Construction and validation of a meropenem-induced liver injury risk prediction model: a multicenter case-control study

Abstract

Objective: To construct and validate a risk prediction model for patients with meropenem-induced liver injury (MiLI).

Methods: A retrospective case-control study was conducted to collect data on inpatients treated with meropenem at Shiyan People's Hospital, Hubei, China from January 2018 to December 2022; this study served as the model construction dataset. Univariate analysis and multiple logistic regression analysis were employed to identify the related factors for MiLI, and a nomogram risk prediction model for MiLI was constructed. The recognition ability and prediction accuracy of the model were evaluated using the receiver operating characteristic (ROC) and calibration curves. The clinical efficacy was assessed via the decision curve analysis (DCA). The internal validation was performed using the bootstrap method, and external validation was conducted based on an external dataset from Shiyan Taihe Hospital between October 2021 and December 2023.

Results: A total of 1,625 individuals were included in the model construction dataset, of which 62 occurred MiLI. The external validation dataset included 1,032 cases, with 74 patients developing liver injury. Six variables were independent factors for MiLI and included in the final prediction model: being male (OR = 2.080, 95% CI: 1.050-4.123, P = 0.036), ICU admission (OR = 8.207, 95% CI: 4.094-16.453, P < 0.001), gallbladder disease (OR = 8.240, 95% CI: 3.605-18.832, P < 0.001), baseline ALP (OR = 1.012, 95% CI: 1.004-1.019, P = 0.004), GGT (OR = 1.010, 95% CI: 1.005-1.015, P < 0.001), and PLT (OR = 0.997, 95% CI: 0.994-0.999, P = 0.020). The c-statistic value for internal validation of the prediction model was 0.821; the sensitivity and specificity were 0.997 and 0.924, respectively. The c-statistic value of the prediction model in the model construction dataset was 0.837 (95% CI, 0.789-0.885), while in the external validation dataset was 0.851 (95% CI, 0.802-0.901). The P-values of the calibration curve in the two datasets were 0.935 and 0.084, respectively.

Conclusion: Being male, ICU admission, gallbladder disease, higher levels of baseline ALP and GGT, and lower levels of baseline PLT were the risk factors for MiLI. The nomogram model built based on these factors demonstrated favorable performance in discrimination, calibration, clinical applicability, and internal-external validation. The nomogram model can assist clinicians in early identification of high-risk patients receiving meropenem, predicting the risk of MiLI, and ensuring safe medication practices.

Keywords: adverse drug reaction; drug safety; drug-induced liver injury; meropenem; prediction model; risk factor.

FIGURE 1

Flow chart for prediction model establishment and validation.

FIGURE 2

Nomogram for predicting MiLI.

FIGURE 3

The ROC curves for the MiLI prediction model in model construction dataset (A) and external validation dataset (B).

FIGURE 4

The calibration curve for the MiLI prediction model in model construction dataset (A) and external validation dataset (B). Note: The calibration curve is the black solid line in the figure, the ideal curve is the grey solid line in the figure, and the nonparametric curve is the dashed line in the figure.

FIGURE 5

Decision curves for the MiLI prediction model in model construction dataset (A) and external validation dataset (B). Note: The DCA curve is the dashed line in the figure. The two extreme reference curves, the None line and the All line, are the grey solid line and the black solid line, respectively.