High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study
Dublin Core
Title
High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study
Subject
Electronic health records, Transportation of patients, Machine learning, Emergency helicopter, Helicopter
ambulance
ambulance
Description
Background: There is limited research on individual patient characteristics, alone or in combination, that contribute
to the higher levels of mortality in post-transfer patients. The purpose of this work is to identify signifcant combinations of diagnoses that identify subgroups of post-interhospital transfer patients experiencing the highest levels of
mortality.
Methods: This was a retrospective cross-sectional study using structured electronic health record data from a
regional health system between 2010–2017. We employed a machine learning approach, association rules mining
using the Apriori algorithm to identify diagnosis combinations.
The study population includes all patients aged 21 and older that were transferred within our health system from a
community hospital to one of three main receiving hospitals.
Results: Overall, 8893 patients were included in the analysis. Patients experiencing mortality post-transfer were on
average older (70.5 vs 62.6 years) and on average had more diagnoses in 5 of the 6 diagnostic subcategories. Within
the diagnostic subcategories, most diagnoses were comorbidities and active medical problems, with hypertension,
atrial fbrillation, and acute respiratory failure being the most common. Several combinations of diagnoses identifed
patients that exceeded 50% post-interhospital transfer mortality.
Conclusions: Comorbid burden, in combination with active medical problems, were most predictive for those
experiencing the highest rates of mortality. Further improving patient level prognostication can facilitate informed
decision making between providers and patients to shift the paradigm from transferring all patients to higher level
care to only transferring those who will beneft or desire continued care, and reduce futile transfers.
to the higher levels of mortality in post-transfer patients. The purpose of this work is to identify signifcant combinations of diagnoses that identify subgroups of post-interhospital transfer patients experiencing the highest levels of
mortality.
Methods: This was a retrospective cross-sectional study using structured electronic health record data from a
regional health system between 2010–2017. We employed a machine learning approach, association rules mining
using the Apriori algorithm to identify diagnosis combinations.
The study population includes all patients aged 21 and older that were transferred within our health system from a
community hospital to one of three main receiving hospitals.
Results: Overall, 8893 patients were included in the analysis. Patients experiencing mortality post-transfer were on
average older (70.5 vs 62.6 years) and on average had more diagnoses in 5 of the 6 diagnostic subcategories. Within
the diagnostic subcategories, most diagnoses were comorbidities and active medical problems, with hypertension,
atrial fbrillation, and acute respiratory failure being the most common. Several combinations of diagnoses identifed
patients that exceeded 50% post-interhospital transfer mortality.
Conclusions: Comorbid burden, in combination with active medical problems, were most predictive for those
experiencing the highest rates of mortality. Further improving patient level prognostication can facilitate informed
decision making between providers and patients to shift the paradigm from transferring all patients to higher level
care to only transferring those who will beneft or desire continued care, and reduce futile transfers.
Creator
Andrew P. Reimer, Nicholas K. Schiltz and Siran M. Koroukian
Publisher
BMC Emergency Medicine
Date
(2022) 22:187
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Andrew P. Reimer, Nicholas K. Schiltz and Siran M. Koroukian, “High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study,” Repository Horizon University Indonesia, accessed February 4, 2025, https://repository.horizon.ac.id/items/show/4264.