Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study

Zhu, Cairong; Xu, Zidu; Gu, Yaowen; Zheng, S. Q.; Sun, Xiangyu; Cao, Jianzhong; Song, Bao; Jin, Jingfen; Liu, Yilan; Wen, Xiaozhong; Cheng, Songbai; Li, Jiao; Wu, Xinjuan

doi:10.1016/j.jhin.2022.01.002

Cited by 28 publications

(17 citation statements)

References 60 publications

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“…Finally, we developed the XGBoost and GBST models to predict the device-associated infection and 30-day survival after initial invasive device procedures in the ICU. 14,30,31 have also utilized neural networks, ensemble learning models, and XGBoost algorithms to develop ML models for predicting CAUTI in hospitalized patients, with AUCs ranging from 0.758 to 0.904.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for individualized prediction of device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients

Su,

Sun,

Sun

et al. 2024

Preprint

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Background Risk evaluation for device-associated infection and 30-day outcomes following the accomplishment of invasive device procedures is essential to patients admitted to the intensive care unit (ICU). We aimed to construct and validate machine learning (ML) models to predict the risk of device-associated infection and 30-day outcomes after invasive device procedures in ICU patients. Methods We included 8574 patients with ICU admission who accepted invasive device procedures from the Medical Information Mart for Intensive Care (MIMIC)-IV version 2.2 database. Enrolled patients were divided into development and test cohorts according to a proportion of 7:3. ML models were created based on the training dataset (n = 6001). We applied seven ML models for device-associated infection, including random forest (RF), logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost), Gaussian naive Bayesian (GNB), decision tree (DT), and recurrent neural networks with long short-term memory (LSTM) algorithm. Five models being used for the 30-day survival outcome, including Cox regression, extra survival trees (EST), survival tree (ST), gradient boosting survival tree (GBST), and deep learning survival neural network (DeepSurv). The primary evaluated approaches to model performance were the receiver operating characteristic (ROC) curve for device-associated infection prediction and the survival model's concordance index (C-index). All models were internally validated in a test cohort (n = 2573). Results During the observation period of 30 days after invasive device procedures, 491 patients developed device-associated infections, and 1329 died. The XGBoost model presented the best-discriminated performance, with the test dataset's highest area under the curve (AUC) of 0.787 (0.787, 0.788), areas under the precision-recall curve (AUPRC) of 0.172 (0.172, 0.172), and the lowest Brier score (BS) of 0.146 (0.145, 0.146). The GBST model revealed the best ability to predict 30-day outcome survival, manifesting the highest C-index of 0.730 (0.728, 0.733) and time-dependent AUC of 0.744 (0.741, 0.748) in the validated cohort. The XGBoost and GBST have been available in the web application. These two models can generate an individual predictive risk of device-associated infection and 30-day survival outcomes for patients with ICU admission experiencing invasive device procedures. Conclusions We developed and internally evaluated XGBoost and GBST models with excellent prediction ability for the risk of device-associated infection and 30-day survival outcomes after invasive device procedures in patients hospitaled to ICU. The predictive result of these two models can help clinicians identify higher-risk patients with adverse events and conduct prevention methods.

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Section: Discussionmentioning

confidence: 99%

Machine learning for individualized prediction of device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients

Su,

Sun,

Sun

et al. 2024

Preprint

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“…However, the study did not explore the meaning of explainability for their models. Zhu et al 33 investigated ML models in the prediction of poststroke urinary tract infection (UTI) risk in immobilized patients, using SHAP for model-agnostic explainability, but without addressing model-specific explainability and only targeting a subgroup of HA-UTI. Jeng et al 34 examined ML for predicting recurrent UTIs caused by Escherichia coli , demonstrating model-specific explainability through decision tree splits.…”

Section: Literature Reviewmentioning

confidence: 99%

A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models

Jakobsen,

Nielsen,

Leutscher

et al. 2024

Health Informatics J

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Early identification of patients at risk of hospital-acquired urinary tract infections (HA-UTI) enables the initiation of timely targeted preventive and therapeutic strategies. Machine learning (ML) models have shown great potential for this purpose. However, existing ML models in infection control have demonstrated poor ability to support explainability, which challenges the interpretation of the result in clinical practice, limiting the adaption of the ML models into a daily clinical routine. In this study, we developed Bayesian Network (BN) models to enable explainable assessment within 24 h of admission for risk of HA-UTI. Our dataset contained 138,250 unique hospital admissions. We included data on admission details, demographics, lifestyle factors, comorbidities, vital parameters, laboratory results, and urinary catheter. Models developed from a reduced set of five features were characterized by transparency compared to models developed from a full set of 50 features. The expert-based clinical BN model over the reduced feature space showed the highest performance (area under the curve = 0.746) compared to the naïve- and tree-augmented-naïve BN models. Moreover, models developed from expert-based knowledge were characterized by enhanced explainability.

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“…Incorporating these additional variables into the nomogram may enhance its predictive performance, providing a more comprehensive and accurate assessment of individual risk. Lastly, future research efforts should explore more advanced prediction model methods (43). Implementing these advanced techniques can help enhance the prediction performance of the nomogram and provide deeper insights into the clinical implications of the model, improving its interpretability and utility in real-world scenarios.…”

Section: Limitationsmentioning

confidence: 99%

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer

Fu,

Shi,

Zhao

et al. 2023

Front. Endocrinol.

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BackgroundHealth-related quality of life (HRQoL) is a critical aspect of overall well-being for patients with lung cancer, particularly those with metastatic spinal cord compression (MSCC). However, there is currently a lack of universal evaluation of HRQoL in this specific patient population. The aim of this study was to develop a nomogram that can accurately predict HRQoL outcomes in patients with lung cancer-related MSCC.MethodsA total of 119 patients diagnosed with MSCC secondary to lung cancer were prospectively collected for analysis in the study. The least absolute shrinkage and selection operator (LASSO) regression analysis, along with 10-fold cross-validation, was employed to select the most significant variables for inclusion in the nomogram. Discriminative and calibration abilities were assessed using the concordance index (C-index), discrimination slope, calibration plots, and goodness-of-fit tests. Net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were conducted to compare the nomogram’s performance with and without the consideration of comorbidities.ResultsFour variables were selected to construct the final nomogram, including the Eastern Cooperative Oncology Group (ECOG) score, targeted therapy, anxiety scale, and number of comorbidities. The C-index was 0.87, with a discrimination slope of 0.47, indicating a favorable discriminative ability. Calibration plots and goodness-of-fit tests revealed a high level of consistency between the predicted and observed probabilities of poor HRQoL. The NRI (0.404, 95% CI: 0.074–0.734, p = 0.016) and the IDI (0.035, 95% CI: 0.004–0.066, p = 0.027) confirmed the superior performance of the nomogram with the consideration of comorbidities.ConclusionsThis study develops a prediction nomogram that can assist clinicians in evaluating postoperative HRQoL in patients with lung cancer-related MSCC. This nomogram provides a valuable tool for risk stratification and personalized treatment planning in this specific patient population.

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Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study

Cited by 28 publications

References 60 publications

Machine learning for individualized prediction of device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients

Machine learning for individualized prediction of device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients

A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer

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