Informatics can identify systemic sclerosis (SSc) patients at risk for scleroderma renal crisis

Advancing rheumatology with natural language processing: insights and prospects from a systematic review

Objectives

Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management.

Methods

We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias.

Results

Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares.

Conclusion

NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice.

Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis

OBJECTIVES

To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden.

METHODS

We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud's syndrome, and esophageal involvement/symptoms.

RESULTS

Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p < .001), organ involvement (pulmonary hypertension 12.7% vs 6% p = .011) and medication use (mycophenolate use 28.7% vs 11.4%, p < .001) than those identified by the ICD codes alone.

CONCLUSION

EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs.

The frequency of Raynaud's phenomenon, very early diagnosis of systemic sclerosis, and systemic sclerosis in a large Veteran Health Administration database

BACKGROUND

We describe Raynauds phenomenon (RP), potential very early diagnosis of systemic sclerosis (VEDOSS), and systemic sclerosis (SSc) in Veterans deployed in support of Post-9/11 operations. We sought to describe the military occupation specialty, clinical features, and vasodilator use across the three diagnoses.

METHODS

Individual Veterans medical records were assessed for RP (ICD-9443.0), VEDOSS with swelling of hands (ICD-9729.81) and RP (ICD-9443.0), and SSc (ICD-9710.1). The distribution of sociodemographic, military service branch, job classification, vasodilator use, and comorbidities were examined across the three classifications of disease. The chi-squared test and Fisher's exact compared frequency of these categorical variables. Logistic regression assessed the likelihood of characteristics of the three classifications.

RESULTS

In this population of 607,665 individual Veteran medical records, 857 had RP, 45 met possible VEDOSS criteria, and 71 had a diagnosis of SSc. The majority of RP, potential VEDOSS and SSc cases were white males. Those in craftworks, engineering or maintenance, and healthcare had a greater likelihood of RP. Less than half of RP and VEDOSS patients were on vasodilators. The most common comorbidities in this population were the diagnostic code for pain (highest in the potential VEDOSS group [81.6%]), followed by depression in all groups.

CONCLUSION

This is a unique Veteran population of predominately-male patients. Our data suggests that vasodilator medications are potentially being under-utilized for RP and potential VEDOSS. Our data highlights mood and pain management as an important aspect of SSc care.

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.

Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record

Background

Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR.

Methods

We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowledge and available data: SSc ICD-9 and ICD-10-CM codes, positive antinuclear antibody (ANA) (titer ≥ 1:80), and a keyword of Raynaud’s phenomenon (RP). We performed both rule-based and machine learning techniques for algorithm development. Positive predictive values (PPVs), sensitivities, and F-scores (which account for PPVs and sensitivities) were calculated for the algorithms.

Results

PPVs were low for algorithms using only 1 count of the SSc ICD-9 code. As code counts increased, the PPVs increased. PPVs were higher for algorithms using ICD-10-CM codes versus the ICD-9 code. Adding a positive ANA and RP keyword increased the PPVs of algorithms only using ICD billing codes. Algorithms using ≥ 3 or ≥ 4 counts of the SSc ICD-9 or ICD-10-CM codes and ANA positivity had the highest PPV at 100% but a low sensitivity at 50%. The algorithm with the highest F-score of 91% was ≥ 4 counts of the ICD-9 or ICD-10-CM codes with an internally validated PPV of 90%. A machine learning method using random forests yielded an algorithm with a PPV of 84%, sensitivity of 92%, and F-score of 88%. The most important feature was RP keyword.

Conclusions

Algorithms using only ICD-9 codes did not perform well to identify SSc patients. The highest performing algorithms incorporated clinical data with billing codes. EHR-based algorithms can identify SSc patients across a healthcare system, enabling researchers to examine important outcomes.

Automatic prediction of coronary artery disease from clinical narratives

Coronary Artery Disease (CAD) is not only the most common form of heart disease, but also the leading cause of death in both men and women (Coronary Artery Disease: MedlinePlus, 2015). We present a system that is able to automatically predict whether patients develop coronary artery disease based on their narrative medical histories, i.e., clinical free text. Although the free text in medical records has been used in several studies for identifying risk factors of coronary artery disease, to the best of our knowledge our work marks the first attempt at automatically predicting development of CAD. We tackle this task on a small corpus of diabetic patients. The size of this corpus makes it important to limit the number of features in order to avoid overfitting. We propose an ontology-guided approach to feature extraction, and compare it with two classic feature selection techniques. Our system achieves state-of-the-art performance of 77.4% F1 score.

Performance of a Natural Language Processing (NLP) Tool to Extract Pulmonary Function Test (PFT) Reports from Structured and Semistructured Veteran Affairs (VA) Data

Introduction/Objective:

Pulmonary function tests (PFTs) are objective estimates of lung function, but are not reliably stored within the Veteran Health Affairs data systems as structured data. The aim of this study was to validate the natural language processing (NLP) tool we developed—which extracts spirometric values and responses to bronchodilator administration—against expert review, and to estimate the number of additional spirometric tests identified beyond the structured data.

Methods:

All patients at seven Veteran Affairs Medical Centers with a diagnostic code for asthma Jan 1, 2006–Dec 31, 2012 were included. Evidence of spirometry with a bronchodilator challenge (BDC) was extracted from structured data as well as clinical documents. NLP’s performance was compared against a human reference standard using a random sample of 1,001 documents.

Results:

In the validation set NLP demonstrated a precision of 98.9 percent (95 percent confidence intervals (CI): 93.9 percent, 99.7 percent), recall of 97.8 percent (95 percent CI: 92.2 percent, 99.7 percent), and an F-measure of 98.3 percent for the forced vital capacity pre- and post pairs and precision of 100 percent (95 percent CI: 96.6 percent, 100 percent), recall of 100 percent (95 percent CI: 96.6 percent, 100 percent), and an F-measure of 100 percent for the forced expiratory volume in one second pre- and post pairs for bronchodilator administration. Application of the NLP increased the proportion identified with complete bronchodilator challenge by 25 percent.

Discussion/Conclusion:

This technology can improve identification of PFTs for epidemiologic research. Caution must be taken in assuming that a single domain of clinical data can completely capture the scope of a disease, treatment, or clinical test.

Google-driven search for big data in autoimmune geoepidemiology: analysis of 394,827 patients with systemic autoimmune diseases

Systemic autoimmune diseases (SADs) are a significant cause of morbidity and mortality worldwide, although their epidemiological profile varies significantly country by country. We explored the potential of the Google search engine to collect and merge large series (>1000 patients) of SADs reported in the Pubmed library, with the aim of obtaining a high-definition geoepidemiological picture of each disease. We collected data from 394,827 patients with SADs. Analysis showed a predominance of medical vs. administrative databases (74% vs. 26%), public health system vs. health insurance resources (88% vs. 12%) and patient-based vs. population-based designs (82% vs. 18%). The most unbalanced gender ratio was found in primary Sjögren syndrome (pSS), with nearly 10 females affected per 1 male, followed by systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and antiphospholipid syndrome (APS) (ratio of nearly 5:1). Each disease predominantly affects a specific age group: children (Kawasaki disease, primary immunodeficiencies and Schonlein-Henoch disease), young people (SLE Behçet disease and sarcoidosis), middle-aged people (SSc, vasculitis and pSS) and the elderly (amyloidosis, polymyalgia rheumatica, and giant cell arteritis). We found significant differences in the geographical distribution of studies for each disease, and a higher frequency of the three SADs with available data (SLE, inflammatory myopathies and Kawasaki disease) in African-American patients. Using a "big data" approach enabled hitherto unseen connections in SADs to emerge.