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A Nomogram Based on a Non-Invasive Method to Distinguish Between Gram-Positive and Gram-Negative Bacterial Infections
Haoran Li1, Xi Chen2, Hui Feng1
1Department of Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China.
Purpose:
The diagnosis of liver abscess (LA) caused by Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB) depends on ultrasonography, but it is difficult to distinguish the overlapping features. Valuable ultrasonic (US) features were extracted to distinguish GPB-LA and GNB-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 GNB-LA and 67 with GPB-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 GNB-LA and GPB-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 GPB-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.
Insights
This study developed a prediction model using ultrasonography features to differentiate Gram-positive bacteria (GPB) liver abscess (LA) from Gram-negative bacteria (GNB) LA. The model aids in early diagnosis and antibiotic selection for liver abscesses.
Area of Science:
- Radiology and Medical Imaging
- Infectious Diseases
- Hepatology
Background:
- Liver abscess (LA) diagnosis often relies on ultrasonography (US), but distinguishing between Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB) etiologies is challenging due to overlapping US features.
- Accurate differentiation is crucial for timely and appropriate antibiotic selection, impacting patient outcomes.
Purpose of the Study:
- To extract valuable ultrasonic (US) features for distinguishing GPB-LA from GNB-LA.
- To establish and validate a prediction model for differentiating the causative organisms of liver abscesses based on US findings.
Main Methods:
- Retrospective analysis of clinical, laboratory, and US features from 436 patients with liver abscess.
- Development of a prediction model using logistic regression and LASSO analysis on a training group (n=262).
- Model performance evaluation using Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA), with validation on a separate group (n=174).
Main Results:
- A total of 436 patients were analyzed (369 GNB-LA, 67 GPB-LA).
- Eleven predictors, including gender, age, liver background, internal gas bubble, echogenic debris, wall thickening, inner wall appearance, temperature, diabetes mellitus, hepatobiliary surgery, and neutrophil count, were identified.
- The developed Nomogram prediction model achieved an AUC of 0.80 (95% CI: 0.73-0.87) for distinguishing GNB-LA from GPB-LA, with an AUC of 0.79 (95% CI: 0.69-0.89) in the validation group.
Conclusions:
- A robust prediction model was established to aid in the early diagnosis of the causative organism in liver abscesses.
- This model can facilitate earlier selection of sensitive antibiotics, potentially shortening treatment duration for patients with liver abscess.
