Predictors of postinjury acute respiratory distress syndrome: Lung injury persists in the era of hemostatic resuscitation

BACKGROUND

Acute respiratory distress syndrome (ARDS) following trauma is historically associated with crystalloid and blood product exposure. Advances in resuscitation have occurred over the last decade, but their impact on ARDS is unknown. We sought to investigate predictors of postinjury ARDS in the era of hemostatic resuscitation.

METHODS

Data were prospectively collected from arrival to 28 days for 914 highest-level trauma activations who required intubation and survived more than 6 hours from 2005 to 2016 at a Level I trauma center. Patients with ratio of partial pressure of oxygen to fraction of inspired oxygen of 300 mmHg or less during the first 8 days were identified. Two blinded expert clinicians adjudicated all chest radiographs for bilateral infiltrates in the first 8 days. Those with left-sided heart failure detected were excluded. Multivariate logistic regression was used to define predictors of ARDS.

RESULTS

Of the 914 intubated patients, 63% had a ratio of partial pressure of oxygen to fraction of inspired oxygen of 300 or less, and 22% developed ARDS; among the ARDS cases, 57% were diagnosed early (in the first 24 hours), and 43% later. Patients with ARDS diagnosed later were more severely injured (ISS 32 vs. 20, p = 0.001), with higher rates of blunt injury (84% vs. 72%, p = 0.008), chest injury (58% vs. 36%, p < 0.001), and traumatic brain injury (72% vs. 48%, p < 0.001) compared with the no ARDS group. In multivariate analysis, head/chest Abbreviated Injury Score scores, crystalloid from 0 to 6 hours, and platelet transfusion from 0 to 6 hours and 7 to 24 hours were independent predictors of ARDS developing after 24 hours.

CONCLUSIONS

Blood and plasma transfusion were not independently associated with ARDS. However, platelet transfusion was a significant independent risk factor. The role of platelets warrants further investigation but may be mechanistically explained by lung injury models of pulmonary platelet sequestration with peripheral thrombocytopenia.

LEVEL OF EVIDENCE

Prognostic study, level IV.

Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma

OBJECTIVE

Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.

METHODS

SuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under the curve was compared to select among predictors. Clinical variables responsible for driving outcomes were estimated at each time point.

RESULTS

SuperLearner fits demonstrated excellent cross-validated prediction of death (overall AUC 0.94-0.97), multi-organ failure (overall AUC 0.84-0.90), and transfusion (overall AUC 0.87-0.9) across multiple post-injury time points, and good prediction of Acute Respiratory Distress Syndrome (overall AUC 0.84-0.89) and venous thromboembolism (overall AUC 0.73-0.83). Outcomes with inferior data quality included coagulopathic trajectory (AUC 0.48-0.88). Key clinical predictors evolved over the post-injury timecourse and included both anticipated and unexpected variables. Non-random missingness of data was identified as a predictor of multiple outcomes over time.

CONCLUSIONS

Machine learning algorithms can be used to generate dynamic prediction after injury while avoiding the risk of over- and under-fitting inherent in ad hoc statistical approaches. SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support.

Health care utilization and the cost of posttraumatic acute respiratory distress syndrome care

BACKGROUND

Posttraumatic acute respiratory distress syndrome (ARDS) is associated with prolonged mechanical ventilation and longer hospitalizations. The relationship between posttraumatic ARDS severity and financial burden has not been previously studied. We hypothesized that increasing ARDS severity is associated with incrementally higher health care costs.

METHODS

Adults arriving as the highest level of trauma activation were enrolled in an ongoing prospective cohort study. Patients who survived 6 hours or longer are included in the analysis. Blinded review of chest radiographs was performed by two independent physicians for any intubated patient with PaO2:FIO2 ratio of 300 mmHg or lower during the first 8 days of admission. The severity of ARDS was classified by the Berlin criteria. Hospital charge data were used to perform standard costing analysis.

RESULTS

Acute respiratory distress syndrome occurred in 13% (203 of 1,586). The distribution of disease severity was 33% mild, 42% moderate, and 25% severe. Patients with ARDS were older (41 years vs. 35 years, p < 0.01), had higher median Injury Severity Score (30 vs. 10, p < 0.01), more chest injury (Abbreviated Injury Scale score, ≥ 3: 51% vs. 21%, p < 0.01), and blunt mechanisms (85% vs. 53%, p < 0.01). By ARDS severity, there was no significant difference in age, mechanism, or rate of traumatic brain injury. Increasing ARDS severity was associated with higher Injury Severity Score and higher mortality rates. Standardized total hospital charges were fourfold higher for patients who developed ARDS compared with those who did not develop ARDS (US $434,000 vs. US $96,000; p < 0.01). Furthermore, the daily hospital charges significantly increased across categories of worsening ARDS severity (mild, US $20,451; moderate, US $23,994; severe, US $33,316; p < 0.01).

CONCLUSION

The development of posttraumatic ARDS is associated with higher health care costs. Among trauma patients who develop ARDS, total hospital charges per day increase with worsening severity of disease. Prevention, early recognition, and treatment of ARDS after trauma are potentially important objectives for efforts to control health care costs in this population.

LEVEL OF EVIDENCE

Economic and value-based evaluations, level IV.

Elevated plasma levels of TIMP-3 are associated with a higher risk of acute respiratory distress syndrome and death following severe isolated traumatic brain injury

Background:

Complications after injury, such as acute respiratory distress syndrome (ARDS), are common after traumatic brain injury (TBI) and associated with poor clinical outcomes. The mechanisms driving non-neurologic organ dysfunction after TBI are not well understood. Tissue inhibitor of matrix metalloproteinase-3 (TIMP-3) is a regulator of matrix metalloproteinase activity, inflammation, and vascular permeability, and hence has plausibility as a biomarker for the systemic response to TBI.

Methods:

In a retrospective study of 182 patients with severe isolated TBI, we measured TIMP-3 in plasma obtained on emergency department arrival. We used non-parametric tests and logistic regression analyses to test the association of TIMP-3 with the incidence of ARDS within 8 days of admission and in-hospital mortality.

Results:

TIMP-3 was significantly higher among subjects who developed ARDS compared with those who did not (median 2810 pg/mL vs. 2260 pg/mL, p=0.008), and significantly higher among subjects who died than among those who survived to discharge (median 2960 pg/mL vs. 2080 pg/mL, p<0.001). In an unadjusted logistic regression model, for each SD increase in plasma TIMP-3, the odds of ARDS increased significantly, OR 1.5 (95% CI 1.1 to 2.1). This association was only attenuated in multivariate models, OR 1.4 (95% CI 1.0 to 2.0). In an unadjusted logistic regression model, for each SD increase in plasma TIMP-3, the odds of death increased significantly, OR 1.7 (95% CI 1.2 to 2.3). The magnitude of this association was greater in a multivariate model adjusted for markers of injury severity, OR 1.9 (95% CI 1.2 to 2.8).

Discussion:

TIMP-3 may play an important role in the biology of the systemic response to brain injury in humans. Along with clinical and demographic data, early measurements of plasma biomarkers such as TIMP-3 may help identify patients at higher risk of ARDS and death after severe isolated TBI.

Level of evidence

III.

Characterizing the gut microbiome in trauma: significant changes in microbial diversity occur early after severe injury

Background

Recent studies have demonstrated the vital influence of commensal microbial communities on human health. The central role of the gut in the response to injury is well described; however, no prior studies have used culture-independent profiling techniques to characterize the gut microbiome after severe trauma. We hypothesized that in critically injured patients, the gut microbiome would undergo significant compositional changes in the first 72 hours after injury.

Methods

Trauma stool samples were prospectively collected via digital rectal examination at the time of presentation (0 hour). Patients admitted to the intensive care unit (n=12) had additional stool samples collected at 24 hours and/or 72 hours. Uninjured patients served as controls (n=10). DNA was extracted from stool samples and 16S rRNA-targeted PCR amplification was performed; amplicons were sequenced and binned into operational taxonomic units (OTUs; 97% sequence similarity). Diversity was analyzed using principle coordinates analyses, and negative binomial regression was used to determine significantly enriched OTUs.

Results

Critically injured patients had a median Injury Severity Score of 27 and suffered polytrauma. At baseline (0 hour), there were no detectable differences in gut microbial community diversity between injured and uninjured patients. Injured patients developed changes in gut microbiome composition within 72 hours, characterized by significant alterations in phylogenetic composition and taxon relative abundance. Members of the bacterial orders Bacteroidales, Fusobacteriales and Verrucomicrobiales were depleted during 72 hours, whereas Clostridiales and Enterococcus members enriched significantly.

Discussion

In this initial study of the gut microbiome after trauma, we demonstrate that significant changes in phylogenetic composition and relative abundance occur in the first 72 hours after injury. This rapid change in intestinal microbiota represents a critical phenomenon that may influence outcomes after severe trauma. A better understanding of the nature of these postinjury changes may lead to the ability to intervene in otherwise pathological clinical trajectories.

Level of evidence

III

Study type

Prognostic/epidemiological

Discovering the truth about life after discharge: Long-term trauma-related mortality

BACKGROUND

Outcome after traumatic injury has typically been limited to the determination at time of discharge or brief follow-up. This study investigates the natural history of long-term survival after trauma.

METHODS

All highest-level activation patients prospectively enrolled in an ongoing cohort study from 2005 to 2012 were selected. To allow for long-term follow-up, patients had to be enrolled at least 1 year before the latest available data from the National Death Index (NDI, 2013). Time and cause of mortality was determined based on death certificates. Survival status was determined by the latest date of either care in our institution or NDI query. Kaplan-Meier curves were created stratified for Injury Severity Score (ISS). Survival was compared with estimated actuarial survival based on age, sex, and race.

RESULTS

A total of 908 highest-level activation patients (median ISS, 18) were followed up for a median 1.7 years (interquartile range 1.0-2.9; maximum, 9.8 years). Survival data were available on 99.8%. Overall survival was 73% (663 of 908). For those with at least 2-year follow-up, survival was only 62% (317 of 509). Severity of injury predicted long-term survival (p < 0.0001) with those having ISS of 25 or greater with the poorest outcome (57% survival at 5 years). For all ISS groups, survival was worse than predicted actuarial survival (p < 0.001). When excluding early deaths (≤30 days), observed survival was still significantly lower than estimated actuarial survival (p < 0.002). Eighteen percent (44 of 245 deaths) of all deaths occurred after 30 days. Among late deaths, 53% occurred between 31 days and 1 year after trauma. Trauma-related mortality was the leading cause of postdischarge death, accounting for 43% of the late deaths.

CONCLUSION

Postdischarge deaths represent a significant percentage of total trauma-related mortality. Despite having "survived" to leave the hospital, long-term survival was worse than predicted actuarial survival, suggesting that the mortality from injury does not end at "successful" hospital discharge.

LEVEL OF EVIDENCE

Prognostic study, level III.