Data Visualization at the Individual Patient Level

2012 ◽  
pp. 273-293 ◽  
Author(s):  
Matthew Austin ◽  
Alicia Zhang
Keyword(s):  
Data Visualization ◽  
Individual Patient Level ◽  
Patient Level ◽  
The Individual

scholarly journals Validation of the howRu and howRwe questionnaires at the individual patient level

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Steven H. Hendriks ◽  
Jojanneke Rutgers ◽  
Peter R. van Dijk ◽  
Klaas H. Groenier ◽  
Henk J. G. Bilo ◽  
...  
Keyword(s):  
Individual Patient Level ◽  
Patient Level ◽  
The Individual

scholarly journals QOLP-03. MEASURING CHANGE IN HEALTH-RELATED QUALITY OF LIFE: THE ADDED VALUE OF ANALYSIS ON THE INDIVIDUAL PATIENT LEVEL IN GLIOMA PATIENTS IN CLINICAL DECISION MAKING

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi197-vi198 ◽  
Author(s):  
Marijke Coomans ◽  
Martin Taphoorn ◽  
Neil Aaronson ◽  
Brigitta Baumert ◽  
Martin van den Bent ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Scale Item ◽  
Individual Patient Level ◽  
Patient Level ◽  
Related Quality ◽  
The Individual ◽  
The Impact ◽  
Over Time

Abstract BACKGROUND: Health-related quality of life (HRQoL) is an important outcome in glioma research, reflecting the impact of disease and treatment on a patient’s functioning and wellbeing. Data on changes in HRQoL scores provide important information for clinical decision-making, but different analytical methods may lead to different interpretations of the impact of treatment on HRQoL. This study aimed to study whether different methods to evaluate change in HRQoL result in different interpretations. Methods: HRQoL and sociodemographical/clinical data from 15 randomized clinical trials were combined. Change in HRQoL scores was analyzed: (1)at the group level, comparing mean changes in scale/item scores between treatment arms over time, (2)at the patient level per scale/item by calculating the percentage of patients that deteriorated, improved or remained stable on a scale/item per scale/item, and (3)at the individual patient level combining all scales/items. Results: Data were available for 3727 patients. At the group scale/item level (method 1), only the item ‘hair loss’ showed a significant and clinically relevant change (i.e. ≥10 points) over time, whereas change scores on the other scales/items showed a statistically significant change only (all p< .001, range in change score:0.1–6.2). Analyses on the patient level per scale (method 2) indicated that, while a large proportion of patients had stable HRQoL over time (range:27–84%), many patients deteriorated (range:6–43%) or improved (range:8–32%) on a specific scale/item. At the individual patient level (method 3), the majority of patients (86%) showed both deterioration and improvement, while only 1% of the patients remained stable on all scales. Conclusion: Different analytical methods of changes in HRQoL result in distinct interpretations of treatment effects, all of which may be relevant for clinical decision-making. Additional information about the joint impact of treatment on all outcomes may help patients and physicians to make the best treatment decision.

scholarly journals OS7.2 Measuring change in health-related quality of life: the added value of analysis on the individual patient level in glioma patients in clinical decision making

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii14-iii14
Author(s):  
M Coomans ◽  
M J B Taphoorn ◽  
N Aaronson ◽  
B G Baumert ◽  
M van den Bent ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Scale Item ◽  
Individual Patient Level ◽  
Patient Level ◽  
Related Quality ◽  
The Individual ◽  
The Impact ◽  
Over Time

Abstract BACKGROUND Health-related quality of life (HRQoL) is often used as an outcome in glioma research, reflecting the impact of disease and treatment on a patient’s functioning and wellbeing. Data on changes in HRQoL scores may provide important information for clinical decision-making, but different analytical methods may lead to different interpretations of the impact of treatment on HRQoL. This study aimed to examine three different methods to evaluate change in HRQoL, and to study whether these methods result in different interpretations. MATERIAL AND METHODS HRQoL and sociodemographical/clinical data from 15 randomized clinical trials were combined. Change in HRQoL scores was analyzed in three ways: (1) at the group level, comparing mean changes in scale/item scores between treatment arms over time, (2) at the patient level per scale/item by calculating the percentage of patients that deteriorated, improved or remained stable on a scale/item per scale/item, and (3) at the individual patient level combining all scales/items. RESULTS Baseline and first follow-up HRQoL data were available for 3727 patients. At the group scale/item level (method 1), only the item ‘hair loss’ showed a significant and clinically relevant change (i.e. ≥10 points) over time, whereas change scores on the other scales/items showed a statistically significant change only (all p<.001, range in change score: 0.1–6.2). Analyses on the patient level per scale (method 2) indicated that, while a large proportion of patients had stable HRQoL over time (range 27–84%), many patients deteriorated (range: 6–43%) or improved (range: 8–32%) on a specific scale/item. At the individual patient level (method 3), the majority of patients (86%) showed both deterioration and improvement, while only 1% of the patients remained stable on all scales. Clustering on clinical characteristics (WHO performance status, sex, tumor type, type of resection, newly diagnosed versus recurrent tumor and age) did not identify subgroups of patients with a specific pattern of change in their HRQoL score. CONCLUSION Different analytical methods of changes in HRQoL result in distinct interpretations of treatment effects, all of which may be relevant for clinical decision-making. Additional information about the joint impact of treatment on all outcomes, showing that most patients experience both deterioration and improvement, may help patients and physicians to make the best treatment decision.

scholarly journals Health status in routine clinical practice: validity of the clinical COPD questionnaire at the individual patient level

2010 ◽  
Vol 8 (1) ◽  
pp. 135 ◽  
Author(s):  
Janwillem WH Kocks ◽  
Huib AM Kerstjens ◽  
Sandra L Snijders ◽  
Barbara de Vos ◽  
Jacqueline J Biermann ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Health Status ◽  
Routine Clinical Practice ◽  
Individual Patient Level ◽  
Patient Level ◽  
The Individual

scholarly journals Event-Free Survival As a Surrogate Endpoint for Overall Survival in Previously Untreated Acute Myeloid Leukemia: An Individual Patient-Level Analysis of Multiple Randomized Trials (Alliance A151614)

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1386-1386
Author(s):  
Jun Yin ◽  
Geoffrey L. Uy ◽  
Betsy Laplant ◽  
Elizabeth Storrick ◽  
Guido Marcucci ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Surrogate Endpoint ◽  
Event Free Survival ◽  
Free Survival ◽  
Individual Patient Level ◽  
Patient Level ◽  
The Individual

Abstract Background: Overall survival (OS) remains the definitive primary efficacy endpoint to evaluate previously untreated acute myeloid leukemia (AML) therapies, but it requires prolonged follow-up. An earlier endpoint assessed post-treatment would expedite clinical trial conduct and accelerate patient access to effective new therapies. Our objective was to formally evaluate event-free survival (EFS) as a surrogate endpoint for OS in untreated AML. Methods: Individual patient data were analyzed from 2,475 patients (pts) from 4 multicenter, randomized controlled phase III trials of active treatment in previously untreated AML using anthracycline and cytarabine induction chemotherapy as the concurrent control (CALGB 10201, n=506, enrollment period 2003-2006, age 60-88 years (y); CALGB 10603, n=717, enrollment period 2008-2015, age 18-60 y, FLT3-mutated pts only; SWOG 0106, n=595, enrollment period 2004-2009, age 18-60 y; ECOG-ACRIN 1900, n=657, enrollment period 2002-2008, age 17-60 y). Individual patient-level surrogacy examines the association between the individual patients' EFS and OS time after adjusting for treatment effect, and was assessed using the copula bivariate survival model (Kendall's tau). Trial-level surrogacy measures how precisely the treatment effect on OS can be predicted on the basis of observed treatment effect on EFS, and was evaluated using both linear regression (R2WLS) weighted by trial size and Copula bivariate (R2Copula) models. Pre-specified criteria for surrogacy required either R2WLS or R2Copula ≥0.80, neither below 0.7, with either lower bound 95% Confidence Interval (CI) >0.60. Sensitivity analyses were conducted using different EFS definitions (Table 1). Results: With a median follow-up of 50.2 months for the 896 patients still alive, the median OS and EFS across all four trials were 20.9 months (95% CI: 19.0-22.7) and 5.6 months (95% CI: 4.5-6.4), respectively. Trial-level surrogacy for EFS was strong (R2WLS=0.79; R2Copula=0.89), indicating a high correlation of treatment effect between EFS and OS. At the individual patient-level, however, EFS showed weak association with OS (tau= 0.52), compared to the strength of trial-level surrogacy. The discrepancy between patient-level EFS and OS was greatest among patients who did not achieve a CR, followed by those who achieved a CR but relapsed (Figure 1). Sensitivity analysis on alternative EFS definitions showed that the trial-level surrogacy was similar, but individual patient-level surrogacy varied across different EFS definitions (Table 1). This is consistent with what we previously reported (ASH 2016): EFS estimates differed considerably based on the definition of induction failure (IF) in a single arm setting, but this had minimal impact on the estimation of the treatment effect using EFS in randomized trials. In addition, when considering only relapse and death as events (definition 4), both individual patient- and trial-level correlations were high. Conclusions: Correlation between EFS and OS was impacted by patients not achieving CR during induction. Despite the lack of patient-level correlation, a strong correlation between hazard ratios for treatment effects was observed between EFS and OS on the trial level. Hence, it remains debatable whether EFS represents a clinical benefit in itself for patient with untreated AML considering the strong correlation in treatment effects. Further validation is needed due to the small number of trials included and the heterogeneity across trials. Acknowledgment: We gratefully acknowledge the important contributions of the late Dr. Stephen H. Petersdorf, SWOG S0106 Study Chair. Support: U10CA180821, U10CA180882, U10CA180794, U10CA180820, U10CA180888; Clinicaltrials.gov Identifiers: NCT00085124 (10201), NCT00651261 (10603), NCT01253070 (11001), NCT00085709 (SWOG S0106), and NCT00049517 (ECOG-ACRIN E1900) Disclosures Uy: Curis: Consultancy; GlycoMimetics: Consultancy. Larson:Pfizer: Consultancy, Research Funding; BristolMyers Squibb: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Ariad/Takeda: Consultancy, Research Funding.

scholarly journals Development of a comprehensive prognostic index for patients with chronic lymphocytic leukemia

Blood ◽  
2014 ◽  
Vol 124 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Natali Pflug ◽  
Jasmin Bahlo ◽  
Tait D. Shanafelt ◽  
Barbara F. Eichhorst ◽  
Manuela A. Bergmann ◽  
...  
Keyword(s):  
Clinical Stage ◽  
Prognostic Index ◽  
Lymphocytic Leukemia ◽  
Clinical Staging ◽  
Individual Patient Level ◽  
Patient Level ◽  
Key Points ◽  
Staging Systems ◽  
High Discriminatory Power ◽  
The Individual

Key Points Prognostic tool for CLL patients with high discriminatory power compared with conventional clinical staging systems. Prognostication on the individual patient level independent of clinical stage.

scholarly journals Optimizing indices of AF susceptibility and burden to evaluate AF severity, risk and outcomes

2021 ◽  
Author(s):  
Giuseppe Boriani ◽  
Marco Vitolo ◽  
Igor Diemberger ◽  
Marco Proietti ◽  
Anna Chiara Valenti ◽  
...  
Keyword(s):  
Risk Factors ◽  
Disease Risk ◽  
Adverse Outcomes ◽  
First Episode ◽  
Individual Patient Level ◽  
Patient Level ◽  
Patients At Risk ◽  
Heart Disease Risk Factors ◽  
The Individual

Abstract Atrial fibrillation (AF) has heterogeneous patterns of presentation concerning symptoms, duration of episodes, AF burden, and the tendency to progress towards the terminal step of permanent AF. AF is associated with a risk of stroke/thromboembolism traditionally considered dependent on patient-level risk factors rather than AF type, AF burden or other characterizations. However, the time spent in AF appears related to an incremental risk of stroke, as suggested by the higher risk of stroke in patients with clinical AF versus subclinical episodes and in patients with non-paroxysmal AF versus paroxysmal AF. In patients with device-detected atrial tachyarrhythmias, AF burden is a dynamic process with potential transitions from a lower to a higher maximum daily arrhythmia burden, thus justifying monitoring its temporal evolution. In clinical terms, the appearance of the first episode of AF, the characterization of the arrhythmia in a specific AF type, the progression of AF, and the response to rhythm control therapies, as well as the clinical outcomes, are all conditioned by underlying heart disease, risk factors, and comorbidities. Improved understanding is needed on how to monitor and modulate the effect of factors that condition AF susceptibility and modulate AF-associated outcomes. The increasing use of wearables and apps in practice and clinical research may be useful to predict and quantify AF burden and assess AF susceptibility at the individual patient level. This may help us reveal why AF stops and starts again, or why AF episodes, or burden, cluster. Additionally, whether the distribution of burden is associated with variations in the propensity to thrombosis or other clinical adverse events. Combining the improved methods for data analysis, clinical and translational science could be the basis for the early identification of the subset of patients at risk of progressing to a longer duration/higher burden of AF and the associated adverse outcomes.

scholarly journals Genomic Profiles of Bone Marrow (BM) Clonal Plasma Cells (PCs) Vs Circulating Tumor Cells (CTCs) and Extramedullary (EM) Plasmacytomas in Multiple Myeloma (MM)

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4442-4442
Author(s):  
Gabriel Bretones ◽  
Bruno Paiva ◽  
Rafael Valdes-Mas ◽  
Diego Alignani ◽  
Miguel Garcia ◽  
...  
Keyword(s):  
Protein Function ◽  
Research Funding ◽  
Somatic Mutations ◽  
Stop Codon ◽  
Clonal Evolution ◽  
Dna Amplification ◽  
Individual Patient Level ◽  
Patient Level ◽  
Recurrent Mutations ◽  
The Individual

Abstract EM disease in MM increases from newly-diagnosed into relapsed patients, and typically predicts for inferior survival. In fact, no treatment seems to be effective in cases with plasma cell (PC) leukemia, which represents the most aggressive form of EM MM. Unfortunately, the mechanisms of extramedullary spread in MM are not well understood, and there is almost no data about the genetic landscape of both forms of EMD (plasmacytomas and peripheral blood CTCs). Here, we performed whole exome sequencing (WES) to analyze the genomic profiles of highly purified FACS sorted BM and EM clonal PCs from 6 patients with relapsed MM. In 5/6 cases we had all three tissue specific clones, whereas in the remaining case no CTCs were detectable. Depending on the amount of genomic DNA from each clone, whole-genome amplification was performed and in such cases, triplicates of 10 ng of DNA were amplified up to 10 µg using the Illustra GenomiPhi HY DNA amplification kit (GE healthcare). Enrichment of exonic sequences was performed for each library using the SureSelectXT Human All Exon V5+UTRs capture kit (Agilent). To identify somatic mutations we used the mutation caller named Varscan. Variants potentially affecting protein function, including non-synonymous variants, frameshifts in the coding sequence, stop codon-introducing (nonsense) or variants potentially affecting splicing, were analyzed. Only those mutations present in 2/3 libraries analyzed per sample were considered positive. Overall, a median of 89 (67 - 474) somatic mutations were detected. EM plasmacytomas showed the highest mutation load, followed by CTCs and BM clonal PCs (85 vs 77 vs 75, respectively; P=.07), supporting a higher genomic instability/evolution of EM clones. Strikingly, all 6 cases showed lack of concordance in the mutation profiles of the three tissue related clones; even 2x2 comparisons between BM clonal PCs vs CTCs or plasmacytomas, or between the two forms of EM MM (ie. plasmacytomas vs CTCs) showed lack of 100% concordance in every single patient. Despite high inter-tissue heterogeneity, it should be noted that whenever present, recurrent and potentially actionable mutations in genes such as KRAS (n=3), KDM4A (n=2), KMT2A (n=2), ARID5B (n=2), TRIO (n=2), BRAF (n=1) or CCND1 (n=1) were detected in all three clones, with the exception of one patient in which CTCs lacked a KRAS mutation. Only one and less frequent actionable mutation in the EPHB2 gene (eg. Herceptin) was exclusively noted in the EM plasmacytoma from one patient. In order to understand the cellular origin of the three tissue related subclones, we then investigated the degree of similarity between each pair of clones at the individual patient level. The pair of clones showing the highest similarity in their mutation profiles were EM plasmacytomas with CTCs (60%, 30% - 94%), followed by BM clonal PCs with CTCs (58%, 35% - 92%), and by BM clonal PCs with EM plasmacytomas (57%, 37% - 91%). Thus, these data suggests that while CTC clones may represent a cellular bridge in between BM and EM plasmacytomas, there is continuous genomic evolution once the different clones have seeded in their respective tissue niches. Accordingly, EM plasmacytomas showed the highest number of specific mutations, followed by BM clonal PCs and CTCs (medians of 9, 4 and 3, respective). We then looked for recurrent mutations specifically present in EM clones, and found that CTCs showed exclusive and recurrent mutations in the CEP152, FSIP2, SYNE1 and TENM1 and ZNF585A genes, whereas EM plasmacytomas showed exclusive and recurrent mutations in the PITRM1 (Metalloprotease 1) gene. Furthermore, ATP7B, MTOR, TBC1D21 and ZNF717 mutations were present in both CTCs and plasmacytomas. In summary, we compared for the first time the genomic profiles of BM vs EM CTCs and plasmacytoma clones, and showed at the individual patient level, the existence of high mutation heterogeneity in between all three tissue related clones. However, actionable mutations in genes such as KRAS or BRAF were typically shared by BM and EM clones. According to the level of similarity between each clone, CTCs would be the most plausible precursor of EM plasmacytomas. Nevertheless, based on the number of specific mutations present in EM clones, it is likely that EM spreading followed by continuous clonal evolution starts much earlier prior to its clinical detection, which emphasizes the need for sensitive techniques to capture the early stages of EM spreading. Disclosures Paiva: Celgene: Honoraria, Research Funding; Janssen: Honoraria; Takeda: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; EngMab: Research Funding; Amgen: Honoraria; Binding Site: Research Funding.

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