| Title |
Why was this transfusion given? Identifying clinical indications for blood transfusion in health care data
|
|---|---|
| Published in |
Clinical Epidemiology, March 2018
|
| DOI | 10.2147/clep.s147142 |
| Pubmed ID | |
| Authors |
Loan R van Hoeven, Aukje L Kreuger, Kit CB Roes, Peter F Kemper, Hendrik Koffijberg, Floris J Kranenburg, Jan MM Rondeel, Mart P Janssen |
| Abstract |
To enhance the utility of transfusion data for research, ideally every transfusion should be linked to a primary clinical indication. In electronic patient records, many diagnostic and procedural codes are registered, but unfortunately, it is usually not specified which one is the reason for transfusion. Therefore, a method is needed to determine the most likely indication for transfusion in an automated way. An algorithm to identify the most likely transfusion indication was developed and evaluated against a gold standard based on the review of medical records for 234 cases by 2 experts. In a second step, information on misclassification was used to fine-tune the initial algorithm. The adapted algorithm predicts, out of all data available, the most likely indication for transfusion using information on medical specialism, surgical procedures, and diagnosis and procedure dates relative to the transfusion date. The adapted algorithm was able to predict 74.4% of indications in the sample correctly (extrapolated to the full data set 75.5%). A kappa score, which corrects for the number of options to choose from, was found of 0.63. This indicates that the algorithm performs substantially better than chance level. It is possible to use an automated algorithm to predict the indication for transfusion in terms of procedures and/or diagnoses. Before implementation of the algorithm in other data sets, the obtained results should be externally validated in an independent hospital data set. |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 10% |
| Unspecified | 1 | 5% |
| Student > Bachelor | 1 | 5% |
| Professor | 1 | 5% |
| Student > Ph. D. Student | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 12 | 57% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 24% |
| Arts and Humanities | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Unspecified | 1 | 5% |
| Decision Sciences | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 11 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 March 2018.
All research outputs
#20,472,403
of 23,031,582 outputs
Outputs from Clinical Epidemiology
#669
of 727 outputs
Outputs of similar age
#292,823
of 331,167 outputs
Outputs of similar age from Clinical Epidemiology
#16
of 17 outputs
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