Abstract

Background

It is unclear whether screening for sepsis using an electronic alert in hospitalized ward patients improves outcomes. The objective of the Stepped-wedge Cluster Randomized Trial of Electronic Early Notification of Sepsis in Hospitalized Ward Patients (SCREEN) trial is to evaluate whether an electronic screening for sepsis compared to no screening among hospitalized ward patients reduces all-cause 90-day in-hospital mortality.

Methods and design

This study is designed as a stepped-wedge cluster randomized trial in which the unit of randomization or cluster is the hospital ward. An electronic alert for sepsis was developed in the electronic medical record (EMR), with the feature of being active (visible to treating team) or masked (inactive in EMR frontend for the treating team but active in the backend of the EMR). Forty-five clusters in 5 hospitals are randomized into 9 sequences of 5 clusters each to receive the intervention (active alert) over 10 periods, 2 months each, the first being the baseline period. Data are extracted from EMR and are compared between the intervention (active alert) and control group (masked alert). During the study period, some of the hospital wards were allocated to manage patients with COVID-19. The primary outcome of all-cause hospital mortality by day 90 will be compared using a generalized linear mixed model with a binary distribution and a log-link function to estimate the relative risk as a measure of effect. We will include two levels of random effects to account for nested clustering within wards and periods and two levels of fixed effects: hospitals and COVID-19 ward status in addition to the intervention. Results will be expressed as relative risk with a 95% confidence interval.

Conclusion

The SCREEN trial provides an opportunity for a novel trial design and analysis of routinely collected and entered data to evaluate the effectiveness of an intervention (alert) for a common medical problem (sepsis in ward patients). In this statistical analysis plan, we outline details of the planned analyses in advance of trial completion. Prior specification of the statistical methods and outcome analysis will facilitate unbiased analyses of these important clinical data.

Trial registration

ClinicalTrials.gov NCT04078594. Registered on September 6, 2019

Details

Title
Statistical analysis plan for the Stepped-wedge Cluster Randomized trial of Electronic Early Notification of sepsis in hospitalized ward patients (SCREEN)
Author
Arabi, Yaseen M. 1   VIAFID ORCID Logo  ; Vishwakarma, Ramesh Kumar 2 ; Al-Dorzi, Hasan M. 1 ; Al Qasim, Eman 1 ; Abdukahil, Sheryl Ann 1 ; Al-Rabeah, Fawaz K. 3 ; Al Ghamdi, Huda 3 ; Al Ghamdi, Ebtisam 3 ; Alsaawi, Abdulmohsen; Al Khathaami, Ali M.; AlHazme, Raed H.; Al Mutrafy, Abdullah; Abuelgasim, Khadega A.; Alatassi, Abdulaleem; Nammour, Georges; Caswell, Angela; Alchin, John; AlKatheri, Mufareh Edah; Alharbi, Ahmad; Al Almoodi, Raed; Jones, Joan; Al-Qahtani, Saad; Salih, Salih Bin; Alselaim, Nahar; Tashkandi, Nabiha; Alyousef, Zeyad; Al Moammary, Mohamed S.; Rasheed, Soud; Matroud, Amal; Al Anazi, Rasha Ebeid; Al Shammari, Haifa; Alshamrani, Majid M.; Qasim, Saleh; Obbed, Saeed; Al Qarni, Mohammed; Jazieh, Abdul Rahman; Al Zahrani, Mohammed; Al-Hameed, Fahad; Munshi, Adnan A.; Jastaniah, Wasil; AlMarhabi, Hassan; AlWafi, Emad; Alyami, Ali H.; Yamani, Arwa O.; Al Tabsh, Hadia; Banat, Basem R.; Abuskout, Omar; Marcelo, Anna Liza; Alhabshi, Mayadah M.; Al Hamdan, Abdullah; Al Qarni, Ali; Al Arfaj, Ahmed; Mustafa, Hani T.; Shahin, Mohammad; Ghamdi, Abdulaziz A.; Yaakob, Jamielah; Chalabi, Jamal; Greyvenstein, Johanna E.; Al Qasem, Abdulaziz; Esilan, Hattan; Aliyyen, Sami Musalam; Sian, Christa Myumi; Alwasaidi, Turki; Alhasani, Amar M.; Jaber, Ibrahim J.; Omaish, Noha; Abdrabo, Mohammad; Qureshi, Ahmad S.; Abdrahim, Azura; Almughamisi, Naif; Rahim, Azurahazri Abd; Al Shouabi, Ahmed; Qahtani, Shaher; Alansari, Mariam; Al Somali, Hanan; Masala, Clara; Madram, Gaber; Nasser, Wafa; Talaqof, Fatimah; Almulhim, Maryam

1  King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, College of Medicine, Riyadh, Saudi Arabia (GRID:grid.412149.b) (ISNI:0000 0004 0608 0662) 
2  King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Biostatistics and Bioinformatics Department, Riyadh, Saudi Arabia (GRID:grid.412149.b) (ISNI:0000 0004 0608 0662); European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, Statistics Department, Brussels, Belgium (GRID:grid.418936.1) (ISNI:0000 0004 0610 0854) 
3  King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Information Systems and Informatics Division, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, College of Health Informatics, Riyadh, Saudi Arabia (GRID:grid.412149.b) (ISNI:0000 0004 0608 0662) 
Pages
828
Publication year
2021
Publication date
Dec 2021
Publisher
Springer Nature B.V.
e-ISSN
17456215
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s13063-021-05788-3
ProQuest document ID
2730344590
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.