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1.
JMIR Med Inform ; 2021 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-33661754

RESUMO

BACKGROUND: SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria and has not been externally validated. OBJECTIVE: Externally validate the C-19 index across a range of healthcare settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. METHODS: We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia. RESULTS: The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68. CONCLUSIONS: The results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 should not be used to aid decision making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

2.
J Biomed Inform ; 113: 103655, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33309898

RESUMO

Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the potential to overcome this issue and can be a step towards trustworthy AI. In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care domain and contribute to formalization of the field of explainable AI. We argue the reason to demand explainability determines what should be explained as this determines the relative importance of the properties of explainability (i.e. interpretability and fidelity). Based on this, we propose a framework to guide the choice between classes of explainable AI methods (explainable modelling versus post-hoc explanation; model-based, attribution-based, or example-based explanations; global and local explanations). Furthermore, we find that quantitative evaluation metrics, which are important for objective standardized evaluation, are still lacking for some properties (e.g. clarity) and types of explanations (e.g. example-based methods). We conclude that explainable modelling can contribute to trustworthy AI, but the benefits of explainability still need to be proven in practice and complementary measures might be needed to create trustworthy AI in health care (e.g. reporting data quality, performing extensive (external) validation, and regulation).

3.
Lancet Digit Health ; 3(2): e98-e114, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33342753

RESUMO

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension. METHODS: In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296. FINDINGS: Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons. INTERPRETATION: No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19. FUNDING: Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.

4.
medRxiv ; 2020 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-33140068

RESUMO

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.

5.
BMC Med Res Methodol ; 20(1): 264, 2020 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-33096986

RESUMO

BACKGROUND: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. METHODS: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. RESULTS: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). CONCLUSIONS: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

6.
Sci Rep ; 10(1): 11115, 2020 07 06.
Artigo em Inglês | MEDLINE | ID: mdl-32632237

RESUMO

Alendronate and raloxifene are among the most popular anti-osteoporosis medications. However, there is a lack of head-to-head comparative effectiveness studies comparing the two treatments. We conducted a retrospective large-scale multicenter study encompassing over 300 million patients across nine databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporotic hip fracture, while secondary outcomes were vertebral fracture, atypical femoral fracture (AFF), osteonecrosis of the jaw (ONJ), and esophageal cancer. We used propensity score trimming and stratification based on an expansive propensity score model with all pre-treatment patient characteritistcs. We accounted for unmeasured confounding using negative control outcomes to estimate and adjust for residual systematic bias in each data source. We identified 283,586 alendronate patients and 40,463 raloxifene patients. There were 7.48 hip fracture, 8.18 vertebral fracture, 1.14 AFF, 0.21 esophageal cancer and 0.09 ONJ events per 1,000 person-years in the alendronate cohort and 6.62, 7.36, 0.69, 0.22 and 0.06 events per 1,000 person-years, respectively, in the raloxifene cohort. Alendronate and raloxifene have a similar hip fracture risk (hazard ratio [HR] 1.03, 95% confidence interval [CI] 0.94-1.13), but alendronate users are more likely to have vertebral fractures (HR 1.07, 95% CI 1.01-1.14). Alendronate has higher risk for AFF (HR 1.51, 95% CI 1.23-1.84) but similar risk for esophageal cancer (HR 0.95, 95% CI 0.53-1.70), and ONJ (HR 1.62, 95% CI 0.78-3.34). We demonstrated substantial control of measured confounding by propensity score adjustment, and minimal residual systematic bias through negative control experiments, lending credibility to our effect estimates. Raloxifene is as effective as alendronate and may remain an option in the prevention of osteoporotic fracture.

7.
medRxiv ; 2020 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-32587982

RESUMO

INTRODUCTION: Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results. METHODS: Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments. RESULTS: Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons. CONCLUSION: There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.

8.
Respir Med ; 165: 105919, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32174450

RESUMO

BACKGROUND: Data on the risk of death following an asthma exacerbation are scarce. With this multinational cohort study, we assessed all-cause mortality rates, mortality rates following an exacerbation, and patient characteristics associated with all-cause mortality in asthma. METHODS: Asthma patients aged ≥18 years and with ≥1 year of follow-up were identified in 5 European electronic databases from the Netherlands, Italy, UK, Denmark and Spain during the study period January 1, 2008-December 31, 2013. Patients with asthma-COPD overlap were excluded. Severe asthma was defined as use of high dose ICS + use of a second controller. Severe asthma exacerbations were defined as emergency department visits, hospitalizations or systemic corticosteroid use, all for reason of asthma. RESULTS: The cohort consisted of 586,436 asthma patients of which 42,611 patients (7.3%) had severe asthma. The age and sex standardized all-cause mortality rates ranged between databases from 5.2 to 9.5/1000 person-years (PY) in asthma, and between 11.3 and 14.8/1000 PY in severe asthma. The all-cause mortality rate in the first week following a severe asthma exacerbation ranged between 14.1 and 59.9/1000 PY. Mortality rates remained high in the first month following a severe asthma exacerbation and decreased thereafter. Higher age, male gender, comorbidity, smoking, and previous severe asthma exacerbations were associated with mortality. CONCLUSION: All-cause mortality following a severe exacerbation is high, especially in the first month following the event. Smoking cessation, comorbidity-management and asthma-treatment focusing on the prevention of exacerbations might reduce associated mortality.

9.
PLoS One ; 15(1): e0226718, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31910437

RESUMO

BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke. METHODS: We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia. RESULTS: In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78. CONCLUSIONS: A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.


Assuntos
Isquemia Encefálica/complicações , Hemorragia Cerebral/diagnóstico , Modelos Estatísticos , Medição de Risco/métodos , Acidente Vascular Cerebral/complicações , Hemorragia Cerebral/etiologia , Feminino , Seguimentos , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Curva ROC , Estudos Retrospectivos , Fatores de Risco
10.
Sci Rep ; 10(1): 555, 2020 01 17.
Artigo em Inglês | MEDLINE | ID: mdl-31953469

RESUMO

Globally, maternal birth season affects fertility later in life. The purpose of this systematic literature review is to comprehensively investigate the birth season and female fertility relationship. Using PubMed, we identified a set of 282 relevant fertility/birth season papers published between 1972 and 2018. We screened all 282 studies and removed 131 non-mammalian species studies on fertility and 122 studies that were on non-human mammals. Our meta-analysis focused on the remaining 29 human studies, including twelve human datasets from around the world (USA, Europe, Asia). The main outcome was change in female fertility as observed by maternal birth month and whether this change was correlated with either temperature or rainfall. We found that temperature was either strongly correlated or anti-correlated in studies, indicating that another factor closely tied to temperature may be the culprit exposure. We found that rainfall only increases fertility in higher altitude locations (New Zealand, Romania, and Northern Vietnam). This suggests the possibility of a combined or multi-factorial mechanism underlying the female fertility - birth season relationship. We discuss other environmental and sociological factors on the birth season - female fertility relationship. Future research should focus on the role of birth season and female fertility adjusting for additional factors that modulate female fertility as discussed in this comprehensive review.


Assuntos
Fertilidade , Taxa de Gravidez/tendências , Adulto , Ásia , Europa (Continente) , Feminino , Humanos , Idade Materna , Pessoa de Meia-Idade , Parto , Gravidez , Estações do Ano , Estados Unidos
12.
Front Physiol ; 10: 1272, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31636572

RESUMO

Aims: QT variability is a promising electrocardiographic marker. It has been studied as a screening tool for coronary artery disease and left ventricular hypertrophy, and increased QT variability is a known risk factor for sudden cardiac death. Considering that comprehensive normal values for QT variability were lacking, we set out to establish these in standard 10-s electrocardiograms (ECGs) covering both sexes and all ages. Methods: Ten-second, 12-lead ECGs were provided by five Dutch population studies (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study). ECGs were recorded digitally and processed by well-validated analysis software. We selected cardiologically healthy participants, 46% being women. Ages ranged from 11 days to 91 years. After quality control, 13,828 ECGs were available. We assessed three markers: standard deviation of QT intervals (SDqt), short-term QT variability (STVqt), and QT variability index (QTVI). Results: For SDqt and STVqt, the median and the lower limit of normal remained stable with age. The upper limit of normal declined until around age 45, and increased strongly in the elderly, notably so in women. This implies that a subset of the population, small enough not to have appreciable effect on the median, shows a high degree of QT variability with a possible risk of arrhythmias or worse, especially in women. Otherwise, sex differences were negligible in all three measurements. For QTVI, median, and normal limits decreased until age 20, and steadily went up afterwards except for the lower limit of normal, which flattens off after age 65. Conclusion: We report the first set of normal values for QT variability based on 10-s ECGs, for all ages and both sexes.

13.
J Biomed Inform ; 97: 103264, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31386904

RESUMO

OBJECTIVES: Smoking status is poorly record in US claims data. IBM MarketScan Commercial is a claims database that can be linked to an additional health risk assessment with self-reported smoking status for a subset of 1,966,174 patients. We investigate whether this subset could be used to learn a smoking status phenotype model generalizable to all US claims data that calculates the probability of being a current smoker. METHODS: 251,643 (12.8%) had self-reported their smoking status as 'current smoker'. A regularized logistic regression model, the Current Risk of Smoking Status (CROSS), was trained using the subset of patients with self-reported smoking status. CROSS considered 53,027 candidate covariates including demographics and conditions/drugs/measurements/procedures/observations recorded in the prior 365 days, The CROSS phenotype model was validated across multiple other claims data. RESULTS: The internal validation showed the CROSS model achieved an area under the receiver operating characteristic curve (AUC) of 0.76 and the calibration plots indicated it was well calibrated. The external validation across three US claims databases obtained AUCs ranging between 0.82 and 0.87 showing the model appears to be transportable across Claims data. CONCLUSION: CROSS predicts current smoking status based on the claims records in the prior year. CROSS can be readily implemented to any US insurance claims mapped to the OMOP common data model and will be a useful way to impute smoking status when conducting epidemiology studies where smoking is a known confounder but smoking status is not recorded. CROSS is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/SmokingModel.


Assuntos
Fumar Cigarros/epidemiologia , Revisão da Utilização de Seguros/estatística & dados numéricos , Modelos Estatísticos , Adulto , Biologia Computacional , Interpretação Estatística de Dados , Bases de Dados Factuais/estatística & dados numéricos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fenótipo , Medição de Risco , Autorrelato/estatística & dados numéricos , Estados Unidos/epidemiologia
14.
Drug Saf ; 42(11): 1377-1386, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31054141

RESUMO

INTRODUCTION: US claims data contain medical data on large heterogeneous populations and are excellent sources for medical research. Some claims data do not contain complete death records, limiting their use for mortality or mortality-related studies. A model to predict whether a patient died at the end of the follow-up time (referred to as the end of observation) is needed to enable mortality-related studies. OBJECTIVE: The objective of this study was to develop a patient-level model to predict whether the end of observation was due to death in US claims data. METHODS: We used a claims dataset with full death records, Optum© De-Identified Clinformatics® Data-Mart-Database-Date of Death mapped to the Observational Medical Outcome Partnership common data model, to develop a model that classifies the end of observations into death or non-death. A regularized logistic regression was trained using 88,514 predictors (recorded within the prior 365 or 30 days) and externally validated by applying the model to three US claims datasets. RESULTS: Approximately 25 in 1000 end of observations in Optum are due to death. The Discriminating End of observation into Alive and Dead (DEAD) model obtained an area under the receiver operating characteristic curve of 0.986. When defining death as a predicted risk of > 0.5, only 2% of the end of observations were predicted to be due to death and the model obtained a sensitivity of 62% and a positive predictive value of 74.8%. The external validation showed the model was transportable, with area under the receiver operating characteristic curves ranging between 0.951 and 0.995 across the US claims databases. CONCLUSIONS: US claims data often lack complete death records. The DEAD model can be used to impute death at various sensitivity, specificity, or positive predictive values depending on the use of the model. The DEAD model can be readily applied to any observational healthcare database mapped to the Observational Medical Outcome Partnership common data model and is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/DeadModel .


Assuntos
Pesquisa Biomédica/métodos , Modelos Biológicos , Mortalidade , Interpretação Estatística de Dados , Bases de Dados Factuais , Humanos , Fatores de Risco , Estados Unidos
15.
J Am Med Inform Assoc ; 25(8): 969-975, 2018 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-29718407

RESUMO

Objective: To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility. Methods: Based on existing best practices we propose a 5 step standardized framework for: (1) transparently defining the problem; (2) selecting suitable datasets; (3) constructing variables from the observational data; (4) learning the predictive model; and (5) validating the model performance. We implemented this framework as open-source software utilizing the Observational Medical Outcomes Partnership Common Data Model to enable convenient sharing of models and reproduction of model evaluation across multiple observational datasets. The software implementation contains default covariates and classifiers but the framework enables customization and extension. Results: As a proof-of-concept, demonstrating the transparency and ease of model dissemination using the software, we developed prediction models for 21 different outcomes within a target population of people suffering from depression across 4 observational databases. All 84 models are available in an accessible online repository to be implemented by anyone with access to an observational database in the Common Data Model format. Conclusions: The proof-of-concept study illustrates the framework's ability to develop reproducible models that can be readily shared and offers the potential to perform extensive external validation of models, and improve their likelihood of clinical uptake. In future work the framework will be applied to perform an "all-by-all" prediction analysis to assess the observational data prediction domain across numerous target populations, outcomes and time, and risk settings.


Assuntos
Aprendizado de Máquina , Observação , Prognóstico , Software , Adulto , Conjuntos de Dados como Assunto , Feminino , Necessidades e Demandas de Serviços de Saúde , Humanos , Masculino , Modelos Teóricos , Estudos Observacionais como Assunto , Medição de Risco , Resultado do Tratamento
16.
Front Physiol ; 9: 424, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29755366

RESUMO

Purpose: Heart-rate variability (HRV) measured on standard 10-s electrocardiograms (ECGs) has been associated with increased risk of cardiac and all-cause mortality, but age- and sex-dependent normal values have not been established. Since heart rate strongly affects HRV, its effect should be taken into account. We determined a comprehensive set of normal values of heart-rate corrected HRV derived from 10-s ECGs for both children and adults, covering both sexes. Methods: Five population studies in the Netherlands (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study) provided 10-s, 12-lead ECGs. ECGs were stored digitally and analyzed by well-validated analysis software. We included cardiologically healthy participants, 42% being men. Their ages ranged from 11 days to 91 years. After quality control, 13,943 ECGs were available. Heart-rate correction formulas were derived using an exponential model. Two time-domain HRV markers were analyzed: the corrected standard deviation of the normal-to-normal RR intervals (SDNNc) and corrected root mean square of successive RR-interval differences (RMSSDc). Results: There was a considerable age effect. For both SDNNc and RMSSDc, the median and the lower limit of normal decreased steadily from birth until old age. The upper limit of normal decreased until the age of 60, but increased markedly after that age. Differences of the median were minimal between men and women. Conclusion: We report the first comprehensive set of normal values for heart-rate corrected 10-s HRV, which can be of value in clinical practice and in further research.

17.
PLoS One ; 12(4): e0175087, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28403196

RESUMO

BACKGROUND: Increased variability of beat-to-beat QT-interval durations on the electrocardiogram (ECG) has been associated with increased risk for fatal and non-fatal cardiac events. However, techniques for the measurement of QT variability (QTV) have not been validated since a gold standard is not available. In this study, we propose a validation method and illustrate its use for the validation of two automatic QTV measurement techniques. METHODS: Our method generates artificial standard 12-lead ECGs based on the averaged P-QRS-T complexes from a variety of existing ECG signals, with simulated intrinsic (QT interval) and extrinsic (noise, baseline wander, signal length) variations. We quantified QTV by a commonly used measure, short-term QT variability (STV). Using 28,800 simulated ECGs, we assessed the performance of a conventional QTV measurement algorithm, resembling a manual QTV measurement approach, and a more advanced algorithm based on fiducial segment averaging (FSA). RESULTS: The results for the conventional algorithm show considerable median absolute differences between the simulated and estimated STV. For the highest noise level, median differences were 4-6 ms in the absence of QTV. Increasing signal length generally yields more accurate STV estimates, but the difference in performance between 30 or 60 beats is small. The FSA algorithm proved to be very accurate, with most median absolute differences less than 0.5 ms, even for the highest levels of disturbance. CONCLUSIONS: Artificially constructed ECGs with a variety of disturbances allow validation of QTV measurement procedures. The FSA algorithm provides highly accurate STV estimates under varying signal conditions, and performs much better than traditional beat-by-beat analysis. The fully automatic operation of the FSA algorithm enables STV measurement in large sets of ECGs.


Assuntos
Cardiopatias/diagnóstico , Algoritmos , Eletrocardiografia , Frequência Cardíaca , Humanos , Contração Miocárdica , Processamento de Sinais Assistido por Computador
18.
Circulation ; 134(10): 713-22, 2016 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-27601558

RESUMO

BACKGROUND: The association between thyroid function and cardiovascular disease is well established, but no study to date has assessed whether it is a risk factor for sudden cardiac death (SCD). Therefore, we studied the association of thyroid function with SCD in a prospective population-based cohort. METHODS: Participants from the Rotterdam Study ≥45 years with thyroid-stimulating hormone or free thyroxine (FT4) measurements and clinical follow-up were eligible. We assessed the association of thyroid-stimulating hormone and FT4 with the risk of SCD by using an age- and sex-adjusted Cox proportional-hazards model, in all participants and also after restricting the analysis to euthyroid participants (defined by thyroid-stimulating hormone 0.4-4.0 mIU/L). Additional adjustment included cardiovascular risk factors, notably hypertension, serum cholesterol, and smoking. We stratified by age and sex and performed sensitivity analyses by excluding participants with abnormal FT4 values (reference range of 0.85-1.95 ng/dL) and including only witnessed SCDs as outcome. Absolute risks were calculated in a competing risk model by taking death by other causes into account. RESULTS: We included 10 318 participants with 261 incident SCDs (median follow-up, 9.1 years). Higher levels of FT4 were associated with an increased SCD risk, even in the normal range of thyroid function (hazard ratio, 2.28 per 1 ng/dL FT4; 95% confidence interval, 1.31-3.97). Stratification by age or sex and sensitivity analyses did not change the risk estimates substantially. The absolute 10-year risk of SCD increased in euthyroid participants from 1% to 4% with increasing FT4 levels. CONCLUSIONS: Higher FT4 levels are associated with an increased risk of SCD, even in euthyroid participants.


Assuntos
Morte Súbita Cardíaca/epidemiologia , Vigilância da População , Glândula Tireoide/fisiologia , Tireotropina/sangue , Tiroxina/sangue , Idoso , Idoso de 80 Anos ou mais , Biomarcadores/sangue , Estudos de Coortes , Morte Súbita Cardíaca/prevenção & controle , Feminino , Seguimentos , Humanos , Masculino , Pessoa de Meia-Idade , Países Baixos/epidemiologia , Vigilância da População/métodos , Estudos Prospectivos , Fatores de Risco , Testes de Função Tireóidea/métodos
19.
Artigo em Inglês | MEDLINE | ID: mdl-26492444

RESUMO

OBJECTIVES: The aim of the study was to assess the prevalence of oral contraceptive (OC) use, user characteristics and prescribing patterns by accessing health care databases of three European countries. METHODS: A retrospective study was performed from 2009 to 2010 in three general practice (GP) databases from the Netherlands, UK and Italy and in one database of linked pharmacy and hospitalisation data in the Netherlands. The presence of selected chronic conditions and diagnoses of diseases associated with OC use were assessed, as were switches, discontinuations and types of OC used during the study period. RESULTS: Among 2.16 million women aged 15 to 49 years, 16.0% were using an OC on 1 January 2010. The prevalence ranged from 19.7% in a Dutch database to 2.6% in the Italian database. During 2009 and 2010, mainly second-generation progestogens were prescribed in the Netherlands (79.4% and 78.3% of users), both second- (57.9%) and third-generation progestogens (43.6%) were prescribed in the UK, and mainly third-generation progestogens in Italy (61.8%). Most switches were to third- or fourth-generation pills. The prevalence of chronic diseases tended to be higher among OC users, and the proportions of women with a history of disease associated with OC use tended to be lower than among non-users. CONCLUSIONS: Second-generation OCs were most frequently prescribed in the Netherlands. In the UK, and even more so in Italy, many women used third- or fourth-generation OCs. Preparation switches were mainly to third- or fourth-generation OCs. Among OC users, a somewhat higher prevalence of chronic diseases was observed; however, information bias cannot be ruled out.


Assuntos
Anticoncepcionais Orais Combinados/administração & dosagem , Prescrições de Medicamentos/estatística & dados numéricos , Vigilância da População , Adulto , Estudos de Casos e Controles , Anticoncepcionais Orais/administração & dosagem , Anticoncepcionais Orais Hormonais/administração & dosagem , Feminino , Conhecimentos, Atitudes e Prática em Saúde , Humanos , Itália/epidemiologia , Pessoa de Meia-Idade , Países Baixos/epidemiologia , Prevalência , Reino Unido/epidemiologia , Saúde da Mulher/estatística & dados numéricos , Adulto Jovem
20.
J Card Fail ; 22(1): 17-23, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26093333

RESUMO

BACKGROUND: Subclinical cardiac dysfunction has been associated with increased mortality, and heart failure increases the risk of sudden cardiac death (SCD). Less well known is whether subclinical cardiac dysfunction is also a risk factor for SCD. Our objective was to assess the association between echocardiographic parameters and SCD in a community-dwelling population free of heart failure. METHODS AND RESULTS: We computed hazard ratios (HRs) for left atrium diameter, left ventricular (LV) end-diastolic dimension, LV end-systolic dimension, LV mass, qualitative LV systolic function, LV fractional shortening, and diastolic function. During a median follow-up of 6.3 years in 4,686 participants, 68 participants died because of SCD. Significant associations with SCD were observed for qualitative LV systolic function and LV fractional shortening. For moderate/poor qualitative LV systolic function, the HR for SCD was 2.54 (95% confidence interval [CI] 1.10-5.87). Each standard deviation decrease in LV fractional shortening was associated with an HR of 1.36 (95% CI 1.09-1.70). CONCLUSIONS: Subclinical abnormalities in LV systolic function were associated with SCD risk in this general population. Although prediction of SCD remains difficult and traditional cardiovascular risk factors are of greatest importance, this knowledge might guide future directions to prevent SCD in persons with subclinical cardiac dysfunction.


Assuntos
Doenças Assintomáticas/epidemiologia , Morte Súbita Cardíaca/epidemiologia , Disfunção Ventricular Esquerda/diagnóstico , Disfunção Ventricular Esquerda/epidemiologia , Idoso , Morte Súbita Cardíaca/prevenção & controle , Diástole , Ecocardiografia , Feminino , Insuficiência Cardíaca/diagnóstico , Insuficiência Cardíaca/fisiopatologia , Humanos , Masculino , Pessoa de Meia-Idade , Países Baixos/epidemiologia , Modelos de Riscos Proporcionais , Estudos Prospectivos , Medição de Risco , Fatores de Risco , Volume Sistólico , Sístole
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