scholarly journals Seven Glycolysis-Related Genes Predict the Prognosis of Patients With Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Han Nie ◽  
Cancan Luo ◽  
Kaili Liao ◽  
Jiasheng Xu ◽  
Xiaozhong Wang
Keyword(s):  
Regression Analysis ◽  
Survival Time ◽  
Predictive Model ◽  
Clinical Characteristics ◽  
Ductal Carcinoma ◽  
Pancreatic Ductal Carcinoma ◽  
Related Gene ◽  
Training Set ◽  
Gene Model ◽  
Glycolysis Pathway

ObjectivesTo identify the key glycolysis-related genes (GRGs) in the occurrence and development of pancreatic ductal carcinoma (PDAC), and to construct a glycolysis-related gene model for predicting the prognosis of PDAC patients.MethodologyPancreatic ductal carcinoma (PDAC) data and that of normal individuals were downloaded from the TCGA database and Genotype-Tissue Expression database, respectively. GSEA analysis of glycolysis-related pathways was then performed on PDAC data to identify significantly enriched GRGs. The genes were combined with other patient’s clinical information and used to construct a glycolysis-related gene model using cox regression analysis. The model was further evaluated using data from the validation group. Mutations in the model genes were subsequently identified using the cBioPortal. In the same line, the expression levels of glycolysis related model genes in PDAC were analyzed and verified using immunohistochemical images. Model prediction for PDAC patients with different clinical characteristics was then done and the relationship between gene expression level, clinical stage and prognosis further discussed. Finally, a nomogram map of the predictive model was constructed to evaluate the prognosis of patients with PDAC.ResultsGSEA results of the training set revealed that genes in the training set were significantly related to glycolysis pathway and iconic glycolysis pathway. There were 108 differentially expressed GRGs. Among them, 29 GRGs were closely related to prognosis based on clinical survival time. Risk regression analysis further revealed that there were seven significantly expressed glycolysis related genes. The genes were subsequently used to construct a predictive model. The model had an AUC value of more than 0.85. It was also significantly correlated with survival time. Further expression analysis revealed that CDK1, DSC2, ERO1A, MET, PYGL, and SLC35A3 were highly expressed in PDAC and CHST12 was highly expressed in normal pancreatic tissues. These results were confirmed using immunohistochemistry images of normal and diseases cells. The model could effectively evaluate the prognosis of PDAC patients with different clinical characteristics.ConclusionThe constructed glycolysis-related gene model effectively predicts the occurrence and development of PDAC. As such, it can be used as a prognostic marker to diagnose patients with PDAC.

scholarly journals A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Li ◽  
Jiajia Du ◽  
Yanhong Wang ◽  
Hongyan Jia
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Prognostic Model ◽  
Ductal Carcinoma ◽  
Risk Group ◽  
Metastatic Breast ◽  
Related Gene ◽  
Training Set ◽  
Gene Set ◽  
Validation Set

Background: Invasive ductal carcinoma (IDC) is the most common type of metastatic breast cancer. Due to the lack of valuable molecular biomarkers, the diagnosis and prognosis of IDC remain a challenge. A large number of studies have confirmed that coagulation is positively correlated with angiogenesis-related factors in metastatic breast cancer. Therefore, the purpose of this study was to construct a COAGULATION-related genes signature for IDC using the bioinformatics approaches.Methods: The 50 hallmark gene sets were obtained from the molecular signature database (MsigDB) to conduct Gene Set Variation Analysis (GSVA). Gene Set Enrichment Analysis (GSEA) was applied to analyze the enrichment of HALLMARK_COAGULATION. The COAGULATION-related genes were extracted from the gene set. Then, Limma Package was used to identify the differentially expressed COAGULATION-related genes (DECGs) between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) samples in GSE26340 data set. A total of 740 IDC samples from The Cancer Genome Atlas (TCGA) database were divided into a training set and a validation set (7:3). The univariate and multivariate Cox regression analyses were performed to construct a risk signature, which divided the IDC samples into the high- and low-risk groups. The overall survival (OS) curve and receiver operating characteristic (ROC) curve were drawn in both training set and validation set. Finally, a nomogram was constructed to predict the 1-, 2-, 3-, 4-, and 5-year survival rates of IDC patients. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic genes.Results: The “HALLMARK_COAGULATION” was significantly activated in IDC. There was a significant difference in the clinicopathological parameters between the DCIS and IDC patients. Twenty-four DECGs were identified, of which five genes (SERPINA1, CAPN2, HMGCS2, MMP7, and PLAT) were screened to construct the prognostic model. The high-risk group showed a significantly lower survival rate than the low-risk group both in the training set and validation set (p=3.5943e-06 and p=0.014243). The risk score was demonstrated to be an independent predictor of IDC prognosis. A nomogram including risk score, pathological_stage, and pathological_N provided a quantitative method to predict the survival probability of 1-, 2-, 3-, 4-, and 5-year in IDC patients. The results of decision curve analysis (DCA) further demonstrated that the nomogram had a high potential for clinical utility.Conclusion: This study established a COAGULATION-related gene signature and showed its prognostic value in IDC through a comprehensive bioinformatics analysis, which may provide a potential new prognostic mean for patients with IDC.

scholarly journals Clinical Characteristics and Prognosis of Renal Cell Carcinoma With Spinal Bone Metastases

2021 ◽  
Vol 11 ◽  
Author(s):  
Jianpo Zhai ◽  
Ning Liu ◽  
Hai Wang ◽  
Guanglin Huang ◽  
Libo Man
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Survival Time ◽  
Median Survival ◽  
Median Survival Time ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Visceral Metastasis ◽  
Cox Regression Analysis

BackgroundThe prognosis of renal cell carcinoma (RCC) with spinal bone metastasis (sBM) varies greatly. In this study, we aimed to define the clinical characteristics and prognostic factors of RCC with spinal bone metastasis (sBM) in our center.MethodsThe clinical and medical records of RCC patients with sBMs were collected. The gender, age, time of BM, the extent of BM, the number of BMs, the presence or absence of visceral metastasis, and the pathological type of BM were investigated. All patients were followed up regularly. Overall survival (OS) was calculated from the date of BMs diagnosis to death or last follow-up using Kaplan-Meier method and modelled with Cox regression analysis.ResultsForty-three RCC patients with sBM were collected. sBM was found synchronously in 30 patients (70%) and metachronously in 13 patients (30%). The median survival time was 30 months in 13 patients (30%) with solitary sBM and 19 months in 30 patients (70%) with multiple sBMs (P = 0.002). Visceral metastasis occurred in 12 patients (28%) with the median survival time of 17 months, while the other 31 patients (72%) had no visceral metastasis with the median survival time of 29 months (P<0.001). En-block resection was done in 10 patients with median survival time of 40.1 months. Non-en-block resection were done in 33 patients with median survival time of 19.7 months (P<0.001). Multivariate COX regression analysis showed that MSKCC score, number of BM, visceral metastasis, and en-block resection are the independent prognosis factors of RCC patients with sBM.ConclusionsMSKCC risk stratification, number of sBM, visceral metastasis and en-block resection are significant prognostic factors for OS in RCC patients with spinal BM. Therefore, for selected patients who has solitary spinal BM with no visceral metastasis, en-block resection of spinal BM can potentially prolong survival and is the treatment of choice.

A Prognostic Nomogram for Predicting Overall Survival in Pediatric Wilms Tumor Based on an Autophagy-related Gene Signature

2021 ◽  
Vol 24 ◽  
Author(s):  
Longkai He ◽  
Xiaotong Wang ◽  
Ya Jin ◽  
Weipeng Xu ◽  
Jun Lyu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Low Risk ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Risk Patients

Background: Wilms tumor (WT) is the most common primary renal malignancy in children. Autophagy plays dual roles in the promotion and suppression of various cancers. Objective: The goal of our study was to develop a novel autophagy-related gene (ARG) prognostic nomogram for WT. Methods: The Cancer Genome Atlas (TCGA) database was used. We screened the expression profiles of ARGs in 136 WT patients. The differentially expressed prognostic ARGs were evaluated by multivariate Cox regression analysis and survival analysis. A novel prognostic nomogram based on the ARGs and clinical characteristics was established using multivariate Cox regression analysis. Results: First, 69 differentially expressed ARGs were identified in WT patients. Then, multivariate Cox regression analysis was used to determine 4 key prognostic ARGs (CC3CL1, ERBB2, HIF-α and CXCR4) in WT. According to their ARG expression levels, the patients were clustered into high- and low-risk groups. Next, survival analysis indicated that high-risk patients had significantly poorer overall survival than low-risk patients. The results of functional enrichment analysis suggested that autophagy may play a tumor-suppressive role in the initiation of WT. Finally, a prognostic nomogram with a Harrell's concordance index (C-index) of 0.841 was used to predict the survival probability of WT patients by integrating clinical characteristics and the 4-ARG signature. The calibration curve indicated its excellent predictive performance. Conclusion: In summary, the ARG signature could be a promising biomarker for monitoring the outcomes of WT. We established a novel nomogram based on the ARG signature, which accurately predicts the overall survival of WT patients.

scholarly journals Clinical characteristics and prognosis of renal cell carcinoma with spinal bone metastases

2019 ◽  
Author(s):  
Jianpo Zhai ◽  
Ning Liu ◽  
Hai Wang ◽  
Haidong Wang ◽  
Guanglin Huang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Survival Time ◽  
Median Survival ◽  
Median Survival Time ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Visceral Metastasis ◽  
Cox Regression Analysis

Abstract Background: The prognosis of renal cell carcinoma (RCC) with spinal bone metastasis (sBM) varies greatly. To define the clinical characteristics and prognostic factors of RCC with spinal bone metastasis (sBM) in our center. Methods: The clinical and medical records of RCC patients with sBMs were collected. The gender, age, time of BM, the extent of BM, the number of BMs, the presence or absence of visceral metastasis and the pathological type of BM were investigated. All patients were followed up regularly. OS was calculated from the date of BMs diagnosis to death or last follow-up using Kaplan-Meier method and modelled with Cox regression analysis. Results: 22 RCC patients with sBM were collected. sBM was found synchronously in 15 patients (68.2%) and metachronously in 7 patients (31.8%) . The median survival time was 30 months in 7 patients (31.8%) with solitary sBM and 19 months in 15 patients (68.2%) with multiple sBMs. Visceral metastasis occurred in 6 patients (27.3%)with the median survival time of 17 months, while the other 16 patients (72.7%) had no visceral metastasis with the median survival time of 29 months ( P =0.006). Enblock resection was done in 7 patients with median survival time of 34 months. Non-Enblock resection were done in 15 patients with median survival time of 18 months( P =0.006). Multivariate COX regression analysis showed that visceral metastasis and Enblock resection are the independent prognostic factors of RCC with sBM. Conclusions: No visceral metastasis, En-block resection are good prognostic factors for RCC with sBM. Therefore En-block resection of sBM is recommended for RCC without visceral metastasis.

scholarly journals Ca2+ Signaling and Its Potential Targeting in Pancreatic Ductal Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3085
Author(s):  
Louay Bettaieb ◽  
Maxime Brulé ◽  
Axel Chomy ◽  
Mel Diedro ◽  
Malory Fruit ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Exocrine Pancreas ◽  
Ductal Carcinoma ◽  
Ductal Adenocarcinoma ◽  
Molecular Devices ◽  
Pancreatic Ductal Carcinoma ◽  
Aberrant Expression ◽  
Common Cause ◽  
Cancer Hallmarks ◽  
Cancer Mutations

Pancreatic cancer (PC) is a major cause of cancer-associated mortality in Western countries (and estimated to be the second cause of cancer deaths by 2030). The main form of PC is pancreatic adenocarcinoma, which is the fourth most common cause of cancer-related death, and this situation has remained virtually unchanged for several decades. Pancreatic ductal adenocarcinoma (PDAC) is inherently linked to the unique physiology and microenvironment of the exocrine pancreas, such as pH, mechanical stress, and hypoxia. Of them, calcium (Ca2+) signals, being pivotal molecular devices in sensing and integrating signals from the microenvironment, are emerging to be particularly relevant in cancer. Mutations or aberrant expression of key proteins that control Ca2+ levels can cause deregulation of Ca2+-dependent effectors that control signaling pathways determining the cells’ behavior in a way that promotes pathophysiological cancer hallmarks, such as enhanced proliferation, survival and invasion. So far, it is essentially unknown how the cancer-associated Ca2+ signaling is regulated within the characteristic landscape of PDAC. This work provides a complete overview of the Ca2+ signaling and its main players in PDAC. Special consideration is given to the Ca2+ signaling as a potential target in PDAC treatment and its role in drug resistance.

scholarly journals Weight loss early after discharge predicts the risk of rehospitalization in non-obese patients with heart failure preserved ejection fraction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K Kamisaka ◽  
K Kamiya ◽  
K Iwatsu ◽  
N Iritani ◽  
Y Iida ◽  
...  
Keyword(s):  
Heart Failure ◽  
Weight Loss ◽  
Regression Analysis ◽  
Ejection Fraction ◽  
Associated Factors ◽  
Clinical Characteristics ◽  
Obese Patients ◽  
Preserved Ejection Fraction ◽  
Kaplan Meier ◽  
Worse Prognosis

Abstract Background Weight loss (WL) has been considered as a prognostic factor in heart failure with reduced ejection fraction (HFrEF). However, the prognosis and associated factors of WL in heart failure with preserved ejection fraction (HFpEF) have remained unclear. Purpose This study aimed to examine the prevalence, prognosis, and clinical characteristics of worse prognosis based on the identified WL after discharge in HFpEF. Methods The study was conducted as a part of a multicenter cohort study (Flagship). The cohort study enrolled ambulatory HF who hospitalized due to acute HF or exacerbation of chronic HF. Patients with severe cognitive, psychological disorders or readmitted within 6-month after discharge were excluded in the study. WL was defined as ≥5% weight loss in 6-month after discharge and HFpEF was defined as left ventricular ejection fraction (LVEF) ≥50% at discharge. Age, gender, etiology, prior HF hospitalization, New York Heart Association (NYHA) class, brain natriuretic peptide (BNP) or N-terminal-proBNP (NT-proBNP), anemia (hemoglobin; male <13g/dL, female <12g/dL), serum albumin, Geriatric Depression Scale, hand grip strength and comorbidities were collected at discharge. Patients were stratified according to their body mass index (BMI) at discharge as non-obese (BMI <25) or obese (BMI ≥25). We analyzed the association between WL and HF rehospitalization from 6 month to 2 years after discharge using Kaplan-Meier curve analysis and Cox regression analysis adjusted for age and gender, and clinical characteristics associated to worse prognosis in WL using logistic regression analysis adjusted for potential confounders in HFpEF. Results A total of 619 patients with HFpEF were included in the analysis. The prevalence of WL was 12.9% in 482 non-obese and 15.3% in 137 obese patients. During 2 years, 72 patients were readmitted for HF (non-obese: 48, obese: 24). WL in non-obese independently associated with poor prognosis (hazard ratio: 2.2: 95% confidence interval: 1.13–4.25) after adjustment for age and sex, while WL in obese patients did not. Logistic regression analysis chose age (odds ratio 1.02 per 1 year; 1.00–1.05), anemia (2.14; 1.32–3.48), and BNP ≥200pg/mL or NT-proBNP ≥900pg/mL (1.83; 1.18–2.86) as independent associated factors for worse prognosis of WL in non-obese patients. Conclusion In HFpEF, WL in early after discharge in non-obese elderly patients may be a prognostic indicator for HF rehospitalization. HF management including WL prevention along with controlling anemia is likely to improve prognosis in this population. Kaplan Meier survival curves Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): A Grant-in-Aid for Scientific Research (A) from the Japan Society for the Promotion of Science

scholarly journals A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110279
Author(s):  
Qinping Guo ◽  
Yinquan Wang ◽  
Jie An ◽  
Siben Wang ◽  
Xiushan Dong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Cox Regression ◽  
Signet Ring Cell Carcinoma ◽  
Signet Ring Cell ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Signet Ring ◽  
Ring Cell ◽  
Independent Risk Factors

Background: The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC). Methods: GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed. Results: A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS. Conclusion: The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

Clinical profile and predictors of severity in paediatric scrub typhus with pulmonary involvement

2021 ◽  
pp. 004947552110125
Author(s):  
Dinesh Kumar Narayanasamy ◽  
Thirunavukkarasu Arun Babu ◽  
Prakash Mathiyalagen
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Interstitial Pneumonitis ◽  
Clinical Characteristics ◽  
Scrub Typhus ◽  
Radiographic Finding ◽  
Maculopapular Rash ◽  
Pulmonary Involvement ◽  
Clearance Time ◽  
Fever Clearance Time

Pulmonary involvement is common in children with scrub typhus. Our paper outlines the clinical characteristics of pulmonary involvement and analyses the predictors of its severity. All scrub typhus serology-positive (optical density >0.5) children with pulmonary symptoms were included. Of 506 serology-positive scrub typhus cases, 256 (50.5%) had pulmonary symptoms, of whom 50 (9.8%) were severe. These severe cases were compared with non-severe cases. Interstitial pneumonitis was the commonest chest radiographic finding. Logistic regression analysis identified ‘fever clearance time’ >48 h, facial puffiness, maculopapular rash and anaemia to be significantly associated with severe pulmonary involvement.

scholarly journals Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunlei Wu ◽  
Quanteng Hu ◽  
Dehua Ma
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

scholarly journals Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tomoyuki Takura ◽  
Keiko Hirano Goto ◽  
Asao Honda
Keyword(s):  
Regression Analysis ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Long Term Care ◽  
Healthcare Resource ◽  
Resource Consumption ◽  
Coefficient Of Determination ◽  
Term Care ◽  
Adherence Score

Abstract Background Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. Methods This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. Results Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). Conclusions ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.

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