A new nomogram to predict the need for tracheostomy in burned patients

Inhalation Injury, Respiratory Failure, and Ventilator Support in Acute Burn Care

Sustaining an inhalation injury increases the risk of severe complications and mortality. Current evidential support to guide treatment of the injury or subsequent complications is lacking, as studies either exclude inhalation injury or design limit inferences that can be made. Conventional ventilator modes are most commonly used, but there is no consensus on optimal strategies. Settings should be customized to patient tolerance and response. Data for pharmacotherapy adjunctive treatments are limited.

A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study

Objective

This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU).

Method

We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility.

Results

The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822-0.886) for the training cohort and 0.865 (95% CI: 0.817-0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit.

Conclusion

We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.

Predicting the Risk of Incorrect Inhalation Technique in Patients with Chronic Airway Diseases by a New Predictive Nomogram

Purpose

To develop and internally validate a nomogram for predicting the risk of incorrect inhalation techniques in patients with chronic airway diseases.

Methods

A total of 206 patients with chronic airway diseases treated with inhaled medications were recruited in this study. Patients were divided into correct (n=129) and incorrect (n=77) cohorts based on their mastery of inhalation devices, which were assessed by medical professionals. Data were collected on the basis of questionnaires and medical records. The least absolute shrinkage and selection operator method (LASSO) and multivariate logistic regression analyses were conducted to identify the risk factors of incorrect inhalation techniques. Then, calibration curve, Harrell's C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and bootstrapping validation were applied to assess the apparent performance, clinical validity and internal validation of the predicting model, respectively.

Results

Seven risk factors including age, education level, drug cognition, self-evaluation of curative effect, inhalation device use instruction before treatment, post-instruction evaluation and evaluation at return visit were finally determined as the predictors of the nomogram prediction model. The ROC curve obtained by this model showed that the AUC was 0.814, with a sensitivity of 0.78 and specificity of 0.75. In addition, the C-index was 0.814, with a Z value of 10.31 (P<0.001). It was confirmed to be 0.783 by bootstrapping validation, indicating that the model had good discrimination and calibration. Furthermore, analysis of DCA showed that the nomogram had good clinical validity.

Conclusion

The application of the developed nomogram to predict the risk of incorrect inhalation techniques during follow-up visits is feasible.

Improving tracheostomy delivery for trauma and surgical critical care patients: timely trach initiative

BACKGROUND

Tracheostomy is recommended within 7 days of intubation for patients with severe traumatic brain injury (TBI) or requiring prolonged mechanical ventilation. A quality improvement project aimed to decrease time to tracheostomy to ≤7 days after intubation for eligible patients requiring tracheostomy in the surgical intensive care unit (SICU).

LOCAL PROBLEM

From January 2017 to June 2018, approximately 85% of tracheostomies were performed >7 days after intubation. The tracheostomy was placed a median of 10 days after intubation (range: 1-57).

METHODS

Quality improvement principles were applied at an American College of Surgeons-verified level I trauma centre to introduce and analyse interventions to improve tracheostomy timing. Using the electronic health record, we analysed changes in tracheostomy timing, hospital length of stay (LOS), ventilator-associated pneumonia and peristomal bleeding rates for three subgroups: patients with TBI, trauma patients and all SICU patients.

INTERVENTIONS

In July 2018, an educational roll-out for SICU residents and staff was launched to inform them of potential benefits of early tracheostomy and potential complications, which they should discuss when counselling patient decision-makers. In July 2019, an early tracheostomy workflow targeting patients with head injury was published in an institutional Trauma Guide app.

RESULTS

Median time from intubation to tracheostomy decreased for all patients from 14 days (range: 4-57) to 8 days (range: 1-32, p≤0.001), and median hospital LOS decreased from 38 days to 24 days (p<0.001, r=0.35). Median time to tracheostomy decreased significantly for trauma patients after publication of the algorithm (10 days (range: 3-21 days) to 6 days (range: 1-15 days), p=0.03). Among patients with TBI, family meetings were held earlier for patients who underwent early versus late tracheostomy (p=0.008).

CONCLUSIONS

We recommend regular educational meetings, enhanced by digitally published guidelines and strategic communication as effective ways to improve tracheostomy timing. These interventions standardised practice and may benefit other institutions.

Management of Airways through Rapid Tracheostomy in a Severely Burnt Patient Attended to via Helicopter

In Catanzaro, Italy, an adult male with severe burns all over his body and in a state of coma was promptly rescued by the medical team at the air ambulance service (HEMS), who provided airway safety through laryngeal mask placement (LMA). The patient was subsequently transferred to the nearest Hub center, where an emergency tracheostomy was performed to ensure better airway management during the flight to the nearest available major burn center. This is the first documented case at regional level of a patient undergoing rapid tracheostomy through an imminent transfer with air ambulance.