The Association between Evidence-Based Healthcare Practices and Outcomes among Preterm Births in China

<b><i>Introduction:</i></b> Very preterm infants are at high risk of early death or severe brain injury, with potential for impaired long-term neurodevelopmental function and physical health. There are evidence-based healthcare practices that can reduce the incidence. <b><i>Materials and Methods:</i></b> Infants born at 24–31⁶ weeks gestational age and admitted within 24 h to NICUs participating in the Chinese Neonatal Network in 2019 were included. We examined the association between 4 evidence-based practices: inborn (born in a tertiary hospital in the Chinese Neonatal Network), ACS (any antenatal corticosteroid), MgSO₄ (prenatal magnesium sulfate), and NT (normothermic temperature [36.0–37.5°C] at admission) and early death and/or severe brain injury in the study population. <b><i>Results:</i></b> Of 6,035 eligible infants, the incidence of early death and/or severe brain injury was 10.6%. Exposure to ACS only was associated with significant lower incidence of death and/or severe brain injury than none (aOR, 0.71; 95% CI: 0.57–0.88), but not MgSO₄ only (aOR, 0.97; 95% CI: 0.81–1.17), NT only (aOR, 0.91; 95% CI: 0.76–1.08), or inborn only (aOR, 0.91; 95% CI: 0.72–1.15). The association between number of practices and incidence of early death and/or severe brain injury is as follows: none = 23% (31/138), any 1 = 14% (84/592), any 2 = 12% (185/1,538), any 3 = 9% (202/2,285), and all 4 = 9% (140/1,482). <b><i>Discussion/Conclusion:</i></b> More comprehensive use of evidence-based practices was associated with improved survival without severe brain injury among very preterm infants born at &#x3c;32 weeks gestational age.

Improving Diagnosis and Prognosis in Acute Severe Brain Injury: A Multimodal Imaging Protocol

Multi-modal neuroimaging techniques have the potential to dramatically improve the diagnosis of the level consciousness and prognostication of neurological outcome for patients with severe brain injury in the intensive care unit (ICU). This protocol describes a study that will utilize functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and functional Near Infrared Spectroscopy (fNIRS) to measure and map the brain activity of acute critically ill patients. Our goal is to investigate whether these modalities can provide objective and quantifiable indicators of good neurological outcome and reliably detect conscious awareness. To this end, we will conduct a prospective longitudinal cohort study to validate the prognostic and diagnostic utility of neuroimaging techniques in the ICU. We will recruit 350 individuals from two ICUs over the course of 7 years. Participants will undergo fMRI, EEG, and fNIRS testing several times over the first 10 days of care to assess for residual cognitive function and evidence of covert awareness. Patients who regain behavioral awareness will be asked to complete web-based neurocognitive tests for 1 year, as well as return for follow up neuroimaging to determine which acute imaging features are most predictive of cognitive and functional recovery. Ultimately, multi-modal neuroimaging techniques may improve the clinical assessments of patients' level of consciousness, aid in the prediction of outcome, and facilitate efforts to find interventional methods that improve recovery and quality of life.

Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

Our goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8–25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale–Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53–0.85]) and consistent (observation windows: 12 min–9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2–6 h of observation (AUC: 0.82 [95% CI: 0.75–0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.