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DOI: 10.4338/ACI-2015-12-RA-0183
Derivation and validation of a search algorithm to retrospectively identify CRRT initiation in the ECMO patients
The study was supported by Mayo Clinic foundation funding through Critical Care research subcommittee.Publication History
received:
06 January 2016
accepted:
28 April 2016
Publication Date:
16 December 2017 (online)
Summary
Background
The role of extracorporeal membrane oxygenation (ECMO) in refractory cardiorespiratory failure is gaining momentum with recent advancements in technology. However, the need for dialysis modes such as continuous renal replacement therapy (CRRT) has also increased in the management for acute kidney injury. Establishing the exact timing of CRRT initiation in these patients from the electronic medical record is vital for automated data extraction for research and quality improvement efforts.
Objectives
We aimed to derive and validate an automated Electronic Health Records (EHR) search strategy for CRRT initiation in patients receiving ECMO.
Methods
We screened 488 patients who received ECMO and a total of 213 patients underwent CRRT. We evaluated random 120 patients, 60 for derivation and 60 for validation cohorts. Following implementation of eligibility criteria, the algorithm was derived in 55 out of 120 ECMO/CRRT patients. The search algorithm was developed using first-time chart entry of ‘access pressure drop’ at CRRT initiation. The algorithm was then validated in an independent subset of 52 patients from the same time period. The overall agreement between electronic search algorithm and a comprehensive manual medical record review in the derivation and validation subsets, using ‘access pressure drop’ as the reference standard, was compared to assess CRRT initiation time.
Results
In the derivation subset (N=55), the automated electronic search strategy achieved an excellent agreement with manual search (D =0.99, 54 were identified electronically, and 55 upon manual review). There was no time difference observed in 49/54(89%) patients, while in the remaining 5 (9%) patients time difference was within 15 minutes. In the validation cohort (N=52), agreement was 100 % (D = 1.0, both methods identified 52 patients). Out of 52 patients, 47 (90%) had no time difference between the methods, for the remaining 5 (10%) patients, differences were within 15 minutes.
Conclusions
The use of an electronic search strategy resulted in determining an accurate CRRT initiation time among ECMO patients.
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References
- 1 Combes A, Pellegrino V. Extracorporeal membrane oxygenation for 2009 influenza A (H1N1)-associated acute respiratory distress syndrome. Seminars in respiratory and critical care medicine 2011; 32: 188-194.
- 2 Zangrillo A, Landoni G, Biondi-Zoccai G, Greco M, Greco T, Frati G, Patroniti N, Antonelli M, Pesenti A, Pappalardo F. A meta-analysis of complications and mortality of extracorporeal membrane oxygenation. Critical care and resuscitation : Journal of the Australasian Academy of Critical Care Medicine 2013; 15: 172-178.
- 3 Askenazi DJ, Ambalavanan N, Hamilton K, Cutter G, Laney D, Kaslow R, Georgeson K, Barnhart DC, Dimmitt RA. Acute kidney injury and renal replacement therapy independently predict mortality in neonatal and pediatric noncardiac patients on extracorporeal membrane oxygenation. Pediatric Critical Care Medicine : A Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2011; 12: e1-e6.
- 4 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. The New England Journal of Medicine 2010; 363: 501-504.
- 5 Kutney-Lee A, Kelly D. The effect of hospital electronic health record adoption on nurse-assessed quality of care and patient safety. The Journal of Nursing Administration 2011; 41: 466-472.
- 6 Smischney NJ, Velagapudi VM, Onigkeit JA, Pickering BW, Herasevich V, Kashyap R. Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit. BMC Medical Informatics and Decision Making 2014; 14: 55.
- 7 Rishi MA, Kashyap R, Wilson G, Hocker S. Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit. BMC Anesthesiology 2014; 14: 41.
- 8 Herasevich V, Pickering BW, Dong Y, Peters SG, Gajic O. Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clinic Proceedings 2010; 85: 247-254.
- 9 Alsara A, Warner DO, Li G, Herasevich V, Gajic O, Kor DJ. Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury. Mayo Clinic Proceedings 2011; 86: 382-388.
- 10 Selewski DT, Cornell TT, Blatt NB, Han YY, Mottes T, Kommareddi M, Gaies MG, Annich GM, Kershaw DB, Shanley TP, Heung M. Fluid overload and fluid removal in pediatric patients on extracorporeal membrane oxygenation requiring continuous renal replacement therapy. Critical Care Medicine 2012; 40: 2694-2699.
- 11 Hei F, Lou S, Li J, Yu K, Liu J, Feng Z, Zhao J, Hu S, Xu J, Chang Q, Liu Y, Wang X, Liu P, Long C. Five-year results of 121 consecutive patients treated with extracorporeal membrane oxygenation at Fu Wai Hospital. Artificial Organs 2011; 35: 572-578.
- 12 Herasevich V, Yilmaz M, Khan H, Hubmayr RD, Gajic O. Validation of an electronic surveillance system for acute lung injury. Intensive Care Medicine 2009; 35: 1018-1023.
- 13 Boyd D, Crawford K. Critical questions for big data. Information, Communication & Society 2012; 15 (05) 662-679.