Identifying acute illness phenotypes via deep temporal interpolation and
  clustering network on physiologic signatures
Yuanfang Ren Yanjun Li Tyler J. Loftus Jeremy Balch Kenneth L. Abbott Shounak Datta Matthew M. Ruppert Ziyuan Guan Benjamin Shickel Parisa Rashidi Tezcan Ozrazgat-Baslanti Azra Bihorac
Initial hours of hospital admission impact clinical trajectory, but early
clinical decisions often suffer due to data paucity. With clustering analysis
for vital signs within six hours of admission, patient phenotypes with distinct
pathophysiological signatures and outcomes may support early clinical
decisions. We created a single-center, longitudinal EHR dataset for 75,762
adults admitted to a tertiary care center for 6+ hours. We proposed a deep
temporal interpolation and clustering network to extract latent representations
from sparse, irregularly sampled vital sign data and derived distinct patient
phenotypes in a training cohort (n=41,502). Model and hyper-parameters were
chosen based on a validation cohort (n=17,415). Test cohort (n=16,845) was used
to analyze reproducibility and correlation with biomarkers. The training,
validation, and testing cohorts had similar distributions of age (54-55 yrs),
sex (55% female), race, comorbidities, and illness severity. Four clusters were
identified. Phenotype A (18%) had most comorbid disease with higher rate of
prolonged respiratory insufficiency, acute kidney injury, sepsis, and
three-year mortality. Phenotypes B (33%) and C (31%) had diffuse patterns of
mild organ dysfunction. Phenotype B had favorable short-term outcomes but
second-highest three-year mortality. Phenotype C had favorable clinical
outcomes. Phenotype D (17%) had early/persistent hypotension, high rate of
early surgery, and substantial biomarker rate of inflammation but second-lowest
three-year mortality. After comparing phenotypes' SOFA scores, clustering
results did not simply repeat other acuity assessments. In a heterogeneous
cohort, four phenotypes with distinct categories of disease and outcomes were
identified by a deep temporal interpolation and clustering network. This tool
may impact triage decisions and clinical decision-support under time
constraints.