Abstract
Objective To develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR). Materials and methods 24 580 adults from the multiparameter intelligent monitoring in intensive care V.6 (MIMIC II) EHR database of critically ill patients were analyzed, with significant temporal associations visualized as a map of associations between hospital length of stay (LOS) and ICD-9-CM codes. An expanded phenotype, using ICD-9-CM, microbiology, and computerized physician order entry data, was defined for hospital-acquired Clostridium difficile (HA-CDI). LOS, estimated costs, 30-day post-discharge mortality, and antecedent medication provider order entry were evaluated for HA-CDI cases compared to randomly selected controls. Results Temporal phenome analysis revealed 191 significant codes (p value, adjusted for false discovery rate, ≤0.05). HA-CDI was identified in 414 cases, and was associated with longer median LOS, 20 versus 9 days, and adjusted HR 0.33 (95% CI 0.28 to 0.39). This prolongation carries an estimated annual incremental cost increase of US$1.2-2.0 billion in the USA alone. Discussion Comprehensive EHR data have made largescale phenome-based analysis feasible. Time-dependent pathological disease states have dynamic phenomic evolution, which may be captured through visual analytical approaches. Although MIMIC II is a single institutional retrospective database, our approach should be portable to other EHR data sources, including prospective 'learning healthcare systems'. For example, interventions to prevent HA-CDI could be dynamically evaluated using the same techniques. Conclusions The new visual analytical method described in this paper led directly to the identification of numerous hospital-acquired conditions, which could be further explored through an expanded phenotype definition.
| Original language | English |
|---|---|
| Journal | Journal of the American Medical Informatics Association |
| Volume | 20 |
| Issue number | E2 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Health Informatics
Cite this
Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. / Warner, Jeremy L.; Zollanvari, Amin; Ding, Quan; Zhang, Peijin; Snyder, Graham M.; Alterovitz, Gil.
In: Journal of the American Medical Informatics Association, Vol. 20, No. E2, 2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications
AU - Warner, Jeremy L.
AU - Zollanvari, Amin
AU - Ding, Quan
AU - Zhang, Peijin
AU - Snyder, Graham M.
AU - Alterovitz, Gil
PY - 2013
Y1 - 2013
N2 - Objective To develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR). Materials and methods 24 580 adults from the multiparameter intelligent monitoring in intensive care V.6 (MIMIC II) EHR database of critically ill patients were analyzed, with significant temporal associations visualized as a map of associations between hospital length of stay (LOS) and ICD-9-CM codes. An expanded phenotype, using ICD-9-CM, microbiology, and computerized physician order entry data, was defined for hospital-acquired Clostridium difficile (HA-CDI). LOS, estimated costs, 30-day post-discharge mortality, and antecedent medication provider order entry were evaluated for HA-CDI cases compared to randomly selected controls. Results Temporal phenome analysis revealed 191 significant codes (p value, adjusted for false discovery rate, ≤0.05). HA-CDI was identified in 414 cases, and was associated with longer median LOS, 20 versus 9 days, and adjusted HR 0.33 (95% CI 0.28 to 0.39). This prolongation carries an estimated annual incremental cost increase of US$1.2-2.0 billion in the USA alone. Discussion Comprehensive EHR data have made largescale phenome-based analysis feasible. Time-dependent pathological disease states have dynamic phenomic evolution, which may be captured through visual analytical approaches. Although MIMIC II is a single institutional retrospective database, our approach should be portable to other EHR data sources, including prospective 'learning healthcare systems'. For example, interventions to prevent HA-CDI could be dynamically evaluated using the same techniques. Conclusions The new visual analytical method described in this paper led directly to the identification of numerous hospital-acquired conditions, which could be further explored through an expanded phenotype definition.
AB - Objective To develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR). Materials and methods 24 580 adults from the multiparameter intelligent monitoring in intensive care V.6 (MIMIC II) EHR database of critically ill patients were analyzed, with significant temporal associations visualized as a map of associations between hospital length of stay (LOS) and ICD-9-CM codes. An expanded phenotype, using ICD-9-CM, microbiology, and computerized physician order entry data, was defined for hospital-acquired Clostridium difficile (HA-CDI). LOS, estimated costs, 30-day post-discharge mortality, and antecedent medication provider order entry were evaluated for HA-CDI cases compared to randomly selected controls. Results Temporal phenome analysis revealed 191 significant codes (p value, adjusted for false discovery rate, ≤0.05). HA-CDI was identified in 414 cases, and was associated with longer median LOS, 20 versus 9 days, and adjusted HR 0.33 (95% CI 0.28 to 0.39). This prolongation carries an estimated annual incremental cost increase of US$1.2-2.0 billion in the USA alone. Discussion Comprehensive EHR data have made largescale phenome-based analysis feasible. Time-dependent pathological disease states have dynamic phenomic evolution, which may be captured through visual analytical approaches. Although MIMIC II is a single institutional retrospective database, our approach should be portable to other EHR data sources, including prospective 'learning healthcare systems'. For example, interventions to prevent HA-CDI could be dynamically evaluated using the same techniques. Conclusions The new visual analytical method described in this paper led directly to the identification of numerous hospital-acquired conditions, which could be further explored through an expanded phenotype definition.
UR - http://www.scopus.com/inward/record.url?scp=84890370603&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890370603&partnerID=8YFLogxK
U2 - 10.1136/amiajnl-2013-001861
DO - 10.1136/amiajnl-2013-001861
M3 - Article
C2 - 23907284
AN - SCOPUS:84890370603
VL - 20
JO - Journal of the American Medical Informatics Association : JAMIA
JF - Journal of the American Medical Informatics Association : JAMIA
SN - 1067-5027
IS - E2
ER -