Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data

Canadian Journal of Kidney Health and Disease

Volumn 5, Issue , 2018, Pages

  Mohamadlou, Hamid ^a   Lynn Palevsky, Anna ^a   Barton, Christopher ^b   Chettipally, Uli ^b,c   Shieh, Lisa ^d   Calvert, Jacob ^a   Saber, Nicholas R ^a   Das, Ritankar ^a  

^a DASCENA INC   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c KAISER PERMANENTE   (United States)

^d STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

acute kidney injury; machine learning

Indexed keywords

ACUTE KIDNEY FAILURE; ADULT; AGED; ARTICLE; BREATHING RATE; CREATININE BLOOD LEVEL; DECISION TREE; ELECTRONIC HEALTH RECORD; EMERGENCY WARD; FEMALE; GLASGOW COMA SCALE; GOLD STANDARD; HEART RATE; HOSPITALIZATION; HUMAN; INTENSIVE CARE UNIT; KIDNEY INJURY; LEARNING ALGORITHM; LENGTH OF STAY; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; NATIONAL HEALTH SERVICE; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SEQUENTIAL ORGAN FAILURE ASSESSMENT SCORE; TEMPERATURE; TREATMENT OUTCOME; YOUNG ADULT;

EID: 85061418276     PISSN: None     EISSN: 20543581     Source Type: Journal    
DOI: 10.1177/2054358118776326     Document Type: Article

References (33)

1. Waikar SS, Liu KD, Chertow GM. Diagnosis, epidemiology, and outcomes of acute kidney injury. Clin J Am Soc Nephrol. 2008;3(3):844-861. doi:10.2215/CJN.05191107.
2. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol. 2005;16(11):3365-3370. doi:10.1681/ASN.2004090740.
3. Silver SA, Long J, Zheng Y, Chertow GM. Cost of acute kidney injury in hospitalized patients. J Hosp Med. 2017;12(2):70-76.
4. Mehta RL, Kellum JA, Shah SV. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007;11:R31. doi:10.1186/cc5713.
5. Metnitz PG, Krenn CG, Steltzer H. Effect of acute renal failure requiring renal replacement therapy on outcome in critically ill patients. Crit Care Med. 2002;30:2051-2058.
6. De Corte W, Dhondt A, Vanholder R. Long-term outcome in ICU patients with acute kidney injury treated with renal replacement therapy: a prospective cohort study. Crit Care. 2016;20:256. doi:10.1186/s13054-016-1409-z.
7. Kolhe NV, Staples D, Reilly T. Impact of compliance with a care bundle on acute kidney injury outcomes: a prospective observational study. PLoS One. 2015;10(7):e0132279. doi:10.1371/journal.pone.0132279.
8. Kellum JA, Lameire N, Aspelin P. Kidney Disease: Improving Global Outcomes (KDIGO) acute kidney injury work group KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1-138.
9. Star RA. Treatment of acute renal failure. Kidney Int. 1998;54(6):1817-1831.
10. Praught ML, Shlipak MG. Are small changes in serum creatinine an important risk factor? Curr Opin Nephrol Hypertens. 2005;14:265-270.
11. Mehta RL, Pascual MT, Soroko S. Spectrum of acute renal failure in the intensive care unit: the PICARD experience. Kidney Int. 2004;66(4):1613-1621. doi:10.1111/j.1523-1755.2004.00927.x.
12. Hoste EAJ, Clermont G, Kersten A. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10(3):R73. doi:10.1186/cc4915.
13. Koyner JL, Adhikari R, Edelson DP, Churpek MM. Development of a multicenter ward-based AKI prediction model. Clin J Am Soc Nephrol. 2016;11:1935-1943.
14. Fletchet M, Guiza F, Chetz M. AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin. Intensive Care Med. 2017;43(6):764-773. doi:10.1007/s00134-017-4678-3.
15. Kate RJ, Perez RM, Mazumdar D, Pasupathy KS, Nilakantan V. Prediction and detection models for acute kidney injury in hospitalized older adults. BMC Med Informat Decis Making. 2016;16:39. doi:10.1186/s12911-016-0277-4.
16. Porter CJ, Juurlink I, Bisset LH, Bavakunji R, Mehta RL, Devonald MA. A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital. Nephrol Dial Transplant. 2014;29(10):1888-1893. doi:10.1093/ndt/gfu082.
17. Vincent JL, Moreno R, Takala J. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the working group on sepsis-related problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707-710. doi:10.1007/BF01709751.
18. De Mendonca A, Vincent JL, Suter PM. Acute renal failure in the ICU: risk factors and outcome evaluated by the SOFA score. Intensive Care Med. 2000;26:915-921.
19. Johnson AEW, Pollard TJ, Shen L. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3: 160035. doi:10.1038/sdata.2016.35.
20. Mao Q, Jay M, Hoffman JL. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. 2017. BMJ Open. 2018;8(1):e017833.
21. Selby NM, Hill R, Fluck RJ. Standardizing the early identification of acute kidney injury: the NHS England national patient safety alert. Nephron. 2015; 131:113-117. doi:10.1159/000439146.
22. Kellum JA, Lameire N. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). On behalf of the KDIGO guideline AKI group. Crit Care. 2013;17(1):204. doi:10.1186/cc11454.
23. Ricci Z, Cruz D, Ronco C. The RIFLE criteria and mortality in acute kidney injury: a systematic review. Kidney Int. 2008;73(5):538-546.
24. Schuur JD, Chambers JG, Hou PC. Urinary catheter use and appropriateness in U.S. emergency departments, 1995-2010. Acad Emerg Med. 2014;21:292-300.
25. Závada J, Hoste E, Cartin-Ceba R. A comparison of three methods to estimate baseline creatinine for RIFLE classification. Nephrol Dial Transplant. 2010;25(12):3911-3918.
26. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Paper presented at the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 2016; San Francisco, CA, USA.
27. Pandharipande PP, Shintani AK, Hagerman HE. Derivation and validation of SpO2/FiO2 ratio to impute for PaO2/FiO2 ratio in the respiratory component of the Sequential Organ Failure Assessment (SOFA) Score. Crit Care Med. 2009;37(4):1317-1321. doi:10.1097/CCM.0b013e31819cefa9.
28. Palevsky PM, Liu KD, Brophy PD. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis. 2013;61(5):649-672.
29. Calvert JS, Price DA, Chettipally UK. A computational approach to early sepsis detection. Comp Biol Med. 2016;74:69-73.
30. Desautels T, Calvert J, Hoffman J. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform. 2016;4(3):e28.
31. Calvert J, Desautels T, Chettipally U. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg. 2016;8:50-55.
32. Shimabukuro D, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomized clinical trial. BMJ Open Respir Res. 2017;4:e000234.
33. McCoy A, Das R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual. 2017;6:e000158. doi:10.1136/bmjoq-2017-000158.