bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deriving real-world oral antineoplastic treatment endpoints: A comparison of automated data extraction versus manual chart abstraction.
Authors: Chen James Lin , Shadra Chhaya , Cho-Phan Cheryl D. , Ghosh Aradhana , Hirsch Jonathan 
Published: May 2019

e18070 Background: Real world data (RWD) is increasingly being used to generate evidence that informs clinical care. Calculating outcomes measures using RWD, such as time-on-treatment (ToT), requires accurate medication start/end dates. Whereas intravenously administered medication dates are typically available, dates for oral antineoplastics (OANs) are challenging as they are filled by specialty pharmacies and documented separately. To determine the feasibility of automated ToT extraction, we used the Syapse Learning Health Network to compare the utility of automated chart extraction (ACE) and of manual chart abstraction (MCA) from the electronic health record (EHR) in providing high-quality OAN data. Methods: We selected cancer patients (pts) from two histologies for whom OANs were prescribed. For these pts, ACE was compared to MCA. ACE data were derived from EHR structured medication lists through existing interoperability pipelines. MCA was performed by 4 trained data abstractors who reviewed the corresponding progress notes per pt. Expected date of OAN start/end, and actual medication start/end, were recorded as available. Chi-square and descriptive statistics were used for analysis. Results: 61 cancer pts (31 lung, 30 breast; mean age 62y [27-90]; 20% male) who received OAN from 3 multi-hospital medical systems were evaluated. ACE detected a greater number of expected start/end dates as compared to MCA (Table, P < 0.001). In contrast, for actual start/end dates, the converse was true; ACE was inferior to MCA (P < 0.001). Noteworthy, only 26% of pts had actual start/end dates using manual chart abstraction only. There was no concordance between the actual ACE and MCA dates. We evaluated if ACE expected ToT dates could be used as a surrogate for actual MCA ToT dates. Of the 12 pts satisfying this criteria, mean discordance in ToT was 19 days (1-71 days). Conclusions: MCA only captured actual ToT in a small minority of pts. ACE expected dates were a poor surrogate for ToT and highly discordant to over 2 months. Neither MCA nor ACE from EHR data were adequate for the majority of pts. Alternative mechanisms such as integration with additional data sources like specialty pharmacy dispensing records are essential. [Table: see text]
Toward early diagnosis decision support for breast cancer: Ontology-based semantic interoperability.
Authors: Gong Mengchun , Wang Zhi , Liu Yefu , Zhou Hong , Wang Fang , Wang Yishang , Hong Na 
Published: May 2019

e18072 Background: Breast cancer is ranked as the most common cancer among women in China, cases in China account for about 300,000 of all newly diagnosed breast cancers each year. Developing Clinical Decision Supporting System (CDSS) to assist early diagnosis breast cancer is valuable, however, lack of semantic interoperability in CDSS has been identified as the main obstacle for broad adoption of CDSS. Ontology is considered to be one of the effective approaches to bridge the terminology gap between various clinical systems and data sources. In this study, we describe our efforts in transforming breast cancer clinical knowledge into computable ontology to support breast cancer early diagnosis. Methods: We have built a breast cancer ontology (BCO) to support our CDSS pipeline. A number of NCCN clinical practice guidelines in Oncology for Breast Cancer and the breast cancer diagnosis and treatment guidelines of China are the main knowledge sources of the BCO. BCO is a manually curated resource that contains concepts and relations of breast cancer clinical findings, demographics, laboratory tests, imaging results, treatments, pathologies, cancer stages, body structure and follow-up information. These curated knowledges are annotated through interoperable standard vocabulary SNOMED CT. Two physicians reviewed and evaluated the BCO. Results: Concepts in BCO currently contains 79 clinical findings, 8 demographics, 42 laboratory tests, 105 imaging results, 332 treatments, 141 pathology and cancer stages, 30 body structures and 3 follow-ups. Relations are defined such as finding site, associated with, etc. The concepts hierarchies and relationships were built by using Protégé to support OWL representation. As an initial evaluation results, most of concepts could be mapped to SNOMED CT, but there are some concepts could not be exactly mapped to SNOMED CT. We created local Chinese Vocabularies for these local terms. Conclusions: Based on interoperable BCO, a group of computable rules are developed by adding statements according to early diagnosis criteria of breast cancer. Diagnosis assistance around complex patients is supported though connecting the Electronic Health Record (EHR). This is an ongoing development that the BCO is continuingly enriched with standard concepts, relations and integrate with individual instances to support broader adoptability.
Patient-reported outcomes during hospitalization for patients with late discharge after an open surgery for GYN tumors.
Authors: Wang Xin Shelley , Shi Qiuling , Cleeland Charles S. , Chen Ting-Yu , Garcia-Gonzalez Araceli , Iniesta-Donate Maria , Chen Tsun Hsuan , Griffin Donna , Meyer Larissa 
Published: May 2019

e18078 Background: Using patient-reported outcomes (PRO) in perioperative care is increasingly common in patients undergoing major surgery. Few reports relate the patient’s symptomatic status using validated PROs during hospitalization with other outcomes, such as length of stay (LOS). Methods: Patients with GYN tumors scheduled for open surgery at MD Anderson Cancer Center were recruited prospectively. The MD Anderson Symptom Inventory gynecology perioperative care module (MDASI-PeriOp-GYN) was used to assess symptoms daily during hospitalization. Longitudinal analysis compared patient’s PROs between groups who were discharged within and beyond the median LOS. Results: From Feb 2018–Dec 2018, 83 patients were evaluable. The median LOS was 3 days. 70% of patients were discharged within 3 days, and 30% were hospitalized longer (defined as late discharge group, range 4-10 days). The median LOS for the two groups was 5.64 vs. 2.41 days, P < .0001. Compared to patients who were discharged within 3 days, late discharge patients had longer surgical time (310 min vs. 209 min, P < .0001), and greater operative blood loss (668mL vs. 289 mL, P < .001). Additionally, more patients in the late discharge group had a worse performance status (ECOG PS 2-3: 24% vs. 3.5%, P < .05), and higher comorbidity (Charlson comorbidity Index > 1: 92% vs. 69%, P < .05), and more used opioids within 6 month pre-surgery (16% vs. 1.7%, P < .05). For all patients, the 5 worst symptoms during the first 3 hospital days were pain, fatigue, drowsiness, dry mouth, bloating. Late discharge patients reported significant more severe pain, distress, dry mouth, and interference with walking during the first 3 days post-surgery (all P < .05). Conclusions: Despite all patients undergoing standardized perioperative care on an Enhanced Recovery pathway, this study demonstrates how baseline and intraoperative factors are associated with longer LOS and more severe symptom burden, especially pain and interference with walking.
Quantifying interoperability: An analysis of oncology practice electronic health record data variability.
Authors: Bernstam Elmer Victor , Warner Jeremy Lyle , Krauss John C. , Ambinder Edward P. , Rubinstein Wendy S. , Komatsoulis George Anthony , Miller Robert S. , Chen James Lin 
Published: May 2019

e18080 Background: Implementation of electronic health records (EHRs) has engendered a large quantity of machine-readable data. However, different practices choose different EHR vendors and the same vendor product may be implemented differently at each practice. Motivated by the desire to facilitate appropriate integration of data, our goal was to describe and quantify the consistency and variation of structured data within EHRs. Methods: De-identified and aggregated CancerLinQ data from 47 practices regarding the standards and variability of structured data including race, diagnoses, encounters, cancer staging, selected cancer-relevant medications, lab values and biomarkers were analyzed. EHR represented included ARIA, MOSAIQ, Allscripts, Centricity, Epic, Intellidose, NextGen, and OncoEMR. Results: De-identified EHR implementations included 23 A, 12 B, C and 5 other vendors. Only 6 practices (13%) used non-standard race representation. All practices used ICD-9/10 for diagnoses. There was variability in coding of encounters. Sixteen practices always used CPT, 5 practices always used SNOMED CT and 26 practices used multiple standards. Multiple staging systems were used. An average of 48% (range 11%-; including patients staged more than once) of patient records included coded staging information. Only one practice used a standard (LOINC) for laboratory data. No standards were used for medications ordered/administered or biomarkers. The table shows the number of distinct names for selected lab tests, medications and biomarkers across systems. Conclusions: In this cross-sectional sample, standards are used consistently for diagnoses and encounter data, often for race and rarely for medications, laboratory tests or biomarkers.[Table: see text]
RDI as a method for reviewer performance monitoring in BICR setup for improving data quality.
Authors: Sharma Manish , Bohnsack Oliver , O'Connor Michael , Shao Yibin , Enus Nicholas , Karve Sayali , Fotinos-Hoyer A. Kassel 
Published: May 2019

e18082 Background: Double read with adjudication is a preferred independent review model of regulatory authorities for blinded independent central review (BICR) in order to minimize reviewer bias in clinical trials. Adjudication rate (AR) and adjudication agreement rate (AAR) are commonly used indicators for monitoring independent reviewer performance quality. We will present data on Reader Disagreement Index (RDI), an innovative indicator for accurately monitoring reviewer performance and triggering timely intervention when applicable. Methods: A detailed review of BICR adjudication data was performed for 12 oncology clinical trials, with a total of 5,369 subjects (ranging from 119 to 894 per individual study) with 27,056 time points using RECIST, the Lugano classification or iwCLL assessment criteria. RDI for each reviewer was calculated as RDI = (# of cases where adjudicator disagreed with given reviewer ÷ Total # of all cases read) × 100, with high RDI indicating high % disagreement. RDI was used to identify the discordant reader (i.e. reviewer with the highest level of cases disagreed with by the adjudicator) when approximately 10% of the total reads were completed for each study. RDI was also calculated and compared with AR and AAR on an ongoing basis throughout the study. Mean RDI + standard deviation (SD) were used to identify outlier readers. Results: RDI reliably identified the most discordant reader consistently across all 12 studies, while AR & AAR did not. The results confirm the advantage of RDI as a lagging and leading indicator for independent reviewer performance across indications and criteria using double read with adjudication review model. RDI, when calculated as early as at the 10% of total reviewed cases benchmark, demonstrated a positive predictive value of 91% and negative predictive value of 93% (Sensitivity 71%; Specificity 98%). Conclusions: Early identification of an outlier reviewer as per RDI (i.e. after reviews completed for ~ 10% study visits), followed by detailed analysis and corrective measures, such as retraining of the reviewer can serve as timely intervention to improve review quality. Thus, RDI proves to be a better indicator for not just monitoring reviewer performance, but also as an excellent tool for triggering timely corrective intervention. [Table: see text]
Creating the real-world medical record: Using digital patient-generated data to create an updated picture of patients outcomes.
Authors: Bivas-Benita Maytal , Deutsch Irad , Malki Eliran , Hasid Lior 
Published: May 2019

e18092 Background: Value-based healthcare requires integration of real-world data (RWD) generated by patients in parallel to clinical documentation. This gives a holistic perspective of the patients’ health and care and could lead to reduced costs and better patient care and safety. Belong digital patient-powered network (PPN) is a social network for cancer patients and caregivers providing them access to other patients, healthcare professionals and disease management tools. In this study we compared the clinical data generated by Belong users with classical clinical documentation. Furthermore we explored the additional information generated in the PPN and its value. Methods: RWD collected from users of the Belong PPN was retrospectively analyzed. We evaluated patient-reported outcomes in parallel to medical records provided voluntarily and anonymously. Data of patient journeys was extracted and mapped from medical records and social engagement in our platform using artificial intelligence (AI) engines. Data was further validated manually by the research team to assure accuracy. Results: We compared 40 treatment journeys reported by patients in Belong to their doctor summary. The data showed that patients can accurately describe their cancer diagnosis and treatment, mentioning 85% and 90% of their diagnosis and treatment, respectively. In 38% of the cases, patients’ engagement data provided additional insights in the categories of OTC drugs, supplements, diet, alternative treatments, lifestyle changes and emotional health. Complementary information from patients with possible clinical consequences (changes in sugar levels, blood pressure, body minerals levels etc.) was also reported and could affect patients’ treatment and well-being. Conclusions: RWD collected in the Belong PPN recreated a comparable clinical description of standard medical documents and provided additional insights that created a more comprehensive picture of patients’ health. Superimposing the social data layer onto the clinical one could provide significant advancement of how we treat patients and result in better cost-efficiency, improved healthcare services and patients’ outcomes.
Harmonization of clinical data across Gen3 data commons.
Authors: Hughes LaRon , Grossman Robert L. , Flamig Zachary , Prokhorenkov Andrew , Lukowski Michael , Fitzsimons Michael , Lichtenberg Tara , Tang Yajing 
Published: May 2019

e18094 Background: Gen3 is an open source software platform for developing and operating data commons. Gen3 systems are now used by a variety of institutions and agencies to share and analyze large biomedical datasets including clinical and genomic data. One of the challenges of working with these datasets is disparate clinical data standards used by researchers across different studies and fields. We have worked to address these hurdles in a variety of ways. Methods: Gen3 is an open source software platform for developing and operating data commons. Detailed specification and features can be found at https://gen3.org/ with code located on GitHub ( https://github.com/UC-cdis ). Results: The Gen3 data model is a graphical representation of the different nodes or classes of data that have been collected. Examples include diagnosis, demographic, exposure, and family history. The properties and values on each node are controlled by the data dictionary specified by the data commons creator. While each commons may have a unique data model and dictionary, specifying external standards allows for easier submission of new data and assists data consumers with interpretation of results. A variety of external references can be supported, but here we demonstrate the use of the National Cancer Institute Thesaurus (NCIt). NCIt provides reference terminologies and biomedical standards that contain a rich set of terms, codes, definitions, and concepts. Using the same reference standards across commons allows for the export of clinical data between commons. The Portable Format for Biomedical Data (PFB) was created to facilitate data export and to allow the data dictionary schema as well as the raw data to be compressed and exported. This new file format, which utilizes an Avro serialization, is small, fast, easy to modify, and enables simple data export and import. PFB also has the ability to house entire external reference ontologies and it is easy to update the PFB references as changes are introduced. Conclusions: We have shown here how the Gen3 data model, use of external reference standards for clinical data, and the export/import format of PFB enable the harmonization of clinical data across different data commons.
An algorithmic approach to deriving line of therapy in a real-world data set for non-small cell lung cancer (NSCLC).
Authors: Shadra Chhaya , Chen James Lin , Cho-Phan Cheryl D. , Ghosh Aradhana , Hirsch Jonathan 
Published: May 2019

e18099 Background: Real World Data (RWD) is being used for outcomes research and regulatory submissions. A key variable needed to understand treatment outcomes is Line of Therapy (LoT). However, LoT is generally not captured in RWD sources such as electronic health records (EHR) or claims data, and is typically derived using manual abstraction. To determine whether an automated approach to LoT derivation is possible, we created an algorithm and applied it to patients (pts) in the Syapse Learning Health Network. Methods: We selected confirmed NSCLC pts from 4 health systems in the RWD set, verifying diagnosis using ICD-9/10/O3 topography and morphology codes. We analyzed the EHR-derived medication list using a regimen-independent algorithm that classified antineoplastic drugs (AD), as defined by ATC L01, into LoT. Within each LoT, we compared the top 80% of AD prescribed (by volume of pts) to the LoT as indicated on each drug’s FDA label. We then used descriptive statistical summaries to outline the alignment between automated algorithmic results and indicated usage within that LoT. Results: In a set of 10,842 NSCLC pts, a total of 106 unique AD were prescribed in the first line as identified by our algorithm, and 13 drugs were prescribed as first line for 80% of the pts. Of those, 9 (69%) of those are indicated for first line, 3 are not indicated for NSCLC, and 1 is indicated for a subsequent NSCLC line, per FDA labels. 82 unique AD were prescribed in the second line as identified by our algorithm, and 15 drugs were prescribed as second line for 80% of the pts. Of those, 11 (73%) are indicated for treatment/continuation therapy for recurrent, advanced or metastatic disease, 3 are not indicated for NSCLC, and 1 is indicated for first line NSCLC per FDA labels. 36 unique AD were prescribed in subsequent line as identified by our algorithm, and 18 drugs were prescribed as subsequent line for 80% of the pts. Of those, 12 (67%) are indicated for treatment of recurrent, advanced or metastatic disease or subsequent systemic therapy, 5 are not indicated for NSCLC and 1 is indicated for first line per FDA labels. Conclusions: An automated algorithmic approach for deriving lines of therapy may be a viable solution to scalably calculate LoT in RWD sets. A deeper analysis using statistical sensitivity and specificity assessment of such algorithms is needed to validate the potential of an algorithmic approach.
Rates and patterns of uninsured cancer survivors before and after implementation of the Affordable Care Act, 2000-2017.
Authors: Sanford Nina Niu , Lam Miranda , Sher David J. , Butler Santino , Ahn Chul , Aizer Ayal Aaron , Mahal Brandon Arvin Virgil 
Published: May 2019

e18105 Background: Cancer survivors experience difficulties in maintaining healthcare coverage, however the reasons and risk factors for lack of insurance are poorly defined. We sought to assess self-reported reasons for not having insurance and to assess demographic and socioeconomic factors associated with non-insurance among cancer survivors, before and after implementation of the Affordable Care Act (ACA) in 2014. Methods: We used the National Health Interview Survey to identify adult participants (18-64 years) reporting a cancer diagnosis between 2000-2017. Multivariable logistic regression was used to define the association between demographic and socioeconomic variables and odds being uninsured. The prevalence of the most common self-reported reasons for not having insurance (unemployment, employment-related reason, family-related) were estimated, with AORs for each of the reasons defined by multivariable logistic regression. Results: Among 17,806 survey participants, 10.3% reported not having health insurance. Individuals surveyed in 2000-2013 had higher odds of not having insurance as compared to those surveyed in 2014-2017 (10.6% vs. 6.2%, AOR 1.75, 95% CI 1.49-2.08). Variables associated with higher odds of non-insurance throughout the entire study interval included younger age, annual family income below the poverty threshold, black race, Hispanic ethnicity, non-citizen status and current smoking (p < 0.001 for all). After implementation of the ACA, increasing interval from cancer diagnosis and black race were no longer associated with not having insurance. The most commonly cited reason for not having insurance were cost followed by unemployment, both of which decreased after ACA implementation (cost: 49.6% vs. 37.6%, AOR 0.62, 95% CI 0.46-0.85; unemployment: 37.1% vs. 28.5%, AOR 0.62, 95% CI 0.45-0.87). Conclusions: The proportion of uninsured cancer survivors decreased after implementation of the ACA, however certain subgroups remain at greater risk of being uninsured. Cost remains the primary barrier to obtaining insurance, although more than half of cancer survivors reported other barriers to coverage. Given the growing number of cancer survivors in conjunction with rising health costs, efforts addressing barriers to insurance coverage are needed for this population.