Heart failure with preserved ejection fraction: recent concepts in diagnosis, mechanisms and management

It is estimated that half of all patients with heart failure (HF) have HF with preserved ejection fraction (HFpEF). Yet this form of HF remains a diagnostic and therapeutic challenge. Differentiating HFpEF from other causes of dyspnoea may require advanced diagnostic methods, such as exercise echocardiography, invasive haemodynamics and investigations for ‘HFpEF mimickers’. While the classification of HF has relied heavily on cut-points in left ventricular ejection fraction (LVEF), recent evidence points towards a gradual shift in underlying mechanisms, phenotypes and response to therapies as LVEF increases. For example, among patients with HF, the proportion of hospitalisations and deaths due to cardiac causes decreases as LVEF increases. Medication classes that are efficacious in HF with reduced ejection fraction (HFrEF) have been less so at higher LVEF ranges, decreasing the risk of HF hospitalisation but not cardiovascular or all-cause death in HFpEF. These observations reflect the burden of non-cardiac comorbidities as LVEF increases and highlight the complex pathophysiological mechanisms, both cardiac and non-cardiac, underpinning HFpEF. Treatment with sodium-glucose cotransporter 2 inhibitors reduces the risk of composite cardiovascular events, driven by a reduction in HF hospitalisations; renin-angiotensin-aldosterone blockers and angiotensin-neprilysin inhibitors result in smaller reductions in HF hospitalisations among patients with HFpEF. Comprehensive management of HFpEF includes exercise as well as treatment of risk factors and comorbidities. Classification based on phenotypes may facilitate a more targeted approach to treatment than LVEF categorisation, which sets arbitrary cut-points when LVEF is a continuum. This narrative review summarises the pathophysiology, diagnosis, classification and management of patients with HFpEF.

Which chart and which cut-point: deciding on the INTERGROWTH, World Health Organization, or Hadlock fetal growth chart

Objective To determine how various centile cut points on the INTERGROWTH-21st (INTERGROWTH), World Health Organization (WHO), and Hadlock fetal growth charts predict perinatal morbidity/mortality, and how this relates to choosing a fetal growth chart for clinical use. Methods We linked antenatal ultrasound measurements for fetuses > 28 weeks’ gestation from the British Columbia Women’s hospital ultrasound unit with the provincial perinatal database. We estimated the risk of perinatal morbidity/mortality (decreased cord pH, neonatal seizures, hypoglycemia, and perinatal death) associated with select centiles on each fetal growth chart (the 3rd, 10th, the centile identifying 10% of the population, and the optimal cut-point by Youden’s Index), and determined how well each centile predicted perinatal morbidity/mortality. Results Among 10,366 pregnancies, the 10th centile cut-point had a sensitivity of 11% (95% CI 8, 14), 13% (95% CI 10, 16), and 12% (95% CI 10, 16), to detect fetuses with perinatal morbidity/mortality on the INTERGROWTH, WHO, and Hadlock charts, respectively. All charts performed similarly in predicting perinatal morbidity/mortality (area under the curve [AUC] =0.54 for all three charts). The statistically optimal cut-points were the 39th, 31st, and 32nd centiles on the INTERGROWTH, WHO, and Hadlock charts respectively. Conclusion The INTERGROWTH, WHO, and Hadlock fetal growth charts performed similarly in predicting perinatal morbidity/mortality, even when evaluating multiple cut points. Deciding which cut-point and chart to use may be guided by other considerations such as impact on workflow and how the chart was derived.

Association between Circulating Osteoprogenitor Cells and Sarcopenia

<b><i>Background:</i></b> Circulating osteoprogenitor (COP) cells are a surrogate of the bone marrow mesenchymal stem cells with high levels observed in osteoporosis and the initial stages of fracture healing. Conversely, a low percentage of COP cells (%COP) is strongly associated with frailty and disability. However, it is unknown whether %COP is associated with sarcopenia, a musculoskeletal disease closely related to frailty. <b><i>Objectives:</i></b> This study sought to determine the associations between %COP and sarcopenia defined using the Sarcopenia Definitions and Outcomes Consortium (SDOC) criteria. <b><i>Methods:</i></b> Data from a random sample of 73 community-dwelling older persons enrolled in the Nepean Osteoporosis and Frailty study (median age 74 years; 60% female) were analyzed. %COP was quantified by flow cytometry using selective gating of CD45/osteocalcin (OCN) + cells. Sarcopenia was defined using handgrip strength and gait speed with cut points as per the SDOC criteria. Linear regression was used for analysis. <b><i>Results:</i></b> Sarcopenia was identified in 19% of participants, all of whom were frail. After adjusting for age, sex, and interleukin 6, sarcopenic participants had 36% lower %COP (95% confidence interval [CI] −56%, −6%, <i>p =</i> 0.024). Both grip strength and gait speed showed associations with %COP (<i>p =</i> 0.065 and 0.002, respectively); however, after adjusting for age and frailty, only gait speed remained associated with %COP (0.1 m/s increase in gait velocity was associated with a 5% increase in %COP cells (95% CI 0%, 10%, <i>p =</i> 0.052). <b><i>Conclusions:</i></b> High levels of %COP are associated with better muscle function. Future longitudinal studies are required to elucidate the clinical utility of %COP as a potential biomarker or disease stratifier for sarcopenia.

Knowledge translation of prediction rules: methods to help health professionals understand their trade-offs

Clinical prediction models are developed with the ultimate aim of improving patient outcomes, and are often turned into prediction rules (e.g. classifying people as low/high risk using cut-points of predicted risk) at some point during the development stage. Prediction rules often have reasonable ability to either rule-in or rule-out disease (or another event), but rarely both. When a prediction model is intended to be used as a prediction rule, conveying its performance using the C-statistic, the most commonly reported model performance measure, does not provide information on the magnitude of the trade-offs. Yet, it is important that these trade-offs are clear, for example, to health professionals who might implement the prediction rule. This can be viewed as a form of knowledge translation. When communicating information on trade-offs to patients and the public there is a large body of evidence that indicates natural frequencies are most easily understood, and one particularly well-received way of depicting the natural frequency information is to use population diagrams. There is also evidence that health professionals benefit from information presented in this way.Here we illustrate how the implications of the trade-offs associated with prediction rules can be more readily appreciated when using natural frequencies. We recommend that the reporting of the performance of prediction rules should (1) present information using natural frequencies across a range of cut-points to inform the choice of plausible cut-points and (2) when the prediction rule is recommended for clinical use at a particular cut-point the implications of the trade-offs are communicated using population diagrams. Using two existing prediction rules, we illustrate how these methods offer a means of effectively and transparently communicating essential information about trade-offs associated with prediction rules.

Identifying youth at high risk for sexually transmitted infections in community-based settings using a risk prediction tool: a validation study

Background Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) are the most common bacterial sexually transmitted infections (STIs) worldwide. In the absence of affordable point-of-care STI tests, WHO recommends STI testing based on risk factors. This study aimed to develop a prediction tool with a sensitivity of > 90% and efficiency (defined as the percentage of individuals that are eligible for diagnostic testing) of < 60%. Methods This study offered CT/NG testing as part of a cluster-randomised trial of community-based delivery of sexual and reproductive health services to youth aged 16–24 years in Zimbabwe. All individuals accepting STI testing completed an STI risk factor questionnaire. The outcome was positivity for either CT or NG. Backwards-stepwise logistic regression was performed with p ≥ 0.05 as criteria for exclusion. Coefficients of variables included in the final multivariable model were multiplied by 10 to generate weights for a STI risk prediction tool. A maximum likelihood Receiver Operating Characteristics (ROC) model was fitted, with the continuous variable score divided into 15 categories of equal size. Sensitivity, efficiency and number needed to screen were calculated for different cut-points. Results From 3 December 2019 to 5 February 2020, 1007 individuals opted for STI testing, of whom 1003 (99.6%) completed the questionnaire. CT/NG prevalence was 17.5% (95% CI 15.1, 19.8) (n = 175). CT/NG positivity was independently associated with being female, number of lifetime sexual partners, relationship status, HIV status, self-assessed STI risk and past or current pregnancy. The STI risk prediction score including those variables ranged from 2 to 46 with an area under the ROC curve of 0.72 (95% CI 0.68, 0.76). Two cut-points were chosen: (i) 23 for optimised sensitivity (75.9%) and specificity (59.3%) and (ii) 19 to maximise sensitivity (82.4%) while keeping efficiency at < 60% (59.4%). Conclusions The high prevalence of STIs among youth, even in those with no or one reported risk factor, may preclude the use of risk prediction tools for selective STI testing. At a cut-point of 19 one in six young people with STIs would be missed.