Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We evaluate the contribution of each element towards prediction performance (ROC-AUC, F1-measure) and cost-savings. We show that the model with all key elements achieves higher discrimination ability (AUC: 0.77; F1: 0.51; Cost: 22% of maximum possible savings) outperforming the reduced models in at least two evaluation metrics. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients.

Objectives The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy. Design Retrospective, population-based registry study. Setting Swedish health services. Primary and secondary outcome measures All cause 30-day mortality. Methods Electronic health records (EHRs) and administrative data were used to train six supervised machine learning models to predict all-cause mortality within 30 days in patients discharged from EDs in southern Sweden, Europe. Participants The models were trained using 65 776 ED visits and validated on 55 164 visits from a separate ED to which the models were not exposed during training. Results The outcome occurred in 136 visits (0.21%) in the development set and in 83 visits (0.15%) in the validation set. The model with highest discrimination attained ROC–AUC 0.95 (95% CI 0.93 to 0.96), with sensitivity 0.87 (95% CI 0.80 to 0.93) and specificity 0.86 (0.86 to 0.86) on the validation set. Conclusions Multiple models displayed excellent discrimination on the validation set and outperformed available indexes for short-term mortality prediction interms of ROC–AUC (by indirect comparison). The practical utility of the models increases as the data they were trained on did not require costly de novo collection but were real-world data generated as a by-product of routine care delivery.

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 with increased mortality and hospitalisation. Improved screening and diagnosis of CKD, identification and management of risk factors and kidney protective treatment could affect clinical and economic outcomes.

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 costs and outcomes. 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 lower costs. Methods and results: A total of 5987 residents of RH in 2016 carried HF diagnoses. Costs were assigned to all health care utilization (inpatient, outpatient, emergency department, primary health care, and medications) using a Patient Encounter Costing methodology. Care of HF patients cost €58.6 M, (€9790/patient) representing 8.7% of RH’s total visit expenses and 14.9% of inpatient care (IPC) expenses. Inpatient care represented 57.2% of this expenditure, totalling €33.5 M (€5601/patient). Receiving AP was associated with significantly lower costs, by €1130 per patient (P < 0.001, 95% confidence interval 574–1687). Comorbidities such as renal failure, diabetes, chronic obstructive pulmonary disease, and cancer were significantly associated with higher costs. Conclusion: Heart failure patients are heavy users of health care, particularly IPC. 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.