Development and validation of a novel predictive score for sepsis risk among trauma patients
Abstract Background Patients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic m...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-03-01
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| Series: | World Journal of Emergency Surgery |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13017-019-0231-8 |
| _version_ | 1811223983448653824 |
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| author | Hong-xiang Lu Juan Du Da-lin Wen Jian-hui Sun Min-jia Chen An-qiang Zhang Jian-xin Jiang |
| author_facet | Hong-xiang Lu Juan Du Da-lin Wen Jian-hui Sun Min-jia Chen An-qiang Zhang Jian-xin Jiang |
| author_sort | Hong-xiang Lu |
| collection | DOAJ |
| description | Abstract Background Patients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma. Materials and methods Clinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test. Results Based on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (P trend = 7.44 × 10−21 for the training cohort and P trend = 1.16 × 10−13 for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757–0.837) and 0.790 (0.736–0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P < 0.001). Moreover, the TSS was well calibrated (P > 0.05). Conclusions We developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data. |
| first_indexed | 2024-04-12T08:41:37Z |
| format | Article |
| id | doaj.art-4de5a2edb3134040a3c321dd0eb227f1 |
| institution | Directory Open Access Journal |
| issn | 1749-7922 |
| language | English |
| last_indexed | 2024-04-12T08:41:37Z |
| publishDate | 2019-03-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Emergency Surgery |
| spelling | doaj.art-4de5a2edb3134040a3c321dd0eb227f12022-12-22T03:39:50ZengBMCWorld Journal of Emergency Surgery1749-79222019-03-011411810.1186/s13017-019-0231-8Development and validation of a novel predictive score for sepsis risk among trauma patientsHong-xiang Lu0Juan Du1Da-lin Wen2Jian-hui Sun3Min-jia Chen4An-qiang Zhang5Jian-xin Jiang6State Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityState Key Laboratory of Trauma, Burns and Combined Injury, Institute of Surgery Research, Daping Hospital, Army Military Medical UniversityAbstract Background Patients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma. Materials and methods Clinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test. Results Based on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (P trend = 7.44 × 10−21 for the training cohort and P trend = 1.16 × 10−13 for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757–0.837) and 0.790 (0.736–0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P < 0.001). Moreover, the TSS was well calibrated (P > 0.05). Conclusions We developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data.http://link.springer.com/article/10.1186/s13017-019-0231-8SepsisTraumaPredictionTraumatic sepsis score |
| spellingShingle | Hong-xiang Lu Juan Du Da-lin Wen Jian-hui Sun Min-jia Chen An-qiang Zhang Jian-xin Jiang Development and validation of a novel predictive score for sepsis risk among trauma patients World Journal of Emergency Surgery Sepsis Trauma Prediction Traumatic sepsis score |
| title | Development and validation of a novel predictive score for sepsis risk among trauma patients |
| title_full | Development and validation of a novel predictive score for sepsis risk among trauma patients |
| title_fullStr | Development and validation of a novel predictive score for sepsis risk among trauma patients |
| title_full_unstemmed | Development and validation of a novel predictive score for sepsis risk among trauma patients |
| title_short | Development and validation of a novel predictive score for sepsis risk among trauma patients |
| title_sort | development and validation of a novel predictive score for sepsis risk among trauma patients |
| topic | Sepsis Trauma Prediction Traumatic sepsis score |
| url | http://link.springer.com/article/10.1186/s13017-019-0231-8 |
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