Diagnosis trajectories of prior multi-morbidity predict sepsis mortality.
Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history to predict sepsis mortality. We benefit from data in electronic medical records covering all hospital encounters in Denmark from 1996 to 2014. This data set included 6.6 million patients of whom almost 120,000 were diagnosed with the ICD-10 code: A41 'Other sepsis'. Interestingly, patients following recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol abuse, Diabetes and Cardio-vascular diagnoses. Many cancers also increased sepsis mortality. Using the trajectory based stratification model we explain contradictory reports in relation to diabetes that recently have appeared in the literature. Finally, we compared the predictive power using 18.5 years of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data.
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark.|Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK2200 Copenhagen, Denmark.
Mette K
MK
Beck
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK2200 Copenhagen, Denmark.
Anders Boeck
AB
Jensen
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK2200 Copenhagen, Denmark.
Annelaura Bach
AB
Nielsen
Department of Intensive Care, Rigshospitalet, University of Copenhagen, DK2100 Copenhagen, Denmark.
Anders
A
Perner
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK2200 Copenhagen, Denmark.|Departments of Medicine and Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, 72205 Arkansas, USA.
Pope L
PL
Moseley
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK2200 Copenhagen, Denmark.
Søren
S
Brunak
D000437:Alcoholism / Q000150:complications / Q000175:diagnosis; D000740:Anemia / Q000150:complications / Q000175:diagnosis*; D015331:Cohort Studies; D003718:Denmark; D003920:Diabetes Mellitus / Q000175:diagnosis; D003933:Diagnosis; D057286:Electronic Health Records; D006801:Humans; D000076322:Multimorbidity; D011379:Prognosis; D018805:Sepsis / Q000209:etiology / Q000401:mortality*