Identification Of Acute Kidney Injury Research Articles

Efficacy of Capacity Building Intervention on Prevention and Identification of Acute Kidney Injury among Nursing Officers of Government Medical College and Hospital Sector-32, Chandigarh

Efficacy of Capacity Building Intervention on Prevention and Identification of Acute Kidney Injury among Nursing Officers of Government Medical College and Hospital Sector-32, Chandigarh

The role of biomarkers in early identification of acute kidney injury among non-critically ill patients.

Prediction and/or early identification of acute kidney injury (AKI) and individuals at greater risk remains of great interest in clinical medicine. Acute kidney injury continues to be a common complication among hospitalized patients, with an incidence ranging from 6 to 58%, depending on the setting.Aim of this study was to determine the performance of Insulin-like growth factor binding protein-7 (IGFBP7), tissue metallopeptidase inhibitor 2 (TIMP2), and urinary neutrophil gelatinase-associated lipocalin (uNGAL) in early detection of AKI among non-critically ill patients. In this prospective observational study at Mayo Clinic Hospitals in Rochester, Minnesota, USA, non-critically ill patients admitted from the emergency department between October 31st, 2016 and May 1st, 2018, who had an acute kidney injury (AKI) probability of 5% or higher were included. Biomarkers were measured in residual urine samples collected in the emergency department. The primary outcome was biomarker performance in predicting AKI development within the first 72h. Among 368 included patients, the mean age was 79 ± 12years, and 160 (43%) were male. Acute kidney injury occurred in 62 (17%) patients; 11.5% stage 1, 2.5% stage 2, and 3% stage 3. Twelve patients (3%) died during hospitalization and 102 (28%) within nine months after admission. The median uNGAL and IGFBP7-TIMP2 were 57 [20-236ng/ml], and 0.3 [0.1-0.8], respectively. The C-statistic of uNGAL and IGFBP7-TIMP2 of > 0.3 and > 2.0 for AKI prediction were 0.56, 0.54, and 0.53, respectively. In a model where one point is assigned to each marker of AKI (elevated serum creatinine, IGFBP7-TIMP2 > 0.3, and uNGAL), a higher score correlated with higher nine-month mortality [OR of 1.32 per point (95% CI 1.02-1.71)]. Among non-critically ill hospitalized patients, the performance of uNGAL and IGFBP7-TIMP2 for AKI prediction within 72h of admission was modest. This suggests a limited role for these biomarkers in AKI risk stratification among non-critically ill patients. Key learning points What was known Acute kidney injury (AKI) is a common complication among hospitalized patients. It is associated with increased morbidity and mortality. Various clinical prediction models and biomarkers have been developed to identify patients in special populations (such as ICU and cardiac surgery) who are at risk of AKI and diagnose AKI early. This study adds The performance of the biomarkers uNGAL, TIMP-2, and IGFBP-7 in predicting AKI within 72h of admission in non-critically ill patients was modest. However, these biomarkers were found to have a prognostic value for predicting 9-month mortality. One potential application of these biomarkers is identifying patients at higher AKI risk before exposing them to nephrotoxic agents. Potential impact This study provides evidence regarding the real-world performance of current FDA-approved biomarkers (uNGAL, TIMP-2, and IGFBP-7) for predicting acute kidney injury (AKI) within 72h of hospital admission among noncritically ill patients. While the performance of these biomarkers for predicting short-term AKI was modest, they may have a prognostic value for predicting 9-month mortality.

Combined Plasma Olink Proteomics and Transcriptomics Identifies CXCL1 and TNFRSF12A as Potential Predictive and Diagnostic Inflammatory Markers for Acute Kidney Injury.

Acute kidney injury (AKI) poses a significant global public health challenge. Current methods for detecting AKI rely on monitoring changes in serum creatinine (Scr), blood urea nitrogen (BUN), urinary output and some commonly employed biomarkers. However, these indicators are usually neither specific nor sensitive to AKI, especially in cases of mild kidney injury. AKI is accompanied by severe inflammatory reactions, resulting in the upregulation of numerous inflammation-associated proteins in the plasma. Plasma biomarkers are a noninvasive method for detecting kidney injury, and to date, plasma inflammation-associated cytokines have not been adequately studied in AKI patients. The objective of our research was to identify novel inflammatory biomarkers for AKI. We utilized Olink proteomics to analyze the alterations in plasma inflammation-related proteins in the serum of healthy mice (n = 2) or mice treated with cisplatin (n = 6). Additionally, transcriptome datasets for the lipopolysaccharide (LPS), cisplatin, and ischemia‒reperfusion injury (IRI) groups were obtained from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We calculated the intersection of differentially expressed proteins (DEPs) and genes (DEGs) from both datasets. In the Olink proteomics analysis, the AKI group had significantly greater levels of 11 DEPs than did the control group. In addition, 56 common upregulated DEGs were obtained from the transcriptome dataset. The expression of CXCL1 and TNFRSF12A overlapped across all the datasets. The transcription and protein expression levels of CXCL1 and TNFRSF12A were detected in vivo. The gene and protein levels of CXCL1 and TNFRSF12A were significantly increased in different AKI mouse models and clinical patients, suggesting that these genes and proteins could be potential specific biomarkers for the identification of AKI.

Arachidonic acid metabolism as a therapeutic target in AKI-to-CKD transition.

Arachidonic acid (AA) is a main component of cell membrane lipids. AA is mainly metabolized by three enzymes: cyclooxygenase (COX), lipoxygenase (LOX) and cytochrome P450 (CYP450). Esterified AA is hydrolysed by phospholipase A2 into a free form that is further metabolized by COX, LOX and CYP450 to a wide range of bioactive mediators, including prostaglandins, lipoxins, thromboxanes, leukotrienes, hydroxyeicosatetraenoic acids and epoxyeicosatrienoic acids. Increased mitochondrial oxidative stress is considered to be a central mechanism in the pathophysiology of the kidney. Along with increased oxidative stress, apoptosis, inflammation and tissue fibrosis drive the progressive loss of kidney function, affecting the glomerular filtration barrier and the tubulointerstitium. Recent studies have shown that AA and its active derivative eicosanoids play important roles in the regulation of physiological kidney function and the pathogenesis of kidney disease. These factors are potentially novel biomarkers, especially in the context of their involvement in inflammatory processes and oxidative stress. In this review, we introduce the three main metabolic pathways of AA and discuss the molecular mechanisms by which these pathways affect the progression of acute kidney injury (AKI), diabetic nephropathy (DN) and renal cell carcinoma (RCC). This review may provide new therapeutic targets for the identification of AKI to CKD continuum.

Effects of inpatient creatinine testing frequency on acute kidney injury identification and staging: a historical cohort study

BackgroundAcute kidney injury (AKI) is a multifactorial condition often induced by drugs commonly used in hospitals. Identifying and staging AKI necessitates frequent monitoring of renal function.AimTo assess the impact of real-world hospital practices regarding serum creatinine (SCr) testing on the identification and staging of AKI, and its implications for adjusting drug doses.MethodA historical cohort study utilizing hospital records from all adult patients admitted between 01/06/2018 and 31/12/2020 was conducted. Patients with no SCr assessment during their stay or those with an SCr at admission ≥ 2 mg/dL were excluded. AKI was determined using two criteria, namely AKIN and KDIGO, considering the time intervals between two SCr tests as outlined in the criteria. Additionally, patients with SCr increases exceeding AKI limits, regardless the time interval, were also identified. The estimated glomerular filtration rate (eGFR) and kinetic eGFR (KeGFR) were calculated.ResultsDuring the study period, 17,269 hospitalizations and 62,255 SCr tests were recorded. Among the 17,032 hospitalizations with a length of stay > 48 h, 46.8% experienced periods with no SCr tests performed for more than 48 h. Any stage of AKI was identified in 7.0% of patients and in 9.1% using AKI and KDIGO criteria, respectively. Ignoring time limits in both criteria revealed potential AKI in 1942 patients (11.2%), indicating a potential underdiagnosis of AKI by 37.5% or 19.1%, depending on the criteria used. A total of 76 drugs requiring dose adjustment in patients with eGFR ≤ 50 ml/min were prescribed in 78.5% admissions. These drugs were prescribed in 87.9% of patients potentially underdiagnosed with AKIN and in 88.9% with KDIGO.ConclusionThere is a need for changes in the established hospital procedures to ensure more frequent testing of SCr levels. Implementing an advanced scope of practice for clinical pharmacists could support these changes.

A systematic review of artificial intelligence algorithms for predicting acute kidney injury.

Acute kidney injury (AKI) increases mortality and costs in hospitalized patients. New methods for early AKI identification have been developed with targeted biomarkers and electronic health records data analysis. Machine learning (ML) use in diagnostics and health data analysis has recently increased. We performed a systematic review to analyze the use of ML for AKI prediction in hospitalized adults. Tubmed, EMBASE, Cochrane, and Web of Science databases were searched until 31st March of 2023. English-language studies using ML in adults for AKI prediction were included using predetermined eligibility search terms such as acute kidney injury, machine learning, and artificial intelligence. Two reviewers evaluated the publications' titles, abstracts, and full texts separately and obtained appropriate data. The main outcome was an area under the curve (AUC) result of at least 0.70. Ten studies in 102 articles were included involving 242,251 patients. Deep learning (AUC 0.907 in critical care AKI; AUC 0.797 in hospitalized patients AKI) was similar to Logistic regression (AUC 0.877 in critical care AKI; AUC 0.789 in hospitalized patients). Decision tree constructions had similar AUC. In this review, most ML models analyzed fulfilled the main outcome. AKI is multifactorial; however, ML performed well with different etiologies, such as cardiac-related AKI, drug-related AKI, and critical care patients. Overfitting data and constructing black box models are limitations that might jeopardize the generalization and comprehension of the results. Most studies were single-center, and three manuscripts used the same database with a predominantly Caucasian population, resulting in a lack of diversity and reducing external generalization. In conclusion, ML could effectively predict AKI in hospitalized adults. Future directions rely on including a more diverse population and completing prospective and controlled trials.

Acute Kidney Injury and Abnormalities on Brain Magnetic Resonance Imaging or Death in Infants with Hypoxic-Ischemic Encephalopathy: A Case-Control Study.

This study aimed to analyze the association between acute kidney injury (AKI) and abnormalities on brain magnetic resonance imaging (MRI) or death in neonates treated with therapeutic hypothermia for hypoxic-ischemic encephalopathy (HIE). This is a retrospective case-control analysis of 380 neonates born at ≥35 weeks' gestation treated with therapeutic hypothermia for HIE. Death or abnormal brain MRI using the basal ganglia watershed scoring system was compared between neonates with and without AKI. A total of 51 (13.4%) neonates had AKI. Infants with AKI had higher rates of the composite of death or abnormal brain MRI (74.5 vs. 38.3%; p < 0.001). Rate of death (21.6 vs. 5.5%; p < 0.001) and severe abnormalities on MRI or death (43.1 vs. 19.1%; p < 0.001) were also higher in neonates with AKI. AKI is strongly associated with abnormalities on brain MRI or death in neonates with HIE. Identification of AKI in this patient population may be helpful in guiding clinical management and predicting potential neurodevelopmental impairment. · Neonates with HIE are at increased risk for AKI.. · AKI is associated with hypoxic-ischemic injury on brain MRI or death among neonates with HIE.. · Identification of AKI in infants with HIE may help predict neurodevelopmental impairment..

Risk Factors and Clinical Characteristics of Acute Kidney Injury in Patients with COVID-19: A Systematic Review and Meta-Analysis.

Acute kidney injury (AKI) is associated with a worse prognosis in coronavirus disease 2019 (COVID-19) patients. Identification of AKI, particularly in COVID-19 patients, is important for improving patients' management. The study aims to assess risk factors and comorbidities of AKI in COVID-19 patients. We systematically searched PubMed and DOAJ databases for relevant studies involving confirmed COVID-19 patients with data on risk factors and comorbidities of AKI. The risk factors and comorbidities were compared between AKI and non-AKI patients. A total of 30 studies involving 22385 confirmed COVID-19 patients were included. Male (OR: 1.74 (1.47, 2.05)), diabetes (OR: 1.65 (1.54, 1.76)), hypertension (OR: 1.82 (1.12, 2.95)), ischemic cardiac disease (OR: 1.70 (1.48, 1.95)), heart failure (OR: 2.29 (2.01, 2.59)), chronic kidney disease (CKD) (OR: 3.24 (2.20, 4.79)), chronic obstructive pulmonary disease (COPD) (OR: 1.86 (1.35, 2.57)), peripheral vascular disease (OR: 2.34 (1.20, 4.56)), and history of nonsteroidal anti-inflammatory drugs (NSAID) (OR: 1.59 (1.29, 1.98)) were independent risk factors associated with COVID-19 patients with AKI. Patients with AKI presented with proteinuria (OR: 3.31 (2.59, 4.23)), hematuria (OR: 3.25 (2.59, 4.08)), and invasive mechanical ventilation (OR: 13.88 (8.23, 23.40)). For COVID-19 patients, male gender, diabetes, hypertension, ischemic cardiac disease, heart failure, CKD, COPD, peripheral vascular disease, and history of use of NSAIDs are associated with a higher risk of AKI.

Acute Kidney Injury: Medical Causes and Pathogenesis.

Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden decline in or loss of kidney function. AKI is not only associated with substantial morbidity and mortality but also with increased risk of chronic kidney disease (CKD). AKI is classically defined and staged based on serum creatinine concentration and urine output rates. The etiology of AKI is conceptually classified into three general categories: prerenal, intrarenal, and postrenal. Although this classification may be useful for establishing a differential diagnosis, AKI has mostly multifactorial, and pathophysiologic features that can be divided into different categories. Acute tubular necrosis, caused by either ischemia or nephrotoxicity, is common in the setting of AKI. The timely and accurate identification of AKI and a better understanding of the pathophysiological mechanisms that cause kidney dysfunction are essential. In this review, we consider various medical causes of AKI and summarize the most recent updates in the pathogenesis of AKI.

Acute kidney injury identification and management: clinical audit

Acute kidney injury identification and management: clinical audit

Urinary Mitochondrial Deoxyribonucleic Acid: A Novel Biomarker of Coronavirus Disease 2019-associated Acute Kidney Injury in Renal Transplant Recipients

Abstract Background: Acute kidney injury (AKI) is one of the common manifestations of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. An early identification of AKI is of paramount importance to modulate the management of AKI, limiting the severity, avoiding nephrotoxic medicines, and modifying the drug dose depending on renal function. Aim: To determine the utility of urinary mitochondrial deoxyribonucleic acid (umt-DNA) and neutrophil gelatinase-associated lipocalin (NGAL) in predicting coronavirus disease 2019 (COVID-19)-associated AKI and mitochondrial stress. Materials and Methods: Live-related renal transplant recipients (RTRs) (n = 66), who acquired SARS-CoV-2 infection and were admitted to a COVID hospital were included and subclassified into AKI (n = 19) with &gt; 1.5-fold rise in serum creatinine level from the pre-COVID-19 serum creatinine level, and non-AKI (n = 47) whose serum creatinine value remained stable, or a rise of &lt;1.5-fold of the baseline values of pre-COVID-19. A 50 mL urine sample was collected and urinary mt-DNA and NGAL was determined by the reverse transcriptase-polymerase chain reaction and enzyme-linked immunosorbent assays methods. Results: Both the urinary mitochondrial gene NADH dehydrogenase (ND-1) and NGAL level were significantly higher in the AKI group compared to non-AKI. The mean ND-1 gene Ct in the AKI group was (19.44 ± 2.58 a.u) compared to non-AKI (21.77 ± 3.60; P = 0.013). The normalized ND-1 gene cycle of threshold (Ct) in AKI was 0.79 ± 0.11 a.u compared to non-AKI (0.89 ± 0.14; P = 0.007). The median urinary NGAL level in AKI group was (453.53; range, 320.22–725.02, 95% confidence interval [CI]) ng/mL compared to non-AKI (212.78; range, 219.80–383.06, 95% CI; P = 0.015). The median urine creatinine normalized uNGAL was 4.78 (0.58–70.39) ng/mg in the AKI group compared to 11.26 ng/mg (0.41–329.71) in the non-AKI group. The area under curve of ND-1 gene Ct was 0.725, normalized ND-1 Ct was 0.713, uNGAL was 0.663, and normalized uNGAL was 0.667 for detecting the AKI and mitochondrial stress. Conclusions: Urinary mt-DNA quantification can detect the post-COVID mitochondrial distress with higher sensitivity than uNGAL in RTRs.

AIE biofluorescent probe based on twisted cucurbit[14]uril for the detection of Fe(CN)63- anion in solutions and live kidney cells

The kidney is one of the important organs for removing metabolites to maintain the stability of the internal environment. In this study, an AIE effect supramolecular fluorescent probe (TPAPy@tQ[14]) was constructed based on twisted cucurbit[14]uril (tQ[14]) and a triphenylamine derivative (TPAPy) through host-guest interaction. TQ[14] could effectively inhibit the rotation of the TPAPy molecule, which triggered the AIE effect. This effect was manifested as the emission of bright yellow fluorescence. TPAPy@tQ[14] could ultrasensitively detect potential cyanide (K3[Fe{CN}6]) in an aqueous solution at a detection limit as low as 3.28 × 10−7 M. The unique nature of the positive charge of TPAPy@tQ[14] could rapidly cross the glomerular filtration barrier into HK-2 cells. In addition, this probe was successfully applied to distinguish excess Fe(CN)63− in HK-2 cells for cell imaging, providing a new detection method for early clinical identification of acute kidney injury (AKI).

610. Low Rates of Acute Kidney Injury (AKI) in Outpatient Vancomycin Managed by Pharmacists using Precision Dosing Software

Abstract Background AKI is a complication of intravenous vancomycin (VAN) occurring in up to 43% of patients (pts) during hospitalization. Data on AKI in pts receiving VAN through outpatient parenteral antimicrobial therapy (OPAT) are limited. There is suggestion that VAN managed through precision dosing pharmacokinetic (PK) software may limit this occurrence. We evaluated the AKI rate of pts receiving VAN as OPAT through Physician Office Infusion Centers (POICs) over a 12-month period. Methods A review was conducted of OPAT VAN pts treated in 2021 with therapeutic drug monitoring (TDM) performed (InsightRX) and ≥2 VAN serum levels. These pts were then evaluated for AKI, defined as increase in serum creatinine (SCr) ≥1.5 times baseline. Pts on dialysis were excluded. Data included comorbidities, diagnosis, VAN regimen, concomitant medications, SCr, and VAN serum levels. PK parameters were collected with trough values and estimated 24-hr area under the curve (AUC24). Outcome of therapy was assessed following identification of AKI. Results A total of 330 pts from 51 POICs had TDM performed. Of these, 27 (8.2%) developed AKI. Median age was 61 years, 67% were male, and baseline SCr was 0.8 mg/dL (IQR, 0.7-1.0). Most pts (89%) were on concomitant nephrotoxic medications. Median duration of OPAT was 34 days (IQR, 18-41) with onset of AKI after 16 days (IQR, 10-21). At time of AKI, the median SCr increase was 1.7 times baseline (IQR, 1.5-2.0) with a corresponding VAN trough of 21 mg/mL (IQR, 15-28) and AUC24 of 572 mg*h/L (IQR, 495-660). As a result, VAN was discontinued in 14 (52%), dose or frequency modified in 10 (37%) with completion of VAN, and no modifications performed in 3 (11%). One pt who discontinued VAN required hospitalization due to AKI with resolution. Conclusion VAN managed by pharmacists in an OPAT setting using precision dosing software resulted in a low rate of AKI. Median rise in SCr was &amp;lt; 2 times baseline for those with AKI. Rapid identification of early AKI by the pharmacist resulted in changes to the regimen or VAN discontinuation, preventing serious patient sequelae. This supports the need for pharmacist-led monitoring in an outpatient setting with long VAN therapy durations. Disclosures Lucinda J. Van Anglen, PharmD, Merck &amp; Co.: Grant/Research Support|Paratek: Grant/Research Support.

Generalizability of an acute kidney injury prediction model across health systems.

Delays in the identification of acute kidney injury (AKI) in hospitalized patients are a major barrier to the development of effective interventions to treat AKI. A recent study by Tomasev and colleagues at DeepMind described a model that achieved a state-of-the-art performance in predicting AKI up to 48 hours in advance.1 Because this model was trained in a population of US Veterans that was 94% male, questions have arisen about its reproducibility and generalizability. In this study, we aimed to reproduce key aspects of this model, trained and evaluated it in a similar population of US Veterans, and evaluated its generalizability in a large academic hospital setting. We found that the model performed worse in predicting AKI in females in both populations, with miscalibration in lower stages of AKI and worse discrimination (a lower area under the curve) in higher stages of AKI. We demonstrate that while this discrepancy in performance can be largely corrected in non-Veterans by updating the original model using data from a sex-balanced academic hospital cohort, the worse model performance persists in Veterans. Our study sheds light on the importance of reproducing artificial intelligence studies, and on the complexity of discrepancies in model performance in subgroups that cannot be explained simply on the basis of sample size.

Update on prognosis driven classification of pediatric AKI.

Acute kidney injury (AKI) affects a large proportion of hospitalized children and increases morbidity and mortality in this population. Initially thought to be a self-limiting condition with uniformly good prognosis, we now know that AKI can persist and progress to acute kidney disease (AKD) and chronic kidney disease (CKD). AKI is presently categorized by stage of injury defined by increase in creatinine, decrease in eGFR, or decrease in urine output. These commonly used biomarkers of acute kidney injury do not change until the injury is well established and are unable to detect early stage of the disease when intervention is likely to reverse injury. The kidneys have the ability to compensate and return serum creatinine to a normal or baseline level despite nephron loss in the setting of AKI possibly masking persistent dysfunction. Though these definitions are important, classifying children by their propensity for progression to AKD and CKD and defining these risk strata by other factors besides creatinine may allow for better prognosis driven discussion, expectation setting, and care for our patients. In order to develop a classification strategy, we must first be able to recognize children who are at risk for AKD and CKD based on modifiable and non-modifiable factors as well as early biomarkers that identify their risk of persistent injury. Prevention of initial injury, prompt evaluation and treatment if injury occurs, and mitigating further injury during the recovery period may be important factors in decreasing risk of AKD and CKD after AKI. This review will cover presently used definitions of AKI, AKD, and CKD, recent findings in epidemiology and risk factors for AKI to AKD to CKD progression, novel biomarkers for early identification of AKI and AKI that may progress to CKD and future directions for improving outcome in children with AKI.

Incidence of Acute Kidney Injury in Polytrauma Patients and Predictive Performance of TIMP2 × IGFBP7 Biomarkers for Early Identification of Acute Kidney Injury.

Background: Acute kidney injury (AKI) is a common cause of organ failure in trauma patients who survive their initial injuries. It is independently associated with increased morbidity and mortality and prolongs the length of hospital stays. The objectives of this study were to describe the incidence of early AKI and influence of risk factors in polytrauma patients and evaluate the predictive potential of TIMP2 × IGFBP7 biomarkers in this patient cohort. Methods: We conducted a retrospective cohort study of severely injured adult patients who were consecutively admitted to a multidisciplinary ICU from May 2017 to May 2019. Detailed patient data was retrieved from ICU medical records. Fluid balance, urinary output, and sCr values up to 72 h were collected. Urine samples for measuring TIMP2 × IGFBP7 concentrations were obtained and analyzed from ICU admission to 72 h. Results: Among the 153 patients eligible for analysis, 45 were included in the AKI, and 108 in the no AKI cohorts. The incidence of AKI within 72 h, based on KDIGO criteria, was 28.8%. There were no differences in ISS, type and mechanism of injury, heart rate, and systolic BP at admission between groups. Patients with early AKI were older (68 vs. 49 years, p < 0.001), obese (BMI 26.2 vs. 24.7, p < 0.048), and more likely to have previous cardiac disease (27% vs. 5.6%, p < 0.001). TIMP2 × IGFBP7 values on ICU admission were associated with subsequent AKI in patients without evidence of AKI at the time of ICU admission. They were also higher in the AKI cohort and significantly correlated with renal replacement therapy (RRT) and episodes of hypotension. Multivariable analysis confirmed age, previous cardiac disease, and NephroCheck as the variables mostly associated with AKI, with AUC 0.792. Conclusions: TIMP2 × IGFBP7 may help identify trauma patients with tubular damage that may evolve into a clinically manifested syndrome. Prospective studies of TIMP2 × IGFBP7, as a trigger for early AKI bundle care, are warranted.

Impact of Different KDIGO Criteria on Clinical Outcomes for Early Identification of Acute Kidney Injury after Non-Cardiac Surgery.

The Kidney Disease Improving Global Outcomes (KDIGO) guidelines are currently used in acute kidney injury (AKI) diagnosis and include both serum creatinine (SCR) and urine output (UO) criteria. Currently, many AKI-related studies have inconsistently defined AKI, which possibly affects the comparison of their results. Therefore, we hypothesized that the different criteria in the KDIGO guidelines vary in measuring the incidence of AKI and its association with clinical outcomes. We retrospectively analyzed that data of patients admitted to the intensive care unit after non-cardiac surgery in 2019. Three different criteria used to define AKI were included: UOmean, mean UO < 0.5 mL/kg/h over time; UOcont, hourly UO < 0.5 mL/kg/h over time; or SCR, KDIGO guidelines SCR criteria. A total of 777 patients were included, and the incidence of UOmean-AKI was 33.1%, the incidence of UOcont-AKI was 7.9%, and the incidence of SCR-AKI was 2.0%. There were differences in the length of ICU stay and hospital stay between AKI and non-AKI patients under different criteria. We found differences in the incidence and clinical outcomes of AKI after non-cardiac surgery when using different KDIGO criteria.

Aetiological factors, outcomes and mortality risk of acute kidney injury in hospitalized patients in a tertiary health centre in Nigeria: An eleven year review

Acute kidney injury is a major public health issue in Nigeria, and it is associated with an increase in mortality. The study’s goal was to look at the most common precipitating factors, outcomes, and risk factors associated with mortality in our patients. This study examined the medical records of 11 years, of of hospitalized adult patients with confirmed Acute Kidney Injury (AKI). The patient record was searched for relevant information. The Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria were used to define AKI. The logistic regression analysis was used to determine the risk factors associated with mortality. A total of 399 patients were analysed. The overall mean age was 45.0 ± 17.3. The older age group (≥ 60years) compared to the younger group (&lt;60 years) developed hospital acquired AKI (10% vs 5%). Pre-existing diseases like, hypertension (p&lt;0.001), diabetes mellitus (p&lt;0.001), anemia (p&lt;0.001), stroke (p&lt;0.001) and malignancy (p&lt;0.001) were significantly higher in the older group. More of the older age group had more than 1 comorbidity (66 vs. 48%), were on diuretic and ACEI. The commonest causes/precipitants of AKI were septicaemia and other infections (62%) and hypovolaemia/hypoperfusion (45%). Overall mortality was 34%. The median length of stay was 11 (7.20) days, 25% had hemodialysis and 16% were admitted in the ICU. The risk factors for mortality identified were, hospital acquired AKI (OR: 6.59, 95% CI: 1.320-32.889, p=0.021), ICU admission (OR: 5.66, 95% CI: 2.061–15.512, p=0.001) and HIV infection (OR: 2.61, 95% CI: 1.063-6.424, p=0.036). The Commonest causes of AKI still remain infections and hypovolaemia and mortality from it was high in our patient population. Early identification of AKI and those at high risk of mortality and provision of adequate treatment are critical to improving outcomes in AKI patients.

Diagnostic Performance of the Acute Kidney Injury Baseline Creatinine Equations in Children and Adolescents with Type 1 Diabetes Mellitus Onset.

Three new equations for calculating the estimated basal serum creatinine (ebSCr) in hospitalized children have been developed: the simplified acute kidney injury (AKI) baseline creatinine (ABC) equation which considered only age in the formula; the equation including age and minimum creatinine (Crmin) within the initial 72 h from hospitalization (ABC-cr); and the equation including Crmin and height, weight, and age as squared values (ABC-advanced). We aimed to test the diagnostic performance of the ABC, ABC-cr and ABC-advanced equations in diagnosing AKI in 163 prospectively enrolled children with type 1 diabetes mellitus (T1DM) onset. We considered measured basal serum creatinine (mbSCr), the creatinine measured 14 days after T1DM onset. AKI was defined by the highest/basal serum creatine (HC/BC) ratio > 1.5. On the basis of the mbSCr, the AKI was diagnosed in 66/163 (40.5%) patients. This prevalence was lower than the prevalence of AKI diagnosed on the basis of ABC ebSCr (122/163 patients; 74.8%) (p < 0.001) and similar to the prevalence of AKI diagnosed on the basis of ABC-cr ebSCr (72/163 patients; 44.2%) (p = 0.5) and to the prevalence of AKI diagnosed on the basis of ABC-advanced ebSCr (69/163; 42.3%) (p = 0.73). AKI determined using ABC ebSCr, ABC-cr ebSCr and ABC-advanced ebSCr showed, respectively, 63.5% (kappa = 0.35; p < 0.001), 87.7% (kappa = 0.75; p < 0.001), and 87.1% (kappa = 0.74; p < 0.001) agreement with AKI determined using mbSCr. Using the HC/BC ratio calculated on the basis of mbSCr as gold standard, for Bland–Altman plots the HC/BC ratio calculated on the basis of ABC formula presented higher bias and wider limits of agreement compared with the HC/BC ratio calculated on the basis of ABC-cr and ABC-advanced formulas. In the receiver–operating characteristics (ROC) curve analysis the HC/BC ratio calculated on the basis of ABC ebSCr presented lower area under the ROC curve (AUROC) (AUROC = 0.89; 95%CI: 0.85–0.95; p < 0.001) compared with HC/BC ratio calculated on the basis of ABC-cr (AUROC = 0.94; 95%CI: 0.91–0.98; p < 0.001) or ABC-advanced ebSCr (AUROC = 0.914; 95%CI: 0.91–0.97; p < 0.001). In both Bland–Altman plots and ROC curve analysis, the ABC-cr and ABC-advanced formulas performed similarly. In conclusion, the ABC-cr and ABC-advanced formulas present very good diagnostic performance toward AKI identification in a population of children with T1DM onset.

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.