Development and validation of a risk prediction model for acute kidney injury in patients with malignant obstructive jaundice: a retrospective matched cohort study

BackgroundTo construct and validate a risk assessment model for acute kidney injury (AKI) in patients with acute pancreatitis (AP) in the intensive care unit (ICU).MethodsA total of 963 patients diagnosed with acute pancreatitis (AP) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database was included. These patients were randomly divided into training set (N = 674) and validation set (N = 289) at a ratio of 7:3. Clinical characteristics were utilized to establish a nomogram for the prediction of AKI during ICU stay. These variables were selected by the least absolute shrinkage and selection operation (LASSO) regression and included in multivariate logistic regression analysis. Variables with P-values less than 0.05 were included in the final model. A nomogram was constructed based on the final model. The predicted accuracy of the nomogram was assessed by calculating the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Moreover, calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) were performed to evaluate model performance. Decision curve analysis (DCA) evaluated the clinical net benefit of the model.ResultsA multivariable model that included 6 variables: weight, SOFA score, white blood cell count, albumin, chronic heart failure, and sepsis. The C-index of the nomogram was 0.82, and the area under the receiver operating characteristic curve (AUC) of the training set and validation set were 0.82 (95% confidence interval:0.79–0.86) and 0.76 (95% confidence interval: 0.70–0.82), respectively. Calibration plots showed good consistency between predicted and observed outcomes in both the training and validation sets. DCA confirmed the clinical value of the model and its good impact on actual decision-making.ConclusionWe identified risk factors associated with the development of AKI in patients with AP. A risk prediction model for AKI in ICU patients with AP was constructed, and improving the treatment strategy of relevant factors in the model can reduce the risk of AKI in AP patients.

A model for acute kidney injury in severe burn patients

Objective Identifying patients at high risk for cardiac arrest-associated acute kidney injury (CA-AKI) helps in early preventive interventions. This study aimed to establish and validate a high-risk nomogram for CA-AKI. Methods In this retrospective dataset, 339 patients after cardiac arrest (CA) were enrolled and randomized into a training or testing dataset. The Student’s t-test, non-parametric Mann-Whitney U test, or χ2 test was used to compare differences between the two groups. Optimal predictors of CA-AKI were determined using the Least Absolute Shrinkage and Selection Operator (LASSO). A nomogram was developed to predict the early onset of CA-AKI. The performance of the nomogram was assessed using metrics such as area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC). Results In total, 150 patients (44.2%) were diagnosed with CA-AKI. Four independent risk predictors were identified and integrated into the nomogram: chronic kidney disease, albumin level, shock, and heart rate. Receiver operating characteristic (ROC) analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset 0.774 (95%CI, 0.715–0.833) and testing dataset 0.763 (95%CI, 0.670–0.856). The AUC values for the two groups were calculated and compared using the Hanley-McNeil test. No statistically significant differences were observed between the groups. The calibration curve demonstrated good agreement between the predicted outcome and actual observations. Good clinical usefulness was identified using DCA and CIC. Conclusion An easy-to-use nomogram for predicting CA-AKI was established and validated, and the prediction efficiency of the clinical model has reasonable clinical practicability.

BackgroundThere are significant differences in the incidence and risk factors of tumor patients, and there is no relevant statistical data. Therefore, this study aims to clarify the incidence and risk factors of acute kidney injury (AKI) in malignant tumor patients and compare critically ill patients with non-critically ill patients.MethodsRelevant literature on the occurrence of AKI in malignant tumors was retrieved from databases. Two authors independently screened and evaluated the eligibility and quality of the literature and extracted the data. The Stata 12.0 software was used for meta-analysis.ResultsA total of 3922 articles were initially retrieved, and 24 articles were finally included, 8 of which were about critically ill malignant tumor patients, and 16 were about malignant tumor patients. Among the 4107 patients included in the 8 studies on critically ill malignant tumors, 1932 developed AKI, with an incidence rate of 52% (95%CI 34–70%, I2 = 99%). The risk factors for AKI in critically ill malignant tumor patients were sepsis and hypovolemia, which were different from those in non-critically ill patients. Among the 292,874 patients included in the 16 studies on malignant tumors, 51,211 developed AKI, and the combined incidence rate was 24% (95%CI 17–30%, I2 = 100%). The risk factors for AKI in critical malignant tumor patients were sepsis and hypovolemia.ConclusionThis meta-analysis shows that the incidence of AKI in critically ill malignant tumor patients is consistent with that in other critically ill patients, and independent risk factors are sepsis and hypovolemia. The incidence of AKI in malignant tumor patients is higher than that in other patients, and tumor is a risk factor for AKI. This study has been registered in INPLASY (INPLASY202320079),Registered February 18,2023.

BackgroundThis 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.MethodsData 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.ResultsMultivariate 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.ConclusionPost-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.

Objective Acute upper gastrointestinal bleeding (AUGIB) is one of the most common critical conditions in clinical practice and is characterized by rapid progression and a high incidence of secondary acute kidney injury (AKI). This study aimed to analyze the clinical characteristics of patients with AUGIB, identify related risk factors for secondary AKI, and develop a predictive model for AKI risk in this patient population. Methods A retrospective analysis was conducted on 300 patients with AUGIB admitted to our department. Patients were categorized based on the occurrence of AKI within 7 days. Univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used for feature selection, followed by multivariable logistic regression to construct a predictive model. The model’s performance was rigorously evaluated through bootstrap internal validation, calibration curves, and decision curve analysis (DCA). Results Seven independent risk factors were identified and incorporated into the SCORE-AKI: a history of renal insufficiency, hypertension, shock index > 1, Glasgow Coma Scale score ≤14, hemoglobin < 60 g/L, platelet count < 50 × 109/L, and serum creatinine > 103 μmol/L. The model showed strong discrimination with a bootstrap-corrected area under the curve (AUC) of 0.808, which was significantly superior to the Glasgow Blatchford score (AUC: 0.722, p < 0.001). The model also demonstrated excellent calibration and a positive net benefit across clinical decision thresholds. Conclusion The SCORE-AKI model is a accurate, well-calibrated, and clinically useful tool that outperforms the GBS for predicting secondary AKI risk in patients with AUGIB, potentially aiding in early risk stratification and preventive intervention.

Background: Acute Kidney Injury (AKI) is a common complication in patients admitted to Intensive Care Units (ICUs), which can lead to increased length of stay, treatment costs, and patient mortality. Specific preventive and therapeutic methods, such as appropriate fluid therapy and reducing the use of nephrotoxic drugs, can help alleviate acute kidney injury. This study aims to investigate the incidence of acute kidney injury in ICU-admitted patients. Methods and Material: This is a cross-sectional study conducted in the ICU of Rasoul Akram Hospital. Patients admitted to this unit were enrolled in the study after obtaining informed consent. Exclusion criteria included the presence of known kidney disease at admission (such as a history of dialysis, hematuria, kidney failure, etc.) and oliguria or creatinine levels above 1.5. Upon entering the study, a checklist was prepared for each patient, including demographic variables (age, gender, body mass index) and relevant medical history (history of kidney disease, hypertension, diabetes), as well as laboratory tests for kidney function. Results: The collected data were analyzed to determine the incidence of acute kidney injury in these patients and to examine its association with demographic and clinical variables. The average age of the 100 participating patients was 65.8 years ± 13.8. Of these, 79% were discharged, and 21% died. The most common reasons for hospitalization included sepsis and decreased consciousness levels. Additionally, 50% of the patients had hypertension, and 40% were diabetic. In-hospital mortality was significantly higher in patients with AKI (p=0.002). The incidence of a history of colicky pain and underlying conditions such as diabetes and hypertension was also considerably higher in patients with AKI. However, no significant difference was observed in the incidence of organ failure. In the group with a history of diabetes, the incidence of AKI was 50%, while in patients without a history of diabetes, this rate was lower at 25%. Conclusion: The study highlighted a high incidence of AKI in the ICU of Rasoul Akram Hospital (35%) and the increased mortality rate in these patients. Therefore, prevention, rapid diagnosis, and treatment methods for acute kidney injury are crucial. Identifying risk factors and implementing appropriate strategies can help reduce complications and improve clinical outcomes in patients hospitalized in the ICU.

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.

To explore the influencing factors of acute kidney injury in elderly patients with diabetic nephropathy and to construct a nomogram model. The research subjects were 680 patients with type 2 diabetic nephropathy admitted to our hospital. The patients were included from May 2018 to August 2023. Patients with acute kidney injury were used as the merge group (n=50), and patients without unmerge group (n=630) was included. The prevalence and predisposing factors of acute kidney injury in diabetic nephropathy were analyzed, multivariate logistic regression were used to analyze the influencing factors of acute kidney injury in patients, and a nomogram risk prediction model was established based on risk factors for verification. Analysis of the factors of acute kidney injury in diabetic nephropathy found that severe infection was the main trigger, accounting for 40.00%, followed by nephrotoxic antibiotics and severe heart failure. The age, urine microalbumin-to-creatinine ratio (ACR), blood urea nitrogen (BUN), uric acid(UA), and cystatin C (CysC) levels of patients in the combined acute kidney injury group were significantly higher than those in the unmerge group (P<0.05), and the left ventricular ejection fraction (LVEF) and epidermal growth factor receptor (eGFR) levels were significantly lower than those in the unmerge group (P<0.05). Age, ACR, and CysC levels are independent risk factors for acute kidney injury in diabetic nephropathy, and LVEF and eGFR are independent protective factors (P<0.05). The C-index of the nomogram risk prediction model in predicting acute kidney injury in diabetic nephropathy is 0.768 (95% CI: 0.663-0.806), and the calibration curve tends to the ideal curve; the prediction threshold is >0.18, and the nomogram risk prediction model provides a clinical net benefits, and clinical net benefits were higher than independent predictors. The establishment of a nomogram model for acute kidney injury in elderly patients with diabetic nephropathy based on age, ACR, CysC, LVEF, and eGFR has a good predictive effect, which can help doctors more accurately assess the patient's condition and provide a basis for formulating personalized treatment plans.

Deterioration of renal function after exposition to contrast media is a common problem in patients with myocardial infarction undergoing percutaneous coronary interventions. The aim of the present study was to assess the incidence of acute kidney injury in patients admitted with ST-elevation-myocardial infarction (STEMI) and its association with infarction severity, comorbidities and treatment modalities, including amount of contrast media applied. All patients with STEMI from the metropolitan area of Bremen, Germany are treated at the Bremen Heart Centre and since 2006 documented in the Bremen STEMI-Registry. Acute kidney injury was graded from stage 0 to 3 following the Kidney-disease-improving-global outcomes criteria from 2012. Data from 3810 patients admitted with STEMI were included in this study. No acute kidney injury was observed in 3120 (82%) patients while acute kidney injury was detected in 690 (18%) patients: Stage 1: n=497 (13%), 2: n=66 (2%), 3: n=127 (3%). Acute kidney injury was associated with elevated 30-day (0: 3%, 1: 20%, 2: 46%, 3: 58%) and one-year mortality rates (0: 6%, 1: 26%, 2: 49%, 3: 66%). Higher acute kidney injury stages were associated with higher peak creatine kinase (in U/l±SEM): stage 0: 1748±33, 1: 2588±127, 2: 3684±395, 3: 3330±399, p (<0.01), lower mean systolic blood pressure at admission (in mmHG±SD): 0: 133±28, 1: 129±31; 2: 121±31, 3: 115±33 ( p<0.01) and higher Thrombolysis in Myocardial Infarction risk score for STEMI (scale 0-14±SD): 0: 2.71±2, 1: 4.08±2, 2: 4.98±2, 3: 5.05±2, ( p<0.01). However, no such association could be found between acute kidney injury stage and amount of contrast media applied (in ml±SD) 0: 138±57, 1: 139±61; 2: 140±76; 3: 145±80 ( p=0.5). Reduced initial glomerular filtration rate was associated with higher incidences of acute kidney injury while again no relation to amount of contrast media could be observed in subgroups ranked by initial glomerular filtration rate. A multivariate analysis confirmed these results: while left-heart-failure/cardiogenic shock (odds ratio (OR) 4.2, 95% confidence interval (CI) 3.3-5.5) as well as larger infarctions (peak creatine kinase >3000 U/l (OR 2.2, 95% CI 1.7-2.8)) were independently associated with a greater risk for acute kidney injury, amount of contrast media applied during angiography was not (150-250 ml, OR 0.95, 95% CI 0.8-1.2 ( p=0.7), >250 ml, OR 1.3, 95% CI 0.8-2.0 ( p=0.5)). Acute kidney injury, which was associated with elevated short- and long-term mortality rates, could be observed in 18% of patients admitted with STEMI. The present data suggest that severity and haemodynamic impairment due to STEMI rather than contrast-media-induced nephropathy is the key contributor for acute kidney injury in STEMI patients. The deleterious effect of the myocardial infarction itself on renal function can be explained through renal hypoperfusion, neurohormonal activation or other pathomechanisms that might have been underestimated in the past.

This study aims to evaluate the value of the platelet-to-albumin ratio (PAR) in predicting acute kidney injury (AKI) in patients with decompensated cirrhosis. A descriptive cross-sectional analysis was conducted at multiple centers on 295 patients with decompensated cirrhosis, treated at the Department of Gastroenterology - Can Tho Central General Hospital and the Department of Gastroenterology - Bac Lieu General Hospital from June 2019 to May 2021. The results showed that the average age of the study subjects was 60.0 ± 12.5 years old, with a male/female ratio of 3/2. The average albumin level was relatively low, at 27.18 ± 6.29 g/L. The median platelet count was 73 x 109/L. The median platelet-to-albumin ratio was 2.99. The incidence of acute kidney injury in patients with decompensated cirrhosis was 33.9%. At a cut-off point of the serum platelet-to-albumin ratio ≥ 3.64, the predictive value for the incidence of acute kidney injury in patients with decompensated cirrhosis was recorded with an area under the ROC curve (AUC) of 96.7% (95%CI: 95% - 98%).

BackgroundThe triglyceride glucose (TyG) index is a novel and reliable alternative marker for insulin resistance. Previous studies have shown that TyG index is closely associated with cardiovascular outcomes in cardiovascular diseases and coronary revascularization. However, the relationship between TyG index and renal outcomes of coronary revascularization is unclear. The purpose of this study was to investigate the correlation between TyG index and the risk of acute kidney injury (AKI) in patients with coronary revascularization.MethodsA retrospective cohort study was conducted to select eligible patients with coronary revascularization admitted to ICU in the medical information mart for intensive care IV (MIMIC-IV). According to the TyG index quartile, these patients were divided into four groups (Q1-Q4). The primary endpoint was the incidence of AKI, and secondary endpoints included 28-day mortality and the rate of renal replacement therapy (RRT) use in the AKI population. Multivariate Cox regression analysis and restricted cubic splines (RCS) were used to analyze TyG index association with AKI risk. Kaplan-Meier survival analysis was performed to assess the incidence of endpoints in the four groups.ResultsIn this study, 790 patients who underwent coronary revascularization surgery were included, and the incidence of AKI was 30.13%. Kaplan-Meier analysis showed that patients with a high TyG index had a significantly increased incidence of AKI (Log-rank P = 0.0045). Multivariate Cox regression analysis showed that whether TyG index was a continuous variable (HR 1.42, 95% CI 1.06–1.92, P = 0.018) or a categorical variable (Q4: HR 1.89, 95% CI 1.12–3.17, P = 0.017), and there was an independent association between TyG index and AKI in patients with coronary revascularization. The RCS curve showed a linear relationship between higher TyG index and AKI in this particular population (P = 0.078). In addition, Kaplan-Meier analysis showed a significantly increased risk of RRT application in a subset of AKI patients based on quartiles of TyG index (P = 0.029).ConclusionTyG index was significantly associated with increased risk of AKI and adverse renal outcomes in patients with coronary revascularization. This finding suggests that the TyG index may be useful in identifying people at high risk for AKI and poor renal outcomes in patients with coronary revascularization.

To evaluate the incidence, risk factors, and outcomes of acute kidney injury (AKI) in patients with decompensated cirrhosis based on the Kidney Disease: Improving Global Outcomes Clinical Practice Guideline. For this retrospective analysis, 923 inpatients were recruited between January 2013 and December 2017. The patients' baseline demographics and clinical information were collected and analyzed. Univariate and multiple logistic regression analyses were conducted to determine the independent risk factors for AKI and in-hospital mortality. Kaplan-Meier survival analyses were used to analyze the between-group differences in mortality. Of the 923 patients, 262 (28.39%) developed AKI. According to the multivariate analysis, an age ≥65 years (odds ratio [OR]: 1.776, 95% confidence interval [CI]: 1.288-2.449, p < 0.001), infection (OR: 1.386, 95% CI: 1.024-1.875, p = 0.034), hypotension (OR: 1.709, 95% CI: 1.091-2.679, p = 0.019), white blood cell count >10 × 109 /L (OR: 4.054, 95% CI: 2.006-8.193, p < 0.001), albumin concentration <35 g/L (OR: 1.931, 95% CI: 1.392-2.680, p < 0.001), baseline serum creatinine concentration >88.4 µmol/L (OR: 2.136, 95% CI: 1.511-3.021, p < 0.001), estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 (OR: 2.384, 95% CI: 1.372-4.145, p = 0.002), and serum sodium concentration <135 mmol/L (OR: 1.686, 95% CI: 1.155-2.459, p = 0.007) were independent risk factors for AKI. Moreover, AKI was significantly associated with in-hospital mortality (OR: 6.934, 95% CI: 1.333-11.052, p = 0.021). Kaplan-Meier survival analysis confirmed that patients with AKI had higher in-hospital mortality than those without AKI. The incidence of AKI was high among patients with decompensated cirrhosis. Infection, an elevated baseline serum creatinine concentration, and decreased eGFR were independent risk factors for both AKI and in-hospital mortality. AKI was an independent risk factor for in-hospital mortality. Based on the risk factors identified, AKI prediction models and treatment approaches care bundles can be used for the early identification and modification of potential predisposing factors and for improving outcomes in these patients in the future.

To examine the performance of the urinary biomarker panel tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 in patients with sepsis at ICU admission. To investigate the effect of nonrenal organ dysfunction on tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 in this population. In this ancillary analysis, we included patients with sepsis who were enrolled in either of two trials including 39 ICUs across Europe and North America. The primary endpoint was moderate-severe acute kidney injury (equivalent to Kidney Disease Improving Global Outcome stage 2-3) within 12 hours of enrollment. We assessed biomarker performance by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, and negative and positive predictive values at three cutoffs: 0.3, 1.0, and 2.0 (ng/mL)/1,000. We also calculated nonrenal Sequential Organ Failure Assessment scores for each patient on enrollment and compared tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 results in patients with and without acute kidney injury and across nonrenal Sequential Organ Failure Assessment scores. Finally, we constructed a clinical model for acute kidney injury in this population and compared the performance of the model with and without tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7. We included 232 patients in the analysis and 40 (17%) developed acute kidney injury. We observed significantly higher urine tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 in patients with acute kidney injury than without acute kidney injury in both patients with low and high nonrenal Sequential Organ Failure Assessment scores (p < 0.001). The area under the receiver operating characteristic curve (95% CI) of tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 was 0.84 (0.73-0.92) and 0.85 (0.76-0.94), in low and high nonrenal Sequential Organ Failure Assessment score subgroups. Performance of the tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 test was not modified by nonrenal Sequential Organ Failure Assessment (p = 0.70). In multivariate analysis, the addition of tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 significantly improved the performance of a clinical model for predicting acute kidney injury (p = 0.015). Urinary tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 accurately predicts acute kidney injury in septic patients with or without other organ failures.

Acute Kidney Injury in Alcohol-Associated Hepatitis: More than a Bystander