Teena Cherian1, Bethany Hedt-Gauthier1,2, Theoneste Nkurunziza3, Kristin Sonderman2,4, Magdalena Anna Gruendl2,5, Edison Nihiwacu3, Bahati Ramadhan3, Erick Gaju6, Evrard Nahimana3, Caste Habiyakare7, Georges Ntakiyiruta8, Alexi Matousek9, Robert Riviello2,10, Fredrick Kateera3. 1. Department of Global Health and Social Medicine and Harvard Medical School, Boston, Massachusetts, USA. 2. Program in Global Surgery and Social Change, Harvard Medical School, Boston, Massachusetts, USA. 3. Partners in Health/Inshuti Mu Buzima, Kigali, Rwanda. 4. Department of Surgery and Brigham and Women's Hospital, Boston, Massachusetts, USA. 5. Department of Epidemiology, Technical University Munich, Munich, Germany. 6. Rwanda Ministry of Health, Kigali, Rwanda. 7. Rwanda Ministry of Health, Kirehe, Rwanda. 8. Rwanda Surgical Society, Kigali, Rwanda. 9. Heart and Lung Institute, Sacred Heart Medical Center, Spokane, Washington, USA. 10. Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Abstract
Background: We aimed to develop and validate a screening algorithm to assist community health workers (CHWs) in identifying surgical site infections (SSIs) after cesarean section (c-section) in rural Africa. Methods:Patients were adult women who underwent c-section at a Rwandan rural district hospital between March and October 2017. A CHW administered a nine-item clinical questionnaire 10 ± 3 days post-operatively. Independently, a general practitioner (GP) administered the same questionnaire and assessed SSI presence by physical examination. The GP's SSI diagnosis was used as the gold standard. Using a simplified Classification and Regression Tree analysis, we identified a subset of screening questions with maximum sensitivity for the GP and CHW and evaluated the subset's sensitivity and specificity in a validation dataset. Then, we compared the subset's results when implemented in the community by CHWs with health center-reported SSI. Results: Of the 596 women enrolled, 525 (88.1%) completed the clinical questionnaire. The combination of questions concerning fever, pain, and discolored drainage maximized sensitivity for both the GPs (sensitivity = 96.8%; specificity = 85.6%) and CHWs (sensitivity = 87.1%; specificity = 73.8%). In the validation dataset, this subset had sensitivity of 95.2% and specificity of 83.3% for the GP-administered questions and sensitivity of 76.2% and specificity of 81.4% for the CHW-administered questions. In the community screening, the overall percent agreement between CHW and health center diagnoses was 81.1% (95% confidence interval: 77.2%-84.6%). Conclusions: We identified a subset of questions that had good predictive features for SSI, but its sensitivity was lower when administered by CHWs in a clinical setting, and it performed poorly in the community. Methods to improve diagnostic ability, including training or telemedicine, must be explored.
RCT Entities:
Background: We aimed to develop and validate a screening algorithm to assist community health workers (CHWs) in identifying surgical site infections (SSIs) after cesarean section (c-section) in rural Africa. Methods:Patients were adult women who underwent c-section at a Rwandan rural district hospital between March and October 2017. A CHW administered a nine-item clinical questionnaire 10 ± 3 days post-operatively. Independently, a general practitioner (GP) administered the same questionnaire and assessed SSI presence by physical examination. The GP's SSI diagnosis was used as the gold standard. Using a simplified Classification and Regression Tree analysis, we identified a subset of screening questions with maximum sensitivity for the GP and CHW and evaluated the subset's sensitivity and specificity in a validation dataset. Then, we compared the subset's results when implemented in the community by CHWs with health center-reported SSI. Results: Of the 596 women enrolled, 525 (88.1%) completed the clinical questionnaire. The combination of questions concerning fever, pain, and discolored drainage maximized sensitivity for both the GPs (sensitivity = 96.8%; specificity = 85.6%) and CHWs (sensitivity = 87.1%; specificity = 73.8%). In the validation dataset, this subset had sensitivity of 95.2% and specificity of 83.3% for the GP-administered questions and sensitivity of 76.2% and specificity of 81.4% for the CHW-administered questions. In the community screening, the overall percent agreement between CHW and health center diagnoses was 81.1% (95% confidence interval: 77.2%-84.6%). Conclusions: We identified a subset of questions that had good predictive features for SSI, but its sensitivity was lower when administered by CHWs in a clinical setting, and it performed poorly in the community. Methods to improve diagnostic ability, including training or telemedicine, must be explored.
Entities:
Keywords:
Cesarean section; community health worker; rural sub-Saharan Africa; screening algorithm; surgical site infection
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