19 research outputs found
Cerebral perfusion in posterior reversible encephalopathy syndrome measured with arterial spin labeling MRI.
Background and purposeThe pathophysiologic basis of posterior reversible encephalopathy syndrome (PRES) remains controversial. Hypertension (HTN)-induced autoregulatory failure with subsequent hyperperfusion is the leading hypothesis, whereas alternative theories suggest vasoconstriction-induced hypoperfusion as the underlying mechanism. Studies using contrast-based CT and MR perfusion imaging have yielded contradictory results supporting both ideas. This work represents one of the first applications of arterial spin labeling (ASL) to evaluate cerebral blood flow (CBF) changes in PRES.Materials and methodsAfter obtaining Institutional Review Board approval, MRI reports at our institution from 07/2015 to 09/2020 were retrospectively searched and reviewed for mention of "PRES" and "posterior reversible encephalopathy syndrome." Of the resulting 103 MRIs (performed on GE 1.5 Tesla or 3 Tesla scanners), 20 MRIs in 18 patients who met the inclusion criteria of clinical and imaging diagnosis of PRES and had diagnostic-quality pseudocontinuous ASL scans were included. Patients with a more likely alternative diagnosis, technically non-diagnostic ASL, or other intracranial abnormalities limiting assessment of underlying PRES features were excluded. Perfusion in FLAIR-affected brain regions was qualitatively assessed using ASL and characterized as hyperperfusion, normal, or hypoperfusion. Additional quantitative analysis was performed by measuring average gray matter CBF in abnormal versus normal brain regions.ResultsHTN was the most common PRES etiology (65%). ASL showed hyperperfusion in 13 cases and normal perfusion in 7 cases. A hypoperfusion pattern was not identified. Quantitative analysis of gray matter CBF among patients with visually apparent hyperperfusion showed statistically higher perfusion in affected versus normal appearing brain regions (median CBF 100.4 ml/100 g-min vs. 61.0 ml/ 100 g-min, p < 0.001).ConclusionElevated ASL CBF was seen in the majority (65%) of patients with PRES, favoring the autoregulatory failure hypothesis as a predominant mechanism. Our data support ASL as a practical way to assess and noninvasively monitor cerebral perfusion in PRES that could potentially alter management strategies
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An efficient risk adjustment model to predict inpatient adverse events after surgery.
BACKGROUND: Risk adjustment is an important component of surgical outcomes and quality analyses. Current models include numerous preoperative variables; however, the relative contribution of these variables may be limited. This research seeks to identify a model with the fewest number of variables necessary to perform an adequate risk adjustment to predict any inpatient adverse event for use in resource-limited settings. METHODS: All patients from the National Surgical Quality Improvement Program (NSQIP) database from 2005 to 2010 were included. Outcomes were inpatient mortality or any surgical complication captured by NSQIP. Models were built by sequential addition of preoperative risk variables selected by their area under the receiver operator characteristic curve (AUC). RESULTS: Among 863,349 patients, the single variable with the highest AUC was American Society of Anesthesiologists (ASA) classification (AUC = 0.7127). AUC values reached 0.7923 with five variables (ASA classification, wound classification, functional status prior to surgery, albumin, and age) and 0.7945 with six variables. The sixth variable was one of the following: alkaline phosphatase, weight loss, principal anesthesia technique, gender, or emergency status. The model with the highest discrimination that did not require laboratories included ASA classification, functional status prior to surgery, wound classification, and age (AUC = 0.7810). Including all 66 preoperative variables produced little additional gain (AUC = 0.8006). CONCLUSIONS: Six variables are sufficient to develop a risk adjustment tool for inpatient surgical mortality and morbidity. This research has important implications for the field of surgical outcomes research by improving efficiency of data collection. This limited model can aid the expansion of risk-adjusted analyses to resource-limited settings worldwide