Pub Date : 2024-04-01Epub Date: 2023-09-25DOI: 10.4103/picr.picr_109_23
Urvashi Gupta, Ashwin Kamath, Priyanka Kamath
Aim: Studies show the presence of a mismatch between drug research and disease burden. A study conducted in the European Union found that new drug development was restricted to certain diseases. A study of biosimilar approvals in India found that 87% of drugs were for treating noncommunicable diseases. This study aimed to determine the new drugs approved in India from 2017 to 2021 and the top ten causes of morbidity and mortality and detect the presence of any discordance between these.
Methods: A descriptive study was conducted using data on new drug approvals accessed from the Central Drugs Standard Control Organization website. The top ten causes of mortality and morbidity in India from 2015 to 2019 were identified from the Global Burden of Diseases database. Descriptive statistics were used to compare the drug approvals and the leading diseases.
Results: One hundred twenty-six drugs were approved during the study period. Antineoplastic drugs constituted 19.84% of the approvals, antimicrobials 18.25%, and cardiovascular drugs 9.52%. Ischemic heart disease and chronic obstructive pulmonary disease were the two leading causes of morbidity and mortality. Diarrheal diseases, lower respiratory tract infection, and drug-susceptible tuberculosis were among the top ten causes. Ten antibacterials, including four antitubercular drugs, were approved during this period. Two drugs were approved for rare diseases.
Conclusion: Our study showed that the drugs approved were largely in line with the prevalent disease burden, and there was no significant discordance observed. Some diseases, such as ischemic stroke/intracranial hemorrhage, require further efforts in bringing forth newer pharmacotherapy options.
{"title":"A descriptive study of new drug approvals during 2017-2021 and disease morbidity and mortality patterns in India.","authors":"Urvashi Gupta, Ashwin Kamath, Priyanka Kamath","doi":"10.4103/picr.picr_109_23","DOIUrl":"10.4103/picr.picr_109_23","url":null,"abstract":"<p><strong>Aim: </strong>Studies show the presence of a mismatch between drug research and disease burden. A study conducted in the European Union found that new drug development was restricted to certain diseases. A study of biosimilar approvals in India found that 87% of drugs were for treating noncommunicable diseases. This study aimed to determine the new drugs approved in India from 2017 to 2021 and the top ten causes of morbidity and mortality and detect the presence of any discordance between these.</p><p><strong>Methods: </strong>A descriptive study was conducted using data on new drug approvals accessed from the Central Drugs Standard Control Organization website. The top ten causes of mortality and morbidity in India from 2015 to 2019 were identified from the Global Burden of Diseases database. Descriptive statistics were used to compare the drug approvals and the leading diseases.</p><p><strong>Results: </strong>One hundred twenty-six drugs were approved during the study period. Antineoplastic drugs constituted 19.84% of the approvals, antimicrobials 18.25%, and cardiovascular drugs 9.52%. Ischemic heart disease and chronic obstructive pulmonary disease were the two leading causes of morbidity and mortality. Diarrheal diseases, lower respiratory tract infection, and drug-susceptible tuberculosis were among the top ten causes. Ten antibacterials, including four antitubercular drugs, were approved during this period. Two drugs were approved for rare diseases.</p><p><strong>Conclusion: </strong>Our study showed that the drugs approved were largely in line with the prevalent disease burden, and there was no significant discordance observed. Some diseases, such as ischemic stroke/intracranial hemorrhage, require further efforts in bringing forth newer pharmacotherapy options.</p>","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"15 2","pages":"66-72"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11101002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.4103/picr.picr_160_23
Khalifah Abdulwahid, Nur Aizati Athirah Daud, Y. Al-Worafi, Mohamed Azmi Ahmad Hassali
{"title":"Impact of education on knowledge and attitude related to pharmacovigilance and reporting of adverse drug reactions among community pharmacists in Yemen: A pre- and postinterventional study","authors":"Khalifah Abdulwahid, Nur Aizati Athirah Daud, Y. Al-Worafi, Mohamed Azmi Ahmad Hassali","doi":"10.4103/picr.picr_160_23","DOIUrl":"https://doi.org/10.4103/picr.picr_160_23","url":null,"abstract":"","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"94 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140771067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.4103/picr.picr_290_23
Mira Kirankumar Desai
Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating “needles from haystack,” is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects – people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.
{"title":"Artificial intelligence in pharmacovigilance – Opportunities and challenges","authors":"Mira Kirankumar Desai","doi":"10.4103/picr.picr_290_23","DOIUrl":"https://doi.org/10.4103/picr.picr_290_23","url":null,"abstract":"\u0000 Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating “needles from haystack,” is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects – people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"36 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.4103/picr.picr_226_23
S. Shivananda, V. Doddawad, C. S. Vidya, J. Chandrakala
Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generating hypotheses, identifying literature, and suggesting methods for data collection and analysis. However, the use of AI in research proposal writing raises important bioethical implications, including the unintentional propagation of bias and questions about the role of human expertise and judgment in the research process. This paper explores the ethical implications of using AI in research proposal writing and proposes guidelines for the responsible and ethical use of AI in this context. The paper will review the potential benefits and challenges associated with using AI in research proposal writing, discuss the role of human expertise and judgment, and propose guidelines for promoting transparency and accountability in developing and using AI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will require ongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency, accountability, and ethical principles.
{"title":"Exploring the bioethical implications of using artificial intelligence in writing research proposals","authors":"S. Shivananda, V. Doddawad, C. S. Vidya, J. Chandrakala","doi":"10.4103/picr.picr_226_23","DOIUrl":"https://doi.org/10.4103/picr.picr_226_23","url":null,"abstract":"\u0000 Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generating hypotheses, identifying literature, and suggesting methods for data collection and analysis. However, the use of AI in research proposal writing raises important bioethical implications, including the unintentional propagation of bias and questions about the role of human expertise and judgment in the research process. This paper explores the ethical implications of using AI in research proposal writing and proposes guidelines for the responsible and ethical use of AI in this context. The paper will review the potential benefits and challenges associated with using AI in research proposal writing, discuss the role of human expertise and judgment, and propose guidelines for promoting transparency and accountability in developing and using AI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will require ongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency, accountability, and ethical principles.","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"38 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.4103/picr.picr_298_23
Philip Mathew, S. Vargese, Litha Mary Mathew, Alice David, J. Saji, Ann Mariam Varghese
Injudicious usage of antibiotics has led to the emergence of antibiotic resistance which is a major health-care problem in developing countries such as India. Our aim was to show how antibiotic therapy based on serial procalcitonin (PCT) assay can help in antibiotic de-escalation in septic patients. A pre–post interventional study was conducted among 300 septic patients admitted to an intensive care unit (ICU). All septic patients admitted 2 months before and 2 months after the introduction of monitoring of PCT were included and they were divided into Group P (with PCT monitoring) and Group C (without PCT monitoring). The proportion of patients for whom antimicrobials were de-escalated, the average time taken to de-escalate antimicrobials, and the average duration of ICU stay were compared. Proportions and averages with standard deviations were calculated to describe the data. A test of proportions was done to compare the proportion de-escalated and a Student’s t-test was done to compare the average duration of antibiotic therapy. The proportion of patients in whom de-escalation of antimicrobials was done was 125 (83.33%) in Group P as compared to 92 (61.33%) in Group C. The time taken to de-escalate was 3.04 ± 0.83 days (95% confidence interval [CI] 2.89–3.18) in Group P compared to 4.7 ± 1.4 days (CI 4.41–4.98) in Group C. The duration of ICU stay was also less in Group P - 3.08 ± 0.91 days (CI 3.08–3.38) as compared to Group C - 5.16 ± 2.17 days (4.80–5.51). Serial PCT assay-based antimicrobial therapy helped to wean patients with sepsis off antimicrobials earlier thus reducing the duration of ICU stay.
抗生素的滥用导致了抗生素耐药性的出现，这是印度等发展中国家的一个主要医疗保健问题。我们的目的是说明基于系列降钙素原 (PCT) 检测的抗生素疗法如何帮助脓毒症患者降低抗生素耐药性。 我们在重症监护室（ICU）收治的 300 名脓毒症患者中开展了一项干预前-干预后研究。所有在引入 PCT 监测前 2 个月和引入 PCT 监测后 2 个月入院的脓毒症患者都被纳入其中，并被分为 P 组（有 PCT 监测）和 C 组（无 PCT 监测）。比较了不再使用抗菌药物的患者比例、不再使用抗菌药物所需的平均时间以及重症监护病房的平均住院时间。通过计算比例、平均值和标准差来描述数据。采用比例检验比较停用抗菌药物的比例，采用学生 t 检验比较抗生素治疗的平均持续时间。 P组患者中使用抗菌药物的比例为125人（83.33%），C组为92人（61.33%）。P 组的重症监护室住院时间为 3.08 ± 0.91 天（CI 3.08-3.38），而 C 组为 5.16 ± 2.17 天（4.80-5.51）。 基于 PCT 检测的系列抗菌疗法有助于脓毒症患者尽早停用抗菌药物，从而缩短重症监护室的住院时间。
{"title":"Procalcitonin-guided antimicrobial stewardship in critically ill patients with sepsis: A pre– post interventional study","authors":"Philip Mathew, S. Vargese, Litha Mary Mathew, Alice David, J. Saji, Ann Mariam Varghese","doi":"10.4103/picr.picr_298_23","DOIUrl":"https://doi.org/10.4103/picr.picr_298_23","url":null,"abstract":"\u0000 \u0000 \u0000 Injudicious usage of antibiotics has led to the emergence of antibiotic resistance which is a major health-care problem in developing countries such as India. Our aim was to show how antibiotic therapy based on serial procalcitonin (PCT) assay can help in antibiotic de-escalation in septic patients.\u0000 \u0000 \u0000 \u0000 A pre–post interventional study was conducted among 300 septic patients admitted to an intensive care unit (ICU). All septic patients admitted 2 months before and 2 months after the introduction of monitoring of PCT were included and they were divided into Group P (with PCT monitoring) and Group C (without PCT monitoring). The proportion of patients for whom antimicrobials were de-escalated, the average time taken to de-escalate antimicrobials, and the average duration of ICU stay were compared. Proportions and averages with standard deviations were calculated to describe the data. A test of proportions was done to compare the proportion de-escalated and a Student’s t-test was done to compare the average duration of antibiotic therapy.\u0000 \u0000 \u0000 \u0000 The proportion of patients in whom de-escalation of antimicrobials was done was 125 (83.33%) in Group P as compared to 92 (61.33%) in Group C. The time taken to de-escalate was 3.04 ± 0.83 days (95% confidence interval [CI] 2.89–3.18) in Group P compared to 4.7 ± 1.4 days (CI 4.41–4.98) in Group C. The duration of ICU stay was also less in Group P - 3.08 ± 0.91 days (CI 3.08–3.38) as compared to Group C - 5.16 ± 2.17 days (4.80–5.51).\u0000 \u0000 \u0000 \u0000 Serial PCT assay-based antimicrobial therapy helped to wean patients with sepsis off antimicrobials earlier thus reducing the duration of ICU stay.\u0000","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-22DOI: 10.4103/picr.picr_256_23
Arun Bhatt
Real-world evidence (RWE) studies are conducted on patient’s data primarily collected for monitoring of health status of patients. The use of real-world data to generate evidence in academic research or for regulatory submission raises a variety of ethical issues such as privacy, confidentiality, data protection, data de-identification, data sharing, scientific design of study, and informed consent requirements. The investigators–researchers and sponsors should adhere to current standards of ethics whilst planning and conduct of RWE studies. The ethics committees should consider ethical issues specific to RWE studies before approval.
{"title":"Ethical considerations for real-world evidence studies","authors":"Arun Bhatt","doi":"10.4103/picr.picr_256_23","DOIUrl":"https://doi.org/10.4103/picr.picr_256_23","url":null,"abstract":"\u0000 Real-world evidence (RWE) studies are conducted on patient’s data primarily collected for monitoring of health status of patients. The use of real-world data to generate evidence in academic research or for regulatory submission raises a variety of ethical issues such as privacy, confidentiality, data protection, data de-identification, data sharing, scientific design of study, and informed consent requirements. The investigators–researchers and sponsors should adhere to current standards of ethics whilst planning and conduct of RWE studies. The ethics committees should consider ethical issues specific to RWE studies before approval.","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"37 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-03DOI: 10.4103/picr.picr_213_23
Y. Shetty, Prajakta D. Auti, Y. Aithal
Over the years, Indian regulations have undergone numerous amendments, including stringent reporting deadlines, relatedness requirements, and compensation obligations for serious adverse event (SAE). A historic change, new drugs and trial rules-2019, was proposed on March 19, 2019. The purpose of the study was to ascertain whether various stakeholders were reporting in accordance with the evolving SAE criteria. Data were retrieved after the Ethics Committee’s approval between August 2014 and December 2021. Data gathered before March 19, 2019, were categorized as “BEFORE” data, while the remaining data were categorized as “AFTER.” Utilizing causality, on-site SAE reporting, and the ethics committee review procedure, we evaluated the compliance. The data were evaluated using descriptive statistics, and the Chi-square or Mann–Whitney tests were used to compare the “BEFORE” and “AFTER” groups. A total of 77 SAEs were reported in 26 clinical trials, where most clinical trials were phase III. Endocrine projects made up 9/26 (34.61%). In the cardiology studies, the greatest SAE distribution was 21 SAEs/89 participants (23.59%) with approximately 48% of these being vascular. The “AFTER” group noticed a decrease in the total number and length of SAE subcommittee meetings. In the “AFTER” group, there was a significantly higher median number of agenda items/meetings (8 [4.5–10.75]) (P < 0.0001). The median interval between the onset of SAE and the first reporting date, however, was just 1 day (interquartile range: 1–5 days). In nondeath SAEs, there was no significant difference in the compensation paid. In the “AFTER” group, there were no discrepancies in reporting SAE. There is acceptable adherence to SAE reporting criteria.
{"title":"Onsite serious adverse events reporting: Seven-year experience of the institutional ethics committee of a tertiary care hospital","authors":"Y. Shetty, Prajakta D. Auti, Y. Aithal","doi":"10.4103/picr.picr_213_23","DOIUrl":"https://doi.org/10.4103/picr.picr_213_23","url":null,"abstract":"\u0000 \u0000 \u0000 Over the years, Indian regulations have undergone numerous amendments, including stringent reporting deadlines, relatedness requirements, and compensation obligations for serious adverse event (SAE). A historic change, new drugs and trial rules-2019, was proposed on March 19, 2019. The purpose of the study was to ascertain whether various stakeholders were reporting in accordance with the evolving SAE criteria.\u0000 \u0000 \u0000 \u0000 Data were retrieved after the Ethics Committee’s approval between August 2014 and December 2021. Data gathered before March 19, 2019, were categorized as “BEFORE” data, while the remaining data were categorized as “AFTER.” Utilizing causality, on-site SAE reporting, and the ethics committee review procedure, we evaluated the compliance. The data were evaluated using descriptive statistics, and the Chi-square or Mann–Whitney tests were used to compare the “BEFORE” and “AFTER” groups.\u0000 \u0000 \u0000 \u0000 A total of 77 SAEs were reported in 26 clinical trials, where most clinical trials were phase III. Endocrine projects made up 9/26 (34.61%). In the cardiology studies, the greatest SAE distribution was 21 SAEs/89 participants (23.59%) with approximately 48% of these being vascular. The “AFTER” group noticed a decrease in the total number and length of SAE subcommittee meetings. In the “AFTER” group, there was a significantly higher median number of agenda items/meetings (8 [4.5–10.75]) (P < 0.0001). The median interval between the onset of SAE and the first reporting date, however, was just 1 day (interquartile range: 1–5 days). In nondeath SAEs, there was no significant difference in the compensation paid. In the “AFTER” group, there were no discrepancies in reporting SAE.\u0000 \u0000 \u0000 \u0000 There is acceptable adherence to SAE reporting criteria.\u0000","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-03DOI: 10.4103/picr.picr_213_23
Y. Shetty, Prajakta D. Auti, Y. Aithal
Over the years, Indian regulations have undergone numerous amendments, including stringent reporting deadlines, relatedness requirements, and compensation obligations for serious adverse event (SAE). A historic change, new drugs and trial rules-2019, was proposed on March 19, 2019. The purpose of the study was to ascertain whether various stakeholders were reporting in accordance with the evolving SAE criteria. Data were retrieved after the Ethics Committee’s approval between August 2014 and December 2021. Data gathered before March 19, 2019, were categorized as “BEFORE” data, while the remaining data were categorized as “AFTER.” Utilizing causality, on-site SAE reporting, and the ethics committee review procedure, we evaluated the compliance. The data were evaluated using descriptive statistics, and the Chi-square or Mann–Whitney tests were used to compare the “BEFORE” and “AFTER” groups. A total of 77 SAEs were reported in 26 clinical trials, where most clinical trials were phase III. Endocrine projects made up 9/26 (34.61%). In the cardiology studies, the greatest SAE distribution was 21 SAEs/89 participants (23.59%) with approximately 48% of these being vascular. The “AFTER” group noticed a decrease in the total number and length of SAE subcommittee meetings. In the “AFTER” group, there was a significantly higher median number of agenda items/meetings (8 [4.5–10.75]) (P < 0.0001). The median interval between the onset of SAE and the first reporting date, however, was just 1 day (interquartile range: 1–5 days). In nondeath SAEs, there was no significant difference in the compensation paid. In the “AFTER” group, there were no discrepancies in reporting SAE. There is acceptable adherence to SAE reporting criteria.
{"title":"Onsite serious adverse events reporting: Seven-year experience of the institutional ethics committee of a tertiary care hospital","authors":"Y. Shetty, Prajakta D. Auti, Y. Aithal","doi":"10.4103/picr.picr_213_23","DOIUrl":"https://doi.org/10.4103/picr.picr_213_23","url":null,"abstract":"\u0000 \u0000 \u0000 Over the years, Indian regulations have undergone numerous amendments, including stringent reporting deadlines, relatedness requirements, and compensation obligations for serious adverse event (SAE). A historic change, new drugs and trial rules-2019, was proposed on March 19, 2019. The purpose of the study was to ascertain whether various stakeholders were reporting in accordance with the evolving SAE criteria.\u0000 \u0000 \u0000 \u0000 Data were retrieved after the Ethics Committee’s approval between August 2014 and December 2021. Data gathered before March 19, 2019, were categorized as “BEFORE” data, while the remaining data were categorized as “AFTER.” Utilizing causality, on-site SAE reporting, and the ethics committee review procedure, we evaluated the compliance. The data were evaluated using descriptive statistics, and the Chi-square or Mann–Whitney tests were used to compare the “BEFORE” and “AFTER” groups.\u0000 \u0000 \u0000 \u0000 A total of 77 SAEs were reported in 26 clinical trials, where most clinical trials were phase III. Endocrine projects made up 9/26 (34.61%). In the cardiology studies, the greatest SAE distribution was 21 SAEs/89 participants (23.59%) with approximately 48% of these being vascular. The “AFTER” group noticed a decrease in the total number and length of SAE subcommittee meetings. In the “AFTER” group, there was a significantly higher median number of agenda items/meetings (8 [4.5–10.75]) (P < 0.0001). The median interval between the onset of SAE and the first reporting date, however, was just 1 day (interquartile range: 1–5 days). In nondeath SAEs, there was no significant difference in the compensation paid. In the “AFTER” group, there were no discrepancies in reporting SAE.\u0000 \u0000 \u0000 \u0000 There is acceptable adherence to SAE reporting criteria.\u0000","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01Epub Date: 2024-01-09DOI: 10.4103/picr.picr_48_23
Sajal De, Dibakar Sahu, Diksha Mahilang, Ranganath T Ganga, Ajoy Kumar Behera
{"title":"Effectiveness of partial COVID-19 vaccination on the outcome of hospitalized COVID-19 patients during the second pandemic in India.","authors":"Sajal De, Dibakar Sahu, Diksha Mahilang, Ranganath T Ganga, Ajoy Kumar Behera","doi":"10.4103/picr.picr_48_23","DOIUrl":"10.4103/picr.picr_48_23","url":null,"abstract":"","PeriodicalId":20015,"journal":{"name":"Perspectives in Clinical Research","volume":"15 1","pages":"46-47"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10810058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01Epub Date: 2024-01-09DOI: 10.4103/picr.picr_331_23
Deepa Chodankar
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