High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study.
High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study.
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DOI:
10.1186/s12873-022-00742-1
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发表时间:
2022-11-24
影响因子:
2.5
通讯作者:
中科院分区:
文献类型:
--
作者:
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 significant combinations of diagnoses that identify subgroups of post-interhospital transfer patients experiencing the highest levels of mortality. 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. 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 fibrillation, and acute respiratory failure being the most common. Several combinations of diagnoses identified patients that exceeded 50% post-interhospital transfer mortality. 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 benefit or desire continued care, and reduce futile transfers.
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影响因子:
5.7
作者:
Ranasinghe, Isuru;Barzi, Federica;Gallagher, Martin
通讯作者:
Gallagher, Martin
DOI:
10.1186/cc13981
发表时间:
2014-07-09
期刊:
Critical care (London, England)
影响因子:
--
作者:
Abe T;Takahashi O;Saitoh D;Tokuda Y
通讯作者:
Tokuda Y
DOI:
10.1161/circoutcomes.116.003414
发表时间:
2017-06-01
影响因子:
6.9
作者:
Kragholm, Kristian;Hansen, Carolina Malta;Granger, Christopher B.
通讯作者:
Granger, Christopher B.
影响因子:
2.6
作者:
Mueller, Stephanie K.;Fiskio, Julie;Schnipper, Jeffrey
通讯作者:
Schnipper, Jeffrey
影响因子:
6.3
作者:
Ho, Vanessa P.;Schiltz, Nicholas K.;Koroukian, Siran M.
通讯作者:
Koroukian, Siran M.