Assessment of risk factors and development and validation of an early prediction model for mortality in patients with severe traumatic liver injury
10.3760/cma.j.cn501098-20230409-00200
- VernacularTitle:严重创伤性肝损伤患者死亡危险因素分析及早期预测模型的建立与验证
- Author:
Bing LIU
1
;
Xiaomei WANG
;
Chuangye SONG
;
Xiaoning LIU
;
Jianjun MIAO
;
Xiaowu LI
;
Peizhong SHANG
Author Information
1. 陆军第八十一集团军医院普通外科，张家口 075000
- Keywords:
Liver;
Wounds and injuries;
Death;
Risk factors;
Nomograms
- From:
Chinese Journal of Trauma
2023;39(6):528-537
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the risk factors associated with mortality in patients with severe traumatic liver injury (TLI) and to establish and validate an early prediction model for mortality.Methods:A retrospective cohort study was conducted to analyze the clinical data of 273 patients with severe TLI admitted to the ICU from the medical information mart for the intensive care-IV (MIMIC-IV) database. The cohort consisted of 176 males and 97 females, with age ranging from 18 to 83 years [35.6 years(25.7,57.5)years]. The patients were divided into two groups based on in-hospital mortality: the survival group (253 patients, 92.7%) and the death group (20 patients, 7.3%). The two groups were compared with regards to gender, age, cause and type of injury, treatment method, massive blood transfusion, comorbidities as well as vital signs and laboratory tests measured within 24 hours of ICU admission. Univariate analysis was used to screen for risk factors associated with mortality in severe TLI patients. Independent risk factors for mortality were determined using multivariate Logistic regression analysis. Lasso regression was used to screen for predictors of mortality, and a nomogram prognostic model was then established through a multivariate Logistic regression analysis. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination of the model, while the Hosmer-Lemeshow goodness-of-fit test and calibration curve were used to evaluate the calibration of the model. The model′s clinical applicability was evaluated through decision curve analysis (DCA). Internal validation was performed by the 200 Bootstrap samples, and external validation was performed by using 163 patients with severe TLI from the emergency ICU collaborative research database (eICU-CRD). Finally, the predictive efficacy of the nomogram model was compared to other trauma or severity scores.Results:Univariate analysis showed that the age, cause of injury, massive blood transfusion, chronic liver disease and laboratory tests measured within 24 hours of ICU admission, including temperature, systolic blood pressure, diastolic blood pressure, mean arterial pressure, shock index, platelets, red blood cell distribution width (RDW), mean red blood cell hemoglobin concentration (MCHC), blood glucose, blood urea nitrogen, creatinine, anion gap, bicarbonate, prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalized ratio (INR) were associated with the mortality of severe TLI patients ( P<0.05 or 0.01). Multivariate Logistic regression analysis revealed that age ( OR=1.08, 95% CI 1.03, 1.12, P<0.01), body temperature <36 ℃ ( OR=8.00, 95% CI 2.17, 29.53, P<0.01), shock index ( OR=9.59, 95% CI 1.76, 52.18, P<0.01) and anion gap ( OR=1.32, 95% CI 1.15, 1.53, P<0.01) were significantly associated with mortality in severe TLI patients. Lasso regression analysis selected 7 predictors, including age, body temperature<36 ℃, shock index, anion gap, chronic liver disease, creatinine and APTT. Based on these 7 predictors, a nomogram prediction model was developed. The AUC of the nomogram for predicting mortality was 0.96 (95% CI 0.94, 0.99), and the Hosmer-Lemeshow goodness-of-fit test indicated a good fit ( P>0.05). The calibration curve demonstrated excellent consistency between the predicted and actual probabilities, and DCA demonstrated that the model had good clinical net benefit at all risk threshold probability ranges. Internal validation confirmed the stability of the model ( AUC=0.96, 95% CI 0.92, 0.98), and external validation demonstrated good generalization ability ( AUC=0.95, 95% CI 0.91, 0.98). Moreover, the nomogram exhibited superior predictive efficacy compared with injury severity score (ISS), revised trauma score (RTS), trauma injury severity score (TRISS), sequential organ failure score (SOFA), acute physiological score III (APS III), Logistic organ dysfunction score (LODS), Oxford acute severity of illness score (OASIS) and simplified acute physiological score II (SAPS II). Conclusions:Age, body temperature <36 ℃, shock index and anion gap are independent risk factors for mortality in severe TLI patients. A nomogram prognosis model based on 7 predictors, namely age, body temperature <36 ℃, shock index, anion gap, chronic liver disease, creatinine and APTT exhibits good predictive efficacy and robustness, and is contributive to accurately assess the risk of mortality in severe TLI patients at an early stage.
