Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. To address these gaps and tackle a common challenge faced by healthcare delivery organizations, a case-based workshop was organized, and a framework was developed to evaluate the potential impact of implementing an AI solution on health equity. The Health Equity Across the AI Lifecycle (HEAAL) is co-designed with extensive engagement of clinical, operational, technical, and regulatory leaders across healthcare delivery organizations and ecosystem partners in the US. It assesses 5 equity assessment domains-accountability, fairness, fitness for purpose, reliability and validity, and transparency-across the span of eight key decision points in the AI adoption lifecycle. It is a process-oriented framework containing 37 step-by-step procedures for evaluating an existing AI solution and 34 procedures for evaluating a new AI solution in total. Within each procedure, it identifies relevant key stakeholders and data sources used to conduct the procedure. HEAAL guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities. It also informs how much resources and support are required to assess the potential impact of AI solutions on health inequities.

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System. We conducted 20 interviews with the team of engineers and scientists that led the multi-year effort to build the tool, integrate it into practice, and maintain the solution. This "Algorithm Journey Map" enumerates all social and technical activities throughout the AI solution's procurement, development, integration, and full lifecycle management. In addition to mapping the "who?" and "what?" of the adoption of the AI tool, we also show several 'lessons learned' throughout the algorithm journey maps including modeling assumptions, stakeholder inclusion, and organizational structure. In doing so, we identify generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings. We expect that this effort will further the development of best practices for operationalizing and sustaining ethical principles-in algorithmic systems.

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare

OBJECTIVE

The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion.

MATERIALS AND METHODS

Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution.

RESULTS

An Implementation Guide articulates evaluation criteria used during review of algorithmic technologies and details what evidence supports the implementation of ethical and quality principles for trustworthy health AI. Application of the processes described in the Implementation Guide can lead to algorithms that are safer as well as more effective, fair, and equitable upon implementation, as illustrated through 4 examples of technologies at different phases of the algorithmic lifecycle that underwent evaluation at our academic medical center.

DISCUSSION

By providing clear descriptions/definitions of evaluation criteria and embedding them within standardized processes, we streamlined oversight processes and educated communities using and developing algorithmic technologies within our institution.

CONCLUSIONS

We developed a scalable, adaptable framework for translating principles into evaluation criteria and specific requirements that support trustworthy implementation of algorithmic technologies in patient care and healthcare operations.

Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration

OBJECTIVES

Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children.

METHODS

Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS).

RESULTS

The LGBM model significantly outperformed I-PEWS based on receiver operating characteristic curve (AUROC) for the composite outcome of ICU transfer or mortality for both internal validation and temporal validation cohorts (AUROC 0.785 95% confidence interval [0.780-0.791] vs 0.708 [0.701-0.715] for temporal validation) as well as lead-time before deterioration events (median 11 hours vs 3 hours; P = .004). However, LGBM performance as evaluated by precision recall curve was lesser in the temporal validation cohort with associated decreased positive predictive value (6% vs 29%) and increased number needed to evaluate (17 vs 3) compared with I-PEWS.

CONCLUSIONS

Our electronic health record based machine learning model demonstrated improved AUROC and lead-time in predicting clinical deterioration in pediatric inpatients 24 to 48 hours in advance compared with I-PEWS. Further work is needed to optimize model positive predictive value to allow for integration into clinical practice.

A framework for understanding label leakage in machine learning for health care

INTRODUCTION

The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule."

FRAMEWORK

We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice.

RECOMMENDATIONS

Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models.

Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study

BACKGROUND

Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes.

OBJECTIVE

This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care.

METHODS

A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS. The stakeholder groups included technical, administrative, and clinical members of the team interacting with the ML-driven CDS. The ML-driven CDS identified patients with a high probability of having PAD, and these patients were then reviewed by an interdisciplinary team that developed a recommended action plan and sent recommendations to the patient's primary care provider. Pseudonymized transcripts were coded, and thematic analysis was conducted by a multidisciplinary research team.

RESULTS

Three themes were identified: positive factors translating in silico performance to real-world efficacy, organizational factors and data structure factors affecting clinical impact, and potential challenges to advancing equity. Our study found that the factors that led to successful translation of in silico algorithm performance to real-world impact were largely nontechnical, given adequate efficacy in retrospective validation, including strong clinical leadership, trustworthy workflows, early consideration of end-user needs, and ensuring that the CDS addresses an actionable problem. Negative factors of integration included failure to incorporate the on-the-ground context, the lack of feedback loops, and data silos limiting the ML-driven CDS. The success criteria for each stakeholder group were also characterized to better understand how teams work together to integrate ML-driven CDS and to understand the varying needs across stakeholder groups.

CONCLUSIONS

Longitudinal and multidisciplinary stakeholder engagement in the development and integration of ML-driven CDS underpins its effective translation into real-world care. Although previous studies have focused on the technical elements of ML-driven CDS, our study demonstrates the importance of including administrative and operational leaders as well as an early consideration of clinicians' needs. Seeing how different stakeholder groups have this more holistic perspective also permits more effective detection of context-driven health care inequities, which are uncovered or exacerbated via ML-driven CDS integration through structural and organizational challenges. Many of the solutions to these inequities lie outside the scope of ML and require coordinated systematic solutions for mitigation to help reduce disparities in the care of patients with PAD.

A Clinicopathological Study with Risk-Stratified Staging of Pediatric Hepatoblastoma: A 5-Year Experience from a Tertiary Cancer Center

Background & Objective

Hepatoblastoma encompasses 1% of pediatric malignancies and is the most common liver malignancy in children. Ninety percent of cases are younger than 5 years of age. Clinical and pathological risk stratification forms a crucial role in determining the treatment strategy. This study aimed to assess the clinicopathological profile of hepatoblastoma with risk stratification and follow-up in children.

Methods

A retrospective evaluation was performed on all pediatric patients diagnosed as hepatoblastoma between 2016 and 2020 in our institution. Clinical, radiological, biochemical, pathological, and treatment data were analyzed. Cases were stratified based on the SIOPEL protocol and compared with the outcome.

Results

The median age of all children was 1 year, the male-to-female ratio was 2.3:1, and elevated α-fetoprotein (AFP) was observed in all cases. SIOPEL risk stratification showed that 50% of children were at high risk. The histopathological types were fetal (30%), embryonal (20%), and macrotrabecular (5%) patterns under epithelial type and mixed epithelial and mesenchymal type (45%) with 1 case showing teratoid features. During the follow-up period, 6 out of the 7 children who died, belonged to the high-risk SIOPEL category, and 5 presented a mixed epithelial and mesenchymal pattern.

Conclusion

Our study found a significant correlation between clinicopathological data, histopathological patterns, and outcomes. Accordingly, histopathological patterns could be considered one of the criteria for risk stratification. Histopathological risk stratification indicators (such as SIOPEL and PRETEXT) have strong prognostic and predictive outcomes; hence, our study emphasizes such parameters to aid oncologists.

Accelerating health system innovation: principles and practices from the Duke Institute for Health Innovation

The Duke Institute for Health Innovation (DIHI) was launched in 2013. Frontline staff members submit proposals for innovation projects that align with strategic priorities set by organizational leadership. Funded projects receive operational and technical support from institute staff members and a transdisciplinary network of collaborators to develop and implement solutions as part of routine clinical care, ranging from machine learning algorithms to mobile applications. DIHI's operations are shaped by four guiding principles: build to show value, build to integrate, build to scale, and build responsibly. Between 2013 and 2021, more than 600 project proposals have been submitted to DIHI. More than 85 innovation projects, both through the application process and other strategic partnerships, have been supported and implemented. DIHI's funding has incubated 12 companies, engaged more than 300 faculty members, staff members, and students, and contributed to more than 50 peer-reviewed publications. DIHI's practices can serve as a model for other health systems to systematically source, develop, implement, and scale innovations.

Development of a Human Immunodeficiency Virus Risk Prediction Model Using Electronic Health Record Data From an Academic Health System in the Southern United States

BACKGROUND

Human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) is underutilized in the southern United States. Rapid identification of individuals vulnerable to diagnosis of HIV using electronic health record (EHR)-based tools may augment PrEP uptake in the region.

METHODS

Using machine learning, we developed EHR-based models to predict incident HIV diagnosis as a surrogate for PrEP candidacy. We included patients from a southern medical system with encounters between October 2014 and August 2016, training the model to predict incident HIV diagnosis between September 2016 and August 2018. We obtained 74 EHR variables as potential predictors. We compared Extreme Gradient Boosting (XGBoost) versus least absolute shrinkage selection operator (LASSO) logistic regression models, and assessed performance, overall and among women, using area under the receiver operating characteristic curve (AUROC) and area under precision recall curve (AUPRC).

RESULTS

Of 998 787 eligible patients, 162 had an incident HIV diagnosis, of whom 49 were women. The XGBoost model outperformed the LASSO model for the total cohort, achieving an AUROC of 0.89 and AUPRC of 0.01. The female-only cohort XGBoost model resulted in an AUROC of 0.78 and AUPRC of 0.00025. The most predictive variables for the overall cohort were race, sex, and male partner. The strongest positive predictors for the female-only cohort were history of pelvic inflammatory disease, drug use, and tobacco use.

CONCLUSIONS

Our machine-learning models were able to effectively predict incident HIV diagnoses including among women. This study establishes feasibility of using these models to identify persons most suitable for PrEP in the South.

Assessment of Practices Affecting Racial and Ethnic COVID-19 Vaccination Equity in 10 Large US Cities

CONTEXT

In the United States, COVID-19 vaccines have been unequally distributed between different racial and ethnic groups. Public reporting of race and ethnicity data for COVID-19 vaccination has the potential to help guide public health responses aimed at promoting vaccination equity. However, there is evidence that such data are not readily available.

OBJECTIVES

This study sought to assess gaps and discrepancies in COVID-19 vaccination reporting in 10 large US cities in July 2021.

DESIGN, SETTING, AND PARTICIPANTS

For the 10 cities selected, we collected COVID-19 vaccination and population data using publicly available resources, such as state health department Web sites and the US Census Bureau American Community Survey. We examined vaccination plans and news sources to identify initial proposals and evidence of implementation of COVID-19 vaccination best practices.

MAIN OUTCOME MEASURE

We performed quantitative assessment of associations of the number of vaccination best practices implemented with COVID-19 racial and ethnic vaccination equity. We additionally assessed gaps and discrepancies in COVID-19 vaccination reporting between states.

RESULTS

Our analysis did not show that COVID-19 vaccination inequity was associated with the number of vaccination best practices implemented. However, gaps and variation in reporting of racial and ethnic demographic vaccination data inhibited our ability to effectively assess whether vaccination programs were reaching minority populations.

CONCLUSIONS

Lack of consistent public reporting and transparency of COVID-19 vaccination data has likely hindered public health responses by impeding the ability to track the effectiveness of strategies that target vaccine equity.

Use of convolutional neural networks in skin lesion analysis using real world image and non-image data

Background

Understanding performance of convolutional neural networks (CNNs) for binary (benign vs. malignant) lesion classification based on real world images is important for developing a meaningful clinical decision support (CDS) tool.

Methods

We developed a CNN based on real world smartphone images with histopathological ground truth and tested the utility of structured electronic health record (EHR) data on model performance. Model accuracy was compared against three board-certified dermatologists for clinical validity.

Results

At a classification threshold of 0.5, the sensitivity was 79 vs. 77 vs. 72%, and specificity was 64 vs. 65 vs. 57% for image-alone vs. combined image and clinical data vs. clinical data-alone models, respectively. The PPV was 68 vs. 69 vs. 62%, AUC was 0.79 vs. 0.79 vs. 0.69, and AP was 0.78 vs. 0.79 vs. 0.64 for image-alone vs. combined data vs. clinical data-alone models. Older age, male sex, and number of prior dermatology visits were important positive predictors for malignancy in the clinical data-alone model.

Conclusion

Additional clinical data did not significantly improve CNN image model performance. Model accuracy for predicting malignant lesions was comparable to dermatologists (model: 71.31% vs. 3 dermatologists: 77.87, 69.88, and 71.93%), validating clinical utility. Prospective validation of the model in primary care setting will enhance understanding of the model’s clinical utility.

Social determinants of health data in solid organ transplantation: National data sources and future directions

Health equity research in transplantation has largely relied on national data sources, yet the availability of social determinants of health (SDOH) data varies widely among these sources. We sought to characterize the extent to which national data sources contain SDOH data applicable to end-stage organ disease (ESOD) and transplant patients. We reviewed 10 active national data sources based in the United States. For each data source, we examined patient inclusion criteria and explored strengths and limitations regarding SDOH data, using the National Institutes of Health PhenX toolkit of SDOH as a data collection instrument. Of the 28 SDOH variables reviewed, eight-core demographic variables were included in ≥80% of the data sources, and seven variables that described elements of social status ranged between 30 and 60% inclusion. Variables regarding identity, healthcare access, and social need were poorly represented (≤20%) across the data sources, and five of these variables were included in none of the data sources. The results of our review highlight the need for improved SDOH data collection systems in ESOD and transplant patients via: enhanced inter-registry collaboration, incorporation of standardized SDOH variables into existing data sources, and transplant center and consortium-based investigation and innovation.

A framework for the oversight and local deployment of safe and high-quality prediction models

Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.

Preliminary results of a clinical research and innovation scholarship to prepare medical students to lead innovations in health care

There is consensus amongst national organizations to integrate health innovation and augmented intelligence (AI) into medical education. However, there is scant evidence to guide policymakers and medical educators working to revise curricula. This study presents academic, operational, and domain understanding outcomes for the first three cohorts of participants in a clinical research and innovation scholarship program.

Assessment of Spanish Translation of Websites at Top-Ranked US Hospitals

This cross-sectional study examines whether Spanish translation of top US hospital websites is associated with hospitals with public medical schools, children’s hospitals, larger Latinx population, or local Immigration and Customs Enforcement activities.

Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study

Background

Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care.

Objective

This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows.

Methods

We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data.

Results

A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes.

Conclusions

This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Background

Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature.

Objective

This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care.

Methods

In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch.

Results

Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch.

Conclusions

Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

Machine learning for early detection of sepsis: an internal and temporal validation study

OBJECTIVE

Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

MATERIALS AND METHODS

We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed.

RESULTS

The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP-RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP-RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP-RNN outperform all 7 clinical risk score and machine learning comparisons.

CONCLUSIONS

We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

The Project Baseline Health Study: a step towards a broader mission to map human health

The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis.

Abstract 327: Electronic Health Record Integration of Predictive Analytics to Select High-risk Stable Patients With Non-st-segment Elevation Myocardial Infarction for Intensive Care Unit Admission

Background: Nationwide, intensive care unit (ICU) utilization for initially stable patients with non-ST-segment elevation myocardial infarction (NSTEMI) is not associated with patient risk. Use of the ACTION ICU risk score, which predicts clinical deterioration requiring ICU care in initially stable NSTEMI patients, could guide admission of high-risk patients to the ICU and low-risk patients to a lower acuity unit.

Methods: We created a modified best practice advisory (BPA) within the electronic health record (EHR) at a single institution. The BPA semi-automatically calculates the ACTION ICU score (5 elements automatically populate from the EHR and 4 are entered manually) and recommends a location for admission based on a 10% risk threshold for clinical deterioration over the course of admission. The BPA was triggered for all ED patients with serum 4 th generation troponin T above the local upper limit of normal. Physicians could temporarily hide the BPA, permanently cancel it if they felt the patient’s presentation was not primarily due to NSTEMI, or generate the ACTION ICU score. We measured how ED physicians used the BPA, and clinical and utilization outcomes for patients admitted through the ED with a discharge diagnosis of NSTEMI in the 12 months before and after BPA roll-out.

Results: Between August 14, 2017 and August 13, 2018, the BPA triggered 972 times. It was hidden until the patient left the ED 230 times (23.7%) and canceled 561 times (57.7%). Providers opted to calculate a risk score 181 times (18.6%), and a score was successfully calculated 146 times. Among 135 patients for whom the BPA triggered that had a final hospital diagnosis of NSTEMI, the BPA was inappropriately canceled in 62 (45.9%) and hidden in 16 (11.9%). Overall, there were 190 NSTEMI admissions through the ED in the year after BPA integration into the EHR and 253 in the year prior. In the year after BPA integration 32.6% of the NSTEMI patients were admitted directly to the ICU compared with 37.5% admitted to ICU prior to BPA (p=0.32). No change was found in the distribution of ACTION ICU scores of NSTEMI patients admitted to the ICU prior to vs after BPA integration, as well as no differences in ICU length of stay (p=0.96), hospital length of stay (p=0.27), the proportion of patients transferred from the ward to the ICU (p=0.78), or in-hospital mortality (p=0.18).

Conclusions: Embedding the ACTION ICU risk score into the EHR did not affect clinical or utilization outcomes for patients presenting to the ED with NSTEMI, but was limited by inappropriate cancellation of the risk score calculator. Better EHR mapping to enable risk calculation without the need for user input may be needed for successful deployment of predictive analytics in this patient population.

Presenting machine learning model information to clinical end users with model facts labels

There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective presents the "Model Facts" label, a systematic effort to ensure that front-line clinicians actually know how, when, how not, and when not to incorporate model output into clinical decisions. The "Model Facts" label was designed for clinicians who make decisions supported by a machine learning model and its purpose is to collate relevant, actionable information in 1-page. Practitioners and regulators must work together to standardize presentation of machine learning model information to clinical end users in order to prevent harm to patients. Efforts to integrate a model into clinical practice should be accompanied by an effort to clearly communicate information about a machine learning model with a "Model Facts" label.

Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission

IMPORTANCE

The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable. Similarly, few have been implemented, and none have been evaluated prospectively and externally validated.

OBJECTIVES

To prospectively and externally validate a machine learning model that predicts in-hospital mortality for all adult patients at the time of hospital admission and to design the model using commonly available electronic health record data and accessible computational methods.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, electronic health record data from a total of 43 180 hospitalizations representing 31 003 unique adult patients admitted to a quaternary academic hospital (hospital A) from October 1, 2014, to December 31, 2015, formed a training and validation cohort. The model was further validated in additional cohorts spanning from March 1, 2018, to August 31, 2018, using 16 122 hospitalizations representing 13 094 unique adult patients admitted to hospital A, 6586 hospitalizations representing 5613 unique adult patients admitted to hospital B, and 4086 hospitalizations representing 3428 unique adult patients admitted to hospital C. The model was integrated into the production electronic health record system and prospectively validated on a cohort of 5273 hospitalizations representing 4525 unique adult patients admitted to hospital A between February 14, 2019, and April 15, 2019.

MAIN OUTCOMES AND MEASURES

The main outcome was in-hospital mortality. Model performance was quantified using the area under the receiver operating characteristic curve and area under the precision recall curve.

RESULTS

A total of 75 247 hospital admissions (median [interquartile range] patient age, 59.5 [29.0] years; 45.9% involving male patients) were included in the study. The in-hospital mortality rates for the training validation; retrospective validations at hospitals A, B, and C; and prospective validation cohorts were 3.0%, 2.7%, 1.8%, 2.1%, and 1.6%, respectively. The area under the receiver operating characteristic curves were 0.87 (95% CI, 0.83-0.89), 0.85 (95% CI, 0.83-0.87), 0.89 (95% CI, 0.86-0.92), 0.84 (95% CI, 0.80-0.89), and 0.86 (95% CI, 0.83-0.90), respectively. The area under the precision recall curves were 0.29 (95% CI, 0.25-0.37), 0.17 (95% CI, 0.13-0.22), 0.22 (95% CI, 0.14-0.31), 0.13 (95% CI, 0.08-0.21), and 0.14 (95% CI, 0.09-0.21), respectively.

CONCLUSIONS AND RELEVANCE

Prospective and multisite retrospective evaluations of a machine learning model demonstrated good discrimination of in-hospital mortality for adult patients at the time of admission. The data elements, methods, and patient selection make the model implementable at a system level.

Abstract P5-15-06: Potential Medicare beneficiary out-of-pocket cost reductions through use of biosimilar filgrastim-sndz over reference filgrastim among breast cancer patients: A simulation model analysis

Rationale & Objective: Granulocyte colony-stimulating factors (G-CSFs) are utilized to decrease the incidence of febrile neutropenia (FN) in patients with cancers undergoing chemotherapy treatments. In 2015 biosimilar filgrastim-sndz was the first biosimilar to be approved and launched in the US market. Limited data exists in ascertaining the impact of biosimilars on patient out-of-pocket (OOP) expenditures. The objective of this simulation model was to estimate potential OOP cost savings through use of filgrastim-sndz over reference filgrastim from a Medicare breast cancer patient perspective.

Methods: An Excel simulation analysis was conducted among breast cancer patients treated with biosimilar filgrastim-sndz or the branded reference filgrastim (identified through HCPCS codes). Data from the 2016 Medicare Limited Data Set (5% sample of the carrier file) was used to populate the model. The payment calculation worksheet within the Medicare carrier file was used to calculate the average Medicare payment to the provider and the average beneficiary OOP responsibility per claim of either filgrastim-sndz or reference filgrastim. The average OOP reduction per claim for a filgrastim-sndz beneficiary relative to a reference filgrastim beneficiary was multiplied to a hypothetical FN prevalent population of 100,000 beneficiaries (average of 10 claims per beneficiary) to estimate the potential OOP savings.

Results: Data for 616 filgrastim-sndz and 1,064 reference filgrastim claims were used to populate the model. The average Medicare allowed charge amount per claim for a filgrastim-sndz beneficiary was $362.8 versus $406.9 for a reference filgrastim beneficiary, while corresponding average Medicare payments to the provider were $284.1 and $316.9, respectively. On an average, OOP responsibility for a filgrastim-sndz beneficiary was lower compared to a reference filgrastim beneficiary ($72.9 versus $82.5) leading to a cost saving per claim of $9.60. When extrapolated to 100,000 beneficiaries (1,000,000 claims), the overall cost saving was projected to be around $9.6 million.

Conclusions: Our simulation model estimated a potential OOP Medicare breast cancer beneficiary saving of around $9.6 million, based on a hypothetical population of 100,000 FN beneficiaries, with the use of biosimilar filgrastim-sndz over reference filgrastim. Further real-world analyses are required to evaluate the true cost saving potential from a breast cancer patient perspective with the use of biosimilars over reference biologics.

Citation Format: Puckrein G, Xu L, Ryan A, Campbell K, Balu S. Potential Medicare beneficiary out-of-pocket cost reductions through use of biosimilar filgrastim-sndz over reference filgrastim among breast cancer patients: A simulation model analysis [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-15-06.

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study

BACKGROUND

Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk.

METHODS AND FINDINGS

A curated data repository of surgical outcomes was created using automated SQL and R code that extracted and processed patient clinical and surgical data across 37 million clinical encounters from the EHRs. A total of 194 clinical features including patient demographics (e.g., age, sex, race), smoking status, medications, comorbidities, procedure information, and proxies for surgical complexity were constructed and aggregated. A cohort of 66,370 patients that had undergone 99,755 invasive procedural encounters between January 1, 2014, and January 31, 2017, was studied further for the purpose of predicting postoperative complications. The average complication and 30-day postoperative mortality rates of this cohort were 16.0% and 0.51%, respectively. Least absolute shrinkage and selection operator (lasso) penalized logistic regression, random forest models, and extreme gradient boosted decision trees were trained on this surgical cohort with cross-validation on 14 specific postoperative outcome groupings. Resulting models had area under the receiver operator characteristic curve (AUC) values ranging between 0.747 and 0.924, calculated on an out-of-sample test set from the last 5 months of data. Lasso penalized regression was identified as a high-performing model, providing clinically interpretable actionable insights. Highest and lowest performing lasso models predicted postoperative shock and genitourinary outcomes with AUCs of 0.924 (95% CI: 0.901, 0.946) and 0.780 (95% CI: 0.752, 0.810), respectively. A calculator requiring input of 9 data fields was created to produce a risk assessment for the 14 groupings of postoperative outcomes. A high-risk threshold (15% risk of any complication) was determined to identify high-risk surgical patients. The model sensitivity was 76%, with a specificity of 76%. Compared to heuristics that identify high-risk patients developed by clinical experts and the ACS NSQIP calculator, this tool performed superiorly, providing an improved approach for clinicians to estimate postoperative risk for patients. Limitations of this study include the missingness of data that were removed for analysis.

CONCLUSIONS

Extracting and curating a large, local institution's EHR data for machine learning purposes resulted in models with strong predictive performance. These models can be used in clinical settings as decision support tools for identification of high-risk patients as well as patient evaluation and care management. Further work is necessary to evaluate the impact of the Pythia risk calculator within the clinical workflow on postoperative outcomes and to optimize this data flow for future machine learning efforts.

Abstract P4-12-07: Cost-minimization of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar ZARXIO® over NEUPOGEN®, NEULASTA®, and NEULASTA/ONPRO®: Breast cancer case study

RATIONALE & OBJECTIVES: Biosimilar filgrastim may offer significant cost advantages over originator filgrastim and pegfilgrastim. The objectives were (1) to evaluate for the US the comparative cost-minimization of chemotherapy-induced (febrile) neutropenia (CIN/FN) prophylaxis with biosimilar filgrastim ZARZIO® over originator filgrastim NEUPOGEN®, and originator pegfilgrastim NEULASTA® and NEULASTA/ONPRO® injection device with the health-care provider (HP) providing full administration, using 3Q2016 average selling price (ASP); and (2) to apply the different savings estimates to a breast cancer case study.

METHODS: Cost-minimization analysis of [1] acquisition costs for one patient for one chemotherapy cycle for 1 to 14 days (d) using per unit dose, and [2] administration costs using Current Procedural Terminology (CPT) codes. We calculated [1] the general cost of prophylaxis for one cycle with each agent, with standard filgrastim administrations ranging from 1-14 days and pegfilgrastim limited to single administration; and [2] the cost-savings that could be accrued from 1-14d prophylaxis with ZARXIO® over the three originator options. The case study concerns a 43 y/o Caucasian female, newly diagnosed with stage 2 HER2-negative breast cancer being started on TAC (FN risk >20%); unremarkable medical history; no comorbidities; with primary prophylaxis initiated in cycle 1 and continued through 6 cycles per local protocol (single NEULASTA® or NEULASTA/ONPRO® or 11d NEUPOGEN® or ZARXIO®).

RESULTS: Using ASP+CPT, prophylaxis cost per dose (rounded) was $260 for ZARXIO®, $326 for NEUPOGEN®, $3,926 for NEULASTA®; $3,910 for NEULASTA®. In general, cost-savings per cycle from ZARXIO® over NEUPOGEN® ranged from $65 (1d) to $916 (14d); over Neulasta®, from $3,666 (1d) to $284 (14d); and over NEULASTA/ONPRO®, from $3,649 (1d) to $267 (14d). In the breast cancer case study, cost of prophylaxis per one cycle was $2,862 for ZARXIO® (11d), $3,582 for NEUPOGEN® (11d) vs. $3926 for NEULASTA® and $3910 for NEULASTA/ONPRO® single-injection. Cost-savings per cycle from ZARXIO® use were $719 vs. NEUPOGEN®, $1,064 vs. NEULASTA®, and $1,047 vs. NEULASTA/ONPRO®. Total savings from ZARXIO® use over all 6 TAC cycles were $4,316 vs. NEUPOGEN®, $6,385 vs. NEULASTA®, and $6,284 vs. NEULASTA/ONPRO®.

CONCLUSIONS: In general, CIN/FN prophylaxis with ZARXIO® for 1-14d generates significant cost savings over NEUPOGEN®, NEULASTA® and NEULASTA/ONPRO generating significant cost-savings. In the case study of the 43 y/o HER-negative breast cancer patient treated with TAC and prescribed 6 cycles of primary prophylaxis with 11d standard or single-administration pegfilgrastim, savings reached as high as $6,385 for the full course of chemotherapy. Given the trial evidence of non-inferiority of pegfilgrastim over filgrastim, the clinical trend for <14d of filgrastim prophylaxis, and payer trends to authorize filgrastim vs. pegfilgrastim prophylaxis, using biosimilar Zarxio® is rational from both a economic perspective; as illustrated also in the breast cancer case study.

Citation Format: McBride A, Campbell K, Bikkina M, MacDonald K, Abraham I, Balu S. Cost-minimization of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar ZARXIO® over NEUPOGEN®, NEULASTA®, and NEULASTA/ONPRO®: Breast cancer case study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-12-07.

Barriers to Achieving Economies of Scale in Analysis of EHR Data. A Cautionary Tale

Signed in 2009, the Health Information Technology for Economic and Clinical Health Act infused $28 billion of federal funds to accelerate adoption of electronic health records (EHRs). Yet, EHRs have produced mixed results and have even raised concern that the current technology ecosystem stifles innovation. We describe the development process and report initial outcomes of a chronic kidney disease analytics application that identifies high-risk patients for nephrology referral. The cost to validate and integrate the analytics application into clinical workflow was $217,138. Despite the success of the program, redundant development and validation efforts will require $38.8 million to scale the application across all multihospital systems in the nation. We address the shortcomings of current technology investments and distill insights from the technology industry. To yield a return on technology investments, we propose policy changes that address the underlying issues now being imposed on the system by an ineffective technology business model.

The impact of specialty pharmaceuticals as drivers of health care costs

The pharmaceutical industry is shifting its focus from blockbuster small molecules to specialty pharmaceuticals. Specialty pharmaceuticals are novel drugs and biologic agents that require special handling and ongoing monitoring, are administered by injection or infusion, and are sold in the marketplace by a small number of distributors. They are frequently identified by having a cost to payers and patients of $600 or more per treatment. The total costs of the new agents are likely to have a substantial impact on overall health care costs and on patients during the next decade, unless steps are taken to align competing interests. We examine the economic and policy issues related to specialty pharmaceuticals, taking care to consider the impact on patients. We assess the role of cost-sharing provisions, legislation that is promoting realignment within the market, the role of biosimilars in price competition, and the potential for novel drug development paradigms to help bend the cost curve. The economic aspects of this analysis highlight the need for a far-reaching discussion of potential novel approaches to innovation pathways in our quest for both affordability and new technology.

Budget impact of rosuvastatin initiation in high-risk hyperlipidemic patients from a US managed care perspective

INTRODUCTION

Statins reduce low-density lipoprotein cholesterol (LDL-C) levels, which, when elevated, represent a significant risk factor for cardiovascular (CV) disease. Hyperlipidemic patients at risk of CV events initiated on simvastatin or atorvastatin may be less likely to meet LDL-C goals (defined in National Cholesterol Education Program guidelines) and more likely to experience CV events than patients initiated on rosuvastatin. A 3-year budget impact model was developed to estimate the clinical impact and cost to a US managed care organization (MCO) with 1 million members of initiating high-risk hyperlipidemic patients on rosuvastatin rather than simvastatin or atorvastatin.

METHODS

A total of 1000 adult patients were assumed to initiate statins. The average baseline LDL-C level was 189 mg/dL. In scenario 1, all patients were initiated on simvastatin or atorvastatin and titrated to a higher dose, or switched to atorvastatin (if initiated on simvastatin) or rosuvastatin; in scenario 2, 50% of the 520 high-risk patients were initiated on rosuvastatin. Drug acquisition and administration costs were considered. Product labeling, clinical trial results, national prescription claims data, and published literature were used to populate the model.

RESULTS

Over 3 years, 75 additional patients reached their LDL-C goal in scenario 2, compared with scenario 1 (633 vs 558, respectively), at an increased cost of $240,628 ($1,415,516 vs $1,174,888, respectively). The additional per member per month (PMPM) cost of scenario 2 was $0.007.

LIMITATIONS

This analysis assumed that statin efficacy is the same in real life as in trials, and used titration and switching patterns not based on patients' goal attainment. However, sensitivity and scenario analyses showed that the model was less sensitive to these parameters than to cost-related parameters.

CONCLUSIONS

Initiating high-risk hyperlipidemic patients on rosuvastatin may increase the number of patients reaching LDL-C goal at a relatively modest increase in PMPM cost to an MCO.

Variation of health-care resource utilization according to GERD-associated complications

Complications associated with gastroesophageal reflux disease (GERD) can include esophageal stricture, Barrett's esophagus, gastrointestinal hemorrhage, and extraesophageal symptoms. The impact of GERD-associated complications on health-care utilization deserves further evaluation. We identified commercial enrollees 18-75 years old with claims for GERD (International Classification of Diseases, Ninth Revision, Clinical Modification Codes: 530.81 or 530.11) and subsequent usage of proton pump inhibitors from 01/01/05 to 06/30/09. The initial GERD diagnosis date was designated as the index date, and patients were studied for 6 months preindex and postindex. Eligible patients were subsequently stratified based on medical claims for GERD-associated complications as follows: stage A (GERD diagnosis, no other symptoms), stage B (GERD + extraesophageal symptoms), stage C (GERD + Barrett's esophagus), stage D (GERD + esophageal stricture), and stage E (GERD + iron-deficiency anemia or acute upper gastrointestinal hemorrhage). Patient characteristics, health-care utilization, and costs were compared between stage A and each stage with complicated GERD (B-D). Of the 174,597 patients who were eligible for analysis, 74% were classified as stage A, 20% stage B, 1% stage C, 2% stage D, and 3% stage E. Relative to stage A, patients in stages C, D, and E were significantly more likely to visit a gastroenterologist (13% vs. 68%, 71%, and 38%, respectively) and had higher rates of esophageal ulcers (0.3% vs. 8%, 5%, and 3%, respectively) and Nissen fundoplication (0.05% vs. 0.6%, 0.3%, and 0.2%, respectively). Six-month GERD-related costs ranged from $615/patient (stage A) to $1714/patient (stage D); all-cause costs ranged from $4195/patient (stage A) to $11,340/patient (stage E). Compared with stage A, all other cohorts had significantly higher all-cause and GERD-related costs (P < 0.0001 for all comparisons). While patients with more severe GERD represented a relatively small portion of the GERD cohort, they demonstrated significantly greater health-care costs and overall utilization than patients with uncomplicated GERD.

Treatment patterns and costs following metastatic breast cancer diagnosis in U.S. women: A SEER-Medicare analysis

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Background: To use SEER-Medicare data to evaluate treatment (tx) patterns and health care costs in U.S. women with metastatic breast cancer (MBC).

Methods: Key inclusion criteria included women diagnosed (dx) with breast cancer in 2001-2005 with 1) enrollment in Medicare 12 mo prior to dx through follow-up (2008) or death; 2) initial dx of “distant” disease or 2 indications of secondary malignancy >2 mo after initial dx; and 3) indication of tx with injectable hormonal, chemotherapy (chemo) or targeted/biologic therapies. Lines of tx were designated as: 1st-line if 1st agent (or agents, if on same day) after dx of MBC; new agents administered > 42 d after the previous agent are a new line, as well as an agent administered > 60 d after last dose of the same agent. Oral medication data were not available. Kaplan-Meier techniques estimated lifetime total health costs by partitioning data into 30-d intervals starting with MBC dx date and then summing the product of mean cost in each interval by the probability of survival to the start of the interval. Bootstrapping methods were used to generate 95% confidence intervals (CI).

Results: The table lists the top 5 injectable tx for first, second, and third line, with associated mean lifetime costs.

Conclusions: In this population, the injectable fulvestrant was the most commonly used first-line tx, with vinorelbine most frequently used in second- and third-line settings for MBC. Mean lifetime cost of MBC was $110,000.

[Table: see text]

Development of a refractory gastro-oesophageal reflux score using an administrative claims database

BACKGROUND

Approximately one-third of gastro-oesophageal reflux disease (GERD) patients demonstrate refractory symptoms following treatment with proton pump inhibitor (PPI) therapy.

AIM

To develop a refractory GERD score that can be applied to predict patients' healthcare utilisation.

METHODS

We enrolled adults (≥18 years) with a diagnosis of GERD. Refractory GERD was evaluated on an 8-point scale where 1 point was given for each of the following criteria: doubling, addition, or switching of GERD medication dose, receipt of a GERD-related endoscopic procedure or surgery, or ≥3 GERD-related outpatient visits. Refractory GERD was defined as the presence of two or more points.

RESULTS

A total of 135,139 GERD patients (44% male) were analysed with a mean (±s.d.) age of 52.9 ± 15 years. The mean overall refractory GERD score was 1.12 ± 1.2 (range 0-8 on an 8-point scale); 31% of patients had refractory GERD with a mean score of 2.56 ± 0.82. Among patients with refractory GERD, 31% doubled their GERD medication, 28% added a new GERD medication, 60% switched GERD medications, 54% had a GERD-related procedure and 1% had a GERD-related surgery. Patients with refractory GERD were more likely to be female (59% vs. 55%, P < 0.001) and had a higher co-morbidity score (0.78 vs. 0.56, P < 0.001). The overall mean costs for refractory patients during the study period were significantly higher compared with treatment-responsive patients ($18,088 ± $36,220 vs. $11,044 ± $22,955, P < 0.001).

CONCLUSIONS

Refractory GERD was present in approximately one-third of the GERD patients. We created a GERD refractory score that could define need for increased anti-reflux therapy and predict higher healthcare resource utilisation.

Palonosetron versus other 5-HT3–receptor antagonists for chemotherapy-induced nausea and vomiting prevention in patients with noncolon gastrointestinal cancers

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Background: This study analyzed the risk of chemotherapy induced nausea and vomiting (N&V) [CINV] associated with palonosetron versus other 5-HT3 receptor antagonists (5-HT3-RAs) initiation among patients with non-colon gastrointestinal cancers receiving chemotherapy (CT) in a hospital outpatient setting.

Methods: Patients diagnosed with any non-colon gastrointestinal cancer initiating any CT and antiemetic prophylaxis with palonosetron (Group 1) or other 5-HT3-RAs (Group 2) for the first time (index date) between 4/1/2007-3/31/2009 were identified from the Premier Perspective database. Patients included were aged ≥ 18 years, with no evidence of N&V, CT, and antiemetic medication in the 6 month pre-index date period, and with at least 36 consecutive months of data. A negative binomial GLM regression analysis was done estimating the number of CINV events (identified through either ICD-9-CM codes for N&V and/or volume depletion or CINV-related rescue medications 1 day after CT administration) in the follow-up period (first of 8 CT cycles or 6 months post index date) between the 2 groups (after matching on CT and specific CT cycle).

Results: Of 658 identified patients, 215 initiated on palonosetron (Group 1; 32.7%). Group 1 patients were significantly younger [61.7 (SD: 11.3) vs. 62.2 (12.1) years; p = 0.0073], a higher percent received highly emetogenic chemotherapy (27.4% vs. 19.4%; p < 0.0001), and comprised of less African Americans (7.0% vs. 13.3%). In the follow-up period, the unadjusted number of CINV events per patient per CT cycle for Group 1 patients was lower versus Group 2 patients, though statistically non-significant (4.7 vs. 5.2; p = 0.1714). However, after controlling for differences in demographic and clinical variables, the regression model predicted a statistically significant reduction (30.9%) in the total CINV events per patient per CT cycle in favor of Group 1 patients; p = 0.0086.

Conclusions: In this analysis, patients with non-colon gastrointestinal cancers initiated on palonosetron were more likely to experience a significantly lower rate of CINV events per patient per CT cycle versus those initiated on other 5-HT3-RAs.

[Table: see text]

Production and characterisation of monoclonal antibodies specific for chicken interleukin-12

Using genetic immunisation of mice, we produced antibodies against chicken interleukin-12p40 (chIL-12p40), also known as IL-12β. After a final injection with a recombinant chIL-12p40 protein, several stable hybridoma cell lines were established which secreted monoclonal antibodies (mAbs) to this component of the heterodimeric IL-12 cytokine. Specific binding of three of the mAbs to COS-7 cell-derived recombinant chIL-12p40 and the chIL-12p70 heterodimer was demonstrated in an indirect ELISA, and in dot blots. Two of the mAbs were used to develop a capture ELISA, suitable for detecting both recombinant protein (chIL-12p40 and the heterodimeric p70 protein) and native chIL-12. The mAbs were further characterised to show utility in immunocytochemistry.

Traditional Medicinal Knowledge on Moringa concanensis Nimmo of Perambalur District, Tamilnadu

Moringa concanensis Nimmo (Moringaceae) is one of the important medicinal plant. It is restricted in its distribution. The present study was aimed at recording traditional knowledge about this plant in various localities of Perambalur district, Tamilnadu. The medicinally useful part, drug preparation, mode of administration and the disease which can be treated have been discussed in this paper.