A novel prognostic model for predicting the mortality risk of patients with sepsis-related acute respiratory failure: a cohort study using the MIMIC-IV database

Abstract

Objectives

Acute respiratory failure increases short-term mortality in sepsis patients. Hence, in this study, we aimed to develop a novel model for predicting the risk of hospital mortality in sepsis patients with acute respiratory failure.

Methods

From the Medical Information Mart for Intensive Care (MIMIC)-IV database, we developed a matched cohort of adult sepsis patients with acute respiratory failure. After applying a multivariate COX regression analysis, we developed a nomogram based on the identified risk factors of mortality. Further, we evaluated the ability of the nomogram in predicting individual hospital death by the area under a receiver operating characteristic (ROC) curve.

Results

A total of 663 sepsis patients with acute respiratory failure were included in this study. Systolic blood pressure, neutrophil percentage, white blood cells count, mechanical ventilation, partial pressure of oxygen < 60 mmHg, abdominal cavity infection, Klebsiella pneumoniae and Acinetobacter baumannii infection, and immunosuppressive diseases were the independent risk factors of mortality in sepsis patients with acute respiratory failure. The area under the ROC curve of the nomogram was 0.880 (95% CI: 0.851–0.908), which provided significantly higher discrimination compared to that of the simplified acute physiology score II [0.656 (95% CI: 0.612–0.701)].

Conclusion

The model shows a good performance in predicting the mortality risk of patients with sepsis-related acute respiratory failure. Hence, this model can be used to evaluate the short-term prognosis of critically ill patients with sepsis and acute respiratory failure.

Keywords:

• Acute respiratory failure
• sepsis
• hospital mortality
• model

Transparency

Declaration of funding

This work was supported by CAMS Innovation Fund for Medical Sciences [CIFMS, grant number:2018-I2M-1-003]. Medical and health science and technology plan project of Health Commission of Inner Mongolia Autonomous Region [grant number: 202202326].

Declaration of financial/other relationships

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Dr. Lina Zhao and Dr. Jing Yang wrote the main manuscript text, included: contributed to the conception, designed the work, analyzed and interpreted data. Dr. Cong Zhou and Yunying Wang collected the data regarding the paper. Dr. Tao Liu ensuring that original data upon which the submission is based is preserved and retrievable for reanalysis; approving data presentation as representative of the original data and foreseeing and minimizing obstacles to the sharing of data described in the work. All authors read and approved the final manuscript.

Acknowledgements

None.

Data availability statement

The MIMIC IV database (version 0.4) is publically available from https://physionet.org/content/mimiciv/0.4/. Any researcher who adheres to the data use requirements is permitted access to the database.

Ethical approval

The MIMIC-IV database (version 0.4) was ethically approval by the Institutional Review Boards of both Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. Patient information in the database was anonymous.