Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease

Genetic correlations and an increased incidence of psychiatric disorders in inflammatory-bowel disease have been reported, but shared molecular mechanisms are unknown. We performed cross-tissue and multiple-gene conditioned transcriptome-wide association studies for 23 tissues of the gut-brain-axis using genome-wide association studies data sets (total 180,592 patients) for Crohn’s disease, ulcerative colitis, primary sclerosing cholangitis, schizophrenia, bipolar disorder, major depressive disorder and attention-deficit/hyperactivity disorder. We identified NR5A2, SATB2, and PPP3CA (encoding a target for calcineurin inhibitors in refractory ulcerative colitis) as shared susceptibility genes with transcriptome-wide significance both for Crohn’s disease, ulcerative colitis and schizophrenia, largely explaining fine-mapped association signals at nearby genome-wide association study susceptibility loci. Analysis of bulk and single-cell RNA-sequencing data showed that PPP3CA expression was strongest in neurons and in enteroendocrine and Paneth-like cells of the ileum, colon, and rectum, indicating a possible link to the gut-brain-axis. PPP3CA together with three further suggestive loci can be linked to calcineurin-related signaling pathways such as NFAT activation or Wnt.

Florian Uellendahl-Werth et al. conduct cross-tissue transcriptome-wide association studies to explore genetic mechanisms shared across immune-related and psychiatric traits. Their results identify several genes (including PPP3CA) that could mediate the interplay between psychiatric and inflammatory disease.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.

The effect of disease onset chronology on mortality among patients with multimorbidity: A Danish nationwide register study

Background

Multimorbidity is associated with increased mortality. Certain combinations of diseases are known to be more lethal than others, but the limited knowledge of how the chronology in which diseases develop impacts mortality may impair the development of effective clinical interventions for patients with multimorbidity.

Objective

To explore if in multimorbidity the chronology of disease onset is associated with mortality. Design: A prospective nationwide cohort study, including 3,986,209 people aged ≥18 years on 1 January 2000, was performed. We included ten diagnosis groups: lung, musculoskeletal, endocrine, mental, cancer, neurological, gastrointestinal, cardiovascular, kidney, and sensory organs. We defined multimorbidity as the presence of at least two diagnoses from two diagnosis groups (out of ten). To determine mortality, logistic regression models were used to calculate odds ratios (OR) and ratio of ORs (RORs).

Results

For most combinations of multimorbidity, the chronology of disease onset does not change mortality. However, when multimorbidity included mental health diagnoses, mortality was in general higher if the mental health diagnosis appeared first. If multimorbidity included heart and sensory diagnoses, mortality was higher if these developed second. For the majority of multimorbidity combinations, there was excess mortality if multimorbidity was diagnosed simultaneously, rather than consecutively, for example, heart and kidney (3.58 ROR; CI 2.39–5.36), or mental health and musculoskeletal diagnoses (2.38 ROR; CI 1.70–3.32).

Conclusions

Overall, in multimorbidity, the chronology in which diseases develop is not associated with mortality, with few exceptions. For almost all combinations of multimorbidity, diagnoses act synergistically in relation to mortality if diagnosed simultaneously.

Optimizing drug selection from a prescription trajectory of one patient

It is unknown how sequential drug patterns convey information on a patient's health status and treatment guidelines rarely account for this. Drug-agnostic longitudinal analyses of prescription trajectories in a population-wide setting are needed. In this cohort study, we used 24 years of data (1.1 billion prescriptions) from the Danish prescription registry to model the risk of sequentially redeeming a drug after another. Drug pairs were used to build multistep longitudinal prescription trajectories. These were subsequently used to stratify patients and calculate survival hazard ratios between the stratified groups. The similarity between prescription histories was used to determine individuals' best treatment option. Over the course of 122 million person-years of observation, we identified 9 million common prescription trajectories and demonstrated their predictive power using hypertension as a case. Among patients treated with agents acting on the renin-angiotensin system we identified four groups: patients prescribed angiotensin converting enzyme (ACE) inhibitor without change, angiotensin receptor blockers (ARBs) without change, ACE with posterior change to ARB, and ARB posteriorly changed to ACE. In an adjusted time-to-event analysis, individuals treated with ACE compared to those treated with ARB had lower survival probability (hazard ratio, 0.73 [95% CI, 0.64-0.82]; P < 1 × 10^-16). Replication in UK Biobank data showed the same trends. Prescription trajectories can provide novel insights into how individuals' drug use change over time, identify suboptimal or futile prescriptions and suggest initial treatments different from first line therapies. Observations of this kind may also be important when updating treatment guidelines.

Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals

Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality trajectory model that represents the temporal ordering of disease appearance, with strong correlations, that terminated in fatal outcomes from one initial diagnosis in a set of patients throughout multiple admissions. Based on longitudinal healthcare records of 10.4 million patients from over 350 hospitals, we profiled 300 mortality trajectories, starting from 118 diseases, in 311,309 patients. Three-quarters (75%) of 59,794 end-stage patients and their deaths accrued throughout 160,360 multiple disease appearances in a short-term period (<4 years, 3.5 diseases per patient). This overlooked and substantial heterogeneity of disease patients and outcomes in the real world is unraveled in our trajectory map at the disease-wide level. For example, the converged dead-end in our trajectory map presents an extreme diversity of sepsis patients based on 43 prior diseases, including lymphoma and cardiac diseases. The trajectories involving the largest number of deaths for each age group highlight the essential predisposing diseases, such as acute myocardial infarction and liver cirrhosis, which lead to over 14,000 deaths. In conclusion, the deciphering of the debilitation processes of patients, consisting of the temporal correlations of diseases that tend towards hospital death at a population-wide level is feasible.

A Poisson binomial-based statistical testing framework for comorbidity discovery across electronic health record datasets

Discovering the concomitant occurrence of distinct medical conditions in a patient, also known as comorbidities, is a prerequisite for creating patient outcome prediction tools. Current comorbidity discovery applications are designed for small datasets and use stratification to control for confounding variables such as age, sex or ancestry. Stratification lowers false positive rates, but reduces power, as the size of the study cohort is decreased. Here we describe a Poisson binomial-based approach to comorbidity discovery (PBC) designed for big-data applications that circumvents the need for stratification. PBC adjusts for confounding demographic variables on a per-patient basis and models temporal relationships. We benchmark PBC using two datasets to compute comorbidity statistics on 4,623,841 pairs of potentially comorbid medical terms. The results of this computation are provided as a searchable web resource. Compared with current methods, the PBC approach reduces false positive associations while retaining statistical power to discover true comorbidities.

Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource

OBJECTIVE

To describe a novel England-wide electronic health record (EHR) resource enabling whole population research on covid-19 and cardiovascular disease while ensuring data security and privacy and maintaining public trust.

DESIGN

Data resource comprising linked person level records from national healthcare settings for the English population, accessible within NHS Digital's new trusted research environment.

SETTING

EHRs from primary care, hospital episodes, death registry, covid-19 laboratory test results, and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular, and covid-19 vaccination data.

PARTICIPANTS

54.4 million people alive on 1 January 2020 and registered with an NHS general practitioner in England.

MAIN MEASURES OF INTEREST

Confirmed and suspected covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack and incident myocardial infarction) and all cause mortality between 1 January and 31 October 2020.

RESULTS

The linked cohort includes more than 96% of the English population. By combining person level data across national healthcare settings, data on age, sex, and ethnicity are complete for around 95% of the population. Among 53.3 million people with no previous diagnosis of stroke or transient ischaemic attack, 98 721 had a first ever incident stroke or transient ischaemic attack between 1 January and 31 October 2020, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.2 million people with no previous diagnosis of myocardial infarction, 62 966 had an incident myocardial infarction during follow-up, of which 8% were recorded only in primary care and 12% only in death registry records. A total of 959 470 people had a confirmed or suspected covid-19 diagnosis (714 162 in primary care data, 126 349 in hospital admission records, 776 503 in covid-19 laboratory test data, and 50 504 in death registry records). Although 58% of these were recorded in both primary care and covid-19 laboratory test data, 15% and 18%, respectively, were recorded in only one.

CONCLUSIONS

This population-wide resource shows the importance of linking person level data across health settings to maximise completeness of key characteristics and to ascertain cardiovascular events and covid-19 diagnoses. Although this resource was initially established to support research on covid-19 and cardiovascular disease to benefit clinical care and public health and to inform healthcare policy, it can broaden further to enable a wide range of research.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

ABSTRACT

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Disease trajectories and mortality among individuals diagnosed with depression: a community-based cohort study in UK Biobank

Patients with depression are at increased risk for a range of comorbid diseases, with, however, unclear explanations. In this large community-based cohort study of the UK Biobank, 24,130 patients diagnosed with depression were compared to 120,366 matched individuals without such a diagnosis. Follow-up was conducted from 6 months after the index date until death or the end of 2019, for the occurrence of 470 medical conditions and 16 specific causes of death. The median age at the time of the depression diagnosis was 62.0 years, and most of the patients were female (63.63%). During a median follow-up of 4.94 years, 129 medical conditions were found to be significantly associated with a prior diagnosis of depression, based on adjusted Cox regression models. Using disease trajectory network analysis to visualize the magnitude of disease-disease associations and the temporal order of the associated medical conditions, we identified three main affected disease clusters after depression (i.e., cardiometabolic diseases, chronic inflammatory diseases, and diseases related to tobacco abuse), which were further linked to a wider range of other conditions. In addition, we also identified three depression-mortality trajectories leading to death due to cardiovascular disease, respiratory system disease and malignant neoplasm. In conclusion, an inpatient diagnosis of depression in later life is associated with three distinct network-based clusters of medical conditions, indicating alterations in the cardiometabolic system, chronic status of inflammation, and tobacco abuse as key pathways to a wide range of other conditions downstream. If replicated, these pathways may constitute promising targets for the health promotion among depression patients.