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
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.
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Frontiers in medicine · Jan 2020
Effect of a Multimodal Movement Intervention in Patients With Neurogenic Claudication Based on Lumbar Spinal Stenosis and/or Degenerative Spondylolisthesis-A Pilot Study.
Chronic low-back pain is a major individual, social, and economic burden. The impairment ranges from deterioration of gait, limited mobility, to psychosocial distress. Due to this complexity, the demand for multimodal treatments is huge. ⋯ For the subsequent study, further kinematic and cognitive parameters should be analyzed, and the number of participants has to be increased. Clinical Trial Registration: German Clinical Trial Register (ID: DRKS00021026/URL: https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00021026).
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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
Unsupervised Clustering Analysis Based on MODS Severity Identifies Four Distinct Organ Dysfunction Patterns in Severely Injured Blunt Trauma Patients.
Purpose: We sought to identify a MODS score parameter that highly correlates with adverse outcomes and then use this parameter to test the hypothesis that multiple severity-based MODS clusters could be identified after blunt trauma. Methods: MOD score across days (D) 2-5 was subjected to Fuzzy C-means Clustering Analysis (FCM) followed by eight Clustering Validity Indices (CVI) to derive organ dysfunction patterns among 376 blunt trauma patients admitted to the intensive care unit (ICU) who survived to discharge. Thirty-one inflammation biomarkers were assayed (Luminex™) in serial blood samples (3 samples within the first 24 h and then daily up to D 5) and were analyzed using Two-Way ANOVA and Dynamic Network analysis (DyNA). ⋯ Interleukin (IL)-6, MCP-1, IL-10, IL-8, IP-10, sST2, and MIG were elevated differentially over time across the four clusters. DyNA identified remarkable differences in inflammatory network interconnectivity. Conclusion: These results suggest the existence of four distinct organ failure patterns based on MOD score magnitude in blunt trauma patients admitted to the ICU who survive to discharge.