Novel Blood Cytokine-Based Model for Predicting Severe Acute Kidney Injury and Poor Outcomes After Cardiac Surgery

Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach

Background

Transvaginal insertion of polypropylene mesh was extensively used in surgical procedures to treat pelvic organ prolapse (POP) due to its cost-efficiency and durability. However, studies have reported a high rate of complications, including mesh exposure through the vaginal wall. Developing predictive models via supervised machine learning holds promise in identifying risk factors associated with such complications, thereby facilitating better informed surgical decisions. Previous studies have demonstrated the efficacy of anticipating medical outcomes by employing supervised machine learning approaches that integrate patient health care data with laboratory findings. However, such an approach has not been adopted within the realm of POP mesh surgery.

Objective

We examined the efficacy of supervised machine learning to predict mesh exposure following transvaginal POP surgery using 3 different datasets: (1) patient medical record data, (2) biomaterial-induced blood cytokine levels, and (3) the integration of both.

Methods

Blood samples and medical record data were collected from 20 female patients who had prior surgical intervention for POP using transvaginal polypropylene mesh. Of these subjects, 10 had experienced mesh exposure through the vaginal wall following surgery, and 10 had not. Standardized medical record data, including vital signs, previous diagnoses, and social history, were acquired from patient records. In addition, cytokine levels in patient blood samples incubated with sterile polypropylene mesh were measured via multiplex assay. Datasets were created with patient medical record data alone, blood cytokine levels alone, and the integration of both data. The data were split into 70% and 30% for training and testing sets, respectively, for machine learning models that predicted the presence or absence of postsurgical mesh exposure.

Results

Upon training the models with patient medical record data, systolic blood pressure, pulse pressure, and a history of alcohol usage emerged as the most significant factors for predicting mesh exposure. Conversely, when the models were trained solely on blood cytokine levels, interleukin (IL)-1β and IL-12 p40 stood out as the most influential cytokines in predicting mesh exposure. Using the combined dataset, new factors emerged as the primary predictors of mesh exposure: IL-8, tumor necrosis factor-α, and the presence of hemorrhoids. Remarkably, models trained on the integrated dataset demonstrated superior predictive capabilities with a prediction accuracy as high as 94%, surpassing the predictive performance of individual datasets.

Conclusions

Supervised machine learning models demonstrated improved prediction accuracy when trained using a composite dataset that combined patient medical record data and biomaterial-induced blood cytokine levels, surpassing the performance of models trained with either dataset in isolation. This result underscores the advantage of integrating health care data with blood biomarkers, presenting a promising avenue for predicting surgical outcomes in not only POP mesh procedures but also other surgeries involving biomaterials. Such an approach has the potential to enhance informed decision-making for both patients and surgeons, ultimately elevating the standard of patient care.

Vasoplegic Syndrome Following Bypass: A Comprehensive Review of Pathophysiology and Proposed Treatments

Following cardiopulmonary bypass (CPB) surgery, patients may experience vasoplegic or vasogenic shock syndrome. This condition has a variable incidence, reaching up to 44% in high-risk patients, with mortality rates ranging from 30% to 50%, primarily due to multiple organ failure. This complex condition is characterized by low arterial pressure, unresponsive vascular collapse to high doses of vasopressors, and biochemical signals of cellular oxygen debt. The cardiac output can either be low or abnormally elevated. A fundamental aspect of the pathophysiology of vasogenic syndrome after CPB is related to the dysfunction of vascular smooth muscle cell contraction. This syndrome is often associated with complex cardiac surgery such as reoperations, long periods of bypass and aorta clamping, and excessive blood transfusion. Some potential triggers that might lead to this condition include the preoperative use of antagonists of the renin-angiotensin system, calcium blockers antagonists, and chronic renal disease. Recent literature has advocated treating vasoplegic syndrome after bypass using oxide nitric synthase inhibitors, such as methylene blue and hydroxocobalamin, along with the progressive escalation of potent vasopressors and intravascular volume adjustment.

Growth Differentiation Factor 15 as a Biomarker for Risk Stratification in the Cardiothoracic Surgery Intensive Care Unit

Growth Differentiation Factor 15 (GDF15) is an emerging biomarker that significantly increases during acute stress responses, such as infections, and is moderately elevated in chronic and inflammation-driven conditions. While evidence suggests that high levels of GDF15 in cardiac surgery are associated with worse outcomes, its utility as an evaluator of early postoperative complications remains unclear. This study aims to characterize the postoperative profile of GDF15 in patients undergoing isolated surgical aortic valve replacement, evaluating its association with short-term outcomes. Serum samples from patients undergoing cardiac surgery were collected preoperatively and at defined postoperative time points (1 h, 6 h, 12 h, 24 h, and 48 h) to measure GDF15 levels. GDF15 levels significantly increased after surgery, peaking at 6 h. A positive correlation was observed between GDF15 levels and both cardiopulmonary bypass and aortic cross-clamp times. Notably, patients who developed postoperative acute kidney injury (AKI) or required prolonged hemodynamic support had significantly higher GDF15 levels, with increased mechanical ventilation time and extended intensive care unit length of stay. Furthermore, GDF15 levels correlated with postoperative SOFA scores at 24 h after surgery. GDF15 may be a valuable biomarker for risk stratification and guiding therapeutic decisions in cardiac surgery patients. Higher GDF15 levels were significantly associated with prolonged hemodynamic support, postoperative AKI, and measures of illness severity.

An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery

Background: Early diagnosis of post-operative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. The purpose of this study was to determine whether an integrated approach based on clinical data, along with metabolites and biomarkers, had greater predictive value than the models built on fewer data in the early diagnosis of post-operative complications after cardiac surgery. Methods: The study included patients (n = 62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass) with (n = 26) or without (n = 36) post-operative complications. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers and metabolites. Results: Multivariate PLS-DA models, predicting the presence or absence of post-operative complications, were built using clinical data, concentrations of metabolites and biomarkers, and the entire data set (ROC-AUC = 0.80, 0.71, and 0.85, respectively). For comparison, we built univariate models using the EuroScore2 and SOFA scales, concentrations of lactate, the dynamic changes of 4-hydroxyphenyllactic acid, and the sum of three sepsis-associated metabolites (ROC-AUC = 0.54, 0.79, 0.62, 0.58, and 0.70, respectively). Conclusions: The proposed complex model using the entire dataset had the best characteristics, which confirms the expediency of searching for new predictive models based on a variety of factors.

Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection

Background

Severe acute kidney injury (AKI) after total aortic arch replacement (TAAR) is related to adverse outcomes in patients with acute type A aortic dissection (ATAAD). However, the early prediction of severe AKI remains a challenge. This study aimed to develop a novel model to predict severe AKI after TAAR in ATAAD patients using machine learning algorithms.

Methods

A total of 572 ATAAD patients undergoing TAAR were enrolled in this retrospective study, and randomly divided into a training set (70 %) and a validation set (30 %). Lasso regression, support vector machine-recursive feature elimination and random forest algorithms were used to screen indicators for severe AKI (defined as AKI stage III) in the training set, respectively. Then the intersection indicators were selected to construct models through artificial neural network (ANN) and logistic regression. The AUC-ROC curve was employed to ascertain the prediction efficacy of the ANN and logistic regression models.

Results

The incidence of severe AKI after TAAR was 22.9 % among ATAAD patients. The intersection predictors identified by different machine learning algorithms were baseline serum creatinine and ICU admission variables, including serum cystatin C, procalcitonin, aspartate transaminase, platelet, lactic dehydrogenase, urine N-acetyl-β-d-glucosidase and Acute Physiology and Chronic Health Evaluation II score. The ANN model showed a higher AUC-ROC than logistic regression (0.938 vs 0.908, p < 0.05). Furthermore, the ANN model could predict 89.1 % of severe AKI cases beforehand. In the validation set, the superior performance of the ANN model was further confirmed in terms of discrimination ability (AUC = 0.916), calibration curve analysis and decision curve analysis.

Conclusion

This study developed a novel and reliable clinical prediction model for severe AKI after TAAR in ATAAD patients using machine learning algorithms. Importantly, the ANN model showed a higher predictive ability for severe AKI than logistic regression.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study

BACKGROUND

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors.

MATERIALS AND METHODS

Adult patients undergoing noncardiac surgery were retrospectively included in the study (76 457 patients in the discovery cohort and 11 910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation.

RESULTS

The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance.

CONCLUSIONS

The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making

OBJECTIVES

The aim of this study using decision curve analysis (DCA) was to evaluate the clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making compared with the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II and to 2 machine-learning models.

METHODS

Using data from a French prospective database, this retrospective study evaluated all patients who underwent cardiac surgery in 43 hospital centers between January 2012 and December 2020. A receiver operating characteristic analysis was performed to compare the accuracy of the EuroSCORE II, machine-learning models, and an adapted Tabular Bidirectional Encoder Representations from Transformers deep-learning model in predicting postoperative in-hospital mortality. The clinical utility of these models for cardiac surgery decision making was compared using DCA.

RESULTS

Over the study period, 165,640 patients underwent cardiac surgery, with a mean EuroSCORE II of 3.99 ± 6.67%. In the receiver operating characteristic analysis, the area under the curve was significantly greater for the deep-learning model (0.834; 95% confidence interval, 0.831-0.838) than the EuroSCORE II (P < .001), the random forest model (P = .03), and the Extreme Gradient Boosting model (P = .03). In the DCA, the clinical utility of the 3 artificial intelligence models was superior to that of the EuroSCORE II, especially when the threshold probability of death was high (>45%). The deep-learning model showed the greatest advantage over the EuroSCORE II.

CONCLUSIONS

The deep-learning model had better predictive accuracy and greater clinical utility than the EuroSCORE II and the 2 machine-learning models. These findings suggest that deep learning with Tabular Bidirectional Encoder Representations from Transformers prediction model could be used in the future as the gold standard for cardiac surgery decision making.

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.

Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury

OBJECTIVE

We aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset.

METHODS

This was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI.

RESULT

A total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI.

CONCLUSIONS

The CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.

Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review

Acute kidney injury (AKI) is a common and serious complication of cardiac surgery and is associated with increased mortality and morbidity, accompanied by a substantial economic burden. The pathogenesis of cardiac surgery-associated acute kidney injury (CSA-AKI) is multifactorial and complex, with a variety of pathophysiological theories. In addition to the existing diagnostic criteria, the exploration and validation of biomarkers is the focus of research in the field of CSA-AKI diagnosis. Prevention remains the key to the management of CSA-AKI, and common strategies include maintenance of renal perfusion, individualized blood pressure targets, balanced fluid management, goal-directed oxygen delivery, and avoidance of nephrotoxins. This article reviews the pathogenesis, definition and diagnosis, and pharmacological and nonpharmacological prevention strategies of AKI in cardiac surgical patients.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.

Risk factors for severe acute kidney injury post complication after total arch replacement combined with frozen elephant trunk, in acute type A aortic dissection

Background

Total arch replacement with the frozen elephant trunk (TAR + FET) technique is a challenging approach for acute type A aortic dissection (ATAAD). Severe acute kidney injury (AKI) adversely affects the prognosis of hospitalized patients. The study aims to evaluate the incidence and risk factors of severe AKI.

Methods

We conducted a retrospective cross-sectional study of the records of ATAAD patients following TAR + FET, admitted between January 2013 and December 2018. A multivariate logistic regression model was used to identify predictors of severe postoperative AKI. Severe postoperative AKI was defined using the Kidney Disease Improving Global Outcomes criteria.

Results

The whole in-hospital mortality rate was 4.3%. Among 670 patients, major adverse outcomes were present in 169 patients (25.2%), 67 patients (10.0%) required renal replacement therapy (RRT), and 80 (11.9%) developed severe postoperative AKI. In-hospital mortality in the severe AKI group (13.8%) was 4.5 times higher than in the non-severe AKI group (3.1%). Compared with the non-severe AKI patients, the severe AKI patients had a higher incidence of major adverse outcomes (100% vs. 15.1%, P<0.001) and more frequent use of RRT (83.8% vs. 0.0%, P<0.001). Multivariate analysis revealed that severe postoperative AKI was predicted by advanced age [odds ratio (OR) =1.029; 95% confidence interval (CI): 1.002-1.056; P=0.032], lower limb symptoms (OR =4.384; 95% CI: 2.240-8.582; P<0.001), coronary artery involvement (OR =2.478; 95% CI: 1.432-4.288; P=0.001), preoperative postoperative serum creatinine (SCr) (OR =1.008; 95% CI: 1.003-1.013; P=0.001), and prolonged cardiopulmonary bypass (CPB) time (OR =1.011; 95% CI: 1.006-1.015; P<0.001).

Conclusions

There was a high incidence of severe AKI and high in-hospital mortality after TAR + FET in ATAAD patients. The risk factors for severe AKI in ATAAD patients undergoing TAR + FET were determined to help identify the high-risk patients and make rational treatment decisions.

Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study

Background: Patients with severe acute kidney injury (AKI) require continuous renal replacement therapy (CRRT) when hemodynamically unstable. We aimed to identify prognostic factors and develop a nomogram that could predict mortality in patients with AKI undergoing CRRT. Methods: Data were extracted from the Dryad Digital Repository. We enrolled 1,002 participants and grouped them randomly into training (n = 670) and verification (n = 332) datasets based on a 2:1 proportion. Based on Cox proportional modeling of the training set, we created a web-based dynamic nomogram to estimate all-cause mortality. Results: The model incorporated phosphate, Charlson comorbidity index, body mass index, mean arterial pressure, levels of creatinine and albumin, and sequential organ failure assessment scores as independent predictive indicators. Model calibration and discrimination were satisfactory. In the training dataset, the area under the curves (AUCs) for estimating the 28-, 56-, and 84-day all-cause mortality were 0.779, 0.780, and 0.787, respectively. The model exhibited excellent calibration and discrimination in the validation dataset, with AUC values of 0.791, 0.778, and 0.806 for estimating 28-, 56-, and 84-day all-cause mortality, respectively. The calibration curves exhibited the consistency of the model between the two cohorts. To visualize the results, we created a web-based calculator. Conclusion: We created a web-based calculator for assessing fatality risk in patients with AKI receiving CRRT, which may help rationalize clinical decision-making and personalized therapy.

Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.

Changes in IL-16 Expression in the Ovary during Aging and Its Potential Consequences to Ovarian Pathology

Aging in females is not only associated with the changes in hormonal status but is also responsible for dysregulation of immune functions in various organs including ovaries. The goal of this study was to determine whether the expression of interleukin 16 (IL-16), a proinflammatory and chemoattractant cytokine, changes during ovarian aging, to determine factors involved in such changes in IL-16 expression, and to examine if changes in IL-16 expression during aging predisposes the ovary to pathologies. Ovarian tissues from premenopausal women (30-50 years old), women at early menopause (55-59 years old), and late menopause (60-85 years old) were used. In addition, tumor tissues from patients with ovarian high-grade serous carcinoma at early stage (n = 5) were also used as reference tissue for comparing the expression of several selected markers in aging ovaries. The expression of IL-16, frequency of macrophages (a source of IL-16) and expression of microRNA (miR) 125a-5p (a regulator of IL-16 gene) were performed by immunohistochemistry, immunoblotting, and gene expression assays. In addition, we examined changes in nuclear expression of IL-16 expression with regards to exposure to follicle-stimulating hormone (FSH) by in vitro cell culture assays with human ovarian cancer cells. The frequencies of IL-16 expressing cells were significantly higher in ovarian stroma in women at early and late menopause as compared with premenopausal women (P < 0.0001). Similar patterns were also observed for macrophages. Expression of miR-125a-5p decreased significantly (P < 0.001) with the increase in IL-16 expression during aging. Furthermore, expression of nuclear IL-16 increased remarkably upon exposure to FSH. Consequently, ovarian aging is associated with increased expression of IL-16 including its nuclear fraction. Therefore, persistent high levels of FSH in postmenopausal women may be a factor for enhanced expression of IL-16. Effects of increased nuclear fraction of IL-16 need to be examined.

Narrative review of the systemic inflammatory reaction to cardiac surgery and cardiopulmonary bypass

Background

Data from large cardiac surgery registries have been depicting a downward trend of mortality and morbidities in the last 20 years. However, despite decades of medical evolution, cardiac surgery and cardiopulmonary bypass still provoke a systemic inflammatory response, which occasionally leads to worsened outcome. This article seeks to outline the mechanism of the phenomenon.

Methods

A thorough review of the literature has been performed. Criteria for considering studies for this non‐systematic review were as follows: observational and interventional studies investigating the systemic inflammatory response to cardiac surgery, experimental studies describing relevant molecular mechanisms, and essential review studies pertinent to the topic.

Results

The intrinsic variability of the inflammatory response to cardiac surgery, together with its heterogenous perception among clinicians, as well as the arduousness to early discriminate high‐responder patients from those who will not develop a clinically relevant reaction, concurred to hitherto unconclusive randomized controlled trials. Furthermore, peremptory knowledge about the pathophysiology of maladaptive inflammation following heart surgery is still lacking.

Conclusions

Systemic inflammation following cardiac surgery is a frequent entity that occasionally becomes clinically relevant. Specific genomic differences, age, and other preoperative factors influence the magnitude of the response, which elements display extreme redundancy and pleiotropism that the target of a single pathway cannot represent a silver bullet.

Chemokine/Cytokine Levels Correlate with Organ Involvement in PR3-ANCA-Associated Vasculitis

Background: ANCA-associated vasculitis (AAV) is a rare small vessel disease characterized by multi-organ involvement. Biomarkers that can measure specific organ involvement are missing. Here, we ask whether certain circulating cytokines and chemokines correlate with renal involvement and if distinct cytokine/chemokine patterns can differentiate between renal, ear/nose/throat, joints, and lung involvement of AAV. Methods: Thirty-two sets of Birmingham vasculitis activity score (BVAS), PR3-ANCA titers, laboratory marker, and different cytokines were obtained from 17 different patients with AAV. BVAS, PR3-ANCA titers, laboratory marker, and cytokine concentrations were correlated to different organ involvements in active AAV. Results: Among patients with active PR3-AAV (BVAS > 0) and kidney involvement we found significant higher concentrations of chemokine ligand (CCL)-1, interleukin (IL)-6, IL21, IL23, IL-28A, IL33, monocyte chemoattractant protein 2 (MCP2), stem cell factor (SCF), thymic stromal lymphopoietin (TSLP), and thrombopoietin (TPO) compared to patients without PR3-ANCA-associated glomerulonephritis. Patients with ear, nose, and throat involvement expressed higher concentrations of MCP2 and of the (C-X-C motif) ligand-12 (CXCL-12) compared to patients with active AAV and no involvement of these organs. Conclusion: We identified distinct cytokine patterns for renal manifestation and for ear, nose and throat involvement of PR3-AAV. Distinct plasma cytokines might be used as non-invasive biomarkers of organ involvement in AAV.

Severe Acute Respiratory Syndrome Coronavirus-2-Associated Acute Kidney Injury: A Narrative Review Focused Upon Pathophysiology

OBJECTIVE

Severe acute respiratory syndrome coronavirus-2 acute kidney injury is a condition that in many ways resembles classical acute kidney injury. The pathophysiology appears to be multifactorial, and accordingly, our main objective was to review possible components of this form of acute kidney injury.

DATA SOURCES

Literature review.

DATA SYNTHESIS

Our principal observation was that the various components of severe acute respiratory syndrome coronavirus-2 acute kidney injury appear to be relatively similar to the classical forms. Temporality of injury is an important factor but is not specific to severe acute respiratory syndrome coronavirus-2 acute kidney injury. Several insults hit the kidney at different moments in the course of disease, some occurring prior to hospital admission, whereas others take place at various stages during hospitalization.

CONCLUSIONS AND RELEVANCE

Treatment of severe acute respiratory syndrome coronavirus-2 acute kidney injury cannot be approached in a "one-size-fits-all" manner. The numerous mechanisms involved do not occur simultaneously, leading to a multiple hit model that may contribute to the prevalence and severity of acute kidney injury. A personalized approach to each patient with acute kidney injury based on the timing and severity of disease is necessary in order to provide appropriate treatment. Although data from the literature regarding the previous coronavirus infections can give some insights, more studies are needed to explore the different mechanisms of acute kidney injury occurring as a result of severe acute respiratory syndrome coronavirus-2.

Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury

OBJECTIVE

We aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients.

METHODS

Clinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model.

RESULTS

The development cohort enrolled 730 patients with a median follow-up time of 87 (40-98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043-1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951-0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678-13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930-0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287-3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p < 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839-0.921) and 0.798 (95% CI = 0.720-0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability.

CONCLUSION

We developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.

Novel Blood Cytokine-Based Model for Predicting Severe Acute Kidney Injury and Poor Outcomes After Cardiac Surgery

Background Alterations in serum creatinine levels delay the identification of severe cardiac surgery-associated acute kidney injury. To provide timely diagnosis, novel predictive tools should be investigated. Methods and Results This prospective observational study consists of a screening cohort (n=204) and a validation cohort (n=198) from 2 centers from our hospital. Thirty-two inflammatory cytokines were measured via a multiplex cytokine assay. Least absolute shrinkage and selection operator regression was conducted to select the cytokine signatures of severe cardiac surgery-associated acute kidney injury. Afterwards, the significant candidates including interferon-γ, interleukin-16, and MIP-1α (macrophage inflammatory protein-1 alpha) were integrated into the logistic regression model to construct a predictive model. The predictive accuracy of the model was evaluated in these 2 cohorts. The cytokine-based model yielded decent performance in both the screening (C-statistic: 0.87, Brier 0.10) and validation cohorts (C-statistic: 0.86, Brier 0.11). Decision curve analysis revealed that the cytokine-based model had a superior net benefit over both the clinical factor-based model and the established plasma biomarker-based model for predicting severe acute kidney injury. In addition, elevated concentrations of each cytokine were associated with longer mechanical ventilation times, intensive care unit stays, and hospital stays. They strongly predicted the risk of composite events (defined as treatment with renal replacement therapy and/or in-hospital death) (OR of the fourth versus the first quartile [95% CI]: interferon-γ, 27.78 [3.61-213.84], interleukin-16, 38.07 [4.98-291.07], and MIP-1α, 9.13 [2.84-29.33]). Conclusions Our study developed and validated a promising blood cytokine-based model for predicting severe acute kidney injury after cardiac surgery and identified prognostic biomarkers for assisting in outcome risk stratification.