The clinical prediction model to distinguish between colonization and infection by

Objective: To develop a machine learning-based prediction model to assist clinicians in accurately determining whether the detection of Research methods: A retrospective analysis was conducted on 8,318 patients with KP cultures admitted to a tertiary hospital in Northeast China from January 2019 to December 2023. After excluding duplicates, other specimen types, cases with substandard specimen quality, and mixed infections, 286 cases with sputum cultures yielding only KP were included, comprising 67 cases in the colonization group and 219 cases in the infection group. Antimicrobial susceptibility testing was performed on the included strains, and through univariate logistic regression analysis, 15 key influencing factors were identified, including: age > 62 years, ESBL, CRKP, number of positive sputum cultures for KP, history of tracheostomy, use of mechanical ventilation for >96 h, indwelling gastric tube, history of craniotomy, recent local glucocorticoid application, altered consciousness, bedridden state, diagnosed with respiratory infectious disease upon admission, electrolyte disorder, hypoalbuminemia, and admission to ICU (all Results: Antimicrobial susceptibility testing indicated that the resistance rates for penicillins, cephalosporins, carbapenems, and quinolones were significantly higher in the infection group compared to the colonization group (all Conclusion: The Random Forest model effectively distinguishes between infection and colonization status of KP, while the Nomogram visually presents the predictive value of various factors, providing clinicians with a reference for formulating treatment plans. In the future, the accuracy of infection diagnosis can be further enhanced through artificial intelligence technology to optimize treatment strategies, thereby improving patient prognosis and reducing healthcare burdens.