ABSTRACT
Background: Nirmatrelvir/ritonavir (NMV/r), a preferred antiviral for high-risk outpatients with COVID-19, is associated with major drug-drug interactions (DDIs). Given the lack of DDI data with short course ritonavir (RTV), initial NMV/r product information was extrapolated from chronic, full dose RTV use. In Jan 2022, DDI experts from the University of Liverpool (UoL), NIH COVID-19 Guidelines Panel, and Ontario Science Table (OST) contributors established a global collaboration to address DDI challenges limiting NMV/r use in real-life settings. We report how safe, pragmatic, and consistent resources were developed to support NMV/r prescribing, and the utilization of these resources globally. Method(s): The 3 teams met monthly to discuss DDIs, review NMV/r DDI literature, and achieve consensus on recommendations. Additional experts were invited as needed. Metrics from the UoL DDI checker guided review of most searched DDIs overall and by severity. 2022 usage metrics for each DDI guide were collected. Differences in recommendations between initial DDI guides and product information were compared. Result(s): In 2022, 12 meetings were convened. Each team's DDI guide was revised and expanded (Table 1). To factor in the lower RTV dose and shorter treatment duration, some recommendations differed from product information. Drug categories that required the most discussion and revision included: anticoagulants (ACs), immunosuppressants, calcium channel blockers. NMV/r accounted for 85% of queries on the UoL site. NMV/r DDI guidance was the most viewed page of the NIH guidelines and among the OST ID/clinical care Science Briefs. Top searched drugs on the UoL site with serious DDIs were certain ACs and statins. Utilization of DDI guides was not limited to in-country resources: 51% and 7% of UoL queries came from the USA and Canada, respectively. NIH users followed links to the UoL and OST sites 161,478 and 37,619 times, respectively. Conclusion(s): Significant efforts have been made by the 3 teams to provide upto-date, complementary DDI guidance. Usage metrics confirm the demand for DDI guidance during the pandemic. Cross-utilization of the DDI guides confirms the need for consistency. DDI recommendations were more permissive than initial product information, expanding clinicians' ability to prescribe NMV/r. DDI guidance for ACs and immunosuppressants was particularly challenging. During drug development, complex interactions likely to be encountered in target populations should be addressed.
ABSTRACT
The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.