Towards Prevention of Acute Syndromes

A. Ahmed; C. Thongprayoon; B.W. Pickering; A. Akhoundi; G. Wilson; D. Pieczkiewicz; V. Herasevich

doi:10.4338/ACI-2013-07-RA-0045

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2014; 05(01): 58-72
DOI: 10.4338/ACI-2013-07-RA-0045

Research Article

Schattauer GmbH

Towards Prevention of Acute Syndromes

Electronic Identification of At-Risk Patients During Hospital Admission

A. Ahmed

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

²Institute for Health Informatics, University of Minnesota, Minneapolis

⁴Department of Medicine, Division of Critical Care Medicine, Mayo Clinic

,

C. Thongprayoon

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

⁴Department of Medicine, Division of Critical Care Medicine, Mayo Clinic

,

B.W. Pickering

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

³Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic

,

A. Akhoundi

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

,

G. Wilson

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

³Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic

,

D. Pieczkiewicz

²Institute for Health Informatics, University of Minnesota, Minneapolis

,

V. Herasevich

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

³Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic

› Author Affiliations

Abstract

Summary

Background: Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predis-posing conditions at the time of hospital admission.

Methods: This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS pre-disposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction.

Results: Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%.

Conclusion: A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.

Citation: Ahmed A, Thongprayoon C, Pickering BW, Akhoundi A, Wilson G, Pieczkiewicz D, Herasevich V. Towards prevention of acute syndromes: Electronic identification of at-risk patients during hospital admission. Appl Clin Inf 2014; 5: 58–72

http://dx.doi.org/10.4338/ACI-2013-07-RA-0045

Keywords

Electronic search - EMR - risk factor - ARDS

Full Text

References

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