression. Medications with anti-inflammatory properties reduced AKI: ketorolac (P = 0.047) and steroids (P = 0.038). Similarly, in an aseptic population, inflammation (POD # 1 NLR) contributed significantly to AKI (P = 0.000). On multivariable analysis for the mixed and aseptic population, the POD #1 WBC and the POD #1 NLR were independently associated with AKI (P = 0.000, P = 0.022), as was procedure duration (P < 0.0001, P < 0.0001). Inflammation-related parameters were the most significant contributors to AKI. AKI correlated with complications: postoperative infections (P = 0.016), chronic renal insufficiency (CRI, P < 0.0001), non-infectious complications (P = 0.010), 30-day readmissions (P = 0.001), and length of stay (LOS, P < 0.0001). Inflammation, in patients with or without sepsis, was similarly a predictor of complications: postoperative infections (P = 0.002, P = 0.008), in-hospital complications (P = 0.000, P = 0.002), 30-day readmissions (P = 0.012, P = 0.371), and LOS (P < 0.0001, P = 0.006), respectively.ConclusionsSystemic inflammation is an important cause of AKI. Limiting early postsurgical inflammation has the potential to improve postoperative outcomes.

Renal arterial resistive index versus novel biomarkers for the early prediction of sepsis-associated acute kidney injury

Acute kidney injury (AKI) is a critical complication of sepsis. There is a continuous need to identify and validate biomarkers for early detection. Serum and urinary biomarkers have been investigated, such as neutrophil gelatinase associated lipocalin (NGAL) and cystatin C (Cys C), but their reliability in the intensive care unit (ICU) remains unknown. Renal hemodynamics can be investigated by measuring the renal resistive index (RRI). This study aimed to compare the performance of RRI, serum NGAL (sNGAL), urinary NGAL (uNGAL), and serum Cys C levels as early predictors of the diagnosis and persistence of sepsis-associated AKI. A total of 166 adult patients with sepsis syndrome were enrolled immediately after ICU admission. Biomarkers were measured directly (T1) and on day 3 (T3). RRI was measured directly (T1) and 24 h later (T2). Patients were categorized (according to the occurrence and persistence of AKI within the first 7 days) into three groups: no AKI, transient AKI, and persistent AKI. The incidence rate of sepsis-associated AKI was 60.2%. Sixty-six patients were categorized as in the no AKI group, while another 61 were in transient AKI and only 39 were in persistent AKI. The RRI value (T1 ≥ 0.72) was the best tool for predicting AKI diagnosis (area under the receiver operating characteristic curve, AUROC = 0.905). Cys C (T1 ≥ 15.1 mg/l) was the best tool to predict the persistence of AKI (AUROC = 0.977). RRI (T1) was the best predictive tool for sepsis-associated AKI, while Cys C was the best predictor of its persistence and 28-day mortality.

Uric acid in predicting the traumatic rhabdomyolysis induced acute kidney injury; a systematic review and meta-analysis

ObjectiveThe objective of this systematic review and meta-analysis was to assess the value of uric acid in predicting acute kidney injury caused by traumatic rhabdomyolysis.MethodsThe search was conducted in MEDLINE, Scopus, Embase and Web of Science until November 1, 2023. Based on the inclusion and exclusion criteria, the articles were included by two independent researchers. Data regarding study design, patient characteristics, number of patients with and without AKI, mean and SD of uric acid and prognostic characteristics of uric acid were extracted from relevant studies. STATA version 17.0 was used to compute pooled measures of standardized mean differences, odds ratios, and diagnostic accuracy. I2 and chi-square tests were used to assess heterogeneity between studies.ResultsWe found 689 non-redundant studies, 44 of them were potentially relevant. Six articles met the inclusion criteria and were included in the review. The results of the meta-analysis confirmed that there was a significant correlation between serum uric acid levels and the occurrence of AKI (SMD = 1.61, 95% CI = 0.69 to 2.54, I2 = 96.94%; p value = 0.001). There were no significant publication biases.ConclusionAccording to this meta-analysis, uric acid levels could be considered as a predictor of acute kidney injury following traumatic rhabdomyolysis.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Adult patients undergoing noncardiac surgery were retrospectively included in the study (76457 patients in the discovery cohort and 11910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Risk factors for postoperative acute kidney injury after major abdominal surgery.

Acute kidney injury (AKI) worsens the outcome in a significant number of hospitalized patients. Risk models mainly address cardiac surgery, while significantly less attention is paid to AKI after major abdominal surgery (MAS). This study aims to evaluate the incidence, along with risk factors, and intrahospital outcomes of AKI after MAS. Our retrospective study included 200 adult patients treated with MAS (in the same institution). Exclusion criteria were obstructive nephropathy, contrast-induced nephropathy, and dialysis dependence whether due to end-stage renal disease (ESRD) or AKI before MAS. Data on preoperative, intraoperative, as well as postoperative variables were collected from patients' medical history and electronic medical records. AKI was diagnosed in 33 (16.5%) patients, with 2 patients treated with hemodialysis. The multivariate logistic regression model showed that the number of intraoperative blood transfusions (p=0.01), pneumonia (p<0.001), and vasoactive drug use (p=0.02) were independently associated with postoperative AKI. Each blood transfusion administered increased the risk of developing AKI by 1.41, vasoactive drug use by 4.13, and the risk of AKI in those with pneumonia was 15.32 times higher. The lethal outcome was observed significantly more frequently in patients with AKI (39.4 vs. 4.8%, p<0.001). Identification of independent predictors of AKI after MAS such as the number of transfusions during surgery, sepsis, pneumonia, and the need for vasoactive drug therapy could help prevent AKI and lower the probability of lethal outcomes after MAS.

Acute Kidney Injury Following Transcatheter Aortic Valve Implantation—A Contemporary Perspective of Incidence, Predictors, and Outcomes

Acute kidney injury (AKI) is a known complication following transcatheter aortic valve implantation (TAVI), associated with increased morbidity and mortality. Most of this data relates to higher-risk patients with early-generation TAVI valves. With TAVI now established as a safe and cost-effective procedure for low-risk patients, there is a distinct need for updated analysis. We aimed to assess the incidence, predictors, and outcomes of AKI in a contemporary cohort of TAVI patients, concurrently examining the role of temporal evolution on AKI. A total of 2,564 patients undergoing TAVI from 2008-2023 included in the Alfred-Cabrini-Epworth (ACE) TAVI Registry were analysed. Patients were divided into AKI and no AKI groups. Outcomes were reported according to the Valve Academic Research Consortium-3 (VARC-3) criteria. Of 2,564 patients, median age 83 (78-87) years, 57.4% men and a median Society of Thoracic Surgeons score of 3.6 (2.4-5.5), 163 (6.4%) patients developed AKI with incidence falling from 9.7% between 2008-2014 to 6% between 2015-2023 (p=0.022). On multivariable analysis, independent predictors of AKI were male sex (adjusted odds ratio [aOR] 1.89, p=0.005), congestive cardiac failure (aOR 1.52, p=0.048), estimated glomerular filtration rate 30-59 (aOR: 2.79, p<0.001), estimated glomerular filtration rate <30 (aOR 8.65, p<0.001), non-femoral access (aOR 5.35, p<0.001), contrast volume (aOR 1.01, p<0.001), self-expanding valve (aOR 1.60, p=0.045), and bleeding (aOR 2.88, p=0.005). Acute kidney injury was an independent predictor of 30-day (aOR: 6.07, p<0.001) and 12-month (aOR: 3.01, p=0.002) mortality, an association that remained consistent when excluding TAVIs performed prior to2015. Acute kidney injury remains a relatively common complication of TAVI, associated with significant morbidity and mortality even in less comorbid, contemporary practice patients.

The relationship between albumin level and the development of acute kidney injury and mortality in critically COVID-19 patients

Aims: The impact of the Coronavirus Pandemic, which began in 2019, has considerably diminished; however, its effects still persist. While respiratory symptoms have been prominent during the pandemic, acute kidney disease has emerged as a significant contributor to morbidity and mortality. This study aims to demonstrate that serum albumin levels can serve as a predictor for acute kidney injury (AKI) and mortality, owing to their cost- effectiveness and feasibility for use across various healthcare centers. The goal is to contribute to the reduction of AKI development and mortality rates. Methods: The study was conducted by analyzing data from a total of 350 patients admitted to intensive care units of a training and research hospital due to COVID-19 between March 1, 2020, and April 30, 2021. Of these patients, 179 (51%) were male, and 171 (49%) were female. The data were examined retrospectively. Patients were categorized into two groups based on serum albumin levels: ?2.5 mg/dl (severe hypoalbuminemia) and 2.5-3.5 mg/dl (mild hypoalbuminemia). Subsequently, the patients were further categorized into groups based on the presence or absence of AKI, and these groups were statistically compared. Demographic data, clinical information, and laboratory values of the patients were assessed. The diagnosis of acute kidney injury (AKI) was made according to KDIGO criteria. Data were compared using the SPSS version 24 software. Results: Among the 350 patients included in the study, 115 had serum albumin levels ?2.5 mg/dl (severe hypoalbuminemia), and 235 had levels in the range of 2.5-3.5 mg/dl (mild hypoalbuminemia). Among those who developed AKI, the average albumin level was found to be 2.68 mg/dl, whereas in the group without AKI, the average albumin level was 2.76 mg/dl. Out of the total, 201 (57%) patients developed AKI, and 82% of them experienced mortality. In contrast, among the 149 (43%) patients who did not develop AKI, the mortality rate was 42%. Further subgroup analysis revealed the highest mortality rate of 88% among patients with severe hypoalbuminemia and AKI development. Conversely, the lowest mortality rate of 40% was observed in patients with mild hypoalbuminemia and no AKI. Conclusion: This study aims to establish that low serum albumin levels should be regarded not only as a negative acute-phase reactant but also as a predictive factor for complications including acute kidney disease development and mortality. Lower albumin values are correlated with higher complication rates and increased mortality. Additionally, due to its widespread availability and low cost, serum albumin is a cost-effective diagnostic tool that can be utilized in almost any healthcare setting.

Preoperative endothelial dysfunction for the prediction of acute kidney injury after cardiac surgery using cardiopulmonary bypass: a pilot study based on a second analysis of the MONS study

BackgroundUp to 42% of patients develop acute kidney injury (AKI) after cardiac surgery. The aim of this study was to describe the relationship between preoperative microcirculatory function and postoperative AKI after cardiac surgery using cardiopulmonary bypass (CPB).MethodsThe prospective observational cohort MONS enrolled 60 patients scheduled for valvular (n = 30, 50%) or coronary (n = 30, 50%) surgery using CPB. Preoperative microcirculation was assessed during preoperative consultation from January 2019 to April 2019 at the University Hospital of Angers, France, using endothelium-dependent and endothelium-independent reactivity tests on the forearm (iontophoresis of acetylcholine (ACh) and sodium nitroprusside (SNP), respectively). Skin blood flow was measured by laser speckle contrast imaging. The primary endpoint was the occurrence of AKI according to the KDIGO classification during the hospital stay.ResultsForty-three (71.7%) patients developed AKI during the in-hospital follow-up, and 15 (25%) were classified as KDIGO stage 1, 20 (33%) KDIGO stage 2, and 8 (13%) KDIGO stage 3. Regarding preoperative microcirculation, a higher peak amplitude of vasodilation in response to iontophoresis of ACh was found in patients with postoperative occurrence of AKI (35 [20–49] vs 23 [9–44] LSPU, p = 0.04). Iontophoresis of SNP was not significantly different according to AKI occurrence (34 [22–49] vs 36 [20–50] LSPU, p = 0.95). In a multivariable model, the preoperative peak amplitude at iontophoresis of ACh was independently associated with postoperative AKI (OR 1.045 [1.001–1.092], p = 0.045).ConclusionsThe preoperative peak amplitude of endothelium-dependent vasodilation is independently associated with the postoperative occurrence of AKI.Trial registrationClinical-Trials.gov, NCT03631797. Registered 15 August 2018, https://clinicaltrials.gov/ct2/show/NCT03631797

Results of the Italian Collaborators for Evar Registry on Acute Kidney Injury After Elective Endovascular Aortic Repair of Infrarenal Abdominal Aortic Aneurysm.

To analyze the incidence and predictive factors of postoperative acute kidney injury (AKI) after elective standard endovascular aortic repair (EVAR) in a large recent, multicenter cohort. This is a multicenter, retrospective, financially unsupported physician-initiated observational cohort study. Between January 2018 and March 2021, only patients treated with elective standard EVAR for infrarenal non-infected abdominal aortic aneurysm were analyzed. Patients already on hemodialysis (HD) were excluded. AKI was defined as an increase in serum creatinine (sCr) ≥0.3 mg/dL within 48 hours or an increase in sCr to ≥1.5 times baseline known or presumed to have occurred within 7 days, or a urine volume of <0.5 mL/kg/h for 6 hours. Primary outcomes of interest were AKI incidence at 30 days and freedom from HD at 1-year follow-up. Secondary outcomes were freedom from severe postoperative complication, and freedom from aorta-related mortality (ARM) at 1 year. The final cohort analyzed 526 (29.8%). There were 489 (93%) males and 37 (7%) females: the median age was 76 years (interquartile range [IQR], 71-81). Chronic kidney disease (CKD) was present in 86 (16.3%) patients. Early mortality was observed in 8 (1.5%) patients, none was aorta-related. Complication rate was 17.1% (n=89). AKI was observed in 17 (3.2%). Renal replacement therapy was needed in 4 (0.8%). HD was transitory in 2 cases and definitive in 1. Binary logistic regression analysis identified CKD (odds ratio [OR]: 4.68, 95% confidence interval [CI]: 2.10-10.45, p<0.001), and the presence of renal artery stenosis (OR: 3.80, 95% CI: 1.35-10.66, p=0.011) to be associated with an increased risk of postoperative AKI. Estimated freedom from ARM was 94.9% at 1 year. Estimated freedom from HD rate at 1 year was 94%: This was significantly different between patients with preoperative CKD and those who did not have preoperative CKD (log-rank, p=0.042). AKI after elective standard EVAR still occurs but with an acceptably low incidence rate. Preoperative CKD is the most important predictor for postoperative AKI, which was not associated with the need for HD at 1-year follow-up but with a higher propensity of mortality. This "real world" experience confirm that EVAR performed with standard contrats agent protocol remains safe for acute kidney injury development. Therefore, only patients presenting with preoperative borderline or ascertained chronic kidney disease will take the most advantage from the use of carbon dioxide contrast.

Artificial intelligence and acute kidney injury

Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in arelevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of athreatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.

Presepsin is a more useful predictor of septic AKI and ARDS for very-old sepsis patients than for young sepsis patients in ICUs: a pilot study

ObjectiveSepsis is a syndrome of life-threatening organ dysfunction. This study aimed to determine whether presepsin is a useful predictor of septic acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), and shock in very-old sepsis patients aged 75 years in intensive care units (ICUs).ResultsA total of 83 adult patients diagnosed with sepsis were prospectively examined and divided into two groups: those aged 75 years and older (over 75 group) and those aged younger than 75 years (under 75 group). Presepsin values were measured after ICU admission. Inflammation-based prognostic scores were also examined. For category classification, total scores (“inflammation-presepsin scores [iPS]”) were calculated. Presepsin values, inflammation-based prognostic scores, and iPS were compared between patients with septic AKI, ARDS, DIC, or shock and those without these disorders in the over 75 and under 75 groups. Areas under the curve of presepsin for predicting septic AKI and ARDS in the over 75 group were both > 0.7, which were significantly higher than those in the under 75 group. In conclusion, presepsin is a more useful predictor of septic AKI and ARDS for very-old sepsis patients (over 75 years) than for younger sepsis patients (under 75 years).

Evaluation of risk stratification for acute kidney injury: a comparative analysis of EKFC, 2009 and 2021 CKD-EPI glomerular filtration estimating equations

BackgroundThe adoption of the 2021 CKD-EPIcr equation for glomerular filtration rate (GFR) estimation provided a race-free eGFR calculation. However, the discriminative performance for AKI risk has been rarely validated. We aimed to evaluate the differences in acute kidney injury (AKI) prediction or reclassification power according to the three eGFR equations.MethodsWe performed a retrospective observational study within a tertiary hospital from 2011 to 2021. Acute kidney injury was defined according to KDIGO serum creatinine criteria. Glomerular filtration rate estimates were calculated by three GFR estimating equations: 2009 and 2021 CKD-EPIcr, and EKFC. In three equations, AKI prediction performance was evaluated with area under receiver operator curves (AUROC) and reclassification power was evaluated with net reclassification improvement analysis.ResultsA total of 187,139 individuals, including 27,447 (14.7%) AKI and 159,692 (85.3%) controls, were enrolled. In the multivariable regression prediction model, the 2009 CKD-EPIcr model (continuous eGFR model 2, 0.7583 [0.755–0.7617]) showed superior performance in AKI prediction to the 2021 CKD-EPIcr (0.7564 [0.7531–0.7597], < 0.001) or EKFC model in AUROC (0.7577 [0.7543–0.761], < 0.001). Moreover, in reclassification of AKI, the 2021 CKD-EPIcr and EKFC models showed a worse classification performance than the 2009 CKD-EPIcr model. (− 7.24 [− 8.21–− 6.21], − 2.38 [− 2.72–− 1.97]).ConclusionRegarding AKI risk stratification, the 2009 CKD-EPIcr equation showed better discriminative performance compared to the 2021 CKD-EPIcr equation in the study population.Graphical abstract

Incidence and predictors of acute kidney injury after elective surgery for lumbar degenerative disease: A 13-year analysis of the US Nationwide Inpatient Sample.

Acute kidney injury (AKI) is a severe postoperative complication associated with poor clinical outcomes, including the development of chronic kidney disease (CKD) and death. This study aimed to investigate the incidence and determinants of AKI following elective surgeries for degenerative lumbar spine disease. All patient data were extracted from the US Nationwide Inpatient Sample database. After surgery, AKI's incidence and risk factors were identified for lumbar degenerative disease. ICD-9 and ICD-10 codes defined lumbar spine degenerative disease, fusion, decompression, and AKI. The study cohort was categorized by type of surgery, that is, decompression alone or spinal fusion. Regression analysis was used to identify associations between AKI and risk factors organized by surgery type. The incidence of AKI after decompression or fusion was 1.1% and 1.8%, respectively. However, the incidence of AKI in the United States is rising. The strongest predictor of AKI was underlying CKD, which was associated with an 9.0- to 12.9-fold more significant risk of AKI than in subjects without comorbid CKD. In this setting, older age, congestive heart failure, anemia, obesity, coagulopathy and hospital-acquired infections were also strong predictors of AKI. In contrast, long-term aspirin/anticoagulant usage was associated with lowered AKI risk. Findings of this study inform risk stratification for AKI and may help to optimize treatment decisions and care planning after elective surgery for lumbar degenerative disease.

Vasoactive-Inotropic Score as a Promising Predictor of Acute Kidney Injury in Adult Patients Requiring Extracorporeal Membrane Oxygenation.

Acute kidney injury (AKI) is a common complication in patients supported by extracorporeal membrane oxygenation (ECMO). Vasoactive-Inotropic Score (VIS) serves as an indicator of the extent of cardiovascular drug support provided. Our objective is to assess the relationship between the VIS and ECMO-associated AKI (EAKI). This single-center retrospective study extracted adult patients treated with ECMO between August 2016 and September 2022 from an intensive care unit (ICU) in a university hospital. A total of 126 patients requiring ECMO support were included in the study, of which 76% developed AKI. Multivariate logistic regression analysis identified VIS-max Day1 (odds ratio [OR]: 1.025, 95% confidence interval [CI]: 1.007-1.044, p = 0.006), VIS-max Day2 (OR: 1.038, 95% CI: 1.007-1.069, p = 0.015), VIS-mean Day1 (OR: 1.048, 95% CI: 1.013-1.084, p = 0.007), and VIS-mean Day2 (OR: 1.059, 95% CI: 1.014-1.107, p = 0.010) as independent risk factors for EAKI. VIS-max Day1 showing the best predictive effect (Area under the receiver operating characteristic curve (AUROC): 0.80, sensitivity: 71.87%, specificity: 80.00%) for EAKI with a cutoff value of 33.33. Surprisingly, VIS-mean Day2 was also excellent at predicting 7 day mortality (AUROC: 0.77, sensitivity: 87.50%, specificity: 56.38%) with a cutoff value of 8.67. In conclusion, VIS could independently predict EAKI and 7 day mortality in patients with ECMO implantation, which may help clinicians to recognize the poor prognosis in time for early intervention.

Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study

Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model’s discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894–0.934) and 0.923 (95% CI, 0.895–0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model’s favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.

Acute kidney injury prediction model utility in premature myocardial infarction

The incidence of premature myocardial infarction (PMI) has been rising and acute kidney injury (AKI) occurring in PMI patients severely impacts prognosis. This study aimed to develop and validate a prediction model for AKI specific to PMI patients. The MIMIC-Ⅲ-CV and MIMIC-Ⅳ databases were utilized for model derivation of PMI patients. Single-center data served for external validation. There were 571 and 182 AKI patients in the training set (n= 937) and external validation set (n= 292) cohorts, respectively. Finally, a 7-variable model consisting of: Sequential Organ Failure Assessment (SOFA) score, coronary artery bypass grafting (CABG), ICU stay time, loop diuretics, estimated glomerular filtration rate (eGFR) HCO3- and Albumin was developed, achieving an AUC of 0.85 (95% CI: 0.83-0.88) in the training set. External validation also confirmed model robustness. This model may assist clinicians in the early identification of patients at elevated risk for PMI. Further validation is warranted before clinical application.

Predictors and Prognostic Impact of Early Acute Kidney Injury in Cardiogenic Shock: Results from a Monocentric, Prospective Registry

Introduction: The presence of acute kidney injury (AKI) was shown to increase the risk of mortality following acute myocardial infarction; however, data regarding the prognostic impact of early AKI in patients with concomitant cardiogenic shock (CS) is limited. The study investigates predictors and the prognostic impact of AKI in patients with CS. Methods: Consecutive patients with CS from 2019 to 2021 were included at one institution. Laboratory values were retrieved from day of disease onset (day 1) and days 2, 3, 4, and 8 thereafter. Predictors for AKI (defined as an increase of plasma creatinine >50% within 48 h referring to pre-admission or baseline creatinine on day 1 and/or the need for continuous veno-venous hemodiafiltration [CVVHDF]) and the prognostic impact of early AKI with regard to 30-day all-cause mortality were assessed. Statistical analyses included t test, Spearman’s correlation, C-statistics, Kaplan-Meier, and Cox proportional regression analyses. Results: A total of 219 CS patients were included with an incidence of early CS-related AKI of 52%. With an area under the curve of up to 0.689 (p = 0.001), creatine discriminated 30-day mortality in CS. Increasing lactate levels (OR = 1.194; 95% CI: 1.083–1.316; p = 0.001; per increase of 1 mmol/L) was associated with the occurrence of AKI. The presence of AKI was associated with an increased risk of 30-day all-cause mortality (63% vs. 36%; HR = 2.138; 95% CI: 1.441–3.171; p = 0.001), even after multivariable adjustment (HR = 1.861; 95% CI: 1.207–2.869; p = 0.005). Finally, highest risk of all-cause mortality was observed in patients with AKI requiring CVVHDF (75% vs. 44%; log rank p = 0.001; HR = 2.211; 95% CI: 1.315–3.718; p = 0.003). Conclusion: Early AKI affects more than half of patients with CS and is independently associated with 30-day all-cause mortality in CS, with highest risk of death among patients with AKI requiring CVVHDF.