Background. Although CSF cytology and MRI are standard methods to diagnose neoplastic meningitis (NM), this complication of neoplastic disease remains difficult to detect. We therefore reevaluated the sensitivity of gadolinium (GD)-enhanced MRI and cerebrospinal-fluid (CSF)-cytology and the relevance of tumor type and CSF cell count. Methods. We retrospectively identified 111 cases of NM diagnosed in our CSF laboratory since 1990 with complete documentation of both MRI and CSF cytology. 37 had haematological and 74 solid neoplasms. CSF cell counts were increased in 74 and normal in 37 patients. Results. In hematological neoplasms, MRI was positive in 49% and CSF cytology in 97%. In solid tumors, the sensitivity of MRI was 80% and of cytology 78%. With normal CSF cell counts, MRI was positive in 59% (50% hematological, 72% solid malignancies) and CSF cytology in 76% (92% in hematological, 68% in solid neoplasms). In cases of elevated cell counts, the sensitivity of MRI was 72% (50% for hematological, 83% for solid malignancies) and of CSF cytology 91% (100% for haematological and 85% for solid neoplasms). 91% of cytologically positive cases were diagnosed at first and another 7% at second lumbar puncture. Routine protein analyses had a low sensitivity in detecting NM. Conclusions. The high overall sensitivity of MRI was only confirmed for NM from solid tumors and for elevated CSF cell counts. With normal cell counts and haematological neoplasms, CSF-cytology was superior to MRI. None of the analysed routine CSF proteins had an acceptable sensitivity and specificity in detecting leptomeningeal disease.
BACKGROUND The Barrow Neurological Institute (BNI) score, measuring maximal thickness of aneurysmal subarachnoid hemorrhage (aSAH), has previously shown to predict symptomatic cerebral vasospasms (CVSs), delayed cerebral ischemia (DCI), and functional outcome. OBJECTIVE To validate the BNI score for prediction of above-mentioned variables and cerebral infarct and evaluate its improvement by integrating further variables which are available within the first 24 h after hemorrhage. METHODS We included patients from a single center. The BNI score for prediction of CVS, DCI, infarct, and functional outcome was validated in our cohort using measurements of calibration and discrimination (area under the curve [AUC]). We improved it by adding additional variables, creating a novel risk score (measure by the dichotomized Glasgow Outcome Scale) and validated it in a small independent cohort. RESULTS Of 646 patients, 41.5% developed symptomatic CVS, 22.9% DCI, 23.5% cerebral infarct, and 29% had an unfavorable outcome. The BNI score was associated with all outcome measurements. We improved functional outcome prediction accuracy by including age, BNI score, World Federation of Neurologic Surgeons, rebleeding, clipping, and hydrocephalus (AUC 0.84, 95% CI 0.8-0.87). Based on this model we created a risk score (HATCH—Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus), ranging 0 to 13 points. We validated it in a small independent cohort. The validated score demonstrated very good discriminative ability (AUC 0.84 [95% CI 0.72-0.96]). CONCLUSION We developed the HATCH score, which is a moderate predictor of DCI, but excellent predictor of functional outcome at 1 yr after aSAH.
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