Development of prostate cancer research database with the clinical data warehouse technology for direct linkage with electronic medical record system

Patient Identification and Tumor Identification Management: Quality Program in a Cancer Multicentric Clinical Data Warehouse

Background

The Regional Basis of Solid Tumor (RBST), a clinical data warehouse, centralizes information related to cancer patient care in 5 health establishments in 2 French departments.

Purpose

To develop algorithms matching heterogeneous data to "real" patients and "real" tumors with respect to patient identification (PI) and tumor identification (TI).

Methods

A graph database programed in java Neo4j was used to build the RBST with data from ~20 000 patients. The PI algorithm using the Levenshtein distance was based on the regulatory criteria identifying a patient. A TI algorithm was built on 6 characteristics: tumor location and laterality, date of diagnosis, histology, primary and metastatic status. Given the heterogeneous nature and semantics of the collected data, the creation of repositories (organ, synonym, and histology repositories) was required. The TI algorithm used the Dice coefficient to match tumors.

Results

Patients matched if there was complete agreement of the given name, surname, sex, and date/month/year of birth. These parameters were assigned weights of 28%, 28%, 21%, and 23% (with 18% for year, 2.5% for month, and 2.5% for day), respectively. The algorithm had a sensitivity of 99.69% (95% confidence interval [CI] [98.89%, 99.96%]) and a specificity of 100% (95% CI [99.72%, 100%]). The TI algorithm used repositories, weights were assigned to the diagnosis date and associated organ (37.5% and 37.5%, respectively), laterality (16%) histology (5%), and metastatic status (4%). This algorithm had a sensitivity of 71% (95% CI [62.68%, 78.25%]) and a specificity of 100% (95% CI [94.31%, 100%]).

Conclusion

The RBST encompasses 2 quality controls: PI and TI. It facilitates the implementation of transversal structuring and assessments of the performance of the provided care.

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center

PURPOSE

This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system.

METHODS

Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine.

RESULTS

The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95.

CONCLUSION

This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Background

Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.

Objective

The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.

Methods

We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.

Results

Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).

Conclusions

IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data

Objectives

Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data.

Materials and Methods

We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients.

Results

There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870.

Discussion and Conclusion

To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.

The Korea Cancer Big Data Platform (K-CBP) for Cancer Research

Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. Data were collected from electronic medical records, genomic sequences, tumor biopsy specimens, and national cancer control initiative databases in the National Cancer Center (NCC), Korea. Data were de-identified and stored in a safe and independent space. Unstructured clinical data were standardized and incorporated into cancer registries and linked to cancer genome sequences and tumor biopsy specimens. Finally, national cancer control initiative data from the public domain were independently organized and linked to cancer registries. We constructed a system for integrating and providing various cancer data called the Korea Cancer Big Data Platform (K-CBP). Although the K-CBP could be used for cancer research, the legal and regulatory aspects of data distribution and usage need to be addressed first. Nonetheless, the system will continue collecting data from cancer-related resources that will hopefully facilitate precision-based research.

Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer

Background

Electronic health record (EHR) based research in oncology can be limited by missing data and a lack of structured data elements. Clinical research data warehouses for specific cancer types can enable the creation of more robust research cohorts.

Methods

We linked data from the Stanford University EHR with the Stanford Cancer Institute Research Database (SCIRDB) and the California Cancer Registry (CCR) to create a research data warehouse for prostate cancer. The database was supplemented with information from clinical trials, natural language processing of clinical notes and surveys on patient-reported outcomes.

Results

11,898 unique prostate cancer patients were identified in the Stanford EHR, of which 3,936 were matched to the Stanford cancer registry and 6153 in the CCR. 7158 patients with EHR data and at least one of SCIRDB and CCR data were initially included in the warehouse.

Conclusions

A disease-specific clinical research data warehouse combining multiple data sources can facilitate secondary data use and enhance observational research in oncology.

Using Electronic Health Records to Build an Ophthalmologic Data Warehouse and Visualize Patients' Data

PURPOSE

To develop a near-real-time data warehouse (DW) in an academic ophthalmologic center to gain scientific use of increasing digital data from electronic medical records (EMR) and diagnostic devices.

DESIGN

Database development.

METHODS

Specific macular clinic user interfaces within the institutional hospital information system were created. Orders for imaging modalities were sent by an EMR-linked picture-archiving and communications system to the respective devices. All data of 325 767 patients since 2002 were gathered in a DW running on an SQL database. A data discovery tool was developed. An exemplary search for patients with age-related macular degeneration, performed cataract surgery, and at least 10 intravitreal (excluding bevacizumab) injections was conducted.

RESULTS

Data related to those patients (3 142 204 diagnoses [including diagnoses from other fields of medicine], 720 721 procedures [eg, surgery], and 45 416 intravitreal injections) were stored, including 81 274 optical coherence tomography measurements. A web-based browsing tool was successfully developed for data visualization and filtering data by several linked criteria, for example, minimum number of intravitreal injections of a specific drug and visual acuity interval. The exemplary search identified 450 patients with 516 eyes meeting all criteria.

CONCLUSIONS

A DW was successfully implemented in an ophthalmologic academic environment to support and facilitate research by using increasing EMR and measurement data. The identification of eligible patients for studies was simplified. In future, software for decision support can be developed based on the DW and its structured data. The improved classification of diseases and semiautomatic validation of data via machine learning are warranted.

Development of Clinical Data Mart of HMG-CoA Reductase Inhibitor for Varied Clinical Research

BACKGROUND

The increasing use of electronic medical record (EMR) systems for documenting clinical medical data has led to EMR data being increasingly accessed for clinical trials. In this study, a database of patients who were prescribed statins for the first time was developed using EMR data. A clinical data mart (CDM) was developed for cohort study researchers.

METHODS

Seoul St. Mary's Hospital implemented a clinical data warehouse (CDW) of data for ~2.8 million patients, 47 million prescription events, and laboratory results for 150 million cases. We developed a research database from a subset of the data on the basis of a study protocol. Data for patients who were prescribed a statin for the first time (between the period from January 1, 2009 to December 31, 2015), including personal data, laboratory data, diagnoses, and medications, were extracted.

RESULTS

We extracted initial clinical data of statin from a CDW that was established to support clinical studies; the data was refined through a data quality management process. Data for 21,368 patients who were prescribed statins for the first time were extracted. We extracted data every 3 months for a period of 1 year. A total of 17 different statins were extracted. It was found that statins were first prescribed by the endocrinology department in most cases (69%, 14,865/21,368).

CONCLUSION

Study researchers can use our CDM for statins. Our EMR data for statins is useful for investigating the effectiveness of treatments and exploring new information on statins. Using EMR is advantageous for compiling an adequate study cohort in a short period.

Improved Prediction of the Pathologic Stage of Patient With Prostate Cancer Using the CART–PSO Optimization Analysis in the Korean Population

Objective:

In current practice, medical experts use the pathological stage predictions provided in the Partin tables to support their decisions. Hence, the Partin tables are based on logistic regression built from the US data. In the present study, we developed a data-mining model to predict the pathologic stage of prostate cancer. In this newly developed model, using the classification and regression tree-particle swarm optimization analysis of the Korean population data, we aim to improve the prediction accuracy of the pathologic state of prostate cancer.

Method:

A total of 467 patients from the smart prostate cancer database were evaluated. The results were intended to predict the pathologic stage of prostate cancer: organ-confined disease and non–organ-confined disease. The accuracy of 4 classification and regression tree-particle swarm optimization models was compared; furthermore, the models were validated with the Partin tables using the receiver operating characteristic curve.

Results:

Among the 467 evaluated patients, 235 patients had organ-confined disease and 232 patients had non–organ-confined disease. The area under the receiver operating characteristic curve of the proposed classification and regression tree-particle swarm optimization model (0.858 ± 0.034) was larger than the 1 in the Partin tables (0.666 ± 0.046).

Conclusion:

The proposed classification and regression tree-particle swarm optimization model was superior to the Partin tables in terms of predicting the risk of prostate cancer. Compared to the validation of the Partin tables for the Korean population, the classification and regression tree-particle swarm optimization model resulted in a larger receiver operating characteristic curve and a more accurate prediction of the pathologic stage of prostate cancer in the Korean population.

The Role of Clinical Virology Laboratory and the Clinical Virology Laboratorian in Ensuring Effective Surveillance for Influenza and Other Respiratory Viruses: Points to Consider and Pitfalls to Avoid

Influenza and respiratory viruses have a global impact on public health. Clinical virology laboratories and laboratorians play an important role in not only the diagnosis but also the surveillance of these pathogens. Surveillance for influenza and other respiratory pathogens is important, as it informs public health decision making in terms of influenza vaccine and antiviral effectiveness, informs clinicians and public health practitioners about the pathogenicity of specific viral strains, guides clinical practice, and supports laboratory panning activities. Key background issues include the following: the fact that the laboratory is only one of several data providers to a surveillance system, the biologic nature of influenza and respiratory viruses and the laboratory needs to keep up to date on the diagnosis of these agents, the need for laboratorians to be involved in case definition development, the impact of push and pull data flow models on laboratory resources, and the fact that laboratories may be asked to provide more than just test results to surveillance programs. This review also identifies some key issues or questions that arise during the pre-analytic, analytic, and post-analytic phases that could impact on the ability of the laboratory to link to surveillance programs. Finally, issues surrounding virus characterization programs and how they link to surveillance programs are identified and discussed.

Is lymphovascular invasion a powerful predictor for biochemical recurrence in pT3 N0 prostate cancer? Results from the K-CaP database

To assess the impact of lymphovascular invasion (LVI) on the risk of biochemical recurrence (BCR) in pT3 N0 prostate cancer, clinical data were extracted from 1,622 patients with pT3 N0 prostate cancer from the K-CaP database. Patients with neoadjuvant androgen deprivation therapy (n = 325) or insufficient pathologic or follow-up data (n = 87) were excluded. The primary endpoint was the oncologic importance of LVI, and the secondary endpoint was the hierarchical relationships for estimating BCR between the evaluated variables. LVI was noted in 260 patients (21.5%) and was significantly associated with other adverse clinicopathologic features. In the multivariate Cox regression analysis, LVI was significantly associated with an increased risk of BCR after adjusting for known prognostic factors. In the Bayesian belief network analysis, LVI and pathologic Gleason score were found to be first-degree associates of BCR, whereas prostate-specific antigen (PSA) level, seminal vesicle invasion, perineural invasion, and high-grade prostatic intraepithelial neoplasia were considered second-degree associates. In the random survival forest, pathologic Gleason score, LVI, and PSA level were three most important variables in determining BCR of patients with pT3 N0 prostate cancer. In conclusion, LVI is one of the most powerful adverse prognostic factors for BCR in patients with pT3 N0 prostate cancer.

Effect of Nerve-Sparing Radical Prostatectomy on Urinary Continence in Patients With Preoperative Erectile Dysfunction

Purpose:

We aimed to assess whether nerve-sparing radical prostatectomy (nsRP) is associated with improved recovery of urinary continence compared to non–nerve-sparing radical prostatectomy (nnsRP) in patients with localized prostate cancer and preoperative erectile dysfunction.

Methods:

A total of 360 patients with organ-confined prostate cancer and an International Index of Erectile Function score of less than 17 were treated with nsRP or nnsRP in Seoul St. Mary’s Hospital. Patients who received neoadjuvant or adjuvant androgen deprivation therapy or had a history of prostate-related surgery were excluded. Recovery of urinary continence was assessed at 0, 1, 3, 6, and 12 months. Postoperative recovery of continence was defined as zero pad usage. The association between nerve-sparing status and urinary continence was assessed by using univariate and multivariate Cox regression analyses after controlling for known predictive factors.

Results:

Urinary continence recovered in 279 patients (77.5%) within the mean follow-up period of 22.5 months (range, 6–123 months). Recovery of urinary continence was reported in 74.6% and 86.4% of patients after nnsRP and nsRP, respectively, at 12 months (P=0.022). All groups had comparable perioperative criteria and had no significant preoperative morbidities. Age, American Society of Anesthesiologists score, and nerve-sparing status were significantly associated with recovery of urinary continence on univariate analysis. On multivariate analysis, age (hazard ratio [HR], 1.254; 95% confidence interval [CI], 1.002–1.478; P=0.026) and nerve-sparing status (HR, 0.713; 95% CI, 0.548–0.929; P=0.012) were independently associated with recovery of urinary continence.

Conclusions:

nsRP, as compared to nnsRP, improves recovery rates of urinary incontinence and decreases surgical morbidity without compromising pathologic outcomes.

Development of South Australian-Victorian Prostate Cancer Health Outcomes Research Dataset

BACKGROUND

Prostate cancer is the most commonly diagnosed and prevalent malignancy reported to Australian cancer registries, with numerous studies from single institutions summarizing patient outcomes at individual hospitals or States. In order to provide an overview of patterns of care of men with prostate cancer across multiple institutions in Australia, a specialized dataset was developed. This dataset, containing amalgamated data from South Australian and Victorian prostate cancer registries, is called the South Australian-Victorian Prostate Cancer Health Outcomes Research Dataset (SA-VIC PCHORD).

RESULTS

A total of 13,598 de-identified records of men with prostate cancer diagnosed and consented between 2008 and 2013 in South Australia and Victoria were merged into the SA-VIC PCHORD. SA-VIC PCHORD contains detailed information about socio-demographic, diagnostic and treatment characteristics of patients with prostate cancer in South Australia and Victoria. Data from individual registries are available to researchers and can be accessed under individual data access policies in each State.

CONCLUSIONS

The SA-VIC PCHORD will be used for numerous studies summarizing trends in diagnostic characteristics, survival and patterns of care in men with prostate cancer in Victoria and South Australia. It is expected that in the future the SA-VIC PCHORD will become a principal component of the recently developed bi-national Australian and New Zealand Prostate Cancer Outcomes Registry to collect and report patterns of care and standardised patient reported outcome measures of men nation-wide in Australia and New Zealand.

Development of a large urban longitudinal HIV clinical cohort using a web-based platform to merge electronically and manually abstracted data from disparate medical record systems: technical challenges and innovative solutions

OBJECTIVE

Electronic medical records (EMRs) are being increasingly utilized to conduct clinical and epidemiologic research in numerous fields. To monitor and improve care of HIV-infected patients in Washington, DC, one of the most severely affected urban areas in the United States, we developed a city-wide database across 13 clinical sites using electronic data abstraction and manual data entry from EMRs.

MATERIALS AND METHODS

To develop this unique longitudinal cohort, a web-based electronic data capture system (Discovere®) was used. An Agile software development methodology was implemented across multiple EMR platforms. Clinical informatics staff worked with information technology specialists from each site to abstract data electronically from each respective site's EMR through an extract, transform, and load process.

RESULTS

Since enrollment began in 2011, more than 7000 patients have been enrolled, with longitudinal clinical data available on all patients. Data sets are produced for scientific analyses on a quarterly basis, and benchmarking reports are generated semi-annually enabling each site to compare their participants' clinical status, treatments, and outcomes to the aggregated summaries from all other sites.

DISCUSSION

Numerous technical challenges were identified and innovative solutions developed to ensure the successful implementation of the DC Cohort. Central to the success of this project was the broad collaboration established between government, academia, clinics, community, information technology staff, and the patients themselves.

CONCLUSIONS

Our experiences may have practical implications for researchers who seek to merge data from diverse clinical databases, and are applicable to the study of health-related issues beyond HIV.

Prostate-specific antigen kinetics after primary stereotactic body radiation therapy using CyberKnife for localized prostate cancer

PURPOSE

To assess prostate-specific antigen (PSA) kinetics and report on the oncologic outcomes for patients with localized prostate cancer treated with stereotactic body radiation therapy (SBRT) using CyberKnife.

METHODS

We extracted the list and data of 39 patients with clinically localized prostate cancer who had undergone primary SBRT using CyberKnife between January 2008 and December 2012 from the Smart Prostate Cancer database system of Seoul St. Mary's Hospital. Changes in PSA over time, PSA velocity, and PSA nadir were evaluated from the completion of SBRT using CyberKnife. Biochemical recurrence (BCR)-free survival after primary SBRT using CyberKnife was determined using Kaplan-Meier analysis.

RESULTS

The rate of PSA decrease was maximal in the first month (median -3.34 ng/mL/mo), which then fell gradually with median values of -1.51, -0.32, -0.28, -0.20, and -0.03 ng/mL/mo for durations of 3, 6, 9, 12, and 24 months after SBRT using CyberKnife, respectively. The median PSA nadir was 0.31 ng/mL after a median 23 months. Kaplan-Meier analysis calculates an actuarial 5-year BCR-free survival after SBRT using CyberKnife as 80.8%.

CONCLUSIONS

PSA decline occurred rapidly in the first month, and then the rate of PSA decline fell off steadily over time throughout 2 years after treatment. Also, SBRT using CyberKnife leads to long-term favorable BCR-free survival in localized prostate cancer.