Studying a Rare Disease Using Multi-Institutional Research Collaborations vs Big Data: Where Lies the Truth?

Johnson, Aileen C.; Ethun, Cecilia G.; Liu, Yuan; Lopez‐Aguiar, Alexandra G.; Tran, Thuy B.; Poultsides, George A.; Grignol, Valerie; Howard, John H.; Bedi, Meena; Gamblin, T. Clark; Tseng, Jennifer F.; Roggin, Kevin K.; Chouliaras, Konstantinos; Votanopoulos, Konstantinos I.; Cullinan, Darren R.; Fields, Ryan C.; Delman, Keith A.; Wood, William C.; Cardona, Kenneth; Maithel, Shishir K.

doi:10.1016/j.jamcollsurg.2018.05.009

Cited by 12 publications

(19 citation statements)

References 33 publications

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“…5 Other data elements are not exempt from similar challenges. 7 It is uncertain which database is more accurate, but this discrepancy brings into question the validity of large registry studies. 5 An analysis of sarcoma data from these large databases may lead to inaccurate predictions and outcomes.…”

Section: Limitations Of Current Large Databasesmentioning

confidence: 99%

“…Currently, populationbased cancer registries in the United States pool a vast amount of data from a heterogeneous group of institutions, and this results in decreased selection bias and increased generalizability. [4][5][6][7] The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. 3 Moreover, the advantage of using large databases for studying rare outcomes and pathologies such as sarcomas has been cited in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…3 Moreover, the advantage of using large databases for studying rare outcomes and pathologies such as sarcomas has been cited in the literature. 7 Subsequently, established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes. Single-institution registries are unlikely to include enough patient data to make statistically significant inferences.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6][7] The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. [6][7][8][9][10] Nevertheless, the validity of large databases across multiple specialties has been questioned and scrutinized because of the inaccuracy and wide variability of coding practices and the absence of clinically relevant variables. 6 However, the acquisition of data from multiple institutions presents many challenges due to limited interoperability and variations in diagnostic methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…Single-institution registries are unlikely to include enough patient data to make statistically significant inferences. 3,7 The aims of this review are 2-fold: 1) to present the current state of cancer databases often used for sarcoma research and to expand on the limitations of the databases and their potential consequences and 2) to introduce current and future initiatives for improving sarcoma data collection and the validity of large oncologic registries. 7 Subsequently, established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes.…”

Section: Introductionmentioning

confidence: 99%

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The opportunities and shortcomings of using big data and national databases for sarcoma research

Lyu

Haider

Landman

et al. 2019

Cancer

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The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. Established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes. However, the validity of large databases has been questioned and scrutinized because of the inaccuracy and wide variability of coding practices and the absence of clinically relevant variables. In addition, the utilization of large databases for the study of rare cancers such as sarcoma may be particularly challenging because of the known limitations of administrative data and poor overall data quality. Currently, there are several large national cancer databases, including the Surveillance, Epidemiology, and End Results database, the National Cancer Data Base of the American College of Surgeons and the American Cancer Society, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention. These databases are often used for sarcoma research, but they are limited by their dependence on administrative or billing data, the lack of agreement between chart abstractors on diagnosis codes, and the use of preexisting documented hospital diagnosis codes for tumor registries, which lead to a significant underestimation of sarcomas in large data sets. Current and future initiatives to improve databases and big data applications for sarcoma research include increasing the utilization of sarcoma-specific registries and encouraging national initiatives to expand on real-world, evidence-based data sets. Cancer 2019;125:2926-2934.

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Section: Limitations Of Current Large Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The opportunities and shortcomings of using big data and national databases for sarcoma research

Lyu

Haider

Landman

et al. 2019

Cancer

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Extremity soft tissue sarcoma in the elderly: Are we overtreating or undertreating this potentially vulnerable patient population?

Gingrich

Bateni

Monjazeb

et al. 2019

Journal of Surgical Oncology

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Background As the U.S. population ages, differences in oncologic outcomes among the elderly have been recognized. Our objective was to analyze the clinical, pathologic, and treatment outcomes for elderly soft tissue sarcoma (STS) patients, hypothesizing significant differences in the management and response to therapy. Methods Using the National Cancer Database, we identified 33 859 patients with nonmetastatic extremity STS. We defined elderly as ≥74 years in age and compared patient and treatment variables between adult and elderly patients. Cox‐proportional hazards analysis was used to determine predictors of overall survival (OS). Results We identified 8504 elderly patients. Significant differences in histologic subtype, grade, and facility type between elderly and nonelderly patients (P < 0.05) exist. Elderly patients were less likely to undergo R0 resection (P = 0.001) and had a higher 90‐day mortality (P = 0.001). Surgical elderly patients experienced superior OS compared with nonsurgical patients (P = 0.001). Among elderly patients, younger age, and female sex, lower Charlson‐Deyo score, lower grade, smaller tumors, surgical resection, negative surgical margins, and radiation therapy were associated with better OS. Conclusions Key differences exist in elderly extremity STS patients, including a narrower benefit/risk ratio with surgical management. These data highlight that elderly patients represent a distinct cohort for whom more careful selection appears indicated.

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The role of radiation therapy and margin width in localized soft‐tissue sarcoma: Analysis from the US Sarcoma Collaborative

Gannon

King

Ethun

et al. 2019

Journal of Surgical Oncology

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Background and Objectives Soft‐tissue sarcomas (STSs) are often treated with resection and radiation (RT)±chemotherapy. The role of RT in decreasing resection width to achieve local control is unclear. We evaluated RT on margin width to achieve local control and local recurrence (LR). Methods From 2000 to 2016, 514 patients with localized STS were identified from the US Sarcoma Collaborative database. Patients were stratified by a margin and local control was compared amongst treatment groups. Results LR was 9% with positive, 4.2% with ≤1 mm, and 9.3% with >1 mm margins (P = .315). In the ≤1 mm group, LR was 5.7% without RT, 0% with preoperative RT, and 0% with postoperative RT (P < .0001). In the >1 mm group, LR was 10.2%, 0%, and 3.7% in the no preoperative and postoperative RT groups, respectively (P = .005). RT did not influence LR in patients with positive margins. In stage I‐III and II‐III patients, local recurrence‐free survival was higher following RT (P = .008 and P = .05, respectively). Conclusions RT may play a larger role in minimizing LR than margin status. In patients with positive margins, RT may decrease LR to similar rates as a negative margin without RT and may be considered to decrease the risk of LR with anticipated close/positive margins.

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Studying a Rare Disease Using Multi-Institutional Research Collaborations vs Big Data: Where Lies the Truth?

Cited by 12 publications

References 33 publications

The opportunities and shortcomings of using big data and national databases for sarcoma research

The opportunities and shortcomings of using big data and national databases for sarcoma research

Extremity soft tissue sarcoma in the elderly: Are we overtreating or undertreating this potentially vulnerable patient population?

The role of radiation therapy and margin width in localized soft‐tissue sarcoma: Analysis from the US Sarcoma Collaborative

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