Factors associated with pulmonary complications after hepatectomy and establishment of nomogram: A real-world retrospective study

Abstract

Background and aims: Hepatectomy is currently the most effective way to treat liver diseases, and its safety has observably improved. However, the incidence of postoperative complications (POCs) remains high. Therefore, exploring the related influencing factors helps identify high-risk groups early and improve patient prognosis.

Methods: Clinical data were retrospectively collected from a real-world setting. Patients were divided into two groups based on the incidence of postoperative pulmonary complications (PPCs). Univariate analysis, LASSO regression, and logistic regression were applied to analyse the correlation between PPCs and perioperative indicators. A nomogram prediction model was constructed, whose discrimination, accuracy, and clinical effectiveness were evaluated.

Results: The incidence of PPCs was 36.33% among the 1244 patients in this study. The total length of hospital stay and perioperative mortality in the PPCs group were markedly higher (P < 0.001) than in the non-PPCs group. Logistic regression showed that surgical method [odds ratio (OR) =2.469 (95% CI: 1.665, 3.748); P < 0.001], duration of surgery [OR = 1.003 (95% CI: 1.002, 1.005); P < 0.001], postoperative patient destination [OR = 1.453 (95% CI: 1.115, 1.893); P = 0.006], and postoperative international normalised ratio (INR) [OR = 2.245 (95% CI: 1.287, 4.120); P = 0.007] were independent risk factors of PPCs; the number of clamping [OR = 0.988 (95% CI: 0.980, 0.995); P = 0.001] was an independent protective factor of PPCs. The area under the receiver operating characteristic (ROC) curve was 0.675 (95% CI: 0.638, 0.703), the consistency index of the calibration curve was 0.675 (95% CI: 0.641, 0.703), and the Hosmer-Lemeshow goodness-of-fit test yielded P = 0.327.

Conclusions: In this study, the incidence of PPCs after hepatectomy was the highest. Our nomogram model can predict the probability of PPCs after hepatectomy.

Keywords: Factors; hepatectomy; nomogram; postoperative pulmonary complications; real-world study; univariate analysis.

Figure 1

Consolidated Standards of Reporting Trials (CONSORT) diagram of patient flow. PPCs: postoperative pulmonary complications

Figure 2

Distribution map of preoperative liver primary disease

Figure 3

Variable selection by the LASSO regression model. a. LASSO coefficient profiles of the 52 variables were produced against the Log Lambda sequence. b. Cross-validation curve of LASSO regression (10-fold). The X-axis is the logarithm of the penalty coefficient Lambda (λ), the Y-axis is the mean-squared error. The top number is the number of variables left in the equation for different λ. The dashed line on the left is λ min, meaning λ with the smallest prediction error, which means that the model fitting effect is the highest. The dashed line on the right is λ-1se, meaning 1 standard error to the right of the smallest λ. At this λ value, the model is simpler with a good fitting effect, too

Figure 4

Nomogram model of predicting PPCs after hepatectomy. INR: international normalised ratio; ALB: albumin; PPCs: postoperative pulmonary complications

Figure 5

Receiver operating characteristic curve of the nomogram predicting PPCsAUC: area under the receiver operating characteristic curve

Figure 6

The calibration curve of the nomogram predicting PPCs (bootstrap 500 repetitions)

Figure 7

Decision curve analysis cure analysis of the model