A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study

Prognostic significance of preoperative nutritional status for postoperative acute kidney injury in older patients undergoing major abdominal surgery: a retrospective cohort study

BACKGROUND

The association between malnutrition and postoperative acute kidney injury (AKI) has not been well studied. In this study, the authors examined the association between preoperative nutritional status and postoperative AKI in older patients who underwent major abdominal surgery, as well as the predictive value of malnutrition for AKI.

MATERIALS AND METHODS

The authors retrospectively included patients aged 65 or older who underwent major elective abdominal surgery. The nutritional status of the patient was evaluated using three objective nutritional indices, such as the geriatric nutritional risk index (GNRI), the prognostic nutritional index (PNI), and the controlling nutritional status (CONUT). AKI was determined using the KDIGO criteria. The authors performed logistic regression analysis to investigate the association between preoperative nutritional status and postoperative AKI, as well as the predictive value of nutritional scores for postoperative AKI.

RESULTS

A total of 2775 patients were included in the study, of which 707 (25.5%), 291 (10.5%), and 517 (18.6%) had moderate to severe malnutrition according to GNRI, PNI, and CONUT calculations. After surgery, 144 (5.2%) patients developed AKI, 86.1% at stage 1, 11.1% at stage 2, and 2.8% at stage 3 as determined by KDIGO criteria. After adjustment for traditional risk factors, worse nutritional scores were associated with a higher AKI risk. In addition to traditional risk factors, these nutritional indices improved the predictive ability of AKI prediction models, as demonstrated by significant improvements in integrated discrimination and net reclassification.

CONCLUSIONS

Poor preoperative nutritional status, as assessed by GNRI, PNI, and CONUT scores, was associated with an increased risk of postoperative AKI. Incorporating these scores into AKI prediction models improved their performance. These findings emphasize the need for screening surgical patients for malnutrition risk. Further research is needed to determine whether preoperative malnutrition assessment and intervention can reduce postoperative AKI incidence.

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.

Intraoperative Hypertension Is Associated with Postoperative Acute Kidney Injury after Laparoscopic Surgery

Background: It is well demonstrated that intraoperative blood pressure is associated with postoperative acute kidney injury (AKI); however, the association between severity and duration of abnormal intraoperative blood pressure (BP) with AKI in patients undergoing laparoscopic surgery remains unknown. Methods: This retrospective cohort study included 12,414 patients aged ≥ 18 years who underwent a single elective laparoscopic abdominal surgery during hospitalization between October 2011 and April 2017. Multivariate stepwise logistic regressions were applied to determine the correlation between the severity and duration of intraoperative mean arterial pressure (MAP, (systolic BP + 2 × diastolic BP)/3), acute intraoperative hypertension (IOTH) and postoperative AKI, in different periods of surgery. Results: A total of 482 hospitalized patients (3.9%) developed surgery-related AKI. Compared with those without IOTH or with preoperative mean MAP (80–85 mmHg), acute elevated IOTH (odds ratio, OR, 1.4, 95% CI, 1.1 to 1.7), mean MAP 95–100 mmHg (OR, 1.8; 95% CI, 1.3 to 2.7), MAP 100–105 mmHg (OR, 2.4; 95% CI, 1.6 to 3.8), and more than 105 mmHg (OR, 1.9; 95% CI, 1.1 to 3.3) were independent of other risk factors in a diverse cohort undergoing laparoscopic surgery. In addition, the risk of postoperative AKI appeared to result from long exposure (≥20 min) to IOTH (OR, 1.9; 95% CI, 1.5 to 2.5) and MAP ≥ 115 mmHg (OR, 2.2; 95% CI, 1.6 to 3.0). Intraoperative hypotension was not found to be associated with AKI in laparoscopic surgery patients. Conclusions: Postoperative AKI correlates positively with intraoperative hypertension in patients undergoing laparoscopic surgery. These findings provide an intraoperative evaluation criterion to predict the occurrence of postoperative AKI.

Risk factors of perioperative acute kidney injury in elderly patients: a single-center retrospective study

PURPOSE

The elderly are vulnerable to perioperative acute kidney injury. The aim of this study was to determine the risk factors that associated with acute kidney injury among elderly patients.

METHODS

Clinical data of elderly patients (> 65 years) who underwent noncardiac, nonrenal surgery between Dec 1 2009 and July 1 2016 were collected for this single-centered historical cohort study. Univariate and multivariate analyses were conducted to explore the risk factors that contribute to acute kidney injury, which was defined as a serum creatinine increase >0.3 mg/dL within 48 h or 1.5 times increase in serum creatinine within 7 days after surgery.

RESULTS

7775 patients were eligible for the final analysis, among which 511 (6.57%) patients developed acute kidney injury. We observed 21 risk factors being associated with perioperative acute kidney injury, with the most important disposing factors being history of kidney disease (adjusted OR = 2.94, 95% CI 2.25-3.84), operation time > 180 min (aOR = 2.93, 95% CI 2.04-4.24), preoperative eGFR [15, 30) (aOR = 2.43, 95% CI 1.29-4.45), and protective factor being intraoperative use of sufentanil (aOR = 0.35, 95% CI 0.23-0.54).

CONCLUSION

This study determined risk factors for perioperative acute kidney injury among the elderly in the Second Xiangya Hospital and visualized the risk factors using nomogram and Excel calculator, which may provide some clues to further investigations. Overall, the prevalence of AKI among this large cohort is 6.57%.

CLINICAL TRIALS REGISTRATION

http://www.chictr.org.cn ; ChiCTR1900027007; October 28, 2019.

Pre-operative proteinuria, postoperative acute kidney injury and mortality: A systematic review and meta-analysis

OBJECTIVE

To investigate the association of pre-operative proteinuria with postoperative acute kidney injury (AKI) development as well as the requirement for a renal replacement therapy (RRT) and mortality at short-term and long-term follow-up.

BACKGROUND

Postoperative AKI is associated with surgical morbidity and mortality. Pre-operative proteinuria is potentially a risk factor for postoperative AKI and mortality. However, the results in literature are conflicting.

METHODS

We searched PubMed, Embase, Scopus, Web of Science and Cochrane Library from the inception through to 3 June 2020. Observational cohort studies investigating the association of pre-operative proteinuria with postoperative AKI development, requirement for RRT, and all-cause mortality at short-term and long-term follow-up were considered eligible. Using inverse variance method with a random-effects model, the pooled effect estimates and 95% confidence interval (CI) were calculated.

RESULTS

Twenty-eight studies were included. Pre-operative proteinuria was associated with postoperative AKI development [odds ratio (OR) 1.74, 95% CI, 1.45 to 2.09], in-hospital RRT (OR 1.70, 95% CI, 1.25 to 2.32), requirement for RRT at long-term follow-up [hazard ratio (HR) 3.72, 95% CI, 2.03 to 6.82], and long-term all-cause mortality (hazard ratio 1.50, 95% CI, 1.30 to 1.73). In the subgroup analysis, pre-operative proteinuria was associated with increased odds of postoperative AKI in both cardiovascular (OR 1.77, 95% CI, 1.47 to 2.14) and noncardiovascular surgery (OR 1.63, 95% CI, 1.01 to 2.63). Moreover, there is a stepwise increase in OR of postoperative AKI development when the quantity of proteinuria increases from trace to 3+.

CONCLUSION

Pre-operative proteinuria is significantly associated with postoperative AKI and long-term mortality. Pre-operative anaesthetic assessment should take into account the presence of proteinuria to identify high-risk patients.

PROSPERO REGISTRATION

CRD42020190065.

Novel risk models to predict acute kidney disease and its outcomes in a Chinese hospitalized population with acute kidney injury

Acute kidney disease (AKD) is a state between acute kidney injury (AKI) and chronic kidney disease (CKD), but the prognosis of AKD is unclear and there are no risk-prediction tools to identify high-risk patients. 2,556 AKI patients were selected from 277,898 inpatients of three affiliated hospitals of Central South University from January 2015 to December 2015. The primary point was whether AKI patients developed AKD. The endpoint was death or end stage renal disease (ESRD) 90 days after AKI diagnosis. Multivariable Cox regression was used for 90-day mortality and two prediction models were established by using multivariable logistic regression. Our study found that the incidence of AKD was 53.17% (1,359/2,556), while the mortality rate and incidence of ESRD in AKD cohort was 19.13% (260/1,359) and 3.02% (41/1,359), respectively. Furthermore, adjusted hazard ratio of mortality for AKD versus no AKD was 1.980 (95% CI 1.427–2.747). In scoring model 1, age, gender, hepatorenal syndromes, organic kidney diseases, oliguria or anuria, respiratory failure, blood urea nitrogen (BUN) and acute kidney injury stage were independently associated with AKI progression into AKD. In addition, oliguria or anuria, respiratory failure, shock, central nervous system failure, malignancy, RDW-CV ≥ 13.7% were independent risk factors for death or ESRD in AKD patients in scoring model 2 (goodness-of fit, P₁ = 0.930, P₂ = 0.105; AUROC₁ = 0.879 (95% CI 0.862–0.896), AUROC₂ = 0.845 (95% CI 0.813–0.877), respectively). Thus, our study demonstrated AKD was independently associated with increased 90-day mortality in hospitalized AKI patients. A new prediction model system was able to predict AKD following AKI and 90-day prognosis of AKD patients to identify high-risk patients.

Big Data in Total Shoulder Arthroplasty: An In-depth Comparison of National Outcomes Databases

INTRODUCTION

The practice of identifying trends in surgical decision-making through large-scale patient databases is commonplace. We hypothesize that notable differences exist between claims-based and prospectively collected clinical registries.

METHODS

We queried the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), a prospective surgical outcomes database, and PearlDiver (PD), a claims-based private insurance database, for patients undergoing primary total shoulder arthroplasties from 2007 to 2016. Comorbidities and 30-day complications were compared. Multiple regression analysis was performed for each cohort to identify notable contributors to 30-day revision surgery.

RESULTS

Significant differences were observed in demographics, comorbidities, and postoperative complications for the age-matched groups between PD and NSQIP (P < 0.05 for all). Multiple regression analysis in PD identified morbid obesity and dyspnea to lead to an increased risk for revision surgery (P = 0.001) in the <65 cohort and dyspnea and diabetes to lead to an increased risk for revision surgery in the ≥65 cohort (P = 0.015, P < 0.001). Multiple regression did not reveal any risk factors for revision surgery in the <65 age group for the NSQIP; however, congestive heart failure was found to have an increased risk for revision surgery in the ≥65 cohort (P < 0.001).

CONCLUSIONS

Notable differences in comorbidities and complications for patients undergoing primary total shoulder arthroplasty were present between PD and NSQIP.

LEVEL OF EVIDENCE

Retrospective cohort study, level III.

Female sex reduces the risk of hospital-associated acute kidney injury: a meta-analysis

Background

Female sex has been included as a risk factor in models developed to predict the development of AKI. In addition, the commentary to the Kidney Disease Improving Global Outcomes Clinical Practice Guideline for AKI concludes that female sex is a risk factor for hospital-acquired AKI. In contrast, a protective effect of female sex has been demonstrated in animal models of ischemic AKI.

Methods

To further explore this issue, we performed a meta-analysis of AKI studies published between January, 1978 and April, 2018 and identified 83 studies reporting sex-stratified data on the incidence of hospital-associated AKI among nearly 240,000,000 patients.

Results

Twenty-eight studies (6,758,124 patients) utilized multivariate analysis to assess risk factors for hospital-associated AKI and provided sex-stratified ORs. Meta-analysis of this cohort showed that the risk of developing hospital-associated AKI was significantly greater in men than in women (OR 1.23 (1.11,1.36). Since AKI is not a single disease but instead represents a heterogeneous group of disorders characterized by an acute reduction in renal function, we performed subgroup meta-analyses. The association of male sex with AKI was strongest among studies of patients who underwent non-cardiac surgery. Male sex was also associated with AKI in studies which included unselected hospitalized patients and in studies of critically ill patients who received care in an intensive care unit. In contrast, cardiac surgery-associated AKI and radiocontrast-induced AKI showed no sexual dimorphism.

Conclusions

Our meta-analysis contradicts the established belief that female sex confers a greater risk of AKI and instead suggests a protective role.

Predicting acute kidney injury in cancer patients using heterogeneous and irregular data

How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days.