An evaluation of the bootstrap for model validation in mixture models

The effects of clinical learning environment and career adaptability on resilience: A mediating analysis based on a survey of nursing interns

BACKGROUND

The resilience education of intern nursing students has significant implications for the development and improvement of the nursing workforce. The clinical internship period is a critical time for enhancing resilience.

AIMS

To evaluate the resilience level of Chinese nursing interns and explore the effects of factors affecting resilience early in their careers, focusing on the mediating roles of career adaptability between clinical learning environment and resilience.

METHODS

The cross-sectional study design was adopted. From March 2022 to May 2023, 512 nursing interns in tertiary care hospitals were surveyed online with the Connor-Davidson Resilience Scale, the Clinical Learning Environment Scale for Nurse and the Career Adapt-Abilities Scale. Structural equation modelling was used to clarify the relationships among these factors. Indirect effects were tested using bootstrapped confidence intervals.

RESULTS

The nursing interns showed a moderately high level of resilience [M (SD) = 70.15 (19.90)]. Gender, scholastic attainment, scholarship, career adaptability and clinical learning environment were influencing factors of nursing interns' resilience. Male interns with good academic performance showed higher levels of resilience. Career adaptability and clinical learning environment positively and directly affected their resilience level (β = 0.62, 0.18, respectively, p < .01). Career adaptability was also positively affected by the clinical learning environment (β = 0.36, p < .01), and mediated the effect of clinical learning environment on resilience (β = 0.22, p < .01).

CONCLUSION

Clinical learning environment can positively affect the resilience level of nursing interns. Career adaptability can affect resilience directly and also play a mediating role between clinical learning environment and resilience. Thus, promotion of career adaptability and clinical teaching environment should be the potential strategies for nursing interns to improve their resilience, especially for female nursing interns with low academic performance.

Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning

OBJECTIVE

We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods.

METHODS

We obtained the endometrial samples from 33 PCOS patients and 7 healthy controls at the Reproductive Center of the Second Hospital of Lanzhou University from September 2019 to September 2020. Liquid chromatography tandem mass spectrometry (LCMS/MS) was conducted to identify the differentially expressed proteins (DEPs) of the two groups. Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to analyze the related pathways and functions of the DEPs. Then, we used machine learning methods to screen the feature proteins. Multivariate Cox regression analysis was also conducted to establish the prognostic models. The performance of the prognostic model was then evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). In addition, the Bootstrap method was conducted to verify the generalization ability of the model. Finally, linear correlation analysis was performed to figure out the correlation between the feature proteins and clinical data.

RESULTS

Four hundred and fifty DEPs in PCOS and controls were screened out, and we obtained some pathways and functions. A prognostic model for the pregnancy loss of PCOS was established, which has good discrimination and generalization ability based on two feature proteins (TIA1, COL5A1). Strong correlation between clinical data and proteins were identified to predict the reproductive outcome in PCOS.

CONCLUSION

The model based on the TIA1 and COL5A1 protein could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a good theoretical foundation for subsequent research.

Development and internal validation of a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas

Aim

To develop and internally validate a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas (PPGLs).

Methods

Clinical data of patients with PPGLs who presented to Peking Union Medical College Hospital from 2013 to 2022 and underwent genetic testing were retrospectively collected. Variables were screened by backward stepwise and clinical significance and were used to construct multivariable logistic models in 50 newly generated datasets after the multiple imputation. Bootstrapping was used for internal validation. A corresponding nomogram was generated based on the model. Sensitivity analyses were also performed.

Results

A total of 556 patients with PPGLs were included, of which 99 had a germline SDHB mutation. The prediction model revealed that younger age of onset [Odds ratio (OR): 0.93, 95% CI: 0.91-0.95], synchronous metastasis (OR: 6.43, 95% CI: 2.62-15.80), multiple lesion (OR: 0.22, 95% CI: 0.09-0.54), retroperitoneal origin (OR: 5.72, 95% CI: 3.13-10.47), negative 131I-meta-iodobenzylguanidine (MIBG) (OR: 0.34, 95% CI: 0.15-0.73), positive octreotide scintigraphy (OR: 3.24, 95% CI: 1.25-8.43), elevated 24h urinary dopamine (DA) (OR: 1.72, 95% CI: 0.93-3.17), NE secretory type (OR: 2.83, 95% CI: 1.22- 6.59), normal secretory function (OR: 3.04, 95% CI: 1.04-8.85) and larger tumor size (OR: 1.09, 95% CI: 0.99-1.20) were predictors of SDHB mutations in PPGLs, and showed good and stable predictive performance with a mean area under the ROC curve (AUC) of 0.865 and coefficient of variation of 2.2%.

Conclusions

This study provided a novel and useful tool for predicting SDHB mutations by integrating easily obtained clinical data. It may help clinicians select suitable genetic testing methods and make appropriate clinical decisions for these high-risk patients.

Analysis of risk factors and establishment of a risk prediction model for post-transplant diabetes mellitus after kidney transplantation

Introduction

Post-transplant diabetes mellitus (PTDM) is a known side effect in transplant recipients administered immunosuppressant drugs, such as tacrolimus. This study aimed to investigate the risk factors related to PTDM, and establish a risk prediction model for PTDM. In addition, we explored the effect of PTDM on the graft survival rate of kidney transplantation recipients.

Methods

Patients with pre-diabetes mellitus before kidney transplant were excluded, and 495 kidney transplant recipients were included in our study, who were assigned to the non-PTDM and PTDM groups. The cumulative incidence was calculated at 3 months, 6 months, 1 year, 2 years, and 3 years post-transplantation. Laboratory tests were performed and the tacrolimus concentration, clinical prognosis, and adverse reactions were analyzed. Furthermore, binary logistic regression analysis was used to identify the independent risk factors of PTDM.

Results

Age ≥ 45 years (adjusted odds ratio [aOR] 2.25, 95% confidence interval [CI] 1.14–3.92; P = 0.015), body mass index (BMI) > 25 kg/m² (aOR 3.12, 95% CI 2.29–5.43, P < 0.001), tacrolimus concentration > 10 ng/mL during the first 3 months post-transplantation (aOR 2.46, 95%CI 1.41–7.38; P < 0.001), transient hyperglycemia (aOR 4.53, 95% CI 1.86–8.03; P < 0.001), delayed graft function (DGF) (aOR 1.31, 95% CI 1.05–2.39; P = 0.019) and acute rejection (aOR 2.16, 95% CI 1.79–4.69; P = 0.005) were identified as independent risk factors of PTDM. The PTDM risk prediction model was developed by including the above six risk factors, and the area under the receiver operating characteristic curve was 0.916 (95% CI 0.862–0.954, P < 0.001). Furthermore, the cumulative graft survival rate was significantly higher in the non- PTDM group than in the PTDM group.

Conclusions

Risk factors related to PTDM were age ≥ 45 years, BMI > 25 kg/m², tacrolimus concentration > 10 ng/mL during the first 3 months post-transplantation, transient hyperglycemia, DGF and acute rejection.

Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches

BACKGROUND

Finite mixture models cluster individuals into latent subgroups based on observed traits. However, inaccurate enumeration of clusters can have lasting implications on policy decisions and allocations of resources. Applied and methodological researchers accept no obvious best model fit statistic, and different measures could suggest different numbers of latent clusters.

OBJECTIVES

The purpose of this article is to evaluate and compare different cluster enumeration techniques.

RESEARCH DESIGN

Study I demonstrates how recently proposed resampling methods result in no precise number of clusters on which all fit statistics agree. We recommend the pre-processing method in Study II as an alternative. Both studies used nationally representative data on working memory, cognitive flexibility, and inhibitory control.

CONCLUSIONS

The data plus priors method shows promise to address inconsistencies among fit measures and help applied researchers using finite mixture models in the future.

Development of a diagnostic model focusing on nutritional indicators for frailty classification in people with chronic heart failure

AIMS

Frailty has a great impact on the quality of life of patients with chronic heart failure (CHF), which needs to be judged in time. To develop a diagnostic model based on nutritional indicators to judge the frailty status of patients with chronic heart failure (Frailty-CHF).

METHODS AND RESULTS

In the data collection part of this study, questionnaire method and biomedical measurement method were adopted. The trace elements in serum samples were detected by high performance liquid chromatography, chemiluminescence, and inductively coupled plasma mass spectrometry. We used Excel for data consolidation, and then imported the data into R software for modelling. Lasso method was used for variable screening, and Logistics regression fitting model was used after variables were determined. The internal validation of the model was completed by Bootstrap re-sampling. A total of 123 patients were included in this study. After variables' screening, age, nutritional status-heart failure, New York Heart Association Functional Class (NYHA), micronutrients B12, Ca, folic acid, and Se were included in the model, the c statistic and Brier score of the original model were 0.9697 and 0.0685, respectively. After Bootstrap re-sampling adjustment, the c statistic and Brier score were 0.8503 and 0.1690.

CONCLUSION

In this study, a diagnostic model of age, nutritional status-heart failure, NYHA, the micronutrients B12, Ca, folic acid, and Se was established. It could help healthcare professionals better identify the frailty status in patients with CHF.

Patients undergoing surgery for oligometastatic oesophageal cancer survive for more than 2 years: bootstrapping systematic review data

OBJECTIVES

Oesophageal cancer oligometastasis is a state of limited systemic disease characterized by ˂5 metastases. Without surgery average survival is 4–12 months. We sought to estimate patient prognosis following the surgical resection of oligometastatic disease from oesophageal cancer.

METHODS

Eligible studies were identified through systematic search of PubMed and the Cochrane Library (end-of-search date: 20 November 2019). We estimated cumulative 1-, 3- and 5-year, as well as overall survival using bootstrap methodology with 1 000 000 repetitions per outcome.

RESULTS

We investigated six studies involving 420 patients who underwent metastasectomy for oligometastasis from oesophageal cancer. Adenocarcinoma [77.3%; 95% confidence interval (CI) 62.8–87.3] was the most prevalent histological type followed by squamous cell carcinoma (22.7%; 95% CI 12.7–37.2). Metastatic lesions were typically synchronous (91.5%; 95% CI 87.5–94.1). Overall, 73.5% (95% CI 67.5–78.6) of the patients underwent resection of the primary and metastatic tumours synchronously. Neoadjuvant chemoradiotherapy was utilized in the majority of the patients (66.7%; 95% CI 49.5–80.3) followed by neoadjuvant chemotherapy (33.3%; 95% CI 19.6–50.5). The mean overall survival was 24.5 months (95% CI 14.4–34.6). One-year survival was 88.3% (95% CI 85.6–90.8). Three-year survival and 5-year survival were 36.3% (95% CI 15.3–7.3) and 23.8% (95% CI 12.0–35.7), respectively.

CONCLUSIONS

Patients undergoing surgical resection of oesophageal oligometastasis survive for more than 24 months. Therefore, loco-regional control of oligometastatic disease appears to improve survival by at least 100%.

ClickGene: an open cloud-based platform for big pan-cancer data genome-wide association study, visualization and exploration

Tremendous amount of whole-genome sequencing data have been provided by large consortium projects such as TCGA (The Cancer Genome Atlas), COSMIC and so on, which creates incredible opportunities for functional gene research and cancer associated mechanism uncovering. While the existing web servers are valuable and widely used, many whole genome analysis functions urgently needed by experimental biologists are still not adequately addressed. A cloud-based platform, named CG (ClickGene), therefore, was developed for DIY analyzing of user's private in-house data or public genome data without any requirement of software installation or system configuration. CG platform provides key interactive and customized functions including Bee-swarm plot, linear regression analyses, Mountain plot, Directional Manhattan plot, Deflection plot and Volcano plot. Using these tools, global profiling or individual gene distributions for expression and copy number variation (CNV) analyses can be generated by only mouse button clicking. The easy accessibility of such comprehensive pan-cancer genome analysis greatly facilitates data mining in wide research areas, such as therapeutic discovery process. Therefore, it fills in the gaps between big cancer genomics data and the delivery of integrated knowledge to end-users, thus helping unleash the value of the current data resources. More importantly, unlike other R-based web platforms, Dubbo, a cloud distributed service governance framework for 'big data' stream global transferring, was used to develop CG platform. After being developed, CG is run on an independent cloud-server, which ensures its steady global accessibility. More than 2 years running history of CG proved that advanced plots for hundreds of whole-genome data can be created through it within seconds by end-users anytime and anywhere. CG is available at http://www.clickgenome.org/.

Breast Cancer Metabolomics: From Analytical Platforms to Multivariate Data Analysis. A Review

Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.