SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care

Autor: Johanna E. Bellon, Tamra E. Minnier, Stefanie C. Altieri Dunn, Joel B. Nelson, Jacob C. Hodges, Daniel E. Hall, Jeffrey D. Borrebach, Matthew E. Harinstein, Mary Kay Wisniewski, Andrew Bilderback
Rok vydání: 2021
Předmět:
Male
Critical Care and Emergency Medicine
Epidemiology
Health Care Providers
Nurses
Cohort Studies
Machine Learning
0302 clinical medicine
Transfer (computing)
Health care
Medicine and Health Sciences
Risk of mortality
Hospital Mortality
Medical Personnel
030212 general & internal medicine
Myocardial infarction
Prospective cohort study
Aged
80 and over
Allied Health Care Professionals
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Mortality rate
Boosting Algorithms
Middle Aged
Hospitals
Hospitalization
Professions
Physical Sciences
Cohort
Medicine
Female
Emergency Service
Hospital
Algorithms
Research Article
Patient Transfer
Computer and Information Sciences
medicine.medical_specialty
Patients
Death Rates
Science
Research and Analysis Methods
Risk Assessment
Machine Learning Algorithms
03 medical and health sciences
Population Metrics
Artificial Intelligence
medicine
Humans
Aged
Retrospective Studies
Inpatients
Models
Statistical
Population Biology
business.industry
Biology and Life Sciences
030208 emergency & critical care medicine
Retrospective cohort study
medicine.disease
Health Care
Health Care Facilities
Medical Risk Factors
People and Places
Emergency medicine
Population Groupings
business
Mathematics
Forecasting
Zdroj: PLoS ONE
PLoS ONE, Vol 16, Iss 2, p e0246669 (2021)
ISSN: 1932-6203
Popis: Background Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. Methods and findings All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively). Conclusions The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.
Databáze: OpenAIRE
Externí odkaz: