Development and validation of a nomogram for assessing survival in acute exacerbation of chronic obstructive pulmonary disease patients

Abstract

Background: Early prediction of survival of hospitalized acute exacerbations of chronic obstructive pulmonary disease (AECOPD) patients is vital. We aimed to establish a nomogram to predict the survival probability of AECOPD patients.

Methods: Retrospectively collected data of 4601 patients hospitalized for AECOPD. These patients were randomly divided into a training and a validation cohort at a 6:4 ratio. In the training cohort, LASSO-Cox regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of AECOPD patients. A model was established based on 3 variables and visualized by nomogram. The performance of the model was assesed by AUC, C-index, calibration curve, decision curve analysis in both cohorts.

Results: Coexisting arrhythmia, invasive mechanical ventilation (IMV) usage and lower serum albumin values were found to be significantly associated with lower survival probability of AECOPD patients, and these 3 predictors were further used to establish a prediction nomogram. The C-indexes of the nomogram were 0.816 in the training cohort and 0.814 in the validation cohort. The AUC in the training cohort was 0.825 for 7-day, 0.807 for 14-day and 0.825 for 21-day survival probability, in the validation cohort this were 0.796 for 7-day, 0.831 for 14-day and 0.841 for 21-day. The calibration of the nomogram showed a good goodness-of-fit and decision curve analysis showed the net clinical benefits achievable at different risk thresholds were excellent.

Conclusion: We established a nomogram based on 3 variables for predicting the survival probability of AECOPD patients. The nomogram showed good performance and was clinically useful.

Keywords: AECOPD; Nomogram; Outcome; Prediction; Survival.

Fig. 1

Patient enrollment flowchart. Legends: The flowchart of study population inclusion. ^a Including patients coexisting multiple diseases

Fig. 2

Hazard Ratios and 95% Confidence Intervals of 8 variables associated with AECOPD survival in the training cohort. Legends: The forest map of Hazard Ratios and 95% Confidence Intervals of 8 variables associated with AECOPD survival in training cohort. N indicates total number of patients in training cohort. Coexisting arrhythmia, IMV usage and lower serum albumin values were significantly associated with lower survival probability of AECOPD patients

Fig. 3

Nomogram for predicting the AECOPD patients survival probability based on training cohort. Legends: The nomogram consisting of 3 variables: arrhythmia, IMV and serum albumin values. To use the nomogram, the specific Points of individual patients are located on each variable axis. Lines and dots are drawn upward to determine the points received by each variable. The sum of these points is located on the Total Points axis. A line is drawn downward to the ‘7-day Survival Probability, 14-day Survival Probability, and 21-day Survival Probability’ axes to determine the survival probability of AECOPD patients. The unit of albumin is g/L

Fig. 4

ROC curve of the nomogram in the training and validation cohort. Legends: The ROC curve and AUC of the nomogram in the training (A) and validation (B) cohort of 7-day, 14-day and 21-day survival

Fig. 5

Calibration curve of the nomogram in the training and validation cohort. Legends: The calibration curve of the nomogram in the training cohort of 7-day (A), 14-day (C) and 21-day (E) survival, and validation cohort of 7-day (B), 14-day (D) and 21-day (F) survival. The overlap between solid and dashed lines in the line graph demonstrates the consistency between the nomogram-predicted 7-day, 14-day, and 21-day survival probabilities of AECOPD patients and the actual survival probabilities of AECOPD patients

Fig. 6

Decision curve analysis of the nomogram in the training and validation cohort. Legends: The DCA of the nomogram in the training cohort of 7-day (A), 14-day(B) and 21-day (C) survival, as well as in the validation cohort of 7-day (D), 14-day (E) and 21-day (F) survival. DCA depicted in the line graph illustrates the clinical net benefit achievable at various risk thresholds. The threshold range for DCA is determined based on the model’s sensitivity and specificity derived from the training and validation cohorts. Interventions are targeted towards patients within the threshold range to assess and manage risks effectively. The net benefit surpasses that of intervening for all patients or not intervening at all