Bethany Hedt-Gauthier1,2,3, Elizabeth Miranda4,5, Theoneste Nkurunziza6,7, Olivia Hughes8, Adeline A Boatin4,9, Erick Gaju10,11, Alexi Matousek12, Teena Cherian13, Robert Riviello13,4,14, Fredrick Kateera6. 1. Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Ave, Boston, MA, 02115, USA. Bethany_Hedt@hms.harvard.edu. 2. Program in Global Surgery and Social Change, Harvard Medical School, Boston, USA. Bethany_Hedt@hms.harvard.edu. 3. Department of Biostatistics, Harvard Chan School, Boston, USA. Bethany_Hedt@hms.harvard.edu. 4. Program in Global Surgery and Social Change, Harvard Medical School, Boston, USA. 5. Division of Vascular Surgery, University of Southern California, Los Angeles, CA, USA. 6. Partners In Health -Rwanda, Kigali, Rwanda. 7. Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany. 8. Department of Biostatistics, Harvard Chan School, Boston, USA. 9. Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, USA. 10. Rwanda Ministry of Health, Kigali, Rwanda. 11. The Global Fund, Geneva, Switzerland. 12. Northwest Heart and Lung Surgical Associates, Providence Sacred Heart Medical Center, Spokane, WA, USA. 13. Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Ave, Boston, MA, 02115, USA. 14. Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, USA.
Abstract
BACKGROUND: In rural Africa where access to medical personnel is limited, telemedicine can be leveraged to empower community health workers (CHWs) to support effective postpartum home-based care after cesarean section (c-section). As a first step toward telemedicine, we assessed the sensitivity, specificity, and interrater reliability of image-based diagnosis of surgical site infections (SSIs) among women delivering via c-section at a rural Rwandan Hospital. METHODS: Women ≥18 years who underwent c-section from March to October 2017 at Kirehe District Hospital (KDH) were enrolled. On postoperative day 10 at KDH, participants underwent a physical examination by a general practitioner, who provided a diagnosis of SSI or no SSI. Trained CHWs photographed patients' incisions and the collected images were shown to six physicians, who upon review, assigned one of the following diagnoses to each image: definite SSI, suspected SSI, suspected no SSI, and definite no SSI, which were compared with the diagnoses based on physical exam. We report the sensitivity and specificity and assessed reviewer agreement using Gwet's AC1. RESULTS: 569 images were included, with 61 women (10.7%) diagnosed with an SSI. Of the 3414 image-reviews, 49 (1.4%) could not be assigned diagnoses due to image quality. The median sensitivity and specificity were 0.83 and 0.69, respectively. The Gwet's AC1 estimate for binary classification was 0.46. CONCLUSIONS: We demonstrate decent accuracy but only moderate consistency for photograph-based SSI diagnosis. Strategies to improve overall agreement include providing clinical information to accompany photographs, providing a baseline photograph for comparison, and implementing photograph-taking processes aimed at improving image quality.
BACKGROUND: In rural Africa where access to medical personnel is limited, telemedicine can be leveraged to empower community health workers (CHWs) to support effective postpartum home-based care after cesarean section (c-section). As a first step toward telemedicine, we assessed the sensitivity, specificity, and interrater reliability of image-based diagnosis of surgical site infections (SSIs) among women delivering via c-section at a rural Rwandan Hospital. METHODS: Women ≥18 years who underwent c-section from March to October 2017 at Kirehe District Hospital (KDH) were enrolled. On postoperative day 10 at KDH, participants underwent a physical examination by a general practitioner, who provided a diagnosis of SSI or no SSI. Trained CHWs photographed patients' incisions and the collected images were shown to six physicians, who upon review, assigned one of the following diagnoses to each image: definite SSI, suspected SSI, suspected no SSI, and definite no SSI, which were compared with the diagnoses based on physical exam. We report the sensitivity and specificity and assessed reviewer agreement using Gwet's AC1. RESULTS: 569 images were included, with 61 women (10.7%) diagnosed with an SSI. Of the 3414 image-reviews, 49 (1.4%) could not be assigned diagnoses due to image quality. The median sensitivity and specificity were 0.83 and 0.69, respectively. The Gwet's AC1 estimate for binary classification was 0.46. CONCLUSIONS: We demonstrate decent accuracy but only moderate consistency for photograph-based SSI diagnosis. Strategies to improve overall agreement include providing clinical information to accompany photographs, providing a baseline photograph for comparison, and implementing photograph-taking processes aimed at improving image quality.
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Authors: Theoneste Nkurunziza; Wendy Williams; Fredrick Kateera; Robert Riviello; Anne Niyigena; Elizabeth Miranda; Laban Bikorimana; Jonathan Nkurunziza; Lotta Velin; Andrea S Goodman; Alex Matousek; Stefanie J Klug; Erick Gaju; Bethany L Hedt-Gauthier Journal: BMJ Glob Health Date: 2022-07