Original Research

A clinical algorithm for triaging patients with significant lymphadenopathy in primary health care settings in Sudan

Eltahir A.G. Khalil, Imad A. El Hag, Kamal E. Elsiddig, Mohamed E.M.O. Elsafi, Mona E.E. Elfaki, Ahmed M. Musa, Brima Y. Musa, Ahmed M. Elhassan
African Journal of Primary Health Care & Family Medicine | Vol 5, No 1 | a435 | DOI: https://doi.org/10.4102/phcfm.v5i1.435 | © 2013 Eltahir A.G. Khalil, Imad A. El Hag, Kamal E. Elsiddig, Mohamed E.M.O. Elsafi, Mona E.E. Elfaki, Ahmed M. Musa, Brima Y. Musa, Ahmed M. Elhassan | This work is licensed under CC Attribution 4.0
Submitted: 25 February 2012 | Published: 21 June 2013

About the author(s)

Eltahir A.G. Khalil, Department of Clinical Pathology & Immunology, University of Khartoum, Sudan
Imad A. El Hag, Riyadh Military Hospital, Saudi Arabia
Kamal E. Elsiddig, Faculty of Medicine, University of Khartoum, Sudan
Mohamed E.M.O. Elsafi, Central Police Hospital, Burri, Khartoum, Sudan
Mona E.E. Elfaki, Department of Clinical Pathology & Immunology, University of Khartoum, Sudan
Ahmed M. Musa, Department of Clinical Pathology & Immunology, University of Khartoum, Sudan
Brima Y. Musa, Department of Clinical Pathology & Immunology, University of Khartoum, Sudan
Ahmed M. Elhassan, Department of Clinical Pathology & Immunology, University of Khartoum, Sudan


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Abstract

Background: Tuberculosis is a major health problem in developing countries. The distinction between tuberculous lymphadenitis, non-specific lymphadenitis and malignant lymph node enlargement has to be made at primary health care levels using easy, simple and cheap methods.

Objective: To develop a reliable clinical algorithm for primary care settings to triage cases ofnon-specific, tuberculous and malignant lymphadenopathies.

Methods: Calculation of the odd ratios (OR) of the chosen predictor variables was carried out using logistic regression. The numerical score values of the predictor variables were weighed against their respective OR. The performance of the score was evaluated by the ROC (ReceiverOperator Characteristic) curve.

Results: Four predictor variables; Mantoux reading, erythrocytes sedimentation rate (ESR),nocturnal fever and discharging sinuses correlated significantly with TB diagnosis and were included in the reduced model to establish score A. For score B, the reduced model included Mantoux reading, ESR, lymph-node size and lymph-node number as predictor variables for malignant lymph nodes. Score A ranged 0 to 12 and a cut-off point of 6 gave a best sensitivity and specificity of 91% and 90% respectively, whilst score B ranged -3 to 8 and a cut-off point of3 gave a best sensitivity and specificity of 83% and 76% respectively. The calculated area underthe ROC curve was 0.964 (95% CI, 0.949 – 0.980) and -0.856 (95% CI, 0.787 ‑ 0.925) for scores Aand B respectively, indicating good performance.

Conclusion: The developed algorithm can efficiently triage cases with tuberculous andmalignant lymphadenopathies for treatment or referral to specialised centres for furtherwork-up.


Keywords

Algorithm; Clinical; Lymphadenopathy; Neoplasm; Tuberculosis

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