• Katja E Wartenberg, David Y Hwang, Karl Georg Haeusler, Susanne Muehlschlegel, Oliver W Sakowitz, Dominik Madžar, Hajo M Hamer, Alejandro A Rabinstein, David M Greer, J Claude Hemphill, Juergen Meixensberger, and Panayiotis N Varelas.
• Neurocritical Care and Stroke Unit, Department of Neurology, University of Leipzig, Liebigstrasse 20, 04103, Leipzig, Germany. katja.wartenberg@medizin.uni-leipzig.de.
• Neurocrit Care. 2019 Oct 1; 31 (2): 231-244.

Background/ObjectivePrognostication is a routine part of the delivery of neurocritical care for most patients with acute neurocritical illnesses. Numerous prognostic models exist for many different conditions. However, there are concerns about significant gaps in knowledge regarding optimal methods of prognostication.MethodsAs part of the Arbeitstagung NeuroIntensivMedizin meeting in February 2018 in Würzburg, Germany, a joint session on prognostication was held between the German NeuroIntensive Care Society and the Neurocritical Care Society. The purpose of this session was to provide presentations and open discussion regarding existing prognostic models for eight common neurocritical care conditions (aneurysmal subarachnoid hemorrhage, intracerebral hemorrhage, acute ischemic stroke, traumatic brain injury, traumatic spinal cord injury, status epilepticus, Guillain-Barré Syndrome, and global cerebral ischemia from cardiac arrest). The goal was to develop a qualitative gap analysis regarding prognostication that could help inform a future framework for clinical studies and guidelines.ResultsPrognostic models exist for all of the conditions presented. However, there are significant gaps in prognostication in each condition. Furthermore, several themes emerged that crossed across several or all diseases presented. Specifically, the self-fulfilling prophecy, lack of accounting for medical comorbidities, and absence of integration of in-hospital care parameters were identified as major gaps in most prognostic models.ConclusionsPrognostication in neurocritical care is important, and current prognostic models are limited. This gap analysis provides a summary assessment of issues that could be addressed in future studies and evidence-based guidelines in order to improve the process of prognostication.

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