Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling

Abstract

Background: There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days.

Methods: We recruited patients with respiratory failure at the First People's Hospital of Yancheng and the People's Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness.

Results: The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value.

Conclusions: A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

Keywords: COX regression modeling; Nomogram; Random survival forest algorithm; Readmission; Respiratory failure.

Fig. 1

Flow chart of this study

Fig. 2

LASSO regression model was used to select feature variables. (A) LASSO coefficient curves for the 11 features. (B) The adjustment parameter (lambda) in the Lasso regression was selected using 10-fold cross-validation

Fig. 3

A nomogram predicting readmission risk for respiratory failure

Fig. 4

Detection of receiver operating characteristic (ROC) curve. (A) the ROC curve of Modeling set. (B) the ROC curve of internal validation set. (C) the ROC curve of external validation set. The red, yellow, and blue AUC curves show the discrimination of the model at 3, 6, and 10 months. The corresponding 95% confidence interval estimates are highlighted in black text

Fig. 5

Calibration curve of risk prediction model for respiratory failure readmission. (A) the calibration curve of the modeling set. (B) the calibration curve of the internal validation set. (C) the calibration curve of the external validation set

Fig. 6

Analysis of decision curve in nomogram. (A) the decision curve analysis of nomogram in the modeling set. (B) the decision curve analysis of nomogram in the internal validation set. (C) the decision curve analysis of nomogram in the external validation set. Solid red lines represent the columns

Fig. 7

The time-dependent AUC of risk prediction model for respiratory failure readmission. (A) the time-dependent AUC of the modeling set. (B) the time-dependent AUC of the internal validation set. (C) the time-dependent AUC of the external validation set

Fig. 8

Individual risk scores obtained from the established nomogram. In the modeling set (A), internal validation set (B) and external validation set (C), individual risk scores were obtained according to the established nomogram, and patients were divided into high-risk group and low-risk group according to the critical value to show the best difference in readmission analysis between risk groups