Application of Functional Data Analysis for the Prediction of Maximum Heart Rate

Maximum heart rate (MHR) is widely used in the prescription and monitoring of exercise intensity, and also as a criterion for the termination of sub-maximal aerobic fitness tests in clinical populations. Traditionally, MHR is predicted from an age-based formula, usually 220−age. These formulae, however, are prone to high predictive errors that potentially could lead to inaccurately prescribed or quantified training or inappropriate fitness test termination. In this paper, we used functional data analysis (FDA) to create a new method to predict MHR. It uses heart rate data gathered every 5 seconds during a low intensity, sub-maximal exercise test. FDA allows the use of all the information recorded by monitoring devices in the form of a function, reducing the amount of information needed to generalize a model, besides minimizing the curse of dimensionality. The functional data model created reduced the predictive error by more than 50% compared to current models within the literature. This new approach has important benefits to clinicians and practitioners when using MHR to test fitness or prescribe exerciseThis work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project TIN2015-73566-JIN, in part by the European Regional Development Fund (ERDF/FEDER), the Consellería de Cultura, Educación e Ordenación Universitaria (2016-2019), under Grant ED431G/08, in part by the Reference Competitive Group (2014-2017) under Grant GRC2014/030, in part by the 2016 Postdoctoral Training Grants, and in part by the European Regional Development Fund (ERDF)S

Trend extraction in functional data of R and T waves amplitudes of exercise electrocardiogram

The R and T waves amplitudes of the electrocardiogram recorded during the exercise test undergo strong modifications in response to stress. We analyze the time series of these amplitudes in a group of normal subjects in the framework of functional data, performing reduction of dimensionality, smoothing and principal component analysis. These methods show that the R and T amplitudes have opposite responses to stress, consisting respectively in a bump and a dip at the early recovery stage. We test these features computing a confidence band for the trend of the population mean and analyzing the zero crossing of its derivative. Our findings support the existence of a relationship between R and T wave amplitudes and respectively diastolic and systolic ventricular volumes

Evaluation of an exercise field test using heart rate monitors to assess cardiorespiratory fitness and heart rate recovery in an asymptomatic population.

PurposeMeasures of cardiorespiratory fitness (CRF) and heart rate recovery (HRR) can improve risk stratification for cardiovascular disease, but these measurements are rarely made in asymptomatic individuals due to cost. An exercise field test (EFT) to assess CRF and HRR would be an inexpensive method for cardiovascular disease risk assessment in large populations. This study assessed 1) the predictive accuracy of a 12-minute run/walk EFT for estimating CRF ([Formula: see text]) and 2) the accuracy of HRR measured after an EFT using a heart rate monitor (HRM) in an asymptomatic population.MethodsFifty subjects (48% women) ages 18-45 years completed a symptom-limited exercise tolerance test (ETT) (Bruce protocol) and an EFT on separate days. During the ETT, [Formula: see text] was measured by a metabolic cart, and heart rate was measured continuously by a HRM and a metabolic cart.ResultsEFT distance and sex independently predicted[Formula: see text]. The average absolute difference between observed and predicted [Formula: see text] was 0.26 ± 3.27 ml·kg-1·min-1 for our model compared to 7.55 ± 3.64 ml·kg-1·min-1 for the Cooper model. HRM HRR data were equivalent to respective metabolic cart values during the ETT. HRR at 1 minute post-exercise during ETT compared to the EFT had a moderate correlation (r=0.75, p<0.001).ConclusionA more accurate model to estimate CRF from a 12-minute run/walk EFT was developed, and HRR can be measured using a HRM in an asymptomatic population outside of clinical settings

Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations

PURPOSE: Predicting the phenotypic effects of mutations has become an important application in clinical genetic diagnostics. Computational tools evaluate the behavior of the variant over evolutionary time and assume that variations seen during the course of evolution are probably benign in humans. However, current tools do not take into account orthologous/paralogous relationships. Paralogs have dramatically different roles in Mendelian diseases. For example, whereas inactivating mutations in the NPC1 gene cause the neurodegenerative disorder Niemann-Pick C, inactivating mutations in its paralog NPC1L1 are not disease-causing and, moreover, are implicated in protection from coronary heart disease. METHODS: We identified major events in NPC1 evolution and revealed and compared orthologs and paralogs of the human NPC1 gene through phylogenetic and protein sequence analyses. We predicted whether an amino acid substitution affects protein function by reducing the organism’s fitness. RESULTS: Removing the paralogs and distant homologs improved the overall performance of categorizing disease-causing and benign amino acid substitutions. CONCLUSION: The results show that a thorough evolutionary analysis followed by identification of orthologs improves the accuracy in predicting disease-causing missense mutations. We anticipate that this approach will be used as a reference in the interpretation of variants in other genetic diseases as well. Genet Med 18 10, 1029–1036

Reduced functional measure of cardiovascular reserve predicts admission to critical care unit following kidney transplantation

Background: There is currently no effective preoperative assessment for patients undergoing kidney transplantation that is able to identify those at high perioperative risk requiring admission to critical care unit (CCU). We sought to determine if functional measures of cardiovascular reserve, in particular the anaerobic threshold (VO2AT) could identify these patients. Methods: Adult patients were assessed within 4 weeks prior to kidney transplantation in a University hospital with a 37-bed CCU, between April 2010 and June 2012. Cardiopulmonary exercise testing (CPET), echocardiography and arterial applanation tonometry were performed. Results: There were 70 participants (age 41.7614.5 years, 60% male, 91.4% living donor kidney recipients, 23.4% were desensitized). 14 patients (20%) required escalation of care from the ward to CCU following transplantation. Reduced anaerobic threshold (VO2AT) was the most significant predictor, independently (OR = 0.43; 95% CI 0.27–0.68; p,0.001) and in the multivariate logistic regression analysis (adjusted OR = 0.26; 95% CI 0.12–0.59; p = 0.001). The area under the receiveroperating- characteristic curve was 0.93, based on a risk prediction model that incorporated VO2AT, body mass index and desensitization status. Neither echocardiographic nor measures of aortic compliance were significantly associated with CCU admission. Conclusions: To our knowledge, this is the first prospective observational study to demonstrate the usefulness of CPET as a preoperative risk stratification tool for patients undergoing kidney transplantation. The study suggests that VO2AT has the potential to predict perioperative morbidity in kidney transplant recipients

Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974

This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974