[Development and validation of a diagnostic model of antimicrobial resistance by extended-spectrum beta-lactamases in community-acquired urinary tract infections. How to adjust the prediction in variable outcome prevalences? The usefulness of contracting predictors with LASSO regression].

BACKGROUND

In the case of community-acquired urinary tract infection, the identification of Enterobacteriaceae with extended spectrum beta-lactamases (ESBL) can optimize treatment, control and follow-up strategies, however the effect of variable prevalences of this resistance pattern has affected the external validity of this type of models.

AIM: To develop a diagnostic predictive model that adjusts the prediction error in variable prevalences using the LASSO regression.

METHODS

A diagnostic predictive model of community-acquired urinary tract infection by infection by ESBL producing Enterobacteriaceae was designed. A cross-sectional study was used for both construction and validation. To assess the effect of the variable prevalence of the outcome, the validation was performed with a population in which the proportion of isolates with this resistance mechanism was lower, the participants were adult patients who consulted the emergency services of two medium-level hospital institutions. complexity of the city of Medellin. To adjust for the effect of an environment with a lower proportion of antimicrobial resistance, we used the contraction of predictors by LASSO regression.

RESULTS

303 patients were included for the construction of the model, six predictors were evaluated and validation was carried out in 220 patients.

CONCLUSION

The adjusted model with LASSO regression favored the external validity of the model in populations with a proportion of ESBL producing isolates in urine culture of outpatients between 11 and 16%. This study provides criteria for early isolation when predictors are present in populations with proportions of resistance in ambulatory urine cultures close to 15% and proposes a methodology for the adjustment of errors in the design of prediction models for antimicrobial resistance.