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• We aimed to capture possible insights from an AI diagnosing without human guidance.• We believe the result mainly aligns with previous knowledge. Though, vital signs and sex did not aid the AI diagnostics.

BACKGROUND: Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. OBJECTIVE: The primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF. Additionally, the study aims to provide explanations for the model predictions by highlighting important features both on a global scale across the patient cohort and on a local level for individual patients. METHODS: The retrospective data for this study were obtained from the Regional Health Care Information Platform in Region Halland, Sweden. The study cohort consisted of patients diagnosed with HF who were over 40 years old and had been hospitalized at least once between 2017 and 2019. Data analysis encompassed the period from January 1, 2017, to December 31, 2019. Two ML models were developed and validated to predict 100-day readmissions, with a focus on the explainability of the model's decisions. These models were built based on decision trees and recurrent neural architecture. Model explainability was obtained using an ML explainer. The predictive performance of these models was compared against 2 risk assessment tools using multiple performance metrics. RESULTS: The retrospective data set included a total of 15,612 admissions, and within these admissions, readmission occurred in 5597 cases, representing a readmission rate of 35.85%. It is noteworthy that a traditional and explainable model, informed by clinical knowledge, exhibited performance comparable to the DL model and surpassed conventional scoring methods in predicting readmission amo

We created an AI support for diagnosis in dyspneic adults at time of triage in the emergency department.Complete data from an entire regional health care system was analyzed, to find AI-derived, unknown, important diagnostic predictors. Most important were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life difficulties and maternal care.Sensitivity for AHF, eCOPD and pneumonia was 75%, 93%, and 54%, respectively, with a specificity above 75%. Each patient visit received an individual graph with the AI´s underlying decision basis.

Objectives: To describe chronic kidney disease (CKD) regarding treatment rates, comorbidities, usage of CKD International Classification of Diseases (ICD) diagnosis, mortality, hospitalisation, evaluate healthcare utilisation and screening for CKD in relation to new nationwide CKD guidelines. Design: Population-based observational study. Setting: Healthcare registry data of patients in Southwest Sweden. Participants: A total cohort of 65 959 individuals aged >18 years of which 20 488 met the criteria for CKD (cohort 1) and 45 470 at risk of CKD (cohort 2). Primary and secondary outcome measures: Data were analysed with regards to prevalence, screening rates of blood pressure, glucose, estimated glomerular filtration rate (eGFR), Urinary-albumin-creatinine ratio (UACR) and usage of ICD-codes for CKD. Mortality and hospitalisation were analysed with logistic regression models. Results: Of the CKD cohort, 18% had CKD ICD-diagnosis and were followed annually for blood pressure (79%), glucose testing (76%), eGFR (65%), UACR (24%). UACR follow-up was two times as common in hypertensive and cardiovascular versus diabetes patients with CKD with a similar pattern in those at risk of CKD. Statin and renin-angiotensin-aldosterone inhibitor appeared in 34% and 43%, respectively. Mortality OR at CKD stage 5 was 1.23 (CI 0.68 to 0.87), diabetes 1.20 (CI 1.04 to 1.38), hypertension 1.63 (CI 1.42 to 1.88), atherosclerotic cardiovascular disease (ASCVD) 1.84 (CI 1.62 to 2.09) associated with highest mortality risk. Hospitalisation OR in CKD stage 5 was 1.96 (CI 1.40 to 2.76), diabetes 1.15 (CI 1.06 to 1.25), hypertension 1.23 (CI 1.13 to 1.33) and ASCVD 1.52 (CI 1.41 to 1.64). Conclusions: The gap between patients with CKD by definition versus those diagnosed as such was large. Compared with recommendations patients with CKD have suboptimal follow-up and treatment with renin-angiotensin-aldosterone system inhibitor and statins. Hypertension, diabetes and ASCVD were associated wit

Background: The vast majority of covid-19 patients experience non-severe disease. Nonetheless, long-term symptoms may be common and the impact on quality of life is uncertain. This study aims to examine these aspects in a prospective, longitudinal cohort. Methods: Non-hospitalised patients with PCR-confirmed covid-19 were prospectively invited to self-report assessments of background data, symptoms and recovery, illness perception (BIPQ) and health-related quality of life (HR-Qol) measured by EQ5D-VAS. Results: 154 patients were included (mean age 46 years, 69% female). The majority of participants (65%) had symptoms for 1–4 weeks and 12% more than 6 months. The most common symptoms were initially malaise, fatigue, headache, fever and cough and the most common long-term symptoms were impaired physical condition, fatigue, anosmia and headache. The BIPQ index had a negative correlation with the EQ5D-VAS score after the infection, but not with long-term symptoms. Mean differences in the EQ5D-VAS score were significantly lower after the infection and patients with long-term symptoms had a more pronounced negative effect in EQ5D-VAS scores. Conclusion: We found that most patients with non-severe covid-19 reported symptoms for 1–4 weeks and approximately 10% developed long-term symptoms. Non-severe covid-19 seems to have a negative influence on HR-Qol, especially in patients with long-term symptoms and with a greater burden from the disease. None of the initial symptoms could predict the presence of long-term symptoms. © 2023 Society for Scandinavian Journal of Infectious Diseases., Funding: The Region Halland Research council, the Foundation of Sparbanken Varberg and the Southern Medical Care Region, Sweden

OBJECTIVE: To assess symptom presentation related to age, sex and previous medical history in patients with chest pain. DESIGN: Prospective observational cohort study. SETTING: Two-centre study in a Swedish county emergency medical service (EMS) organisation. PARTICIPANTS: Unselected inclusion of 2917 patients with chest pain cared for by the EMS during 2018. DATA ANALYSIS: Multivariate analysis on the association between symptom characteristics, patients' sex, age, previous acute coronary syndrome (ACS) or diabetes and the final outcome of acute myocardial infarction (AMI). RESULTS: Symptomology in patients assessed by the EMS due to acute chest pain varied with sex and age and also with previous ACS or diabetes. Women suffered more often from nausea (OR 1.6) and pain in throat (OR 2.1) or back (OR 2.1). Their pain was more often affected by palpation (1.7) or movement (OR 1.4). Older patients more often described pain onset while sleeping (OR 1.5) and that the onset of symptoms was slow, over hours rather than minutes (OR 1.4). They were less likely to report pain in other parts of their body than their chest (OR 1.4). They were to a lesser extent clammy (OR 0.6) or nauseous (OR 0.6). These differences were present regardless of whether the symptoms were caused by AMI or not. CONCLUSIONS: A number of aspects of the symptom of chest pain appear to differ in unselected prehospital patients with chest pain in relation to age, sex and medical history, regardless of whether the chest pain was caused by a myocardial infarction or not. This complicates the possibility in prehospital care of using symptoms to predict the underlying aetiology of acute chest pain.

Introduction Chest pain is one of the most common reasons for contacting the emergency medical services (EMS). About 15% of these chest pain patients have a high-risk condition, while many of them have a low-risk condition with no need for acute hospital care. It is challenging to at an early stage distinguish whether patients have a low- or high-risk condition. The objective of this study has been to develop prediction models for optimising the identification of patients with low- respectively high-risk conditions in acute chest pain early in the EMS work flow. Methods This prospective observational cohort study included 2578 EMS missions concerning patients who contacted the EMS in a Swedish region due to chest pain in 2018. All the patients were assessed as having a low-, intermediate- or high-risk condition, i.e. occurrence of a time-sensitive diagnosis at discharge from hospital. Multivariate regression analyses using data on symptoms and symptom onset, clinical findings including ECG, previous medical history and Troponin T were carried out to develop models for identification of patients with low- respectively high-risk conditions. Developed models where then tested hold-out data set for internal validation and assessing their accuracy. Results Prediction models for risk-stratification based on variables mutual for both low- and high-risk prediction were developed. The variables included were: age, sex, previous medical history of kidney disease, atrial fibrillation or heart failure, Troponin T, ST-depression on ECG, paleness, pain debut during activity, constant pain, pain in right arm and pressuring pain quality. The high-risk model had an area under the receiving operating characteristic curve of 0.85 and the corresponding figure for the low-risk model was 0.78. Conclusions Models based on readily available information in the EMS setting can identify high- and low-risk conditions with acceptable accuracy. A clinical decision support tool based on developed, Fujnding: The Department of Ambulance and Prehospital Care, Region Halland, and the Scientific Council of Region Halland (HALLAND-209901).

Aims: Patients with heart failure (HF) have high costs, morbidity and mortality, but it is not known if appropriate pharmacotherapy (AP), defined as compliance with international evidence-based guidelines, is associated with improved. The purpose of this study was to evaluate HF patients’ health care utilization, cost and outcomes in Region Halland (RH), Sweden, and if AP was associated with costs. Methods and Results: 5 987 residents of RH in 2016 carried HF diagnoses. Costs were assigned to all healthcare utilization (inpatient, outpatient, emergency department, primary health care and medications) using a Patient Encounter Costing methodology. Care of HF patients cost €58.6M, (€9 790/patient) representing 8.7% of RH’s total visit expenses and 14.9% of inpatient care expenses. Inpatient care represented 57.2% of this expenditure, totaling €33.5M (€5,601/patient). Receiving AP was associated with significantly lower costs, by €1 130 per patient (p < 0.001, 95% Confidence Interval 574,1 687) Comorbidities such as renal failure, diabetes, COPD and cancer were significantly associated with higher costs. Conclusion: HF patients are heavy users of healthcare, particularly inpatient care. Receiving AP is associated with lower costs even adjusting for comorbidities, although causality cannot be proven from an observational study. There may be an opportunity to decrease overall costs and improve outcomes by improving prescribing patterns and associated high-quality care. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020.

Background: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit. Objectives: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge. Methods: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either “surprising” or “unsurprising” by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression(LR), Random Forest (RF), and Support Vector Machine (SVM). Results: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models. Conclusion: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly. © 2021 The Author(s). Published by Elsevier Inc.

Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence. Key messages Structured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date. Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care. © The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

AIMS AND OBJECTIVES: To explore the symptoms descriptions and situational information provided by patients during ongoing chest pain events caused by a high-risk condition. BACKGROUND: Chest pain is a common symptom in patients contacting emergency dispatch centres. Only 15% of these patients are later classified as suffering from a high-risk condition. Prehospital personnel are largely dependent on symptom characteristics when trying to identify these patients. DESIGN: Qualitative descriptive. METHODS: Manifest content analysis of 56 emergency medical calls involving patients with chest pain was carried out. A stratified purposive sampling was used to obtain calls concerning patients with high-risk conditions. These calls were then listened to and transcribed. Thereafter, meaning units were identified and coded and finally categorised. Consolidated criteria for reporting qualitative studies guidelines have been applied. RESULTS: A wide range of situational information and symptoms descriptions was found. Pain and affected breathing were dominating aspects, but other situational information and several other symptoms were also reported. The situational information and these symptoms were classified into seven categories: Pain narrative, Affected breathing, Bodily reactions, Time, Bodily whereabouts, Fear and concern and Situation management. The seven categories consisted of 17 subcategories. CONCLUSIONS: Patients with chest pain caused by a high-risk condition present a wide range of symptoms which are described in a variety of ways. They describe different kinds of chest pain accompanied by pain from other parts of the body. Breathing difficulties and bodily reactions such as muscle weakness are also reported. The variety of symptoms and the absence of a typical symptomatology make risk stratification on the basis of symptoms alone difficult. RELEVANCE TO CLINICAL PRACTICE: This study highlights the importance of an open mind when assessing patients with chest pai

Background: Aggressive treatment at end-of-life (EOL) can be traumatic to patients and may not add clinical benefit. Absent an accurate prognosis of death, individual level biases may prevent timely discussions about the scope of EOL care and patients are at risk of being subject to care against their desire. The aim of this work is to develop predictive algorithms for identifying patients at EOL, with clinically meaningful discriminatory power. Methods: Retrospective, population-based study of patients utilizing emergency departments (EDs) in Sweden, Europe. Electronic health records (EHRs) were used to train supervised learning algorithms to predict all-cause mortality within 30 days following ED discharge. Algorithm performance was validated out of sample on EHRs from a separate hospital, to which the algorithms were previously unexposed. Results: Of 65,776 visits in the development set, 136 (0.21%) experienced the outcome. The algorithm with highest discrimination attained ROC-AUC 0.945 (95% CI 0.933 - 0.956), with sensitivity 0.869 (95% CI 0.802, 0.931) and specificity 0.858 (0.855, 0.860) on the validation set. Conclusions: Multiple algorithms displayed excellent discrimination and outperformed available indexes for short-term mortality prediction. The practical utility of the algorithms increases as the required data were captured electronically and did not require de novo data collection. Trial registration number: Not applicable., Funding: This work was partly funded by Region Halland, Sweden.The initial stage of MCBs involvement in the work was funded by a grant for post-doctoral research from the Tegger Foundation.