Leave-one-out prediction error of systolic arterial pressure time series under paced breathing

Incidence and mortality related risk factors in patients with severe traumatic brain injury: A meta‑analysis

The present study aimed to clarify the onset of traumatic brain injury (TBI) and identify mortality-related risk factors in patients with severe TBI, to enable the early identification of high-risk individuals and timely implementation of prevention and treatment strategies to minimize mortality rates. Comprehensive database searches were conducted across Web of Science, PubMed, CINAHL and EMBASE, covering publications from database inception until October 17, 2023. Search terms in English included 'head trauma', 'brain trauma', 'mortality', 'death' and 'risk factor'. In total, two independent researchers screened and extracted the data on mortality onset and associated risk factors in patients with severe TBI. Meta-analysis was performed using R 4.2.2. A total of 33 cohort studies, including 71,718 patients with severe TBI, were selected for meta-analysis. The data indicated an overall mortality rate of 27.8% (95%CI: 22.5-33.2%) from database inception until October 17, 2023. Subgroup analysis revealed a mortality rate of 25.2% (95%CI: 20.2-30.1%) in developed countries, compared with 38.0% (95%CI: 21.4-54.7%) in developing countries. Additionally, the mean age of deceased patients was significantly higher compared with that of survivors (41.53±16.47). Key risk factors found to be associated with mortality included anemia [relative risk (RR), 1.42; 95%CI, 1.04-1.93], diabetes mellitus (RR, 1.40; 95%CI, 1.00-1.96), coagulopathy (RR, 4.31; 95%CI, 2.31-8.05), shock (RR, 3.41; 95%CI, 2.31-5.04) and systolic blood pressure≤90 mmHg (RR, 2.32; 95%CI, 1.65-3.27). Furthermore, pre-hospital intubation (RR, 1.48; 95%CI, 1.13-1.92),hypotension (RR, 2.04; 95%CI: 1.58, 2.63), hypoxemia (RR, 1.42; 95%CI: 1.13, 1.79), subdural hemorrhage (RR, 1.99; 95%CI: 1.50, 2.62), subarachnoid hemorrhage (RR, 1.64; 95%CI: 1.09, 2.47) and subdural hematoma (SDH; RR, 1.50; 95%CI: 1.04, 2.17). was identified to be a significant risk factor during hospitalization treatment. These results suggest that various factors, such as age, anemia, diabetes, shock, hypotension, hypoxemia, trauma scores and brain injury types, can all contribute to mortality risk in patients with severe TBI. Addressing these risk factors will likely be important for reducing mortality in this patient population.

Detection of synchronization from univariate data using wavelet transform

A method is proposed for detecting from univariate data the presence of synchronization of a self-sustained oscillator by external driving with varying frequency. The method is based on the analysis of difference between the oscillator instantaneous phases calculated using continuous wavelet transform at time moments shifted by a certain constant value relative to each other. We apply our method to a driven asymmetric van der Pol oscillator, experimental data from a driven electronic oscillator with delayed feedback and human heartbeat time series. In the latest case, the analysis of the heart rate variability data reveals synchronous regimes between the respiration and slow oscillations in blood pressure.

Detecting synchronization of self-sustained oscillators by external driving with varying frequency

We propose a method for detecting the presence of a synchronization of a self-sustained oscillator by external driving with linearly varying frequency. The method is based on a continuous wavelet transform of the signals of the self-sustained oscillator and external force and allows one to distinguish the case of true synchronization from the case of spurious synchronization caused by linear mixing of the signals. We apply the method to a driven van der Pol oscillator and to experimental data of human heart rate variability and respiration.