Prediction of Perioperative Transfusions Using an Artificial Neural Network

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2020 Feb 25

PMID 32092108

Citations 14

Authors

Steven Walczak

Vic Velanovich

Affiliations

Soon will be listed here.

Abstract

Background: Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations.

Methods: Over 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients.

Results: Various ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity.

Conclusion: ANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.

Citing Articles

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

Sheikhalishahi S, Goss S, Seidlmayer L, Zaghdoudi S, Hinske L, Kaspar M BMC Med Inform Decis Mak. 2024; 24(1):397.

PMID: 39695617 PMC: 11657635. DOI: 10.1186/s12911-024-02800-z.

Robust Meta-Model for Predicting the Likelihood of Receiving Blood Transfusion in Non-traumatic Intensive Care Unit Patients.

Rafiei A, Moore R, Choudhary T, Marshall C, Smith G, Roback J Health Data Sci. 2024; 4:0197.

PMID: 39507297 PMC: 11538953. DOI: 10.34133/hds.0197.

A Novel Scoring System Predicting Red Blood Cell Transfusion Requirements in Patients Undergoing Invasive Spine Surgery.

Schenk A, Ende J, Hoch J, Guresir E, Grabert J, Coburn M J Clin Med. 2024; 13(4).

PMID: 38398261 PMC: 10888619. DOI: 10.3390/jcm13040948.

Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.

Ansari M, Jain D, Budhiraja S Hematol Transfus Cell Ther. 2023; 46 Suppl 5:S13-S23.

PMID: 37996385 PMC: 11670722. DOI: 10.1016/j.htct.2023.09.2365.

Machine learning in transfusion medicine: A scoping review.

Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong W Transfusion. 2023; 64(1):162-184.

PMID: 37950535 PMC: 11497333. DOI: 10.1111/trf.17582.

References

Das A, Ben-Menachem T, Cooper G, Chak A, Sivak Jr M, Gonet J . Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet. 2003; 362(9392):1261-6. DOI: 10.1016/S0140-6736(03)14568-0. View

Litmathe J, Boeken U, Feindt P, Gams E . Predictors of homologous blood transfusion for patients undergoing open heart surgery. Thorac Cardiovasc Surg. 2003; 51(1):17-21. DOI: 10.1055/s-2003-37281. View

Spinella P, Holcomb J . Resuscitation and transfusion principles for traumatic hemorrhagic shock. Blood Rev. 2009; 23(6):231-40. PMC: 3159517. DOI: 10.1016/j.blre.2009.07.003. View

Frisch N, Wessell N, Charters M, Yu S, Jeffries J, Silverton C . Predictors and complications of blood transfusion in total hip and knee arthroplasty. J Arthroplasty. 2014; 29(9 Suppl):189-92. DOI: 10.1016/j.arth.2014.03.048. View

Hayn D, Kreiner K, Kastner P, Breznik N, Hofmann A, Gombotz H . Data Driven Methods for Predicting Blood Transfusion Needs in Elective Surgery. Stud Health Technol Inform. 2016; 223:9-16. View

Barr P, Donnelly M, Cardwell C, Alam S, Morris K, Parker M . Drivers of transfusion decision making and quality of the evidence in orthopedic surgery: a systematic review of the literature. Transfus Med Rev. 2011; 25(4):304-16.e1-6. DOI: 10.1016/j.tmrv.2011.04.003. View

Carson J, Grossman B, Kleinman S, Tinmouth A, Marques M, Fung M . Red blood cell transfusion: a clinical practice guideline from the AABB*. Ann Intern Med. 2012; 157(1):49-58. DOI: 10.7326/0003-4819-157-1-201206190-00429. View

Benarroch-Gampel J, Sheffield K, Duncan C, Brown K, Han Y, Townsend Jr C . Preoperative laboratory testing in patients undergoing elective, low-risk ambulatory surgery. Ann Surg. 2012; 256(3):518-28. PMC: 3488956. DOI: 10.1097/SLA.0b013e318265bcdb. View

Feldman K, Davis D, Chawla N . Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration. J Biomed Inform. 2015; 57:377-85. DOI: 10.1016/j.jbi.2015.07.017. View

10.

Madhu Krishna N, Nagaraja P, Singh N, Nanjappa S, Kumar K, Prabhakar V . Evaluation of risk scores in predicting perioperative blood transfusions in adult cardiac surgery. Ann Card Anaesth. 2019; 22(1):73-78. PMC: 6350431. DOI: 10.4103/aca.ACA_18_18. View

11.

Massicotte L, Sassine M, Lenis S, Roy A . Transfusion predictors in liver transplant. Anesth Analg. 2004; 98(5):1245-51, table of contents. DOI: 10.1213/01.ane.0000111184.21278.07. View

12.

Dreiseitl S, Ohno-Machado L . Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform. 2003; 35(5-6):352-9. DOI: 10.1016/s1532-0464(03)00034-0. View

13.

Magovern J, Sakert T, Benckart D, Burkholder J, Liebler G, Magovern Sr G . A model for predicting transfusion after coronary artery bypass grafting. Ann Thorac Surg. 1996; 61(1):27-32. DOI: 10.1016/0003-4975(95)00808-X. View

14.

Luo J, Leu H, Huang K, Huang C, Hou M, Lin H . Incidence of bleeding from gastroduodenal ulcers in patients with end-stage renal disease receiving hemodialysis. CMAJ. 2011; 183(18):E1345-51. PMC: 3255160. DOI: 10.1503/cmaj.110299. View

15.

Tobian A, Heddle N, Wiegmann T, Carson J . Red blood cell transfusion: 2016 clinical practice guidelines from AABB. Transfusion. 2016; 56(10):2627-2630. DOI: 10.1111/trf.13735. View

16.

Wu W, Trivedi A, Friedmann P, Henderson W, Smith T, Poses R . Association between hospital intraoperative blood transfusion practices for surgical blood loss and hospital surgical mortality rates. Ann Surg. 2012; 255(4):708-14. DOI: 10.1097/SLA.0b013e31824a55b9. View

17.

Goodnough L, Maggio P, Hadhazy E, Shieh L, Hernandez-Boussard T, Khari P . Restrictive blood transfusion practices are associated with improved patient outcomes. Transfusion. 2014; 54(10 Pt 2):2753-9. DOI: 10.1111/trf.12723. View

18.

Lisboa P, Taktak A . The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw. 2006; 19(4):408-15. DOI: 10.1016/j.neunet.2005.10.007. View

19.

Zheng F, Cammisa Jr F, Sandhu H, Girardi F, Khan S . Factors predicting hospital stay, operative time, blood loss, and transfusion in patients undergoing revision posterior lumbar spine decompression, fusion, and segmental instrumentation. Spine (Phila Pa 1976). 2002; 27(8):818-24. DOI: 10.1097/00007632-200204150-00008. View

20.

Park J, Sandberg I . Universal Approximation Using Radial-Basis-Function Networks. Neural Comput. 2019; 3(2):246-257. DOI: 10.1162/neco.1991.3.2.246. View