Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Physiology and Big Data

BACKGROUND

The implementation of multimodality monitoring in the clinical management of patients with disorders of consciousness (DoC) results in physiological measurements that can be collected in a continuous and regular fashion or even at waveform resolution. Such data are considered part of the "Big Data" available in intensive care units and are potentially suitable for health care-focused artificial intelligence research. Despite the richness in content of the physiological measurements, and the clinical implications shown by derived metrics based on those measurements, they have been largely neglected from previous attempts in harmonizing data collection and standardizing reporting of results as part of common data elements (CDEs) efforts. CDEs aim to provide a framework for unifying data in clinical research and help in implementing a systematic approach that can facilitate reliable comparison of results from clinical studies in DoC as well in international research collaborations.

METHODS

To address this need, the Neurocritical Care Society's Curing Coma Campaign convened a multidisciplinary panel of DoC "Physiology and Big Data" experts to propose CDEs for data collection and reporting in this field.

RESULTS

We report the recommendations of this CDE development panel and disseminate CDEs to be used in physiologic and big data studies of patients with DoC.

CONCLUSIONS

These CDEs will support progress in the field of DoC physiologic and big data and facilitate international collaboration.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Exploring Plan of Care Communication With a Multidisciplinary Rounding Plan to Nursing Care Plans

ABSTRACT

BACKGROUND: The nursing care plan (NCP) was developed approximately 100 years ago as a teaching tool for nursing students. Our neuroscience intensive care unit (NSICU) uses a multidisciplinary rounding plan (MDRP) that may provide more relevant and up-to-date information than the standard NCP. METHODS: In this prospective single-blind randomized pilot study, we examined nurses' abilities to respond to 7 clinical scenarios common to the NSICU. The NCPs and MDRPs from 70 patients were randomly assigned to 14 nurses (10 per nurse) who answered each of the 7 questions using only data from an NCP or data from an MDR. RESULTS: The MDRP mean score of 4.51 (1.50) correct answers was statistically significantly higher than the NCP mean score of 0.31 (0.71) correct answer (P < .0001). CONCLUSION: The MDRP was designed to address the modern-day communication needs of NSICU staff by leveraging technological advances. Data from this study suggest that the MDRP may have advantages over the NCP in providing contextually relevant information. Additional research is warranted to develop the MDRP as a replacement for the NCP in the NSICU setting.