2 results
Remote and semi-automated methods to conduct a decentralized randomized clinical trial
- Teresa Cafaro, Patrick J. LaRiccia, Brigid Bandomer, Helen Goldstein, Tracy L. Brobyn, Krystal Hunter, Satyajeet Roy, Kevin Q. Ng, Ludmil V. Mitrev, Alan Tsai, Denise Thwing, Mary Ann Maag, Myung K. Chung, Noud van Helmond
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- Journal:
- Journal of Clinical and Translational Science / Volume 7 / Issue 1 / 2023
- Published online by Cambridge University Press:
- 07 June 2023, e153
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Introduction:
Designing and conducting clinical trials is challenging for some institutions and researchers due to associated time and personnel requirements. We conducted recruitment, screening, informed consent, study product distribution, and data collection remotely. Our objective is to describe how to conduct a randomized clinical trial using remote and automated methods.
Methods:A randomized clinical trial in healthcare workers is used as a model. A random group of workers were invited to participate in the study through email. Following an automated process, interested individuals scheduled consent/screening interviews. Enrollees received study product by mail and surveys via email. Adherence to study product and safety were monitored with survey data review and via real-time safety alerts to study staff.
Results:A staff of 10 remotely screened 406 subjects and enrolled 299 over a 3-month period. Adherence to study product was 87%, and survey data completeness was 98.5% over 9 months. Participants and study staff scored the System Usability Scale 93.8% and 90%, respectively. The automated and remote methods allowed the study maintenance period to be managed by a small study team of two members, while safety monitoring was conducted by three to four team members. Conception of the trial to study completion was 21 months.
Conclusions:The remote and automated methods produced efficient subject recruitment with excellent study product adherence and data completeness. These methods can improve efficiency without sacrificing safety or quality. We share our XML file for researchers to use as a template for learning purposes or designing their own clinical trials.
Surveillance of Healthcare-Associated Bloodstream and Urinary Tract Infections in a National Level Network of Indian Hospitals
- Purva Mathur, Paul Malpiedi, Kamini Walia, Rajesh Malhotra, Padmini Srikantiah, Omika Katoch, Sonal Katyal, Surbhi Khurana, Mahesh Chandra Misra, Sunil Gupta, Subodh Kumar, Sushma Sagar, Naveet Vig, Pramod Garg, Arti Kapil, Manoj Sahu, Arunaloke Chakrabarti, Pallab Ray, Manisha Biswal, Neelam Taneja, Priscilla Rupali, Vellore Binila Chacko, Joy Sarojini Michael, Veeraraghavan Balaji, Camilla Rodrigues, Vijaya Lakshmi Nag, Vibhor Tak, Vimala Venkatesh, Chiranjay Mukhopadhyay, KE Vandana, Muralidhar Varma, Vijayshri Deotale, Ruchita Attal, Kanne Padmaja, Chand Wattal, Neeraj Goel, Sanjay Bhattacharya, Tadepalli Karuna, Saurabh Saigal, Bijayini Behera, Sanjeev Singh, MA Thirunarayan, Reema Nath, Raja Ray, Sujata Baveja, Mammen Chandy, Sudipta Mukherjee, Manas Roy, Gaurav Goel, Swagata Tripathy, Satyajeet Misra, Anupam Dey, Tushar Mishra, Hirak Raj, Bashir Fomda, Gulnaz Bashir, Shaista Nazir, Sulochana Devi, Khuraijam Ranjana Devi, Langpoklakpam Chaoba Singh, Padma Das, Anudita Bhargava, Ujjwala Gaikwad, Neeta Khandelwal, Geeta Vaghela, Tanvi Sukharamwala, Prachi Verma, Mamta Lamba, Shristi Jain, Prithwis Bhattacharyya, Anil Phukan, Clarissa Lyngdoh, Rajeev Sharma, Rajni Gaind, Rushika Saksena, Lata Kapoor, Neil Gupta, Aditya Sharma, Daniel VanderEnde, Anoop Velayudhan, Valan Siromany, Kayla Laserson, Randeep Guleria
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s398-s399
- Print publication:
- October 2020
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Background: Healthcare-associated infections (HAIs) are a major global threat to patient safety. Systematic surveillance is crucial for understanding HAI rates and antimicrobial resistance trends and to guide infection prevention and control (IPC) activities based on local epidemiology. In India, no standardized national HAI surveillance system was in place before 2017. Methods: Public and private hospitals from across 21 states in India were recruited to participate in an HAI surveillance network. Baseline assessments followed by trainings ensured that basic microbiology and IPC implementation capacity existed at all sites. Standardized surveillance protocols for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) were modified from the NHSN for the Indian context. IPC nurses were trained to implement surveillance protocols. Data were reported through a locally developed web portal. Standardized external data quality checks were performed to assure data quality. Results: Between May 2017 and April 2019, 109 ICUs from 37 hospitals (29 public and 8 private) enrolled in the network, of which 33 were teaching hospitals with >500 beds. The network recorded 679,109 patient days, 212,081 central-line days, and 387,092 urinary catheter days. Overall, 4,301 bloodstream infection (BSI) events and 1,402 urinary tract infection (UTI) events were reported. The network CLABSI rate was 9.4 per 1,000 central-line days and the CAUTI rate was 3.4 per 1,000 catheter days. The central-line utilization ratio was 0.31 and the urinary catheter utilization ratio was 0.57. Moreover, 3,542 (73%) of 4,742 pathogens reported from BSIs and 868 (53%) of 1,644 pathogens reported from UTIs were gram negative. Also, 1,680 (26.3%) of all 6,386 pathogens reported were Enterobacteriaceae. Of 1,486 Enterobacteriaceae with complete antibiotic susceptibility testing data reported, 832 (57%) were carbapenem resistant. Of 951 Enterobacteriaceae subjected to colistin broth microdilution testing, 62 (7%) were colistin resistant. The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia. Phased expansion of surveillance to additional hospitals continues. Conclusions: HAI surveillance was successfully implemented across a national network of diverse hospitals using modified NHSN protocols. Surveillance data are being used to understand HAI burden and trends at the facility and national levels, to inform public policy, and to direct efforts to implement effective hospital IPC activities. This network approach to HAI surveillance may provide lessons to other countries or contexts with limited surveillance capacity.
Funding: None
Disclosures: None