Nigel B. Neely

Bio: Nigel B. Neely is an academic researcher from Durham University. The author has contributed to research in topics: Internal medicine & Oncology. The author has an hindex of 1, co-authored 2 publications receiving 16 citations.

Utilization of neoadjuvant chemotherapy in high‐risk, node‐negative early breast cancer

Abstract: Controversy exists regarding the optimal sequence of chemotherapy among women with operable node‐negative breast cancers with high‐risk tumor biology. We evaluated national patterns of neoadjuvant chemotherapy (NACT) use among women with early‐stage HER2+, triple‐negative (TNBC), and high‐risk hormone receptor‐positive (HR+) invasive breast cancers.

Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow-up care.

Abstract: BACKGROUND This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to stratify survivors based on late-effect risk, analyze follow-up care patterns, and determine factors associated with suboptimal follow-up care. PROCEDURE The survivorship cohort included patients ≤18 years of age reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. International Classification of Diseases for Oncology, third revision (ICD-O-3) coding and treatment exposures facilitated risk stratification of survivors. The EHR was linked to the cancer registry based on medical record number (MRN) to extract clinic visits. RESULTS Five hundred and ninety pediatric hematology-oncology (PHO) and 275 pediatric neuro-oncology (PNO) survivors were included in the final analytic cohort. Two hundred and eight-two survivors (32.6%) were not seen in any oncology-related subspecialty clinic at Duke 5-7 years after initial diagnosis. Factors associated with follow-up included age (p = .008), diagnosis (p < .001), race/ethnicity (p = .010), late-effect risk strata (p = .001), distance to treatment center (p < .0001), and area deprivation index (ADI) (p = .011). Multivariable logistic modeling attenuated the association for high-risk (OR 1.72; 95% CI 0.805, 3.66) and intermediate-risk (OR 1.23, 95% CI 0.644, 2.36) survivors compared to survivors at low risk of late effects among the PHO cohort. PNO survivors at high risk for late effects were more likely to follow up (adjusted OR 3.66; 95% CI 1.76, 7.61). CONCLUSIONS Nearly a third of survivors received suboptimal follow-up care. This study provides a reproducible model to integrate cancer registry and EHR data to construct risk-stratified survivorship cohorts to assess follow-up care.

Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference.

Abstract: Electronic health record (EHR) data are becoming a primary resource for clinical research. Compared to traditional research data, such as those from clinical trials and epidemiologic cohorts, EHR data have a number of appealing characteristics. However, because they do not have mechanisms set in place to ensure that the appropriate data are collected, they also pose a number of analytic challenges. In this paper, we illustrate that how a patient interacts with a health system influences which data are recorded in the EHR. These interactions are typically informative, potentially resulting in bias. We term the overall set of induced biases informed presence. To illustrate this, we use examples from EHR based analyses. Specifically, we show that: 1) Where a patient receives services within a health facility can induce selection bias; 2) Which health system a patient chooses for an encounter can result in information bias; and 3) Referral encounters can create an admixture bias. While often times addressing these biases can be straightforward, it is important to understand how they are induced in any EHR based analysis.

Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study

Abstract: Background: Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. Objective: This study aimed to derive and validate an electronic health record–based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. Methods: A 2-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in 2 large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, 2 emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). Results: Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). Conclusions: This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.

Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.

Abstract: This manuscript reviews the use of electronic medical record (EMR) data for HIV care and research along the HIV care continuum with a specific focus on machine learning methods and clinical informatics interventions. EMR-based clinical decision support tools and electronic alerts have been effectively utilized to improve HIV care continuum outcomes. Accurate EMR-based machine learning models have been developed to predict HIV diagnosis, retention in care, and viral suppression. Natural language processing (NLP) of clinical notes and data sharing between healthcare systems and public health agencies can enhance models for identifying people living with HIV who are undiagnosed or in need of relinkage to care. Challenges related to using these technologies include inconsistent EMR documentation, alert fatigue, and the potential for bias. Clinical informatics and machine learning models are promising tools for improving HIV care continuum outcomes. Future research should focus on methods for combining EMR data with additional data sources (e.g., social media, geospatial data) and studying how to effectively implement predictive models for HIV care into clinical practice.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.

Abstract: Background: Electronic medical records (EMRs) contain large amounts of rich clinical information Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research Objective: This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions Methods: A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed This study covered articles published between January 2000 and April 2020, both inclusive Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines Results: A total of 274 articles representing 299 algorithms were assessed and summarized Most studies were undertaken in the United States (181/299, 605%), followed by the United Kingdom (42/299, 140%) and Canada (15/299, 50%) These algorithms were mostly developed either in primary care (103/299, 344%) or inpatient (168/299, 562%) settings Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms Data-driven and clinical rule–based approaches have been identified EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance Conclusions: Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies Several strategies to assist with phenotype-based case definitions have been proposed Trial Registration: