Electronic Healthcare Record Research Articles

Standards for a New Healthcare World: Towards Nationwide Patient Access

The goal of achieving nationwide patient access is getting closer to reality with the establishment of nationwide electronic healthcare record networks, and with the emergence of TEFCA. Interoperability and privacy standards are critical to achieve efficiency and to provide secure patient access applications. Topics will include current and future developments, including:
 
 CommonWell Health Alliance
 TEFCA
 Specific Functional Use Cases and Applications
 Blockchain Opportunities

Estimating the pertussis burden in adolescents and adults in the United States between 2007 and 2019

ABSTRACT We developed a machine learning algorithm to identify undiagnosed pertussis episodes in adolescent and adult patients with reported acute respiratory disease (ARD) using clinician notes in an electronic healthcare record (EHR) database. Here, we utilized the algorithm to better estimate the overall pertussis incidence within the Optum Humedica clinical repository from 1 January 2007 through 31 December 2019. The incidence of diagnosed pertussis episodes was 1–5 per 100,000 annually, consistent with data registered by the US Centers for Disease Control and Prevention (CDC) over the same time period. Among 18,573,496 ARD episodes assessed, 1,053,946 were identified (i.e. algorithm-identified) as likely undiagnosed pertussis episodes. Accounting for these undiagnosed pertussis episodes increased the estimated pertussis incidence by 110-fold on average (34–474 per 100,000 annually). Risk factors for pertussis episodes (diagnosed and algorithm-identified) included asthma (Odds ratio [OR] 2.14; 2.12–2.16), immunodeficiency (OR 1.85; 1.78–1.91), chronic obstructive pulmonary disease (OR 1.63; 1.61–1.65), obesity (OR 1.44; 1.43–1.45), Crohn’s disease (OR 1.39; 1.33–1.45), diabetes type 1 (OR 1.21; 1.17–1.24) and type 2 (OR 1.12; 1.1–1.13). Of note, all these risk factors, except Crohn’s disease, increased the likelihood of severe pertussis. In conclusion, the incidence of pertussis in the adolescent and adult population in the USA is likely substantial, but considerably under-recognized, highlighting the need for improved clinical awareness of the disease and for improved control strategies in this population. These results will help better inform public health vaccination and booster programs, particularly in those with underlying comorbidities.

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector. Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records. The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks. A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system. This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges. The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner. The efficacy of the proposed framework is evaluated in the context of service execution time and throughput. The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

Context Parameters Retrieval Framework from Electronic Healthcare Record through Biomedical NLP for Clinical Support

Context Parameters Retrieval Framework from Electronic Healthcare Record through Biomedical NLP for Clinical Support

Context parameters retrieval framework from electronic healthcare record through biomedical NLP for clinical support

This paper presents architecture for extracting context features from electronic health records (EHR) to design a clinical support system using transfer learning for natural language processing (NLP). The system is trained to provide a supporting summary to the medical practioners on the basis of the ICD 9 codes and respective symptoms of the patient. The BioALBERT model has been trained over biomedical corpora and the proposed model uses improvised parameter sharing techniques and requires less physical memory. The theoretical analysis of the proposed system is supported by the experimental analysis. MIMIC-III database has been used to fine train the proposed models and to assess the efficiency and efficacy of the proposed work. This study introduced a context-aware approach for extracting useful context from EHR, which can be used to acquire a basic understanding of the treatment path.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Predicting Visit Cost of Obstructive Sleep Apnea Using Electronic Healthcare Records With Transformer.

Obstructive sleep apnea (OSA) is growing increasingly prevalent in many countries as obesity rises. Sufficient, effective treatment of OSA entails high social and financial costs for healthcare. For treatment purposes, predicting OSA patients' visit expenses for the coming year is crucial. Reliable estimates enable healthcare decision-makers to perform careful fiscal management and budget well for effective distribution of resources to hospitals. The challenges created by scarcity of high-quality patient data are exacerbated by the fact that just a third of those data from OSA patients can be used to train analytics models: only OSA patients with more than 365 days of follow-up are relevant for predicting a year's expenditures. The authors propose a translational engineering method applying two Transformer models, one for augmenting the input via data from shorter visit histories and the other predicting the costs by considering both the material thus enriched and cases with more than a year's follow-up. This method effectively adapts state-of-the-art Transformer models to create practical cost prediction solutions that can be implemented in OSA management, potentially enhancing patient care and resource allocation. The two-model solution permits putting the limited body of OSA patient data to productive use. Relative to a single-Transformer solution using only a third of the high-quality patient data, the solution with two models improved the prediction performance's [Formula: see text] from 88.8% to 97.5%. Even using baseline models with the model-augmented data improved the [Formula: see text] considerably, from 61.6% to 81.9%. The proposed method makes prediction with the most of the available high-quality data by carefully exploiting details, which are not directly relevant for answering the question of the next year's likely expenditure. Clinical and Translational Impact Statement: Public Health- Lack of high-quality source data hinders data-driven analytics-based research in healthcare. The paper presents a method that couples data augmentation and prediction in cases of scant healthcare data.

Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission.

A Brief History of Public Health Informatics-Lessons for Leaders and a Look Into the Future.

A Brief History of Public Health Informatics-Lessons for Leaders and a Look Into the Future.

Contemporary Databases in Real-world Studies Regarding the Diverse Health Care Systems of India, Thailand, and Taiwan: Protocol for a Scoping Review.

Real-world data (RWD) related to patient health status or health care delivery can be broadly defined as data collected outside of conventional clinical trials, including those from databases, treatment and disease registries, electronic medical records, insurance claims, and information directly contributed by health care professionals or patients. RWD are used to generate real-world evidence (RWE), which is increasingly relevant to policy makers in Asia, who use RWE to support decision-making in several areas, including public health policy, regulatory health technology assessment, and reimbursement; set priorities; or inform clinical practice. To support the achievement of the benefits of RWE in Asian health care strategies and policies, we sought to identify the linked contemporary databases used in real-world studies from three representative countries-India, Thailand, and Taiwan-and explore variations in results based on these countries' economies and health care reimbursement systems by performing a systematic scoping review. Herein, we describe the protocol and preliminary findings of our scoping review. The PubMed search strategy covered 3 concepts. Concept 1 was designed to identify potential RWE and RWD studies by applying various Medical Subject Headings (MeSH) terms ("Treatment Outcome," "Evidence-Based Medicine," "Retrospective Studies," and "Time Factors") and related keywords (eg, "real-world," "actual life," and "actual practice"). Concept 2 introduced the three countries-India, Taiwan, and Thailand. Concept 3 focused on data types, using a combination of MeSH terms ("Electronic Health Records," "Insurance, Health," "Registries," "Databases, Pharmaceutical," and "Pharmaceutical Services") and related keywords (eg, "electronic medical record," "electronic healthcare record," "EMR," "EHR," "administrative database," and "registry"). These searches were conducted with filters for language (English) and publication date (publications in the last 5 years before the search). The retrieved articles will undergo 2 screening phases (phase 1: review of titles and abstracts; phase 2: review of full texts) to identify relevant and eligible articles for data extraction. The data to be extracted from eligible studies will include the characteristics of databases, the regions covered, and the patient populations. The literature search was conducted on September 27, 2022. We retrieved 3,172,434, 1,094,125, and 672,794 articles for concepts 1, 2, and 3, respectively. After applying all 3 concepts and the language and publication date filters, 2277 articles were identified. These will be further screened to identify eligible studies. Based on phase 1 screening and our progress to date, approximately 44% (1003/2277) of articles have undergone phase 2 screening to judge their eligibility. Around 800 studies will be used for data extraction. Our research will be crucial for nurturing advancement in RWD generation within Asia by identifying linked clinical RWD databases and new avenues for public-private partnerships and multiple collaborations for expanding the scope and spectrum of high-quality, robust RWE generation in Asia. DERR1-10.2196/43741.

Predicting Kidney Transplant Recipient Cohorts’ 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data

Rehospitalization after kidney transplant is costly to patients and health care systems and is associated with poor outcomes. Few prediction model studies have examined whether inclusion of clinical notes data from the electronic medical record (EMR) enhances prediction of rehospitalization. In a retrospective, observational study of first-time, adult kidney transplant recipients at a large, urban hospital in southeastern United States (2005-2015), we examined 30-day rehospitalization (30DR) using structured EMR and unstructured (i.e., clinical notes) data. We used natural language processing (NLP) methods on 8 types of clinical notes and included terms in predictive models using unsupervised machine learning approaches. Both the area under the receiver operating curve and precision-recall curve (ROC and PRC, respectively) were used to determine and compare model accuracy, and 5-fold cross-validation tested model performance. Among 2060 kidney transplant recipients, 30.7% were readmitted within 30 days. Predictive models using clinical notes did not meaningfully improve performance over previous models using structured data alone (ROC 0.6821; 95% confidence interval [CI]: 0.6644, 0.6998). Predictive models built using solely clinical notes performed worse than models using both clinical notes and structured data. The data that contributed to the top performing models were not identical but both included structured data and progress notes (ROC 0.6902; 95% CI: 0.6699, 0.7105). Including new features from clinical notes in risk prediction models did not substantially increase predictive accuracy for 30DR for kidney transplant recipients. Future research should consider pooling data from multiple institutions to increase sample size and avoid overfitting models.

Neural-signature methods for structured EHR prediction

Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain.

Concordance and discordance in disease severity classification between clinician judgments and cognitive testing scores for Alzheimer’s disease in the United States Veterans Affairs Healthcare System

AbstractBackgroundThe current standard of practice for assessment of Alzheimer’s disease (AD) severity in the Department of Veterans Affairs (VA) Healthcare System is primarily based on clinical examination and cognitive testing. We aimed to assess the consistency between clinician judgements of AD severity compared with that classified by cognitive test scores in the VA system.MethodWe identified medical notes for patients with mild, moderate, or severe AD from the VA electronic healthcare records (EHR) database between March 2008 and October 2021, using the natural language processing technology. Mini‐Mental Status Exam (MMSE) and Montreal Cognitive Assessment (MoCA) scores from the corresponding medical notes with physician judgments were classified and used for comparisons of severity levels. A binary variable was coded to indicate concordance or discordance for each paired comparison.ResultWe screened clinical notes from 51,809 AD patients and analyzed 8,888 clinical notes that had either an MMSE or MoCA test score from 5,150 AD patients (3.5% female; average age at 78.1±9.4 years). Clinician judgements and test‐score‐based classification of AD severity were concordant in 53.2% of comparisons (weighted Kappa = 0.39, 95% CI:0.38‐0.41, p = 0.009); whereas clinician judgments were less severe in 25.7% and more severe in 21.1% comparisons, relative to the test‐score‐based severity level. Clinician judgments were concordant with 54.1%, 51.8%, and 53.0% cognitive test‐score‐based classifications, of mild, moderate, and severe AD, respectively. Concordance was observed in 55.9% clinician judgments involving internist, 53.0% involving psychiatrist or neurologist, and 51.7% involving psychologist specialties, and in 50.4% involving nurse practitioners. Among discordant severity classifications, clinician judgments tended to be less severe than test scores (Figure).ConclusionExamination of VA EHR reveals that nearly one half of clinician judgments of AD severity are discordant with severity classification based on the MMSE or MoCA test scores. Discordance is more pronounced in the moderate relative to mild and severe AD categories but does not vary substantially over clinicians’ specialty background. Research will aim to find plausible explanations for the variation of concordance vs discordance by further examining the relative contributions of patient, clinician, and system characteristics in the process of AD clinical assessments.

Role of Electronic Healthcare Record Adoption in Enhancing the Relationship between Quality Measures and Hospital Financial Performance

Electronic Health Records (EHRs) are designed as a real-time digital record to streamline distinctive and valuable patient information across multiple stakeholders in the healthcare delivery channel. The stakeholders include hospitals, healthcare providers and patients, as well as a myriad of third-party providers (i.e., insurance companies, Medicare). Academicians, practitioners and public policy makers are grappling with uneven experiences and empirical findings regarding the relationship between technology-enabled information sharing and the ensuing quality of healthcare outcomes. The most significant government-mandated technology is the implementation and adoption of EHR. The present research examines EHR through the lens of Resource Advantage Theory –– to empirically assess how partial and comprehensive implementation levels of EHR adoption influence quality management and financial performance of hospitals. Based on archival data attained from 210 hospitals in the state of Texas, the results indicate positive relationships between EHR and the quality of care. While it is generally recognized that EHR and quality management affect a hospital’s performance, this research investigates the moderating effect that EHR has on quality management and a hospital’s performance. These findings provide hospital administrators, practitioners, and third-party payers with an integrative and parsimonious model to understand the impact of partial and comprehensive levels of EHR implementation on the relationship of healthcare quality and hospital performance.

Digital Rectal Exam in the Acute Hospital Setting: Bridging Patient Experience and the Physician Perspective.

The digital rectal examination is one of the key physical examination tools used in the assessment of gastrointestinal bleed, different rectal diseases and prostate disease. It is an invasive diagnostic test that has a significant level of patient anxiety and discomfort involved. Despite its many diagnostic utilities this exam has been vastly underutilised due to many limiting factors including lack of physician confidence with this bedside physical maneuver. This review includes patient's point of view and providers perspective on digital rectal examination in the inpatient hospital setting. Some of the contributors to negative patient experience include opposite gender of the physician performing the procedure, lack of procedural awareness and expectations and repeat examinations due to improper electronic health care record charting. A better understanding of the limitations and constraints involved from both the patient and physician perspective can help improve patient experience and overall clinical outcomes.

Abstract 14412: Statin Use and Incident Heart Failure by Race and Cardiovascular Disease Risk in a Real World Primary Prevention Cohort

Background: Statins are indicated for primary prevention in patients with elevated risk of atherosclerotic CVD (ASCVD). However, statin associated risk reduction in heart failure (HF) is uncertain. There remain racial disparities in statin prescription and associated ASCVD outcomes. We evaluated the association of statin therapy and incident HF stratified by race and estimated ASCVD risk in a primary prevention cohort. Methods: Statin use in Black and White patients without ASCVD and HF was evaluated (2013-2019) based on the ACC/AHA 10y risk groups ( Borderline [5%-7.4%], Intermediate [7.5-19.9%], or High [≥20%]). Guideline Directed Statin Intensity (GDSI) was defined as “at least moderate” for intermediate and high risk patients. Statin use and HF outcome was assessed via electronic health care record and ICD-10 codes. Cox regression models, fully adjusted for CVD risk factors, were used to calculate HRs for the association between statin use and incident HF. Results: In 270,079 patients, 39% Black and 32% White patients were categorized as intermediate or high ASCVD risk. Proportionally fewer Black patients in intermediate (22% vs. 31%) and high (40% vs. 44%) risk groups received "any statin" (all p<0.001). At 6y follow up, incident rate of HF was 12.9 per 1000-person-y in those with highest ASCVD risk. Black patients had higher risk of HF events [(HR HF 1.11 (1.05-1.17), p<0.001)]. In both populations, high risk patients not on any statin had significantly higher risk of HF [(HR HF 1.42 (1.04-1.93) in Black and 1.22(1.08-1.37) in White] ( Table ). The interaction between race and statin use was not a significant predictor of incident HF. Conclusion: In a large healthcare system, statin use was low among White and Black patients. Lack of statin therapy was associated with a higher risk of incident HF amongst those at highest ASCVD risk. Further research is needed to study the role of statins in primary prevention of HF, especially in higher risk populations.

Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system.

Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence-driven tools for early detection. The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008-July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence-driven early detection of iNPH and prompt treatment.

87 VERTEBRAL FRACTURES IN OLDER PATIENTS WITH COPD: AN UNDER-DIAGNOSED AND UNDER-TREATED ENTITY

Abstract Background Patients with Chronic Obstructive Pulmonary Disease (COPD) are at increased risk of osteoporosis. Vertebral Fractures (VF) are the most common manifestation of osteoporosis. Only one fourth to one third of incident radiographically identified VF are clinically diagnosed. The aim of this study is to evaluate patients the prevalence of VF in a cohort of patients admitted with Acute Exacerbation of COPD (AECOPD). Methods A retrospective review of AECOPD admissions over a four-month period was performed. Information on baseline characteristics, prescribed medications and reports of radiological imaging were obtained from Electronic Healthcare Records, discharge summaries and clinic letters. Results In total 116 patients were included over the study period. In 24% (n=28), VF was present on imaging. In those with VF, females accounted for 64.3% (n=18). Median age was 80 (76-85) and median CFS was 6. In 12 cases a single VF was present, in 9 two VFs and in 7 patients 3+ VF were present. There was no documented diagnosis of osteoporosis in 67.8% (n=19). VF were initially detected on CT thorax imaging performed to assess for pulmonary pathology in 35.7% (n=10) cases. Vitamin D3 was prescribed in 60.7% (n=17), concurrently with calcium in 28.6% (n=8). Antiresorptive therapy was prescribed in 39.3% (n=11), oral bisphosphonates in 4 patients and denosumab in 7. Vitamin D (OH) level was measured in 78.6% (n= 22) patients and was insufficient (<50mmol) in 25% (n=7). Conclusion Almost one-quarter of patients who were admitted with an AECOPD had vertebral insufficiency fracture on imaging. Identification and treatment of VF was suboptimal. Most were not identified as having osteoporosis nor prescribed antiresorptive therapy. CT imaging should be routinely reviewed for the presence of insufficiency fractures as part of case finding initiatives. Bone health assessment should be routinely performed in patients with COPD, particularly those with frequent corticosteroid usage.

Residential Proximity to Urban Play Spaces and Childhood Overweight and Obesity in Barcelona, Spain: A Population-Based Longitudinal Study.

Findings on the relationship between play spaces and childhood overweight and obesity are mixed and scarce. This study aimed to investigate the associations between residential proximity to play spaces and the risk of childhood overweight or obesity and potential effect modifiers. This longitudinal study included children living in the city of Barcelona identified in an electronic primary healthcare record database between 2011 and 2018 (N = 75,608). Overweight and obesity were defined according to the WHO standards and we used 300 m network buffers to assess residential proximity to play spaces. We calculated the risk of developing overweight or obesity using Cox proportional hazard models. A share of 29.4% of the study population developed overweight or obesity, but we did not find consistent associations between play space indicators and overweight or obesity. We did not find any consistent sign of effect modification by sex, and only some indications of the modifying role of area socioeconomic status and level of exposure. Although it is not possible to draw clear conclusions from our study, we call for cities to continue increasing and improving urban play spaces with an equitable, healthy, and child-friendly perspective.

Real-world outcomes among patients with advanced or metastatic biliary tract cancers initiating second-line treatment.

Limited data are available regarding second-line (2L) treatment for advanced or metastatic biliary tract cancers (BTC) in the US real-world setting. This study explores the rapidly evolving and growing treatment landscape in the 2L setting for advanced or metastatic BTC with a large cohort of patients treated in a community oncology setting. Adult patients with BTC initiating 2L treatment after a platinum-containing first-line between 1/1/10- and 6/30/19 were identified from the US Oncology Network electronic healthcare record database and followed through 12/31/19. Baseline patient and treatment characteristics were analyzed descriptively, including overall response rate (ORR) in the real-world clinical setting. Kaplan-Meier methods were used to measure duration of response, progression-free survival (PFS), and overall survival (OS). The overall population (N=160) included 74 patients (46.3%) with intrahepatic cholangiocarcinoma, 41 (25.6%) with extrahepatic cholangiocarcinoma, and 45 (28.1%) with gallbladder cancer. Thirty unique 2L regimens were recorded for the study population, with folinic acid, fluorouracil and oxaliplatin (FOLFOX, 34.4%) and capecitabine monotherapy (20.0%) being the most common. ORR was 7.5% (95% CI, 3.9%-12.7%). From 2L initiation, median PFS was 2.8months (95% CI, 2.4-3.3months), and median OS was 5.2months (95% CI, 4.2-6.7months). Results from this study provide real-world evidence that although patients treated in the community oncology setting receive a wide variety of 2L treatments, the regimens are consistent with those recommended by guidelines. Although responses are observed with 2L treatment, duration is brief and associated with poor OS in patients with advanced or metastatic disease.