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Late mortality prediction using blood cell counts in trauma patients who underwent emergency surgery
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 채윤정 | - |
| dc.contributor.author | 이인경 | - |
| dc.date.accessioned | 2022-11-29T03:01:21Z | - |
| dc.date.available | 2022-11-29T03:01:21Z | - |
| dc.date.issued | 2022-08 | - |
| dc.identifier.other | 32054 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/21051 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :의학과,2022. 8 | - |
| dc.description.tableofcontents | I. Background 1 II. Methods 3 A. Study protocol 3 B. Statistical analysis 4 III. Results 5 A. Patient demographics 5 B. Prediction power 5 C. NLP and outcome 6 IV. Discussion 8 V. Conclusion 11 VI. Reference 13 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Late mortality prediction using blood cell counts in trauma patients who underwent emergency surgery | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | In Kyong Yi | - |
| dc.contributor.department | 일반대학원 의학과 | - |
| dc.date.awarded | 2022. 8 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 1254147 | - |
| dc.identifier.uci | I804:41038-000000032054 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000032054 | - |
| dc.subject.keyword | Inflammation | - |
| dc.subject.keyword | injury severity | - |
| dc.subject.keyword | mortality | - |
| dc.subject.keyword | neutrophil | - |
| dc.subject.keyword | platelet | - |
| dc.subject.keyword | trauma | - |
| dc.description.alternativeAbstract | Background: Late mortality, following major trauma, is mainly caused by multi-organ dysfunction syndrome that is associated with systemic inflammation. Although neutrophil/lymphocyte (NL) and neutrophil/lymphocyte platelet (NLP) ratios are known to reflect systemic inflammation, they have not been evaluated as predictors of late mortality in such patients. This study was designed to evaluate the usefulness of NL and NLP in predicting late mortality of trauma patients after emergency surgery. Methods: Adult trauma patients after emergency surgery at a level I trauma center were evaluated retrospectively. Patients who expired within 48 hours were excluded. Blood count ratios (NL, and NLP at initial, 48-hours and 1-week of hospitalization), and the preexisting trauma scores were evaluated. Results: Enrolled patients were 209. Baseline characteristics of patients were as follows: median age, 49 [interquartile range 37.5–61.0] years; Injury Severity Score (ISS), 23 [16–34]; Revised Trauma Score (RTS), 7.11 [5.44–7.84]; Trauma Injury Severity Score (TRISS), 91.0 [70.2–97.8]; and mortality rate 11.5%. NLP at 1 week, NL at 1 week, TRISS, RTS, and ISS were related to late death. Area under the curves for predicting mortality was greatest for NLP at 1 week (0.867 [95% confidence interval 0.798–0.936], p < .001). Based on cutoff value (9.3, sensitivity 77.3%, specificity 83.1%), the high-NLP-at-1-week group showed higher death rate than the low-NLP-at-1-week group (35.4% vs. 3.2%, p < .001). Conclusions: Preexisting trauma scores, NL at 1 week, and NLP at 1 week were significantly related to late mortality in trauma patients after emergency surgery. However, the prediction power of NLP at 1 week was the highest compared to other tools. | - |
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- Graduate School of Ajou University > Department of Medicine > 4. Theses(Ph.D)
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