Category: Statistics

Insights Imaging. 2024 Jul 6;15(1):167. doi: 10.1186/s13244-024-01753-1.

ABSTRACT

OBJECTIVES: Detection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.

METHODS: CT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.

RESULTS: A higher number of metastases was detected by one reader with DLIR-high: 7 (2-10) (median (Q₁-Q₃); total 733) versus 5 (2-10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.

CONCLUSION: DLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected.

CRITICAL RELEVANCE STATEMENT: Deep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT.

KEY POINTS: Detection of liver metastases is crucial but limited with standard CT reconstructions. More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction. Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.

PMID:38971933 | DOI:10.1186/s13244-024-01753-1

Commun Med (Lond). 2024 Jul 6;4(1):132. doi: 10.1038/s43856-024-00537-4.

ABSTRACT

BACKGROUND: The widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.

METHODS: We used 24-h steps data from the National Steps Challenge^TM Season 3, wherein participants were rewarded with vouchers when achieving specified goals of 5000, 7500, and 10,000 steps per day. We extracted data from 3075 participants’ including a total of 52,346 participant-days. We modelled the hourly step counts using a two-part model, in which the distribution for step counts was allowed to depend on the sum of step counts up to the previous hour and participant demographics.

RESULTS: Participants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.

CONCLUSIONS: We provide recommendations on the hourly targets to achieve daily goals, based on different participants’ characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.

PMID:38971929 | DOI:10.1038/s43856-024-00537-4

Aesthetic Plast Surg. 2024 Jul 7. doi: 10.1007/s00266-024-04202-y. Online ahead of print.

ABSTRACT

INTRODUCTION: The rising use of soft tissue fillers for aesthetic procedures has seen an increase in complications, including vascular occlusions and neurological symptoms that resemble stroke. This study synthesizes information on central nervous system (CNS) complications post-filler injections and evaluates the effectiveness of hyaluronidase (HYAL) treatment.

METHODS: A thorough search of multiple databases, including PubMed, EMBASE, Scopus, Web of Science, Google Scholar, and Cochrane, focused on publications from January 2014 to January 2024. Criteria for inclusion covered reviews and case reports that documented CNS complications related to soft tissue fillers. Advanced statistical and computational techniques, including logistic regression, machine learning, and Bayesian analysis, were utilized to dissect the factors influencing therapeutic outcomes.

RESULTS: The analysis integrated findings from 20 reviews and systematic analyses, with 379 cases reported since 2018. Hyaluronic acid (HA) was the most commonly used filler, particularly in nasal region injections. The average age of patients was 38, with a notable increase in case reports in 2020. Initial presentation data revealed that 60.9% of patients experienced no light perception, while ptosis and ophthalmoplegia were present in 54.3 and 42.7% of cases, respectively. The statistical and machine learning analyses did not establish a significant linkage between the HYAL dosage and patient recovery; however, the injection site emerged as a critical determinant.

CONCLUSION: The study concludes that HYAL treatment, while vital for managing complications, varies in effectiveness based on the injection site and the timing of administration. The non-Newtonian characteristics of HA fillers may also affect the incidence of complications. The findings advocate for tailored treatment strategies incorporating individual patient variables, emphasizing prompt and precise intervention to mitigate the adverse effects of soft tissue fillers.

LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

PMID:38971925 | DOI:10.1007/s00266-024-04202-y

Mol Psychiatry. 2024 Jul 6. doi: 10.1038/s41380-024-02640-3. Online ahead of print.

ABSTRACT

Previous studies have shown an association between the thalamocortical dysconnectivity and treatment-resistant depression (TRD). Whether a single subanesthetic dose of ketamine may change thalamocortical connectivity among patients with TRD is unclear. Whether these changes in thalamocortical connectivity is associated with the antidepressant and antisuicidal effects of ketamine treatment is also unclear. Two resting-state functional MRIs were collected in two clinical trials of 48 patients with TRD (clinical trial 1; 32 receiving ketamine, 16 receiving a normal saline placebo) and 48 patients with TRD and strong suicidal ideation (clinical trial 2; 24 receiving ketamine, 24 receiving midazolam), respectively. All participants underwent rs-fMRI before and 3 days after infusion. Seed-based functional connectivity (FC) was analyzed in the left/right thalamus. FCs between the bilateral thalamus and right middle frontal cortex (BA46) and between the left thalamus and left anterior paracingulate gyrus (BA8) increased among patients in the ketamine group in clinical trials 1 and 2, respectively. FCs between the right thalamus and bilateral frontal pole (BA9) and between the right thalamus and left rostral paracingulate gyrus (BA10) decreased among patients in the ketamine group in clinical trials 1 and 2, respectively. However, the associations between those FC changes and clinical symptom changes did not survive statistical significance after multiple comparison corrections. Whether ketamine-related changes in thalamocortical connectivity may be associated with ketamine’s antidepressant and antisuicidal effects would need further investigation. Clinical trials registration: UMIN Clinical Trials Registry (UMIN-CTR): Registration number: UMIN000016985 and UMIN000033916.

PMID:38971895 | DOI:10.1038/s41380-024-02640-3

Psychometrika. 2024 Jul 6. doi: 10.1007/s11336-024-09985-2. Online ahead of print.

ABSTRACT

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method’s performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

PMID:38971882 | DOI:10.1007/s11336-024-09985-2

Sci Rep. 2024 Jul 6;14(1):15589. doi: 10.1038/s41598-024-66596-8.

ABSTRACT

Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and treatment. The goal of this study is to develop a privacy-preserving federated learning framework for predicting candidemia in ICU patients. This approach aims to enhance the accuracy of antifungal drug prescriptions and patient outcomes. This study involved the creation of four predictive FL models for candidemia using data from ICU patients across three hospitals in China. The models were designed to prioritize patient privacy while aggregating learnings across different sites. A unique ensemble feature selection strategy was implemented, combining the strengths of XGBoost’s feature importance and statistical test p values. This strategy aimed to optimize the selection of relevant features for accurate predictions. The federated learning models demonstrated significant improvements over locally trained models, with a 9% increase in the area under the curve (AUC) and a 24% rise in true positive ratio (TPR). Notably, the FL models excelled in the combined TPR + TNR metric, which is critical for feature selection in candidemia prediction. The ensemble feature selection method proved more efficient than previous approaches, achieving comparable performance. The study successfully developed a set of federated learning models that significantly enhance the prediction of candidemia in ICU patients. By leveraging a novel feature selection method and maintaining patient privacy, the models provide a robust framework for improved clinical decision-making in the treatment of candidemia.

PMID:38971879 | DOI:10.1038/s41598-024-66596-8

Insights Imaging. 2024 Jul 6;15(1):171. doi: 10.1186/s13244-024-01749-x.

ABSTRACT

OBJECTIVES: To evaluate the effectiveness of cryoablation compared to partial nephrectomy in patients with stage IA papillary and chromophobe renal cell carcinoma (pRCC; chRCC).

MATERIAL AND METHODS: The 2004-2016 National Cancer Database was queried for adult patients with stage IA pRCC or chRCC treated with cryoablation or partial nephrectomy. Patients receiving systemic therapy or radiotherapy, as well as those with bilateral RCC or prior malignant disease were excluded. Overall survival (OS) was assessed using Kaplan-Meier plots and Cox proportional hazard regression models. Nearest neighbor propensity matching (1:1 cryoablation:partial nephrectomy, stratified for pRCC and chRCC) was used to account for potential confounders.

RESULTS: A total of 11122 stage IA renal cell carcinoma patients were included (pRCC 8030; chRCC 3092). Cryoablation was performed in 607 (5.5%) patients, and partial nephrectomy in 10515 (94.5%) patients. A higher likelihood of cryoablation treatment was observed in older patients with non-private healthcare insurance, as well as in those with smaller diameter low-grade pRCC treated at non-academic centers in specific US geographic regions. After propensity score matching to account for confounders, there was no statistically significant difference in OS comparing cryoablation vs partial nephrectomy in patients with pRCC (HR = 1.3, 95% CI: 0.96-1.75, p = 0.09) and those with chRCC (HR = 1.38, 95% CI: 0.67-2.82, p = 0.38).

CONCLUSION: After accounting for confounders, cryoablation, and partial nephrectomy demonstrated comparable OS in patients with stage IA papillary and chromophobe RCC. Cryoablation is a reasonable treatment alternative to partial nephrectomy for these histological RCC subtypes when radiologically suspected or diagnosed after biopsy.

CRITICAL RELEVANCE STATEMENT: Cryoablation might be considered as an upfront treatment alternative to partial nephrectomy in patients with papillary and chromophobe stage IA renal cell carcinoma, as both treatment approaches yield comparable oncological outcomes.

KEY POINTS: The utilization of cryoablation for stage IA papillary and chromophobe RCC increases. In the National Cancer Database, we found specific patterns of use of cryoablation. Cryoablation and partial nephrectomy demonstrate comparable outcomes after accounting for confounders.

PMID:38971873 | DOI:10.1186/s13244-024-01749-x

Sci Rep. 2024 Jul 6;14(1):15563. doi: 10.1038/s41598-024-66550-8.

ABSTRACT

Influenza viruses are responsible for a high number of infections and hospitalizations every year. In this study, we aimed to identify clinical and host-specific factors that influence the duration of hospitalization and the progression to acute respiratory failure (ARF) in influenza. We performed an analysis of data from a prospective active influenza surveillance study that was conducted over five seasons (2018/19 to 2022/23). A total of 1402 patients with influenza were included in the analysis, the majority of which (64.5%) were children (under 18 years), and 9.1% were elderly. At least one chronic condition was present in 29.2% of patients, and 9.9% of patients developed ARF. The median hospital stay was 4 days (IQR: 3, 6 days). The most important predictors of prolonged hospital stay and development of ARF were extremes of age (infants and elderly), presence of chronic diseases, particularly the cumulus of at least 3 chronic diseases, and late presentation to hospital. Among the chronic diseases, chronic obstructive pulmonary disease, cardiovascular disease, cancer, diabetes, obesity, and chronic kidney disease were strongly associated with a longer duration of hospitalization and occurrence of ARF. In this context, interventions aimed at chronic disease management, promoting influenza vaccination, and improving awareness and access to health services may contribute to reducing the impact of influenza not only in Romania but globally. In addition, continued monitoring of the circulation of influenza viruses is essential to limit their spread among vulnerable populations.

PMID:38971866 | DOI:10.1038/s41598-024-66550-8

Sci Rep. 2024 Jul 6;14(1):15593. doi: 10.1038/s41598-024-66671-0.

ABSTRACT

This research examines the psychometric characteristics and reliability of the 6-item turnover intention scale (TIS-6) by Bothma and Roodt (SA J Hum Resour Manag 11:a507, 2013) on a Hungarian sample. The internal validity of the TIS-6 was assessed using data from 269 Hungarian elderly care institution workers. Confirmatory factor analysis was performed to analyse the structural validity. Convergent and discriminant validity were examined with questions on job characteristics and using the Maslach Burnout Inventory and Effort-Reward Imbalance Scale. IBM SPSS 28.0 software was used for the statistical analysis, and the results were considered significant at p < 0.05. The internal consistency of the questionnaire’s scale proved to be acceptable (α = 0.826). Convergent validity was confirmed by the relationships between the components of the questionnaire and burnout (r_s = 0.512; p < 0.001; r_s = 0.419; p < 0.001) and workplace stress (r_s = 0.565; p < 0.001; r_s = 0.310; p < 0.001). There were significant differences between the TIS-6 scores among the groups with different degrees of burnout (p < 0.001), which indicated adequate discriminant validity of the questionnaire. The structural validity of the questionnaire was acceptable, and the scale questions fit well. The Hungarian version of the TIS-6 scale is a valid and reliable tool for assessing turnover intention among elderly care institution workers in Hungary.

PMID:38971853 | DOI:10.1038/s41598-024-66671-0

Sci Rep. 2024 Jul 6;14(1):15567. doi: 10.1038/s41598-024-66291-8.

ABSTRACT

The novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R² = 0.605), (RMSE = 6.644, R² = 0.681), (RMSE = 6.386, R² = 0.727) and (RMSE = 1.754, R² = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.

PMID:38971824 | DOI:10.1038/s41598-024-66291-8