scholarly journals Machine Learning Model for Predicting Outcomes of Biologic Therapy in Psoriasis

2021 ◽  
Author(s):  
Amy X. Du ◽  
Zarqa Ali ◽  
Kawa K. Ajgeiy ◽  
Maiken G. Dalager ◽  
Tomas N. Dam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Biologic Therapy ◽  
Characteristic Curve ◽  
Drug Discontinuation ◽  
Biologic Treatment ◽  
Cox Regression Analysis ◽  
Biologic Drug

AbstractBackgroundBiological agents used for the therapy of psoriasis lose efficacy over time, which leads to discontinuation of the drug. Optimization of long-term biologic treatment is an area of medical need but there are currently no prediction tools for biologic drug discontinuation.ObjectiveTo compare the accuracy of the risk factor-based frequentist statistical model to machine learning to predict the 5-year probability of biologic drug discontinuation.MethodsThe national Danish psoriasis biologic therapy registry, Dermbio, comprising 6,172 treatment series with anti-TNF (Etanercept, Infliximab, Adalimumab), Ustekinumab, Guselkumab and anti-IL17 (Secukinumab and Ixekizumab) in 3,388 unique patients was used as data source. Hazard ratios (HR) were computed for all available predictive factors using Cox regression analysis. Different machine learning (ML) models for the prediction of 5-year risk of drug discontinuation were trained using the 5-fold cross validation technique and using 10 clinical features routinely assessed in psoriasis patients as input variables. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe lowest 5-year risk of discontinuation was associated with therapy with ustekinumab or ixekizumab, male sex and no previous exposure to biologic therapy. The predictive model based on those risk factors had an AUC of 0.61. The best ML model (gradient boosted tree) had an AUC of 0.85.ConclusionsA machine learning-based approach, more than a statistical model, accurately predicts the risk of discontinuation of biologic therapy based on simple patient variables available in clinical practice. ML might be incorporated into clinical decision making.

scholarly journals Prognostic Association between Common Laboratory Tests and Overall Survival in Elderly Men with De Novo Metastatic Castration Sensitive Prostate Cancer: A Population-Based Study in Canada

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2844
Author(s):  
Christopher J. D. Wallis ◽  
Bobby Shayegan ◽  
Scott C. Morgan ◽  
Robert J. Hamilton ◽  
Ilias Cagiannos ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
De Novo ◽  
Population Based ◽  
Population Based Study ◽  
Cox Regression Analysis ◽  
Lymphocyte Ratio ◽  
Laboratory Markers

De novo cases of metastatic prostate cancer (mCSPC) are associated with poorer prognosis. To assist in clinical decision-making, we aimed to determine the prognostic utility of commonly available laboratory-based markers with overall survival (OS). In a retrospective population-based study, a cohort of 3556 men aged ≥66 years diagnosed with de novo mCSPC between 2014 and 2019 was identified in Ontario (Canada) administrative database. OS was assessed by using the Kaplan–Meier method. Multivariate Cox regression analysis was performed to evaluate the association between laboratory markers and OS adjusting for patient and disease characteristics. Laboratory markers that were assessed include neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin, hemoglobin, serum testosterone and PSA kinetics. Among the 3556 older men with de novo mCSPC, their median age was 77 years (IQR: 71–83). The median survival was 18 months (IQR: 10–31). In multivariate analysis, a statistically significant association with OS was observed with all the markers (NLR, PLR, albumin, hemoglobin, PSA decrease, reaching PSA nadir and a 50% PSA decline), except for testosterone levels. Our findings support the use of markers of systemic inflammation (NLR, PLR and albumin), hemoglobin and PSA metrics as prognostic indicators for OS in de novo mCSPC.

scholarly journals Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10884
Author(s):  
Xin Yu ◽  
Qian Yang ◽  
Dong Wang ◽  
Zhaoyang Li ◽  
Nianhang Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Training Set ◽  
Cpg Sites

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named “stacked ensemble of machine learning models for methylation-correlated blocks” (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

scholarly journals Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Daojun Lv ◽  
Zanfeng Cao ◽  
Wenjie Li ◽  
Haige Zheng ◽  
Xiangkun Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Survival Analysis ◽  
Biochemical Recurrence ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Medical Decision ◽  
Regression Analyses ◽  
Cox Regression Analysis ◽  
Protein Signature

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.

scholarly journals Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zi-Qi Pan ◽  
Shu-Jun Zhang ◽  
Xiang-Lian Wang ◽  
Yu-Xin Jiao ◽  
Jian-Jian Qiu
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cox Regression ◽  
Clinical Risk Factors ◽  
Cox Regression Analysis ◽  
Clinical Risk ◽  
Independent Test ◽  
Independent Test Dataset ◽  
Multiple Regions ◽  
Radiomics Signature

Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: n = 82 ; validation set: n = 40 ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The C -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 ( P < 0.001 ) and 0.757 ( P = 0.001 ), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, P < 0.001 ), age (HR: 1.023, P = 0.01 ), and KPS (HR: 0.968, P < 0.001 ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( C ‐ index = 0.764 and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.

Inflammatory markers in cerebrospinal fluid: independent prognostic biomarkers in amyotrophic lateral sclerosis?

2019 ◽  
pp. jnnp-2018-319586 ◽  
Author(s):  
Benjamin Gille ◽  
Maxim De Schaepdryver ◽  
Lieselot Dedeene ◽  
Janne Goossens ◽  
Kristl G Claeys ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Cerebrospinal Fluid ◽  
Disease Progression ◽  
Inflammatory Markers ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Progression Rate ◽  
Cox Regression Analysis ◽  
Lateral Sclerosis ◽  
Discriminatory Performance

ObjectiveInflammation is a key pathological hallmark in amyotrophic lateral sclerosis (ALS), which seems to be linked to the disease progression. It is not clear what the added diagnostic and prognostic value are of inflammatory markers in the cerebrospinal fluid (CSF) of patients with ALS.MethodsChitotriosidase-1 (CHIT1), chitinase-3-like protein 1 (YKL-40) and monocyte chemoattractant protein-1 (MCP-1) were measured in CSF and serum of patients with ALS (n=105), disease controls (n=102) and patients with a disease mimicking ALS (n=16). The discriminatory performance was evaluated by means of a receiver operating characteristic curve analysis. CSF and serum levels were correlated with several clinical parameters. A multivariate Cox regression analysis, including eight other established prognostic markers, was used to evaluate survival in ALS.ResultsIn CSF, CHIT1, YKL-40 and MCP-1 showed a weak discriminatory performance between ALS and ALS mimics (area under the curve: 0.79, p<0.0001; 0.72, p=0.001; 0.75, p=0.001, respectively). CHIT1 and YKL-40 correlated with the disease progression rate (ρ=0.28, p=0.009; ρ=0.34, p=0.002, respectively). CHIT1 levels were elevated in patients with a higher number of regions displaying motor neuron degeneration (one vs three regions: 4248 vs 13 518 pg/mL, p = 0.0075). In CSF, YKL-40 and MCP-1 were independently associated with survival (HR: 29.7, p=0.0003; 6.14, p=0.001, respectively).ConclusionsOur findings show that inflammation in patients with ALS reflects the disease progression as an independent predictor of survival. Our data encourage the use of inflammatory markers in patient stratification and as surrogate markers of therapy response in clinical trials.

scholarly journals Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096357
Author(s):  
Xiaoyong Gong ◽  
Bobin Ning
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Normal Prostate ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

P1900Amiodarone treatment duration and reasons for permanent drug discontinuation in patients with atrial fibrillation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Mihajlovic ◽  
A Mihajlovic ◽  
M Marinkovic ◽  
V Kovacevic ◽  
L Vajagic ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Clinical Practice ◽  
Side Effects ◽  
Cox Regression ◽  
Daily Practice ◽  
Arterial Disease ◽  
Drug Discontinuation ◽  
Cox Regression Analysis ◽  
Coronary Arterial Disease ◽  
History Of

Abstract Background and purpose Amiodarone is commonly use in patients with atrial fibrillation (AF), but the organ toxicity side effects limit its long-term use. We investigated the rates of and reasons for permanent amiodarone discontinuation among patients with AF in contemporary clinical practice. Methods A single-centre, ongoing, registry-based observational longitudinal study included consecutive AF patients prescribed with amiodarone in our hospital from January 2015 to December 2017. All patients underwent a loading protocol of 400–600 mg of amiodarone daily for 1–2 weeks, followed by 200–400mg daily for 4–8 weeks and 200mg daily or 1000mg weekly thereafter. Results Of 657 AF patients taking amiodarone (Mean age 62.2±11.0, female n=215 (32.6%), hypertension n=504 (76.7%), diabetes mellitus n=107 (16.3%), coronary arterial disease n=139 (19.8%), History of Myocardial infarction 86 (13.1%), Stroke/TIA 60 (9.1%), chronic kidney disease 157 (23.9%)), the drug was permanently discontinued in total of 248 patients (37.7%). The reasons for amiodarone discontinuation are shown in Figure. On multivariable Cox-regression analysis, physician's decision (HR 5.6; 95% CI 3.9–7.9, p<0.001) and amiodarone side effects (HR 3.9; 95% CI 2.9–5.1, p<0.001) were significantly associated with permanent amiodarone discontinuation. The overall time to discontinuation was 23.2±24.1 months. Compared with others, time to discontinuation was shorter in patients post AF ablation (17.3±21.3 vs 24.5±24.5, p=0.05), longer in those with AF progression (29.2±31.0 vs 20.9±20.3, p=0.014) and similar in patients with amiodarone side effects (23.7±17.7 vs 23.0±26.8, p=0.813). Pulmonary toxicity and proarrhythmia were not observed among study patients (Figure). Chart 1 Conclusion Our study showed that permanent discontinuation of amiodarone in contemporary clinical practice was due to the drug side effects in 12% of amiodarone-treated AF patients, occurring after a mean 2-year treatment course. The most prevalent side effect was thyroid dysfunction, whereas the prevalence of proarrhythmic effect was low. Notably, physician's fear of complications (which may not always be justified), also was an independent driver of permanent amiodarone discontinuation. More data are needed to inform optimal amiodarone use in AF patients in daily practice.

scholarly journals A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 414 ◽  
Author(s):  
Feng Liu ◽  
Lu Xing ◽  
Xiaoqian Zhang ◽  
Xiaoqi Zhang
Keyword(s):  
Machine Learning ◽  
Expression Analysis ◽  
Cox Regression ◽  
High Sensitivity ◽  
Rna Seq ◽  
Cox Regression Analysis ◽  
Potential Marker ◽  
Risk Patients ◽  
Common Malignancy

Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients’ RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44–0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14–0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35–2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37–0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

scholarly journals Proactive Infliximab Monitoring Following Reactive Testing is Associated With Better Clinical Outcomes Than Reactive Testing Alone in Patients With Inflammatory Bowel Disease

2018 ◽  
Vol 12 (7) ◽  
pp. 804-810 ◽  
Author(s):  
Konstantinos Papamichael ◽  
Ravy K Vajravelu ◽  
Byron P Vaughn ◽  
Mark T Osterman ◽  
Adam S Cheifetz
Keyword(s):  
Inflammatory Bowel Disease ◽  
Treatment Failure ◽  
Bowel Disease ◽  
Cox Regression ◽  
Drug Discontinuation ◽  
Cox Regression Analysis ◽  
Loss Of Response ◽  
Group A ◽  
Inflammatory Bowel ◽  
Group B

Abstract Background and Aims Reactive testing has emerged as the new standard of care for managing loss of response to infliximab in inflammatory bowel disease [IBD]. Recent data suggest that proactive infliximab monitoring is associated with better therapeutic outcomes in IBD. Nevertheless, there are no data regarding the clinical utility of proactive infliximab monitoring after first reactive testing. We aimed to evaluate long-term outcomes of proactive infliximab monitoring following reactive testing compared with reactive testing alone in patients with IBD. Methods This was a retrospective multicenter cohort study of consecutive IBD patients on infliximab maintenance therapy receiving a first reactive testing between September 2006 and January 2015. Patients were divided into two groups; Group A [proactive infliximab monitoring after reactive testing] and Group B [reactive testing alone]. Patients were followed through December 2015. Time-to-event analysis for treatment failure and IBD-related surgery and hospitalization was performed. Treatment failure was defined as drug discontinuation due to either loss of response or serious adverse event. Results The study population consisted of 102 [n = 70, 69% with CD] patients [Group A, n = 33 and Group B, n = 69] who were followed for (median, interquartile range [IQR]) 2.7 [1.4–3.8] years. Multiple Cox regression analysis identified proactive following reactive TDM as independently associated with less treatment failure (hazard ratio [HR] 0.15; 95% confidence interval [CI] 0.05–0.51; p = 0.002) and fewer IBD-related hospitalizations [HR: 0.18; 95% CI 0.05–0.99; p = 0.007]. Conclusions This study showed that proactive infliximab monitoring following reactive testing was associated with greater drug persistence and fewer IBD-related hospitalizations than reactive testing alone.

scholarly journals Biologic Drug Survival in Psoriasis: A Systematic Review & Comparative Meta-Analysis

2020 ◽  
Author(s):  
Ahmed I Mourad ◽  
Robert Gniadecki
Keyword(s):  
Real World ◽  
Survival Data ◽  
Clinical Decision Making ◽  
Meta Analysis ◽  
Survival Rates ◽  
Clinical Decision ◽  
Drug Discontinuation ◽  
Large Variability ◽  
Drug Survival ◽  
Biologic Drug

Background: Drug survival studies have been utilized to evaluate the real-world effectiveness of biologics used in psoriasis. However, the increasing volume of drug survival data suffers from large variability due to regional differences in drug availability, patient selection and biologic reimbursement. Objectives: To conduct a meta-analysis of biologic drug survival to determine comparative effectiveness of the biologics in a real-world setting. Methods: Studies reporting drug survival for biologic therapy in psoriasis were identified by a systematic literature search. Hazard ratio data for drug discontinuation were estimated directly from published Kaplan-Meier estimator curves at year 1, 2 and 5 of treatment and compared pairwise for the following biologics: ustekinumab, adalimumab, etanercept, infliximab, secukinumab and ixekizumab. This pooled hazard ratios were used to estimate 2- and 5- year overall drug survival rates. Results: Ustekinumab had the longest persistence at 2 years and 5 years among all biologics included in this meta-analysis. Adalimumab was superior to etanercept and infliximab at 5 years. Pooled 5-year drug survival rates for adalimumab, etanercept, and infliximab were 46.3%, 35.9% and 34.7%, respectively. 2- and 5-year data were not available for anti-IL-17 drugs, but at 1-year ustekinumab outperformed secukinumab, the latter being equal to anti-TNFs. Conclusions: Ustekinumab is characterized by longer drug survival than TNF inhibitors and IL-17 inhibitors. Estimated pooled 2- and 5- year drug survival rates may serve as a useful tool for patient communication and clinical decision-making.

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