The Long-Term Outcome of Boys With Partial Androgen Insensitivity Syndrome and a Mutation in the Androgen Receptor Gene

BACKGROUND

In boys with suspected partial androgen insensitivity syndrome (PAIS), systematic evidence that supports the long-term prognostic value of identifying a mutation in the androgen receptor gene (AR) is lacking.

OBJECTIVE

To assess the clinical characteristics and long-term outcomes in young men with suspected PAIS in relation to the results of AR analysis.

METHODS

Through the International Disorders of Sex Development Registry, clinical information was gathered on young men suspected of having PAIS (n = 52) who presented before the age of 16 years and had genetic analysis of AR.

RESULTS

The median ages at presentation and at the time of the study were 1 month (range, 1 day to 16 years) and 22 years (range, 16 to 52 years), respectively. Of the cohort, 29 men (56%) had 20 different AR mutations reported. At diagnosis, the median external masculinization scores were 7 and 6 in cases with and without AR mutation, respectively (P = .9), and median current external masculinization scores were 9 and 10, respectively (P = .28). Thirty-five men (67%) required at least one surgical procedure, and those with a mutation were more likely to require multiple surgeries for hypospadias (P = .004). All cases with an AR mutation had gynecomastia, compared to 9% of those without an AR mutation. Of the six men who had a mastectomy, five (83%) had an AR mutation.

CONCLUSIONS

Boys with genetically confirmed PAIS are likely to have a poorer clinical outcome than those with XY DSD, with normal T synthesis, and without an identifiable AR mutation. Routine genetic analysis of AR to confirm PAIS informs long-term prognosis and management.

Early warning of EUSIG-defined hypotensive events using a Bayesian Artificial Neural Network

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 study shows a model sensitivity of 40.95% (SE 6%) and specificity of 86.46% (SE 3%) Although this figure is low it is considered clinically useful for this dangerous condition, provided the false positive rate can be kept sufficiently low as to be practical in an intensive care environment.

CONCLUSION

We have shown that using advanced statistical modeling techniques can provide clinical teams with useful information that will assist clinical care.

Early warning of EUSIG-defined hypotensive events using a Bayesian Artificial Neural Network

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 study shows a model sensitivity of 40.95% (SE 6%) and specificity of 86.46% (SE 3%) Although this figure is low it is considered clinically useful for this dangerous condition, provided the false positive rate can be kept sufficiently low as to be practical in an intensive care environment.

CONCLUSION

We have shown that using advanced statistical modeling techniques can provide clinical teams with useful information that will assist clinical care.

Trigger characteristics of EUSIG-defined hypotensive events

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.

Towards a virtual anonymisation grid for unified access to remote clinical data

Grid technologies provide an infrastructure through which, amongst other things, data access and integration is facilitated across highly distributed and heterogeneous resources. Different domains have their own requirements on the nature of this data access and integration. The clinical domain offers arguably the greatest challenges facing the roll-out and adoption of Grid technologies to meet the changing face of post-genomic clinical research, especially with regard to information governance, ethics and hence security solutions. This paper outlines a novel system design for secure anonymous data access and linkage that meets the needs of key stakeholders in this space including end user researchers, data providers and owners and ethical oversight bodies amongst others. We identify how existing solutions developed within the Medical Research Council funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) project are being re-factored to meet the needs of these players and to address information governance criteria.

Open pelvic fracture: an injury for trauma centers

Twenty-seven blunt trauma patients with open pelvic fracture who were seen in a trauma center over a 10-year period were characterized for treatment and resource consumption. Age, injury severity score, mortality, mechanism of injury, associated injuries, blood requirement, length of stay (LOS), surgical procedures, and complications were analyzed. There was a mean of 26 units of blood transfused, 9 operative procedures, and LOS of 43 days. Associated injuries were common. Aggressive hemorrhage and sepsis control, including 2 hemipelvectomies, resulted in an overall survival rate of 85%, with no mortality occurring in the last 20 patients. The reduced mortality obtained in the treatment of this highly resource consumptive injury suggests that open pelvic fracture should be managed at a trauma center, where these resources are immediately available.