Abstract:
:Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement. The estimated trajectories were used for a subsequent K-medians cluster analysis to categorize the longitudinal pain score patterns into distinct clusters. For each cluster, a mixture regression model estimated the association between pain score and time to discharge adjusting for confounding. The fitted regression model generated the pain trajectory pattern for given cluster. Finally, regression analyses examined the association between pain trajectories and patient outcomes. A total of 3442 surgeries were identified with a median of 22 pain scores at an academic hospital during 2009-2016. Four pain trajectory patterns were identified and one was associated with higher rates of outcomes. In conclusion, we described a novel approach with fast implementation to model patients' pain experience using electronic health records. In the era of big data science, clinical research should be learning from all available data regarding a patient's episode of care instead of focusing on the "average" patient outcomes.
journal_name
Health Informatics Jjournal_title
Health informatics journalauthors
Weng Y,Tian L,Tedesco D,Desai K,Asch SM,Carroll I,Curtin C,McDonald KM,Hernandez-Boussard Tdoi
10.1177/1460458219881339subject
Has Abstractpub_date
2020-06-01 00:00:00pages
1404-1418issue
2eissn
1460-4582issn
1741-2811journal_volume
26pub_type
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