Improving Tracheostomy Decannulation Rate in Trauma Patients Objectives: Identify the effect of a multidisciplinary tracheostomy decannulation protocol in the trauma population. Design: Single-center retrospective review. Setting: American College of Surgeons level 1 trauma center; large academic associated community hospital. Patients: Adult trauma patients who required a tracheostomy. Interventions: A tracheostomy decannulation protocol empowering respiratory therapists to move patients toward tracheostomy decannulation. Measurements Main Results: Tracheostomy decannulation rate, time to tracheostomy decannulation, length of stay, and reintubation and recannulation rates. A total of 252 patients met inclusion criteria during the study period with 134 presenting after the tracheostomy decannulation protocol was available. Since the tracheostomy decannulation protocol was implemented, patients managed by the tracheostomy decannulation protocol had a 50% higher chance of tracheostomy decannulation during the hospital stay (p < 0.001). The time to tracheostomy decannulation was 1 day shorter with the tracheostomy decannulation protocol (p = 0.54). There was no difference in time to discharge after ventilator liberation (p = 0.91) or in discharge disposition (p = 0.66). When comparing all patients, the development of a tracheostomy decannulation protocol, regardless if a patient was managed by the tracheostomy decannulation protocol, resulted in an 18% higher chance of tracheostomy decannulation (p = 0.003). Time to tracheostomy decannulation was 5 days shorter in the postintervention period (p = 0.07). There was no difference in discharge disposition (p = 0.88) but the time to discharge after ventilator liberation was shorter post protocol initiation (p = 0.04). Conclusions: In a trauma population, implementation of a tracheostomy decannulation protocol significantly improves tracheostomy decannulation rates during the same hospital stay. A larger population will be required to identify patient predictive factors for earlier successful tracheostomy decannulation. |
Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call Objectives: Machine learning models have been used to predict mortality among patients requiring rapid response team activation. The goal of our study was to assess the impact of adding laboratory values into the model. Design: A gradient boosted decision tree model was derived and internally validated to predict a primary outcome of in-hospital mortality. The base model was then augmented with laboratory values. Setting: Two tertiary care hospitals within The Ottawa Hospital network. Patients: Inpatients over the age of 18 years who experienced a rapid response team activation between January 1, 2015, and May 31, 2016. Interventions: None. Measurements and Main Results: A total of 2,061 rapid response team activations occurred during the study period. The in-hospital mortality rate was 29.4%. Patients who died were older (median age, 72 vs 68 yr; p < 0.001), had a longer length of stay (length of stay) prior to rapid response team activation (4 vs 2 d; p < 0.001), and more often had respiratory distress (31% vs 22%; p < 0.001). Our base model without laboratory values performed with an area under the receiver operating curve of 0.71 (95% CI, 0.71–0.72). When the base model was augmented with laboratory values, the area under the receiver operating curve improved to 0.77 (95% CI, 0.77–0.78). Important mortality predictors in the base model were age, estimated ratio of PaO2 to FIO2 (calculated using oxygen saturation and estimated FIO2), length of stay prior to rapid response team activation, and systolic blood pressure. Conclusions: Machine learning models can identify rapid response team patients at a high risk of mortality and potentially supplement clinical decision making. Incorporating laboratory values into model development significantly improved predictive performance in this study. |
Estimated Effects of Early Diuretic Use in Critical Illness Objectives: To estimate the effects of diuretic use during the first 24 hours of an ICU stay on in-hospital mortality and other clinical outcomes including acute kidney injury and duration of mechanical ventilation. Design: Retrospective cohort study. Setting: Urban, academic medical center. Patients: Adult patients admitted to medical or cardiac ICUs between 2001 and 2012, excluding those on maintenance dialysis or with ICU length of stay less than 24 hours. Interventions: None. Measurements and Main Results: We included 13,589 patients: 2,606 with and 10,983 without early diuretic use (loop diuretic exposure during the first 24 hr of an ICU stay). Propensity score matching generated 2,523 pairs with well-balanced baseline characteristics. Early diuretic use was unassociated with in-hospital mortality (risk ratio, 1.01; 99.5% CI, 0.83–1.22). We found no evidence of associations with ICU or hospital length of stay, or duration or provision of mechanical ventilation. Early diuretic use was associated with higher rates of subsequent acute kidney injury (risk ratio, 1.41; 99.5% CI, 1.25–1.59) and electrolyte abnormalities. Results were not materially different in subgroups of patients with heart failure, chronic kidney disease, or acute lung injury. Conclusions: Early diuretic use in critical illness was unassociated with in-hospital mortality, ICU or hospital length of stay, or duration of mechanical ventilation, but risks of acute kidney injury and electrolyte abnormalities were higher. |
Mechanical Ventilation Guided by Electrical Impedance Tomography in Children With Acute Lung Injury Objectives: To provide proof-of-concept for a protocol applying a strategy of personalized mechanical ventilation in children with acute respiratory distress syndrome. Positive end-expiratory pressure and inspiratory pressure settings were optimized using real-time electrical impedance tomography aiming to maximize lung recruitment while minimizing lung overdistension. Design: Prospective interventional trial. Setting: Two PICUs. Patients: Eight children with early acute respiratory distress syndrome (< 72 hr). Interventions: On 3 consecutive days, electrical impedance tomography-guided positive end-expiratory pressure titration was performed by using regional compliance analysis. The Acute Respiratory Distress Network high/low positive end-expiratory pressure tables were used as patient’s safety guardrails. Driving pressure was maintained constant. Algorithm includes the following: 1) recruitment of atelectasis: increasing positive end-expiratory pressure in steps of 4 mbar; 2) reduction of overdistension: decreasing positive end-expiratory pressure in steps of 2 mbar until electrical impedance tomography shows collapse; and 3) maintaining current positive end-expiratory pressure and check regional compliance every hour. In case of derecruitment start at step 1. Measurements and Main Results: Lung areas classified by electrical impedance tomography as collapsed or overdistended were changed on average by –9.1% (95% CI, –13.7 to –4.4; p < 0.001) during titration. Collapse was changed by –9.9% (95% CI, –15.3 to –4.5; p < 0.001), while overdistension did not increase significantly (0.8%; 95% CI, –2.9 to 4.5; p = 0.650). A mean increase of the positive end-expiratory pressure level (1.4 mbar; 95% CI, 0.6–2.2; p = 0.008) occurred after titration. Global respiratory system compliance and gas exchange improved (global respiratory system compliance: 1.3 mL/mbar, 95% CI [–0.3 to 3.0], p = 0.026; PaO2: 17.6 mm Hg, 95% CI [7.8–27.5], p = 0.0039; and PaO2/FIO2 ratio: 55.2 mm Hg, 95% CI [27.3–83.2], p < 0.001, all values are change in pre vs post). Conclusions: Electrical impedance tomography-guided positive end-expiratory pressure titration reduced regional lung collapse without significant increase of overdistension, while improving global compliance and gas exchange in children with acute respiratory distress syndrome. |
| ||||||||||||||||||||||||
| ||||||||||||||||||||||||
|
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου