Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury.

Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models that predict CSA-AKI. We introduced a novel approach for extracting relevant features from high-resolution intraoperative biosignals to reflect the patient's baseline status, the extent of unfavorable conditions encountered intraoperatively, and data variability. We developed XGBoost models from 2,003 patients across three consecutive perioperative phases using: 1) only preoperative, 2) pre- and intraoperative, and 3) pre-, intra-, and postoperative variables. The predictive performance progressively improved throughout the three consecutive perioperative phases (e.g., AUROC of 0.767 to 0.797 and 0.840), all surpassing the Thakar Score's performance. According to the SHAP method, intraoperative perfusion pressure was most important in the prediction, highlighting the importance of intraoperative patient management and the use of high-resolution biosignal data in predictive modeling to analyze hemodynamic fluctuations during surgery. Early postoperative biomarkers were also important predictors, highlighting the importance of intensified monitoring early after surgery.

Development and validation of a predictive model for acute kidney injury in patients with ureterolithiasis.

OBJECTIVES: This study aims to identify risk factors for acute kidney injury (AKI) in patients with ureterolithiasis and to develop a predictive model for early AKI detection in this population. METHODS: A retrospective analysis was conducted on data from 1,016 patients with ureterolithiasis who presented to our outpatient emergency department between January 2021 and December 2022. Using multifactorial logistic regression, we identified independent risk factors for AKI and constructed a nomogram to predict AKI risk. The predictive model's efficacy was assessed through the area under the ROC curve, calibration curves, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA). RESULTS: AKI was diagnosed in 18.7% of the patients. Independent risk factors identified included age, fever, diabetes, hyperuricemia, bilateral calculi, functional solitary kidney, self-medication, and prehospital delay. The nomogram demonstrated excellent discriminatory capabilities, with AUCs of 0.818 (95% CI, 0.775-0.861) for the modeling set and 0.782 (95% CI, 0.708-0.856) for the validation set. Both calibration curve and HL test results confirmed strong concordance between the model's predictions and actual observations. DCA highlighted the model's significant clinical utility. CONCLUSIONS: The predictive model developed in this study provides clinicians with a valuable tool for early identification and management of patients at high risk for AKI, thereby potentially enhancing patient outcomes.

Hashimoto's thyroiditis masquerading as acute tubular injury and rhabdomyolysis.

Hashimoto's thyroiditis manifesting as hypothyroidism has been implicated in glomerular disorders due to autoantibody formation. Here we present the case of a 26-year-old male without any comorbidities presenting with easy fatiguability and weight gain for 2 months. He was found to have a creatinine of 2.1 mg/dL with a history of rhinitis treated with anti-histaminic three days prior to the hospital visit. He had symptoms of intermittent myalgia for the past two weeks. On laboratory evaluation, he was found to have raised CPK, elevated TSH, low normal T4, and positive anti-TPO and anti-Tg antibodies. Neck ultrasound revealed linear echogenic septations in the thyroid gland. Renal biopsy revealed acute tubular injury. Appropriate thyroxine supplementation was started and his creatinine decreased to 1.2 mg/dL after 1 month. It is important that clinicians should be aware of this rare kidney presentation in Hashimoto's thyroiditis.

The Need to Identify Novel Markers for Early Renal Injury in Cardiorenal Syndrome.

The term "Cardiorenal Syndrome" (CRS) refers to the complex interplay between heart and kidney dysfunction. First described by Robert Bright in 1836, CRS was brought to its modern view by Ronco et al. in 2008, who defined it as one organ's primary dysfunction leading to secondary dysfunction in the other, a view that led to the distinction of five different types depending on the organ of primary dysfunction and the temporal pattern (acute vs. chronic). Their pathophysiology is intricate, involving various hemodynamic, neurohormonal, and inflammatory processes that result in damage to both organs. While traditional biomarkers have been utilized for diagnosing and prognosticating CRS, they are inadequate for the early detection of acute renal damage. Hence, there is a pressing need to discover new biomarkers to enhance clinical outcomes and treatment approaches.

Redefining urine output thresholds for acute kidney injury criteria in critically Ill patients: a derivation and validation study.

INTRODUCTION: The current definition of acute kidney injury (AKI) includes increased serum creatinine (sCr) concentration and decreased urinary output (UO). Recent studies suggest that the standard UO threshold of 0.5 ml/kg/h may be suboptimal. This study aimed to develop and validate a novel UO-based AKI classification system that improves mortality prediction and patient stratification. METHODS: Data were obtained from the MIMIC-IV and eICU databases. The development process included (1) evaluating UO as a continuous variable over 3-, 6-, 12-, and 24-h periods; (2) identifying 3 optimal UO cutoff points for each time window (stages 1, 2, and 3); (3) comparing sensitivity and specificity to develop a unified staging system; (4) assessing average versus persistent reduced UO hourly; (5) comparing the new UO-AKI system to the KDIGO UO-AKI system; (6) integrating sCr criteria with both systems and comparing them; and (7) validating the new classification with an independent cohort. In all these steps, the outcome was hospital mortality. Another analyzed outcome was 90-day mortality. The analyses included ROC curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and logistic and Cox regression analyses. RESULTS: From the MIMIC-IV database, 35,845 patients were included in the development cohort. After comparing the sensitivity and specificity of 12 different lowest UO thresholds across four time frames, 3 cutoff points were selected to compose the proposed UO-AKI classification: stage 1 (0.2-0.3 mL/kg/h), stage 2 (0.1-0.2 mL/kg/h), and stage 3 (< 0.1 mL/kg/h) over 6 h. The proposed classification had better discrimination when the average was used than when the persistent method was used. The adjusted odds ratio demonstrated a significant stepwise increase in hospital mortality with advancing UO-AKI stage. The proposed classification combined or not with the sCr criterion outperformed the KDIGO criteria in terms of predictive accuracy-AUC-ROC 0.75 (0.74-0.76) vs. 0.69 (0.68-0.70); NRI: 25.4% (95% CI: 23.3-27.6); and IDI: 4.0% (95% CI: 3.6-4.5). External validation with the eICU database confirmed the superior performance of the new classification system. CONCLUSION: The proposed UO-AKI classification enhances mortality prediction and patient stratification in critically ill patients, offering a more accurate and practical approach than the current KDIGO criteria.

Predictive value of the early postoperative hemoglobin-to-red blood cell distribution width ratio for acute kidney injury in elderly intertrochanteric fracture patients.

BACKGROUND: Hemoglobin-to-red blood cell distribution width ratio (HRR) had great predictive value for the prognosis of malignant tumors and cardiovascular disease. However, its predictive value for the occurrence of acute kidney injury (AKI) in elderly intertrochanteric fracture patients remains unclear. This study aims to analyze the correlation between the early postoperative HRR and the risk of postoperative AKI in elderly intertrochanteric fracture patients. METHODS: We reviewed the medical records of 307 elderly intertrochanteric fracture patients in this single-center retrospective cohort study. We performed univariate analysis on the relevant parameters, and parameters with significant differences were included in the following logistic regression model for multivariate analysis. Then, we used a receiver operating characteristic (ROC) curve to evaluate the predictive value of the early postoperative HRR level for AKI in elderly intertrochanteric fracture patients. Patients were divided into a high HRR group and a low HRR group according to the cutoff point determined by ROC curve analysis. Subsequently, the relevance between postoperative HRR and AKI was further determined using propensity score matching (PSM) and inverse probability of treatment weighting (IPTW). RESULTS: The area under the curve of the early postoperative HRR for predicting postoperative AKI was 0.714 (95% CI: 0.618-0.809). The cutoff value was 5.44. The sensitivity was 72.7%, and the specificity was 70.8%. Patients were divided into low HRR (⩽ 5.44) and high HRR (> 5.44) groups according to the cutoff value. PSM and IPTW analysis indicated that the risk of AKI in the low HRR group was significantly higher than that in the high HRR group in both the matched cohort (OR = 6.914, 95% CI: 1.714-46.603, p = 0.016) and the weighted group (OR = 2.784, 95% CI: 1.415-5.811, p = 0.040). CONCLUSIONS: Early postoperative HRR is an accurate, accessible, and economical blood test parameter that can predict the risk of postoperative AKI in elderly patients with femoral intertrochanteric fracture.

Development and evaluation of the model for acute kidney injury in patients with cardiac arrest after successful resuscitation.

BACKGROUND: This study aims to construct a clinical prediction model and create a visual line chart depicting the risk of acute kidney injury (AKI) following resuscitation in cardiac arrest (CA) patients. Additionally, the study aims to validate the clinical predictive accuracy of the developed model. METHODS: Data were retrieved from the Dryad database, and publicly shared data were downloaded. This retrospective cohort study included 347 successfully resuscitated patients post-cardiac arrest from the Dryad database. Demographic and clinical data of patients in the database, along with their renal function during hospitalization, were included. Through data analysis, the study aimed to explore the relevant influencing factors of acute kidney injury (AKI) in patients after cardiopulmonary resuscitation. The study constructed a line chart prediction model using multivariate logistic regression analysis with post-resuscitation shock status (Post-resuscitation shock refers to the condition where, following successful cardiopulmonary resuscitation after cardiac arrest, some patients develop cardiogenic shock.), C reactive protein (CRP), Lactate dehydrogenase (LDH), and Alkaline phosphatase (ALP) identified as predictive factors. The predictive efficiency of the fitted model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: Multivariate logistic regression analysis showed that post-resuscitation shock status, CRP, LDH, and PAL were the influencing factors of AKI after resuscitation in CA patients. The calibration curve test indicated that the prediction model was well-calibrated, and the results of the Decision Curve Analysis (DCA) demonstrated the clinical utility of the model constructed in this study. CONCLUSION: Post-resuscitation shock status, CRP, LDH, and ALPare the influencing factors for AKI after resuscitation in CA patients. The clinical prediction model constructed based on the above indicators has good clinical discriminability and practicality.

Changes in urinary renal injury markers in children with Mycoplasma pneumoniae pneumonia and a prediction model for related early renal injury.

BACKGROUND: This study aims to analyse changes in urinary kidney injury markers in children with Mycoplasma pneumoniae pneumonia (MPP), investigate the risk factors for MPP-related acute kidney injury (AKI) and establish a model to predict MPP-related AKI. METHODS: Ninety-five children were enrolled based on the study's inclusion and exclusion criteria. They were divided into a severe MPP (SMPP) group and a non-SMPP group and then into an AKI group and a non-AKI group according to the presence of AKI. A univariate logistic regression analysis was performed to explore the early risk factors for AKI. Based on a multivariate logistic regression analysis and a least absolute shrinkage and selection operator regression analysis, appropriate variables were selected to establish a prediction model, and R 4.2.2 software was used to draw nomograms and generate a dynamic nomogram website. RESULTS: Seven urinary kidney injury markers were abnormally elevated in the SMPP group and the non-SMPP group: urinary N-acetyl-ß-D-glucosaminidase (NAG), ß2-microglobulin, α1-microglobulin, retinol-binding protein, urinary immunoglobulin G, urinary transferrin and urinary microalbumin. Sixteen children were identified with AKI during hospitalisation. The AKI group had higher levels of urinary NAG, α1-microglobulin, ß2-microglobulin, urinary microalbumin, urinary transferrin and retinol-binding protein than the non-AKI group (P < 0.05). The MPP-related AKI prediction model consists of four indicators (serum immunoglobulin M [IgM], C-reactive protein [CRP], urine NAG and sputum plug presence) and a dynamic nomogram. CONCLUSION: Urinary kidney injury markers are often elevated in children with MPP; urinary NAG is the marker most likely to be elevated, and it is especially evident in severe cases. The nomogram of the prediction model, comprising serum IgM, CRP, urinary NAG and sputum plug presence, can predict the probability of AKI in children with MPP.

Assessing the predictive value of elevated postoperative syndecan-1 levels for progressive acute kidney injury and kidney replacement therapy necessity in adult cardiac surgery patients.

BACKGROUND: The development of acute kidney injury (AKI) post-cardiac surgery significantly increases patient morbidity and healthcare costs. Prior researches have established Syndecan-1 (SDC-1) as a potential biomarker for endothelial injury and subsequent acute kidney injury development. This study assessed whether postoperative SDC-1 levels could further predict AKI requiring kidney replacement therapy (AKI-KRT) and AKI progression. METHODS: In this prospective study, 122 adult cardiac surgery patients, who underwent valve or coronary artery bypass grafting (CABG) or a combination thereof and developed AKI within 48 h post-operation from May to September 2021, were monitored for the progression to stage 2-3 AKI or the need for KRT. We analyzed the predictive value of postoperative serum SDC-1 levels in relation to multiple endpoints. RESULTS: In the study population, 110 patients (90.2%) underwent cardiopulmonary bypass, of which thirty received CABG or combined surgery. Fifteen patients (12.3%) required KRT, and thirty-eight (31.1%) developed progressive AKI, underscoring the severe AKI incidence. Multivariate logistic regression indicated that elevated SDC-1 levels were independent risk factors for progressive AKI (OR = 1.006) and AKI-KRT (OR = 1.011). The AUROC for SDC-1 levels in predicting AKI-KRT and AKI progression was 0.892 and 0.73, respectively, outperforming the inflammatory cytokines. Linear regression revealed a positive correlation between SDC-1 levels and both hospital (ß = 0.014, p = 0.022) and ICU stays (ß = 0.013, p < 0.001). CONCLUSION: Elevated postoperative SDC-1 levels significantly predict AKI progression and AKI-KRT in patients following cardiac surgery. The study's findings support incorporating SDC-1 level monitoring into post-surgical care to improve early detection and intervention for severe AKI.

Linear Correlation Between Mean Arterial Pressure and Urine Output in Critically Ill Patients.

OBJECTIVE: Mean arterial pressure (MAP) plays a significant role in regulating tissue perfusion and urine output (UO). The optimal MAP target in critically ill patients remains a subject of debate. We aimed to explore the relationship between MAP and UO. DESIGN: A retrospective observational study. SETTING: A general ICU in a tertiary medical center. PATIENTS: All critically ill patients admitted to the ICU for more than 10 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: MAP values and hourly UO were collected in 5,207 patients. MAP levels were categorized into 10 groups of 5 mm Hg (from MAP < 60 mm Hg to MAP > 100 mg Hg), and 656,423 coupled hourly mean MAP and UO measurements were analyzed. Additionally, we compared the UO of individual patients in each MAP group with or without norepinephrine (NE) support or diuretics, as well as in patients with acute kidney injury (AKI).Hourly UO rose incrementally between MAP values of 65-100 mm Hg. Among 2,226 patients treated with NE infusion, mean UO was significantly lower in the MAP less than 60 mm Hg group (53.4 mL/hr; 95% CI, 49.3-57.5) compared with all other groups (p < 0.001), but no differences were found between groups of 75 less than or equal to MAP. Among 2500 patients with AKI, there was a linear increase in average UO from the MAP less than 60 mm Hg group (57.1 mL/hr; 95% CI, 54.2-60.0) to the group with MAP greater than or equal to 100 mm Hg (89.4 mL/hr; 95% CI, 85.7-93.1). When MAP was greater than or equal to 65 mm Hg, we observed a statistically significant trend of increased UO in periods without NE infusion. CONCLUSIONS: Our analysis revealed a linear correlation between MAP and UO within the range of 65-100 mm Hg, also observed in the subgroup of patients treated with NE or diuretics and in those with AKI. These findings highlight the importance of tissue perfusion to the maintenance of diuresis and achieving adequate fluid balance in critically ill patients.

Clinical features associated with immune checkpoint inhibitor nephritis: a single-center clinical case series.

BACKGROUND: Acute kidney injury (AKI) has been well described as a complication of immune checkpoint inhibitor therapy. We present a series of patients, the majority with lung adenocarcinoma, who developed AKI while actively receiving immune checkpoint inhibitors. METHODS: This is a retrospectively analyzed clinical case series of six patients treated at City of Hope Comprehensive Cancer Center. Data were collected on gender, age, ethnicity, comorbidities, concomitant medications, type of malignancy, treatments, and renal function. All patients underwent renal biopsy for classification of the mechanism of AKI. Comprehensive genomic profiling (CGP) was performed on tumor tissue for all patients. RESULTS: Patterns of AKI included acute interstitial nephritis and acute tubular necrosis. Contributing factors included the use of concomitant medications known to contribute to AKI. All but two patients had full resolution of the AKI with the use of steroids. There were several mutations found on CGP that was notable including an Exon 20 insertion as well as multiple NF1 and TP53 mutations. There was high PD-L1 expression on tumor tissue noted in two out of six patients. In addition to AKI, a subset of patients had proteinuria with biopsies revealing corresponding glomerular lesions of minimal change disease and focal and segmental glomerulosclerosis. CONCLUSIONS: Our case series demonstrates that AKI from immune checkpoint inhibitors has a variable presentation that may require an individualized treatment approach. Further studies are needed to identify biomarkers that may help identify those at risk and guide the management of this condition.

Complications of new-onset atrial fibrillation in critically ill COVID-19 patients admitted to the intensive care unit (ICU): a meta-analysis.

BACKGROUND: COVID-19 infections can result in severe acute respiratory distress syndrome (ARDS) requiring admission to the intensive care unit (ICU). Cardiovascular manifestation or exacerbation of cardiovascular diseases could be another complication. Cardiac arrhythmias including New-Onset Atrial Fibrillation (NOAF), have been observed in hospitalized patients with COVID-19 infections. In this analysis, we aimed to systematically compare the complications associated with NOAF in critically ill COVID-19 patients admitted to the ICU. METHODS: MEDLINE, EMBASE, Web of Science, the Cochrane database, http://www. CLINICALTRIALS: gov , Google Scholar and Mendeley were searched for relevant publications based on COVID-19 patients with NOAF admitted to the ICU. Complications including in-hospital mortality, ICU mortality, patients requiring mechanical ventilation, acute myocardial infarction, acute kidney injury, renal replacement therapy and pulmonary embolism were assessed. This is a meta-analysis and the analytical tool which was used was the RevMan software version 5.4. Risk ratios (RR) and 95% confidence intervals (CIs) were used to represent the data post analysis. RESULTS: In critically ill COVID-19 patients with NOAF admitted to the ICU, the risks of ICU mortality (RR: 1.39, 95% CI: 1.07 - 1.80; P = 0.01), in-hospital mortality (RR: 1.56, 95% CI: 1.20 - 2.04; P = 0.001), patients requiring mechanical ventilation (RR: 1.32, 95% CI: 1.04 - 1.66; P = 0.02) were significantly higher when compared to the control group without AF. Acute myocardial infarction (RR: 1.54, 95% CI: 1.31 - 1.81; P = 0.00001), the risk for acute kidney injury (RR: 1.31, 95% CI: 1.11 - 1.55; P = 0.002) and patients requiring renal replacement therapy (RR: 1.83, 95% CI: 1.60 - 2.09; P = 0.00001) were also significantly higher in patients with NOAF. CONCLUSIONS: Critically ill COVID-19 patients with NOAF admitted to the ICU were at significantly higher risks of developing complications and death compared to similar patients without AF.

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

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

Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.

BACKGROUND: The successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future. METHODS: In this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients. RESULTS: We noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects. CONCLUSIONS: This study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an in vivo RS method to detect and monitor AKI effectively.

Association between platelet count and neonatal acute kidney injury: a cohort study using the medical information mart for intensive care III database.

OBJECTIVE: A decrease in platelet count has been reported to be associated with several neonatal inflammatory diseases, including sepsis and necrotizing enterocolitis; while its association with neonatal acute kidney injury (AKI) has not been reported. This study aims to explore the association between platelet count and neonatal AKI. METHODS: This was a retrospective cohort study based on the Medical Information Mart for Intensive Care III (MIMIC-III) database. Data were extracted based on baseline characteristics, comorbidities, vital signs, laboratory parameters, and intervention measures. Logistic regression analysis was used to assess the association between platelet count and AKI, and results were shown as odds ratios (OR) with 95% confidence intervals (CI). RESULTS: A total of 1,576 neonates were finally included in the analysis. After adjusting birth weight, sepsis, patent ductus arteriosus, hematocrit, percentage of neutrophils, and vasopressor use, we found that platelet count in the lowest quartile (Q1) was significantly associated with the higher odds of AKI than platelet count in the highest quartile (Q4) (OR = 1.70, 95% CI: 1.01-2.87). CONCLUSIONS: Low platelet count was associated with the high odds of AKI in the neonatal intensive care unit (NICU), indicating that platelet count might be a biomarker for neonatal AKI. Large-scale multicenter studies should be performed to verify the results.

Association between the triglyceride glucose index and the risk of acute kidney injury in critically ill patients with hypertension: analysis of the MIMIC-IV database.

Background: The triglyceride glucose (TyG) index, a metric computed from the levels of fasting triglyceride (TG) and fasting plasma glucose (FPG), has emerged as a simple surrogate measure for insulin resistance (IR) in recent years. In multiple critical care scenarios, such as contrast-induced acute kidney injury (AKI) and cardiorenal syndrome, a high TyG index levels shows a notable correlation with AKI incidence. However, its predictive value for AKI in critically ill hypertensive patients remains uncertain. Methods: Participants were selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and divided into quartiles based on the TyG index. The primary focus of the study was to investigate the risk of acute kidney injury (AKI), with in-hospital mortality as a secondary endpoint, assessed among all study subjects as well as specifically among AKI patients. The use of renal replacement therapy (RRT), indicative of AKI progression, was also considered a secondary endpoint reflecting renal outcomes. To explore the correlation between the TyG index and AKI risk in critically ill hypertensive patients, the study employed a restricted cubic splines model and Cox proportional hazards (CPH) models. Additionally, Kaplan-Meier survival analysis was utilized to assess differences in primary and secondary outcomes across groups categorized by their TyG index. Analyses were conducted to ensure the consistency of the predictive capability of TyG index across various subgroups. Results: Our study included 4,418 participants, with 57% being male patients. AKI occurred in 56.1% of cases. Through the CPH analysis, we identified a significant association between the TyG index and AKI occurrence in critically ill hypertensive patients. With the help of a restricted cubic splines model, we observed a direct relationship between an elevated TyG index and an increased AKI. Subgroup examinations consistently proved the predictive value of the TyG index across categories. Furthermore, Kaplan-Meier survival analysis revealed notable differences in RRT among AKI patients. Conclusion: The findings underscore the importance of the TyG index as a reliable predictor for the occurrence of AKI and adverse renal outcomes among hypertensive patients in critical ill states. Nevertheless, validating causality mandates extensive prospective investigations.

Prognostic significance of mean platelet volume to platelet count ratio in pediatric patients with acute kidney injury.

BACKGROUND: Mean platelet volume (MPV), which is regarded as a marker of thrombocyte function and activation, is related to increased morbidity and mortality. In critically ill patients, the ratio of MPV to platelets can independently predict adverse outcomes. This study aimed to investigate the prognostic value of the mean platelet volume/platelet count ratio (MPR) for mortality in children with acute kidney injury (AKI). METHODS: In this retrospective study, patients hospitalized in the pediatric intensive care unit (PICU) between March 2020 and June 2022 were evaluated. Patients between 1 month and 18 years of age with AKI were enrolled. Clinical and laboratory data were compared between survivors and non-survivors. The MPR ratio was calculated on the first and third days of admission to the intensive care unit. A multiple logistic regression analysis was used to determine the association between MPR and mortality. ROC curves were used for the prediction performance of the logistic regression models and cut-off values of the thrombocyte indices. RESULTS: Sixty-three children with AKI were included in the study. The total mortality rate was 34.9% (n=22). MPR ratios were significantly higher in the non-survivors at admission (p=0.042) and at the 72nd hour (p=0.003). In the multiple logistic regression analysis, thrombocyte counts and MPR72h ratio were found to be independent risk parameters for adverse outcomes in children with AKI. CONCLUSIONS: MPR is an inexpensive and practical marker that may predict the outcome of children with AKI.

Upconverting Nanoparticle-based Enhanced Luminescence Lateral-Flow Assay for Urinary Biomarker Monitoring.

Development of efficient portable sensors for accurately detecting biomarkers is crucial for early disease diagnosis, yet remains a significant challenge. To address this need, we introduce the enhanced luminescence lateral-flow assay, which leverages highly luminescent upconverting nanoparticles (UCNPs) alongside a portable reader and a smartphone app. The sensor's efficiency and versatility were shown for kidney health monitoring as a proof of concept. We engineered Er3+- and Tm3+-doped UCNPs coated with multiple layers, including an undoped inert matrix shell, a mesoporous silica shell, and an outer layer of gold (UCNP@mSiO2@Au). These coatings synergistically enhance emission by over 40-fold and facilitate biomolecule conjugation, rendering UCNP@mSiO2@Au easy to use and suitable for a broad range of bioapplications. Employing these optimized nanoparticles in lateral-flow assays, we successfully detected two acute kidney injury-related biomarkers─kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL)─in urine samples. Using our sensor platform, KIM-1 and NGAL can be accurately detected and quantified within the range of 0.1 to 20 ng/mL, boasting impressively low limits of detection at 0.28 and 0.23 ng/mL, respectively. Validating our approach, we analyzed clinical urine samples, achieving biomarker concentrations that closely correlated with results obtained via ELISA. Importantly, our system enables biomarker quantification in less than 15 min, underscoring the performance of our novel UCNP-based approach and its potential as reliable, rapid, and user-friendly diagnostics.