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

A spatio-temporally constrained gene regulatory network directed by PBX1/2 acquires limb patterning specificity via HAND2.

A lingering question in developmental biology has centered on how transcription factors with widespread distribution in vertebrate embryos can perform tissue-specific functions. Here, using the murine hindlimb as a model, we investigate the elusive mechanisms whereby PBX TALE homeoproteins, viewed primarily as HOX cofactors, attain context-specific developmental roles despite ubiquitous presence in the embryo. We first demonstrate that mesenchymal-specific loss of PBX1/2 or the transcriptional regulator HAND2 generates similar limb phenotypes. By combining tissue-specific and temporally controlled mutagenesis with multi-omics approaches, we reconstruct a gene regulatory network (GRN) at organismal-level resolution that is collaboratively directed by PBX1/2 and HAND2 interactions in subsets of posterior hindlimb mesenchymal cells. Genome-wide profiling of PBX1 binding across multiple embryonic tissues further reveals that HAND2 interacts with subsets of PBX-bound regions to regulate limb-specific GRNs. Our research elucidates fundamental principles by which promiscuous transcription factors cooperate with cofactors that display domain-restricted localization to instruct tissue-specific developmental programs

Clinical predictors of 3- and 6-month outcome for mild traumatic brain injury patients with a negative head CT scan in the emergency department: A TRACK-TBI pilot study

Aconsiderable subset of mild traumatic brain injury (mTBI) patients fail to return to baseline functional status at or beyond 3 months postinjury. Identifying at-risk patients for poor outcome in the emergency department (ED) may improve surveillance strategies and referral to care. Subjects with mTBI (Glasgow Coma Scale 13–15) and negative ED initial head CT < 24 h of injury, completing 3- or 6-month functional outcome (Glasgow Outcome Scale-Extended; GOSE), were extracted from the prospective, multicenter Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot study. Outcomes were dichotomized to full recovery (GOSE = 8) vs functional deficits (GOSE < 8). Univariate predictors with p < 0.10 were considered for multivariable regression. Adjusted odds ratios (AOR) were reported for outcome predictors. Significance was assessed at p < 0.05. Subjects who completed GOSE at 3- and 6-month were 211 (GOSE < 8: 60%) and 185 (GOSE < 8: 65%). Risk factors for 6-month GOSE < 8 included less education (AOR = 0.85 per-year increase, 95% CI: (0.74–0.98)), prior psychiatric history (AOR = 3.75 (1.73–8.12)), Asian/minority race (American Indian/Alaskan/Hawaiian/Pacific Islander) (AOR = 23.99 (2.93–196.84)), and Hispanic ethnicity (AOR = 3.48 (1.29–9.37)). Risk factors for 3-month GOSE < 8 were similar with the addition of injury by assault predicting poorer outcome (AOR = 3.53 (1.17–10.63)). In mTBI patients seen in urban trauma center EDs with negative CT, education, injury by assault, Asian/minority race, and prior psychiatric history emerged as risk factors for prolonged disability

Variably protease-sensitive prionopathy, a unique prion variant with inefficient transmission properties

Variably protease-sensitive prionopathy (VPSPr) can occur in persons of all codon 129 genotypes in the human prion protein gene (PRNP) and is characterized by a unique biochemical profile when compared with other human prion diseases. We investigated transmission properties of VPSPr by inoculating transgenic mice expressing human PRNP with brain tissue from 2 persons with the valine-homozygous (VV) and 1 with the heterozygous methionine/valine codon 129 genotype. No clinical signs or vacuolar pathology were observed in any inoculated mice. Small deposits of prion protein accumulated in the brains of inoculated mice after challenge with brain material from VV VPSPr patients. Some of these deposits resembled microplaques that occur in the brains of VPSPr patients. Comparison of these transmission properties with those of sporadic Creutzfeldt-Jakob disease in the same lines of mice indicated that VPSPr has distinct biological properties. Moreover, we established that VPSPr has limited potential for human-to-human transmission

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 &gt;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 &lt; 0.0083 (Bonferroni correction).Results: In 260 subjects sustaining blunt mTBI, mean age was 44.0-years and 70.4% were male. Baseline comorbidities &gt;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

Probabilistic Modeling of Sensor Artifacts in Critical Care

We describe an application of probabilistic modeling and inference technology to the problem of analyzing sensor data in the setting of an intensive care unit (ICU). In particular, we consider the arterial-line blood pressure sensor, which is subject to frequent data artifacts that cause false alarms in the ICU and make the raw data almost useless for automated decision making. The problem is complicated by the fact that the sensor data are acquired at fixed intervals whereas the events causing data artifacts may occur at any time and have durations that may be significantly shorter than the data collection inter- val. We show that careful modeling of the sensor, combined with a general technique for detecting sub-interval events and estimating their duration, enables effective detection of artifacts and accurate estimation of the underlying blood pressure values