Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

IntroductionAdvances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.MethodsMultivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality.ResultsWe identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.ConclusionsHere we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients

Pre-injury Comorbidities Are Associated With Functional Impairment and Post-concussive Symptoms at 3- and 6-Months After Mild Traumatic Brain Injury: A TRACK-TBI Study.

Introduction: Over 70% of traumatic brain injuries (TBI) are classified as mild (mTBI), which present heterogeneously. Associations between pre-injury comorbidities and outcomes are not well-understood, and understanding their status as risk factors may improve mTBI management and prognostication. Methods: mTBI subjects (GCS 13-15) from TRACK-TBI Pilot completing 3- and 6-month functional [Glasgow Outcome Scale-Extended (GOSE)] and post-concussive outcomes [Acute Concussion Evaluation (ACE) physical/cognitive/sleep/emotional subdomains] were extracted. Pre-injury comorbidities >10% incidence were included in regressions for functional disability (GOSE ≤ 6) and post-concussive symptoms by subdomain. Odds ratios (OR) and mean differences (B) were reported. Significance was assessed at p 10% incidence included psychiatric-30.0%, cardiac (hypertension)-23.8%, cardiac (structural/valvular/ischemic)-20.4%, gastrointestinal-15.8%, pulmonary-15.0%, and headache/migraine-11.5%. At 3- and 6-months separately, 30.8% had GOSE ≤ 6. At 3-months, psychiatric (GOSE ≤ 6: OR = 2.75, 95% CI [1.44-5.27]; ACE-physical: B = 1.06 [0.38-1.73]; ACE-cognitive: B = 0.72 [0.26-1.17]; ACE-sleep: B = 0.46 [0.17-0.75]; ACE-emotional: B = 0.64 [0.25-1.03]), headache/migraine (GOSE ≤ 6: OR = 4.10 [1.67-10.07]; ACE-sleep: B = 0.57 [0.15-1.00]; ACE-emotional: B = 0.92 [0.35-1.49]), and gastrointestinal history (ACE-physical: B = 1.25 [0.41-2.10]) were multivariable predictors of worse outcomes. At 6-months, psychiatric (GOSE ≤ 6: OR = 2.57 [1.38-4.77]; ACE-physical: B = 1.38 [0.68-2.09]; ACE-cognitive: B = 0.74 [0.28-1.20]; ACE-sleep: B = 0.51 [0.20-0.83]; ACE-emotional: B = 0.93 [0.53-1.33]), and headache/migraine history (ACE-physical: B = 1.81 [0.79-2.84]) predicted worse outcomes. Conclusions: Pre-injury psychiatric and pre-injury headache/migraine symptoms are risk factors for worse functional and post-concussive outcomes at 3- and 6-months post-mTBI. mTBI patients presenting to acute care should be evaluated for psychiatric and headache/migraine history, with lower thresholds for providing TBI education/resources, surveillance, and follow-up/referrals. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT01565551

Pre-injury Comorbidities Are Associated With Functional Impairment and Post-concussive Symptoms at 3- and 6-Months After Mild Traumatic Brain Injury: A TRACK-TBI Study

Introduction: Over 70% of traumatic brain injuries (TBI) are classified as mild (mTBI), which present heterogeneously. Associations between pre-injury comorbidities and outcomes are not well-understood, and understanding their status as risk factors may improve mTBI management and prognostication.Methods: mTBI subjects (GCS 13–15) from TRACK-TBI Pilot completing 3- and 6-month functional [Glasgow Outcome Scale-Extended (GOSE)] and post-concussive outcomes [Acute Concussion Evaluation (ACE) physical/cognitive/sleep/emotional subdomains] were extracted. Pre-injury comorbidities >10% incidence were included in regressions for functional disability (GOSE ≤ 6) and post-concussive symptoms by subdomain. Odds ratios (OR) and mean differences (B) were reported. Significance was assessed at p < 0.0083 (Bonferroni correction).Results: In 260 subjects sustaining blunt mTBI, mean age was 44.0-years and 70.4% were male. Baseline comorbidities >10% incidence included psychiatric-30.0%, cardiac (hypertension)-23.8%, cardiac (structural/valvular/ischemic)-20.4%, gastrointestinal-15.8%, pulmonary-15.0%, and headache/migraine-11.5%. At 3- and 6-months separately, 30.8% had GOSE ≤ 6. At 3-months, psychiatric (GOSE ≤ 6: OR = 2.75, 95% CI [1.44–5.27]; ACE-physical: B = 1.06 [0.38–1.73]; ACE-cognitive: B = 0.72 [0.26–1.17]; ACE-sleep: B = 0.46 [0.17–0.75]; ACE-emotional: B = 0.64 [0.25–1.03]), headache/migraine (GOSE ≤ 6: OR = 4.10 [1.67–10.07]; ACE-sleep: B = 0.57 [0.15–1.00]; ACE-emotional: B = 0.92 [0.35–1.49]), and gastrointestinal history (ACE-physical: B = 1.25 [0.41–2.10]) were multivariable predictors of worse outcomes. At 6-months, psychiatric (GOSE ≤ 6: OR = 2.57 [1.38–4.77]; ACE-physical: B = 1.38 [0.68–2.09]; ACE-cognitive: B = 0.74 [0.28–1.20]; ACE-sleep: B = 0.51 [0.20–0.83]; ACE-emotional: B = 0.93 [0.53–1.33]), and headache/migraine history (ACE-physical: B = 1.81 [0.79–2.84]) predicted worse outcomes.Conclusions: Pre-injury psychiatric and pre-injury headache/migraine symptoms are risk factors for worse functional and post-concussive outcomes at 3- and 6-months post-mTBI. mTBI patients presenting to acute care should be evaluated for psychiatric and headache/migraine history, with lower thresholds for providing TBI education/resources, surveillance, and follow-up/referrals.Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT01565551

The new Licox combined brain tissue oxygen and brain temperature monitor: assessment of in vitro accuracy and clinical experience in severe traumatic brain injury.

OBJECTIVE: Monitoring of brain tissue oxygen tension is increasingly being used to monitor patients after severe traumatic brain injury and to guide therapies aimed at maintaining brain tissue oxygen tension above threshold levels. The new Licox PMO combined oxygen and temperature catheter (Integra LifeSciences, Plainsboro, NJ) combines measurements of oxygen tension and temperature in a single probe inserted through a bolt mechanism. In this study, we sought to evaluate the accuracy of the new Licox PMO probe under controlled laboratory conditions and to assess the accuracy of oxygen tension and temperature measurements and the new automated card calibration system. We also describe our clinical experience with the Licox PMO probe. METHODS: Oxygen tension was measured in a 2-chambered apparatus at different oxygen tensions and temperatures. The new card calibration system was compared with a manually calibrated system. Rates of hematoma, infection, and dislodgement in our clinical experience were recorded. RESULTS: The new Licox PMO probe accurately measures oxygen tension over a wide range of oxygen concentrations and physiological temperatures, but it does have a small tendency to underestimate oxygen tension (mean error, -3.8 ± 3.5%) that is more pronounced between the temperatures of 33 and 39°C. The thermistor of the PMO probe also has a tendency to underestimate temperature when compared with a resistance thermometer (mean error, -0.67 ± 0.22°C). The card calibration system was also found to introduce some variability in measurements of oxygen tension when compared with a manually calibrated system. Clinical experience with the new probe indicates good placement within the white matter using the improved bolt system and low rates of hematoma (2.9%), infection (0%), and dislodgement (5.9%). CONCLUSION: The new Licox PMO probe is accurate but has a small, consistent tendency to under-read oxygen tension that is more pronounced at higher temperatures. The probe tends to under-read temperature by 0.5 to 0.8°C across temperatures, suggesting that caution should be used when brain temperature is measured with the Licox PMO probe and used to guide temperature-directed treatment strategies. The Licox PMO probe improves upon previous models in allowing consistent and accurate placement in the white matter and obviating the need for placement of 2 separate probes to measure oxygen tension and temperature.</p

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

IntroductionAdvances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.MethodsMultivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality.ResultsWe identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.ConclusionsHere we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients

EFFECTS OF 23.4% SODIUM CHLORIDE SOLUTION IN REDUCING INTRACRANIAL PRESSURE IN PATIENTS WITH TRAUMATIC BRAIN INJURY: A PRELIMINARY STUDY

OBJECTIVE: Mannitol is the standard of care for patients with increased intracranial pressure (ICP), but multiple administrations of mannitol risk renal toxicity and fluid accumulation in the brain parenchyma with consequent worsening of cerebral edema. This preliminary study assessed the safety and efficacy of small-volume injections of 23.4% sodium chloride solution for the treatment of intracranial hypertension in patients with traumatic brain injury who became tolerant to mannitol. METHODS: We retrospectively reviewed the charts of 13 adult patients with traumatic brain injury who received mannitol and 23.4% sodium chloride independently for the treatment of intracranial hypertension at San Francisco General Hospital between January and October 2003. Charts were reviewed to determine ICP, cerebral perfusion pressure, mean arterial pressure, serum sodium values, and serum osmolarity before and after treatment with 23.4% sodium chloride and mannitol. Complications were noted. RESULTS: The mean reductions in ICP after treatment were significant for both mannitol (PϽ0.001) and hypertonic saline (PϽ0.001); there were no significant differences between reductions in ICP when comparing the two agents (P ϭ 0.174). The ICP reduction observed for hypertonic saline was durable, and its mean duration of effect (96 min) was significantly longer than that of mannitol treatment (59 min) (P ϭ 0.016). No complications were associated with treatment with hypertonic saline. CONCLUSION: This study suggests that 23.4% hypertonic saline is a safe and effective treatment for elevated ICP in patients after traumatic brain injury. These results warrant a rigorous evaluation of its efficacy as compared to mannitol in a prospective randomized controlled trial