Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases

A Potential Role for MAGI-1 in the Bi-Directional Relationship Between Major Depressive Disorder and Cardiovascular Disease

PURPOSE OF REVIEW

Major Depressive Disorder (MDD) is characterized by persistent symptoms such as fatigue, loss of interest in activities, feelings of sadness and worthlessness. MDD often coexist with cardiovascular disease (CVD), yet the precise link between these conditions remains unclear. This review explores factors underlying the development of MDD and CVD, including genetic, epigenetic, platelet activation, inflammation, hypothalamic-pituitary-adrenal (HPA) axis activation, endothelial cell (EC) dysfunction, and blood-brain barrier (BBB) disruption.

RECENT FINDINGS

Single nucleotide polymorphisms (SNPs) in the membrane-associated guanylate kinase WW and PDZ domain-containing protein 1 (MAGI-1) are associated with neuroticism and psychiatric disorders including MDD. SNPs in MAGI-1 are also linked to chronic inflammatory disorders such as spontaneous glomerulosclerosis, celiac disease, ulcerative colitis, and Crohn's disease. Increased MAGI-1 expression has been observed in colonic epithelial samples from Crohn's disease and ulcerative colitis patients. MAGI-1 also plays a role in regulating EC activation and atherogenesis in mice and is essential for Influenza A virus (IAV) infection, endoplasmic reticulum stress-induced EC apoptosis, and thrombin-induced EC permeability. Despite being understudied in human disease; evidence suggests that MAGI-1 may play a role in linking CVD and MDD. Therefore, further investigation of MAG-1 could be warranted to elucidate its potential involvement in these conditions.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network

BACKGROUND

Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability.

METHODS

Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts.

RESULTS

Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05).

CONCLUSIONS

Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Machine-learning-based adverse drug event prediction from observational health data: A review

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.

Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection

BACKGROUND

With the widespread adoption of electronic healthcare records (EHRs) by US hospitals, there is an opportunity to leverage this data for the development of predictive algorithms to improve clinical care. A key barrier in model development and implementation includes the external validation of model discrimination, which is rare and often results in worse performance. One reason why machine learning models are not externally generalizable is data heterogeneity. A potential solution to address the substantial data heterogeneity between health care systems is to use standard vocabularies to map EHR data elements. The advantage of these vocabularies is a hierarchical relationship between elements, which allows the aggregation of specific clinical features to more general grouped concepts.

OBJECTIVE

This study aimed to evaluate grouping EHR data using standard vocabularies to improve the transferability of machine learning models for the detection of postoperative health care-associated infections across institutions with different EHR systems.

METHODS

Patients who underwent surgery from the University of Utah Health and Intermountain Healthcare from July 2014 to August 2017 with complete follow-up data were included. The primary outcome was a health care-associated infection within 30 days of the procedure. EHR data from 0-30 days after the operation were mapped to standard vocabularies and grouped using the hierarchical relationships of the vocabularies. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and F₁-score in internal and external validations. To evaluate model transferability, a difference-in-difference metric was defined as the difference in performance drop between internal and external validations for the baseline and grouped models.

RESULTS

A total of 5775 patients from the University of Utah and 15,434 patients from Intermountain Healthcare were included. The prevalence of selected outcomes was from 4.9% (761/15,434) to 5% (291/5775) for surgical site infections, from 0.8% (44/5775) to 1.1% (171/15,434) for pneumonia, from 2.6% (400/15,434) to 3% (175/5775) for sepsis, and from 0.8% (125/15,434) to 0.9% (50/5775) for urinary tract infections. In all outcomes, the grouping of data using standard vocabularies resulted in a reduced drop in AUC and F₁-score in external validation compared to baseline features (all P<.001, except urinary tract infection AUC: P=.002). The difference-in-difference metrics ranged from 0.005 to 0.248 for AUC and from 0.075 to 0.216 for F₁-score.

CONCLUSIONS

We demonstrated that grouping machine learning model features based on standard vocabularies improved model transferability between data sets across 2 institutions. Improving model transferability using standard vocabularies has the potential to improve the generalization of clinical prediction models across the health care system.

Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma

Background

This study aimed to develop and validate machine learning (ML)-based prediction models for lung metastasis (LM) in patients with Ewing sarcoma (ES), and to deploy the best model as an open access web tool.

Methods

We retrospectively analyzed data from the Surveillance Epidemiology and End Results (SEER) Database from 2010 to 2016 and from four medical institutions to develop and validate predictive models for LM in patients with ES. Patient data from the SEER database was used as the training group (n = 929). Using demographic and clinicopathologic variables six ML-based models for predicting LM were developed, and internally validated using 10-fold cross validation. All ML-based models were subsequently externally validated using multiple data from four medical institutions (the validation group, n = 51). The predictive power of the models was evaluated by the area under receiver operating characteristic curve (AUC). The best-performing model was used to produce an online tool for use by clinicians to identify ES patients at risk from lung metastasis, to improve decision making and optimize individual treatment.

Results

The study cohort consisted of 929 patients from the SEER database and 51 patients from multiple medical centers, a total of 980 ES patients. Of these, 175 (18.8%) had lung metastasis. Multivariate logistic regression analysis was performed with survival time, T-stage, N-stage, surgery, and bone metastasis providing the independent predictive factors of LM. The AUC value of six predictive models ranged from 0.585 to 0.705. The Random Forest (RF) model (AUC = 0.705) using 4 variables was identified as the best predictive model of LM in ES patients and was employed to construct an online tool to assist clinicians in optimizing patient treatment. (https://share.streamlit.io/liuwencai123/es_lm/main/es_lm.py).

Conclusions

Machine learning were found to have utility for predicting LM in patients with Ewing sarcoma, and the RF model gave the best performance. The accessibility of the predictive model as a web-based tool offers clear opportunities for improving the personalized treatment of patients with ES.

Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's Common Data Model: Pilot Feasibility Study

BACKGROUND

Falls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication.

OBJECTIVE

The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods.

METHODS

As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest).

RESULTS

In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models.

CONCLUSIONS

To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.

Sestrin2 reduces cancer stemness via Wnt/β-catenin signaling in colorectal cancer

Background

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in both men and women in China. In previous studies, Sestrin2 was demonstrated to have functions in CRC. However, the relationship between Sestrin2 and cancer stemness has not been reported.

Methods and results

To investigate the contribution of Sestrin2 in CRC, we performed bioinformatics analysis of The Cancer Genome Atlas datasets and found that Sestrin2 was downregulated in CRC. Using a lentivirus vector, we verified that Sestrin2 suppressed CRC cell proliferation, migration, and colony formation. Furthermore, sphere formation, flow cytometry, quantitative PCR, and western blot analysis verified the influence of Sestrin2 on cancer stemness, including the expression of cluster of differentiation 44, octamer-binding transcription factor 4, sex-determining region Y-Box 2, CXC chemokine receptor 4, and the Wnt pathway downstream factors β-catenin and c-Myc. Consistently, the Wnt pathway activator BML-284 partially rescued the effects of Sestrin2 on the expression of proteins related to cancer stemness. Furthermore, in a mouse xenoplant model, tumors expressing Sestrin2 were significantly reduced in size with corresponding changes in cancer stemness.

Conclusions

Collectively, our results suggest that Sestrin2 inhibits CRC cell progression by downregulating the Wnt signaling pathway. Thus, Sestrin2 may be a promising therapeutic target for CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02498-x.

Predictive Genetic Variations in the Kynurenine Pathway for Interferon-α-Induced Depression in Patients with Hepatitis C Viral Infection

Importance: The high incidence of major depressive episodes during interferon-α (IFN-α) therapy is considered the most powerful supportive evidence for the inflammation theory of depression. As the kynurenine pathway plays an important role connecting inflammation and depression, it is plausible to investigate this pathway for predictive genetic markers for IFN-α-induced depression. Methods: In this prospective case-control study, we assessed 291 patients with chronic hepatitis C viral infection taking IFN-α therapy and analyzed the single nucleotide polymorphisms (SNPs) in genes in the kynurenine pathway. Our case group contained patients who developed IFN-α-induced depression during the treatment, and others were defined as the control group. Genomic DNA was extracted from leukocytes in the peripheral blood and analyzed by Affymetrix TWB array. We first tested allelic, dominant, and recessive models on each of our SNPs using Fisher’s exact test. We then conducted 5000 gene-wide max(T) permutations based on the best model of each SNP to provide strong gene-wide family-wise error rate control. Finally, we preformed logistic regression for the significant SNPs acquired in previous procedures, with sex and education level as covariates to build predictive models. Additional haplotype analyses were conducted with Haploview 4.2 to investigate the combining effect of multiple significant SNPs within a gene. Results: With sex and education level as covariates, rs8082252 (p = 0.0015, odds ratio = 2.716), rs8082142 (p = 0.0031, odds ratio = 2.499) in arylformamidase (AFMID), and rs12477181 (p = 0.0004, odds ratio = 0.3478) in kynureninase (KYNU) were significant in logistic regression models with dominant modes of inheritance. Haplotype analyses showed the two significant SNPs in AFMID to be in the same haploblock and highly correlated (r² = 0.99). There were two significant haplotypes (by the sequence of rs8082252, rs8082142): AT (χ2 = 7.734, p = 0.0054) and GC (χ2 = 6.874, p = 0.0087). Conclusions: This study provided supportive evidence of the involvement of the kynurenine pathway in IFN-α-induced depression. SNPs in this pathway were also predictive of this disease.

Neuropsychiatric Consequences of Lipophilic Beta-Blockers

Beta-blockers are a class of drugs with important benefits in cardiovascular pathology. In this paper, we aim to highlight their adverse and therapeutic effects in the neuropsychiatric field. With respect to permeability, we would like to mention that most beta-blockers are lipophilic and can cross the blood–brain barrier. Observational studies show the presence of neuropsychiatric side effects when taking beta-blockers, and is the reason for which caution is recommended in their use in patients with depressive syndrome. From a therapeutic point of view, most current evidence is for the use of beta-blockers in migraine attacks, essential tremor, and akathisia. Beta-blockers appear to be effective in the treatment of aggressive behavior, beneficial in the prevention of posttraumatic stress syndrome and may play a role in the adjuvant treatment of obsessive–compulsive disorder, which is refractory to standard therapy. In conclusion, the relationship between beta-blockers and the central nervous system appears as a two-sided coin. Summarizing the neuropsychiatric side effects of beta-blockers, we suggest that clinicians pay special attention to the pharmacological properties of different beta-blockers.