Neck CT imaging and correlation with thyroid cancer incidence across age, gender and race.

OBJECTIVE: Incidental detection of thyroid cancers has been proposed as a cause of thyroid cancer increases over past decades, but few studies assess the impact of imaging utilization on thyroid cancer incidence. This study quantifies neck CT prevalence and its relationship with thyroid cancer incidence as a function of age, sex and race. DESIGN AND PATIENTS: Medical records of over 1 million patients at our institution were retrospectively analysed to quantify neck CT prevalence from 2004 to 2011 (study period). A national cancer database was used to compute thyroid cancer incidences over the study period and a reference period (1974-81) and to calculate change in thyroid incidence between the two periods. Both populations were partitioned into demographic subgroups of varying age, sex and race. Linear correlation between neck imaging and thyroid cancer incidence changes among subgroups was assessed using Pearson's correlation. RESULTS: Neck CT imaging and change in thyroid cancer incidence varied across all examined demographic variables, particularly age. When stratifying by age, CT use correlated strongly with recent national thyroid cancer incidence (R = .97) and with 30-year change in thyroid cancer incidence (R = .87). Across all demographic subgroups, CT prevalence correlated strongly and positively with change in thyroid cancer incidence (R = .60), greater for whites (R = .60) and blacks (R = .70) than other races (R = .28). CONCLUSION: Differences in neck CT usage strongly and positively correlates with the variation in thyroid cancer trends based on age, gender and race.

A knowledge-anchored integrative image search and retrieval system.

Clinical data that may be used in a secondary capacity to support research activities are regularly stored in three significantly different formats: (1) structured, codified data elements; (2) semi-structured or unstructured narrative text; and (3) multi-modal images. In this manuscript, we will describe the design of a computational system that is intended to support the ontology-anchored query and integration of such data types from multiple source systems. Additional features of the described system include (1) the use of Grid services-based electronic data interchange models to enable the use of our system in multi-site settings and (2) the use of a software framework intended to address both potential security and patient confidentiality concerns that arise when transmitting or otherwise manipulating potentially privileged personal health information. We will frame our discussion within the specific experimental context of the concept-oriented query and integration of correlated structured data, narrative text, and images for cancer research.

Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases.

An effective analysis of clinical trials data involves analyzing different types of data such as heterogeneous and high dimensional time series data. The current time series analysis methods generally assume that the series at hand have sufficient length to apply statistical techniques to them. Other ideal case assumptions are that data are collected in equal length intervals, and while comparing time series, the lengths are usually expected to be equal to each other. However, these assumptions are not valid for many real data sets, especially for the clinical trials data sets. An addition, the data sources are different from each other, the data are heterogeneous, and the sensitivity of the experiments varies by the source. Approaches for mining time series data need to be revisited, keeping the wide range of requirements in mind. In this paper, we propose a novel approach for information mining that involves two major steps: applying a data mining algorithm over homogeneous subsets of data, and identifying common or distinct patterns over the information gathered in the first step. Our approach is implemented specifically for heterogeneous and high dimensional time series clinical trials data. Using this framework, we propose a new way of utilizing frequent itemset mining, as well as clustering and declustering techniques with novel distance metrics for measuring similarity between time series data. By clustering the data, we find groups of analytes (substances in blood) that are most strongly correlated. Most of these relationships already known are verified by the clinical panels, and, in addition, we identify novel groups that need further biomedical analysis. A slight modification to our algorithm results an effective declustering of high dimensional time series data, which is then used for "feature selection." Using industry-sponsored clinical trials data sets, we are able to identify a small set of analytes that effectively models the state of normal health.

Pharmacobiologically based scheduling of capecitabine and docetaxel results in antitumor activity in resistant human malignancies.

PURPOSE: Capecitabine and docetaxel have demonstrated preclinical antitumor synergy. This synergy is thought to occur from docetaxel-mediated upregulation of thymidine phosphorylase (dThdPase), an enzyme responsible for the relative tumor selectivity of capecitabine. On the basis of the time-dependency and transiency for this upregulation, we performed a phase I study of capecitabine in combination with weekly docetaxel. We hypothesized that weekly docetaxel would result in sustained dThdPase expression and that capecitabine administration at times of maximum dThdPase upregulation would increase the therapeutic index for this combination. PATIENTS AND METHODS: Patients with advanced solid malignancies received docetaxel on days 1, 8, and 15, and capecitabine bid on days 5 to 18, every 4 weeks. Docetaxel was fixed at 36 mg/m(2)/wk, whereas capecitabine was escalated in successive patients cohorts. RESULTS: Sixteen patients received 77 courses at capecitabine doses from 950 to 1,500 mg/m(2)/d. The most common toxicities were hand-foot syndrome, diarrhea, nausea/vomiting, and asthenia. Grades 3 to 4 hematologic toxicities were infrequent and no treatment-related hospitalizations occurred. Three of three patients treated at 1,500/36 mg/m(2) capecitabine/docetaxel developed grade 3 hand-foot syndrome or diarrhea during either their first or second course, whereas only two of 13 patients at 1,250/36 mg/m(2) doses developed significant toxicity. Antitumor responses (n = 7) occurred in patients with hepatocellular, non-small-cell lung, and chemotherapy-refractory breast, bladder, and colorectal carcinomas. Prolonged stabilizations occurred in patients with metastatic mesothelioma (n = 2), chemorefractory non-small-cell lung carcinoma, and bronchioloalveolar carcinoma. CONCLUSION: Capecitabine in combination with weekly docetaxel is well tolerated. Recommended doses are capecitabine 1,250 mg/m(2)/d (625 mg/m(2) bid) with docetaxel 36 mg/m(2)/wk. The acceptable toxicity profile in this dose schedule, and the antitumor activity observed, warrant further evaluation of this regimen.

Toward a fully de-identified biomedical information warehouse.

The Information Warehouse at the Ohio State University Medical Center is a comprehensive repository of business, clinical, and research data from various source systems. Data collected here is a valuable resource that facilitates both translational research and personalized healthcare. The use of such data in research is governed by federal privacy regulations with oversight by the Institutional Review Board. In 2006, the Information Warehouse was recognized by the OSU IRB as an "Honest Broker" of clinical data, providing investigators with de-identified or limited datasets under stipulations contained in a signed data use agreement. In order to streamline this process even further, the Information Warehouse is developing a de-identified data warehouse that is suitable for direct user access through a controlled query tool that is aimed to support both research and education activities. In this paper we report our findings on performance evaluation of different de-identification schemes that may be used to ensure regulatory compliance while also facilitating practical database updating and querying. We also discuss how date-shifting in the de-identification process can impact other data elements such as diagnosis and procedure codes and consider a possible solution to those problems.

Identification of potential surgical site infections leveraging an enterprise clinical information warehouse.

At The Ohio State University Medical Center (OSUMC), infection control practitioners (ICPs) need an accurate list of patients undergoing defined operative procedures to track surgical site infections. Using data from the OSUMC Information Warehouse (IW), we have created an automated report detailing required data. This report also displays associated surgical and pathology text or dictated reports providing additional information to the ICPs.

Use of synthesized data to support complex ad-hoc queries in an enterprise information warehouse: a diabetes use case.

The Ohio State University Medical Center (OSUMC) Information Warehouse (IW) is a comprehensive data warehousing facility incorporating operational, clinical, and biological data sets from multiple enterprise system. It is common for users of the IW to request complex ad-hoc queries that often require significant intervention by data analyst. In response to this challenge, we have designed a workflow that leverages synthesized data elements to support such queries in an more timely, efficient manner.

Data delivery workflow in an academic information warehouse.

The Ohio State University Medical Center (OSUMC) Information Warehouse (IW) collects data from many systems throughout the OSUMC on load cycles ranging from real-time to on-demand. The data then is prepared for delivery to diversity of customers across the clinical, education, and research sectors of the OSUMC. Some of the data collected at the IW include patient management, billing and finance, procedures, medications, lab results, clinical reports, physician order entry, outcomes, demographics, and so on. This data is made available to the users of the IW in variety of formats and methods.

The design of a pre-encounter clinical trial screening tool: ASAP.

Manually screening patients for clinical trials eligibility prior to their clinical encounters is labor-intensive and time-consuming. In order to increase the efficiency of such processes, we have developed a web-based system, called Advanced Screening for Active Protocols (ASAP).

Innovative applications of an enterprise-wide information warehouse.

The Information Warehouse at The Ohio State University Medical Center is a comprehensive effort integrating data from over 70 sources throughout the enterprise. The IW serves a broad diversity of customers in all mission areas of the medical center, from clinical operations and administration to education, to research. This comprehensiveness has facilitated an innovative application of cross-disciplinary technologies and methodologies to problem domains beyond the roles traditionally envisioned for data warehousing.

Augmenting Oracle Text with the UMLS for enhanced searching of free-text medical reports.

The intrinsic complexity of free-text medical reports imposes great challenges for information retrieval systems. We have developed a prototype search engine for retrieving clinical reports that leverages the powerful indexing and querying capabilities of Oracle Text, and the rich biomedical domain knowledge and semantic structures that are captured in the UMLS Metathesaurus.

ICD9 Code Assistant: A prototype.

At The Ohio State University Medical Center (OSUMC) patient reports are available in real time along with other clinical and financial data in the OSUMC Information Warehouse (IW). Using the UMLS Meta Thesaurus we have leveraged the IW to develop a tool that can assist the medical record coders as well as administrators, physicians and researchers to quickly identify clinical concepts and their associated ICD-9 codes.

An indexing scheme for medical free text searches: a prototype.

At the Ohio State University Medical Center, a significant amount of valuable data pertaining to a patient's visit is stored in the form of dictated reports such as discharge summaries, cardiology reports, and radiology reports. We have implemented conceptual search capability to facilitate more comprehensive content mining from clinical free text.