Journal Article > ResearchFull Text
Health Sci Rep. 2023 February 17; Volume 6 (Issue 2); e1119.; DOI:doi.org/10.1002/hsr2.1119
Swe TM, Johnson DC, Mar HT, Thit P, Homan T, et al.
Health Sci Rep. 2023 February 17; Volume 6 (Issue 2); e1119.; DOI:doi.org/10.1002/hsr2.1119
BACKGROUND AND AIMS
In Myanmar, public sector treatment programs for hepatitis C virus (HCV) infection were nonexistent until June 2017. WHO highlights the importance of simplification of HCV service delivery through task-shifting among health workers and decentralization to the primary health care level. Between November 2016 and November 2017, a study was conducted to describe the epidemiological data and real-world outcomes of treating HIV/HCV coinfected patients with generic direct acting antiviral (DAA) based regimens in the three HIV clinics run by nonspecialist medical doctors in Myanmar.
METHODS
HCV co-infection among people living with HIV (PLHIV) from two clinics in Yangon city and one clinic in Dawei city was screened by rapid diagnostic tests and confirmed by testing for viral RNA. Nonspecialist medical doctors prescribed sofosbuvir and daclatasvir based regimens (with or without ribavirin) for 12 or 24 weeks based on the HCV genotype and liver fibrosis status. Sustained virologic response at 12 weeks after treatment (SVR12) was assessed to determine cure.
RESULTS
About 6.5% (1417/21,777) of PLHIV were co-infected with HCV. Of 864 patients enrolled in the study, 50.8% reported history of substance use, 27% history of invasive medical procedures and 25.6% history of incarceration. Data on treatment outcomes were collected from 267 patients of which 257 (96.3%) achieved SVR12, 7 (2.6%) failed treatment, 2 (0.7%) died and 1 (0.4%) became loss to follow-up.
CONCLUSION
The study results support the integration of hepatitis C diagnosis and treatment with DAA-based regimens into existing HIV clinics run by nonspecialist medical doctors in a resource-limited setting. Epidemiological data on HIV/HCV co-infection call for comprehensive HCV care services among key populations like drug users and prisoners in Yangon and Dawei.
In Myanmar, public sector treatment programs for hepatitis C virus (HCV) infection were nonexistent until June 2017. WHO highlights the importance of simplification of HCV service delivery through task-shifting among health workers and decentralization to the primary health care level. Between November 2016 and November 2017, a study was conducted to describe the epidemiological data and real-world outcomes of treating HIV/HCV coinfected patients with generic direct acting antiviral (DAA) based regimens in the three HIV clinics run by nonspecialist medical doctors in Myanmar.
METHODS
HCV co-infection among people living with HIV (PLHIV) from two clinics in Yangon city and one clinic in Dawei city was screened by rapid diagnostic tests and confirmed by testing for viral RNA. Nonspecialist medical doctors prescribed sofosbuvir and daclatasvir based regimens (with or without ribavirin) for 12 or 24 weeks based on the HCV genotype and liver fibrosis status. Sustained virologic response at 12 weeks after treatment (SVR12) was assessed to determine cure.
RESULTS
About 6.5% (1417/21,777) of PLHIV were co-infected with HCV. Of 864 patients enrolled in the study, 50.8% reported history of substance use, 27% history of invasive medical procedures and 25.6% history of incarceration. Data on treatment outcomes were collected from 267 patients of which 257 (96.3%) achieved SVR12, 7 (2.6%) failed treatment, 2 (0.7%) died and 1 (0.4%) became loss to follow-up.
CONCLUSION
The study results support the integration of hepatitis C diagnosis and treatment with DAA-based regimens into existing HIV clinics run by nonspecialist medical doctors in a resource-limited setting. Epidemiological data on HIV/HCV co-infection call for comprehensive HCV care services among key populations like drug users and prisoners in Yangon and Dawei.
Journal Article > ResearchFull Text
Health Sci Rep. 2023 March 30; Volume 6 (Issue 4); e1165.; DOI:10.1002/hsr2.1165
Loarec A, Gutierrez AG, Muvale G, Couto AM, Nguyen AP, et al.
Health Sci Rep. 2023 March 30; Volume 6 (Issue 4); e1165.; DOI:10.1002/hsr2.1165
BACKGROUND AND AIMS
Hepatitis C (HCV) programs face challenges, especially linked to key populations to achieve World Health Organization (WHO) goals of eliminating hepatitis. Médecins Sans Frontières and Mozambique's Ministry of Health first implemented HCV treatment in Maputo, in 2016 and harm reduction activities in 2017.
METHODS
We retrospectively analyzed routine data of patients enrolled between December 2016 and July 2021. Genotyping was systematically requested up to 2018 and subsequently in cases of treatment failure. Sustainable virological response was assessed 12 weeks after the end of treatment by sofosbuvir-daclatasvir or sofosbuvir-velpatasvir.
RESULTS
Two hundred and two patients were enrolled, with 159 (78.71%) males (median age: 41 years [interquartile range (IQR): 37.10, 47.00]). Risk factors included drug use (142/202; 70.29%). One hundred and eleven genotyping results indicated genotype 1 predominant (87/111; 78.37%). Sixteen patients presented genotype 4, with various subtypes. The people who used drugs and HIV coinfected patients were found more likely to present a genotype 1. Intention-to-treat analysis showed 68.99% (89/129) cure rate among the patients initiated and per-protocol analysis, 88.12% (89/101) cure rate. Nineteen patients received treatment integrated with opioid substitution therapy, with a 100% cure rate versus 59.37% (38/64) for initiated ones without substitution therapy (p < 0.001). Among the resistance testing performed, NS5A resistance-associated substitutions were found in seven patients among the nine tested patients and NS5B ones in one patient.
CONCLUSION
We found varied genotypes, including some identified as difficult-to-treat subtypes. People who used drugs were more likely to present genotype 1. In addition, opioid substitution therapy was key for these patients to achieve cure. Access to second-generation direct-acting antivirals (DAAs) and integration of HCV care with harm reduction are crucial to program effectiveness.
Hepatitis C (HCV) programs face challenges, especially linked to key populations to achieve World Health Organization (WHO) goals of eliminating hepatitis. Médecins Sans Frontières and Mozambique's Ministry of Health first implemented HCV treatment in Maputo, in 2016 and harm reduction activities in 2017.
METHODS
We retrospectively analyzed routine data of patients enrolled between December 2016 and July 2021. Genotyping was systematically requested up to 2018 and subsequently in cases of treatment failure. Sustainable virological response was assessed 12 weeks after the end of treatment by sofosbuvir-daclatasvir or sofosbuvir-velpatasvir.
RESULTS
Two hundred and two patients were enrolled, with 159 (78.71%) males (median age: 41 years [interquartile range (IQR): 37.10, 47.00]). Risk factors included drug use (142/202; 70.29%). One hundred and eleven genotyping results indicated genotype 1 predominant (87/111; 78.37%). Sixteen patients presented genotype 4, with various subtypes. The people who used drugs and HIV coinfected patients were found more likely to present a genotype 1. Intention-to-treat analysis showed 68.99% (89/129) cure rate among the patients initiated and per-protocol analysis, 88.12% (89/101) cure rate. Nineteen patients received treatment integrated with opioid substitution therapy, with a 100% cure rate versus 59.37% (38/64) for initiated ones without substitution therapy (p < 0.001). Among the resistance testing performed, NS5A resistance-associated substitutions were found in seven patients among the nine tested patients and NS5B ones in one patient.
CONCLUSION
We found varied genotypes, including some identified as difficult-to-treat subtypes. People who used drugs were more likely to present genotype 1. In addition, opioid substitution therapy was key for these patients to achieve cure. Access to second-generation direct-acting antivirals (DAAs) and integration of HCV care with harm reduction are crucial to program effectiveness.
Journal Article > ReviewFull Text
Health Sci Rep. 2024 January 4; Volume 7 (Issue 1); e1794.; DOI:10.1002/hsr2.1794
Oduoye MO, Fatima E, Muzammil MA, Dave T, Irfan H, et al.
Health Sci Rep. 2024 January 4; Volume 7 (Issue 1); e1794.; DOI:10.1002/hsr2.1794
BACKGROUND AND AIMS
Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low‐ and middle‐income countries (LMICs).
METHODS
In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision‐making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.
RESULTS
In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource‐constrained LMICs.
CONCLUSION
While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data‐sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.
Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low‐ and middle‐income countries (LMICs).
METHODS
In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision‐making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.
RESULTS
In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource‐constrained LMICs.
CONCLUSION
While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data‐sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.