The addition of ROTEM parameter did not significantly improve the massive transfusion prediction in severe trauma patients Article Swipe
Dongyup Baik
,
Seok Ran Yeom
,
Sung Wook Park
,
Youngmo Cho
,
Wook Tae Yang
,
Hoon Kwon
,
Jae Il Lee
,
Jun Kyeung Ko
,
Hyuk Jin Choi
,
Up Huh
,
Tae Sik Goh
,
Chanhee Song
,
Lee Hwangbo
,
Il Jae Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1951300/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1951300/v1
ObjectiveThis study aimed to assess whether rotational thermoelectrometry (ROTEM) data could improve the massive transfusion (MT) prediction model.MethodThis was a single-center, retrospective study. Patients who presented to the trauma center and underwent ROTEM between 2016 and 2020 were included. The primary and secondary outcomes were massive transfusion and in-hospital mortality, respectively. We constructed two models using multivariate logistic regression with backward conditional stepwise elimination (Model 1: without ROTEM parameter and Model 2: with ROTEM parameters). The area under the receiver operating characteristic curve (AUROC) was calculated to assess the predictive ability of the models.ResultIn total, 969 patients were included; 196 (20.2%) received MT. The in-hospital mortality rate was 14.1%. For MT, the AUROC was 0.854 (95% confidence interval [CI], 0.825-0.883) and 0.860 (95% CI, 0.832-0.888) for Model 1 and 2, respectively. For in-hospital mortality, the AUROC was 0.886 (95% CI, 0.857-0.915) and 0.889 (95% CI, 0.861-0.918) for Model 1 and 2, respectively. The AUROC values for Models 1 and 2 were not statistically different for either MT or in-hospital mortality.ConclusionWe found that addition of the ROTEM parameter did not significantly improve the predictive power of MT and in-hospital mortality in trauma patients.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1951300/v1
- https://www.researchsquare.com/article/rs-1951300/latest.pdf
- OA Status
- green
- Cited By
- 2
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294904546
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4294904546Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1951300/v1Digital Object Identifier
- Title
-
The addition of ROTEM parameter did not significantly improve the massive transfusion prediction in severe trauma patientsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-07Full publication date if available
- Authors
-
Dongyup Baik, Seok Ran Yeom, Sung Wook Park, Youngmo Cho, Wook Tae Yang, Hoon Kwon, Jae Il Lee, Jun Kyeung Ko, Hyuk Jin Choi, Up Huh, Tae Sik Goh, Chanhee Song, Lee Hwangbo, Il Jae WangList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-1951300/v1Publisher landing page
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https://www.researchsquare.com/article/rs-1951300/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-1951300/latest.pdfDirect OA link when available
- Concepts
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Medicine, Receiver operating characteristic, Confidence interval, Logistic regression, Trauma center, Stepwise regression, Emergency medicine, Retrospective cohort study, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2022: 1Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study. | 23 |
| abstract_inverted_index.total, | 95 |
| abstract_inverted_index.trauma | 29, 191 |
| abstract_inverted_index.values | 155 |
| abstract_inverted_index.(20.2%) | 101 |
| abstract_inverted_index.(AUROC) | 84 |
| abstract_inverted_index.(ROTEM) | 9 |
| abstract_inverted_index.ability | 91 |
| abstract_inverted_index.between | 34 |
| abstract_inverted_index.improve | 12, 181 |
| abstract_inverted_index.massive | 14, 46 |
| abstract_inverted_index.primary | 41 |
| abstract_inverted_index.whether | 6 |
| abstract_inverted_index.without | 67 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Patients | 24 |
| abstract_inverted_index.addition | 173 |
| abstract_inverted_index.backward | 61 |
| abstract_inverted_index.interval | 118 |
| abstract_inverted_index.logistic | 58 |
| abstract_inverted_index.outcomes | 44 |
| abstract_inverted_index.patients | 97 |
| abstract_inverted_index.received | 102 |
| abstract_inverted_index.receiver | 80 |
| abstract_inverted_index.stepwise | 63 |
| abstract_inverted_index.different | 164 |
| abstract_inverted_index.included. | 39 |
| abstract_inverted_index.included; | 99 |
| abstract_inverted_index.mortality | 106, 189 |
| abstract_inverted_index.operating | 81 |
| abstract_inverted_index.parameter | 69, 177 |
| abstract_inverted_index.patients. | 192 |
| abstract_inverted_index.presented | 26 |
| abstract_inverted_index.secondary | 43 |
| abstract_inverted_index.underwent | 32 |
| abstract_inverted_index.calculated | 86 |
| abstract_inverted_index.confidence | 117 |
| abstract_inverted_index.mortality, | 50, 134 |
| abstract_inverted_index.prediction | 17 |
| abstract_inverted_index.predictive | 90, 183 |
| abstract_inverted_index.regression | 59 |
| abstract_inverted_index.rotational | 7 |
| abstract_inverted_index.conditional | 62 |
| abstract_inverted_index.constructed | 53 |
| abstract_inverted_index.elimination | 64 |
| abstract_inverted_index.in-hospital | 49, 105, 133, 169, 188 |
| abstract_inverted_index.transfusion | 15, 47 |
| abstract_inverted_index.0.825-0.883) | 120 |
| abstract_inverted_index.0.832-0.888) | 125 |
| abstract_inverted_index.0.857-0.915) | 141 |
| abstract_inverted_index.0.861-0.918) | 146 |
| abstract_inverted_index.multivariate | 57 |
| abstract_inverted_index.parameters). | 75 |
| abstract_inverted_index.ObjectiveThis | 1 |
| abstract_inverted_index.respectively. | 51, 131, 152 |
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| abstract_inverted_index.statistically | 163 |
| abstract_inverted_index.characteristic | 82 |
| abstract_inverted_index.single-center, | 21 |
| abstract_inverted_index.models.ResultIn | 94 |
| abstract_inverted_index.model.MethodThis | 18 |
| abstract_inverted_index.thermoelectrometry | 8 |
| abstract_inverted_index.mortality.ConclusionWe | 170 |
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| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 14 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.66925924 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |