Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application

Referral Criteria to Specialist Palliative Care for People with Advanced Chronic Kidney Disease: A Systematic Review

CONTEXT

People with advanced chronic kidney disease (CKD) have significant morbidity, yet for many, access to palliative care occurs late, if at all.

OBJECTIVES

This study sought to examine criteria for referral to specialist palliative care for adults with advanced CKD with a view to improving use of these essential services.

METHODS

Systematic review of studies detailing referral criteria to palliative care in advanced CKD conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guideline and registered (PROSPERO: CRD42021230751).

DATA SOURCES

Electronic databases (Ovid, MEDLINE, Ovid Embase, and PubMed) were used to identify potential studies, which were subjected to double review, data extraction, thematic coding, and descriptive analyses.

RESULTS

Searches yielded 650 unique titles ultimately resulting in 56 studies addressing referral criteria to specialist palliative care in advanced CKD. Of 10 categories of referral criteria, most commonly discussed were: Critical times of treatment decision making (n = 23, 41%); physical or emotional symptoms (n = 22, 39%); limited prognosis (n = 18, 32%); patient age and comorbidities (n = 18, 32%); category of CKD/ biochemical criteria (n = 13, 23%); functional decline (n = 13, 23); psychosocial needs (n = 9, 16%); future care planning (n = 9, 16%); anticipated decline in illness course (n = 8, 14%); and hospital use (n = 8, 14%).

CONCLUSION

Clinicians consider referral to specialist palliative care for a wide range of reasons, with many related to care needs. As palliative care continues to integrate with nephrology, our findings represent a key step towards developing consensus criteria to standardize referral for patients with chronic kidney diseases.

The use of natural language processing in palliative care research: A scoping review

BACKGROUND

Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy.

AIM

To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed.

DESIGN

A scoping review using the framework by Arksey and O'Malley and the updated recommendations proposed by Levac et al. was conducted.

SOURCES

PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified.

RESULTS

197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings.

CONCLUSIONS

We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

Background

Electronic health records can be used for population-wide identification and monitoring of disease. The Territory Kidney Care project developed algorithms to identify individuals with chronic kidney disease (CKD) and several commonly comorbid chronic diseases. This study aims to describe the development and validation of our algorithms for CKD, diabetes, hypertension, and cardiovascular disease. A secondary aim of the study was to describe data completeness of the Territory Kidney Care database.

Methods

The Territory Kidney Care database consolidates electronic health records from multiple health services including public hospitals (n = 6) and primary care health services (> 60) across the Northern Territory, Australia. Using the database (n = 48,569) we selected a stratified random sample of patients (n = 288), which included individuals with mild to end-stage CKD. Diagnostic accuracy of the algorithms was tested against blinded manual chart reviews. Data completeness of the database was also described.

Results

For CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR < 60 ml/min/1.73², including renal replacement therapy) overall algorithm sensitivity was 93% (95%CI 89 to 96%) and specificity was 73% (95%CI 64 to 82%). For CKD defined as CKD stage 3a or higher (eGFR < 60 ml/min/1.73²) algorithm sensitivity and specificity were 93% and 97% respectively. Among the CKD 1 to 5 staging algorithms, the CKD stage 5 algorithm was most accurate with > 99% sensitivity and specificity. For related comorbidities – algorithm sensitivity and specificity results were 75% and 97% for diabetes; 85% and 88% for hypertension; and 79% and 96% for cardiovascular disease.

Conclusions

We developed and validated algorithms to identify CKD and related chronic diseases within electronic health records. Validation results showed that CKD algorithms have a high degree of diagnostic accuracy compared to traditional administrative codes. Our highly accurate algorithms present new opportunities in early kidney disease detection, monitoring, and epidemiological research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02947-9.

Identification of Uncontrolled Symptoms in Cancer Patients Using Natural Language Processing

CONTEXT

For patients with cancer, uncontrolled pain and other symptoms are the leading cause of unplanned hospitalizations. Early access to specialty palliative care (PC) is effective to reduce symptom burden, but more efficient approaches are needed for rapid identification and referral. Information on symptom burden largely exists in free-text notes, limiting its utility as a trigger for best practice alerts or automated referrals.

OBJECTIVES

To evaluate whether natural language processing (NLP) can be used to identify uncontrolled symptoms (pain, dyspnea, or nausea/vomiting) in the electronic health record (EHR) among hospitalized cancer patients with advanced disease.

METHODS

The dataset included 1,644 hospitalization encounters for cancer patients admitted from 1/2017 -6/2019. We randomly sampled 296 encounters, which included 15,580 clinical notes. We manually reviewed the notes and recorded symptom severity. The primary endpoint was an indicator for whether a symptom was labeled as "controlled" (none, mild, not reported) or as "uncontrolled" (moderate or severe). We randomly split the data into training and test sets and used the Random Forest algorithm to evaluate final model performance.

RESULTS

Our models predicted presence of an uncontrolled symptom with the following performance: pain with 61% accuracy, 69% sensitivity, and 46% specificity (F1: 69.5); nausea/vomiting with 68% accuracy, 21% sensitivity, and 90% specificity (F1: 29.4); and dyspnea with 80% accuracy, 22% sensitivity, and 88% specificity (F1: 21.1).

CONCLUSION

This study demonstrated initial feasibility of using NLP to identify hospitalized cancer patients with uncontrolled symptoms. Further model development is needed before these algorithms could be implemented to trigger early access to PC.

Precision Medicine Landscape of Genomic Testing for Patients With Cancer in the National Institutes of Health All of Us Database Using Informatics Approaches

PURPOSE

The rapid growth of biomedical data ecosystems has catalyzed research for oncology and precision medicine. We leverage federal cloud-based precision medicine databases and tools to better understand the current landscape of precision medicine and genomic testing for patients with cancer.

METHODS

Retrospective observational study of genomic testing for patients with cancer in the National Institutes of Health All of Us Research Program, with the cancer cohort defined as having at least two documented or reported cancer diagnoses.

RESULTS

There were 5,678 (1.8%) All of Us participants in the cancer cohort, with a significant difference between cancer status by age category, sex, race, and ethnicity (P < .001 for all). There were 295 (5.2%) patients with cancer who received genomic testing compared with 6,734 (2.2%) of noncancer patients, with 752 genomic tests commonly focused on gene mutations (primarily pharmacogenomics), molecular pathology, or clinical cytogenetic reports.

CONCLUSION

Although not yet ubiquitous, diverse clinical genomic analyses in oncology can set the stage to grow the practice of precision medicine by integrating research patient data repositories, cancer data ecosystems, and biomedical informatics.

Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research

PURPOSE

The Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics.

METHODS

We identified peer-reviewed publications that used EMERSE through online searches as well as through direct e-mails to users based on audit logs. These logs were also used to summarize use at each of the three sites. Search terms for two of the sites were characterized using the natural language processing tool MetaMap to determine to which semantic types the terms could be mapped.

RESULTS

We identified a total of 326 peer-reviewed publications that used EMERSE through August 2019, although this is likely an underestimation of the true total based on the use log analysis. Oncology-related research comprised nearly one third (n = 105; 32.2%) of all research output. The use logs showed that EMERSE had been used by multiple people at each site (nearly 3,500 across all three) who had collectively logged into the system > 100,000 times. Many user-entered search queries could not be mapped to a semantic type, but the most common semantic type for terms that did match was “disease or syndrome,” followed by “pharmacologic substance.”

CONCLUSION

EMERSE has been shown to be a valuable tool for supporting cancer research. It has been successfully deployed at other sites, despite some implementation challenges unique to each deployment environment.

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

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Does Receipt of Recommended Elements of Palliative Care Precede In-Hospital Death or Hospice Referral?

CONTEXT

Palliative care aligns treatments with patients' values and improves quality of life, yet whether receipt of recommended elements of palliative care is associated with end-of-life outcomes is understudied.

OBJECTIVES

To assess whether recommended elements of palliative care (pain and symptom management, goals of care, and spiritual care) precede in-hospital death and hospice referral and whether delivery by specialty palliative care affects that relationship.

METHODS

We conducted structured chart reviews for decedents with late-stage cancer, dementia, and chronic kidney disease with a hospital admission during the six months preceding death. Measures included receipt of recommended elements of palliative care delivered by any clinician and specialty palliative care consult. We assessed associations between recommended elements of palliative care and in-hospital death and hospice referral using multivariable Poisson regression models.

RESULTS

Of 402 decedents, 67 (16.7%) died in hospital, and 168 (41.8%) had hospice referral. Among elements of palliative care, only goals-of-care discussion was associated with in-hospital death (incidence rate ratio [IRR] 1.37; 95% CI 1.01-1.84) and hospice referral (IRR 1.85; 95% CI 1.31-2.61). Specialty palliative care consult was associated with a lower likelihood of in-hospital death (IRR 0.57; 95% CI 0.44-0.73) and a higher likelihood of hospice referral (IRR 1.45; 95% CI 1.12-1.89) compared with no consult.

CONCLUSION

Goals-of-care discussions by different types of clinicians commonly precede end-of-life care in hospital or hospice. However, engagement with specialty palliative care reduced in-hospital death and increased hospice referral. Understanding the causal pathways of goals-of-care discussions may help build primary palliative care interventions to support patients near the end of life.