Development and validation of a nomogram for predicting persistent inflammation, immunosuppression, and catabolism syndrome in trauma patients

Ligang Xu; Zhaofeng Kang; Dongfang Wang; Yukun Liu; Chuntao Wang; Zhanfei Li; Xiangjun Bai; Yuchang Wang

doi:10.3389/fmed.2023.1249724

Development and validation of a nomogram for predicting persistent inflammation, immunosuppression, and catabolism syndrome in trauma patients

Front Med (Lausanne). 2023 Aug 24:10:1249724. doi: 10.3389/fmed.2023.1249724. eCollection 2023.

Authors

Ligang Xu^{1

2}, Zhaofeng Kang^{1

2}, Dongfang Wang^{1

2}, Yukun Liu³, Chuntao Wang^{1

2}, Zhanfei Li^{1

2}, Xiangjun Bai^{1

2}, Yuchang Wang^{1

2}

Affiliations

¹ Division of Trauma Surgery, Emergency Surgery and Surgical Critical, Tongji Trauma Center, Wuhan, China.
² Department of Emergency and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Abstract

Background: Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS) is a significant contributor to adverse long-term outcomes in severe trauma patients.

Objective: The objective of this study was to establish and validate a PIICS predictive model in severe trauma patients, providing a practical tool for early clinical prediction.

Patients and methods: Adult severe trauma patients with an Injury Severity Score (ISS) of ≥16, admitted between October 2020 and December 2022, were randomly divided into a training set and a validation set in a 7:3 ratio. Patients were classified into PIICS and non-PIICS groups based on diagnostic criteria. LASSO regression was used to select appropriate variables for constructing the prognostic model. A logistic regression model was developed and presented in the form of a nomogram. The performance of the model was evaluated using calibration and ROC curves.

Results: A total of 215 patients were included, consisting of 155 males (72.1%) and 60 females (27.9%), with a median age of 51 years (range: 38-59). NRS2002, ISS, APACHE II, and SOFA scores were selected using LASSO regression to construct the prognostic model. The AUC of the ROC analysis for the predictive model in the validation set was 0.84 (95% CI 0.72-0.95). The Hosmer-Lemeshow test in the validation set yielded a χ² value of 14.74, with a value of p of 0.098.

Conclusion: An accurate and easily implementable PIICS risk prediction model was established. It can enhance risk stratification during hospitalization for severe trauma patients, providing a novel approach for prognostic prediction.

Keywords: ICUAW; catabolism syndrome; immunosuppression; nomogram; persistent inflammation; trauma prediction; trauma score.