Development and validation of a prediction model based on clinical and CT features for invasiveness of K. pneumoniae
Hairui Wang1, Yawen Guo1, Bin Yan2
1Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, China.
Objectives:
Klebsiella pneumoniae liver abscess (KPLA) complicated with extrahepatic migratory infection (EMI) is defined as invasive KPLA. The current study aimed to develop and validate a risk prediction model for the invasiveness of KPLA.
Methods:
From 2010 to 2020, KPLA patients from four institutes were selected retrospectively. In the development cohort, risk factors from a logistic regression analysis were utilized to develop the prediction model. External validation was performed using an independent cohort.
Results:
A total of 382 KPLA patients comprised two separate cohorts: development cohort (institute 1, n = 286) and validation cohort (institute 2-4, n = 86). The overall incidence of EMI was 19.1% (development cohort, n = 55; validation cohort, n = 18, p > 0.05). In the development cohort, four risk factors (age ≤ 40 years, fasting blood glucose (FBG) > 7 mmol/L, no rim enhancement, and thrombophlebitis on CT), significantly associated with EMI, were incorporated into the scoring system. The area under curve (AUC) of the receiver operating characteristic curve (ROC) in the development and validation cohorts was 0.931 (95% confidence interval [CI]: 0.93-0.95) and 0.831 (95% CI: 0.86-0.91), respectively. The calibration curves fitted well. The incidence of EMI was 3.3% and 56.5% for the low- (total scores ≤ 4) and high-risk (total scores > 4) groups in the development cohort, and 3.2% and 66.7% in the validation cohort (all p < 0.001), respectively.
Conclusions:
Age ≤ 40 years, FBG > 7 mmol/L, no rim enhancement, and thrombophlebitis were independent risk factors for EMI. This validated prediction model may aid clinicians in identifying KPLA patients at increased risk for invasiveness.
Key Points:
• Four risk factors are significantly associated with extrahepatic migratory infections (EMI): age ≤ 40 years, fasting blood glucose (FBG) > 7 mmol/L, no rim enhancement, and thrombophlebitis on CT. • Based on these risk factors, the current study developed and validated a prediction model for the invasiveness of Klebsiella pneumoniae liver abscess (KPLA). • This validated prediction model may in the help early identification of KPLA patients at increased risk for invasiveness.
Insights
A new model identifies invasive Klebsiella pneumoniae liver abscess (KPLA) by assessing four risk factors: young age, high blood glucose, lack of rim enhancement, and thrombophlebitis. This tool aids clinicians in predicting KPLA invasiveness and extrahepatic migratory infections (EMI).
Area of Science:
- Infectious Diseases
- Hepatology
- Radiology
Background:
- Klebsiella pneumoniae liver abscess (KPLA) can lead to invasive disease with extrahepatic migratory infections (EMI).
- Accurate prediction of KPLA invasiveness is crucial for timely clinical intervention.
Purpose of the Study:
- To develop and validate a risk prediction model for identifying invasive KPLA.
- To identify independent risk factors associated with EMI in KPLA patients.
Main Methods:
- Retrospective analysis of 382 KPLA patients from 2010-2020 across four institutes.
- Logistic regression analysis to identify risk factors for EMI.
- Development and external validation of a risk prediction score.
Main Results:
- Four independent risk factors for EMI were identified: age ≤ 40 years, fasting blood glucose (FBG) > 7 mmol/L, no rim enhancement on CT, and thrombophlebitis.
- The prediction model demonstrated good performance with an AUC of 0.931 in the development cohort and 0.831 in the validation cohort.
- High-risk groups (score > 4) showed significantly higher EMI incidence (56.5% and 66.7%) compared to low-risk groups (3.3% and 3.2%).
Conclusions:
- Age, FBG, CT findings (rim enhancement, thrombophlebitis) are key predictors of KPLA invasiveness.
- The validated prediction model effectively stratifies KPLA patients into low and high-risk categories for EMI.
- This model can assist clinicians in early identification and management of invasive KPLA.
