Development and validation of a nomogram for predicting pulmonary infection in patients receiving immunosuppressive drugs

Chuxuan Luo; Yue Zhang; Jiajie Zhang; Chen Jin; Xiaolan Ye; Yan Ren; Huajuan Shen; Maosheng Chen; Yiwen Li; Qiang He; Guangbiao Xu; Lina Shao

doi:10.3389/fphar.2023.1255609

Development and validation of a nomogram for predicting pulmonary infection in patients receiving immunosuppressive drugs

Front Pharmacol. 2024 Jan 16:14:1255609. doi: 10.3389/fphar.2023.1255609. eCollection 2023.

Authors

Chuxuan Luo¹, Yue Zhang², Jiajie Zhang³, Chen Jin², Xiaolan Ye⁴, Yan Ren², Huajuan Shen², Maosheng Chen², Yiwen Li², Qiang He⁵, Guangbiao Xu¹, Lina Shao²

Affiliations

¹ Department of Nephrology, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Taizhou, Zhejiang, China.
² Urology and Nephrology Center, Department of Nephrology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.
³ Center for General Practice Medicine, Department of Infectious Diseases, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.
⁴ Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.
⁵ Department of Nephrology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.

Abstract

Objective: Pulmonary infection (PI), a severe complication of immunosuppressive therapy, affects patients' prognosis. As part of this study, we aimed to construct a pulmonary infection prediction (PIP) model and validate it in patients receiving immunosuppressive drugs (ISDs). Methods: Totally, 7,977 patients being treated with ISDs were randomised 7:3 to the developing (n = 5,583) versus validation datasets (n = 2,394). Our predictive nomogram was established using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. With the use of the concordance index (C-index) and calibration curve, the prediction performance of the final model was evaluated. Results: Among the patients taking immunosuppressive medication, PI was observed in 548 (6.9%). The median time of PI occurrence after immunosuppressive therapy was 123.0 (interquartile range: 63.0, 436.0) days. Thirteen statistically significant independent predictors (sex, age, hypertension, DM, malignant tumour, use of biologics, use of CNIs, use of methylprednisolone at 500 mg, use of methylprednisolone at 40 mg, use of methylprednisolone at 40 mg total dose, use of oral glucocorticoids, albumin level, and haemoglobin level) were screened using the LASSO algorithm and multivariate COX regression analysis. The PIP model built on these features performed reasonably well, with the developing C-index of 0.87 (sensitivity: 85.4%; specificity: 81.0%) and validation C-indices of 0.837, 0.829, 0.832 and 0.830 for predicting 90-, 180-, 270- and 360-day PI probability, respectively. The decision curve analysis (DCA) and calibration curves displayed excellent clinical utility and calibration performance of the nomogram. Conclusion: The PIP model presented herein could aid in the prediction of PI risk in individual patients who receive immunosuppressive treatment and help personalise clinical decision-making.

Keywords: LASSO; immunosuppressive drugs; nomogram; predictive model; pulmonary infection.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Medical and Health Technology Program of Zhejiang Province (Grant Nos 2021KY468 and 2023KY523), and the National Natural Science Foundation of China (Grant No. 82202042).