Predictors of Bacterial Meningitis in Resource-Limited Contexts: An Angolan Case

Abstract

BackgroundDespite the great morbidity and mortality that childhood bacterial meningitis (BM) is experiencing in Africa, diagnosis of BM in resource-limited contexts is still a challenge. Several algorithms and clinical predictors have been proposed to help physicians in decision-making but a lot of these markers used variables that are calculable only in well-equipped laboratories. Predictors or algorithm based on parameters that can be easily performed in basic laboratories can help significantly in BM diagnosis, even in resource-limited settings, rural hospitals or health centers.ResultsThis retrospective study examined 145 cerebral-spinal fluid (CSF) specimens from children from 2 months to 14 years. CSF specimens were divided into two groups, according to the presence or not of a clinical diagnosis of BM. For each specimen, CSF aspect, CSF white blood cells (WBC) count, CSF glucose and protein concentration were analyzed and statistical analysis were performed. CSF WBC count ≥10/µl is no more a valuable predictor of BM. CSF protein concentration ≥50 mg/dl has a better sensitivity for BM diagnosis and when used with CSF glucose concentration ≤40 mg/dl, can help to diagnose correctly almost all the BM cases. An algorithm including CSF protein concentration, glucose concentration and WBC count has been proposed to rule out BM and to correctly diagnose it.ConclusionsIn resource-limited health centers, the availability of a combination of easy-to-obtain parameters can significantly help physicians in BM diagnosis. The prompt identification of a BM case can be rapid treated or transferred to adequate structures and can modify the outcome in the patient.

Diagnostic Value of Clinical and Laboratory Findings in Childhood Meningitis

“…Increased CSF protein levels and decreased glucose concentrations are observed in bacterial meningitis. 12,13 In the present study, there was no difference between meningitis and nonmeningitis patients in terms of CSF glucose concentration. However, the CSF protein concentration was significantly higher in patients with meningitis (median 26.15 vs. 18.9 mg/dL, p < 0.001).…”

Diagnosis of bacterial meningitis in Ghana: Polymerase chain reaction versus latex agglutination methods

“…Although rapid and cheap, the technique is limited in its identification of serogroups [21, 22], which makes it unreliable in the event of an outbreak as it is unable to guide the choice of a vaccine. The introduction of latex agglutination tests (LATs) for the identification of bacterial antigens in cerebrospinal fluid (CSF) of patients is gradually replacing the leukocyte cell count and Gram stain technique at the District laboratories [23, 24]. The LATs employ antigen-specific antibodies on latex particles to detect bacterial antigens.…”

Performance of Thirteen Clinical Rules to Distinguish Bacterial and Presumed Viral Meningitis in Vietnamese Children

“…More recently, numerous researchers have investigated potential clinical decision rules that recognize ABM from pAVM including: Thome [9], Spanos [10], Hoen [11] (also called Jaeger et al [12]), Freedman [13], Nigrovic [14], Oostenbrink [15], Bonsu 2004 [16], Brivet [17], Schmidt [18], De Cauwer [19], Chavanet [20], Dubos [21], Bonsu 2008[22], Tokuda [23], and Lussiana [24]. A few rules have included complicated multivariate models that require the use of a computer [10], [11], while others have used scoring systems [9], [15], tree model decisions [23], or a simple list of items [13], [14], [17], [18], [19], [20], [21], [22].…”

“…These clinical decision rules require extensive test prior to their use in hospitals [25] and have rarely been compared in a single study. In addition, several rules are yet to be tested by independent researchers [17], [21], [22], [23], [24] or tested in children [17], [23]. The Nigrovic's rule, also called Bacterial Meningitis Score (BMS) [14], performed perfectly in several studies [8], [26], [27], [28], [29], [30], but failed to provide 100% sensitivity in other independent data sets [7], [19], [20], [31].…”