Presenting a “pleiotropic messenger” strategy using near-infrared–triggered on-demand NO release for spinal cord injury repair.

Aims: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.Methods: A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy‐related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four‐fold cross‐validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score.Results: Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913‐0.950), precision (92.4%, 95% CI: 0.897‐0.951), F1 score (91.5%, 95% CI: 0.889‐0.964), and recall score (93.6%, 95% CI: 0.909‐0.964), and yielded higher area under the receiver operating characteristic curve (AU‐ROC) (0.962, 95% CI: 0.942‐0.982).Conclusion: The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.

Aims: Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis.Methods: This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best‐performed prediction model for in‐hospital hypokalemia. The internal fivefold cross‐validation and external validation were performed to demonstrate the interpretability and generalizability.Results: A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate‐to‐severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open‐assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions.Conclusions: The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.

Objective: To generate an optimal prediction model along with identifying major contributors to intracranial infection among patients under external ventricular drainage and neurological intensive care. Methods: A retrospective cohort study was conducted among patients admitted into neurointensive care units between 1 January 2015 and 31 December 2020 who underwent external ventricular drainage due to traumatic brain injury, hydrocephalus, and nonaneurysmal spontaneous intracranial hemorrhage. Multivariate logistic regression in combination with the least absolute shrinkage and selection operator regression was applied to derive prediction models and optimize variable selections. Other machine-learning algorithms, including the support vector machine and K-nearest neighbor, were also applied to derive alternative prediction models. Five-fold cross-validation was used to train and validate each model. Model performance was assessed by calibration plots, receiver operating characteristic curves, and decision curves. A nomogram analysis was developed to explicate the weights of selected features for the optimal model. Results: Multivariate logistic regression showed the best performance among the three tested models with an area under curve of 0.846 ± 0.006. Six variables, including hemoglobin, albumin, length of operation time, American Society of Anesthesiologists grades, presence of traumatic subarachnoid hemorrhage, and a history of diabetes, were selected from 37 variable candidates as the top-weighted prediction features. The decision curve analysis showed that the nomogram could be applied clinically when the risk threshold is between 20% and 100%. Conclusions: The occurrence of external ventricular-drainage-associated intracranial infections could be predicted using optimal models and feature-selection approaches, which would be helpful for the prevention and treatment of this complication in neurointensive care units.

Objective: To date, there is no consensus on the surgery strategies of cranioplasty (CP) and ventriculoperitoneal shunt (VPS) placement. This meta‐analysis aimed to investigate the safety of staged and simultaneous operation in patients with comorbid cranial defects with hydrocephalus to inform future surgery protocols.Methods: A meta‐analysis of PubMed, Ovid, Web of Science, and Cochrane Library databases from the inception dates to February 8, 2023 adherent to PRISMA guidelines was conducted. The pooled analyses were conducted using RevMan 5.3 software. The outcomes included postoperative infection, reoperation, shunt obstruction, hematoma, and subdural effusion.Results: Of the 956 studies initially retrieved, 10 articles encompassing 515 patients were included. Among the total patients, 193 (37.48%) and 322 (62.52%), respectively, underwent simultaneous and staged surgeries. The finding of pooled analysis indicated that staged surgery was associated with lower rate of subdural effusion (14% in the simultaneous groups vs. 5.4% in the staged groups; OR = 2.39, 95% CI: 1.04–5.49, p = 0.04). However, there were no significant differences in overall infection (OR = 1.92, 95% CI: 0.74–4.97, p = 0.18), central nervous system infection (OR = 1.50, 95% CI: 0.68–3.31, p = 0.31), cranioplasty infection (OR = 1.58, 95% CI: 0.50–5.00, p = 0.44), shunt infection (OR = 1.30, 95% CI: 0.38–4.52, p = 0.67), reoperation (OR = 1.51, 95% CI: 0.38–6.00, p = 0.55), shunt obstruction (OR = 0.73, 95% CI: 0.25–2.16, p = 0.57), epidural hematoma (OR = 2.20, 95% CI: 0.62–7.86, p = 0.22), subdural hematoma (OR = 1.20, 95% CI: 0.10–14.19, p = 0.88), and intracranial hematoma (OR = 1.31, 95% CI: 0.42–4.07, p = 0.64). Moreover, subgroup analysis failed to yield new insights.Conclusions: Staged surgery is associated with a lower rate of postoperative subdural effusion. However, from the evidence of sensitivity analysis, this result is not stable. Therefore, our conclusion should be viewed with caution, and neurosurgeons in practice should make individualized decisions based on each patient's condition and cerebrospinal fluid tap test.