An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

BACKGROUND: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. METHODS: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. RESULTS: A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]). CONCLUSION: We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

BACKGROUND: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Study on tumour cell-derived hybrid exosomes as dasatinib nanocarriers for pancreatic cancer therapy.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death. Therefore, we intend to explore novel strategies against PDAC. The exosomes-based biomimetic nanoparticle is an appealing candidate served as a drug carrier in cancer treatment, due to its inherit abilities. In the present study, we designed dasatinib-loaded hybrid exosomes by fusing human pancreatic cancer cells derived exosomes with dasatinib-loaded liposomes, followed by characterization for particle size (119.9 ± 6.10 nm) and zeta potential (-11.45 ± 2.24 mV). Major protein analysis from western blot techniques reveal the presence of exosome marker proteins CD9 and CD81. PEGylated hybrid exosomes showed pH-sensitive drug release in acidic condition, benefiting drug delivery to acidic cancer environment. Dasatinib-loaded hybrid exosomes exhibited significantly higher uptake rates and cytotoxicity to parent PDAC cells by two-sample t-test or by one-way ANOVA analysis of variance, as compared to free drug or liposomal formulations. The results from our computational analysis demonstrated that the drug-likeness, ADMET, and protein-ligand binding affinity of dasatinib are verified successfully. Cancer derived hybrid exosomes may serve as a potential therapeutic candidate for pancreatic cancer treatment.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusion: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases.

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets.

BACKGROUND: Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. METHODS: We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. RESULTS: Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. CONCLUSIONS: We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Catastrophic Cardiac Events During Transcatheter Aortic Valve Replacement.

BACKGROUND: Perioperative complications of transcatheter aortic valve replacement (TAVR) are decreasing but can be catastrophic when they occur. Systematic reports of the nature of these events are lacking in the contemporary era. Our study aimed to report the incidence, outcomes, and perioperative management of catastrophic cardiac events in patients undergoing TAVR and to propose a working strategy to address these complications. METHODS: This is a retrospective cohort study of patients who developed catastrophic cardiac events during or immediately after TAVR between 2015 and 2019 at a single academic centre. RESULTS: Of 2102 patients who underwent TAVR, 51 (2.5%) developed catastrophic cardiac events. The causes included cardiac perforation and tamponade (n = 19, 37.3%), acute left- ventricular failure (n = 10, 19.6%), coronary artery obstruction (n = 10, 19.6%), aortic-root disruption (n = 7, 13.7%), and device embolization (n = 5, 9.8%). Twenty-four patients (47.0%) with catastrophic cardiac events required stabilization by either intra-aortic balloon counter-pulsation or extracorporeal membrane oxygenation. The in-hospital mortality rate increased by 11.7-fold for patients with catastrophic cardiac events compared with those without (25.5% vs 2.0%, P < 0.001). Patients who developed aortic root disruption had the highest mortality rate (42.8%) compared with the others. The incidence of catastrophic cardiac events remained stable over a 5-year period, but the associated mortality decreased from 38.5% in 2015 to 9.1% in 2019. CONCLUSIONS: Catastrophic cardiac events during TAVR are rare, but they account for a dramatic increase in perioperative mortality. Early recognition and development of a standardized perioperative team approach can help manage patients experiencing these complications.

Outcomes of Patients Undergoing Transcatheter Aortic Valve Implantation With Incidentally Discovered Masses on Computed Tomography.

Routine preprocedural chest and abdomen computed tomography is done prior to transcatheter aortic valve implantation (TAVI), which, in turn, have led to the discovery of radiographic potentially malignant incidental masses (pMIM). It is largely unknown whether pMIM impact the outcomes of patients undergoing TAVI. In this retrospective cohort study from a single center, 1,081 patients underwent TAVI from 2012 to 2016, who had available computed tomographies, survived the index hospitalization, and also had 1 year follow-up data for review. Machine learning (backward propagation neural network)-augmented multivariable regression for mortality by pMIM was conducted. In this cohort of 1,081 patients, the mean age was 79.1 (± 9.0), 48.8% were females, 16.8% had a history of prior malignancy, and 21.1% had pMIM. One-year mortality for the entire cohort was 12.6%. The most common prior malignancies were prostate, breast, and lymphoma and the most common pMIM were present in the lung, kidneys, and thyroid. In a fully adjusted regression analysis, neither prior malignancy nor pMIM increased mortality odds. However, having both was associated with a higher 1-year mortality (odds ratio 4.02, 95% confidence interval 1.50 to 10.73, p = 0.006). In conclusion, presence of pMIM alone was not associated with an increased 1-year mortality among patients undergoing TAVI. However, the presence of pMIM and a history of prior malignancy was associated with a significant increase in 1-year mortality.

Rates, predictors, and outcomes of early readmissions after tricuspid valve surgery.

BACKGROUND: The data on readmissions following tricuspid valve repair/replacement (TVR) are scarce. We examined rates, predictors, causes, and outcomes of readmissions after TVR, using the National Readmission Database. METHODS: The International Classification of Diseases-9th version was used to identify the patients who underwent isolated TVR or concomitant aortic, mitral, and coronary bypass surgeries. Rates, causes, and outcomes were assessed using the analysis of variance and the χ2 test, and predictors of readmissions were evaluated using multivariate analysis. RESULTS: A total of 8254 patients who underwent TVR during 2013 to 2014 were included, of whom 1994 (24.16%) were isolated, and 6260 (75.84%) were performed concomitantly with other heart valve or coronary bypass surgery. A total of 1720 (20.84%) patients were readmitted within 30 days. The readmission rates were 448 (22.46%) after isolated TVR and similar after concomitant TVR (TVR + aortic valve replacement, TVR + mitral valve repair, TVR + coronary artery bypass graft, and TVR + multiple) (P = .194); whereas 1305 (20.11%) and 414 (23.45%) were after tricuspid valve repair and replacement (P = .080), respectively. The independent predictors of readmission were acute kidney injury during index visit and Charlson comorbidity index of more than 2. Mean time to readmission and median length of stay during readmission were 13.02 (±7.93) and 5 (interquartile range: 3-9) days, respectively. Total mortality during rehospitalization was 105 (6.1%), a very high (26.86%) number of patients were discharged to skilled facilities after readmission. CONCLUSIONS: One out of five patients were readmitted within 30 days after the TVR, associated with 6.1% mortality during rehospitalization, and very high need for skilled facility placement.

Validation of scoring system predicting permanent pacemaker implantation after transcatheter aortic valve replacement.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is being increasingly performed in patients with severe aortic stenosis. Despite newer generation valves, atrioventricular (AV) conduction disturbance is a common complication, necessitating permanent pacemaker (PPM) implantation in about 10% of patients. Hence, it is imperative to improve periprocedural risk stratification to predict PPM implantation after TAVR. The objective of this study was to externally validate a novel risk-stratification model derived from the National Inpatient Sample (NIS) database that predicts risk of PPM from TAVR. METHODS: Components of the score included pre-TAVR left and right bundle branch block, sinus bradycardia, second-degree AV block, and transfemoral approach. The scoring system was applied to 917 patients undergoing TAVR at our institution from November 2011 to February 2017. We assessed its predictive accuracy by looking at two components: discrimination using the C-statistic and calibration using the Hosmer-Lemeshow goodness of fit test. RESULTS: Ninety patients (9.8%) required PPM. The scoring system showed good discrimination with C-statistic score of 0.6743 (95% CI: 0.618-0.729). Higher scores suggested increased PPM risk, that is, 7.3% with score ⩽3, 19.23% with score 4-6, and 37.50% with score ≥7. Patients requiring PPM were older (81.4 versus 78.7 years, P = .002). Length of stay and in-hospital mortality was significantly higher in PPM group. CONCLUSIONS: The NIS database derived PPM risk prediction model was successfully validated in our database with acceptable discriminative and gradation capacity. It is a simple but valuable tool for patient counseling pre-TAVR and in identifying high-risk patients.

The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is associated with high mortality. Current methods for predicting mortality post-arrest require data unavailable at the time of initial medical contact. We created and validated a risk prediction model for patients experiencing OHCA who achieved return of spontaneous circulation (ROSC) which relies only on objective information routinely obtained at first medical contact. METHODS: We performed a retrospective evaluation of 14,892 OHCA patients in a large metropolitan cardiac arrest registry, of which 3952 patients had usable data. This population was divided into a derivation cohort (n = 2,635) and a verification cohort (n = 1,317) in a 2:1 ratio. Backward stepwise logistic regression was used to identify baseline factors independently associated with death after sustained ROSC in the derivation cohort. The cardiac arrest survival score (CASS) was created from the model and its association with in-hospital mortality was examined in both the derivation and verification cohorts. RESULTS: Baseline characteristics of the derivation and verification cohorts were not different. The final CASS model included age >75 years (odds ratio [OR] = 1.61, confidence interval [CI][1.30-1.99], p < 0.001), unwitnessed arrest (OR = 1.95, CI[1.58-2.40], p < 0.001), home arrest (OR = 1.28, CI[1.07-1.53], p = 0.008), absence of bystander CPR (OR = 1.35, CI[1.12-1.64], p = 0.003), and non-shockable initial rhythm (OR = 3.81, CI[3.19-4.56], p < 0.001). The area under the curve for the model derivation and model verification cohorts were 0.7172 and 0.7081, respectively. CONCLUSION: CASS accurately predicts mortality in OHCA patients. The model uses only binary, objective clinical data routinely obtained at first medical contact. Early risk stratification may allow identification of more patients in whom timely and aggressive invasive management may improve outcomes.

National 10-year trends and outcomes of isolated and concomitant tricuspid valve surgery.

BACKGROUND: The data on the trends and comparative outcomes after isolated and concomitant tricuspid valve repair/replacement (TVR) is scarce. METHODS: The International Classification of Diseases - 9th version was used to identify the patients who underwent TVR, using the National Inpatient Sample. Outcomes were evaluated using the analysis of variance and Chi-square test, and trends across the years were tested via Cochran-Armitage Test. RESULTS: Of 6868 patients who underwent TVR between 2005-14, 1601 (23%) were isolated. Over the 10-years period, the number of total and isolated TVR has steadily increased (P<0.001). The proportion of patients undergoing repair has increased compared to replacement. Overall in-hospital mortality was 8.5% and 7.7% (p=0.28), permanent pacemaker requirement was 11.24% and 10.69% (P=0.53), blood transfusion rates were 32.6% and 37.9% (P<0.001), and the post-procedure length of stay (LOS) was 14.1±0.44 and 12.5±0.17 days (P<0.001) after isolated and concomitant TVR respectively. High (26.25%) number of patients were discharged to skilled facility. The operative mortality associated with TV repair was lower than with replacement (6.8% versus 11.15%, P<0.001), but the mortality trend over the 10-years has stayed relatively stable. Independent predictors of mortality were age >50 years, heart failure, cerebrovascular accident, end-stage renal disease, peripheral vascular disease, liver disease, and TV replacement. CONCLUSIONS: Both isolated and concomitant TVR has increased over the last 10 years, however is associated with high mortality, complications, need for skilled facility placement, and longer LOS. The mortality after TV replacement was significantly higher than that after repair.

Yellow Means Caution: Correlations Between Liver Injury and Mortality with the Use of VA-ECMO.

Abnormalities in markers of liver injury after venoarterial extracorporeal membrane oxygenation (VA-ECMO) initiation are of unclear distribution and clinical significance. This study included all consecutive adult patients from a single institution who underwent VA-ECMO cannulation between May 2012 and September 2016 and had liver function panels drawn during their admission (n = 223). Data points include: age, sex, body mass index, diagnosis, duration of ECMO cannulation, duration of hospitalization, pre-ECMO cardiac arrest, central nervous system (CNS) injury, the presence of chronic kidney disease or acute renal failure, renal replacement therapy utilization, lactate levels, duration of pre-ECMO intubation, admission and peak bilirubin/aspartate aminotransferase (AST)/alanine aminotransferase (ALT)/alkaline phosphatase (ALP) levels, and time to peak bilirubin/AST/ALT/ALP in relation to cannulation. Multivariate Poisson regression analyses were performed to determine associations with mortality. In-hospital mortality was 66%. Serum bilirubin elevation appeared to significantly correlate continuously with mortality. Other markers of liver injury were not significant in final multivariate models. As a univariate factor, no patient survived with a total serum bilirubin greater than 30 mg/dl, and specificity for 90% mortality was crossed at 11 mg/dl. Mortality was also significantly associated with the presence of CNS injury and elevation of lactic acid levels. Postcannulation liver injury is significantly associated with increased mortality and total serum bilirubin appears to be a biomarker of considerable clinical significance.

Minimally Invasive Versus Transcatheter and Surgical Aortic Valve Replacement: A Propensity Matched Study.

BACKGROUND: An increasing number of options exist for the treatment of severe symptomatic aortic stenosis. The study aim was to compare short-term outcomes in patients undergoing surgical aortic valve replacement (SAVR), minimally invasive aortic valve replacement (MIAVR), and transcatheter aortic valve replacement (TAVR). METHODS: A multi-institutional retrospective review of 2,571 patients undergoing SAVR (n = 842), MIAVR via right anterior thoracotomy (n = 699) and TAVR (n = 1,030) between 2011 and 2014 was conducted. TAVR patients were further stratified as either transfemoral (TF) or transapical (TA). Propensity matching was performed between MIAVR and SAVR (384 pairs), MIAVR and TA-TAVR (115 pairs), and MIAVR and TF-TAVR (247 pairs). RESULTS: Total numbers of AVR increased between 2011 and 2014. When stratified by procedure type, MIAVR and TF-TAVR accounted for most of the growth, while TA-TAVR and SAVR each experienced a decreased volume. Propensity matched comparisons of SAVR, TF-TAVR, and TA-TAVR versus MIAVR revealed no difference in 30-day mortality. TF-TAVR versus MIAVR revealed that MIAVR had a decreased rate of stroke (0.4% versus 3.6%, p = 0.02) and increased atrial fibrillation (AF; 19.4% versus 4%, p <0.01). When compared to SAVR, MIAVR had a lower incidence of AF (19% versus 32.6%, p <0.01). MIAVR exhibited decreased ventilation time (27.2 versus 134 h, p = 0.03) and intensive care unit time (63.7 versus 92.7 h, p = 0.02) compared to TA-TAVR. CONCLUSIONS: During recent years, MIAVR and TFTAVR have experienced significant growth in volume with near-comparable short-term outcomes, while SAVR and TA-TAVR volumes have declined. These results underscore the importance of surgeons adopting MIAVR and TF-TAVR techniques in order to offer patients optimal outcomes.

The Society of Thoracic Surgery Risk Score as a Predictor of 30-Day Mortality in Transcatheter vs Surgical Aortic Valve Replacement: A Single-Center Experience and its Implications for the Development of a TAVR Risk-Prediction Model.

BACKGROUND: The Society of Thoracic Surgery (STS) risk score serves as an important determinant of eligibility for transcatheter aortic valve replacement (TAVR). The STS score's validity for predicting TAVR mortality, however, is incompletely understood. This study compares the STS score's discriminatory power for TAVR mortality as compared with surgical aortic valve replacement (SAVR) mortality. METHODS: A retrospective analysis of STS score and 30-day mortality for TAVR patients (n = 426) and SAVR patients (n = 297) at a single institution was performed. The performance of the STS score was evaluated from the standpoint of discriminatory power. The predictive ability of STS for 30-day mortality was detected by generation of receiver operator characteristic (ROC) curves. RESULTS: The STS score possesses predictive ability for 30-day SAVR mortality with an area under the ROC curve of 0.791 (95% confidence interval [CI], 0.690-0.893). The STS score also possesses predictive ability for 30-day TAVR mortality with an area under the ROC curve of 0.674 (95% CI, 0.541-0.807). When stratifying TAVR by access route, the STS score for transfemoral TAVR provides an area under the ROC curve of 0.789 (95% CI, 0.569-1.000). There is not a statistically significant difference in predictive ability between SAVR and TAVR. CONCLUSION: The STS score possesses predictive value for 30-day mortality in both SAVR and TAVR. Although not designed for TAVR, the STS score may provide some insight into TAVR mortality, and therefore serves as an appropriate model for efforts to develop a TAVR-specific risk prediction instrument.

Clinical trends in surgical, minimally invasive and transcatheter aortic valve replacement†.

OBJECTIVES: Transcatheter aortic valve replacement (TAVR) and minimally invasive aortic valve replacement (MIAVR) have emerged as alternatives to surgical aortic valve replacement (SAVR) via traditional sternotomy. However, their effect on clinical practice remains unclear. The study's objective is to describe clinical trends between TAVR, MIAVR and SAVR in patients with severe aortic stenosis (AS). METHODS: This retrospective observational study analyzed trends in isolated severe aortic valve replacement (AVR) among three high volume TAVR, MIAVR and SAVR centres in the United States. The cohort included 2571 patients from 2011 through 2014 undergoing SAVR ( n = 842), MIAVR ( n = 699) and TAVR ( n = 1030) further stratified into transapical (TA-TAVR) and trans-femoral (TF-TAVR). RESULTS: Total AVR volume increased +107% with increases in TF-TAVR (+595%) and MIAVR (+57%). However, SAVR (-15%) and TA-TAVR (-49%) decreased from 2013 to 2014. In the final year, risk stratification by age ≥ 80, redo AVR, patients receiving dialysis and STS score >8% revealed increases in TF-TAVR and MIAVR, while SAVR decreased for all groups. CONCLUSIONS: TF-TAVR and MIAVR increased while SAVR and TA-TAVR trended down in the latter periods, which underscore a paradigm shift in the treatment of severe AS and the importance of surgeon adoption of TF-TAVR and MIAVR techniques. As the demand for minimally invasive modalities increases, further studies comparing MIAVR versus TF-TAVR in low and intermediate risk patients are warranted.