Conference Material > Slide Presentation
Malou N, Al Asmar M, Fakhri RM, Badaro N, Kanapathipillai R, et al.
MSF Scientific Days International 2021: Innovation. 2021 May 20
Conference Material > Slide Presentation
Rapoud D, Cramer E, Al Asmar M, Sagara F, Ndiaye B, et al.
MSF Scientific Day International 2024. 2024 May 16; DOI:10.57740/2acXDPpuix
Conference Material > Abstract
Rapoud D, Cramer E, Al Asmar M, Sagara F, Ndiaye B, et al.
MSF Scientific Day International 2024. 2024 May 16; DOI:10.57740/rxwuURR8
INTRODUCTION
Antimicrobial resistance (AMR) is a major threat to public health and could cause 10 million deaths per year by 2050. Access to high-quality diagnostic tests is a key intervention to tackle AMR, leading to better patient care, provision of data for global surveillance, and more rational use of antibiotics. Despite technological advances, antimicrobial susceptibility testing (AST) interpretation is complex and requires expert clinical microbiologists, which are lacking in low- and middle-income countries (LMIC). To fill the gap, The Médecins Sans Frontières (MSF) Foundation developed Antibiogo, a smartphone-based application to support laboratory technicians with AST interpretation. We aimed to assess the clinical performance of Antibiogo in intended use settings as per European regulations for in-vitro diagnostic medical devices.
METHODS
Antibiogo combines image processing, machine learning, and expert system technologies for the provision of final results (S/I/R: Susceptible, Intermediate, or Resistant). In 2022, we assessed the clinical performance of Antibiogo according to European regulations in three microbiology laboratories in Jordan (MSF Reconstructive Surgery Hospital, Amman), Mali (MSF Paediatric Hospital, Koutiala), and Senegal (Pasteur Institute, Dakar). In each site, clinical AST performed for routine purposes was processed in parallel with Antibiogo. AST pictures and inhibition zone diameter values measured with Antibiogo were interpreted by an expert microbiologist who was masked to Antibiogo interpretation. We calculated S/I/R category agreement between the microbiologist and Antibiogo, as well as minor (mD), major (MD) and very major discrepancies (VMD).
RESULTS
We included 378 fresh isolates in the study, representing 11 different pathogens. The overall category agreement was 88.8% (95% CI 87.9–89.7), ranging per pathogen from 67.1% (63.2–70.8) (for Pseudomonas aeruginosa) to 98.1% (94.4–99.6) (for Haemophilus influenzae), with 10.2% (9.4–11.1) mD, 1.6% MD (1.2–2.3), and 0.25% VMD (0.08–0.59). From these results, Antibiogo was validated for 11 WHO priority pathogens. From an operational need identified, to proof of concept and evaluation, it became the first MSF CE-marked in-vitro diagnostic (IVD) test in May 2022. As of January 2024, it has been implemented in five MSF laboratories (in Central African Republic, Democratic Republic of the Congo, Jordan, Mali, and Yemen), and in public laboratories in Mali upon request from the Ministry of Health.
CONCLUSION
It will take 400 years to address the shortfall of microbiologists in LMIC at the present rate of training. In the meantime, technology can help fill the gap. In parallel to deployment of Antibiogo in additional countries and regions, developments are ongoing, and an improved version of the app will be released in 2024.
Antimicrobial resistance (AMR) is a major threat to public health and could cause 10 million deaths per year by 2050. Access to high-quality diagnostic tests is a key intervention to tackle AMR, leading to better patient care, provision of data for global surveillance, and more rational use of antibiotics. Despite technological advances, antimicrobial susceptibility testing (AST) interpretation is complex and requires expert clinical microbiologists, which are lacking in low- and middle-income countries (LMIC). To fill the gap, The Médecins Sans Frontières (MSF) Foundation developed Antibiogo, a smartphone-based application to support laboratory technicians with AST interpretation. We aimed to assess the clinical performance of Antibiogo in intended use settings as per European regulations for in-vitro diagnostic medical devices.
METHODS
Antibiogo combines image processing, machine learning, and expert system technologies for the provision of final results (S/I/R: Susceptible, Intermediate, or Resistant). In 2022, we assessed the clinical performance of Antibiogo according to European regulations in three microbiology laboratories in Jordan (MSF Reconstructive Surgery Hospital, Amman), Mali (MSF Paediatric Hospital, Koutiala), and Senegal (Pasteur Institute, Dakar). In each site, clinical AST performed for routine purposes was processed in parallel with Antibiogo. AST pictures and inhibition zone diameter values measured with Antibiogo were interpreted by an expert microbiologist who was masked to Antibiogo interpretation. We calculated S/I/R category agreement between the microbiologist and Antibiogo, as well as minor (mD), major (MD) and very major discrepancies (VMD).
RESULTS
We included 378 fresh isolates in the study, representing 11 different pathogens. The overall category agreement was 88.8% (95% CI 87.9–89.7), ranging per pathogen from 67.1% (63.2–70.8) (for Pseudomonas aeruginosa) to 98.1% (94.4–99.6) (for Haemophilus influenzae), with 10.2% (9.4–11.1) mD, 1.6% MD (1.2–2.3), and 0.25% VMD (0.08–0.59). From these results, Antibiogo was validated for 11 WHO priority pathogens. From an operational need identified, to proof of concept and evaluation, it became the first MSF CE-marked in-vitro diagnostic (IVD) test in May 2022. As of January 2024, it has been implemented in five MSF laboratories (in Central African Republic, Democratic Republic of the Congo, Jordan, Mali, and Yemen), and in public laboratories in Mali upon request from the Ministry of Health.
CONCLUSION
It will take 400 years to address the shortfall of microbiologists in LMIC at the present rate of training. In the meantime, technology can help fill the gap. In parallel to deployment of Antibiogo in additional countries and regions, developments are ongoing, and an improved version of the app will be released in 2024.
Conference Material > Abstract
Malou N, Al Asmar M, Fakhri RM, Badaro N, Kanapathipillai R, et al.
MSF Scientific Days International 2021: Innovation. 2021 May 20
INTRODUCTION
Timely and accurate identification of microorganisms and assessment of antimicrobial susceptibility in clinical specimens help clinicians in selecting the most appropriate treatment for their patients. In low-to-middle income countries (LMIC), bacteriological testing is generally not performed routinely due to technological challenges. This contributes to treatment delays and consequent clinical complications, extended hospital stays, and the global spread of multidrug resistance (MDR). The MSF Foundation has developed Antibiogo, an offline smartphone-based application that allows non-microbiologists to carry out antimicrobial susceptibility testing (AST) and interpret the results. We are presenting the preliminary results of the Antibiogo performance evaluation.
METHODS
Antibiogo comprises several components: the Image Analysis Program (IAP) that detects and measures inhibition zone diameters (IZDs); the Expert System (ES) that adjusts AST results based on the application of expert rules and identifies resistance mechanisms; and the Selective Reporting Program. For the evaluation of the IAP, we used collection isolates (n=8) and compared the automatic measurement of IZDs using Antibiogo with the readings made by eight laboratory technicians who inspected the plates manually. For evaluation of the ES, we used Antibiogo to assess 60 pathogens isolated from bone and tissues from patients admitted to MSF’s Reconstructive Surgical Project in Amman, Jordan, between February and September 2020. In parallel, pictures of AST were shared with an external clinical microbiologist who performed an independent and blinded interpretation. Results of the two parallel interpretations were compared and the discordances categorised (minor, major, very major).
RESULTS
Evaluation of the IAP showed good concordance of measurements between technicians and Antibiogo (Krippendorff’s alpha value of 0.957, 95% confidence interval [CI] 0.94-0.97; p<0.001). These results indicate excellent inter-rater agreement between human raters and the Antibiogo platform for these pathogen-antibiotic pairs. For evaluation of the ES, 509 paired samples were read in parallel, and agreement of the measured diameters was excellent (R2=0.95). The ES correctly classified 474 (95.2%) of 498 interpretable samples (95% CI 92.9- 97.4), corresponding to a Krippendorff’s alpha value of 90.6% (95% CI 87%-94%). This indicates excellent to near-perfect agreement. Further investigation of the samples showing non-agreement is underway.
CONCLUSIONS
Preliminary results suggest that Antibiogo is a very promising tool that can be used for the interpretation of antibiograms. This could improve access to microbiology diagnostic tests and the rational use of antibiotics in LMIC. The application currently undergoing further evaluation using a diverse set of pathogens isolated from multiple sites.
ETHICS
This study was approved by the MSF Ethics Review Board and the Hospital Director of Al Mowasah Hospital, Amman, Jordan.
Timely and accurate identification of microorganisms and assessment of antimicrobial susceptibility in clinical specimens help clinicians in selecting the most appropriate treatment for their patients. In low-to-middle income countries (LMIC), bacteriological testing is generally not performed routinely due to technological challenges. This contributes to treatment delays and consequent clinical complications, extended hospital stays, and the global spread of multidrug resistance (MDR). The MSF Foundation has developed Antibiogo, an offline smartphone-based application that allows non-microbiologists to carry out antimicrobial susceptibility testing (AST) and interpret the results. We are presenting the preliminary results of the Antibiogo performance evaluation.
METHODS
Antibiogo comprises several components: the Image Analysis Program (IAP) that detects and measures inhibition zone diameters (IZDs); the Expert System (ES) that adjusts AST results based on the application of expert rules and identifies resistance mechanisms; and the Selective Reporting Program. For the evaluation of the IAP, we used collection isolates (n=8) and compared the automatic measurement of IZDs using Antibiogo with the readings made by eight laboratory technicians who inspected the plates manually. For evaluation of the ES, we used Antibiogo to assess 60 pathogens isolated from bone and tissues from patients admitted to MSF’s Reconstructive Surgical Project in Amman, Jordan, between February and September 2020. In parallel, pictures of AST were shared with an external clinical microbiologist who performed an independent and blinded interpretation. Results of the two parallel interpretations were compared and the discordances categorised (minor, major, very major).
RESULTS
Evaluation of the IAP showed good concordance of measurements between technicians and Antibiogo (Krippendorff’s alpha value of 0.957, 95% confidence interval [CI] 0.94-0.97; p<0.001). These results indicate excellent inter-rater agreement between human raters and the Antibiogo platform for these pathogen-antibiotic pairs. For evaluation of the ES, 509 paired samples were read in parallel, and agreement of the measured diameters was excellent (R2=0.95). The ES correctly classified 474 (95.2%) of 498 interpretable samples (95% CI 92.9- 97.4), corresponding to a Krippendorff’s alpha value of 90.6% (95% CI 87%-94%). This indicates excellent to near-perfect agreement. Further investigation of the samples showing non-agreement is underway.
CONCLUSIONS
Preliminary results suggest that Antibiogo is a very promising tool that can be used for the interpretation of antibiograms. This could improve access to microbiology diagnostic tests and the rational use of antibiotics in LMIC. The application currently undergoing further evaluation using a diverse set of pathogens isolated from multiple sites.
ETHICS
This study was approved by the MSF Ethics Review Board and the Hospital Director of Al Mowasah Hospital, Amman, Jordan.
Journal Article > ResearchFull Text
BMC Public Health. 2014 June 28; Volume 14 (Issue 1); DOI:10.1186/1471-2458-14-658
Lover AA, Buchy P, Rachline A, Moniboth D, Huy R, et al.
BMC Public Health. 2014 June 28; Volume 14 (Issue 1); DOI:10.1186/1471-2458-14-658
Background: Dengue is a major contributor to morbidity in children aged twelve and below throughout Cambodia; the 2012 epidemic season was the most severe in the country since 2007, with more than 42,000 reported (suspect or confirmed) cases.
Methods: We report basic epidemiological characteristics in a series of 701 patients at the National Paediatric Hospital in Cambodia, recruited during a prospective clinical study (2011-2012). To more fully explore this cohort, we examined climatic factors using multivariate negative binomial models and spatial clustering of cases using spatial scan statistics to place the clinical study within a larger epidemiological framework.
Results: We identify statistically significant spatial clusters at the urban village scale, and find that the key climatic predictors of increasing cases are weekly minimum temperature, median relative humidity, but find a negative association with rainfall maximum, all at lag times of 1-6 weeks, with significant effects extending to 10 weeks.
Conclusions: Our results identify clustering of infections at the neighbourhood scale, suggesting points for targeted interventions, and we find that the complex interactions of vectors and climatic conditions in this setting may be best captured by rising minimum temperature, and median (as opposed to mean) relative humidity, with complex and limited effects from rainfall. These results suggest that real-time cluster detection during epidemics should be considered in Cambodia, and that improvements in weather data reporting could benefit national control programs by allow greater prioritization of limited health resources to both vulnerable populations and time periods of greatest risk. Finally, these results add to the increasing body of knowledge suggesting complex interactions between climate and dengue cases that require further targeted research.
Methods: We report basic epidemiological characteristics in a series of 701 patients at the National Paediatric Hospital in Cambodia, recruited during a prospective clinical study (2011-2012). To more fully explore this cohort, we examined climatic factors using multivariate negative binomial models and spatial clustering of cases using spatial scan statistics to place the clinical study within a larger epidemiological framework.
Results: We identify statistically significant spatial clusters at the urban village scale, and find that the key climatic predictors of increasing cases are weekly minimum temperature, median relative humidity, but find a negative association with rainfall maximum, all at lag times of 1-6 weeks, with significant effects extending to 10 weeks.
Conclusions: Our results identify clustering of infections at the neighbourhood scale, suggesting points for targeted interventions, and we find that the complex interactions of vectors and climatic conditions in this setting may be best captured by rising minimum temperature, and median (as opposed to mean) relative humidity, with complex and limited effects from rainfall. These results suggest that real-time cluster detection during epidemics should be considered in Cambodia, and that improvements in weather data reporting could benefit national control programs by allow greater prioritization of limited health resources to both vulnerable populations and time periods of greatest risk. Finally, these results add to the increasing body of knowledge suggesting complex interactions between climate and dengue cases that require further targeted research.