Faculty Bibliography

OBJECTIVES/OBJECTIVE:Hospital readmissions are associated with poor health outcomes including illness severity and medical complications. The objective of this study was to identify characteristics associated with 30-day post-stroke readmission in an academic urban hospital network. MATERIALS AND METHODS/METHODS:We collected data on patients admitted with stroke from 2017 through 2022 who were readmitted within 30 days of discharge and compared them to a subset of non-readmitted stroke patients. Chart review was used to collect demographics, characteristics of the stroke, co-morbid conditions, in-hospital complications, and post-discharge care. Univariate analyses followed by regression analysis were used to assess characteristics associated with post-stroke readmission. RESULTS:We identified 4743 patients with stroke (18 % hemorrhagic, mean age 70.1 (standard deviation (SD) 17.2), 47.3 % female) discharged from the stroke services, of whom 282 (5.9 %) patients were readmitted within 30 days of index hospitalization. Univariate analyses identified 18 significantly different features between admitted and readmitted patients. Regression analysis revealed characteristics associated with readmission included private insurance (odds ratio (OR) 0.4, confidence interval (CI) 0.3-0.6, p < 0.001), comorbid peripheral vascular disease (PVD) (OR 2.7, CI 1.3-5.5, p = 0.009), malignancy (OR 1.6, CI 1.0-2.6, p = 0.04), seizure (OR 3.4, CI 1.4-8.2, p = 0.007), thrombolytic administration (OR 0.4, CI 0.2-0.7, p = 0.003), undergoing thrombectomy (OR 5.4, CI 2.9-10.1, p < 0.001), and higher discharge modified Rankin Scale score (OR 1.2, CI 1.0-1.3, p = 0.047). CONCLUSIONS:Our data demonstrate that thrombectomy, high discharge Rankin score, comorbid malignancy, seizure or PVD, and lack of thrombolytic administration or private insurance predict readmission.

In 1972, Maynard and Stolov showed that the experimental error in calculating nerve conduction velocity (NCV) depends on errors of latency and distance measurements. Their data suggested that a minimum distance of 10 cm should be used when calculating NCV because of an increase in error >/= 25% at shorter distances. The object of this study was to reestablish the minimum distance using current technology. Twenty physicians measured the proximal and distal onset latencies of the same stored ulnar compound muscle action potentials, as well as the forearm skin distance. The mean and standard deviation of the errors in conduction time and distance were determined. A spreadsheet was constructed, determining the error in NCV for a variety of distances and NCVs. The average conduction time between proximal and distal stimulation was 3.23 ms, with a standard deviation of 0.09 ms. The mean distance measurement was 212.6 +/- 2.1 mm. The errors in NCV were significantly less than previously reported. The experimental error increased as distance decreased, reaching 20% with distances less than 6 cm. The latency error accounted for 71% of the total experimental error, as opposed to 90% in the previous study. With advances in electrodiagnostic equipment, NCV can reasonably be calculated at distances less than 10 cm, perhaps as low as 5-6 cm.

One diagnostic criterion for ulnar nerve mononeuropathy at the elbow (UNE) is a decrease in across-elbow nerve conduction velocity (NCV) > 10 m/s compared to the forearm segment. Distance and latency measurement errors are an inherent part of NCV calculations. Twenty electromyographers measured the latencies of stored ulnar compound muscle action potentials and measured the forearm and across-elbow distances along the ulnar nerve. Based on previously published equations, experimental error in NCV was calculated for various NCVs. The mean distances and standard deviations for the forearm and elbow segments were 212.5 +/- 2.1 mm and 86.7 +/- 4.2 mm, respectively. For an NCV of 55 m/s, a difference of 14 m/s between the two segments can occur from measurement error alone. Distance measurements about the elbow are fraught with interobserver errors rendering the resultant NCV of that segment of limited value as a sole criterion for the diagnosis of UNE.

Deferoxamine is a potent chelator of ferric iron. Past studies have shown that deferoxamine interferes with acute inflammatory tissue injury in a number of animal models. In cell culture, it inhibits neutrophil-medicated killing of endothelial cells. Both the animal model and cell culture data are thought to reflect the capacity of deferoxamine to interfere with the superoxide anion- and and ferric iron-dependent reduction of hydrogen peroxide to the hydroxyl radical (Fenton Reaction). The present study describes a second mechanism by which deferoxamine may interfere with the acute inflammatory response. Here it is shown that deferoxamine has the capacity to inhibit neutrophil adhesion to lung epithelial cells and vascular endothelial cells. Adhesion of phorbol ester-stimulated neutrophils to both cell types is reduced by 70-80%. The inhibitory effects are reversible and are overcome when ferric iron is present along with deferoxamine in a 2:1 molar ratio. Concentrations of deferoxamine that prevent neutrophil adhesion also prevent neutrophil-mediated killing of the same target cells. In contrast, deferoxamine does not significantly inhibit activation-induced up-regulation of neutrophil surface adhesion structures (CD11b/CD18) and does not prevent binding of a monoclonal antibody that recognizes beta 2 integrins in the high-affinity state. Release of proteolytic enzymes from activated cells is also not significantly inhibited by deferoxamine. Taken together, these data indicate that deferoxamine modulates neutrophil adhesive functions associated with the activated state. The ability of deferoxamine to interfere with neutrophil binding to target cells may contribute to its anti-inflammatory activity.