Catheter related thrombosis in hospitalized infants: A neural network approach to predict risk factors

Development and validation of machine learning-based prediction model for central venous access device-related thrombosis in children

BACKGROUND

Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely preventive interventions and reducing the incidence of CRT. Approaches for identifying the risk of CRT in children have not been well-researched.

OBJECTIVE

To identify the critical risk factors for CRT in children and to construct machine learning-based prediction models tailored to this group, providing a theoretical basis and technical support for the prediction and prevention of CRT in these patients.

STUDY DESIGN

Retrospective data of pediatric patients receiving CVAD catheterization from January 1, 2018 to June 31, 2023 in Tongji Hospital were collected and divided into a training set and an internal validation set in a ratio of 7:3. Relevant data from July 1, 2023 to July 1, 2024 were prospectively collected for external validation of the model. LASSO regression was applied to determine CRT independent risk factors. Subsequently, four prediction models were constructed using logistic regression (LR), random forest, artificial neural network, and eXtreme Gradient Boosting.

RESULTS

A total of 1445 children were included in this study and the overall incidence of CRT was 17.4 %. The LASSO regression screened out 11 critical variables, including history of thrombosis, leukemia, number of catheters, history of catheterization, chemotherapy, parenteral nutrition, mechanical prophylaxis, dialysis, hypertonic liquid, anticoagulants, and post-catheterization D-dimer. The LR model outperformed the other models in both internal and external validation and was considered the best model for this study, which was transformed into a nomogram.

CONCLUSIONS

This study identified 11 independent risk factors for CRT in children. The prediction model developed using LR algorithm demonstrated excellent clinical applicability and may provide valuable support for early prediction of CRT.

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.

Risk factors and incidence of central venous access device-related thrombosis in hospitalized children: a systematic review and meta-analysis

BACKGROUND

The risk factors for central venous access device-related thrombosis (CRT) in children are not fully understood. We used evidence-based medicine to find the risk factors for CRT by pooling current studies reporting risk factors of CRT, aiming to guide clinical diagnosis and treatment.

METHODS

A systematic search of PubMed, Web of Science, Embase, Cochrane Library, Scopus, CNKI, Sinomed, and Wanfang databases was conducted. RevMan 5.4 was employed for data analysis.

RESULTS

The review included 47 studies evaluating 262,587 children with CVAD placement. Qualitative synthesis and quantitative meta-analysis identified D-dimer, location of insertion, type of catheter, number of lumens, catheter indwelling time, and central line-associated bloodstream infection as the most critical risk factors for CRT. Primarily due to observational design, the quality of evidence was regarded as low certainty for these risk factors according to the GRADE approach.

CONCLUSION

Because fewer high-quality studies are available, larger sample sizes and well-designed prospective studies are still needed to clarify the risk factors affecting CRT. In the future, developing pediatric-specific CRT risk assessment tools is important. Appropriate stratified preventive strategies for CRT according to risk assessment level will help improve clinical efficiency, avoid the occurrence of CRT, and alleviate unnecessary suffering of children.

IMPACT

This is the latest systematic review of risk factors and incidence of CRT in children. A total of 47 studies involving 262,587 patients were included in our meta-analysis, according to which the pooled prevalence of CRT was 9.1%. This study identified several of the most critical risk factors affecting CRT in children, including D-dimer, insertion location, type of catheter, number of lumens, catheter indwelling time, and central line-associated bloodstream infection (CLABSI).

Multi-omics analysis reveals the impact of gut microbiota on antipsychotic-induced weight gain in schizophrenia

Emerging evidence indicates that gut microbial dysbiosis is associated with the development of antipsychotic-induced weight gain in schizophrenia (SZ). However, the exact taxonomic composition and functionality that constitute the "obesogenic" microbial profile remain elusive. Our retrospective survey identified two groups of the SZ population separated by BMI, with 1/3 of patients developing overweight/obesity after chronic antipsychotic treatment. Based on multi-omics analysis, we observed altered gut microbiota in SZ patients with overweight/obesity, characterized by a reduction in several beneficial bacteria genera, including Bacteroides, Parabacteroides, Akkermansia, and Clostridium. This microbial dysbiosis was accompanied by disrupted energy expenditure and nutritional metabolism, worsened metabolic indices, and reduced levels of beneficial metabolites, e.g. indole-3-carboxylic acid and propionic acid. Moreover, leveraging data from first-episode drug-naïve schizophrenia (FSZ) patients at one-month and one-year follow-up, both artificial neural network and random forest classifier-based prediction models demonstrated a strong ability of microbial profiles to predict antipsychotic-induced weight gain. Importantly, FSZ patients with higher relative abundance of Parabacteria distasonis were less susceptible to antipsychotic-induced weight gain. Thus, gut microbiota could serve as a noninvasive approach to predict antipsychotic-induced weight gain, guiding clinical antipsychotics administration and developing novel therapeutic strategies for weight management in SZ.

Machine Learning Predicts Peripherally Inserted Central Catheters-Related Deep Vein Thrombosis Using Patient Features and Catheterization Technology Features

This study aims to use patient feature and catheterization technology feature variables to train the corresponding machine learning (ML) models to predict peripherally inserted central catheters-deep vein thrombosis (PICCs-DVT) and analyze the importance of the two types of features to PICCs-DVT from the aspect of "input-output" correlation. To comprehensively and systematically summarize the variables used to describe patient features and catheterization technical features, this study combined 18 literature involving the two types of features in predicting PICCs-DVT. A total of 21 variables used to describe the two types of features were summarized, and feature values were extracted from the data of 1,065 PICCs patients from January 1, 2021 to August 31, 2022, to construct a data sample set. Then, 70% of the sample set is used for model training and hyperparameter optimization, and 30% of the sample set is used for PICCs-DVT prediction and feature importance analysis of three common ML classification models (i.e. support vector classifier [SVC], random forest [RF], and artificial neural network [ANN]). In terms of prediction performance, this study selected four metrics to evaluate the prediction performance of the model: precision (P), recall (R), accuracy (ACC), and area under the curve (AUC). In terms of feature importance analysis, this study chooses a single feature analysis method based on the "input-output" sensitivity principle-Permutation Importance. For the mean model performance, the three ML models on the test set are P = 0.92, R = 0.95, ACC = 0.88, and AUC = 0.81. Specifically, the RF model is P = 0.95, R = 0.96, ACC = 0.92, AUC = 0.86; the ANN model is P = 0.92, R = 0.95, ACC = 0.88, AUC = 0.81; the SVC model is P = 0.88, R = 0.94, ACC = 0.85, AUC = 0.77. For feature importance analysis, Catheter-to-vein rate (RF: 91.55%, ANN: 82.25%, SVC: 87.71%), Zubrod-ECOG-WHO score (RF: 66.35%, ANN: 82.25%, SVC: 44.35%), and insertion attempt (RF: 44.35%, ANN: 37.65%, SVC: 65.80%) all occupy the top three in the ML models prediction task of PICCs-DVT, showing relatively consistent ranking results. The ML models show good performance in predicting PICCs-DVT and reveal a relatively consistent ranking of feature importance from the data. The important features revealed might help clinical medical staff to better understand and analyze the formation mechanism of PICCs-DVT from a data-driven perspective.

Trends in the prevalence and risk factors for peripherally inserted central catheter-related complications in cancer patients from 2016 to 2022: a multicenter study

PURPOSE

To identify the trends in the prevalence of peripherally inserted central catheter (PICC) related complications in cancer patients and explore the risk factors for complications and occurrence speed.

METHODS

A total of 3573 cancer patients with PICC were recruited at 17 hospitals from 2016 to 2022. Logistic and COX regression were performed to identify influencing factors of PICC-related complications and incidence speed, respectively.

RESULTS

The proportion of symptomatic PICC-related thrombosis, phlebitis, and infections reported had decreased from 59.1% (in 2017), 11.9% (in 2016), and 11.1% (in 2016) to 15.3% (in 2022), 2.9% (in 2022), and 7.4% (in 2022), and adhesive-related skin injuries and bleeding/oozing reported had increased from 4.8% (in 2016) and 0.0% (in 2016) to 45.5% (in 2022) and 3.4% (in 2022), respectively. Catheter occlusion showed a trend of first increasing and then decreasing from 2.4 (in 2016) to 12.0 (in 2020) to 5.8% (in 2022). Logistic regression showed that hospital level, nature, the patient's gender, age, diagnosis, history of deep vein catheterization, chemotherapy drug administration, and type of PICC were influencing factors of complications. COX regression showed that the patient's gender, age, diagnosis, history of deep vein thrombosis and thrombophlebitis, history of deep vein catheterization, chemotherapy drug administration, type of PICC, type of connector, and StatLock used for fixation were influencing factors of incidence speed.

CONCLUSION

The composition ratios of PICC-related complications in cancer patients in China have changed in recent years. Chemotherapy drug administration was a significant risk factor accelerating the occurrence of complications. Maintenance factors had the maximum weight on the COX model, followed by patient factors. It is suggested that patients with high-risk factors be closely monitored and proper maintenance be performed to prevent and delay the occurrence of PICC-related complications.

Device Related Thrombosis and Bleeding in Pediatric Health Care: A Meta-analysis

CONTEXT

The risk of invasive device-related thrombosis and bleeding contributes to morbidity and mortality, yet their prevalence by device-types is poorly understood.

OBJECTIVES

This study aimed to estimate pooled proportions and rates of thrombotic and bleeding complications associated with invasive devices in pediatric health care.

DATA SOURCES

Medline, CINAHL, Embase, Web of Science, Scopus, Cochrane CENTRAL, clinical trial registries, and unpublished study databases were searched.

STUDY SELECTION

Cohort studies and trials published from January 2011 to June 2022, including (1) indwelling invasive devices, (2) pediatric participants admitted to a hospital, (3) reporting thrombotic and bleeding complications, and (4) published in English, were included.

DATA EXTRACTION

Meta-analysis of observational studies in epidemiology guidelines for abstracting and assessing data quality and validity were used.

MAIN OUTCOMES AND MEASURES

Device-specific pooled thromboses (symptomatic, asymptomatic, unspecified) and bleeding (major, minor).

RESULTS

Of the 107 studies, 71 (66%) focused on central venous access devices. Symptomatic venous thromboembolism in central venous access devices was 4% (95% confidence interval [CI], 3-5; incidence rate 0.03 per 1000 device-days, 95% CI, 0.00-0.07), whereas asymptomatic was 10% (95% CI, 7-13; incidence rate 0.25 per 1000 device-days, 95% CI, 0.14-0.36). Both ventricular assist devices (28%; 95% CI, 19-39) and extracorporeal membrane oxygenation (67%; 95% CI, 52-81) were often associated with major bleeding complications.

CONCLUSIONS

This comprehensive estimate of the incidence and prevalence of device-related thrombosis and bleeding complications in children can inform clinical decision-making, guide risk assessment, and surveillance.

Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study

BACKGROUND

Cancer indeed represents a significant public health challenge, and unplanned extubation of peripherally inserted central catheter (PICC-UE) is a critical concern in patient safety. Identifying independent risk factors and implementing high-quality assessment tools for early detection in high-risk populations can play a crucial role in reducing the incidence of PICC-UE among patients with cancer. Precise prevention and treatment strategies are essential to improve patient outcomes and safety in clinical settings.

OBJECTIVE

This study aims to identify the independent risk factors associated with PICC-UE in patients with cancer and to construct a predictive model tailored to this group, offering a theoretical framework for anticipating and preventing PICC-UE in these patients.

METHODS

Prospective data were gathered from January to December 2022, encompassing patients with cancer with PICC at Xiangya Hospital, Central South University. Each patient underwent continuous monitoring until the catheter's removal. The patients were categorized into 2 groups: the UE group (n=3107) and the non-UE group (n=284). Independent risk factors were identified through univariate analysis, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate analysis. Subsequently, the 3391 patients were classified into a train set and a test set in a 7:3 ratio. Utilizing the identified predictors, 3 predictive models were constructed using the logistic regression, support vector machine, and random forest algorithms. The ultimate model was selected based on the receiver operating characteristic (ROC) curve and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) synthesis analysis. To further validate the model, we gathered prospective data from 600 patients with cancer at the Affiliated Hospital of Qinghai University and Hainan Provincial People's Hospital from June to December 2022. We assessed the model's performance using the area under the curve of the ROC to evaluate differentiation, the calibration curve for calibration capability, and decision curve analysis (DCA) to gauge the model's clinical applicability.

RESULTS

Independent risk factors for PICC-UE in patients with cancer were identified, including impaired physical mobility (odds ratio [OR] 2.775, 95% CI 1.951-3.946), diabetes (OR 1.754, 95% CI 1.134-2.712), surgical history (OR 1.734, 95% CI 1.313-2.290), elevated D-dimer concentration (OR 2.376, 95% CI 1.778-3.176), targeted therapy (OR 1.441, 95% CI 1.104-1.881), surgical treatment (OR 1.543, 95% CI 1.152-2.066), and more than 1 catheter puncture (OR 1.715, 95% CI 1.121-2.624). Protective factors were normal BMI (OR 0.449, 95% CI 0.342-0.590), polyurethane catheter material (OR 0.305, 95% CI 0.228-0.408), and valved catheter (OR 0.639, 95% CI 0.480-0.851). The TOPSIS synthesis analysis results showed that in the train set, the composite index (Ci) values were 0.00 for the logistic model, 0.82 for the support vector machine model, and 0.85 for the random forest model. In the test set, the Ci values were 0.00 for the logistic model, 1.00 for the support vector machine model, and 0.81 for the random forest model. The optimal model, constructed based on the support vector machine, was obtained and validated externally. The ROC curve, calibration curve, and DCA curve demonstrated that the model exhibited excellent accuracy, stability, generalizability, and clinical applicability.

CONCLUSIONS

In summary, this study identified 10 independent risk factors for PICC-UE in patients with cancer. The predictive model developed using the support vector machine algorithm demonstrated excellent clinical applicability and was validated externally, providing valuable support for the early prediction of PICC-UE in patients with cancer.

Central venous catheter-associated deep vein thrombosis in critically ill pediatric patients: risk factors, prevention, and treatment

PURPOSE OF REVIEW

Central venous catheter (CVC) placement and ICU admission are the two most important risk factors for pediatric deep vein thrombosis. The optimal prevention and treatment strategies for CVC-associated deep vein thrombosis (CADVT) are unclear, but recently, seminal studies have been published. This review aims to summarize the recent literature on CADVT in critically ill children.

RECENT FINDINGS

Recent publications focused on three themes: risk factors, prevention, and treatment of CADVT. Newly identified risk factors for CADVT relate to Virchow's triad of hemostasis, blood vessel injury, and hypercoagulability. New risk prediction models have moderately good accuracy in predicting CADVT. Though previous data on pharmacologic CADVT prophylaxis was equivocal, recent studies indicate that low-molecular-weight heparin may be effective in preventing CADVT, particularly in critically ill children. Finally, new studies suggest that direct oral anticoagulants and shorter treatment times are noninferior to traditional agents and treatment durations in the treatment of CADVT.

SUMMARY

Recent research suggests new ways to accurately identify children at high risk of CADVT, effectively prevent CADVT, and optimize CADVT treatment. Future research should focus on understanding the pathobiology of CADVT formation, prevention, and treatment in critically ill children.

Gut Microbiota Serves a Predictable Outcome of Short-Term Low-Carbohydrate Diet (LCD) Intervention for Patients with Obesity

To date, much progress has been made in dietary therapy for obese patients. A low-carbohydrate diet (LCD) has reached a revival in its clinical use during the past decade with undefined mechanisms and debatable efficacy. The gut microbiota has been suggested to promote energy harvesting. Here, we propose that the gut microbiota contributes to the inconsistent outcome under an LCD. To test this hypothesis, patients with obesity or patients who were overweight were randomly assigned to a normal diet (ND) or an LCD group with ad libitum energy intake for 12 weeks. Using matched sampling, the microbiome profile at baseline and end stage was examined. The relative abundance of butyrate-producing bacteria, including Porphyromonadaceae Parabacteroides and Ruminococcaceae Oscillospira, was markedly increased after LCD intervention for 12 weeks. Moreover, within the LCD group, participants with a higher relative abundance of Bacteroidaceae Bacteroides at baseline exhibited a better response to LCD intervention and achieved greater weight loss outcomes. Nevertheless, the adoption of an artificial neural network (ANN)-based prediction model greatly surpasses a general linear model in predicting weight loss outcomes after LCD intervention. Therefore, the gut microbiota served as a positive outcome predictor and has the potential to predict weight loss outcomes after short-term LCD intervention. Gut microbiota may help to guide the clinical application of short-term LCD intervention to develop effective weight loss strategies. (This study has been registered at the China Clinical Trial Registry under approval no. ChiCTR1800015156). IMPORTANCE Obesity and its related complications pose a serious threat to human health. Short-term low-carbohydrate diet (LCD) intervention without calorie restriction has a significant weight loss effect for overweight/obese people. Furthermore, the relative abundance of Bacteroidaceae Bacteroides is a positive outcome predictor of individual weight loss after short-term LCD intervention. Moreover, leveraging on these distinct gut microbial structures at baseline, we have established a prediction model based on the artificial neural network (ANN) algorithm that could be used to estimate weight loss potential before each clinical trial (with Chinese patent number 2021104655623). This will help to guide the clinical application of short-term LCD intervention to improve weight loss strategies.