Cerebral microbleeds after use of extracorporeal membrane oxygenation in children.

Cerebral microbleeds (CMB) on gradient-recalled echo (GRE) magnetic resonance imaging (MRI) are rarely seen in children, yet have been described following vascular procedures in adults. Extracorporeal membrane oxygenation (ECMO) has been associated with vascular injury and neurological events in children, but there have been no reports to date of GRE MRI findings in children treated with ECMO. We reviewed MRI scans for all vascular neurology consultations in children treated with ECMO at an academic medical center over a 5-year period. In 6 of 12 cases, GRE was acquired as others were unstable or had contraindications to MRI. All 6 of 6 (100%) GRE cases (mean age 2.1 years, 7 female, 5 male) demonstrated CMB. CMB were multiple (>3 lesions), situated in cortical or lobar regions, with a striking predominance (5/6 cases) for the right carotid distribution. Other than CMB, no cases demonstrated intracranial hemorrhage. CMB may be noted on GRE MRI after ECMO and may reflect vascular damage from gaseous emboli.

Predictive combinations of monitor alarms preceding in-hospital code blue events.

Bedside monitors are ubiquitous in acute care units of modern healthcare enterprises. However, they have been criticized for generating an excessive number of false positive alarms causing alarm fatigue among care givers and potentially compromising patient safety. We hypothesize that combinations of regular monitor alarms denoted as SuperAlarm set may be more indicative of ongoing patient deteriorations and hence predictive of in-hospital code blue events. The present work develops and assesses an alarm mining approach based on finding frequent combinations of single alarms that are also specific to code blue events to compose a SuperAlarm set. We use 4-way analysis of variance (ANOVA) to investigate the influence of four algorithm parameters on the performance of the data mining approach. The results are obtained from millions of monitor alarms from a cohort of 223 adult code blue and 1768 control patients using a multiple 10-fold cross-validation experiment setup. Using the optimal setting of parameters determined in the cross-validation experiment, final SuperAlarm sets are mined from the training data and used on an independent test data set to simulate running a SuperAlarm set against live regular monitor alarms. The ANOVA shows that the content of a SuperAlarm set is influenced by a subset of key algorithm parameters. Simulation of the extracted SuperAlarm set shows that it can predict code blue events one hour ahead with sensitivity between 66.7% and 90.9% while producing false SuperAlarms for control patients that account for between 2.2% and 11.2% of regular monitor alarms depending on user-supplied acceptable false positive rate. We conclude that even though the present work is still preliminary due to the usage of a moderately-sized database to test our hypothesis it represents an effort to develop algorithms to alleviate the alarm fatigue issue in a unique way.

Curriculum-based neurosurgery digital library.

BACKGROUND: Recent work-hour restrictions and the constantly evolving body of knowledge are challenging the current ways of teaching neurosurgery residents. OBJECTIVE: To develop a curriculum-based digital library of multimedia content to face the challenges in neurosurgery education. METHOD: We used the residency program curriculum developed by the Congress of Neurological Surgeons to structure the library and Microsoft Sharepoint as the user interface. RESULTS: This project led to the creation of a user-friendly and searchable digital library that could be accessed remotely and throughout the hospital, including the operating rooms. CONCLUSION: The electronic format allows standardization of the content and transformation of the operating room into a classroom. This in turn facilitates the implementation of a curriculum within the training program and improves teaching efficiency. Future work will focus on evaluating the efficacy of the library as a teaching tool for residents.

A comparison of vital signs charted by nurses with automated acquired values using waveform quality indices.

OBJECTIVE: (1) To investigate if there exist any discrepancies between the values of vital signs charted by nurses and those recorded by bedside monitors for a group of patients admitted for neurocritical care. (2) To investigate possible interpretations of discrepancies by exploring information in the alarm messages and the raw waveform data from monitors. METHODS: Each charted vital sign value was paired with a corresponding value from data collected by an archival program of bedside monitors such that the automatically archived data preceded the charted data and had minimal time lag to the charted value. Next, the absolute differences between the paired values were taken as the discrepancy between charted and automatically-collected data. Archived alarm messages were searched for technical alarms of sensor/lead failure types. Additionally, 7-min waveform data around the place of large discrepancy were analyzed using signal abnormality indices (SAI) for quantifying the quality of recorded signals. RESULTS: About 31,145 pairs of systolic blood pressure (BP-S) and 67,097 pairs of SpO(2) were investigated. Seven and a half percent of systolic blood pressure pairs had a discrepancy greater than 20 mmHg and less than one percent of the SpO2 pairs had a discrepancy greater than 10. We could not find any technical alarms from the monitors that could explain the large difference. However, SAI calculated for the waveforms associated with this group of cases was significantly larger than the SAI values calculated for the control waveform data of the same patients with small discrepancies. CONCLUSION: Charted vital signs reflect in large the raw data as reported by bedside monitors. Poor signal quality could partially explain the existence of cases of large discrepancies.