European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2023
Risk exploration and prediction model construction for linezolid-resistant Enterococcus faecalis based on big data in a province in southern China.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Peijun, Zeng Bangwei, Wu Xiaoyan, Zheng Feng, Zhang Yangmei, Liao Xiaohua
Аннотация
BACKGROUND: Enterococcus faecalis is a common cause of healthcare-associated infections. Its resistance to linezolid, the antibiotic of last resort for vancomycin-resistant enterococci, has become a growing threat in healthcare settings.
METHODS: We analyzed the data of E. faecalis isolates from 26 medical institutions between 2018 and 2020 and performed univariate and multivariate logistic regression analyses to determine the independent predictors for linezolid-resistant E. faecalis (LREFs). Then, we used the artificial neural network (ANN) and logistic regression (LR) to build a prediction model for linezolid resistance and performed a performance evaluation and comparison.
RESULTS: Of 12,089 E. faecalis strains, 755 (6.25%) were resistant to linezolid. Among vancomycin-resistant E. faecalis, the linezolid-resistant rate was 24.44%, higher than that of vancomycin-susceptible E. faecalis (p < 0.0001). Univariate and multivariate regression analyses showed that gender, age, specimen type, length of stay before culture, season, region, GDP (gross domestic product), number of beds, and hospital level were predictors of linezolid resistance. Both the ANN and LR models constructed in the study performed well in predicting linezolid resistance in E. faecalis, with AUCs of 0.754 and 0.741 in the validation set, respectively. However, synthetic minority oversampling technique (SMOTE) did not improve the prediction ability of the models.
CONCLUSION: E. faecalis linezolid-resistant rates varied by specimen site, geographic region, GDP level, facility level, and the number of beds. At the same time, community-acquired E. faecalis with linezolid resistance should be monitored closely. We can use the prediction model to guide clinical medication and take timely prevention and control measures.
METHODS: We analyzed the data of E. faecalis isolates from 26 medical institutions between 2018 and 2020 and performed univariate and multivariate logistic regression analyses to determine the independent predictors for linezolid-resistant E. faecalis (LREFs). Then, we used the artificial neural network (ANN) and logistic regression (LR) to build a prediction model for linezolid resistance and performed a performance evaluation and comparison.
RESULTS: Of 12,089 E. faecalis strains, 755 (6.25%) were resistant to linezolid. Among vancomycin-resistant E. faecalis, the linezolid-resistant rate was 24.44%, higher than that of vancomycin-susceptible E. faecalis (p < 0.0001). Univariate and multivariate regression analyses showed that gender, age, specimen type, length of stay before culture, season, region, GDP (gross domestic product), number of beds, and hospital level were predictors of linezolid resistance. Both the ANN and LR models constructed in the study performed well in predicting linezolid resistance in E. faecalis, with AUCs of 0.754 and 0.741 in the validation set, respectively. However, synthetic minority oversampling technique (SMOTE) did not improve the prediction ability of the models.
CONCLUSION: E. faecalis linezolid-resistant rates varied by specimen site, geographic region, GDP level, facility level, and the number of beds. At the same time, community-acquired E. faecalis with linezolid resistance should be monitored closely. We can use the prediction model to guide clinical medication and take timely prevention and control measures.