Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections

Die Grundlagen der Theorie des Mikroskops (Bookreview)

Kurt Michel. Die Grundlagen der Theorie des Mikroskops. Wissensch. Verlagsgesellschaft m.b.H., m.b.H.: Stuttgart, I950. 314 Seiten, 160 Abbildungen, 27 DM

Surgical instrument tracking optimizes trans-sphenoidal endoscopic treatment of petrous apex cholesterol granuloma

During endoscopic trans-sphenoidal treatment of petrous apex cholesterol granuloma, the challenge for the surgeon is to drill the posterior wall of the sphenoid sinus to reach the lesion while attempting to avoid the internal carotid artery (ICA). A refined neuronavigation technique is presented that diminishes bonework needed for exposure and marsupialization, and simultaneously minimizes risks of accidental harm to the ICA. The technique utilizes real-time intraoperative instrument tracking of a drill, enabling safe creation of a direct canal toward the cyst just medial to the paraclival ICA and of a curette for entirely image-guided marsupialization of the cyst's deep areas through the canal

Familial occurrence of brain arteriovenous malformations: a systematic review

Item does not contain fulltextBACKGROUND: Brain arteriovenous malformations (BAVMs) are thought to be sporadic developmental vascular lesions, but familial occurrence has been described. We compared the characteristics of patients with familial BAVMs with those of patients with sporadic BAVMs. METHODS: We systematically reviewed the literature on patients with familial BAVMs. Three families that were found in our centre were added. Age, sex distribution and clinical presentation of the identified patients were compared with those in population based series of patients with sporadic BAVMs. Furthermore, we calculated the difference in mean age at diagnosis of parents and children to study possible anticipation. RESULTS: We identified 53 patients in 25 families with BAVMs. Mean age at diagnosis of patients with familial BAVMs was 27 years (range 9 months to 58 years), which was younger than in the reference population (difference between means 8 years, 95% CI 3 to 13 years). Patients with familial BAVMs did not differ from the reference populations with respect to sex or mode of presentation. In families with BAVMs in successive generations, the age of the child at diagnosis was younger than the age of the parent (difference between means 22 years, 95% CI 13 to 30 years), which suggests clinical anticipation. CONCLUSIONS: Few patients with familial BAVMs have been described. These patients were diagnosed at a younger age than sporadic BAVMs whereas their mode of presentation was similar. Although there are indications of anticipation, it remains as yet unclear whether the described families represent accidental aggregation or indicate true familial occurrence of BAVMs

Comparison of observed and predicted overall yearly infection rates.

<p>Observed and predicted infection rates at the patient level (panel A) and expressed per 1000 drainage days at risk (panel B), including 95% confidence intervals. Predicted yearly infection rates are determined by the summed predicted probabilities and show good concordance with observed rates.</p

Outcome of backward stepwise logistic regression predicting the risk of drain-related meningitis.

<p>Outcome of backward stepwise logistic regression, cut-off for exclusion p<0.05. Odd's ratio and confidence intervals are after bootstrapping, p-values and predictor selection are prior to bootstrapping and shrinkage. Predictors not retained in model: indication for drain placement, duration of admission, total drainage duration, number of days in intensive care unit, CSF glucose, CSF total protein.</p><p>Abbreviations: CI – confidence interval, CRP – C-reactive protein, CSF – cerebrospinal fluid, EVD – external ventricular drain, OR – Odd's ratio.</p