It is unclear who can benefit from tracheal intubation in the moderate (mTBI) traumatic brain injury (TBI) population. Given that mTBI patients are conscious, intubation can cause intense stress, possibly triggering neurological deterioration. Therefore, identifying potential risk factors for intubation in mTBI patients can serve as a valuable clinical warning. We sought to investigate whether elevated D-dimer is a possible risk factor for intubation in mTBI patients. Using the STROBE statement, adult patients with isolated TBI (Glasgow Coma Scale [GCS] score 9–13) treated at a high-volume neurotrauma center between January 2015 and December 2020 were reviewed. The demographics, clinical presentation, neuroimaging, and laboratory information were collected based on the patients' electronic medical record. D-dimer values were assessed from serum when patients were admitted to the hospital. The primary study end-point was that the mTBI patient was intubated within 72 h upon admission. A total of 557 patients with mTBI were finally included in this study. Of these, 85 (15.3%) patients were intubated. Multi-variate logistic regression analysis showed that high-level D-dimer (≥17.9mg/L) was significantly associated with early tracheal intubation in mTBI patients (odds ratio, 3.10 [1.16–8.25]; p = 0.024) after adjusting for age, sex, GCS scores, Marshall scores, and Injury Severity Scores. Sensitivity analysis showed that high-level D-dimer had a robust correlation with intubation in the different subgroups or after propensity score matching. High-level D-dimer on admission is an independent risk factor for early tracheal intubation in isolated mTBI patients.

Acute respiratory failure (ARF) with a high incidence among moderate-to-severe traumatic brain injury (M-STBI) patients plays a pivotal role in worsening neurological outcomes. Traumatic subarachnoid hemorrhage (tSAH) is highly prevalent in M-STBI, which is associated with significant adverse outcomes. In this retrospective cohort study, we aimed to explore the association between the severity of the tSAH and ARF in the M-STBI population. A total of 771 subjects were reviewed. Clinical and neuroimaging data of M-STBI patients were retrospectively collected, and ARF was ascertained retrospectively based on their electronic medical record. The degree of tSAH was classified according to Fisher’s criteria, and the grade of tSAH was dichotomized to a low Fisher grade (Fisher grade 1–2) and a high Fisher grade (Fisher grade 3–4). After exclusion procedures, the data of 695 M-STBI patients were analyzed. A total of 284 (30.8%) had a high Fisher grade on admission. The overall rate of ARF within 48 h upon admission was 34.4% (239/695); it was 29.5% (142/481) and 46.3% (99/214) for the low and high Fisher groups, respectively. In a full cohort, a high Fisher grade was associated with ARF after adjusting for age, gender, GCS, smoking history, comorbidities, multiple injuries, characteristics of TBI, and pulmonary factors (OR 1.78; 95% CI, 1.11–2.85, p = 0.016). This result remained robust in the comparisons after PSM (71/132, 42.8% vs. 53/132, 31.9%; OR, 1.59; 95% CI, 1.02–2.49, p = 0.042). A high Fisher SAH grade exposure on admission is associated with ARF in M-STBI patients.

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

Background: Cerebral fat embolism (CFE) syndrome at high altitude was rare complicated with paroxysmal sympathetic hyperactivity (PSH) syndrome and septic shock. It is a challenge to differential diagnosis and treatment at high altitude.Case presentation: This case presents a CFE with PSH and septic shock of a 23-year-old man occurred at high altitude of 3800 m above sea level, transferred by airplane successfully and cured in the department of neurosurgery, Xi’an Tangdu Hospital.Conclusions: It is key that CFE with PSH can be rapid diagnosed and treatment bundles of septic shock should be initiated as soon as possible. Early neurological rehabilitation played an important role for good outcome.

Background: Readmission due to heart failure (HF) remains a major clinical and economic challenge. The red cell distribution width-to-albumin ratio (RAR) has been proposed as a novel biomarker reflecting chronic inflammation, nutritional status, and multi-organ involvement in cardiovascular diseases. However, the association between RAR and readmission risk, particularly in Asian populations with HF, remains unclear. This study aimed to evaluate the relationship between RAR levels and the 6-month readmission risk among Chinese patients with heart failure. Methods: We conducted a retrospective cohort study using data from the Zigong Heart Failure Database, which included 2008 patients hospitalized for HF between December 2016 and June 2019. After excluding patients with missing RDW, albumin, or readmission data, 1854 patients were included in the final analytical cohort. Based on RAR values, patients were divided into four quartiles. Multivariable Cox proportional hazards regression models were used to assess the association between RAR and 6-month readmission risk, adjusting for demographics, clinical parameters, comorbidities, and medications. Subgroup analyses were performed to examine the consistency of associations across different patient characteristics. Results: The overall 6-month readmission rate was 38.5% (714/1854). Patients in higher RAR quartiles had a significantly greater risk of readmission than those in lower quartiles ( P = 0.0025). In unadjusted analyses, each unit increase in RAR was associated with a 57% higher risk of 6-month readmission (HR 1.57, 95% CI: 1.27–1.93, P < 0.001). This association remained significant after adjusting for confounding variables (HR 1.52, 95% CI: 1.21–1.92, P < 0.001). After full adjustment, patients in the highest quartile (Q4) had a 39% higher risk of readmission compared with those in the lowest quartile (Q1) (HR 1.39, 95% CI: 1.10–1.76, P < 0.001). Subgroup analyses showed consistent associations across sex, age, BMI, NYHA classification, and medication use, with no significant interactions observed. Conclusion: Elevated RAR was independently associated with an increased 6-month readmission risk among Chinese patients with HF. As a simple and cost-effective biomarker, RAR may enhance risk stratification and inform clinical decision-making in HF management, potentially improving patient outcomes and reducing healthcare burden.

Objectives: Low lean mass (LLM), as a critical element of sarcopenia, is associated with chronic inflammation and malnutrition and presents substantial risks to ageing populations. Although the red blood cell distribution width-to-albumin ratio (RAR) has been identified as a promising biomarker in various inflammatory and nutritional contexts, its relationship with LLM has not yet been investigated. This study seeks to examine the association between RAR and the risk of LLM. Methods: A cross-sectional analysis was conducted using data from 22,299 adults participating in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. LLM was defined according to the criteria established by the Foundation for the National Institutes of Health. Multivariable logistic regression and restricted cubic spline models were employed to assess the relationship between RAR and LLM, adjusting for variables including age, gender, marital status, race and ethnicity, body mass index, smoking status, alcohol consumption, educational attainment, diabetes mellitus, cardiovascular disease, moderate-to-vigorous physical activity, and time spent in sedentary activities. Subgroup analyses and propensity score matching analyses were also conducted. Results: The prevalence of LLM was 10.3% in NHANES 1999–2018. Restricted cubic splines analysis revealed a significant dose-response relationship between RAR and LLM risk ( P < .001). Based on multivariate logistic regression, elevated RAR levels were linked to a higher odds of LLM (OR 1.84, 95% CI 1.64–2.06, P < .001). Specifically, individuals in the highest RAR quartile (Q4) had an OR of 2.38 (95% CI 2.00–2.83, P < .001) for developing LLM compared to those in the lowest quartile (Q1). Sensitivity analysis confirmed the robustness of this positive association in several subgroup analyses and propensity score matching analysis. Conclusion: High RAR is associated with higher odds of LLM risk in community populations. These findings serve as a basis for further research on sarcopenia and need thorough validation in various populations.