Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity

Nareg H Roubinian, Edward L Murphy, Bix E Swain, Marla N Gardner, Vincent Liu, Gabriel J Escobar, NHLBI Recipient Epidemiology and Donor Evaluation Study-III (REDS-III), Northern California Kaiser Permanente DOR Systems Research Initiative, A E Mast, J L Gottschall, D J Triulzi, J E Kiss, E L Murphy, E W Fiebig, E L Snyder, R G Cable, D J Brambilla, M T Sullivan, M P Busch, P J Norris, R Y Dodd, S H Kleinman, S A Glynn, A M Cristman, Nareg H Roubinian, Edward L Murphy, Bix E Swain, Marla N Gardner, Vincent Liu, Gabriel J Escobar, NHLBI Recipient Epidemiology and Donor Evaluation Study-III (REDS-III), Northern California Kaiser Permanente DOR Systems Research Initiative, A E Mast, J L Gottschall, D J Triulzi, J E Kiss, E L Murphy, E W Fiebig, E L Snyder, R G Cable, D J Brambilla, M T Sullivan, M P Busch, P J Norris, R Y Dodd, S H Kleinman, S A Glynn, A M Cristman

Abstract

Background: Randomized controlled trial evidence supports a restrictive strategy of red blood cell (RBC) transfusion, but significant variation in clinical transfusion practice persists. Patient characteristics other than hemoglobin levels may influence the decision to transfuse RBCs and explain some of this variation. Our objective was to evaluate the role of patient comorbidities and severity of illness in predicting inpatient red blood cell transfusion events.

Methods: We developed a predictive model of inpatient RBC transfusion using comprehensive electronic medical record (EMR) data from 21 hospitals over a four year period (2008-2011). Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization. Model predictors included administrative data (age, sex, comorbid conditions, admission type, and admission diagnosis), admission hemoglobin, severity of illness, prior inpatient RBC transfusion, admission ward, and hospital.

Results: The study cohort included 275,874 patients who experienced 444,969 hospitalizations. The 24 hour and overall inpatient RBC transfusion rates were 7.2% and 13.9%, respectively. A predictive model for transfusion within 24 hours of hospital admission had a C-statistic of 0.928 and pseudo-R2 of 0.542; corresponding values for the model examining transfusion through the entire hospitalization were 0.872 and 0.437. Inclusion of the admission hemoglobin resulted in the greatest improvement in model performance relative to patient comorbidities and severity of illness.

Conclusions: Data from electronic medical records at the time of admission predicts with very high likelihood the incidence of red blood transfusion events in the first 24 hours and throughout hospitalization. Patient comorbidities and severity of illness on admission play a small role in predicting the likelihood of RBC transfusion relative to the admission hemoglobin.

Figures

Figure 1
Figure 1
Probability of Red Blood Cell Transfusion as a Function of Admission Hemoglobin and Severity of Illness. A) The left panel shows that the likelihood of transfusion is tightly linked to the degree of anemia and that it falls exponentially in the first 24 hours, after which the rate of decrease is linear. B) The right panel shows that trends in severity of illness, within varying strata of admission hemoglobin, do not explain differences in overall rates of RBC transfusion. Severity of Illness refers to ranges of Laboratory Acute Physiology Score, version 2 (LAPS2) a physiology-based score which includes vital signs, neurological status, and laboratory results [19]. Increasing degrees of physiologic derangement are reflected in a higher LAPS2. Ranges of Severity of Illness (LAPS2) were defined as: Low (0-75), Moderate (75-125), and High (>125), associated with 30-day mortality rates of 2%, 9%, and 30%, respectively.

References

    1. Bennett-Guerrero E, Zhao Y, O'Brien SM, Ferguson TB Jr, Peterson ED, Gammie S, Song HK. Variation in use of blood transfusion in coronary artery bypass graft surgery. JAMA. 2010 Oct 13;14(14):1568–1575. doi: 10.1001/jama.2010.1406.
    1. Ibrahim HN, Ishani A, Foley RN, Guo H, Liu J, Collins AJ. Temporal trends in red blood transfusion among US dialysis patients, 1992-2005. Am J Kidney Dis. 2008 Dec;14(6):1115–1121. doi: 10.1053/j.ajkd.2008.07.022.
    1. Wass CT, Long TR, Faust RJ, Yaszemski MJ, Joyner MJ. Changes in red blood cell transfusion practice during the past two decades: a retrospective analysis, with the Mayo database, of adult patients undergoing major spine surgery. Transfusion. 2007 Jun;14(6):1022–1027. doi: 10.1111/j.1537-2995.2007.01231.x.
    1. Rana R, Afessa B, Keegan MT, Whalen FX, Nuttall GA, Evenson LK, Peters SG, Winters JL, Hubmayr RD, Moore SB, Gajic O. Evidence-based red cell transfusion in the critically ill: quality improvement using computerized physician order entry. Crit Care Med. 2006;14:7.
    1. Vincent JL, Baron JF, Reinhart K, Gattinoni L, Thijs L, Webb A, Meier-Hellmann A, Nollet G, Peres-Bota D. ABC (Anemia and Blood Transfusion in Critical Care) Investigators. Anemia and blood transfusion in critically ill patients. JAMA. 2002;14:1499–1507. doi: 10.1001/jama.288.12.1499.
    1. Corwin HL, Gettinger A, Pearl RG, Fink MP, Levy MM, Abraham E, MacIntyre NR, Shabot MM, Duh MS, Shapiro MJ. The CRIT study: anemia and blood transfusion in the critically ill - current clinical practice in the United States. Crit Care Med. 2004;14:39–52. doi: 10.1097/01.CCM.0000104112.34142.79.
    1. Carson JL, Terrin ML, Barton FB, Aaron R, Greenburg AG, Heck DA, Magaziner J, Merlino FE, Bunce G, McClelland B, Duff A, Noveck H. A pilot randomized trial comparing symptomatic vs. hemoglobin-level-driven red blood cell transfusions following hip fracture. Transfusion. 1998;14:522–529. doi: 10.1046/j.1537-2995.1998.38698326331.x.
    1. Hébert PC, Wells G, Blajchman MA, Marshall J, Martin C, Pagliarello G, Tweeddale M, Schweitzer I, Yetisir E. A multicenter, randomized, controlled clinical trial of transfusion requirements in critical care. Transfusion requirements in critical care investigators, Canadian critical care trials group. N Engl J Med. 1999;14:409–417. doi: 10.1056/NEJM199902113400601.
    1. Hajjar LA, Vincent JL, Galas FR, Nakamura RE, Silva CM, Santos MH, Fukushima J, Kalil Filho R, Sierra DB, Lopes NH, Mauad T, Roquim AC, Sundin MR, Leão WC, Almeida JP, Pomerantzeff PM, Dallan LO, Jatene FB, Stolf NA, Auler JO Jr. Transfusion requirements after cardiac surgery: the TRACS randomized controlled trial. JAMA. 2010 Oct 13;14(14):1559–1567. doi: 10.1001/jama.2010.1446.
    1. Carson JL, Terrin ML, Noveck H, Sanders DW, Chaitman BR, Rhoads GG, Nemo G, Dragert K, Beaupre L, Hildebrand K, Macaulay W, Lewis C, Cook DR, Dobbin G, Zakriya KJ, Apple FS, Horney RA, Magaziner J, Investigators FOCUS. FOCUS Investigators. Liberal or restrictive transfusion in high-risk patients after hip surgery. N Engl J Med. 2011;14(26):2453–2462. doi: 10.1056/NEJMoa1012452.
    1. Napolitano LM, Kurek S, Luchette FA, Corwin HL, Barie PS, Tisherman SA, Hebert PC, Anderson GL, Bard MR, Bromberg W, Chiu WC, Cipolle MD, Clancy KD, Diebel L, Hoff WS, Hughes KM, Munshi I, Nayduch D, Sandhu R, Yelon JA. American College of Critical Care Medicine of the Society of Critical Care Medicine; Eastern Association for the Surgery of Trauma Practice Management Workgroup. Clinical practice guideline: red blood cell transfusion in adult trauma and critical care. Crit Care Med. 2009;14(12):3124–3157. doi: 10.1097/CCM.0b013e3181b39f1b.
    1. Hill SR, Carless PA, Henry DA, Carson JL, Hebert PC, McClelland DB, Henderson KM. Transfusion thresholds and other strategies for guiding allogeneic red blood cell transfusion. Cochrane Database Syst Rev. 2002;14 CD002042.
    1. Carless PA, Henry DA, Carson JL, Hebert PP, McClelland B, Ker K. Transfusion thresholds and other strategies for guiding allogeneic red blood cell transfusion. Cochrane Database Syst Rev. 2010 Oct 6;14 CD002042.
    1. Carson JL, Grossman BJ, Kleinman S, Tinmouth AT, Marques MB, Fung MK, Holcomb JB, Illoh O, Kaplan LJ, Katz LM, Rao SV, Roback JD, Shander A, Tobian AA, Weinstein R, Swinton McLaughlin LG, Djulbegovic B. Clinical Transfusion Medicine Committee of the AABB. Clinical transfusion medicine committee of the AABB. Red blood cell transfusion: a clinical practice guideline from the AABB. Ann Intern Med. 2012;14(1):49–58. doi: 10.7326/0003-4819-157-1-201206190-00429.
    1. Gouëzec H, Berger E, Bergoin-Costello V, Betbèze V, Bourcier V, Damais A, Drouet N, Ducroz S, Fialon P, Hervé I, Huchet C, Lassale B, Léo S, Lovi V, Le Niger C, Moron S, Renom P, Delaunay C, Turmel V. Groupe des Hémobiologistes et Correspondants d'Hémovigilance. [Suitability of red blood cell transfusion: a multicenter study] Transfus Clin Biol. 2010;14(5-6):318–330. doi: 10.1016/j.tracli.2010.09.154.
    1. Shander A, Fink A, Javidroozi M, Erhard J, Farmer SL, Corwin H, Goodnough LT, Hofmann A, Isbister J, Ozawa S, Spahn DR. International Consensus Conference on Transfusion Outcomes Group. Appropriateness of allogeneic red blood cell transfusion: the international consensus conference on transfusion outcomes. Transfus Med Rev. 2011;14(3):232–246. doi: 10.1016/j.tmrv.2011.02.001.
    1. So-Osman C, Cicilia J, Brand A, Schipperus M, Berning B, Scherjon S. Triggers and appropriateness of red blood cell transfusions in the postpartum patient—a retrospective audit. Vox Sang. 2010 Jan;14(1):65–69. doi: 10.1111/j.1423-0410.2009.01231.x.
    1. Rothschild JM, McGurk S, Honour M, Lu L, McClendon AA, Srivastava P, Churchill WH, Kaufman RM, Avorn J, Cook EF, Bates DW. Assessment of education and computerized decision support interventions for improving transfusion practice. Transfusion. 2007;14(2):228–239. doi: 10.1111/j.1537-2995.2007.01093.x.
    1. Escobar GJ, Gardner MN, Greene JD, Draper D, Kipnis P. Risk-adjusting hospital mortality using a comprehensive electronic record in an integrated healthcare delivery system. Med Care. May 2013;14(5):446–453. doi: 10.1097/MLR.0b013e3182881c8e.
    1. Escobar GJ, Greene JD, Scheirer P, Gardner M, Draper D, Kipnis P. Risk adjusting hospital inpatient mortality using automated Inpatient, outpatient, and laboratory databases. Med Care. Mar 2008;14(3):232–239. doi: 10.1097/MLR.0b013e3181589bb6.
    1. Van Walraven C, Escobar GJ, Greene JD, Forster AJ. The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. Jul 2010;14(7):798–803.
    1. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992 Jun;14(6):613–619. doi: 10.1016/0895-4356(92)90133-8.
    1. Pencina MJ, D'Agostino RB Sr, Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med. 2012 Jan 30;14(2):101–113. doi: 10.1002/sim.4348.
    1. Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012 Sep 15;14(6):473–481. doi: 10.1093/aje/kws207.
    1. Welsby I, Crow J, Bandarenko N, Lappas G, Phillips-Bute B, Stafford-Smith M. A clinical prediction tool to estimate the number of units of red blood cells needed in primary elective coronary artery bypass surgery. Transfusion. 2010 Nov;14(11):2337–2343. doi: 10.1111/j.1537-2995.2010.02711.x.
    1. Elmistekawy EM, Errett L, Fawzy HF. Predictors of packed red cell transfusion after isolated primary coronary artery bypass grafting–the experience of a single cardiac center: a prospective observational study. J Cardiothorac Surg. 2009 May 7;14:20. doi: 10.1186/1749-8090-4-20.
    1. Alghamdi AA, Davis A, Brister S, Corey P, Logan A. Development and validation of Transfusion Risk Understanding Scoring Tool (TRUST) to stratify cardiac surgery patients according to their blood transfusion needs. Transfusion. 2006 Jul;14(7):1120–1129. doi: 10.1111/j.1537-2995.2006.00860.x.
    1. Mitra B, Rainer TH, Cameron PA. Predicting massive blood transfusion using clinical scores post-trauma. Vox Sang. 2012 May;14(4):324–330. doi: 10.1111/j.1423-0410.2011.01564.x.
    1. Larson CR, White CE, Spinella PC, Jones JA, Holcomb JB, Blackbourne LH, Wade CE. Association of shock, coagulopathy, and initial vital signs with massive transfusion in combat casualties. J Trauma. 2010 Jul;14(Suppl 1):S26–S32.
    1. Dente CJ, Shaz BH, Nicholas JM, Harris RS, Wyrzykowski AD, Ficke BW, Vercruysse GA, Feliciano DV, Rozycki GS, Salomone JP, Ingram WL. Early predictors of massive transfusion in patients sustaining torso gunshot wounds in a civilian level I trauma center. J Trauma. 2010 Feb;14(2):298–304. doi: 10.1097/TA.0b013e3181cf7f2a.
    1. McLaughlin DF, Niles SE, Salinas J, Perkins JG, Cox ED, Wade CE, Holcomb JB. A predictive model for massive transfusion in combat casualty patients. J Trauma. 2008;14(2 Suppl):S57–S63.
    1. Nunez TC, Voskresensky IV, Dossett LA, Shinall R, Dutton WD, Cotton BA. Early prediction of massive transfusion in trauma: simple as ABC (assessment of blood consumption) J Trauma. 2009;14:346–352. doi: 10.1097/TA.0b013e3181961c35.
    1. Yucel N, Lefering R, Maegele M. Polytrauma Study Group of the German Trauma Society et al.Trauma Associated Severe Hemorrhage (TASH)-Score: probability of mass transfusion as surrogate for life threatening hemorrhage after multiple trauma. J Trauma. 2006;14:1236–1237.
    1. Eastridge BJ, Salinas J, McManus JG, Blackburn L, Bugler EM, Cooke WH, Convertino VA, Wade CE, Holcomb JB. Hypotension begins at 110 mm Hg: redefining “hypotension” with data. J Trauma. 2007;14:291–299. doi: 10.1097/TA.0b013e31809ed924.
    1. Kuzniewicz M, Draper D, Escobar GJ. Incorporation of physiologic trend and interaction effects in neonatal severity of illness scores: an experiment using a variant of the Richardson score. Intensive Care Med. 2007;14(9):1602–1608. doi: 10.1007/s00134-007-0714-z.
    1. Villanueva C, Colomo A, Bosch A, Concepción M, Hernandez-Gea V, Aracil C, Graupera I, Poca M, Alvarez-Urturi C, Gordillo J, Guarner-Argente C, Santaló M, Muñiz E, Guarner C. Transfusion strategies for acute upper gastrointestinal bleeding. N Engl J Med. 2013 Jan 3;14(1):11–21. doi: 10.1056/NEJMoa1211801.

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