OBJECTIVES: To investigate the clinical sub-phenotype (SP) of pediatric acute kidney injury (AKI) and their association with clinical outcomes. METHODS: General status and initial values of laboratory markers within 24 hours after admission to the pediatric intensive care unit (PICU) were recorded for children with AKI in the derivation cohort (n=650) and the validation cohort (n=177). In the derivation cohort, a least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify death-related indicators, and a two-step cluster analysis was employed to obtain the clinical SP of AKI. A logistic regression analysis was used to develop a parsimonious classifier model with simplified metrics, and the area under the curve (AUC) was used to assess the value of this model. This model was then applied to the validation cohort and the combined derivation and validation cohort. The association between SPs and clinical outcomes was analyzed with all children with AKI as subjects. RESULTS: In the derivation cohort, two clinical SPs of AKI (SP1 and SP2) were identified by the two-step cluster analysis using the 20 variables screened by LASSO regression, namely SP_d1 group (n=536) and SP_d2 group (n=114). The simplified classifier model containing eight variables (P<0.05) had an AUC of 0.965 in identifying the two clinical SPs of AKI (P<0.001). The validation cohort was clustered into SP_v1 group (n=156) and SP_v2 group (n=21), and the combined derivation and validation cohort was clustered into SP1 group (n=694) and SP2 group (n=133). After adjustment for confounding factors, compared with the SP1 group, the SP2 group had significantly higher incidence rates of multiple organ dysfunction syndrome and death during the PICU stay (P<0.001), and SP2 was significantly associated with the risk of death within 28 days after admission to the PICU (P<0.001). CONCLUSIONS This study establishes a parsimonious classifier model and identifies two clinical SPs of AKI with different clinical features and outcomes.The SP2 group has more severe disease and worse clinical prognosis.
Humans ; Acute Kidney Injury/diagnosis* ; Prognosis ; Male ; Female ; Child ; Child, Preschool ; Phenotype ; Infant ; Logistic Models ; Adolescent

OBJECTIVE: To explore the influencing factors of prognosis in patients with sepsis-induced acute kidney injury undergoing continuous renal replacement therapy (CRRT), and to construct a mortality risk prediction model. METHODS: A retrospective research method was adopted, patients with sepsis-induced acute kidney injury who received CRRT at Fuyang People's Hospital from February 2021 to September 2023 were included in this study. Collect general information, comorbidities, vital signs, laboratory indicators, disease severity scores, treatment status, length of stay in the intensive care unit (ICU), and 28-day prognosis were collected within 24 hours of patient enrollment. The Cox regression model was used to identify the factors influencing prognosis in patients with sepsis-induced acute kidney injury, and a nomogram model was developed to predict mortality in these patients. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the nomogram model. RESULTS: A total of 146 patients with sepsis-induced acute kidney injury were included, of which 98 survived and 48 died (with a mortality of 32.88%) after 28 days of treatment. The blood lactic acid, interleukin-6 (IL-6), serum cystatin C, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), and proportion of mechanical ventilation in the death group were significantly higher than those in the survival group. The ICU stay was significantly longer than that in the survival group, and the glomerular filtration rate was significantly lower than that in the survival group. Cox regression analysis showed that blood lactic acid [odds ratio (OR) = 2.992, 95% confidence interval (95%CI) was 1.023-8.754], IL-6 (OR = 3.522, 95%CI was 1.039-11.929), serum cystatin C (OR = 3.999, 95%CI was 1.367-11.699), mechanical ventilation (OR = 4.133, 95%CI was 1.413-12.092), APACHE II score (OR = 5.013, 95%CI was 1.713-14.667), SOFA score (OR = 3.404, 95%CI was 1.634-9.959) were risk factors for mortality in patients with sepsis-induced acute kidney injury (all P < 0.05), glomerular filtration rate (OR = 0.294, 95%CI was 0.101-0.860) was a protective factor for mortality in patients with sepsis-induced acute kidney injury (P < 0.05). The ROC curve showed that the column chart model has a sensitivity of 80.0% (95%CI was 69.1%-89.2%) and a specificity of 89.3% (95%CI was 83.1%-95.2%) in predicting 28-day mortality in patients with acute kidney injury caused by sepsis. CONCLUSIONS Blood lactic acid, IL-6, mechanical ventilation, APACHEII score, SOFA score, glomerular filtration rate, and serum cystatin C are associated with the risk of death in patients with sepsis-induced acute kidney injury. The nomogram model could help early identification of mortality risk in these patients.
Humans ; Acute Kidney Injury/diagnosis* ; Sepsis/therapy* ; Retrospective Studies ; Prognosis ; Continuous Renal Replacement Therapy/methods* ; Nomograms ; Intensive Care Units ; ROC Curve ; Interleukin-6/blood* ; Proportional Hazards Models ; Female ; Male ; Cystatin C/blood* ; Middle Aged ; Risk Factors ; Lactic Acid/blood*

OBJECTIVE: To explore the risk factors for poor prognosis in sepsis-associated acute kidney injury (SA-AKI) and establish a nomogram predictive model. METHODS: The clinical data of patients with SA-AKI admitted to the department of critical care medicine of Shandong Provincial Hospital Affiliated to Shandong First Medical University from January 2019 to September 2022 were retrospectively analyzed, including demographic information, worst values of blood cell counts and biochemical indicators within 24 hours of SA-AKI diagnosis, whether the patient received renal replacement therapy (RRT), mechanical ventilation, vasopressor therapy during hospitalization, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), fibrinogen-to-albumin ratio (FAR) within 24 hours of diagnosis, acute kidney injury (AKI) staging, total length of hospital stay, length of intensive care unit (ICU) stay, and others. According to the 28-day outcome, the patients were divided into survival group and death group, and the indicators between the two groups were compared. Univariate and multivariate Logistic regression analyses were used to screen for risk factors associated with mortality in SA-AKI patients. A nomogram predictive model for SA-AKI prognosis was constructed based on the identified risk factors. Receiver operator characteristic curve (ROC curve) and calibration plots were generated to evaluate the predictive value of the nomogram model for SA-AKI prognosis. RESULTS: A total of 113 SA-AKI patients were included, with 67 in the survival group and 46 in the death group. The 28-day mortality among SA-AKI patients was 40.7%. The comparison between the two groups showed that there were statistically significant differences in age ≥ 65 years, AKI stage, mechanical ventilation, vasopressors, RRT, length of ICU stay, and laboratory indicators cystatin C (Cys C), fibrinogen (Fib), and FAR. Multivariate Logistic regression analysis showed that age ≥ 65 years [odds ratio (OR) = 7.967, 95% confidence interval (95%CI) was 1.803-35.203, P = 0.006], cystatin C (OR = 7.202, 95%CI was 1.756-29.534, P = 0.006), FAR (OR = 2.444, 95%CI was 1.506-3.968, P < 0.001), and RRT (OR = 7.639, 95%CI was 1.391-41.951, P = 0.019) were independent risk factors for mortality in SA-AKI patients. ROC curve analysis showed that the area under the ROC curve (AUC) for age ≥ 65 years, cystatin C, FAR, and RRT in predicting SA-AKI patient mortality were 0.713, 0.856, 0.911, and 0.701, respectively. A nomogram predictive model for SA-AKI patient prognosis was constructed based on age ≥ 65 years, cystatin C, FAR, and RRT, with an AUC of 0.967 (95%CI was 0.932-1.000) according to ROC curve analysis. The calibration plot indicated good consistency between predicted and actual probabilities. CONCLUSIONS Age ≥ 65 years, cystatin C, FAR, and RRT are independent risk factors for mortality in SA-AKI patients. The nomogram predictive model based on these four factors can accurately predict SA-AKI patient prognosis, helping physicians adjust treatment strategies in a timely manner and improve patient outcomes.
Humans ; Aged ; Cystatin C ; Retrospective Studies ; Intensive Care Units ; Sepsis/diagnosis* ; Acute Kidney Injury/therapy* ; Prognosis ; ROC Curve ; Fibrinogen

OBJECTIVE: To investigate the association between the glucose-to-lymphocyte ratio (GLR) and prognosis of patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), SA-AKI patients aged ≥ 18 years were selected. According to the tertiles of GLR, the patients were divided into GLR1 group (GLR ≤ 4.97×10^-9 mmol), GLR2 group (4.97×10^-9 mmol < GLR < 9.75×10^-9 mmol) and GLR3 group (GLR ≥ 9.75×10^-9 mmol). Patients with SA-AKI were divided into survival group and death group according to whether they survived 28 days after admission. The patient's gender, age, vital signs, laboratory test results, comorbidities, sequential organ failure assessment (SOFA), acute physiology score III (APS III) score and treatment measures were extracted from the database. Kaplan-Meier survival analysis was used to make the survival curves of patients with SA-AKI at 28 days, 90 days, 180 days and 1 year. Multivariate Logistic regression analysis model was used to explore the independent risk factors of 28-day mortality in patients with SA-AKI. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive efficacy of GLR for the prognosis of patients with SA-AKI. RESULTS: A total of 1 524 patients with SA-AKI were included, with a median age of 68.28 (58.96, 77.24) years old, including 612 females (40.16%) and 912 males (59.84%). There were 507 patients in the GLR1 group, 509 patients in the GLR2 group and 508 patients in the GLR3 group. There were 1 181 patients in the 28-day survival group and 343 patients in the death group. Grouping according to GLR tertiles showed that with the increase of GLR, the 28-day, 90-day, 180-day and 1-year mortality of SA-AKI patients gradually increased (28-day mortality were 11.64%, 22.00%, 33.86%, respectively; 90-day mortality were 15.98%, 26.72%, 40.55%, respectively; 180-day mortality were 17.16%, 28.29% and 41.73%, and the 1-year mortality were 17.95%, 29.27% and 42.72%, respectively, all P < 0.01). According to 28-day survival status, the GLR of the death group was significantly higher than that of the survival group [×10^-9 mmol: 9.81 (5.75, 20.01) vs. 6.44 (3.64, 10.78), P < 0.01]. Multivariate Logistic regression analysis showed that GLR was an independent risk factor for 28-day mortality in patients with SA-AKI [when GLR was used as a continuous variable: odds ratio (OR) = 1.065, 95% confidence interval (95%CI) was 1.045-1.085, P < 0.001; when GLR was used as a categorical variable, compared with GLR1 group: GLR2 group OR = 1.782, 95%CI was 1.200-2.647, P = 0.004; GLR3 group OR = 2.727, 95%CI was 1.857-4.005, P < 0.001]. ROC curve analysis showed that the area under the ROC curve (AUC) of GLR for predicting 28-day mortality in patients with SA-AKI was 0.674, when the optimal cut-off value was 8.769×10^-9 mmol, the sensitivity was 57.1% and the specificity was 67.1%. The predictive performance was improved when GLR was combined with APS III score and SOFA score, and the AUC was 0.806, the sensitivity was 74.6% and the specificity was 71.4%. CONCLUSIONS GLR is an independent risk factor of 28-day mortality in patients with SA-AKI, and high GLR is associated with poor prognosis in patients with SA-AKI.
Male ; Female ; Humans ; Blood Glucose ; Glucose ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Acute Kidney Injury ; Retrospective Studies ; Intensive Care Units

INTRODUCTION: Creatinine has limitations in identifying and predicting acute kidney injury (AKI). Our study examined the utility of neutrophil gelatinase-associated lipocalin (NGAL) in predicting AKI in patients presenting to the emergency department (ED), and in predicting the need for renal replacement therapy (RRT), occurrence of major adverse cardiac events (MACE) and all-cause mortality at three months post visit. METHODS: This is a single-centre prospective cohort study conducted at Singapore General Hospital (SGH). Patients presenting to SGH ED from July 2011 to August 2012 were recruited. They were aged ≥21 years, with an estimated glomerular filtration rate <60 mL/min/1.73 m², and had congestive cardiac failure, systemic inflammatory response syndrome or required hospital admission. AKI was diagnosed by researchers blinded to experimental measurements. Serum NGAL was measured as a point-of-care test. RESULTS: A total of 784 patients were enrolled, of whom 107 (13.6%) had AKI. Mean serum NGAL levels were raised (P < 0.001) in patients with AKI (670.0 ± 431.9 ng/dL) compared with patients without AKI (490.3 ± 391.6 ng/dL). The sensitivity and specificity of NGAL levels >490 ng/dL for AKI were 59% (95% confidence interval [CI] 49%-68%) and 65% (95% CI 61%-68%), respectively. Need for RRT increased 21% per 100 ng/dL increase in NGAL (P < 0.001), whereas odds of death in three months increased 10% per 100 ng/dL increase in NGAL (P = 0.028). No clear relationship was observed between NGAL levels and MACE. CONCLUSION Serum NGAL identifies AKI and predicts three-month mortality.
Humans ; Lipocalin-2 ; Prospective Studies ; Lipocalins ; Proto-Oncogene Proteins ; Acute-Phase Proteins ; Biomarkers ; Acute Kidney Injury/diagnosis* ; Emergency Service, Hospital ; Predictive Value of Tests

Few studies have described the key features and prognostic roles of lung microbiota in patients with severe community-acquired pneumonia (SCAP). We prospectively enrolled consecutive SCAP patients admitted to ICU. Bronchoscopy was performed at bedside within 48 h of ICU admission, and 16S rRNA gene sequencing was applied to the collected bronchoalveolar lavage fluid. The primary outcome was clinical improvements defined as a decrease of 2 categories and above on a 7-category ordinal scale within 14 days following bronchoscopy. Sixty-seven patients were included. Multivariable permutational multivariate analysis of variance found that positive bacteria lab test results had the strongest independent association with lung microbiota (R² = 0.033; P = 0.018), followed by acute kidney injury (AKI; R² = 0.032; P = 0.011) and plasma MIP-1β level (R² = 0.027; P = 0.044). Random forest identified that the families Prevotellaceae, Moraxellaceae, and Staphylococcaceae were the biomarkers related to the positive bacteria lab test results. Multivariable Cox regression showed that the increase in α-diversity and the abundance of the families Prevotellaceae and Actinomycetaceae were associated with clinical improvements. The positive bacteria lab test results, AKI, and plasma MIP-1β level were associated with patients' lung microbiota composition on ICU admission. The families Prevotellaceae and Actinomycetaceae on admission predicted clinical improvements.
Acute Kidney Injury/complications* ; Bacteria/classification* ; Chemokine CCL4/blood* ; Community-Acquired Infections/microbiology* ; Humans ; Lung ; Microbiota/genetics* ; Pneumonia, Bacterial/diagnosis* ; Prognosis ; RNA, Ribosomal, 16S/genetics*

PURPOSE: To investigate the clinical value of urine interleukin-18 (IL-8), neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) for the early diagnosis of acute kidney injury (AKI) in patients with ureteroscopic lithotripsy (URL) related urosepsis. METHODS: A retrospective study was carried out in 157 patients with urosepsis after URL. The patients were divided into AKI group and non-AKI group according to the Kidigo guideline and urine IL-8, NGAL and KIM-1 levels were detected by enzyme-linked immunosorbent assay at 0, 4, 12, 24 and 48 h after the surgery. Receiver operating characteristic curve (ROC) was used to evaluate the diagnostic value of these three biomarkers for postoperative AKI. RESULTS: The level of urine IL-8, NGAL and KIM-1 in AKI group was significantly higher than that in non-AKI group at 4, 12, 24 and 48 h (p < 0.01). The ROC analysis showed the combined detection of urine IL-8, NGAL and KIM-1 at 12 h had a larger area under curve (AUC) than a single marker (0.997, 95% CI: 0.991-0.998), and the sensitivity and specificity were 98.2% and 96.7%, respectively. Pearson correlation analysis showed that the levels of urine NGAL at 4, 12, 24 and 48 h in AKI patients were positively correlated with the levels of urine KIM-1 and IL-18 (p < 0.01). CONCLUSION AKI could be quickly recognized by the elevated level of urine IL-8, NGAL and KIM-1 in patients with URL-related urosepsis. Combined detection of the three urine biomarkers at 12 h after surgery had a better diagnostic performance, which may be an important reference for the early diagnosis of AKI.
Acute Kidney Injury/etiology* ; Biomarkers ; Early Diagnosis ; Hepatitis A Virus Cellular Receptor 1 ; Humans ; Interleukin-18 ; Interleukin-8 ; Lipocalin-2 ; Lithotripsy ; Retrospective Studies ; Ureteroscopy

Objective: To investigate the changes of high density lipoprotein cholesterol (HDL-C) in sepsis patients and its early predictive value for secondary acute kidney injury (AKI) in such patients. Methods: A retrospective case series study was conducted. From June 2019 to June 2021, 232 sepsis patients who met the inclusion criteria were admitted to the Second Hospital of Hebei Medical University, including 126 males and 106 females, aged 24 to 71 years. According to whether complicating secondary AKI, the patients were divided into non-AKI group (n=158) and AKI group (n=74). Data of patients between the two groups were compared and statistically analyzed with independent sample t test or chi-square test, including the sex, age, body mass index (BMI), body temperature, heart rate, primary infection site, combined underlying diseases, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score and sepsis-related organ failure assessment (SOFA) score at admission, and the serum levels of C-reactive protein (CRP), procalcitonin, creatinine, cystatin C, and HDL-C measured at diagnosis of sepsis. The multivariate logistic regression analysis was performed on the indicators with statistically significant differences between the two groups to screen the independent risk factors for developing secondary AKI in 232 sepsis patients, and the joint prediction model was established based on the independent risk factors. The receiver operating characteristic (ROC) curve of the independent risk factors and the joint prediction model predicting secondary AKI in 232 sepsis patients were drawn, and the area under the curve (AUC), the optimal threshold, and the sensitivity and specificity under the optimal threshold were calculated. The quality of the above-mentioned AUC was compared by Delong test, and the sensitivity and specificity under the optimal threshold were compared using chi-square test. Results: The sex, age, BMI, body temperature, heart rate, primary infection site, combined underlying diseases, and CRP level of patients between the two groups were similar (P>0.05). The procalcitonin, creatinine, cystatin C, and scores of APACHE Ⅱ and SOFA of patients in AKI group were all significantly higher than those in non-AKI group (with t values of -3.21, -16.14, -12.75, -11.13, and -12.88 respectively, P<0.01), while the HDL-C level of patients in AKI group was significantly lower than that in non-AKI group (t=6.33, P<0.01). Multivariate logistic regression analysis showed that creatinine, cystatin C, and HDL-C were the independent risk factors for secondary AKI in 232 sepsis patients (with odds ratios of 2.45, 1.68, and 2.12, respectively, 95% confidence intervals of 1.38-15.35, 1.06-3.86, and 0.86-2.56, respectively, P<0.01). The AUCs of ROC curves of creatinine, cystatin C, HDL-C, and the joint prediction model for predicting secondary AKI in 232 sepsis patients were 0.69, 0.79, 0.89, and 0.93, respectively (with 95% confidence intervals of 0.61-0.76, 0.72-0.85, 0.84-0.92, and 0.89-0.96, respectively, P values all below 0.01); the optimal threshold were 389.53 μmol/L, 1.56 mg/L, 0.63 mmol/L, and 0.48, respectively; the sensitivity under the optimal threshold were 76.6%, 81.4%, 89.7%, and 95.5%, respectively; the specificity under the optimal threshold values were 78.6%, 86.7%, 88.6%, and 96.6%, respectively. The AUC quality of cystatin C was significantly better than that of creatinine (z=2.34, P<0.05), the AUC quality and sensitivity and specificity under the optimal threshold of HDL-C were all significantly better than those of cystatin C (z=3.33, with χ² values of 6.43 and 7.87, respectively, P<0.01) and creatinine (z=5.34, with χ² values of 6.32 and 6.41, respectively, P<0.01); the AUC quality and sensitivity and specificity under the optimal threshold of the joint prediction model were all significantly better than those of creatinine, cystatin C, and HDL-C (with z values of 6.18, 4.50, and 2.06, respectively, χ² values of 5.31, 7.23, 3.99, 6.56, 7.34, and 4.00, respectively, P<0.05 or P<0.01). Conclusions: HDL-C level in sepsis patients with secondary AKI is significantly lower than that in patients without secondary AKI. This is an independent risk factor for secondary AKI in sepsis patients with a diagnostic value being superior to that of creatinine and cystatin C. The combination of the aforementioned three indicators would have higher predicative valuable for secondary AKI in sepsis patients.
Acute Kidney Injury/etiology* ; Adult ; Aged ; Cholesterol, HDL ; Female ; Humans ; Male ; Middle Aged ; Prognosis ; ROC Curve ; Retrospective Studies ; Sepsis/diagnosis* ; Young Adult

To maintain homeostasis of the cardiovascular system, the heart and kidney act bidirectionally. Therefore, acute or chronic dysfunction of one organ can cause dysfunction in the other. This phenomenon is characterized as cardiorenal syndrome (CRS). Concurrent dysfunction of the heart and kidney adversely affects one another and eventually worsens patient outcomes through a vicious cycle. Although a CRS classification system has been proposed, the underlying pathophysiology is multifactorial and clinical access continues to be difficult. Although several therapies, including agents that target the renin-angiotensin-aldosterone system, have been utilized, there is not enough evidence to demonstrate their effectiveness for CRS. Thus, more effort should be made to optimize the diagnosis and treatment strategies for CRS patients. This review will introduce CRS as it is currently understood, discuss the pathophysiology, and examine management strategies.
Acute Kidney Injury ; Cardio-Renal Syndrome ; Cardiovascular System ; Classification ; Diagnosis ; Heart ; Heart Failure ; Homeostasis ; Humans ; Kidney ; Renal Insufficiency, Chronic ; Renin-Angiotensin System

Diagnosis of thrombotic microangiopathy (TMA) is challenging due to its close association with other forms of microangiopathic hemolytic anemia, such as malignant hypertension and disseminated intravascular coagulation, and because other manifestations including cytopenia and acute kidney injury are manifestations of other medical comorbidities. Further challenges for accurate diagnosis include distinguishing between primary and secondary TMA, as well as between hemolytic uremic syndrome (HUS) and thrombotic thrombocytopenic purpura (TTP). TTP is typically differentiated from HUS by the presence of more severe thrombocytopenia, along with a higher frequency of altered mental status with relatively preserved renal function. However, the clinical course can vary among patients, requiring polymerase chain reaction testing of patient stools for enterohemorrhagic Escherichia coli and a disintegrin and metalloproteinase with thrombospondin type 1 motif 13 (ADAMTS13) assay. To reduce the mortality rate, prompt initiation of plasmapheresis is important in cases where TPP cannot be excluded. Future advances enabling more rapid testing for ADAMTS13 levels will reduce the need for unnecessary plasmapheresis, so that treatment strategy can be more optimized.
Acute Kidney Injury ; Anemia, Hemolytic ; Comorbidity ; Diagnosis ; Diagnosis, Differential ; Disseminated Intravascular Coagulation ; Enterohemorrhagic Escherichia coli ; Hemolytic-Uremic Syndrome ; Humans ; Hypertension, Malignant ; Mortality ; Plasma Exchange ; Plasmapheresis ; Polymerase Chain Reaction ; Purpura, Thrombotic Thrombocytopenic ; Thrombocytopenia ; Thrombospondins ; Thrombotic Microangiopathies