scholarly journals Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information

2021 ◽  
Vol 12 ◽  
Author(s):  
Dan Dai ◽  
Huiyao Chen ◽  
Xinran Dong ◽  
Jinglong Chen ◽  
Mei Mei ◽  
...  
Keyword(s):  
Risk Factors ◽  
Predictive Model ◽  
Risk Prediction ◽  
Bronchopulmonary Dysplasia ◽  
Premature Infants ◽  
Genetic Factors ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Clinical Risk Factors ◽  
Clinical Risk

BackgroundAn early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. The risk prediction models nowadays for BPD risk that included only clinical factors but without genetic factors are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately.MethodsExome sequencing was performed in a cohort of 245 premature infants (gestational age <32 weeks), with 131 BPD infants and 114 infants without BPD as controls. A gene burden test was performed to find risk genes with loss-of-function mutations or missense mutations over-represented in BPD and severe BPD (sBPD) patients, with risk gene sets (RGS) defined as BPD–RGS and sBPD–RGS, respectively. We then developed two predictive models for the risk of BPD and sBPD by integrating patient clinical and genetic features. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC).ResultsThirty and 21 genes were included in BPD–RGS and sBPD–RGS, respectively. The predictive model for BPD, which combined the BPD–RGS and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUROC, 0.915 vs. AUROC, 0.814, P = 0.013, respectively) in the independent testing dataset. The same was observed in the predictive model for sBPD (AUROC, 0.907 vs. AUROC, 0.826; P = 0.016).ConclusionThis study suggests that genetic information contributes to susceptibility to BPD. The predictive model in this study, which combined BPD–RGS with basic clinical risk factors, can thus accurately stratify BPD risk in premature infants.

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Standardised measurement of coronary inflammation using cardiovascular CT: integration in clinical care as a prognostic medical device

2021 ◽  
Author(s):  
Evangelos K Oikonomou ◽  
Alexios S Antonopoulos ◽  
David Schottlander ◽  
Mohammad Marwan ◽  
Chris Mathers ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Medical Device ◽  
Risk Prediction ◽  
Clinical Care ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Cardiovascular Risk Prediction ◽  
Perivascular Fat

Abstract Aims Coronary CT angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular Fat Attenuation Index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide individualised cardiovascular risk prediction. Methods and Results The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the United States (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardised interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model (Δ[C-statistic] of 0.085, P = 0.01 in the US Cohort and 0.149, P &lt; 0.001 in the European cohort) and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. Conclusion CaRi-Heart® reliably improves cardiovascular risk prediction by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a screening tool among patients referred for CCTA. Translational Perspective Mapping of perivascular Fat Attenuation Index (FAI) on coronary computed tomography angiography (CCTA) enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide age-standardised reference maps and individualised cardiovascular risk prediction. This integration advances the prognostic value of CCTA and paves the way for its use as a screening tool among patients referred for CCTA.

scholarly journals Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li-Na Liao ◽  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Conventional Risk Factors ◽  
Diabetes Patients

AbstractWe evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72–0.78), 0.64 (0.60–0.68), and 0.78 (0.75–0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65–0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

An evaluation of ML-based clinical risk prediction models in live EHR systems: a study on multiple use cases in different hospitals (Preprint)

2021 ◽  
Author(s):  
Hong Sun ◽  
Kristof Depraetere ◽  
Laurent Meesseman ◽  
Patricia Cabanillas Silva ◽  
Ralph Szymanowsky ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Use Cases ◽  
Generic Model ◽  
Retrospective Data ◽  
Clinical Workflow ◽  
Risk Prediction Models ◽  
Clinical Risk ◽  
The Cross

BACKGROUND Machine learning (ML) algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. We provide detailed evaluations of clinical risk prediction models in live clinical workflows for three different use cases in three different hospitals. OBJECTIVE The main objective of this study is to evaluate the clinical risk prediction models in live clinical workflows and compare with their performance on retrospective data. We also aimed at generalizing the results by applying our investigation to three different use cases in three different hospitals. METHODS We trained clinical risk prediction models for three use cases (delirium, sepsis and acute kidney injury (AKI)) in three different hospitals with retrospective data. The models are deployed in these three hospitals and used in daily clinical practice. The predictions made by these models are logged and correlated with the diagnosis at discharge. We compared the performance with evaluations on retrospective data and conducted cross-hospital evaluations. RESULTS The performance of the prediction models in live clinical workflows is similar to the performance with retrospective data. The average value of area under the receiver-operating characteristic curve (AUROC) decreases slightly by 0.8 percentage point (from 89.4 % to 88.6%). The cross-hospital evaluations exhibit severe reduced performance, the averaged AUROC decreased by 8 percentage point (from 94.2% to 86.3%), which indicates the importance of model calibration with data from deployment hospitals. CONCLUSIONS Calibrating the prediction model with data from different deployment hospitals leads to a good performance in live settings. The performance degradation in the cross-hospital evaluation indicates limitations in developing a generic model for different hospitals. Designing a generic model development process to generate specialized prediction models for each hospital guarantees the model performance in different hospitals.

Abstract P077: Hypertension And Ischemic Stroke; The Effect Of A Combined Anti-hypertension Medications And Thrombolytic Therapy

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Nicolas Poupore ◽  
Bridgette Allen ◽  
Thomas I Nathaniel
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Thrombolytic Therapy ◽  
Characteristic Curve ◽  
Management Strategies ◽  
Recombinant Tissue Plasminogen Activator ◽  
Clinical Risk Factors ◽  
High Density Lipoproteins ◽  
Clinical Risk

Background: The aim of this study is to identify clinical risk factors in acute ischemic stroke ( AIS) pretreated with anti-hypertensive (anti-HTN) medications with a subsequent thrombolytic therapy that are associated with potential worsening or improving neurological functions. Methods: We analyzed retrospectively collected data from consecutive AIS patients with a combined anti-HTN medications and recombinant tissue plasminogen activator (rtPA) therapy for AIS. We used logistic regression model to identify demographic and clinical risk factors that are associated with improving or worsening neurologic functions in AIS patients with a combined anti-HTN and thrombolytic therapy. The overall correct classification of the logistic regression models was determined using the Hosmer-Lemeshow test, while the area under the receiver operating characteristic curve was used to test the sensitivity of the model. The variance inflation factor was used to check for multicollinearity. Results: In the adjusted analysis, patients with increasing age (Odd ratio (OR)=1.035, 95% CI, 1.022-1.049, P <0.001), female AIS patients (OR = 1.630, 95% CI, 1.182-2.248, P =0.002) with a history of substance abuse (OR = 2.315, 95% CI, 1.107-4.842, P = 0.026) were associated with worsening neurologic functions. However, Caucasians (OR = 0.535, 95% CI, 0.361-0.793, P = 0.002) with dyslipidemia (OR = 0.655, 95% CI, 0.479-0.897, P = 0.008), obesity (OR = 0.642, 95% CI, 0.472-0.873, P = 0.005), high-density lipoproteins (HDL; OR = 0.988, 95% CI, 0.976-1.000, P = 0.045), and with a direct admission (OR = 0.509, 95% CI, 0.341-0.761, P = 0.001) were associated with improving neurologic function in AIS patients with a combined anti-HTN medications and rtPA therapy. Conclusion: Our findings reveal specific demographic and clinical risk factors that are associated with worsening or improving neurological functions in AIS pretreated anti-HTN medications with a subsequent thrombolytic therapy. This finding suggests the development of management strategies to manage identified clinical risk factors in AIS patients pretreated with anti-HTN medications prior to thrombolytic therapy.

scholarly journals Fracture risk prediction using BMD and clinical risk factors in early postmenopausal women: Sensitivity of the WHO FRAX tool

2010 ◽  
Vol 25 (5) ◽  
pp. 1002-1009 ◽  
Author(s):  
Florence A Trémollieres ◽  
Jean-Michel Pouillès ◽  
Nicolas Drewniak ◽  
Jacques Laparra ◽  
Claude A Ribot ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Postmenopausal Women ◽  
Risk Prediction ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Early Postmenopausal Women ◽  
Fracture Risk Prediction

scholarly journals Prediction of Venous Thromboembolism Based on Clinical and Genetic Factors

2020 ◽  
Author(s):  
David A. Kolin ◽  
Scott Kulm ◽  
Olivier Elemento
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Hazard Ratio ◽  
Low Risk ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Increased Risk

BACKGROUNDBoth clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown.METHODSUsing Cox proportional-hazard models, we analyzed the association of risk factors with the likelihood of venous thromboembolism in U.K. Biobank, a large prospective cohort. We created a novel ten point clinical score using seven established clinical risk factors for venous thromboembolism. We also generated a polygenic risk score of 21 single nucleotide polymorphisms to quantify genetic risk. The genetic score was categorized into high risk (top two deciles of scores), intermediate risk (deciles three to eight), and low risk (lowest two deciles). The discrete clinical score led to the following approximate decile categorizations: high risk (5 to 10 points), intermediate risk (3 to 4 points), and low risk (0 to 2 points).RESULTSAmongst the 502,536 participants in the U.K. Biobank, there were 4,843 events of venous thromboembolism. Analyses of established clinical risk factors and the most commonly used medications revealed that participants were at decreased risk of venous thromboembolism if they had ever used oral contraceptive pills (hazard ratio, 0.88; 95% confidence interval [CI], 0.79 to 0.99) or if they currently used bendroflumethiazide (hazard ratio, 0.84; 95% CI, 0.74 to 0.95), cod liver oil capsules (hazard ratio, 0.87; 95% CI, 0.77 to 0.99), or atenolol (hazard ratio, 0.79; 95% CI, 0.68 to 0.91). Participants were at significantly increased risk of venous thromboembolism if they were at high clinical risk (hazard ratio, 5.98; 95% CI, 5.43 to 6.59) or high genetic risk (hazard ratio, 2.28; 95% CI, 2.07 to 2.51) relative to participants at low clinical or genetic risk, respectively. Combining clinical risk factors with genetic risk factors produced a model that better predicted risk of venous thromboembolism than either model alone (P<0.001). Participants at high clinical and genetic risk in the combined score had over an eightfold increased risk of venous thromboembolism relative to participants at low risk (hazard ratio, 8.27; 95% CI 7.59 to 9.00).CONCLUSIONSBy assessing venous thromboembolic events in over 500,000 participants, we identified several known and novel associations between risk factors and venous thromboembolism. Participants in the high risk group of a combined score, consisting of clinical and genetic factors, were over eight times more likely to experience venous thromboembolism than participants in the low risk group.

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