A Nomogram Based on a Non-Invasive Method to Distinguish Between Gram-Positive and Gram-Negative Bacterial Infections of Liver Abscess.

PURPOSE

The diagnosis of liver abscess (LA) caused by and depends on ultrasonography, but it is difficult to distinguish the overlapping features. Valuable ultrasonic (US) features were extracted to distinguish -LA and -LA and establish the relevant prediction model.

MATERIALS AND METHODS

We retrospectively analyzed seven clinical features, three laboratory indicators and 11 US features of consecutive patients with LA from April 2013 to December 2023. Patients with LA were randomly divided into training group (n=262) and validation group (n=174) according to a ratio of 6:4. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group.

RESULTS

A total of 436 participants (median age: 55 years; range: 42-68 years; 144 women) were evaluated, including 369 participants with -LA and 67 with -LA, respectively. A total of 11 predictors by LASSO regression analysis, which included gender, age, the liver background, internal gas bubble, echogenic debris, wall thickening, whether the inner wall is worm-eaten, temperature, diabetes mellitus, hepatobiliary surgery and neutrophil(NEUT). The performance of the Nomogram prediction model distinguished between -LA and -LA was 0.80, 95% confidence interval [CI] (0.73-0.87). In the validation group, the AUC of GNB was 0.79, 95% CI (0.69-0.89).

CONCLUSION

A model for predicting the risk of -LA was established to help diagnose pathogenic organism of LA earlier, which could help select sensitive antibiotics before the results of drug-sensitive culture available, thereby shorten the treatment time of patients.