An electronic health record based on structured narrative

OBJECTIVE

To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse.

DESIGN

We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry.

VALIDATION

The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules.

DISCUSSION

The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale.

CONCLUSION

Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

Childhood Asthma Incidence, Early and Persistent Wheeze, and Neighborhood Socioeconomic Factors in the ECHO/CREW Consortium

Importance

In the United States, Black and Hispanic children have higher rates of asthma and asthma-related morbidity compared with White children and disproportionately reside in communities with economic deprivation.

Objective

To determine the extent to which neighborhood-level socioeconomic indicators explain racial and ethnic disparities in childhood wheezing and asthma.

Design, Setting, and Participants

The study population comprised children in birth cohorts located throughout the United States that are part of the Children's Respiratory and Environmental Workgroup consortium. Cox proportional hazard models were used to estimate hazard ratios (HRs) of asthma incidence, and logistic regression was used to estimate odds ratios of early and persistent wheeze prevalence accounting for mother's education, parental asthma, smoking during pregnancy, child's race and ethnicity, sex, and region and decade of birth.

Exposures

Neighborhood-level socioeconomic indicators defined by US census tracts calculated as z scores for multiple tract-level variables relative to the US average linked to participants' birth record address and decade of birth. The parent or caregiver reported the child's race and ethnicity.

Main Outcomes and Measures

Prevalence of early and persistent childhood wheeze and asthma incidence.

Results

Of 5809 children, 46% reported wheezing before age 2 years, and 26% reported persistent wheeze through age 11 years. Asthma prevalence by age 11 years varied by cohort, with an overall median prevalence of 25%. Black children (HR, 1.47; 95% CI, 1.26-1.73) and Hispanic children (HR, 1.29; 95% CI, 1.09-1.53) were at significantly increased risk for asthma incidence compared with White children, with onset occurring earlier in childhood. Children born in tracts with a greater proportion of low-income households, population density, and poverty had increased asthma incidence. Results for early and persistent wheeze were similar. In effect modification analysis, census variables did not significantly modify the association between race and ethnicity and risk for asthma incidence; Black and Hispanic children remained at higher risk for asthma compared with White children across census tracts socioeconomic levels.

Conclusions and Relevance

Adjusting for individual-level characteristics, we observed neighborhood socioeconomic disparities in childhood wheeze and asthma. Black and Hispanic children had more asthma in neighborhoods of all income levels. Neighborhood- and individual-level characteristics and their root causes should be considered as sources of respiratory health inequities.

Extracting information on pneumonia in infants using natural language processing of radiology reports

Natural language processing (NLP) is critical for improvement of the healthcare process because it can encode clinical data in patient documents. Many clinical applications such as decision support require coded data to function appropriately. However, in order to be applicable for healthcare, performance must be adequate. A valuable automated application is the detection of infectious diseases, such as surveillance of pneumonia in newborns (e.g., neonates) because the disease produces significant rates of morbidity and mortality, and manual surveillance is challenging. Studies have demonstrated that automated surveillance using NLP is a useful adjunct to manual surveillance and an effective tool for infection control practitioners. This paper presents a study evaluating the feasibility of an NLP-based monitoring system to screen for healthcare-associated pneumonia in neonates. We estimated sensitivity, specificity, and positive predictive value by comparing results with clinicians' judgments. Sensitivity was 71% and specificity was 99%. Our results demonstrated that the automated method was feasible.

Searching for RET/PTC rearrangements and BRAF V599E mutation in thyroid aspirates might contribute to establish a preoperative diagnosis of papillary thyroid carcinoma

OBJECTIVE

Searching for multiple molecular markers in thyroid aspirates appears to be a promising approach for establishing a preoperative diagnosis of papillary thyroid carcinoma (PTC).

METHODS

Based on this hypothesis, a total of 63 samples from 55 patients, were collected at random. RNA was extracted from the residue cells inside the needle used for fine needle aspiration cytology (FNAC) and thereafter molecular analysis was carried out both for RETrearrangements (type 1, 2, 3) and BRAF codon 599 mutation molecule. Results were compared with the cytological and histopathological diagnoses in 24 patients submitted to surgery.

RESULTS

58% PTCs presented a genetic alteration either RET/PTC rearrangement, BRAF V599E mutation or both: three cases of PTCs (25%) presented a RET/PTC rearrangement; three cases of PTCs (25%) presented a BRAF V599E mutation and in one case (8%) both alterations were identified.

CONCLUSIONS

The present results suggest that searching for multiple molecular markers in thyroid aspirates may enhance the accuracy of FNAC and refine preoperative diagnosis of PTC.

PalmCIS: a wireless handheld application for satisfying clinician information needs

Wireless handheld technology provides new ways to deliver and present information. As with any technology, its unique features must be taken into consideration and its applications designed accordingly. In the clinical setting, availability of needed information can be crucial during the decision-making process. Preliminary studies performed at New York Presbyterian Hospital (NYPH) determined that there are inadequate access to information and ineffective communication among clinicians (potential proximal causes of medical errors). In response to these findings, the authors have been developing extensions to their Web-based clinical information system including PalmCIS, an application that provides access to needed patient information via a wireless personal digital assistant (PDA). The focus was on achieving end-to-end security and developing a highly usable system. This report discusses the motivation behind PalmCIS, design and development of the system, and future directions.

Neuroblastoma Patients' KIR and KIR-Ligand Genotypes Influence Clinical Outcome for Dinutuximab-based Immunotherapy: A Report from the Children's Oncology Group

Purpose: In 2010, a Children's Oncology Group (COG) phase III randomized trial for patients with high-risk neuroblastoma (ANBL0032) demonstrated improved event-free survival (EFS) and overall survival (OS) following treatment with an immunotherapy regimen of dinutuximab, GM-CSF, IL2, and isotretinoin compared with treatment with isotretinoin alone. Dinutuximab, a chimeric anti-GD2 monoclonal antibody, acts in part via natural killer (NK) cells. Killer immunoglobulin-like receptors (KIR) on NK cells and their interactions with KIR-ligands can influence NK cell function. We investigated whether KIR/KIR-ligand genotypes were associated with EFS or OS in this trial.Experimental Design: We genotyped patients from COG study ANBL0032 and evaluated the effect of KIR/KIR-ligand genotypes on clinical outcomes. Cox regression models and log-rank tests were used to evaluate associations of EFS and OS with KIR/KIR-ligand genotypes.Results: In this trial, patients with the "all KIR-ligands present" genotype as well as patients with inhibitory KIR2DL2 with its ligand (HLA-C1) together with inhibitory KIR3DL1 with its ligand (HLA-Bw4) were associated with improved outcome if they received immunotherapy. In contrast, for patients with the complementary KIR/KIR-ligand genotypes, clinical outcome was not significantly different for patients who received immunotherapy versus those receiving isotretinoin alone.Conclusions: These data show that administration of immunotherapy is associated with improved outcome for neuroblastoma patients with certain KIR/KIR-ligand genotypes, although this was not seen for patients with other KIR/KIR-ligand genotypes. Further investigation of KIR/KIR-ligand genotypes may clarify their role in cancer immunotherapy and may enable KIR/KIR-ligand genotyping to be used prospectively for identifying patients likely to benefit from certain cancer immunotherapy regimens. Clin Cancer Res; 24(1); 189-96. ©2017 AACRSee related commentary by Cheung and Hsu, p. 3.

A phase III skin cancer chemoprevention study of DFMO: long-term follow-up of skin cancer events and toxicity

Decreasing the incidence of nonmelanoma skin cancer (NMSC) is of great importance in regards to future healthcare services. Given the previously reported preventive effects of α-difluoromethylornithine (DFMO) in skin and colon cancer trials, we determined appropriate cause to update the clinical data on the subjects from the recently reported randomized, double-blind, placebo-controlled phase III skin cancer prevention study of DFMO. Our intention was to retrospectively assess the further incidence of skin cancer, other malignancies, and adverse events of patients accrued to our phase III skin cancer prevention study of DFMO. Clinical records of 209 University of Wisconsin (UW) Health subjects were reviewed, and 2,092.7 person years of on study (884.3 person years) and poststudy (1,208.4 person years) follow-up for these patients were assessed for new NMSC events and recurrence rates from the on study period, the poststudy period, and the two study periods combined. No evidence of increased significant diagnoses or serious adverse events was observed in the DFMO participants. The initially observed, marginally significant reduction (P = 0.069) in NMSC rates for DFMO subjects relative to placebo continued without evidence of rebound. Event rates after discontinuation from study for total NMSCs (DFMO 0.236 NMSC/person/year, placebo 0.297, P = 0.48) or the subtypes of basal cell carcinomas (BCC; DFMO 0.179 BCC/person/year, placebo 0.190, P = 0.77) and squamous cell carcinomas (SCC; DFMO 0.057 SCC/person/year, placebo 0.107, P = 0.43) are listed. Follow-up data revealed a persistent but insignificant reduction in new NMSCs occurring in DFMO subjects without evidence of latent or cumulative toxicity relative to placebo subjects.

Gene symbol disambiguation using knowledge-based profiles

MOTIVATION

The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols.

RESULTS

For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast.

AVAILABILITY

The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006

Use of computerized surveillance to detect nosocomial pneumonia in neonatal intensive care unit patients

BACKGROUND

Pneumonia surveillance is difficult and time-consuming. The definition is complicated, and there are many opportunities for subjectivity in determining infection status.

OBJECTIVE

To compare traditional infection control professional (ICP) surveillance for pneumonia among neonatal intensive care unit (NICU) patients with computerized surveillance of chest x-ray reports using an automated detection system based on a natural language processor.

METHODS

This system evaluated chest x-rays from 2 NICUs over a 2-year period. It flagged x-rays indicative of pneumonia according to rules derived from the National Nosocomial Infection Surveillance System definition as applied to radiology reports. Data from the automated system were compared with pneumonia data collected prospectively by an ICP.

RESULTS

Sensitivity of the computerized surveillance in NICU 1 was 71%, and specificity was 99.8%. The positive predictive value was 7.9%, and the negative predictive value (NPV) was >99%. Data from NICU 2 were incomplete.

CONCLUSIONS

Computer-assisted surveillance has the potential to decrease ICP workload and make pneumonia surveillance feasible. The high NPV means the system can safely screen out many chest x-rays of noninfected patients. However, all data must be available to the computer system and must be analyzed the same way for results to be comparable.

Approach to mobile information and communication for health care

Evidence suggests that inadequate access to information and ineffective communication are proximal causes of errors and other adverse events in-patient care. Within the context of reducing these proximal causes of errors, we explore the use of novel information-based approaches to improve information access and communication in health care settings. This paper describes the approaches for and the design of extensions to a clinical information system used to improve information access and communication at the point of care using information-based handheld wireless applications. These extensions include clinical and information resources, event monitoring, and a virtual whiteboard (VWB).

PhenoGO: an integrated resource for the multiscale mining of clinical and biological data

The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset .

Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits

BACKGROUND

Machine learning is increasingly used for risk stratification in health care. Achieving accurate predictive models do not improve outcomes if they cannot be translated into efficacious intervention. Here we examine the potential utility of automated risk stratification and referral intervention to screen older adults for fall risk after emergency department (ED) visits.

OBJECTIVE

This study evaluated several machine learning methodologies for the creation of a risk stratification algorithm using electronic health record data and estimated the effects of a resultant intervention based on algorithm performance in test data.

METHODS

Data available at the time of ED discharge were retrospectively collected and separated into training and test datasets. Algorithms were developed to predict the outcome of a return visit for fall within 6 months of an ED index visit. Models included random forests, AdaBoost, and regression-based methods. We evaluated models both by the area under the receiver operating characteristic (ROC) curve, also referred to as area under the curve (AUC), and by projected clinical impact, estimating number needed to treat (NNT) and referrals per week for a fall risk intervention.

RESULTS

The random forest model achieved an AUC of 0.78, with slightly lower performance in regression-based models. Algorithms with similar performance, when evaluated by AUC, differed when placed into a clinical context with the defined task of estimated NNT in a real-world scenario.

CONCLUSION

The ability to translate the results of our analysis to the potential tradeoff between referral numbers and NNT offers decisionmakers the ability to envision the effects of a proposed intervention before implementation.

Metaplastic carcinomas of the breast--fine needle aspiration (FNA) cytology findings

Metaplastic carcinomas of the breast are defined by mesenchymal and/or squamous cell components associated with ductal carcinoma and may raise diagnostic problems in FNA cytology. We reviewed FNA smears of a series of nine cases; seven were compared with histological sections and two with cell-block sections. The cytological pattern was diagnostic of carcinoma in six cases; in two cases a diagnosis of sarcoma/phyllodes tumour was considered, as cells were predominantly spindle-shaped. One case had a pleomorphic adenoma type pattern. The cytological findings suggesting a diagnosis of metaplastic carcinoma include a liquid aspirate, a proteinaceous or chondromyxoid background and a poorly differentiated tumour with multinucleated giant cells, neoplastic or histiocytic. A definite diagnosis requires the presence of both carcinomatous and metaplastic (squamous/mesenchymal) components.

Accessing heterogeneous sources of evidence to answer clinical questions

The large and rapidly growing number of information sources relevant to health care, and the increasing amounts of new evidence produced by researchers, are improving the access of professionals and students to valuable information. However, seeking and filtering useful, valid information can be still very difficult. An online information system that conducts searches based on individual patient data can have a beneficial influence on the particular patient's outcome and educate the healthcare worker. In this paper, we describe the underlying model for a system that aims to facilitate the search for evidence based on clinicians' needs. This paper reviews studies of information needs of clinicians, describes principles of information retrieval, and examines the role that standardized terminologies can play in the integration between a clinical system and literature resources, as well as in the information retrieval process. The paper also describes a model for a digital library system that supports the integration of clinical systems with online information sources, making use of information available in the electronic medical record to enhance searches and information retrieval. The model builds on several different, previously developed techniques to identify information themes that are relevant to specific clinical data. Using a framework of evidence-based practice, the system generates well-structured questions with the intent of enhancing information retrieval. We believe that by helping clinicians to pose well-structured clinical queries and including in them relevant information from individual patients' medical records, we can enhance information retrieval and thus can improve patient-care.

Conceptual knowledge acquisition in biomedicine: A methodological review

The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.

Documented lifestyle education among young adults with incident hypertension

BACKGROUND

Only 38% of young adults with hypertension have controlled blood pressure. Lifestyle education is a critical initial step for hypertension control. Previous studies have not assessed the type and frequency of lifestyle education in young adults with incident hypertension.

OBJECTIVE

The purpose of this study was to determine patient, provider, and visit predictors of documented lifestyle education among young adults with incident hypertension.

DESIGN

We conducted a retrospective analysis of manually abstracted electronic health record data.

PARTICIPANTS

A random selection of adults 18-39 years old (n = 500), managed by a large academic practice from 2008 to 2011 and who met JNC 7 clinical criteria for incident hypertension, participated in the study.

MAIN MEASURES

The primary outcome was the presence of any documented lifestyle education during one year after meeting criteria for incident hypertension. Abstracted topics included documented patient education for exercise, tobacco cessation, alcohol use, stress management/stress reduction, Dietary Approaches to Stop Hypertension (DASH) diet, and weight loss. Clinic visits were categorized based upon a modified established taxonomy to characterize patients' patterns of outpatient service. We excluded patients with previous hypertension diagnoses, previous antihypertensive medications, or pregnancy. Logistic regression was used to identify predictors of documented education.

KEY RESULTS

Overall, 55% (n = 275) of patients had documented lifestyle education within one year of incident hypertension. Exercise was the most frequent topic (64%). Young adult males had significantly decreased odds of receiving documented education. Patients with a previous diagnosis of hyperlipidemia or a family history of hypertension or coronary artery disease had increased odds of documented education. Among visit types, chronic disease visits predicted documented lifestyle education, but not acute or other/preventive visits.

CONCLUSIONS

Among young adults with incident hypertension, only 55% had documented lifestyle education within one year. Knowledge of patient, provider, and visit predictors of education can help better target the development of interventions to improve young adult health education and hypertension control.

Medullary thyroid carcinoma: an accurate pre-operative diagnosis by reverse transcription-PCR

OBJECTIVE

To study the expression of calcitonin (CT) and thyroglobulin mRNA in samples of leftover cells in needles used for fine-needle aspiration biopsy either from thyroid tumours or cervical lymph nodes.

PATIENTS AND METHODS

Specimens were analysed using reverse transcription-polymerase chain reaction; 12 samples from 11 patients were included and molecular diagnosis was compared with cytological or histological diagnosis and serum CT measurements.

RESULTS

Transcripts of the CT gene were detected in all six patients with medullary thyroid carcinoma (MTC) but in none of the other patients.

CONCLUSIONS

Present data reinforce this technique as a reliable and alternative tool to establish the pre-operative diagnosis of MTC, especially when cytological examination is not conclusive or when cytological information is not in agreement with clinical data. Furthermore, it may be clinically useful to identify those conditions in which increased serum CT in the presence of a thyroid nodule is not due to MTC.

AI-Enabled Medical Education: Threads of Change, Promising Futures, and Risky Realities Across Four Potential Future Worlds

BACKGROUND

The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI?

OBJECTIVE

The purpose of this perspective is to plan for the intersection of health care and medical education in the future.

METHODS

We used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation.

RESULTS

The following 4 worlds were created using OpenAI's GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research.

CONCLUSIONS

To ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity.

Flow cytometric DNA hypertetraploidy is associated with unfavourable prognostic features in breast cancer

AIM

Breast tumours with a DNA content higher than 4N (hypertetraploidy) are not well characterised. The aim of this study was to evaluate the clinical and biological characteristics of 51 hypertetraploid breast carcinomas selected from a series of 860 consecutive cases analysed by flow cytometry.

METHODS

The clinicopathological characteristics of the hypertetraploid group were compared with those of a control group of 138 non-hypertetraploid breast carcinomas. Breast tumours from patients submitted to surgery as primary therapeutic approach (15 hypertetraploid and the 138 non-hypertetraploid) were TNM staged and classified according to the histological type and grade. The remaining 36 patients had advanced neoplastic disease at presentation and were classified by cytological criteria only. DNA flow cytometric analysis was performed on fresh-frozen samples stained with propidium iodide. Hormone receptors were analysed by immunocytochemistry.

RESULTS

The incidence of hypertetraploid breast tumours was 5.9% (51 of 860). All the patients were women and the mean age at diagnosis was 65 years. There was a family history of breast cancer in 21.6% of cases. In the group of operated patients, 33.3% had pT3 tumours and 53.3% had axillary lymph node metastases. All but one tumour were invasive ductal carcinomas; the remaining was an invasive papillary carcinoma. Ten (66.7%) tumours were classified as poorly differentiated carcinomas. Oestrogen and progesterone receptors were negative in 33 (64.7%) and 38 (74.5%) tumours, respectively. At last follow up, 35 (72.9%) patients were alive, while 13 (27.1%) died of disease within three years of diagnosis. Statistical comparison of the clinicopathological features of hypertetraploid v non-hypertetraploid breast carcinomas yielded a significant difference in tumour size (p < 0.001), histological grade (p < 0.001), hormone receptor status (p < 0.001), and overall survival (p < 0.001) between the two groups.

CONCLUSION

Flow cytometric DNA hypertetraploidy is related to clinicopathological features of breast cancer usually associated with unfavourable prognosis.

Conflicting biomedical assumptions for mathematical modeling: the case of cancer metastasis

Computational models in biomedicine rely on biological and clinical assumptions. The selection of these assumptions contributes substantially to modeling success or failure. Assumptions used by experts at the cutting edge of research, however, are rarely explicitly described in scientific publications. One can directly collect and assess some of these assumptions through interviews and surveys. Here we investigate diversity in expert views about a complex biological phenomenon, the process of cancer metastasis. We harvested individual viewpoints from 28 experts in clinical and molecular aspects of cancer metastasis and summarized them computationally. While experts predominantly agreed on the definition of individual steps involved in metastasis, no two expert scenarios for metastasis were identical. We computed the probability that any two experts would disagree on k or fewer metastatic stages and found that any two randomly selected experts are likely to disagree about several assumptions. Considering the probability that two or more of these experts review an article or a proposal about metastatic cascades, the probability that they will disagree with elements of a proposed model approaches 1. This diversity of conceptions has clear consequences for advance and deadlock in the field. We suggest that strong, incompatible views are common in biomedicine but largely invisible to biomedical experts themselves. We built a formal Markov model of metastasis to encapsulate expert convergence and divergence regarding the entire sequence of metastatic stages. This model revealed stages of greatest disagreement, including the points at which cancer enters and leaves the bloodstream. The model provides a formal probabilistic hypothesis against which researchers can evaluate data on the process of metastasis. This would enable subsequent improvement of the model through Bayesian probabilistic update. Practically, we propose that model assumptions and hunches be harvested systematically and made available for modelers and scientists.

Mathematical models and scientific theories fail not only from internal inconsistency, but also from the poor selection of basic assumptions. Assumptions in computational models of biomedicine are typically provided by scientists who interact directly with empirical data. If we seek to model the dynamics of cancer metastasis and ask experts regarding valid assumptions, how widely will they agree and on which assumptions? To answer this question, we queried 28 faculty-level experts about the progression of metastasis. We demonstrate an unexpected diversity of assumptions across experts leading to a striking lack of agreement over the basic stages and sequence of metastasis. We suggest a formal model and framework that builds on this diversity and enables researchers to evaluate divergent hypotheses about metastasis with experimental data. We conclude that modeling biomedical processes could be substantially improved by harvesting scientific assumptions and exposing them for formalization and experiment.

Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits

OBJECTIVES

To evaluate the utility of routinely collected Hendrich II fall scores in predicting returns to the emergency department (ED) for falls within 6 months.

DESIGN

Retrospective electronic record review.

SETTING

Academic medical center ED.

PARTICIPANTS

Individuals aged 65 and older seen in the ED from January 1, 2013, through September 30, 2015.

MEASUREMENTS

We evaluated the utility of routinely collected Hendrich II fall risk scores in predicting ED visits for a fall within 6 months of an all-cause index ED visit.

RESULTS

For in-network patient visits resulting in discharge with a completed Hendrich II score (N = 4,366), the return rate for a fall within 6 months was 8.3%. When applying the score alone to predict revisit for falls among the study population the resultant receiver operating characteristic (ROC) plot had an area under the curve (AUC) of 0.64. In a univariate model, the odds of returning to the ED for a fall in 6 months were 1.23 times as high for every 1-point increase in Hendrich II score (odds ratio (OR)=1.23 (95% confidence interval (CI)=1.19-1.28). When included in a model with other potential confounders or predictors of falls, the Hendrich II score is a significant predictor of a return ED visit for fall (adjusted OR=1.15, 95% CI=1.10-1.20, AUC=0.75).

CONCLUSION

Routinely collected Hendrich II scores were correlated with outpatient falls, but it is likely that they would have little utility as a stand-alone fall risk screen. When combined with easily extractable covariates, the screen performs much better. These results highlight the potential for secondary use of electronic health record data for risk stratification of individuals in the ED. Using data already routinely collected, individuals at high risk of falls after discharge could be identified for referral without requiring additional screening resources.

Ecotoxicological monitoring of remediation in a coke oven soil

Assessment of contaminated sites is usually based on chemical analyses of hazardous compounds in soil. This is enough either to assess the environmental hazard of contaminated soil nor to evaluate the efficiency of applied remediation techniques. Information on the bioavailability of complex mixtures of xenobiotics and degradation products cannot be provided by chemical analytical data, but results from bioassays can integrate the effects of pollutants in mixtures. In the preservation of human health and environmental quality, it is important to evaluate ecotoxicological effects of contaminated industrial soils to complement the techniques of analytical chemistry. The monitoring of a coke oven soil and the evaluation of the landfarming treatment technique, conducted on-site in a pilot scale installation, was done by a battery of ecotoxicological tests: acute, chronic, and genotoxicity tests. Contaminated soil samples revealed toxic effects for different species mainly due to high concentrations of polycyclic aromatic hydrocarbons (PAHs). After landfarming treatment, soil samples presented significant reduction in toxicity, confirming the effectiveness of the landfarming process pointed out by a significant reduction in low molecular weight PAH concentrations. Comparative analysis of the different ecotoxicological tests allowed the establishment and validation of a more suitable procedure for the monitoring of PAH contaminated soils.

Assessing HER-2 status in fresh frozen and archival cytological samples obtained by fine needle aspiration cytology

Determination of HER-2 status is an essential prerequisite in considering a patient's eligibility for anti-HER-2 therapy; few studies have focused on its evaluation on cytological material. We present a new method of assessing HER-2 status on cytological samples, by fluorescence in situ hybridization (FISH) with an automated detection system, using fresh frozen (FF) and May-Grunwald-Giemsa (MGG) stained smears, and we evaluate the reliability of HER-2 determination on fine needle aspiration cytology (FNAC). The pre-treatment protocol for FF smears is easier and faster than for MGG stained slides. However, with the described procedure, FISH is also feasible on archival MGG stained slides. We conclude that with this method, cytological samples obtained by FNAC, either FF or MGG, are a reliable option for assessing HER-2 status.

Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department

BACKGROUND

Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification.

METHODS

In this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results.

RESULTS

The NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders.

CONCLUSIONS

Our pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance.