Association Between Elevated Echocardiographic Index of Left Ventricular Filling Pressure and Acute Kidney Injury After Off-Pump Coronary Artery Surgery

Association Between Left Ventricular Relative Wall Thickness and Acute Kidney Injury After Noncardiac Surgery

BACKGROUND

Acute kidney injury (AKI) after major noncardiac surgery is commonly attributed to cardiovascular dysfunction. Identifying novel associations between preoperative cardiovascular markers and kidney injury may guide risk stratification and perioperative intervention. Increased left ventricular relative wall thickness (RWT), routinely measured on echocardiography, is associated with myocardial dysfunction and long-term risk of heart failure in patients with preserved left ventricular ejection fraction (LVEF); however, its relationship to postoperative complications has not been studied. We evaluated the association between preoperative RWT and AKI in high-risk noncardiac surgical patients with preserved LVEF.

METHODS

Patients ≥18 years of age having major noncardiac surgery (high-risk elective intra-abdominal or noncardiac intrathoracic surgery) between July 1, 2016, and June 30, 2018, who had transthoracic echocardiography in the previous 12 months were eligible. Patients with preoperative creatinine ≥2 mg/dL or reduced LVEF (<50%) were excluded. The association between RWT and AKI, defined as an increase in serum creatinine by 0.3 mg/dL from baseline within 48 hours or by 50% within 7 days after surgery, was assessed using multivariable logistic regression adjusted for preoperative covariates. An additional model adjusted for intraoperative covariates, which are strongly associated with AKI, especially hypotension. RWT was modeled continuously, associating the change in odds of AKI for each 0.1 increase in RWT.

RESULTS

The study included 1041 patients (mean ± standard deviation [SD] age 62 ± 15 years; 59% female). A total of 145 subjects (13.9%) developed AKI within 7 days. For RWT quartiles 1 through 4, respectively, 20 of 262 (7.6%), 40 of 259 (15.4%), 39 of 263 (14.8%), and 46 of 257 (17.9%) developed AKI. Log-odds and proportion with AKI increased across the observed RWT values. After adjusting for confounders (demographics, American Society of Anesthesiologists [ASA] physical status, comorbidities, baseline creatinine, antihypertensive medications, and left ventricular mass index), each RWT increase of 0.1 was associated with an estimated 26% increased odds of developing AKI (odds ratio [OR]; 95% confidence interval [CI]) of 1.26 (1.09-1.46; P = .002). After adjusting for intraoperative covariates (length of surgery, presence of an arterial line, intraoperative hypotension, crystalloid administration, transfusion, and urine output), RWT remained independently associated with the odds of AKI (OR; 95% CI) of 1.28 (1.13-1.47; P = .001). Increased RWT was also independently associated with hospital length of stay and adjusted hazard ratio (HR [95% CI]) of 0.94 (0.89-0.99; P = .018).

CONCLUSIONS

Left ventricular RWT is a novel cardiovascular factor associated with AKI within 7 days after high-risk noncardiac surgery among patients with preserved LVEF. Application of this commonly available measurement of risk stratification or perioperative intervention warrants further investigation.

A Novel Radiomics-Based Machine Learning Framework for Prediction of Acute Kidney Injury-Related Delirium in Patients Who Underwent Cardiovascular Surgery

Acute kidney injury (AKI) can be caused by multiple etiologies and is characterized by a sudden and severe decrease in kidney function. Understanding the independent risk factors associated with the development of AKI and its early detection can refine the risk management and clinical decision-making of high-risk patients after cardiovascular surgery. A retrospective analysis was performed in a single teaching hospital between December 1, 2019, and December 31, 2020. The diagnostic performance of novel biomarkers was assessed using random forest, support vector machine, and multivariate logistic regression. The nomogram from multivariate analysis of risk factors associated with AKI indicated that only LVEF, red blood cell input, and ICUmvat contribute to AKI differentiation and that the difference is statistically significant (P < 0.05). Seven radiomics biomarkers were found among 65 patients to be highly correlated with AKI-associated delirium. The importance of the variables was determined using the multilayer perceptron model; fivefold cross-validation was applied to determine the most important delirium risk factors in radiomics of the hippocampus. Finally, we established a radiomics-based machine learning framework to predict AKI-induced delirium in patients who underwent cardiovascular surgery.

Association between left ventricular diastolic dysfunction and septic acute kidney injury in severe sepsis and septic shock: A multicenter retrospective study

INTRODUCTION

Left ventricular diastolic dysfunction (LVDD) adversely impacts renal function, and E/e' is a significant predictor of adverse kidney events under different clinical conditions. However, no studies have evaluated the association between LVDD and septic acute kidney injury (AKI) among patients with severe sepsis and septic shock.

METHODS

This multicenter retrospective study evaluated adult patients with severe sepsis or septic shock between January 1, 2013, and December 31, 2019, who underwent echocardiography within 24 hours after admission to an intensive care unit.

RESULTS

A total of 495 adult patients were enrolled in the study. LVDD grades II and III were associated with severe (stage 3) AKI (p < 0.001, p for trend < 0.001). E/e' and e' were risk factors for septic AKI (OR, 1.155; 95% CI, 1.088-1.226, p < 0.001; and OR, 7.218; 95% CI, 2.942-17.712, p < 0.001, respectively) in the multivariate logistic regression analysis. The area under the receiver operating characteristic curve of E/e' and e' was 0.728 (95% CI, 0.680-0.777, p < 0.001) and 0.715 (95% CI, 0.665-0.764, p < 0.001), respectively.

CONCLUSIONS

LVDD was associated with septic AKI, and E/e' and e' are useful predictors of septic AKI among patients with severe sepsis or septic shock.

TRIAL REGISTRATION

The study was registered at the Chinese Clinical Trial Registry (Protocol No. ChiCTR2000033083).

Diastolic dysfunction and acute kidney injury in elderly patients with femoral neck fracture

Background

Femoral neck fracture is common in the elderly population. Acute kidney injury (AKI) is an important risk factor for mortality in patients who have had such fracture. We evaluated the incidence of AKI in patients who had femoral neck fracture and identified risk factors for AKI and mortality.

Methods

This was an observational cohort study including 285 patients who were ≥ 65 years of age and who underwent femoral neck fracture surgery between 2013 and 2017.

Results

The mean age was 78.63 ± 6.75 years. A total of 67 (23.5%) patients developed AKI during the hospital stay: 57 (85.1%), 5 (7.5%), and 5 (7.5%) patients were classified as having stage 1, 2, and 3 AKI, respectively. Patients with AKI had a lower baseline estimated glomerular filtration rate and higher left atrial dimension, left ventricular mass index, pulmonary artery pressure, and the ratio of early mitral inflow velocity to early diastolic mitral annulus velocity (E/e’) and were more likely to have diabetes or hypertension (HTN) (P < 0.05). The presence of HTN (odds ratio [OR], 4.570; 95% confidence interval [CI], 1.632–12.797) higher E/e’ (OR, 1.105; 95% CI, 1.019–1.198), and lower hemoglobin (OR, 0.704; 95% CI, 0.528–0.938) were independently associated with a higher risk for developing AKI. Severe AKI (OR, 24.743; 95% CI, 2.822–212.401) was associated with a higher risk of mortality.

Conclusion

Elderly patients with femoral neck fracture had a high incidence of AKI. Diastolic dysfunction was associated with AKI. Severe AKI was associated with in-hospital mortality.

Systolic and diastolic dysfunction affects kidney outcomes in hospitalized patients

Backgrounds

Knowledge on cross-talk between the heart and kidney has been established by basic and clinical research. Nevertheless, the effects of systolic and diastolic heart dysfunctions on the development of acute kidney injury (AKI) and end-stage renal disease (ESRD) remain unresolved in hospitalized patients.

Methods

A total of 1327 hospitalized patients who had baseline transthoracic echocardiography performed were retrospectively analyzed. Patients were categorized by the quartiles of ejection fraction (EF) and the ratio of the early transmitral blood flow velocity to early diastolic velocity of the mitral annulus (E/e’). The odds ratios (ORs) for AKI and the hazard ratios (HRs) for ESRD were calculated after adjustment of multiple covariates.

Results

During hospital admission, AKI occurred in 210 (15.8%) patients. The lowest quartile of EF was associated with a risk of AKI (OR, 1.60 [1.07–2.41]) and the highest quartile of E/e’ was associated with a risk of AKI (OR, 1.90 [1.26–2.41]). When two echocardiographic parameters were combined, patients with a low EF (first to second quartiles) and high E/e’ (fourth quartile) showed the highest OR for AKI (OR, 2.27 [1.49–3.45]) compared with the counterpart patients. When the risk of ESRD was evaluated, E/e’, but not EF, was a significant parameter of high risk (fourth vs. first quartiles: HR, 4.13 [1.17–14.64]).

Conclusions

Baseline systolic and diastolic dysfunction is related to subsequent risks of AKI and ESRD in hospitalized patients. Monitoring of these parameters may be a useful strategy to predict the risk of these adverse events in the kidney.

Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data were obtained. The primary outcome was postoperative acute kidney injury (AKI) defined according to the Kidney Disease Improving Global Outcomes criteria. The following machine learning techniques were used: decision tree, random forest, extreme gradient boosting, support vector machine, neural network classifier, and deep learning. The performance of these techniques was compared with that of logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUC). During the first postoperative week, AKI occurred in 770 patients (38.3%). The best performance regarding AUC was achieved by the gradient boosting machine to predict the AKI of all stages (0.78, 95% confidence interval (CI) 0.75–0.80) or stage 2 or 3 AKI. The AUC of logistic regression analysis was 0.69 (95% CI 0.66–0.72). Decision tree, random forest, and support vector machine showed similar performance to logistic regression. In our comprehensive comparison of machine learning approaches with logistic regression analysis, gradient boosting technique showed the best performance with the highest AUC and lower error rate. We developed an Internet–based risk estimator which could be used for real-time processing of patient data to estimate the risk of AKI at the end of surgery.

Perioperative Echocardiographic Index of Left Ventricular Filling Pressure in Cardiac Surgery

BACKGROUND

The elevated preoperative ratio of early transmitral flow velocity to early diastolic velocity of the mitral annulus (E/e') as an echocardiographic index of left ventricular filling pressure is known to be associated with poor postoperative outcomes. We investigated the association between preoperative and postoperative E/e' elevation and clinical outcomes after cardiac operations.

METHODS

The study divided 1,353 patients who underwent cardiac operations into four groups: preoperative and postoperative E/e' ≤15 (low-low), preoperative E/e' ≤15 but postoperative E/e' >15 (low-high), preoperative E/e' >15 but postoperative E/e' ≤15 (high-low), and preoperative and postoperative E/e' >15 (high-high). Cox proportional hazard analysis was performed. Kaplan-Meier curve analysis was performed before and after propensity score matching.

RESULTS

The four perioperative E/e' categories were independently associated with 5-year mortality (hazard ratio, high-high vs low-low: 3.58; low-high vs low-low: 3.75; high-low vs low-low: 1.18). Kaplan-Meier curves showed that mortality was significantly different between the groups (log-rank test: high-high vs. low-low, p < 0.001; low-high vs low-low, p < 0.001). Postoperative intensive care unit and hospital lengths of stay, incidence of acute kidney injury, and 1-year mortality were significantly different. However, after propensity score matching, mortality and the incidence of postoperative acute kidney injury were significantly different only between postoperative E/e' ≤15 and E/e' >15, but not between preoperative E/e' ≤15 and E/e' >15.

CONCLUSIONS

Postoperative E/e' >15 was more strongly associated with mortality and acute kidney injury than preoperative E/e' >15. Measurement of the postoperative E/e' ratio may help in assessing the risk of these patients.