Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Introduction: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD. Methods: The data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total). Results: The ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = - 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811). Discussion: Presence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.

Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study.

Background: Health care budgets are limited, requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources. Objective: We assessed the applicability of selected ML tools to evaluate the contribution of known risk markers for prognosis of coronary artery disease to predict health care costs for all reasons in patients with a recent acute coronary syndrome (n = 65, aged 65 ± 9 years) for 1-year follow-up. Methods: Risk markers were assessed at baseline, and health care costs were collected from electronic health registries. The Cross-decomposition algorithms were used to rank the considered risk markers based on their impacts on variances. Then regression analysis was performed to predict costs by entering the first top-ranking risk marker and adding the next-best markers, one by one, to build up altogether 13 predictive models. Results: The average annual health care costs were €2601 ± €5378 per patient. The Depression Scale showed the highest predictive value (r = 0.395), accounting for 16% of the costs (P = .001). When the next 2 ranked markers (LDL cholesterol, r = 0.230; and left ventricular ejection fraction, r = -0.227, respectively) were added to the model, the predictive value was 24% for the costs (P = .001). Conclusion: Higher depression score is the primary variable forecasting health care costs in 1-year follow-up among acute coronary syndrome patients. The ML tools may help decision-making when planning optimal utilization of treatment strategies.

Cardiac rehabilitation availability and delivery in Europe: How does it differ by region and compare with other high-income countries?: Endorsed by the European Association of Preventive Cardiology.

AIMS: The aims of this study were to establish cardiac rehabilitation availability and density, as well as the nature of programmes, and to compare these by European region (geoscheme) and with other high-income countries. METHODS: A survey was administered to cardiac rehabilitation programmes globally. Cardiac associations were engaged to facilitate programme identification. Density was computed using global burden of disease study ischaemic heart disease incidence estimates. Four high-income countries were selected for comparison (N = 790 programmes) to European data, and multilevel analyses were performed. RESULTS: Cardiac rehabilitation was available in 40/44 (90.9%) European countries. Data were collected in 37 (94.8% country response rate). A total of 455/1538 (29.6% response rate) programme respondents initiated the survey. Programme volumes (median 300) were greatest in western European countries, but overall were higher than in other high-income countries (P < 0.001). Across all Europe, there was on average only 1 CR spot per 7 IHD patients, with an unmet regional need of 3,449,460 spots annually. Most programmes were funded by social security (n = 25, 59.5%; with significant regional variation, P < 0.001), but in 72 (16.0%) patients paid some or all of the programme costs (or ∼18.5% of the ∼€150.0/programme) out of pocket. Guideline-indicated conditions were accepted in 70% or more of programmes (lower for stable coronary disease), with no regional variation. Programmes had a multidisciplinary team of 6.5 ± 3.0 staff (number and type varied regionally; and European programmes had more staff than other high-income countries), offering 8.5 ± 1.5/10 core components (consistent with other high-income countries) over 24.8 ± 26.0 hours (regional differences, P < 0.05). CONCLUSION: European cardiac rehabilitation capacity must be augmented. Where available, services were consistent with guidelines, but varied regionally.

Short-term assessment of autonomic nervous system as a potential tool to quantify pain experience.

Various pain conditions may result in altered autonomic nervous system regulation. We evaluated whether autonomic regulation, analyzed with short-term heart rate variability (HRV), differs between low back pain (LBP) patients and healthy controls. Additionally, we tested if short-term HRV recordings are feasible and informative in planning home monitoring of LBP patients. The study population consisted of 16 volunteers (8 LBP patients and 8 healthy subjects) (age 42±10 years, body mass index 26±4 kg/m2, 7 men and 9 women). Usually 3- to 5-minute R-R interval recordings have been used as short-term recordings of HRV, but recent evidence supports even shorter R-R interval recording procedure for short-term HRV assessment. We collected R-R interval data for 1 minute in sitting, standing and bending down tasks. Mean heart rate (HR) and vagally mediated beat-to-beat variability (SD1 and rMSSD) were analyzed. The results showed that autonomic nervous system function assessed with the short-term measurement HRV method differentiates LBP patients from healthy controls in sitting and standing. Vagally mediated SD1 and rMSSD were significantly lower and the HR was higher among the patients compared to the controls. A novel finding was also the feasibility of 1-minute measurement of HRV, which may open entirely new opportunities to assess accurately concomitant changes in autonomic nervous system function and self-reported individual pain experience. This could lead to a more personalized pain treatment and more efficient health care resource allocation as the new measurement methods is more suitable for home monitoring than the previously used ones.