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Background: Multimorbidity, defined as the coexistence of multiple chronic conditions, poses significant challenges to health care systems on a global scale. It is associated with increased mortality, reduced quality of life, and increased health care costs. The burden of multimorbidity is expected to worsen if no effective intervention is taken. Machine learning has the potential to assist in addressing these challenges since it offers advanced analysis and decision-making capabilities, such as disease prediction, treatment development, and clinical strategies. Objective: This paper represents the protocol of a scoping review that aims to identify and explore the current literature concerning the use of machine learning for patients with multimorbidity. More precisely, the objective is to recognize various machine learning models, the patient groups involved, features considered, types of input data, the maturity of the machine learning algorithms, and the outcomes from these machine learning models. Methods: The scoping review will be based on the guidelines of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Five databases (PubMed, Embase, IEEE, Web of Science, and Scopus) are chosen to conduct a literature search. Two reviewers will independently screen the titles, abstracts, and full texts of identified studies based on predefined eligibility criteria. Covidence (Veritas Health Innovation Ltd) will be used as a tool for managing and screening papers. Only studies that examine more than 1 chronic disease or individuals with a single chronic condition at risk of developing another will be included in the scoping review. Data from the included studies will be collected using Microsoft Excel (Microsoft Corp). The focus of the data extraction will be on bibliographical information, objectives, study populations, types of input data, types of algorithm, performance, maturi, Background: Multimorbidity, defined as the coexistence of multiple chronic conditions, poses significant challenges to health care systems on a global scale. It is associated with increased mortality, reduced quality of life, and increased health care costs. The burden of multimorbidity is expected to worsen if no effective intervention is taken. Machine learning has the potential to assist in addressing these challenges since it offers advanced analysis and decision-making capabilities, such as disease prediction, treatment development, and clinical strategies. Objective: This paper represents the protocol of a scoping review that aims to identify and explore the current literature concerning the use of machine learning for patients with multimorbidity. More precisely, the objective is to recognize various machine learning models, the patient groups involved, features considered, types of input data, the maturity of the machine learning algorithms, and the outcomes from these machine learning models. Methods: The scoping review will be based on the guidelines of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Five databases (PubMed, Embase, IEEE, Web of Science, and Scopus) are chosen to conduct a literature search. Two reviewers will independently screen the titles, abstracts, and full texts of identified studies based on predefined eligibility criteria. Covidence (Veritas Health Innovation Ltd) will be used as a tool for managing and screening papers. Only studies that examine more than 1 chronic disease or individuals with a single chronic condition at risk of developing another will be included in the scoping review. Data from the included studies will be collected using Microsoft Excel (Microsoft Corp). The focus of the data extraction will be on bibliographical information, objectives, study populations, types of input data, types of algorithm, performance, maturity of the algorithms, and o

Patient-reported outcomes (PROs) provide important insights into patients own perspectives about their health and medical condition, and there is evidence that their use can lead to improvements in the quality of care and to better-informed clinical decisions. Their application in cardiovascular populations has grown over the past decades. This statement describes what PROs are, and it provides an inventory of disease-specific and domain-specific PROs that have been developed for cardiovascular populations. International standards and quality indices have been published, which can guide the selection of PROs for clinical practice and in clinical trials and research; patients as well as experts in psychometrics should be involved in choosing which are most appropriate. Collaborations are needed to define criteria for using PROs to guide regulatory decisions, and the utility of PROs for comparing and monitoring the quality of care and for allocating resources should be evaluated. New sources for recording PROs include wearable digital health devices, medical registries, and electronic health record. Advice is given for the optimal use of PROs in shared clinical decision-making in cardiovascular medicine, and concerning future directions for their wider application.

Aims The aim of this study was to evaluate if a combination of World Health Organization-5 (WHO-5), Anxiety Symptom Scale-2 (ASS-2), and Major Depression Inventory-2 (MDI-2) can replace the Hospital Anxiety and Depression Scale (HADS) as screening tool for anxiety and depression in cardiac patients across diagnoses and whether it is feasible to generate crosswalks (translation tables) for use in clinical practice. Methods and results We used data from the Danish ‘Life with a heart disease’ survey, in which 10 000 patients with a hospital contact and discharge diagnosis of ischaemic heart disease, heart failure, heart valve disease, or atrial fibrillation in 2018 were invited. Potential participants received an electronic questionnaire including 51 questions on health, well-being, and evaluation of the health care system. Crosswalks between WHO-5/ASS-2 and HADS anxiety dimension (HADS-A) and between WHO-5/MDI-2 and HADS depression dimension (HADS-D) were generated and tested using item response theory (IRT). A total of 4346 patients responded to HADS, WHO-5, ASS-2, and MDI-2. Model fit of the bi-factor IRT models illustrated appropriateness of a bi-factor structure and thus of essential uni-dimensionality [root mean square error of approximation (RMSEA) (P value) range 0.000–0.053 (0.0099–0.7529) for anxiety and 0.033–0.061 (0.0168–0.2233) for depression]. A combination of WHO-5 and ASS-2 measured the same trait as HADS-A, and a combination of WHO-5 and MDI-2 measured the same trait as HADS-D. Consequently, crosswalks (translation tables) were generated. Conclusions Our study shows that it is feasible to use crosswalks between HADS-A and WHO-5/ASS-2 and HADS-D and WHO-5/MDI-2 for screening cardiac patients across diagnoses for anxiety and depression in clinical practice., AIM: The aim of this study was to evaluate if a combination of World Health Organization-5 (WHO-5), Anxiety Symptom Scale-2 (ASS-2) and Major Depression Inventory-2 (MDI-2) can replace the Hospital Anxiety and Depression Scale (HADS) as screening tool for anxiety and depression in cardiac patients across diagnoses, and whether it is feasible to generate crosswalks (translation tables) for use in clinical practice.METHODS: We used data from the Danish 'Life with a heart disease' survey, in which 10,000 patients with a hospital contact and discharge diagnosis of ischemic heart disease (IHD), heart failure (HF), heart valve disease (HVD) or atrial fibrillation (AF) in 2018 were invited. Potential participants received an electronic questionnaire including 51 questions on health, well-being, and evaluation of the health care system. Crosswalks between WHO-5/ASS-2 and HADS-A, and between WHO-5/MDI-2 and HADS-D were generated and tested using item response theory (IRT).RESULTS: A total of 4346 patients responded to HADS, WHO-5, ASS-2, and MDI-2. Model fit of the bi-factor IRT models illustrated appropriateness of a bi-factor structure and thus of essential uni-dimensionality (RMSEA(p value) range 0.000-0.053(0.0099-0.7529) for anxiety, and 0.033-0.061(0.0168-0.2233) for depression). A combination of WHO-5 and ASS-2 measured the same trait as HADS-A, and a combination of WHO-5 and MDI-2 measured the same trait as HADS-D. Consequently, crosswalks (translation tables) were generated.CONCLUSIONS: Our study shows that it is feasible to use crosswalks between HADS-A and WHO-5/ASS-2, and HADS-D and WHO-5/MDI-2 for screening cardiac patients across diagnoses for anxiety and depression in clinical practice.

Importance Allocating resources to increase survival after cardiac arrest requires survivors to have a good quality of life, but long-term data are lacking. Objective To determine the quality of life of survivors of out-of-hospital cardiac arrest from 2001 to 2019. Design, Setting, and Participants This survey study used the EuroQol Health Questionnaire, 12-Item Short Form Health Survey (SF-12), and Hospital Anxiety and Depression Scale (HADS) to assess the health-related quality of life of all adult survivors of out-of-hospital cardiac arrest included in the Danish Cardiac Arrest Registry between June 1, 2001, and August 31, 2019, who were alive in October 2020 (follow-up periods, 0-1, >1-2, >2-4, >4-6, >6-8, >8-10, >10-15, and >15-20 years since arrest). The survey was conducted from October 1, 2020, through May 31, 2021. Exposure All patients who experienced an out-of-hospital cardiac arrest. Main Outcome and Measures Self-reported health was measured using the EuroQol Health Questionnaire index (EQ index) score and EQ visual analog scale. Physical and mental health were measured using the SF-12, and anxiety and depression were measured using the HADS. Descriptive statistics were used for the analysis. Results Of 4545 survivors, 2552 (56.1%) completed the survey, with a median follow-up since their event of 5.5 years (IQR, 2.9-8.9 years). Age was comparable between responders and nonresponders (median [IQR], 67 [58-74] years vs 68 [56-78] years), and 2075 responders (81.3%) were men and 477 (18.7%) women (vs 1473 male [73.9%] and 520 female [26.1%] nonresponders). For the shortest follow-up (0-1 year) and longest follow-up (>15-20 years) groups, the median EQ index score was 0.9 (IQR, 0.7-1.0) and 0.9 (0.8-1.0), respectively. For all responders, the mean (SD) SF-12 physical health score was 43.3 (12.3) and SF-12 mental health score, 52.9 (8.3). All 3 scores were comparable to a general Danish re, IMPORTANCE: Allocating resources to increase survival after cardiac arrest requires survivors to have a good quality of life, but long-term data are lacking.OBJECTIVE: To determine the quality of life of survivors of out-of-hospital cardiac arrest from 2001 to 2019.DESIGN, SETTING, AND PARTICIPANTS: This survey study used the EuroQol Health Questionnaire, 12-Item Short Form Health Survey (SF-12), and Hospital Anxiety and Depression Scale (HADS) to assess the health-related quality of life of all adult survivors of out-of-hospital cardiac arrest included in the Danish Cardiac Arrest Registry between June 1, 2001, and August 31, 2019, who were alive in October 2020 (follow-up periods, 0-1, >1-2, >2-4, >4-6, >6-8, >8-10, >10-15, and >15-20 years since arrest). The survey was conducted from October 1, 2020, through May 31, 2021.EXPOSURE: All patients who experienced an out-of-hospital cardiac arrest.MAIN OUTCOME AND MEASURES: Self-reported health was measured using the EuroQol Health Questionnaire index (EQ index) score and EQ visual analog scale. Physical and mental health were measured using the SF-12, and anxiety and depression were measured using the HADS. Descriptive statistics were used for the analysis.RESULTS: Of 4545 survivors, 2552 (56.1%) completed the survey, with a median follow-up since their event of 5.5 years (IQR, 2.9-8.9 years). Age was comparable between responders and nonresponders (median [IQR], 67 [58-74] years vs 68 [56-78] years), and 2075 responders (81.3%) were men and 477 (18.7%) women (vs 1473 male [73.9%] and 520 female [26.1%] nonresponders). For the shortest follow-up (0-1 year) and longest follow-up (>15-20 years) groups, the median EQ index score was 0.9 (IQR, 0.7-1.0) and 0.9 (0.8-1.0), respectively. For all responders, the mean (SD) SF-12 physical health score was 43.3 (12.3) and SF-12 mental health score, 52.9 (8.3). All 3 scores were comparable to a general Danish referenc

Aims: Out-of-hospital cardiac arrest (OHCA) survivors may suffer short-term fatigue, psychological, cognitive and disability problems, but we lack information on the proportion of survivors with these problems in the long-term. Hence, we investigated these problems in survivors 1–5 years post-OHCA and whether the results are different at different time points post-OHCA. Methods: All adults who survived an OHCA in Denmark from 2016 to 2019 were identified using the Danish Cardiac Arrest Registry and invited to participate in a survey between October 2020 and March 2021. The survey included the Modified Fatigue Impact Scale, Hospital Anxiety and Depression Scale, “Two simple questions” (everyday activities and mental recovery), and the 12-item World Health Organisation Disability Assessment Schedule 2.0. To investigate results at different time points, survivors were divided into four time-groups (12–24, 25–36, 37–48 and 49–56 months post-OHCA). Differences between time-groups were determined using the Kruskall-Wallis test for the mean scores and Chi-square test for the proportion of survivors with symptoms. Results: Total eligible survey population was 2116, of which 1258 survivors (60 %) responded. Overall, 29 % of survivors reported fatigue, 20 % anxiety, 15 % depression, and 27 % disability. When survivors were sub-divided by time since OHCA, no significant difference was found on either means scores or proportion between time groups (p = 0.28 to 0.88). Conclusion: Up to a third of survivors report fatigue, anxiety, depression, reduced mental function and disability 1–5 years after OHCA. This proportion is the same regardless of how much time has passed supporting early screening and tailored post-OHCA interventions to help survivors adapt to their new situation.