Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS)

Journal of the American Medical Informatics Association

Volumn 21, Issue 3, 2014, Pages 455-463

  Tabak, Ying P ^a   Sun, Xiaowu ^a   Nunez, Carlos M ^a,b   Johannes, Richard S ^a,c  

^a Department of Clinical Research CareFusion ^*  (United States)

^b SAN DIEGO STATE UNIVERSITY   (United States)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALBUMIN; AMINO TERMINAL PRO BRAIN NATRIURETIC PEPTIDE; OXYGEN; TROPONIN I;

ACUTE LABORATORY RISK OF MORTALITY SCORE; ADULT; ARTERIAL PH; ARTICLE; COMORBIDITY; DECISION SUPPORT SYSTEM; DISEASE CLASSIFICATION; ELECTRONIC MEDICAL RECORD; FEMALE; HEALTH CARE QUALITY; HOSPITAL ADMISSION; HOSPITAL DISCHARGE; HOSPITAL PATIENT; HUMAN; LEUKOCYTE COUNT; MAJOR CLINICAL STUDY; MALE; MORTALITY; NAMED INVENTORIES, QUESTIONNAIRES AND RATING SCALES; PRESSURE; THROMBOCYTE; UREA NITROGEN BLOOD LEVEL; AGE; AGED; BLOOD EXAMINATION; COMPUTER PROGRAM; ELECTRONIC HEALTH RECORD (EHR); HOSPITALIZATION; LABORATORY DATA; METHODOLOGY; MIDDLE AGED; MORTALITY RISK MODEL; OUTCOME RESEARCH; PROGNOSIS; RISK ASSESSMENT; THEORETICAL MODEL;

DECISION SUPPORT; ELECTRONIC HEALTH RECORD (EHR); LABORATORY DATA; MORTALITY RISK MODEL; OUTCOME RESEARCH;

ADULT; AGE FACTORS; AGED; ELECTRONIC HEALTH RECORDS; FEMALE; HEMATOLOGIC TESTS; HOSPITAL MORTALITY; HOSPITALIZATION; HUMANS; MALE; MIDDLE AGED; MODELS, THEORETICAL; PROGNOSIS; RISK ASSESSMENT; SOFTWARE;

EID: 84901819329     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-001790     Document Type: Article

References (25)

1. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993;270:2957-63.
2. Zimmerman JE, Kramer AA, McNair DS, et al. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit Care Med 2006;34:1297-310.
3. Escobar GJ, Greene JD, Scheirer P, et al. Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care 2008;46:232-9.
4. Tabak YP, Johannes RS, Silber JH. Using automated clinical data for risk adjustment: development and validation of six disease-specific mortality predictive models for pay-for-performance. Med Care 2007;45:789-805.
5. Pine M, Jordan HS, Elixhauser A, et al. Enhancement of claims data to improve risk adjustment of hospital mortality. JAMA 2007;297:71-6.
6. Tabak YP, Sun X, Derby KG, et al. Development and validation of a disease-specific risk adjustment system using automated clinical data. Health Serv Res 2010;45:1815-35.
7. Froom P, Shimoni Z. Prediction of hospital mortality rates by admission laboratory tests. Clin Chem 2006;52:325-8.
8. Asadollahi K, Hastings IM, Gill GV, et al. Prediction of hospital mortality from admission laboratory data and patient age: a simple model. Emerg Med Australas 2011;23:354-63.
9. Asiimwe AC, Brims FJ, Andrews NP, et al. Routine laboratory tests can predict in-hospital mortality in acute exacerbations of COPD. Lung 2011;189:225-32.
10. AHRQ. Comorbidity Software, Version 3.7. 2012 [updated 2012; cited December 6, 2012]. http://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity. jsp#overview.
11. Elixhauser A, Steiner C, Palmer L. Clinical Classifications Software (CCS), 2013. U.S. Agency for Healthcare Research and Quality. 2012 [updated 2012; cited December 6, 2012]. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp.
12. Fine MJ, Auble TE, Yealy DM, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997;336:243-50.
13. Hollenbeak CS, Gorton CP, Tabak YP, et al. Reductions in mortality associated with intensive public reporting of hospital outcomes. Am J Med Qual 2008;23:279-86.
14. Iezzoni LI, Moskowitz MA. A clinical assessment of MedisGroups. JAMA 1988;260:3159-63.
15. Kollef MH, Shorr A, Tabak YP, et al. Epidemiology and outcomes of health-care-associated pneumonia: results from a large US database of culture-positive pneumonia. Chest 2005;128:3854-62.
16. Shorr AF, Gupta V, Sun X, et al. Burden of early-onset candidemia: analysis of culture-positive bloodstream infections from a large U.S. database. Crit Care Med 2009;37:2519-26.
17. Silber JH, Rosenbaum PR, Schwartz JS, et al. Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery. JAMA 1995;274:317-23.
18. Tabak YP, Sun X, Johannes RS, et al. Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score. Arch Intern Med 2009;169:1595-602.
19. Weigelt JA, Lipsky BA, Tabak YP, et al. Surgical site infections: causative pathogens and associated outcomes. Am J Infect Control 2010;38:112-20.
20. Sullivan LM, Massaro JM, D'Agostino RB Sr. Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med 2004;23:1631-60.
21. Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72; discussion 207-12.
22. Krumholz HM, Wang Y, Mattera JA, et al. An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure. Circulation 2006;113:1693-701.
23. Krumholz HM, Wang Y, Mattera JA, et al. An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction. Circulation 2006;113:1683-92.
24. Bratzler DW, Normand SL, Wang Y, et al. An administrative claims model for profiling hospital 30-day mortality rates for pneumonia patients. PLoS One 2011;6: e17401-1.
25. CDC. National Vital Statistics Reports, Volume 60, No. 4, January 11, 2012. 2012 [updated 2012; cited December 7, 2012]. http://www.cdc.gov/nchs/data/nvsr/ nvsr60/nvsr60_04.pdf.