Risk factors for tracheostomy after traumatic cervical spinal cord injury

Long-term trends and risk factors of tracheostomy and decannulation in patients with cervical spinal cord Injury

STUDY DESIGN

Retrospective study.

OBJECTIVES

To investigate the risk factors of tracheostomy and decannulation after cervical spinal cord injury (CSCI) and their epidemiological changes over the past 8 years in Beijing Bo'ai Hospital, China Rehabilitation Research Center (CRRC), China.

SETTING

Beijing Bo'ai Hospital, CRRC.

METHODS

We reviewed 8 years of patient data (2013.1.1 to 2020.12.31) at CRRC, focusing on those hospitalized and diagnosed with CSCI. We analyzed changes in demographic and clinical data's trends. Logistic regression analysis was used to determine factors impacting tracheostomy and decannulation.

RESULTS

Finally, 1641 CSCI patients met the inclusion criteria. Over the past 8 years, the proportion of tracheostomized patients with CSCI was 16.3%, and the proportion of successfully decannulated of tracheostomized patients with TCSCI was 77.9%. We found that Traumatic (OR = 1.8, 95% CI = 1.06, 3.22; p = 0.046), Motor level of injury (C5-C8) (OR = 0.32, 95% CI = -1.91,-0.34; p = 0.005), AIS = A/B/C (OR = 22.7/11.1/4.2, 95% CI = 12.16,42.26/5.74,21.56/2.23,7.89; p < 0.001/p < 0.001/p < 0.001), age > 56 (OR = 1.6, 95% CI = 1.04, 2.32; p = 0.031) were the risk factors for tracheostomy. By analyzing the risk factors of decannulation failure in tracheostomized patients with TCSCI through multivariable logistic regression, statistically significant differences were found in age > 45 (OR = 4.1, 95% CI = 1.44, 11.81; p = 0.008), complete injury (OR = 2.7, 95% CI = 1.26, 5.95; p = 0.011), facet dislocation (OR = 2.8, 95% CI = 1.13,7.07; p = 0.027).

CONCLUSIONS

Recent years have witnessed shifts in the epidemiological characteristics of CSCI. Identifying the factors influencing tracheostomy and decannulation in CSCI can aid in improving patient prognosis.

Nomogram and Risk Calculator for Postoperative Tracheostomy after Heart Valve Surgery

Postoperative tracheostomy (POT) is an important indicator of critical illness, associated with poorer prognoses and increased medical burdens. However, studies on POTs after heart valve surgery (HVS) have not been reported. The objectives of this study were first to identify the risk factors and develop a risk prediction model for POTs after HVS, and second to clarify the relationship between POTs and clinical outcomes. Consecutive adults undergoing HVS from January 2016 to December 2019 in a single cardiovascular center were enrolled, and a POT was performed in 1.8% of the included patients (68/3853). Compared to patients without POTs, the patients with POTs had higher rates of readmission to the ICU and in-hospital mortality, as well as longer ICU and hospital stays. Five factors were identified to be significantly associated with POTs after HVS by our multivariate analysis, including age, diabetes mellitus, pulmonary edema, intraoperative transfusion of red blood cells, and surgical types. A nomogram and a risk calculator were constructed based on the five factors, showing excellent discrimination, calibration, and clinical utility. Three risk intervals were defined as low-, medium-, and high-risk groups according to the nomogram and clinical practice. The findings of this study may be helpful for early risk assessment and perioperative management.

Respiratory Complications and Weaning Considerations for Patients with Spinal Cord Injuries: A Narrative Review

Respiratory complications following traumatic spinal cord injury are common and are associated with high morbidity and mortality. The inability to cough and clear secretions coupled with weakened respiratory and abdominal muscles commonly leads to respiratory failure, pulmonary edema, and pneumonia. Higher level and severity of the spinal cord injury, history of underlying lung pathology, history of smoking, and poor baseline health status are potential predictors for patients that will experience respiratory complications. For patients who may require prolonged intubation, early tracheostomy has been shown to lead to improved outcomes. Prediction models to aid clinicians with the decision and timing of tracheostomy have been shown to be successful but require larger validation studies in the future. Mechanical ventilation weaning strategies also require further investigation but should focus on a combination of optimizing ventilator setting, pulmonary toilet techniques, psychosocial well-being, and an aggressive bowel regimen.

Tracheostomy in traumatic cervical spinal cord injury: Early versus late tracheostomy

OBJECTIVE

The purpose of this study was to characterize the relationship between predictors and the time of tracheostomy after traumatic cervical spinal cord injury (TCSCI).

METHODS

Five hundred twenty-six patients with TCSCI treated between January,2012 and December, 2021 were retrospectively reviewed. Patients were subdivided into two groups: early tracheostomy (≤7 days from initiation of endotracheal intubation) and late tracheostomy. Comparisons between early tracheostomy and late tracheostomy were statistically analyzed. Logistic regression analysis was applied to identify independent predictors of tracheostomy and calculate probability for different grades of combining predictors to predict tracheostomy. Spearman's correlation coefficient was used to evaluate the association between the grade of combining predictors and the time to tracheostomy.

RESULTS

Among 526 eligible patients, 63(12.0%) had a tracheostomy performed. Compared with late tracheostomy group, patients in early tracheostomy group had higher ISS, more severe neurological status while fewer In-hospital LOS days and ICU LOS days. By Logistic regression analysis, severe American Spinal Injury Association Impairment Scale (AIS A), the neurological level of injury (NLI>C5), higher Injury Severity Score (ISS>16) and advanced age (over 50 years old) were identified as independent predictors for tracheostomy. Depending on the likelihood of tracheostomy, the combining predictors were graded into five categories. As the value of probability was higher than 50%, Grade I-III made optimistic predictions about tracheostomy. According to Spearman's correlation analysis, early tracheostomy had a strong association with Grade I, while late tracheostomy was closely correlated with Grade III.

CONCLUSIONS

Factors related to the decision of tracheostomy were ASIA impairment scale, neurological level of injury, injury severity score and age. The grades of combining predictors could support indication for predicting the time of tracheostomy.

Recent advances in nanomaterials for the treatment of spinal cord injury

Spinal cord injuries (SCIs) are devastating. In SCIs, a powerful traumatic force impacting the spinal cord results in the permanent loss of nerve function below the injury level, leaving the patient paralyzed and wheelchair-bound for the remainder of his/her life. Unfortunately, clinical treatment that depends on surgical decompression appears to be unable to handle damaged nerves, and high-dose methylprednisolone-based therapy is also associated with problems, such as infection, gastrointestinal bleeding, femoral head necrosis, obesity, and hyperglycemia. Nanomaterials have opened new avenues for SCI treatment. Among them, performance-based nanomaterials derived from a variety of materials facilitate improvements in the microenvironment of traumatic injury and, in some cases, promote neuron regeneration. Nanoparticulate drug delivery systems enable the optimization of drug effects and drug bioavailability, thus contributing to the development of novel treatments. The improved efficiency and accuracy of gene delivery will also benefit the exploration of SCI mechanisms and the understanding of key genes and signaling pathways. Herein, we reviewed different types of nanomaterials applied to the treatment of SCI and summarized their functions and advantages to provide new perspectives for future clinical therapies.

A Nomogram Model for Prediction of Tracheostomy in Patients With Traumatic Cervical Spinal Cord Injury

OBJECTIVE

To develop a nomogram for the prediction of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI).

METHODS

A total of 689 TCSCI patients were included in our study. First, the variable selection was performed using between-group comparisons and LASSO regression analysis. Second, a multivariate logistic regression analysis (MLRA) with a step-by-step method was performed. A nomogram model was developed based on the MLRA. Finally, the model was validated on the training set and validation set.

RESULTS

The nomogram prediction model incorporated 5 predictors, including smoking history, dislocation, thoracic injury, American Spinal Injury Association (ASIA) grade, and neurological level of injury (NLI). The area under curve in the training group and in the validation group were 0.883 and 0.909, respectively. The Hosmer-Lemeshow test result was p = 0.153. From the decision curve analysis curve, the model performed well and was feasible to make beneficial clinical decisions.

CONCLUSION

The nomogram combining dislocation, thoracic injury, ASIA grade A, NLI, and smoking history was validated as a reliable model for the prediction of tracheostomy.

The shoulder abductor strength is a novel predictor of tracheostomy in patients with traumatic cervical spinal cord injury

BACKGROUND

Early prediction of tracheostomy in traumatic cervical spinal cord injury (TCSCI) patients is often difficult. This study aims to clarify the association between shoulder abductor strength (SAS) and tracheostomy in patients with TCSCI.

METHODS

We retrospectively analyzed 513 TCSCI patients who were treated in our hospital. All patients were divided into a tracheostomy group and a non-tracheostomy group. The SAS was assessed using the Medical Research Council (MRC) Scale for Muscle Strength grading. Potential predictors were assessed for their association with tracheostomy in patients. A nomogram was developed based on multivariable logistic regression analysis (MLRA) to visualize the predictive ability of the SAS. Validation of the nomogram was performed to judge whether the nomogram was reliable for visual analysis of the SAS. Receiver operating characteristics curve, specificity, and sensitivity were also performed to assess the predictive ability of the SAS.

RESULTS

The proportion of patients with the SAS grade 0-2 was significantly higher in the tracheostomy group than in the non-tracheostomy group (88.1% vs. 54.8%, p = 0.001). The SAS grade 0-2 was identified as a significant predictor of the tracheostomy (OR: 4.505; 95% CI: 2.080-9.758; p = 0.001). Points corresponding to both the SAS grade 0-2 and the neurological level of injury at C2-C4 were between 60 and 70 in the nomogram. The area under the curve for the SAS grade 0-2 was 0.692. The sensitivity of SAS grade 0-2 was 0.239. The specificity of SAS grade 0-2 was 0.951.

CONCLUSIONS

SAS is a novel predictor of tracheostomy in patients after TCSCI. The SAS grade 0-2 had a good predictive ability of tracheostomy.

Classification and regression tree (CART) model to assist clinical prediction for tracheostomy in patients with traumatic cervical spinal cord injury: a 7-year study of 340 patients

OBJECTIVE

To develop a classification and regression tree (CART) model to predict the need of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI) and to quantify scores of risk factors to make individualized clinical assessments.

METHODS

The clinical characteristics of patients with TCSCI admitted to our hospital from January 2014 to December 2020 were retrospectively analyzed. The demographic characteristics (gender, age, smoking history), mechanism of injury, injury characteristics (ASIA impairment grades, neurological level of impairment, injury severity score), preexisting lung disease and preexisting medical conditions were statistically analyzed. The risk factors of tracheostomy were analyzed by univariate logistic regression analysis (ULRA) and multiple logistic regression analysis (MLRA). The CART model was established to predict tracheostomy.

RESULTS

Three hundred and forty patients with TCSCI met the inclusion criteria, in which 41 patients underwent the tracheostomy. ULRA and MLRA showed that age > 50, ISS > 16, NLI > C5 and AIS A were significantly associated with tracheostomy. The CART model showed that AIS A and NLI > C5 were at the first and second decision node, which had a significant influence on the decision of tracheostomy. The final scores for tracheostomy from CART algorithm, composed of age, ISS, NLI and AIS A with a sensitivity of 0.78 and a specificity of 0.96, could also predict tracheostomy.

CONCLUSION

The establishment of CART model provided a certain clinical guidance for the prediction of tracheostomy in TCSCI. Quantifications of risk factors enable accurate prediction of individual patient risk of need for tracheostomy.

Predictors and outcomes of postoperative tracheostomy in patients undergoing acute type A aortic dissection surgery

Background

Despite surgical advances, acute type A aortic dissection remains a life-threatening disease with high mortality and morbidity. Tracheostomy is usually used for patients who need prolonged mechanical ventilation in the intensive care unit (ICU). However, data on the risk factors for requiring tracheostomy and the impact of tracheostomy on outcomes in patients after Stanford type A acute aortic dissection surgery (AADS) are limited.

Methods

A retrospective single-institutional study including consecutive patients who underwent AADS between January 2016 and December 2019 was conducted. Patients who died intraoperatively were excluded. Univariate analysis and multivariate logistic regression analysis were used to identify independent risk factors for postoperative tracheostomy (POT). A nomogram to predict the probability of POT was constructed based on independent predictors and their beta-coefficients. The area under the receiver operating characteristic curve (AUC) was performed to assess the discrimination of the model. Calibration plots and the Hosmer–Lemeshow test were used to evaluate calibration. Clinical usefulness of the nomogram was assessed by decision curve analysis. Propensity score matching analysis was used to analyze the correlation between requiring tracheostomy and clinical prognosis.

Results

There were 492 patients included in this study for analysis, including 55 patients (11.2%) requiring tracheostomy after AADS. Compared with patients without POT, patients with POT experienced longer ICU and hospital stay and higher mortality. Age, cerebrovascular disease history, preoperative white blood cell (WBC) count and renal insufficiency, intraoperative amount of red blood cell (RBC) transfusion and platelet transfusion were identified as independent risk factors for POT. Our constructed nomogram had good discrimination with an AUC = 0.793 (0.729–0.856). Good calibration and clinical utility were observed through the calibration and decision curves, respectively. For better clinical application, we defined four intervals that stratified patients from very low to high risk for occurrence of POT.

Conclusions

Our study identified preoperative and intraoperative risk factors for POT and found that requiring tracheostomy was related to the poor outcomes in patients undergoing AADS. The established prediction model was validated with well predictive performance and clinical utility, and it may be useful for individual risk assessment and early clinical decision-making to reduce the incidence of tracheostomy.