Author:
Ren Yuanfang,Li Yanjun,Loftus Tyler J.,Balch Jeremy,Abbott Kenneth L.,Ruppert Matthew M.,Guan Ziyuan,Shickel Benjamin,Rashidi Parisa,Ozrazgat-Baslanti Tezcan,Bihorac Azra
Abstract
AbstractUsing clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically sampled sporadically. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from irregularly sampled vital signs and derive phenotypes. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (3-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B’s favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the sequential organ failure assessment scores, the clustering results did not simply provide a recapitulation of previous acuity assessments. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.
Funder
National Center for Advancing Translational Sciences of the National Institutes of Health under University of Florida Clinical and Translational Science Awards
Publisher
Springer Science and Business Media LLC
Reference37 articles.
1. Graber, M. L., Franklin, N. & Gordon, R. Diagnostic error in internal medicine. Arch. Intern. Med. 165, 1493–1499. https://doi.org/10.1001/archinte.165.13.1493 (2005).
2. Hall, M. J., Levant, S. & DeFrances, C. J. Trends in inpatient hospital deaths: National Hospital Discharge Survey, 2000–2010. NCHS Data Brief, 1–8 (2013).
3. Weiss, A. J. & Elixhauser, A. Overview of hospital stays in the United States, 2012. In HCUP Statistical Brief #180 (Agency for Healthcare Research and Quality, 2014).
4. Abe, T. et al. In-hospital mortality associated with the misdiagnosis or unidentified site of infection at admission. Crit. Care 23, 202. https://doi.org/10.1186/s13054-019-2475-9 (2019).
5. Jhanji, S. et al. Mortality and utilisation of critical care resources amongst high-risk surgical patients in a large NHS trust*. Anaesthesia 63, 695–700. https://doi.org/10.1111/j.1365-2044.2008.05560.x (2008).