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

IntroductionExercise-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.MethodsThe 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).ResultsThe 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).DiscussionPresence 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

Effects of bright light treatment on psychomotor speed in athletes

Purpose: A recent study suggests that transcranial brain targeted light treatment via ear canals may have physiological effects on brain function studied by functional magnetic resonance imaging (fMRI) techniques in humans. We tested the hypothesis that bright light treatment could improve psychomotor speed in professional ice hockey players. Methods: Psychomotor speed tests with audio and visual warning signals were administered to a Finnish National Ice Hockey League team before and after 24 days of transcranial bright light or sham treatment. The treatments were given during seasonal darkness in the Oulu region (latitude 65 degrees north) when the strain on the players was also very high (10 matches during 24 days). A daily 12-min dose of bright light or sham (n = 11 for both) treatment was given every morning between 8–12 am at home with a transcranial bright light device. Mean reaction time and motor time were analyzed separately for both psychomotor tests. Analysis of variance for repeated measures adjusted for age was performed. Results: Time x group interaction for motor time with a visual warning signal was p = 0.024 after adjustment for age. In Bonferroni post-hoc analysis, motor time with a visual warning signal decreased in the bright light treatment group from 127 ± 43 to 94 ± 26 ms (p = 0.024) but did not change significantly in the sham group 121 ± 23 vs. 110 ± 32 ms (p = 0.308). Reaction time with a visual signal did not change in either group. Reaction or motor time with an audio warning signal did not change in either the treatment or sham group. Conclusion: Psychomotor speed, particularly motor time with a visual warning signal, improves after transcranial bright light treatment in professional ice-hockey players during the competition season in the dark time of the year

Postexercise Heart Rate Recovery in Adults Born Preterm

Objective To evaluate postexercise heart rate recovery (HRR) in adults born preterm. Study design We studied the association between preterm birth and postexercise HRR in 545 adults (267 women) at 23.3 years of age (range 19.9-26.3 years). One hundred three participants were born early preterm ( Results Mean peak HR was 159.5 bpm in the early preterm (P = .16 with controls), 157.8 bpm in the late preterm (P = .56), and 157.0 bpm in the control group. Mean HRR 30 seconds after exercise was 3.2 bpm (95% CI 1.1-5.2) lower in the early preterm group and 2.1 bpm (0.3-3.8) lower in the late preterm group than the full term controls. Mean 60s HRR was 2.5 (-0.1 to 5.1) lower in the early preterm group and 2.8 bpm (0.6-4.9) lower in the late preterm group. Mean maximum slope after exercise was 0.10 beats/s (0.02-0.17) lower in the early preterm group and 0.06 beats/s (0.00-0.12) lower in the late preterm group. Conclusions Our results suggest reduced HRR after exercise in adults born preterm, including those born late preterm. This suggests altered reactivation of the parasympathetic nervous system, which may contribute to cardiovascular risk among adults born preterm.Peer reviewe

Heart Rate Dynamics after Exercise in Cardiac Patients with and without Type 2 Diabetes

Purpose: The incidence of cardiovascular events is higher in coronary artery disease patients with type 2 diabetes (CAD + T2D) than in CAD patients without T2D. There is increasing evidence that the recovery phase after exercise is a vulnerable phase for various cardiovascular events. We hypothesized that autonomic regulation differs in CAD patients with and without T2D during post-exercise condition. Methods: A symptom-limited maximal exercise test on a bicycle ergometer was performed for 68 CAD + T2D patients (age 61 ± 5 years, 78% males, ejection fraction (EF) 67 ± 8, 100% on β-blockade), and 64 CAD patients (age 62 ± 5 years, 80% males, EF 64 ± 8, 100% on β-blockade). Heart rate (HR) recovery after exercise was calculated as the slope of HR during the first 60 s after cessation of exercise (HRRslope). R–R intervals were measured before (5 min) and after exercise from 3 to 8 min, both in a supine position. R–R intervals were analyzed using time and frequency methods and a detrended fluctuation method (α1). Results: BMI was 30 ± 4 vs. 27 ± 3 kg m2 (p < 0.001); maximal exercise capacity, 6.5 ± 1.7 vs. 7.7 ± 1.9 METs (p < 0.001); maximal HR, 128 ± 19 vs. 132 ± 18 bpm (p = ns); and HRRslope, −0.53 ± 0.17 vs. −0.62 ± 0.15 beats/s (p = 0.004), for CAD patients with and without T2D, respectively. There was no differences between the groups in HRRslope after adjustment for METs, BMI, and medication (ANCOVA, p = 0.228 for T2D and, e.g., p = 0.030 for METs). CAD + T2D patients had a higher HR at rest than non-diabetic patients (57 ± 10 vs. 54 ± 6 bpm, p = 0.030), but no other differences were observed in HR dynamics at rest or in post-exercise condition. Conclusion: HR recovery is delayed in CAD + T2D patients, suggesting impairment of vagal activity and/or augmented sympathetic activity after exercise. Blunted HR recovery after exercise in diabetic patients compared with non-diabetic patients is more closely related to low exercise capacity and obesity than to T2D itself

Commentary on Viewpoint: Exercise and cardiovascular risk reduction: Time to update the rationale for exercise?\ud

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Saturation of high-frequency oscillations of R-R intervals in healthy subjects and patients after acute myocardial infarction during ambulatory conditions

Saturation of high-frequency oscillations of R-R intervals in healthy subjects and patients after acute myocardial infarction during ambulatory conditions. Am J Physiol Heart Circ Physiol 287: H1921-H1927, 2004. First published July 8, 2004 doi:10.1152/ajpheart.00433.2004.-This study was designed to assess the relationship between R-R interval length and heart rate (HR) variability in healthy subjects and patients after an acute myocardial infarction (AMI). Twenty-four-hour ambulatory ECG recordings were obtained for 76 healthy subjects and 82 post-AMI patients. The high-frequency (HF, 0.15-0.4 Hz) spectral power of R-R intervals was analyzed in 5-min sequences over 24 h and plotted as a function of the corresponding mean R-R interval length. Quadratic regression model was used to study the relationship between R-R interval length and HF power. If a distinct deflection point (R-R 0) occurred in the quadratic regression (r Ͼ 0.50) model before maximum R-R interval, indicating the plateau of HF power, the relationship between R-R interval and HF power was defined as saturated. Otherwise, the relationship was defined as linear (r Ͼ 0.50) or low correlated (r Ͼ 0.50). The relationship was saturated in 35, linear in 38, and low correlated in 3 healthy subjects. In post-AMI patients, the relationship was saturated in 9 subjects, linear in 44 subjects, and low correlated in 29 patients. The HF power analyzed from the 24-h period did not differ between the saturated and linear groups, but when analyzed from the linear portion only, HF spectral power was smaller in the linear than the saturated group both among healthy subjects (P Ͻ 0.05) and post-AMI patients (P Ͻ 0.05). Saturation of the HF oscillations of R-R intervals is a common phenomenon in healthy subjects and also present in post-AMI patients during ambulatory conditions. This saturation effect may bias the quantification of cardiac vagal function when HR variability is analyzed from Holter recordings. heart rate variability; cardiovascular regulation; vagal activity ANALYSIS OF HEART RATE (HR) variability from 24-h ambulatory recordings is a widely used noninvasive tool in the assessment of autonomic regulation in various physiological and clinical settings (13). The ability of HR variability, particularly the measurement of high-frequency (HF, 0.15-0.4 Hz) oscillations of HR, to quantify vagal outflow to the sinus node has been documented in several previous studies METHODS Subjects. Healthy adults (n ϭ 83) were recruited by advertising in a newspaper. All subjects were nonsmokers and without any medication or cardiovascular disorder

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 one-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 built-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=0.001). When the next two 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=0.001). Conclusion Higher depression score is the primary variable forecasting health care costs in one-year follow-up among acute coronary syndrome patients. The ML tools may help decision-making when planning optimal utilization of treatment strategies.peerReviewe