scholarly journals Effects of Anesthetic Technique on the Occurrence of Acute Kidney Injury after Spine Surgery: A Retrospective Cohort Study

2021 ◽  
Vol 10 (23) ◽  
pp. 5653
Author(s):  
Jiwon Han ◽  
Ah-Young Oh ◽  
Chang-Hoon Koo ◽  
Yu Kyung Bae ◽  
Yong-Tae Jeon
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Spine Surgery ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Volatile Anesthetics

The effects of anesthetics on acute kidney injury (AKI) after spine surgery have not been evaluated fully. This study compared propofol-based total intravenous anesthesia (TIVA) and volatile anesthetics in the development of AKI after spine surgery. This retrospective study reviewed patients who underwent spine surgery between 2015 and 2019. A logistic regression analysis was performed to identify risk factors for AKI. Additionally, after propensity score matching, the incidence of AKI was compared between TIVA and volatile groups. Of the 4473 patients, 709 were excluded and 3764 were included in the logistic regression. After propensity score matching, 766 patients from each group were compared, and we found that the incidence of AKI was significantly lower in the TIVA group (1% vs. 4.2%, p < 0.001). In the multivariate logistic regression analysis, the risk factors for postoperative AKI were male sex (OR 1.85, 95% CI 1.18–3.06), hypertension (OR 2.48, 95% CI 1.56–3.94), anemia (OR 2.66, 95% CI 1.76–4.04), and volatile anesthetics (OR 4.69, 95% CI 2.24–9.84). Compared with volatile anesthetics, TIVA is associated with a reduced risk of AKI for patients who have undergone spine surgery.

scholarly journals Elevated De Ritis Ratio as a Predictor for Acute Kidney Injury after Radical Retropubic Prostatectomy

2021 ◽  
Vol 11 (9) ◽  
pp. 836
Author(s):  
Jun-Young Park ◽  
Jihion Yu ◽  
Jun Hyuk Hong ◽  
Bumjin Lim ◽  
Youngdo Kim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Kidney Injury ◽  
Radical Retropubic Prostatectomy ◽  
Multivariable Logistic Regression Analysis ◽  
Retropubic Prostatectomy

Acute kidney injury (AKI) is related to mortality and morbidity. The De Ritis ratio, calculated by dividing the aspartate aminotransferase by the alanine aminotransferase, is used as a prognostic indicator. We evaluated risk factors for AKI after radical retropubic prostatectomy (RRP). This retrospective study included patients who performed RRP. Multivariable logistic regression analysis and a receiver operating characteristic (ROC) curve analysis were conducted. Other postoperative outcomes were also evaluated. Among the 1415 patients, 77 (5.4%) had AKI postoperatively. The multivariable logistic regression analysis showed that estimated glomerular filtration rate, albumin level, and the De Ritis ratio at postoperative day 1 were risk factors for AKI. The area under the ROC curve of the De Ritis ratio at postoperative day 1 was 0.801 (cutoff = 1.2). Multivariable-adjusted analysis revealed that the De Ritis ratio at ≥1.2 was significantly related to AKI (odds ratio = 8.637, p < 0.001). Postoperative AKI was associated with longer hospitalization duration (11 ± 5 days vs. 10 ± 4 days, p = 0.002). These results collectively show that an elevated De Ritis ratio at postoperative day 1 is associated with AKI after RRP in patients with prostate cancer.

scholarly journals Incidence of Contrast Induced Acute Kidney Injury, Its Risk Factors and In-Hospital Outcomes in Patients Undergoing Coronary Angiography and Angioplasty

2020 ◽  
Vol 42 (2) ◽  
pp. 59-63
Author(s):  
Prashun Upadhaya ◽  
Pradeep Thapa ◽  
Ratna M Gajurel ◽  
Mahesh R Sigdel
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Analysis ◽  
Coronary Angiography ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Increased Risk ◽  
Contrast Exposure

Introduction Contrast-induced acute kidney injury (CI-AKI) is a serious complication of angiographic procedures with significant morbidity and mortality. We aimed to find the incidence, risk factors and outcomes of CI-AKI in patients who have undergone coronary angiography/angioplasty in a referral hospital in Nepal. MethodsIt was a descriptive observational study of consenting consecutive patients above 18 years undergoing coronary angiography/angioplasty at Manmohan Cardiothoracic Vascular and Transplant Centre, Nepal from July 2015 to September 2017. CI AKI was defined as an elevation of serum creatinine of >25% or ≥0.5 mg/dl (44 μmol/L) from baseline within 48 hour of exposure to contrast. Statistical analysis was performed using SPSS 18 software. Statistical analysis was completed using Student’s t-test, chi-square test and multivariable logistic regression analysis. ResultsOut of 240 patients, 156 (65%) were male, mean age was 60.36±11.29 years. Eighteen patients (7.5%) developed CI-AKI. Incidence of CI-AKI was 20% in patients with chronic kidney disease (CKD), 5.4% in diabetics, 13.6% in patients >70 years, 12.79 % in patients with anaemia and 12.3% in patients with prior contrast exposure. Multivariate logistic regression analysis found smoking and history of prior contrast exposure to be independent predictors for development of CI-AKI. Among patients with CI-AKI, one (5.88%) required dialysis and one (5.88%) died. ConclusionIncidence of CI-AKI after coronary angiography/angioplasty was 7.5%. Patients with prior contrast exposure and smoking were at significantly increased risk of CI-AKI; higher trend of CI-AKI was seen in patients with CKD, diabetes, elderly and anaemia.

scholarly journals Late Acute Kidney Injury is a Poor Prognosis Sign in Severe Burns: A Retrospective Cohort Study

2020 ◽  
Author(s):  
Bo You ◽  
Zi Chen Yang ◽  
Yu Long Zhang ◽  
Yu Chen ◽  
Yun Long Shi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Poor Prognosis ◽  
Multiple Logistic Regression Analysis ◽  
Kidney Injury ◽  
Occurrence Rate ◽  
Severe Burns ◽  
Epidemiological Characteristics

Abstract BackgroundAcute kidney injury (AKI) is a morbid complication and the main cause of multiple organ failure and death in severely burned patients. The objective of this study was to explore the epidemiological characteristics, the risk factors, and impact of both early and late AKIs, respectively.MethodsThis retrospective study was performed with prospectively collected data of severely burned patients from the Institute of Burn Research in Southwest Hospital during 2011-2017. AKI was diagnosed according to Kidney Disease Improving Global Outcomes (KDIGO) criteria (2012), and it was divided into early and late AKIs depending on its onset time (within the first 3 days or >3 days post burn). The baseline characteristics, clinical data, and outcomes of the three groups (early AKI, late AKI and non-AKI) were compared using logistic regression analysis. Mortality predictors of patients with AKI were assessed.ResultsA total of 637 patients were included in analysis. The incidence of AKI was 36.9% (early AKI 29.4%, late AKI 10.0%). The mortality of patients with AKI was 32.3% (early AKI 25.7%, late AKI 56.3%), and that of patients without AKI was 2.5%. AKI was independently associated with obviously increased mortality of severely burned patients [early AKI, OR = 12.98 (6.08-27.72); late AKI, OR = 34.02 (15.69-73.75)]. Multiple logistic regression analysis revealed that age, gender, total burn surface area (TBSA), full-thickness burns of TBSA, chronic comorbidities (hypertension or/and diabetes), hypovolemic shock of early burn, and tracheotomy were independent risk factors for both early and late AKIs. However, sepsis was only a risk factor for late AKI. Decompression escharotomy was a protective factor for both AKIs. ConclusionsAKI remains prevalent and is associated with high mortality in severely burned patients. Compared with early AKI, late AKI has a lower occurrence rate, but greater severity and worse prognosis，is a devastating complication. Late AKI is a poor prognosis sign in severe burns.

scholarly journals Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

2018 ◽  
Vol 7 (11) ◽  
pp. 428 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Soo Yoon ◽  
Seong-Mi Yang ◽  
Won Kim ◽  
Ho-Geol Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Gradient Boosting Machine

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

How Reliable Is Automated Urinalysis in Acute Kidney Injury?

2020 ◽  
Author(s):  
Vani Chandrashekar ◽  
Anil Tarigopula ◽  
Vikram Prabhakar
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Epithelial Cells ◽  
Logistic Regression Analysis ◽  
Blood Cells ◽  
White Blood Cells ◽  
Kidney Injury ◽  
Tubular Epithelial Cells ◽  
Spearman Test

Abstract Objective Examination of urine sediment is crucial in acute kidney injury (AKI). In such renal injury, tubular epithelial cells, epithelial cell casts, and dysmorphic red cells may provide clues to etiology. The aim of this study was to compare automated urinalysis findings with manual microscopic analysis in AKI. Methods Samples from patients diagnosed with AKI and control patients were included in the study. Red blood cells, white blood cells, renal tubular epithelial cells/small round cells, casts, and pathologic (path) cast counts obtained microscopically and by a UF1000i cytometer were compared by Spearman test. Logistic regression analysis was used to assess the ability to predict AKI from parameters obtained from the UF1000i. Results There was poor correlation between manual and automated analysis in AKI. None of the parameters could predict AKI using logistic regression analysis. However, the increment in the automated path cast count increased the odds of AKI 93 times. Conclusion Automated urinalysis parameters are poor predictors of AKI, and there is no agreement with manual microscopy.

scholarly journals Impaired T Lymphocyte Subsets Early Predict Acute Kidney Injury in Sepsis Patients

2021 ◽  
Author(s):  
Guanglan Li ◽  
Yu Zhang ◽  
Ganyuan He ◽  
Wenke Hao ◽  
Wenxue Hu
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
T Lymphocyte ◽  
Logistic Regression Analysis ◽  
Lymphocyte Subsets ◽  
Kidney Injury ◽  
High Morbidity ◽  
T Helper ◽  
T Lymphocyte Subsets

Abstract Objective: Acute kidney injury (AKI) is a frequent complication of sepsis patients and is associated with high morbidity and mortality. Early recognition of sepsis-associated AKI (SA-AKI) is crucial to provide supportive treatment and improve prognosis. Thus, the objective is to analyze the early discriminative predictive information regarding T lymphocyte subsets of SA-AKI.Methods: We evaluated the relationships of T lymphocyte subsets and clinical parameters of sepsis patients, and assessed their potential roles in SA-AKI diagnosis. The following T lymphocyte subsets were studied: total T lymphocyte (CD3+), helper T lymphocyte (T helper, CD3+CD4+), cytotoxic T lymphocyte (CTL, CD3+CD8+), totally activated T lymphocyte (CD3+HLADR+), early activated T lymphocyte (CD4+CD69+, CD8+CD69+), regulatory T lymphocyte (Treg, CD4+CD25+, CD8+CD25+).Results: A total of 171 patients with sepsis were enrolled. The incidence of AKI was 80.1%. The percentages of total T lymphocyte, CTL, and totally activated T lymphocyte of SA-AKI patients were lower than those of sepsis patients without AKI (61.95±19.65 % vs 68.80±18.57 %, 19.95±17.22 % vs 26.48±18.31 %, 19.00±14.21 % vs 30.88±28.86 %, respectively, P<0.05). There were no significant differences in the percentages of T helper, early activated T lymphocyte, and Tregs between SA-AKI group and non-SA-AKI group. Univariate logistic regression analysis showed that percentages of total T lymphocyte, CTL, and totally activated T lymphocyte were protective factors for SA-AKI. Multivariate logistic regression analysis revealed that percentage of totally activated T lymphocyte had a negative association with SA-AKI independently (OR: 0.952, 95% CI: 0.926-0.978, P=0.000). Moreover, ROC analysis showed that total T lymphocyte, CTL, and totally activated T lymphocyte had discriminatory abilities, with areas under the curve (AUC) value of 0.638, 0.615, and 0.661, respectively (P<0.05). Conclusions: Impaired total T lymphocyte, CTL, and totally activated T lymphocyte could contribute to early diagnosis for SA-AKI.

scholarly journals Risk factors for acute kidney injury and mortality in high risk patients undergoing cardiac surgery

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252209
Author(s):  
Giuseppe Filiberto Serraino ◽  
Michele Provenzano ◽  
Federica Jiritano ◽  
Ashour Michael ◽  
Nicola Ielapi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cardiac Surgery ◽  
Blood Glucose ◽  
Kidney Injury ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Risk Patients

Background Acute Kidney Injury (AKI) represents a clinical condition with poor prognosis. The incidence of AKI in hospitalized patients was about 22–57%. Patients undergoing cardiac surgery (CS) are particularly exposed to AKI because of the related oxidative stress, inflammation and ischemia-reperfusion damage. Hence, the risk profile of patients undergoing CS who develop AKI and who are consequently at increased mortality risk deserves further investigation. Methods We designed a retrospective study examining consecutive patients undergoing any type of open-heart surgery from January to December 2018. Patients with a history of AKI were excluded. AKI was diagnosed according to KDIGO criteria. Univariate associations between clinical variables and AKI were tested using logistic regression analysis. Variable thresholds maximizing the association with AKI were measured with the Youden index. Multivariable logistic regression analysis was performed to assess predictors of AKI through backward selection. Mortality risk factors were assessed through the Cox proportional hazard model. Results We studied 158 patients (mean age 51.2±9.7 years) of which 74.7% were males. Types of procedures performed were: isolated coronary artery bypass (CABG, 50.6%), valve (28.5%), aortic (3.2%) and combined (17.7%) surgery. Overall, incidence of AKI was 34.2%. At multivariable analysis, young age (p = 0.016), low blood glucose levels (p = 0.028), estimated Glomerular Filtration Rate (p = 0.007), pH (p = 0.008), type of intervention (p = 0.031), prolonged extracorporeal circulation (ECC, p = 0.028) and cross-clamp (p = 0.021) times were associated with AKI. The threshold for detecting AKI were 91 and 51 minutes for ECC and cross-clamp times, respectively. At survival analysis, the presence of AKI, prolonged ECC and cross-clamp times, and low blood glucose levels forecasted mortality. Conclusions AKI is common among CS patients and associates with shortened life-expectancy. Several pre-operative and intra-operative predictors are associated with AKI and future mortality. Future studies, aiming at improving prognosis in high-risk patients, by a stricter control of these factors, are awaited.

scholarly journals Risk Factors and Outcomes of Acute Kidney Injury in Critically Ill Patients with Coronavirus Disease 2019

2020 ◽  
pp. 1-9
Author(s):  
Yichun Cheng ◽  
Nanhui Zhang ◽  
Ran Luo ◽  
Meng Zhang ◽  
Zhixiang Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Kidney Injury ◽  
Stage 1

<b><i>Background:</i></b> Coronavirus disease 2019 (COVID-19) has emerged as a major global health threat with a great number of deaths worldwide. Acute kidney injury (AKI) is a common complication in patients admitted to the intensive care unit. We aimed to assess the incidence, risk factors and in-hospital outcomes of AKI in COVID-19 patients admitted to the intensive care unit. <b><i>Methods:</i></b> We conducted a retrospective observational study in the intensive care unit of Tongji Hospital, which was assigned responsibility for the treatments of severe COVID-19 patients by the Wuhan government. AKI was defined and staged based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Mild AKI was defined as stage 1, and severe AKI was defined as stage 2 or stage 3. Logistic regression analysis was used to evaluate AKI risk factors, and Cox proportional hazards model was used to assess the association between AKI and in-hospital mortality. <b><i>Results:</i></b> A total of 119 patients with COVID-19 were included in our study. The median patient age was 70 years (interquartile range, 59–77) and 61.3% were male. Fifty-one (42.8%) patients developed AKI during hospitalization, corresponding to 14.3% in stage 1, 28.6% in stage 2 and 18.5% in stage 3, respectively. Compared to patients without AKI, patients with AKI had a higher proportion of mechanical ventilation mortality and higher in-hospital mortality. A total of 97.1% of patients with severe AKI received mechanical ventilation and in-hospital mortality was up to 79.4%. Severe AKI was independently associated with high in-hospital mortality (OR: 1.82; 95% CI: 1.06–3.13). Logistic regression analysis demonstrated that high serum interleukin-8 (OR: 4.21; 95% CI: 1.23–14.38), interleukin-10 (OR: 3.32; 95% CI: 1.04–10.59) and interleukin-2 receptor (OR: 4.50; 95% CI: 0.73–6.78) were risk factors for severe AKI development. <b><i>Conclusions:</i></b> Severe AKI was associated with high in-hospital mortality, and inflammatory response may play a role in AKI development in critically ill patients with COVID-19.

scholarly journals A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

2021 ◽  
Author(s):  
Francesca Alfieri ◽  
Andrea Ancona ◽  
Giovanni Tripepi ◽  
Dario Crosetto ◽  
Vincenzo Randazzo ◽  
...  
Keyword(s):  
At Risk ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Deep Learning ◽  
Logistic Regression Analysis ◽  
Urine Output ◽  
Kidney Injury ◽  
Learning Model ◽  
Deep Learning Model

Abstract Background Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions. Methods The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to  the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model. Results The deep learning model defined an area under the curve (AUC) of 0.89 (± 0.01), sensitivity = 0.8 and specificity = 0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12 h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI. Conclusion In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12 h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated in the ICU setting to better manage, and potentially prevent, AKI episodes. Graphic abstract

Sign in / Sign up

Export Citation Format

Share Document