Common data elements for predictors of pediatric sepsis: A framework to standardize data collection

Background Standardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings. Methods A preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings. Results We generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information. Conclusion Routine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.

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TBI surveillance using the common data elements for traumatic brain injury: a population study

International Journal of Emergency Medicine ◽

10.1186/1865-1380-6-5 ◽

2013 ◽

Vol 6 (1) ◽

Cited By ~ 11

Author(s):

Latha Ganti Stead ◽

◽

Aakash N Bodhit ◽

Pratik Shashikant Patel ◽

Yasamin Daneshvar ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Population Study ◽

Common Data Elements ◽

The Common ◽

Data Elements

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Feasible Surgical Site Infection Surveillance in Resource-Limited Settings: A Pilot in Sierra Leone

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.517 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s38-s38

Author(s):

Matthew Westercamp ◽

Aqueelah Barrie ◽

Christiana Conteh ◽

Danica Gomes ◽

Hassan Benya ◽

...

Keyword(s):

Data Collection ◽

Cesarean Delivery ◽

Sierra Leone ◽

Clinical Decision Making ◽

Case Finding ◽

Surveillance Strategy ◽

Case Definitions ◽

Patient Interviews ◽

Resource Limited ◽

Infection Surveillance

Background: Surgical site infections (SSIs) are among the most common healthcare-associated infections (HAIs) in low- and middle-income countries (LMICs). SSI surveillance can be challenging and resource-intensive to implement in LMICs. To support feasible LMIC SSI surveillance, we piloted a multisite SSI surveillance protocol using simplified case definitions and methodology in Sierra Leone. Methods: A standardized evaluation tool was used to assess SSI surveillance knowledge, capacity, and attitudes at 5 proposed facilities. We used simplified case definitions restricted to objective, observable criteria (eg, wound purulence or intentional reopening) without considering the depth of infection. Surveillance was limited to post-cesarean delivery patients to control variability of patient-level infection risk and to decrease data collection requirements. Phone-based patient interviews at 30-days facilitated postdischarge case finding. Surveillance activities utilized existing clinical staff without monetary incentives. The Ministry of Health provided training and support for data management and analysis. Results: Three facilities were selected for initial implementation. At all facilities, administration and surgical staff described most, or all, infections as “preventable” and all considered SSIs an “important problem” at their facility. However, capacity assessments revealed limited staff availability to support surveillance activities, limited experience in systematic data collection, nonstandardized patient records as the basis for data collection, lack of unique and consistent patient identifiers to link patient encounters, and no quality-assured microbiology services. To limit system demands and to maximize usefulness, our surveillance data collection elements were built into a newly developed clinical surgical safety checklist that was designed to support surgeons’ clinical decision making. Following implementation and 2 months of SSI surveillance activities, 77% (392 of 509) of post-cesarean delivery patients had a checklist completed within the surveillance system. Only 145 of 392 patients (37%) under surveillance were contacted for final 30-day phone interview. Combined SSI rate for the initial 2-months of data collection in Sierra Leone was 8% (32 of 392) with 31% (10 of 32) identified through postdischarge case finding. Discussion: The surveillance strategy piloted in Sierra Leone represents a departure from established HAI strategies in the use of simplified case definitions and implementation methods that prioritize current feasibility in a resource-limited setting. However, our pilot implementation results suggest that even these simplified SSI surveillance methods may lack sustainability without additional resources, especially in postdischarge case finding. However, even limited phone-based patient interviews identified a substantial number of infections in this population. Although it was not addressed in this pilot study, feasible laboratory capacity building to support HAI surveillance efforts and promote appropriate treatment should be explored.Funding: NoneDisclosures: None

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71 Challenges to the Standardization of Trauma Data Collection: A Call for Common Data Elements for Acute and Long-Term Trauma Databases

Journal of Burn Care & Research ◽

10.1093/jbcr/iry006.074 ◽

2018 ◽

Vol 39 (suppl_1) ◽

pp. S40-S41

Author(s):

L C Simko ◽

L A Chen ◽

R Friedman ◽

D Amtmann ◽

K Kowalske ◽

...

Keyword(s):

Data Collection ◽

Common Data Elements ◽

Data Elements

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Feasibility of routine data collection on icu performance and activity in resource limited settings

Intensive Care Medicine Experimental ◽

10.1186/2197-425x-3-s1-a162 ◽

2015 ◽

Vol 3 (S1) ◽

Author(s):

SMM Ali ◽

MI Memon ◽

V Ioos ◽

TMU Pasha ◽

S Afghan ◽

...

Keyword(s):

Data Collection ◽

Routine Data ◽

Resource Limited Settings ◽

Resource Limited ◽

Routine Data Collection

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Pediatric sepsis and septic shock management in resource-limited settings

Intensive Care Medicine ◽

10.1007/s00134-016-4382-8 ◽

2016 ◽

Vol 42 (12) ◽

pp. 2037-2039 ◽

Cited By ~ 9

Author(s):

Ndidiamaka Musa ◽

Srinivas Murthy ◽

Niranjan Kissoon

Keyword(s):

Septic Shock ◽

Resource Limited Settings ◽

Resource Limited ◽

Pediatric Sepsis ◽

Sepsis And Septic Shock

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Pediatric Sepsis and Septic Shock Management in Resource-Limited Settings

Sepsis Management in Resource-limited Settings ◽

10.1007/978-3-030-03143-5_10 ◽

2019 ◽

pp. 197-216 ◽

Cited By ~ 1

Author(s):

Ndidiamaka Musa ◽

Srinivas Murthy ◽

Niranjan Kissoon ◽

Rakesh Lodha ◽

Suchitra Ranjit

Keyword(s):

Septic Shock ◽

Resource Limited Settings ◽

Resource Limited ◽

Pediatric Sepsis ◽

Sepsis And Septic Shock

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The Common Data Elements for Cancer Research: Remarks on Functions and Structure

Methods of Information in Medicine ◽

10.1055/s-0038-1634121 ◽

2006 ◽

Vol 45 (06) ◽

pp. 594-601 ◽

Cited By ~ 30

Author(s):

C. A. Brandt ◽

P. M. Nadkarni

Keyword(s):

Cancer Research ◽

Community Integration ◽

Controlled Vocabulary ◽

Common Data Elements ◽

Metadata Model ◽

Data Interchange ◽

The Common ◽

Set Up ◽

Definition Of ◽

Data Elements

Summary Objectives: The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-oper-ability between cancer research centers. We evaluated CDE’s structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality. Methods: Evaluation was performed on CDE content downloaded via the NCI’s CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research. Results: Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure – particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements. Conclusions: While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter’s links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.

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Abstract WP203: National Institute of Neurological Disorders and Stroke Common Data Elements: Public Review and Updates to the Stroke Recommendations

Stroke ◽

10.1161/str.51.suppl_1.wp203 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Katelyn Gay ◽

Damon Collie ◽

Muniza Sheikh ◽

Joy Esterlitz ◽

Jeffrey Saver ◽

...

Keyword(s):

Data Collection ◽

Clinical Research ◽

Neurological Disorders ◽

Vital Signs ◽

Cerebral Aneurysms ◽

Research Data ◽

Common Data Element ◽

Common Data Elements ◽

Stroke Research ◽

Data Elements

Objective: The National Institute of Neurological Disorders and Stroke (NINDS) initiated the Common Data Element (CDE) project to provide standardized clinical research data collection formats that increase the efficiency and effectiveness of studies and reduce start-up time, as well as improve data quality and facilitate and accelerate data sharing. In 2010, Stroke-specific CDEs were posted on the NINDS CDE website. The Stroke Oversight Committee (OC) reviewed Core CDEs in 2015; and in 2018, recommended that Stroke CDEs undergo a comprehensive review and update to Version 2.0. Background: In August 2018, a Stroke V2.0 Working Group (WG) consisting of over 50 worldwide subject matter experts was convened by NINDS. The WG was asked to review all current Stroke CDEs and subarachnoid hemorrhage and unruptured cerebral aneurysms (SAH) CDEs (developed in 2017) for harmonization and inclusion within Stroke V2.0. Methods: The Stroke V2.0 WG divided into eight domain-specific subgroups: Biospecimens, Biomarkers, and Laboratory Tests; Hospital Course and Acute Therapies; Imaging; Long Term Therapies; Medical History and Prior Health Status; Outcomes and Endpoints; Stroke Presentation and Vital Signs; and Stroke Types and Subtypes. Subgroups met regularly to review, revise and add to the existing Stroke CDEs based on developments in stroke research. Following an internal WG review, a public review of the draft updates will be held. The WG will consider public feedback before V2.0 is finalized. The Stroke OC plans to review the project status at the 2020 International Stroke Conference. Results: The Stroke V2.0 CDE recommendations will include updated and new template case report forms, data dictionaries, instrument informational documents and guideline documents. The updates will reflect the current state of science, streamline CDE recommendations, and incorporate SAH CDEs. Stroke V2.0 CDEs will be available on the NINDS CDE website in 2020. Conclusions: The NINDS CDEs are periodically revised as research progresses. Through the update of the Stroke CDEs to V2.0, the initiative strives to maintain the utility of CDEs as a valuable clinical research resource. NINDS encourages use of CDEs to standardize research data collection across studies.

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