Abstract
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.
Highlights
Sepsis is a major cause of death, contributing to almost half of deaths in hospitals[1]
In the literature several approaches have been suggested to define groups of sepsis patients based on data in medical records coded in the ICD-9 and ICD-10 terminologies[7,8,9]
This approach includes most patients diagnosed with A40 (‘streptococcal sepsis’) or A41 (‘other sepsis’)
Summary
Sepsis is a major cause of death, contributing to almost half of deaths in hospitals[1]. The medical history of patients is increasingly becoming available in many registries and in hospital EHR systems for possible use in new and more advanced risk scores This kind of data has for example been used to uncover temporal patterns of disease development[5] and associations between diseases[6]. We present a proof of concept for utilization of full patient disease history data collected over 18.5 years to predict 30-day mortality in patients with sepsis In prior work, it was demonstrated how large amounts of data from a population-wide disease registry can be condensed and organized into time dependent diagnosis trajectories that uncover recurrent, temporal disease associations[5]. We investigated whether such a time dependent, full disease history strategy could provide a useful model to predict sepsis mortality
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