Dataset Information

Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

ABSTRACT: Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations.We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each cancer in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by cancer type/data source, and contrasted with an existing binary detection rule.Detection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by cancer type and dataset challenged efforts to create 1 common algorithm for all scenarios.Valid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and colorectal cancer patients and those who develop recurrence.

SUBMITTER: Hassett MJ

PROVIDER: S-EPMC4732933 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

Hassett Michael J MJ Uno Hajime H Cronin Angel M AM Carroll Nikki M NM Hornbrook Mark C MC Ritzwoller Debra D

Medical care 20171201 12

<h4>Introduction</h4>Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations.<h4>Methods</h4>We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and c ...[more]

PMID: 29135771

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Project description:BackgroundChinese Center for Disease Control and Prevention (China CDC) introduced the Structured Operational Research Training Initiative (SORT IT) into China to build a special capacity and equip public health professionals with an effective tool to support developing countries in strengthening their operational research. The paper aims to investigate and analyze the implementation, outcomes and challenges of the first cycle of SORT IT in China.Main textAs a result of the successful implementation, SORT IT China, Cycle 1 has demonstrated fruitful outputs as exemplified by the 18-month follow-up to the post-training initiatives of the twelve participants, who all achieved the four milestones required by SORT IT. Eleven of twelve (92%) manuscripts generated that focused on the prevention and control of malaria, influenza, HIV/AIDS, hepatitis B, schistosomiasis, tuberculosis and Japanese encephalitis were published by peer-reviewed international journals with the impact factor ranging from 2.6 to 4.8. The most up-to-date citation count on February 19, 2021 was 53 times out of which 31 times were cited by Science Citation Index papers with 94.827 impact factor in total. Six senior professionals from China CDC also facilitated the whole SORT IT training scheme as co-mentors under the guidance of SORT IT mentors. The twelve participants who gained familiarity with the SORT IT courses and training principles are likely become potential mentors for future SORT IT, but they as the non-first language speakers/users of English also faced the challenge in thoroughly understanding the modules delivered in English and writing English academically to draft the manuscripts.ConclusionThe outcomes from the first cycle of SORT IT in China have led to studies contributing to narrowing the knowledge gap among numerous public health challenges nationally and internationally. It is believed the researchers who participated will continue to apply the skills learned within their domain and help build the training capacity for future operational research courses both in China and in developing countries with similar needs.

Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Project description:ObjectiveTo evaluate the diagnostic accuracy of a single CEA (carcinoembryonic antigen) blood test in detecting colorectal cancer recurrence.BackgroundPatients who have undergone curative resection for primary colorectal cancer are typically followed up with scheduled CEA testing for 5 years. Decisions to investigate further (usually by CT imaging) are based on single test results, reflecting international guidelines.MethodsA secondary analysis was undertaken of data from the FACS trial (two arms included CEA testing). The composite reference standard applied included CT-CAP imaging, clinical assessment and colonoscopy. Accuracy in detecting recurrence was evaluated in terms of sensitivity, specificity, likelihood ratios, predictive values, time-dependent area under the ROC curves, and operational performance when used prospectively in clinical practice are reported.ResultsOf 582 patients, 104 (17.9%) developed recurrence during the 5 year follow-up period. Applying the recommended threshold of 5?g/L achieves at best 50.0% sensitivity (95% CI: 40.1-59.9%); in prospective use in clinical practice it would lead to 56 missed recurrences (53.8%; 95% CI: 44.2-64.4%) and 89 false alarms (56.7% of 157 patients referred for investigation). Applying a lower threshold of 2.5?g/L would reduce the number of missed recurrences to 36.5% (95% CI: 26.5-46.5%) but would increase the false alarms to 84.2% (924/1097 referred). Some patients are more prone to false alarms than others-at the 5?g/L threshold, the 89 episodes of unnecessary investigation were clustered in 29 individuals.ConclusionOur results demonstrated very low sensitivity for CEA, bringing to question whether it could ever be used as an independent triage test. It is not feasible to improve the diagnostic performance of a single test result by reducing the recommended action threshold because of the workload and false alarms generated. Current national and international guidelines merit re-evaluation and options to improve performance, such as making clinical decisions on the basis of CEA trend, should be further assessed.

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Dataset Information

Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

Publications

Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

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