Early prediction of central line associated bloodstream infection using machine learning

Incidence, Risk Factors, and Outcomes of Central Line-Associated Bloodstream Infections in Trauma Patients

Introduction: Central line-associated blood stream infection (CLABSI) is a hospital-acquired infection (HAI) associated with increased morbidity and mortality among the general patient population. However, few studies have evaluated the incidence, outcomes, and risk factors for CLABSI in trauma patients. This study aimed to identify the rate of positive (+)CLABSI in trauma patients and risk factors associated with (+)CLABSI. Methods: The 2017-2021 Trauma Quality Improvement Program database was queried for trauma patients aged ≥18 years undergoing central-line placement. We compared patients with (+)CLABSI vs. (-)CLABSI patients. Bivariate and multivariable logistic regression analyses were performed. Results: From 175,538 patients undergoing central-line placement, 469 (<0.1%) developed CLABSI. The (+)CLABSI patients had higher rates of cirrhosis (3.9% vs. 2.0%, p = 0.003) and chronic kidney disease (CKD) (4.3% vs. 2.6%, p = 0.02). The (+)CLABSI group had increased injury severity score (median: 25 vs. 13, p < 0.001), length of stay (LOS) (median 33.5 vs. 8 days, p < 0.001), intensive care unit LOS (median 21 vs. 6 days, p < 0.001), and mortality (23.7% vs. 19.6%, p = 0.03). Independent associated risk factors for (+)CLABSI included catheter-associated urinary tract infection (CAUTI) (odds ratio [OR] = 5.52, confidence interval [CI] = 3.81-8.01), ventilator-associated pneumonia (VAP) (OR = 4.43, CI = 3.42-5.75), surgical site infection (SSI) (OR = 3.66, CI = 2.55-5.25), small intestine injury (OR = 1.91, CI = 1.29-2.84), CKD (OR = 2.08, CI = 1.25-3.47), and cirrhosis (OR = 1.81, CI = 1.08-3.02) (all p < 0.05). Conclusion: Although CLABSI occurs in <0.1% of trauma patients with central-lines, it significantly impacts LOS and morbidity/mortality. The strongest associated risk factors for (+)CLABSI included HAIs (CAUTI/VAP/SSI), specific injuries (small intestine), and comorbidities. Providers should be aware of these risk factors with efforts made to prevent CLABSI in these patients.

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes

Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Systematic review finds risk of bias and applicability concerns for models predicting central line-associated bloodstream infection

OBJECTIVES

To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients.

STUDY DESIGN AND SETTING

Systematic review of literature in PubMed, Embase, Web of Science Core Collection, and Scopus up to July 10, 2023. Two authors independently appraised risk models using CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and assessed their risk of bias and applicability using Prediction model Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

Sixteen studies were included, describing 37 models. When studies presented multiple algorithms, we focused on the model that was selected as the best by the study authors. Eventually we appraised 19 models, among which 15 were regression models and four machine learning models. All models were at a high risk of bias, primarily due to inappropriate proxy outcomes, predictors that are unavailable at prediction time in clinical practice, inadequate sample size, negligence of missing data, lack of model validation, and absence of calibration assessment. 18 out of 19 models had a high concern for applicability, one model had unclear concern for applicability due to incomplete reporting.

CONCLUSION

We did not identify a prediction model of potential clinical use. There is a pressing need to develop an applicable model for CLA-BSI.

Early identification of bloodstream infection in hemodialysis patients by machine learning

Background

Bloodstream infection (BSI) is a prevalent cause of admission in hemodialysis (HD) patients and is associated with increased morbidity and mortality. This study aimed to establish a diagnostic, predictive model for the early identification of BSI in HD patients.

Methods

HD patients who underwent blood culture testing between August 2018 and March 2022 were enrolled in this study. Machine learning algorithms, including stepwise logistic regression (SLR), Lasso logistic regression (LLR), support vector machine (SVM), decision tree, random forest (RF), and gradient boosting machine (XGboost), were used to predict the risk of developing BSI from the patient's clinical data. The accuracy (ACC) and area under the subject working curve (AUC) were used to evaluate the performance of such models. The Shapley Additive Explanation (SHAP) values were used to explain each feature's predictive value on the models' output. Finally, a simplified nomogram for predicting BSI was devised.

Results

A total of 391 HD patients were enrolled in this study, of whom 74 (18.9%) were diagnosed with BSI. The XGboost model achieved the highest AUC (0.914, 95% confidence interval [CI]: 0.861-0.964) and ACC (86.3%) for BSI prediction. The four most significant co-variables in both the significance matrix plot of the XGboost model variables and the SHAP summary plot were body temperature, dialysis access via a non-arteriovenous fistula (non-AVF), the procalcitonin levels (PCT), and neutrophil-lymphocyte ratio (NLR).

Conclusions

This study created an effective machine-learning model for predicting BSI in HD patients. The model could be used to detect BSI at an early stage and hence guide antibiotic treatment in HD patients.

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms

Background

Lumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms.

Methods

We utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed.

Results

In the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy.

Conclusions

The RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

Background

Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.

Methods

A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.

Results

After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.

Conclusion

MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

Multi-Task Learning with Recurrent Neural Networks for ARDS Prediction using only EHR Data: Model Development and Validation Study (Preprint)

Background

Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes.

Objective

The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients.

Methods

The electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia, and COVID-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic curve. Heat maps to visualize attention scores were generated to provide interpretability to the neural networks. Finally, cluster analysis was performed to identify potential phenotypic subgroups of patients with ARDS.

Results

The single RNN model trained to classify 13 outputs outperformed the individual XGBoost models for ARDS prediction, achieving an area under the receiver operating characteristic curve of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in improved performance. Earlier prediction of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations.

Conclusions

The RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with the means to take early action.