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Randomized Controlled Trial
. 2023 Mar 8;23(1):78.
doi: 10.1186/s12890-023-02365-z.

A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study

Chi Xu  1 , Lei Zheng  2 , Yicheng Jiang  3 , Li Jin  4
Affiliations
Randomized Controlled Trial

A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study

Chi Xu et al. BMC Pulm Med. .

Abstract

Background: The risk of death in sepsis patients with acute respiratory distress syndrome (ARDS) was as high as 20-50%. Few studies focused on the risk identification of ARDS among sepsis patients. This study aimed to develop and validate a nomogram to predict the ARDS risk in sepsis patients based on the Medical Information Mart for Intensive Care IV database.

Methods: A total of 16,523 sepsis patients were included and randomly divided into the training and testing sets with a ratio of 7:3 in this retrospective cohort study. The outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. Univariate and multivariate logistic regression analyses were used in the training set to identify the factors that were associated with ARDS risk, which were adopted to establish the nomogram. The receiver operating characteristic and calibration curves were used to assess the predictive performance of nomogram.

Results: Totally 2422 (20.66%) sepsis patients occurred ARDS, with the median follow-up time of 8.47 (5.20, 16.20) days. The results found that body mass index, respiratory rate, urine output, partial pressure of carbon dioxide, blood urea nitrogen, vasopressin, continuous renal replacement therapy, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors. The area under the curve of developed model were 0.811 (95% CI 0.802-0.820) in the training set and 0.812 (95% CI 0.798-0.826) in the testing set. The calibration curve showed a good concordance between the predicted and observed ARDS among sepsis patients.

Conclusion: We developed a model incorporating thirteen clinical features to predict the ARDS risk in patients with sepsis. The model showed a good predictive ability by internal validation.

Keywords: Acute respiratory distress syndrome; MIMIC-IV; Nomogram; Prediction; Sepsis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The flow-chart for population selection
Fig. 2
Fig. 2
The nomogram for predicting the ARDS risk in ICU patients with sepsis
Fig. 3
Fig. 3
An example for the application of the nomogram
Fig. 4
Fig. 4
ROC curves of a established model in the training set; b established model in the testing set; c SOFA in the testing set; d SAPSII in the testing set
Fig. 5
Fig. 5
Calibration curves of a the training set and b testing set
See this image and copyright information in PMC

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