Volume 22, Issue 1 (Spring 2025)                   2025, 22(1): 54-62 | Back to browse issues page

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Abdolrahimzadeh H, Naderi N, Gheibi Z, Kasraian L. Forecasting Blood and Blood Product Demand: A Step Towards Optimal Resource Management in Trauma Patients. Journal of Iranian Blood Transfusion 2025; 22 (1) :54-62
URL: http://bloodjournal.ir/article-1-1565-en.html
Forecasting Blood and Blood Product Demand: A Step Towards Optimal Resource Management in Trauma Patients
Hossein Abdolrahimzadeh , Nima Naderi , Zahra Gheibi , Leila Kasraian *
Keywords: Trauma, Forecasting, Blood Transfusion, Patients
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Forecasting Blood and Blood Product Demand:
A Step Towards Optimal Resource Management
 in Trauma Patients
 ‎
Hossein Abdul Rahimzadeh1, Nima Naderi2        , Zahran Ghaybi3, Leila Kasraian4,5

1Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
2Shiraz Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
3Shiraz University of Medical Sciences, Shiraz, Iran
4Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran
5Shiraz Blood Transfusion Center, Shiraz, Iran







http://dx.doi.org/10.61186/bloodj.22.1.54
    



Citation:
Abdul Rahimzadeh H, Naderi N, Ghaybi Z, Kasraian L. Forecasting Blood and Blood Product Demand: A Step Towards Optimal Resource Management in Trauma Patients . J Iran Blood Transfus. 2025: 22 (1) : 54-62.
    



Correspondence: Kasraian L., Associate Professor of Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine and Shiraz Blood Transfusion Center.
P.O.Box: 1153, Shiraz, Iran.
Tel: (+9871) 6273445
E-mail:
1- Acridine Orange
1- Biological safety cabinet
1- Platelet Concentrate
2- Food and Drug Administration
3- Normal Skin Flora
4- Platelet Rich Plasma-Platelet Concentrate
5- Eosin-Methylene blue
6- Thioglycolate
1- Acridine Orange
1- Biological safety cabinet
1- Platelet Concentrate
2- Food and Drug Administration
3- Normal Skin Flora
4- Platelet Rich Plasma-Platelet Concentrate
5- Eosin-Methylene blue
6- Thioglycolate
l.kasraian@tmi.ac.ir  
Received: 2024/12/08
Accepted: 2025/02/18
 
 A B S T R A C T
Background and Objectives
Severe bleeding due to trauma remains one of the leading preventable causes of mortality. Ensuring the timely and accurate availability of blood and blood components poses a major challenge in healthcare centers. This study aimed to forecast blood and blood products demand in trauma patients at Shahid Rajaee Hospital in Shiraz and assess the accuracy of these prediction to enhance resource and paitent outcome .
Materials and Methods
This cross-sectional analytical study collected data on the request and consumption of blood, platelets, and plasma units among trauma patients during 2017-2020. The ARIMA (Autoregressive Integrated Moving Average) time-series model was applied to forecast blood product demand for 2021–2022. To assess the prediction accuracy, the mean number of predicted units was compared with actual requests and usage using an independent t-test.
Results
During the study period, the average consumption of blood and platelets was 6,266 and 1,697 units, respectively, with no significant differences observed. Plasma consumption averaged 3,356 units, showing a slight increase. The predicted mean request for blood units was 10,790, closely aligned with the actual mean of 9,276. Similarly, the predicted and actual platelet requests were 1,781 and 1,891 units, respectively. The predicted plasma request (4,262 units) exceeded the actual request (3,189 units). The predicted cryoprecipitate request (279 units) showed no significant difference from the actual mean of 348 units.
Conclusions 
The ARIMA time-series model appears to provide reasonably reliable forecasts of blood and blood product demand based on historical patterns. This method can enhance planning and optimizing resource management, helping to minimize strategic shortages and waste of blood products.
Key words: Trauma, Forecasting, Blood Transfusion, Patients

      References:
  1. Cornero SG, Maegele M, Lefering R, Abbati C, Gupta S, Sammartano F, et al. predictive factors for massive transfusion in trauma: a novel clinical score from an Italian trauma center and German trauma registry. J Clin Med 2020; 9(10): 3235. [DOI:10.3390/jcm9103235] [PMID]
  2. Rossaint R, Afshari A, Bouillon B, Cerny V, Cimpoesu D, Curry N, et al. The European guideline on management of major bleeding and coagulopathy following trauma. Crit Care 2023; 27(1): 80. [DOI:10.1186/s13054-023-04327-7] [PMID]
  3. Cantle PM, Cotton BA. Prediction of massive transfusion in trauma. Crit Care Clin 2017; 33(1): 71-84. [DOI:10.1016/j.ccc.2016.08.002] [PMID]
  4. Saviano A, Perotti C, Zanza C, Longhitano Y, Ojetti V, Franceschi F, et al. Blood Transfusion for Major Trauma in Emergency Department. Diagnostics 2024; 14(7): 708. [DOI:10.3390/diagnostics14070708] [PMID]
  5. White NJ, Ward KR, Pati S, Strandenes G, Cap AP. Hemorrhagic blood failure: oxygen debt, coagulopathy, and endothelial damage. J Trauma Acute Care Surg 2017; 82(6S): S41-S49. [DOI:10.1097/TA.0000000000001436] [PMID]
  6. Miri-Moghaddam E, Bizhaem SK, Moezzifar Z, Salmani F. Long-term prediction of Iranian bloodproduct supply using LSTM: a 5-year forecast. BMC Med Inform Decis Mak 2024; 24(1): 213. [DOI:10.1186/s12911-024-02614-z] [PMID]
  7. Nandi AK, Roberts DJ, Nandi AK. Prediction paradigm involving time series applied to total blood issues data from England. Transfusion 2020; 60 (3): 535-543. [DOI:10.1111/trf.15705] [PMID]
  8. Hanna M, Knittel J, Gillihan J. The use of whole blood transfusion in trauma. Curr Anesthesiol Rep 2022; 12(2): 234-9. [DOI:10.1007/s40140-021-00514-w] [PMID]
  9. Kasraian L, Naderi N, Hosseini M, Taheri Akerdi A, Paydar S, Abdolrahimzadeh Fard H. A novel scoring system for early prediction of massive transfusion requirement in trauma patients. Intern Emerg Med 2024; 19(5): 1431-8. https://doi.org/10.1007/s11739-024-03541-7 [DOI:10.1007/s11739-024-03650-3] [PMID]
  10. Rafieemehr H, Golparian M, Jiriaei Sharahi N, Vaseie M, Salimi R. The rate of blood products transfusion in the treatment ofpatients with acute trauma referred to Besat Hospital in Hamadan in 2018-2019. Sci J Iran Blood Transfus Organ 2022; 19(4): 270-83. [Article in Farsi]
  1. Drackley A, Newbold KB, Paez A, Heddle N. Forecasting Ontario's blood supply and demand. Transfusion 2012; 52(2): 366-74. [DOI:10.1111/j.1537-2995.2011.03280.x] [PMID]
  2. Crowe E, DeSantis SM, Bonnette A, Jansen JO, Yamal JM, Holcomb JB, et al. Whole blood transfusion versus component therapy in trauma resuscitation: a systematic review and meta‐analysis. J Am Coll Emerg Physicians Open 2020; 1(4): 633-41. [DOI:10.1002/emp2.12089] [PMID]
  3. Lier H, Fries D. Emergency blood transfusion for trauma and perioperative resuscitation: standard of care. Transfus Med Hemother 2021; 48(6): 366-76. [DOI:10.1159/000519696] [PMID]
  4. Shackelford S, Yang S, Hu P, Miller C, Anazodo A, Galvagno S, et al. Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values. J Trauma Acute Care Surg 2015; 79(4): S175-S80. [DOI:10.1097/TA.0000000000000738] [PMID]
  5. Mishra S, Daga A, Gupta A. Inventory management practices in the blood bank of an institute of national importance in India. J Family Med Prim Care 2021; 10(12): 4489-92. [DOI:10.4103/jfmpc.jfmpc_1000_21] [PMID]
  6. Sarvestani SE, Hatam N, Seif M, Kasraian L, Lari FS, Bayati M. Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches. Scientific Reports. 2022 Dec 20;12(1): 22031. [DOI:10.1038/s41598-022-26461-y] [PMID]
  7. Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, et al. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64(1): 162-84. [DOI:10.1111/trf.17582] [PMID]
  8. Khaldi R, El Afia A, Chiheb R. Artificial neural networks for blood demand forecasting. Transfus Med 2022; 32(4): 245-52.

 
Type of Study: Research | Subject: Blood transfusion medicine
References
1. Cornero SG, Maegele M, Lefering R, Abbati C, Gupta S, Sammartano F, et al. predictive factors for massive transfusion in trauma: a novel clinical score from an Italian trauma center and German trauma registry. J Clin Med 2020; 9(10): 3235. [DOI:10.3390/jcm9103235] [PMID] []
2. Rossaint R, Afshari A, Bouillon B, Cerny V, Cimpoesu D, Curry N, et al. The European guideline on management of major bleeding and coagulopathy following trauma. Crit Care 2023; 27(1): 80. [DOI:10.1186/s13054-023-04327-7] [PMID] []
3. Cantle PM, Cotton BA. Prediction of massive transfusion in trauma. Crit Care Clin 2017; 33(1): 71-84. [DOI:10.1016/j.ccc.2016.08.002] [PMID]
4. Saviano A, Perotti C, Zanza C, Longhitano Y, Ojetti V, Franceschi F, et al. Blood Transfusion for Major Trauma in Emergency Department. Diagnostics 2024; 14(7): 708. [DOI:10.3390/diagnostics14070708] [PMID] []
5. White NJ, Ward KR, Pati S, Strandenes G, Cap AP. Hemorrhagic blood failure: oxygen debt, coagulopathy, and endothelial damage. J Trauma Acute Care Surg 2017; 82(6S): S41-S49. [DOI:10.1097/TA.0000000000001436] [PMID] []
6. Miri-Moghaddam E, Bizhaem SK, Moezzifar Z, Salmani F. Long-term prediction of Iranian bloodproduct supply using LSTM: a 5-year forecast. BMC Med Inform Decis Mak 2024; 24(1): 213. [DOI:10.1186/s12911-024-02614-z] [PMID] []
7. Nandi AK, Roberts DJ, Nandi AK. Prediction paradigm involving time series applied to total blood issues data from England. Transfusion 2020; 60 (3): 535-543. [DOI:10.1111/trf.15705] [PMID] []
8. Hanna M, Knittel J, Gillihan J. The use of whole blood transfusion in trauma. Curr Anesthesiol Rep 2022; 12(2): 234-9. [DOI:10.1007/s40140-021-00514-w] [PMID] []
9. Kasraian L, Naderi N, Hosseini M, Taheri Akerdi A, Paydar S, Abdolrahimzadeh Fard H. A novel scoring system for early prediction of massive transfusion requirement in trauma patients. Intern Emerg Med 2024; 19(5): 1431-8. https://doi.org/10.1007/s11739-024-03541-7 [DOI:10.1007/s11739-024-03650-3] [PMID]
10. Rafieemehr H, Golparian M, Jiriaei Sharahi N, Vaseie M, Salimi R. The rate of blood products transfusion in the treatment ofpatients with acute trauma referred to Besat Hospital in Hamadan in 2018-2019. Sci J Iran Blood Transfus Organ 2022; 19(4): 270-83. [Article in Farsi]
11. Drackley A, Newbold KB, Paez A, Heddle N. Forecasting Ontario's blood supply and demand. Transfusion 2012; 52(2): 366-74. [DOI:10.1111/j.1537-2995.2011.03280.x] [PMID]
12. Crowe E, DeSantis SM, Bonnette A, Jansen JO, Yamal JM, Holcomb JB, et al. Whole blood transfusion versus component therapy in trauma resuscitation: a systematic review and meta‐analysis. J Am Coll Emerg Physicians Open 2020; 1(4): 633-41. [DOI:10.1002/emp2.12089] [PMID] []
13. Lier H, Fries D. Emergency blood transfusion for trauma and perioperative resuscitation: standard of care. Transfus Med Hemother 2021; 48(6): 366-76. [DOI:10.1159/000519696] [PMID] []
14. Shackelford S, Yang S, Hu P, Miller C, Anazodo A, Galvagno S, et al. Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values. J Trauma Acute Care Surg 2015; 79(4): S175-S80. [DOI:10.1097/TA.0000000000000738] [PMID]
15. Mishra S, Daga A, Gupta A. Inventory management practices in the blood bank of an institute of national importance in India. J Family Med Prim Care 2021; 10(12): 4489-92. [DOI:10.4103/jfmpc.jfmpc_1000_21] [PMID] []
16. Sarvestani SE, Hatam N, Seif M, Kasraian L, Lari FS, Bayati M. Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches. Scientific Reports. 2022 Dec 20;12(1): 22031. [DOI:10.1038/s41598-022-26461-y] [PMID] []
17. Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, et al. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64(1): 162-84. [DOI:10.1111/trf.17582] [PMID] []
18. Khaldi R, El Afia A, Chiheb R. Artificial neural networks for blood demand forecasting. Transfus Med 2022; 32(4): 245-52.
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