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Vickers A, Vertosick E, Langsetmo L, Dahm P, Steineck G, Wilt TJ. Estimating the Effect of Radical Prostatectomy: Combining Data From the SPCG4 and PIVOT Randomized Trials With Contemporary Cohorts. J Urol 2024; 212:310-319. [PMID: 38865734 PMCID: PMC11233245 DOI: 10.1097/ju.0000000000004039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/06/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE Two randomized trials (SPCG4 and PIVOT) have compared surgery to conservative management for localized prostate cancer. The applicability of these trials to contemporary practice remains uncertain. We aimed to develop an individualized prediction model for prostate cancer mortality comparing immediate surgery at a high-volume center to active surveillance. MATERIALS AND METHODS We determined whether the relative risk of prostate cancer mortality with surgery vs observation varied by baseline risk. We then used various estimates of relative risk to estimate 15-year mortality with and without surgery using, as a predictor, risk of biochemical recurrence calculated from a model. RESULTS We saw no evidence that relative risk varied by baseline risk, supporting the use of a constant relative risk. Compared with observation, surgery was associated with negligible benefit for patients with Grade Group (GG) 1 disease (0.2% mortality reduction at 15 years) and small benefit for patients with GG2 with lower PSA and stage (≤5% mortality reduction). Benefit was greater (6%-9%) for patients with GG3 or GG4 though still modest, but effect estimates varied widely depending on choice of hazard ratio for surgery (6%-36% absolute risk reduction). CONCLUSIONS Surgery should be avoided for men with low-risk (GG1) prostate cancer and for many men with GG2 disease. Surgical benefits are greater in men with higher-risk disease. Integration of findings with a life expectancy model will allow patients to make informed treatment decisions given their oncologic risk, risk of death from other causes, and estimated effects of surgery.
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Affiliation(s)
- Andrew Vickers
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily Vertosick
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lisa Langsetmo
- Minneapolis VA Health Care System, Minneapolis, Minnesota
- Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Philipp Dahm
- Minneapolis VA Health Care System, Minneapolis, Minnesota
- Department of Urology, University of Minnesota, Minneapolis, Minnesota
| | - Gunnar Steineck
- Department of Oncology, University of Gothenburg, Gothenburg, Sweden
| | - Timothy J Wilt
- Minneapolis VA Health Care System, Minneapolis, Minnesota
- Department of Medicine, University of Minnesota, Minneapolis, Minnesota
- Department of Public Health, University of Minnesota, Minneapolis, Minnesota
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Justice AC, Tate JP, Howland F, Gaziano JM, Kelley MJ, McMahon B, Haiman C, Wadia R, Madduri R, Danciu I, Leppert JT, Leapman MS, Thurtle D, Gnanapragasam VJ. Adaption and National Validation of a Tool for Predicting Mortality from Other Causes Among Men with Nonmetastatic Prostate Cancer. Eur Urol Oncol 2024; 7:923-932. [PMID: 38171965 DOI: 10.1016/j.euo.2023.11.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND An electronic health record-based tool could improve accuracy and eliminate bias in provider estimation of the risk of death from other causes among men with nonmetastatic cancer. OBJECTIVE To recalibrate and validate the Veterans Aging Cohort Study Charlson Comorbidity Index (VACS-CCI) to predict non-prostate cancer mortality (non-PCM) and to compare it with a tool predicting prostate cancer mortality (PCM). DESIGN, SETTING, AND PARTICIPANTS An observational cohort of men with biopsy-confirmed nonmetastatic prostate cancer, enrolled from 2001 to 2018 in the national US Veterans Health Administration (VA), was divided by the year of diagnosis into the development (2001-2006 and 2008-2018) and validation (2007) sets. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Mortality (all cause, non-PCM, and PCM) was evaluated. Accuracy was assessed using calibration curves and C statistic in the development, validation, and combined sets; overall; and by age (<65 and 65+ yr), race (White and Black), Hispanic ethnicity, and treatment groups. RESULTS AND LIMITATIONS Among 107 370 individuals, we observed 24 977 deaths (86% non-PCM). The median age was 65 yr, 4947 were Black, and 5010 were Hispanic. Compared with CCI and age alone (C statistic 0.67, 95% confidence interval [CI] 0.67-0.68), VACS-CCI demonstrated improved validated discrimination (C statistic 0.75, 95% CI 0.74-0.75 for non-PCM). The prostate cancer mortality tool also discriminated well in validation (C statistic 0.81, 95% CI 0.78-0.83). Both were well calibrated overall and within subgroups. Owing to missing data, 18 009/125 379 (14%) were excluded, and VACS-CCI should be validated outside the VA prior to outside application. CONCLUSIONS VACS-CCI is ready for implementation within the VA. Electronic health record-assisted calculation is feasible, improves accuracy over age and CCI alone, and could mitigate inaccuracy and bias in provider estimation. PATIENT SUMMARY Veterans Aging Cohort Study Charlson Comorbidity Index is ready for application within the Veterans Health Administration. Electronic health record-assisted calculation is feasible, improves accuracy over age and Charlson Comorbidity Index alone, and might help mitigate inaccuracy and bias in provider estimation of the risk of non-prostate cancer mortality.
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Affiliation(s)
- Amy C Justice
- VA Connecticut Healthcare, West Haven, CT, USA; Pain Research, Informatics, Multimorbidities, Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA; School of Public Health, Yale University, New Haven, CT, USA.
| | - Janet P Tate
- VA Connecticut Healthcare, West Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Frank Howland
- Wabash College Economics Department, Crawfordsville, IN, USA
| | | | - Michael J Kelley
- Durham VA Health Care System, Durham, NC, USA; Cancer Institute and Department of Medicine, Duke University, Durham, NC, USA
| | | | - Christopher Haiman
- Center for Genetic Epidemiology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Roxanne Wadia
- Department of Anatomic Pathology and Lab Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ravi Madduri
- Data Science Learning Division, Argonne Research Library, Lemont, IL, USA
| | - Ioana Danciu
- Oak Ridge National Laboratory, Oak Ridge, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John T Leppert
- Department of Urology, Stanford University, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Michael S Leapman
- VA Connecticut Healthcare, West Haven, CT, USA; Department of Urology, Yale School of Medicine, New Haven, CT, USA
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Gnanapragasam V. Shifting the paradigm in the management of early prostate cancer. Br J Cancer 2024; 130:1075-1077. [PMID: 38448750 PMCID: PMC10991531 DOI: 10.1038/s41416-024-02641-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/21/2024] [Accepted: 02/23/2024] [Indexed: 03/08/2024] Open
Abstract
Outcomes from active surveillance have clearly shown that it is the optimal method of managing many early prostate cancers. Yet, clinician training and healthcare systems are still primarily focused on the "need to treat". This comment explores the challenges and resource issues in future implementation of high-quality surveillance programmes.
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Affiliation(s)
- Vincent Gnanapragasam
- Cambridge Urology Translational Research and Clinical Trials Office, Cambridge, UK.
- Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK.
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Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer. NPJ Digit Med 2022; 5:110. [PMID: 35933478 PMCID: PMC9357044 DOI: 10.1038/s41746-022-00659-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/14/2022] [Indexed: 11/15/2022] Open
Abstract
Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility. We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (±0.11) compared to 0.70 (±0.15) for landmarking Cox and 0.67 (±0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years. In summary, we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time.
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Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets. Cancers (Basel) 2022; 14:cancers14061564. [PMID: 35326714 PMCID: PMC8945961 DOI: 10.3390/cancers14061564] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs. Abstract Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.
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Thurtle D, Jenkins V, Freeman A, Pearson M, Recchia G, Tamer P, Leonard K, Pharoah P, Aning J, Madaan S, Goh C, Hilman S, McCracken S, Ilie PC, Lazarowicz H, Gnanapragasam V. Clinical Impact of the Predict Prostate Risk Communication Tool in Men Newly Diagnosed with Nonmetastatic Prostate Cancer: A Multicentre Randomised Controlled Trial. Eur Urol 2021; 80:661-669. [PMID: 34493413 DOI: 10.1016/j.eururo.2021.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Predict Prostate is a freely available online personalised risk communication tool for men with nonmetastatic prostate cancer. Its accuracy has been assessed in multiple validation studies, but its clinical impact among patients has not hitherto been assessed. OBJECTIVE To assess the impact of the tool on patient decision-making and disease perception. DESIGN, SETTING, AND PARTICIPANTS A multicentre randomised controlled trial was performed across eight UK centres among newly diagnosed men considering either active surveillance or radical treatment. A total of 145 patients were included between 2018 and 2020, with median age 67 yr (interquartile range [IQR] 61-72) and prostate-specific antigen 6.8 ng/ml (IQR 5.1-8.8). INTERVENTION Participants were randomised to either standard of care (SOC) information or SOC and a structured presentation of the Predict Prostate tool. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Validated questionnaires were completed by assessing the impact of the tool on decisional conflict, uncertainty, anxiety, and perception of survival. RESULTS AND LIMITATIONS Mean Decisional Conflict Scale scores were 26% lower in the Predict Prostate group (mean = 16.1) than in the SOC group (mean = 21.7; p = 0.027). Scores on the "support", "uncertainty", and "value clarity" subscales all favoured Predict Prostate (all p < 0.05). There was no significant difference in anxiety scores or final treatment selection between the two groups. Patient perception of 15-yr prostate cancer-specific mortality (PCSM) and overall survival benefit from radical treatment were considerably lower and more accurate among men in the Predict Prostate group (p < 0.001). In total, 57% of men reported that the Predict Prostate estimates for PCSM were lower than expected, and 36% reported being less likely to select radical treatment. Over 90% of patients in the intervention group found it useful and 94% would recommend it to others. CONCLUSIONS Predict Prostate reduces decisional conflict and uncertainty, and shifts patient perception around prognosis to be more realistic. This randomised trial demonstrates that Predict Prostate can directly inform the complex decision-making process in prostate cancer and is felt to be useful by patients. Future larger trials are warranted to test its impact upon final treatment decisions. PATIENT SUMMARY In this national study, we assessed the impact of an individualised risk communication tool, called Predict Prostate, on patient decision-making after a diagnosis of localised prostate cancer. Men were randomly assigned to two groups, which received either standard counselling and information, or this in addition to a structured presentation of the Predict Prostate tool. Men who saw the tool were less conflicted and uncertain in their decision-making, and recommended the tool highly. Those who saw the tool had more realistic perception about their long-term survival and the potential impact of treatment upon this. TAKE HOME MESSAGE The use of an individualised risk communication tool, such as Predict Prostate, reduces patient decisional conflict and uncertainty when deciding about treatment for nonmetastatic prostate cancer. The tool leads to more realistic perceptions about survival outcomes and prognosis.
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Affiliation(s)
- David Thurtle
- Department of Surgery, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Val Jenkins
- Brighton and Sussex Medical School, Brighton, UK
| | - Alex Freeman
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - Mike Pearson
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - Gabriel Recchia
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - Priya Tamer
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Kelly Leonard
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Paul Pharoah
- Department of Community Medicine, University of Cambridge, Cambridge, UK; Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Aning
- University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | | | - Chee Goh
- Surrey and Sussex Healthcare NHS Trust, Surrey, UK
| | - Serena Hilman
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | | | | | - Henry Lazarowicz
- Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Vincent Gnanapragasam
- Department of Surgery, University of Cambridge School of Clinical Medicine, Cambridge, UK
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Farmer GD, Pearson M, Skylark WJ, Freeman ALJ, Spiegelhalter DJ. Redevelopment of the Predict: Breast Cancer website and recommendations for developing interfaces to support decision-making. Cancer Med 2021; 10:5141-5153. [PMID: 34152085 PMCID: PMC8335820 DOI: 10.1002/cam4.4072] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 05/30/2021] [Accepted: 05/31/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives To develop a new interface for the widely used prognostic breast cancer tool: Predict: Breast Cancer. To facilitate decision‐making around post‐surgery breast cancer treatments. To derive recommendations for communicating the outputs of prognostic models to patients and their clinicians. Method We employed a user‐centred design process comprised of background research and iterative testing of prototypes with clinicians and patients. Methods included surveys, focus groups and usability testing. Results The updated interface now caters to the needs of a wider audience through the addition of new visualisations, instantaneous updating of results, enhanced explanatory information and the addition of new predictors and outputs. A programme of future research was identified and is now underway, including the provision of quantitative data on the adverse effects of adjuvant breast cancer treatments. Based on our user‐centred design process, we identify six recommendations for communicating the outputs of prognostic models including the need to contextualise statistics, identify and address gaps in knowledge, and the critical importance of engaging with prospective users when designing communications. Conclusions For prognostic algorithms to fulfil their potential to assist with decision‐making they need carefully designed interfaces. User‐centred design puts patients and clinicians needs at the forefront, allowing them to derive the maximum benefit from prognostic models.
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Affiliation(s)
- George D Farmer
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK.,Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Mike Pearson
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | | | - Alexandra L J Freeman
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - David J Spiegelhalter
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
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Nie Z, Zhao P, Shang Y, Sun B. Nomograms to predict the prognosis in locally advanced oral squamous cell carcinoma after curative resection. BMC Cancer 2021; 21:372. [PMID: 33827452 PMCID: PMC8028060 DOI: 10.1186/s12885-021-08106-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/24/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Oral squamous cell carcinoma (OSCC) is the dominant histologic type of oral cancer. Locally advanced OSCC remains a major therapeutic challenge. Our study aimed to develop and validate nomograms predicting survival prognosis in patients with locally advanced oral squamous cell carcinoma (OSCC) after curative resection. METHODS A total of 269 consecutive patients with primary OSCC who received curative resection between September 2007 and March 2020 were retrospectively enrolled in our study. Patients were randomly assigned to the training cohort (n = 201) or the validation cohort (n = 68). Multivariate Cox regression analyses were conducted to determine independent prognostic factors for overall survival (OS) and cancer specific survival (CSS) in the training set, which were used to develop nomogram models estimating 3-, and 5-year OS and CSS. We also evaluated the nomograms using concordance indices (c-index), calibration curves, and decision curve analyses (DCA), and compared those with the AJCC 8th staging system. The results were externally validated in the validation cohort. RESULTS Age, Kaplan-Feinstein (KFI) index, pT, the number of positive nodes and systemic inflammatory index (SII) were significant prognostic predictors for OS and CSS. The OS nomogram had c-index values of 0.712 in the training set and 0.697 in the validation set, while the CSS nomogram exhibited c-index values of 0.709 in the training set and 0.675 in the validation set. These data were superior to those of AJCC 8th staging system, suggesting high discriminative ability of the nomograms. Calibration curves exhibited good agreement between observed and predicted survival. DCA curves indicated the nomograms were with potential clinical usefulness. These results were validated in the validation set. CONCLUSIONS The novel nomograms incorporating clinically available characteristics for OS and CSS prediction were developed in the locally advanced OSCC patients after curative surgery. Validation revealed good discrimination and calibration, indicating the clinical utility of the nomograms in the individualized prognosis prediction of locally advanced OSCC after curative surgery.
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Affiliation(s)
- Zhiliang Nie
- First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
| | - Pengcheng Zhao
- School of Stomatology, Dalian Medical University, Dalian, Liaoning, China
| | - Yishan Shang
- Dalian Municipal Women and Children's Medical Center, Dalian, Liaoning, China
| | - Bo Sun
- The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
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Kosch R, Schiffmann I, Daumer M, Lederer C, Scalfari A, Galea I, Scheiderbauer J, Rahn A, Heesen C. Long-term prognostic counselling in people with multiple sclerosis using an online analytical processing tool. Mult Scler 2020; 27:1442-1450. [PMID: 33103987 DOI: 10.1177/1352458520964774] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Prognostic counselling is a sensitive issue in medicine and especially so in MS due to the highly heterogeneous disease course. However, people with MS (pwMS) seek prognostic information. The web-based 'Evidence-Based Decision Support Tool in Multiple Sclerosis' (EBDiMS) uses data of 717 patients from the London/Ontario cohort to calculate personalized long-term prognostic information. OBJECTIVE The aim of this study was to investigate the feasibility and effect of long-term prognostic counselling in pwMS using EBDiMS. METHODS Ninety consecutive pwMS were provided with personalized estimations of expected time to reach Expanded Disability Status Scale (EDSS) scores of 6 and 8 and time to conversion to secondary-progressive MS. Participants gave estimates on their own putative prognosis and rated the tool's acceptability on six-step Likert-type scales. RESULTS Participants rated EBDiMS as highly understandable, interesting and relevant for patient-physician encounters, coping and therapy decisions. Although it provoked a certain degree of worry in some participants, 95% would recommend using the tool. Participants' own prognosis estimates did not change significantly following EBDiMS. CONCLUSION Long-term prognostic counselling using an online tool has been shown to be feasible in a clinical setting. EBDiMS provides pwMS with relevant, easy-to-understand, long-term prognostic information without causing relevant anxiety.
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Affiliation(s)
- Ricardo Kosch
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Insa Schiffmann
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany/Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martin Daumer
- Sylvia Lawry Center for Multiple Sclerosis Research & Human Motion Institute, Munich, Germany
| | - Christian Lederer
- Sylvia Lawry Center for Multiple Sclerosis Research & Human Motion Institute, Munich, Germany
| | - Antonio Scalfari
- Division of Neuroinflammation and Neurodegeneration, Department of Medicine, Imperial College London, London, UK
| | - Ian Galea
- Clinical Neurosciences, Clinical & Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Jutta Scheiderbauer
- Stiftung für Selbstbestimmung und Selbstvertretung von MS-Betroffenen, Trier, Germany
| | - Anne Rahn
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Heesen
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany/Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Thurtle D, Bratt O, Stattin P, Pharoah P, Gnanapragasam V. Comparative performance and external validation of the multivariable PREDICT Prostate tool for non-metastatic prostate cancer: a study in 69,206 men from Prostate Cancer data Base Sweden (PCBaSe). BMC Med 2020; 18:139. [PMID: 32539712 PMCID: PMC7296776 DOI: 10.1186/s12916-020-01606-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/27/2020] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND PREDICT Prostate is an endorsed prognostic model that provides individualised long-term prostate cancer-specific and overall survival estimates. The model, derived from UK data, estimates potential treatment benefit on overall survival. In this study, we externally validated the model in a large independent dataset and compared performance to existing models and within treatment groups. METHODS Men with non-metastatic prostate cancer and prostate-specific antigen (PSA) < 100 ng/ml diagnosed between 2000 and 2010 in the nationwide population-based Prostate Cancer data Base Sweden (PCBaSe) were included. Data on age, PSA, clinical stage, grade group, biopsy involvement, primary treatment and comorbidity were retrieved. Sixty-nine thousand two hundred six men were included with 13.9 years of median follow-up. Fifteen-year survival estimates were calculated using PREDICT Prostate for prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM). Discrimination was assessed using Harrell's concordance (c)-index in R. Calibration was evaluated using cumulative available follow-up in Stata (TX, USA). RESULTS Overall discrimination of PREDICT Prostate was good with c-indices of 0.85 (95% CI 0.85-0.86) for PCSM and 0.79 (95% CI 0.79-0.80) for ACM. Overall calibration of the model was excellent with 25,925 deaths predicted and 25,849 deaths observed. Within the conservative management and radical treatment groups, c-indices for 15-year PCSM were 0.81 and 0.78, respectively. Calibration also remained good within treatment groups. The discrimination of PREDICT Prostate significantly outperformed the EAU, NCCN and CAPRA scores for both PCSM and ACM within this cohort overall. A key limitation is the use of retrospective cohort data. CONCLUSIONS This large external validation demonstrates that PREDICT Prostate is a robust and generalisable model to aid clinical decision-making.
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Affiliation(s)
- David Thurtle
- Academic Urology Group, University of Cambridge, Norman Bleehan Offices, Addenbrookes Hospital, Hills Road, Cambridge, CB2 0QQ, UK.
| | - Ola Bratt
- Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, and Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Pär Stattin
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Paul Pharoah
- Department of Cancer Epidemiology, University of Cambridge, Cambridge, UK
| | - Vincent Gnanapragasam
- Vincent Gnanapragasam, Academic Urology Group, University of Cambridge, Cambridge, UK
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11
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Prostate cancer treatment choices: the GP's role in shared decision making. Br J Gen Pract 2019; 69:588-589. [PMID: 31780467 DOI: 10.3399/bjgp19x706685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
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