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| 3. |
- Mechler, Jakob, et al.
(författare)
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Therapist-guided internet-based psychodynamic therapy versus cognitive behavioural therapy for adolescent depression in Sweden : a randomised, clinical, non-inferiority trial
- 2022
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Ingår i: The Lancet Digital Health. - : Elsevier. - 2589-7500. ; 4:8, s. E594-E603
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Tidskriftsartikel (refereegranskat)abstract
- Background Adolescent major depressive disorder (MDD) is highly prevalent and associated with lifelong adversity. Evidence-based treatments exist, but accessible treatment alternatives are needed. We aimed to compare internet-based psychodynamic therapy (IPDT) with an established evidence-based treatment (internet-based cognitive behavioural therapy [ICBT]) for the treatment of adolescents with depression. Methods In this randomised, clinical trial, we tested whether IPDT was non-inferior to ICBT in the treatment of adolescent MDD. Eligible participants were 15-19 years old, presenting with a primary diagnosis of MDD according to DSM-5. Participants were recruited nationwide in Sweden through advertisements on social media, as well as contacts with junior and senior high schools, youth associations, social workers, and health-care providers. Adolescents who scored 9 or higher on the Quick Inventory of Depressive Symptomatology for Adolescents (QIDS-A17-SR) in an initial online screening were contacted by telephone for a diagnostic assessment using the Mini International Neuropsychiatric Interview. Participants were randomly assigned to ICBT or IPDT. Both interventions comprised eight self-help modules delivered over 10 weeks on a secure online platform. The primary outcome was change in depression severity measured weekly by the QIDS-A17-SR. Primary analyses were based on an intention -to-treat sample including all participants randomly assigned. A non-inferiority margin of Cohen's d=0.30 was predefined. The study is registered at ISRCTN, ISRCTN12552584. Findings Between Aug 19, 2019, and Oct 7, 2020, 996 young people completed screening; 516 (52%) were contacted for a diagnostic interview. 272 participants were eligible and randomly assigned to ICBT (n=136) or IPDT (n=136). In the ICBT group, 51 (38%) of 136 participants were classified as remitted, and 54 (40%) of 136 participants were classified as remitted in the IPDT group. Within-group effects were large (ICBT: within-group d=1.75, 95% CI 1.49 to 2.01; IPDT: within-group d=1.93, 1.67 to 2.20; both p<0.0001). No statistically significant treatment difference was found in the intention-to-treat analysis. Non-inferiority for IPDT was shown for the estimated change in depression during treatment (d=-0.18, 90% CI -0.49 to 0.13; p=0.34). All secondary outcomes showed non-significant between-group differences. Interpretation IPDT was non-inferior to ICBT in terms of change in depression for the treatment of adolescents with MDD. This finding increases the range of accessible and effective treatment alternatives for adolescents with depression. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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| 5. |
- Mohammad, Moman A., et al.
(författare)
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Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction : a nationwide population-based study
- 2022
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Ingår i: The Lancet Digital Health. - : Elsevier. - 2589-7500. ; 4:1, s. 37-45
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Tidskriftsartikel (refereegranskat)abstract
- Background: Patients have an estimated mortality of 15–20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing the risk of long-term mortality. We aimed to establish the accuracy of an artificial neural network (ANN) algorithm in predicting 1-year mortality and admission to hospital for heart failure after myocardial infarction. Methods: In this nationwide population-based study, we used data for all patients admitted to hospital for myocardial infarction and discharged alive from a coronary care unit in Sweden (n=139 288) between Jan 1, 2008, and April 1, 2017, from the Swedish Web system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) nationwide registry; these patients were randomly divided into training (80%) and testing (20%) datasets. We developed an ANN using 21 variables (including age, sex, medical history, previous medications, in-hospital characteristics, and discharge medications) associated with the outcomes of interest with a back-propagation algorithm in the training dataset and tested it in the testing dataset. The ANN algorithm was then validated in patients with incident myocardial infarction enrolled in the Western Denmark Heart Registry (external validation cohort) between Jan 1, 2008, and Dec 31, 2016. The predictive ability of the model was evaluated using area under the receiver operating characteristic curve (AUROC) and Youden's index was established as a means of identifying an empirical dichotomous cutoff, allowing further evaluation of model performance. Findings: 139 288 patients who were admitted to hospital for myocardial infarction in the SWEDEHEART registry were randomly divided into a training dataset of 111 558 (80%) patients and a testing dataset of 27 730 (20%) patients. 30 971 patients with myocardial infarction who were enrolled in the Western Denmark Heart Registry were included in the external validation cohort. A first event, either all-cause mortality or admission to hospital for heart failure 1 year after myocardial infarction, occurred in 32 308 (23·2%) patients in the testing and training cohorts only. For 1-year all-cause mortality, the ANN had an AUROC of 0·85 (95% CI 0·84–0·85) in the testing dataset and 0·84 (0·83–0·84) in the external validation cohort. The AUROC for admission to hospital for heart failure within 1 year was 0·82 (0·81–0·82) in the testing dataset and 0·78 (0·77–0·79) in the external validation dataset. With an empirical cutoff the ANN algorithm correctly classified 73·6% of patients with regard to all-cause mortality and 61·5% of patients with regard to admission to hospital for heart failure in the external validation cohort, ruling out adverse outcomes with 97·1–98·7% probability in the external validation cohort. Interpretation: Identifying patients at a high risk of developing heart failure or death after myocardial infarction could result in tailored therapies and monitoring by the allocation of resources to those at greatest risk. Funding: The Swedish Heart and Lung Foundation, Swedish Scientific Research Council, Swedish Foundation for Strategic Research, Knut and Alice Wallenberg Foundation, ALF Agreement on Medical Education and Research, Skane University Hospital, The Bundy Academy, the Märta Winkler Foundation, the Anna-Lisa and Sven-Eric Lundgren Foundation for Medical Research.
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- Raket, Lars Lau, et al.
(författare)
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Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis : a case-control development and validation study
- 2020
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Ingår i: The Lancet Digital Health. - 2589-7500. ; 2:5, s. 229-239
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Tidskriftsartikel (refereegranskat)abstract
- Background: Many individuals who will experience a first episode of psychosis (FEP) are not detected before occurrence, limiting the effect of preventive interventions. The combination of machine-learning methods and electronic health records (EHRs) could help address this gap. Methods: This case-control development and validation study is based on EHR data from IBM Explorys. The IBM Explorys Platform holds standardised, longitudinal, de-identified, patient-level EHR data pooled from different health-care systems with distinct EHRs. The present EHR-based studies were retrospective, matched (1:1), case-control studies compliant with RECORD, STROBE, and TRIPOD statements. The study included individuals in the IBM Explorys database who at some point between 1990 and 2018 had a diagnosis of FEP followed by schizophrenia, and psychosis-free matched control individuals from a random subsample of the full cohort. For every individual in the FEP cohort, the individual in the control cohort was matched to have a similar date for inclusion in the database and a similar total observation time. Individuals in the FEP cohort had their index date defined as the first diagnosis of psychosis or the first prescription of antipsychotic medication. Individuals in the control cohort had their index date defined to occur the same number of days after inclusion in the database as their matching FEP individual. The FEP and control cohorts were both randomly split into development and validation datasets in a ratio of 7:3. The subset of individuals in the validation dataset who had all their health-care encounters at providers that were not seen in the development dataset made up the external validation subset. A novel recurrent neural network model was developed to predict the risk of FEP 1 year before the index date by employing demographics and medical events (in the categories diagnoses, prescriptions, procedures, encounters and admissions, observations, and laboratory test results) dynamically collected in the EHR as part of clinical routine. We named the recurrent neural network Dynamic ElecTronic hEalth reCord deTection (DETECT). The main outcomes were accuracy and area under receiver operating characteristic curve (AUROC). Decision-curve analyses and dynamic patient journey plots were used to evaluate clinical usefulness. Findings: The FEP and control cohorts each comprised 72 860 individuals. 102 030 individuals (51 015 matching pairs) were randomly allocated to the development dataset and the remaining 43 690 to the validation dataset. In the validation dataset, 4770 individuals had all their encounters outside of the 118 790 health-care providers that were encountered in the development dataset. The data from these individuals made up the external validation subset. The median follow-up (observation time before index date) was 6·0 years (IQR 3·0–10·4). In the development dataset, DETECT's prognostic accuracy was 0·787 and AUROC was 0·868. In the validation dataset, DETECT's prognostic accuracy was 0·774 and AUROC was 0·856. In the external test subset, DETECT's balanced prognostic accuracy was 0·724 and AUROC was 0·799. Prevalence-adjusted decision-curve analyses suggested that DETECT was associated with a positive net benefit in two different scenarios for FEP detection. Interpretation: DETECT showed adequate prognostic accuracy to detect individuals at risk of developing a FEP in primary and secondary care. Replication and refinement in a population-based setting are needed to consolidate these findings. Funding: Lundbeck.
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| 7. |
- Spada, Cristiano, et al.
(författare)
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AI-assisted capsule endoscopy reading in suspected small bowel bleeding : a multicentre prospective study
- 2024
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Ingår i: The Lancet Digital Health. - 2589-7500. ; 6:5, s. 345-353
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Tidskriftsartikel (refereegranskat)abstract
- Background: Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. Methods: Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. Findings: From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6–19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). Interpretation: AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. Funding: ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.
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| 13. |
- Dembrower, Karin, et al.
(författare)
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Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload : a retrospective simulation study
- 2020
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Ingår i: The Lancet Digital Health. - : Elsevier. - 2589-7500. ; 2:9, s. E468-E474
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Tidskriftsartikel (refereegranskat)abstract
- Background We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection. Methods In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0.7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer. Findings 7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0.3% (95% CI 0.0-4.3), or 2.6% (1.1-5.4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively. Interpretation Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later.
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- Häggström, Ida, 1982, et al.
(författare)
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Deep learning for [ 18 F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
- 2024
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Ingår i: The Lancet Digital Health. - 2589-7500. ; 6:2, s. e114-e125
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Tidskriftsartikel (refereegranskat)abstract
- Background : The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. Methods : In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1–3 vs 4–5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. Findings : In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942–0·956), accuracy of 0·890 (0·879–0·901), sensitivity of 0·868 (0·851–0·885), and specificity of 0·913 (0·899–0·925); LARS-max achieved an AUC of 0·949 (0·942–0·956), accuracy of 0·868 (0·858–0·879), sensitivity of 0·909 (0·896–0·924), and specificity of 0·826 (0·808–0·843); and LARS-ptct achieved an AUC of 0·939 (0·930–0·948), accuracy of 0·875 (0·864–0·887), sensitivity of 0·836 (0·817–0·855), and specificity of 0·915 (0·901–0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938–0·966), accuracy of 0·907 (0·888–0·925), sensitivity of 0·874 (0·843–0·904), and specificity of 0·949 (0·921–0·960); LARS-max achieved an AUC of 0·952 (0·937–0·965), accuracy of 0·898 (0·878–0·916), sensitivity of 0·899 (0·871–0·926), and specificity of 0·897 (0·871–0·922); and LARS-ptct achieved an AUC of 0·932 (0·915–0·948), accuracy of 0·870 (0·850–0·891), sensitivity of 0·827 (0·793–0·863), and specificity of 0·913 (0·889–0·937). Interpretation : Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. Funding: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.
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| 22. |
- Mechler, Jakob, et al.
(författare)
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Therapist-guided internet-based psychodynamic therapy versus cognitive behavioural therapy for adolescent depression in Sweden : a randomised, clinical, non-inferiority trial
- 2022
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Ingår i: The Lancet Digital Health. - : Elsevier. - 2589-7500. ; 4:8, s. e594-e603
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Tidskriftsartikel (refereegranskat)abstract
- Background: Adolescent major depressive disorder (MDD) is highly prevalent and associated with lifelong adversity. Evidence-based treatments exist, but accessible treatment alternatives are needed. We aimed to compare internet-based psychodynamictherapy (IPDT) with an established evidence-based treatment (internet-based cognitive behavioural therapy [ICBT]) for the treatment of adolescents with depression.Methods: In this randomised, clinical trial, we tested whether IPDT was non-inferior to ICBT in the treatment of adolescent MDD. Eligible participants were 15–19 years old, presenting with a primary diagnosis of MDD according to DSM-5. Participants were recruited nationwide in Sweden through advertisements on social media, as well as contacts with junior and senior high schools, youth associations, social workers, and health-care providers. Adolescents who scored 9 or higher on the Quick Inventory of Depressive Symptomatology for Adolescents (QIDS-A17-SR) in an initial online screening were contacted by telephone for a diagnostic assessment using the Mini International Neuropsychiatric Interview. Participants were randomly assigned to ICBT or IPDT. Both interventions comprised eight self-help modules delivered over 10 weeks on a secure online platform. The primary outcome was change in depression severity measured weekly by the QIDS-A17-SR. Primary analyses were based on an intention-to-treat sample including all participants randomly assigned. A non-inferiority margin of Cohen's d=0·30 was predefined. The study is registered at ISRCTN, ISRCTN12552584.Findings: Between Aug 19, 2019, and Oct 7, 2020, 996 young people completed screening; 516 (52%) were contacted for a diagnostic interview. 272 participants were eligible and randomly assigned to ICBT (n=136) or IPDT (n=136). In the ICBT group, 51 (38%) of 136 participants were classified as remitted, and 54 (40%) of 136 participants were classified as remitted in the IPDT group. Within-group effects were large (ICBT: within-group d=1·75, 95% CI 1·49 to 2·01; IPDT: within-group d=1·93, 1·67 to 2·20; both p<0·0001). No statistically significant treatment difference was found in the intention-to-treat analysis. Non-inferiority for IPDT was shown for the estimated change in depression during treatment (d=–0·18, 90% CI –0·49 to 0·13; p=0·34). All secondary outcomes showed non-significant between-group differences.Interpretation: IPDT was non-inferior to ICBT in terms of change in depression for the treatment of adolescents with MDD. This finding increases the range of accessible and effective treatment alternatives for adolescents with depression.
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| 23. |
- Moghadam, Saeed Montazeri, et al.
(författare)
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An automated bedside measure for monitoring neonatal cortical activity : a supervised deep learning-based electroencephalogram classifier with external cohort validation
- 2022
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Ingår i: The Lancet Digital Health. - : Elsevier. - 2589-7500. ; 4:12, s. E884-E892
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Tidskriftsartikel (refereegranskat)abstract
- Background Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors. Methods In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend. Findings We collected 2561 h of EEG from 39 infants (gestational age 35 center dot 0-42 center dot 1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes. Interpretation The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials. Copyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.
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| 24. |
- Morales, Daniel R, et al.
(författare)
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Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis.
- 2021
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Ingår i: The Lancet Digital health. - 2589-7500. ; 3:2
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Tidskriftsartikel (refereegranskat)abstract
- 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.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.Among 1355349 antihypertensive users (363785 ACEI or ARB monotherapy users, 248915 CCB or THZ monotherapy users, 711799 ACEI or ARB combination users, and 473076 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.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.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.
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| 25. |
- Richard, Edo, et al.
(författare)
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Healthy ageing through internet counselling in the elderly (HATICE) : a multinational, randomised controlled trial
- 2019
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Ingår i: The Lancet Digital Health. - 2589-7500. ; 1:8, s. e424-e434
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Tidskriftsartikel (refereegranskat)abstract
- Background Although web-based interventions have been promoted for cardiovascular risk management over the past decade, there is limited evidence for effectiveness of these interventions in people older than 65 years. The healthy ageing through internet counselling in the elderly (HATICE) trial aimed to determine whether a coach-supported internet intervention for self-management can reduce cardiovascular risk in community-dwelling older people.Methods This prospective open-label, blinded endpoint clinical trial among people age 65 years or over at increased risk of cardiovascular disease randomly assigned participants in the Netherlands, Finland, and France to an interactive internet intervention stimulating coach-supported self-management or a control platform. Primary outcome was the difference from baseline to 18 months on a standardised composite score (Z score) of systolic blood pressure, LDL cholesterol, and body-mass index (BMI). Secondary outcomes included individual risk factors and cardiovascular endpoints. This trial is registered with the ISRCTN registry, 48151589, and is closed to accrual.Findings Among 2724 participants, complete primary outcome data were available for 2398 (88%). After 18 months, the primary outcome improved in the intervention group versus the control group (0.09 vs 0.04, respectively; mean difference -0.05, 95% CI -0.08 to -0.01; p=0.008). For individual components of the primary outcome, mean differences (intervention vs control) were systolic blood pressure -1.79 mm Hg versus -0.67 mm Hg (-1.12, -2.51 to 0.27); BMI -0.23 kg/m(2) versus -0.08 kg/m(2) (-0.15, -0.28 to -0.01); and LDL -0.12 mmol/L versus -0.07 mmol/L (-0.05, -0.11 to 0.01). Cardiovascular disease occurred in 30 (2.2%) of 1382 patients in the intervention versus 32 (2.4%) of 1333 patients in the control group (hazard ratio 0.86, 95% CI 0.52 to 1.43).Interpretation Coach-supported self-management of cardiovascular risk factors using an interactive internet intervention is feasible in an older population, and leads to a modest improvement of cardiovascular risk profile. When implemented on a large scale this could potentially reduce the burden of cardiovascular disease.
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