Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records.

Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, 'Blood pressure reading without diagnosis', 'Abnormalities of heartbeat', and 'Intestinal obstruction' were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories 'Cough→Jaundice→Intestinal obstruction' and 'Pain→Jaundice→Abnormal results of function studies'. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer.

Pancreatic cancer is one of the deadliest cancer types. Scientists predict it will become the second largest cause of cancer-related deaths in 2030. It has few or no symptoms at early stages and often goes undetected for an extended period. As a result, patients are often diagnosed at an advanced stage when they have few treatment options and lower survival rates. Only 11 percent of patients with pancreatic cancer survive five years past their diagnosis. Earlier detection and surgery to remove the tumor increase patient survival to 42% at five years. Those who undergo surgery at the earliest stage have an 84% survival rate at five years. Developing ways to screen for and detect pancreatic cancer early could improve patient survival. Identifying early symptoms is critical. So far, studies show links between weight loss, abdominal pain, lower back pain, and new-onset diabetes and pancreatic cancer. But clinicians often overlook these symptoms or do not associate them with cancer. National health registries may be data sources that scientists can use to zoom in on early pancreatic symptoms and create alerts for clinicians. Hjaltelin, Novitski et al. identified potential pancreatic cancer symptoms using patient registry data and electronic health records. Hjaltelin, Novitski et al. extracted potential pancreatic cancer-related disease or symptom trajectories from 7 million patients listed in the Danish National Patient Registry. They also scoured clinical notes in 34,000 patients' electronic health records for symptoms. The electronic health records yielded more promising symptoms than the registry. But both data sources produced complementary information. The analysis showed that some symptoms, like jaundice, were associated with higher survival rates because they may lead to earlier diagnosis. The data so far suggest that symptoms leading up to a pancreatic cancer diagnosis may be nonspecific and not occur in a particular order. As the cancer progresses, symptoms may become more specific and severe. Further assessment of the study's results is necessary. Tools like artificial intelligence or advanced text mining may allow scientists identify more definitive early symptom trajectories and help clinicians identify patients earlier.

Text mining of electronic health records can validate a register-based diagnosis of epilepsy and subgroup into focal and generalized epilepsy.

OBJECTIVE: Combining population-based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD-10)-registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types. METHODS: Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD-10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining-based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text-mined diagnoses. RESULTS: We identified 23 632 patients with an ICD-10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining-assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining-eligible patients registered with unspecified epilepsy. SIGNIFICANCE: Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD-10 epilepsy codes alone. We believe the concept will be essential for future genome-wide and phenome-wide association studies and subsequently the development of precision medicine for epilepsy 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.

An Increase in Specialist Treatment for Onychomycosis: An Unexplained Tendency. A Retrospective Study of Patients Treated for Onychomycosis in Danish Hospitals from 1994 to 2018.

Onychomycosis is a common disease with a significant negative impact on quality of life. While the disease is usually manageable in general practice, a proportion of patients need specialist treatment in academic hospital clinics. However, it is an unknown question whether the incidence in those needing specialist treatments is changing. Furthermore, the comorbidity burden in this patient population severely affected by onychomycosis has never been characterized. We conducted a retrospective study on patients treated for onychomycosis in Danish hospitals from 1994 to 2018. The cohort was observed for 24 years, and the data comprise 7.2 million Danes and their hospital diagnoses. A disease trajectory algorithm was used to examine the comorbidity burden in the cohort. A total of 2,271 patients received hospital treatment for onychomycosis during the time period, of which 1358 (59.8%) were men. The data show an increase in the incidence of hospital-treated cases since 2012 and that the most common comorbidities in this patient population include cardiovascular disease, alcohol-related diagnoses, and diabetes. One explanation of the increase in specialist treatment may include a general increase in patients with decreased resilience to fungal disease. This lack of resilience may both include an increasing elderly population with atherosclerosis, diabetes, and immunosuppression but also a potential increase in patients treated with immunosuppressive agents. Another possible explanation may include a shift in patient expectations in the case of treatment failure. Thus, patients may have an increasing demand for specialist treatment. While our data document an increase in the number of patients in need of specialist treatment for onychomycosis, we suggest future research to examine the general incidence of onychomycosis but also whether this increase in an apparently recalcitrant disease may be attributed to increased antifungal resistance, more specialist treatment options, or increased attention to dermatomycoses.

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.

Genome-wide analysis of 944 133 individuals provides insights into the etiology of haemorrhoidal disease.

OBJECTIVE: Haemorrhoidal disease (HEM) affects a large and silently suffering fraction of the population but its aetiology, including suspected genetic predisposition, is poorly understood. We report the first genome-wide association study (GWAS) meta-analysis to identify genetic risk factors for HEM to date. DESIGN: We conducted a GWAS meta-analysis of 218 920 patients with HEM and 725 213 controls of European ancestry. Using GWAS summary statistics, we performed multiple genetic correlation analyses between HEM and other traits as well as calculated HEM polygenic risk scores (PRS) and evaluated their translational potential in independent datasets. Using functional annotation of GWAS results, we identified HEM candidate genes, which differential expression and coexpression in HEM tissues were evaluated employing RNA-seq analyses. The localisation of expressed proteins at selected loci was investigated by immunohistochemistry. RESULTS: We demonstrate modest heritability and genetic correlation of HEM with several other diseases from the GI, neuroaffective and cardiovascular domains. HEM PRS validated in 180 435 individuals from independent datasets allowed the identification of those at risk and correlated with younger age of onset and recurrent surgery. We identified 102 independent HEM risk loci harbouring genes whose expression is enriched in blood vessels and GI tissues, and in pathways associated with smooth muscles, epithelial and endothelial development and morphogenesis. Network transcriptomic analyses highlighted HEM gene coexpression modules that are relevant to the development and integrity of the musculoskeletal and epidermal systems, and the organisation of the extracellular matrix. CONCLUSION: HEM has a genetic component that predisposes to smooth muscle, epithelial and connective tissue dysfunction.

Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis.

Diagnostic errors are common and can lead to harmful treatments. We present a data-driven, generic approach for identifying patients at risk of being mis- or overdiagnosed, here exemplified by chronic obstructive pulmonary disease (COPD). It has been estimated that 5-60% of all COPD cases are misdiagnosed. High-throughput methods are therefore needed in this domain. We have used a national patient registry, which contains hospital diagnoses for 6.9 million patients across the entire Danish population for 21 years and identified statistically significant disease trajectories for COPD patients. Using 284,154 patients diagnosed with COPD, we identified frequent disease trajectories comprising time-ordered comorbidities. Interestingly, as many as 42,459 patients did not present with these time-ordered, common comorbidities. Comparison of the individual disease history for each non-follower to the COPD trajectories, demonstrated that 9597 patients were unusual. Survival analysis showed that this group died significantly earlier than COPD patients following a trajectory. Out of the 9597 patients, we identified one subgroup comprising 2185 patients at risk of misdiagnosed COPD without the typical events of COPD patients. In all, 10% of these patients were diagnosed with lung cancer, and it seems likely that they are underdiagnosed for lung cancer as their laboratory test values and survival pattern are similar to such patients. Furthermore, only 4% had a lung function test to confirm the COPD diagnosis. Another subgroup with 2368 patients were found to be at risk of "classically" overdiagnosed COPD that survive >5.5 years after the COPD diagnosis, but without the typical complications of COPD.

Disease Trajectories for Hidradenitis Suppurativa in the Danish Population.

Importance: Hidradenitis suppurativa (HS) is a chronic skin disease characterized by recurrent inflamed nodular lesions and is associated with multiple comorbidities; previous studies have been of cross-sectional design, and the temporal association of HS with multiple comorbidities remains undetermined. Objective: To evaluate and characterize disease trajectories in patients with HS using population-wide disease registry data. Design, Setting, and Participants: This retrospective registry-based cohort study included the entire Danish population alive between January 1, 1994, and April 10, 2018 (7 191 519 unique individuals). Among these, 14 488 Danish inhabitants were diagnosed with HS or fulfilled diagnostic criteria identified through surgical procedure codes. Exposures: Citizens of Denmark with a diagnosis code of HS as defined by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) or as identified through surgical procedures. Main Outcomes and Measures: Disease trajectories experienced more frequently by patients with HS than by the overall Danish population. Strength of associations between disease co-occurrences was evaluated using relative risk (RR). All significant disease pairs were tested for directionality using a binomial test, and pairs with directionality were merged into disease trajectories of 3 consecutive diseases. Numerous disease trajectories were combined into a disease progression network showing the most frequent disease paths over time for patients with HS. Results: A total of 11 929 individuals were identified by ICD-10 diagnosis codes (8392 [70.3%] female; mean [SD] age, 37.72 [13.01] years), and 2791 were identified by procedural codes (1686 [60.4%] female; mean [SD] age, 37.38 [15.83]). The set of most common temporal disease trajectories included 25 diagnoses and had a characteristic appearance in which genitourinary, respiratory, or mental and behavioral disorders preceded the diagnosis of HS and chronic obstructive pulmonary disease (604 cases [4.2%]; RR, 1.57; 95% CI, 1.55-1.59; P < .001), pneumonia (827 [5.7%]; RR, 1.18; 95% CI, 1.15-1.20; P < .001), and acute myocardial infarction (293 [2.0%]; RR, 1.37; 95% CI, 1.35-1.39; P < .001) developed after the diagnosis. Conclusions and Relevance: The findings suggest that patients with newly diagnosed HS may have a high frequency of manifest type 1 diabetes and subsequent high risk of acute myocardial infarction, pneumonia, and chronic obstructive pulmonary disease.

Age-stratified longitudinal study of Alzheimer's and vascular dementia patients.

INTRODUCTION: Similar symptoms, comorbidities and suboptimal diagnostic tests make the distinction between different types of dementia difficult, although this is essential for improved work-up and treatment optimization. METHODS: We calculated temporal disease trajectories of earlier multi-morbidities in Alzheimer's disease (AD) dementia and vascular dementia (VaD) patients using the Danish National Patient Registry covering all hospital encounters in Denmark (1994 to 2016). Subsequently, we reduced the comorbidity space dimensionality using a non-linear technique, uniform manifold approximation and projection. RESULTS: We found 49,112 and 24,101 patients that were diagnosed with AD or VaD, respectively. Temporal disease trajectories showed very similar disease patterns before the dementia diagnosis. Stratifying patients by age and reducing the comorbidity space to two dimensions, showed better discrimination between AD and VaD patients in early-onset dementia. DISCUSSION: Similar age-associated comorbidities, the phenomenon of mixed dementia, and misdiagnosis create great challenges in discriminating between classical subtypes of dementia.

Comorbidity landscape of the Danish patient population affected by chromosome abnormalities.

PURPOSE: Most chromosome abnormality patients require long-term clinical care. Awareness of mosaicism and comorbidities can potentially guide such health care. Here we present a population-wide analysis of direct and inverse comorbidities affecting patients with chromosome abnormalities. METHODS: We extracted direct and inverse comorbidities for the 11 most prevalent chromosome abnormalities from the Danish National Patient Registry (covering 6.9 million patients hospitalized between 1994 and 2015): trisomy 13, 18, and 21, Klinefelter (47,XXY), triple X, XYY, Turner (45,X), Wolf-Hirschhorn, Cri-du-chat, Angelman, and Fragile X syndromes (FXS). We also performed four sub-analyses for male/female Down syndrome (DS) and FXS and non-mosaic/mosaic DS and Turner syndrome. RESULTS: Our data cover 9,003 patients diagnosed with at least one chromosome abnormality. Each abnormality showed a unique comorbidity signature, but clustering of their profiles underlined common risk profiles for chromosome abnormalities with similar genetic backgrounds. We found that DS had a decreased risk for three inverse cancer comorbidities (lung, breast, and skin) and that male FXS and non-mosaic patients have a much more severe phenotype than female FXS and mosaic patients, respectively. CONCLUSION: Our study underlines the importance of considering mosaicism, sex, and the associated comorbidity profiles of chromosome abnormalities to guide long-term health care of affected patients.