1,2*Yao Bernard Fionko,1,2Serge Konan Yao,3,4Raissa Diaby,1,2Koffi Jean-Jacques Kouassi,1,2Adama Simaga,1,2Aderehime Haidara
1Department of Neurosurgery, Bouaké University Hospital, Bouaké, Côte d’Ivoire
2Faculty of Medical Sciences, Alassane Ouattara University, Bouaké, Côte d’Ivoire
3Department of Neurosurgery, Yopougon University Hospital, Abidjan, Côte d’Ivoire
4Faculty of Medical Sciences, Felix Houphouët Boigny University, Abidjan, Côte d’Ivoire
ABSTRACT
Background. Studies of intracranial suppurative infections (ISI) in sub-Saharan Africa look at isolated variables — a delay, a Glasgow Coma Scale (GCS) score, a mortality rate — and rarely describe how a hospital stay unfolds. We asked whether a day-by-day reconstruction of the pathway tells us something that conventional prognostic analyses do not.
Methods. Single-centre cohort of 150 admissions (148 patients) for ISI at Bouaké University Hospital, Côte d’Ivoire, 2016–2026. Each stay became a 31-day sequence on two channels, care (seven states) and neurological severity (three GCS bands), using rules fixed before clustering. Sequences were compared by optimal matching and grouped by partitioning around medoids. Stability was tested by bootstrap and eleven sensitivity analyses, admission factors by discrepancy analysis and Firth regressions. The study is exploratory.
Results. The 150 admissions produced 100 distinct sequences, grouped into four profiles: rapid recovery (T1, 38.7%), prolonged convalescence (T2, 35.3%), complicated course (T3, 15.3%) and death (T4, 10.7%). Length of stay, GCS, complication and death build the sequences, so their differences between groups are expected and validate nothing. Among other variables, only lesion diameter clearly separated the groups (ε² = 0.185). Symptom-to-admission delay had only a weak effect (ε² = 0.049; unadjusted p = 0.017) that disappeared after adjustment, and referral status was linked to nothing (p = 0.55). Entropy formed a plateau from day 2 to day 12, peaking at 0.72 on day 10. On bootstrap, only T4 was stable (Jaccard 0.985; T1 0.741, T3 0.672, T2 0.599). Four of eleven sensitivity analyses departed clearly from the main solution.
Conclusion. Stays fall into three survivor profiles of uneven clarity and one profile of death. The registry dates neither complications nor critical care, so the sequences mainly describe length of stay, complications and outcome; they say nothing about the order of events or about when to intervene. Multicentre validation and a registry that dates complications and critical care are needed.
KEYWORDS
intracranial suppurative infection; brain abscess; subdural empyema; care trajectory; sequence analysis; optimal matching; care pathway; sub-Saharan Africa; implementation science; global neurosurgery
REFERENCES
1) Brouwer MC, Tunkel AR, McKhann GM, van de Beek D. Brain abscess. N Engl J Med. 2014;371:447-56.
2) [Brouwer MC, Coutinho JM, van de Beek D. Clinical characteristics and outcome of brain abscess: systematic review and meta-analysis. Neurology. 2014;82:806-13.
3) Nathoo N, Nadvi SS, van Dellen JR, Gouws E. Intracranial subdural empyemas in the era of computed tomography: a review of 699 cases. Neurosurgery. 1999;44:529-35.
4) 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;75:716-26.
5) Kanu OO, Nnoli C, Olowoyeye O, Arigbabu S. Infratentorial brain abscess: management challenges in a resource-limited setting. Niger J Clin Pract. 2012;15:228-32.
6) Sanoussi S, Rabiou MS, Bawa KM, Kelani A, Sani R, Chaibou MS. Les suppurations intracrâniennes : aspects épidémiologiques, diagnostiques et thérapeutiques à Niamey. Afr J Neurol Sci. 2011;30.
7) Studer M, Ritschard G. What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures. J R Stat Soc Ser A Stat Soc. 2016;179:481-511.
8) Abbott A, Hrycak A. Measuring resemblance in sequence data: an optimal matching analysis of musicians’ careers. Am J Sociol. 1990;96:144-85.
9) Abbott A, Tsay A. Sequence analysis and optimal matching methods in sociology: review and prospect. Sociol Methods Res. 2000;29:3-33.
10) Aisenbrey S, Fasang AE. New life for old ideas: the second wave of sequence analysis bringing the course back into the life course. Sociol Methods Res. 2010;38:420-62.
11) Gabadinho A, Ritschard G, Müller NS, Studer M. Analyzing and visualizing state sequences in R with TraMineR. J Stat Softw. 2011;40:1-37.
12) Liao TF, Bolano D, Brzinsky-Fay C, Cornwell B, Fasang AE, Helske S, et al. Sequence analysis: its past, present, and future. Soc Sci Res. 2022;107:102772.
13) Ritschard G, Studer M, editors. Sequence analysis and related approaches: innovative methods and applications. Cham: Springer; 2018.
14) Cornwell B. Social sequence analysis: methods and applications. Cambridge: Cambridge University Press; 2015.
15) Vanasse A, Courteau J, Courteau M, Benigeri M, Chiu YM, Dufour I, et al. Healthcare utilization after a first hospitalization for COPD: a new approach of state sequence analysis based on the 6W multidimensional model of care trajectories. BMC Health Serv Res. 2020;20:177.
16) Vanasse A, Courteau J, Courteau M, Roy MA, Stip E, Fleury MJ, et al. Multidimensional analysis of adult patients’ care trajectories before a first diagnosis of schizophrenia. Schizophrenia (Heidelb). 2022;8:52.
17) McKay R, Letarte L, Lebel A, Quesnel-Vallée A; TORSADE Cohort Working Group. Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data. BMC Health Serv Res. 2022;22:131.
18) Brodeur S, Vanasse A, Courteau J, Courteau M, Stip E, Fleury MJ, et al. Antipsychotic utilization trajectories three years after initiating or reinitiating treatment of schizophrenia: a state sequence analysis approach. Acta Psychiatr Scand. 2022;145:469-80.
19) Vanoli J, Nava CR, Airoldi C, Ucciero A, Salvi V, Barone-Adesi F. Use of state sequence analysis in pharmacoepidemiology: a tutorial. Int J Environ Res Public Health. 2021;18:13398.
20) Le Meur N, Gao F, Bayat S. Mining care trajectories using health administrative information systems: the use of state sequence analysis to assess disparities in prenatal care consumption. BMC Health Serv Res. 2015;15:200.
21) Nguena Nguefack HL, Pagé MG, Katz J, Choinière M, Vanasse A, Dorais M, et al. Trajectory modelling techniques useful to epidemiological research: a comparative narrative review of approaches. Clin Epidemiol. 2020;12:1205-22.
22) Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109-38.
23) Piccarreta R, Studer M. Holistic analysis of the life course: methodological challenges and new perspectives. Adv Life Course Res. 2019;41:100251.
24) Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38:65-76.
25) Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
26) Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50:217-26.
27) Plsek PE, Greenhalgh T. Complexity science: the challenge of complexity in health care. BMJ. 2001;323:625-8.
28) Braithwaite J, Churruca K, Long JC, Ellis LA, Herkes J. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med. 2018;16:63.
29) Flothow A, Novelli A, Sundmacher L. Analytical methods for identifying sequences of utilization in health data: a scoping review. BMC Med Res Methodol. 2023;23:212. https://doi.org/10.1186/s12874-023-02019-y
30) Mathew S, Peat G, Parry E, Singh Sokhal B, Yu D. Applying sequence analysis to uncover ‘Real-World’ clinical pathways from routinely collected data: a systematic review. J Clin Epidemiol. 2024;166:111226. https://doi.org/10.1016/j.jclinepi.2023.111226
31) von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453-7.
32) Lesnard L. Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns. Sociol Methods Res. 2010;38:389-419.
33) Elzinga CH, Studer M. Spell sequences, state proximities, and distance metrics. Sociol Methods Res. 2015;44:3-47.
34) Hollister M. Is optimal matching suboptimal? Sociol Methods Res. 2009;38:235-64.
35) Wu LL. Some comments on sequence analysis and optimal matching methods in sociology: review and prospect. Sociol Methods Res. 2000;29:41-64.
36) Kaufman L, Rousseeuw PJ. Finding groups in data: an introduction to cluster analysis. New York: Wiley; 1990.
37) Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53-65.
38) Hennig C. Cluster-wise assessment of cluster stability. Comput Stat Data Anal. 2007;52:258-71.
39) Hubert L, Arabie P. Comparing partitions. J Classif. 1985;2:193-218.
40) Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc. 1927;22:209-12.
41) Tomczak M, Tomczak E. The need to report effect size estimates revisited: an overview of some recommended measures of effect size. Trends Sport Sci. 2014;1:19-25.
42) Studer M, Ritschard G, Gabadinho A, Müller NS. Discrepancy analysis of state sequences. Sociol Methods Res. 2011;40:471-510.
43) Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27-38.
44) Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21:2409-19.
45) Studer M, Struffolino E, Fasang AE. Estimating the relationship between time-varying covariates and trajectories: the sequence analysis multistate model procedure. Sociol Methodol. 2018;48:103-35.
46) Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D. Process mining in healthcare: a literature review. J Biomed Inform. 2016;61:224-36.
47) Munoz-Gama J, Martin N, Fernandez-Llatas C, Johnson OA, Sepúlveda M, Helm E, et al. Process mining for healthcare: characteristics and challenges. J Biomed Inform. 2022;127:103994.
48) Helske S, Helske J. Mixture hidden Markov models for sequence data: the seqHMM package in R. J Stat Softw. 2019;88:1-32.
49) Murphy K, Murphy TB, Piccarreta R, Gormley IC. Clustering longitudinal life-course sequences using mixtures of exponential-distance models. J R Stat Soc Ser A Stat Soc. 2021;184:1414-51.
50) Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58:236-44.
51) Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Soc Sci Med. 1994;38:1091-110.
52) Vanasse A, Courteau M, Ethier JF. The 6W multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions. Public Health. 2018;157:53-61.
53) Nguena Nguefack HL, Pagé MG, Choinière M, Vanasse A, Deslauriers S, Angarita-Fonseca A, et al. Distinct care trajectories among persons living with arthritic conditions: a two-year state sequence analysis. Front Pain Res. 2022;3:1014793. https://doi.org/10.3389/fpain.2022.1014793
54) Roux J, Grimaud O, Leray E. Use of state sequence analysis for care pathway analysis: the example of multiple sclerosis. Stat Methods Med Res. 2019;28:1651-63.
55) Kuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Bhatti O, Husain S, et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit Health. 2019;1:e63-77.
56) Martin GL, Atramont A, Mazars M, Tajahmady A, Agamaliyev E, Singer M, et al. Days spent at home and mortality after critical illness: a cluster analysis using nationwide data. Chest. 2023;163:826-42.
57) Dagliati A, Malovini A, Tibollo V, Bellazzi R. A process mining pipeline to characterise COVID-19 patients’ trajectories and identify relevant temporal phenotypes from EHR data. Front Public Health. 2022;10:815674.
58) Manktelow M, Iftikhar A, Bucholc M, McCann M, O’Kane M. Clinical and operational insights from data-driven care pathway mapping: a systematic review. BMC Med Inform Decis Mak. 2022;22:43.
59) Najjar A, Reinharz D, Girouard C, Gagné C. A two-step approach for mining patient treatment pathways in administrative healthcare databases. Artif Intell Med. 2018;87:34-48.
60) Adams JN, Ziegler J, McDermott M, Douglas MJ, Eber R, Gichoya JW, et al. A health equity monitoring framework based on process mining. PLOS Digit Health. 2024;3:e0000575.
61) Muyama L, Neuraz A, Coulet A. Machine learning approaches for the discovery of clinical pathways from patient data. J Biomed Inform. 2024;160:104746.
62) van der Aalst WMP. A practitioner’s guide to process mining: limitations of the directly-follows graph. Procedia Comput Sci. 2019;164:321-8.
63) Guzzo A, Rullo A, Vocaturo E. Process mining applications in the healthcare domain: a comprehensive review. WIREs Data Min Knowl Discov. 2022;12:e1442.
64) Crozes F, de Boissezon X, Demeulemeester R, Mounie M, Montane E, Azema O, et al. Identifying pre- and posttraumatic brain injury pathways with a multidomain sequence analysis: a retrospective cohort study. Ann Phys Rehabil Med. 2026;69:102148. https://doi.org/10.1016/j.rehab.2026.102148
65) Bodilsen J, Dalager-Pedersen M, van de Beek D, Brouwer MC, Nielsen H. Incidence and mortality of brain abscess in Denmark: a nationwide population-based study. Clin Microbiol Infect. 2020;26:95-100.
66) Bodilsen J, D’Alessandris QG, Humphreys H, Iro MA, Klein M, Last K, et al. European Society of Clinical Microbiology and Infectious Diseases guidelines on diagnosis and treatment of brain abscess in children and adults. Clin Microbiol Infect. 2024;30:66-89.
67) Corsini Campioli C, Castillo Almeida NE, O’Horo JC, Wilson WR, Cano E, DeSimone DC, et al. Bacterial brain abscess: an outline for diagnosis and management. Am J Med. 2021;134:1210-7.
68) Muzumdar D, Jhawar S, Goel A. Brain abscess: an overview. Int J Surg. 2011;9:136-44.
69) Sharma BS, Gupta SK, Khosla VK. Current concepts in the management of pyogenic brain abscess. Neurol India. 2000;48:105-11.
70) Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q. 1966;44:166-206.
71) Kruk ME, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-DeWan S, et al. High-quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob Health. 2018;6:e1196-252.
72) Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis. 2011;52:793-800.
73) Haber N, Pillay D, Porter K, Bärnighausen T. Constructing the cascade of HIV care: methods for measurement. Curr Opin HIV AIDS. 2016;11:102-8.
74) Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med. 2010;2:57cm29.
75) Institute of Medicine. Best care at lower cost: the path to continuously learning health care in America. Washington (DC): National Academies Press; 2013.
76) Ovretveit J. Improving complex systems with improve-mentation: challenges and solutions. Front Health Serv. 2026;5:1724893.
77) Punchak M, Mukhopadhyay S, Sachdev S, Hung YC, Peeters S, Rattani A, et al. Neurosurgical care: availability and access in low-income and middle-income countries. World Neurosurg. 2018;112:e240-54.
78) Fuller A, Tran T, Muhumuza M, Haglund MM. Building neurosurgical capacity in low and middle income countries. eNeurologicalSci. 2015;3:1-6.
79) Park KB, Johnson WD, Dempsey RJ. Global neurosurgery: the unmet need. World Neurosurg. 2016;88:32-5.
80) Kanmounye US, Robertson FC, Thango NS, Doe AN, Bankole NDA, Ginette PA, et al. Needs of young African neurosurgeons and residents: a cross-sectional study. Front Surg. 2021;8:647279.
81) Ukachukwu AK, Still MEH, Seas A, von Isenburg M, Fieggen G, Malomo AO, et al. Fulfilling the specialist neurosurgical workforce needs in Africa: a systematic review and projection toward 2030. J Neurosurg. 2022;138:1102-13.
82) Barthelemy EJ, Diouf SA, Silva AS, Mukasa J, Kanmounye US, Ogunfolaji O, et al. Historical and current state of neurosurgery in sub-Saharan Africa. World Neurosurg. 2022;167:e670-8.
83) Meara JG, Leather AJM, Hagander L, Alkire BC, Alonso N, Ameh EA, et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet. 2015;386:569-624.
84) Dewan MC, Rattani A, Fieggen G, Arraez MA, Servadei F, Boop FA, et al. Global neurosurgery: the current capacity and deficit in the provision of essential neurosurgical care. J Neurosurg. 2018;130:1055-64.
85) Rudolfson N, Dewan MC, Park KB, Shrime MG, Meara JG, Alkire BC. The economic consequences of neurosurgical disease in low- and middle-income countries. J Neurosurg. 2018;130:1149-56.
86) Mensah EO, Mehta NH, Gal ZT, Pacheco-Barrios N, Hamzah R, Rosseau G, et al. The rising bibliometric profile of global neurosurgery. J Clin Neurosci. 2026;143:111725.
87) Rose J, Gelb AW, Davies JI, Martin J, McIntyre K, Mellin-Olsen J; Utstein Surgical Metrics Group. From definitions to implementation: a guide to collect and apply the Lancet Commission on Global Surgery indicators — an Utstein consensus report. J Data Inf Sci. 2026;11:46-62. https://doi.org/10.2478/jdis-2025-0053
88) Davies JI, Gelb AW, Gore-Booth J, Martin J, Mellin-Olsen J, Åkerman C, et al. Global surgery, obstetric, and anaesthesia indicator definitions and reporting: an Utstein consensus report. PLoS Med. 2021;18:e1003749.
89) Sonderman KA, Citron I, Meara JG. National surgical, obstetric and anaesthesia planning in the context of global surgery: the way forward. World J Surg. 2019;43:412-6.
90) Truché P, Shoman H, Starr N, Choa R, Ashby T, Citron I, et al. Globalization of national surgical, obstetric and anesthesia plans: the critical link between health policy and action in global surgery. Global Health. 2020;16:1.
91) World Health Assembly. Resolution WHA68.15: strengthening emergency and essential surgical care and anaesthesia as a component of universal health coverage. Geneva: World Health Organization; 2015.
92) Levesque JF, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12:18.
93) Hirschhorn LR, Baynes C, Sherr K, Chintu N, Awoonor-Williams JK, Finnegan K, et al. Approaches to ensuring and improving quality in the context of health system strengthening. BMC Health Serv Res. 2013;13:S8.
Cite this article
Fionko, Y. B., Yao, S. K., Diaby, R., Kouassi, K. J. J., Simaga, A., & Haidara, A. (2026). Care Trajectories Rather than Outcomes: Sequence Analysis Identifies Four Hospital Pathways in Intracranial Suppurative Infections. INTERNATIONAL JOURNAL OF HEALTH & MEDICAL RESEARCH, 5(10), 989-1018. https://doi.org/10.58806/ijhmr.2026.v5i10n04
