Intraoperative Data Enhance the Detection of High-Risk Acute Kidney Injury Patients When Added to a Baseline Prediction Model

Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function

Background

The study aimed to develop a risk prediction model through screening preoperative risk factors for acute kidney injury (AKI) after heart valve replacement in patients with normal renal function.

Methods

A total of 608 patients with normal renal function who underwent heart valve replacement from November 2013 to June 2022 were analyzed retrospectively. The Lasso regression was used to preliminarily screen potential risk factors, which were entered into the multivariable logistic regression analysis to identify preoperative independent risk factors for postoperative AKI. Based on the results, a risk prediction model was developed, and traditional and dynamic nomograms were constructed. The risk prediction model was evaluated using receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA).

Results

220 patients (36.2%) developed AKI after surgery. Current smoker, hypertension, heart failure, previous myocardial infarction, cerebrovascular disease, CysC, and NT-proBNP were selected as independent risk factors for AKI. A risk prediction model, a traditional and a dynamic nomogram were developed based on the above factors. The area under the curve (AUC) of the ROC for predicting the risk of postoperative AKI was 0.803 (95% CI 0.769-0.836), with sensitivity and specificity of 84.9% and 63.4%, respectively. The calibration curve slope was close to 1, and the DCA showed that the model produced better clinical benefits when the probability threshold was set at 10%-82%.

Conclusions

We developed a preoperative risk prediction model for AKI after heart valve replacement in patients with normal renal function, which demonstrated satisfactory discrimination and calibration.

Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study

BACKGROUND

Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them.

METHODS

We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases.

RESULTS

Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis.

CONCLUSIONS

We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.

Intraoperative hypotension and postoperative acute kidney injury: A systematic review

BACKGROUND

There is no consensus regarding safe intraoperative blood pressure thresholds that protect against postoperative acute kidney injury (AKI). This review aims to examine the existing literature to delineate safe intraoperative hypotension (IOH) parameters to prevent postoperative AKI.

METHODS

PubMed, Cochrane Central, and Web of Science were systematically searched for articles published between 2015 and 2022 relating the effects of IOH on postoperative AKI.

RESULTS

Our search yielded 19 articles. IOH risk thresholds ranged from <50 to <75 ​mmHg for mean arterial pressure (MAP) and from <70 to <100 ​mmHg for systolic blood pressure (SBP). MAP below 65 ​mmHg for over 5 ​min was the most cited threshold (N ​= ​13) consistently associated with increased postoperative AKI. Greater magnitude and duration of MAP and SBP below the thresholds were generally associated with a dose-dependent increase in postoperative AKI incidence.

CONCLUSIONS

While a consistent definition for IOH remains elusive, the evidence suggests that MAP below 65 ​mmHg for over 5 ​min is strongly associated with postoperative AKI, with the risk increasing with the magnitude and duration of IOH.

An online-predictive model of acute kidney injury after pancreatic surgery

OBJECTIVE

Acute kidney injury(AKI) after pancreatic surgery is associated with increased mortality, longer hospital stays and poor prognosis. This study aims to identify the risk factors and establish an easy-to-use prediction calculator by the nomogram to predict the risk of AKI after pancreatic surgery.

METHODS

From January 2016 to June 2018, 1504 patients who underwent pancreatic surgery in our center were included in this retrospective analysis and randomly assigned to primary (1054 patients) and validation (450 patients) cohorts. The independent risk factors of AKI were identified using univariate and multivariate analyses. A risk-predicted nomogram for AKI was developed through multivariate logistic regression analysis in the primary cohort while the nomogram was evaluated in the validation cohort. Nomogram discrimination and calibration were assessed using C-index and calibration curves in the primary and validation cohorts. The clinical utility of the final nomogram was evaluated using decision curve analysis.

RESULTS

The overall incidence of AKI after pancreatic surgery was 5.3% (79/1504). Independent risk factors including smoking history, cardiovascular disease, ASA score, baseline eGFR, bilirubin＞2 ​mg/dL, undergoing pancreaticoduodenectomy, and intraoperative blood loss＞400 ​mL were identified by multivariate analysis. Nomogram revealed moderate discrimination and calibration in estimating the risk of AKI, with an unadjusted C-index of 0.79 (95 %CI, 0.73-0.85). Application of the nomogram in the validation cohort provided moderate discrimination (C-index,0.80 [95% CI, 0.72-0.88]) and good calibration. Besides, the decision curve analysis (DCA) confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

An easy-to-use online prediction calculator comprised of preoperative and intraoperative factors was able to individually predict the occurrence risk of AKI among patients with pancreatic surgery, which may help identify reasonable risk judgments and develop proper treatment strategies to a certain extent.

A Risk-Prediction Platform for Acute Kidney Injury and 30-Day Readmission After Colorectal Surgery

INTRODUCTION

Few known risk factors for certain surgical complications are prospectively analyzed to ascertain their influence on outcomes. Health systems can use integrated machine-learning-derived algorithms to provide information regarding patients' risk status in real time and pair this data with interventions to improve outcomes. The purpose of this work was to evaluate whether real-time knowledge of patients' calculated risk status paired with a stratified intervention was associated with a reduction in acute kidney injury and 30-d readmission following colorectal surgery.

METHODS

Unblinded, retrospective study, evaluating the impact of an electronic health record-integrated and autonomous algorithm-based clinical decision support tool (KelaHealth, San Francisco, California) on acute kidney injury and 30-d readmission following colorectal surgery at a single academic medical center between January 1, 2020, and December 31, 2020, relative to a propensity-matched historical cohort (2014-2018) prior to algorithm integration (January 11, 2019).

RESULTS

3617 patients underwent colorectal surgery during the control period and 665 underwent surgery during the treatment period; 1437 historical control patients were matched to 479 risk-based patients for the study. Utilization of the risk-based management platform was associated with a 2.5% decrease in the rate of acute kidney injury (11.3% to 8.8%) and 3.1% decrease in rate of readmissions (12% to 8.9%).

CONCLUSIONS

In this study, we found significant reductions in postoperative acute kidney injury (AKI) and unplanned readmissions after the implementation of an algorithm based clinical decision support tool that risk-stratified populations and offered stratified interventions. This opens up an opportunity for further investigation in translating similar risk platform approaches across surgical specialties.

Effect of preoperative risk on the association between intraoperative hypotension and postoperative acute kidney injury in cardiac surgery

BACKGROUND

Acute kidney injury (AKI), a common and severe complication after cardiac surgery, has been demonstrated to be associated with intraoperative hypotension (IOH). The reproducibility of this finding and whether preoperative risk modifies the association remain unclear. We hypothesised that the relationship between IOH and AKI after cardiac surgery varies by preoperative risk.

METHODS

We conducted a single-centre, retrospective cohort study to analyse the association between IOH and postoperative AKI by stratifying patients using preoperative risk factors. IOH was defined as a mean arterial pressure (MAP) of less than 65 mmHg and characterised by the cumulative duration and area under the curve (AUC).

RESULTS

Ten variables could be identified as risk factors: age, smoking status, NYHA III/Ⅳ, emergency surgery, peripheral vascular disease, cerebrovascular disease, heart failure, hypertension, previous cardiac surgery, and NT-proBNP concentration. The risk prediction model divided the patients into three equal-sized preoperative risk groups. Low-risk patients demonstrated no association between AKI and IOH of any severity, while high-risk patients demonstrated a statistically significant association between AKI and IOH with a cumulative duration greater than 104 min (adjusted odds ratio [OR]: 2.27, 95% confidence interval [CI]: 1.10-4.74; and adjusted OR: 3.63, 95% CI: 1.77-7.58) and an AUC greater than 905 mmHg min (adjusted OR: 2.08, 95% CI: 1.01-4.36; and adjusted OR: 4.00, 95% CI: 1.95-8.43).

CONCLUSION

IOH is a significant independent risk factor for AKI after cardiac surgery. Patients with higher baseline risk showed a more prominent relationship between IOH and postoperative AKI than low-risk patients.

Evaluation of neonatal acute kidney injury (AKI) after emergency gastrointestinal surgery

BACKGROUND

The overall incidence of acute kidney injury (AKI) in neonates undergoing emergency gastrointestinal surgery is yet to be determined. The study aims are to analyze our experience in emergency gastrointestinal surgery for neonates and to evaluate the incidence of AKI.

METHODS

We conducted a retrospective study of neonates undergoing emergency gastrointestinal surgery between June 31, 2018 and May 10, 2022 (N = 329). The primary outcome was the overall incidence of AKI. The diagnostic AKI was based on the Modified Kidney Disease: Improving Global Outcomes (KDIGO) definition of neonatal AKI. The secondary outcomes, including the postoperative length of hospital stay (PLOS), 24-h mortality, in-hospital mortality, and total in-hospital cost, were analyzed. The risk factors associated with the development of postoperative AKI were also analyzed.

RESULTS

The incidence of postoperative AKI was 9.1% (30/329). No significant differences were detected in the 24-h mortality and in-hospital mortality between the two cohorts. In the final model, patients undergoing mechanical ventilation before surgery, vasopressor support, surgical duration, intraoperative oliguria and preoperative lowest serum creatinine (SCr), were independently associated with AKI.

CONCLUSION

Our study found that patients undergoing mechanical ventilation before surgery, vasopressor support, surgical duration, intraoperative oliguria and preoperative lowest SCr were independently associated with postoperative AKI in neonates who accepted emergency gastrointestinal surgeries.

Fluids, vasopressors, and acute kidney injury after major abdominal surgery between 2015 and 2019: a multicentre retrospective analysis

BACKGROUND

Practice patterns related to intraoperative fluid administration and vasopressor use have potentially evolved over recent years. However, the extent of such changes and their association with perioperative outcomes, such as the development of acute kidney injury (AKI), have not been studied.

METHODS

We performed a retrospective analysis of major abdominal surgeries in adults across 26 US hospitals between 2015 and 2019. The primary outcome was AKI as defined by the Kidney Disease Improving Global Outcomes definition (KDIGO) using only serum creatinine criteria. Univariable linear predictive additive models were used to describe the dose-dependent risk of AKI given fluid administration or vasopressor use.

RESULTS

Over the study period, we observed a decrease in the volume of crystalloid administered, a decrease in the proportion of patients receiving more than 10 ml kg^-1 h^-1 of crystalloid, an increase in the amount of norepinephrine equivalents administered, and a decreased duration of hypotension. The incidence of AKI increased between 2016 and 2019. An increase of crystalloid administration from 1 to 10 ml kg^-1 h^-1 was associated with a 58% decreased risk of AKI.

CONCLUSIONS

Despite decreased duration of hypotension during the study period, decreased fluid administration and increased vasopressor use were associated with increased incidence of AKI. Crystalloid administration below 10 ml kg^-1 h^-1 was associated with an increased risk of AKI. Although no causality can be concluded, these data suggest that prevention and treatment of hypotension during abdominal surgery with liberal use of vasopressors at the expense of fluid administration is associated with an increased risk of postoperative AKI.

The Predictive Value of Pre-operative N-Terminal Pro-B-Type Natriuretic Peptide in the Risk of Acute Kidney Injury After Non-cardiac Surgery

Objective

To evaluate the association between N-terminal pro-B-type natriuretic peptide (NT-proBNP) and risk of post-operative acute kidney injury (PO-AKI).

Methods

The electronic medical records and laboratory results were obtained from 3,949 adult patients (≥18 years) undergoing non-cardiac surgery performed between 1 October 2012 to 1 October 2019 at the Third Xiangya Hospital, Central South University, China. Collected data were analyzed retrospectively.

Results

In all, 5.3% (209 of 3,949) of patients developed PO-AKI. Pre-operative NT-proBNP was an independent predictor of PO-AKI. After adjustment for significant variables, OR for AKI of highest and lowest NT-proBNP quintiles was 1.96 (95% CI, 1.04–3.68, P = 0.008), OR per 1-unit increment in natural log transformed NT-proBNP was 1.20 (95% CI, 1.09–1.32, P < 0.001). Compared with clinical variables alone, the addition of NT-proBNP modestly improved the discrimination [change in area under the curve(AUC) from 0.82 to 0.83, ΔAUC=0.01, P = 0.024] and the reclassification (continuous net reclassification improvement 0.15, 95% CI, 0.01–0.29, P = 0.034, improved integrated discrimination 0.01, 95% CI, 0.002–0.02, P = 0.017) of AKI and non-AKI cases.

Conclusions

Results from our retrospective cohort study showed that the addition of pre-operative NT-proBNP concentrations could better predict post-operative AKI in a cohort of non-cardiac surgery patients and achieve higher net benefit in decision curve analysis.

Haemodynamic predisposition to acute kidney injury: Shadow and light!

Acute kidney injury (AKI) could well be regarded as a sentinel complication given it is relatively common and associated with a substantial risk of subsequent morbidity and mortality. On the aegis of 'prevention is better than cure', there has been a wide interest in evaluating haemodynamic predisposition to AKI so as to provide a favourable renoprotective haemodynamic milieu to the subset of patients presenting a significant risk of developing AKI. In this context, the last decade has witnessed a series of evaluation of the hypotension value and duration cut-offs associated with risk of AKI across diverse non-operative and operative settings. Nevertheless, a holistic comprehension of the haemodynamic predisposition to AKI has been a laggard with only few reports highlighting the potential of elevated central venous pressure, intra-abdominal hypertension and high mean airway pressures in considerably attenuating the effective renal perfusion, particularly in scenarios where kidneys are highly sensitive to any untoward elevation in the afterload. Despite the inherent autoregulatory mechanisms, the effective renal perfusion pressure (RPP) can be modulated by a number of haemodynamic factors in addition to mean arterial pressure (MAP) as the escalation of renal interstitial pressure, in particular hampers kidney perfusion which in itself is a dynamic interplay of a number of innate pressures. The present article aims to review the subject of haemodynamic predisposition to AKI centralising the focus on effective RPP (over and above the conventional 'tunnel-vision' for MAP) and discuss the relevant literature accumulating in this area of ever-growing clinical interest.

Impact of Intraoperative Data on Risk Prediction for Mortality After Intra-Abdominal Surgery

BACKGROUND

Risk prediction models for postoperative mortality after intra-abdominal surgery have typically been developed using preoperative variables. It is unclear if intraoperative data add significant value to these risk prediction models.

METHODS

With IRB approval, an institutional retrospective cohort of intra-abdominal surgery patients in the 2005 to 2015 American College of Surgeons National Surgical Quality Improvement Program was identified. Intraoperative data were obtained from the electronic health record. The primary outcome was 30-day mortality. We evaluated the performance of machine learning algorithms to predict 30-day mortality using: 1) baseline variables and 2) baseline + intraoperative variables. Algorithms evaluated were: 1) logistic regression with elastic net selection, 2) random forest (RF), 3) gradient boosting machine (GBM), 4) support vector machine (SVM), and 5) convolutional neural networks (CNNs). Model performance was evaluated using the area under the receiver operator characteristic curve (AUROC). The sample was randomly divided into a training/testing split with 80%/20% probabilities. Repeated 10-fold cross-validation identified the optimal model hyperparameters in the training dataset for each model, which were then applied to the entire training dataset to train the model. Trained models were applied to the test cohort to evaluate model performance. Statistical significance was evaluated using P < .05.

RESULTS

The training and testing cohorts contained 4322 and 1079 patients, respectively, with 62 (1.4%) and 15 (1.4%) experiencing 30-day mortality, respectively. When using only baseline variables to predict mortality, all algorithms except SVM (area under the receiver operator characteristic curve [AUROC], 0.83 [95% confidence interval {CI}, 0.69-0.97]) had AUROC >0.9: GBM (AUROC, 0.96 [0.94-1.0]), RF (AUROC, 0.96 [0.92-1.0]), CNN (AUROC, 0.96 [0.92-0.99]), and logistic regression (AUROC, 0.95 [0.91-0.99]). AUROC significantly increased with intraoperative variables with CNN (AUROC, 0.97 [0.96-0.99]; P = .047 versus baseline), but there was no improvement with GBM (AUROC, 0.97 [0.95-0.99]; P = .3 versus baseline), RF (AUROC, 0.96 [0.93-1.0]; P = .5 versus baseline), and logistic regression (AUROC, 0.94 [0.90-0.99]; P = .6 versus baseline).

CONCLUSIONS

Postoperative mortality is predicted with excellent discrimination in intra-abdominal surgery patients using only preoperative variables in various machine learning algorithms. The addition of intraoperative data to preoperative data also resulted in models with excellent discrimination, but model performance did not improve.

External Validation of a Prediction Model for Acute Kidney Injury Following Noncardiac Surgery

IMPORTANCE

The Simple Postoperative AKI Risk (SPARK) index is a prediction model for postoperative acute kidney injury (PO-AKI) in patients undergoing noncardiac surgery. External validation has not been performed.

OBJECTIVE

To externally validate the SPARK index.

DESIGN, SETTING, AND PARTICIPANTS

This single-center retrospective cohort study included adults who underwent noncardiac surgery under general anesthesia from 2007 to 2011. Those with obstetric or urological surgery, estimated glomerular filtration rate (eGFR) of less than 15 mL/min/1.73 m2, preoperative dialysis, or an expected surgical duration of less than 1 hour were excluded. The study was conducted at Nara Medical University Hospital. Data analysis was conducted from January to July 2021.

EXPOSURES

Risk factors for AKI included in SPARK index.

MAIN OUTCOMES AND MEASURES

PO-AKI, defined as an increase in serum creatinine of at least 0.3 mg/dL within 48 hours or 150% compared with preoperative baseline value or urine output of less than 0.5 mL/kg/h for at least 6 hours within 1 week after surgery, and critical AKI, defined as either AKI stage 2 or greater and/or any AKI connected to postoperative death or requiring kidney replacement therapy before discharge. The discrimination and calibration of the SPARK index were examined with area under the receiver operating characteristic curves (AUC) and calibration plots, respectively.

RESULTS

Among 5135 participants (2410 [46.9%] men), 303 (5.9%) developed PO-AKI, and 137 (2.7%) developed critical AKI. Compared with the SPARK cohort, participants in our cohort were older (median [IQR] age, 56 [44-66] years vs 63 [50-73] years), had lower baseline eGFR (median [IQR], 82.1 [71.4-95.1] mL/min/1.73 m2 vs 78.2 [65.6-92.2] mL/min/1.73 m2), and had a higher prevalence of comorbidities (eg, diabetes: 3956 of 51 041 [7.8%] vs 802 [15.6%]). The incidence of PO-AKI and critical AKI increased as the scores on the SPARK index increased. For example, 10 of 593 participants (1.7%) in SPARK class A, indicating lowest risk, experienced PO-AKI, while 53 of 332 (16.0%) in SPARK class D, indicating highest risk, experienced PO-AKI. However, AUCs for PO-AKI and critical AKI were 0.67 (95% CI, 0.63-0.70) and 0.62 (95% CI, 0.57-0.67), respectively, and the calibration was poor (PO-AKI: y = 0.24x + 3.28; R2 = 0.86; critical AKI: y = 0.20x + 2.08; R2 = 0.51). Older age, diabetes, expected surgical duration, emergency surgery, renin-angiotensin-aldosterone system blockade use, and hyponatremia were not associated with PO-AKI in our cohort, resulting in overestimation of the predicted probability of AKI in our cohort.

CONCLUSIONS AND RELEVANCE

In this study, the incidence of PO-AKI increased as the scores on the SPARK index increased. However, the predicted probability might not be accurate in cohorts with older patients with more comorbidities.