Modeling Brain-Heart Crosstalk Information in Patients with Traumatic Brain Injury

Heart rate variability for neuro-prognostication after CA: Insight from the Parisian registry

BACKGROUND

Hypoxic ischemic brain injury (HIBI) induced by cardiac arrest (CA) seems to predominate in cortical areas and to a lesser extent in the brainstem. These regions play key roles in modulating the activity of the autonomic nervous system (ANS), that can be assessed through analyses of heart rate variability (HRV). The objective was to evaluate the prognostic value of various HRV parameters to predict neurological outcome after CA.

METHODS

Retrospective monocentric study assessing the prognostic value of HRV markers and their association with HIBI severity. Patients admitted for CA who underwent EEG for persistent coma after CA were included. HRV markers were computed from 5 min signal of the ECG lead of the EEG recording. HRV indices were calculated in the time-, frequency-, and non-linear domains. Frequency-domain analyses differentiated very low frequency (VLF 0.003-0.04 Hz), low frequency (LF 0.04-0.15 Hz), high frequency (HF 0.15-0.4 Hz), and LF/HF ratio. HRV indices were compared to other prognostic markers: pupillary light reflex, EEG, N20 on somatosensory evoked potentials (SSEP) and biomarkers (neuron specific enolase-NSE). Neurological outcome at 3 months was defined as unfavorable in case of best CPC 3-4-5.

RESULTS

Between 2007 and 2021, 199 patients were included. Patients were predominantly male (64%), with a median age of 60 [48.9-71.7] years. 76% were out-of-hospital CA, and 30% had an initial shockable rhythm. Neurological outcome was unfavorable in 73%. Compared to poor outcome, patients with a good outcome had higher VLF (0.21 vs 0.09 ms2/Hz, p < 0.01), LF (0.07 vs 0.04 ms2/Hz, p = 0.003), and higher LF/HF ratio (2.01 vs 1.01, p = 0.008). Several non-linear domain indices were also higher in the good outcome group, such as SD2 (15.1 vs 10.2, p = 0.016) and DFA α1 (1.03 vs 0.78, p = 0.002). These indices also differed depending on the severity of EEG pattern and abolition of pupillary light reflex. These time-frequency and non-linear domains HRV parameters were predictive of poor neurological outcome, with high specificity despite a low sensitivity.

CONCLUSION

In comatose patients after CA, some HRV markers appear to be associated with unfavorable outcome, EEG severity and PLR abolition, although the sensitivity of these HRV markers remains limited.

Refining outcome prediction after traumatic brain injury with machine learning algorithms

Outcome after traumatic brain injury (TBI) is typically assessed using the Glasgow outcome scale extended (GOSE) with levels from 1 (death) to 8 (upper good recovery). Outcome prediction has classically been dichotomized into either dead/alive or favorable/unfavorable outcome. Binary outcome prediction models limit the possibility of detecting subtle yet significant improvements. We set out to explore different machine learning methods with the purpose of mapping their predictions to the full 8 grade scale GOSE following TBI. The models were set up using the variables: age, GCS-motor score, pupillary reaction, and Marshall CT score. For model setup and internal validation, a total of 866 patients could be included. For external validation, a cohort of 369 patients were included from Leuven, Belgium, and a cohort of 573 patients from the US multi-center ProTECT III study. Our findings indicate that proportional odds logistic regression (POLR), random forest regression, and a neural network model achieved accuracy values of 0.3-0.35 when applied to internal data, compared to the random baseline which is 0.125 for eight categories. The models demonstrated satisfactory performance during external validation in the data from Leuven, however, their performance were not satisfactory when applied to the ProTECT III dataset.

Crosstalk Between the Nervous System and Systemic Organs in Acute Brain Injury

Organ crosstalk is a complex biological communication between distal organs mediated via cellular, soluble, and neurohormonal actions, based on a two-way pathway. The communication between the central nervous system and peripheral organs involves nerves, endocrine, and immunity systems as well as the emotional and cognitive centers of the brain. Particularly, acute brain injury is complicated by neuroinflammation and neurodegeneration causing multiorgan inflammation, microbial dysbiosis, gastrointestinal dysfunction and dysmotility, liver dysfunction, acute kidney injury, and cardiac dysfunction. Organ crosstalk has become increasingly popular, although the information is still limited. The present narrative review provides an update on the crosstalk between the nervous system and systemic organs after acute brain injury. Future research might help to target this pathophysiological process, preventing the progression toward multiorgan dysfunction in critically ill patients with brain injury.

Predictive factors for cerebrocardiac syndrome in patients with severe traumatic brain injury: a retrospective cohort study

Background and objective

Cerebrocardiac syndrome (CCS) is a severe complication of severe traumatic brain injury (sTBI) that carries high mortality and disability rates. Early identification of CCS poses a significant clinical challenge. The main objective of this study was to investigate potential risk factors associated with the development of secondary CCS in patients with sTBI. It was hypothesized that elevated right heart Tei index (TI), lower Glasgow Coma Scale (GCS) scores, and elevated cardiac troponin-I (cTnI) levels would independently contribute to the occurrence of CCS in sTBI patients.

Methods

A retrospective cohort study was conducted to identify risk factors for CCS secondary to sTBI. One hundred and fifty-five patients were enrolled with sTBI admitted to the hospital between January 2016 and December 2020 and divided them into a CCS group (n = 75) and a non-CCS group (n = 80) based on the presence of CCS. This study involved the analysis and comparison of clinical data from two patient groups, encompassing demographic characteristics, peripheral oxygen saturation (SPO2), neuron-specific enolase (NSE), cardiac troponin-I (cTnI), N-terminal pro-brain natriuretic peptide (NT-proBNP), optic nerve sheath diameter (ONSD), cardiac ultrasound, acute physiology and chronic health evaluation (APACHE II) scores, and GCS scores and so on. Multivariate logistic regression was employed to identify independent risk factors for CCS, and receiver operating characteristic (ROC) curves were used to assess their predictive value for CCS secondary to sTBI.

Results

The study revealed that 48.4% of sTBI patients developed secondary CCS. In the multivariate analysis model 1 that does not include NT-proBNP and cTnI, ONSD (OR = 2.582, 95% CI: 1.054-6.327, P = 0.038), right heart Tei index (OR = 2.81, 95% CI: 1.288-6.129, P = 0.009), and GCS (OR = 0.212, 95% CI: 0.086-0.521, P = 0.001) were independent risk factors for secondary CCS in sTBI patients. In multivariate analysis model 2 that includes NT-proBNP and cTnI, cTnI (OR = 27.711, 95%CI: 3.086-248.795, P = 0.003), right heart Tei index (OR = 2.736, 95% CI: 1.056-7.091, P = 0.038), and GCS (OR = 0.147, 95% CI: 0.045-0.481, P = 0.002) were independent risk factors for secondary CCS in sTBI patients. The area under the ROC curve for ONSD, Tei index, GCS, and cTnI were 0.596, 0.613, 0.635, and 0.881, respectively. ONSD exhibited a positive predictive value (PPV) of 0.704 and a negative predictive value (NPV) of 0.634. The Tei index demonstrated a PPV of 0.624 and an NPV of 0.726, while GCS had a PPV of 0.644 and an NPV of 0.815. On the other hand, cTnI exhibited a significantly higher PPV of 0.936 and an NPV of 0.817. These findings indicate that the Tei index, GCS score, and cTnI possess certain predictive value for secondary CCS in patients with sTBI.

Conclusions

The study provides valuable insights into the identification of independent risk factors for CCS secondary to sTBI. The findings highlight the significance of right heart Tei index, GCS score, and cTnI as potential predictive factors for CCS in sTBI patients. Further larger-scale studies are warranted to corroborate these findings and to provide robust evidence for the development of early intervention strategies aimed at reducing the incidence of CCS in this patient population.

Treatment of insomnia based on the mechanism of pathophysiology by acupuncture combined with herbal medicine: A review

Insomnia is a sleep disorder which severely affects patients mood, quality of life and social functioning, serves as a trigger or risk factor to a variety of diseases such as depression, cardiovascular and cerebrovascular diseases, obesity and diabetes, and even increases the risk of suicide, and has become an increasingly widespread concern worldwide. Considerable research on insomnia has been conducted in modern medicine in recent years and encouraging results have been achieved in the fields of genetics and neurobiology. Unfortunately, however, the pathogenesis of insomnia remains elusive to modern medicine, and pharmacological treatment of insomnia has been regarded as conventional. However, in the course of treatment, pharmacological treatment itself is increasingly being questioned due to potential dependence and drug resistance and is now being replaced by cognitive behavior therapy as the first-line treatment. As an important component of complementary and alternative medicine, traditional Chinese medicine, especially non-pharmacological treatment methods such as acupuncture, is gaining increasing attention worldwide. In this article, we discuss the combination of traditional Chinese medicine, acupuncture, and medicine to treat insomnia based on neurobiology in the context of modern medicine.

Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications

Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. Work from our group and others has demonstrated that data-analytic techniques can identify quantitative physiologic changes that precede clinical detection of meaningful events, and therefore may provide an important window for time-sensitive therapies. Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.

LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.