Dynamic Three-Dimensional Scoring of Cerebral Perfusion Pressure and Intracranial Pressure Provides a Brain Trauma Index That Predicts Outcome in Patients With Severe Traumatic Brain Injury

A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study

BACKGROUND

Clinicians working in intensive care units (ICUs) are immersed in a cacophony of alarms and a relentless onslaught of data. Within this frenetic environment, clinicians make high-stakes decisions using many data sources and are often oversaturated with information of varying quality. Traditional bedside monitors only depict static vital signs data, and these data are not easily viewable remotely. Clinicians must rely on separate nursing charts-handwritten or electric-to review physiological patterns, including signs of potential clinical deterioration. An automated physiological data viewer has been developed to provide at-a-glance summaries and to assist with prioritizing care for multiple patients who are critically ill.

OBJECTIVE

This study aims to evaluate a novel vital signs viewer system in a level 1 trauma center by subjectively assessing the viewer's utility in a high-volume ICU setting.

METHODS

ICU attendings were surveyed during morning rounds. Physicians were asked to conduct rounds normally, using data reported from nurse charts and briefs from fellows to inform their clinical decisions. After the physician finished their assessment and plan for the patient, they were asked to complete a questionnaire. Following completion of the questionnaire, the viewer was presented to ICU physicians on a tablet personal computer that displayed the patient's physiologic data (ie, shock index, blood pressure, heart rate, temperature, respiratory rate, and pulse oximetry), summarized for up to 72 hours. After examining the viewer, ICU physicians completed a postview questionnaire. In both questionnaires, the physicians were asked questions regarding the patient's stability, status, and need for a higher or lower level of care. A hierarchical clustering analysis was used to group participating ICU physicians and assess their general reception of the viewer.

RESULTS

A total of 908 anonymous surveys were collected from 28 ICU physicians from February 2015 to June 2017. Regarding physicians' perception of whether the viewer enhanced the ability to assess multiple patients in the ICU, 5% (45/908) strongly agreed, 56.6% (514/908) agreed, 35.3% (321/908) were neutral, 2.9% (26/908) disagreed, and 0.2% (2/908) strongly disagreed.

CONCLUSIONS

Morning rounds in a trauma center ICU are conducted in a busy environment with many data sources. This study demonstrates that organized physiologic data and visual assessment can improve situation awareness, assist clinicians with recognizing changes in patient status, and prioritize care.

Chinese Admission Warning Strategy for Predicting the Hospital Discharge Outcome in Patients with Traumatic Brain Injury

Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury

Gold standard prognostic models for long-term outcome in patients with severe traumatic brain injury (TBI) use admission characteristics and are considered useful in some areas but not for clinical practice. In this study, we aimed to build prognostic models for 6-month Glasgow Outcome Score (GOS) in patients with severe TBI, combining baseline characteristics with physiological, treatment, and injury severity data collected during the first 24 h after injury. We used a training dataset of 472 TBI subjects and several data mining algorithms to predict the long-term neurological outcome. Performance of these algorithms was assessed in an independent (test) sample of 158 subjects. The least absolute shrinkage and selection operator (LASSO) led to the highest prediction accuracy (area under the receiving operating characteristic curve = 0.86) in the test set. The most important post-baseline predictor of GOS was the best motor Glasgow Coma Scale (GCS) recorded in the first day post-injury. The LASSO model containing the best motor GCS and baseline variables as predictors outperformed a model with baseline data only. TBI patient physiology of the first day-post-injury did not have a major contribution to patient prognosis six months after injury. In conclusion, 6-month GOS in patients with TBI can be predicted with good accuracy by the end of the first day post-injury, using hospital admission data and information on the best motor GCS achieved during those first 24 h post-injury. Passed the first day after injury, important physiological predictors could emerge from landmark analyses, leading to prediction models of higher accuracy than the one proposed in the current research.

A joint logistic regression and covariate-adjusted continuous-time Markov chain model

The use of longitudinal measurements to predict a categorical outcome is an increasingly common goal in research studies. Joint models are commonly used to describe two or more models simultaneously by considering the correlated nature of their outcomes and the random error present in the longitudinal measurements. However, there is limited research on joint models with longitudinal predictors and categorical cross-sectional outcomes. Perhaps the most challenging task is how to model the longitudinal predictor process such that it represents the true biological mechanism that dictates the association with the categorical response. We propose a joint logistic regression and Markov chain model to describe a binary cross-sectional response, where the unobserved transition rates of a two-state continuous-time Markov chain are included as covariates. We use the method of maximum likelihood to estimate the parameters of our model. In a simulation study, coverage probabilities of about 95%, standard deviations close to standard errors, and low biases for the parameter values show that our estimation method is adequate. We apply the proposed joint model to a dataset of patients with traumatic brain injury to describe and predict a 6-month outcome based on physiological data collected post-injury and admission characteristics. Our analysis indicates that the information provided by physiological changes over time may help improve prediction of long-term functional status of these severely ill subjects. Copyright © 2017 John Wiley & Sons, Ltd.

Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a "Monitor of Monitors" System (MoMs)

Research and practice based on automated electronic patient monitoring and data collection systems is significantly limited by system down time. We asked whether a triple-redundant Monitor of Monitors System (MoMs) to collect and summarize key information from system-wide data sources could achieve high fault tolerance, early diagnosis of system failure, and improve data collection rates. In our Level I trauma center, patient vital signs(VS) monitors were networked to collect real time patient physiologic data streams from 94 bed units in our various resuscitation, operating, and critical care units. To minimize the impact of server collection failure, three BedMaster® VS servers were used in parallel to collect data from all bed units. To locate and diagnose system failures, we summarized critical information from high throughput datastreams in real-time in a dashboard viewer and compared the before and post MoMs phases to evaluate data collection performance as availability time, active collection rates, and gap duration, occurrence, and categories. Single-server collection rates in the 3-month period before MoMs deployment ranged from 27.8 % to 40.5 % with combined 79.1 % collection rate. Reasons for gaps included collection server failure, software instability, individual bed setting inconsistency, and monitor servicing. In the 6-month post MoMs deployment period, average collection rates were 99.9 %. A triple redundant patient data collection system with real-time diagnostic information summarization and representation improved the reliability of massive clinical data collection to nearly 100 % in a Level I trauma center. Such data collection framework may also increase the automation level of hospital-wise information aggregation for optimal allocation of health care resources.

Fentanyl and Midazolam Are Ineffective in Reducing Episodic Intracranial Hypertension in Severe Pediatric Traumatic Brain Injury

OBJECTIVE

To evaluate the clinical effectiveness of bolus-dose fentanyl and midazolam to treat episodic intracranial hypertension in children with severe traumatic brain injury.

DESIGN

Retrospective cohort.

SETTING

PICU in a university-affiliated children's hospital level I trauma center.

PATIENTS

Thirty-one children 0-18 years of age with severe traumatic brain injury (Glasgow Coma Scale score of ≤ 8) who received bolus doses of fentanyl and/or midazolam for treatment of episodic intracranial hypertension.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The area under the curve from high-resolution intracranial pressure-time plots was calculated to represent cumulative intracranial hypertension exposure: area under the curve for intracranial pressure above 20 mm Hg (area under the curve-intracranial hypertension) was calculated in 15-minute epochs before and after administration of fentanyl and/or midazolam for the treatment of episodic intracranial hypertension. Our primary outcome measure, the difference between predrug and postdrug administration epochs (Δarea under the curve-intracranial hypertension), was calculated for all occurrences. We examined potential covariates including age, injury severity, mechanism, and time after injury; time after injury correlated with Δarea under the curve-intracranial hypertension. In a mixed-effects model, with patient as a random effect, drug/dose combination as a fixed effect, and time after injury as a covariate, intracranial hypertension increased after administration of fentanyl and/or midazolam (overall aggregate mean Δarea under the curve-intracranial hypertension = +17 mm Hg × min, 95% CI, 0-34 mm Hg × min; p = 0.04). The mean Δarea under the curve-intracranial hypertension increased significantly after administration of high-dose fentanyl (p = 0.02), low-dose midazolam (p = 0.006), and high-dose fentanyl plus low-dose midazolam (0.007). Secondary analysis using age-dependent thresholds showed no significant impact on cerebral perfusion pressure deficit (mean Δarea under the curve-cerebral perfusion pressure).

CONCLUSIONS

Bolus dosing of fentanyl and midazolam fails to reduce the intracranial hypertension burden when administered for episodic intracranial hypertension. Paradoxically, we observed an overall increase in intracranial hypertension burden following drug administration, even after accounting for within-subject effects and time after injury. Future work is needed to confirm these findings in a prospective study design.

Computer Modelling Using Prehospital Vitals Predicts Transfusion and Mortality

OBJECTIVE

Test computer-assisted modeling techniques using prehospital vital signs of injured patients to predict emergency transfusion requirements, number of intensive care days, and mortality, compared to vital signs alone.

METHODS

This single-center retrospective analysis of 17,988 trauma patients used vital signs data collected between 2006 and 2012 to predict which patients would receive transfusion, require 3 or more days of intensive care, or die. Standard transmitted prehospital vital signs (heart rate, blood pressure, shock index, and respiratory rate) were used to create a regression model (PH-VS) that was internally validated and evaluated using area under the receiver operating curve (AUROC). Transfusion records were matched with blood bank records. Documentation of death and duration of intensive care were obtained from the trauma registry.

RESULTS

During the course of their hospital stay, 720 of the 17,988 patients in the study population died (4%), 2,266 (12.6%) required at least a 3-day stay in the intensive care unit (ICU), 1,171 (6.5%) required transfusions, and 210 (1.2%) received massive transfusions. The PH-VS model significantly outperformed any individual vital sign across all outcomes (average AUROC = 0.82), The PH-VS model correctly predicted that 512 of 777 (65.9%) and 580 of 931 (62.3%) patients in the study population would receive transfusions within the first 2 and 6 hours of admission, respectively.

CONCLUSIONS

The predictive ability of individual vital signs to predict outcomes is significantly enhanced with the model. This could support prehospital triage by enhancing decision makers' ability to match critically injured patients with appropriate resources with minimal delays.

Intracranial pressure response after pharmacologic treatment of intracranial hypertension

BACKGROUND

The accepted treatment of increased intracranial pressure (ICP) in patients experiencing severe traumatic brain injury is multimodal and algorithmic, obscuring individual effects of treatment. Using continuous vital signs monitoring, we sought to measure treatment effect and ascertain the accuracy of manual data recording.

METHODS

Patients older than 17 years, admitted and requiring ICP monitoring between 2008 and 2010 at a high-volume urban trauma center, were retrospectively evaluated. Timing and dose of ICP-directed therapy were recorded from paper and electronic medical records. ICP data were collected automatically at 6-second intervals and from manual charts. A statistical mixed model was applied to all data to account for multiple sampling.

RESULTS

A total of 117 patients met inclusion criteria; 450 treatments were administered when nursing records indicate an ICP greater than 20 mm Hg, while 968 treatments were given when ICP was greater than 20 mm Hg by automated data. Pharmacologic treatments identified include hypertonic saline (HTS), mannitol, barbiturates, and dose escalations of propofol or fentanyl infusions. Treatment with HTS resulted in the largest ICP decrease of the treatments examined, with a 1-hour ICP reduction of 8.8/9.9 mm Hg (for a small/large dose) according to manual data and a reduction of 3.0/2.4 mm Hg according to automated data. Propofol and fentanyl escalations resulted in smaller but significant ICP reductions. Mannitol (n = 8) resulted in statistically insignificant trends down in the first hour but rebounded by the second hour after administration. The average ICP in the hour before medication administration was higher for barbiturates (27 mm Hg) and mannitol (32 mm Hg) than for the other interventions (18-19 mm Hg).

CONCLUSION

ICP fell after administration of HTS, mannitol, or barbiturates and showed continued improvement after 2 hours. ICP fell initially after treatment with short-acting propofol and fentanyl but trended back up after 2 hours. Manually recorded data consistently overestimated treatment effectiveness. Automated data collection gives a more accurate assessment of patient status and responsiveness to treatment.

LEVEL OF EVIDENCE

Therapeutic study, level IV.

Novel methods to predict increased intracranial pressure during intensive care and long-term neurologic outcome after traumatic brain injury: development and validation in a multicenter dataset

OBJECTIVE

Intracranial pressure monitoring is standard of care after severe traumatic brain injury. Episodes of increased intracranial pressure are secondary injuries associated with poor outcome. We developed a model to predict increased intracranial pressure episodes 30 mins in advance, by using the dynamic characteristics of continuous intracranial pressure and mean arterial pressure monitoring. In addition, we hypothesized that performance of current models to predict long-term neurologic outcome could be substantially improved by adding dynamic characteristics of continuous intracranial pressure and mean arterial pressure monitoring during the first 24 hrs in the ICU.

DESIGN

Prognostic modeling. Noninterventional, observational, retrospective study.

SETTING AND PATIENTS

The Brain Monitoring with Information Technology dataset consisted of 264 traumatic brain injury patients admitted to 22 neuro-ICUs from 11 European countries.

INTERVENTIONS

None.

MEASUREMENTS

Predictive models were built with multivariate logistic regression and Gaussian processes, a machine learning technique. Predictive attributes were Corticosteroid Randomisation After Significant Head Injury-basic and International Mission for Prognosis and Clinical Trial design in TBI-core predictors, together with time-series summary statistics of minute-by-minute mean arterial pressure and intracranial pressure.

MAIN RESULTS

Increased intracranial pressure episodes could be predicted 30 mins ahead with good calibration (Hosmer-Lemeshow p value 0.12, calibration slope 1.02, calibration-in-the-large -0.02) and discrimination (area under the receiver operating curve = 0.87) on an external validation dataset. Models for prediction of poor neurologic outcome at six months (Glasgow Outcome Score 1-2) based only on static admission data had 0.72 area under the receiver operating curve; adding dynamic information of intracranial pressure and mean arterial pressure during the first 24 hrs increased performance to 0.90. Similarly, prediction of Glasgow Outcome Score 1-3 was improved from 0.68 to 0.87 when including dynamic information.

CONCLUSION

The dynamic information in continuous mean arterial pressure and intracranial pressure monitoring allows to accurately predict increased intracranial pressure in the neuro-ICU. Adding information of the first 24 hrs of intracranial pressure and mean arterial pressure monitoring to known baseline risk factors allows very accurate prediction of long-term neurologic outcome at 6 months.

Computational gene mapping to analyze continuous automated physiologic monitoring data in neuro-trauma intensive care

BACKGROUND

We asked whether the advanced machine learning applications used in microarray gene profiling could assess critical thresholds in the massive databases generated by continuous electronic physiologic vital signs (VS) monitoring in the neuro-trauma intensive care unit.

METHODS

We used Class Prediction Analysis to predict binary outcomes (life/death, good/bad Extended Glasgow Outcome Score, etc.) based on data accrued within 12, 24, 48, and 72 hours after admission to the neuro-trauma intensive care unit. Univariate analyses selected "features," discriminator VS segments or "genes," in each individual's data set. Prediction models using these selected features were then constructed using six different statistical modeling techniques to predict outcome for other individuals in the sample cohort based on the selected features of each individual then cross-validated with a leave-one-out method.

RESULTS

We gleaned complete sets of 588 VS monitoring segment features for each of four periods and outcomes from 52 of 60 patients with severe traumatic brain injury who met study inclusion criteria. Overall, intracranial pressures and blood pressures over time (e.g., intracranial pressure >20 mm Hg for 20 minutes) provided the best discrimination for outcomes. Modeling performed best in the first 12 hours of care and for mortality. The mean number of selected features included 76 predicting 14-day hospital stay in that period, 11 predicting mortality, and 4 predicting 3-month Extended Glasgow Outcome Score. Four of the six techniques constructed models that correctly identified mortality by 12 hours 75% of the time or higher.

CONCLUSION

Our results suggest that valid prediction models after severe traumatic brain injury can be constructed using gene mapping techniques to analyze large data sets from conventional electronic monitoring data, but that this methodology needs validation in larger data sets, and that additional unstructured learning techniques may also prove useful.