Shifts in mutation spectra enhance access to beneficial mutations.

Biased mutation spectra are pervasive, with wide variation in the magnitude of mutational biases that influence genome evolution and adaptation. How do such diverse biases evolve? Our experiments show that changing the mutation spectrum allows populations to sample previously undersampled mutational space, including beneficial mutations. The resulting shift in the distribution of fitness effects is advantageous: Beneficial mutation supply and beneficial pleiotropy both increase, while deleterious load reduces. More broadly, simulations indicate that reducing or reversing the direction of a long-term bias is always selectively favored. Such changes in mutation bias can occur easily via altered function of DNA repair genes. A phylogenetic analysis shows that these genes are repeatedly gained and lost in bacterial lineages, leading to frequent bias shifts in opposite directions. Thus, shifts in mutation spectra may evolve under selection and can directly alter the outcome of adaptive evolution by facilitating access to beneficial mutations.

Statistical modeling of mRNP transport in dendrites: A comparative analysis of ß-actin and Arc mRNP dynamics.

Localization of messenger RNA (mRNA) in dendrites is crucial for regulating gene expression during long-term memory formation. mRNA binds to RNA-binding proteins (RBPs) to form messenger ribonucleoprotein (mRNP) complexes that are transported by motor proteins along microtubules to their target synapses. However, the dynamics by which mRNPs find their target locations in the dendrite have not been well understood. Here, we investigated the motion of endogenous ß-actin and Arc mRNPs in dissociated mouse hippocampal neurons using the MS2 and PP7 stem-loop systems, respectively. By evaluating the statistical properties of mRNP movement, we found that the aging Lévy walk model effectively describes both ß-actin and Arc mRNP transport in proximal dendrites. A critical difference between ß-actin and Arc mRNPs was the aging time, the time lag between transport initiation and measurement initiation. The longer mean aging time of ß-actin mRNP (~100 s) compared with that of Arc mRNP (~30 s) reflects the longer half-life of constitutively expressed ß-actin mRNP. Furthermore, our model also permitted us to estimate the ratio of newly generated and pre-existing ß-actin mRNPs in the dendrites. This study offers a robust theoretical framework for mRNP transport, which provides insight into how mRNPs locate their targets in neurons.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

Lifestyle Walking Intervention for Patients With Heart Failure With Reduced Ejection Fraction: The WATCHFUL Trial.

BACKGROUND: Physical activity is pivotal in managing heart failure with reduced ejection fraction, and walking integrated into daily life is an especially suitable form of physical activity. This study aimed to determine whether a 6-month lifestyle walking intervention combining self-monitoring and regular telephone counseling improves functional capacity assessed by the 6-minute walk test (6MWT) in patients with stable heart failure with reduced ejection fraction compared with usual care. METHODS: The WATCHFUL trial (Pedometer-Based Walking Intervention in Patients With Chronic Heart Failure With Reduced Ejection Fraction) was a 6-month multicenter, parallel-group randomized controlled trial recruiting patients with heart failure with reduced ejection fraction from 6 cardiovascular centers in the Czech Republic. Eligible participants were ≥18 years of age, had left ventricular ejection fraction <40%, and had New York Heart Association class II or III symptoms on guidelines-recommended medication. Individuals exceeding 450 meters on the baseline 6MWT were excluded. Patients in the intervention group were equipped with a Garmin vívofit activity tracker and received monthly telephone counseling from research nurses who encouraged them to use behavior change techniques such as self-monitoring, goal-setting, and action planning to increase their daily step count. The patients in the control group continued usual care. The primary outcome was the between-group difference in the distance walked during the 6MWT at 6 months. Secondary outcomes included daily step count and minutes of moderate to vigorous physical activity as measured by the hip-worn Actigraph wGT3X-BT accelerometer, NT-proBNP (N-terminal pro-B-type natriuretic peptide) and high-sensitivity C-reactive protein biomarkers, ejection fraction, anthropometric measures, depression score, self-efficacy, quality of life, and survival risk score. The primary analysis was conducted by intention to treat. RESULTS: Of 218 screened patients, 202 were randomized (mean age, 65 years; 22.8% female; 90.6% New York Heart Association class II; median left ventricular ejection fraction, 32.5%; median 6MWT, 385 meters; average 5071 steps/day; average 10.9 minutes of moderate to vigorous physical activity per day). At 6 months, no between-group differences were detected in the 6MWT (mean 7.4 meters [95% CI, -8.0 to 22.7]; P=0.345, n=186). The intervention group increased their average daily step count by 1420 (95% CI, 749 to 2091) and daily minutes of moderate to vigorous physical activity by 8.2 (95% CI, 3.0 to 13.3) over the control group. No between-group differences were detected for any other secondary outcomes. CONCLUSIONS: Whereas the lifestyle intervention in patients with heart failure with reduced ejection fraction improved daily steps by about 25%, it failed to demonstrate a corresponding improvement in functional capacity. Further research is needed to understand the lack of association between increased physical activity and functional outcomes. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03041610.

iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network.

Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).

A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning.

Combination therapies have brought significant advancements to the treatment of various diseases in the medical field. However, searching for effective drug combinations remains a major challenge due to the vast number of possible combinations. Biomedical knowledge graph (KG)-based methods have shown potential in predicting effective combinations for wide spectrum of diseases, but the lack of credible negative samples has limited the prediction performance of machine learning models. To address this issue, we propose a novel model-agnostic framework that leverages existing drug-drug interaction (DDI) data as a reliable negative dataset and employs supervised contrastive learning (SCL) to transform drug embedding vectors to be more suitable for drug combination prediction. We conducted extensive experiments using various network embedding algorithms, including random walk and graph neural networks, on a biomedical KG. Our framework significantly improved performance metrics compared to the baseline framework. We also provide embedding space visualizations and case studies that demonstrate the effectiveness of our approach. This work highlights the potential of using DDI data and SCL in finding tighter decision boundaries for predicting effective drug combinations.

LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data.

With the development of chromosome conformation capture technique, the study of spatial conformation of a genome based on Hi-C technique has made a quantum leap. Previous studies reveal that genomes are folded into hierarchy of three-dimensional (3D) structures associated with topologically associating domains (TADs), and detecting TAD boundaries is of great significance in the chromosome-level analysis of 3D genome architecture. In this paper, we propose a novel TAD identification method, LPAD, which first extracts node correlations from global interactions of chromosomes based on the random walk with restart and then builds an undirected graph from Hi-C contact matrix. Next, LPAD designs a label propagation-based approach to discover communities and generates TADs. Experimental results verify the effectiveness and quality of TAD detections compared with existing methods. Furthermore, experimental evaluation of chromatin immunoprecipitation sequencing data shows that LPAD performs high enrichment of histone modifications remarkably nearby the TAD boundaries, and these results demonstrate LPAD's advantages on TAD identification accuracy.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond.

BACKGROUND: Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. RESULTS: We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. CONCLUSION: Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

Genetic Architecture of Flowering Time Differs Between Populations With Contrasting Demographic and Selective Histories.

Understanding the evolutionary factors that impact the genetic architecture of traits is a central goal of evolutionary genetics. Here, we investigate how quantitative trait variation accumulated over time in populations that colonized a novel environment. We compare the genetic architecture of flowering time in Arabidopsis populations from the drought-prone Cape Verde Islands and their closest outgroup population from North Africa. We find that trait polygenicity is severely reduced in the island populations compared to the continental North African population. Further, trait architectures and reconstructed allelic histories best fit a model of strong directional selection in the islands in accord with a Fisher-Orr adaptive walk. Consistent with this, we find that large-effect variants that disrupt major flowering time genes (FRI and FLC) arose first, followed by smaller effect variants, including ATX2 L125F, which is associated with a 4-day reduction in flowering time. The most recently arising flowering time-associated loci are not known to be directly involved in flowering time, consistent with an omnigenic signature developing as the population approaches its trait optimum. Surprisingly, we find no effect in the natural population of EDI-Cvi-0 (CRY2 V367M), an allele for which an effect was previously validated by introgression into a Eurasian line. Instead, our results suggest the previously observed effect of the EDI-Cvi-0 allele on flowering time likely depends on genetic background, due to an epistatic interaction. Altogether, our results provide an empirical example of the effects demographic history and selection has on trait architecture.

MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization.

Subcellular localization of microRNAs (miRNAs) is an important reflection of their biological functions. Considering the spatio-temporal specificity of miRNA subcellular localization, experimental detection techniques are expensive and time-consuming, which strongly motivates an efficient and economical computational method to predict miRNA subcellular localization. In this paper, we describe a computational framework, MiRLoc, to predict the subcellular localization of miRNAs. In contrast to existing methods, MiRLoc uses the functional similarity between miRNAs instead of sequence features and incorporates information about the subcellular localization of the corresponding target mRNAs. The results show that miRNA functional similarity data can be effectively used to predict miRNA subcellular localization, and that inclusion of subcellular localization information of target mRNAs greatly improves prediction performance.

Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities.

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are more and more popular in recent years. Conventional computational methods almost simply view heterogeneous networks which integrate diverse drug-related and target-related dataset instead of fully exploring drug and target similarities. In this paper, we propose a new method, named DTIHNC, for $\mathbf{D}$rug-$\mathbf{T}$arget $\mathbf{I}$nteraction identification, which integrates $\mathbf{H}$eterogeneous $\mathbf{N}$etworks and $\mathbf{C}$ross-modal similarities calculated by relations between drugs, proteins, diseases and side effects. Firstly, the low-dimensional features of drugs, proteins, diseases and side effects are obtained from original features by a denoising autoencoder. Then, we construct a heterogeneous network across drug, protein, disease and side-effect nodes. In heterogeneous network, we exploit the heterogeneous graph attention operations to update the embedding of a node based on information in its 1-hop neighbors, and for multi-hop neighbor information, we propose random walk with restart aware graph attention to integrate more information through a larger neighborhood region. Next, we calculate cross-modal drug and protein similarities from cross-scale relations between drugs, proteins, diseases and side effects. Finally, a multiple-layer convolutional neural network deeply integrates similarity information of drugs and proteins with the embedding features obtained from heterogeneous graph attention network. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods. Datasets and a stand-alone package are provided on Github with website https://github.com/ningq669/DTIHNC.

Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field.

Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA-miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.

Discordance of patient-reported outcome measures with objectively assessed walking decline in peripheral artery disease.

OBJECTIVE: Among people with peripheral artery disease (PAD), perceived change in walking difficulty over time, compared with people without PAD, is unclear. Among people reporting no change in walking difficulty over time, differences in objectively measured change in walking performance between people with and without PAD are unknown. METHODS: A total of 1289 participants were included. Eight hundred seventy-four participants with PAD (aged 71.1 ± 9.1 years) were identified from noninvasive vascular laboratories and 415 without PAD (aged 69.9 ± 7.6 years) were identified from people with normal vascular laboratory testing or general medical practices in Chicago. The Walking Impairment Questionnaire and 6-minute walk were completed at baseline and 1-year follow-up. The Walking Impairment Questionnaire assessed perceived difficulty walking due to symptoms in the calves or buttocks on a Likert scale (range, 0-4). Symptom change was determined by comparing difficulty reported at 1-year follow-up to difficulty reported at baseline. RESULTS: At 1-year follow-up, 31.9% of participants with and 20.6% of participants without PAD reported walking difficulty that was improved (P < .01), whereas 41.2% vs 55%, respectively, reported walking difficulty that was unchanged (P < .01). Among all reporting no change in walking difficulty, participants with PAD declined in 6-minute walk, whereas participants without PAD improved (-10 vs +15 meters; mean difference, -25; 95% confidence interval, -38 to -13; P < .01). CONCLUSIONS: Most people with PAD reported improvement or no change in walking difficulty from calf or buttock symptoms at one-year follow-up. Among all participants who perceived stable walking ability, those with PAD had significant greater declines in objectively measured walking performance, compared with people without PAD.

Establishing how much improvement in lung function and distance walked is clinically important for adult patients with Pompe disease.

BACKGROUND AND PURPOSE: Pompe disease is a rare, inheritable, progressive metabolic myopathy. This study aimed to estimate the minimal clinically important difference (MCID) for an improvement in forced vital capacity in the upright seated position (FVCup) and the 6-min walk test (6MWT) after a year of treatment with enzyme replacement therapy. METHODS: Data were obtained from two prospective follow-up studies. Between-group and within-group MCIDs were estimated using anchor-based methods. Additionally, a distribution-based method was used to generate supportive evidence. As anchors, self-reported change in health and in physical functioning, shortness of breath and a categorization of the Short-Form 36 Physical Component Summary score were used. Anchor appropriateness was assessed using Spearman correlations (absolute values ≥0.29) and a sufficient number of observations in each category. RESULTS: In all, 102 patients had at least one FVCup or 6MWT measurement during enzyme replacement therapy. Based on the anchors assessed as appropriate, the between-group MCID for an improvement in FVCup ranged from 2.47% to 4.83% points. For the 6MWT, it ranged from 0.35% to 7.47% points which is equivalent to a distance of 2.18-46.61 m and 1.97-42.13 m for, respectively, a man and a woman of age 50, height 1.75 m and weight 80 kg. The results of the distribution-based method were within these ranges when applied to change in the outcome values. CONCLUSION: The MCIDs for FVCup and 6MWT derived in this study can be used to interpret differences between and within groups of patients with Pompe disease in clinical trials and cohort studies.

What Happens to Frailty in the First Year After Lung Transplantation?

BACKGROUND: Frailty is prevalent in lung transplant (LTx) candidates, but the impact and subsequent frailty trajectory is unclear. This study aimed to investigate frailty over the first year after LTx. METHOD: Post-LTx recipients completed a thrice weekly 12-week directly supervised exercise rehabilitation program. Edmonton Frail Scale (EFS) was used to assess frailty. Primary outcome was 6-Minute Walk Distance (6MWD) measured at pre-LTx, prerehabilitation, postrehabilitation, and 1 year post-LTx. RESULTS: 106 of 139 recruited participants underwent LTx: mean age 58 years, 48% male, 52% with chronic obstructive pulmonary disease. Mean (± SD) frailty scores pre-LTx and 1 year post-LTx were 5.54 ± 2.4 and 3.28 ±1.5. Mean 6MWD improved significantly for all: prerehabilitation 326 m (SD 116), versus postrehabilitation 523 m (SD 101) (p < 0.001) versus 1 year 512 m (SD 120) (p < 0.001). There were significant differences between an EFS > 7 (frail) and EFS ≤ 7 (not frail) for 6MWD, grip strength (GS), anxiety, and depression. Postrehabilitation, there were no significant differences in 6MWD, GS, anxiety, or depression while comparing EFS > 7 versus ≤ 7. At 1 year, there was a significant difference in depression but not 6MWD, GS, or anxiety between those EFS ≤ 7 and > 7 (p = 0.017). CONCLUSION: Participants in a structured post-LTx rehabilitation program improved in functional exercise capacity (6MWD), GS, depression, and anxiety. For frail participants exercise capacity, depression, anxiety, and GS were well managed in rehabilitation with no significant differences between those who were not frail. Pre-LTx frailty may be reversible post-LTx and should not be an absolute contraindication to LTx.

Hippocampal functional connectivity mediates the association between cardiorespiratory fitness and cognitive function in healthy young adults.

OBJECTIVE: Higher cardiorespiratory fitness (CRF) induces neuroprotective effects in the hippocampus, a key brain region for memory and learning. We investigated the association between CRF and functional connectivity (FC) of the hippocampus in healthy young adults. We also examined the association between hippocampal FC and neurocognitive function. Lastly, we tested whether hippocampal FC mediates the association between 2-Min Walk Test (2MWT) and neurocognitive function. METHODS: 913 young adults (28.7 ± 3.7 years) from the Human Connectome Project were included in the analyses. The 2MWT performance result was used as a proxy for cardiovascular endurance. Fluid and crystalized composite neurocognitive scores were used to assess cognition. Resting-state functional MRI data were processed to measure hippocampal FC. Linear regression was used to examine the association between 2MWT, hippocampal FC, and neurocognitive outcomes after controlling for age, sex, years of education, body mass index, systolic blood pressure, and gait speed. RESULTS: Better 2MWT performance was associated with greater FC between the anterior hippocampus and right posterior cingulate and left middle temporal gyrus. No associations between 2MWT and posterior hippocampal FC, whole hippocampal FC, and caudate FC (control region) were observed. Greater anterior hippocampal FC was associated with better crystalized cognition scores. Lastly, greater FC between the anterior hippocampus and right posterior cingulate mediated the association between better 2MWT scores and higher crystalized cognition scores. CONCLUSIONS: Anterior hippocampal FC may be one underlying neurophysiological mechanism that promotes the association between 2MWT performance and crystalized composite cognitive function in healthy young adults.

Responsiveness and minimal clinically important difference of the 6-minute walk distance in patients undergoing colorectal cancer surgery.

PURPOSE: This study aimed to clarify the responsiveness and minimal clinically important difference (MCID) of the 6-minute walk distance (6MWD) from before and 1 week after surgery in patients with colorectal cancer (CRC). METHODS: This retrospective cohort study enrolled 97 patients with primary CRC scheduled for surgery. An anchor-based approach estimated the MCID of the 6MWD, with postoperative physical recovery and EuroQol 5-dimension 5L questionnaire assessments serving as anchors. Effect size (ES) and standardized response mean (SRM) of the 6MWD were calculated to evaluate responsiveness, and the receiver operating characteristic (ROC) curve was used to estimate the MCID of the 6MWD. RESULTS: Of the 97 patients, 72 were included in the analysis. The absolute value of ES and SRM of the 6MWD were 0.69 and 0.91, respectively. The ROC curve indicated that the optimal cut-off values for estimating the MCID of the 6MWD were -60 m (area under the curve [AUC] = 0.753 [95% CI: 0.640-0.866]) and -75 m (AUC = 0.870 [95% CI: 0.779-0.961]) at each anchor. CONCLUSION: From before to 1 week after surgery, the responsiveness of the 6MWD was favorable, and the MCID of the 6MWD was -75 to -60 m in patients with CRC.

Recovery of surgery in the elderly (ROSE) program: The efficacy of a multi-modal prehabilitation program implemented in frail and pre-frail elderly undergoing major abdominal surgery.

BACKGROUND: Major abdominal surgery is associated with a high rate of post-operative complications with increased risk of adverse surgical outcomes due to the presence of frailty. This study aims to evaluate the effectiveness of the multimodal Recovery of Surgery in the Elderly (ROSE) prehabilitation program with supervised exercise in mitigating postoperative functional decline when compared to standard care. METHOD: The ROSE program enrolled ambulant patients who were 65 years and above, had a Clinical Frailty Scale score of 4 or more and were planned for major abdominal surgery. Participation in supervised exercise sessions before surgery were compared with standard physiotherapy advice. The primary outcome was 6-min walk test (6MWT) distance assessed at baseline, after prehabilitation and 30 days follow-up after surgery. Secondary outcomes included physical performance, length of hospital stay and postoperative morbidity. RESULTS: Data from 74 eligible patients, 37 in each group, were included. Median age was 78 years old. Forty-two patients (22 in Prehab group and 20 in control group) with complete 6MWT follow-up data at 30 days follow-up were analysed for outcomes. Most patients underwent laparoscopic surgery (63.5%) and almost all of the surgeries were for abdominal malignancies (97.3%). The Prehab group had an increase in 6MWT distance at the 30-day follow up, from a baseline mean (SD) of 277.4 (125) m to 287.6 (143.5) m (p = 0.415). The 6MWT distance in the control group decreased from a baseline mean (SD) of 281.7 (100.5) m to 260.1 (78.6) m at the 30-day follow up (p = 0.086). After adjusting for baseline 6MWT distance and frailty score, the Prehab group had significantly higher 6MWT distance at 30-day follow-up than control (difference in adjusted means 41.7 m, 95% confidence interval 8.7-74.8 m, p = 0.015). There were no significant between-group differences in the secondary outcomes. CONCLUSION: A multimodal prehabilitation program with supervised exercise within a short time frame can improve preoperative functional capacity and maintain baseline functional capacity in frail older adults undergoing major abdominal surgery.

Mapping Kansas City cardiomyopathy, Seattle Angina, and minnesota living with heart failure to the MacNew-7D in patients with heart disease.

INTRODUCTION: The Kansas City Cardiomyopathy Questionnaire (KCCQ), Seattle Angina Questionnaire (SAQ), and Minnesota Living with Heart Failure Questionnaire (MLHFQ) are widely used non-preference-based instruments that measure health-related quality of life (QOL) in people with heart disease. However, currently it is not possible to estimate quality-adjusted life-years (QALYs) for economic evaluation using these instruments as the summary scores produced are not preference-based. The MacNew-7D is a heart disease-specific preference-based instrument. This study provides different mapping algorithms for allocating utility scores to KCCQ, MLHFQ, and SAQ from MacNew-7D to calculate QALYs for economic evaluations. METHODS: The study included 493 participants with heart failure or angina who completed the KCCQ, MLHFQ, SAQ, and MacNew-7D questionnaires. Regression techniques, namely, Gamma Generalized Linear Model (GLM), Bayesian GLM, Linear regression with stepwise selection and Random Forest were used to develop direct mapping algorithms. Cross-validation was employed due to the absence of an external validation dataset. The study followed the Mapping onto Preference-based measures reporting Standards checklist. RESULTS: The best models to predict MacNew-7D utility scores were determined using KCCQ, MLHFQ, and SAQ item and domain scores. Random Forest performed well for item scores for all questionnaires and domain score for KCCQ, while Bayesian GLM and Linear Regression were best for MLHFQ and SAQ domain scores. However, models tended to over-predict severe health states. CONCLUSION: The three cardiac-specific non-preference-based QOL instruments can be mapped onto MacNew-7D utilities with good predictive accuracy using both direct response mapping techniques. The reported mapping algorithms may facilitate estimation of health utility for economic evaluations that have used these QOL instruments.

Use of 6-minute walk distance to predict lung transplant-free survival in fibrosing non-IPF interstitial lung diseases.

BACKGROUND AND OBJECTIVE: The identification of progression in patients with fibrosing non-idiopathic pulmonary fibrosis (IPF) interstitial lung diseases (ILDs) represents an ongoing clinical challenge. Lung function decline alone may have significant limitations in the detection of clinically significant progression. We hypothesized that longitudinal changes of 6-min walk distance (6MWD) from baseline, simultaneously considered with measures of lung function, may independently predict survival and identifying clinically significant progression of disease. METHODS: Forced vital capacity (FVC), diffusing lung capacity (DLCO) and 6MWD were considered both at baseline and at 1 year in a discovery cohort (n = 105) and in a validation cohort (n = 138) from different centres. The primary endpoint was lung transplant (LTx)-free survival. RESULTS: Average follow-up was 3 years in both cohorts. Combined incidence of deaths and LTx was 29% and 21%, respectively. No collinearity and no strong correlations were observed among FVC, DLCO and 6MWD longitudinal changes. While age, gender and BMI were not significant, 6MWD decline ≥24 m predicted LTx-free-survival significantly and independently from FVC and DLCO declines, with high sensitivity and specificity, in both the discovery and the validation cohorts. Although FVC and DLCO declines remained significant predictors of LTx-free survival, 6MWD decline was more accurate than the proposed ATS/ERS/JRS/ALAT functional criteria. Results were confirmed after stratifying patients by baseline FVC. CONCLUSION: Longitudinal declines of 6MWD are associated with poor survival in fibrosing ILDs across a wide range of baseline severity, with high accuracy. 6MWD longitudinal decline is largely independent from lung function decline and may be integrated into the routine assessment of progression.