clinical outcome prediction
Recently Published Documents


TOTAL DOCUMENTS

54
(FIVE YEARS 20)

H-INDEX

11
(FIVE YEARS 1)

2022 ◽  
Vol 123 ◽  
pp. 102230
Author(s):  
Shuchao Pang ◽  
Matthew Field ◽  
Jason Dowling ◽  
Shalini Vinod ◽  
Lois Holloway ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Joana Fino ◽  
Bárbara Marques ◽  
Zirui Dong ◽  
Dezső David

With the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV’s clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present SVInterpreter, a free Web application, which analyzes both balanced and unbalanced SVs using topologically associated domains (TADs) as genome units. Among others, gene-associated data (as function and dosage sensitivity), phenotype similarity scores, and copy number variants (CNVs) scoring metrics are retrieved for an informed SV interpretation. For evaluation, we retrospectively applied SVInterpreter to 97 balanced (translocations and inversions) and 125 unbalanced (deletions, duplications, and insertions) previously published SVs, and 145 SVs identified from 20 clinical samples. Our results showed the ability of SVInterpreter to support the evaluation of SVs by (1) confirming more than half of the predictions of the original studies, (2) decreasing 40% of the variants of uncertain significance, and (3) indicating several potential position effect events. To our knowledge, SVInterpreter is the most comprehensive TAD-based tool to identify the possible disease-causing candidate genes and to assist prediction of the clinical outcome of SVs. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py.


2021 ◽  
pp. 1-27
Author(s):  
Lasse Hansen ◽  
Kenneth C. Enevoldsen ◽  
Martin Bernstorff ◽  
Kristoffer L. Nielbo ◽  
Andreas A. Danielsen ◽  
...  

Abstract Background The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalized predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years. Methods PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011 to October 28, 2020 (n=119,291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation. Discussion We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative dataset. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.


2021 ◽  
Author(s):  
Jakob Ledwoch ◽  
Anna Krauth ◽  
Jana Kraxenberger ◽  
Alisa Schneider ◽  
Katharina Leidgschwendner ◽  
...  

AbstractHigh-sensitive troponin T (hs-TnT) is increasingly used for clinical outcome prediction in patients with acute heart failure (AHF). However, there is an ongoing debate regarding the potential impact of renal function on the prognostic accuracy of hs-TnT in this setting. The aim of the present study was to assess the prognostic value of hs-TnT within 6 h of admission for the prediction of 30-day mortality depending on renal function in patients with AHF. Patients admitted to our institution due to AHF were retrospectively included. Clinical information was gathered from electronic and paper-based patient charts. Patients with myocardial infarction were excluded. A total of 971 patients were enrolled in the present study. A negative correlation between estimated glomerular filtration rate (eGFR) and hsTnT was identified (Pearson r = − 0.16; p < 0.001) and eGFR was the only variable to be independently associated with hsTnT. The area under the curve (AUC) of hs-TnT for the prediction of 30-mortality was significantly higher in patients with an eGFR ≥ 45 ml/min (AUC 0.74) compared to those with an eGFR < 45 ml/min (AUC 0.63; p = 0.049). Sensitivity and specificity of the Youden Index derived optimal cut-off for hs-TnT was higher in patients with an eGFR ≥ 45 ml/min (40 ng/l: sensitivity 73%, specificity 71%) compared to patients with an eGFR < 45 ml/min (55 ng/l: sensitivity 63%, specificity 62%). Prognostic accuracy of hs-TnT in patients hospitalized for AHF regarding 30-day mortality is significantly lower in patients with reduced renal function.


2021 ◽  
Author(s):  
Betty van Aken ◽  
Jens-Michalis Papaioannou ◽  
Manuel Mayrdorfer ◽  
Klemens Budde ◽  
Felix Gers ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document