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:Breast, lung and colorectal cancers constitute the most common cancers worldwide and their epidemiology, related health outcomes and quality indicators can be studied using administrative healthcare databases. To constitute a reliable source for research, administrative healthcare databases need to be validated. The aim of this protocol is to perform the first systematic review of studies reporting the validation of International Classification of Diseases 9th and 10th revision codes to identify breast, lung and colorectal cancer diagnoses in administrative healthcare databases.This review protocol has been developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) 2015 statement. We will search the following databases: MEDLINE, EMBASE, Web of Science and the Cochrane Library, using appropriate search strategies. We will include validation studies that used administrative data to identify breast, lung and colorectal cancer diagnoses or studies that evaluated the validity of breast, lung and colorectal cancer codes in administrative data. The following inclusion criteria will be used: (1) the presence of a reference standard case definition for the disease of interest; (2) the presence of at least one test measure (eg, sensitivity, positive predictive values, etc) and (3) the use of data source from an administrative database. Pairs of reviewers will independently abstract data using standardised forms and will assess quality using a checklist based on the Standards for Reporting of Diagnostic accuracy (STARD) criteria.Ethics approval is not required. We will submit results of this study to a peer-reviewed journal for publication. The results will serve as a guide to identify appropriate case definitions and algorithms of breast, lung and colorectal cancers for researchers involved in validating administrative healthcare databases as well as for outcome research on these conditions that used administrative healthcare databases.CRD42015026881.

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:Background and aimColorectal cancer (CRC) accounts for over 8% of all deaths each year, with 1.2 million new cases diagnosed annually worldwide. It represents the seventh most common cancer in Egypt. Early detection of peritoneal metastasis is a major challenge in such cases. It helps with the decision of the immediate application of intraperitoneal chemotherapy after resection. Meta-analysis studies reported contrast evidence for a possible prognostic role of intraperitoneal free cancer cells (IPCCs) in peritoneal recurrence and survival after curative resection. In this work, we aim to evaluate the prevalence and impact of detecting free malignant cells in peritoneal fluid on survival and local recurrence and to estimate the incidence of peritoneal carcinomatosis (PC) during follow up.MethodsDesign: This was a prospective cohort study. Settings: From June 2016 to December 2018, samples were collected from 104 patients who underwent abdominal surgery for colorectal cancer in the Egyptian National Cancer Institute. A total of 96 Egyptian CRC patients who underwent curative resection were enrolled. Intraoperative peritoneal lavage was performed to detect IPCC by conventional cytology. Patients with no residual tumor after surgery and no evidence of PC were followed up for a median 14 months. The cumulative 12-month overall survival rate for patients with IPCC was 100% versus 86% for patients with negative cytology.ResultsOur results demonstrated that the prevalence of IPCC in the peritoneal lavage was 11.5%. Peritoneal and local recurrence occurred at a higher rate in patients with cytology positive lavage (9.1% vs 6.3% and 9.1% vs 3.8%, respectively), although this was statistically insignificant. Distant metastasis occurred significantly in patients with positive cytology (45.5% vs 8.9%) with P-value <0.001.The conventional cytology technique has a high specificity but less sensitivity.ConclusionsThe presence of IPCC using conventional cytology was not an independent prognostic factor for the development of PC or survival.

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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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