Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study.

BACKGROUND: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. METHODS AND RESULTS: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly. CONCLUSIONS: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease.

AIMS: Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs). METHODS AND RESULTS: Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACEs: fatal and non-fatal myocardial infarction, stroke, and coronary artery revascularization) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. Data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC = 0.771, P < 001, NRI = 0.140, and AUC = 0.767, P = 0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not. CONCLUSION: Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

The risk of having a myocardial infarction or stroke is usually assessed by clinical scores including traditional risk factors for cardiovascular disease. The development of new technologies enables the rapid measurement of an increasing number of blood proteins. In this study, we applied machine learning techniques in a UK-based cohort of 38 380 participants with 2919 blood proteins measured. We obtained a set of 114 proteins that improved the prediction of the 10-year risk of major cardiovascular event when added to traditional risk factors. Improvements were also achieved using a set of 113 proteins found in previous studies. However, the magnitude of these improvements was relatively low and the clinical utility of combining these proteins with traditional risk factors in primary prevention will have to be further investigated.

A deep multi-stream model for robust prediction of left ventricular ejection fraction in 2D echocardiography.

We propose a deep multi-stream model for left ventricular ejection fraction (LVEF) prediction in 2D echocardiographic (2DE) examinations. We use four standard 2DE views as model input, which are automatically selected from the full 2DE examination. The LVEF prediction model processes eight streams of data (images + optical flow) and consists of convolutional neural networks terminated with transformer layers. The model is made robust to missing, misclassified and duplicate views via pre-training, sampling strategies and parameter sharing. The model is trained and evaluated on an existing clinical dataset (12,648 unique examinations) with varying properties in terms of quality, examining physician, and ultrasound system. We report [Formula: see text] and mean absolute error = 4.0% points for the test set. When evaluated on two public benchmarks, the model performs on par or better than all previous attempts on fully automatic LVEF prediction. Code and trained models are available on a public project repository .

Semi-supervised learning with natural language processing for right ventricle classification in echocardiography-a scalable approach.

We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed.

Cardiorenal function and survival in in-hospital cardiac arrest: A nationwide study of 22,819 cases.

BACKGROUND: We studied the association between cardiorenal function and survival, neurological outcome and trends in survival after in-hospital cardiac arrest (IHCA). METHODS: We included cases aged ≥ 18 years in the Swedish Cardiopulmonary Resuscitation Registry during 2008 to 2020. The CKD-EPI equation was used to calculate estimated glomerular filtration rate (eGFR). A history of heart failure was defined according to contemporary guideline criteria. Logistic regression was used to study survival. Neurological outcome was assessed using cerebral performance category (CPC). RESULTS: We studied 22,819 patients with IHCA. The 30-day survival was 19.3%, 16.6%, 22.5%, 28.8%, 39.3%, 44.8% and 38.4% in cases with eGFR < 15, 15-29, 30-44, 45-59, 60-89, 90-130 and 130-150 ml/min/1.73 m2, respectively. All eGFR levels below and above 90 ml/min/1.73 m2 were associated with increased mortality. Probability of survival at 30 days was 62% lower in cases with eGFR < 15 ml/min/1.73 m2, compared with normal kidney function. At every level of eGFR, presence of heart failure increased mortality markedly; patients without heart failure displayed higher mortality only at eGFR below 30 ml/min/1.73 m2. Among survivors with eGFR < 15 ml/min/1.73 m2, good neurological outcome was noted in 87.2%. Survival increased in most groups over time, but most for those with eGFR < 15 ml/min/1.73 m2, and least for those with normal eGFR. CONCLUSIONS: All eGFR levels below and above normal range are associated with increased mortality and this association is modified by the presence of heart failure. Neurological outcome is good in the majority of cases, across kidney function levels and survival is increasing.

Cardiac involvement in critically ill and mechanically ventilated patients with COVID-19 - a prospective, observational echocardiographic study.

INTRODUCTION: In this prospective, observational study, we have evaluated right (RV) and left (LV) ventricular function with echocardiography and correlated it to the levels of biomarkers, hs-TNT, NT-pro-BNP, D-dimer and fibrinogen. In a subgroup, we have evaluated the effect of inhaled milrinone on RV afterload and function. METHODS: Thirty-one ICU patients with COVID-19 in need of mechanical ventilation and norepinephrine infusion were prospectively included. Hemodynamic and respiratory variables were measured at the time of the echocardiographic examination and biomarkers were obtained on arrival at the ICU and then followed up routinely. Eight patients received inhaled aerosolized milrinone at a dose of 2.5 mg/hour. RESULTS: The most common echocardiographic pattern was RV dilation with or without systolic dysfunction, which was found in 62% of patients. Pulmonary acceleration time was abnormal in 55% and indices of RV systolic function, such as fractional area of change, RV strain, were abnormal in 30% and 31% of patients respectively. A cardiac index of < 2.5 l/min*m2 was seen in 58% of the patients. Left ventricular ejection fraction and global left ventricular strain were impaired in 10% and 16% respectively. The correlation between echocardiographic variables and cardiac biomarkers was poor. RV afterload correlated well to the levels of D-dimer. Milrinone inhalation did not improve RV function or afterload. CONCLUSION: RV dysfunction was the most common finding. The poor correlation to cardiac biomarkers argues against extensive myocardial involvement. The lack of improvement in RV function after milrinone inhalation suggests that the most likely cause of RV dysfunction is increased RV afterload caused by pulmonary thrombosis/embolism.

Severe COVID-19 in people with type 1 and type 2 diabetes in Sweden: A nationwide retrospective cohort study.

BACKGROUND: Whether infection with SARS-CoV-2 leads to excess risk of requiring hospitalization or intensive care in persons with diabetes has not been reported, nor have risk factors in diabetes associated with increased risk for these outcomes. METHODS: We included 44,639 and 411,976 adult patients with type 1 and type 2 diabetes alive on Jan 1, 2020, and compared them to controls matched for age, sex, and county of residence (n=204,919 and 1,948,900). Age- and sex-standardized rates for COVID-19 related hospitalizations, admissions to intensive care and death, were estimated and hazard ratios were calculated using Cox regression analyses. FINDINGS: There were 10,486 hospitalizations and 1,416 admissions into intensive care. A total of 1,175 patients with diabetes and 1,820 matched controls died from COVID-19, of these 53•2% had been hospitalized and 10•7% had been in intensive care. Patients with type 2 diabetes, compared to controls, displayed an age- and sex-adjusted hazard ratio (HR) of 2•22, 95%CI 2•13-2•32) of being hospitalized for COVID-19, which decreased to HR 1•40, 95%CI 1•34-1•47) after further adjustment for sociodemographic factors, pharmacological treatment and comorbidities, had higher risk for admission to ICU due to COVID-19 (age- and sex-adjusted HR 2•49, 95%CI 2•22-2•79, decreasing to 1•42, 95%CI 1•25-1•62 after adjustment, and increased risk for death due to COVID-19 (age- and sex-adjusted HR 2•19, 95%CI 2•03-2•36, complete adjustment 1•50, 95%CI 1•39-1•63). Age- and sex-adjusted HR for COVID-19 hospitalization for type 1 diabetes was 2•10, 95%CI 1•72-2•57), decreasing to 1•25, 95%CI 0•3097-1•62) after adjustment• Patients with diabetes type 1 were twice as likely to require intensive care for COVID-19, however, not after adjustment (HR 1•49, 95%CI 0•75-2•92), and more likely to die (HR 2•90, 95% CI 1•6554-5•47) from COVID-19, but not independently of other factors (HR 1•38, 95% CI 0•64-2•99). Among patients with diabetes, elevated glycated hemoglobin levels were associated with higher risk for most outcomes. INTERPRETATION: In this nationwide study, type 2 diabetes was independently associated with increased risk of hospitalization, admission to intensive care and death for COVID-19. There were few admissions into intensive care and deaths in type 1 diabetes, and although hazards were significantly raised for all three outcomes, there was no independent risk persisting after adjustment for confounding factors.

Cardiac arrest in COVID-19: characteristics and outcomes of in- and out-of-hospital cardiac arrest. A report from the Swedish Registry for Cardiopulmonary Resuscitation.

AIM: To study the characteristics and outcome among cardiac arrest cases with COVID-19 and differences between the pre-pandemic and the pandemic period in out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA). METHOD AND RESULTS: We included all patients reported to the Swedish Registry for Cardiopulmonary Resuscitation from 1 January to 20 July 2020. We defined 16 March 2020 as the start of the pandemic. We assessed overall and 30-day mortality using Cox regression and logistic regression, respectively. We studied 1946 cases of OHCA and 1080 cases of IHCA during the entire period. During the pandemic, 88 (10.0%) of OHCAs and 72 (16.1%) of IHCAs had ongoing COVID-19. With regards to OHCA during the pandemic, the odds ratio for 30-day mortality in COVID-19-positive cases, compared with COVID-19-negative cases, was 3.40 [95% confidence interval (CI) 1.31-11.64]; the corresponding hazard ratio was 1.45 (95% CI 1.13-1.85). Adjusted 30-day survival was 4.7% for patients with COVID-19, 9.8% for patients without COVID-19, and 7.6% in the pre-pandemic period. With regards to IHCA during the pandemic, the odds ratio for COVID-19-positive cases, compared with COVID-19-negative cases, was 2.27 (95% CI 1.27-4.24); the corresponding hazard ratio was 1.48 (95% CI 1.09-2.01). Adjusted 30-day survival was 23.1% in COVID-19-positive cases, 39.5% in patients without COVID-19, and 36.4% in the pre-pandemic period. CONCLUSION: During the pandemic phase, COVID-19 was involved in at least 10% of all OHCAs and 16% of IHCAs, and, among COVID-19 cases, 30-day mortality was increased 3.4-fold in OHCA and 2.3-fold in IHCA.

Shortening Ambulance Response Time Increases Survival in Out-of-Hospital Cardiac Arrest.

Background The ambulance response time in out-of-hospital cardiac arrest (OHCA) has doubled over the past 30 years in Sweden. At the same time, the chances of surviving an OHCA have increased substantially. A correct understanding of the effect of ambulance response time on the outcome after OHCA is fundamental for further advancement in cardiac arrest care. Methods and Results We used data from the SRCR (Swedish Registry of Cardiopulmonary Resuscitation) to determine the effect of ambulance response time on 30-day survival after OHCA. We included 20 420 cases of OHCA occurring in Sweden between 2008 and 2017. Survival to 30 days was our primary outcome. Stratification and multiple logistic regression were used to control for confounding variables. In a model adjusted for age, sex, calendar year, and place of collapse, survival to 30 days is presented for 4 different groups of emergency medical services (EMS)-crew response time: 0 to 6 minutes, 7 to 9 minutes, 10 to 15 minutes, and >15 minutes. Survival to 30 days after a witnessed OHCA decreased as ambulance response time increased. For EMS response times of >10 minutes, the overall survival among those receiving cardiopulmonary resuscitation before EMS arrival was slightly higher than survival for the sub-group of patients treated with compressions-only cardiopulmonary resuscitation. Conclusions Survival to 30 days after a witnessed OHCA decreases as ambulance response times increase. This correlation was seen independently of initial rhythm and whether cardiopulmonary resuscitation was performed before EMS-crew arrival. Shortening EMS response times is likely to be a fast and effective way of increasing survival in OHCA.

The influence of time-to-surgery on mortality after a hip fracture.

BACKGROUND: The effect of time-to-surgery on mortality in acute hip fracture (AHF) patients has been debated and studies are inconsistent regarding from what time limit mortality starts to increase. At Sahlgrenska University Hospital/Mölndal, surgery is recommended within 24 hours leaving little time for pre-operative optimization. However, internationally the definition of early surgery varies between 24 and 48 hours and over. This retrospective study was initiated to investigate the relation between time-to-surgery and 30-day mortality. METHOD: Data of AHF patients from January 2007 through December 2016 were collected. The variables analysed were: age, gender, American Society of Anesthesiologists physical status classification, surgical method (prosthesis or osteosynthesis) and time-to-surgery, along with 30-day mortality. Primary outcome was 30-day mortality related to time-to-surgery divided into groups. Secondary outcome was 30-day mortality related to time-to-surgery analysed hour-by-hour. RESULTS: From 10,844 eligible patients, 9,270 patients were included into the study. Mean time-to-surgery was 19.4 hours and overall 30-day mortality was 7.6%. Adjusted Cox regression analysis revealed an increased mortality rate in patients with time-to-surgery >48 hours. In the hour-by-hour analysis, significant mortality increase was observed at 39 hours of time-to-surgery. Patients with time-to-surgery >24 hours did not have increased mortality compared to patients with time-to-surgery <24 hours. CONCLUSION: In AHF patients, a time-to-surgery exceeding 39-48 hours was associated with increased mortality. Patients with surgeries performed before 39-48 hours did not have increased mortality and this time may, in some patients, be used for optimization prior surgery even if time-to-surgery exceeds 24 hours.

Prolonged vasopressor support during hip-fracture surgery is a risk factor for enhanced mortality.

BACKGROUND: Hip fracture is a common injury in the elderly population and is associated with high morbidity and mortality. Intraoperative hypotension is commonly noted, and is often treated with vasopressors (VP), however, to what extent is unknown. We set out to examine retrospectively how many hip fracture-patients received VP perioperatively and further to investigate if VP treatment is connected to increased mortality. METHOD: Data on VP treatment were captured from medical and anaesthesia journals, and if so, data were investigated to find potential confounders. Patients were divided into (a) no VP, (b) VP by injection, (c) VP by infusion <3 hours, and (d) VP by infusion ≥3 hours to achieve stratification. RESULTS: Nine hundred and ninety-seven patients were included. About 80.4% received VP treatment. The 30-day mortality rates in subgroups were 3.6%, 5.4%, 6.4% and 19.1% respectively. The 90-day mortality rates were 6.7%, 10.3%, 11.6% and 30.3% respectively. Finally, the same patient groups had 365-day mortality rates of 12.8%, 20.0%, 23.3% and 44.9% respectively. We found a significant increase in mortality (30-90-365 days) in patients receiving VP infusion ≥3 hours, after adjusting for confounding factors. There was no increased mortality in patients treated by injection and by infusion <3 hours after adjustment for confounding factors vs untreated patients. CONCLUSION: Vasopressor treatment is common during hip fracture surgery. Patients treated with VP infusion ≥3 hours have increased mortality, while patients treated with injections or infusion <3 hours have not. We suggest that the prolonged use of VP treatment is linked to increased mortality.