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- 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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- 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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- 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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