High titers of neutralizing SARS-CoV-2 antibodies six months after symptom onset are associated with increased severity in COVID-19 hospitalized patients.

BACKGROUND: Viral shedding and neutralizing antibody (NAb) dynamics among patients hospitalized with severe coronavirus disease 2019 (COVID-19) and immune correlates of protection have been key questions throughout the pandemic. We investigated the duration of reverse transcriptase-polymerase chain reaction (RT-PCR) positivity, infectious viral shedding and NAb titers as well as the association between NAb titers and disease severity in hospitalized COVID-19 patients in Denmark 2020-2021. MATERIALS AND METHODS: Prospective single-center observational cohort study of 47 hospitalized COVID-19 patients. Oropharyngeal swabs were collected at eight time points during the initial 30 days of inclusion. Serum samples were collected after a median time of 7 (IQR 5 - 10), 37 (IQR 35 - 38), 97 (IQR 95 - 100), and 187 (IQR 185 - 190) days after symptom onset. NAb titers were determined by an in-house live virus microneutralization assay. Viral culturing was performed in Vero E6 cells. RESULTS: Patients with high disease severity had higher mean log2 NAb titers at day 37 (1.58, 95% CI [0.34 -2.81]), 97 (2.07, 95% CI [0.53-3.62]) and 187 (2.49, 95% CI [0.20- 4.78]) after symptom onset, compared to patients with low disease severity. Peak viral load (0.072, 95% CI [- 0.627 - 0.728]), expressed as log10 SARS-CoV-2 copies/ml, was not associated with disease severity. Virus cultivation attempts were unsuccessful in almost all (60/61) oropharyngeal samples collected shortly after hospital admission. CONCLUSIONS: We document an association between high disease severity and high mean NAb titers at days 37, 97 and 187 after symptom onset. However, peak viral load during admission was not associated with disease severity. TRIAL REGISTRATION: The study is registered at https://clinicaltrials.gov/ (NCT05274373).

A unidirectional mapping of ICD-8 to ICD-10 codes, for harmonized longitudinal analysis of diseases.

Periodic revisions of the international classification of diseases (ICD) ensure that the classification reflects new practices and knowledge; however, this complicates retrospective research as diagnoses are coded in different versions. For longitudinal disease trajectory studies, a crosswalk is an essential tool and a comprehensive mapping between ICD-8 and ICD-10 has until now been lacking. In this study, we map all ICD-8 morbidity codes to ICD-10 in the expanded Danish ICD version. We mapped ICD-8 codes to ICD-10, using a many-to-one system inspired by general equivalence mappings such that each ICD-8 code maps to a single ICD-10 code. Each ICD-8 code was manually and unidirectionally mapped to a single ICD-10 code based on medical setting and context. Each match was assigned a score (1 of 4 levels) reflecting the quality of the match and, if applicable, a "flag" signalling choices made in the mapping. We provide the first complete mapping of the 8596 ICD-8 morbidity codes to ICD-10 codes. All Danish ICD-8 codes representing diseases were mapped and 5106 (59.4%) achieved the highest consistency score. Only 334 (3.9%) of the ICD-8 codes received the lowest mapping consistency score. The mapping provides a scaffold for translation of ICD-8 to ICD-10, which enable longitudinal disease studies back to and 1969 in Denmark and to 1965 internationally with further adaption.

Temporal patterns of multi-morbidity in 570157 ischemic heart disease patients: a nationwide cohort study.

BACKGROUND: Patients diagnosed with ischemic heart disease (IHD) are becoming increasingly multi-morbid, and studies designed to analyze the full spectrum are few. METHODS: Disease trajectories, defined as time-ordered series of diagnoses, were used to study the temporality of multi-morbidity. The main data source was The Danish National Patient Register (NPR) comprising 7,179,538 individuals in the period 1994-2018. Patients with a diagnosis code for IHD were included. Relative risks were used to quantify the strength of the association between diagnostic co-occurrences comprised of two diagnoses that were overrepresented in the same patients. Multiple linear regression models were then fitted to test for temporal associations among the diagnostic co-occurrences, termed length two disease trajectories. Length two disease trajectories were then used as basis for constructing disease trajectories of three diagnoses. RESULTS: In a cohort of 570,157 IHD disease patients, we identified 1447 length two disease trajectories and 4729 significant length three disease trajectories. These included 459 distinct diagnoses. Disease trajectories were dominated by chronic diseases and not by common, acute diseases such as pneumonia. The temporal association of atrial fibrillation (AF) and IHD differed in different IHD subpopulations. We found an association between osteoarthritis (OA) and heart failure (HF) among patients diagnosed with OA, IHD, and then HF only. CONCLUSIONS: The sequence of diagnoses is important in characterization of multi-morbidity in IHD patients as the disease trajectories. The study provides evidence that the timing of AF in IHD marks distinct IHD subpopulations; and secondly that the association between osteoarthritis and heart failure is dependent on IHD.

Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes.

PURPOSE: While the beneficial effects of medications are numerous, drug-drug interactions may lead to adverse drug reactions that are preventable causes of morbidity and mortality. Our goal was to quantify the prevalence of potential drug-drug interactions in drug prescriptions at Danish hospitals, estimate the risk of adverse outcomes associated with discouraged drug combinations, and highlight the patient types (defined by the primary diagnosis of the admission) that appear to be more affected. METHODS: This cross-sectional (descriptive part) and cohort study (adverse outcomes part) used hospital electronic health records from two Danish regions (~2.5 million people) from January 2008 through June 2016. We included all inpatients receiving two or more medications during their admission and considered concomitant prescriptions of potentially interacting drugs as per the Danish Drug Interaction Database. We measured the prevalence of potential drug-drug interactions in general and discouraged drug pairs in particular during admissions and associations with adverse outcomes: post-discharge all-cause mortality rate, readmission rate and length-of-stay. RESULTS: Among 2 886 227 hospital admissions (945 475 patients; median age 62 years [IQR: 41-74]; 54% female; median number of drugs 7 [IQR: 4-11]), patients in 1 836 170 admissions were exposed to at least one potential drug-drug interaction (659 525 patients; median age 65 years [IQR: 49-77]; 54% female; median number of drugs 9 [IQR: 6-13]) and in 27 605 admissions to a discouraged drug pair (18 192 patients; median age 68 years [IQR: 58-77]; female 46%; median number of drugs 16 [IQR: 11-22]). Meropenem-valproic acid (HR: 1.5, 95% CI: 1.1-1.9), domperidone-fluconazole (HR: 2.5, 95% CI: 2.1-3.1), imipramine-terbinafine (HR: 3.8, 95% CI: 1.2-12), agomelatine-ciprofloxacin (HR: 2.6, 95% CI: 1.3-5.5), clarithromycin-quetiapine (HR: 1.7, 95% CI: 1.1-2.7) and piroxicam-warfarin (HR: 3.4, 95% CI: 1-11.4) were associated with elevated mortality. Confidence interval bounds of pairs associated with readmission were close to 1; length-of-stay results were inconclusive. CONCLUSIONS: Well-described potential drug-drug interactions are still missed and alerts at point of prescription may reduce the risk of harming patients; prescribing clinicians should be alert when using strong inhibitor/inducer drugs (i.e. clarithromycin, valproic acid, terbinafine) and prevalent anticoagulants (i.e. warfarin and non-steroidal anti-inflammatory drugs - NSAIDs) due to their great potential for dangerous interactions. The most prominent CYP isoenzyme involved in mortality and readmission rates was 3A4.

First wave of COVID-19 hospital admissions in Denmark: a Nationwide population-based cohort study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease coronavirus disease 2019 (COVID-19), is a worldwide emergency. Demographic, comorbidity and laboratory determinants of death and of ICU admission were explored in all Danish hospitalised patients. METHODS: National health registries were used to identify all hospitalized patients with a COVID-19 diagnosis. We obtained demographics, Charlson Comorbidity Index (CCI), and laboratory results on admission and explored prognostic factors for death using multivariate Cox proportional hazard regression and competing risk survival analysis. RESULTS: Among 2431 hospitalised patients with COVID-19 between February 27 and July 8 (median age 69 years [IQR 53-80], 54.1% males), 359 (14.8%) needed admission to an intensive care unit (ICU) and 455 (18.7%) died within 30 days of follow-up. The seven-day cumulative incidence of ICU admission was lower for females (7.9%) than for males (16.7%), (p < 0.001). Age, high CCI, elevated C-reactive protein (CRP), ferritin, D-dimer, lactate dehydrogenase (LDH), urea, creatinine, lymphopenia, neutrophilia and thrombocytopenia within ±24-h of admission were independently associated with death within the first week in the multivariate analysis. Conditional upon surviving the first week, male sex, age, high CCI, elevated CRP, LDH, creatinine, urea and neutrophil count were independently associated with death within 30 days. Males presented with more pronounced laboratory abnormalities on admission. CONCLUSIONS: Advanced age, male sex, comorbidity, higher levels of systemic inflammation and cell-turnover were independent factors for mortality. Age was the strongest predictor for death, moderate to high level of comorbidity were associated with a nearly two-fold increase in mortality. Mortality was significantly higher in males after surviving the first week.

Association between antipsychotic drug dose and length of clinical notes: a proxy of disease severity?

BACKGROUND: Most structured clinical data, such as diagnosis codes, are not sufficient to obtain precise phenotypes and assess disease burden. Text mining of clinical notes could provide a basis for detailed profiles of phenotypic traits. The objective of the current study was to determine whether drug dose, regardless of polypharmacy, is associated with the length of clinical notes, and to determine the frequency of adverse events per word in clinical notes. METHODS: In this observational study, we utilized restricted-access data from an electronic patient record system. Using three methods (defined daily dose, olanzapine equivalents, and chlorpromazine equivalents) we calculated antipsychotic dose equivalents and compared these with the number of words recorded per treatment day. For each normalization method, the frequencies of adverse events per word in manually curated samples were compared to dose intervals. RESULTS: The length of clinical notes per treatment day was positively associated with the prescribed dose for all normalization methods. The number of adverse events per word was stable over the analyzed dose spectrum. CONCLUSIONS: Assuming that drug dose increases with the severity of disease, the length of clinical notes can serve as a proxy for disease severity. Due to the near-linear relationship, correction of daily word count is unnecessary when text mining for potential adverse drug reactions.

Implementation and comparison of two text mining methods with a standard pharmacovigilance method for signal detection of medication errors.

BACKGROUND: Medication errors have been identified as the most common preventable cause of adverse events. The lack of granularity in medication error terminology has led pharmacovigilance experts to rely on information in individual case safety reports' (ICSRs) codes and narratives for signal detection, which is both time consuming and labour intensive. Thus, there is a need for complementary methods for the detection of medication errors from ICSRs. The aim of this study is to evaluate the utility of two natural language processing text mining methods as complementary tools to the traditional approach followed by pharmacovigilance experts for medication error signal detection. METHODS: The safety surveillance advisor (SSA) method, I2E text mining and University of Copenhagen Center for Protein Research (CPR) text mining, were evaluated for their ability to extract cases containing a type of medication error where patients extracted insulin from a prefilled pen or cartridge by a syringe. A total of 154,209 ICSRs were retrieved from Novo Nordisk's safety database from January 1987 to February 2018. Each method was evaluated by recall (sensitivity) and precision (positive predictive value). RESULTS: We manually annotated 2533 ICSRs to investigate whether these contained the sought medication error. All these ICSRs were then analysed using the three methods. The recall was 90.4, 88.1 and 78.5% for the CPR text mining, the SSA method and the I2E text mining, respectively. Precision was low for all three methods ranging from 3.4% for the SSA method to 1.9 and 1.6% for the CPR and I2E text mining methods, respectively. CONCLUSIONS: Text mining methods can, with advantage, be used for the detection of complex signals relying on information found in unstructured text (e.g., ICSR narratives) as standardised and both less labour-intensive and time-consuming methods compared to traditional pharmacovigilance methods. The employment of text mining in pharmacovigilance need not be limited to the surveillance of potential medication errors but can be used for the ongoing regulatory requests, e.g., obligations in risk management plans and may thus be utilised broadly for signal detection and ongoing surveillance activities.

Enlarged cross-sectional area in peripheral nerves in Swedish patients with hereditary V30M transthyretin amyloidosis.

INTRODUCTION: In hereditary transthyretin amyloidosis (ATTRv), two different fibrillar forms causing the amyloid deposition, have been identified, displaying substantially cardiac or neuropathic symptoms. Neuropathic symptoms are more frequent in early-onset patients, whereas late-onset patients, besides cardiac symptoms, seem to develop carpal tunnel syndrome, more often. With ultrasonography (US) of peripheral nerves, it is possible to distinguish structural changes, and enlarged cross-sectional area (CSA). The main purpose of this study was, for the first time, to elucidate US of peripheral nerves in Swedish ATTRv patients at an early stage of the disease, and to evaluate possible early enlarged CSA. MATERIAL AND METHODS: This prospective study included first visit data of 13 patients, aged 30-88 years, of which 11 with late-onset age. All had a positive V30M mutation. Eight men and six women (aged 28-74 years) served as controls. RESULTS: Significantly enlarged CSA was seen in ATTRv patients for the tibial nerve at the ankle (p = .001), the sural nerve (p < .001), the peroneal nerve at the popliteal fossa (p = .003), and the ulnar nerve at the middle upper arm (p = .007). CONCLUSION: US of peripheral nerves could be a valuable tool in disease evaluation and could facilitate monitoring of disease progression.

Drug dosage modifications in 24 million in-patient prescriptions covering eight years: A Danish population-wide study of polypharmacy.

Polypharmacy has generally been assessed by raw counts of different drugs administered concomitantly to the same patients; not with respect to the likelihood of dosage-adjustments. To address this aspect of polypharmacy, the objective of the present study was to identify co-medications associated with more frequent dosage adjustments. The data foundation was electronic health records from 3.2 million inpatient admissions at Danish hospitals (2008-2016). The likelihood of dosage-adjustments when two drugs were administered concomitantly were computed using Bayesian logistic regressions. We identified 3,993 co-medication pairs that associate significantly with dosage changes when administered together. Of these pairs, 2,412 (60%) did associate with readmission, mortality or longer stays, while 308 (8%) associated with reduced kidney function. In comparison to co-medications pairs that were previously classified as drug-drug interactions, pairs not classified as drug-drug interactions had higher odds ratios of dosage modifications than drug pairs with an established interaction. Drug pairs not corresponding to known drug-drug interactions while still being associated significantly with dosage changes were prescribed to fewer patients and mentioned more rarely together in the literature. We hypothesize that some of these pairs could be associated with yet to be discovered interactions as they may be harder to identify in smaller-scale studies.

Host Genetics and Antiviral Immune Responses in Adult Patients With Multisystem Inflammatory Syndrome.

COVID-19 associated multisystem inflammatory syndrome (MIS) is a rare condition mostly affecting children but also adults (MIS-A). Although severe systemic inflammation and multiorgan dysfunction are hallmarks of the syndrome, the underlying pathogenesis is unclear. We aimed to provide novel immunological and genetic descriptions of MIS-A patients. Cytokine responses (IL-6, IL-1ß, TNFα, CXCL10, type I, II and III interferons) following SARS-CoV-2 infection of peripheral blood mononuclear cells in vitro were analyzed as well as antibodies against IFNα and IFNω (by ELISA) in patients and healthy controls. We also performed whole exome sequencing (WES) of patient DNA. A total of five patients (ages 19, 23, 33, 38, 50 years) were included. The patients shared characteristic features, although organ involvement and the time course of disease varied slightly. SARS-CoV-2 in vitro infection of patient PBMCs revealed impaired type I and III interferon responses and reduced CXCL10 expression, whereas production of proinflammatory cytokines were less affected, compared to healthy controls. Presence of interferon autoantibodies was not detected. Whole exome sequencing analysis of patient DNA revealed 12 rare potentially disease-causing variants in genes related to autophagy, classical Kawasaki disease, restriction factors and immune responses. In conclusion, we observed an impaired production of type I and III interferons in response to SARS-CoV-2 infection and detected several rare potentially disease-causing gene variants potentially contributing to MIS-A.

Sex and age differences among tramadol users in three Nordic countries.

INTRODUCTION: The aim of this study was to examine the prevalence and characteristics of tramadol users in Denmark, Norway and Sweden. METHODS: Data from the national prescription databases comprising the entire population of Denmark, Norway and Sweden between 2007 and 2015 were used to assess prescription medicine use and sold amount (in defined daily doses (DDDs)) of tramadol, other opioids and non-steroidal anti-inflammatory drugs. RESULTS: From 2007 to 2015 the prevalence of tramadol users increased in Denmark from 45 to 52 per 1,000 residents, and in Norway from 20 to 41 per 1,000 residents. In Sweden, the prevalence decreased from 36 to 17 per 1,000 residents. In comparison, the prevalence of other opioid users decreased in Denmark and Norway, but increased in Sweden. During the study period, there were more female than male tramadol users in all three countries, and the prevalence of tramadol users tended to increase with age. The average tramadol DDD per treated patient remained fairly constant in Norway, while it increased in Denmark and Sweden. In Denmark and Norway, women received a higher DDD than men. The amount of sold tramadol and other opioids combined per 1,000 residents was highest in Denmark. CONCLUSIONS: From 2007 to 2015, the prescription patterns of tramadol and other opioids differed between the three countries. Tramadol was generally used more frequently by women. Women received higher DDD then men in Norway and Denmark, but not in Sweden. The prevalence of tramadol users tended to increase with age in all countries. FUNDING: none. TRIAL REGISTRATION: not relevant.

[Pulmonary thromboembolism as contributing cause of severe respiratory insufficiency in a patient with COVID-19 infection].

We report a case of multiple peripheral pulmonary thromboembolisms in a 69-year-old male hospitalised due to SARS-CoV-2 infection. There were no evident risk factors for pulmonary thromboembolism, the patient had a Wells' score of zero, and the diagnosis only became evident after repeated CT pulmonary angiographies.

Sex differences in text-mined possible adverse drug events associated with drugs for psychosis.

BACKGROUND: Understanding sex differences in adverse drug reactions to drugs for psychosis could potentially guide clinicians in optimal drug choices. AIMS: By applying a text-mining approach, this study aimed to investigate the relationship between drugs for psychosis and biological sex differences in frequencies and co-occurrences of potential adverse drug events (ADEs). METHODS: Electronic patient records of a psychiatric population (1427 men and 727 women) were text mined for potential ADEs. The relative risk of experiencing specific ADEs and co-occurrence of ADEs were calculated for each sex. RESULTS: Findings included 55 potential ADEs with significantly different frequencies between the two sexes. Of these, 20 were more frequent in men, with relative risks of 1.10-7.64, and 35 were more frequent in women, with relative risks of 1.19-21.58. Frequent potential ADEs were psychiatric symptoms, including sexual dysfunction and disturbances in men, and gastrointestinal symptoms, suicidal and self-injurious behaviour and hyperprolactinemia-related events in women. Mention of different hyperprolactinemia-related ADEs often co-occurred in female patients but not in male patients. CONCLUSION: Several known sex-related ADEs were identified, as well as some previously not reported. When considering the risk-benefit profile of drugs for psychosis, the patient's sex should be considered.

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining.

Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.

Bone Status in Obese, Non-diabetic, Antipsychotic-Treated Patients, and Effects of the Glucagon-Like Peptide-1 Receptor Agonist Exenatide on Bone Turnover Markers and Bone Mineral Density.

Background: Low bone mineral density (BMD) may constitute an underestimated comorbidity in schizophrenia patients undergoing long-term antipsychotic treatment. Glucagon-like peptide 1 (GLP-1) receptor agonists are antidiabetic drugs, which may also affect bone turnover. Methods: In planned secondary analyses of a 3 months, double-blind, randomized, placebo-controlled trial (n = 45), we explored effects of the GLP-1 receptor agonist exenatide 2 mg once-weekly (n = 23), or placebo (n = 22) on bone turnover markers (BTMs) and BMD in chronic, obese, antipsychotic-treated patients with schizophrenia spectrum disorder. Baseline BTMs were compared to sex- and age-adjusted reference values from a Danish population cohort, and T- and Z-scores were calculated for BMD. Results: In women (n = 24), all baseline BTM measurements of procollagen type I N-terminal propeptide (PINP) and C-terminal cross-linking telopeptide of type I collagen (CTX) were within reference values. In men (n = 21), 5% displayed lower PINP and 14% displayed lower CTX. One patient displayed BMD Z-score < -2, and 23% of patients (17% of women and 29% of men) displayed -2.5 < T-scores < -1 indicating osteopenia, but none had osteoporosis. After treatment, PINP decreased at trend level significance (P = 0.05), and body mass index BMD increased for L2-L4 (P = 0.016). No changes in bone markers were significant after correction for mean prolactin levels. Conclusions: Sex- and age-adjusted measures of bone status in chronic, obese, antipsychotic-treated patients appeared comparable to the reference population. Subtle changes in bone markers during 3 months exenatide treatment may suggest beneficial effects of GLP-1 receptor agonists on bone status in antipsychotic-treated patients, and further studies should consider the potential influence of prolactin.

Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events.

PURPOSE: New pharmacovigilance methods are needed as a consequence of the morbidity caused by drugs. We exploit fine-grained drug related adverse event information extracted by text mining from electronic medical records (EMRs) to stratify patients based on their adverse events and to determine adverse event co-occurrences. METHODS: We analyzed the similarity of adverse event profiles of 2347 patients extracted from EMRs from a mental health center in Denmark. The patients were clustered based on their adverse event profiles and the similarities were presented as a network. The set of adverse events in each main patient cluster was evaluated. Co-occurrences of adverse events in patients (p-value < 0.01) were identified and presented as well. RESULTS: We found that each cluster of patients typically had a most distinguishing adverse event. Examination of the co-occurrences of adverse events in patients led to the identification of potentially interesting adverse event correlations that may be further investigated as well as provide further patient stratification opportunities. CONCLUSIONS: We have demonstrated the feasibility of a novel approach in pharmacovigilance to stratify patients based on fine-grained adverse event profiles, which also makes it possible to identify adverse event correlations. Used on larger data sets, this data-driven method has the potential to reveal unknown patterns concerning adverse event occurrences.

Dose-specific adverse drug reaction identification in electronic patient records: temporal data mining in an inpatient psychiatric population.

BACKGROUND: Data collected for medical, filing and administrative purposes in electronic patient records (EPRs) represent a rich source of individualised clinical data, which has great potential for improved detection of patients experiencing adverse drug reactions (ADRs), across all approved drugs and across all indication areas. OBJECTIVES: The aim of this study was to take advantage of techniques for temporal data mining of EPRs in order to detect ADRs in a patient- and dose-specific manner. METHODS: We used a psychiatric hospital's EPR system to investigate undesired drug effects. Within one workflow the method identified patient-specific adverse events (AEs) and links these to specific drugs and dosages in a temporal manner, based on integration of text mining results and structured data. The structured data contained precise information on drug identity, dosage and strength. RESULTS: When applying the method to the 3,394 patients in the cohort, we identified AEs linked with a drug in 2,402 patients (70.8 %). Of the 43,528 patient-specific drug substances prescribed, 14,736 (33.9 %) were linked with AEs. From these links we identified multiple ADRs (p < 0.05) and found them to occur at similar frequencies, as stated by the manufacturer and in the literature. We showed that drugs displaying similar ADR profiles share targets, and we compared submitted spontaneous AE reports with our findings. For nine of the ten most prescribed antipsychotics in the patient population, larger doses were prescribed to sedated patients than non-sedated patients; five antipsychotics [corrected] exhibited a significant difference (p<0.05). Finally, we present two cases (p < 0.05) identified by the workflow. The method identified the potentially fatal AE QT prolongation caused by methadone, and a non-described likely ADR between levomepromazine and nightmares found among the hundreds of identified novel links between drugs and AEs (p < 0.05). CONCLUSIONS: The developed method can be used to extract dose-dependent ADR information from already collected EPR data. Large-scale AE extraction from EPRs may complement or even replace current drug safety monitoring methods in the future, reducing or eliminating manual reporting and enabling much faster ADR detection.

Discrepancies in listed adverse drug reactions in pharmaceutical product information supplied by the regulatory authorities in Denmark and the USA.

Pharmaceutical product information (PI) supplied by the regulatory authorities serves as a source of information on safe and effective use of drugs. The objectives of this study were to qualitatively and quantitatively compare PIs for selected drugs marketed in both Denmark and the USA with respect to consistency and discrepancy of listed adverse drug reaction (ADR) information. We compared individual ADRs listed in PIs from Denmark and the USA with respect to type and frequency. Consistency was defined as match of ADRs and of ADR frequency or match could not be ruled out. Discrepancies were defined as ADRs listed only in one country or listed with different frequencies. We analyzed PIs for 40 separate drugs from ten therapeutic groups and assigned the 4003 identified ADRs to System Organ Classes (Medical Dictionary for Regulatory Activities [MedDRA] terminology). Less than half of listed ADRs (n = 1874; 47%) showed consistency. Discrepancies (n = 2129; 53%) were split into ADRs listed only in the USA (n = 1558; 39%), ADRs listed only in Denmark (n = 325; 8%) and ADRs listed with different frequencies (n = 246; 6%). The majority of listed ADRs were of the type "gastrointestinal disorders" and "nervous system disorders". Our results show great differences in PIs for drugs approved in both Denmark and the USA illuminating concerns about the credibility of the publicly available PIs. The results also represent an argument for further harmonization across borders to improve consistency between authority-supplied information.

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients.

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.

Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text.

OBJECTIVE: Drugs have tremendous potential to cure and relieve disease, but the risk of unintended effects is always present. Healthcare providers increasingly record data in electronic patient records (EPRs), in which we aim to identify possible adverse events (AEs) and, specifically, possible adverse drug events (ADEs). MATERIALS AND METHODS: Based on the undesirable effects section from the summary of product characteristics (SPC) of 7446 drugs, we have built a Danish ADE dictionary. Starting from this dictionary we have developed a pipeline for identifying possible ADEs in unstructured clinical narrative text. We use a named entity recognition (NER) tagger to identify dictionary matches in the text and post-coordination rules to construct ADE compound terms. Finally, we apply post-processing rules and filters to handle, for example, negations and sentences about subjects other than the patient. Moreover, this method allows synonyms to be identified and anatomical location descriptions can be merged to allow appropriate grouping of effects in the same location. RESULTS: The method identified 1 970 731 (35 477 unique) possible ADEs in a large corpus of 6011 psychiatric hospital patient records. Validation was performed through manual inspection of possible ADEs, resulting in precision of 89% and recall of 75%. DISCUSSION: The presented dictionary-building method could be used to construct other ADE dictionaries. The complication of compound words in Germanic languages was addressed. Additionally, the synonym and anatomical location collapse improve the method. CONCLUSIONS: The developed dictionary and method can be used to identify possible ADEs in Danish clinical narratives.