Frontiers in medicine
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Frontiers in medicine · Jan 2020
A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). ⋯ The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837-0.846) and 0.803 (95% CI: 0.798-0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653-0.667) and SIC scores (0.752; 95% CI: 0.747-0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable. Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.
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Frontiers in medicine · Jan 2020
A Systematic Review and Meta-Analysis of Machine Perfusion vs. Static Cold Storage of Liver Allografts on Liver Transplantation Outcomes: The Future Direction of Graft Preservation.
Background: Machine perfusion (MP) and static cold storage (CS) are two prevalent methods for liver allograft preservation. However, the preferred method remains controversial. Aim: To conduct a meta-analysis on the impact of MP preservation on liver transplant outcome. ⋯ Conclusions: Machine perfusion is superior to CS on improving short-term outcomes for human liver transplantation, with a less clear effect in the longer term. Hypothermic machine perfusion but not NMP conducted significantly protective effects on EAD and biliary complications. Further RCTs are warranted to confirm MP's superiority and applications.
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Frontiers in medicine · Jan 2020
Performance of Two Risk-Stratification Models in Hospitalized Patients With Coronavirus Disease.
Background: Despite an increase in the familiarity of the medical community with the epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19), there is presently a lack of rapid and effective risk stratification indicators to predict the poor clinical outcomes of COVID-19 especially in severe patients. Methods: In this retrospective single-center study, we included 117 cases confirmed with COVID-19. The clinical, laboratory, and imaging features were collected and analyzed during admission. ⋯ The K-M survival analysis showed that patients with MuLBSTA score ≥ 12 had higher risk of ICU (log-rank, P = 0.001) and high risk of death (log-rank, P = 0.000). Conclusions: The MuLBSTA score is valuable for risk stratification and could effectively screen high-risk patients at admission. The higher score at admission have higher risk of ICU care and death in patients infected with COVID.
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Frontiers in medicine · Jan 2020
Classification of Patients With Sepsis According to Immune Cell Characteristics: A Bioinformatic Analysis of Two Cohort Studies.
Background: Sepsis is well-known to alter innate and adaptive immune responses for sustained periods after initiation by an invading pathogen. Identification of immune cell characteristics may shed light on the immune signature of patients with sepsis and further indicate the appropriate immune-modulatory therapy for distinct populations. Therefore, we aimed to establish an immune model to classify sepsis into different immune endotypes via transcriptomics data analysis of previously published cohort studies. ⋯ External validation further demonstrated that the present model could categorize sepsis into the immunoparalysis and immunocompetent type precisely and efficiently. The percentages of 4 types of immune cells (M0 macrophages, M2 macrophages, naïve B cells, and naïve CD4 T cells) were significantly associated with 28-day cumulative mortality (P < 0.05). Conclusion: The present study developed a comprehensive tool to identify the immunoparalysis endotype and immunocompetent status in hospitalized patients with sepsis and provides novel clues for further targeting of therapeutic approaches.
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Frontiers in medicine · Jan 2020
Serum N-terminal Pro-B-type Natriuretic Peptide Predicts Mortality in Cardiac Surgery Patients Receiving Renal Replacement Therapy.
Background: N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a useful cardiac biomarker that is associated with acute kidney injury (AKI) and mortality after cardiac surgery. However, its prognostic value in cardiac surgical patients receiving renal replacement therapy (RRT) remains unclear. Objectives: Our study aimed to assess the prognostic value of NT-proBNP in patients with established AKI receiving RRT after cardiac surgery. ⋯ Consistently, Cox regression revealed that NT-proBNP levels before surgery (HR: 1.27, 95% CI, 1.06-1.52), at RRT initiation (HR: 1.11, 95% CI, 1.06-1.17), and on the first day after RRT (HR: 1.17, 95% CI, 1.11-1.23) were independently associated with 28-day mortality. Conclusions: Serum NT-proBNP was an independent predictor of 28-day mortality in cardiac surgical patients with AKI requiring RRT. The prognostic role of NT-proBNP needs to be confirmed in the future.