Tag: pubmed

Immunotherapy. 2022 Jul 7. doi: 10.2217/imt-2022-0082. Online ahead of print.

ABSTRACT

Objective: This study evaluated the cost-effectiveness of pembrolizumab/chemotherapy combinations for previously untreated metastatic triple-negative breast cancer patients in the USA with PD-L1 combined positive score ≥10. Methods: A partitioned-survival model was developed to project health outcomes and direct medical costs over a 20-year time horizon. Efficacy and safety data were from randomized clinical trials. Comparative effectiveness of indirect comparators was assessed using network meta-analyses. A series of sensitivity analyses were performed to test the robustness of the results. Results: Pembrolizumab/chemotherapy resulted in total quality-adjusted life-year (QALY) gains of 0.70 years and incremental cost-effectiveness ratio of US$182,732/QALY compared with chemotherapy alone. The incremental cost-effectiveness ratio for pembrolizumab/nab-paclitaxel versus atezolizumab/nab-paclitaxel was US$44,157/QALY. Sensitivity analyses showed the results were robust over plausible values of model inputs. Conclusion: Pembrolizumab/chemotherapy is cost effective compared with chemotherapy as well as atezolizumab/nab-paclitaxel as first-line treatment for PD-L1-positive metastatic triple-negative breast cancer from a US payer perspective.

PMID:35796042 | DOI:10.2217/imt-2022-0082

Psychol Med. 2022 Jul 7:1-13. doi: 10.1017/S003329172200160X. Online ahead of print.

ABSTRACT

BACKGROUND: While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need.

METHODS: Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FA_T) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL’s Randomise. FA_T and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence.

RESULTS: Individuals with adolescent-onset psychosis exhibited pronounced FW and FA_T abnormalities compared to HCs. FA_T reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FA_T was positively related to age.

CONCLUSIONS: We observe evidence for cellular (FA_T) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.

PMID:35796024 | DOI:10.1017/S003329172200160X

Brief Bioinform. 2022 Jul 6:bbac259. doi: 10.1093/bib/bbac259. Online ahead of print.

ABSTRACT

In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.

PMID:35794711 | DOI:10.1093/bib/bbac259

Genomics Inform. 2022 Jun;20(2):e22. doi: 10.5808/gi.22025. Epub 2022 Jun 22.

ABSTRACT

The rise of newer coronavirus disease 2019 (COVID-19) variants has brought a challenge to ending the spread of COVID-19. The variants have a different fatality, morbidity, and transmission rates and affect vaccine efficacy differently. Therefore, the impact of each new variant on the spread of COVID-19 is of interest to governments and scientists. Here, we proposed mathematical SEIQRDVP and SEIQRDV3P models to predict the impact of the Omicron variant on the spread of the COVID-19 situation in South Korea. SEIQEDVP considers one vaccine level at a time while SEIQRDV3P considers three vaccination levels (only one dose received, full doses received, and full doses + booster shots received) simultaneously. The omicron variant’s effect was contemplated as a weighted sum of the delta and omicron variants’ transmission rate and tuned using a hyperparameter k. Our models’ performances were compared with common models like SEIR, SEIQR, and SEIQRDVUP using the root mean square error (RMSE). SEIQRDV3P performed better than the SEIQRDVP model. Without consideration of the variant effect, we don’t see a rapid rise in COVID-19 cases and high RMSE values. But, with consideration of the omicron variant, we predicted a continuous rapid rise in COVID-19 cases until maybe herd immunity is developed in the population. Also, the RMSE value for the SEIQRDV3P model decreased by 27.4%. Therefore, modeling the impact of any new risen variant is crucial in determining the trajectory of the spread of COVID-19 and determining policies to be implemented.

PMID:35794702 | DOI:10.5808/gi.22025

Genomics Inform. 2022 Jun;20(2):e23. doi: 10.5808/gi.22036. Epub 2022 Jun 30.

ABSTRACT

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

PMID:35794703 | DOI:10.5808/gi.22036

Genomics Inform. 2022 Jun;20(2):e17. doi: 10.5808/gi.22033. Epub 2022 Jun 30.

ABSTRACT

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

PMID:35794697 | DOI:10.5808/gi.22033

Genomics Inform. 2022 Jun;20(2):e16. doi: 10.5808/gi.22022. Epub 2022 Jun 30.

ABSTRACT

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

PMID:35794696 | DOI:10.5808/gi.22022

Genomics Inform. 2022 Jun;20(2):e15. doi: 10.5808/gi.20.2.e1. Epub 2022 Jun 30.

NO ABSTRACT

PMID:35794695 | DOI:10.5808/gi.20.2.e1

J Orthop Surg Res. 2022 Jul 6;17(1):337. doi: 10.1186/s13018-022-03227-w.

ABSTRACT

BACKGROUND: Recovery from knee surgery or injury can be hindered by knee arthrofibrosis, which can lead to motion limitations, pain and delayed recovery. Surgery or prolonged physical therapy are often treatment options for arthrofibrosis, but they can result in increased costs and decreased quality of life. A treatment option that can regain lost motion without surgery would help minimize risks and costs for the patient. The purpose of this study was to determine treatment efficacy of high-intensity home mechanical stretch therapy in patients with knee arthrofibrosis.

METHODS: Records were reviewed for 11,000+ patients who were prescribed a high-intensity stretch device to regain knee flexion. Initial and last recorded knee flexion and days between measurements were available for 9842 patients (Dataset 1). Dataset 2 was a subset of 966 patients from Dataset 1. These 966 patients had separate more rigorous measurements available from physical therapy notes (Dataset 3) in addition to data from the internal database (Dataset 2). Within and between dataset statistics were calculated using t tests for comparison of means and Cohen’s d for determination of effect size.

RESULTS: All dataset showed significant gains in flexion (p < 0.01). Mean initial flexion, last recorded flexion and flexion gain were 79.5°, 108.4°, and 29.9°, respectively in Dataset 1. Differences between Datasets 2 and 3 had small effect sizes (Cohen’s d < 0.17). The were no significant differences when comparing workers’ compensation and non-workers’ compensation patients. The average last recorded flexion for all datasets was above the level required to perform activities of daily living. Motion gains were recorded in under 60 days from device delivery.

CONCLUSIONS: High-intensity home mechanical stretch therapy was effective in restoring knee flexion, generally in 2 months or less, and in avoiding additional surgery in severe motion loss patients regardless of sex, age, or workers’ compensation status. We believe high-intensity stretching should be considered in any patient who is at risk for a secondary motion loss surgery, because in over 90% of these patients, the complications and costs associated with surgery can be avoided.

PMID:35794671 | DOI:10.1186/s13018-022-03227-w

Diagn Progn Res. 2022 Jul 7;6(1):13. doi: 10.1186/s41512-022-00126-w.

ABSTRACT

BACKGROUND: Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain.

METHODS: We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately.

RESULTS: We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation.

CONCLUSIONS: The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.

PMID:35794668 | DOI:10.1186/s41512-022-00126-w