Monocytes to lymphocytes multiplying platelets ratio as an early indicator of acute kidney injury in cardiac surgery with cardiopulmonary bypass: a retrospective analysis.

OBJECTIVE: The monocyte-to-lymphocyte multiplying platelets ratio (MLPR) is a novel systemic inflammatory marker, deriving from the monocyte-to-lymphocyte ratio (MLR). However, the link between MLPR and acute kidney injury following cardiac surgery (CSA-AKI) with cardiopulmonary bypass (CPB) has not been investigated yet. We comprehensively explored the potential linear and nonlinear relationship between MLPR or MLR and CSA-AKI. METHODS: Data of patients who underwent cardiac surgery with CPB between December 2018 and April 2021 were retrospectively collected at Fuwai Hospital, Beijing, China. MLPR was defined as monocyte count (×109/L) × 1000/(lymphocyte count (×109/L) × platelets (×109/L)). MLR was defined as monocyte count (×109/L)/lymphocyte count (×109/L). Logistic regression and restricted cubic spline (RCS) were used for linear and nonlinear analysis. The primary outcome was postoperative AKI within 48 h of after cardiac surgery. RESULTS: Of the 2420 patients screened, 2387 eligible patients were enrolled in the final analysis; the mean age was 54.7 years, and 1501 [62.9%] were men. The incidence of AKI was 25.8%. Logistic regression showed that MLPR (odds ratio [OR] = 1.31, 95% confidence interval [CI]: 1.16-1.48, p < .001) and MLR (OR = 3.06, 95% CI: 1.29-7.29, p = .012) were independent risk factors for AKI. Moreover, in the RCS model with adjustment for age (median: 56), female sex, and history of diabetes, a significant statistical difference was detected between preoperative MLPR, MLR, and AKI (p for non-linearity <.001). The subgroup analyses revealed similar results. CONCLUSIONS: The study revealed a nonlinear relationship between MLPR and MLR with AKI. MLPR exhibited a J-shaped curve, and MLR showed a favorable S-shaped curve in relation to AKI. Particularly, MLPR emerges as a promising clinical composite index for early CSA-AKI prediction. These findings emphasize the significance of MLPR as a valuable tool in clinical practice for timely identification and management of CSA-AKI.

Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass.

BACKGROUND: Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). METHODS: This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70 years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). RESULTS: A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. CONCLUSIONS: In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.

Development and validation of a machine learning predictive model for perioperative myocardial injury in cardiac surgery with cardiopulmonary bypass.

BACKGROUND: Perioperative myocardial injury (PMI) with different cut-off values has showed to be associated with different prognostic effect after cardiac surgery. Machine learning (ML) method has been widely used in perioperative risk predictions during cardiac surgery. However, the utilization of ML in PMI has not been studied yet. Therefore, we sought to develop and validate the performances of ML for PMI with different cut-off values in cardiac surgery with cardiopulmonary bypass (CPB). METHODS: This was a second analysis of a multicenter clinical trial (OPTIMAL) and requirement for written informed consent was waived due to the retrospective design. Patients aged 18-70 undergoing elective cardiac surgery with CPB from December 2018 to April 2021 were enrolled in China. The models were developed using the data from Fuwai Hospital and externally validated by the other three cardiac centres. Traditional logistic regression (LR) and eleven ML models were constructed. The primary outcome was PMI, defined as the postoperative maximum cardiac Troponin I beyond different times of upper reference limit (40x, 70x, 100x, 130x) We measured the model performance by examining the area under the receiver operating characteristic curve (AUROC), precision-recall curve (AUPRC), and calibration brier score. RESULTS: A total of 2983 eligible patients eventually participated in both the model development (n = 2420) and external validation (n = 563). The CatboostClassifier and RandomForestClassifier emerged as potential alternatives to the LR model for predicting PMI. The AUROC demonstrated an increase with each of the four cutoffs, peaking at 100x URL in the testing dataset and at 70x URL in the external validation dataset. However, it's worth noting that the AUPRC decreased with each cutoff increment. Additionally, the Brier loss score decreased as the cutoffs increased, reaching its lowest point at 0.16 with a 130x URL cutoff. Moreover, extended CPB time, aortic duration, elevated preoperative N-terminal brain sodium peptide, reduced preoperative neutrophil count, higher body mass index, and increased high-sensitivity C-reactive protein levels were identified as risk factors for PMI across all four cutoff values. CONCLUSIONS: The CatboostClassifier and RandomForestClassifer algorithms could be an alternative for LR in prediction of PMI. Furthermore, preoperative higher N-terminal brain sodium peptide and lower high-sensitivity C-reactive protein were strong risk factor for PMI, the underlying mechanism require further investigation.

Effect of Perioperative Nicorandil on Myocardial Protection in Patients Undergoing Cardiac Surgery with Cardiopulmonary Bypass, a Retrospective Study.

Background: The potential myocardial protective effect of nicorandil (NICD) in patients undergoing percutaneous coronary intervention has been established. However, its efficacy in the context of cardiac surgery remains controversial. The present study aimed to evaluate the myocardial protective effect of perioperative NICD use in patients undergoing cardiac surgery with cardiopulmonary bypass (CPB). Methods: We retrospectively gathered data from patients undergoing cardiac bypass surgery between 12/2018 and 04/2021 in Fuwai Hospital. Subsequently, the patients were divided into two groups, NICD group and non-nicorandil (non-NICD) group. A 1, 3 propensity score matching (PSM) was conducted. The primary outcome was the incidence of myocardial injury. The secondary outcomes included the mechanical ventilation (MV) duration, intensive care unit (ICU) length of stay (LOS), hospital LOS, duration of chest drainage, the drainage volume, the total cost, the incidence of acute kidney injury (AKI), and the incidence of acute liver injury (ALI). Subsequently, we divided the entire population into two distinct subgroups based on their administration of NICD, and performed a comprehensive subgroup analysis. Results: A total of 2406 patients were ultimately included in the study. After PSM, 250 patients in NICD group and 750 patients in non-NICD group were included in the analysis. Perioperative NICD reduced the incidence of myocardial injury (47.2% versus 38.8%, P=0.025). Our subgroup analysis revealed that preoperative NICD administration not only provided myocardial protection benefits (45.7% vs 35.8%, OR 0.66, 95% CI [0.45-0.97], P=0.041), but also demonstrated statistically significant reduction in ALI, the ICU and hospital LOS, and the duration of chest drainage (all P<0.05). Conclusion: The perioperative NICD administration may confer myocardial protection in patients undergoing cardiac surgery with CPB. Furthermore, the preoperative utilization of NICD has the potential to mitigate the incidence of postoperative ALI, a reduction in the ICU and hospital LOS, and the duration of chest drainage.

Development and validation of a machine learning prediction model for perioperative red blood cell transfusions in cardiac surgery.

OBJECTIVE: Several machine learning (ML) models have been used in perioperative red blood cell (RBC) transfusion risk for cardiac surgery with limited generalizability and no external validation. Hence, we sought to develop and comprehensively externally validate a ML model in a large dataset to estimate RBC transfusion in cardiac surgery with cardiopulmonary bypass (CPB). DESIGN: A retrospective analysis of a multicenter clinical trial (NCT03782350). PATIENTS: The study patients who underwent cardiac surgery with CPB came from four cardiac centers in China and Medical Information Mart for Intensive Cared (MIMIC-IV) dataset. MEASUREMENTS: Data from Fuwai Hospital were used to develop an individualized prediction model for RBC transfusion. The model was externally validated in the data from three other centers and MIMIC-IV dataset. Twelve models were constructed. MAIN RESULTS: A total of 11,201 eligible patients were included in the model development (2420 in Fuwai Hospital) and external validation (563 in the other three centers and 8218 in the MIMIC-IV dataset). A significant difference was observed between the Logistic Regression and CatboostClassifier (0.72 Vs. 0.74, P = 0.031) or RandomForestClassifier (0.72 Vs. 0.75 p = 0.012) in the external validation and MIMIV-IV datasets (age ≤ 70:0.63 Vs. 0.71, p < 0.001; age > 70:0.63 Vs. 0.70, 0.63 Vs. 0.71, p < 0.001). The CatboostClassifier and RandomForestClassifier model was comparable in development (0.83 Vs. 0.82, p = 0.419), external (0.74 Vs. 0.75, p = 0.268), and MIMIC-IV datasets (age ≤ 70: 0.71 Vs. 0.71, p = 0.574; age > 70: 0.70 Vs. 0.71, p = 0.981). Of note, they outperformed other ML models with excellent discrimination and calibration. The CatboostClassifier and RandomForestClassifier models achieved higher area under precision-recall curve and lower brier loss score in validation and MIMIC-IV datasets. Additionally, we confirmed that low preoperative hemoglobin, low body mass index, old age, and female sex increased the risk of RBC transfusion. CONCLUSIONS: In our study, enrolling a broad range of cardiovascular surgeries with CPB and utilizing a restrictive RBC transfusion strategy, robustly validates the generalizability of ML algorithms for predicting RBC transfusion risk. Notably, the CatboostClassifier and RandomForestClassifier exhibit strong external clinical applicability, underscoring their potential for widespread adoption. This study provides compelling evidence supporting the efficacy and practical value of ML-based approaches in enhancing transfusion risk prediction in clinical practice.

Rational design of a cross-type HPV vaccine through immunodominance shift guided by a cross-neutralizing antibody.

In vaccine development, broadly or cross-type neutralizing antibodies (bnAbs or cnAbs) are frequently targeted to enhance protection. Utilizing immunodominant antibodies could help fine-tune vaccine immunogenicity and augment the precision of immunization strategies. However, the methodologies to capitalize on the attributes of bnAbs in vaccine design have not been clearly elucidated. In this study, we discovered a cross-type neutralizing monoclonal antibody, 13H5, against human papillomavirus 6 (HPV6) and HPV11. This nAb exhibited a marked preference for HPV6, demonstrating superior binding activity to virus-like particles (VLPs) and significantly higher prevalence in anti-HPV6 human serum as compared to HPV11 antiserum (90% vs. 31%). Through co-crystal structural analysis of the HPV6 L1 pentamer:13H5 complex, we delineated the epitope as spanning four segments of amino acids (Phe42-Ala47, Gly172-Asp173, Glu255-Val275, and Val337-Tyr351) on the L1 surface loops. Further interaction analysis and site-directed mutagenesis revealed that the Ser341 residue in the HPV6 HI loop plays a critical role in the interaction between 13H5 and L1. Substituting Ser341 with alanine, which is the residue type present in HPV11 L1, almost completely abolished binding activity to 13H5. By swapping amino acids in the HPV11 HI loop with corresponding residues in HPV6 L1 (Ser341, Thr338, and Thr339), we engineered chimeric HPV11-6HI VLPs. Remarkably, the chimeric HPV11-6HI VLPs shifted the high immunodominance of 13H5 from HPV6 to the engineered VLPs and yielded comparable neutralization titers for both HPV6 and HPV11 in mice and non-human primates. This approach paves the way for the design of broadly protective vaccines from antibodies within the main immunization reservoir.

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.

Incidence, risk factors, and outcomes in electroencephalographic seizures after mechanical circulatory support: A systematic review and meta-analysis.

Purpose: To estimate the overall incidence, risk factors, and clinical outcomes of electroencephalographic (EEG) seizures for adults and children after mechanical circulatory support (MCS). Method and measurements: This systematic review and meta-analysis were carried out in accordance with the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidance document. MEDLINE EMBASE and CENTRAL were investigated for relevant studies. The related information was retrieved by two independent reviewers and all analyses were conducted by STATA (version 16.0; Stata Corporation, College Station, TX, United States). Result: Sixty studies including 36,191 adult and 55,475 pediatric patients with MCS were enrolled for evaluation. The study showed that the overall incidence of EEG seizures in adults was 2% (95%CI: 1-3%), in which 1% (95%CI: 1-2%) after cardiopulmonary bypass (CPB), and 3% (95%CI: 1-6%) after extracorporeal membrane oxygenation (ECMO). For pediatrics patients, the incidence of EEG seizures was 12% (95%CI: 11-14%), among which 12% (9-15%) after CPB and 13% (11-15%) after ECMO. The major risk factors of EEG seizures after MCS in adults were redo surgery (coefficient = 0.0436, p = 0.044), and COPD (coefficient = 0.0749, p = 0.069). In addition, the gestational week of CPB (coefficient = 0.0544, p = 0.080) and respiratory failure of ECMO (coefficient = -0.262, p = 0.019) were also indicated to be associated with EEG seizures in pediatrics. Conclusion: EEG seizures after MCS were more common in pediatrics than in adults. In addition, the incidence of EEG seizure after ECMO was higher than CPB both in adults and children. It is expected that appropriate measures should be taken to control modifiable risk factors, thus improving the prognosis and increasing the long-term survival rate of MCS patients. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero], identifier [CRD42021287288].

Characterization of an Escherichia coli-derived triple-type chimeric vaccine against human papillomavirus types 39, 68 and 70.

In vaccinology, a potent immunogen has two prerequisite attributes-antigenicity and immunogenicity. We have rational designed a triple-type HPV vaccine against HPV58, -33 and -52 covered in Gardasil 9 based on the sequence homology and similar surface loop structure of L1 protein, which is related to cross-type antigenicity. Here, we design another triple-type vaccine against non-vaccine types HPV39, -68 and -70 by immunogenicity optimization considering type specific immunodominant epitopes located in separate region for different types. First, we optimized the expression of wild-type HPV39, -68 and -70 L1-only virus-like particles (VLPs) in E. coli through N-terminal truncation of HPV L1 proteins and non-fusion soluble expression. Second, based on genetic relationships and an L1 homologous loop-swapping rationale, we constructed several triple-type chimeric VLPs for HPV39, -68 and -70, and obtained the lead candidate named H39-68FG-70DE by the immunogenicity optimization using reactivity profile of a panel type-specific monoclonal antibodies. Through comprehensive characterization using various biochemical, VLP-based analyses and immune assays, we show that H39-68FG-70DE assumes similar particulate properties as that of its parental VLPs, along with comparable neutralization immunogenicity for all three HPV types. Overall, this study shows the promise and translatability of an HPV39/68/70 triple-type vaccine, and the possibility of expanding the type-coverage of current HPV vaccines. Our study further expanded the essential criteria on the rational design of a cross-type vaccine, i.e. separate sites with inter-type similar sequence and structure as well as type-specific immunodominant epitope to be clustered together.