Posttraumatic Stress Symptoms after Exposure to Two Fire Disasters : Comparative Study

This study investigated traumatic stress symptoms in severely burned survivors of two fire disasters and two comparison groups of patients with "non-disaster" burn injuries, as well as risk factors associated with acute and chronic stress symptoms. Patients were admitted to one out of eight burn centers in the Netherlands or Belgium. The Impact of Event Scale (IES) was administered to 61 and 33 survivors respectively of two fire disasters and 54 and 57 patients with "nondisaster" burn etiologies at 2 weeks, 3, 6, 12 and 24 months after the event. We used latent growth modeling (LGM) analyses to investigate the stress trajectories and predictors in the two disaster and two comparison groups. The results showed that initial traumatic stress reactions in disaster survivors with severe burns are more intense and prolonged during several months relative to survivors of "non-disaster" burn injuries. Excluding the industrial fire group, all participants' symptoms on average decreased over the two year period. Burn severity, peritraumatic anxiety and dissociation predicted the long-term negative outcomes only in the industrial fire group. In conclusion, fire disaster survivors appear to experience higher levels of traumatic stress symptoms on the short term, but the long-term outcome appears dependent on factors different from the first response. Likely, the younger age, and several beneficial post-disaster factors such as psychosocial aftercare and social support, along with swift judicial procedures, contributed to the positive outcome in one disaster cohort

Estimated means and observed individual values representing four classes of response patterns.

<p>2a. Low stress trajectory (resilience); 2b. Acute stress trajectory, 2c. Chronic stress trajectory, 2d. Delayed onset trajectory.</p

Mean estimated IES scores in two disaster groups and two comparison groups over time and predictors (only 12 months study period).

*<p>one tailed tested; ** two tailed tested.</p

Trajectories of traumatic stress symptoms.

<p>(A) Represents trajectories in the café fire group and its comparison group; (B) Represents trajectories in the industrial fire group and its comparison group. Chronic stress trajectory is depicted by black and white rectangles, acute stress trajectory is depicted by circles, resilient trajectory is depicted by triangles, delayed onset trajectory is depicted by grey line. Thick lines represent stress scores in fire disaster survivors, thin lines represent stress scores in regular burn survivors.</p

Observed mean IES scores in four groups during the two year follow-up.

<p>The café fire group is depicted by the black circles, its comparison group by the white circles. The industrial fire group is depicted by the black rectangles, its comparison group by the white rectangles.</p

Group descriptive statistics.

*<p>Statistically significant differences between disaster group and its comparison group (p<.01); TBSA: Total Body Surface Area burned, LOS: Length of stay in hospital.</p

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes