Main Article Content
Abstract
ABSTRACT
Introduction. Microbiological yield in intracranial suppurative infections (ISI) is low in resource-limited settings, where empirical antibiotics often precede sampling. We quantified this yield, sought factors associated with culture positivity, and assessed the adequacy of empirical therapy against isolated organisms. Methods. Secondary analysis of a single-centre cohort of 150 episodes (148 patients; 2016–2026, Bouaké Teaching Hospital, Côte d’Ivoire). Factors associated with positivity were examined among sampled patients (n = 116; 50 events) using Firth penalized logistic regression with internal bootstrap validation. Adequacy was assessed both by recorded in-vitro activity against the antibiogram and by a prespecified expert classification of expected spectrum-based coverage. Results. Pus was sampled in 77.3 % of episodes; cultures were positive in 50/116 sampled patients (43.1 %; 95 % CI 34.5–52.2) and in 33.3 % of the cohort. Gram-positive cocci predominated (82 %): streptococci 42 %, Staphylococcus aureus 34 %. No prespecified factor was associated with positivity; only continuous age was (aOR 1.23 per decade; 95 % CI 1.03–1.48), without usable discrimination (corrected AUC 0.58–0.61). Initial therapy included at least one in-vitro-active agent in 34/44 episodes with an antibiogram (77.3 %); expected spectrum-based coverage was 74–76 % but showed no individual concordance with the antibiogram (kappa −0.04). The main gap concerned S. aureus (53 % covered). Two-thirds of the cohort (100/150) were treated without an exploitable isolate; 93 % of regimens contained a WHO “Watch” antibiotic. Conclusion. Routine variables did not identify culture-positive episodes, arguing for deep sampling whenever feasible rather than clinical selection. Empirical therapy was broadly adequate, antistaphylococcal coverage being the one easily correctable weakness. Strengthening microbiological capacity and structured stewardship is the priority.
RÉSUMÉ
Introduction. Le rendement microbiologique des suppurations intracrâniennes (SIC) est faible dans les contextes de ressources limitées, où l’antibiothérapie probabiliste précède souvent le prélèvement. Nous avons quantifié ce rendement, recherché les facteurs associés à une culture positive et évalué l’adéquation de l’antibiothérapie probabiliste vis-à-vis des germes isolés. Méthodes. Analyse secondaire d’une cohorte monocentrique de 150 observations (148 patients ; 2016–2026, CHU de Bouaké). Les facteurs associés à la positivité ont été recherchés parmi les patients prélevés (n = 116 ; 50 événements) par régression logistique pénalisée de Firth, avec validation interne par bootstrap. L’adéquation a été évaluée par l’adéquation enregistrée vis-à-vis de l’antibiogramme et par une classification experte préspécifiée de la couverture attendue selon le spectre. Résultats. Un prélèvement était réalisé dans 77,3 % des cas ; les cultures étaient positives chez 50/116 patients prélevés (43,1 % ; IC95 % 34,5–52,2). Les cocci à Gram positif prédominaient (82 %) : streptocoques 42 %, Staphylococcus aureus 34 %. Aucun facteur préspécifié n’était associé à la positivité ; seul l’âge en continu l’était (ORa 1,23 par décennie ; IC95 % 1,03–1,48), sans discrimination exploitable (AUC corrigée 0,58–0,61). L’adéquation vis-à-vis de l’antibiogramme était de 77,3 % (34/44) ; la couverture attendue selon le spectre était de 74–76 % mais sans concordance individuelle (kappa −0,04). La principale faille concernait S. aureus (couvert dans 53 %). Deux tiers de la cohorte (100/150) ont été traités sans isolat exploitable ; 93 % des schémas comportaient un antibiotique « Watch » de l’OMS. Conclusion. Les variables de routine ne permettaient pas d’identifier les épisodes à culture positive, ce qui plaide pour un prélèvement profond chaque fois qu’il est techniquement possible plutôt que pour une sélection clinique. L’antibiothérapie probabiliste était globalement adéquate, la couverture antistaphylococcique constituant le seul point faible aisément corrigeable. Le renforcement des capacités microbiologiques et des programmes structurés de bon usage des antibiotiques apparaît prioritaire.
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References
1. Brouwer MC, Tunkel AR, McKhann GM 2nd, van de Beek D. Brain abscess. N Engl J Med. 2014;371(5):447-456. doi:10.1056/NEJMra1301635
2. Brouwer MC, Coutinho JM, van de Beek D. Clinical characteristics and outcome of brain abscess: systematic review and meta-analysis. Neurology. 2014;82(9):806-813. doi:10.1212/WNL.0000000000000172
3. Brouwer MC, van de Beek D. Epidemiology, diagnosis, and treatment of brain abscess. Curr Opin Infect Dis. 2017;30(1):129-134. doi:10.1097/QCO.0000000000000334
4. Bodilsen J, Dalager-Pedersen M, Schønheyder HC, Nielsen H. Incidence and mortality of brain abscess in Denmark: a nationwide population-based study. Clin Microbiol Infect. 2020;26(1):95-100. doi: 10.1016/j.cmi.2019.05.016
5. Bodilsen J, Duerlund LS, Mariager T, Brandt CT, Petersen PT, Larsen L, et al. Clinical features and prognostic factors in adults with brain abscess. Brain. 2023;146(4):1637-1647. doi:10.1093/brain/awac312
6. Nathoo N, Nadvi SS, Narotam PK, van Dellen JR. Brain abscess: management and outcome analysis of a computed tomography era experience with 973 patients. World Neurosurg. 2011;75(5-6):716-726. doi: 10.1016/j.wneu.2010.11.043
7. Helweg-Larsen J, Astradsson A, Richhall H, Erdal J, Laursen A, Brennum J. Pyogenic brain abscess, a 15 year survey. BMC Infect Dis. 2012;12:332. doi:10.1186/1471-2334-12-332
8. Mathisen GE, Johnson JP. Brain abscess. Clin Infect Dis. 1997;25(4):763-779. doi:10.1086/515541
9. Sonneville R, Ruimy R, Benzonana N, Riahi H, Wolff M. An update on bacterial brain abscess in immunocompetent patients. Clin Microbiol Infect. 2017;23(9):614-620. doi: 10.1016/j.cmi.2017.05.004
10. Moorthy RK, Rajshekhar V. Management of brain abscess: an overview. Neurosurg Focus. 2008;24(6):E3. doi:10.3171/FOC/2008/24/6/E3
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20. Udoh A, Ernawati DK, Akpan M, Galbraith K, Hussein R, Ashiru-Oredope D. Implementation of antimicrobial stewardship programmes in African countries: a systematic literature review. J Glob Antimicrob Resist. 2020; 22:317-324. doi: 10.1016/j.jgar.2020.03.009
21. Miller JM, Binnicker MJ, Campbell S, Carroll KC, Chapin KC, Gonzalez MD, et al. A guide to utilization of the microbiology laboratory for diagnosis of infectious diseases. Clin Infect Dis. 2018;67(6):e1-e94. doi:10.1093/cid/ciy381
22. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Lancet. 2007;370(9596):1453-1457. doi:10.1016/S0140-6736(07)61602-X
23. Brook I. Microbiology and treatment of brain abscess. J Clin Neurosci. 2017;38:8-12. doi: 10.1016/j.jocn.2016.12.035
24. Brook I. Antimicrobial treatment of anaerobic infections. Expert Opin Pharmacother. 2011;12(11):1691-1707. doi:10.1517/14656566.2011.576672
25. Nau R, Sörgel F, Eiffert H. Penetration of drugs through the blood-cerebrospinal fluid/blood-brain barrier for treatment of central nervous system infections. Clin Microbiol Rev. 2010;23(4):858-883. doi:10.1128/CMR.00007-10
26. Newcombe RG. Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med. 1998;17(8):857-872. doi:10.1002/(SICI)1097-0258(19980430)17:8<857::AID-SIM777>3.0.CO;2-E
27. Riley RD, Snell KIE, Ensor J, Burke DL, Harrell FE Jr, Moons KGM, et al. Minimum sample size for developing a multivariable prediction model. Stat Med. 2019;38(7):1276-1296. doi:10.1002/sim.7992
28. Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80(1):27-38. doi:10.1093/biomet/80.1.27
29. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409-2419. doi:10.1002/sim.1047
30. Harrell FE Jr. Regression modeling strategies. 2nd ed. Cham: Springer; 2015. doi:10.1007/978-3-319-19425-7
31. Steyerberg EW. Clinical prediction models. 2nd ed. Cham: Springer; 2019. doi:10.1007/978-3-030-16399-0
32. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):230. doi:10.1186/s12916-019-1466-7
33. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55-63. doi:10.7326/M14-0697
34. Boukassa L, Ngackosso OB, Kinata SB, Ekouele Mbaki HB. Intracranial suppurations in Africa: systematic review. Open J Mod Neurosurg. 2020;10(2):244-253. doi:10.4236/ojmn.2020.102023
35. Ooi SZY, Sichimba D, Dalle DU, Higginbotham G, Takoutsing BD, Bankole NDA, et al. Management and outcomes of paediatric intracranial suppurations in low- and middle-income countries: a scoping review. Front Surg. 2021;8:690895. doi:10.3389/fsurg.2021.690895
36. Kabré A, Zabsonré DS, Diallo O, Cissé R. Prise en charge médico-chirurgicale des abcès du cerveau à l'ère du scanner en Afrique subsaharienne. Neurochirurgie. 2014;60(5):249-253.
37. Al Masalma M, Armougom F, Scheld WM, Dufour H, Roche PH, Drancourt M, et al. The expansion of the microbiological spectrum of brain abscesses with use of multiple 16S ribosomal DNA sequencing. Clin Infect Dis. 2009;48(9):1169-1178. doi:10.1086/597578
38. Al Masalma M, Lonjon M, Richet H, Dufour H, Roche PH, Drancourt M, et al. Metagenomic analysis of brain abscesses identifies specific bacterial associations. Clin Infect Dis. 2012;54(2):202-210. doi:10.1093/cid/cir797
39. Kommedal Ø, Wilhelmsen MT, Skrede S, Meisal R, Jakovljev A, Gaustad P, et al. Massive parallel sequencing provides new perspectives on bacterial brain abscesses. J Clin Microbiol. 2014;52(6):1990-1997. doi:10.1128/JCM.00346-14
40. Stebner A, Ensser A, Geißdörfer W, Bozhkov Y, Lang R. Molecular diagnosis of polymicrobial brain abscesses with 16S-rRNA-based next-generation sequencing. Clin Microbiol Infect. 2021;27(1):76-82. doi: 10.1016/j.cmi.2020.03.028
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