Humans ; Retrospective Studies ; Cardiopulmonary Bypass/adverse effects* ; Coronary Artery Bypass/adverse effects* ; Coronary Artery Disease/surgery* ; Acute Kidney Injury/etiology* ; Postoperative Complications ; Risk Factors ; Treatment Outcome

OBJECTIVE: To investigate the predictive value of pulse infusion index (PPI) in the short-term prognosis of patients with sepsis-induced acute kidney injury (AKI). METHODS: A retrospective cohort study was conducted. The clinical data of patients with sepsis-induced AKI admitted to intensive care unit (ICU) of the First Affiliated Hospital of Soochow University from July 2021 to December 2022 were enrolled. The basic information of the patients were collect, including age, gender, site of infection, underlying disease, mean arterial pressure (MAP) and heart rate (HR) at admission, as well as the use of mechanical ventilation and vasoactive drugs, and norepinephrine (NE) dosage. Laboratory indicators, sequential organ failure assessment (SOFA) score and PPI within 24 hours of admission were also recorded, and the patient's prognosis during ICU hospitalization was also recorded. The differences in clinical data between the patients of two groups with different prognosis were compared. Spearman correlation method was used to analyze the correlation between PPI and SOFA score. Binary multivariate Logistic regression analysis was used to screen independent risk factors for death during ICU hospitalization in sepsis patients with AKI. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive value of PPI for the short-term prognosis of patients with sepsis-induced AKI. RESULTS: A total of 102 patients with sepsis-induced AKI were enrolled, of which 70 patients in the survival group and 32 patients in the death group, with ICU mortality of 31.4. Compared with the survival group, SOFA score, HR, procalcitonin (PCT), serum creatinine (SCr), and NE dosage in the death group were significantly increased [SOFA score: 11.22±2.48 vs. 8.56±2.01, HR (bpm): 103.80±12.47 vs. 97.41±9.73, PCT (μg/L): 9.22 (5.24, 17.84) vs. 6.19 (3.86, 7.71), SCr (μmol/L): 163.2 (104.7, 307.9) vs. 125.5 (89.3, 221.0), Lac (mmol/L): 2.81 (1.95, 4.22) vs. 2.13 (1.74, 2.89), NE usage (μg×kg^-1×min^-1): 0.7 (0.4, 1.1) vs. 0.5 (0.2, 0.6), all P < 0.05], while PPI was significantly lower than that in survival group [0.83 (0.42, 1.55) vs. 1.70 (1.14, 2.20), P < 0.01]. Spearman correlation analysis showed that based on SOFA score, PPI was closely related to the severity of patients with sepsis-induced AKI (r = -0.328, P < 0.05). Binary multivariate Logistic regression analysis showed that PPI [odds ratio (OR) = 0.590, 95% confidence interval (95%CI) was 0.361-0.966, P = 0.002], SOFA score (OR = 1.406, 95%CI was 1.280-1.545, P < 0.001), PCT (OR = 2.061, 95%CI was 1.267-3.350, P = 0.006) were independent risk factors of the short-term prognosis of patients with sepsis-induced AKI. ROC curve analysis showed that the area under the ROC curve (AUC) of PPI for death during ICU hospitalization in patients with sepsis-induced AKI was 0.779 (95%CI was 0.686-0.855, P < 0.001), which superior to PCT (AUC = 0.677, 95%CI was 0.577-0.766, P = 0.004), and similar to SOFA score (AUC = 0.794, 95%CI was 0.703-0.868, P < 0.001). When the cut-off value of PPI was 0.72, the sensitivity was 50.0%, and the specificity was 97.1%. CONCLUSIONS PPI has a good predictive value for the short-term prognosis of patients with sepsis-induced AKI during ICU hospitalization.
Humans ; Heart Rate ; Retrospective Studies ; ROC Curve ; Sepsis/complications* ; Prognosis ; Procalcitonin ; Acute Kidney Injury/etiology* ; Intensive Care Units

Humans ; Child ; COVID-19/complications* ; Brain Diseases ; Myositis/complications* ; Kidney ; Liver Failure, Acute/complications* ; Acute Kidney Injury/complications*

OBJECTIVES: Abdominal aortic aneurysm is a pathological condition in which the abdominal aorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSR is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, this study aims to perform tests on the efficacy of different machine learning models. METHODS: Perioperative data of 80 OSR patients were retrospectively collected from January 2009 to December 2021 at Xiangya Hospital, Central South University. The vascular surgeon performed the surgical operation. Four commonly used machine learning classification models (logistic regression, linear kernel support vector machine, Gaussian kernel support vector machine, and random forest) were chosen to predict AKI. The efficacy of the models was validated by five-fold cross-validation. RESULTS: AKI was identified in 33 patients. Five-fold cross-validation showed that among the 4 classification models, random forest was the most precise model for predicting AKI, with an area under the curve of 0.90±0.12. CONCLUSIONS Machine learning models can precisely predict AKI during early stages after surgery, which allows vascular surgeons to address complications earlier and may help improve the clinical outcomes of OSR.
Humans ; Aortic Aneurysm, Abdominal/complications* ; Endovascular Procedures/methods* ; Retrospective Studies ; Blood Vessel Prosthesis Implantation/adverse effects* ; Acute Kidney Injury/etiology* ; Machine Learning ; Treatment Outcome ; Postoperative Complications/etiology* ; Risk Factors

The risk of developing perioperative acute kidney injury (AKI) in elderly patients increases with age. The combined involvement of aging kidneys, coexisting multiple underlying chronic diseases, and increased exposure to potential renal stressors and nephrotoxic drugs or invasive procedures constitute susceptibility factors for AKI in elderly patients. The perioperative AKI in elderly patients undergoing noncardiac surgery has its own specific population characteristics, so it is necessary to further explore the characteristics of AKI in elderly patients in terms of epidemiology, clinical diagnosis, risk factors, and preventive and curative measures to provide meaningful clinical advice to improve prognosis, accelerate recovery, and reduce medical burden in elderly patients. Since AKI has the fastest-growing incidence in older patients and is associated with a worse prognosis, early detection, early diagnosis, and prevention of AKI are important for elderly patients in the perioperative period. Large, multicenter, randomized controlled clinical studies in elderly non-cardiac surgery patients with AKI can be conducted in the future, with the aim of providing the evidence to reduce of the incidence of AKI and to improve the prognosis of patients.
Humans ; Aged ; Acute Kidney Injury/prevention & control* ; Kidney ; Risk Factors ; Prognosis ; Incidence ; Postoperative Complications/prevention & control*

OBJECTIVE: To establish a prediction model of acute kidney injury (AKI) in moderate and severe burn patients, so as to provide basic research evidence for early identification of burn-related AKI. METHODS: Patients who were admitted to the department of plastic burn surgery of the Affiliated Hospital of Southwest Medical University from November 2018 to January 2021 were selected, and their clinical characteristics, laboratory examinations and other indicators were recorded. Multivariate Logistic regression analysis was used to screen out the risk factors of AKI related to moderate and severe burns, and R software was used to establish the nomogram of moderate and severe burn patients complicated with AKI. The Bootstrap method model was used for internal verification by repeating sample for 1 000 times. Consistency index and calibration curve were used to evaluate the accuracy of the model, and the receiver operator characteristic curve (ROC curve) and the area under the curve (AUC) were used to evaluate the prediction efficiency, decision curve analysis (DCA) was used to evaluate the clinical utility of the model. RESULTS: A total of 186 patients with moderate and severe burn were included, among which 54 patients suffered from AKI, and the incidence rate was 29.03%. Multivariate Logistic regression analysis showed that the total burn surface area [TBSA; odds ratio (OR) = 1.072, 95% confidence interval (95%CI) was 1.031-1.115, P = 0.001], estimated glomerular filtration rate (eGFR; OR = 0.960, 95%CI was 0.931-0.990, P = 0.010), neutrophil (NEU; OR = 1.190, 95%CI was 1.021-1.386, P = 0.026), neutrophil/lymphocyte ratio (NLR; OR = 0.867, 95%CI was 0.770-0.977, P = 0.019), D-dimer (OR = 4.603, 95%CI was 1.792-11.822, P = 0.002) were the risk factors for patients with moderate and severe burn complicated with AKI. Taking the above indexes as predictive factors, a nomogram prediction model was established, the ROC curve was plotted with AUC of 0.998 (95%CI was 0.988-1.000). Optimum threshold of ROC curve was -0.862, the sensitivity was 98.0% and the specificity was 98.2%, and the consistency index was 0.998 (95%CI was 0.988-1.000). The calibration curve showed that the prognostic nomogram model was accurate, DCA showed that most patients can benefit from this model. CONCLUSIONS The burned patients with higher TBSA, NEU, NLR, D-dimer and lower eGFR tend to suffer from AKI. The nomogram based on the above five risk factors has high accuracy and clinical value, which can be used as a predictive tool to evaluate the risk of AKI in moderate and severe burn patients.
Humans ; Prognosis ; Nomograms ; Retrospective Studies ; Burns/complications* ; Acute Kidney Injury/etiology* ; ROC Curve

Few studies have described the key features and prognostic roles of lung microbiota in patients with severe community-acquired pneumonia (SCAP). We prospectively enrolled consecutive SCAP patients admitted to ICU. Bronchoscopy was performed at bedside within 48 h of ICU admission, and 16S rRNA gene sequencing was applied to the collected bronchoalveolar lavage fluid. The primary outcome was clinical improvements defined as a decrease of 2 categories and above on a 7-category ordinal scale within 14 days following bronchoscopy. Sixty-seven patients were included. Multivariable permutational multivariate analysis of variance found that positive bacteria lab test results had the strongest independent association with lung microbiota (R² = 0.033; P = 0.018), followed by acute kidney injury (AKI; R² = 0.032; P = 0.011) and plasma MIP-1β level (R² = 0.027; P = 0.044). Random forest identified that the families Prevotellaceae, Moraxellaceae, and Staphylococcaceae were the biomarkers related to the positive bacteria lab test results. Multivariable Cox regression showed that the increase in α-diversity and the abundance of the families Prevotellaceae and Actinomycetaceae were associated with clinical improvements. The positive bacteria lab test results, AKI, and plasma MIP-1β level were associated with patients' lung microbiota composition on ICU admission. The families Prevotellaceae and Actinomycetaceae on admission predicted clinical improvements.
Acute Kidney Injury/complications* ; Bacteria/classification* ; Chemokine CCL4/blood* ; Community-Acquired Infections/microbiology* ; Humans ; Lung ; Microbiota/genetics* ; Pneumonia, Bacterial/diagnosis* ; Prognosis ; RNA, Ribosomal, 16S/genetics*

OBJECTIVES: To investigate the risk factors for acute kidney injury (AKI) after hematopoietic stem cell transplantation (HSCT) in children. METHODS: A retrospective analysis was performed on the medical data of 111 children who underwent HSCT from January 2018 to January 2020. A multivariate logistic regression analysis was used to identify the risk factors for AKI. The Kaplan-Meier survival analysis was used to compare the prognosis in children with different grades of AKI. RESULTS: Graft-versus-host disease (grade Ⅱ-Ⅳ) (OR=4.406, 95%CI: 1.501-12.933, P=0.007), hepatic veno-occlusive disease (OR=4.190, 95%CI: 1.191-14.740, P=0.026), and thrombotic microangiopathy (OR=10.441, 95%CI: 1.148-94.995, P=0.037) were closely associated with the development of AKI after HSCT. The children with stage Ⅲ AKI had a lower 1-year survival rate than those without AKI or with stage Ⅰ AKI or stage Ⅱ AKI (28.6%±12.1% vs 82.8%±5.2%/81.7%±7.4%/68.8%±11.6%; P<0.05). CONCLUSIONS Children with stage Ⅲ AKI after HSCT have a higher mortality rate. Graft-versus-host disease, hepatic veno-occlusive disease, and thrombotic microangiopathy are closely associated with the development of AKI after HSCT.
Child ; Humans ; Retrospective Studies ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Graft vs Host Disease/complications* ; Risk Factors ; Acute Kidney Injury/therapy* ; Thrombotic Microangiopathies/complications*

OBJECTIVES: To investigate the risk factors for acute kidney injury (AKI) in children with cardiac arrest (CA) and the influencing factors for prognosis. METHODS: A retrospective analysis was performed on the medical records of the children who developed CA in the pediatric intensive care unit (PICU) of Hunan Children's Hospital from June 2016 to June 2021. According to the presence or absence of AKI within 48 hours after return of spontaneous circulation (ROSC) for CA, the children were divided into two groups: AKI (n=50) and non-AKI (n=113). According to their prognosis on day 7 after ROSC, the AKI group was further divided into a survival group (n=21) and a death group (n=29). The multivariate logistic regression analysis was used to investigate the risk factors for early AKI in the children with CA and the influencing factors for prognosis. RESULTS: The incidence rate of AKI after CA was 30.7% (50/163). The AKI group had a 7-day mortality rate of 58.0% (29/50) and a 28-day mortality rate of 78.0% (39/50), and the non-AKI group had a 7-day mortality rate of 31.9% (36/113) and a 28-day mortality rate of 58.4% (66/113). The multivariate logistic regression analysis showed that long duration of cardiopulmonary resuscitation (OR=1.164, 95%CI: 1.088-1.246, P<0.001), low baseline albumin (OR=0.879, 95%CI: 0.806-0.958, P=0.003), and adrenaline administration before CA (OR=2.791, 95%CI: 1.119-6.961, P=0.028) were closely associated with the development of AKI after CA, and that low baseline pediatric critical illness score (OR=0.761, 95%CI: 0.612-0.945, P=0.014), adrenaline administration before CA (OR=7.018, 95%CI: 1.196-41.188, P=0.031), and mechanical ventilation before CA (OR=7.875, 95%CI: 1.358-45.672, P=0.021) were closely associated with the death of the children with AKI after CA. CONCLUSIONS Albumin should be closely monitored for children with ROSC after CA, especially for those with long duration of cardiopulmonary resuscitation, low baseline pediatric critical illness score, adrenaline administration before CA, and mechanical ventilation before CA, and such children should be identified and intervened as early as possible to reduce the incidence of AKI and the mortality rate.
Child ; Humans ; Prognosis ; Retrospective Studies ; Critical Illness ; Heart Arrest/complications* ; Acute Kidney Injury/epidemiology* ; Intensive Care Units, Pediatric ; Risk Factors ; Epinephrine ; Albumins

With the advancement of disease treatments, the number of patients undergoing surgery worldwide is increasing. However, many patients still experience severe perioperative complications. Perioperative hypotension is one of the common side effects during surgery. Physiologically, perioperative hypotension can lead to insufficient perfusion of important organs and result in acute and chronic irreversible organ injury, which cause serious consequences for the patient's postoperative hospitalization and even the long-term outcome. Therefore, in order to optimize perioperative circulation management and improve the quality of life for patients after surgery, it is of great importance to investigate the relationship between perioperative hypotension and postoperative myocardial injury, ischemic stroke, postoperative delirium, acute kidney injury, and postoperative mortality. Individualized circulation management and reasonable application of vasoactive drugs may be the key point to early prevention and correct treatment of perioperative hypotension, which is of great significance for reducing perioperative related morbidity and mortality and improving the prognosis for the surgical patients.
Acute Kidney Injury/etiology* ; Humans ; Hypotension/etiology* ; Postoperative Complications/etiology* ; Quality of Life