Cell-free DNA in patients with sepsis: long term trajectory and association with 28-day mortality and sepsis-associated acute kidney injury.

Introduction: Outcome-prediction in patients with sepsis is challenging and currently relies on the serial measurement of many parameters. Standard diagnostic tools, such as serum creatinine (SCr), lack sensitivity and specificity for acute kidney injury (AKI). Circulating cell-free DNA (cfDNA), which can be obtained from liquid biopsies, can potentially contribute to the quantification of tissue damage and the prediction of sepsis mortality and sepsis-associated AKI (SA-AKI). Methods: We investigated the clinical significance of cfDNA levels as a predictor of 28-day mortality, the occurrence of SA-AKI and the initiation of renal replacement therapy (RRT) in patients with sepsis. Furthermore, we investigated the long-term course of cfDNA levels in sepsis survivors at 6 and 12 months after sepsis onset. Specifically, we measured mitochondrial DNA (mitochondrially encoded NADH-ubiquinone oxidoreductase chain 1, mt-ND1, and mitochondrially encoded cytochrome C oxidase subunit III, mt-CO3) and nuclear DNA (nuclear ribosomal protein S18, n-Rps18) in 81 healthy controls and all available samples of 150 intensive care unit patients with sepsis obtained at 3 ± 1 days, 7 ± 1 days, 6 ± 2 months and 12 ± 2 months after sepsis onset. Results: Our analysis revealed that, at day 3, patients with sepsis had elevated levels of cfDNA (mt-ND1, and n-Rps18, all p<0.001) which decreased after the acute phase of sepsis. 28-day non-survivors of sepsis (16%) had higher levels of cfDNA (all p<0.05) compared with 28-day survivors (84%). Patients with SA-AKI had higher levels of cfDNA compared to patients without AKI (all p<0.05). Cell-free DNA was also significantly increased in patients requiring RRT (all p<0.05). All parameters improved the AUC for SCr in predicting RRT (AUC=0.88) as well as APACHE II in predicting mortality (AUC=0.86). Conclusion: In summary, cfDNA could potentially improve risk prediction models for mortality, SA-AKI and RRT in patients with sepsis. The predictive value of cfDNA, even with a single measurement at the onset of sepsis, could offer a significant advantage over conventional diagnostic methods that require repeated measurements or a baseline value for risk assessment. Considering that our data show that cfDNA levels decrease after the first insult, future studies could investigate cfDNA as a "memoryless" marker and thus bring further innovation to the complex field of SA-AKI diagnostics.

Hepatic function markers as prognostic factors in patients with acute kidney injury undergoing continuous renal replacement therapy.

Acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT), secondary to cardiovascular disease and sepsis, is associated with high in-hospital mortality. Although studies have examined cardiovascular disease and sepsis in AKI, the association between AKI and hepatic functional impairment remains unclear. We hypothesized that hepatic function markers would predict mortality in patients undergoing CRRT. We included 1,899 CRRT patients from a multi-centre database. In Phase 1, participants were classified according to the total bilirubin (T-Bil) levels on the day of, and 3 days after, CRRT initiation: T-Bil < 1.2, 1.2 ≤ T-Bil < 2, and T-Bil ≥ 2 mg/dL. In Phase 2, propensity score matching (PSM) was performed to examine the effect of a T-Bil cutoff of 1.2 mg/dL (supported by the Sequential Organ Failure Assessment score); creating two groups based on a T-Bil cutoff of 1.2 mg/dL 3 days after CRRT initiation. The primary endpoint was total mortality 90 days after CRRT initiation, which was 34.7% (n = 571). In Phase 1, the T-Bil, aspartate transaminase (AST), alanine transaminase (ALT), and AST/ALT (De Ritis ratio) levels at CRRT initiation were not associated with the prognosis, while T-Bil, AST, and the De Ritis ratio 3 days after CRRT initiation were independent factors. In Phase 2, T-Bil ≥1.2 mg/dL on day 3 was a significant independent prognostic factor, even after PSM [hazard ratio: 2.41 (95% CI; 1.84-3.17), p < 0.001]. T-Bil ≥1.2 mg/dL 3 days after CRRT initiation predicted 90-day mortality. Changes in hepatic function markers in acute renal failure may enable stratification of high-risk patients.

Plasma proenkephalin and neutrophil gelatinase-associated lipocalin predict mortality in ICU patients with acute kidney injury.

BACKGROUND: Acute kidney injury (AKI) is a common complication in patients admitted to intensive care unit (ICU) and mortality rates for this condition are high. To reduce the high incidence of short-term mortality, reliable prognostic indicators are required to facilitate early diagnosis and treatment of AKI. We assessed the ability of plasma proenkephalin (p­PENK) and plasma neutrophil gelatinase-associated lipocalin (p­NGAL) to predict 28-day mortality in AKI patients in intensive care. METHODS: This prospective study, carried out between January 2019 and December 2019, comprised 150 patients (100 male) diagnosed with AKI after excluding 20 patients discharged within 24 h and those with missing hospitalization data. Blood samples were collected to determine admission p-PENK and p-NGAL levels. The study outcome was 28­day mortality. RESULTS: The mean patient age was 68 years (female, 33%). The average P­PENK and p­NGAL levels were 0.24 ng/µL and 223.70 ng/mL, respectively. P­PENK levels >0.36 ng/µL and p­NGAL levels >230.30 ng/mL were used as critical values to reliably indicate 28­day mortality for patients with AKI (adjusted hazard ratios 0.785 [95% confidence interval 0.706-0.865, P<0.001] and 0.700 [95% confidence interval 0.611-0.789, P<0.001], respectively). This association was significant for mortality in patients in intensive care with AKI. Baseline p-PENK (0.36 ng/µL) and p-NGAL (230.30 ng/mL) levels and their respective cut-off values showed clinical value in predicting 28-day mortality. CONCLUSION: Serum PENK and NGAL levels, when used in conjunction, improved the accuracy of predicting 28-day mortality in patients with AKI while retaining sensitivity and specificity.

Sex bias in prediction and diagnosis of cardiac surgery associated acute kidney injury.

BACKGROUND: Female sex has been recognized as a risk factor for cardiac surgery associated acute kidney injury (CS-AKI). The current study sought to evaluate whether female sex is a risk factor for CS-AKI, or modifies the association of peri-operative change in serum creatinine with CS-AKI. METHODS: Observational study of adult patients undergoing cardiac surgery between 2000 and 2019 in a single U.S. center. The main variable of interest was registered patient sex, identified from electronic medical records. The main outcome was CS-AKI within 2 weeks of surgery. RESULTS: Of 58526 patients, 19353 (33%) were female; 12934 (22%) incurred AKI based on ≥ 0.3 mg/dL or ≥ 50% rise in serum creatinine (any AKI), 3320 (5.7%) had moderate to severe AKI, and 1018 (1.7%) required dialysis within 2 weeks of surgery. Female sex was associated with higher risk for AKI in models that were based on preoperative serum creatinine (OR, 1.35; 95% CI, 1.29-1.42), and lower risk with the use of estimated glomerular filtration, (OR, 0.90; 95% CI, 0.86-0.95). The risk for moderate to severe CS-AKI for a given immediate peri-operative change in serum creatinine was higher in female compared to male patients (p < .0001 and p < .0001 for non-linearity), and the association was modified by pre-operative kidney function (p < .0001 for interaction). CONCLUSIONS: The association of patient sex with CS-AKI and its direction was dependent on the operational definition of pre-operative kidney function, and differential outcome misclassification due to AKI defined by absolute change in serum creatinine.

Acute kidney injury: the experience of a tertiary center of Pediatric Nephrology.

INTRODUCTION: Acute kidney injury (AKI) is an abrupt deterioration of kidney function. The incidence of pediatric AKI is increasing worldwide, both in critically and non-critically ill settings. We aimed to characterize the presentation, etiology, evolution, and outcome of AKI in pediatric patients admitted to a tertiary care center. METHODS: We performed a retrospective observational single-center study of patients aged 29 days to 17 years and 365 days admitted to our Pediatric Nephrology Unit from January 2012 to December 2021, with the diagnosis of AKI. AKI severity was categorized according to Kidney Disease Improving Global Outcomes (KDIGO) criteria. The outcomes considered were death or sequelae (proteinuria, hypertension, or changes in renal function at 3 to 6 months follow-up assessments). RESULTS: Forty-six patients with a median age of 13.0 (3.5-15.5) years were included. About half of the patients (n = 24, 52.2%) had an identifiable risk factor for the development of AKI. Thirteen patients (28.3%) were anuric, and all of those were categorized as AKI KDIGO stage 3 (p < 0.001). Almost one quarter (n = 10, 21.7%) of patients required renal replacement therapy. Approximately 60% of patients (n = 26) had at least one sequelae, with proteinuria being the most common (n = 15, 38.5%; median (P25-75) urinary protein-to-creatinine ratio 0.30 (0.27-0.44) mg/mg), followed by reduced glomerular filtration rate (GFR) (n = 11, 27.5%; median (P25-75) GFR 75 (62-83) mL/min/1.73 m2). CONCLUSIONS: Pediatric AKI is associated with substantial morbidity, with potential for proteinuria development and renal function impairment and a relevant impact on long-term prognosis.

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.

BACKGROUND: Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. OBJECTIVE: We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. METHODS: Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps. RESULTS: For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO2). CONCLUSIONS: We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

Urinary L-FABP as an Early Biomarker for Pediatric Acute Kidney Injury Following Cardiac Surgery with Cardiopulmonary Bypass: A Systematic Review and Meta-Analysis.

Acute kidney injury (AKI) following surgery with cardiopulmonary bypass (CPB-AKI) is common in pediatrics. Urinary liver-type fatty acid binding protein (uL-FABP) increases in some kidney diseases and may indicate CPB-AKI earlier than current methods. The aim of this systematic review with meta-analysis was to evaluate the potential role of uL-FABP in the early diagnosis and prediction of CPB-AKI. Databases Pubmed/MEDLINE, Scopus, and Web of Science were searched on 12 November 2023, using the MeSH terms "Children", "CPB", "L-FABP", and "Acute Kidney Injury". Included papers were revised. AUC values from similar studies were pooled by meta-analysis, performed using random- and fixed-effect models, with p < 0.05. Of 508 studies assessed, nine were included, comprising 1658 children, of whom 561 (33.8%) developed CPB-AKI. Significantly higher uL-FABP levels in AKI versus non-AKI patients first manifested at baseline to 6 h post-CPB. At 6 h, uL-FABP correlated with CPB duration (r = 0.498, p = 0.036), postoperative serum creatinine (r = 0.567, p < 0.010), and length of hospital stay (r = 0.722, p < 0.0001). Importantly, uL-FABP at baseline (AUC = 0.77, 95% CI: 0.64-0.89, n = 365), 2 h (AUC = 0.71, 95% CI: 0.52-0.90, n = 509), and 6 h (AUC = 0.76, 95% CI: 0.72-0.80, n = 509) diagnosed CPB-AKI earlier. Hence, higher uL-FABP levels associate with worse clinical parameters and may diagnose and predict CPB-AKI earlier.

Risk factors and early prediction of cardiorenal syndrome type 3 among acute kidney injury patients: a cohort study.

BACKGROUND: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram. METHODS: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort. RESULTS: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility. CONCLUSIONS: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.

Clinical significance of the lactate-to-albumin ratio on prognosis in critically ill patients with acute kidney injury.

OBJECTIVE: To explore the relationship between lactate-to-albumin ratio (LAR) at ICU admission and prognosis in critically ill patients with acute kidney injury (AKI). METHODS: A retrospective analysis was conducted. Patients were divided into low (<0.659) LAR and high LAR (≥0.659) groups. Least absolute shrinkage and selection operator regression analysis was conducted to select variables associated with the 30-day prognosis. Cox regression analyses were performed to assess the association between LAR and mortality. Kaplan-Meier curves were plotted to compare cumulative survival rates between high and low LAR groups. Subgroup analysis was employed to assess the stability of the results. ROC curve was used to determine the diagnostic efficacy of LAR on prognosis. RESULTS: A nonlinear relationship was observed between LAR and the risk of 30-day and 360-day all-cause mortality in AKI patients (p < 0.001). Cox regulation showed that high LAR (≥ 0.659) was an independent risk factor for 30-day and 360-day all-cause mortality in patients with AKI (p < 0.001). The Kaplan-Meier survival curves demonstrated a noteworthy decrease in cumulative survival rates at both 30 and 360 days for the high LAR group in comparison to the low LAR group (p < 0.001). Subgroup analyses demonstrated the stability of the results. ROC curves showed that LAR had a diagnostic advantage when compared with lactate or albumin alone (p < 0.001). CONCLUSION: High LAR (≥0.659) at ICU admission was an independent risk factor for both short-term (30-day) and long-term (360-day) all-cause mortality in patients with AKI.

Predictive performance of two types of urinary biomarkers for renal non-recovery in sepsis-associated acute kidney injury: a prospective observational study.

BACKGROUND AND PURPOSE: Renal non-recovery is known to have negative prognostic implications in patients suffering from acute kidney injury (AKI). Nevertheless, the identification of biomarkers for predicting renal non-recovery in sepsis-associated AKI (SA-AKI) within clinical settings remains unresolved. This study aims to evaluate and compare the predictive ability for renal non-recovery, use of kidney replacement therapy (KRT) in the Intensive Care Unit (ICU), and 30-day mortality after SA-AKI by two urinary biomarkers, namely C-C motif chemokine ligand 14 (CCL14) and [TIMP-2]•[IGFBP7]. METHODS: We prospectively screened adult patients who met the criteria for AKI stage 2-3 and Sepsis-3.0 in two ICUs from January 2019 to May 2022. Patients who developed new-onset SA-AKI after ICU admission were enrolled and urinary biomarkers including [TIMP-2]•[IGFBP7] and CCL14 were detected at the time of SA-AKI diagnosis. The primary endpoint was non-recovery from SA-AKI within 7 days. The secondary endpoints were the use of KRT in the ICU and 30-day mortality after SA-AKI. The individual discriminative ability of [TIMP-2]•[IGFBP7] and CCL14 to predict renal non-recovery were evaluated by the area under receiver operating characteristics curve (AUC). RESULTS: 141 patients with stage 2-3 SA-AKI were finally included, among whom 54 (38.3%) experienced renal non-recovery. Urinary CCL14 exhibited a higher predictive capability for renal non-recovery compared to [TIMP-2]•[IGFBP7], with CCL14 showing an AUC of 0.901, versus an AUC of 0.730 for [TIMP-2]•[IGFBP7] (P = 0.001). Urinary CCL14 and [TIMP-2]•[IGFBP7] demonstrated a moderate predictive value for the need for KRT in ICU, with AUC values of 0.794 and 0.725, respectively; The AUC of [TIMP-2]•[IGFBP7] combined with CCL14 reached up to 0.816. Urinary CCL14 and [TIMP-2]•[IGFBP7] exhibited poor predictive power for 30-day mortality, with respective AUC values of 0.623 and 0.593. CONCLUSION: Urinary CCL14 had excellent predictive value for renal non-recovery in SA-AKI patients. For predicting the use of KRT in the ICU, the predictive capability of urinary [TIMP-2]•[IGFBP7] or CCL14 was fair. However, a combination of [TIMP-2]•[IGFBP7] and CCL14 showed good predictive ability for the use of KRT.

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. METHODS: The prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. RESULTS: In total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. CONCLUSION: The model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Mechanism and clinical role of TIMP-2 and IGFBP-7 in cardiac surgery-associated acute kidney injury: A review.

Acute kidney injury (AKI) is a common postoperative complication, but there is still a lack of accurate biomarkers. Cardiac surgery-associated AKI is the most common cause of major-surgery-related AKI, and patients requiring renal replacement therapy have high mortality rates. Early diagnosis, intervention, and management are crucial for improving patient prognosis. However, diagnosing AKI based solely on changes in serum creatinine level and urine output is insufficient, as these changes often lag behind actual kidney damage, making early detection challenging. Biomarkers such as tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein-7 (IGFBP-7) have been found to be significant predictors of moderate-to-severe AKI when combined with urine content analysis. This article reviews the mechanism of biomarkers TIMP-2 and IGFBP-7 in AKI and provides a comprehensive overview of the clinical effects of TIMP-2 and IGFBP-7 in cardiac surgery-associated AKI, including prediction, diagnosis, and progression.

Safety of ultra-low contrast coronary angiography in patients with acute kidney injury.

BACKGROUND: Ultra-low contrast administration during coronary angiography has been previously shown to be feasible and safe among patients with stable chronic kidney disease. In the present study, we investigate the safety of ultra-low contrast coronary angiography in patients with pre-existing acute kidney injury (AKI). METHODS: The study was a retrospective single-center evaluation of hospitalized patients who had AKI and required coronary angiography. Ultra-low contrast use was defined as ≤18 mL of contrast media. RESULTS: The cohort consisted of a case series of eight inpatients with AKI who required coronary angiography. The mean age was 57 (±16) years and half were females. All patients had chronic kidney disease with a mean baseline estimated glomerular filtration rate of 34 (±17) mL/min/1.73 m2. The mean creatinine before angiography was 3 (±1) mg/dL and volume of contrast administered was 14 (±4) mL. One patient had a 0.1 mg/dL increase in creatinine during admission, and no patients had further AKI up to 1-week postprocedure. CONCLUSIONS: The current data suggest that ultra-low contrast coronary angiography can be safely performed in patients with pre-existing AKI The study should be viewed as hypothesis-generating due to its small sample size. A larger cohort is required to validate the results.

Too Much of a Good Thing: Severe Hypercalcemia Presenting with Lethargy and Kidney Failure.

INTRODUCTION: We present a case of a 58-year-old man with a history of laryngo-pharyngectomy including bilateral thyroidectomy due to hypopharyngeal cancer presenting with lethargy, acute kidney failure, and hypercalcemia. Milk alkali syndrome was diagnosed given the history of high-dose calcium / vitamin D supplementation after ruling out other causes of hypercalcemia. After initial treatment with normal saline, furosemide and denosumab, the patient developed severe symptomatic hypocalcemia as a rare adverse effect of denosumab.

Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients.

Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non-race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%-100%) and 99% (95% CI 97%-100%) and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.

APASL clinical practice guidelines on the management of acute kidney injury in acute-on-chronic liver failure.

Acute-on-chronic liver failure (ACLF) is a syndrome that is characterized by the rapid development of organ failures predisposing these patients to a high risk of short-term early death. The main causes of organ failure in these patients are bacterial infections and systemic inflammation, both of which can be severe. For the majority of these patients, a prompt liver transplant is still the only effective course of treatment. Kidneys are one of the most frequent extrahepatic organs that are affected in patients with ACLF, since acute kidney injury (AKI) is reported in 22.8-34% of patients with ACLF. Approach and management of kidney injury could improve overall outcomes in these patients. Importantly, patients with ACLF more frequently have stage 3 AKI with a low rate of response to the current treatment modalities. The objective of the present position paper is to critically review and analyze the published data on AKI in ACLF, evolve a consensus, and provide recommendations for early diagnosis, pathophysiology, prevention, and management of AKI in patients with ACLF. In the absence of direct evidence, we propose expert opinions for guidance in managing AKI in this very challenging group of patients and focus on areas of future research. This consensus will be of major importance to all hepatologists, liver transplant surgeons, and intensivists across the globe.