Do optimal prognostic thresholds in continuous physiological variables really exist? Analysis of origin of apparent thresholds, with systematic review for peak oxygen consumption, ejection fraction and BNP

Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes

Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study, we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data, we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility.

SIGNIFICANCE

When a non-monotonic relationship to survival is present it is not possible to divide patients by a single value of a predictor. Neural networks allow for complex transformations and can be used to correctly split patients when a non-monotonic relationship is present.

The influence of birthweight on mortality and severe neonatal morbidity in late preterm and term infants: an Australian cohort study

Background

The aim of this study was to detail incidence rates and relative risks for severe adverse perinatal outcomes by birthweight centile categories in a large Australian cohort of late preterm and term infants.

Methods

This was a retrospective cohort study of singleton infants (≥34+0 weeks gestation) between 2000 and 2018 in Queensland, Australia. Study outcomes were perinatal mortality, severe neurological morbidity, and other severe morbidity. Categorical outcomes were compared using Chi-squared tests. Continuous outcomes were compared using t-tests. Multinomial logistic regression investigated the effect of birthweight centile on study outcomes.

Findings

The final cohort comprised 991,042 infants. Perinatal mortality occurred in 1944 infants (0.19%). The incidence and risk of perinatal mortality increased as birthweight decreased, peaking for infants <1st centile (perinatal mortality rate 13.2/1000 births, adjusted Relative Risk Ratio (aRRR) of 12.96 (95% CI 10.14, 16.57) for stillbirth and aRRR 7.55 (95% CI 3.78, 15.08) for neonatal death). Severe neurological morbidity occurred in 7311 infants (0.74%), with the highest rate (19.6/1000 live births) in <1st centile cohort. There were 75,243 cases of severe morbidity (7.59% livebirths), with the peak incidence occurring in the <1st centile category (12.3% livebirths). The majority of adverse outcomes occurred in infants with birthweights between 10 and 90th centile. Almost 2 in 3 stillbirths, and approximately 3 in 4 cases of neonatal death, severe neurological morbidity or other severe morbidity occurred within this birthweight range.

Interpretation

Although the incidence and risk of perinatal mortality, severe neurological morbidity and severe morbidity increased at the extremes of birthweight centiles, the majority of these outcomes occurred in infants that were apparently "appropriately grown" (i.e., birthweight 10th-90th centile).

Funding

National Health and Medical Research Council, Mater Foundation, Royal Australian College of Obstetricians and Gynaecologists Women's Health Foundation - Norman Beischer Clinical Research Scholarship, Cerebral Palsy Alliance, University of Queensland Research Scholarship.

Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome

Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle-Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package, which includes all methods discussed in this paper.

How robust are recommended waiting times to pacing after cardiac surgery that are derived from observational data?

AIMS

For bradycardic patients after cardiac surgery, it is unknown how long to wait before implanting a permanent pacemaker (PPM). Current recommendations vary and are based on observational studies. This study aims to examine why this variation may exist.

METHODS AND RESULTS

We conducted first a study of patients in our institution and second a systematic review of studies examining conduction disturbance and pacing after cardiac surgery. Of 5849 operations over a 6-year period, 103 (1.8%) patients required PPM implantation. Only pacing dependence at implant and time from surgery to implant were associated with 30-day pacing dependence. The only predictor of regression of pacing dependence was time from surgery to implant. We then applied the conventional procedure of receiver operating characteristic (ROC) analysis, seeking an optimal time point for decision-making. This suggested the optimal waiting time was 12.5 days for predicting pacing dependence at 30 days for all patients (area under the ROC curve (AUC) 0.620, P = 0.031) and for predicting regression of pacing dependence in patients who were pacing-dependent at implant (AUC 0.769, P < 0.001). However, our systematic review showed that recommended optimal decision-making time points were strongly correlated with the average implant time point of those individual studies (R = 0.96, P < 0.001). We further conducted modelling which revealed that in any such study, the ROC method is strongly biased to indicate a value near to the median time to implant as optimal.

CONCLUSION

When commonly used automated statistical methods are applied to observational data with the aim of defining the optimal time to pacing after cardiac surgery, the suggested answer is likely to be similar to the average time to pacing in that cohort.

FasterRib: A deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans

OBJECTIVE

Characterizing and enumerating rib fractures are critical to informing clinical decisions, yet in-depth characterization is rarely performed because of the manual burden of annotating these injuries on computed tomography (CT) scans. We hypothesized that our deep learning model, FasterRib , could predict the location and percentage displacement of rib fractures using chest CT scans.

METHODS

The development and internal validation cohort comprised more than 4,700 annotated rib fractures from 500 chest CT scans within the public RibFrac. We trained a convolutional neural network to predict bounding boxes around each fracture per CT slice. Adapting an existing rib segmentation model, FasterRib outputs the three-dimensional locations of each fracture (rib number and laterality). A deterministic formula analyzed cortical contact between bone segments to compute percentage displacements. We externally validated our model on our institution's data set.

RESULTS

FasterRib predicted precise rib fracture locations with 0.95 sensitivity, 0.90 precision, 0.92 f1 score, with an average of 1.3 false-positive fractures per scan. On external validation, FasterRib achieved 0.97 sensitivity, 0.96 precision, and 0.97 f1 score, and 2.24 false-positive fractures per scan. Our publicly available algorithm automatically outputs the location and percent displacement of each predicted rib fracture for multiple input CT scans.

CONCLUSION

We built a deep learning algorithm that automates rib fracture detection and characterization using chest CT scans. FasterRib achieved the highest recall and the second highest precision among known algorithms in literature. Our open source code could facilitate FasterRib's adaptation for similar computer vision tasks and further improvements via large-scale external validation.

LEVEL OF EVIDENCE

Diagnostic Tests/Criteria; Level III.

Development and validation of a physical frailty phenotype index-based model to estimate the frailty index

BACKGROUND

The conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors-an approach that creates information loss and depends on the availability of population-derived cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization.

METHODS

A sample of 998 community-dwelling older adults (mean [SD], 68 [7] years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI ("model-based PFP"). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting.

RESULTS

The model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R², 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen's k_w, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18-28%) net increase in FI-defined "prefrail/frail" participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP.

CONCLUSION

The developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.

How Much Blood Loss Is Too Much for a 1-Level Open Lumbar Fusion?

BACKGROUND

Despite the known association between increased estimated blood loss (EBL) and suboptimal perioperative outcomes, the exact threshold of EBL that impacts outcomes following elective spine surgery remains unknown. In a cohort of patients undergoing elective 1-level open posterior lumbar fusion, we sought to identify EBL thresholds associated with: (1) prolonged length of stay (LOS), (2) postoperative complications, and (3) patient-reported outcomes (PROs).

METHODS

A retrospective, single-center study was performed of patients undergoing elective, 1-level open posterior lumbar fusion with and without interbody fusion between October 2010 and April 2021. The primary exposure variable was EBL. Primary outcomes included: (1) LOS, (2) 30-day complications, and (3) 3-month PROs. Minimum clinically important difference was set at 30% improvement from baseline. For purposes of receiver-operating characteristic curves, LOS was dichotomized as 1 vs ≥2 days.

RESULTS

Of the 2028 patients undergoing posterior lumbar fusion surgery, 1183 underwent 1-level fusions, 763 (64.5%) with interbody fusion and 420 (35.5%) without. With interbody fusion: Median (interquartile range [IQR]) EBL was 350 mL (200-600), and median (IQR) LOS was 2 days (2-3). A positive linear association was found between EBL and LOS (P < 0.001) but not with PROs. EBL above 275 mL was associated with LOS beyond postoperative day 1 (POD1) (area under the curve [AUC] = 0.73, 95% CI 0.68-0.78, P < 0.001), with no significant association with overall complications or PROs. Without interbody fusion: Median EBL (IQR) was 300 mL (150-500), and median (IQR) LOS was 3 days (2-4). A positive linear association was found between EBL and LOS (P < 0.001) but not with PROs. EBL above 238 mL was associated with LOS beyond POD1 (AUC = 0.78, 95% CI 0.71-0.85, P < 0.001), with no impact on overall complications or PROs.

CONCLUSIONS

In patients undergoing 1-level posterior lumbar fusion, EBL volumes greater than 275 and 238 mL in patients with and without interbody fusion, respectively, were associated with increased LOS beyond POD1. No effect was found regarding 30-day complications and 3-month PROs. Although EBL did not directly impact complications or PROs, surgeons may expect longer LOS when higher EBL is reported.

CLINICAL RELEVANCE

EBL above 275 mL with an interbody and 238 mL without an interbody were associated with prolonged LOS beyond POD1 in 1-level open lumbar fusion.

LEVEL OF EVIDENCE: 3

Screening accuracy of percentage predicted gait speed for prefrailty/frailty in community-dwelling older adults

AIM

In order to account for the variability in gait speed due to demographic factors, an observed gait speed value can be compared with its predicted value based on age, sex, and body height (observed gait speed divided by predicted gait speed, termed "GS%predicted" henceforth). This study aimed to examine the screening accuracy of an optimal GS%predicted threshold for prefrailty/frailty.

METHODS

This cross-sectional study included 998 community-dwelling ambulant participants aged >50 years (mean age = 68 years). Participants completed a multi-domain geriatric screen and a physical fitness assessment, from which the 10-m habitual gait speed, GS%predicted, Physical Frailty Phenotype (PFP) index, and 36-item Frailty Index (FI) were computed.

RESULTS

Based on the FI, ~49% of participants had pre-frailty or frailty. The optimal threshold of GS%predicted (0.93) had greater screening accuracy than the 1.0 m/s fixed threshold for gait speed (AUC, 0.65 vs. 0.60; DeLong's P < 0.001). Replacing gait speed with GS%predicted in the PFP improved its overall discrimination (AUC, 0.70 vs. 0.67 of original PFP; DeLong's P < 0.001).

CONCLUSIONS

Defining a "slow" gait speed by a GS%predicted value of <0.93 provided greater screening accuracy than the traditional 1.0 m/s threshold for gait speed. Our results also support the use of GS%predicted-derived PFP to identify older adults at risk of prefrailty/frailty. Geriatr Gerontol Int 2022; ••: ••-••.

Scalable Deep Learning Algorithm to Compute Percent Pulmonary Contusion among Patients with Rib Fractures

BACKGROUND

Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a deep learning algorithm to automate percent pulmonary contusion computation and exemplify how transfer learning could facilitate large-scale validation. We hypothesized our deep learning algorithm could automate percent pulmonary contusion computation and that greater percent contusion would be associated with higher odds of adverse inpatient outcomes among patients with rib fractures.

METHODS

We evaluated admission-day chest computed tomography (CT) scans of adults aged ≥18 years admitted to our institution with multiple rib fractures and pulmonary contusions (2010-2020). We adapted a pre-trained convolutional neural network that segments 3-dimensional lung volumes and segmented contused lung parenchyma, pulmonary blood vessels, and computed percent pulmonary contusion. Exploratory analysis evaluated associations between percent pulmonary contusion (quartiles) and odds of mechanical ventilation, mortality, and prolonged hospital length-of-stay using multivariable logistic regression. Sensitivity analysis included pulmonary blood vessel volumes during percent contusion computation.

RESULTS

A total of 332 patients met inclusion criteria (median 5 rib fractures), among whom 28% underwent mechanical ventilation and 6% died. The study population's median (IQR) percent pulmonary contusion was 4(2-8)%. Compared to the lowest quartile of percent pulmonary contusion, each increasing quartile was associated with higher adjusted odds of undergoing mechanical ventilation (OR[95%CI]: 1.5[1.1-2.1]) and prolonged hospitalization (OR[95%CI]: 1.6[1.1-2.2]), but not with mortality (OR[95%CI]: 1.1 [0.6-2.0]. Findings were similar on sensitivity analysis.

CONCLUSION

We developed a scalable deep learning algorithm to automate percent pulmonary contusion calculating using chest CTs of adults admitted with rib fractures. Open code sharing and collaborative research is needed to validate our algorithm and exploratory analysis at large scale. Transfer learning can help harness the full potential of big data and high-performing algorithms to bring precision medicine to the bedside.

LEVEL OF EVIDENCE

IV.

Low Programmed Death-Ligand 1-Expressing Subgroup Outcomes of First-Line Immune Checkpoint Inhibitors in Gastric or Esophageal Adenocarcinoma

PURPOSE

The US Food and Drug Administration has granted regulatory approval for the use of nivolumab-an immune checkpoint inhibitor (ICI)-in the first-line treatment of advanced gastric or esophageal adenocarcinoma (GEAC), regardless of programmed death-ligand 1 (PD-L1) expression. However, the efficacy of ICIs in low PD-L1-expressing tumors remains unclear.

MATERIALS AND METHODS

This study aims to reconstruct unreported Kaplan-Meier (KM) plots of PD-L1 combined positive score (CPS) subgroups of randomized phase III trials comparing the addition of ICIs with conventional chemotherapy in the first-line treatment of GEAC. A graphical reconstructive algorithm was adopted to estimate time-to-event outcomes from reported overall survival and progression-free survival (OS and PFS) KM plots describing overall or subgroup cohorts. Using reconstructed time-to-event outcomes, KMSubtraction conducts bipartite matching of patients from the reported subgroup among the overall cohort. By excluding matched patients, KM plots and survival analyses of the unreported subgroups were retrieved.

RESULTS

CheckMate-649, KEYNOTE-062, and KEYNOTE-590 were included. Two PD-L1 subgroups were identified with data unreported in the primary manuscripts: PD-L1 CPS 1-4 from CheckMate-649 and PD-L1 CPS 1-9 from KEYNOTE-062. No significant differences in OS and PFS were demonstrated in ICI-chemotherapy combinations when compared with chemotherapy among CheckMate-649 PD-L1 CPS 1-4 (OS: hazard ratio [HR] = 0.950, 95% CI, 0.747 to 1.209, P = .678; PFS: HR = 0.958, 95% CI, 0.743 to 1.236, P = .743) and KEYNOTE-062 PD-L1 CPS 1-9 subgroups. In the KEYNOTE-062 PD-L1 CPS 1-9 subgroup, patients treated with pembrolizumab had an increased hazard of tumor progression (HR = 2.092, 95% CI, 1.661 to 2.635, P < .001).

CONCLUSION

Using KMSubtraction, data of PD-L1 subgroups previously unreported by primary manuscripts of pivotal clinical trials were retrieved. These data suggest the lack of benefit in the addition of ICI to chemotherapy in low PD-L1-expressing GEAC tumors.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Rapid changes of miRNAs-20, -30, -410, -515, -134, and -183 and telomerase with psychological activity: A one year study on the relaxation response and epistemological considerations

Background and aim

Mental stress represents a pivotal factor in cardiovascular diseases. The mechanism by which stress produces its deleterious effects is still under study, but one of the most explored pathways is inflammation-aging and cell senescence. In this scenario, circulating microRNAs appear to be regulatory elements of the telomerase activity and alternative splicing within the nuclear factor kappa-light-chain-enhancer (NF-κB) network. Anti-stress techniques appeared to be able to slow down the inflammatory and aging processes. As we recently verified, the practice of the relaxation response (RR) counteracted psychological stress and determined favorable changes of the NF-κB, p53, and toll-like receptor-4 (TLR-4) gene expression and in neurotransmitters, hormones, cytokines, and inflammatory circulating microRNAs. We aimed to verify a possible change in the serum levels of six other micro-RNAs of cardiovascular interest, involved in cell senescence and in the NF-κB network (miRNAs -20, -30, -410, -515, -134, and -183), and tested the activity of telomerase in peripheral blood mononuclear cells (PBMCs).

Experimental procedure

We measured the aforementioned molecules in the serum of patients with ischemic heart disease (and healthy controls) immediately before and after a relaxation response session, three times (after the baseline), in one year of follow-up.

Results

According to our data, the miRNA-20 and -30 levels and PBMCs-telomerase activity increased during the RR while the -410 and -515 levels decreased. During the RR sessions, both miRNA-134 and -183 decreased.

Conclusion

The mediators considered in this exploratory work appeared to vary rapidly with the psychological activity (in particular when focused on relaxation techniques) showing that psychological activity should be part of the future research on epigenetics. Epistemological perspectives are also discussed.

Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation

BACKGROUND

Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation.

OBJECTIVE

Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies.

METHODS

We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables.

RESULTS

We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data.

CONCLUSIONS

This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

Epidemiological and clinical boundaries of heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is highly prevalent and is associated with relevant morbidity and mortality. However, an evidence-based treatment is still absent. The heterogeneous definitions, differences in aetiology/pathophysiology, and diagnostic challenges of HFpEF made it difficult to define its epidemiological landmarks so far. Several large registries and observational studies have recently disclosed an increasing incidence/prevalence, as well as its prognostic significance. An accurate definition of HFpEF epidemiological boundaries and phenotypes is mandatory to develop novel effective and rational therapeutic approaches.

Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis

BACKGROUND

Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer.

OBJECTIVE

The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation.

METHODS

We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer.

RESULTS

During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness.

CONCLUSIONS

This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.

Revisiting the basis for haemoglobin screening in pregnancy

PURPOSE OF REVIEW

Anaemia affects up to 50% of pregnancies worldwide, and is associated with maternal and neonatal morbidity and mortality. Prevention and management of anaemia remains a priority. Despite this, there is ongoing debate on the optimal approach to identifying anaemia in pregnant women and the best strategies for prevention and management. The objective of this review is to describe the current landscape of haemoglobin testing in pregnancy in low and high-income countries.

RECENT FINDINGS

Current definitions of anaemia in pregnancy comprise a laboratory threshold of haemoglobin below which treatment is offered. Haemoglobin measurement is not sensitive in detecting iron deficiency - the most common cause of maternal anaemia. Furthermore, these historical thresholds were derived from heterogeneous populations comprising men and women. Women with anaemia in pregnancy are offered iron therapy, without testing for the underlying cause. This may be appropriate in high-income settings, where iron deficiency is the likely cause, but may not address the complex causes of anaemia in other geographical areas.

SUMMARY

Current thresholds of haemoglobin defining anaemia in pregnancy are under review. Further research and policy should focus on optimal strategies to identify women at risk of anaemia from all causes.

Left ventricular dysfunction in COPD without pulmonary hypertension

Objectives

We aimed to assess prevalence of left ventricular (LV) systolic and diastolic function in stable cohort of COPD patients, where LV disease had been thoroughly excluded in advance.

Methods

100 COPD outpatients in GOLD II-IV and 34 controls were included. Patients were divided by invasive mean pulmonary artery pressure (mPAP) in COPD-PH (≥25 mmHg) and COPD-non-PH (<25 mmHg), which was subdivided in mPAP ≤20 mmHg and 21–24 mmHg. LV myocardial performance index (LV MPI) and strain by tissue Doppler imaging (TDI) were used for evaluation of LV global and systolic function, respectively. LV MPI ≥0.51 and strain ≤-15.8% were considered abnormal. LV diastolic function was assessed by the ratio between peak early (E) and late (A) velocity, early TDI E´, E/E´, isovolumic relaxation time, and left atrium volume.

Results

LV MPI ≥0.51 was found in 64.9% and 88.5% and LV strain ≤-15.8% in 62.2.% and 76.9% in the COPD-non-PH and COPD-PH patients, respectively. Similarly, LV MPI and LV strain were impaired even in patients with mPAP <20 mmHg. In multiple regression analyses, residual volume and stroke volume were best associated to LV MPI and LV strain, respectively. Except for isovolumic relaxation time, standard diastolic echo indices as E/A, E´, E/E´ and left atrium volume did not change from normal individuals to COPD-non-PH.

Conclusions

Subclinical LV systolic dysfunction was a frequent finding in this cohort of COPD patients, even in those with normal pulmonary artery pressure. Evidence of LV diastolic dysfunction was hardly present as measured by conventional echo indices.

Body composition measured by computed tomography is associated with colorectal cancer survival, also in early-stage disease

Background: Cachexia and sarcopenia are associated with poor survival after colorectal cancer (CRC) diagnosis. Computed tomography (CT) can be used to measure aspects of cachexia including sarcopenia, myosteatosis and the amount of subcutaneous and visceral adipose tissue. The aim of this study was to relate CT-based body composition variables with survival outcomes in CRC.Material and methods: In this population-based, retrospective cohort study, CT scans of 974 patients with pathological stages I-IV CRCs, collected at or very near diagnosis (years 2000-2016), were used to measure cross-sectional fat and muscle tissue areas. Body composition variables based on these measurements were assessed in relation to tumor stage and site and cancer-specific survival in stages I-III CRC (n = 728) using Cox proportional hazards models and Kaplan-Meier estimators.Results: Sarcopenia was associated with decreased cancer-specific survival, especially in patients with stages I-II tumors. The hazard ratio (HR) for the lowest versus highest tertile of skeletal muscle index (SMI) was 1.67; 95% confidence interval (CI), 1.08-2.58 for all stages, and HR 2.22; 95% CI 1.06-4.68, for stages I-II. Myosteatosis was also associated with decreased cancer-specific survival [(HR 2.03; 95% CI 1.20-3.34 for the lowest versus the highest tertile of skeletal muscle radiodensity (SMR)]. SMI and SMR were lower in patients with right-sided CRC, independent of age and sex. No adipose tissue measurement was significantly associated with cancer-specific survival.Conclusion: In concordance with previous studies, sarcopenia and myosteatosis were associated with decreased cancer-specific survival. The strong association between sarcopenia and poor cancer-specific survival in early-stage disease could have clinical implications for personalizing therapy decisions, including nutritional support.

Body mass index and outcomes in ischaemic versus non-ischaemic heart failure across the spectrum of ejection fraction

Aims

Obesity is related to better prognosis in heart failure with either reduced (HFrEF; left ventricular ejection fraction (LVEF) &lt; 40%) or preserved LVEF (HFpEF; LVEF ≥50%). Whether the obesity paradox exists in patients with heart failure and mid-range LVEF (HFmrEF; LVEF 40–49%) and whether it is independent of heart failure aetiology is unknown. Therefore, we aimed to test the prognostic value of body mass index (BMI) in ischaemic and non-ischaemic heart failure patients across the whole spectrum of LVEF.

Methods

Consecutive ambulatory heart failure patients were enrolled in two tertiary centres in Italy and Spain and classified as HFrEF, HFmrEF or HFpEF, of either ischaemic or non-ischaemic aetiology. Patients were stratified into underweight (BMI &lt; 18.5 kg/m2), normal-weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), mild-obese (BMI 30–34.9 kg/m2), moderate-obese (BMI 35–39.9 kg/m2) and severe-obese (BMI ≥40 kg/m2) and followed up for the end-point of five-year all-cause mortality.

Results

We enrolled 5155 patients (age 70 years (60–77); 71% males; LVEF 35% (27–45); 63% HFrEF, 18% HFmrEF, 19% HFpEF). At multivariable analysis, mild obesity was independently associated with a lower risk of all-cause mortality in HFrEF (hazard ratio, 0.78 (95% confidence interval (CI) 0.64–0.95), p = 0.020), HFmrEF (hazard ratio 0.63 (95% CI 0.41–0.96), p = 0.029), and HFpEF (hazard ratio 0.60 (95% CI 0.42–0.88), p = 0.008). Both overweight and mild-to-moderate obesity were associated with better outcome in non-ischaemic heart failure, but not in ischaemic heart failure.

Conclusions

Mild obesity is independently associated with better survival in heart failure across the whole spectrum of LVEF. Prognostic benefit of obesity is maintained only in non-ischaemic heart failure.

Risk Stratification Using Multivariable Fractional Polynomials in Diffuse Large B-Cell Lymphoma

The risk stratification of diffuse large B-cell lymphoma (DLBCL) is crucial. The International Prognostic Index, the most commonly used and the traditional risk stratification system, is composed of fixed and artificially dichotomized attributes. We aimed to develop a novel prognostic model that allows the incorporation of up-to-date attributes comprehensively without information loss. We analyzed 204 patients with primary DLBCL who were uniformly treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) from 2007 to 2012 at Asan Medical Center. Using the multivariable fractional polynomial (MFP) method and bootstrap resampling, we selected the variables of significance and the best fitted functional form in fractional polynomials. Age, serum β2-microglobulin, serum lactate dehydrogenase, and BCL2 expression were selected as significant variables in predicting overall survival (OS), while age was excluded in predicting 2-years event-free survival. The prognostic score calculated by the MFP model effectively classifies patients into four risk groups with 5-years OS of 89.91% (low risk), 81.21% (low-intermediate risk), 66.40% (high-intermediate risk), and 37.89% (high risk). We suggest a new prognostic model that is simple and flexible. By using the MFP method, we can incorporate various clinicopathologic factors into a risk stratification system without arbitrary dichotomization.

Dobutamine Stress Echocardiography Ischemia as a Predictor of the Placebo-Controlled Efficacy of Percutaneous Coronary Intervention in Stable Coronary Artery Disease: The Stress Echocardiography-Stratified Analysis of ORBITA

BACKGROUND

Dobutamine stress echocardiography is widely used to test for ischemia in patients with stable coronary artery disease. In this analysis, we studied the ability of the prerandomization stress echocardiography score to predict the placebo-controlled efficacy of percutaneous coronary intervention (PCI) within the ORBITA trial (Objective Randomised Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina).

METHODS

One hundred eighty-three patients underwent dobutamine stress echocardiography before randomization. The stress echocardiography score is broadly the number of segments abnormal at peak stress, with akinetic segments counting double and dyskinetic segments counting triple. The ability of prerandomization stress echocardiography to predict the placebo-controlled effect of PCI on response variables was tested by using regression modeling.

RESULTS

At prerandomization, the stress echocardiography score was 1.56±1.77 in the PCI arm (n=98) and 1.61±1.73 in the placebo arm (n=85). There was a detectable interaction between prerandomization stress echocardiography score and the effect of PCI on angina frequency score with a larger placebo-controlled effect in patients with the highest stress echocardiography score (P_interaction=0.031). With our sample size, we were unable to detect an interaction between stress echocardiography score and any other patient-reported response variables: freedom from angina (P_interaction=0.116), physical limitation (P_interaction=0.461), quality of life (P_interaction=0.689), EuroQOL 5 quality-of-life score (P_interaction=0.789), or between stress echocardiography score and physician-assessed Canadian Cardiovascular Society angina class (P_interaction=0.693), and treadmill exercise time (P_interaction=0.426).

CONCLUSIONS

The degree of ischemia assessed by dobutamine stress echocardiography predicts the placebo-controlled efficacy of PCI on patient-reported angina frequency. The greater the downstream stress echocardiography abnormality caused by a stenosis, the greater the reduction in symptoms from PCI.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT02062593.

The Heart as a Psychoneuroendocrine and Immunoregulatory Organ

The heart can be viewed not just as muscle pump but also as an important checkpoint for a complex network of nervous, endocrine, and immune signals. The heart is able to process neurological signals independently from the brain and to crosstalk with the endocrine and immune systems. The heart communicates with the psyche through the neuro-endocrine-immune system in a highly integrated way, in order to maintain the homeostasis of the whole body with peculiarities specific to males and females.

Thoughts modulate the expression of inflammatory genes and may improve the coronary blood flow in patients after a myocardial infarction

BACKGROUND

Mental stress is one of the main risk factors for cardiovascular disease. Meditation and music listening are two techniques that are able to counteract it through the activation of specific brain areas, eliciting the so-called Relaxing Response (RR). Epidemiological evidence reveals that the RR practice has a beneficial prognostic impact on patients after myocardial infarction. We aimed to study the possible molecular mechanisms of RR underlying these findings.

METHODS

We enrolled 30 consecutive patients after myocardial infarction and 10 healthy controls. 10 patients were taught to meditate, 10 to appreciate music and 10 did not carry out any intervention and served as controls. After training, and after 60 days of RR practice, we studied the individual variations, before and after the relaxation sessions, of the vital signs, the electrocardiographic and echocardiographic parameters along with coronary flow reserve (CFR) and the carotid's intima media thickness (IMT). Neuro-endocrine-immune (NEI) messengers and the expression of inflammatory genes (p53, Nuclear factor Kappa B (NfKB), and toll like receptor 4 (TLR4)) in circulating peripheral blood mononuclear cells were also all observed.

RESULTS

The RR results in a reduction of NEI molecules (p < 0.05) and oxidative stress (p < 0.001). The expression of the genes p53, NFkB and TLR4 is reduced after the RR and also at 60 days (p < 0.001). The CFR increases with the relaxation (p < 0.001) and the IMT regressed significantly (p < 0.001) after 6 months of RR practice.

CONCLUSIONS

The RR helps to advantageously modulate the expression of inflammatory genes through a cascade of NEI messengers improving, over time, microvascular function and the arteriosclerotic process.

Echocardiographic assessment of maximum and minimum left atrial volumes: a population-based study of middle-aged and older subjects without apparent cardiovascular disease

The aim of the present study was to obtain reference values of maximum and minimum left atrial volumes (maxLAV and minLAV, respectively) in a population-based subset without apparent cardiovascular disease or other factors potentially associated with left atrial enlargement. Because left ventricular diastolic dysfunction is commonly found in elderly subjects, we also tried to identify the presence of possible preclinical diastolic dysfunction in the study population. A population-based sample of 168 subjects (127 men and 41 women) underwent two-dimensional echocardiography using the single-plane disc method to determine maxLAV and minLAV. maxLAV and minLAV were indexed to body surface area (maxLAVi and minLAVi, respectively). maxLAVi was independent of age and sex, and produced reference limits (mean ± 1.96 SD) of 15–37 mL/m². minLAVi was correlated with age, and produced estimated reference limits of 3–15 and 7–23 mL/m² in 40- and 80-year-old subjects, respectively. Based on the age-dependent reference values from the European Association of Cardiovascular Imaging, <5 % of the study population had possible preclinical left ventricular diastolic dysfunction. The present study established normal ranges for maxLAVi and minLAVi in a well-characterized population-based subset without apparent cardiovascular disease or other factors potentially associated with left atrial volume enlargement.