Predictive factors of bacterial meningitis in the patients seen in emergency departments. / Factores predictores de meningitis bacteriana en los pacientes atendidos en urgencias.

OBJETIVES: To analyse and compare predictive factors of bacterial meningitis in the patients seen in the Emergency Departments (ED) due to an episode of acute meningitis (AM). METHODS: A prospective, observational study was carried out in patients aged 15 years and older seen in ED due to AM between August 2009 and November 2015. Thirty-two variables for predicting bacterial meningitis were assessed. They covered epidemiological, comorbidity, clinical and analytical factors. Multivariate logistic regression analysis was performed. RESULTS: The study included 154 patients. The diagnosis was bacterial meningitis in 53 (34.4%) patients. Four variables were significantly associated with bacterial aetiology: cerebrospinal fluid (CSF) lactate concentration ≥33mg/dl (odds ratio [OR] 50.84; 95% confidence interval [CI]: 21.63-119.47, P<.001), serum procalcitonin (PCT) ≥0.8ng/ml (OR 46.34; 95%CI: 19.71-108.89; P<.001), CSF glucose <60% of blood value (OR 20.82; 95%CI: 8.86-48.96; P=.001), CSF polymorphonuclears greater than 50% (OR 20.19; 95%CI: 8.31-49.09; P=.002]. The area under the curve for the model serum PCT≥0.8ng/ml plus CSF lactate ≥33mg/dl was 0.992 (95%CI: 0.979-1; P<.001), and achieved 99% sensitivity and 98% specificity for predicting bacterial meningitis. CONCLUSIONS: Serum PCT with CSF lactate, CSF glucose and CSF polymorphonuclears evaluated in an initial assessment in the ED for patients with AM, achieved an excellent diagnostic usefulness for predicting bacterial meningitis.

[Use of the insertable Holter with remote detection in the etiological diagnosis of cryptogenic stroke: Analysis of 73 patients]. / Utilidad del Holter insertable con detección remota en el diagnóstico etiológico del ictus criptogénico: análisis de 73 pacientes.

INTRODUCTION: Cryptogenic stroke constitutes 25% of all ischemic strokes, of which 20-30% are due to atrial fibrillation (AF). With the aim of increasing the detection rate, implantable long-term monitoring devices have emerged. The study of the profile of the ideal candidate subsidiary to such monitoring would provide a better understanding of the mechanisms underlying this subtype of stroke. OBJECTIVE: To determine which variables are related and can predict the detection of silent AF in patients with cryptogenic stroke. PATIENTS AND METHODS: This is a longitudinal cohort with recruitment from March 2017 to May 2022. They are patients with an implantable monitoring device and cryptogenic stroke with a minimum monitoring of one year. RESULTS: The total number of patients included was 73, with a mean age of 58.8 years, 56.2% were male. AF was detected in 21 patients (28.8%). The most frequent cardiovascular risk factors were hypertension (47.9%) and dyslipidemia (45.2%). The most frequent topography was cortical (52%). Regarding the echocardiographic parameters, 22% had a dilated left atrium, 19% had a patent foramen ovale, and 22% had high-density supraventricular tachycardia (>1%) on Holter monitoring. In the multivariate analysis, the only variable that predicts AF is the presence of high-density supraventricular tachycardia, with an area under the curve of 0.726 (CI 0.57-0.87, p=0.04), sensitivity of 47.6%, specificity of 97.5%, positive predictive value of 90.9%, negative predictive value of 78.8%, and accuracy of 80.9%. CONCLUSIONS: The presence of high-density supraventricular tachycardia can be indicative for predicting silent AF. No other variables have been observed that allow us to predict detection of AF in these patients.