de Rooij NK, Greving JP, Rinkel GJE, Frijns CJM. Early prediction of delayed cerebral ischemia after SAH: development and validation of a practical risk chart. Stroke. 2013.
Delayed cerebral ischemia (DCI) is a common complication in the setting of subarachnoid hemorrhage and it may be a major determinant of morbidity and mortality. De Rooij et al. report the development and validation of a risk chart that incorporates only variables collected at admission and aims to predict the risk of infarcts related to delayed cerebral ischemia.
The authors carefully studied data derived from 371 prospectively collected patients in order to create a prognostic model, and validated the findings in an additional cohort of 255 patients. Not unexpectedly, the variables that more robustly predicted DCI-related infarcts were poor clinical grade, larger amount of blood on CT, and age. Based on these factors, the chart predicted a chance as low as 12% of developing DCI-related infarct in patients with low Fisher Scale/ low World Federation of Neurological Surgeons Scale (WFNS) grade and age ≥55, and as high as 61% in patients with thick subarachnoid hemorrhage, high WFNS grade and younger age. The authors suggest that lower risk patients could be considered for an earlier discharge from the unit, and state that the chart should be used to “predict absolute risks of DCI in individual patients”.
In practice, it works as a well-ordered paradigm, however it is intriguing to consider its application to certain “individual” cases, such as a 60 years-old patient with modified Fisher II and WFNS V (in whom the risk of infarct from DCI would be read as low). Moreover, multiple medical variables such as fever, anemia, hyperglycemia, cardiac and pulmonary issues, hemodynamic and/or electrolyte changes, etc, are considered to influence the morbidity of these patients and need to be intensively followed. Therefore, the benefit of early identification of an increased risk of DCI/infarct based on admission variables would not be relatively so important, and providers could have time to incorporate variables collected within the few days into the risk stratification. In summary, Rooij et al. provide us with solid and meticulously analyzed data that may aid in the decision making of such a commonly encountered scenario.
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