Temporal trends of post-contrast acute kidney injury in patients with intravenous administration of iodinated contrast medium.

Little is known about whether preventative practices for post-contrast acute kidney injury (PC-AKI) recommended in guidelines have been adopted in clinical practice and translated into a lower incidence of PC-AKI. The aim of this study was to examine the yearly trends in the incidence of PC-AKI, and comorbidities and care practices associated with PC-AKI in hospitalized patients who received intravenous administration of iodinated contrast medium (ICM). Adult patients receiving intravenous ICM at the Second Xiangya Hospital of Central South University in China between 2015 and 2021 were included. Temporal trends in the incidence and risk factors for PC-AKI were evaluated using logistic regression analyses with adjustments for relevant variables. The incidence of PC-AKI has declined significantly from 5.3% in 2015 to 4.1% in 2021 (p < 0.001). This decreasing trend persisted after extensive multivariable adjustments. Of the comorbidities associated with PC-AKI, the proportion of patients with congestive heart failure or hypertension increased, while the proportion of patients older than 75 years, or with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2, diabetic nephropathy, or renal stone disease decreased. Among the care practices associated with PC-AKI, the proportion of patients using nephrotoxic drugs decreased, whereas the proportion of patients receiving intravenous fluids > 1000 mL on the day of ICM administration or using iso-osmolar ICM increased. In conclusion, a declining trend in PC-AKI incidence was observed in patients receiving intravenous ICM between 2015 and 2021, which may be related to increased awareness and efforts to prevent PC-AKI.

Effects of intravenous hydration in preventing post-contrast acute kidney injury in patients with eGFR < 30 mL/min/1.73 m2.

OBJECTIVES: To investigate the effects of intravenous hydration in preventing post-contrast outcomes in patients with estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 undergoing intravenous administration of iodinated contrast media (ICM). METHODS: Hospitalized patients with eGFR < 30 mL/min/1.73 m2 and intravenous ICM exposure between 2015 and 2021 were included. Post-contrast outcomes include post-contrast acute kidney injury (PC-AKI) (defined by 2012 Kidney Disease: Improving Global Outcomes (KDIGO) or European Society of Urogenital Radiology (ESUR)), chronic dialysis at discharge, and in-hospital mortality. Confounding effects between the two groups were reduced to a minimum using propensity score-based matching and overlap weighting. Association between intravenous hydration and outcomes was analyzed using logistic regression. RESULTS: In total, 794 patients were included in the study, with 284 receiving intravenous hydration, and 510 not. After 1:1 propensity score matching, 210 pairs were generated. No significant differences were found in the outcomes between the intravenous hydration and no intravenous hydration groups: PC-AKI by KDIGO, 25.2% vs 24.8% (odds ratio (OR), 0.93; 95% confidence interval (CI), 0.57-1.50); PC-AKI by ESUR, 31.0% vs 25.2% (OR, 1.34; 95% CI, 0.86-2.08); chronic dialysis at discharge, 4.3% vs 3.3% (OR, 1.56; 95% CI, 0.56-4.50); in-hospital mortality, 1.9% vs 0.5% (OR, 4.08; 95% CI, 0.58-81.08). Overlap propensity score-weighted analysis also showed no significant effects of intravenous hydration on the incidences of the post-contrast outcomes. CONCLUSIONS: Intravenous hydration was not associated with lower risks of PC-AKI, chronic dialysis at discharge, and in-hospital mortality in patients with eGFR < 30 mL/min/1.73 m2 undergoing intravenous administration of ICM. CLINICAL RELEVANCE STATEMENT: This study provides new evidence in supporting that intravenous hydration is not beneficial to patients with eGFR < 30 mL/min/1.73 m2 before and after intravenous administration of iodinated contrast media. KEY POINTS: • Intravenous hydration before and after intravenous administration of ICM is not associated with lower risks in PC-AKI, chronic dialysis at discharge, and in-hospital mortality in patients with eGFR < 30 mL/min/1.73 m2. • Withholding intravenous hydration may be considered in patients with eGFR < 30 mL/min/1.73 m2 around intravenous administration of ICM.

Association of underweight and obesity with adverse postoperative renal outcomes in infants and young children undergoing congenital heart surgery.

Postoperative acute kidney injury (AKI) is a prevalent condition and associated with increased morbidity and mortality following cardiac surgery. This study aimed to investigate the association of underweight and obesity with adverse postoperative renal outcomes in infants and young children undergoing congenital heart surgery. This retrospective cohort study included patients aged from 1 month to 5 years who underwent congenital heart surgery with cardiopulmonary bypass at the Second Xiangya Hospital of Central South University from January 2016 to March 2022. On the basis of the percentile of body mass index (BMI) for age and sex, eligible participants were divided into three nutritional groups: normal bodyweight, underweight (BMI P5), and obesity (BMI P95). Primary outcomes included postoperative AKI and major adverse kidney events within 30 days (MAKE30). Multivariable logistic regression was performed to determine the association of underweight and obesity with postoperative outcomes. The same analyses were reproduced for classifying patients using weight-for-height instead of BMI. A total of 2,079 eligible patients were included in the analysis, including 1,341 (65%) patients in the normal bodyweight group, 683 (33%) patients in the underweight group, and 55 (2.6%) patients in the obesity group. Postoperative AKI (16% vs. 26% vs. 38%; P < 0.001) and MAKE30 (2.5% vs. 6.4% vs. 9.1%; P < 0.001) were more likely to occur in the underweight and obesity groups. After adjusting for potential confounders, underweight (OR1.39; 95% CI 1.08-1.79; P = 0.008) and obesity (OR 3.85; 95% CI 1.97-7.50; P < 0.001) were found to be associated with an increased risk of postoperative AKI. In addition, both underweight (OR 1.89; 95% CI 1.14-3.14; P = 0.014) and obesity (OR 3.14; 95% CI 1.08-9.09; P = 0.035) were independently associated with MAKE30. Similar results were also found when weight-for-height was used instead of BMI.    Conclusion: In infants and young children undergoing congenital heart surgery, underweight and obesity are independently associated with postoperative AKI and MAKE30. These results may help assess prognosis in underweight and obese patients, and will guide future quality improvement efforts. What is Known: • Postoperative acute kidney injury (AKI) is prevalent and associated with increased morbidity and mortality following pediatric cardiac surgery. • Major adverse kidney events within 30 days (MAKE30) have been recommended as a patient-centered endpoint for evaluating AKI clinical trajectories. A growing concern arises for underweight and obesity in children with congenital heart disease. What is New: • Prevalence of underweight and obesity among infants and young children undergoing congenital heart surgery was 33% and 2.6%, respectively. • Both underweight and obesity were independently associated with postoperative AKI and MAKE30 following congenital heart surgery.

Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care.

Major adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted to the Second Xiangya Hospital of Central South University between January 2020 and December 2021. Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. Out of the 2366 patients, 1656 were used for model derivation and 710 for testing. The incidence of MAKE30 was 13.8% in the derivation cohort and 13.2% in the test cohort. The average area under the receiver operating characteristic curve of the XGBoost model was 0.930 (95% CI: 0.912-0.946) in the training set and 0.851 (95% CI: 0.810-0.890) in the test set. The top 8 predictors of MAKE30 tentatively identified by the Shapley additive explanations method were Acute Physiology and Chronic Health Evaluation II score, serum creatinine, blood urea nitrogen, Simplified Acute Physiology Score II score, Sequential Organ Failure Assessment score, aspartate aminotransferase, arterial blood bicarbonate, and albumin. The XGBoost model accurately predicted the occurrence of MAKE30 in elderly ICU patients, and the findings of this study provide valuable information to clinicians for making informed clinical decisions.

Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.

BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication following pediatric cardiac surgery, which is associated with increased morbidity and mortality. The early prediction of CSA-AKI before and immediately after surgery could significantly improve the implementation of preventive and therapeutic strategies during the perioperative periods. However, there is limited clinical information on how to identify pediatric patients at high risk of CSA-AKI. OBJECTIVE: The study aims to develop and validate machine learning models to predict the development of CSA-AKI in the pediatric population. METHODS: This retrospective cohort study enrolled patients aged 1 month to 18 years who underwent cardiac surgery with cardiopulmonary bypass at 3 medical centers of Central South University in China. CSA-AKI was defined according to the 2012 Kidney Disease: Improving Global Outcomes criteria. Feature selection was applied separately to 2 data sets: the preoperative data set and the combined preoperative and intraoperative data set. Multiple machine learning algorithms were tested, including K-nearest neighbor, naive Bayes, support vector machines, random forest, extreme gradient boosting (XGBoost), and neural networks. The best performing model was identified in cross-validation by using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using the Shapley additive explanations (SHAP) method. RESULTS: A total of 3278 patients from one of the centers were used for model derivation, while 585 patients from another 2 centers served as the external validation cohort. CSA-AKI occurred in 564 (17.2%) patients in the derivation cohort and 51 (8.7%) patients in the external validation cohort. Among the considered machine learning models, the XGBoost models achieved the best predictive performance in cross-validation. The AUROC of the XGBoost model using only the preoperative variables was 0.890 (95% CI 0.876-0.906) in the derivation cohort and 0.857 (95% CI 0.800-0.903) in the external validation cohort. When the intraoperative variables were included, the AUROC increased to 0.912 (95% CI 0.899-0.924) and 0.889 (95% CI 0.844-0.920) in the 2 cohorts, respectively. The SHAP method revealed that baseline serum creatinine level, perfusion time, body length, operation time, and intraoperative blood loss were the top 5 predictors of CSA-AKI. CONCLUSIONS: The interpretable XGBoost models provide practical tools for the early prediction of CSA-AKI, which are valuable for risk stratification and perioperative management of pediatric patients undergoing cardiac surgery.

Development and validation of a deep neural network-based model to predict acute kidney injury following intravenous administration of iodinated contrast media in hospitalized patients with chronic kidney disease: a multicohort analysis.

BACKGROUND: Stratification of chronic kidney disease (CKD) patients [estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2] at risk for post-contrast acute kidney injury (PC-AKI) following intravenous administration of iodinated contrast media (ICM) is important for clinical decision-making and clinical trial enrollment. METHODS: The derivation and internal validation cohorts originated from the Second Xiangya Hospital. The external validation cohort was generated from the Xiangya Hospital and the openly accessible database Medical Information Mart for Intensive CareIV. PC-AKI was defined based on the serum creatinine criteria of the Kidney Disease: Improving Global Outcomes (KDIGO). Six feature selection methods were used to identify the most influential predictors from 79 candidate variables. Deep neural networks (DNNs) were used to establish the model and compared with logistic regression analyses. Model discrimination was evaluated by area under the receiver operating characteristic curve (AUC). Low-risk and high-risk cutoff points were set to stratify patients. RESULTS: Among 4218 encounters studied, PC-AKI occurred in 10.3, 10.4 and 11.4% of encounters in the derivation, internal and external validation cohorts, respectively. The 14 variables-based DNN model had significantly better performance than the logistic regression model with AUC being 0.939 (95% confidence interval: 0.916-0.958) and 0.940 (95% confidence interval: 0.909-0.954) in the internal and external validation cohorts, respectively, and showed promising discrimination in subgroup analyses (AUC ≥ 0.800). The observed PC-AKI risks increased significantly from the low- to intermediate- to high-risk group (<1.0 to >50%) and the accuracy of patients not developing PC-AKI was 99% in the low-risk category in both the internal and external validation cohorts. CONCLUSIONS: A DNN model using routinely available variables can accurately discriminate the risk of PC-AKI of hospitalized CKD patients following intravenous administration of ICM.

Prediction of Mortality Risk After Ischemic Acute Kidney Injury With a Novel Prognostic Model: A Multivariable Prediction Model Development and Validation Study.

Background and Objectives: Acute kidney injury (AKI) that results from ischemia is a common clinical syndrome and correlates with high morbidity and mortality among hospitalized patients. However, a clinical tool to predict mortality risk of ischemic AKI is not available. In this study, we aimed to develop and validate models to predict the 30-day and 1-year mortality risk of hospitalized patients with ischemic AKI. Methods: A total of 1,836 admissions with ischemic AKI were recruited from 277,898 inpatients admitted to three affiliated tertiary general hospitals of Central South University in China between January 2015 and December 2015. Patients in the final analysis were followed up for 1 year. Study patients were randomly divided in a 7:3 ratio to form the training cohort and validation cohort. Multivariable regression analyses were used for developing mortality prediction models. Results: Hepatorenal syndrome, shock, central nervous system failure, Charlson comorbidity index (≥2 points), mechanical ventilation, renal function at discharge were independent risk factors for 30-day mortality after ischemic AKI, while malignancy, sepsis, heart failure, liver failure, Charlson comorbidity index (≥2 points), mechanical ventilation, and renal function at discharge were predictors for 1-year mortality. The area under the receiver operating characteristic curves (AUROCs) of 30-day prediction model were 0.878 (95% confidence interval (CI): 0.849-0.908) in the training cohort and 0.867 (95% CI: 0.820-0.913) in the validation cohort. The AUROCs of the 1-year mortality prediction in the training and validation cohort were 0.803 (95% CI: 0.772-0.834) and 0.788 (95% CI: 0.741-0.835), respectively. Conclusion: Our easily applied prediction models can effectively identify individuals at high mortality risk within 30 days or 1 year in hospitalized patients with ischemic AKI. It can guide the optimal clinical management to minimize mortality after an episode of ischemic AKI.

Acute Kidney Disease in Hospitalized Pediatric Patients With Acute Kidney Injury in China.

Objective: The epidemiology and outcomes of acute kidney disease (AKD) after acute kidney injury (AKI) in hospitalized children are poorly described. The aim of this study is to investigate the prevalence, predictive factors, and clinical outcomes of AKD in hospitalized children with AKI. Methods: Children (1 month-18 years) with AKI during hospitalization in the Second Xiangya Hospital from January 2015 to December 2020 were identified. AKD was defined based on the consensus report of the Acute Disease Quality Initiative 16 workgroup. The endpoints include adverse outcomes in 30 and 90 days. Multivariable logistic regression analyses were used to estimate the odds ratio of 30- and 90-day adverse outcomes associated with AKD and identify the risk factors of AKD. Results: AKD was developed in 42.3% (419/990) of the study patients, with 186 in AKD stage 1, 107 in AKD stage 2, and 126 in AKD stage 3. Pediatric patients with AKD stages 2-3 had significantly higher rates of developing 30- and 90-day adverse outcomes than those with AKD stage 0 and 1. The adjusted odds ratio of AKD stage 2-3 was 12.18 (95% confidence interval (CI), 7.38 - 20.09) for 30-day adverse outcomes and decreased to 2.49 (95% CI, 1.26 - 4.91) for 90-day adverse outcomes. AKI stages 2 and 3, as well as glomerulonephritis, were the only predictive factors for AKD stage 2-3. Conclusion: AKD is frequent among hospitalized pediatric AKI patients. AKD stage 2-3 represents a high-risk subpopulation among pediatric AKI survivors and is independently associated with 30- and 90-day adverse outcomes. Awareness of the potential risks associated with AKD stage 2-3 and its risk factors may help improve outcomes through careful monitoring and timely intervention.

Outcome prediction for acute kidney injury among hospitalized children via eXtreme Gradient Boosting algorithm.

Acute kidney injury (AKI) is common among hospitalized children and is associated with a poor prognosis. The study sought to develop machine learning-based models for predicting adverse outcomes among hospitalized AKI children. We performed a retrospective study of hospitalized AKI patients aged 1 month to 18 years in the Second Xiangya Hospital of Central South University in China from 2015 to 2020. The primary outcomes included major adverse kidney events within 30 days (MAKE30) (death, new renal replacement therapy, and persistent renal dysfunction) and 90-day adverse outcomes (chronic dialysis and death). The state-of-the-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), and the traditional logistic regression were used to establish prediction models for MAKE30 and 90-day adverse outcomes. The models' performance was evaluated by split-set test. A total of 1394 pediatric AKI patients were included in the study. The incidence of MAKE30 and 90-day adverse outcomes was 24.1% and 8.1%, respectively. In the test set, the area under the receiver operating characteristic curve (AUC) of the XGBoost model was 0.810 (95% CI 0.763-0.857) for MAKE30 and 0.851 (95% CI 0.785-0.916) for 90-day adverse outcomes, The AUC of the logistic regression model was 0.786 (95% CI 0.731-0.841) for MAKE30 and 0.759 (95% CI 0.654-0.864) for 90-day adverse outcomes. A web-based risk calculator can facilitate the application of the XGBoost models in daily clinical practice. In conclusion, XGBoost showed good performance in predicting MAKE30 and 90-day adverse outcomes, which provided clinicians with useful tools for prognostic assessment in hospitalized AKI children.

Is intravenous iodinated contrast medium administration really harmful in hospitalized acute kidney injury patients: a propensity score-matched study.

OBJECTIVES: To evaluate the effects of intravenous iodinated contrast medium (ICM) administration on the deterioration of renal function (DRF), new renal replacement therapy (RRT) induction and mortality of hospitalized acute kidney injury (AKI) patients. METHODS: Adult hospitalized patients undergoing a contrast-enhanced or unenhanced CT scan within 7 days after AKI diagnosis from January 2015 to December 2019 were identified in this retrospective study. Propensity score matching was performed. Outcomes in 7 and 30 days after CT scan were compared between the contrast and non-contrast groups. Additional analyses were also performed in patients stratified by SCr levels at AKI diagnosis, times and time of CT scan, and in patients without chronic kidney disease or RRT requirement prior to CT scan. RESULTS: In total, 1172 pairs were generated after 1:1 propensity score matching from 1336 cases exposed to ICM and 2724 unexposed. No significant differences were found in the outcomes between the two groups: DRF, 7.8% vs 9.0% (odds ratio (OR) 0.83, 95% confidence interval (CI) 0.62-1.11) in 7 days, 5.1% vs 5.4% (OR 0.93, 95%CI 0.64-1.34) in 30 days; new RRT induction, 2.3% vs 3.3% (OR 0.72,95%CI 0.43-1.18) in 7 days, 4.2% vs 4.5% (OR 0.95,95%CI 0.64-1.41) in 30 days; and mortality, 3.9% vs 4.8% (OR 0.83,95%CI 0.56-1.22) in 7 days, 9.0% vs 10.2% (OR 0.88,95%CI 0.68-1.15) in 30 days. Subset analyses showed similar results. CONCLUSION: Intravenous ICM administration during AKI duration did not increase the risks of DRF, new RRT induction, and mortality in 7 and 30 days after CT scan. KEY POINTS: • Intravenous ICM administration in hospitalized AKI patients does not increase the risks of deterioration of renal function, RRT induction, and mortality in 7 and 30 days after CT scan. • The effects of intravenous ICM on adverse outcomes are minimal even in AKI patients with high level of SCr values or multiple CT scans.

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.

Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74-0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73-0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

Development of mortality prediction model in the elderly hospitalized AKI patients.

Acute kidney injury (AKI) correlates with increased health-care costs and poor outcomes in older adults. However, there is no good scoring system to predict mortality within 30-day, 1-year after AKI in older adults. We performed a retrospective analysis screening data of 53,944 hospitalized elderly patients (age > 65 years) from multi-centers in China. 944 patients with AKI (acute kidney disease) were included and followed up for 1 year. Multivariable regression analysis was used for developing scoring models in the test group (a randomly 70% of all the patients). The established models have been verified in the validation group (a randomly 30% of all the patients). Model 1 that consisted of the risk factors for death within 30 days after AKI had accurate discrimination (The area under the receiver operating characteristic curves, AUROC: 0.90 (95% CI 0.875-0.932)) in the test group, and performed well in the validation groups (AUROC: 0.907 (95% CI 0.865-0.949)). The scoring formula of all-cause death within 1 year (model 2) is a seven-variable model including AKI type, solid tumor, renal replacement therapy, acute myocardial infarction, mechanical ventilation, the number of organ failures, and proteinuria. The area under the receiver operating characteristic (AUROC) curves of model 2 was > 0.80 both in the test and validation groups. Our newly established risk models can well predict the risk of all-cause death in older hospitalized AKI patients within 30 days or 1 year.

Acute kidney disease in hospitalized acute kidney injury patients.

BACKGROUND: Acute kidney injury (AKI) and chronic kidney disease (CKD) have become worldwide public health problems, but little information is known about the epidemiology of acute kidney disease (AKD)-a state in between AKI and CKD. We aimed to explore the incidence and outcomes of hospitalized patients with AKD after AKI, and investigate the prognostic value of AKD in predicting 30-day and one-year adverse outcomes. METHODS: A total of 2,556 hospitalized AKI patients were identified from three tertiary hospitals in China in 2015 and followed up for one year.AKD and AKD stage were defined according to the consensus report of the Acute Disease Quality Initiative 16 workgroup. Multivariable regression analyses adjusted for confounding variables were used to examine the association of AKD with adverse outcomes. RESULTS: AKD occurred in 45.4% (1161/2556) of all AKI patients, 14.5% (141/971) of AKI stage 1 patients, 44.6% (308/691) of AKI stage 2 patients and 79.6% (712/894) of AKI stage 3 patients. AKD stage 1 conferred a greater risk of Major Adverse Kidney Events within 30 days (MAKE30) (odds ratio [OR], 2.36; 95% confidence interval 95% CI [1.66-3.36]) than AKD stage 0 but the association only maintained in AKI stage 3 when patients were stratified by AKI stage. However, compared with AKD stage 0, AKD stage 2-3 was associated with higher risks of both MAKE30 and one-year chronic dialysis and mortality independent of the effects of AKI stage with OR being 31.35 (95% CI [23.42-41.98]) and 2.68 (95% CI [2.07-3.48]) respectively. The association between AKD stage and adverse outcomes in 30 days and one year was not significantly changed in critically ill and non-critically ill AKI patients. The results indicated that AKD is common among hospitalized AKI patients. AKD stage 2-3 provides additional information in predicting 30-day and one-year adverse outcomes over AKI stage. Enhanced follow-up of renal function of these patients may be warranted.

Novel risk models to predict acute kidney disease and its outcomes in a Chinese hospitalized population with acute kidney injury.

Acute kidney disease (AKD) is a state between acute kidney injury (AKI) and chronic kidney disease (CKD), but the prognosis of AKD is unclear and there are no risk-prediction tools to identify high-risk patients. 2,556 AKI patients were selected from 277,898 inpatients of three affiliated hospitals of Central South University from January 2015 to December 2015. The primary point was whether AKI patients developed AKD. The endpoint was death or end stage renal disease (ESRD) 90 days after AKI diagnosis. Multivariable Cox regression was used for 90-day mortality and two prediction models were established by using multivariable logistic regression. Our study found that the incidence of AKD was 53.17% (1,359/2,556), while the mortality rate and incidence of ESRD in AKD cohort was 19.13% (260/1,359) and 3.02% (41/1,359), respectively. Furthermore, adjusted hazard ratio of mortality for AKD versus no AKD was 1.980 (95% CI 1.427-2.747). In scoring model 1, age, gender, hepatorenal syndromes, organic kidney diseases, oliguria or anuria, respiratory failure, blood urea nitrogen (BUN) and acute kidney injury stage were independently associated with AKI progression into AKD. In addition, oliguria or anuria, respiratory failure, shock, central nervous system failure, malignancy, RDW-CV ≥ 13.7% were independent risk factors for death or ESRD in AKD patients in scoring model 2 (goodness-of fit, P1 = 0.930, P2 = 0.105; AUROC1 = 0.879 (95% CI 0.862-0.896), AUROC2 = 0.845 (95% CI 0.813-0.877), respectively). Thus, our study demonstrated AKD was independently associated with increased 90-day mortality in hospitalized AKI patients. A new prediction model system was able to predict AKD following AKI and 90-day prognosis of AKD patients to identify high-risk patients.

Post-contrast acute kidney injury in a hospitalized population: short-, mid-, and long-term outcome and risk factors for adverse events.

OBJECTIVES: To investigate the prognosis including major adverse kidney events within 30 days (MAKE30) and 90-day and 1-year adverse outcome in hospitalized patients with post-contrast acute kidney injury (PC-AKI) to identify high-risk factors. METHODS: This retrospective observational study included 288 PC-AKI patients selected from 277,898 patients admitted to hospitals from January 2015 to December 2015. PC-AKI was defined according to the 2018 guideline of European Society of Urogenital Radiology. Multivariable Cox regression and logistic regression analyses were used to analyze main outcome and risk factors. RESULTS: PC-AKI patients with AKI stage ≥ 2 had much higher incidence of MAKE30 than those with AKI stage 1 (RR = 7.027, 95% CI 4.918-10.039). Persistent renal dysfunction, heart failure, central nervous system failure, baseline eGFR < 60 mL/min/1.73 m2, oliguria or anuria, blood urea nitrogen ≥ 7.14 mmol/L, respiratory failure, and shock were independent risk factors of 90-day or 1-year adverse prognosis (p < 0.05). Compared with transient renal dysfunction, PC-AKI patients with persistent renal dysfunction had a higher all-cause mortality rate (RR = 3.768, 95% CI 1.612-8.810; RR = 4.106, 95% CI 1.765-9.551) as well as combined endpoints of death, chronic kidney disease, or end-stage renal disease (OR = 3.685, 95% CI 1.628-8.340; OR = 5.209, 95% CI 1.730-15.681) within 90 days or 1 year. CONCLUSIONS: PC-AKI is not always a transient, benign creatininopathy, but can result in adverse outcome. AKI stage is independently correlated to MAKE30 and persistent renal dysfunction may exaggerate the risk of long-term adverse events. KEY POINTS: • PC-AKI can result in adverse outcome such as persistent renal dysfunction, dialysis, chronic kidney disease (CKD), end-stage renal disease (ESRD), or death. • AKI stage is independently correlated to MAKE30. • Persistent renal dysfunction may exaggerate the risk of long-term adverse events.