Risk factors of cardiac surgery-associated acute kidney injury: development and validation of a perioperative predictive nomogram

Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Pediatric Patients Undergoing Cardiac Surgery

Acute kidney injury (AKI) is a common complication after cardiac surgery and associated with adverse outcomes. The purpose of this study is to construct a nomogram to predict the probability of postoperative AKI in pediatric patients undergoing cardiac surgery. We conducted a single-center retrospective cohort study of 1137 children having cardiac surgery under cardiopulmonary bypass. We randomly divided the included patients into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator regression model was used for feature selection. We constructed a multivariable logistic regression model to select predictors and develop a nomogram to predict AKI risk. Discrimination, calibration and clinical benefit of the final prediction model were evaluated in the development and validation cohorts. A simple nomogram was developed to predict risk of postoperative AKI using six predictors including age at operation, cyanosis, CPB duration longer than 120 min, cross-clamp time, baseline albumin and baseline creatinine levels. The area under the receiver operator characteristic curve of the nomogram was 0.739 (95% CI 0.693-0.786) and 0.755 (95% CI 0.694-0.816) for the development and validation cohort, respectively. The calibration curve showed a good correlation between predicted and observed risk of postoperative AKI. Decision curve analysis presented great clinical benefit of the nomogram. This novel nomogram for predicting AKI after pediatric cardiac surgery showed good discrimination, calibration and clinical practicability.

Uric Acid to Albumin Ratio: A Predictive Marker for Acute Kidney Injury in Isolated Tricuspid Valve Surgery

Background

The plasma uric acid/albumin ratio (UAR) has emerged as a novel inflammatory biomarker for predicting the development of acute kidney injury (AKI) following percutaneous coronary intervention. However, the potential of the UAR to serve as a predictive marker for AKI in patients undergoing isolated tricuspid valve (TV) surgery remains unknown. This study aimed to explore the association between the UAR and AKI and to assess whether the UAR can predict AKI in these patients.

Methods

We conducted a retrospective analysis of patients who underwent isolated TV surgery between January 2018 and June 2019. The patients were divided into three groups based on the tertiles of the UAR. We utilized multivariate logistic regression and restricted cubic spline analysis to examine the association between the UAR and AKI. Additionally, we used the receiver operating characteristic (ROC) curve analysis to assess the predictive accuracy of the UAR for AKI.

Results

A total of 224 patients were enrolled in this study, of whom 41 developed AKI. The incidence of AKI across the three UAR tertiles was 3.8%, 22.2%, and 29.7%, with a significant difference between the group (p < 0.001). In the multivariate analysis, UAR ≥8.5 was associated with a 7-fold increased risk of AKI (odds ratio (OR): 7.73, 95% confidence interval (CI): 1.61-37.14), while a UAR ≥10.8 was a linked to a 9-fold increased risk (OR: 9.34, 95% CI: 1.96-44.60). The restricted cubic spline model showed a linear association between the UAR and AKI development. The area under the curve (AUC) value for the UAR was 0.713 (95% CI: 0.633-0.793; p < 0.001) with a cutoff value of 8.89.

Conclusions

An increased UAR was significantly associated with a higher risk of AKI in patients undergoing isolated TV surgery; however, while the UAR could serve as a marker to predict AKI, it was not superior to uric acid alone.

Development and validation of a prediction model for acute kidney injury following cardiac valve surgery

Background

Acute kidney injury (AKI) often accompanies cardiac valve surgery, and worsens patient outcome. The aim of our study is to identify preoperative and intraoperative independent risk factors for AKI in patients undergoing cardiac valve surgery. Using these factors, we developed a risk prediction model for AKI after cardiac valve surgery and conducted external validation.

Methods

Our retrospective study recruited 497 adult patients undergoing cardiac valve surgery as a derivation cohort between February and August 2023. Patient demographics, including medical history and perioperative clinical information, were acquired, and patients were classified into one of two cohorts, AKI and non-AKI, according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Using binary logistic stepwise regression analysis, we identified independent AKI risk factors after cardiac valve surgery. Lastly, we constructed a nomogram and conducted external validation in a validation cohort comprising 200 patients. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).

Results

In the derivation cohort, 172 developed AKI (34.6%). Relative to non-AKI patients, the AKI patients exhibited elevated postoperative complication incidences and worse outcome. Based on multivariate analysis, advanced age (OR: 1.855; p = 0.011), preoperative hypertension (OR: 1.91; p = 0.017), coronary heart disease (OR: 6.773; p < 0.001), preoperative albumin (OR: 0.924; p = 0.015), D-Dimer (OR: 1.001; p = 0.038), plasma creatinine (OR: 1.025; p = 0.001), cardiopulmonary bypass (CPB) duration (OR: 1.011; p = 0.001), repeat CPB (OR: 6.195; p = 0.010), intraoperative red blood cell transfusion (OR: 2.560; p < 0.001), urine volume (OR: 0.406 p < 0.001) and vasoactive-inotropic score (OR: 1.135; p = 0.009) were independent risk factors for AKI. The AUC of the nomogram in the derivation and validation cohorts were 0.814 (95%CI: 0.775-0.854) and 0.798 (95%CI: 0.726-0.871), respectively. Furthermore, the calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value.

Conclusion

Several perioperative factors modulate AKI development following cardiac valve surgery, resulting in poor patient prognosis. The proposed AKI predictive model is both sensitive and precise, and can assist in high-risk patient screening in the clinics.

Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study

Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.

Association between preoperative proton pump inhibitor use and postoperative acute kidney injury in patients undergoing major surgery

BACKGROUND

Acute kidney injury (AKI) is a severe postoperative complication in patients undergoing major surgery. Proton pump inhibitors (PPIs) are used preoperatively as prophylaxis for postoperative gastrointestinal bleeding. Whether preoperative PPI use is associated with an increased risk of postoperative AKI remains uncertain.

METHODS

This retrospective cohort study used electronic medical records from the clinical data warehouse of Peking University First Hospital to screen all adult hospitalizations undergoing major surgery between 1 January 2018 and 31 December 2020. Exposure was preoperative PPI use, defined as PPI use within 7 days before major surgery. The primary outcome was postoperative AKI, defined as AKI occurring within 7 days after major surgery; secondary outcomes included in-hospital AKI and in-hospital mortality.

RESULTS

A total of 21,533 patients were included in the study (mean [SD] age, 57.8 [15.0] years; 51.2% male), of which 944 (4.4%) were prescribed PPI within 7 days before major surgery (PPI users). Overall, 72 PPI users (7.6%) and 356 non-users (1.7%) developed postoperative AKI. After adjustment, preoperative PPI use was associated with an increased risk of postoperative AKI (adjusted OR, 1.47; 95% CI, 1.04-2.07) and in-hospital AKI (adjusted OR, 1.41; 95% CI, 1.03-1.94). Moreover, subgroup analyses showed that the risk of PPI on postoperative AKI was amplified by the concomitant use of non-steroidal anti-inflammatory drugs or diuretics. No significant difference was observed between preoperative PPI use and in-hospital mortality in the fully adjusted model (adjusted OR 1.63; 95% CI, 0.55-4.85).

CONCLUSIONS

Preoperative PPI use was associated with an increased risk of AKI in patients undergoing major surgery. This risk may be enhanced by the concomitant use of other nephrotoxic drugs. Clinicians should weigh the pros and cons before initiating PPI prophylaxis.

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.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury

BACKGROUND

The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early.

METHODS

This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram.

RESULTS

The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value.

CONCLUSIONS

We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

Does dexmedetomidine reduce the risk of acute kidney injury after cardiac surgery? A meta-analysis of randomized controlled trials

BACKGROUND

Acute Kidney Injury (AKI) is a common complication after cardiac surgery and has been associated with poor outcomes. Dexmedetomidine (DEX) has been shown to confer direct renoprotection based on some animal and clinical studies, but data from other trials came to the opposite conclusion following cardiac surgery. This meta-analysis was conducted to evaluate the effects of perioperative DEX administration on the occurrence of AKI and the outcomes after cardiac surgery.

METHODS

We searched databases including EMBASE, PubMed, and Cochrane CENTRAL for Randomized Controlled Trials (RCTs) focused on DEX for AKI in adult patients after cardiac surgery. The primary outcome was incidence of AKI. Secondary outcomes were Mechanical Ventilation (MV) duration, Intensive Care Unit (ICU) Length Of Stay (LOS), hospital LOS and mortality.

RESULTS

Fifteen trials enrolling 2907 study patients were collected in the meta-analyses. Compared with controls, DEX reduced the incidence of postoperative AKI (Odds Ratio [OR = 0.66]; 95% Confidence Interval [95% CI 0.48-0.91]; p = 0.01), and there was no significant difference between groups in postoperative mortality (OR = 0.63; 95% CI 0.32-1.26; p = 0.19), MV duration (Weighted Mean Difference [WMD = -0.44]; 95% CI -1.50-0.63; p = 0.42), ICU LOS (WMD = -1.19; 95% CI -2.89-0.51; p = 0.17), and hospital LOS (WMD = -0.31; 95% CI -0.76-0.15; p = 0.19).

CONCLUSIONS

Perioperative DEX reduced the incidence of postoperative AKI in adult patients undergoing cardiac surgery. No significant decrease existed in mortality, MV duration, ICU LOS and hospital LOS owing to DEX administration.

A pilot study to explore patterns and predictors of delayed kidney decline after cardiopulmonary bypass

There is no current consensus on the follow up of kidney function in patients undergoing cardiopulmonary bypass (CPB). The main objectives of this pilot study is to collect preliminary data on kidney function decline encountered on the first postoperative visit of patients who have had CPB and to identify predictors of kidney function decline post hospital discharge. Design: Retrospective chart review. Adult patients undergoing open heart procedures utilizing CPB. Patient demographics, type of procedure, pre-, intra-, and postoperative clinical, hemodynamic echocardiographic, and laboratory data were abstracted from electronic medical records. Acute kidney disease (AKD), and chronic kidney disease (CKD) were diagnosed based on standardized criteria. Interval change in medications, hospital admissions, and exposure to contrast, from hospital discharge till first postoperative visit were collected. AKD, and CKD as defined by standardized criteria on first postoperative visit. 83 patients were available for analysis. AKD occurred in 27 (54%) of 50 patients and CKD developed in 12 (42%) out of 28 patients. Older age was associated with the development of both AKD and CKD. Reduction in right ventricular cardiac output at baseline was associated with AKD (OR: 0.5, 95% CI: 0.3, 0.79, P = 0.01). Prolongation of transmitral early diastolic filling wave deceleration time was associated with CKD (OR: 1.02, 95% CI: 1.01, 1.05, P = 0.03). In-hospital acute kidney injury (AKI) was a predictor of neither AKD nor CKD. AKD and CKD occur after CPB and may not be predicted by in-hospital AKI. Older age, right ventricular dysfunction and diastolic dysfunction are important disease predictors. An adequately powered longitudinal study is underway to study more sensitive predictors of delayed forms of kidney decline after CPB.

Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery

OBJECTIVE

Postoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI.

METHODS

The retrospective cohort study included 2310 adult patients undergoing cardiac surgery in a tertiary teaching hospital, China. Postoperative AKI and severe AKI were identified by the modified KDIGO definition. The sample was randomly divided into a derivation set and a validation set based on a ratio of 4:1. Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO-AKI. We implemented the interpretation of models using SHapley Additive exPlanation (SHAP) analysis.

RESULTS

Postoperative AKI and severe AKI occurred in 1020 (44.2%) and 286 (12.4%) patients, respectively. Compared with the five ML models, LR model for PO-AKI exhibited the largest AUC (0.812, 95%CI: 0.756, 0.860, all P < 0.05), sensitivity (0.774, 95%CI: 0.719, 0.813), accuracy (0.753, 95%CI: 0.719, 0.781) and Youden index (0.513, 95%CI: 0.451, 0.573). Regarding severe AKI, GBC algorithm showed a significantly higher AUC than the other four ML models (all P < 0.05). Although no significant difference (P = 0.173) was observed in AUCs between GBC (0.86, 95%CI: 0.808, 0.902) and conventional logistic regression (0.803, 95%CI: 0.746, 0.852), GBC achieved greater sensitivity, accuracy and Youden index than conventional LR. Notably, SHAP analyses showed that preoperative serum creatinine, hyperlipidemia, lipid-lowering agents and assisted ventilation time were consistently among the top five important predictors for both postoperative AKI and severe AKI.

CONCLUSION

Logistic regression and GBC algorithm demonstrated moderate to good discrimination and superior performance in predicting PO-AKI and severe AKI, respectively. Interpretation of the models identified the key contributors to the predictions, which could potentially inform clinical interventions.

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.

Effects of Ulinastatin on Postoperative Renal Function in Patients Undergoing Cardiac Surgery with Cardiopulmonary Bypass: A Prospective Cohort Study with 10-Year Follow-Up

INTRODUCTION

The present study aimed to explore the potential effect of ulinastatin on renal function and long-term survival in patients receiving cardiac surgery with cardiopulmonary bypass (CPB).

METHODS

This prospective cohort study was conducted at Fuwai Hospital, Beijing, China. Ulinastatin was applied after induction anesthesia. The primary outcome was the rate of new-onset postoperative acute kidney injury (AKI). Moreover, a 10-year follow-up was conducted until January 2021.

RESULTS

The rate of new-onset AKI was significantly lower in the ulinastatin group than in the control group (20.00 vs. 32.40%, p = 0.009). There was no significant difference in renal replacement therapy between the two groups (0.00 vs. 2.16%, p = 0.09). The postoperative plasma neutrophil gelatinase-associated lipocalin (pNGAL) and IL-6 levels were significantly lower in the ulinastatin group compared with the control group (pNGAL: p = 0.007; IL-6: p = 0.001). A significantly lower incidence of respiratory failure in the ulinastatin group compared with the control group (0.76 vs. 5.40%, p = 0.02). The nearly 10-year follow-up (median: 9.37, 95% confidence interval: 9.17-9.57) survival rates did not differ significantly between the two groups (p = 0.076).

CONCLUSIONS

Ulinastatin significantly reduced postoperative AKI and respiratory failure in patients receiving cardiac surgery with CPB. However, ulinastatin did not reduce intensive care unit and hospital stays, mortality, and long-term survival rate.

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

Comparison between ketorolac- and fentanyl-based patient-controlled analgesia for acute kidney injury after robot-assisted radical prostatectomy: a retrospective propensity score-matched analysis

PURPOSE

It is unclear whether ketorolac-based patient-controlled analgesia (PCA) leads to acute kidney injury (AKI) after robot-assisted radical prostatectomy (RARP) in patients susceptible to AKI. We compared the postoperative AKI incidence with ketorolac- and fentanyl-based PCA after RARP.

METHODS

After medical record review, eligible patients were divided in ketorolac and fentanyl groups. We conducted propensity score matching of 3239 patients and assigned 641 matched patients to each group, and compared the AKI incidence. We investigated potential risk factors for postoperative AKI, defined according to the Kidney Disease Improving Global Outcomes criteria. We collected preoperative data (age, height, weight, body mass index, American Society of Anesthesiologists physical status, medical history, creatinine level, estimated glomerular filtration rate, and hemoglobin level) and intraoperative data (maintenance anesthetics, surgery duration, anesthesia duration, crystalloid amount, colloid use, total amount of fluid administered, estimated blood loss, norepinephrine use, phenylephrine use, and PCA type).

RESULTS

The postoperative AKI incidence was significantly higher in the ketorolac than in the fentanyl group, both before (31.1% vs. 20.4%; p < 0.001) and after (31.5% vs. 22.6%; p < 0.001) matching. In the univariate analysis, ketorolac was significantly associated with postoperative AKI, both before (odds ratio [OR], 1.762; 95% confidence interval [CI], 1.475-2.105; p < 0.001) and after (OR, 1.574; 95% CI, 1.227-2.019; p < 0.001) matching. In the multivariate analysis, ketorolac-based PCA was independently associated with development of postoperative AKI in the matched groups (OR, 1.659; 95% CI, 1.283-2.147; p < 0.001).

CONCLUSION

Ketorolac-based PCA may increase postoperative AKI incidence after RARP; thus, renal function should be monitored in these patients.

Increased numbers of pre-operative circulating monocytes predict risk of developing cardiac surgery-associated acute kidney injury in conditions requiring cardio pulmonary bypass

BACKGROUND

Evaluating patients' risk for acute kidney injury (AKI) is crucial for positive outcomes following cardiac surgery. Our aims were first to select candidate risk factors from pre- or intra-operative real-world parameters collected from routine medical care and then evaluate potential associations between those parameters and risk of onset of post-operative cardiac surgery-associated AKI (CSA-AKI).

METHOD

We conducted two cohort studies in Japan. The first was a single-center prospective cohort study (n = 145) to assess potential association between 115 clinical parameters collected from routine medical care and CSA-AKI (≥ Stage1) risk in the population of patients undergoing cardiac surgery involving cardiopulmonary bypass (CPB). To select candidate risk factors, we employed random forest analysis and applied survival analyses to evaluate association strength. In a second retrospective cohort study, we targeted patients undergoing cardiac surgery with CPB (n = 619) and evaluated potential positive associations between CSA-AKI incidence and risk factors suggested by the first cohort study.

RESULTS

Variable selection analysis revealed that parameters in clinical categories such as circulating inflammatory cells, CPB-related parameters, ventilation, or aging were potential CSA-AKI risk factors. Survival analyses revealed that increased counts of pre-operative circulating monocytes and neutrophils were associated with CSA-AKI incidence. Finally, in the second cohort study, we found that increased pre-operative circulating monocyte counts were associated with increased CSA-AKI incidence.

CONCLUSIONS

Circulating monocyte counts in the pre-operative state are associated with increased risk of CSA-AKI development. This finding may be useful in stratifying patients for risk of developing CSA-AKI in routine clinical practice.

Association of Preoperative Neutrophil-to-Lymphocyte Ratio with Postoperative Acute Kidney Injury and Mortality Following Major Noncardiac Surgeries

BACKGROUND

Acute kidney injury (AKI) is a major complication that occurs following an operation. Therefore, there is an increasing need to discover new predictors of AKI. We hypothesized that the preoperative neutrophil-to-lymphocyte ratio (NLR) was associated with postoperative AKI and in-hospital mortality following noncardiac surgery.

METHODS

This is a retrospective observational study of patients who underwent noncardiac surgery at Sichuan University West China Hospital from 2018 to 2020. Multivariable logistic regression was performed as the major analytic method. In addition, sensitivity and subgroup analyses were performed to validate the results.

RESULTS

A total of 44,065 patients were included in this study. The prevalence of postoperative AKI was 5.62%, and the in-hospital mortality was 1.58%. Multivariable analysis demonstrated that NLR ≥ 5 was independently associated with the development of postoperative AKI (OR 1.42, 1.24-1.73; P < 0.001) and in-hospital mortality (OR 2.03, 1.63-2.52; P < 0.001). Similar results were achieved when propensity-score matching was performed for patients with NLR ≥ 5 and < 5 on the baseline. In stratified analysis, the associations remained persistent in most subgroups. For the sensitivity analysis, we took NLR as a continuous variable and demonstrated the potential linear relationship between NLR and postoperative AKI and mortality.

CONCLUSIONS

Our results indicated that preoperative NLR is associated with the prevalence of postoperative AKI and in-hospital mortality that occur after major noncardiac surgery. These findings suggest that NLR has the potential to be a significant correlation biomarker associated with perioperative risk assessment of patients undergoing noncardiac surgeries.

Development and validation of a nomogram for predicting in-hospital mortality of elderly patients with persistent sepsis-associated acute kidney injury in intensive care units: a retrospective cohort study using the MIMIC-IV database

OBJECTIVES

To identify the clinical risk factors that influence in-hospital mortality in elderly patients with persistent sepsis-associated acute kidney injury (S-AKI) and to establish and validate a nomogram to predict in-hospital mortality.

DESIGN

Retrospective cohort analysis.

SETTING

Data from critically ill patients at a US centre between 2008 and 2021 were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database (V.1.0).

PARTICIPANTS

Data from 1519 patients with persistent S-AKI were extracted from the MIMIC-IV database.

PRIMARY OUTCOME

All-cause in-hospital death from persistent S-AKI.

RESULTS

Multiple logistic regression revealed that gender (OR 0.63, 95% CI 0.45-0.88), cancer (2.5, 1.69-3.71), respiratory rate (1.06, 1.01-1.12), AKI stage (2.01, 1.24-3.24), blood urea nitrogen (1.01, 1.01-1.02), Glasgow Coma Scale score (0.75, 0.70-0.81), mechanical ventilation (1.57, 1.01-2.46) and continuous renal replacement therapy within 48 hours (9.97, 3.39-33.9) were independent risk factors for mortality from persistent S-AKI. The consistency indices of the prediction and the validation cohorts were 0.780 (95% CI: 0.75-0.82) and 0.80 (95% CI: 0.75-0.85), respectively. The model's calibration plot suggested excellent consistency between the predicted and actual probabilities.

CONCLUSIONS

This study's prediction model demonstrated good discrimination and calibration abilities to predict in-hospital mortality of elderly patients with persistent S-AKI, although it warrants further external validation to verify its accuracy and applicability.

Monocyte to high density lipoprotein ratio in patients with acute kidney injury after cardiac surgery

BACKGROUND

The Monocyte to high density lipoprotein ratio (MHR) has been postulated as a novel parameter associated with adverse renal and cardiovascular outcomes. In this study we investigated the association of MHR with cardiac surgery-associated acute kidney injury (CSA-AKI).

METHODS

In this retrospective study, we analyzed the data pertaining to 1505 patients undergoing cardiopulmonary bypass (CPB) surgery. The CSA-AKI, which was defined using Kidney Disease Improving Global Outcomes criteria. Concurrently, a retrospective scan of patient files was conducted and information relevant to nephropathy such as the level of their serum creatinine (SCr), Blood urea nitrogen (BUN), uric acid (UA), serum cystatin C (Cys-C), total cholesterol (TC), triglycerides (TG), glucose and MHR, ejection fraction, CPB duration time, and other indicators.

RESULTS

About 1505 patients were studied of whom 195 developed AKI. MHR was significantly higher in the AKI patients (p = 0.001). In multivariate logistic regression analysis, MHR, UA, Cys-C, age, glucose, and history of chronic kidney disease or hypertension were independently correlated with CSA-AKI.

CONCLUSIONS

As a laboratory index, the elevated MHR is convenient, independent, and a useful predictor for CSA-AKI.

A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment

BACKGROUND

Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.

METHODS

This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.

RESULTS

A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168-7.904), hematuria (OR = 3.656, 95%CI 1.325-10.083), CYS-C (OR = 4.416, 95%CI 2.296-8.491), and CA-125 (OR = 3.93, 95%CI 1.436-10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650-0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941-0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).

CONCLUSIONS

Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.

Impact of Diabetes on the Recurrence and Prognosis of Acute Kidney Injury in Older Male Patients: A 10-Year Retrospective Cohort Study

INTRODUCTION

While patients with diabetes are at higher risk of developing acute kidney injury (AKI), there are few studies on the recurrence of AKI in older adult patients. This study therefore aimed to examine the impact of diabetes on AKI recurrence and long-term outcomes in older male patients.

METHODS

This retrospective cohort study included older male patients who experienced AKI during hospitalization from July 2007 to August 2011. Medical records of all patients were followed up for 10 years. Patients with AKI were classified into groups with and without diabetes. We analyzed differences in common geriatric comorbidities, AKI recurrence frequency, and severity between the two groups, identified risk factors affecting recurrence frequency, and assessed outcomes.

RESULTS

Of all 266 patients, 128 had diabetes and 138 did not. The AKI recurrence rate was significantly higher in the group with diabetes (80.5 vs. 66.7%; P = 0.011). There was a significantly higher proportion of AKI caused by infections in patients with diabetes (43.3 vs. 33.2%, P = 0.006). The proportion of patients with an AKI recurrence frequency ≥ 3 was significantly higher in the group with diabetes (44.7 vs. 29.4%, P = 0.027). Diabetes and coronary heart disease were independent risk factors for AKI recurrence (P < 0.05), diabetes control was associated with multiple AKI recurrences (P = 0.016), and no significant difference was found between the groups regarding the 10-year prognosis (P = 0.522). However, a subgroup analysis showed that patients with multiple AKI recurrences within 2 years had the worst survival outcome (P = 0.004).

CONCLUSIONS

Older male patients with diabetes are prone to AKI recurrence after initial onset of AKI. Diabetes is an independent risk factor for AKI recurrence, and active diabetes control (HbA1c < 7%) may thus reduce the recurrence of AKI and improve the very poor outcomes of patients with multiple recurrences of AKI within 2 years.

Diagnosis of Cardiac Surgery-Associated Acute Kidney Injury: State of the Art and Perspectives

Diagnosis of cardiac surgery-associated acute kidney injury (CSA-AKI), a syndrome of sudden renal dysfunction occurring in the immediate post-operative period, is still sub-optimal. Standard CSA-AKI diagnosis is performed according to the international criteria for AKI diagnosis, afflicted with insufficient sensitivity, specificity, and prognostic capacity. In this article, we describe the limitations of current diagnostic procedures and of the so-called injury biomarkers and analyze new strategies under development for a conceptually enhanced diagnosis of CSA-AKI. Specifically, early pathophysiological diagnosis and patient stratification based on the underlying mechanisms of disease are presented as ongoing developments. This new approach should be underpinned by process-specific biomarkers including, but not limited to, glomerular filtration rate (GFR) to other functions of renal excretion causing GFR-independent hydro-electrolytic and acid-based disorders. In addition, biomarker-based strategies for the assessment of AKI evolution and prognosis are also discussed. Finally, special focus is devoted to the novel concept of pre-emptive diagnosis of acquired risk of AKI, a premorbid condition of renal frailty providing interesting prophylactic opportunities to prevent disease through diagnosis-guided personalized patient handling. Indeed, a new strategy of risk assessment complementing the traditional scores based on the computing of risk factors is advanced. The new strategy pinpoints the assessment of the status of the primary mechanisms of renal function regulation on which the impact of risk factors converges, namely renal hemodynamics and tubular competence, to generate a composite and personalized estimation of individual risk.

Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation

BACKGROUND

Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features.

METHODS

Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model.

RESULTS

Post-transplant AKI occurred in 76 (71.0%) patients including 15 (14.0%) stage 1, 18 (16.8%) stage 2, and 43 (40.2%) stage 3 cases. The top six features selected for the ML model to predicate AKI stage 3 were serum cystatin C, estimated glomerular filtration rate (eGFR), right atrial long-axis dimension, left atrial anteroposterior dimension, serum creatinine (SCr) and FVII. The predictive performance of the ML model (AUC: 0.821; 95% confidence interval [CI]: 0.740-0.901) was significantly higher compared with that of the Cleveland-clinical model (AUC: 0.654; 95% [CI]: 0.545-0.763, p < 0.05).

CONCLUSIONS

The ML model, which achieved an effective predictive performance for post-transplant AKI stage 3, may be helpful for timely intervention to improve the patient's prognosis.

A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database

OBJECTIVES

We aimed to develop an effective tool for predicting severe acute kidney injury (AKI) in patients admitted to the cardiac surgery recovery unit (CSRU).

DESIGN

A retrospective cohort study.

SETTING

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database, consisting of critically ill participants between 2001 and 2012 in the USA.

PARTICIPANTS

A total of 6271 patients admitted to the CSRU were enrolled from the MIMIC-III database.

PRIMARY AND SECONDARY OUTCOME

Stages 2-3 AKI.

RESULT

As identified by least absolute shrinkage and selection operator (LASSO) and logistic regression, risk factors for AKI included age, sex, weight, respiratory rate, systolic blood pressure, diastolic blood pressure, central venous pressure, urine output, partial pressure of oxygen, sedative use, furosemide use, atrial fibrillation, congestive heart failure and left heart catheterisation, all of which were used to establish a clinical score. The areas under the receiver operating characteristic curve of the model were 0.779 (95% CI: 0.766 to 0.793) for the primary cohort and 0.778 (95% CI: 0.757 to 0.799) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Decision curve analysis demonstrated that the model could achieve a net benefit.

CONCLUSION

A clinical score built by using LASSO regression and logistic regression to screen multiple clinical risk factors was established to estimate the probability of severe AKI in CSRU patients. This may be an intuitive and practical tool for severe AKI prediction in the CSRU.

Analysis of prognostic factors for in-hospital mortality in patients with unplanned re-exploration after cardiovascular surgery

Objective

To explore the prognostic factors for in-hospital mortality in patients with unplanned re-exploration after cardiovascular surgery.

Methods

We retrospectively analyzed the data of 100 patients who underwent unplanned re-exploration after cardiovascular surgery in our hospital between May 2010 and May 2020. There were 77 males and 23 females, aged (55.1 ± 15.2) years. Demographic characteristics, surgical information, perioperative complications were collected to establish a database. These patients were divided into surviving and non-surviving groups according to in-hospital mortality. Logistic regression was used for multivariable analysis to explore the prognostic factors of in-hospital mortality. These statistically significant indicators were selected for drawing the receiver operating characteristic curve of the evaluation model, calculating the area under the curve (AUC) and evaluating the effectiveness of the new model with Hosmer–Lemeshow C-statistic.

Results

In-hospital mortality in patients with unplanned re-exploration after cardiovascular surgery was 26.0% (26/100). Multivariate logistics regression revealed that the operation time of unplanned re-exploration, the worst blood creatinine value within 48 h before the re-exploration, the worst lactate value within 24 h after the re-exploration, cardiac insufficiency, respiratory insufficiency, and acute kidney injury were independent prognostic factors (P < 0.05). The AUC of the new assessment model constituted by these prognostic factors was 0.910, and the Hosmer–Lemeshow C-statistic was 4.153 (P = 0.762).

Conclusions

Operation time of unplanned re-exploration, worst serum creatinine value within 48 h before re-exploration, worst lactate value within 24 h after re-exploration, cardiac insufficiency, respiratory insufficiency, and acute kidney injury are the main prognostic factors for in-hospital mortality in patients with unplanned re-exploration after cardiovascular surgery. Identifying these prognostic factors can effectively facilitate preventive measures and improve patient outcomes.

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset

Background

Acute kidney injury (AKI) is a major complication following cardiac surgery that substantially increases morbidity and mortality. Current diagnostic guidelines based on elevated serum creatinine and/or the presence of oliguria potentially delay its diagnosis. We presented a series of models for predicting AKI after cardiac surgery based on electronic health record data.

Methods

We enrolled 1457 adult patients who underwent cardiac surgery at Nanjing First Hospital from January 2017 to June 2019. 193 clinical features, including demographic characteristics, comorbidities and hospital evaluation, laboratory test, medication, and surgical information, were available for each patient. The number of important variables was determined using the sliding windows sequential forward feature selection technique (SWSFS). The following model development methods were introduced: extreme gradient boosting (XGBoost), random forest (RF), deep forest (DF), and logistic regression. Model performance was accessed using the area under the receiver operating characteristic curve (AUROC). We additionally applied SHapley Additive exPlanation (SHAP) values to explain the RF model. AKI was defined according to Kidney Disease Improving Global Outcomes guidelines.

Results

In the discovery set, SWSFS identified 16 important variables. The top 5 variables in the RF importance matrix plot were central venous pressure, intraoperative urine output, hemoglobin, serum potassium, and lactic dehydrogenase. In the validation set, the DF model exhibited the highest AUROC (0.881, 95% confidence interval [CI] 0.831–0.930), followed by RF (0.872, 95% CI 0.820–0.923) and XGBoost (0.857, 95% CI 0.802–0.912). A nomogram model was constructed based on intraoperative longitudinal features, achieving an AUROC of 0.824 (95% CI 0.763–0.885) in the validation set. The SHAP values successfully illustrated the positive or negative contribution of the 16 variables attributed to the output of the RF model and the individual variable’s effect on model prediction.

Conclusions

Our study identified 16 important predictors and provided a series of prediction models to enhance risk stratification of AKI after cardiac surgery. These novel predictors might aid in choosing proper preventive and therapeutic strategies in the perioperative management of AKI patients.

Risk of bias in non-randomized observational studies assessing the relationship between proton-pump inhibitors and adverse kidney outcomes: a systematic review

BACKGROUND

Proton-pump inhibitors (PPIs) are widely prescribed as acid-suppression therapy. Some observational studies suggest that long-term use of PPIs is potentially associated with certain adverse kidney outcomes. We conducted a systematic literature review to assess potential bias in non-randomized studies reporting on putative associations between PPIs and adverse kidney outcomes (acute kidney injury, acute interstitial nephritis, chronic interstitial nephritis, acute tubular necrosis, chronic kidney disease, and end-stage renal disease).

METHODS

We searched the medical literature within 10 years of 17 December 2020. Pre-specified criteria guided identification of relevant English language articles for assessment. Risk of bias on an outcome-specific basis was evaluated using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool by two independent reviewers.

RESULTS

Of 620 initially identified records, 26 studies met a priori eligibility criteria and underwent risk of bias assessment. Nineteen studies were judged as having a moderate risk of bias for reported adverse kidney outcomes, while six studies were judged as having a serious risk of bias (mainly due to inadequate control of confounders and selection bias). We were unable to determine the overall risk of bias in two studies (one of which was assessed as having a moderate risk of bias for a different adverse kidney outcome) due to insufficient information presented. Effect estimates for PPIs in relation to adverse kidney outcomes varied widely (0.24-7.34) but associations mostly showed increased risk.

CONCLUSION

Using ROBINS-I, we found that non-randomized observational studies suggesting kidney harm by PPIs have moderate to serious risk of bias, making it challenging to establish causality. Additional high-quality, real-world evidence among generalizable populations are needed to better understand the relation between PPI treatment and acute and chronic kidney outcomes, accounting for the effects of varying durations of PPI treatment, self-treatment with over-the-counter PPIs, and potential critical confounders.

CSA-AKI: Incidence, Epidemiology, Clinical Outcomes, and Economic Impact

Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication following cardiac surgery and reflects a complex biological combination of patient pathology, perioperative stress, and medical management. Current diagnostic criteria, though increasingly standardized, are predicated on loss of renal function (as measured by functional biomarkers of the kidney). The addition of new diagnostic injury biomarkers to clinical practice has shown promise in identifying patients at risk of renal injury earlier in their course. The accurate and timely identification of a high-risk population may allow for bundled interventions to prevent the development of CSA-AKI, but further validation of these interventions is necessary. Once the diagnosis of CSA-AKI is established, evidence-based treatment is limited to supportive care. The cost of CSA-AKI is difficult to accurately estimate, given the diverse ways in which it impacts patient outcomes, from ICU length of stay to post-hospital rehabilitation to progression to CKD and ESRD. However, with the global rise in cardiac surgery volume, these costs are large and growing.

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.

Persistent Pain After Cardiac Surgery: Prevention and Management

Persistent postoperative pain (PPP) after cardiac surgery is a significant complication that negatively affects patient quality of life and increases health care system burden. However, there are no standards or guidelines to inform how to mitigate these effects. Therefore, in this review, we will discuss strategies to prevent and manage PPP after cardiac surgery. Adequate perioperative analgesia may prove instrumental in the prevention of PPP. Although opioids have historically been the primary analgesic approach to cardiac surgery, an opioid-sparing strategy may prove advantageous in reducing side effects, avoiding secondary hyperalgesia, and decreasing risk of PPP. Implementing a multimodal analgesic plan using alternative medications and regional anesthetic techniques may offer superior efficacy while reducing adverse effects.

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Background: Drug-induced acute kidney injury (D-AKI) is associated with increased mortality and longer hospital stays. This study aims to establish a nomogram to predict the occurrence of D-AKI in hospitalized patients in a multi-drug environment.

Methods: A single center retrospective study among adult hospitalized patients was conducted from July 2019 to September 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by our hospital. According to the propensity score matching algorithm, four controls per case were matched to eliminate the confounding bias caused by individual baseline variables. The predictors for D-AKI were obtained by logistic regression equation and used to establish the nomogram.

Results: Among 51,772 hospitalized patients, 332 were diagnosed with D-AKI. After matching, 288 pairs and 1,440 patients were included in the study, including 1,005 cases in the development group and 435 cases in the validation group. Six variables were independent predictors for D-AKI: alcohol abuse, the concurrent use of nonsteroidal anti-inflammatory drugs or diuretics, chronic kidney disease, lower baseline red blood cell count and neutrophil count ≥7 × 10⁹/L. The area under the curve (AUC) of the prediction model in the development group and validation group were 0.787 (95%CI, 0.752–0.823) and 0.788 (95%CI, 0.736–0.840), respectively. The GiViTI calibration belts showed that the model had a good prediction accuracy for the occurrence of D-AKI (p > 0.05).

Conclusion: This nomogram can help identify patients at high risk of D-AKI, which was useful in preventing the progression of D-AKI and treating it in the early stages.

A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients

Background

Combining tubular damage and functional biomarkers may improve prediction precision of acute kidney injury (AKI). Serum cystatin C (sCysC) represents functional damage of kidney, while urinary N-acetyl-β-D-glucosaminidase (uNAG) is considered as a tubular damage biomarker. So far, there is no nomogram containing this combination to predict AKI in septic cohort. We aimed to compare the performance of AKI prediction models with or without incorporating these two biomarkers and develop an effective nomogram for septic patients in intensive care unit (ICU).

Methods

This was a prospective study conducted in the mixed medical-surgical ICU of a tertiary care hospital. Adults with sepsis were enrolled. The patients were divided into development and validation cohorts in chronological order of ICU admission. A logistic regression model for AKI prediction was first constructed in the development cohort. The contribution of the biomarkers (sCysC, uNAG) to this model for AKI prediction was assessed with the area under the receiver operator characteristic curve (AUC), continuous net reclassification index (cNRI), and incremental discrimination improvement (IDI). Then nomogram was established based on the model with the best performance. This nomogram was validated in the validation cohort in terms of discrimination and calibration. The decision curve analysis (DCA) was performed to evaluate the nomogram’s clinical utility.

Results

Of 358 enrolled patients, 232 were in the development cohort (69 AKI), while 126 in the validation cohort (52 AKI). The first clinical model included the APACHE II score, serum creatinine, and vasopressor used at ICU admission. Adding sCysC and uNAG to this model improved the AUC to 0.831. Furthermore, incorporating them significantly improved risk reclassification over the predictive model alone, with cNRI (0.575) and IDI (0.085). A nomogram was then established based on the new model including sCysC and uNAG. Application of this nomogram in the validation cohort yielded fair discrimination with an AUC of 0.784 and good calibration. The DCA revealed good clinical utility of this nomogram.

Conclusions

A nomogram that incorporates functional marker (sCysC) and tubular damage marker (uNAG), together with routine clinical factors may be a useful prognostic tool for individualized prediction of AKI in septic patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02388-w.

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database

BACKGROUND

This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA).

METHODS

A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P < 0.1 were included in the final model. Then, a nomogram was constructed applying these significant risk predictors based on a multivariate logistic regression model. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the area under the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) were conducted to evaluate the performance of our newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit.

RESULTS

A multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706-0.789) in the training dataset, and 0.712 (95% CI, 0.642-0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P > 0.05). DCA showed that our model was clinically useful.

CONCLUSION

The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis.

Serum lactate dehydrogenase level is associated with in-hospital mortality in critically Ill patients with acute kidney injury

Objective

Sixty percent of critically ill patients suffer from acute kidney injury (AKI) and 12% of them require renal replacement therapy during their ICU stay. However, we lack effective biomarkers to predict the mortality of critically ill patients with AKI. Few studies have investigated the association between lactate dehydrogenase levels and mortality in patients with AKI.

Methods

We conducted a retrospective cohort study with large samples, using a large database, the Multi parameter Intelligent Monitoring in Intensive Care III project. Clinical and demographic data were collected from the database by structure query language. Multiple models were constructed by stepwise methods to examine the association between lactate dehydrogenase (LDH) and in-hospital mortality. The predictive performance of LDH was assessed by ROC analysis and p values were calculated for trends.

Results

In the final analysis, 8436 patients met the inclusion criteria, and 1519 patients died during their hospital stay. The mortality rate increased with increasing LDH levels. The association between LDH and in-hospital mortality was almost linear (p < 0.001). A multiple logistic regression model indicated that LDH level was an independent predictor of in-hospital mortality (OR = 1.56, 95% CI (1.39–1.73), p < 0.001) and this effect remained stable in the subgroup analysis. Moreover, the combined AUC of LDH and SAPSII was 0.83.

Conclusions

The LDH level, which can be easily assessed, is significantly and independently associated with in-hospital mortality, and could increase the predictive ability of SAPSII for in-hospital mortality in our study.

Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury

OBJECTIVE

We aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients.

METHODS

Clinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model.

RESULTS

The development cohort enrolled 730 patients with a median follow-up time of 87 (40-98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043-1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951-0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678-13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930-0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287-3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p < 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839-0.921) and 0.798 (95% CI = 0.720-0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability.

CONCLUSION

We developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.

A visual risk assessment tool for acute kidney injury after intracranial aneurysm clipping surgery

Objective

The aim of the study was to establish a predictive postoperative nomogram for acute kidney injury (AKI) after intracranial aneurysm clipping surgery, in order to early identify patients with high postoperative AKI risk.

Methods

This is a retrospective study, which included patients who underwent intracranial aneurysm clipping surgery. Multivariate logistic regression was employed to select confound factors that associated with AKI, then incorporated into the nomogram. The predictive accuracy of the model was assessed by concordance index (C-Index).

Results

A total of 365 patients after intracranial aneurysm clipping surgery were enrolled in the study eventually, of which 68 (18.63%) suffered postoperative AKI, and the incidence of stage 1, stage 2 and stage 3 were 92.65% (63/68), 5.88% (4/68), and 1.47% (1/68), respectively. Univariate logistic regression revealed that high density lipoprotein (HDL), prothrombin time (PT), estimated glomerular filtration rate (eGFR), size of aneurysm ≥10 mm, and aneurysm ruptured before surgery were associated with AKI after surgery, while multivariate logistic regression showed same results as the size of aneurysm ≥10 mm and aneurysm ruptured were independent AKI risk factors. In addition, the nomogram demonstrated a good accuracy in estimating intracranial aneurysm clipping associated AKI, as a C-Index and a bootstrap-corrected one of 0.772 and 0.737, respectively. Moreover, calibration plots showed consistency with the actual presence of AKI.

Conclusion

The novel nomogram model can serve as a promising predictive tool to improve the identification of AKI among those who underwent intracranial aneurysm clipping surgery.

Acute Kidney Injury in Asia: Disease Burden

Acute kidney injury (AKI) is a common and critical clinical disorder with non-negligible morbidity and mortality and remains a large public health problem. Asia, as the world's largest and most populous continent, is crucial in eliminating unsatisfactory outcomes of AKI. The diversities in climate, customs, and economic status lead to various clinical features of AKI across Asia. In this review, we focus on the epidemiologic data and clinical features of AKI in different Asian countries and clinical settings, and we show the huge medical and economic burden of AKI in Asian countries. Drugs and sepsis are the most common etiologies for AKI, however, an adequate surveillance system has not been well established. There is significant undertreatment of AKI in many regions, and medical resources for renal replacement therapy are not universally available. Although substantial improvement has been achieved, health care for AKI still needs improvement, especially in developing regions.

Preoperative platelet count, preoperative hemoglobin concentration and deep hypothermic circulatory arrest duration are risk factors for acute kidney injury after pulmonary endarterectomy: a retrospective cohort study

Background

Acute kidney injury (AKI) is a major postoperative morbidity of patients undergoing cardiac surgery and has a negative effect on prognosis. The kidney outcomes after pulmonary endarterectomy (PEA) have not yet been reported; However, several perioperative characteristics of PEA may induce postoperative AKI. The objective of our study was to identify the incidence and risk factors for postoperative AKI and its association with short-term outcomes.

Methods

This was a single-center, retrospective, observational, cohort study. Assessments of AKI diagnosis was executed based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria.

Results

A total of 123 consecutive patients who underwent PEA between 2014 and 2018 were included.

The incidence of postoperative AKI was 45% in the study population. Stage 3 AKI was associated with worse short-term outcomes and 90-day mortality (p < 0.001, p = 0.002, respectively). The independent predictors of postoperative AKI were the preoperative platelet count (OR 0.992; 95%CI 0.984–0.999; P = 0.022), preoperative hemoglobin concentration (OR 0.969; 95%CI 0.946–0.993; P = 0.01) and deep hypothermic circulatory arrest (DHCA) time (OR 1.197; 95%CI 1.052–1.362; P = 0.006) in the multivariate analysis.

Conclusion

The incidence of postoperative AKI was relatively high after PEA compared with other types of cardiothoracic surgeries. The preoperative platelet count, preoperative hemoglobin concentration and DHCA duration were modifiable predictors of AKI, and patients may benefit from some low-risk, low-cost perioperative measures.