Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission

The Value of Strength as a Predictor of Musculoskeletal Injury in Canadian Armed Forces Basic Infantry Candidates

INTRODUCTION

Musculoskeletal injuries (MSKI) impact military organizations by threatening their operational readiness, warranting investigation into relevant factors to inform risk reduction strategies. While several self-reported and physical performance measures have been associated with MSKI among military personnel, few have been validated and none have been reported in Canadian basic infantry candidates. The purpose of this study was to investigate associations between self-reported and physical performance measures and MSKI, and determine their validity as predictors of MSKI, in Canadian basic infantry candidates.

METHODS

This was a planned secondary analysis of a study tracking MSKI at a basic infantry training facility in Ontario, Canada approved by Defence Research & Development Canada. Before the basic infantry training, consenting candidates completed a baseline testing session including self-reported questionnaires, measures of anthropometry, and physical performance previously associated with MSKI (ankle dorsiflexion test, Y-Balance Test, Isometric Mid-Thigh Pull, and the Fitness for Operational Requirements of CAF Employment (FORCE) evaluation). All MSKI reported by candidates were diagnosed by licensed healthcare providers. From a total sample of 129 candidates, 76% (n = 98) were used to determine any associations between baseline testing variables and MSKI and to develop a predictive model (Development Sample), while 24% (n = 33) were used to offer preliminary validation of the same predictive model (Validation Sample). The binary logistic regression and independent sample t-testing determined independent associations with MSKI in the Development Sample. All continuous variables and dichotomous variables previously associated with MSKI risk (Smoker Yes/No, previous history of MSKI, and physical inactivity) were entered into a backward stepwise logistic regression analysis to assess the predictive association with MSKI incidence in the Development Sample. The regression model was then applied to the Validation Sample.

RESULTS

A total of 35 MSKI were diagnosed by Health Services Centre staff. The majority of the MSKI were acute (63%), sustained to the hip, knee, and ankle (74%). The most common diagnoses were strains and sprains (71%). Uninjured participants performed significantly better on the Relative Isometric Mid-Thigh Pull, FORCE 20 mR, FORCE ILS, and FORCE Estimated VO2peak compared to injured participants. Logistic regression analysis showed that the only variable with significant independent association with diagnosed MSKI incidence was self-reported previous history of MSKI. However, the backward stepwise logistic regression analysis retained self-reported previous history of MSKI, FORCE SBD, FORCE Estimated VO2peak, and Isometric Mid-Thigh Pull Peak Force as predictors of MKSI. The logistic regression model including these variables could predict MSKI with an accuracy of 79% in the Development Sample and 67% in the Validation Sample.

CONCLUSION

This study provides preliminary support for the value of measures of absolute muscular strength and cardiorespiratory fitness as predictors of MSKI in Canadian basic infantry candidates. Given the associations between physical performance measures and MSKI, and their necessity during occupational tasks, it is recommended that Canadian basic infantry training facilities integrate resistance training with external loads to best prepare their candidates to meet their occupational demands and potentially minimize MSKI. Further investigations to confirm the predictive capacity of these variables in a larger sample across additional facilities are warranted.

Revealing Novel Genes Related to Parkinson's Disease Pathogenesis and Establishing an associated Model

Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.

Higher in-hospital mortality in SARS-CoV-2 omicron variant infection compared to influenza infection-Insights from the CORONA Germany study

BACKGROUND

With the emergence of new subvariants, the disease severity of Severe Acute Respiratory Syndrome Coronavirus-2 has attenuated. This study aimed to compare the disease severity in patients hospitalized with omicron variant infection to those with influenza infection.

METHODS

We compared data from the multicenter observational, prospective, epidemiological "CORONA Germany" (Clinical Outcome and Risk in hospitalized COVID-19 patients) study on patients infected with Severe Acute Respiratory Syndrome Coronavirus-2 to retrospective data on influenza infection cases from November 2016 to August 2022. Severe Acute Respiratory Syndrome Coronavirus-2 cases were classified as wild-type/delta variant before January 2022, or omicron variant from January 2022 onward. The primary outcome was in-hospital mortality, adjusted for age, gender, and comorbidities.

RESULTS

The study included 35,806 patients from 53 hospitals in Germany, including 4,916 patients (13.7%) with influenza infection, 16,654 patients (46.5%) with wild-type/delta variant infection, and 14,236 patients (39.8%) with omicron variant infection. In-hospital mortality was highest in patients with wild-type/delta variant infection (16.8%), followed by patients with omicron variant infection (8.4%) and patients with influenza infection (4.7%). In the adjusted analysis, higher age was the strongest predictor for in-hospital mortality (age 80 years vs. age 50 years: OR 4.25, 95% CI 3.10-5.83). Both, patients with wild-type/delta variant infection (OR 3.54, 95% CI 3.02-4.15) and patients with omicron variant infection (OR 1.56, 95% CI 1.32-1.84) had a higher risk for in-hospital mortality than patients with influenza infection.

CONCLUSION

After adjusting for age, gender and comorbidities, patients with wild-type/delta variant infection had the highest risk for in-hospital mortality compared to patients with influenza infection. Even for patients with omicron variant infection, the adjusted risk for in-hospital mortality was higher than for patients with influenza infection. The adjusted risk for in-hospital mortality showed a strong age dependency across all virus types and variants.

TRIAL REGISTRATION NUMBER

NCT04659187.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.