Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Li Y

PROVIDER: S-EPMC8841094 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Li Yaling Y Wu Yutong Y Gao Yali Y Niu Xueli X Li Jingyi J Tang Mingsui M Fu Chang C Qi Ruiqun R Song Bing B Chen Hongduo H Gao Xinghua X Yang Ying Y Guan Xiuhao X

BMC infectious diseases 20220213 1

<h4>Background</h4>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.<h4>Methods</h4>We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 20 ...[more]

PMID: 35152879

Similar Datasets

Project description:BackgroundInvasive candidiasis is a common cancer-related complication with a high fatality rate. If patients with a high risk of dying in the hospital are identified early and accurately, physicians can make better clinical judgments. However, epidemiological analyses and mortality prediction models of cancer patients with invasive candidiasis remain limited.MethodA set of 40 potential risk factors was acquired in a sample of 258 patients with both invasive candidiasis and cancer. To begin, risk factors for Candida albicans vs. non-Candida albicans infections and persistent vs. nonpersistent Candida infections were analysed using classic statistical methods. Then, we applied three machine learning models (random forest, logistic regression, and support vector machine) to identify prognostic indicators related to mortality. Prediction performance of different models was assessed by precision, recall, F1 score, accuracy, and AUC.ResultsOf the 258 patients both with invasive candidiasis and cancer included in the analysis. The median age of patients was 62 years, and 95 (36.82%) patients were older than 65 years, of which 178 (66.28%) were male. And 186 (72.1%) patients underwent surgery 2 weeks before data collection, 100 (39.1%) patients stayed in ICU during hospitalisation, 99 (38.4%) patients had bacterial blood infection, 85 (32.9%) patients had persistent invasive candidiasis, and 41 (15.9%) patients died within 30 days. The usage of drainage catheter and prolonged length of hospitalisation are the dominant risk factors for non-Candida albicans infections and persistent Candida infections, respectively. Risk factors, such as septic shock, history of surgery within the past 2 weeks, usage of drainage tubes, length of stay in ICU, total parenteral nutrition, serum creatinine level, fungal antigen, stay in ICU during hospitalisation, and total bilirubin level, were significant predictors of death. The RF model outperformed the LR and SVM models. Precision, recall, F1 score, accuracy, and AUC for RF were 64.29%, 75.63%, 69.23%, 89.61%, and 91.28%.ConclusionsIn this study, the machine learning-based models accurately predicted the prognosis of cancer and invasive candidiasis patients. The algorithm could be used to help clinicians in high-risk patients' early intervention.

Project description:BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Project description:BackgroundBreast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data.MethodsA total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluate the predictive performance of the models. In addition, to investigate the sensitivity of different training/testing set radio to model performance, we examined five sets of ratios: 50:50, 60:40, 70:30, 80:20, 90:10.ResultsAmong these models, XGBoost demonstrated the highest performance with receiver operating characteristic (ROC) area under the curve (AUC) of 0.813, accuracy of 0.739, sensitivity of 0.815, and specificity of 0.735. Further statistical analysis identified several significant predictors of prognosis, including age, tumor size, lymph node status, and hormone receptor status. The XGBoost model was found to exhibit superior predictive power compared to established prognostic models such as the Nottingham Prognostic Index (NPI) and Predict Breast. Based on the successful performance of the XGBoost model, we developed a prognosis prediction tool specifically designed for breast cancer, providing valuable insights to clinicians, and aiding them in making informed treatment decisions tailored to individual patients.ConclusionsOur study highlights the potential of machine learning models in accurately predicting prognosis for breast cancer patients, ultimately facilitating personalized treatment strategies. Further research and validation are warranted to fully integrate these models into clinical practice.

Project description:Acinetobacter baumannii is emerging as a leading global multiple-antibiotic-resistant nosocomial pathogen. The identity of genes essential for pathogenesis in a mammalian host remains largely unknown. Using transposon-directed insertion-site sequencing (TraDIS), we identified A. baumannii genes involved in bacterial survival in a leukopenic mouse model of bloodstream infection. Mice were inoculated with a pooled transposon mutant library derived from 109,000 mutants, and TraDIS was used to map transposon insertion sites in the genomes of bacteria in the inoculum and of bacteria recovered from mouse spleens. Unique transposon insertion sites were mapped and used to calculate a fitness factor for every insertion site based on its relative abundance in the inoculum and postinfection libraries. Eighty-nine transposon insertion mutants that were underrepresented after experimental infection in mice compared to their presence in the inocula were delineated as candidates for further evaluation. Genetically defined mutants lacking feoB (ferrous iron import), ddc (d-ala-d-ala-carboxypeptidase), and pntB (pyridine nucleotide transhydrogenase subunit) exhibited a fitness defect during systemic infection resulting from bacteremia. In vitro, these mutants, as well as a fepA (ferric enterobactin receptor) mutant, are defective in survival in human serum and within macrophages and are hypersensitive to killing by antimicrobial peptides compared to the survival of the parental strain under these conditions. Our data demonstrate that FepA is involved in the uptake of exogenous enterobactin in A. baumannii. Genetic complementation rescues the phenotypes of mutants in assays that emulate conditions encountered during infection. In summary, we have determined novel A. baumannii fitness genes involved in the pathogenesis of mammalian infection. IMPORTANCE A. baumannii is a significant cause of bacterial bloodstream infection in humans. Since multiple antibiotic resistance is becoming more common among strains of A. baumannii, there is an urgent need to develop novel tools to treat infections caused by this dangerous pathogen. To develop knowledge-guided treatment approaches for A. baumannii, a thorough understanding of the mechanism by which this pathogen causes bloodstream infection is required. Here, using a mouse model of infection, we report the identification of A. baumannii genes that are critical for the ability of this pathogen to cause bloodstream infections. This study lays the foundation for future research on A. baumannii genes that can be targeted to develop novel therapeutics against this emerging human pathogen.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets