Discovering novel disease comorbidities using electronic medical records

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records

BACKGROUND

Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2).

METHODS

De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention.

RESULTS

In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention.

CONCLUSIONS

Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.

Health Conditions Associated with Alzheimer's Disease and Vascular Dementia

OBJECTIVE

We examined medical records to determine health conditions associated with dementia at varied intervals prior to dementia diagnosis in participants from the Baltimore Longitudinal Study of Aging (BLSA).

METHODS

Data were available for 347 Alzheimer's disease (AD), 76 vascular dementia (VaD), and 811 control participants without dementia. Logistic regressions were performed associating International Classification of Diseases, 9th Revision (ICD-9) health codes with dementia status across all time points, at 5 and 1 year(s) prior to dementia diagnosis, and at the year of diagnosis, controlling for age, sex, and follow-up length of the medical record.

RESULTS

In AD, the earliest and most consistent associations across all time points included depression, erectile dysfunction, gait abnormalities, hearing loss, and nervous and musculoskeletal symptoms. Cardiomegaly, urinary incontinence, non-epithelial skin cancer, and pneumonia were not significant until 1 year before dementia diagnosis. In VaD, the earliest and most consistent associations across all time points included abnormal electrocardiogram (EKG), cardiac dysrhythmias, cerebrovascular disease, non-epithelial skin cancer, depression, and hearing loss. Atrial fibrillation, occlusion of cerebral arteries, essential tremor, and abnormal reflexes were not significant until 1 year before dementia diagnosis.

INTERPRETATION

These findings suggest that some health conditions are associated with future dementia beginning at least 5 years before dementia diagnosis and are consistently seen over time, while others only reach significance closer to the date of diagnosis. These results also show that there are both shared and distinctive health conditions associated with AD and VaD. These results reinforce the need for medical intervention and treatment to lessen the impact of health comorbidities in the aging population. ANN NEUROL 2023;93:805-818.

From real-world electronic health record data to real-world results using artificial intelligence

With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures almost the complete variety of patients, leading to more generalisable results. For rheumatology, these data are particularly interesting as our diseases are uncommon and often take years to develop. In this review, we discuss the following concepts related to the use of EHR for research and considerations for translation into clinical care: EHR data contain a broad collection of healthcare data covering the multitude of real-life patients and the healthcare processes related to their care. Machine learning (ML) is a powerful method that allows us to leverage a large amount of heterogeneous clinical data for clinical algorithms, but requires extensive training, testing, and validation. Patterns discovered in EHR data using ML are applicable to real life settings, however, are also prone to capturing the local EHR structure and limiting generalisability outside the EHR(s) from which they were developed. Population studies on EHR necessitates knowledge on the factors influencing the data available in the EHR to circumvent biases, for example, access to medical care, insurance status. In summary, EHR data represent a rapidly growing and key resource for real-world studies. However, transforming RWD EHR data for research and for real-world evidence using ML requires knowledge of the EHR system and their differences from existing observational data to ensure that studies incorporate rigorous methods that acknowledge or address factors such as access to care, noise in the data, missingness and indication bias.

pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis

Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .

Gender-sensitive word embeddings for healthcare

OBJECTIVE

To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance.

MATERIALS AND METHODS

We analyze gender bias in clinical trials described by 16 772 PubMed abstracts (2008-2018). We present a method to augment word embeddings, the core building block of NLP-centric representations, by weighting abstracts by the number of women participants in the trial. We evaluate the resulting gender-sensitive embeddings performance on several clinical prediction tasks: comorbidity classification, hospital length of stay prediction, and intensive care unit (ICU) readmission prediction.

RESULTS

For female patients, the gender-sensitive model area under the receiver-operator characteristic (AUROC) is 0.86 versus the baseline of 0.81 for comorbidity classification, mean absolute error 4.59 versus the baseline of 4.66 for length of stay prediction, and AUROC 0.69 versus 0.67 for ICU readmission. All results are statistically significant.

DISCUSSION

Women have been underrepresented in clinical trials. Thus, using the broad clinical trials literature as training data for statistical language models could result in biased models, with deficits in knowledge about women. The method presented enables gender-sensitive use of publications as training data for word embeddings. In experiments, the gender-sensitive embeddings show better performance than baseline embeddings for the clinical tasks studied. The results highlight opportunities for recognizing and addressing gender and other representational biases in the clinical trials literature.

CONCLUSION

Addressing representational biases in data for training NLP embeddings can lead to better results on downstream tasks for underrepresented populations.

Trends in Optic Neuritis Incidence and Prevalence in the UK and Association With Systemic and Neurologic Disease

Importance

Epidemiologic data on optic neuritis (ON) incidence and associations with immune-mediated inflammatory diseases (IMIDs) are sparse.

Objective

To estimate 22-year trends in ON prevalence and incidence and association with IMIDs in the United Kingdom.

Design, Setting, and Participants

This cohort study analyzed data from The Health Improvement Network from January 1, 1995, to September 1, 2019. The study included 10 937 511 patients 1 year or older with 75.2 million person-years' follow-up. Annual ON incidence rates were estimated yearly (January 1, 1997, to December 31, 2018), and annual ON prevalence was estimated by performing sequential cross-sectional studies on data collected on January 1 each year for the same period. Data for 1995, 1996, and 2019 were excluded as incomplete. Risk factors for ON were explored in a cohort analysis from January 1, 1997, to December 31, 2018. Matched case-control and retrospective cohort studies were performed using data from January 1, 1995, to September 1, 2019, to explore the odds of antecedent diagnosis and hazard of incident diagnosis of 66 IMIDs in patients compared with controls.

Exposures

Optic neuritis.

Main Outcomes and Measures

Annual point prevalence and incidence rates of ON, adjusted incident rate ratios (IRRs) for risk factors, and adjusted odds ratios (ORs) and adjusted hazard ratios (HRs) for 66 IMIDs.

Results

A total of 10 937 511 patients (median [IQR] age at cohort entry, 32.6 [18.0-50.4] years; 5 571 282 [50.9%] female) were studied. A total of 1962 of 2826 patients (69.4%) with incident ON were female and 1192 of 1290 92.4%) were White, with a mean (SD) age of 35.6 (15.6) years. Overall incidence across 22 years was stable at 3.7 (95% CI, 3.6-3.9) per 100 000 person-years. Annual point prevalence (per 100 000 population) increased with database maturity, from 69.3 (95% CI, 57.2-81.3) in 1997 to 114.8 (95% CI, 111.0-118.6) in 2018. The highest risk of incident ON was associated with female sex, obesity, reproductive age, smoking, and residence at higher latitude, with significantly lower risk in South Asian or mixed race/ethnicity compared with White people. Patients with ON had significantly higher odds of prior multiple sclerosis (MS) (OR, 98.22; 95% CI, 65.40-147.52), syphilis (OR, 5.76; 95% CI, 1.39-23.96), Mycoplasma (OR, 3.90; 95% CI, 1.09-13.93), vasculitis (OR, 3.70; 95% CI, 1.68-8.15), sarcoidosis (OR, 2.50; 95% CI, 1.21-5.18), Epstein-Barr virus (OR, 2.29; 95% CI, 1.80-2.92), Crohn disease (OR, 1.97; 95% CI, 1.13-3.43), and psoriasis (OR, 1.28; 95% CI, 1.03-1.58). Patients with ON had a significantly higher hazard of incident MS (HR, 284.97; 95% CI, 167.85-483.81), Behçet disease (HR, 17.39; 95% CI, 1.55-195.53), sarcoidosis (HR, 14.80; 95% CI, 4.86-45.08), vasculitis (HR, 4.89; 95% CI, 1.82-13.10), Sjögren syndrome (HR, 3.48; 95% CI, 1.38-8.76), and herpetic infection (HR, 1.68; 95% CI, 1.24-2.28).

Conclusions and Relevance

The UK incidence of ON is stable. Even though predominantly associated with MS, ON has numerous other associations with IMIDs. Although individually rare, together these associations outnumber MS-associated ON and typically require urgent management to preserve sight.

Precision medicine in the era of artificial intelligence: implications in chronic disease management

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.