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Objectives: RESCUEicp studied decompressive craniectomy (DC) applied as third-tier option in severe traumatic brain injury (TBI) patients in a randomized controlled setting and demonstrated a decrease in mortality with similar rates of favorable outcome in the DC group compared to the medical management group. In many centers, DC is being used in combination with other second/third-tier therapies. The aim of the present study is to investigate outcomes from DC in a prospective non-RCT context. Methods: This is a prospective observational study of 2 patient cohorts: one from the University Hospitals Leuven (2008–2016) and one from the Brain-IT study, a European multicenter database (2003–2005). In thirty-seven patients with refractory elevated intracranial pressure who underwent DC as a second/third-tier intervention, patient, injury and management variables including physiological monitoring data and administration of thiopental were analysed, as well as Extended Glasgow Outcome score (GOSE) at 6 months. Results: In the current cohorts, patients were older than in the surgical RESCUEicp cohort (mean 39.6 vs. 32.3; p < 0.001), had higher Glasgow Motor Score on admission (GMS < 3 in 24.3% vs. 53.0%; p = 0.003) and 37.8% received thiopental (vs. 9.4%; p < 0.001). Other variables were not significantly different. GOSE distribution was: death 24.3%; vegetative 2.7%; lower severe disability 10.8%; upper severe disability 13.5%; lower moderate disability 5.4%; upper moderate disability 2.7%, lower good recovery 35.1%; and upper good recovery 5.4%. The outcome was unfavorable in 51.4% and favorable in 48.6%, as opposed to 72.6% and 27.4% respectively in RESCUEicp (p = 0.02). Conclusion: Outcomes in DC patients from two prospective cohorts reflecting everyday practice were better than in RESCUEicp surgical patients. Mortality was similar, but fewer patients remained vegetative or severely disabled and more patients had a good recovery. Although patients were older a

Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. A Bayesian artificial neural network (BANN) model predicting episodes of hypotension was developed using data from 104 patients selected from the BrainIT multi-center database. Arterial hypotension events were recorded and defined using the Edinburgh University Secondary Insult Grades (EUSIG) physiological adverse event scoring system. The BANN was trained on a random selection of 50% of the available patients (n = 52) and validated on the remaining cohort. A multi-center prospective pilot study (Phase 1, n = 30) was then conducted with the system running live in the clinical environment, followed by a second validation pilot study (Phase 2, n = 49). From these prospectively collected data, a final evaluation study was done on 69 of these patients with 10 patients excluded from the Phase 2 study because of insufficient or invalid data. Each data collection phase was a prospective non-interventional observational study conducted in a live clinical setting to test the data collection systems and the model performance. No prediction information was available to the clinical teams during a patient's stay in the ICU. The final cohort (n = 69), using a decision threshold of 0.4, and including false positive checks, gave a sensitivity of 39.3% (95% CI 32.9-46.1) and a specificity of 91.5% (95% CI 89.0-93.7). Using a decision threshold of 0.3, and false positive correction, gave a sensitivity of 46.6% (95% CI 40.1-53.2) and specificity of 85.6% (95% CI 82.3-88.8). With a decision threshold of 0.3, > 15min warn

BACKGROUND: Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. METHODS: We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days. RESULTS: From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from 'fair' to 'very good' and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination. CONCLUSIONS: Stroke aetiological cl

Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. A Bayesian artificial neural network (BANN) model predicting episodes of hypotension was developed using data from 104 patients selected from the BrainIT multi-center database. Arterial hypotension events were recorded and defined using the Edinburgh University Secondary Insult Grades (EUSIG) physiological adverse event scoring system. The BANN was trained on a random selection of 50% of the available patients (n = 52) and validated on the remaining cohort. A multi-center prospective pilot study (Phase 1, n = 30) was then conducted with the system running live in the clinical environment, followed by a second validation pilot study (Phase 2, n = 49). From these prospectively collected data, a final evaluation study was done on 69 of these patients with 10 patients excluded from the Phase 2 study because of insufficient or invalid data. Each data collection phase was a prospective non-interventional observational study conducted in a live clinical setting to test the data collection systems and the model performance. No prediction information was available to the clinical teams during a patient’s stay in the ICU. The final cohort (n = 69), using a decision threshold of 0.4, and including false positive checks, gave a sensitivity of 39.3% (95% CI 32.9–46.1) and a specificity of 91.5% (95% CI 89.0–93.7). Using a decision threshold of 0.3, and false positive correction, gave a sensitivity of 46.6% (95% CI 40.1–53.2) and specificity of 85.6% (95% CI 82.3–88.8). With a decision threshold of 0.3, > 15 min warnin

Objectives: Cardiac surgical risk models predict mortality preoperatively, whereas intensive care unit (ICU) models predict mortality postoperatively. Finding a large difference between the 2 (an acute risk change [ARC]) may reflect an alteration in the status of the patient related to the surgery. An adverse ARC was associated with morbidity and mortality in an Australian population. The aims of this study were to validate ARC in a UK population and to investigate the possible mechanisms behind ARC. Design(s): This was a retrospective case-control study. Setting(s): Single, high-volume cardiothoracic hospital. Participant(s): Data from 4,842 cardiac surgical patients were collected between 2013 and 2015. Intervention(s): None. Measurements and Main Results: EuroSCORE was recalibrated to each preceding year's data. ARC was defined as postoperative minus preoperative percentage mortality risk. Association among ARC, morbidity, and mortality was tested. Cases with large adverse ARC (greater than +15%) were compared with cases with large favorable ARC (less than -10%) with regard to intraoperative adverse events, unmeasured patient risk factors, and postoperative events. Adverse ARC was associated with hospital mortality, ICU stay, ICU readmission, renal support, prolonged intubation and return to the operating room (p < 0.001). Intraoperative adverse events occurred in 23 of 33 patients with adverse ARC; however, only 2 of 17 patients with favorable ARC reported adverse events (p < 0.001). Unmeasured risk factors were present in 48% of patients in the adverse ARC group. Conclusion(s): ARC is a readily available and sensitive marker that correlates strongly with morbidity and mortality. The use of ARC in local and national quality monitoring could identify areas for improvement of the quality of cardiac surgical care.Copyright © 2018 Elsevier Inc.

Background Intracerebral haemorrhage growth is associated with poor clinical outcome and is a therapeutic target for improving outcome. We aimed to determine the absolute risk and predictors of intracerebral haemorrhage growth, develop and validate prediction models, and evaluate the added value of CT angiography. Methods In a systematic review of OVID MEDLINE—with additional hand-searching of relevant studies' bibliographies— from Jan 1, 1970, to Dec 31, 2015, we identified observational cohorts and randomised trials with repeat scanning protocols that included at least ten patients with acute intracerebral haemorrhage. We sought individual patient-level data from corresponding authors for patients aged 18 years or older with data available from brain imaging initially done 0·5–24 h and repeated fewer than 6 days after symptom onset, who had baseline intracerebral haemorrhage volume of less than 150 mL, and did not undergo acute treatment that might reduce intracerebral haemorrhage volume. We estimated the absolute risk and predictors of the primary outcome of intracerebral haemorrhage growth (defined as >6 mL increase in intracerebral haemorrhage volume on repeat imaging) using multivariable logistic regression models in development and validation cohorts in four subgroups of patients, using a hierarchical approach: patients not taking anticoagulant therapy at intracerebral haemorrhage onset (who constituted the largest subgroup), patients taking anticoagulant therapy at intracerebral haemorrhage onset, patients from cohorts that included at least some patients taking anticoagulant therapy at intracerebral haemorrhage onset, and patients for whom both information about anticoagulant therapy at intracerebral haemorrhage onset and spot sign on acute CT angiography were known. Findings Of 4191 studies identified, 77 were eligible for inclusion. Overall, 36 (47%) cohorts provided data on 5435 eligible patients. 5076 of these patients were not taking anticoagulant ther

Objectives: Cardiac surgical risk models predict mortality preoperatively, whereas intensive care unit (ICU) models predict mortality postoperatively. Finding a large difference between the 2 (an acute risk change [ARC]) may reflect an alteration in the status of the patient related to the surgery. An adverse ARC was associated with morbidity and mortality in an Australian population. The aims of this study were to validate ARC in a UK population and to investigate the possible mechanisms behind ARC. Design: This was a retrospective case-control study. Setting: Single, high-volume cardiothoracic hospital. Participants: Data from 4,842 cardiac surgical patients were collected between 2013 and 2015. Interventions: None. Measurements and main results: EuroSCORE was recalibrated to each preceding year's data. ARC was defined as postoperative minus preoperative percentage mortality risk. Association among ARC, morbidity, and mortality was tested. Cases with large adverse ARC (greater than +15%) were compared with cases with large favorable ARC (less than -10%) with regard to intraoperative adverse events, unmeasured patient risk factors, and postoperative events. Adverse ARC was associated with hospital mortality, ICU stay, ICU readmission, renal support, prolonged intubation and return to the operating room (p < 0.001). Intraoperative adverse events occurred in 23 of 33 patients with adverse ARC; however, only 2 of 17 patients with favorable ARC reported adverse events (p < 0.001). Unmeasured risk factors were present in 48% of patients in the adverse ARC group. Conclusion: ARC is a readily available and sensitive marker that correlates strongly with morbidity and mortality. The use of ARC in local and national quality monitoring could identify areas for improvement of the quality of cardiac surgical care.

Background and aim: The availability and access of hospital administrative data [coding for Charlson comorbidity index (CCI)] in large data form has resulted in a surge of interest in using this information to predict mortality from stroke. The aims of this study were to determine the minimum clinical data set to be included in models for predicting disability after ischemic stroke adjusting for CCI and clinical variables and to evaluate the impact of CCI on prediction of outcome. Method(s): We leverage anonymized clinical trial data in the Virtual International Stroke Trials Archive. This repository contains prospective data on stroke severity and outcome. The inclusion criteria were patients with available stroke severity score such as National Institutes of Health Stroke Scale (NIHSS), imaging data, and outcome disability score such as 90-day Rankin Scale. We calculate CCI based on comorbidity data in this data set. For logistic regression, we used these calibration statistics: Nagelkerke generalised R2 and Brier score; and for discrimination we used: area under the receiver operating characteristics curve (AUC) and integrated discrimination improvement (IDI). The IDI was used to evaluate improvement in disability prediction above baseline model containing age, sex, and CCI. Result(s): The clinical data among 5,206 patients (55% males) were as follows: mean age 69 +/- 13 years, CCI 4.2 +/- 0.8, and median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 h. In Model 2, adding admission NIHSS to the baseline model improved AUC from 0.67 (95% CI 0.65-0.68) to 0.79 (95% CI 0.78-0.81). In Model 3, adding 24-h NIHSS to the baseline model resulted in substantial improvement in AUC to 0.90 (95% CI 0.89-0.91) and increased IDI by 0.23 (95% CI 0.22-0.24). Adding the variable recombinant tissue plasminogen activator did not result in a further change in AUC or IDI to this regression model. In Model 3, the variable NIHSS at 24 h explains 87.3% of the variance of

There are many serious and acute physiological conditions about which we have incomplete medical knowledge. To address this and develop effective treatments it is often the case that a wealth of clinical data is required for collection, analysis and feedback. Whilst such data often exists it is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data infrastructure (ICUnet) to address the specific issue of prediction of hypotensive events in the brain trauma domain. This system has been used to support a major multi-centre clinical trial. In this paper, the implementation and application of the ICUnet system is described, followed by the design and results of the clinical trial. © 2012 IEEE.

There are many serious and acute physiological conditions about which we have incomplete medical knowledge that can support optimal healthcare intervention. To develop effective treatments a wealth of clinical data is required for collection, analysis and feedback. Such data often does exist but is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data grid infrastructure (HypoNet) to address the specific issue of prediction of hypotensive events in the brain trauma domain and is currently being used as part of a large multi-centre clinical trial. The implementation and application of the HypoNet system is described here. © 2010 IEEE.

Background The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health technology. Materials and methods Over a 2 year period, core dataset data (grouped by nine categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data was compared with original data sent and percentage error rates calculated by data category. Findings Comparisons, 19,461, were made in proportion to the size of the data received with the largest number checked in laboratory data (5,667) and the least in the surgery data (567). Error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found. Conclusions The BrainIT core dataset (with the exception of the surgery classification) is feasible and accurate to collect. The surgery classification needs to be revised. © 2008 Springer-Verlag/Wien.

OBJECTIVE: The aim of this study was to evaluate the robustness and zero-drift of an intracranial pressure sensor, Neurovent-P (Raumedic AG, Münchberg, Germany), when used in the clinical environment. METHODS: A prospective multicenter trial, conforming to the International Organization for Standardization 14155 Standard, was conducted in 6 European BrainIT centers between July 2005 and December 2006. Ninety-nine catheters were used. The study was observational, followed by a centralized sensor bench test after catheter removal. RESULTS: The mean recorded value before probe insertion was 0.17 ± 1.1 mm Hg. Readings outside the range ±1 mm Hg were recorded in only 3 centers on a total of 15 catheters. Complications were minimal and mainly related to the insertion bolt. The mean recorded pressure value at removal was 0.8 ± 2.2 mm Hg. No relationship was identified between postremoval reading and length of monitoring. The postremoval bench test indicated the probability of a system failure, defined as a drift of more than 3 mm Hg, at a range between 12 and 17%. CONCLUSION: The Neurovent-P catheter performed well in clinical use in terms of robustness. The majority of technical complications were associated with the bolt fixation technology. Adverse events were rare and clinically nonsignificant. Despite the earlier reported excellent bench test zero-drift rates, under the more demanding clinical conditions, zero-drift rate remains a concern with catheter tip strain gauge technology. This performance is similar, and not superior, to other intracranial pressure devices.

The summed Alberta Stroke Program Early CT Score (ASPECTS) is useful for predicting stroke outcome. The anatomical information in the CT template is rarely used for this purpose because traditional regression methods are not adept at handling collinearity (relatedness) among brain regions. While penalized logistic regression (PLR) can handle collinearity, it does not provide an intuitive understanding of the interaction among network structures in a way that eigenvector method such as PageRank can (used in Google search engine). In this exploratory analysis we applied graph theoretical analysis to explore the relationship among ASPECTS regions with respect to disability outcome. The Virtual International Stroke Trials Archive (VISTA) was searched for patients who had infarct in at least one ASPECTS region (ASPECTS <=9, ASPECTS=10 were excluded), and disability (modified Rankin score/mRS). A directed graph was created from a cross correlation matrix (thresholded at false discovery rate of 0.01) of the ASPECTS regions and demographic variables and disability (mRS>2). We estimated the network-based importance of each ASPECTS region by comparing PageRank and node strength measures. These results were compared with those from PLR. There were 185 subjects, average age 67.5+/- 12.8 years (55% Males). Model 1: demographic variables having no direct connection with disability, the highest PageRank was M2 (0.225, bootstrap 95% CI 0.215-0.347). Model 2: demographic variables having direct connection with disability, the highest PageRank were M2 (0.205, bootstrap 95% CI 0.194-0.367) and M5 (0.125, bootstrap 95% CI 0.096-0.204). Both models illustrate the importance of M2 region to disability. The PageRank method reveals complex interaction among ASPECTS regions with respects to disability. This approach may help to understand the infarcted brain network involved in stroke disability.Copyright © 2015 Beare et al.

The summed Alberta Stroke Program Early CT Score (ASPECTS) is useful for predicting stroke outcome. The anatomical information in the CT template is rarely used for this purpose because traditional regression methods are not adept at handling collinearity (relatedness) among brain regions. While penalized logistic regression (PLR) can handle collinearity, it does not provide an intuitive understanding of the interaction among network structures in a way that eigenvector method such as PageRank can (used in Google search engine). In this exploratory analysis we applied graph theoretical analysis to explore the relationship among ASPECTS regions with respect to disability outcome. The Virtual International Stroke Trials Archive (VISTA) was searched for patients who had infarct in at least one ASPECTS region (ASPECTS 9, ASPECTS=10 were excluded), and disability (modified Rankin score/mRS). A directed graph was created from a cross correlation matrix (thresholded at false discovery rate of 0.01) of the ASPECTS regions and demographic variables and disability (mRS>2). We estimated the network-based importance of each ASPECTS region by comparing PageRank and node strength measures. These results were compared with those from PLR. There were 185 subjects, average age 67.5+/- 12.8 years (55% Males). Model 1: demographic variables having no direct connection with disability, the highest PageRank was M2 (0.225, bootstrap 95% CI 0.215-0.347). Model 2: demographic variables having direct connection with disability, the highest PageRank were M2 (0.205, bootstrap 95% CI 0.194-0.367) and M5 (0.125, bootstrap 95% CI 0.096-0.204). Both models illustrate the importance of M2 region to disability. The PageRank method reveals complex interaction among ASPECTS regions with respects to disability. This approach may help to understand the infarcted brain network involved in stroke disability.Copyright © 2015 Beare et al. This is an open access article distributed under the terms of the Cr

There are many serious and acute physiological conditions about which we have incomplete medical knowledge. To address this and develop effective treatments it is often the case that a wealth of clinical data is required for collection, analysis and feedback. Whilst such data often exists it is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data infrastructure (ICUnet) to address the specific issue of prediction of hypotensive events in the brain trauma domain. This system has been used to support a major multi-centre clinical trial. In this paper, the implementation and application of the ICUnet system is described, followed by the design and results of the clinical trial.

BACKGROUND: Hypotension is a recognized -secondary insult after traumatic brain injury (TBI). There are many definitions of hypotension, an often cited example being the Brain Trauma Foundation's current (2007) "Guidelines for the Management of Severe Traumatic Brain Injury," which defines hypotension as systolic pressure <90 mmHg. However, this same document declares "The importance of mean arterial pressure, as opposed to systolic pressure should also be stressed, …." Our work shows that when using the Edinburgh University Secondary Insult Grades (EUSIG) definitions, which require monitoring of both systolic and mean arterial pressures, that most hypotensive events are in fact triggered by a breach of the mean arterial level of 70 mmHg. We suggest that close monitoring of mean arterial pressure would enable clinical teams to avoid more potentially damaging hypotensive events. MATERIALS AND METHODS: An analysis of 100 patients from the Brain-IT database was performed. Using the EUSIG definitions, 2,081 events can be obtained by analyzing the systolic and mean blood pressures on a minute by minute basis. A software program was written to identify and classify the trigger pattern for each event. A categorical analysis of these triggering patterns has been carried out. KEY RESULTS: Our analysis shows that most events are triggered by a drop in mean arterial pressure. In fact a large number of events (91%) occur where the mean arterial pressure is below the threshold limits whereas the systolic pressure does not cross the 90 mmHg limit at all. CONCLUSION: We suggest that more emphasis should be placed on closely monitoring mean arterial pressure as well as systolic pressure when trying to guard against hypotensive problems in traumatically brain injured patients. In future work we will study the underlying physiological mechanisms and attempt to further classify concomitant conditions that may be contributing to the onset of a hypotensive event.

Hypotension is a recognized -secondary insult after traumatic brain injury (TBI). There are many definitions of hypotension, an often cited example being the Brain Trauma Foundation's current (2007) "Guidelines for the Management of Severe Traumatic Brain Injury," which defines hypotension as systolic pressure <90 mmHg. However, this same document declares "The importance of mean arterial pressure, as opposed to systolic pressure should also be stressed, ...." Our work shows that when using the Edinburgh University Secondary Insult Grades (EUSIG) definitions, which require monitoring of both systolic and mean arterial pressures, that most hypotensive events are in fact triggered by a breach of the mean arterial level of 70 mmHg. We suggest that close monitoring of mean arterial pressure would enable clinical teams to avoid more potentially damaging hypotensive events. An analysis of 100 patients from the Brain-IT database was performed. Using the EUSIG definitions, 2,081 events can be obtained by analyzing the systolic and mean blood pressures on a minute by minute basis. A software program was written to identify and classify the trigger pattern for each event. A categorical analysis of these triggering patterns has been carried out. Our analysis shows that most events are triggered by a drop in mean arterial pressure. In fact a large number of events (91%) occur where the mean arterial pressure is below the threshold limits whereas the systolic pressure does not cross the 90 mmHg limit at all. We suggest that more emphasis should be placed on closely monitoring mean arterial pressure as well as systolic pressure when trying to guard against hypotensive problems in traumatically brain injured patients. In future work we will study the underlying physiological mechanisms and attempt to further classify concomitant conditions that may be contributing to the onset of a hypotensive event

Background: Hypotension is recognized as a potentially damaging secondary insult after traumatic brain injury. Systems to give clinical teams some early warning of likely hypotensive instability could be added to the range of existing techniques used in the management of this group of patients. By using the Edinburgh University Secondary Insult Grades (EUSIG) definitions for-hypotension (systolic arterial pressure <90 mmHg OR mean arterial-pressure <70 mmHg) we collected a group of 2,000 events by analyzing the Brain-IT database. We then constructed a Bayesian Artificial Neural Network (an advanced statistical modeling technique) that is able to provide some early warning when trained on this previously collected demographic and physiological data. Materials and Methods: Using EUSIG defined event data from the Brain-IT database, we identified a Bayesian artificial neural network (BANN) topology and constructed a series of datasets using a group of clinically guided input variables. This allowed us to train a BANN, which was then tested on an unseen set of patients from the Brain-IT database. The initial tests used a particularly harsh assessment criterion whereby a true positive prediction was only allowed if the BANN predicted an upcoming event to the exact minute. We have now developed the system to the point where it is about to be used in a two-stage Phase II clinical trial and we are also researching a more realistic assessment technique. Key Results: We have constructed a BANN that is able to provide early warning to the clinicians based on a model that uses information from the physiological inputs; systolic and mean arterial pressure and heart rate; and demographic variables age and gender. We use 15-min SubWindows starting at 15 and 30 min before an event and process mean, slope and standard deviations. Based on 10 simulation runs, our current sensitivity is 36.25% (SE 1.31) with a specificity of 90.82% (SE 0.85). Initial results from a Phase I clinical stu