Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis

Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.

Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms

Background and Purpose: Treatment for mild stroke remains an open question. We aim to develop a decision support tool based on machine learning (ML) algorithms, called DAMS (Disability After Mild Stroke), to identify mild stroke patients who would be at high risk of post-stroke disability (PSD) if they only received medical therapy and, more importantly, to aid neurologists in making individual clinical decisions in emergency contexts.Methods: Ischemic stroke patients were prospectively recorded in the National Advanced Stroke Center of Nanjing First Hospital (China) between July 2016 and September 2020. The exclusion criteria were patients who received thrombolytic therapy, age <18 years, lack of 3-month modified Rankin Scale (mRS), disabled before the index stroke, with an admission National Institute of Health stroke scale (NIHSS) > 5. The primary outcome was PSD, corresponding to 3-month mRS ≥ 2. We developed five ML models and assessed the area under curve (AUC) of receiver operating characteristic, calibration curve, and decision curve analysis. The optimal ML model was selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. Finally, rapid-DAMS (R-DAMS) was constructed for a more urgent situation based on DAMS.Results: A total of 1,905 mild stroke patients were enrolled in this study, and patients with PSD accounted for 23.4% (447). There was no difference in AUCs between the five models (ranged from 0.691 to 0.823). Although there was similar discriminative performance between ML models, the support vector machine model exhibited higher net benefit and better calibration (Brier score, 0.159, calibration slope, 0.935, calibration intercept, 0.035). Therefore, this model was selected for DAMS. In addition, SHAP approach showed that the most crucial feature was NIHSS on admission. Finally, R-DAMS was constructed and there was similar discriminative performance between R-DAMS and DAMS, but the former performed worse on calibration.Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, were designed to aid clinical decision-making for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.

IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

Mild Coronavirus Disease 2019 (COVID-19) Is Marked by Systemic Oxidative Stress: A Pilot Study

Oxidative stress has been implicated to play a critical role in the pathophysiology of coronavirus disease 2019 (COVID-19) and may therefore be considered as a relevant therapeutic target. Serum free thiols (R-SH, sulfhydryl groups) comprise a robust marker of systemic oxidative stress, since they are readily oxidized by reactive oxygen species (ROS). In this study, serum free thiol concentrations were measured in hospitalized and non-hospitalized patients with COVID-19 and healthy controls and their associations with relevant clinical parameters were examined. Serum free thiol concentrations were measured colorimetrically (Ellman’s method) in 29 non-hospitalized COVID-19 subjects and 30 age-, sex-, and body-mass index (BMI)-matched healthy controls and analyzed for associations with clinical and biochemical disease parameters. Additional free thiol measurements were performed on seven serum samples from COVID-19 subjects who required hospitalization to examine their correlation with disease severity. Non-hospitalized subjects with COVID-19 had significantly lower concentrations of serum free thiols compared to healthy controls (p = 0.014), indicating oxidative stress. Serum free thiols were positively associated with albumin (St. β = 0.710, p < 0.001) and inversely associated with CRP (St. β = −0.434, p = 0.027), and showed significant discriminative ability to differentiate subjects with COVID-19 from healthy controls (AUC = 0.69, p = 0.011), which was slightly higher than the discriminative performance of CRP concentrations regarding COVID-19 diagnosis (AUC = 0.66, p = 0.042). This study concludes that systemic oxidative stress is increased in patients with COVID-19 compared with healthy controls. This opens an avenue of treatment options since free thiols are amenable to therapeutic modulation.

Identifying weather variables as important clinical predictors of bacterial diarrhea among international travelers to Nepal and Thailand

Background: Clinicians and travelers often have limited tools to differentiate bacterial from non-bacterial causes of travelers' diarrhea (TD). Development of a clinical prediction rule assessing the etiology of TD may help identify episodes of bacterial diarrhea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhea among clinical, demographic, and weather variables, as well as to develop and cross-validate a parsimonious predictive model. Methods: We collected de-identified clinical data from 457 international travelers with acute diarrhea presenting to two healthcare centers in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal etiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhea. Results: We identified 195 cases of bacterial etiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite, and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected etiologies were average location-specific environmental temperature and RBC on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an AUC of 0.73 using 3 variables with calibration intercept -0.01 (SD 0.31) and slope 0.95 (SD 0.36). Conclusions: We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of etiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.

Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania

ObjectivesWe aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms.SettingWe analysed the birth registry data from a referral hospital in northern Tanzania. Since July 2000, every birth at this facility has been recorded in a specific database.Participants21 578 deliveries between 2000 and 2015 were included. Deliveries that lacked information regarding the labour induction status were excluded.Primary outcomeDeliveries involving labour induction intervention.ResultsParity, maternal age, body mass index, gestational age and birth weight were all found to be important predictors of labour induction. Boosting method demonstrated the best discriminative performance (area under curve, AUC=0.75: 95% CI (0.73 to 0.76)) while logistic regression presented the least (AUC=0.71: 95% CI (0.70 to 0.73)). Random forest and boosting algorithms showed the highest net-benefits as per the decision curve analysis.ConclusionAll of the machine learning algorithms performed well in predicting the likelihood of labour induction intervention. Further optimisation of these classifiers through hyperparameter tuning may result in an improved performance. Extensive research into the performance of other classifier algorithms is warranted.

Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma

ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p &lt;0.05 was set as the threshold for significance.ResultsThe classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was &lt;0.75 in cross-validation. The clinical–radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819–0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707–0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively.ConclusionRadiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical–radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

VALIDATION OF A DYNAMIC RISK CLASSIFICATION SYSTEM FOR IN-HOSPITAL DEATH, BASED ON ELECTRONIC RECORDS OF NON-SURGICAL ADMISSIONS TO GENERAL HOSPITALS

Objective: To develop and validate a risk-classification system for in-hospital death, clinically useful for general hospital adult primarily non-surgical cases. Methods: Admissions for non-surgical conditions at 5 public general hospitals of Minas Gerais were included. Procedures: Build a predictive model for death during admission, using logistic regression; Create a severity index based on the independent effect of the selected variables, and then, validate its ability to predict in-hospital death during index admission; Validate the predictive scale by challenging it with a new dataset. Results: The final multivariate model included seven significant predictive variables: age, gender, diagnostic-related group, hospital of index admission, admission to the ICU, total length of stay, and unplanned surgical procedure. This model presented adequate fit and fair discriminative performance (AUC=0.78). Temporal validation with a new sample also presented an adequate fit, and the discriminative performance was again fair (AUC=0.76). Conclusions: A dynamic and clinically useful risk classification system for in-hospital death of non-surgical admissions has been validated.

Predictive Value of MR-proADM in the Risk Stratification of COVID-19 Patients Assessed at the Triage of the Emergency Department

Background In the last two pandemic years, the Emergency Departments (ED) have been overrun with COVID-19 suspicious patients, creating a pressing need to optimize resources through risk stratification for those patients. For this reason, the assessment of prognostic tools and biomarkers have been necessary. Some dataon the role played by laboratory biomarkers in the early risk stratification of COVID-19 patients have been recently published. The aim of this study is to assess the potential role of the new biomarker mid-regional proadrenomedullim (MR-proADM) in stratifying the in-hospital mortality risk of COVID-19 patients at the triage in order to help the emergency physician in the decision-making process. A further goal of the present study is to evaluate whether MR-proADM together with other biochemical markers could play a key role in assessing the correct care level of these patients by predicting who could need intensive care and ventilation. Methods Data from 321 consecutive patients admitted to the triage of the emergency department with a COVID-19 infection were analyzed. Epidemiological, demographic, clinical, laboratory, and outcome data were assessed. C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), d-dimer and MR-proADM blood levels were also evaluated. Results All the biomarkers evaluated showed significant increased values at admission in the emergency department in non-survivorsvs survivors as well in ventilated as compared to non-ventilated patients. Moreover, all the biomarkers analyzed showed animportant role in predicting mortality, need of invasive mechanical ventilation (IMV) and non-invasive mechanical ventilation (NIMV) in patients admitted at the emergency department with COVID-19 infection as analyzed by the univariate Cox regression analysis. Pooling together both clinical and laboratory variables in a multivariate analysis, all biomarkers, except for PCT, seem to play a significant role in the mortality risk stratification at admission in the emergency department. Similarly, an increase of MR-proADM level at ED admission resulted independently associated with a threefold times higher risk of IMV. LDH showed a smaller but still significant power. CRP only showed a significant predictive value for the need of NIMV. In patients COVID-19 positive, MR-proADM assessed at the admission in the triage showed a good discriminative performance both for in-hospital mortality (AUC 0,85) and for prediction of IMV (AUC 0,81), whereas it was less effective for NIMV prediction (AUC 0,71). ROC curves and AUC resulted significantly greater for MR-proADM as compared to other laboratory biomarkers for the primary endpoint, i.e. in-hospital mortality, with the exception of CRP. Conclusion This study shows that MR-proADM seems to be particularly effective for early predicting mortality and the need of ventilation in COVID-19 patients admitted to the emergency department.

Added Value of Cognition in the Prediction of Survival in Low and High Grade Glioma

Background: Diffuse gliomas, which are at WHO grade II-IV, are progressive primary brain tumors with great variability in prognosis. Our aim was to investigate whether pre-operative cognitive functioning is of added value in survival prediction in these patients.Methods: In a retrospective cohort study of patients undergoing awake craniotomy between 2010 and 2019 we performed pre-operative neuropsychological assessments in five cognitive domains. Their added prognostic value on top of known prognostic factors was assessed in two patient groups [low- (LGG) and high-grade gliomas (HGG]). We compared Cox proportional hazards regression models with and without the cognitive domain by means of loglikelihood ratios tests (LRT), discriminative performance measures (by AUC), and risk classification [by Integrated Discrimination Index (IDI)].Results: We included 109 LGG and 145 HGG patients with a median survival time of 1,490 and 511 days, respectively. The domain memory had a significant added prognostic value in HGG as indicated by an LRT (p-value = 0.018). The cumulative AUC for HGG with memory included was.78 (SD = 0.017) and without cognition 0.77 (SD = 0.018), IDI was 0.043 (0.000–0.102). In LGG none of the cognitive domains added prognostic value.Conclusions: Our findings indicated that memory deficits, which were revealed with the neuropsychological examination, were of additional prognostic value in HGG to other well-known predictors of survival.