Correction to: Standards for Neurologic Critical Care Units: A Statement for Healthcare Professionals from The Neurocritical Care Society

Quality Improvement in Neurocritical Care: a Review of the Current Landscape and Best Practices

Purpose of Review

The field of neurocritical care (NCC) has grown such that there is now a substantial body of literature on quality improvement specific to NCC. This review will discuss the development of this literature over time and highlight current best practices with practical tips for providers.

Recent Findings

There is tremendous variability in patient care models for NCC patients, despite evidence showing that certain structural elements are associated with better outcomes. There now also exist evidence-based recommendations for neurocritical care unit (NCCU) structure and processes, as well as NCC-specific performance measure (PM) sets; however, awareness of these is variable among care providers. The evidence-based literature on NCC structure, staffing, training, standardized order sets and bundles, transitions of care including handoff, prevention of bounce backs, bed flow optimization, and inter-hospital transfers is growing and offers many examples of successful performance improvement initiatives in NCCUs.

Summary

NCC providers care for patients with life-threatening conditions like intracerebral and subarachnoid hemorrhages, ischemic stroke, and traumatic brain injury, which are associated with high morbidity, complexity of treatment, and cost. Quality improvement initiatives have been successful in improving many aspects of NCC patient care, and NCC providers should continue to update and standardize their practices with consideration of this data. More research is needed to continue to identify high-risk and high-cost NCCU structures and processes and strategies to optimize them, validate current NCC PMs, and encourage clinical adoption of those that prove to be associated with improved outcomes.

A Perspective from the Neurocritical Care Society and the Society of Critical Care Medicine: Team-Based Care for Neurological Critical Illness

The Neurocritical Care Society and the Society of Critical Care Medicine have worked together to create a perspective regarding the Standards of Neurologic Critical Care Units (Moheet et al. in Neurocrit Care 29:145-160, 2018). The most neurologically ill or injured patients warrant the highest standard of care available; this supports the need for defining and establishing specialized neurological critical care units. Rather than interpreting the Standards as being exclusionary, it is most appropriate to embrace them in the setting of team-based care. Since there are many more patients than there are highly specialized beds, collaborative care and appropriate transfer agreements are essential in promoting excellent patient outcomes. This viewpoint addresses areas of clarification and emphasizes the need for collegiality and partnership in delivering the best specialty critical care to our patients.

The Neurological Examination Improves Cranial Accelerometry Large Vessel Occlusion Prediction Accuracy

BACKGROUND/OBJECTIVE

We combined cranial accelerometry, a device-based approach to large vessel occlusion (LVO) prediction, with neurological examination findings to determine if this improves diagnostic accuracy compared to either alone.

METHODS

Cranial accelerometry recordings and NIHSS scores were obtained during stroke codes and thrombectomy transfers at an academic medical center using convenience sampling. The reference standard was discharge diagnosis of LVO stroke. We compared accuracy statistics between machine learning models trained using cranial accelerometry alone, with asymmetric arm weakness added, with NIHSS scores added, and retrospective examination only LVO prediction scales. An exploratory analysis required asymmetric arm weakness prior to model training or scale testing.

RESULTS

Of 68 patients, there were 23 LVO strokes. Cranial accelerometry was 65% sensitive (95% CI 43-84%) and 87% specific (95% CI 73-95%). Adding asymmetric arm weakness increased specificity to 91% (95% CI 79-98%). Adding asymmetric arm weakness and the NIHSS increased sensitivity to 74% (95% CI 52-90%) and decreased specificity to 89% (95% CI 76-96%). LVO prediction scales had wide sensitivity and specificity ranges. The exploratory analysis improved sensitivity to 91% (95% CI 72-99%) and specificity to 93% (95% CI 92-99%) with only three false positives and two false negatives.

CONCLUSIONS

Cranial accelerometry models are improved by various additions of asymmetric arm weakness and the NIHSS. An exploratory analysis requiring asymmetric arm weakness prior to cranial accelerometry model training minimized false positives and negatives.