Predicting central line-associated bloodstream infections and mortality using supervised machine learning

Parreco, Joshua; Hidalgo, Antonio E.; Badilla, Alejandro D.; Ilyas, Omar; Rattan, Rishi

doi:10.1016/j.jcrc.2018.02.010

Cited by 56 publications

(44 citation statements)

References 16 publications

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“…Different databases include different amounts of dimensions. In the critical area, a large number of articles have been published on the public database MIMIC, but the data may not apply to the Chinese population 25,26 . CRKP bloodstream infection is gradually increasing in recent years, but the epidemic trend is quite different from that of foreign countries.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

Liang

Qian

Chen

et al. 2019

Preprint

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Purpose To establish mortality prediction models in 14 days of Carbapenem-Resistant Klebsiella Pneumoniae bacteremia using Machine learning.Materials and Methods It is a single-center retrospective study. We collect the relevant clinical information of all patients with Carbapenem-Resistant Klebsiella Pneumoniae (CRKP) bacteremia in the past 5 years using the local database. Data analysis and verification are carried out by multiple logical regression, decision tree, random forest, support vector machine (SVM), and XGBoost.Result This study includes 187 patients with 40 related variables. In multiple logical regression, acute renal injury (P=0.003), Apache II score (P=0.036), immunodeficiency (P=0.025), severe thrombocytopenia (P=0.025) and septic shock (P=0.044) are the high-risk factors for 14 days mortality of CRKP bloodstream infections. According to the importance of those parameters, risk scoring is established to predict the survival rate of CRKP bacteremia. The analysis of the five models, with 70% training set and 30% test set, show the comprehensive performance of random forest (AUROC=0.953, precision=91.85%) is slightly better than that of XGBoost (AUROC=0.912, precision=86.41%) and SVM (AUROC=0.936, precision=79.89%) in predicting 14-day mortality of CRKP bacteremia. The multiple logical regression model (AUROC=0.825, precision=81.52%) is the second, and the decision tree model (AUROC=0.712, precision=79.89%) is not very ideal.Conclusion Machine learning has good performances in predicting 14-day mortality of CRKP bacteremia than multiple logical regression. Acute renal injury, severe thrombocytopenia, and septic shock are the high-risk factors of CRKP bacteremia mortality.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

Liang

Qian

Chen

et al. 2019

Preprint

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“…This phenomenon is also underscored by a study in the HAI group predicting central line placement and central-line-associated bloodstream infections (CLABSIs) [10]. The ML model achieved an AUROC of only 0.64 when modelling CLABSI but 0.82 for modelling the placement of a central line.…”

Section: Challenges In Predicted Outcome Definitionsmentioning

confidence: 99%

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Luz

Vollmer

Decruyenaere

et al. 2020

Clinical Microbiology and Infection

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Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection*, and infectious disease* for the years 2014e2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n ¼ 19), hospital-acquired infections (n ¼ 11), surgical site infections and other postoperative infections (n ¼ 11), microbiological test results (n ¼ 4), infections in general (n ¼ 2), musculoskeletal infections (n ¼ 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n ¼ 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

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“…In total, 21 studies were retrospective 33,35,70-88 and six were prospective [89][90][91][92][93][94] . There were 21 single-center studies 33,70-75, [77][78][79][80][81][82][83][86][87][88][90][91][92]94 and six multi-center studies 35,76,84,85,89,93 . Seven studies were time series 71,78,82,[84][85][86]92 , 18 studies were case series 33,35,70, [72][73][74][75][76]80,81,83,[87][88][89][90][91]93,94 , one was a case-control 77 and one was a matched-controlled study …”

Section: Study Characteristicsmentioning

confidence: 99%

“…Majority of the published studies evaluated data from different wards; several studies included patients admitted only to the ICU 70, 72,81,[84][85][86]93 and surgical ward 73,76,78,87,91,92 , less often the General ward 33 and Emergency Department 74 . Of these, 23 studies included data collected at their own hospital; and four utilized previously collated databases 76,81,84,86 .…”

Section: Study Characteristicsmentioning

confidence: 99%

“…The machine learning algorithms evaluated in the studies were developed to predict a range of diseases. These included sepsis 33,35,72,78,81,85,93,94 , acute kidney injury 70, [78][79][80]82,84,91 , surgical site infections 33,73,76,87,92 , central line-associated bloodstream infections 77,86 , Clostridium difficile 83,88 , pulmonary aspergillosis 89 , bacteremia 90 , fibrosis 71 , urine tract infection 33, 74 and infections in general 75 .…”

Section: Study Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Medic

Atallah

et al. 2019

F1000Res

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Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

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Predicting central line-associated bloodstream infections and mortality using supervised machine learning

Cited by 56 publications

References 16 publications

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

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