Olivier Marcy1,2, Laurence Borand3, Vibol Ung4,5, Philippe Msellati6, Mathurin Tejiokem7, Khanh Truong Huu8, Viet Do Chau9, Duong Ngoc Tran10, Francis Ateba-Ndongo11, Suzie Tetang-Ndiang12, Boubacar Nacro13, Bintou Sanogo13, Leakhena Neou14, Sophie Goyet3, Bunnet Dim3, Polidy Pean15, Catherine Quillet16, Isabelle Fournier17, Laureline Berteloot18, Guislaine Carcelain19, Sylvain Godreuil20, Stéphane Blanche21, Christophe Delacourt22. 1. Epidemiology and Public Health Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia; olivier.marcy@u-bordeaux.fr. 2. Centre INSERM U1219, Bordeaux Population Health, University of Bordeaux, Bordeaux, France. 3. Epidemiology and Public Health Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia. 4. Tuberculosis and HIV Department, National Pediatric Hospital, Phnom Penh, Cambodia. 5. University of Health Sciences, Phnom Penh, Cambodia. 6. UMI 233-U1175 TransVIHMI, IRD, Université de Montpellier, Montpellier, France. 7. Service d'Epidémiologie et de Santé Publique, Centre Pasteur du Cameroun, Réseau International des Instituts Pasteur, Yaounde, Cameroon. 8. Infectious Disease Department, Pediatric Hospital Nhi Dong 1, Ho Chi Minh City, Vietnam. 9. Infectious Disease Department, Pediatric Hospital Nhi Dong 2, Ho Chi Minh City, Vietnam. 10. Pediatric Department, Pham Ngoc Thach Hospital, Ho Chi Minh City, Vietnam. 11. Centre Mère et Enfant de la Fondation Chantal Biya, Yaounde, Cameroon. 12. Service de Pédiatrie, Centre Hospitalier d'Essos, Yaounde, Cameroon. 13. Service de Pédiatrie, Centre Hospitalier Universitaire Souro Sanou, Bobo Dioulasso, Burkina Faso. 14. Angkor Hospital for Children, Siem Reap, Cambodia. 15. Immunology Laboratory, Institut Pasteur du Cambodge, Phnom Penh, Cambodia. 16. ANRS Research Site, Pham Ngoc Thach Hospital, Ho Chi Minh City, Vietnam. 17. Inserm US19, Villejuif, France. 18. Service de Radiologie Pédiatrique. 19. Immunologie Biologique, Hôpital Robert Debré, Assistance Publique-Hôpitaux de Paris, Paris, France; and. 20. Département de Bactériologie-Virologie, Hôpital Arnaud de Villeneuve, Centre Hospitalier Régional Universitaire de Montpellier, Montpellier, France. 21. Unité d'Immunologie Hématologie Rhumatologie Pédiatrique, Hôpital Necker Enfants Malades and. 22. Service de Pneumologie et d'Allergologie Pédiatriques, and.
Abstract
BACKGROUND: Diagnosis of tuberculosis should be improved in children infected with HIV to reduce mortality. We developed prediction scores to guide antituberculosis treatment decision in HIV-infected children with suspected tuberculosis. METHODS: HIV-infected children with suspected tuberculosis enrolled in Burkina Faso, Cambodia, Cameroon, and Vietnam (ANRS 12229 PAANTHER 01 Study), underwent clinical assessment, chest radiography, Quantiferon Gold In-Tube (QFT), abdominal ultrasonography, and sample collection for microbiology, including Xpert MTB/RIF (Xpert). We developed 4 tuberculosis diagnostic models using logistic regression: (1) all predictors included, (2) QFT excluded, (3) ultrasonography excluded, and (4) QFT and ultrasonography excluded. We internally validated the models using resampling. We built a score on the basis of the model with the best area under the receiver operating characteristic curve and parsimony. RESULTS: A total of 438 children were enrolled in the study; 251 (57.3%) had tuberculosis, including 55 (12.6%) with culture- or Xpert-confirmed tuberculosis. The final 4 models included Xpert, fever lasting >2 weeks, unremitting cough, hemoptysis and weight loss in the past 4 weeks, contact with a patient with smear-positive tuberculosis, tachycardia, miliary tuberculosis, alveolar opacities, and lymph nodes on the chest radiograph, together with abdominal lymph nodes on the ultrasound and QFT results. The areas under the receiver operating characteristic curves were 0.866, 0.861, 0.850, and 0.846, for models 1, 2, 3, and 4, respectively. The score developed on model 2 had a sensitivity of 88.6% and a specificity of 61.2% for a tuberculosis diagnosis. CONCLUSIONS: Our score had a good diagnostic performance. Used in an algorithm, it should enable prompt treatment decision in children with suspected tuberculosis and a high mortality risk, thus contributing to significant public health benefits.
BACKGROUND: Diagnosis of tuberculosis should be improved in childreninfected with HIV to reduce mortality. We developed prediction scores to guide antituberculosis treatment decision in HIV-infectedchildren with suspected tuberculosis. METHODS:HIV-infectedchildren with suspected tuberculosis enrolled in Burkina Faso, Cambodia, Cameroon, and Vietnam (ANRS 12229 PAANTHER 01 Study), underwent clinical assessment, chest radiography, Quantiferon Gold In-Tube (QFT), abdominal ultrasonography, and sample collection for microbiology, including Xpert MTB/RIF (Xpert). We developed 4 tuberculosis diagnostic models using logistic regression: (1) all predictors included, (2) QFT excluded, (3) ultrasonography excluded, and (4) QFT and ultrasonography excluded. We internally validated the models using resampling. We built a score on the basis of the model with the best area under the receiver operating characteristic curve and parsimony. RESULTS: A total of 438 children were enrolled in the study; 251 (57.3%) had tuberculosis, including 55 (12.6%) with culture- or Xpert-confirmed tuberculosis. The final 4 models included Xpert, fever lasting >2 weeks, unremitting cough, hemoptysis and weight loss in the past 4 weeks, contact with a patient with smear-positive tuberculosis, tachycardia, miliary tuberculosis, alveolar opacities, and lymph nodes on the chest radiograph, together with abdominal lymph nodes on the ultrasound and QFT results. The areas under the receiver operating characteristic curves were 0.866, 0.861, 0.850, and 0.846, for models 1, 2, 3, and 4, respectively. The score developed on model 2 had a sensitivity of 88.6% and a specificity of 61.2% for a tuberculosis diagnosis. CONCLUSIONS: Our score had a good diagnostic performance. Used in an algorithm, it should enable prompt treatment decision in children with suspected tuberculosis and a high mortality risk, thus contributing to significant public health benefits.
Authors: Bryan J Vonasek; Kendra K Radtke; Paula Vaz; W Chris Buck; Chishala Chabala; Eric D McCollum; Olivier Marcy; Elizabeth Fitzgerald; Alexander Kondwani; Anthony J Garcia-Prats Journal: Expert Rev Respir Med Date: 2022-02-28 Impact factor: 4.300
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Authors: Alexander W Kay; Helena Rabie; Elizabeth Maleche-Obimbo; Moorine Penninah Sekadde; Mark F Cotton; Anna M Mandalakas Journal: Pathogens Date: 2021-12-29