Screening for cardiac allograft rejection among heart transplant recipients using an electrocardiogram-based deep learning model

Background

Current approaches utilizing non-invasive methods to screen for cardiac allograft rejection (gene expression profiling and cell free DNA) have yet to be broadly integrated into heart transplant management and have shown limited discrimination (AUCs of 0.68 and 0.77, respectively). Changes in the electrocardiogram (ECG) have been reported at the time of severe cardiac rejection, including low voltages and conduction abnormalities. It remains unknown if subtle ECG changes correlating with cardiac allograft rejection can be detected earlier using machine learning methods.

Purpose

We sought to develop an artificial intelligence (AI) model to detect cardiac allograft rejection based on the 12 lead ECG.

Methods

We identified all patients who underwent a heart transplant at 3 hospital sites within a single health system from Jan 1998 through Apr 2021 and extracted digital 12-lead ECG data as well as endomyocardial biopsy pathology results from the electronic medical record. We partitioned our data into a training (80%), validation (10%), and test set (10%) based on a group stratification sampling. Each patient was present in only one set and each set had a positivity rate of 2.6% with 6,074/758/758 ECGs belonging to 1,146/140/141 unique patients in each set respectively. Cardiac allograft rejection was defined as moderate or severe acute cellular rejection based on International Society for Heart and Lung Transplantation (ISHLT) guidelines. A convolutional neural network, using the 12-lead ECG data as input, was trained with hyperparameter optimization for regularization, learning rate adjustments, and class weights. Model performance metrics were based on the test data and estimated using the final model architecture.

Results

1,587 heart transplant recipients who had at least one endomyocardial biopsy were evaluated for inclusion. We limited our sample to ECGs performed within 30 days of the biopsy date (7,590 ECGs, representing 1,425 unique patients). Our study population had a median age of 55.8 years and 28.7% were female. The median number of ECG-biopsy pairs per patient was 5. The majority of endomyocardial biopsy results were classified as none or mild rejection (97.1%), and 2.9% had moderate/severe rejection. The ECG-based AI model detected cardiac allograft rejection with an area under the receiver operative curve (AUC) of 0.84 in the test set. The sensitivity, specificity, positive and negative predictive values were 95%, 52.6%. 5.2% and 99.7% respectively.

Conclusions

An AI-ECG model appears to outperform novel non-invasive laboratory tests (gene expression profiling and cell free DNA) for detecting cardiac allograft rejection and does not require a blood draw or the additional complexities surrounding sample processing. This model relies on a readily available and relatively inexpensive test, the ECG. In addition, AI predictions can be made available within a few minutes following ECG acquisition.

Funding Acknowledgement

Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic

Detecting cardiomyopathies in pregnancy and the postpartum period using ECG

Background

Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms.

Purpose

The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women.

Methods

We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters.

Results

1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively.

Conclusions

A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Evaluating the sensitivity of the ACC/AHA pooled cohort risk calculator to predict atherosclerotic cardiovascular events within 10 years: how many events are we failing to predict?

Background

The ACC/AHA Pooled Cohort Equation (PCE) for atherosclerotic cardiovascular disease (ASCVD) has been recommended as the initial step in cardiovascular risk assessment. The sensitivity of this tool to detect those who will develop ASCVD within 10-years, while considering age and sex groups, has not been extensively studied.

Methods

Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, between the years 1998–2000 and were followed up through March 1st 2016. Inclusion criteria were ages 40–79 and complete data to calculate the PCE. We excluded those with known ASCVD, atrial fibrillation or heart failure. Criteria were similar to those used to derive the PCE. Events were validated in duplicate and included fatal and non-fatal myocardial infarction and ischemic stroke. Patient information was ascertained using the record linkage system of the REP. Follow-up was truncated at 10 years. We assessed the ASCVD predicted risk (categorized as low <5%, intermediate 5–9.9%, high 10–19.9%, and very high ≥20% risk) at baseline, in subjects having an ASCVD event within 10-years in the community across age (<65 years) and sex categories. We also categorized ideal cardiovascular health as ≥4 metrics [non-smoker, body mass index <25 kg/m2, and not having of elevated blood pressure (≥130/80 mmHg), LDL cholesterol (>100 mg/dL), or fasting blood glucose (>100 mg/dL), in the absence of a medical diagnosis or treatment].

Results

We included 30,042 adults, mean ± SD age 48.5±12.2 years, 54% women, with a median follow-up of 16.5±5.3 years. There were 1,555 ASCVD events (5.2%) at 10 years of follow-up. The performance of the PCE was similar to what was described in the original report (0.78 vs 0.79). Overall, among those who suffered an ASCVD, 54% of women and 41% of men were not high risk as predicted by PCE (Figure 1A). Most women (73%) <65 years of age would had been considered low risk within 10-years before the event, and only 10% would have been considered to be high risks (Figure 1B). Nonetheless, women <65 years who had an ASCVD event and low 10-year predicted ASCVD risk by PCE were less likely to have ideal cardiovascular health [55 (0.40%) vs 3884 (28.39%), p-value<0.0001], when compared to women in the low risk category without an event.

Conclusion

The PCE fails to identify most women who will develop an ASCVD event, particularly women <65 years of age. These results underscore the importance of using additional information when estimating ASCVD risk among women and the need for better cardiovascular risk prediction tools.

Figure 1

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): Mayo Clinic

External validation of an electrocardiography artificial intelligence-generated algorithm to detect left ventricular systolic function in a general cardiac clinic in Uganda

Background

Left ventricular systolic dysfunction (LVSD) is associated with increased morbidity and mortality. Although there are effective treatments for patients with LVSD to prevent mortality, heart failure and to improve symptoms, many patients remain undetected and untreated. We have recently derived a deep learning algorithm to detect LVSD using the electrocardiogram (ECG) which could have an important screening role, particularly in limited resources settings. We evaluated the accuracy of this algorithm for the first time in Africa in a sample of subjects attending a cardiology clinic.

Methods

We conducted a retrospective study in a general cardiac clinic in Uganda. Consecutive patients ≥18 years who had a digital ECG and echocardiogram done within two days of each other were included. We excluded patients with pacemakers or missing information regarding left ventricular ejection fraction (LVEF). Routine 10-second, twelve-lead surface rest ECG were performed using an Edan PC ECG Model SE-1515, Hamburg, Germany. The probability of LVSD was estimated with the Mayo Clinic artificial intelligence (AI) ECG algorithm. LVEF was calculated by the MMode (Teichholz method) using a Philips Ultrasound system, HD7XE, Bothel, Washington, USA. LVSD was defined as a LVEF≤35%. We assessed the overall diagnostic performance of the algorithm to identify LVSD in this population with the area under the receiver operating curve (AUC), and estimated sensitivity, specificity and accuracy using a pre-specified cut-off based on the probability for LVSD generated by the algorithm. We conducted secondary analyses using different LVEF cutoff values.

Results

We included 634 subjects, 32% (200) of whom had hypertension and 12% (77) clinical heart failure. Mean age was 57±18.8 years, 58% were women and the overall prevalence of LVSD was 4%. The AI-ECG had an AUC of 0.866 (see figure below), sensitivity 73.08%, specificity 91.10%, negative predictive value 98.75%, positive predictive value 26.03% and an accuracy of 90.96% using the original threshold. Using the optimal cutoff based on the AUCs, the sensitivity was 80.77% and specificity was 81.05% with a negative predictive value of 98.99%. The ROC for the detection of LVEF of 40% or below was 0.821.

Conclusion

The Mayo AI-ECG algorithm demonstrated good accuracy, sensitivity and specificity to detect LVSD in patients seen in a clinical setting in Uganda. This tool may facilitate the identification of people at a high risk for LVSD in settings with low resources.

ROC

Funding Acknowledgement

Type of funding source: None

Artificial intelligence-enabled detection of paroxysmal atrial fibrillation from normal sinus ECGs in patients with coronary microvascular dysfunction

Background

Artificial Intelligence (AI) algorithms enabled the detection of patients with paroxysmal atrial fibrillation (PAF) from a single normal sinus rhythm (NSR) ECG. Coronary microvascular dysfunction (CMD) is a precursor for coronary artery disease, which is a known risk factor for AF.

Purpose

The aim of this study is to examine the probability of PAF, according to AI-enabled algorithm estimation, in patients with CMD.

Methods

1858 patients without persistent atrial fibrillation with signs and/or symptoms of ischemia and with non-obstructive CAD (<40% stenosis) who underwent invasive coronary microvascular functional assessment and the ECG closest to the functional assessment were included in this analysis. Patients with coronary flow velocity reserve (CFR) <2 in response to adenosine were labelled as endothelial-independent CMD; % increase in coronary blood flow (%ΔCBF) <50% in response to acetylcholine were labelled as endothelial-dependent CMD. Patients were categorized into 4 groups. G1: Normal (NL) CFR/NL %ΔCBF; G2: Abnormal (ABN) %ΔCBF only; G3: ABN CFR only; G4: ABL CFR & %ΔCBF. The probability of having PAF (%probAF) was calculated by a previously-trained and validated AI algorithm. AF Flag = %probAF >9%; which is a pre-set cut-off found to have the highest accuracy of identifying patients with PAF (Area Under the Curve = 0.87).

Results

Mean age for patients was 51.2±12.4 and 66.3% were females. 835 (45%) were in G1, 39 (2%) in G2, 911 (49%) in G3, and 73 (4%) in G4. Compared to G1 and G2, G3 and G4 were older, had more diabetes and higher smoking rates (p<0.05 for all). Furthermore, G4 had a significantly higher %probAF compared to other groups (Fig. 1). G4 were also more likely to be flagged by the algorithm as having PAF, even after adjusting for cardiovascular risk factors, with an odds ratio of 1.9 [CI 95% 1.1–3.3; p=0.03]) (Fig. 2).

Conclusion

Patients with combined CMD have a significantly higher probability of having PAF based on an AI-enabled algorithm. Further research is warranted to know if patients with CMD would benefit from formal AF screening at the time of diagnosis.

Funding Acknowledgement

Type of funding source: None

Characteristics of cardiac rehabilitation programs in Latin America and the Caribbean, and estimation of capacity and needs in the region

Background

Cardiac rehabilitation (CR) is an established model of cardiovascular (CV) prevention that has proven benefits. Availability, characteristics and need of CR programs in Latin-American and Caribbean (LAC) countries remains poorly characterized. This study aims to establish the availability, capacity, density and aspects of CR delivery in LAC.

Methods

A cross-sectional survey was administered to CR programs in 24 LAC. Local CV organizations and societies identified CR programs. Characteristics of individual CR program were reviewed including: funding sources, core components, healthcare providers, and dose (number of sessions per weeks X total number of weeks) of CR. National CR capacity (median number of patients a program could serve per year X number of programs per country), density (Ischemic Heart Disease [IHD] incidence per year/ national capacity), need (IHD incidence per year- national capacity) and occupancy (median number patients program served per year/national capacity) were computed based on survey responses.

Results

At least one CR program was identified per LAC country (total 255 programs across 24 countries). Data was collected in 20 of the 24 countries. Responses were received from 139/255 programs (median program response rate=55%; Table 1). Over 50% (n=73) of programs were funded by multiple sources (government, hospital/clinic, private health insurance); Self-payment was reported by 63% programs, in which 24 (33.8%) patients paid over 50% of the cost. Guideline-indicated conditions were accepted in 77% or more programs. Physiotherapists (n=106, 76.3%), cardiologists (n=105, 75.5%) and dietitians (n=79, 56.8%) were the most common healthcare providers on CR teams. Regionally, programs offered 9 (IQR = 8–10) core components (patient education, exercise prescription and initial assessment delivered by nearly all programs). Median CR was 36 (IQR = 24–56) sessions/patient. Twenty-seven (20.9%) programs offered alternative CR models (e.g., home or community-based and hybrid models). Median national capacity was 500 CR spots/country (IQR= 200–2300). Regional density was 1 CR spot per 24 incident IHD patients per year. Greatest need in absolute terms for CR was observed in Brazil, Dominican Republic and Mexico (all with >150,000 spots needed per year to manage incident IHD patients; Table 1). Occupancy ranged from over 100% in Colombia to 15% in Chile (median=60%, IQR = 32%–81%), Table 1.

Conclusion

In LAC countries, there is very limited capacity to meet the need for CR. Nature of CR services varied regionally.

Funding Acknowledgement

Type of funding source: None

Artificial intelligence-derived physiological aging is associated with coronary endothelial dysfunction

Background

An Artificial intelligence (AI)-enabled algorithm can provide an age estimate from a single standard resting ECG. The gap between AI-estimated age (ECG age) and chronological age (Age-Gap) has been associated with total and cardiovascular mortality. We hypothesized that coronary endothelial dysfunction (CED), an early feature of coronary atherosclerosis, is associated with physiological aging, as measured by ECG age.

Purpose

The aim of this study is to investigate the association of CED, a potential index of cardiac aging, and AI-estimated physiological aging.

Methods

1902 patients with signs and/or symptoms of ischemia and with non-obstructive coronary artery disease (<40% angiographic stenosis) who underwent functional coronary angiography with intracoronary (IC) acetylcholine (Ach) infusions and a resting ECG within one year were included in this analysis. CED was defined as coronary artery constriction [%ΔCoronary artery diameter (CAD) <0] in response to IC Ach. Patients were categorized as CED+ or CED−. The ECG age were calculated by a previously-trained AI algorithm that was built using the Keras framework with Tensorflow backend and Python. The association between ECG-age and Age-Gap, with CED was investigated.

Results

Average chronological age was 50.9±12.5, ECG age was 54.1±11.6, 1261 (66%) were females. Compared to CED−, the CED+ group had more males and hyperlipidaemia (p<0.05 for both). Age-gap was significantly higher in patients with CED+ (Fig. A). Multiple regression analysis showed that CED+ was associated with Age-gap (standardized β [sβ] coefficient = 0.08, P=0.001) even after adjustment for chronological age (sβ coefficient = 0.07, P=0.001), and even after adjusting for other cardiovascular risk factors like gender and obesity (sβ coefficient = 0.07, P<0.0001). Furthermore, there is a significant gradual increase in vasoconstriction, in response to IC Ach, with increasing Age-gap (Fig. B).

Conclusion

Abnormal coronary endothelial function is associated with an increased AI-estimated age which could indicate a higher physiological age.

Funding Acknowledgement

Type of funding source: None

The association of artificial intelligence-enabled electrocardiogram-derived age (physiologic age) with atherosclerotic cardiovascular events in the community

Background

We have demonstrated that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased mortality. We hypothesized that Age-Gap would predict long-term atherosclerotic cardiovascular disease (ASCVD) and that Age-Gap would refine the ACC/AHA Pooled Cohort Equations' (PCE) predictive abilities.

Methods

Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients seeking primary care between 1998–2000 and followed through March 2016. Inclusion criteria were age 40–79 and complete data to calculate PCE. We excluded those with known ASCVD, AF, HF or an event within 30 days of baseline.A neural network, trained, validated, and tested in an independent cohort of ∼ 500,000 independent patients, using 10-second digital samples of raw, 12 lead ECGs. PCE was categorized as low<5%, intermediate 5–9.9%, high 10–19.9%, and very high≥20%. The primary endpoint was ASCVD and included fatal and non-fatal myocardial infarction and ischemic stroke; the secondary endpoint also included coronary revascularization [Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Graft (CABG)], TIA and Cardiovascular mortality. Events were validated in duplicate. Follow-up was truncated at 10 years for PCE analysis. The association between Age-Gap with ASCVD and expanded ASCVD was assessed with cox proportional hazard models that adjusted for chronological age, sex and risk factors. Models were stratified by PCE risk categories to evaluate the effect of PCE predicted risk.

Results

We included 24,793 patients (54% women, 95% Caucasian) with mean follow up of 12.6±5.1 years. 2,366 (9.5%) developed ASCVD events and 3,401 (13.7%) the expanded ASCVD. Mean chronologic age was 53.6±11.6 years and the AI-ECG age was 54.5±10.9 years, R2=0.7865, p<0.0001. The mean Age-Gap was 0.87±7.38 years. After adjusting for age and sex, those considered older by ECG, compared to their chronologic age had a higher risk for ASCVD when compared to those with <−2 SD age gap (considered younger by ECG). (Figure 1A), with similar results when using the expanded definition of ASCVD (data not shown). Furthermore, Age-Gap enhanced predicted capabilities of the PCE among those with low 10-year predicted risk (<5%): Age and sex adjusted HR 4.73, 95% CI 1.42–15.74, p-value=0.01 and among those with high predicted risk (>20%) age and sex adjusted HR 6.90, 95% CI 1.98–24.08, p-value=0.0006, when comparing those older to younger by ECG respectively (Figure 1B).

Conclusion

The difference between physiologic AI-ECG age and chronologic age is associated with long-term ASCVD, and enhances current risk calculators (PCE) ability to identify high and low risk individuals. This may help identify individuals who should or should not be treated with newer, expensive risk-reducing therapies.

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): Mayo Clinic

0719 The Association of Sleep Apnea and Cardiorespiratory Fitness with Long-Term Major Cardiovascular Events

Introduction

Sleep disordered breathing (SDB) is associated with adverse cardiovascular outcomes and decreased cardiorespiratory fitness (CRF). The risk of long-term major adverse cardiovascular events (MACE) when SDB and decreased CRF co-occur has not been determined.

Methods

We included consecutive patients that underwent a symptom-limited cardiopulmonary exercise test followed by first-time diagnostic polysomnography within 6 months. Patients were stratified based on the presence of moderate-severe SDB (apnea/hypopnea index ≥15/hour) and decreased CRF defined as <70% predicted peak oxygen consumption (VO2). MACE was a composite outcome of myocardial infarction (MI), coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), stroke/transient ischemic attack (TIA) and death. Cox-proportional hazard models adjusting for factors known to influence CRF and MACE were constructed.

Results

Of 498 included patients (60±13 years, 28.1% female), 175 (35%) had MACE (MI=17, PCI=14, CABG=13, stroke=20, TIA=12, deaths=99) at a median follow-up of 8.7 years (interquartile range=6.5-10.3 years). After adjusting for age, sex, beta-blockers, systemic hypertension, diabetes mellitus, coronary artery disease, cardiac arrhythmia, chronic obstructive pulmonary disease, smoking and positive airway pressure (PAP) usage, decreased CRF alone (HR=1.91, 95%CI=1.15-3.18, p=0.012), but not SDB alone (HR=1.26, 95%CI=0.75-2.13, p=0.389) was associated with increased risk of MACE. Those with SDB and decreased CRF had increased risk of MACE compared to patients with decreased CRF alone (HR=1.85, 95%CI=1.21-2.84, p<0.005) after accounting for these confounders; the risk was attenuated after additionally adjusting for adequate adherence to PAP (HR=1.85, 95%CI=0.99-3.05, p=0.05).

Conclusion

The incidence of MACE, including mortality, was high in this sample. Moderate-severe SDB with concurrent decreased CRF was associated with higher risk of MACE than decreased CRF alone. These results highlight the importance of including CRF in the risk assessment of patients with SDB, and conversely, that of screening for SDB in patients with low peak VO2.

Support

None.

Association between eating time interval and frequency with ideal cardiovascular health: Results from a random sample Czech urban population

BACKGROUND AND AIMS

The frequency and timing of meals may affect cardiovascular health (CVH) outcomes, but large-scale epidemiological studies are lacking. The aim of this study was to understand the relationship between eating time interval and frequency, and measures of ideal CVH in the Kardiovize Brno cohort study, a random urban sample population in Central Europe.

METHODS AND RESULTS

1659 members of the Kardiovize Brno 2030 cohort were included in a cross-sectional study (mean age = 46.86 years; 44.6% male). Exposure variables were eating time interval and frequency, and skipping meals. Primary outcomes were indices of CVH, including body mass index, diet, physical activity, smoking, blood pressure, glucose and cholesterol, and the composite CVH score. Cluster analysis and binary logistic regression analysis were used to evaluate eating habits and the association between variables. After adjustment for well-known risk factors, subjects who skipped breakfast or the afternoon snack had a higher risk of poor CVH (OR = 1.613; 95%CI = 1.121-2.320; p = 0.010; OR = 1.409; 95%CI = 1.110-1.788; p = 0.005, respectively). Moreover, we identified three clusters of individuals based on eating habits; from cluster 1 to cluster 3, eating time interval and frequency increased and this was associated with increases in CVH score from 8.70 (SEM = 0.10) in cluster 1, and 9.06 (SEM = 0.08) in cluster 2 to 9.42 (SEM = 0.09) in cluster 3 (p-trend = 0.019).

CONCLUSIONS

Our findings suggest that skipping breakfast or the afternoon snack are risk factors for poor CVH, while higher eating time interval and frequency may promote ideal CVH.

Association of adiposity, telomere length and mortality: data from the NHANES 1999-2002

BACKGROUND/OBJECTIVES

Telomere shortening is associated with age and risk of medical comorbidity. We assessed the relationship between measures of adiposity, leukocyte telomere length, and mortality and whether it is modified by age.

SUBJECTS/METHODS

Subjects with dual-energy X-ray absorptiometry measures were identified using the National Health and Nutrition Examination Survey 1999-2002. Obesity was categorized using two body fat definitions (BF1%: men ⩾25%; females ⩾35%; BF2% ⩾28% and ⩾38%, respectively), body mass index (BMI) and waist circumference (WC; men ⩾102 cm; females ⩾88 cm). Telomere length relative to standard reference DNA (T/S ratio) was assessed using quantitative PCR. Weighted multivariable regression models evaluated the association of telomere length with adiposity, both continuously and categorically (low/normal BF%, low/high WC and standard BMI categories). Differences in telomere length by age and adiposity were ascertained and subsequent models were stratified by age. Proportional hazard models assessed the risk of mortality by adiposity status. A telomere by adiposity interaction was tested in the entire cohort and by age category (<60 vs ⩾60 years; <70 vs ⩾70 years).

RESULTS

We identified 7827 subjects. Mean age was 46.1 years. Overall telomere length was 1.05±0.01 (s.e.) that differed by BF1% (low/high: 1.12±0.02 vs 1.03±0.02; P<0.001), BF2% (1.02±0.02 vs 1.11±0.02; P<0.001), BMI (underweight 1.08±0.03; normal 1.09±0.02; overweight 1.04±0.02; and obese 1.03±0.02;P<0.001) and WC (low/high 1.09±0.02 vs 1.02±0.02; P<0.001). Adjusted β-coefficients evaluating the relationship between telomere length and adiposity (measured continuously) were as follows: BF1% (β=-0.0033±0.0008; P<0.001), BF2% (-0.041±0.008; P<0.001), BMI (β=-0.025±0.0008; P=0.005) and WC (β=-0.0011±0.0004; P=0.007). High BF% (BF1%: β=-0.035±0.011; P=0.002; BF2%: β=-0.041±0.008; P<0.001) and WC (β=-0.035±0.011; P=0.008) were inversely related to telomere length (TL). Stratifying by age, high BF1% (-0.061±0.013), BF2% (-0.065±0.01), BMI-obesity (-0.07±0.015) and high WC (-0.048±0.013) were significant (all P<0.001). This association diminished with increasing age. In older participants, TL was inversely related to mortality (hazard ratio 0.36 (0.27, 0.49)), as were those classified by BF1% (0.68 (0.56, 0.81)), BF2% (0.75 (0.65, 0.80)), BMI (0.50 (0.42, 0.60)) and WC (0.72 (0.63, 0.83)). No interaction was observed between adiposity status, telomere length and mortality.

CONCLUSIONS

Obesity is associated with shorter telomere length in young participants, a relationship that diminishes with increasing age. It does not moderate the relationship with mortality.

Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis

BACKGROUND

The ideal means of identifying obesity in children and adolescents has not been determined although body mass index (BMI) is the most widely used screening tool.

OBJECTIVE

We performed a systematic review and meta-analysis of studies assessing the diagnostic performance of BMI to detect adiposity in children up to 18 years.

METHODS

Data sources were EMBASE, MEDLINE, Cochrane, Database of Systematic Reviews Cochrane CENTRAL, Web of Science and SCOPUS up to March 2013. Studies providing measures of diagnostic performance of BMI and using body composition technique for body fat percentage measurement were included.

RESULTS

Thirty-seven eligible studies that evaluated 53 521 patients, with mean age ranging from 4 to 18 years were included in the meta-analysis. Commonly used BMI cut-offs for obesity showed pooled sensitivity to detect high adiposity of 0.73 (confidence interval [CI] 0.67-0.79), specificity of 0.93 (CI 0.88-0.96) and diagnostic odds ratio of 36.93 (CI 20.75-65.71). Males had lower sensitivity. Moderate heterogeneity was observed (I(2) = 48%) explained in meta-regression by differences across studies in race, BMI cut-off, BMI reference criteria (Center for Disease Control vs. International Obesity Task Force) and reference standard method assessing adiposity.

CONCLUSION

BMI has high specificity but low sensitivity to detect excess adiposity and fails to identify over a quarter of children with excess body fat percentage.

Anthropometric measurements and survival in older Americans: Results from the third National Health and Nutrition Examination Survey

OBJECTIVES

The impact of adiposity on mortality in older adults remains controversial. Some reports suggest that measures of general adiposity such as body mass index (BMI) predict better survival. We assessed the relationship between measures of adiposity and mortality in older adults.

DESIGN

Cross-sectional analysis of a population-based sample.

SETTING

Non-institutionalized persons in the United States participating in the National Health and Nutrition Examination Surveys III and its linked mortality dataset.

PARTICIPANTS

A subsample of 4,489 non-institutionalized survey participants aged >60 years with measures of body composition using bioimpedance. To account for possible residual confounding, smokers, subjects with heart failure, respiratory disease, kidney disease and cancer were excluded (n=2,920). Data from 1569 subjects were analysed.

MEASUREMENTS

BMI, waist circumference (WC), waist-hip ratio (WHR), lean mass (LM) and % Body Fat (BF) were classified by tertiles (lowest=referent). Proportional-hazard models evaluated the association of anthropometric indices with overall and cardiovascular mortality.

RESULTS

Mean age was 69.4years, and 265(16.9%) were >80 years. There were 717(47.6%) women and 792 deaths of which 284 [35.9%] were cardiovascular related. Elevated BMI was associated with reduced cardiovascular mortality (HR 0.53 [0.30-0.84]), and remained significant after adjusting for LM (HR 0.54 [0.31-0.93]). Elevated %BF was associated with reduced mortality from cardiovascular causes (HR 0.52 [0.29-0.91]). Low BMI was associated with higher risk of cardiovascular (HR 3.66 [1.25-10.69]) and overall death (HR 2.44 [1.22-4.90]).

CONCLUSION

Measures of adiposity in older participants are associated with lower mortality from cardiovascular causes that cannot be explained by major known confounders between obesity and mortality. Further studies need to elucidate a possible protective role and interplay between adiposity and skeletal muscle in older adults.

Accuracy of body mass index in diagnosing obesity in the adult general population

BACKGROUND

Body mass index (BMI) is the most widely used measure to diagnose obesity. However, the accuracy of BMI in detecting excess body adiposity in the adult general population is largely unknown.

METHODS

A cross-sectional design of 13 601 subjects (age 20-79.9 years; 49% men) from the Third National Health and Nutrition Examination Survey. Bioelectrical impedance analysis was used to estimate body fat percent (BF%). We assessed the diagnostic performance of BMI using the World Health Organization reference standard for obesity of BF%>25% in men and>35% in women. We tested the correlation between BMI and both BF% and lean mass by sex and age groups adjusted for race.

RESULTS

BMI-defined obesity (> or =30 kg m(-2)) was present in 19.1% of men and 24.7% of women, while BF%-defined obesity was present in 43.9% of men and 52.3% of women. A BMI> or =30 had a high specificity (men=95%, 95% confidence interval (CI), 94-96 and women=99%, 95% CI, 98-100), but a poor sensitivity (men=36%, 95% CI, 35-37 and women=49%, 95% CI, 48-50) to detect BF%-defined obesity. The diagnostic performance of BMI diminished as age increased. In men, BMI had a better correlation with lean mass than with BF%, while in women BMI correlated better with BF% than with lean mass. However, in the intermediate range of BMI (25-29.9 kg m(-2)), BMI failed to discriminate between BF% and lean mass in both sexes.

CONCLUSIONS

The accuracy of BMI in diagnosing obesity is limited, particularly for individuals in the intermediate BMI ranges, in men and in the elderly. A BMI cutoff of> or =30 kg m(-2) has good specificity but misses more than half of people with excess fat. These results may help to explain the unexpected better survival in overweight/mild obese patients.

Metabolic syndrome: from global epidemiology to individualized medicine

The metabolic syndrome (MetS) encompasses a constellation of metabolic abnormalities that are thought to place patients at higher risk for the development of diabetes and cardiovascular (CV) disease. The underlying pathophysiology is still a point of contention among various professional organizations leading to inconsistencies in the manner in which MetS is defined. Each definition has its advantages and disadvantages. Nonetheless, there is an agreement that insulin resistance and obesity are likely the central contributing factors. Because the prevalence of obesity has been increasing at a frightening rate in the past few decades, MetS represents a major public health problem that should be identified clinically in individual patients. This review describes the changing epidemiology of obesity and of MetS and discusses its importance in CV disease. We outline the existing controversies that surround MetS and discuss the role of lifestyle, pharmacological, surgical, and novel approaches in its management.

Effect of pravastatin on blood pressure in people with cardiovascular disease

Experimental evidence and several small studies in humans suggest that HMG-CoA (3-hydroxy 3-methylglutaryl coenzyme A) reductase inhibitors (statins) reduce blood pressure, perhaps through effects on endothelial function or by reducing inflammation. We tested the hypothesis that pravastatin would reduce blood pressure at 3 months and the risk of developing new hypertension over a follow-up period of 5 years. This was a post hoc subgroup analysis of a randomized double-blind placebo-controlled trial of pravastatin 40 mg daily vs placebo in 4159 participants with previous myocardial infarction and total plasma cholesterol <240 mg/dl (6.2 mmol/l). The primary outcome was the unadjusted change in mean arterial pressure (MAP) from baseline to 3 months. We also considered systolic and diastolic blood pressure (SBP and DBP) and pulse pressure. Analysis of covariance was used to calculate the adjusted effect of treatment on change in these outcomes at 3, 6, 12 and 24 months postrandomization, after controlling for potential confounders. Logistic regression was used to calculate the adjusted effect of treatment on incident hypertension (blood pressure > or =140/90 in those without known hypertension at baseline). This analysis included 4126/4159 (99.2%) participants for whom blood pressure was measured at baseline and during at least one follow-up visit. Median duration of follow-up was 57.8 months. The unadjusted and adjusted change in MAP, SBP, DBP or pulse pressure from baseline was not significantly different for pravastatin or placebo recipients at 3, 6, 12 or 24 months after randomization, or at last follow-up. Pravastatin did not reduce the adjusted risk of incident systolic hypertension (odds ratio 0.99, 95% CI 0.80-1.23), or incident diastolic hypertension (odds ratio 0.97, 95% CI 0.73-1.27). In summary, pravastatin 40 mg daily did not reduce blood pressure in survivors of myocardial infarction without overt hypercholesterolaemia.

Recognition, diagnosis and management of obesity after myocardial infarction

OBJECTIVE

We investigated the documentation of obesity as a medical problem, and subsequent management recommendations, in patients after myocardial infarction (MI).

DESIGN

We performed a cross-sectional analysis of a randomly selected sample of 627 patients discharged after an MI, from five US teaching hospitals between 1/1/01 and 12/31/02. Information was extracted from clinical notes using standardized definitions.

RESULTS

Mean body mass index (BMI) was 31+/-13 kg/m2, which was documented in only 14% of patients and had to be calculated post hoc in the rest. Waist circumference and waist/hip ratio were not documented at all; 83% of patients were overweight, 55% obese, and 8% morbidly obese. In only 20% of patients with BMI> or =30 kg/m2 was the diagnosis of obesity documented either as a current medical problem, as part of past medical history or as a final diagnosis. A dietary counseling was carried out in 61% of patients with BMI> or =25 kg/m2 and in 61% of patients with BMI<25 kg/m2, P=0.96. Weight loss was described as part of the goals/plan at discharge in 7% of overweight and 9% of obese patients. There was no change in either the level of recognition of obesity (22 vs 19%, P=0.3) or in the proportion of obese patients for whom weight loss was described as part of the goals/plan at discharge (8 vs 10%, P=0.7) before (n=301) compared to after (n=326) the Call to Action in Obesity by the Surgeon General in December 2001.

CONCLUSION

Obesity is underecognized, underdiagnosed and undertreated in persons with acute MI.

Repair of massive rotator cuff tears in patients older than 70 years

Major ruptures of the rotator cuff are common in elderly patients, and their management has been a challenge for orthopaedic surgeons. This study was undertaken to evaluate the results obtained in patients older than 70 years who were treated with open surgical repair for massive ruptures of the rotator cuff tendons and to correlate the functional outcomes with the quality of the repairs. Between January 1991 and November 1994, 69 patients older than 70 years of age underwent rotator cuff repairs of massive tears with a minimum follow-up of 2 years (mean of 3 years). These patients were evaluated before and after surgery with the University of California at Los Angeles score. The mean age was 75 years (range 70 to 90 years), and female sex and right shoulder were predominant. A good bone-tendon repair was achieved in 55 patients, a fair bone-tendon repair in 5 patients, a poor bone tendon-repair in 8 patients, and 1 patient had no repair. The mean University of California at Los Angeles rating score before surgery was 9.4 points, and the mean postoperative score at final follow-up was 30.9 points, with an average improvement of 21.5 points (P = .0001). Satisfactory results were achieved in 78.2% of the patients (University of California at Los Angeles score equal or over 28). The patients' clinical results and postoperative arthrograms are evaluated with regard to the quality of cuff repair. Painful massive rotator cuff tears can be repaired in patients older than 70 years with satisfactory results.

Effect of diabetes mellitus on the presentation and triage of patients with acute chest pain without known coronary artery disease

PURPOSE

Patients with diabetes and acute chest pain may be admitted to hospitals more frequently than patients without diabetes because physicians suspect atypical presentations for ischemic heart disease. This study aimed to determine whether the presentation of acute myocardial infarction and risk for major cardiac complications differs among patients without known coronary artery disease who do or do not have diabetes.

PATIENTS AND METHODS

Data from an emergency department of an urban teaching hospital on the medical histories, physical examinations, and electrocardiograms of 2,694 subjects with acute chest pain and without known coronary artery disease were prospectively recorded.

RESULTS

Diabetes was present in 301 (11%) patients. Compared with patients without diabetes, patients with diabetes were more likely to be < or = 60 years old (51% versus 20%) and to have a history of hypertension (70% versus 35%) or high blood cholesterol (35% versus 19%). A discharge diagnosis of acute myocardial infarction was made in 25 diabetic (8%) and in 148 nondiabetic (6%; P = 0.16) patients. A major cardiac complication occurred in two patients with diabetes (0.7%) and in 20 patients without diabetes (0.8%; P = 1.0). Patients with and without diabetes who had atypical chest pain complaints had similar rates of myocardial infarction (3% and 4%, respectively; P = 0.6). Patients with diabetes were more likely to be hospitalized (67% versus 47%; P = 0.001) both before and after adjusting for clinical and electrocardiographic data.

CONCLUSIONS

For patients with acute chest pain without a prior history of coronary artery disease, diabetes was not associated with a higher rate of acute myocardial infarction or complications. However, diabetes was associated with a higher rate of hospitalization in this population, suggesting that physicians have a lower threshold for admission to the hospital of patients with diabetes.

Home continuous passive motion machine versus professional physical therapy following total knee replacement

A vigorous rehabilitation program following discharge from the hospital is necessary for patients having a total knee arthroplasty to maintain and improve range of motion and function. To compare the effectiveness of the continuous passive motion (CPM) machine as a home therapy program versus professional physical therapy, a prospective, comparative, randomized clinical study of 103 consecutive primary total knee arthroplasties in 80 patients (23 bilateral) was performed. The CPM group consisted of 37 patients (49 knees), and the physical therapy group consisted of 43 patients (54 knees). At 2 weeks, knee flexion was similar in the two groups, but a flexion contracture was noted in the CPM group (4.2 degrees). This difference is felt by the authors to be clinically insignificant. At 6 months, there were no differences in knee scores, knee flexion, presence of flexion contracture, or extensor lag between the two groups. The cost for the CPM machine group was $10,582 ($286 per patient), and the cost for professional therapy was $23,994 ($558 per patient). We conclude that the CPM machine after the hospital discharge of patients having total knee replacement is an adequate rehabilitation alternative with lower cost and with no difference in results compared with professional therapy.

Frontiers in Internal Medicine

Clinical research in Internal Medicine has provided many scientific advances during the past few years. However, the newly generated information overrides the time available to read all of the medical literature regarding advances in Internal Medicine. The goal of this review is to summarize some of the most relevant improvements in clinical practice published over the last few years. From Cardiology to Pulmonology, the authors of this review expose in a succinct way what they and many of their peers consider to be the most transcendental information gathered from thousands of publications. The authors of this review article have attempted to avoid sensationalism by including facts instead of just simply optimistic preliminary findings that can mislead clinicians' decision making. The review is focused on information obtained through well-designed, prospective clinical trials and cohorts where the effectiveness of medical interventions and diagnostic procedures were tested.

Thigh pain following tourniquet application in simultaneous bilateral total knee replacement arthroplasty

Thigh pain following tourniquet application is a common patient complaint in the early postoperative period following total knee arthroplasty. Postoperative thigh pain was evaluated in 28 consecutive simultaneous bilateral total knee arthroplasty patients between April 1996 and October 1996. A prospective, double-blind, randomized clinical trial was performed. Tourniquet pressure of 350 mmHg was used on 1 thigh (thigh 1) and 100 mmHg plus systolic blood pressure on the other (thigh 2). A scale of pain (no pain, mild, moderate, or severe) was applied on the first, second, and third days, as well as 2 and 6 weeks after surgery. There were 16 men and 12 women with a mean age of 72 years (range, 55-85 years). The mean tourniquet time was similar in both groups (thigh 1 = 23 minutes, thigh 2 = 22 minutes). The mean tourniquet pressure in thigh 2 was 230 mmHg (range, 212-260 mmHg). There was a statistically significant difference in thigh pain on the first (P = .01), second (P = .01), and third (P = .001) postoperative days between both groups, with more thigh pain on the 350 mmHg side. At 6 weeks after surgery, the difference in thigh pain was gone. For total knee arthroplasty, using the tourniquet at a pressure of 100 mmHg above the systolic blood pressure is recommended. This is adequate to provide a bloodless field and will result in a less unpleasant postoperative period.

Prognostic value of cardiac troponin T after noncardiac surgery: 6-month follow-up data

OBJECTIVES

We sought to evaluate the prognostic significance of cardiac troponin T (TnT) serum levels after noncardiac surgery.

BACKGROUND

Cardiac TnT has been found to be marker for myocardial injury, but elevations of TnT are common in patients undergoing noncardiac surgery without clinical evidence of severe ischemia.

METHODS

We studied 772 patients who underwent major noncardiac procedures and did not have major cardiovascular complications during their inpatient course. Total serum creatine kinase (CK) and cardiac TnT were measured according to a protocol that included sampling in the recovery room and during the next 2 days. A 6-month follow-up interview was performed for 722 (94%) of the patients.

RESULTS

Elevated cardiac TnT and CK-MB results were detected for 92 (12%) and 211 (27%) patients, respectively. During the follow-up period, there were 19 (2.5%) major cardiac complications, including 14 cardiac deaths, 3 nonfatal myocardial infarctions and 2 admissions for unstable angina. Compared with patients with cardiac TnT values < 0.1 ng/ml, patients with elevated TnT had a relative risk for cardiac events of 5.4 (95% confidence interval: 2.2 to 13, p = 0.001), whereas CK-MB was not correlated with postdischarge cardiac events. In multivariate logistic regression analysis adjusting for preoperative clinical and CK-MB data, a cardiac TnT value > 0.1 ng/ml was in independent correlate of cardiac events (adjusted odds ratio 4.6, p < 0.05). This correlation was a function of the relation of elevated TnT levels with postoperative in-hospital congestive heart failure and new sustained arrhythmias, suggesting that elevated postoperative TnT levels detected myocardial ischemia during these clinical events.

CONCLUSIONS

We conclude that an abnormal TnT level in patients undergoing noncardiac surgery may be a useful marker of ischemic disease and a predictor of 6-month prognosis.