Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia

Machine‑learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Background Invasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited. Methods We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3). Results Of the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25–74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level. Conclusion Our results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level

Seasonal Variation of Midgut Bacterial Diversity in Culex quinquefasciatus Populations in Haikou City, Hainan Province, China

Culex quinquefasciatus, one of the most significant mosquito vectors in the world, is widespread in most parts of southern China. A variety of diseases including Bancroft’s filariasis, West Nile disease, and St. Louis encephalitis could be transmitted by the vector. Mosquitoes have been shown to host diverse bacterial communities that vary depending on environmental factors such as temperature and rainfall. In this work, 16S rDNA sequencing was used to analyze the seasonal variation of midgut bacterial diversity of Cx. Quinquefasciatus in Haikou City, Hainan Province, China. Proteobacteria was the dominant phylum, accounting for 79.7% (autumn), 73% (winter), 80.4% (spring), and 84.5% (summer). The abundance of Bacteroidetes in autumn and winter was higher than in others. Interestingly, Epsilonbacteraeota, which only exists in autumn and winter, was discovered accidentally in the midgut. We speculated that this might participate in the nutritional supply of adult mosquitoes when temperatures drop. Wolbachia is the most abundant in autumn, accounting for 31.6% of bacteria. The content of Pantoea was highest in the summer group, which might be related to the enhancement of the ability of mosquitoes as temperatures increased. Pseudomonas is carried out as the highest level in winter. On the contrary, in spring and summer, the genus in highest abundance is Enterobacter. Acinetobacter enriches in the spring when it turns from cold to hot. By studying the diversity of midgut bacteria of Cx. quinquefasciatus, we can further understand the co-evolution of mosquitoes and their symbiotic microbes. This is necessary to discuss the seasonal variation of microorganisms and ultimately provide a new perspective for the control of Cx. quinquefasciatus to reduce the spread of the diseases which have notably vital practical significance for the effective prevention of Cx. quinquefasciatus