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Background The variability in treatment response in people with rheumatoid arthritis (RA) warrants the prediction of patients at high risk of treatment failure. Identification of biomarkers linked to clinical remission in RA is currently a challenge. Metabolomics may help to identify such biomarkers as it allows for a comprehensive exploration of disease-related variations that extends beyond the genome and proteome. This hypothesis-free exploratory metabolomics study aimed to profile serum metabolic alterations in early RA to understand the metabolic changes associated with disease activity and therapeutic response. Methods The study included 220 early RA participants from the NORD-STAR study, randomized at baseline into four arms, ranging from conventional anti-rheumatic treatment to biological drugs: methotrexate combined with prednisolone (1), certolizumab (2), abatacept (3), or tocilizumab (4). Untargeted metabolomics was performed in serum samples at baseline and 24-week follow-up. Participants achieving clinical disease activity index remission at 24 weeks were defined as responders. Machine learning models for treatment response were constructed using random forest, logistic regression, support vector machine and extreme gradient boosting algorithms based on selected features. Results We identified 278 metabolites, of which 39 were associated with baseline disease activity, including several acylcarnitines and amino acids. We also found 17 baseline metabolites associated with remission at 24 weeks in the overall cohort, including malic acid (β=-0.4), cytidine (β = 0.4), arginine (β = 0.3), and citrulline (β = 0.2), as well as specific metabolites and metabolic pathways associated with remission in the four treatment arms. Fifteen features were identified using machine learning-based multivariable selection. The best predictive model using logistic regression achieved AUC of 0.75 in training and 0.73 in the test set. Conclusions Our study has identified several baseline metabolites and metabolic pathways associated with disease activity and response to different treatments in early RA. By integrating metabolomics and clinical data, we developed predictive models for response to treatment in early RA, though their predictive performance remains limited.

Background There has been in increase in the use of systems for organizing lay responders for suspected out-of-hospital cardiac arrests (OHCAs) dispatch using smartphone-based technology. The purpose is to increase survival rates; however, such systems are dependent on people’s commitment to becoming a lay responder. Knowledge about the characteristics of such volunteers and their motivational factors is lacking. Therefore, we explored characteristics and quantified the underlying motivational factors for joining a smartphone-based cardiopulmonary resuscitation (CPR) lay responder system. Methods In this descriptive cross-sectional study, 800 consecutively recruited lay responders in a smartphone-based mobile positioning first-responder system (SMS-lifesavers) were surveyed. Data on characteristics and motivational factors were collected, the latter through a modified version of the validated survey “Volunteer Motivation Inventory” (VMI). The statements in the VMI, ranked on a Likert scale (1–5), corresponded to(a) intrinsic (an inner belief of doing good for others) or (b) extrinsic (earning some kind of reward from the act) motivational factors. Results A total of 461 participants were included in the final analysis. Among respondents, 59% were women, 48% between 25 and 39 years of age, 37% worked within health care, and 66% had undergone post-secondary school. The most common way (44%) to learn about the lay responder system was from a CPR instructor. A majority (77%) had undergone CPR training at their workplace. In terms of motivation, where higher scores reflect greater importance to the participant, intrinsic factors scored highest, represented by the category values (mean 3.97) followed by extrinsic categories reciprocity (mean 3.88) and self-esteem (mean 3.22). Conclusion This study indicates that motivation to join a first responder system mainly depends on intrinsic factors, i.e. an inner belief of doing good, but there are also extrinsic factors, such as earning some kind of reward from the act, to consider. Focusing information campaigns on intrinsic factors may be the most important factor for successful recruitment. When implementing a smartphone-based lay responder system, CPR instructors, as a main information source to potential lay responders, as well as the workplace, are crucial for successful recruitment.

Patients with type 1 or type 2 diabetes in Sweden were studied to examine trends in mortality and cardiovascular disease incidence between 1998 and 2014. Both outcomes declined substantially, although fatal outcomes declined less among patients with type 2 diabetes than among controls. Diabetes mellitus is a complex and heterogeneous group of chronic metabolic diseases that are characterized by hyperglycemia. Type 1 diabetes occurs predominantly in young people (diagnosis at 30 years of age or younger) and is generally thought to be precipitated by an immune-associated destruction of insulin-producing pancreatic beta cells, leading to insulin deficiency and an absolute need for exogenous insulin replacement. 1 Type 2 diabetes is a progressive metabolic disease that is characterized by insulin resistance and eventual functional failure of pancreatic beta cells. 2 The prevalence of type 2 diabetes has been increasing dramatically over the past few decades, 3 with projections . . .

People with type 1 diabetes are at elevated risk of mortality and cardiovascular disease, yet current guidelines do not consider age of onset as an important risk stratifier. We aimed to examine how age at diagnosis of type 1 diabetes relates to excess mortality and cardiovascular risk. We did a nationwide, register-based cohort study of individuals with type 1 diabetes in the Swedish National Diabetes Register and matched controls from the general population. We included patients with at least one registration between Jan 1, 1998, and Dec 31, 2012. Using Cox regression, and with adjustment for diabetes duration, we estimated the excess risk of all-cause mortality, cardiovascular mortality, non-cardiovascular mortality, acute myocardial infarction, stroke, cardiovascular disease (a composite of acute myocardial infarction and stroke), coronary heart disease, heart failure, and atrial fibrillation. Individuals with type 1 diabetes were categorised into five groups, according to age at diagnosis: 0–10 years, 11–15 years, 16–20 years, 21–25 years, and 26–30 years. 27 195 individuals with type 1 diabetes and 135 178 matched controls were selected for this study. 959 individuals with type 1 diabetes and 1501 controls died during follow-up (median follow-up was 10 years). Patients who developed type 1 diabetes at 0–10 years of age had hazard ratios of 4·11 (95% CI 3·24–5·22) for all-cause mortality, 7·38 (3·65–14·94) for cardiovascular mortality, 3·96 (3·06–5·11) for non-cardiovascular mortality, 11·44 (7·95–16·44) for cardiovascular disease, 30·50 (19·98–46·57) for coronary heart disease, 30·95 (17·59–54·45) for acute myocardial infarction, 6·45 (4·04–10·31) for stroke, 12·90 (7·39–22·51) for heart failure, and 1·17 (0·62–2·20) for atrial fibrillation. Corresponding hazard ratios for individuals who developed type 1 diabetes aged 26–30 years were 2·83 (95% CI 2·38–3·37) for all-cause mortality, 3·64 (2·34–5·66) for cardiovascular mortality, 2·78 (2·29–3·38) for non-cardiovascular mortality, 3·85 (3·05–4·87) for cardiovascular disease, 6·08 (4·71–7·84) for coronary heart disease, 5·77 (4·08–8·16) for acute myocardial infarction, 3·22 (2·35–4·42) for stroke, 5·07 (3·55–7·22) for heart failure, and 1·18 (0·79–1·77) for atrial fibrillation; hence the excess risk differed by up to five times across the diagnosis age groups. The highest overall incidence rate, noted for all-cause mortality, was 1·9 (95% CI 1·71–2·11) per 100 000 person-years for people with type 1 diabetes. Development of type 1 diabetes before 10 years of age resulted in a loss of 17·7 life-years (95% CI 14·5–20·4) for women and 14·2 life-years (12·1–18·2) for men. Age at onset of type 1 diabetes is an important determinant of survival, as well as all cardiovascular outcomes, with highest excess risk in women. Greater focus on cardioprotection might be warranted in people with early-onset type 1 diabetes. Swedish Heart and Lung Foundation.

Cardiomyopathy is the fourth most common cause of heart failure. The spectrum of cardiomyopathies may be impacted by changes in environmental factors and the prognosis may be influenced by modern treatment. The aim of this study is to create a prospective clinical cohort, the Sahlgrenska CardioMyoPathy Centre (SCMPC) study, and compare patients with cardiomyopathies in terms of phenotype, symptoms, and survival. The SCMPC study was founded in 2018 by including patients with all types of suspected cardiomyopathies. This study included data on patient characteristics, background, family history, symptoms, diagnostic examinations, and treatment including heart transplantation and mechanical circulatory support (MCS). Patients were categorized by the type of cardiomyopathy on the basis of the diagnostic criteria laid down by the European Society of Cardiology (ESC) working group on myocardial and pericardial diseases. The primary outcomes were death, heart transplantation, or MCS, analyzed by Kaplan-Meier and Cox proportional regression, adjusted for age, gender, LVEF and QRS width on ECG in milliseconds. In all, 461 patients and 73.1% men with a mean age of 53.6 ± 16 years were included in the study. The most common diagnosis was dilated cardiomyopathy (DCM), followed by cardiac sarcoidosis and myocarditis. Dyspnea was the most common initial symptom in patients with DCM and amyloidosis, while patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) presented with ventricular arrythmias. Patients with ARVC, left-ventricular non-compaction cardiomyopathy (LVNC), hypertrophic cardiomyopathy (HCM), and DCM had the longest time from the debut of symptoms until inclusion in the study. Overall, 86% of the patients survived without heart transplantation or MCS after 2.5 years. The primary outcome differed among the cardiomyopathies, where the worst prognosis was reported for ARVC, LVNC, and cardiac amyloidosis. In a Cox regression analysis, it was found that ARVC and LVNC were independently associated with an increased risk of death, heart transplantation, or MCS compared with DCM. Further, female gender, a lower LVEF, and a wider QRS width were associated with an increased risk of the primary outcome. The SCMPC database offers a unique opportunity to explore the spectrum of cardiomyopathies over time. There is a large difference in characteristics and symptoms at debut and a remarkable difference in outcome, where the worst prognosis was reported for ARVC, LVNC, and cardiac amyloidosis.

Guidelines for the management of cardiac arrest play a crucial role in guiding clinical decisions and care. We examined the strength and quality of evidence underlying these recommendations in order to elucidate strengths and gaps in knowledge. Using the 2020 American Heart Association (AHA) Guidelines for Adult CPR, we subdivided all recommendations into advanced life support (ALS), basic life support (BLS), and recovery after cardiac arrest, as well as a more granular categorization by topic (i.e. the intervention or evaluation recommended). The Class of Recommendation (COR) and Level of Evidence (LOE) for each were reviewed. Additionally, we reviewed the 2023 guidelines to ensure the inclusion of the most recent updates. We noted 254 recommendations, of which 181 were ALS, 69 were BLS, and 4 were recovery after resuscitation. In total, only 2 (1%) had the most robust evidence (LOE A), while 23% were at LOE B-NR (Non-Randomized), 15% at LOE B-R (Randomized), 50% at LOE C-LD (Limited Data), and 12% relied on expert opinion LOE C-EO (Expert Opinion). Despite the strength of ALS recommendations (Class 1, 2a, or 2b), none had LOE A. In BLS, no recommendations were supported by LOE A. For BLS, 7% of recommendations had LOE C (C-LD or C-EO). The evidence for specific BLS topics, such as airway management, was notably low. Among ALS topics, neurological prognostication had relatively stronger evidence. Only 26 out of the 81 COR 1 recommendations (32%) were supported by LOE A or B, indicating a strong discrepancy between the strength of recommendation and the underlying evidence in cardiac arrest guidelines. The findings underscore a pressing need for more rigorous research, particularly randomized trials.

Objective To investigate the long term effects of continuous subcutaneous insulin infusion (insulin pump therapy) on cardiovascular diseases and mortality in people with type 1 diabetes.Design Observational study.Setting Swedish National Diabetes Register, Sweden 2005-12.Participants 18 168 people with type 1 diabetes, 2441 using insulin pump therapy and 15 727 using multiple daily insulin injections.Main outcome measures Cox regression analysis was used to estimate hazard ratios for the outcomes, with stratification of propensity scores including clinical characteristics, risk factors for cardiovascular disease, treatments, and previous diseases.Results Follow-up was for a mean of 6.8 years until December 2012, with 114 135 person years. With multiple daily injections as reference, the adjusted hazard ratios for insulin pump treatment were significantly lower: 0.55 (95% confidence interval 0.36 to 0.83) for fatal coronary heart disease, 0.58 (0.40 to 0.85) for fatal cardiovascular disease (coronary heart disease or stroke), and 0.73 (0.58 to 0.92) for all cause mortality. Hazard ratios were lower, but not significantly so, for fatal or non-fatal coronary heart disease and fatal or non-fatal cardiovascular disease. Unadjusted absolute differences were 3.0 events of fatal coronary heart disease per 1000 person years; corresponding figures were 3.3 for fatal cardiovascular disease and 5.7 for all cause mortality. When lower body mass index and previous cardiovascular diseases were excluded, results of subgroup analyses were similar to the results from complete data. A sensitivity analysis of unmeasured confounders in all individuals showed that an unmeasured confounders with hazard ratio of 1.3 would have to be present in >80% of the individuals treated with multiple daily injections versus not presence in those treated with pump therapy to invalidate the significantly lower hazard ratios for fatal cardiovascular disease. Data on patient education and frequency of blood glucose monitoring were missing, which might have influenced the observed association.Conclusion Among people with type 1 diabetes use of insulin pump therapy is associated with lower cardiovascular mortality than treatment with multiple daily insulin injections.

ObjectivesTo evaluate the effects of diabetes mellitus (DM) and body mass index (BMI) on long-term all-cause mortality in chronic total occlusion (CTO) patients.DesignRetrospective, nationwide cohort study.SettingSwedish Coronary Angiography and Angioplasty Registry, between June 2015 and December 2021.Participants24 284 patients with angiographically confirmed CTO. Prior coronary artery bypass graft surgery excluded. Subgroups were defined by DM status and BMI categories (underweight, healthy weight, overweight, obesity).Primary outcome measuresLong-term all-cause mortality, assessed by Kaplan-Meier analysis and multivariable Cox proportional hazards regression.ResultsDM was present in 30.3% of patients and conferred a 31% higher risk of mortality (HR: 1.31, 95% CI: 1.20 to 1.42; p<0.001). Insulin use among patients with diabetes added a 52% increase in hazard (HR: 1.52; 95% CI: 1.38 to 1.67; p<0.001). BMI demonstrated a non-linear association with mortality: overweight (HR: 0.70, 95% CI: 0.64 to 0.77; p<0.001) and obese (HR: 0.74, 95% CI: 0.68 to 0.81; p<0.001) groups had lower risk compared with the healthy-weight group, whereas underweight individuals faced the highest risk (HR: 1.61, 95% CI: 1.25 to 2.08; p<0.001). A continuous BMI spline revealed an asymmetric U-shaped association: a steep increase in mortality below 23 kg/m2, lowest risk (nadir) at 32 kg/m2 and modest rise above 35 kg/m2.ConclusionsIn this nationwide CTO cohort, DM independently predicted higher long-term mortality, accompanied by more severe comorbidities and greater CTO complexity, and insulin therapy further elevated hazard. Overweight and obese patients had better survival, while underweight individuals had the poorest prognosis. These findings underscore the importance of individualised risk assessment and management strategies in CTO patients, particularly those with DM or low BMI.

Introduction Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and whether they were associated with survival. Unfortunately, this approach does not measure how strong each factor is in predicting survival after OHCA. Aim To investigate the relative importance of 16 well-recognized factors in OHCA at the time point of ambulance arrival, and before any interventions or medications were given, by using a machine learning approach that implies building models directly from the data, and arranging those factors in order of importance in predicting survival. Methods Using a data-driven approach with a machine learning algorithm, we studied the relative importance of 16 factors assessed during the pre-hospital phase of OHCA. We examined 45,000 cases of OHCA between 2008 and 2016. Results Overall, the top five factors to predict survival in order of importance were: initial rhythm, age, early Cardiopulmonary Resuscitation (CPR, time to CPR and CPR before arrival of EMS), time from EMS dispatch until EMS arrival, and place of cardiac arrest. The largest difference in importance was noted between initial rhythm and the remaining predictors. A number of factors, including time of arrest and sex were of little importance. Conclusion Using machine learning, we confirm that the most important predictor of survival in OHCA is initial rhythm, followed by age, time to start of CPR, EMS response time and place of OHCA. Several factors traditionally viewed as important, e.g. sex, were of little importance.

ObjectiveIn-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in resuscitation, predicting 30-day survival remains difficult, and current methods lack interpretability for timely decision-making. This study developed a machine learning (ML) model to predict 30-day survival after IHCA, using peri-arrest variables available on the rescue team’s arrival, while ensuring a balance between predictive accuracy and clinical interpretability through Shapley Additive Explanations (SHAP).DesignA nationwide, registry-based observational study.SettingData were sourced from the Swedish Cardiopulmonary Resuscitation Registry (2010–2020), merged with the Patient Registry.ParticipantsWe analysed 25 905 IHCA cases with attempted resuscitation, of which 8166 patients survived for 30 days.Outcome measure and analysis30-day survival after IHCA was the outcome measure. An ML model was developed using fivefold cross-validation. Key predictors were identified through in-built variable importance and validated using SHAP. Model performance was evaluated with metrics such as area under the receiver operating characteristics (AUROC), calibration, sensitivity, specificity, false negative rate (FNR) and F-score.ResultsThe CatBoost model achieved an AUROC of 0.9136 (95% CI 0.9075 to 0.9191) with all features, and 0.9034 (95% CI 0.8955 to 0.9037) with the top 15 features, along with Brier scores of 0.1028 and 0.1103, respectively. Performance plateaued after including the top 15 predictors, with few key variables, such as epinephrine administration, age, initial rhythm, ROSC within 15 min, breathing on rescue team arrival and witnessed cardiac arrest, being most influential. The model showed strong calibration for patients with low predicted survival probabilities and demonstrated high sensitivity with a low FNR across relevant survival thresholds.ConclusionThe CatBoost model provides an effective and interpretable tool for predicting 30-day survival after IHCA. Key predictors such as epinephrine administration, age and initial rhythm inform clinical decision-making. This model has strong clinical utility and can be externally validated via the open-access Application Programming Interface (API) at www.gocares.se