Informatics Infrastructure for Syndrome Surveillance, Decision Support, Reporting, and Modeling of Critical Illness

Overview

Journal Mayo Clin Proc

Specialty General Medicine

Date 2010 Mar 3

PMID 20194152

Citations 90

Authors

Vitaly Herasevich

Brian W Pickering

Yue Dong

Steve G Peters

Ognjen Gajic

Affiliations

Soon will be listed here.

Abstract

Objective: To develop and validate an informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness.

Methods: Using open-schema data feeds imported from electronic medical records (EMRs), we developed a near-real-time relational database (Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart). Imported data domains included physiologic monitoring, medication orders, laboratory and radiologic investigations, and physician and nursing notes. Open database connectivity supported the use of Boolean combinations of data that allowed authorized users to develop syndrome surveillance, decision support, and reporting (data "sniffers") routines. Random samples of database entries in each category were validated against corresponding independent manual reviews.

Results: The Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart accommodates, on average, 15,000 admissions to the intensive care unit (ICU) per year and 200,000 vital records per day. Agreement between database entries and manual EMR audits was high for sex, mortality, and use of mechanical ventilation (kappa, 1.0 for all) and for age and laboratory and monitored data (Bland-Altman mean difference +/- SD, 1(0) for all). Agreement was lower for interpreted or calculated variables, such as specific syndrome diagnoses (kappa, 0.5 for acute lung injury), duration of ICU stay (mean difference +/- SD, 0.43+/-0.2), or duration of mechanical ventilation (mean difference +/- SD, 0.2+/-0.9).

Conclusion: Extraction of essential ICU data from a hospital EMR into an open, integrative database facilitates process control, reporting, syndrome surveillance, decision support, and outcome research in the ICU.

Citing Articles

Individualized prediction of critical illness in older adults: Validation of an elders risk assessment model.

Herasevich S, Minteer S, Boswell C, Hanson A, Dong Y, Gajic O J Am Geriatr Soc. 2024; 72(6):1839-1846.

PMID: 38450712 PMC: 11187675. DOI: 10.1111/jgs.18861.

Serum lactate and mean arterial pressure thresholds in patients with cirrhosis and septic shock.

Smith T, Choi C, Rattan P, Piccolo Serafim L, Kassmeyer B, Lennon R Hepatol Commun. 2024; 8(1).

PMID: 38180993 PMC: 10781124. DOI: 10.1097/HC9.0000000000000353.

Temporal trends in critical care utilization and outcomes in allogeneic hematopoietic stem cell transplant recipients.

Heybati K, Ochal D, Hogan W, Al-Khateeb H, Sklar D, Herasevich S Ann Hematol. 2024; 103(3):957-967.

PMID: 38170240 DOI: 10.1007/s00277-023-05612-9.

Impact of Various Estimated Glomerular Filtration Rate Equations on the Pharmacokinetics of Meropenem in Critically Ill Adults.

Barreto E, Chang J, Rule A, Mara K, Meade L, Paul J Crit Care Explor. 2023; 5(12):e1011.

PMID: 38107538 PMC: 10723891. DOI: 10.1097/CCE.0000000000001011.

Relationship between the rate of fluid resuscitation and acute kidney injury: A retrospective cohort study.

Reddy S, Hu B, Kashani K Int J Crit Illn Inj Sci. 2023; 13(3):104-110.

PMID: 38023572 PMC: 10664038. DOI: 10.4103/ijciis.ijciis_7_23.

References

Toy P, Gajic O . Transfusion-related acute lung injury. Anesth Analg. 2004; 99(6):1623-1624. DOI: 10.1213/01.ANE.0000138033.24633.4E. View

Azoulay E, Pochard F, Kentish-Barnes N, Chevret S, Aboab J, Adrie C . Risk of post-traumatic stress symptoms in family members of intensive care unit patients. Am J Respir Crit Care Med. 2005; 171(9):987-94. DOI: 10.1164/rccm.200409-1295OC. View

Schoenberg R, Sands D, Safran C . Making ICU alarms meaningful: a comparison of traditional vs. trend-based algorithms. Proc AMIA Symp. 1999; :379-83. PMC: 2232567. View

Chen L, Martin C, Morrison T, Sibbald W . Interobserver variability in data collection of the APACHE II score in teaching and community hospitals. Crit Care Med. 1999; 27(9):1999-2004. DOI: 10.1097/00003246-199909000-00046. View

Lockley S, Barger L, Ayas N, Rothschild J, Czeisler C, Landrigan C . Effects of health care provider work hours and sleep deprivation on safety and performance. Jt Comm J Qual Patient Saf. 2008; 33(11 Suppl):7-18. DOI: 10.1016/s1553-7250(07)33109-7. View

Halpern N, Pastores S, Greenstein R . Critical care medicine in the United States 1985-2000: an analysis of bed numbers, use, and costs. Crit Care Med. 2004; 32(6):1254-9. DOI: 10.1097/01.ccm.0000128577.31689.4c. View

Herasevich V, Yilmaz M, Khan H, Hubmayr R, Gajic O . Validation of an electronic surveillance system for acute lung injury. Intensive Care Med. 2009; 35(6):1018-23. PMC: 2730460. DOI: 10.1007/s00134-009-1460-1. View

Bernard G, Artigas A, Brigham K, Carlet J, Falke K, Hudson L . The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med. 1994; 149(3 Pt 1):818-24. DOI: 10.1164/ajrccm.149.3.7509706. View

Gajic O, Malinchoc M, Comfere T, Harris M, Achouiti A, Yilmaz M . The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008; 36(3):676-82. DOI: 10.1097/CCM.0B013E318164E3B0. View

10.

Pakhomov S, Weston S, Jacobsen S, Chute C, Meverden R, Roger V . Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care. 2007; 13(6 Part 1):281-8. View

11.

Kozak L, Hall M, Owings M . National Hospital Discharge Survey: 2000 annual summary with detailed diagnosis and procedure data. Vital Health Stat 13. 2003; (153):1-194. View

12.

Afessa B, Keegan M, Gajic O, Hubmayr R, Peters S . The influence of missing components of the Acute Physiology Score of APACHE III on the measurement of ICU performance. Intensive Care Med. 2005; 31(11):1537-43. DOI: 10.1007/s00134-005-2751-9. View

13.

Embriaco N, Azoulay E, Barrau K, Kentish N, Pochard F, Loundou A . High level of burnout in intensivists: prevalence and associated factors. Am J Respir Crit Care Med. 2007; 175(7):686-92. DOI: 10.1164/rccm.200608-1184OC. View

14.

Angus D, Barnato A, Linde-Zwirble W, Weissfeld L, Watson R, Rickert T . Use of intensive care at the end of life in the United States: an epidemiologic study. Crit Care Med. 2004; 32(3):638-43. DOI: 10.1097/01.ccm.0000114816.62331.08. View

15.

Metnitz P, Lenz K . Patient data management systems in intensive care--the situation in Europe. Intensive Care Med. 1995; 21(9):703-15. DOI: 10.1007/BF01704737. View

16.

Safran C, Bloomrosen M, Hammond W, Labkoff S, Markel-Fox S, Tang P . Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc. 2006; 14(1):1-9. PMC: 2329823. DOI: 10.1197/jamia.M2273. View

17.

Montgomery V . Effect of fatigue, workload, and environment on patient safety in the pediatric intensive care unit. Pediatr Crit Care Med. 2007; 8(2 Suppl):S11-6. DOI: 10.1097/01.PCC.0000257735.49562.8F. View

18.

Herasevich V, Afessa B, Chute C, Gajic O . Designing and testing computer based screening engine for severe sepsis/septic shock. AMIA Annu Symp Proc. 2008; :966. View

19.

McIntosh N . Intensive care monitoring: past, present and future. Clin Med (Lond). 2002; 2(4):349-55. PMC: 4953397. DOI: 10.7861/clinmedicine.2-4-349. View

20.

Polderman K, Jorna E, Girbes A . Inter-observer variability in APACHE II scoring: effect of strict guidelines and training. Intensive Care Med. 2001; 27(8):1365-9. DOI: 10.1007/s001340101012. View