Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform.

Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.

Markov modeling for cost-effectiveness using federated health data network.

OBJECTIVE: To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. MATERIALS AND METHODS: We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. RESULTS: We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. DISCUSSION: Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. CONCLUSION: We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.

The role of COVID-19 vaccines in preventing post-COVID-19 thromboembolic and cardiovascular complications.

OBJECTIVE: To study the association between COVID-19 vaccination and the risk of post-COVID-19 cardiac and thromboembolic complications. METHODS: We conducted a staggered cohort study based on national vaccination campaigns using electronic health records from the UK, Spain and Estonia. Vaccine rollout was grouped into four stages with predefined enrolment periods. Each stage included all individuals eligible for vaccination, with no previous SARS-CoV-2 infection or COVID-19 vaccine at the start date. Vaccination status was used as a time-varying exposure. Outcomes included heart failure (HF), venous thromboembolism (VTE) and arterial thrombosis/thromboembolism (ATE) recorded in four time windows after SARS-CoV-2 infection: 0-30, 31-90, 91-180 and 181-365 days. Propensity score overlap weighting and empirical calibration were used to minimise observed and unobserved confounding, respectively.Fine-Gray models estimated subdistribution hazard ratios (sHR). Random effect meta-analyses were conducted across staggered cohorts and databases. RESULTS: The study included 10.17 million vaccinated and 10.39 million unvaccinated people. Vaccination was associated with reduced risks of acute (30-day) and post-acute COVID-19 VTE, ATE and HF: for example, meta-analytic sHR of 0.22 (95% CI 0.17 to 0.29), 0.53 (0.44 to 0.63) and 0.45 (0.38 to 0.53), respectively, for 0-30 days after SARS-CoV-2 infection, while in the 91-180 days sHR were 0.53 (0.40 to 0.70), 0.72 (0.58 to 0.88) and 0.61 (0.51 to 0.73), respectively. CONCLUSIONS: COVID-19 vaccination reduced the risk of post-COVID-19 cardiac and thromboembolic outcomes. These effects were more pronounced for acute COVID-19 outcomes, consistent with known reductions in disease severity following breakthrough versus unvaccinated SARS-CoV-2 infection.

Real-world treatment trajectories of adults with newly diagnosed asthma or COPD.

BACKGROUND: There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice. METHODS: This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.3 million patients with asthma or COPD. We analysed treatment trajectories at drug class level from first diagnosis and visualised these in sunburst plots. RESULTS: In four countries (USA, UK, Spain and the Netherlands), most adults with asthma initiate treatment with short-acting ß2 agonists monotherapy (20.8%-47.4% of first-line treatments). For COPD, the most frequent first-line treatment varies by country. The largest percentages of untreated patients (for asthma and COPD) were found in claims databases (14.5%-33.2% for asthma and 27.0%-52.2% for COPD) from the USA as compared with EHR databases (6.9%-15.2% for asthma and 4.4%-17.5% for COPD) from European countries. The treatment trajectories showed step-up as well as step-down in treatments. CONCLUSION: Real-world data from claims and EHRs indicate that first-line treatments of asthma and COPD vary widely across countries. We found evidence of a stepwise approach in the pharmacological treatment of asthma and COPD, suggesting that treatments may be tailored to patients' needs.

The effectiveness of COVID-19 vaccines to prevent long COVID symptoms: staggered cohort study of data from the UK, Spain, and Estonia.

BACKGROUND: Although vaccines have proved effective to prevent severe COVID-19, their effect on preventing long-term symptoms is not yet fully understood. We aimed to evaluate the overall effect of vaccination to prevent long COVID symptoms and assess comparative effectiveness of the most used vaccines (ChAdOx1 and BNT162b2). METHODS: We conducted a staggered cohort study using primary care records from the UK (Clinical Practice Research Datalink [CPRD] GOLD and AURUM), Catalonia, Spain (Information System for Research in Primary Care [SIDIAP]), and national health insurance claims from Estonia (CORIVA database). All adults who were registered for at least 180 days as of Jan 4, 2021 (the UK), Feb 20, 2021 (Spain), and Jan 28, 2021 (Estonia) comprised the source population. Vaccination status was used as a time-varying exposure, staggered by vaccine rollout period. Vaccinated people were further classified by vaccine brand according to their first dose received. The primary outcome definition of long COVID was defined as having at least one of 25 WHO-listed symptoms between 90 and 365 days after the date of a PCR-positive test or clinical diagnosis of COVID-19, with no history of that symptom 180 days before SARS-Cov-2 infection. Propensity score overlap weighting was applied separately for each cohort to minimise confounding. Sub-distribution hazard ratios (sHRs) were calculated to estimate vaccine effectiveness against long COVID, and empirically calibrated using negative control outcomes. Random effects meta-analyses across staggered cohorts were conducted to pool overall effect estimates. FINDINGS: A total of 1 618 395 (CPRD GOLD), 5 729 800 (CPRD AURUM), 2 744 821 (SIDIAP), and 77 603 (CORIVA) vaccinated people and 1 640 371 (CPRD GOLD), 5 860 564 (CPRD AURUM), 2 588 518 (SIDIAP), and 302 267 (CORIVA) unvaccinated people were included. Compared with unvaccinated people, overall HRs for long COVID symptoms in people vaccinated with a first dose of any COVID-19 vaccine were 0·54 (95% CI 0·44-0·67) in CPRD GOLD, 0·48 (0·34-0·68) in CPRD AURUM, 0·71 (0·55-0·91) in SIDIAP, and 0·59 (0·40-0·87) in CORIVA. A slightly stronger preventative effect was seen for the first dose of BNT162b2 than for ChAdOx1 (sHR 0·85 [0·60-1·20] in CPRD GOLD and 0·84 [0·74-0·94] in CPRD AURUM). INTERPRETATION: Vaccination against COVID-19 consistently reduced the risk of long COVID symptoms, which highlights the importance of vaccination to prevent persistent COVID-19 symptoms, particularly in adults. FUNDING: National Institute for Health and Care Research.

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.

Objective: To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. Materials and Methods: We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150 824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. Results: In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. Discussion: During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. Conclusion: For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.

Risk of SARS-CoV-2 infection and hospitalization in individuals with natural, vaccine-induced and hybrid immunity: a retrospective population-based cohort study from Estonia.

A large proportion of the world's population has some form of immunity against SARS-CoV-2, through either infection ('natural'), vaccination or both ('hybrid'). This retrospective cohort study used data on SARS-CoV-2, vaccination, and hospitalization from national health system from February 2020 to June 2022 and Cox regression modelling to compare those with natural immunity to those with no (Cohort1, n = 94,982), hybrid (Cohort2, n = 47,342), and vaccine (Cohort3, n = 254,920) immunity. In Cohort 1, those with natural immunity were at lower risk for infection during the Delta (aHR 0.17, 95%CI 0.15-0.18) and higher risk (aHR 1.24, 95%CI 1.18-1.32) during the Omicron period than those with no immunity. Natural immunity conferred substantial protection against COVID-19-hospitalization. Cohort 2-in comparison to natural immunity hybrid immunity offered strong protection during the Delta (aHR 0.61, 95%CI 0.46-0.80) but not the Omicron (aHR 1.05, 95%CI 0.93-1.1) period. COVID-19-hospitalization was extremely rare among individuals with hybrid immunity. In Cohort 3, individuals with vaccine-induced immunity were at higher risk than those with natural immunity for infection (Delta aHR 4.90, 95%CI 4.48-5.36; Omicron 1.13, 95%CI 1.06-1.21) and hospitalization (Delta aHR 7.19, 95%CI 4.02-12.84). These results show that risk of infection and severe COVID-19 are driven by personal immunity history and the variant of SARS-CoV-2 causing infection.

The use of prescription drugs and health care services during the 6-month post-COVID-19 period.

COVID-19 and other acute respiratory viruses can have a long-term impact on health. We aimed to assess the common features and differences in the post-acute phase of COVID-19 compared with other non-chronic respiratory infections (RESP) using population-based electronic health data. We applied the self-controlled case series method where prescription drugs and health care utilisation were used as indicators of health outcomes during the six-month-long post-acute period. The incidence rate ratios of COVID-19 and RESP groups were compared. The analysis included 146 314 individuals. Out of 5452 drugs analysed, 14 had increased administration after COVID-19 with drugs for cardiovascular diseases (trimetazidine, metoprolol, rosuvastatin) and psychotropic drugs (alprazolam, zolpidem, melatonin) being most prevalent. The health impact of COVID-19 was more apparent among females and individuals with non-severe COVID-19. The increased risk of exacerbating pre-existing conditions was observed for the COVID-19 group. COVID-19 vaccination did not have effect on drug prescriptions but lowered the health care utilisation during post-acute period. Compared with RESP, COVID-19 increased the use of outpatient services during the post-infection period. The long-term negative impact of COVID-19 on life quality must be acknowledged, and supportive health care and public health services provided.

A retrospective cohort study of incidence and risk factors for severe SARS-CoV-2 breakthrough infection among fully vaccinated people.

SARS-CoV-2 vaccination is currently the mainstay in combating the COVID-19 pandemic. However, there are still people among vaccinated individuals suffering from severe forms of the disease. We conducted a retrospective cohort study based on data from nationwide e-health databases. The study included 184,132 individuals who were SARS-CoV-2 infection-naive and had received at least a primary series of COVID-19 vaccination. The incidence of BTI (breakthrough infection) was 8.03 (95% CI [confidence interval] 7.95⎼8.13/10,000 person-days), and for severe COVID-19 it was 0.093 (95% CI 0.084⎼ 0.104/10,000 person-days). The protective effect of vaccination against severe COVID-19 remained constant for up to six months, and the booster dose offered an additional pronounced benefit (hospitalization aHR 0.32, 95% CI 0.19⎼0.54). The risk of severe COVID-19 was higher among those ≥ 50 years of age (aHR [adjusted hazard ratio] 2.06, 95% CI 1.25⎼3.42) and increased constantly with every decade of life. Male sex (aHR 1.32, 95% CI 1.16⎼1.45), CCI (The Charlson Comorbidity Index) score ≥ 1 (aHR 2.09, 95% CI 1.54⎼2.83), and a range of comorbidities were associated with an increased risk of COVID-19 hospitalization. There are identifiable subgroups of COVID-19-vaccinated individuals at high risk of hospitalization due to SARS-CoV-2 infection. This information is crucial to driving vaccination programs and planning treatment strategies.

Modeling Patient Treatment Trajectories Using Markov Chains for Cost Analysis.

Electronically stored medical records offer a rich source of data for investigating treatment trajectories and identifying best practices in healthcare. These trajectories, which consist of medical interventions, give us a foundation to evaluate the economics of treatment patterns and model the treatment paths. The aim of this work is to introduce a technical solution for the aforementioned tasks. The developed tools use the open source Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership Common Data Model to construct treatment trajectories and implement these to compose Markov models for composing financial analysis between standard of care and alternatives.

Information Extraction from Medical Texts with BERT Using Human-in-the-Loop Labeling.

Neural network language models, such as BERT, can be used for information extraction from medical texts with unstructured free text. These models can be pre-trained on a large corpus to learn the language and characteristics of the relevant domain and then fine-tuned with labeled data for a specific task. We propose a pipeline using human-in-the-loop labeling to create annotated data for Estonian healthcare information extraction. This method is particularly useful for low-resource languages and is more accessible to those in the medical field than rule-based methods like regular expressions.

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study.

Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. Methods: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. Findings: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism. Interpretation: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term. Funding: None.

Prevaccination Prevalence of Type-Specific Human Papillomavirus Infection by Grade of Cervical Cytology in Estonia.

Importance: Large-scale data on type-specific human papillomavirus (HPV) prevalence and disease burden worldwide are needed to guide cervical cancer prevention efforts. Promoting the research and application of health care big data has become a key factor in modern medical research. Objective: To examine the prevaccination prevalence of high-risk HPV (hrHPV) and type distribution by cervical cytology grade in Estonia. Design, Setting, and Participants: This cross-sectional study used text mining and the linking of data from electronic health records and health care claims to examine type-specific hrHPV positivity in Estonia from 2012 to 2019. Participants were women aged at least 18 years. Statistical analysis was performed from September 2021 to August 2022. Main Outcomes and Measures: Type-specific hrHPV positivity rate by cervical cytological grade. Results: A total of 11 017 cases of cervical cytology complemented with data on hrHPV testing results between 2012 and 2019 from 66 451 women aged at least 18 years (mean [SD] age, 48.1 [21.0] years) were included. The most common hrHPV types were HPV16, 18, 31, 33, 51 and 52, which accounted for 73.8% of all hrHPV types detected. There was a marked decline in the positivity rate of hrHPV infection with increasing age, but the proportion did not vary significantly based on HPV type. Implementation of nonavalent prophylactic vaccination was estimated to reduce the number of women with high-grade cytology by 50.5% (95% CI, 47.4%-53.6%) and the number with low-grade cytology by 27.8% (95% CI, 26.3%-29.3%), giving an overall estimated reduction of 33.1% (95% CI, 31.7%-34.5%) in the number of women with precancerous cervical cytology findings. Conclusions and Relevance: In this cross-sectional study, text mining and natural language processing techniques allowed the detection of precursors to cervical cancer based on data stored by the nationwide health system. These findings contribute to the literature on type-specific HPV distribution by cervical cytology grade and document that α-9 phylogenetic group HPV types 16, 31, 33, 52 and α-7 phylogenetic group HPV 18 are the most frequently detected in normal-to-high-grade precancerous lesions in Estonia.

Post-acute sequelae of COVID-19 among hospitalized patients in Estonia: Nationwide matched cohort study.

BACKGROUND: Post-acute COVID-19 sequelae refers to a variety of health complications involving different organ systems that have been described among individuals after acute phase of illness. Data from unselected population groups with long-time follow up is needed to comprehensively describe the full spectrum of post-acute COVID-19 complications. METHODS: In this retrospective nationwide cohort study, we used data obtained from electronic health record database. Our primary cohort were adults hospitalized with confirmed COVID-19 and matched (age, sex, Charlson Comorbidity Index) unaffected controls from general population. Individuals included from February 2020 until March 2021 were followed up for 12 months. We estimated risks of all-cause mortality, readmission and incidence of 16 clinical sequelae after acute COVID-19 phase. Using a frailty Cox model, we compared incidences of outcomes in two cohorts. RESULTS: The cohort comprised 3949 patients older than 18 years who were alive 30 days after COVID-19 hospital admission and 15511 controls. Among cases 40.3% developed at least one incident clinical sequelae after the acute phase of SARS-CoV-2 infection, which was two times higher than in general population group. We report substantially higher risk of all-cause mortality (adjusted hazard ratio (aHR) = 2.57 (95%CI 2.23-2.96) and hospital readmission aHR = 1.73 (95%CI 1.58-1.90) among hospitalized COVID-19 patients. We found that the risks for new clinical sequalae were significantly higher in COVID-19 patients than their controls, especially for dementia aHR = 4.50 (95% CI 2.35-8.64), chronic lower respiratory disease aHR = 4.39 (95% CI 3.09-6.22), liver disease aHR 4.20 (95% CI 2.01-8.77) and other (than ischemic) forms of heart diseases aHR = 3.39 (95%CI 2.58-4.44). CONCLUSION: Our results provide evidence that the post-acute COVID-19 morbidity within the first year after COVID-19 hospitalization is substantial. Risks of all-cause mortality, hospitalisation and majority of clinical sequelae were significantly higher in hospitalized COVID-19 patients than in general population controls and warrant targeted prevention efforts.

Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin.

Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

Clinical characteristics and risk factors for COVID-19 infection and disease severity: A nationwide observational study in Estonia.

BACKGROUND: COVID-19 pandemic has led to overloading of health systems all over the world. For reliable risk stratification, knowledge on factors predisposing to SARS-CoV-2 infection and to severe COVID-19 disease course is needed for decision-making at the individual, provider, and government levels. Data to identify these factors should be easily obtainable. METHODS AND FINDINGS: Retrospective cohort study of nationwide e-health databases in Estonia. We used longitudinal health records from 66,295 people tested positive for SARS-CoV-2 RNA from 26 February 2020 to 28 February 2021 and 254,958 randomly selected controls from the reference population with no known history of SARS-CoV-2 infection or clinical COVID-19 diagnosis (case to control ratio 1:4) to predict risk factors of infection and severe course of COVID-19. We analysed sociodemographic and health characteristics of study participants. The SARS-CoV-2 infection risk was slightly higher among women, and was higher among those with comorbid conditions or obesity. Dementia (RRR 3.77, 95%CI 3.30⎼4.31), renal disease (RRR 1.88, 95%CI 1.56⎼2.26), and cerebrovascular disease (RRR 1.81, 95%CI 1.64⎼2.00) increased the risk of infection. Of all SARS-CoV-2 infected people, 92% had a non-severe disease course, 4.8% severe disease (requiring hospitalisation), 1.7% critical disease (needing intensive care), and 1.5% died. Male sex, increasing age and comorbid burden contributed significantly to more severe COVID-19, and the strength of association for male sex increased with the increasing severity of COVID-19 outcome. The strongest contributors to critical illness (expressed as RRR with 95% CI) were renal disease (7.71, 4.71⎼12.62), the history of previous myocardial infarction (3.54, 2.49⎼5.02) and obesity (3.56, 2.82⎼4.49). The strongest contributors to a lethal outcome were renal disease (6.48, 3.74⎼11.23), cancer (3.81, 3.06⎼4.75), liver disease (3.51, 1.36⎼9.02) and cerebrovascular disease (3.00, 2.31⎼3.89). CONCLUSIONS: We found divergent effect of age and gender on infection risk and severity of COVID-19. Age and gender did not contribute substantially to infection risk, but did so for the risk of severe disease Co-morbid health conditions, especially those affecting renin-angiotensin system, had an impact on both the risk of infection and severe disease course. Age and male sex had the most significant impact on the risk of severe COVID-19. Taking into account the role of ACE2 receptors in the pathogenesis of SARS-CoV-2 infection, as well as its modulating action on the renin-angiotensin system in cardiovascular and renal diseases, further research is needed to investigate the influence of hormonal status on ACE2 expression in different tissues, which may be the basis for the development of COVID-19 therapies.

Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis.

BACKGROUND: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. METHODS: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. FINDINGS: Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. INTERPRETATION: We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. FUNDING: This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

Long-term mortality following SARS-CoV-2 infection: A national cohort study from Estonia.

Background: The objective of this study was to describe 12-month mortality following SARS-CoV-2 infection compared with a reference population with no history of SARS-CoV-2. Methods: Nationwide cohort study using electronic health care data on SARS-CoV-2 RNA positive cases (n= 66,287) and reference group subjects (n=254,969) with linkage to SARS-CoV-2 testing and death records. Findings: People infected with SARS-COV-2 had more than three times the risk of dying over the following year compared with those who remained uninfected (aHR 3·1, 95%CI 2·9-3·3). Short-term mortality (up to 5 weeks post-infection) was significantly higher among COVID-19 group (1623·0/10 000) than in the reference group (118/10 000). For COVID-19 cases aged 60 years or older, increased mortality persisted until the end of the first year after infection, and was related to increased risk for cardiovascular (aHR 2·1, 95%CI 1·8-2·3), cancer (aHR 1·5, 95%CI 1·2-1·9), respiratory system diseases (aHR 1·9, 95%CI 1·2-3·0), and other causes of death (aHR 1·8, 95%CI 1·4-2·2). Interpretation: Increased risk of death from SARS-CoV-2 is not limited to the acute illness: SARS-CoV-2 infection carries a substantially increased mortality in the following 12 months. This excess death mainly occurs in older people and is driven by broad array of causes of death. Funding: Research was carried out with the support of Estonian Research Council (grants PRG1197, PRG198), European Regional Development Fund (RITA 1/02-120) and European Social Fund via IT Academy program.

Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model.

Objective: To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods: A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results: As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions: For the first time, there is a complete software package for detecting disease trajectories in health data.

A core genome approach that enables prospective and dynamic monitoring of infectious outbreaks.

Whole-genome sequencing is increasingly adopted in clinical settings to identify pathogen transmissions, though largely as a retrospective tool. Prospective monitoring, in which samples are continuously added and compared to previous samples, can generate more actionable information. To enable prospective pathogen comparison, genomic relatedness metrics based on single-nucleotide differences must be consistent across time, efficient to compute and reliable for a large variety of samples. The choice of genomic regions to compare, i.e., the core genome, is critical to obtain a good metric. We propose a novel core genome method that selects conserved sequences in the reference genome by comparing its k-mer content to that of publicly available genome assemblies. The conserved-sequence genome is sample set-independent, which enables prospective pathogen monitoring. Based on clinical data sets of 3436 S. aureus, 1362 K. pneumoniae and 348 E. faecium samples, ROC curves demonstrate that the conserved-sequence genome disambiguates same-patient samples better than a core genome consisting of conserved genes. The conserved-sequence genome confirms outbreak samples with high sensitivity: in a set of 2335 S. aureus samples, it correctly identifies 44 out of 44 known outbreak samples, whereas the conserved-gene method confirms 38 known outbreak samples.