Acute kidney injury after nephrectomy: a new nomogram to predict postoperative renal function

Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients

Introduction

Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients.

Methods

We retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method.

Results

A total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models.

Discussion

The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

Hospitalized patients received furosemide undergoing acute kidney injury: the risk and prediction tool

PURPOSE

Furosemide, a frequently prescribed diuretic for managing congestive heart failure and edema, remains a topic of debate regarding its potential risk of inducing acute kidney injury (AKI) in patients. Consequently, this study aims to examine the occurrence of hospital-acquired AKI (HA-AKI) in hospitalized patients who are administered furosemide and to investigate potential risk factors associated with this outcome.

METHODS

This study encompassed a cohort of 22374 hospitalized patients who either received furosemide treatment or not from June 1, 2012, to December 31, 2017. Propensity score matching was employed to establish comparability between the two groups regarding covariates. Subsequently, a nomogram was constructed to predict the probability of AKI occurrence among patients who underwent furosemide treatment.

RESULTS

The regression analysis identified the single-day total dose of furosemide as the most significant factor for AKI, followed by ICU administration, estimated glomerular filtration rate, antibiotic, statin, NSAIDs, β-blockers, proton pump inhibitor, chronic kidney disease, and 7 other indicators. Subgroup analysis revealed a synergistic effect of furosemide with surgical operation, previous treatment with β-blockers, ACEI/ARB and antibiotics, leading to an increased risk of AKI when used in combination. Subsequently, a visually represented prognostic nomogram was developed to predict AKI occurrence in furosemide users. The predictive accuracy of the nomogram was assessed through calibration analyses, demonstrating an excellent agreement between the nomogram predictions and the actual likelihood of AKI, with a probability of 77.40%.

CONCLUSIONS

Careful consideration of factors such as dosage, concurrent medication use, and renal function of the patient is necessary for clinical practice when using furosemide. Our practical prognostic model for HA-AKI associated with furosemide use can be utilized to assist clinicians in making informed decisions about patient care and treatment.

Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy

Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R2 = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed.

Safety of Nephrectomy Performed During CRS/HIPEC: A Propensity Score-Matched Study

BACKGROUND

Surgeons may hesitate to perform nephrectomy (NE) during cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) due to a potential increase in morbidity. However, no data are available regarding the impact of NE on outcomes, so the authors decided to assess its safety during CRS/HIPEC.

METHODS

A single-center propensity score-matched study was conducted using a prospective database (1994-2021). The study included patients who underwent NE during CRS/HIPEC with completeness of cytoreduction (CC) of 0, 1, or 2. Control subjects (no-NE) were selected in a 1:3 ratio using propensity score-matching weighted by age, histology, peritoneal cancer index (PCI), CC-0 or CC-1 rate, and length of surgery.

RESULTS

Among 828 patients, 13 NE and 39 no-NE control subjects were identified. The indications for NE included tumor involvement of the ureter, hilum, and/or kidney with preserved (n = 8), decreased (n = 2), or absent (n = 3) function. NE patients received more intraoperative intravenous (IV) fluids (16,000 vs 11,500 mL; p = 0.045) and had a greater urine output (3200 vs 1913 mL; p = 0.008). NE patients received mitomycin C (40 mg for 90 min) or melphalan (50 mg/m² for 90 min) without reduction of dose or time. Major morbidity (p = 0.435) and mortality (p = 1.000) were comparable between the two groups. No postoperative acute kidney injury was seen in either group. Adjuvant chemotherapy was administered to 46.2% of the NE and 35.9% of the no-NE patients (p = 0.553), with similar starting times (p = 0.903) between the groups.

CONCLUSIONS

Nephrectomy performed during CRS/HIPEC does not seem to increase postoperative morbidity or to delay adjuvant chemotherapy, and NE can be performed if required for complete cytoreduction. The NE patients in our cohort did not have a reduction of mitomycin C or melphalan dose or perfusion time.

Development and validation of short-term renal prognosis prediction model in diabetic patients with acute kidney injury

OBJECTIVE

Diabetes is a major cause of the progression of acute kidney injury (AKI). Few prediction models have been developed to predict the renal prognosis in diabetic patients with AKI so far. The aim of this study was to develop and validate a predictive model to identify high-risk individuals with non-recovery of renal function at 90 days in diabetic patients with AKI.

METHODS

Demographic data and related laboratory indicators of diabetic patients with AKI in the First Affiliated Hospital of Guangxi Medical University from January 31, 2012 to January 31, 2022 were retrospectively analysed, and patients were followed up to 90 days after AKI diagnosis. Based on the results of Logistic regression, a model predicting the risk of non-recovery of renal function at 90 days in diabetic patients with AKI was developed and internal validated. Consistency index (C-index), calibration curve, and decision curve analysis were used to evaluate the differentiation, accuracy, and clinical utility of the prediction model, respectively.

RESULTS

A total of 916 diabetic patients with AKI were enrolled, with a male to female ratio of 2.14:1. The rate of non-recovery of renal function at 90 days was 66.8% (612/916). There were 641 in development cohort and 275 in validation cohort (ration of 7:3). In the development cohort, a prediction model was developed based on the results of Logistic regression analysis. The variables included in the model were: diabetes duration (OR = 1.022, 95% CI  1.012-1.032), hypertension (OR = 1.574, 95% CI 1.043-2.377), chronic kidney disease (OR = 2.241, 95% CI 1.399-3.591), platelet (OR = 0.997, 95% CI 0.995-1.000), 25-hydroxyvitamin D3 (OR = 0.966, 95% CI  0.956-0.976), postprandial blood glucose (OR = 1.104, 95% CI 1.032-1.181), discharged serum creatinine (OR = 1.003, 95% CI 1.001-1.005). The C-indices of the prediction model were 0.807 (95% CI 0.738-0.875) and 0.803 (95% CI 0.713-0.893) in the development and validation cohorts, respectively. The calibration curves were all close to the straight line with slope 1. The decision curve analysis showed that in a wide range of threshold probabilities.

CONCLUSION

A prediction model was developed to help predict short-term renal prognosis of diabetic patients with AKI, which has been verified to have good differentiation, calibration degree and clinical practicability.

The incidence and risk factors of chronic kidney disease after radical nephrectomy in patients with renal cell carcinoma

Background

Radical nephrectomy (RN) is the standard treatment for localized renal cell carcinoma. The decrease in nephrons from RN could lead to postoperative chronic kidney disease (CKD). In this study, we aim to investigate the incidence and risk factors for CKD in patients who have received RN.

Methods

A Total of 1233 patients underwent radical nephrectomy in Zhejiang Provincial People’s Hospital from January 2010 to December 2018. Those who had an abnormal renal function before surgery or were lost to follow-up were excluded. Five hundred patients were enrolled in the end. eGFR was calculated using the abbreviated MDRD equation. CKD was defined as eGFR less than 60 ml/min/1.73m². The incidence of postoperative CKD was estimated using the Kaplan-Meier method. The independent risk factors for CKD occurrence were determined through logistic multivariate regression analysis.

Results

Patients were followed up for a median of 40 month (3–96 months), with CKD occurring in 189 cases. The 5-year cumulative incidence of CKD was 43.4%. There was a significant difference between these189 patients and the remaining patients without post nephrectomy CKD in terms of age, sex, weight, and preoperative eGFR(P<0.05). Multivariate regression analysis showed that age (OR = 1.038, 95%CI = 1.002–1.076), preoperative eGFR of the contralateral kidney (OR = 0.934, 95%CI = 0.884–0.988) and Immediate postoperative eGFR (OR = 0.892, 95%CI = 0.854–0.931) were independent risk factors for postoperative CKD.

Conclusions

The incidence of CKD after radical nephrectomy was not uncommon. Age, preoperative eGFR of the contralateral kidney and Immediate postoperative eGFR are independent risk factors for postoperative CKD.

Prediction of significant renal function decline after open, laparoscopic, and robotic partial nephrectomy: External validation of the Martini's nomogram on the RECORD2 project cohort

OBJECTIVES

Martini et al. developed a nomogram to predict significant (>25%) renal function loss after robot-assisted partial nephrectomy and identified four risk categories. We aimed to externally validate Martini's nomogram on a large, national, multi-institutional data set including open, laparoscopic, and robot-assisted partial nephrectomy.

METHODS

Data of 2584 patients treated with partial nephrectomy for renal masses at 26 urological Italian centers (RECORD2 project) were collected. Renal function was assessed at baseline, on third postoperative day, and then at 6, 12, 24, and 48 months postoperatively. Multivariable models accounting for variables included in the Martini's nomogram were applied to each approach predicting renal function loss at all the specific timeframes.

RESULTS

Multivariable models showed high area under the curve for robot-assisted partial nephrectomy at 6- and 12-month (87.3% and 83.6%) and for laparoscopic partial nephrectomy (83.2% and 75.4%), whereas area under the curves were lower in open partial nephrectomy (78.4% and 75.2%). The predictive ability of the model decreased in all the surgical approaches at 48 months from surgery. Each Martini risk group showed an increasing percentage of patients developing a significant renal function reduction in the open, laparoscopic and robot-assisted partial nephrectomy group, as well as an increased probability to develop a significant estimated glomerular filtration rate reduction in the considered time cutoffs, although the predictive ability of the classes was <70% at 48 months of follow-up.

CONCLUSIONS

Martini's nomogram is a valid tool for predicting the decline in renal function at 6 and 12 months after robot-assisted partial nephrectomy and laparoscopic partial nephrectomy, whereas it showed a lower performance at longer follow-up and in patients treated with open approach at all these time cutoffs.

Blood Urea Nitrogen to Serum Albumin Ratio in the Prediction of Acute Kidney Injury of Patients with Rib Fracture in Intensive Care Unit

Background

We hypothesized that the blood urea nitrogen (BUN) to serum albumin ratio (BAR) could serve as an independent predictor for incident acute kidney injury (AKI) in intensive care unit (ICU) patients with rib fracture.

Methods

Rib fracture patients in ICU were extracted from Medical Information Mart for Intensive Care IV (MIMIC-IV v1.0) database. The primary outcome in this study was the incidence of AKI. Univariate and multivariate logistic regression analyses were used to determine the relationship between BAR and AKI and propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were also applied to assure the robustness of our results.

Results

The optimal cut-off value for BAR was 5.26 based on receiver operator characteristic curve. Among the 953 patients who diagnosed with rib fracture, 197 high-BAR group (≥5.26) patients and 197 low-BAR group (<5.26) patients who had similar propensity scores were finally included in the matched cohort. High-BAR group patients had a significantly higher incidence of AKI (odds ratio, OR, 3.85, 95% confidence index, 95% CI, 2.58–5.79, P<0.001) in the original cohort, in the matched cohort (OR, 4.47, 95% CI 2.71–7.53, P<0.001), and in the weighted cohort (OR, 4.28, 95% CI 2.80–6.53, P<0.001). Furthermore, BAR was superior to that of acute physiology score III for predicting AKI and could add more net benefit for incident AKI in critical care patients with rib fracture.

Conclusion

As an easily access and cost-effective parameter, BAR could serve as a good diagnostic predictor for AKI in ICU patients with rib fracture.

Development and validation of a nomogram for predicting acute kidney injury after orthotopic liver transplantation

BACKGROUND

We aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT).

METHODS

A total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD).

RESULTS

Overall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients' BMI (OR 1.10, 95% CI 1.04-1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22-4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95-0.97, p < 0.001), and intraoperative fresh frozen plasm (FFP) transfusion (OR for each 1000 ml increase 1.34, 95% CI 1.03-1.75, p = 0.031) were independent risk factors for post-OLT severe AKI. They were all incorporated into the nomogram. The area under the ROC curve (AUC) was 0.73 (p < 0.05) and 0.81 (p < 0.05) in the development and validation cohort. The calibration curve demonstrated the predicted probabilities of severe AKI agreed with the observed probabilities (p > 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p < 0.01). The cumulative risk of CKD was higher in the severe AKI group than no severe AKI group after competitive risk analysis (HR 1.48, p < 0.05).

CONCLUSIONS

With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for severe AKI and poor long-term prognosis after OLT.

Development of a Nomogram Model for Predicting the Risk of In-Hospital Death in Patients with Acute Kidney Injury

OBJECTIVE

To analyze the risk factors of in-hospital death in patients with acute kidney injury (AKI) in the intensive care unit (ICU), and to develop a personalized risk prediction model.

METHODS

The clinical data of 137 AKI patients hospitalized in the ICU of Anhui provincial hospital from January 2018 to December 2020 were retrospectively analyzed. Patients were divided into two groups: those that survived to discharge ("survival" group, 100 cases) and those that died while in hospital ("death" group, 37 cases), and risk factors for in-hospital death analyzed.

RESULTS

The in-hospital mortality of AKI patients in the ICU was 27.01% (37/137). A multivariate logistic regression analysis indicated age, mechanical ventilation and vasoactive drugs were significant risk factors for in-hospital death in AKI patients, and a nomogram risk prediction model was developed. The Harrell's C-index of the nomogram model was 0.891 (95% CI: 0.837-0.945), and the area under the receiver operating characteristic (ROC) curve was 0.886 (95% CI: 0.823-0.936) after internal validation, indicating that the nomogram model had good discrimination. The Hosmer-Lemeshow goodness of fit test and calibration curve indicated the predicted probability of the nomogram model was consistent with the actual frequency of death in ICU patients with AKI. The decision curve analysis (DCA) showed that the clinical net benefit level of the nomogram model is highest when the probability threshold of AKI is between 0.01 and 0.75.

CONCLUSION

Patients in the ICU with AKI had high in-hospital mortality and were affected by a variety of risk factors. The nomogram prediction model based on the risk factors of AKI showed good prediction efficiency and clinical applicability, which could help medical staff in the ICU to identify AKI patients with high-risk, allowing early prevention, detection and intervention, and reducing the risk of in-hospital deaths in ICU patients with AKI.

Predicting the risk of postoperative acute kidney injury: development and assessment of a novel predictive nomogram

OBJECTIVE

This study aimed to establish and internally verify the risk nomogram of postoperative acute kidney injury (AKI) in patients with renal cell carcinoma.

METHODS

We retrospectively collected data from 559 patients with renal cell carcinoma from June 2016 to May 2019 and established a prediction model. Twenty-six clinical variables were examined by least absolute shrinkage and selection operator regression analysis, and variables related to postoperative AKI were determined. The prediction model was established by multiple logistic regression analysis. Decision curve analysis was conducted to evaluate the nomogram.

RESULTS

Independent predictors of postoperative AKI were smoking, hypertension, surgical time, blood glucose, blood uric acid, alanine aminotransferase, estimated glomerular filtration rate, and radical nephrectomy. The C index of the nomogram was 0.825 (0.790-0.860) and 0.814 was still obtained in the internal validation. The nomogram had better clinical benefit when the intervention was decided at the threshold probabilities of >4% and <79% for patients and doctors, respectively.

CONCLUSIONS

This novel postoperative AKI nomogram incorporating smoking, hypertension, the surgical time, blood glucose, blood uric acid, alanine aminotransferase, the estimated glomerular filtration rate, and radical nephrectomy is convenient for facilitating the individual postoperative risk prediction of AKI in patients with renal cell carcinoma.

Epidemiology and Outcomes of Acute Kidney Diseases: A Comparative Analysis

INTRODUCTION

Acute kidney diseases and disorders (AKD) encompass acute kidney injury (AKI) and subacute or persistent alterations in kidney function that occur after an initiating event. Unlike AKI, accurate estimates of the incidence and prognosis of AKD are not available and its clinical significance is uncertain.

METHODS

We studied the epidemiology and long-term outcome of AKD (as defined by the KDIGO criteria), with or without AKI, in a retrospective cohort of adults hospitalized at a single centre for >24 h between 2012 and 2016 who had a baseline eGFR ≥60 mL/min/1.73 m2 and were alive at 30 days. In patients for whom follow-up data were available, the risks of major adverse kidney events (MAKEs), CKD, kidney failure, and death were examined by Cox and competing risk regression analyses.

RESULTS

Among 62,977 patients, 906 (1%) had AKD with AKI and 485 (1%) had AKD without AKI. Follow-up data were available for 36,118 patients. In this cohort, compared to no kidney disease, AKD with AKI was associated with a higher risk of MAKEs (40.25 per 100 person-years; hazard ratio [HR] 2.51, 95% confidence interval [CI] 2.16-2.91), CKD (27.84 per 100 person-years); subhazard ratio [SHR] 3.18, 95% CI 2.60-3.89), kidney failure (0.56 per 100 person-years; SHR 24.84, 95% CI 5.93-104.03), and death (14.86 per 100 person-years; HR 1.52, 95% CI 1.20-1.92). Patients who had AKD without AKI also had a higher risk of MAKEs (36.21 per 100 person-years; HR 2.26, 95% CI 1.89-2.70), CKD (22.94 per 100 person-years; SHR 2.69, 95% CI 2.11-3.43), kidney failure (0.28 per 100 person-years; SHR 12.63, 95% CI 1.48-107.64), and death (14.86 per 100 person-years; HR 1.57, 95% CI 1.19-2.07). MAKEs after AKD were driven by CKD, especially in the first 3 months.

CONCLUSIONS

These findings establish the burden and poor prognosis of AKD and support prioritisation of clinical initiatives and research strategies to mitigate such risk.

The impact of visceral adipose tissue on post -operative renal Function after Radical Nephrectomy for renal cell carcinoma

BACKGROUND

The objective of this study was to evaluate the usefulness of pre-operative visceral (VAT) and subcutaneous adipose tissue (SAT) evaluation in the prediction of Acute Kidney Injury (AKI) and decrease of eGFR at 12 months after radical nephrectomy (RN).

METHODS

We relied on 112 patients who underwent RN between January 2010 and March 2017 at a single institution. Images from the pre-operatory CT scan were analyzed and both SAT and VAT assessments were carried out on a cross-sectional plane. eGFR was measured before surgery, at 7 days, and 12 months after surgery. ROC analysis was used to compare the diagnostic value of BMI, VAT ratio, and abdominal circumference in predicting AKI. Logistic regression models were fitted to predict the new onset of AKI, and the progression from chronic kidney disease (CKD) stage 1-3a to CKD stage 3b or from 3b to 4 at 12 months follow-up. Two logistic regression models were also performed to assess the predictors for AKI and CKD stage progression. The predictive accuracy was quantified using the receiver operating characteristic-derived area under the curve.

RESULTS

Sixty-six patients (58.9%) had AKI after RN. Thirty-five (31.3%) patients were upgraded to CKD IIIb or from CKD stage IIIb to CKD IV. In the ROC analysis, VAT% performed better than the BMI and abdominal circumference (AUC = 0.66 vs 0.49 and 0.54, respectively). At multivariable analyses, VAT reached an independent predictor status for AKI (OR: 1.03) and for CKD stage at 12 months Follow-up (OR: 1.05). Inclusion of VAT% into the multivariable models was associated with the highest accuracy both for AKI (AUC = 0.700 vs 0.570) and CKD stage progression (AUC = 0.848 vs 0.800).

CONCLUSIONS

In patients undergoing RN, preoperative visceral adipose tissue ratio significantly predicts AKI incidence and is significantly predictive of 12 months CKD stage worsening.

A Simple Nomogram to Predict Contrast-Induced Acute Kidney Injury in Patients with Congestive Heart Failure Undergoing Coronary Angiography

Background

Patients with congestive heart failure (CHF) are vulnerable to contrast-induced kidney injury (CI-AKI), but few prediction models are currently available. Therefore, we aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography.

Methods

A total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48–72 hours following the procedure. Predictors for the simple nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer–Lemeshow test and 1000 bootstrap samples.

Results

The incidence of CI-AKI was 9.06% (170) in the total sample, 8.64% (n = 109) in the development cohort, and 9.92% (n = 61) in the validation cohort (P=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction, and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs. 0.75, P=0.061), as well as a well-fitted calibration curve.

Conclusions

The present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed.