Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Background and purpose: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. / Methods: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. / Results: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. / Conclusion: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features

The transition experience of rural older persons with advanced cancer and their families: a grounded theory study

BACKGROUND: Transitions often occur suddenly and can be traumatic to both patients with advanced disease and their families. The purpose of this study was to explore the transition experience of older rural persons with advanced cancer and their families from the perspective of palliative home care patients, bereaved family caregivers, and health care professionals. The specific aims were to: (1) describe the experience of significant transitions experienced by older rural persons who were receiving palliative home care and their families and (2) develop a substantive theory of transitions in this population. METHODS: Using a grounded theory approach, 27 open-ended individual audio-taped interviews were conducted with six older rural persons with advanced cancer and 10 bereaved family caregivers. Four focus group interviews were conducted with 12 palliative care health care professionals. All interviews were transcribed verbatim, coded, and analyzed using Charmaz\u27s constructivist grounded theory approach. RESULTS: Within a rural context of isolation, lack of information and limited accessibility to services, and values of individuality and community connectedness, older rural palliative patients and their families experienced multiple complex transitions in environment, roles/relationships, activities of daily living, and physical and mental health. Transitions disrupted the lives of palliative patients and their caregivers, resulting in distress and uncertainty. Rural palliative patients and their families adapted to transitions through the processes of Navigating Unknown Waters . This tentative theory includes processes of coming to terms with their situation, connecting, and redefining normal. Timely communication, provision of information and support networks facilitated the processes. CONCLUSION: The emerging theory provides a foundation for future research. Significant transitions identified in this study may serve as a focus for improving delivery of palliative and end of life care in rural areas. Improved understanding of the transitions experienced by advanced cancer palliative care patients and their families, as well as the psychological processes involved in adapting to the transitions, will help health care providers address the unique needs of this vulnerable population

Framework and baseline examination of the German National Cohort (NAKO)

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19–74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2–3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4–5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00890-5

Bewertung einer Leitlinie zur Datenqualität durch Vertreter epidemiologischer Kohortenstudien in Deutschland

High data quality is a precondition for valid scientific conclusions. Indicators should therefore routinely be used to evaluate data quality within the life cycle of health studies. In this project, 15 representatives of seven German population-based cohort studies assessed 51 quality indicators that were proposed in a guideline for networked medical research. The applicability of the indicators to primary data collections was assessed. In addition, their importance was evaluated using a scale ranging from 1 (essential) to 4 (not important). Moreover, their implementation in data quality assessments in the participating studies was evaluated. Comments on potential improvements could be made. Forty-three indicators were rated as applicable. Of these, 29 received a mean importance score of 2 (important) or better, nine received a mean importance score of 1.5 or better. The latter represent a potential core set of data quality indicators for cohort studies. Most indicators that were rated as highly important were used in data quality assessments of the participating studies. Points of criticism regarding the guideline related to its structure and the understandability of some indicators. It was concluded that further improvement of the data quality indicator set will increase its usefulness and applicability in primary data collections. In practice, a small subset of data quality indicators may suffice to capture the most important aspects of data quality in cohort studies.Eine hohe Datenqualität ist wesentlich für valide wissenschaftliche Schlussfolgerungen. Indikatoren sollten daher routinemäßig angewendet werden, um die Datenqualität innerhalb des Lebenszyklus von Gesundheitsstudien zu beurteilen. In dem hier beschriebenen Projekt haben 15 Vertreter von sieben bevölkerungsbezogenen Kohortenstudien in Deutschland 51 Qualitätsindikatoren bewertet, die im Rahmen einer deutschen Leitlinie für die vernetzte medizinische Forschung vorgeschlagen wurden. Die Evaluation betraf die Anwendbarkeit der Indikatoren für primäre Datenerhebungen, deren Wichtigkeit auf einer Skala von 1 (essentiell) bis 4 (nicht wichtig) sowie deren Implementation in den teilnehmenden Kohorten. Verbesserungsvorschläge konnten gemacht werden. 43 Indikatoren wurden als anwendbar angesehen. Davon erhielten 29 eine durchschnittliche Wichtigkeit von mindestens 2 (wichtig), neun eine durchschnittliche Wichtigkeit von mindestens 1,5. Die als am wichtigsten bewerteten Indikatoren geben Hinweise auf einen für Kohortenstudien relevanten Kernsatz von Indikatoren zur Erfassung der Datenqualität. Die Mehrzahl der hoch bewerteten Indikatoren wurde in den teilnehmenden Kohorten im Rahmen der Datenqualitätssicherung betrachtet. Als Schwächen der Leitlinie wurden die Verständlichkeit einzelner Indikatoren sowie die Struktur der Leitlinie identifiziert. Das Konzept von Indikatoren zur Datenqualität sollte weiter verbessert werden, um den Nutzwert und die Anwendbarkeit im Rahmen primärer Datenerhebungen zu erhöhen. In der praktischen Anwendung reicht eine Teilmenge der Indikatoren aus, um wesentliche Aspekte der Datenqualität in Kohortenstudien zu beschreiben

Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: A scoping review

Aims: Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. Methods and results: PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. Conclusion: AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: creation of a benchmark

ObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated.Methods & resultsA total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12‑lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18-75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04). The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II.ConclusionThe application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments.Medicinal Chemistr