Novel Prediction Score Including Pre- and Intraoperative Parameters Best Predicts Acute Kidney Injury after Liver Surgery

Renal Function in Sequential Living Kidney-Then-Liver Donors Undergoing Right Lobe Donation: A Two-Center Case Study

BACKGROUND

There are concerns regarding the potential impact of living donor hepatectomy on the kidney function of prior kidney donors. The current literature lacks comprehensive data on living liver following living kidney donation. Furthermore, the focus on left lobe donation in the literature does not fully represent the prevalent use of the right lobe graft for living liver transplants in the United States.

METHODS

We performed a retrospective chart review on all living liver donors who had previously donated a kidney at two US centers.

RESULTS

There were 14 sequential living kidney-then-liver donors. The median donor age was 49 years (range 35-59). Most of these (12 donors) were nondirected donations. The median follow-up period was 24 months (range 1-129). The median interval between the donations was 32 months (range 17-154 months). All donors donated the right lobe with 43.5% (range 31.4%-49.9 %) of remnant liver volume. The overall donor complication rate was 43%, seen in six donors, with one Clavien-Dindo Grade IIIa complication (suture granuloma removal under local anesthesia). Two donors (14%) experienced stage 1 AKI, both resolving with supportive care. A decrease in eGFR greater than 10 mL/min/1.73 m2 over the follow-up was observed in only one donor, who gained weight and was lost to follow-up. Compensatory kidney hypertrophy was observed, with kidney volumetry showing an increase of 1.27 (1.09-1.39) times pre- versus post-kidney donation and 1.08 times pre- versus post-liver donation (1.01-1.16).

CONCLUSIONS

Right lobe living liver donation in previous kidney donors might be safely performed in terms of midterm kidney function. Longer-term assessment in a larger cohort would be necessary to have better insight into this unique donor group.

The significance of the modified surgical apgar score in predicting postoperative acute kidney injury among patients undergoing hepatectomy

AIM

The incidence of acute kidney injury (AKI) following hepatectomy ranges from 0.9 % to 21.6 %. Postoperative AKI is associated with increased mortality, prolonged hospital stays, and more healthcare costs. Previous predictive models either neglected intraoperative factors or were excessively complicated for application. Based on estimated blood loss, minimum heart rate, and minimum mean arterial pressure, the Surgical Apgar Score (SAS) has been validated as an indicator of major complications and outcomes following surgeries. Furthermore, previous studies have linked hematocrit levels to the incidence of AKI. Our aim was to determine whether the modified SAS, calculated using both SAS and hematocrit, could accurately predict AKI following hepatectomy.

METHODS

This retrospective study ultimately enrolled 960 patients who underwent hepatectomy. The study included a total of 28 preoperative and intraoperative variables. Univariate and multivariate logistic regression analyses were performed to determine the predictive ability of the modified SAS.

RESULTS

We demonstrated significant associations between the modified SAS and the incidence of AKI (OR 0.65, 95 % CI 0.54-0.78, p < 0.001). A lower total score increases the likelihood of postoperative AKI, with a cutoff value set at 9.

CONCLUSIONS

The modified SAS appears to be a valid predictive factor for AKI following hepatectomy.

An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy

BACKGROUND

Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. This retrospective study aimed to create a model predicting AKI through machine learning and identify features that contribute to the development of early and late AKI.

METHODS

Patients that underwent major hepatectomy were categorized into the No-AKI, Early-AKI (within 48 h) or Late-AKI group (between 48 h and 7 days). Modeling was done with 20 perioperative features and the performance of prediction models were measured by the area under the receiver operating characteristic curve (AUROCC). Shapley Additive Explanation (SHAP) values were utilized to explain the outcome of the prediction model.

RESULTS

Of the 1383 patients included in this study, 1229, 110 and 44 patients were categorized into the No-AKI, Early-AKI and Late-AKI group, respectively. The CatBoost classifier exhibited the greatest AUROCC of 0.758 (95% CI: 0.671-0.847) and was found to differentiate well between Early and Late-AKI. We identified different perioperative features for predicting each outcome and found 1-year mortality to be greater for Early-AKI.

CONCLUSIONS

Our results suggest that risk factors are different for Early and Late-AKI after major hepatectomy, and 1-year mortality is greater for Early-AKI.

Machine learning in liver surgery: Benefits and pitfalls

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

A nomogram for predicting acute kidney injury following hepatectomy: A propensity score matching analysis

STUDY OBJECTIVE

The low central venous pressure (LCVP) technique is a key technique in hepatectomy, but its impact on acute kidney injury (AKI) is unclear. The purpose of this study was to explore risk factors (in particular LCVP time) for AKI following hepatectomy.

DESIGN

A retrospective case-control study with propensity score matching.

SETTING

Operating room.

PATIENTS

A total of 1949 patients who underwent hepatectomy were studied.

INTERVENTIONS

The patients were grouped with or without AKI within 7 days after surgery. Univariable and multivariable analyses were performed, including recognized intraoperative predictors. The final result is represented as a nomogram.

MEASUREMENTS

Preoperative, intraoperative and postoperative data were collected. LCVP is monitored directly through a central venous catheter via the right internal jugular vein.

MAIN RESULTS

AKI occurred in 148 patients (7.59%). Surgery time, minimum SBP, furosemide administration and norepinephrine were identified as independent risk factors. The area under the curve for the receiver operating characteristic curves was 0.726 (95% CI 0.668-0.783).

CONCLUSION

Intraoperative parameters can be used to predict the probability of postoperative AKI. Although AKI increases the length of stay, it may not increase in-hospital mortality. LCVP time was not confirmed to be a risk factor for AKI.

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.

Decision curve analysis confirms higher clinical utility of multi-domain versus single-domain prediction models in patients with open abdomen treatment for peritonitis

BACKGROUND

Prediction modelling increasingly becomes an important risk assessment tool in perioperative systems approaches, e.g. in complex patients with open abdomen treatment for peritonitis. In this population, combining predictors from multiple medical domains (i.e. demographical, physiological and surgical variables) outperforms the prediction capabilities of single-domain prediction models. However, the benefit of these prediction models for clinical decision-making remains to be investigated. We therefore examined the clinical utility of mortality prediction models in patients suffering from peritonitis with a decision curve analysis.

METHODS

In this secondary analysis of a large dataset, a traditional logistic regression approach, three machine learning methods and a stacked ensemble were employed to examine the predictive capability of demographic, physiological and surgical variables in predicting mortality under open abdomen treatment for peritonitis. Calibration was examined with calibration belts and predictive performance was assessed with the area both under the receiver operating characteristic curve (AUROC) and under the precision recall curve (AUPRC) and with the Brier Score. Clinical utility of the prediction models was examined by means of a decision curve analysis (DCA) within a treatment threshold range of interest of 0-30%, where threshold probabilities are traditionally defined as the minimum probability of disease at which further intervention would be warranted.

RESULTS

Machine learning methods supported available evidence of a higher prediction performance of a multi- versus single-domain prediction models. Interestingly, their prediction performance was similar to a logistic regression model. The DCA demonstrated that the overall net benefit is largest for a multi-domain prediction model and that this benefit is larger compared to the default "treat all" strategy only for treatment threshold probabilities above about 10%. Importantly, the net benefit for low threshold probabilities is dominated by physiological predictors: surgical and demographics predictors provide only secondary decision-analytic benefit.

CONCLUSIONS

DCA provides a valuable tool to compare single-domain and multi-domain prediction models and demonstrates overall higher decision-analytic value of the latter. Importantly, DCA provides a means to clinically differentiate the risks associated with each of these domains in more depth than with traditional performance metrics and highlighted the importance of physiological predictors for conservative intervention strategies for low treatment thresholds. Further, machine learning methods did not add significant benefit either in prediction performance or decision-analytic utility compared to logistic regression in these data.

Association between intraoperative oliguria and postoperative acute kidney injury in non-cardiac surgical patients: a systematic review and meta-analysis

PURPOSE

This systematic review and meta-analysis aimed to evaluate the association between intraoperative oliguria and the risk of postoperative acute kidney injury (AKI) in patients undergoing non-cardiac surgery.

METHODS

The MEDLINE and EMBASE databases were searched up to August 2022 for studies in adult patients undergoing non-cardiac surgery, where the association between intraoperative urine output and the risk of postoperative AKI was assessed. Both randomised and non-randomised studies were eligible for inclusion. Study selection and risk of bias assessment were independently performed by two investigators. The risk of bias was evaluated using the Newcastle-Ottawa scale. We performed meta-analysis of the reported multivariate adjusted odds ratios for the association between intraoperative oliguria (defined as urine output < 0.5 mL/kg/hr) and the risk of postoperative AKI using the inverse-variance method with random effects models. We conducted sensitivity analyses using varying definitions of oliguria as well as by pooling unadjusted odds ratios to establish the robustness of the primary meta-analysis. We also conducted subgroup analyses according to surgery type and definition of AKI to explore potential sources of clinical or methodological heterogeneity.

RESULTS

Eleven studies (total 49,252 patients from 11 observational studies including a post hoc analysis of a randomised controlled trial) met the selection criteria. Seven of these studies contributed data from a total 17,148 patients to the primary meta-analysis. Intraoperative oliguria was associated with a significantly elevated risk of postoperative AKI (pooled adjusted odds ratio [OR] 1.74; 95% confidence interval [CI] 1.36-2.23, p < 0.0001, 8 studies). Sensitivity analyses supported the robustness of the primary meta-analysis. There was no evidence of any significant subgroup differences according to surgery type or definition of AKI.

CONCLUSIONS

This study demonstrated a significant association between intraoperative oliguria and the risk of postoperative AKI, regardless of the definitions of oliguria or AKI used. Further prospective and multi-centre studies using standardised definitions of intraoperative oliguria are required to define the thresholds of oliguria and establish strategies to minimise the risk of AKI.

Role of proenkephalin in the diagnosis of severe and subclinical acute kidney injury during the perioperative period of liver transplantation

In recent decades, clinical research on early biomarkers of renal injury has been frequent and intensive, with proenkephalin (PENK) being indicated as a promising filtration biomarker (BM). From a cohort of 57 patients, blood samples were collected preoperatively and 48 h after liver transplantation (LT). The following BMs were analyzed: PENK, cystatin-C (CYS-C), and serum creatinine (Scr). Diagnosis of AKI was based on the KDIGO criteria. Of the 57 patients undergoing LT, 50 (88%) developed acute kidney injury (AKI) and were categorized as follows: no-AKI/mild-AKI - 21 (36.8%) and severe-AKI 36 (63.2%). During the preoperative period, only PENK was significantly higher in patients with severe AKI, with an AUC of 0.69 (CI 0.54–0.83), a cutoff of 55.30 pmol/l, a sensitivity of 0.86, a specificity of 0.52, and an accuracy of 0.75. In addition, subclinical AKI was determined preoperatively in 32 patients. Forty-eight hours after LT, PENK maintained its performance in determining severe AKI, with an AUC of 0.83 (CI 0.72–0.94), a cutoff of 119.05 pmol/l, a sensitivity of 0.81, a specificity of 0.90, and an accuracy of 0.84. PENK detected AKI 48 h earlier than serum creatinine. In a multivariate linear regression analysis, PENK was an independent predictor of severe AKI. This small study suggests that the filtration biomarker PENK shows promise for detecting AKI in patients undergoing LT, revealing greater accuracy and an earlier rise in patients with severe AKI. The combination of kidney functional and filtration BMs may aid in the management and prevention of AKI progression.

Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients

Objective

There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients’ early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models.

Methods

We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n=799) and test (20%;n=201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively.

Results

In predicting AKI, nine ML algorithms posted AUC of 0.656–1.000 in the training cohort, with the randomforest standing out and AUC of 0.674–0.821 in the test cohort, with the logistic regression model standing out. Thus, we applied the logistic regression model to establish nomogram. The nomogram was comprised of ten variables: age, body mass index, American Society of Anesthesiologists, hypoproteinemia, hypertension, diabetes, anemia, duration of low mean arterial pressure, mean arterial pressure, transfusion.The calibration curves showed good agreement between prediction and observation in both the training and test sets.

Conclusion

By including intraoperative and preoperative risk factors, ML algorithm can predict AKI and logistic regression model performing the best. Our prediction model and nomogram that are based on this ML algorithm can help lead decision-making for strategies to inhibit AKI over the perioperative duration.

Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study

BACKGROUND

Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients.

METHODS

A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients.

RESULTS

Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P <  0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68-0.78) and validation (0.71, 95% CI: 0.63-0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66-0.76).

CONCLUSION

This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection.

TRIAL REGISTRATION

NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021.

Skin autofluorescence as a novel predictor of acute kidney injury after liver resection

BACKGROUND

Skin autofluorescence (SAF) reflects accumulation of advanced glycation end-products (AGEs). The aim of this study was to evaluate predictive usefulness of SAF measurement in prediction of acute kidney injury (AKI) after liver resection.

METHODS

This prospective observational study included 130 patients undergoing liver resection. The primary outcome measure was AKI. SAF was measured preoperatively and expressed in arbitrary units (AU).

RESULTS

AKI was observed in 32 of 130 patients (24.6%). SAF independently predicted AKI (p = 0.047), along with extent of resection (p = 0.019) and operative time (p = 0.046). Optimal cut-off for SAF in prediction of AKI was 2.7 AU (area under the curve [AUC] 0.611), with AKI rates of 38.7% and 20.2% in patients with high and low SAF, respectively (p = 0.037). Score based on 3 independent predictors (SAF, extent of resection, and operative time) well stratified the risk of AKI (AUC 0.756), with positive and negative predictive values of 59.3% and 84.0%, respectively. In particular, SAF predicted AKI in patients undergoing major and prolonged resections (p = 0.010, AUC 0.733) with positive and negative predictive values of 81.8%, and 62.5%, respectively.

CONCLUSIONS

AGEs accumulation negatively affects renal function in patients undergoing liver resection. SAF measurement may be used to predict AKI after liver resection, particularly in high-risk patients.

Impact of acute kidney injury after extended liver resections

BACKGROUND

Complex liver resection is a risk factor for the development of AKI, which is associated with increased morbidity and mortality. Aim of this study was to assess risk factors for acute kidney injury (AKI) and its impact on outcome for patients undergoing complex liver surgery.

METHODS

AKI was defined according to the KDIGO criteria. Primary endpoint was the occurrence of AKI after liver resection. Secondary endpoints were complications and mortality.

RESULTS

Overall, 146 patients undergoing extended liver resection were included in the study. The incidence of AKI was 21%. The incidence of chronic kidney disease (CKD) and hepatocellular carcinoma were significantly higher in patients with AKI. In the AKI group, the proportion of extended right hepatectomies was the highest (53%), followed by ALPPS (43%). Increased intraoperative blood loss, increased postoperative complications and perioperative mortality was associated with AKI. Besides age and CKD, ALPPS was an independent risk factor for postoperative AKI. A small future liver remnant seemed to increase the risk of AKI in patients undergoing ALPPS.

CONCLUSION

Following extended liver resection, AKI is associated with an increased morbidity and mortality. ALPPS is a major independent risk factor for the development of AKI and a sufficient future liver remnant could avoid postoperative AKI.

Association of Preoperative Prognostic Nutritional Index and Postoperative Acute Kidney Injury in Patients Who Underwent Hepatectomy for Hepatocellular Carcinoma

Various biological indicators are reportedly associated with postoperative acute kidney injury (AKI) in the surgical treatment of hepatocellular carcinoma (HCC). However, only a few studies have evaluated the association between the preoperative prognostic nutritional index (PNI) and postoperative AKI. This study evaluated the association of the preoperative PNI and postoperative AKI in HCC patients. We retrospectively analyzed 817 patients who underwent open hepatectomy between December 2007 and December 2015. Multivariate regression analysis was performed to evaluate the association between the PNI and postoperative AKI. Additionally, we evaluated the association between the PNI and outcomes such as postoperative renal replacement therapy (RRT) and mortality. Cox regression analysis was performed to assess the risk factors for one-year and five-year mortality. In the multivariate analysis, high preoperative PNI was significantly associated with a lower incidence of postoperative AKI (odds ratio (OR): 0.92, 95% confidence interval (CI): 0.85 to 0.99, p = 0.021). Additionally, diabetes mellitus and the use of synthetic colloids were significantly associated with postoperative AKI. PNI was associated with postoperative RRT (OR: 0.76, 95% CI: 0.60 to 0.98, p = 0.032) even after adjusting for other potential confounding variables. In the Cox regression analysis, high PNI was significantly associated with low one-year mortality (Hazard ratio (HR): 0.87, 95% CI: 0.81 to 0.94, p < 0.001), and five-year mortality (HR: 0.93, 95% CI: 0.90–0.97, p < 0.001). High preoperative PNI was significantly associated with a lower incidence of postoperative AKI and low mortality. These results suggest that the preoperative PNI might be a predictor of postoperative AKI and surgical prognosis in HCC patients undergoing open hepatectomy.

Assessment of prognostic value of intraoperative oliguria for postoperative acute kidney injury: a retrospective cohort study

BACKGROUND

Oliguria is often viewed as a sign of renal hypoperfusion and an indicator for volume expansion during surgery. However, the prognostic association and the predictive utility of intraoperative oliguria for postoperative acute kidney injury (AKI) are unclear.

METHODS

We conducted a retrospective cohort study on patients undergoing major thoracic surgery in an academic hospital to assess the association of intraoperative oliguria with postoperative AKI and its predictive value. To contextualise our findings, we included our results in a meta-analysis of observational studies on the importance of oliguria during noncardiac surgery.

RESULTS

In our cohort study, 3862 patients were included; 205 (5.3%) developed AKI after surgery. Intraoperative urine output of 0.3 ml kg-1 h-1 was the optimal threshold for oliguria in multivariable analysis. Patients with oliguria had an increased risk of AKI (adjusted odds ratio: 2.60; 95% confidence interval: 1.24-5.05). However, intraoperative oliguria had a sensitivity of 5.9%, specificity of 98%, positive likelihood ratio of 2.74, and negative likelihood ratio of 0.96, suggesting poor predictive ability. Moreover, it did not improve upon the predictive performance of a multivariable model, based on discrimination and reclassification indices. Our findings were generally consistent with the results of a systematic review and meta-analysis, including six additional studies.

CONCLUSIONS

Intraoperative oliguria has moderate association with, but poor predictive ability for, postoperative AKI. It remains of clinical interest as a risk factor potentially modifiable to interventions.

Renal Impairment Is Associated with Reduced Outcome After Associating Liver Partition and Portal Vein Ligation for Staged Hepatectomy

BACKGROUND

Impaired postoperative renal function is associated with increased morbidity and mortality after liver resection. The role of impaired renal function in the two-stage hepatectomy setting of associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) is unknown.

METHODS

An international multicenter cohort of ALPPS patients captured in the ALPPS Registry was analyzed. Particular attention was drawn to the renal function in the interstage interval to determine outcome after stage 2 surgery. Interstage renal impairment (RI) was defined as an increase of serum creatinine of ≥ 0.3 mg/dl referring to a preoperative value or an increase of serum creatinine of ≥ 1.5× of the preoperative value on the fifth postoperative day after stage 1.

RESULTS

A total of 705 patients were identified of which 7.5% had an interstage RI. Patients developing an interstage RI were significantly older. During stage 1, a longer operation time, higher rate of intraoperative transfusions, and additional procedures were observed in patients that developed interstage RI. After stage 1, interstage RI patients had more major complications and higher interstage mortality (1% vs. 8%, p < 0.001). Furthermore, these patients developed more and severe complications after completion of stage 2. Mortality of patients with interstage RI was 38% vs. 8% without interstage RI. In 41% of patients with interstage RI, the renal function recovered before stage 2; however, the mortality after stage 2 remained 28% in those patients. Risk factors for the development of an interstage RI were age over 67 years, prolonged operative time, and additional procedure during stage 1.

CONCLUSION

This study shows that interstage RI is a predictor for interstage and post-stage 2 morbidity and perioperative mortality. The causality of impaired renal function on outcome, however, remains unknown. Interstage RI may directly cause adverse outcome but may also be a surrogate marker for major complications.

Analysis of the risk factors of acute kidney injury after total hip or knee replacement surgery

Background

Postoperative acute kidney injury (AKI), which increases the risk of postoperative morbidity and mortality, poses a major concern to surgeons. We conducted this study to analyze the risk factors associated with the occurrence of AKI after orthopedic surgery.

Methods

This was a retrospective study that included 351 patients who underwent total hip or knee replacement surgery at Inje University Haeundae Paik Hospital between January 2012 and December 2016.

Results

AKI occurred in 13 (3.7%) of the 351 patients. The patients’ preoperative estimated glomerular filtration rate (eGFR) was 66.66±34.02 mL/min/1.73 m² in the AKI group and 78.07±21.23 mL/min/1.73 m² in the non-AKI group. The hemoglobin levels were 11.21±1.65 g/dL in the AKI group and 12.39±1.52 g/dL in the non-AKI group. Hemoglobin level was related to increased risk of AKI (odds ratio [OR], 0.13; 95% confidence interval [CI], 0.02–0.68; p=0.016). Administration of crystalloid or colloid fluid alone and the perioperative amount of fluid did not show any significant relationship with AKI. Further analysis of the changes in eGFR was performed using a cutoff value of 7.54. The changes in eGFR were significantly related to decreased risk of AKI (OR, 0.74; 95% CI, 0.61–0.89; p=0.002).

Conclusion

Renal function should be monitored closely after orthopedic surgery if patients have chronic kidney disease and low hemoglobin level. Predicting the likelihood of AKI occurrence, early treatment of high-risk patients, and monitoring perioperative laboratory test results, including eGFR, will help improve patient prognosis.

Preoperative Risk and the Association between Hypotension and Postoperative Acute Kidney Injury

BACKGROUND

Despite the significant healthcare impact of acute kidney injury, little is known regarding prevention. Single-center data have implicated hypotension in developing postoperative acute kidney injury. The generalizability of this finding and the interaction between hypotension and baseline patient disease burden remain unknown. The authors sought to determine whether the association between intraoperative hypotension and acute kidney injury varies by preoperative risk.

METHODS

Major noncardiac surgical procedures performed on adult patients across eight hospitals between 2008 and 2015 were reviewed. Derivation and validation cohorts were used, and cases were stratified into preoperative risk quartiles based upon comorbidities and surgical procedure. After preoperative risk stratification, associations between intraoperative hypotension and acute kidney injury were analyzed. Hypotension was defined as the lowest mean arterial pressure range achieved for more than 10 min; ranges were defined as absolute (mmHg) or relative (percentage of decrease from baseline).

RESULTS

Among 138,021 cases reviewed, 12,431 (9.0%) developed postoperative acute kidney injury. Major risk factors included anemia, estimated glomerular filtration rate, surgery type, American Society of Anesthesiologists Physical Status, and expected anesthesia duration. Using such factors and others for risk stratification, patients with low baseline risk demonstrated no associations between intraoperative hypotension and acute kidney injury. Patients with medium risk demonstrated associations between severe-range intraoperative hypotension (mean arterial pressure less than 50 mmHg) and acute kidney injury (adjusted odds ratio, 2.62; 95% CI, 1.65 to 4.16 in validation cohort). In patients with the highest risk, mild hypotension ranges (mean arterial pressure 55 to 59 mmHg) were associated with acute kidney injury (adjusted odds ratio, 1.34; 95% CI, 1.16 to 1.56). Compared with absolute hypotension, relative hypotension demonstrated weak associations with acute kidney injury not replicable in the validation cohort.

CONCLUSIONS

Adult patients undergoing noncardiac surgery demonstrate varying associations with distinct levels of hypotension when stratified by preoperative risk factors. Specific levels of absolute hypotension, but not relative hypotension, are an important independent risk factor for acute kidney injury.

Acute kidney injury risk prediction score for critically-ill surgical patients

Background

There has been a global increase in the incidence of acute kidney injury (AKI), including among critically-ill surgical patients. AKI prediction score provides an opportunity for early detection of patients who are at risk of AKI; however, most of the AKI prediction scores were derived from cardiothoracic surgery. Therefore, we aimed to develop an AKI prediction score for major non-cardiothoracic surgery patients who were admitted to the intensive care unit (ICU).

Methods

The data of critically-ill patients from non-cardiothoracic operations in the Thai Surgical Intensive Care Unit (THAI-SICU) study were used to develop an AKI prediction score. Independent prognostic factors from regression analysis were included as predictors in the model. The outcome of interest was AKI within 7 days after the ICU admission. The AKI diagnosis was made according to the Kidney Disease Improving Global Outcomes (KDIGO)-2012 serum creatinine criteria. Diagnostic function of the model was determined by area under the Receiver Operating Curve (AuROC). Risk scores were categorized into four risk probability levels: low (0–2.5), moderate (3.0–8.5), high (9.0–11.5), and very high (12.0–16.5) risk. Risk of AKI was presented as likelihood ratios of positive (LH+).

Results

A total of 3474 critically-ill surgical patients were included in the model; 333 (9.6%) developed AKI. Using multivariable logistic regression analysis, older age, high Sequential Organ Failure Assessment (SOFA) non-renal score, emergency surgery, large volume of perioperative blood loss, less urine output, and sepsis were identified as independent predictors for AKI. Then AKI prediction score was created from these predictors. The summation of the score was 16.5 and had a discriminative ability for predicting AKI at AuROC = 0.839 (95% CI 0.825–0.852). LH+ for AKI were: low risk = 0.117 (0.063–0.200); moderate risk = 0.927 (0.745–1.148); high risk = 5.190 (3.881–6.910); and very high risk = 9.892 (6.230–15.695), respectively.

Conclusions

The function of AKI prediction score to predict AKI among critically ill patients who underwent non-cardiothoracic surgery was good. It can aid in early recognition of critically-ill surgical patients who are at risk from ICU admission. The scores could guide decision making for aggressive strategies to prevent AKI during the perioperative period or at ICU admission.

Trial registration

TCTR20190408004, registered on April 4, 2019.

Strategies to Reduce Perioperative Nephrotoxicity

Postoperative acute kidney injury (AKI) is a common complication of surgery that is associated with significant adverse outcomes, including increased morbidity and mortality. The perioperative burden of AKI risk factors is complex and potentially large, including high-risk nephrotoxic medications, hypotension, hypovolemia, radiologic contrast, anemia, and surgery-specific factors. Understanding the pathogenesis, risk factors, and potential cumulative impact of perioperative nephrotoxic exposures is particularly important in the prevention and reduction of perioperative AKI. This review outlines the possible strategies to reduce perioperative nephrotoxicity and the development of postoperative AKI.

Acute kidney injury after liver surgery: does postoperative urine output correlate with postoperative serum creatinine?

BACKGROUND

Acute kidney injury (AKI) after hepatectomy occurs in around 10% of cases. AKI is often defined based only on postoperative serum creatinine increase. This study aimed to assess if postoperative urine output (UO) correlated with serum creatinine after hepatectomy.

METHODS

All consecutive hepatectomy patients (2010-2016) were assessed. AKI was defined according to KDIGO criteria: serum creatinine increase ≥26.5 μmol/l, creatinine increase ≥1.5x baseline creatinine, or postoperative oliguria. Oliguria was defined as daily mean UO <0.5 mL/kg/h. AKI was subdivided into creatinine-based or oliguria-based AKI according to the defining criterion.

RESULTS

Out of 285 patients, AKI was observed in 79 cases (28%). Creatinine-based AKI occurred in 25 patients (9%) and oliguria-based only AKI in 54 patients (19%). Ten patients fulfilled both criteria (4%). Postoperative UO correlated poorly with postoperative serum creatinine level in both whole cohort (rho = -0.34, p <0.001) and AKI subgroup (rho = -0.189, p = 0.124). No association was found between postoperative oliguria and postoperative serum creatinine increase (HR = 0.5, 95%CI: 0.2-1.9, p = 0.341). On multivariable analysis, operation duration >360 minutes was the only predictor of creatinine increase (HR = 3.6, 95%CI: 1.1-11.4, p = 0.032).

CONCLUSION

Postoperative UO showed poor correlation with postoperative serum creatinine both in all patients and AKI patients. Surgery duration >360 minutes appeared as the only independent predictor of postoperative serum creatinine increase.

Developing and testing models to predict mortality in the general population

We have previously proposed an approach using information collected from published reports to generate prediction models. The goal of this project was to validate this technique to develop and test various prediction models. A risk indicator (R) is calculated as a linear combination of the hazard ratios for the following predictors: age, male gender, diabetes, albuminuria, and either CKD, CVD or both. We developed a linear and two exponential expressions to predict the probability of the outcome of 2-year mortality and compared to actual outcome in the target dataset from NHANES. The risk indicator demonstrated good performance with area under ROC curve of 0.84. The linear and two exponential expressions generated similar predictions in the lower categories of risk indicator (R ≤ 6). However, in the groups with higher R value, the linear expression tends to predict lower, and the exponential expressions higher, probabilities than the observed outcome. A Combined model which averaged the linear and logistic expressions was shown to approximate the actual outcome data the best. A simple technique (named Woodpecker™) allows derivation functional prediction models and risk stratification tools from reports of clinical outcome studies and their application to new populations by using only summary statistics of the new population.

Acute kidney injury following resection of hepatocellular carcinoma: prognostic value of the acute kidney injury network criteria

Background

Acute kidney injury (AKI) is associated with increased morbidity and mortality after liver resection. Patients with hepatocellular carcinoma (HCC) have a higher risk of AKI owing to the underlying association between hepatic and renal dysfunction. Use of the Acute Kidney Injury Network (AKIN) diagnostic criteria is recommended for patients with cirrhosis, but remains poorly studied following liver resection. We compared the prognostic value of the AKIN creatinine and urine output criteria in terms of postoperative outcomes following liver resection for HCC.

Methods

All patients who underwent a liver resection for HCC from January 2010 to June 2016 were included. We used AKIN urine output and creatinine criteria to assess for AKI within 48 hours of surgery.

Results

Eighty liver resections were performed during the study period. Cirrhosis was confirmed in 80%. Median hospital stay was 9 (interquartile range 7–12) days, and 30-day mortality was 2.5%. The incidence of AKI was higher based on the urine output than on the creatinine criterion (53.8% v. 20%), and was associated with prolonged hospitalization and 30-day postoperative mortality when defined by serum creatinine (hospital stay: 11.2 v. 20.1 d, p = 0.01; mortality: 12.5% v. 0%, p < 0.01), but not urine output (hospital stay: 15.6 v. 10 d, p = 0.05; mortality: 2.3% v. 2.7%, p > 0.99).

Conclusion

The urine output criterion resulted in an overestimation of AKI and compromised the prognostic value of AKIN criteria. Revision may be required to account for the exacerbated physiologic postoperative reduction in urine output in patients with HCC.

Early Clinical and Economic Outcomes of Prophylactic and Acute Pathologic Fracture Treatment

INTRODUCTION:

Pathologic fractures often contribute to adverse events in metastatic bone disease, and prophylactic fixation offers to mitigate their effects. This study aims to analyze patient selection, complications, and in-hospital costs that are associated with prophylactic fixation compared with traditional acute fixation after completed fracture.

MATERIALS AND METHODS:

The Nationwide Inpatient Sample database was queried from 2002 to 2014 for patients with major extremity pathologic fractures. Patients were divided by fixation technique (prophylactic or acute) and fracture location (upper or lower extremity). Patient demographics, comorbidities, complications, hospitalization length, and hospital charges were compared between cohorts. Preoperative variables were analyzed for potential confounding, and χ2 tests and Student’s t tests were used to compare fixation techniques.

RESULTS:

Cumulatively, 43,920 patients were identified, with 14,318 and 28,602 undergoing prophylactic and acute fixation, respectively. Lower extremity fractures occurred in 33,582 patients, and 10,333 patients had upper extremity fractures. A higher proportion of prophylactic fixation patients were white ( P = .043), male ( P = .046), age 74 years or younger ( P < .001), and privately insured ( P < .001), with decreased prevalence of obesity ( P = .003) and/or preoperative renal disease ( P = .008). Prophylactic fixation was also associated with decreased peri- and postoperative blood transfusions ( P < .001), anemia ( P < .001), acute renal failure ( P = .010), and in-hospital mortality ( P = .031). Finally, prophylactic fixation had decreased total charges (−$3,405; P = .001), hospitalization length ( P = .004), and extended length of stay (greater than 75th percentile; P = .012).

CONCLUSION:

Prophylactic fixation of impending pathologic fractures is associated with decreased complications, hospitalization length, and total charges, and should be considered in appropriate patients.

Assessing the Clinical Impact of Risk Models for Opting Out of Treatment

Decision curves are a tool for evaluating the population impact of using a risk model for deciding whether to undergo some intervention, which might be a treatment to help prevent an unwanted clinical event or invasive diagnostic testing such as biopsy. The common formulation of decision curves is based on an opt-in framework. That is, a risk model is evaluated based on the population impact of using the model to opt high-risk patients into treatment in a setting where the standard of care is not to treat. Opt-in decision curves display the population net benefit of the risk model in comparison to the reference policy of treating no patients. In some contexts, however, the standard of care in the absence of a risk model is to treat everyone, and the potential use of the risk model would be to opt low-risk patients out of treatment. Although opt-out settings were discussed in the original decision curve paper, opt-out decision curves are underused. We review the formulation of opt-out decision curves and discuss their advantages for interpretation and inference when treat-all is the standard.

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, similarity between selection results, and AKI prediction performance. Prediction accuracy did not intrinsically guarantee the feature ranking stability. Across different FS methods, the prediction performance did not change significantly, while the importance rankings of features were quite different. A positive correlation was observed between the complexity of suitable FS method and sample size. This study provides several practical implications, including recognizing the importance of feature stability as it is desirable for model reproducibility, identifying important AKI risk factors for further investigation, and facilitating early prediction of AKI.

Severe acute kidney injury following major liver resection without portal clamping: incidence, risk factors, and impact on short-term outcomes

BACKGROUND

Acute kidney injury (AKI) following major hepatectomy (MH) remains inadequately investigated. This retrospective study aimed to assess the risk factors and prognostic value of AKI on short-term outcomes following MH without portal pedicle clamping.

METHODS

From January 2014 through June 2017, 111 consecutive patients underwent MH without portal pedicle clamping, but with intraoperative low-crystalloid infusion. Kidney Disease Improving Global Outcomes stages II and III were classified as severe AKI.

RESULTS

A total of 102 patients did not develop AKI or only AKI stage I (92%, control group), whereas 9 patients developed severe AKI (8%, severe AKI group). Hepatectomy (P = 0.002) and surgery (P = 0.011) durations were longer in the severe AKI group. Clavien-Dindo grades 3 to 5 morbidity (55% versus 9%, P = 0.001), liver failure (P = 0.017), and 90-day mortality (33% versus 2%, P = 0.003) were significantly higher in the severe AKI group. After a multivariate analysis, the duration of hepatectomy (cut-off: 250 min; P = 0.029) and urea serum levels on postoperative day 3 (P = 0.006) were identified as independent predictors of severe AKI.

DISCUSSION

Severe AKI, is common with increased duration of hepatectomy, was associated with poor short-term outcomes, and can be predicted by operative duration greater than 250 minutes.

Calibration drift in regression and machine learning models for acute kidney injury

Objective

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.

Materials and Methods

Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years.

Results

Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods.

Conclusions

Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

Risk factors for and the prevention of acute kidney injury after abdominal surgery

Postoperative acute kidney injury in patients undergoing abdominal surgery is not rare and often results in bad outcomes for patients. The incidence of postoperative acute kidney injury is hard to evaluate reliably due to its non-unified definitions in different studies. Risk factors for acute kidney injury specific to abdominal surgery include preoperative renal insufficiency, intraabdominal hypertension, blood transfusion, bowel preparation, perioperative dehydration, contrast agent and nephrotoxic drug use. Among these, preoperative renal insufficiency is the strongest predictor of acute kidney injury. The peri-operative management of high-risk patients should include meticulous selection of fluid solutions. Balanced crystalloid solutions and albumin are generally thought to be relatively safe, while the safety of hydroxyethyl starch solutions has been controversial. The purpose of the present review is to discuss the current knowledge regarding postoperative acute kidney injury in abdominal surgical settings to help surgeons make better decisions concerning the peri-operative management.

Diagnostic value of plasma NGAL and intraoperative diuresis for AKI after major gynecological surgery in patients treated within an intraoperative goal-directed hemodynamic algorithm: A substudy of a randomized controlled trial

Data on early markers for acute kidney injury (AKI) after noncardiovascular surgery are still limited. This study aimed to determine the diagnostic value of plasma neutrophil-gelatinase-associated lipocalin (pNGAL) and intraoperative diuresis for AKI in patients undergoing major abdominal surgery treated within a goal-directed hemodynamic algorithm.This study is a post-hoc analysis of a randomized controlled pilot trial comparing intravenous solutions within a hemodynamic goal-directed algorithm based on the esophageal Doppler in patients undergoing epithelial ovarian cancer surgery. The diagnostic value of plasma NGAL obtained at ICU admission and intraoperative diuresis was determined with respect to patients already meeting AKI criteria 6 hours after surgery (AKI6h) and to all patients meeting AKI criteria at least once during the postoperative course (AKItotal). AKI was diagnosed by the definition of the Kidney Disease Improving Global Outcome (KDIGO) group creatinine criteria and was screened up to postoperative day 3. Receiver operating characteristic curves including a gray zone approach were performed.A total of 48 patients were analyzed. None of the patients had increased creatinine levels before surgery and 14 patients (29.2%) developed AKI after surgery. Plasma NGAL was predictive for AKI6h (AUCAKI6h 0.832 (95% confidence interval [CI], 0.629-0.976), P = .001) and AKItotal (AUCAKItotal 0.710 (CI 0.511-0.878), P = .023). The gray zones of pNGAL calculated for AKI6h and AKItotal were 210 to 245 and 207 to 274 ng mL, respectively. The lower cutoffs of the gray zone at 207 and 210 ng mL had a negative predictive value (NPV) (i.e., no AKI during the postoperative course) of 96.8% (CI 90-100) and 87.1% (CI 78-97), respectively. Intraoperative diuresis was also predictive for AKI6h (AUCAKI6h 0.742 (CI 0.581-0.871), P = .019) with a gray zone of 0.5 to 2.0 mL kg h. At the lower cutoff of the gray zone at 0.5 mL kg h, corresponding to the oliguric threshold, the NPV was 84.2% (78-92).This study indicates that pNGAL can be used as an early marker to rule out AKI occurring within 3 days after major abdominal surgery. Intraoperative diuresis can be used to rule out AKI occurring up to 6 hours after surgery.

TRIAL REGISTRATION

ISRCTN 53154834.

Acute kidney injury following hepatectomy for hepatocellular carcinoma: incidence, risk factors and prognostic value

BACKGROUND

Acute kidney injury (AKI) following hepatectomy remains understudied in terms of diagnosis, severity, recovery and prognostic value. The aim of this study was to assess the risk factors and prognostic value of AKI on short- and long-term outcomes following hepatectomy for hepatocellular carcinoma (HCC).

METHOD

This is a retrospective analysis of a single-center cohort of 457 consecutive patients who underwent hepatectomy for HCC. The KDIGO criteria were used for AKI diagnosis. The incidence, risk factors, and prognostic value of AKI were investigated.

RESULTS

AKI occurred in 67 patients (15%). The mortality and major morbidity rates were significantly higher in patients with AKI (37% and 69%) than in those without (6% and 22%; p < 0.001). Renal recovery was complete in 35 (52%), partial in 25 (37%), and absent in 7 (11%) patients. Advanced age, an increased MELD score, major hepatectomy and prolonged duration of operation were identified as independent predictors of AKI. AKI was identified as the strongest independent predictor of postoperative mortality but did not impact survival.

CONCLUSION

AKI is a common complication after hepatectomy for HCC. Although its development is associated with poor short-term outcomes, it does not appear to be predictive of impaired long-term survival.

Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man's risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.

Risk prediction models for acute kidney injury following major noncardiac surgery: systematic review

BACKGROUND

Acute kidney injury (AKI) is a serious complication of major noncardiac surgery. Risk prediction models for AKI following noncardiac surgery may be useful for identifying high-risk patients to target with prevention strategies.

METHODS

We conducted a systematic review of risk prediction models for AKI following major noncardiac surgery. MEDLINE, EMBASE, BIOSIS Previews and Web of Science were searched for articles that (i) developed or validated a prediction model for AKI following major noncardiac surgery or (ii) assessed the impact of a model for predicting AKI following major noncardiac surgery that has been implemented in a clinical setting.

RESULTS

We identified seven models from six articles that described a risk prediction model for AKI following major noncardiac surgeries. Three studies developed prediction models for AKI requiring renal replacement therapy following liver transplantation, three derived prediction models for AKI based on the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE) criteria following liver resection and one study developed a prediction model for AKI following major noncardiac surgical procedures. The final models included between 4 and 11 independent variables, and c-statistics ranged from 0.79 to 0.90. None of the models were externally validated.

CONCLUSIONS

Risk prediction models for AKI after major noncardiac surgery are available; however, these models lack validation, studies of clinical implementation and impact analyses. Further research is needed to develop, validate and study the clinical impact of such models before broad clinical uptake.

Risk of postoperative acute kidney injury in patients undergoing orthopaedic surgery--development and validation of a risk score and effect of acute kidney injury on survival: observational cohort study

STUDY QUESTION

What is the predicted risk of acute kidney injury after orthopaedic surgery and does it affect short term and long term survival?

METHODS

The cohort comprised adults resident in the National Health Service Tayside region of Scotland who underwent orthopaedic surgery from 1 January 2005 to 31 December 2011. The model was developed in 6220 patients (two hospitals) and externally validated in 4395 patients from a third hospital. Several preoperative variables were selected for candidate predictors, based on literature, clinical expertise, and availability in the orthopaedic surgery setting. The main outcomes were the development of any severity of acute kidney injury (stages 1-3) within the first postoperative week, and 90 day, one year, and longer term survival.

STUDY ANSWER AND LIMITATIONS

Using logistic regression analysis, independent predictors of acute kidney injury were older age, male sex, diabetes, number of prescribed drugs, lower estimated glomerular filtration rate, use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers, and American Society of Anesthesiologists grade. The model's predictive performance for discrimination was good (C statistic 0.74 in development cohort, 0.70 in validation cohort). Calibration was good in the development cohort and after recalibration in the validation cohort. Only the highest risks were over-predicted. Survival was worse in patients with acute kidney injury compared with those without (adjusted hazard ratio 1.53, 95% confidence interval 1.38 to 1.70). This was most noticeable in the short term (adjusted hazard ratio: 90 day 2.36, 1.94 to 2.87) and diminished over time (90 day-one year 1.40, 1.10 to 1.79; >1 year 1.28, 1.10 to 1.48). The model used routinely collected data in the orthopaedic surgery setting therefore some variables that could potentially improve predictive performance were not available. However, the readily available predictors make the model easily applicable.

WHAT THIS STUDY ADDS

A preoperative risk prediction model consisting of seven predictors for acute kidney injury was developed, with good predictive performance in patients undergoing orthopaedic surgery. Survival was significantly poorer in patients even with mild (stage 1) postoperative acute kidney injury.

FUNDING, COMPETING INTERESTS, DATA SHARING

SB received grants from Tenovus Tayside, Chief Scientist Office, and the Royal College of Physicians and Surgeons of Glasgow; PT receives grants from Novo Nordisk, GlaxoSmithKline, and the New Drugs Committee of the Scottish Medicines Consortium. No additional data are available.

National Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury

OBJECTIVE

Hospital-acquired acute kidney injury (HA-AKI) is a potentially preventable cause of morbidity and mortality. Identifying high-risk patients prior to the onset of kidney injury is a key step towards AKI prevention.

MATERIALS AND METHODS

A national retrospective cohort of 1,620,898 patient hospitalizations from 116 Veterans Affairs hospitals was assembled from electronic health record (EHR) data collected from 2003 to 2012. HA-AKI was defined at stage 1+, stage 2+, and dialysis. EHR-based predictors were identified through logistic regression, least absolute shrinkage and selection operator (lasso) regression, and random forests, and pair-wise comparisons between each were made. Calibration and discrimination metrics were calculated using 50 bootstrap iterations. In the final models, we report odds ratios, 95% confidence intervals, and importance rankings for predictor variables to evaluate their significance.

RESULTS

The area under the receiver operating characteristic curve (AUC) for the different model outcomes ranged from 0.746 to 0.758 in stage 1+, 0.714 to 0.720 in stage 2+, and 0.823 to 0.825 in dialysis. Logistic regression had the best AUC in stage 1+ and dialysis. Random forests had the best AUC in stage 2+ but the least favorable calibration plots. Multiple risk factors were significant in our models, including some nonsteroidal anti-inflammatory drugs, blood pressure medications, antibiotics, and intravenous fluids given during the first 48 h of admission.

CONCLUSIONS

This study demonstrated that, although all the models tested had good discrimination, performance characteristics varied between methods, and the random forests models did not calibrate as well as the lasso or logistic regression models. In addition, novel modifiable risk factors were explored and found to be significant.

Epidural analgesia and perioperative kidney function after major liver resection

BACKGROUND

Epidural analgesia (EDA) is a common analgesia regimen in liver resection, and is accompanied by sympathicolysis, peripheral vasodilatation and hypotension in the context of deliberate intraoperative low central venous pressure. This associated fall in mean arterial pressure may compromise renal blood pressure autoregulation and lead to acute kidney injury (AKI). This study investigated whether EDA is a risk factor for postoperative AKI after liver surgery.

METHODS

The incidence of AKI was investigated retrospectively in patients who underwent liver resection with or without EDA between 2002 and 2012. Univariable and multivariable analyses were performed including recognized preoperative and intraoperative predictors of posthepatectomy renal failure.

RESULTS

A series of 1153 patients was investigated. AKI occurred in 8·2 per cent of patients and was associated with increased morbidity (71 versus 47·3 per cent; P = 0·003) and mortality (21 versus 0·3 per cent; P <  0·001) rates. The incidence of AKI was significantly higher in the EDA group (10·1 versus 3·7 per cent; P = 0·003). Although there was no significant difference in the incidence of AKI between patients undergoing minor hepatectomy with or without EDA (5·2 versus 2·7 per cent; P = 0·421), a substantial difference in AKI rates occurred in patients undergoing major hepatectomy (13·8 versus 5·0 per cent; P = 0·025). In multivariable analysis, EDA remained an independent risk factor for AKI after hepatectomy (P = 0·040).

CONCLUSION

EDA may be a risk factor for postoperative AKI after major hepatectomy.