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How to Improve Blood Component Wastage Reporting in Transfusion Medicine? An AI-Assisted Optimization for Reporting Frequency of Blood Products

Submitted:

09 July 2026

Posted:

14 July 2026

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Abstract
Background: Wastage as a Percentage of Issue (WAPI) is a widely used parameter for benchmarking blood product disposal rates and a quality indicator commonly reported across institutions and studies using various time frames. Because of the proportional calculation, the number of issued blood products is closely related to the statistical variability. Therefore, in this study, the aim was to evaluate the influence of reporting intervals across different blood components and to determine statistical reporting frequencies for transfusion medicine. Material and Methods: A three-year dataset from the institutional blood bank was used for testing. WAPI was calculated using different time intervals: weekly, monthly, quarterly, and yearly. Statistical reliability was evaluated in confidence interval–based analysis for precision. After the model was developed and the precision of the values was tested, independent verification was performed by the statistical department and an independent AI model. Results: The precision of the values demonstrated a volume-dependent pattern across blood components. WAPI calculations for high-volume products maintained high precision across all time intervals. A consistent ±1 % reliability was achieved in weekly reporting. Intermediate-volume products, such as fresh-frozen plasma, showed progressive improvement in stability with increasing aggregation and were highly reliable in monthly reporting. Apheresis platelet suspension required longer periods for stable reporting. Low-volume products showed greater variability over shorter intervals and were suggested for reporting on a quarterly or yearly basis. Discussion: Widening the time frame reduced variability and improved interpretability across all products. Reliability is primarily dependent on the issuance volume more than the reporting interval. Independent verification converged on the findings, supporting the robustness of the statistical methods. Equal reporting frequencies may not be appropriate for all blood components and are not recommended for quality improvement. Adapting reporting intervals according to product may improve the interpretability, comparability, and clinical utility of transfusion quality metrics.
Keywords: 
blood component wastage
;  
wastage as percentage of issue
;  
WAPI
;  
transfusion medicine
;  
reporting frequency
;  
quality indicators
;  
benchmarking
;  
blood bank management
;  
artificial intelligence
Subject: 
Public Health and Healthcare  -   Health Policy and Services

Introduction

Blood product wastage represents a critical performance indicator in transfusion medicine, directly affecting both resource utilization and patient safety. Because blood components are inherently perishable and require strict storage and handling conditions, minimizing unnecessary discard while maintaining adequate availability remains a central objective of blood bank management. [1,2,3,4,5]
Wastage as a Percentage of Issue (WAPI) is widely used to quantify the efficiency of blood component utilization. By relating discarded units to issued units, WAPI provides a proportional metric that enables comparisons across products, institutions, and time periods. [1,2,3,4,5,6,7,8,9,10] Owing to its intuitive percentage-based structure, WAPI is often interpreted as a straightforward quality indicator in both clinical practice and institutional benchmarking.
Despite its widespread use, there is no consensus regarding the optimal temporal resolution for WAPI reporting. Existing guidelines and institutional practices demonstrate considerable heterogeneity. While the College of American Pathologists (CAP) recommends monthly data collection, the National Accreditation Board for Hospitals and Healthcare Providers (NABH) mandates semi-annual reporting of selected quality indicators. [11,12] Operational toolkits such as those developed by Transfusion Ontario emphasize continuous monitoring with periodic review, without defining a fixed reporting interval. [13] Similarly, the Blood Stocks Management Scheme (BSMS) promotes continuous daily data entry combined with structured monthly reporting and additional periodic analyses. [14] Collectively, these approaches illustrate that temporal resolution in blood inventory and wastage monitoring is determined by institutional practice rather than by a standardized statistical framework.
This variability reflects an underlying methodological gap. While shorter reporting intervals may provide greater temporal sensitivity and enable timely operational interventions, they are also more susceptible to statistical instability. Conversely, longer aggregation periods increase denominator size and reduce variability, but may obscure short-term fluctuations and delay detection of clinically relevant trends. Consequently, different institutions adopt reporting frequencies based on practical considerations rather than on formal statistical justification.
Importantly, WAPI is fundamentally a proportion-based metric, and its statistical precision is intrinsically dependent on the denominator—the number of issued units within the selected time window. [15,16] Small denominators can lead to substantial variability, unstable estimates, and wide confidence intervals, particularly for low-volume blood products. In contrast, larger denominators improve precision but reduce temporal resolution. Despite this well-recognized trade-off, the relationship between temporal aggregation, denominator size, and statistical precision has not been systematically quantified in the context of WAPI reporting. [6,7,10,17]
The present study addresses this gap by evaluating the effect of temporal aggregation on the statistical precision of WAPI estimates across multiple blood product categories using a three-year institutional dataset. By comparing weekly, monthly, quarterly, and yearly aggregation levels, we aim to determine whether reporting intervals can be rationally aligned with predefined precision thresholds. Ultimately, this study proposes a denominator-based framework to guide the selection of optimal reporting intervals for transfusion quality performance metrics.

Material and Methods

Study Design, Dataset, and Ethical Considerations

This retrospective methodological study used a three-year dataset from an institutional blood bank, comprising daily records of blood component issuance and wastage. Data were extracted from the in-hospital Transfusion Unit of a 2750-bed, Level-1 trauma hospital and structured at the daily level for five blood components: erythrocyte suspension, apheresis platelet suspension, pooled platelet concentrate, fresh frozen plasma, and cryoprecipitate. For each blood component and each calendar day, the numbers of issued and discarded units were recorded. All data were presented in the Supplementary Material for tertiary verification and further analysis.
The study was approved by the Ethics Committee of the conducting institution’s hospital (approval date and number: 2025-350) and was conducted in accordance with the principles of the World Medical Association Declaration of Helsinki. The study used blood bank data with anonymized transfusion records. The requirement for informed consent was waived.

Definition of WAPI

The primary metric of interest was Wastage as Percentage of Issue (WAPI), defined as the number of discarded units divided by the number of issued units, multiplied by 100:
W A P I   % =   D i s c a r d e d   u n i t s I s s u e d   u n i t s ×   100
WAPI was calculated separately for each blood component for the primary analysis and for two wastage reasons in the secondary analysis: time-expired only and all-causes.

Temporal Aggregation Strategy

To evaluate the effect of reporting interval on WAPI stability and precision, the daily dataset was aggregated into weekly, monthly, quarterly, and yearly reporting intervals. Weekly aggregation was performed using consecutive seven-day blocks, whereas monthly, quarterly, and yearly aggregations followed calendar-based definitions. [1,2,4,6,7,9,18]
For each reporting interval, issued and discarded units were first summed within the relevant time window. WAPI was then recalculated from these aggregated numerator and denominator values:
W A P I p e r i o d   ( % ) = ∑ D i s c a r d e d   u n i t s ∑ I s s u e d   u n i t s × 100
Importantly, period-specific WAPI values were not obtained by averaging daily WAPI values. This approach was used to avoid denominator-induced weighting bias, since days or periods with low issuance volumes may produce unstable WAPI estimates.

Precision-Based Statistical Framework

Because WAPI is a proportion derived from discarded units over issued units, its statistical precision depends strongly on the denominator, namely, the number of issued units within each reporting interval. For each blood component and reporting interval, the observed WAPI proportion ( ρ ) was defined as:
ρ = W a s t e I s s u e
where n represents the number of issued units. The standard error of the proportion was calculated using the binomial standard error (SE) formula:
S E = ρ 1 − ρ n
The 95% confidence interval (CI) half-width was then calculated and expressed in percentage points:
C I h a l f − w i d t h = 100   ×   1.96 × S E
This confidence interval half-width was interpreted as the expected margin of error around the reported WAPI value. Smaller half-widths indicated more precise and therefore more reliable WAPI estimates.

Dominator-Based Precision Thresholds

To determine whether a given reporting interval provided sufficient precision, two predefined absolute precision thresholds were used: ±1 percentage point and ±5 percentage points. The ±1 percentage-point threshold was considered high precision, whereas the ±5 percentage-point threshold was considered operationally acceptable precision. The minimum number of issued units (n required) required to achieve a predefined margin of error was calculated as:
n r e q u i r e d = 1,96 2 × ρ ( 1 − ρ ) ( H / 100 ) 2
where p represents the observed product-specific WAPI proportion and H represents the desired absolute precision margin in percentage points.
For each blood component and reporting interval, the observed mean issuance volume was compared with the required denominator. If the observed issuance volume met the denominator requirements for ±1 percentage-point precision, the interval was classified as high precision. If it met only the ±5 percentage point threshold, it was classified as operationally acceptable. If neither threshold was met, the reporting interval was classified as insufficiently precise.

Use of Artificial Intelligence (AI) for Methodological Development

An AI-assisted methodological development process was used during the planning phase of the statistical framework. ChatGPT (OpenAI) was used as a guidance tool to develop the stepwise analytical workflow. ChatGPT did not access the dataset or perform the statistical calculations directly. Instead, it provided procedural guidance, while the authors executed all calculations in Microsoft Excel and IBM SPSS Version 29 Statistics. Descriptive statistics were presented as means, standard deviations, medians, and interquartile ranges, as appropriate.

Use of Artificial Intelligence (AI) for Secondary Verification

To independently evaluate the robustness of the proposed framework, the same methodological question and dataset structure were subsequently assessed using NotebookLM (Google). In contrast to ChatGPT, Notebook LM was given access to the complete dataset via Google Drive and generated independently an alternative exploratory model that included direct calculations.
This model emphasized cumulative WAPI estimation to determine the baseline value for both discard reasons: all causes and time expiration. A Margin of Error analysis was performed at a 95% confidence interval for testing the reliability of the different reporting time frames. A highly reliable reporting interval was assumed when the margin of error was found under 1%, whereas a reliable reporting interval was assumed when the margin of error was found between %1 and %5. Additionally, a formula was created to calculate the minimum issuance required for highly sensitive weekly reporting for any product.
The outputs generated by NotebookLM were not accepted as final statistical results without verification; however, the results were reported to use as an independent methodological comparator. A final verification of the statistical methodology was controlled and approved by all authors, one of whom is a professional statistician.

Results

The primary analysis was the calculation of denominator-dependent precision across four temporal aggregations: weekly (n=156), monthly (n=36), quarterly (n=12), and yearly (n=3). The mean and standard deviation of issuance, WAPI, precision percentage, suggested proportions, required issuance for %1 and %5 precision, and finally achieved categorical precision were listed for each product according to the specified time frames, as shown in Table 1. The achieved precision results, presented categorically in the last column of Table 1, were also used to generate a grey-scale heatmap in Table 2.
The margins of error calculated from the secondary analyses were presented in Table 3 and Table 4, respectively, in previously defined grey-scale categories for all-cause and time-expiration reasons. For all causes and time-expiration discards, weekly reporting was highly reliable for ES and FFP, with an extremely low percentage points within a 95% confidence interval.

Discussion

Interpretation of Findings

The central finding of this study is that the reliability of WAPI is strongly influenced by the volume of issued blood components, and that this relationship has direct implications for how frequently different products should be reported. In routine practice, WAPI is often presented at fixed intervals, such as weekly or monthly, without considering whether the underlying data volume is sufficient to support stable interpretation. [1,2,4,6,7,8,9,10,17,18]
Our results demonstrate that this approach may lead to misleading conclusions, particularly for low-volume products. Cryoprecipitate and pooled platelet concentrate showed substantial variability when evaluated at shorter time intervals, with wide confidence ranges and occasional extreme values. In contrast, high-volume components, such as erythrocyte suspensions, remained stable even at a weekly resolution. This difference reflects a practical reality familiar to clinicians: fluctuations in low-frequency events tend to appear exaggerated when examined over short periods.
From a clinical perspective, this means that reporting frequency should not be uniform across all blood products. Weekly reporting may be appropriate and informative for erythrocyte suspension, allowing timely identification of operational changes. However, applying the same reporting frequency to low-volume products may lead to overinterpretation of random variation, potentially resulting in unnecessary interventions.

Implications for Practice and Benchmarking

These findings have important implications for transfusion services and benchmarking studies. Current reporting practices vary widely across institutions and guidelines, often reflecting local habits rather than data-driven decisions. Our results suggest that a more tailored approach is needed.
For high-volume components, such as erythrocyte suspension, frequent reporting (including weekly analysis) appears both feasible and reliable. For intermediate-volume products, such as plasma and apheresis platelets, monthly or quarterly aggregation provides a more balanced representation of performance. In contrast, for low-volume products such as cryoprecipitate, meaningful interpretation requires longer observation periods, with quarterly or even yearly aggregation offering more stable estimates.
Importantly, these recommendations are not based on arbitrary time intervals, but on the relationship between data volume and interpretability. In practical terms, this means that the same reporting interval may be appropriate in one institution but not in another, depending on case volume.

Interpretation of Methodological Comparison

An additional strength of this study is the comparison of two independently developed analytical approaches. Although these approaches differed in their statistical structure, both led to the same practical conclusion: the frequency of reporting should be aligned with the volume of available data.
This convergence is clinically reassuring. It suggests that the observed patterns are not dependent on a specific analytical method, but rather reflect an inherent property of WAPI as a proportion-based indicator. While more complex statistical measures can be used to describe variability, the key message remains simple and clinically intuitive: smaller datasets produce less stable results.

Relevance to Existing Literature

WAPI has been widely used as a quality indicator in transfusion medicine, yet reporting practices remain inconsistent. [19,20,21,22] Some studies report annual values, while others focus on shorter intervals for operational monitoring. [3,23,24,25]. However, few studies have addressed whether these intervals are appropriate from a statistical or clinical standpoint. [15,16]
Our findings help to bridge this gap by providing a practical framework for interpreting WAPI results in relation to reporting frequency. Rather than focusing solely on the numerical value of WAPI, clinicians and transfusion specialists should consider whether the underlying data volume is sufficient to support reliable interpretation.

Practical Perspective

In daily practice, transfusion teams often review wastage data in response to perceived increases or fluctuations. Our results suggest that such fluctuations, particularly in low-volume products, may reflect random variation rather than true performance changes. Recognizing this distinction is essential to avoid unnecessary corrective actions.
At the same time, the ability to estimate the minimum data volume required for reliable interpretation offers a practical tool for planning audits and benchmarking studies. This approach may help institutions align their reporting practices with the scale of their activity, improving both the accuracy and the usefulness of performance metrics.

Conclusion

In summary, WAPI remains a valuable and widely used indicator of blood utilization efficiency. However, its interpretation is closely tied to the volume of underlying data. Adapting reporting frequency to product-specific volume characteristics may enhance the clinical relevance of WAPI and reduce the risk of misinterpretation in transfusion practice.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org.

Conflicts of Interest Statement

The authors declare no conflicts of interest related to this study.

Data Collection and Declaration of Artificial Intelligence Use

The authors would like to thank the staff of the hospital blood bank and transfusion medicine unit for their contributions to data collection and routine operational management. The authors also acknowledge the use of artificial intelligence–assisted analytical tools during the exploratory methodological development phase of this study. All statistical calculations, interpretations, and final methodological decisions were independently reviewed and performed by the authors.

Declaration of Funding

No funding was received or used for this study.

Competing Interest

None.

Declaration of Funding, Suport or Drug

None.

Decleration of Artificial Intelligence Use

During the preparation of this manuscript, the authors used ChatGPT to for language editing and readability check. All manuscript were critically reviewed and revised by the authors. All statistical analyses were conducted and verified in collaboration with the Department of Statistics. The authors take full responsibility for the accuracy, integrity, and content of the published work.

References

  1. Chapman, J. Unlocking the essentials of effective blood inventory management. Transfusion 2007, 47((2) Suppl, 190S–6S; discussion 201S. [Google Scholar] [CrossRef] [PubMed]
  2. Perera, G.; Hyam, C.; Taylor, C.; Chapman, J.F. Hospital blood inventory practice: the factors affecting stock level and wastage. Transfus. Med. 2009, 19(2), 99–104. [Google Scholar] [CrossRef] [PubMed]
  3. Heitmiller, E.S.; Hill, R.B.; Marshall, C.E.; Parsons, B.J.; Berkow, L.C.; Barrasso, C.A.; et al. Blood wastage reduction using Lean Sigma methodology. Transfusion 2010, 50(9), 1887–96. [Google Scholar] [CrossRef] [PubMed]
  4. Stanger, S.H.; Yates, N.; Wilding, R.; Cotton, S. Blood inventory management: hospital best practice. Transfus. Med. Rev. 2012, 26(2), 153–63. [Google Scholar] [CrossRef] [PubMed]
  5. Kelly, S.L.; Reed, M.J.; Innes, C.J.; Manson, L. A review of blood component usage in a large UK emergency department after implementation of simple measures. Emerg. Med. J. 2013, 30(10), 842–5. [Google Scholar] [CrossRef] [PubMed]
  6. Collins, R.A.; Wisniewski, M.K.; Waters, J.H.; Triulzi, D.J.; Yazer, M.H. Effectiveness of multiple initiatives to reduce blood component wastage. Am. J. Clin. Pathol. 2015, 143(3), 329–35. [Google Scholar] [CrossRef] [PubMed]
  7. Bedi, R.K.; Mittal, K.; Sood, T.; Kaur, P.; Kaur, G. Segregation of blood inventory: A key driver for optimum blood stock management in a resource-poor setting. Int. J. Appl. Basic Med. Res. 2016, 6(2), 119–22. [Google Scholar] [CrossRef] [PubMed]
  8. Yazer, M.H.; Abraham, S.; Beckman, N.; Folléa, G. International Society for Blood Transfusion international survey on blood product wastage in hospitals. ISBT Sci. Ser. 2016, 11(1), 24–31. [Google Scholar] [CrossRef]
  9. Staples, S.; Evans, H.; Caulfield, J.; Bend, M.; Foy, R.; Murphy, M.F.; et al. Opportunities to improve feedback to reduce blood component wastage: Results of a national scheme evaluation. Transfusion 2024, 64(7), 1223–32. [Google Scholar] [CrossRef] [PubMed]
  10. Chien, J.H.; Yao, C.Y.; Chen, H.F.; Ho, T.F. Trends in blood transfusion and causes of blood wastage: a retrospective analysis in a teaching hospital. BMC Health Serv. Res. 2025, 25(1), 67. [Google Scholar] [CrossRef] [PubMed]
  11. Providers NABfHaH. Quality Indicators for Blood Bank (QIBB); NABH: India, n.d.; Available online: https://international.nabh.co/H-Doc/QIBB.pdf.
  12. CoA, Pathologist. Blood Product Wastage Monitor; College of American Pathologist: Northfield, IL, n.d.; Available online: https://estore.cap.org/OA_HTML/xxCAPibeCCtpItmDspRte.jsp?item=689232&sitex=10020:22372:US.
  13. Ontario, T. Blood Inventory Management Best Practices for Hospital Transfusion Services. Transfusion Ontario. 2017. Available online: https://transfusionontario.org/wp-content/uploads/2020/08/InventoryManagementToolkit_rev2017-3.pdf.
  14. Transplant, NBa. Blood Stocks Management Scheme: Inventory Management Best Practice Review. United Kingdom, 2024. Available online: https://nhsbtdbe.blob.core.windows.net/umbraco-assets-corp/34578/bsms-inventory-management-best-practice-review-september-2024.pdf.
  15. Spiegelhalter, D.J. Funnel plots for comparing institutional performance. Stat. Med. 2005, 24(8), 1185–202. [Google Scholar] [CrossRef] [PubMed]
  16. Vach, W.; Wehberg, S.; Güntert, B.; Jakob, M.; Luta, G. Healthcare provider profiling: fixing observation period or fixing sample size? BMJ Open Qual. 2022, 11(2). [Google Scholar] [CrossRef] [PubMed]
  17. Tounsi, W.A.; Alzahrani, O.A.; Bukhari, A.G.; Garout, R.M.; Felimban, R.I.; Al-Musallam, R.H.; et al. Reducing blood component wastage through targeted interventions: A four-year retrospective study at a Saudi tertiary hospital. Transfus. Med. 2025, 35(6), 556–63. [Google Scholar] [CrossRef] [PubMed]
  18. Amini Kafi-Abad, S.; Omidkhoda, A.; Pourfatollah, A.A. Analysis of hospital blood components wastage in Iran (2005-2015). Transfus. Apher. Sci. 2019, 58(1), 34–8. [Google Scholar] [CrossRef] [PubMed]
  19. Dunbar, N.M.; Olson, N.J.; Szczepiorkowski, Z.M.; Martin, E.D.; Tysarcyk, R.M.; Triulzi, D.J.; et al. Blood component transfusion and wastage rates in the setting of massive transfusion in three regional trauma centers. Transfusion 2017, 57(1), 45–52. [Google Scholar] [CrossRef] [PubMed]
  20. Khan, S.; Allard, S.; Weaver, A.; Barber, C.; Davenport, R.; Brohi, K. A major haemorrhage protocol improves the delivery of blood component therapy and reduces waste in trauma massive transfusion. Injury 2013, 44(5), 587–92. [Google Scholar] [CrossRef] [PubMed]
  21. Mitra, B.; Garland, R.; Catalano, J.; O'Reilly, G.; Nevill, A. Reducing blood wastage through introduction of a transfusion team. Scand. J. Trauma Resusc. Emerg. Med. 2025, 33(1), 136. [Google Scholar] [CrossRef] [PubMed]
  22. Paganini, M.; Abowali, H.; Bosco, G.; Balouch, M.; Enten, G.; Deng, J.; et al. Quality Improvement Project of a Massive Transfusion Protocol (MTP) to Reduce Wastage of Blood Components. Int. J. Env. Res. Public Health 2021, 18(1). [Google Scholar] [CrossRef] [PubMed]
  23. Haran H, I SK, J S. Stemming the Flow: Causes and Solutions for Blood and Blood Component Wastage in a Tertiary Care Hospital. Cureus. Cureus, 2024; 16, 5, p. e59493. [CrossRef] [PubMed]
  24. Heddle, N.M.; Liu, Y.; Barty, R.; Webert, K.E.; Whittaker, S.; Gagliardi, K.; et al. Factors affecting the frequency of red blood cell outdates: an approach to establish benchmarking targets. Transfusion 2009, 49(2), 219–26. [Google Scholar] [CrossRef] [PubMed]
  25. McCullagh, J.; Proudlove, N.; Tucker, H.; Davies, J.; Edmondson, D.; Lancut, J.; et al. Making every drop count: reducing wastage of a novel blood component for transfusion of trauma patients. BMJ Open Qual. 2021, 10(3). [Google Scholar] [CrossRef] [PubMed]
Table 1. WAPI Performance and Precision Metrics Across Temporal Aggregation Levels.
Table 1. WAPI Performance and Precision Metrics Across Temporal Aggregation Levels.
Product* Period** N Mean Issueance SD Issuance Mean WAPI (%) SD WAPI Mean %95 CI Half-Width SD CI Half-Width Proportion (p)± Required Issuance (±1%) Required Issuance (±5% Precision Achieved***
RBC (w) 156 964,69 141,78 0,87 0,44 0,19 0,08 0,01 330,09 13,20 Yes (±1%)
(m) 36 4180,33 482,89 0,86 0,29 0,28 0,05 0,01 325,76 13,03 Yes (±1%)
(q) 12 12541,00 1370,35 0,85 0,21 0,16 0,03 0,01 325,08 13,00 Yes (±1%)
(y) 3 50164,00 5438,59 0,85 0,09 0,08 0,01 0,01 323,53 12,94 Yes (±1%)
FFP (w) 156 425,97 110,46 5,36 2,55 2,14 0,59 0,05 1948,11 77,92 Yes (±5%)
(m) 36 1845,86 347,59 5,30 1,47 1,03 0,19 0,05 1927,07 77,08 Yes (±5%)
(q) 12 5537,58 872,67 5,26 1,19 0,59 0,09 0,05 1915,80 76,63 Yes (±1%)
(y) 3 22150,33 2685,14 5,21 0,52 0,29 0,01 0,05 1895,85 75,83 Yes (±1%)
APLT (w) 156 81,16 29,70 4,79 3,97 4,64 3,00 0,05 1751,93 70,08 Yes (±5%)
(m) 36 351,69 120,38 4,63 2,18 2,31 0,91 0,05 1697,39 67,90 Yes (±5%)
(q) 12 1055,08 356,37 4,61 1,83 1,34 0,49 0,05 1689,34 67,57 Yes (±5%)
(y) 3 4220,33 1526,10 4,55 1,55 0,66 0,22 0,05 1669,20 66,77 Yes (±1%)
PPLT (w) 156 75,16 30,99 9,20 8,58 6,96 5,32 0,09 3208,90 128,36 No
(m) 36 325,69 121,52 8,75 5,59 3,39 2,05 0,09 3068,64 122,75 Yes (±5%)
(q) 12 977,08 337,47 8,46 3,94 1,91 1,08 0,08 2974,12 118,96 Yes (±5%)
(y) 3 3908,33 796,73 7,93 2,85 0,86 0,24 0,08 2803,74 112,15 Yes (±1%)
CRYO (w) 156 14,78 11,61 11,73 18,54 11,17 12,98 0,12 3976,58 159,06 No
(m) 36 64,06 30,35 9,51 5,87 7,29 2,47 0,10 3306,89 132,28 No
(q) 12 192,17 64,92 9,63 3,47 4,24 0,87 0,10 3341,90 133,68 Yes (±5%)
(y) 3 768,67 60,70 9,92 1,69 2,11 0,18 0,10 3433,81 137,35 Yes (±5%)
*RBC, erythrocyte suspension; FFP, fresh frozen plasma; APLT, apheresis platelet suspension; PPLT, pooled platelet concentrate; CRYO, cryoprecipitate; ** (w) weekly; (m) monthly; (q) quarterly; (y) yearly. ***Precision was classified as ±1% if the observed mean issuance exceeded the required issuance threshold for a ±1% margin of error; ±5% if exceeding the ±5% threshold only; and No if neither threshold was met.±Proportion (p) represents the mean WAPI expressed as a decimal proportion (mean WAPI/100)
Table 2. A grey-scale heat map of denominator-dependent precision percentages of WAPI. A lighter shade corresponds to higher precision and a narrower confidence interval, which is classified as high precision for reporting. The middle shaded cells represent operationally acceptable precisions. The darker shade reflects greater uncertainty driven by smaller denominators and is suggested to be classified as insufficiently precise.
Table 2. A grey-scale heat map of denominator-dependent precision percentages of WAPI. A lighter shade corresponds to higher precision and a narrower confidence interval, which is classified as high precision for reporting. The middle shaded cells represent operationally acceptable precisions. The darker shade reflects greater uncertainty driven by smaller denominators and is suggested to be classified as insufficiently precise.
Table 2: A grey-scale heat map of denominator-dependent precision percentages of WAPI.
Product Weekly Monthly Quarterly Yearly
Erythrocyte Suspension ±1% ±1% ±1% ±1%
Fresh Frozen Plasma ±5% ±5% ±1% ±1%
Apheresis Platelet Suspension ±5% ±5% ±5% ±1%
Pooled Platelet Concentrate >%5 ±5% ±5% ±1%
Cryo-Precipitate >%5 >%5 ±5% ±5%
Table 3. Heatmap of precision point of margin-of-error and volatility across temporal aggregations for all-cause wastage. All values represent the half-width of the 95% confidence interval expressed in percentage points. Precision categories (±1% and ±5%) are indicated for interpretability. Pink colored cells indicate the first stability for reliable reporting.
Table 3. Heatmap of precision point of margin-of-error and volatility across temporal aggregations for all-cause wastage. All values represent the half-width of the 95% confidence interval expressed in percentage points. Precision categories (±1% and ±5%) are indicated for interpretability. Pink colored cells indicate the first stability for reliable reporting.
Table 3: Volatility ( Coefficient of Variance) of the WAPI values and Precision Points of Margins of Errors in specified time frames for all-reason discards.
ProductType Weekly Monthly Quarterly Yearly
CV MoE CV MoE CV MoE CV MoE
Erythrocyte Suspension 0.52 0.07 (±1%) 0.34 0.09 (±1%) 0.24 0.11 (±1%) 0.11 0.10 (±1%)
Fresh Frozen Plasma 0.47 0.39 (±1%) 0.28 0.48 (±1%) 0.23 0.67 (±1%) 0.10 0.58 (±1%)
Apheresis Platelet Suspension 0.81 0.60 (±1%) 0.47 0.71 (±1%) 0.40 1.03 (±1%) 0.34 1.76 (±5%)
Pooled Platelet Concentrate 0.83 1.16 (±5%) 0.64 1.83 (±5%) 0.47 2.23 (±5%) 0.36 3.22 (±5%)
Cryo-Precipitate 1.53 2.75 (±5%) 0.62 1.92 (±5%) 0.36 1.96 (±5%) 0.17 1.91 (±5%)
CV: Coefficient of Variance, indicator of Volatiliy. Calculated by dividing standard deviation to mean of WAPI. MoE: Margin of Error was calculated by multiplying Standart Error and 1.96. Value at the pink cells show first stability for reliable reporting in %95 confidence interval.
Table 4. Heatmap of precision point of margin-of-error and volatility across temporal aggregations for time-expirated wastage. All values represent the half-width of the 95% confidence interval expressed in percentage points. Precision categories (±1% and ±5%) are indicated for interpretability. Pink colored cells indicate the first stability for reliable reporting.
Table 4. Heatmap of precision point of margin-of-error and volatility across temporal aggregations for time-expirated wastage. All values represent the half-width of the 95% confidence interval expressed in percentage points. Precision categories (±1% and ±5%) are indicated for interpretability. Pink colored cells indicate the first stability for reliable reporting.
Table 4: Volatility ( Coefficient of Variance) of the WAPI values and Precision Points of Margins of Errors in specified time frames for time-expiration caused discards.
ProductType Weekly Monthly Quarterly Yearly
CV MoE CV MoE CV MoE CV MoE
Erythrocyte Suspension 1.13 0.05 (±1%) 0.71 0.07 (±1%) 0.46 0.07 (±1%) 0.19 0.06 (±1%)
Fresh Frozen Plasma 1.19 0.10 (±1%) 0.64 0.12 (±1%) 0.28 0.09 (±1%) 0.11 0.07 (±1%)
Apheresis Platelet Suspension 0.87 0.59 (±1%) 0.50 0.70 (±1%) 0.43 1.04 (±5%) 0.37 1.76 (±5%)
Pooled Platelet Concentrate 0.91 1.44 (±5%) 0.70 0.98 (±5%) 0.51 1.09 (±5%) 0.35 0.87 (±1%)
Cryo-Precipitate 3.46 1.16 (±5%) 1.53 1.82 (±5%) 1.00 2.15 (±5%) 0.42 2.80 (±5%)
CV: Coefficient of Variance, indicator of Volatiliy. Calculated by dividing standard deviation to mean of WAPI. MoE: Margin of Error was calculated by multiplying Standart Error and 1.96. Value at the pink cells show first stability for reliable reporting in %95 confidence interval.
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