***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background — Long-term follow-up after  percutaneous mitral commissurotomy  enables predictive factors of late results to be identified . Methods and Results — Late results of percutaneous mitral commissurotomy were assessed in 1024 consecutive patients . Good immediate results , defined as valve area ≥1.5 cm2 without mitral regurgitation > 2/4 , were obtained in 912 patients ( 89 % ) . These 912 patients were r and omly split into 2 cohorts comprising 609 and 303 patients that were used to develop and vali date , respectively , a scoring system predicting late functional results . The 20-year rate of good functional results ( survival without cardiovascular death , mitral surgery , or repeat percutaneous mitral commissurotomy and in New York Heart Association class I or II ) was 30.2±2.0 % . A multivariable Cox model identified 7 predictive factors of poor late functional results : higher final mean gradient ( P<0.0001 ) , interaction between age and final mitral valve area ( P<0.0001 ) showing that the impact of valve area decreases with age , interaction between sex and valve calcification ( P<0.0001 ) showing that the impact of valve anatomy is stronger in men , and interaction between rhythm and New York Heart Association class showing an impact of New York Heart Association class only in patients in atrial fibrillation ( P<0.0001 ) . A 13-point score enabled 3 risk groups to be defined , corresponding to predicted good functional results of 55.1 % , 29.1 % , and 10.5 % at 20 years in the validation cohort . Conclusions — Twenty years after  percutaneous mitral commissurotomy in a population of patients with varied characteristics , 30 % still had good functional results . Prediction of late functional results is multifactorial and strongly determined by age and the quality of immediate results . A simple vali date d scoring system is useful for estimating individual patient outcome OBJECTIVE To develop a multivariate prediction model for in-hospital mortality following aortic valve replacement . METHODS Retrospective analysis of prospect ively collected data on 4550 consecutive patients undergoing aortic valve replacement between 1 April 1997 and 31 March 2004 at four hospitals . A multivariate logistic regression analysis was undertaken , using the forward stepwise technique , to identify independent risk factors for in-hospital mortality . The area under the receiver operating characteristic ( ROC ) curve was calculated to assess the performance of the model . The statistical model was internally vali date d using the technique of bootstrap resampling , which involved creating 100 r and om sample s , with replacement , of 70 % of the entire data set . The model was also vali date d on 816 consecutive patients undergoing aortic valve replacement between 1 April 2004 and 31 March 2005 from the same four hospitals . RESULTS Two hundred and seven ( 4.6 % ) in-hospital deaths occurred . Independent variables identified with in-hospital mortality are shown with relevant co-efficient values and p-values as follows : ( 1 ) age 70 - 75 years : 0.7046 , p<0.001 ; ( 2 ) age 75 - 85 years : 1.1714 , p<0.001 ; ( 3 ) age>85 years : 2.0339 , p<0.001 ; ( 4 ) renal dysfunction : 1.2307 , p<0.001 ; ( 5 ) New York Heart Association class IV : 0.5782 , p=0.003 ; ( 6 ) hypertension : 0.4203 , p=0.006 ; ( 7 ) atrial fibrillation : 0.604 , p=0.002 ; ( 8) ejection fraction<30 % : 0.571 , p=0.012 ; ( 9 ) previous cardiac surgery : 0.9193 , p<0.001 ; ( 10 ) non-elective surgery : 0.5735 , p<0.001 ; ( 11 ) cardiogenic shock : 1.1291 , p=0.009 ; ( 12 ) concomitant CABG : 0.6436 , p<0.001 . Intercept : -4.8092 . A simplified additive scoring system was also developed . The ROC curve was 0.78 , indicating a good discrimination power . Bootstrapping demonstrated that estimates were stable with an average ROC curve of 0.76 , with a st and ard deviation of 0.025 . Validation on 2004 - 2005 data revealed a ROC curve of 0.78 and an expected mortality of 4.7 % compared to the observed rate of 4.1 % . CONCLUSIONS We developed a contemporaneous multivariate prediction model for in-hospital mortality following  aortic valve replacement .  This tool can be used in day-to-day practice to calculate patient-specific risk by the logistic equation or a simple scoring system with an equivalent predicted risk OBJECTIVE To assess  soluble suppression of tumorigenicity 2 ( sST2 ) serum concentrations and predict mortality in patients undergoing transcatheter aortic valve implantation ( TAVI ) . METHODS We prospect ively enrolled 74 patients with severe aortic stenosis ( AS ) who underwent TAVI and matched them to patients without aortic valve disease ( n=74 ) . AS patients underwent comprehensive echocardiographic and cardiac magnetic resonance imaging and laboratory examinations .  sST2  levels were determined by enzyme-linked immunosorbent assay ( ELISA ) , their association with post procedural mortality was investigated using logistic and Cox regression analyses , and the prognostic performance compared to established risk scores . RESULTS AS patients had substantially higher sST2 levels than controls ( 39.5 vs. 17.8ng/mL , p<0.001 ) . sST2 significantly correlated with left and right atrial sizes ( r=0.25 , p=0.033 and r=0.38 , p=0.001 ) . At one and two years , 10 ( 13.9 % ) and 18 ( 25 % ) patients had died , respectively . sST2 significantly predicted survival in uni- and multivariate Cox regression analyses in our cohort ( p=0.005 and p=0.025 ) . sST2 also predicted major adverse cardiovascular events ( MACE , p=0.046 ) . Adding sST2 to the established STS score improved prediction of two-year mortality in our cohort ( ΔAUC=0.108 ; 95 % CI -0.066 - 0.281 ; continuous NRI=0.778 ; 95 % CI : 0.277 - 1.278 and IDI=0.141 ; 95 % CI : 0.031 - 0.251 ) , and a model containing both sST2 and the STS score had a negative predictive value of 96.1 % and 86.3 % regarding one and two-year mortality , respectively . CONCLUSIONS sST2 is elevated in AS patients and a prognostic marker of survival after TAVI . Implementation of this marker in routine pre-TAVI workup may improve risk prediction and patient selection BACKGROUND Symptomatic mitral regurgitation ( MR ) is associated with high morbidity and mortality that can be ameliorated by surgical valve repair or replacement . Despite this , many patients with MR do not undergo surgery . Transcatheter mitral valve replacement ( TMVR ) may be an option for selected patients with severe MR . OBJECTIVES This study aim ed to examine the effectiveness and safety of TMVR in a cohort of patients with native valve MR who were at high risk for cardiac surgery . METHODS Patients underwent transcatheter , transapical delivery of a self-exp and ing mitral valve prosthesis and were examined in a prospect i ve registry for short-term and 30-day outcomes . RESULTS Thirty patients ( age 75.6 ± 9.2 years ; 25 men ) with grade 3 or 4 MR underwent TMVR .  The MR etiology was secondary ( n = 23 ) , primary ( n = 3 ) , or mixed pathology ( n = 4 ) . The Society of Thoracic Surgeons Predicted Risk of Mortality was 7.3 ± 5.7 % . Successful device implantation was achieved in 28 patients ( 93.3 % ) . There were no acute deaths , strokes , or myocardial infa rct ions . One patient died 13 days after TMVR  from hospital-acquired pneumonia . Prosthetic leaflet thrombosis was detected in 1 patient at follow-up and resolved after increased oral anticoagulation with warfarin . At 30 days , transthoracic echocardiography showed mild ( 1 + ) central MR in 1 patient , and no residual MR in the remaining 26 patients with valves in situ . The left ventricular end-diastolic volume index decreased ( 90.1 ± 28.2 ml/m2 at baseline vs. 72.1 ± 19.3 ml/m2 at follow-up ; p = 0.0012 ) , as did the left ventricular end-systolic volume index ( 48.4 ± 19.7 ml/m2 vs. 43.1 ± 16.2 ml/m2 ; p = 0.18 ) . Seventy-five percent of the patients reported mild or no symptoms at follow-up ( New York Heart Association functional class I or II ) . Successful device implantation free of cardiovascular mortality , stroke , and device malfunction at 30 days was 86.6 % . CONCLUSIONS TMVR is an effective and safe therapy for selected patients with symptomatic native MR . Further evaluation of TMVR using prostheses specifically design ed for the mitral valve is warranted . This intervention may help address an unmet need in patients at high risk for surgery . ( Early Feasibility Study of the Tendyne Mitral Valve System [ Global Feasibility Study ] ; NCT02321514 ) OBJECTIVES We sought to develop national benchmarks for valve replacement surgery by developing statistical risk models of operative mortality . BACKGROUND National risk models for coronary artery bypass graft surgery ( CABG ) have gained widespread acceptance , but there are no similar models for valve replacement surgery . METHODS The Society of Thoracic Surgeons National Cardiac Surgery Data base was used to identify risk factors associated with valve surgery from 1994 through 1997 . The population was drawn from 49,073 patients undergoing isolated aortic valve replacement ( AVR ) or mitral valve replacement ( MVR ) and from 43,463 patients undergoing CABG combined with AVR or MVR .  Two multivariable risk models were developed : one for isolated AVR or MVR and one for CABG plus AVR or CABG plus MVR . RESULTS Operative mortality rates for AVR , MVR , combined CABG/AVR and combined CABG/ MVR were 4.00 % , 6.04 % , 6.80 % and 13.29 % , respectively . The strongest independent risk factors were emergency/salvage procedures , recent infa rct ion , reoperations and renal failure . The c-indexes were 0.77 and 0.74 for the isolated valve replacement and combined CABG/valve replacement models , respectively . These models retained their predictive accuracy when applied to a prospect i ve patient population undergoing operation from 1998 to 1999 . The Hosmer-Lemeshow goodness-of-fit statistic was 10.6 ( p = 0.225 ) for the isolated valve replacement model and 12.2 ( p = 0.141 ) for the CABG/valve replacement model . CONCLUSIONS Statistical models have been developed to accurately predict operative mortality after valve replacement surgery . These models can be used to enhance quality by providing a national benchmark for valve replacement surgery Background — The management of patients with asymptomatic severe aortic stenosis remains controversial . We sought to develop a continuous risk score for predicting the midterm development of symptoms or adverse events in this setting . Methods and Results — We prospect ively followed 107 patients with asymptomatic aortic stenosis ( aged 72 years [ 63 to 77 ] ; 35 women ; aortic-jet velocity , 4.1 m/s [ 3.5 to 4.4 ] ) at a single center in France . Predefined end points for assessing outcome were the occurrence within 24 months of death or aortic valve replacement necessitated by symptoms or by a positive exercise test . Variables independently associated with outcome were used to build a score that was vali date d in an independent cohort of 107 patients from Belgium . Independent predictors of outcome were female sex , peak aortic-jet velocity , and B-type natriuretic peptide at baseline . Accordingly , the score could be calculated as follows : Score=[peak velocity (m/s) × 2]+(natural logarithm of B-type natriuretic peptide × 1.5)+1.5 ( if female sex ) . Event-free survival after 20 months was 80 % for patients within the first score quartile compared with only 7 % for the fourth quartile . Areas under the receiver operating characteristic curve for the score were 0.90 and 0.89 in the development and validation cohorts , respectively . Conclusions — If further validation is achieved , this score may be useful to predict outcome in individual patients with asymptomatic aortic stenosis to 
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Conclusions Many  CPMs for patients with valvular heart disease have never been externally vali date d and isolated external validations appear insufficient to assess the trustworthiness of predictions .