The ICEL Healthcare-Associated Infection Probability Equation. (October 2020)

• Record Type: Journal Article
• Authors: Moore, Mark
• Is Part Of: Infection control and hospital epidemiology. Volume 41(2020)Supplement 1
• Page Start: s405
• Page End: s406
• Publication Date: 2020-10
• Subjects: Nosocomial infections -- Epidemiology -- Periodicals; Health facilities -- Sanitation -- Periodicals; Hospital buildings -- Sanitation -- Periodicals; Cross Infection -- Periodicals; Epidemiology -- Periodicals; Hospitals -- Periodicals; Infection Control -- Periodicals; 614.44
• DOI: 10.1017/ice.2020.1056
• Languages: English
• ISSNs: 0899-823X

Abstract

Abstract : Backround: In American hospitals alone, the CDC estimates that hospital acquired infections (HAIs) account for an estimated 1.7 million infections and 99, 000 associated deaths each year. 1 Although the United states and most industrialized nations have made strides in lowering the overall HAI rate by taking critical steps to reduce HAIs, an overall formula that combines a global risk assessment per patient for HAI acquisition has yet to be established. To address this issue, we developed the ICEL equation. This equation uses a probabilistic argument to estimate the likelihood of HAI acquisition and to promote infection control dialogue among healthcare practitioners from diverse healthcare disciplines. Methods: We defined HAI risk using the ICEL acronym as follows: HAI risk = (I + C + E + L), where I is invasive devices present; C is patient-specific characteristics; E is the average number of pathogenic organisms in the patient environment; and L is the length of stay. A simple scale of 1–10 points is subjectively assigned for each of the following categories: I = (number of invasive devices / surgeries / % body surface areas open) C = Patient specific characteristics (immune system integrity / immunomodulators / radiation exposure / chemotherapy, etc) E = Environmental conditions / cleaning (average number of pathogenic bacteria in room, 100% hand hygiene compliance, patient / staff colonization, etc) L = Length of stay days risk, where 0–3 days is low risk, 4–7 is moderate risk, and 8–10+ is high risk Summing the points for each of the 4 categories, the greatest possible total is 40. A total score of 0–10 indicates low risk of HAI; 11–20 indicates low-to-medium risk of HAI; 20–30 indicates a high risk of HAI; and 30–40 indicates a very high risk of HAI. Results: This equation was designed to stimulate thought and encourage multidisciplinary cooperation among providers, nursing, environmental services, and facilities departments rather than provide an exact number for HAI risk. All of these categories are key players in the determining patient risk of acquiring an HAI. If any of the 4 hospital departments mentioned fails in their duties, the patient is at higher risk of HAI. Conclusions: This categorical HAI risk assessment relies on the subjective medical and environmental knowledge of the assessor to assign risk across the continuum of the healthcare environment. Although it is nearly impossible to provide exact numbers regarding total risk in these risk categories, the goal of the scoring system is to encourage clinical dialogue among hospital staff so that they communicate and collaborate within their specialties and with their peers to assure that each category poses as low a risk as possible, thus driving the total risk for HAI lower. 1. https://www.cdc.gov/hai/data/portal/progress-report.html Funding: None Disclosures: None