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openMar 2024View details →

Evolution of retinal degeneration and prediction of disease activity in relapsing and progressive multiple sclerosis

Retinal optical coherence tomography has been identified as biomarker for disease progression in relapsing-remitting multiple sclerosis (RRMS), while the dynamics of retinal atrophy in progressive MS are less clear. We investigated retinal layer thickness changes in RRMS, primary and secondary progressive MS (PPMS, SPMS), and their prognostic value for disease activity. After quality control, 2651 OCT measurements of 195 RRMS, 87 SPMS, 125 PPMS patients, and 98 controls from five German MS centers were analyzed. Peripapillary and macular retinal nerve fiber layer (pRNFL, mRNFL) thickness predicted future relapses in all MS and RRMS patients without history of optic neuritis while mRNFL and ganglion cell-inner plexiform layer (GCIPL) thickness predicted future MRI progression/activity in RRMS without history of optic neuritis (mRNFL, GCIPL) and PPMS (GCIPL). mRNFL thickness predicted future disability progression in PPMS: However, thickness change rates were subject to considerable amounts of measurement variability. In conclusion, retinal degeneration, most pronounced of pRNFL and GCIPL, occurs in all subtypes. Using the current state of technology, longitudinal assessments of retinal thickness may not be suitable on a single patient level.

dryad24/100

Cerebral small vessel disease and functional outcome prediction after intracerebral haemorrhage

Background. It is unknown whether adding cerebral small vessel disease (SVD) biomarkers can improve the performance of intracerebral hemorrhage (ICH) outcome predictive scores. Purpose. To determine whether: (1) CT-based SVD biomarkers are associated with 6-month functional outcome after ICH and (2) whether these biomarkers improve the performance of pre-existing ICH score. Methods. We included 864 patients with acute ICH from a multicentre, hospital-based prospective cohort study. We evaluated CT-based SVD biomarkers (white matter hypodensities [WMH]; lacunes; brain atrophy; and a composite SVD burden score) and their associations with poor 6-month functional outcome (modified Rankin Scale [mRS] score >2). The area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow test were used to assess discrimination and calibration of the ICH score with and without SVD biomarkers. Results. In multivariable models (adjusted for ICH score components), WMH presence (OR 1.52, 95%CI 1.12-2.06), cortical atrophy presence (OR 1.80, 95%CI 1.19-2.73), deep atrophy presence (OR 1.66, 95%CI 1.17-2.34), and severe atrophy (either deep or cortical) (OR 1.94, 95%CI 1.36-2.74) were independently associated with poor functional outcome. For the ICH score, the AUROC was 0.71 (95%CI 0.68-0.74). Adding SVD markers did not significantly improve ICH score discrimination; for the best model (adding severe atrophy) the AUROC was 0.73 (95%CI 0.69-0.76). These results were confirmed when considering lobar and non-lobar ICH, separately. Conclusions. The ICH score has acceptable discrimination for predicting 6-month functional outcome after ICH. CT biomarkers of SVD are associated with functional outcome but adding them does not significantly improve ICH score discrimination.

opencc-zeroJan 2022View details →

Figure 4 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162

Figure 4 Tornado diagram for LYS

Figure 2 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162

Figure 2 CEAC of all data points

Figure 1 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162

Figure 1 ICER points and dispersion cloud of Monte-Carlo simulation

Figure 3 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162

Figure 3 Tornado diagram for QALYs

openother-ncJul 2018View details →

Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)

(This is the updated version that has been converted a standard pytorch model format) This is the deep learning sequence model used in  Jian Zhou, Chandra L. Theesfeld, Kevin Yao, Kathleen M. Chen, Aaron K. Wong, and Olga G. Troyanskaya, Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk, Nature Genetics, 2018. Note the full software is available from https://github.com/FunctionLab/ExPecto and this release is created for the convenience of use and under the same non-commercial license. The model weights can be loaded with pytorch load_state_dict function (for an example please find <a href="https://github.com/FunctionLab/ExPecto/blob/master/chromatin.py">https://github.com/FunctionLab/ExPecto/blob/master/chromatin.py</a>). We also provide a web server for browsing mutations with strong predicted effects at https://hb.flatironinstitute.org/expecto/, which are currently limited to mutations within 1kb to TSS or are 1000 Genomes variants. Trivia: we code-named our models with whale names. This model has an unofficial codename DeepSEA "Beluga".

restrictedcc-by-4.0Nov 2024View details →

Dataset related to: "Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease"

We provide the raw data used for the following article: Vitacca M, Malovini A, Paneroni M, Spanevello A, Ceriana P, Capelli A, Murgia R, Ambrosino N. Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease. Arch Bronconeumol. 2024 Mar;60(3):153-160. English, Spanish. doi: 10.1016/j.arbres.2024.01.001. <h2>Abstract</h2> <div> Background: Predicting the response to pulmonary rehabilitation (PR) could be valuable in defining admission priorities. We aimed to investigate whether the response of individuals recovering from a COPD exacerbation (ECOPD) could be forecasted using machine learning approaches. Method: This multicenter, retrospective study recorded data on anthropometrics, demographics, physiological characteristics, post-PR changes in six-minute walking distance test (6MWT), Medical Research Council scale for dyspnea (MRC), Barthel Index dyspnea (BId), COPD assessment test (CAT) and proportion of participants reaching the minimal clinically important difference (MCID). The ability of multivariate approaches (linear regression, quantile regression, regression trees, and conditional inference trees) in predicting changes in each outcome measure has been assessed. Results: Individuals with lower baseline 6MWT, as well as those with less severe airway obstruction or admitted from acute care hospitals, exhibited greater improvements in 6MWT, whereas older as well as more dyspnoeic individuals had a lower forecasted improvement. Individuals with more severe CAT and dyspnea, and lower 6MWT had a greater potential improvement in CAT. More dyspnoeic individuals were also more likely to show improvement in BId and MRC. The Mean Absolute Error estimates of change prediction were 44.70m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId, and MRC respectively. Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61.78% to 98.99% and from 14.00% to 71.20%, respectively. Conclusion: While the assessed models were not entirely satisfactory, predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD. Future larger studies will be essential to confirm the methodology, variables, and utility. </div>

Genetic Test Based Risk Prediction of Early Calcific Aortic Valve Disease in Patients With Bicuspid Aortic Valve

ClinicalTrials.gov study NCT06153407. IPD Sharing: NO. Countries: 1. Publications: 0.

Development of a Non-invasive Screening Tool to Predict Metabolic Dysfunction-associated Steatotic Liver Disease

ClinicalTrials.gov study NCT04873258. IPD Sharing: NO. Countries: 1. Publications: 0.

Prediction and Close Monitoring of Postoperative Recurrence by Intestinal Ultrasound After Ileocecal Resection in Crohn's Disease Patients

ClinicalTrials.gov study NCT05713409. IPD Sharing: UNDECIDED. Countries: 4. Publications: 0.

a Foundational Model for Cardiovascular Disease Diagnosis and Prediction

ClinicalTrials.gov study NCT06591923. IPD Sharing: NO. Countries: 1. Publications: 0.

Prediction of Cognitive Properties of New Drug Candidates for Neurodegenerative Diseases in Early Clinical Development

ClinicalTrials.gov study NCT01487395. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Predicting Outcomes After Lumbar Fusion for Degenerative Disease

ClinicalTrials.gov study NCT05161130. IPD Sharing: NO. Countries: 3. Publications: 0.

Study of Imaginomics Predicting Early Surgical Rates in Crohn's Disease

ClinicalTrials.gov study NCT05174208. IPD Sharing: Not stated. Countries: 1. Publications: 0.

PET Imaging to Determine the Role of PET in the Assessment of Regional Disease in Breast Cancer (PET PREDICT Trial)

ClinicalTrials.gov study NCT00201942. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Early Diagnosis and Prediction of Maternal and Neonatal Diseases:

ClinicalTrials.gov study NCT06791343. IPD Sharing: NO. Countries: 1. Publications: 0.

AI-based Progression and Medication Response Prediction Study in Parkinson's Disease

ClinicalTrials.gov study NCT07189468. IPD Sharing: NO. Countries: 4. Publications: 0.

Evaluation of the Effectiveness of Platelet-based and Microvesicle-based Assays to Predict Thrombotic and Bleeding Risk in Chronic Kidney Disease Patients With Acute Coronary Syndrome

ClinicalTrials.gov study NCT06026436. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Liver and Splenic Stiffness in Predicting Esophageal Varices Needing Treatment in MASLD Related Compensated Advanced Chronic Liver Disease.

ClinicalTrials.gov study NCT05044663. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.