domain: chim sde

text:
Tao Peng

Jian Zhang

Srimanta Ray

Fatemeh Saadat

Ghareh Bagh

Houssam Fakhouri

Farzaneh Arefi-Khonsari
email: farzaneh.arefi@upmc.fr

Jerald A Lalman
email: lalman@uwindsor.ca

Optimizing one-dimensional TiO2 for photocatalytic hydrogen production from a water-ethanol mixture and other electron donors

Keywords: 1D titanium dioxide, photocatalyst, anatase, TiO2-B, hydrothermal, hydrogen production, quantum yield

This work is focused on synthesizing and employing one-dimensional (1D) titanium dioxide (TiO2) for hydrogen (H2) production. Based on using electron donors (EDs) (ethanol, methanol, fomic acid and 1,2,3 propanetriol), the increased H2 production, when compared to P25 TiO2 nanoparticles, was due to the large specific surface area (SSA) and enhanced electron mobility of 1D TiO2. The impact of the 1D TiO2 synthesis reaction conditions (temperature, NaOH concentration and the TiO2 precursor concentration) on the photocatalytic H2 production rate was evaluated using a 3-factor 3-level Box Behnken design (BBD). The BBD model demonstrated that the temperature and the NaOH concentration significantly affected the 1D TiO2 phase structure, crystal size, SSA, bandgap and the photocatalytic H2 production rate. The phase structure and crystal size of 1D TiO2 were key factors affecting the H2 production rate. 1D TiO2 containing an anatase phase with a mean crystal size of 20.1±0.2 nm was synthesized at 126 o C, 15 M NaOH and 49 g•L -1 TiO2. The maximum H2 production rate of 475±12 µmol•h -1 (quantum efficiency (ε) = 20.2±0.5%) for the 1D TiO2 sample was significantly enhanced when compared to commercial TiO2 P25. The H2 production rate for the optimized 1D TiO2 was significantly enhanced by decorating the structure with Pt and Au. Hydrothermal synthesized of 1D TiO2 provided an efficient and low cost method for producing H2 from ethanol, methanol, fomic acid and 1,2,3 propanetriol.

Introduction

Hydrogen is considered as a promising fuel for the future. 1D TiO2 nanomaterials have shown significant potential in producing H2 because of their high electron mobility, large SSA and high mechanical strength [START_REF] Roy | TiO2 nanotubes: synthesis and applications[END_REF][START_REF] Bavykin | The effect of hydrothermal conditions on the mesoporous structure of TiO2 nanotubes[END_REF][START_REF] Zhu | Phase transition between nanostructures of titanate and titanium dioxides via simple wet-chemical reactions[END_REF][START_REF] Chen | Titanium dioxide nanomaterials: Synthesis, properties, modifications, and applications[END_REF][START_REF] Chen | Formation mechanism of TiO2 nanotubes and their applications in photoelectrochemical water splitting and supercapacitors[END_REF]. Producing 1D TiO2 using the alkaline hydrothermal method is a relatively facile process with advantages of large-scale production and varied crystal phase [START_REF] Roy | TiO2 nanotubes: synthesis and applications[END_REF][START_REF] Bavykin | The effect of hydrothermal conditions on the mesoporous structure of TiO2 nanotubes[END_REF][START_REF] Chen | Titanium dioxide nanomaterials: Synthesis, properties, modifications, and applications[END_REF][START_REF] Chen | Formation mechanism of TiO2 nanotubes and their applications in photoelectrochemical water splitting and supercapacitors[END_REF][START_REF] Kasuga | Formation of titanium oxide nanotube[END_REF][START_REF] Tang | Mechanical force-driven growth of elongated bending TiO2-based nanotubular materials for ultrafast rechargeable lithium ion batteries[END_REF][START_REF] Kolen'ko | Hydrothermal synthesis and characterization of nanorods of various titanates and titanium dioxide[END_REF][START_REF] Shim | Facile hydrothermal synthesis of porous TiO2 nanowire electrodes with high-rate capability for Li ion batteries[END_REF][START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF][START_REF] Armstrong | TiO2-B nanowires[END_REF]. In studies employing the hydrothermal method, 1D TiO2 nanomaterials containing anatase [START_REF] Roy | TiO2 nanotubes: synthesis and applications[END_REF][START_REF] Kukovecz | Atomic scale characterization and surface chemistry of metal modified titanate nanotubes and nanowires[END_REF] or TiO2-B [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF][START_REF] Armstrong | TiO2-B nanowires[END_REF] were produced under selected synthesis conditions. These materials have been utilized to produce hydrogen (H2) from the photocatalytic splitting of water or from other substrates such as ethanol [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF][START_REF] Jitputti | Synthesis of TiO2 nanotubes and its photocatalytic activity for H2 evolution[END_REF]. In comparison to TiO2 nanoparticles, studies have reported improved H2 production from ethanol under UV light conditions when employing 1D TiO2 [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF][START_REF] Jitputti | Synthesis of TiO2 nanotubes and its photocatalytic activity for H2 evolution[END_REF]. Hydrothermal synthesis factors such as reaction temperature and the TiO2-to-NaOH molar ratio can significantly affect the 1D TiO2 phase structure, crystal size, SSA [START_REF] Bavykin | The effect of hydrothermal conditions on the mesoporous structure of TiO2 nanotubes[END_REF][START_REF] Zhu | Phase transition between nanostructures of titanate and titanium dioxides via simple wet-chemical reactions[END_REF][START_REF] Kasuga | Formation of titanium oxide nanotube[END_REF][START_REF] Peng | The role of hydrothermal conditions in determining 1D TiO2 nanomaterials bandgap energies and crystal phases[END_REF] and, subsequently the H2 production rate. However, the impact of hydrothermal synthesis factors on the 1D TiO2 photocatalytic H2 production rate and optimization of the hydrothermal conditions have not been addressed in previous studies.

Photocatalytic production of H2 and other fuels is affected by phase structure, crystal size, BET SSA, bandgap [START_REF] Lavanya | Superior photocatalytic performance of graphene wrapped anatase/rutile mixed phase TiO2 nanofibers synthesized by a simple and facile route[END_REF][START_REF] Hussein | Mesoporous coupled ZnO/TiO2 photocatalyst nanocomposites for hydrogen generation[END_REF][START_REF] Linsebigler | Photocatalysis on TiO2 surfaces: Principles, mechanisms, and selected results[END_REF], band edge positions [START_REF] Linsebigler | Photocatalysis on TiO2 surfaces: Principles, mechanisms, and selected results[END_REF][START_REF] Zhang | The characterization of nitrogen doped TiO2 photoanodes and its application in the dye sensitized solar cells[END_REF], charge transfer [START_REF] Xiang | Graphene based photocatalysts for solar fuel generation[END_REF], material stability [START_REF] Wang | A metal-free polymeric photocatalyst for hydrogen production from water under visible light[END_REF][START_REF] Cao | In-situ growth of CdS quantum dots on g-C3N4 nanosheets for highly efficient photocatalytic hydrogen generation under visible light irradiation[END_REF], cocatalyst [START_REF] Nasirian | Modification of TiO2 to enhance photocatalytic degradation of organics in aqueous solutions[END_REF][START_REF] Nada | Studies on the photocatalytic hydrogen production using suspended modified photocatalysts[END_REF][START_REF] Murdoch | The effect of gold loading and particle size on photocatalytic hydrogen production from ethanol over Au/TiO2 nanoparticles[END_REF][START_REF] Connelly | Photoreaction of Au/TiO2 for hydrogen production from renewables: a review on the synergistic effect between anatase and rutile phases of TiO2[END_REF], availability [START_REF] Hashimoto | TiO2 photocatalysis: a historical overview and future prospects[END_REF], complementary metal oxide semiconductor [START_REF] Qin | One-dimensional CdS/TiO2 nanofiber composites as efficient visible-light-driven photocatalysts for selective organic transformation: synthesis, characterization, and performance[END_REF][START_REF] Wang | One-dimensional titanium dioxide nanomaterials: Nanowires, nanorods, and nanobelts[END_REF] and toxicity [START_REF] Hashimoto | TiO2 photocatalysis: a historical overview and future prospects[END_REF]. Substrates such as water [START_REF] Xiang | Graphene based photocatalysts for solar fuel generation[END_REF][START_REF] Priebe | Water reduction with visible light: synergy between optical transitions and electron transfer in Au-TiO2 catalysts visualized by in situ EPR spectroscopy[END_REF] and ethanol [START_REF] Murdoch | The effect of gold loading and particle size on photocatalytic hydrogen production from ethanol over Au/TiO2 nanoparticles[END_REF] have been used to produce H2 using photocatalysts. Other electron donors (ED) employed to produce H2 utilizing TiO2 under UV light conditions include methanol, 1,2,3 propanetriol and glucose [START_REF] Xie | Graphene-based materials for hydrogen generation from light-driven water splitting[END_REF].

Next, the photogenerated electrons reduce H + to generate H2. The water splitting reaction is described in the following half-cell reactions:

2 + 2 →
(photocathode) [START_REF] Hashimoto | TiO2 photocatalysis: a historical overview and future prospects[END_REF] (1)

+ 2ℎ → + 2
(photoanode) [START_REF] Hashimoto | TiO2 photocatalysis: a historical overview and future prospects[END_REF] (2)

In the presence of sacrificial ED [START_REF] Xie | Graphene-based materials for hydrogen generation from light-driven water splitting[END_REF][START_REF] Daskalaki | Efficient production of hydrogen by photo-induced reforming of glycerol at ambient conditions[END_REF] such as ethanol, the photogenerated holes react with the ED and subsequently, the recombination of electrons and holes in photocatalyst are suppressed [START_REF] Xie | Graphene-based materials for hydrogen generation from light-driven water splitting[END_REF][START_REF] Daskalaki | Efficient production of hydrogen by photo-induced reforming of glycerol at ambient conditions[END_REF]. This suppression process significantly increases the H2 production rate as well as photocatalytic stability [START_REF] Xie | Graphene-based materials for hydrogen generation from light-driven water splitting[END_REF][START_REF] Daskalaki | Efficient production of hydrogen by photo-induced reforming of glycerol at ambient conditions[END_REF]. The photoanode water splitting half reaction is as follows [START_REF] Murdoch | The effect of gold loading and particle size on photocatalytic hydrogen production from ethanol over Au/TiO2 nanoparticles[END_REF][START_REF] Strataki | Hydrogen production by photocatalytic alcohol reforming employing highly efficient nanocrystalline titania films[END_REF]:

+ 2ℎ → 2 + ( ℎ ! "# $ ) [24, 32] (3)
Ethanol, a renewable fuel produced mainly from corn, with a specific heating value of approximately 29.7 MJ•kg -1 is less when compared to H2 with a specific heating value of approximately 141.8 MJ•kg -1 [START_REF] Mcallister | Fundamentals of Combustion Processes[END_REF]. In addition, the decomposition of ethanol contributes to a high H2 production rate and quantum efficiency [START_REF] Zhang | TiO2 single crystal with fourtruncated-bipyramid morphology as an efficient photocatalyst for hydrogen production[END_REF]. Hence, H2 with a larger energy content is preferred when compared to ethanol as a fuel source.

The first objective of this study was to employ an alkaline hydrothermal method to synthesize 1D TiO2 photocatalysts under variable reaction conditions. The second objective was to employ a three-factor and three-level Box Behnken design (BBD) to evaluate the effects of selected hydrothermal process conditions (reaction temperature, the NaOH concentration and the TiO2 precursor concentration) on the photocatalytic 1D TiO2 H2 production rate. The third objective was to optimize the hydrothermal conditions and synthesize 1D TiO2 with a maximum H2 production rate using the BBD model. The final objective was to decorate the optimized BBD TiO2 catalyst with Pt and Au noble metals and investigate the photocatalytic H2 production using ethanol, methanol, formic acid and 1,2,3 propanetriol.

Experimental

Commercial TiO2 nanoparticles (P25) were purchased from Degussa (Evonik Industries, Germany). NaOH (97% purity), ethanol (95% purity), HAuCl4 (99.9% purity), (99.5% purity), methanol (99.8% purity), formic acid (95% purity), 1,2,3 propanetriol (99.5% purity), F-doped SnO2-coated (FTO) glass, polyethylene glycol (PEG, molecular weight 20 000) and HCl (37 wt%) were purchased from Fischer Scientific (Ontario, Canada).

Preparation of 1D nanometric TiO2 photocatalyst

1D TiO2 samples were prepared using the alkaline hydrothermal route [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF][START_REF] Kukovecz | Atomic scale characterization and surface chemistry of metal modified titanate nanotubes and nanowires[END_REF]. Briefly, TiO2 nanoparticles (Aeroxide TiO2 P25, Evonik Corporation, Parsippany, New Jersey) were added into 70 mL of a NaOH solution. The TiO2 concentration and the NaOH concentration used in the study are shown in Table 1. The mixture (total volume 70 mL) was poured into a 100-mL Teflon® capped container. The Teflon ® container was placed into a stainless-steel bomb, capped then heated at desired temperature for 48 h and subsequently, cooled to room temperature. The resulting product was centrifuged and washed with 0.1 M HCl solution. The white paste was repeatedly washed with deionized water until pH = 7 and calcined at 400 o C for 2 h to produce 1D TiO2 nanomaterials. The hydrothermal conditions for temperature, NaOH concentration and TiO2 concentration (Table 1) were based on previous research [START_REF] Peng | The role of hydrothermal conditions in determining 1D TiO2 nanomaterials bandgap energies and crystal phases[END_REF].

Characterization studies

The 1D TiO2 photocatalyst morphology was characterized using field emission scanning electron microscopy (FESEM; JEOL, Japan) and high resolution transmission electron microscopy (HRTEM) (300kV, JEOL 3010, Japan). X-ray diffraction (XRD) analysis of the samples was performed using an X-ray diffractometer (Rigaku, MI) equipped with Cu K∂ radiation source. An Alpha300 RA argon laser Raman spectrometer (WITec, Germany) with an excitation wavelength set at 532 nm was used to conduct the Raman studies. UV-Vis spectra analysis was recorded on a Cary 300 UV-Vis (Agilent Technologies, Santa Clara, CA). A Micromeritics ASAP 2020 Brunauer-Emmett-Teller (BET) analyzer (Micromeritics Instrument, Norcross, GA) was used to obtain nitrogen adsorption-desorption isotherms at 77K. The BET SSA for each sample is provided in Table 2.

Photocatalytic hydrogen production

Photocatalytic experiments were conducted using quartz tubes (25 mm ID × 250 mm length) (Technical Glass Products Inc., Painesville, OH) sealed with aluminum crimp caps and Teflon® lined rubber septa. All reaction conditions were examined in triplicate conditions. The sealed photocatalytic reaction tubes were irradiated with a 300-nm UV light source (LZC-UVB-016 lamp, Luzchem, ON, Canada) as described in a previous study by Ray et al. [START_REF] Ray | Using the Box-Benkhen technique to statistically model phenol photocatalytic degradation by titanium dioxide nanoparticles[END_REF] . The average intensity of UV irradiance was 9 mW•cm -2 . The reaction mixture was stirred to maintain the photocatalyst in suspension, minimize particle agglomeration and mass transfer limitation. The reactor temperature was maintained at 37±2 o C. The 50-mL reaction mixture contains 0.2 g•L -1 of the photocatalyst and 20% (v/v) ethanol as ED. The reaction mixture was purged with nitrogen (N2) gas to remove oxygen. Next, the reaction mixture was ultrasonicated and magnetically stirred under dark condition for 1 h before initiation with UV light. Next, the reaction mixture was exposed to UV light and maintained at 37±2 o C. At specific time intervals, headspace samples were collected and the H2 concentration was determined using a gas chromatograph (GC) (Varian CP-3800). The GC was configured with a 2 m long x 2 mm I.D. Carbon Shin Column (Alltech, Deerfield, IL) and a thermal conductivity detector (TCD) at 200 o C. The oven and injector temperatures were set at 200 o C and 150 o C, respectively. Nitrogen gas was used as a carrier gas at a flowrate of 15 mL•min -1 . The GC detection limit was approximately 310 umol•L -1 for H2. The photocatalyst reaction mixture was irradiated for 5 h using a 300-nm monochromatic UV light source. The irradiation area for reaction mixture in each tube was 58 cm 2 . The H2 production rate (µmol•h -1 ) for each sample is shown in Table 2.

The quantum efficiency (ε) was calculated using Eq. ( 4) and (5) [START_REF] Liu | Metal-free efficient photocatalyst for stable visible water splitting via a two-electron pathway[END_REF].

( = *+ ,- =
.× 0 12 ×34× 500× 00× 0 16 5.5 5× 0 128 × × 0 9 = 2.83 × 10 (4) < = ×=,> ?@AB>C DE >FDGF>H ,IHCDJ>? ADG>-@G>K =,> ?@AB>C DE L?-LH>?= M,D=D?K × 100% = ×5.0 × 0 O2 × P O MCDH@-=LD? CQ=> × , .4 × 0 OR × 100%

(5) Where N is the photon number in 1 h; E is the energy rate of incident photons; T is the wavelength in nm; h is Planck's constant; and c is the photon velocity. Additional photocatalytic H2 production experiments were conducted using solar irradiation (310-800 nm) (LZC-VIS-016, Luzchem, ON, Canada) with average intensity of 2 mW•cm -2 . Other EDs examined in the study included 20% (v/v) methanol, 20% (v/v) formic acid and 20% (v/v) 1,2,3 propanetriol.

Optimization study

The experimental design was based on a 3-factor and 3-level Box-Behnken design (BBD) [START_REF] Veeravalli | Optimizing hydrogen production from a switchgrass steam exploded liquor using a mixed anaerobic culture in an upflow anaerobic sludge blanket reactor[END_REF]. The experimental design was used to optimize key process factors (Table 1) to improve the H2 production rate with using ethanol as the ED. A total of 15 experimental points (#1 to #15) were conducted with each condition under triplicate (Table 2). Experiments, designated as #13, #14 and #15, were conducted as central points to evaluate the magnitude of the error in the study.

The experimental factors and H2 production rate (Y) (response variable) were modeled using Minitab 15 (Minitab Inc., State College, PA) to fit a full quadratic equation (Eq. ( 6)): [START_REF] Kasuga | Formation of titanium oxide nanotube[END_REF] where Xi's are input variables which influence the response variable Y, аo is an offset term, аi is the i th linear coefficient, аii is the quadratic coefficient, and аij is the ij th interaction coefficient. The X1, X2 and X3 represent the synthesis temperature, NaOH concentration and TiO2 concentration, respectively (Table 2).

U = а 0 + ∑ а L X L LY + ∑ а LL X L LY + ∑ ∑ а LZ X L X Z L[ZY LY
The quadratic model was employed to predict reaction conditions which produced 1D TiO2 with a maximum H2 production rate. A D-optimality analysis [START_REF] Shanmugam | Statistical optimization of conditions for minimum H2 consumption in mixed anaerobic cultures: effect on homoacetogenesis and methanogenesis[END_REF] was performed to find optimal conditions for the three factors under consideration (maximize the H2 production rate).

The coefficient values for fitting the full quadratic model were determined using the H2 production rate and a multiple regression analysis. An analysis of variance (ANOVA) was conducted using the observed responses for samples #1 to #15) (Table 2). Only significant terms with p values < 0.05 were included into the model. A normal distribution plot together with the Anderson-Darling (AD) [START_REF] Ray | Using the Box-Benkhen technique to statistically model phenol photocatalytic degradation by titanium dioxide nanoparticles[END_REF] test was used to determine the adequacy of the model.

TiO2 photocatalyst decorated with Au and Pt nanoparticles

The TiO2 photocatalyst sample was decorated with Au nanoparticles based on work by Pu et al. [START_REF] Pu | Au-decorated NaxH2-xTi3O7 nanobelts exhibiting remarkable photocatalytic properties under visible-light illumination[END_REF]. 1D TiO2 (500 mg of optimized 1D TiO2 synthesized at 126 o C, 15 M NaOH and 49 g•L -1 TiO2) was first dispersed in the reaction mixture consisting of 500 mL deionized water and 250 mL ethanol followed by the addition of 10 mL of 0.1 M NaOH solution. The mixture was heated to 50 o C, followed by addition of 6 mL of a 5 mM HAuCl4 solution. Ethanol was used as a reducing agent to facilitate Au growth. The mixed solution was maintained at 50 o C for 3 h with vigorous stirring. The product (Au-decorated 1D TiO2 with the Au content of 2 wt%) was filtered, washed with deionized water and ethanol, and then dried at 60 o C in air for 12 h. For the Pt-decorated 1D TiO2 with a Pt content of 2 wt%, a similar procedure was employed with using H2PtCl6 as Pt source and UV light was used to assist in the reduction of H2PtCl6 to Pt.

Results and discussion

1D TiO2 morphology

Field emission scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM) images for selected 1D TiO2 samples are shown in Figure 1. 1D structures (nanorods and nanowires) with large differences in the diameter and length were characteristic of TiO2 synthesized under different reaction conditions. Nanorods morphology with lengths ranging from 20 nm to 100 nm and the diameter of approximately 10 nm were observed in the sample (#1) (Figure 1a) synthesized at 120 o C and with 5 M NaOH plus 43 g•L -1 TiO2 (Figure 1e). Lattice fringes observed with 0.35 nm spacing (Figure 1e) indicate the presence of anatase in the 1D TiO2 structure (sample #1) [START_REF] Zhang | Importance of the relationship between surface phases and photocatalytic activity of TiO2[END_REF]. Increasing reaction temperatures and NaOH concentrations caused increasing length, increasing diameter and phase structure changes. Nanowire morphology with lengths reaching 3 μm and diameter ranging from 10 nm to 100 nm was observed for sample #4 (synthesized at 190 o C, 15 M and 43 g•L -1 ) (Figure 1b). The FESEM image for sample #8 (190 o C, 10 M and 100 g L -1 ) (Figure 1c) indicates nanowires with lengths in the micrometer range. The wide distribution of diameter for sample #8 was variable from 10 nm to hundreds of nanometers. The lattice fringes of with 0.62 nm spacing (Figure 1f) indicate the TiO2 nanowire (sample #8) consisted of the TiO2-B phase [START_REF] Kogure | Formation of TiO2(B) nanocrystallites in sol-gel-derived SiO2-TiO2 film[END_REF]. The FESEM image for sample #9 (150 o C, 5 M and 14 g L -1 ) (Figure 1d

Photocatalytic hydrogen production

Significant differences in H2 production were observed for 1D TiO2 samples prepared using different reaction conditions. Hydrogen production versus time profiles are shown in Figure 2a for BBD sample #3 plus the P25 control. Control experiments were conducted employing with and without P25 nanoparticles. The mean H2 production rate (Y value) was 0 µmol•h -1 without a photocatalyst; however, with the addition of P25 nanoparticles, the mean production rate increased to 235±8 μmol•h -1 . In comparison, the production rate of 463±14 μmol•h -1 (ε = 19.3 ± 0.6%) for sample #3 (synthesized at 120 o C, 15 M NaOH and 43 g•L -1 TiO2) was approximately 2-fold larger than P25 (ε = 10.0 ± 0.4%) using ethanol as an ED. Notice sample #3 was characteristic with nanorods structure of approximately 10 nm diameter (Figure 2b). The ε value for the Pt-TiO2 catalyst was 5-fold greater than the ε value for TiO2 when employing pure water as a substrate under UV light conditions . The photocatalytic H2 production rate showed an approximate 110-fold increase using 20 v/v% ethanol as an ED when compared to using water (Figure S1). The H2 production rates are listed in Table 2. The effect of ethanol concentration on H2 production rate was evaluated (Figure S1). For various ethanol concentrations tested, the highest H2 production rate was observed when using a 20 v/v% aqueous ethanol solution.

Modeling and effects of factors on the H2 production rate (response variable) for ethanol

For the response surface optimization study, three sets (replicates) of 15 experiments were conducted to compute the H2 production rate under 15 conditions (Table 2). A multiple regression analysis was used to develop a full quadratic equation [START_REF] Tang | Mechanical force-driven growth of elongated bending TiO2-based nanotubular materials for ultrafast rechargeable lithium ion batteries[END_REF] which describes the H2 production rate, Y, as a function of the hydrothermal process conditions. Y = -2964 + 34.28X + 124.34X + 8.55X -0.7X X + 0.006X X -0.239X X -0.096X -0.269X -0.058X [START_REF] Tang | Mechanical force-driven growth of elongated bending TiO2-based nanotubular materials for ultrafast rechargeable lithium ion batteries[END_REF] where Y is the H2 production rate (µmol•h -1 ), X1, X2 and X3 represent the reaction temperature ( o C), NaOH concentration (M) and TiO2 concentration (g•L -1 ), respectively.

The relationship between the synthesis factors, phase structure and the H2 production rate is shown in the 3D surface plots and the corresponding contour plots (Figure 3). Interaction between the temperature and the NaOH concentration (Figures 3a andb) suggests that both parameters significantly affected the 1D TiO2 phase structure and the H2 production rate (Figure S2). The D-optimality index was used to locate the maximum response (Y) region within the factor space under consideration (Figure S3). A maximum response of 470 µmol•h -1 was predicted for 1D TiO2 synthesized at 126 o C, 15 M NaOH and 49 g•L -1 TiO2. Additional experiments were conducted to verify the maximum response when using the 1D TiO2 photocatalyst under the predicted conditions. The predicted value was slightly underestimated when compared to the observed response of 475±12 µmol•h -1 (ε=20.2±0.5%). Recall, pure anatase with a mean crystal size of 20.1±0.2 nm and BET SSA of 140±3 m 2 •g -1 was observed for 1D TiO2 synthesized under optimum conditions. The opitimum H2 production rate value was approximately 2-fold larger when compared to commercial P25 when employing a 20 v/v% ethanol feed. This H2 production rate was significantly improved when compared to reported production rates of 348 µmol•h -1 for biphasic anatase-TiO2-B [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF]. The optimum H2 production rate of 470 µmol•h -1 for the optimized 1D TiO2 was normalized to a mass basis. The specific H2 production rate (per g of photocatalyst) attained was 23,500 µmol•h -1 •g -1 . The maxmum specific H2 production rate was significantly enhanced when compared to data reported for pure anatase, pure rutile, flame spray pyrolysis-synthesised TiO2 and Au/TiO2 [START_REF] Chiarello | Photocatalytic hydrogen production over flame spray pyrolysis-synthesised TiO2 and Au/TiO2[END_REF]. 380 381

Response surface model development

An analysis of variance (ANOVA) (Table S1) of the model full quadratic equation ( 7) was employed to determine the significance and adequacy of the model. The quadratic model with an overall p-value ≤ 0.05 [START_REF] Veeravalli | Optimizing hydrogen production from a switchgrass steam exploded liquor using a mixed anaerobic culture in an upflow anaerobic sludge blanket reactor[END_REF][START_REF] Shanmugam | Statistical optimization of conditions for minimum H2 consumption in mixed anaerobic cultures: effect on homoacetogenesis and methanogenesis[END_REF][START_REF] Lai | Optimization of key factors affecting hydrogen production from sugarcane bagasse by a thermophilic anaerobic pure culture[END_REF][START_REF] Reungsang | Optimization of key factors affecting methane production from acidic effluent coming from the sugarcane juice hydrogen fermentation process[END_REF] and a F-value ≤ to the critical value (2.01 at 5% level of significance) [START_REF] Veeravalli | Optimizing hydrogen production from a switchgrass steam exploded liquor using a mixed anaerobic culture in an upflow anaerobic sludge blanket reactor[END_REF][START_REF] Shanmugam | Statistical optimization of conditions for minimum H2 consumption in mixed anaerobic cultures: effect on homoacetogenesis and methanogenesis[END_REF][START_REF] Lai | Optimization of key factors affecting hydrogen production from sugarcane bagasse by a thermophilic anaerobic pure culture[END_REF][START_REF] Reungsang | Optimization of key factors affecting methane production from acidic effluent coming from the sugarcane juice hydrogen fermentation process[END_REF] indicate the model was statistically significant. However, two variables (X and X1X3) with p-value ≥ 0.05 were statistically insignificant with no individual effect on the full quadratic model. These terms were deleted from model using a backward elimination method [START_REF] Shanmugam | Statistical optimization of conditions for minimum H2 consumption in mixed anaerobic cultures: effect on homoacetogenesis and methanogenesis[END_REF]. The revised response surface model is designated as equation ( 8): U = -2974 + 34.43X + 118.96X + 9.40X -0.7X X -0.239X X -0.095X -0.058X [START_REF] Kolen'ko | Hydrothermal synthesis and characterization of nanorods of various titanates and titanium dioxide[END_REF] where Y is the H2 production rate (µmol•h -1 ), X1, X2 and X3 represent temperature ( o C), NaOH concentration (M) and TiO2 concentration (g•L -1 ), respectively.

Response surface model verification

A scatter plot comparing the experimental and the model predicted values (Figure 4a) indicate a reasonable correlation at each level. The Anderson-Darling (AD) statistic (Figure 4b) was employed to assess the adequacy of fitting the model to the experimental data by determining the normal distribution of the residuals (difference between the predicted and experimental values) [START_REF] Stephens | EDF statistics for goodness of fit and some comparisons[END_REF]. An AD value of 0.379 (≤ the critical value of 0.735 for 45 samples and at a 5% level of significance) [START_REF] Stephens | EDF statistics for goodness of fit and some comparisons[END_REF] and a p-value of 0.391 (≥ 0.05) suggest the model's predicted valued correlated with the experimental values. 5). The model predicted trend was comparable to the experimental trend for varying each factor. For a temperature range from 120 to 190 o C (Figure 5a), the model predicted values were close to the experimental values with a slight overestimate at each temperature level.

For the NaOH concentration (Figure 5b), the predicted values were in agreement with the experimental data with a slight overestimate at 5 M NaOH. For the TiO2 concentration, the model predicted values which were reasonably close to the observed values for each level of TiO2 concentration with a slight underestimate at 100 g•L -1 TiO2 and a slight overestimate at 14 and 43 g•L -1 (Figure 5c). Additional experiments (Table S2) were conducted to compare the 3 for all the samples. The three phase structures observed in this study included pure anatase [START_REF] Cheng | Hydrothermal preparation of uniform nanosize rutile and anatase particles[END_REF], biphasic anatase-rutile [START_REF] Zhang | Understanding polymorphic phase transformation behavior during growth of nanocrystalline aggregates: insights from TiO2[END_REF][START_REF] Hidalgo | Photocatalytic properties of surface modified platinised TiO2: effects of particle size and structural composition[END_REF][START_REF] Tian | Preparation and characterization of stable biphase TiO2 photocatalyst with high crystallinity, large surface area, and enhanced photoactivity[END_REF] and the biphasic anatase-TiO2-B [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF] structures (Figure 6a). The biphasic anatase-rutile structure, similar to that in P25, was only detected in sample #1 [START_REF] Zhang | Understanding polymorphic phase transformation behavior during growth of nanocrystalline aggregates: insights from TiO2[END_REF][START_REF] Hidalgo | Photocatalytic properties of surface modified platinised TiO2: effects of particle size and structural composition[END_REF][START_REF] Tian | Preparation and characterization of stable biphase TiO2 photocatalyst with high crystallinity, large surface area, and enhanced photoactivity[END_REF] (Figure S4), while pure anatase phase and the biphasic anatase-TiO2-B structures were observed in a majority of the samples. The mean crystal sizes (L, nm) for sample #1-15 (Table 2) were calculated using the Scherrer equation (equation 9) and XRD data. L=Kλ/β.cosθ [START_REF] Monshi | Modified Scherrer equation to estimate more accurately nano-crystallite size using XRD[END_REF] (

where K, λ and β represent the shape factor (0.89), the wavelength of XRD radiation (0.154 nm)

and the half maximum of a full peak, respectively. Photon energy (eV) (b) Figure 6: X-ray diffraction (XRD) patterns and αkmhv) 1/2 versus absorbed energy for samples #1, #3, and #8: (a) XRD spectra and (b) αkmhv) 1/2 versus absorbed energy profiles. Notes: a . Synthesis parameter details are listed in Table 2; b . A(101) peak is positioned on the shoulder and 3. A, R and B represents the anatase, rutile and TiO2-B phases, respectively.

37±3 to 73±3 72±4 to 103±5

Note: Sample order number is defined in Table 2.

Diffuse reflectance UV-Vis spectroscopic profiles for selected BBD samples are shown in Figure S5. All the BBD samples with an optical absorption below 400 nm was likely caused by their band-band electron transition [START_REF] Zhang | Understanding polymorphic phase transformation behavior during growth of nanocrystalline aggregates: insights from TiO2[END_REF][START_REF] Luo | Synthesis of mesostructured titania with controlled crystalline framework[END_REF]. The diffuse reflectance spectra was transformed into corresponding absorption spectra (Figure 6b) using Kubelka-Munk function [START_REF] Lopez | Band-gap energy estimation from diffuse reflectance measurements on sol-gel and commercial TiO2: a comparative study[END_REF]. The bandgap energy is closely correlated with the phase structure and chemical composition (Table 3). The pure anatase (e.g. sample #3) phase was observed with a large bandgap energy ranging from 3.258±0.003 to 3.298±0.005 eV [START_REF] Tao | A two-dimensional phase of TiO2 with a reduced bandgap[END_REF][START_REF] Fan | Nanocomposites of TiO2 and reduced graphene oxide as efficient photocatalysts for hydrogen evolution[END_REF], while the biphasic structure anatase-rutile (sample #1)

and anatase-TiO2-B (samples #4, #6 and #8) showed decreasing bandgap energies of 3.067±0.006 and values ranging from 3.210±0.007 to 3.229±0.009 eV, respectively. In the Ti2p XPS spectra for sample #1 and the optimized 1D TiO2 (Figure S6) sample, the two main peaks at 458.9 eV (Ti2p3/2) and 464.5 eV (Ti2p1/2) indicate the presence of Ti 4+ . In comparison, the deconvoluted peaks located at 457.4 eV (Ti2p3/2) and 463.0 eV (Ti2p1/2) were due to the presence of Ti 3+ . The low peak intensity attributed to Ti 3+ indicate sample #1 and the optimized 1D TiO2 contained a small amount of Ti 3+ and oxygen vacancy.

The effect on phase structure and crystal size

The H2 production rates, crystal size and BET SSA are summarized in Table 3 for the BBD 1D TiO2 samples synthesized under different reaction conditions. Depending on the hydrothermal synthesis conditions, the H2 production rates was variable from 72±4 to 475±12 μmol•h -1 . The 1D TiO2 phase structures marked in the 3D response surface plot (Figure 3a) The H2 production rate (μmol•h -1 ) for the pure anatase phase samples (Figure 7) suggests the greatest H2 production rate of 475±12 μmol•h -1 was observed when the mean crystal size was

shows
Where S (m 2 g -1 ) and L (nm) represent the BET SSA and the mean crystal size, respectively. ) S=1518 L -0.7931 R 2 =95.6% Fitted line Figure 7: Hydrogen production rate and specific surface area versus anatase mean crystal size. Note: All samples counted in the figure is anatase phase. The L and S represent crystal size and BET SSA, respectively. 16 ≤L≤56.

Increasing the H2 production rate was closely correlated with decreasing the anatase mean crystal size from 58.3±0.3 to 20.1±0.2 nm. The increasing the H2 production rate was due to increasing the SSA [START_REF] Okura | Photocatalysis science and technology[END_REF] and faster rates of e-and h+ surface reactions with the substrate. The optimum anatase mean crystal size of 20.1±0.2 nm was correlated with a maximum H2 production rate. However, note the lower H2 production rate observed below the optimum anatase mean crystal size was caused by fast e --h + recombination [START_REF] Okura | Photocatalysis science and technology[END_REF]. A decreasing H2 production rate which was correlated with fast e --h + recombination at defective sites on the photocatalyst surface [START_REF] Okura | Photocatalysis science and technology[END_REF]. The significantly great number of defective sites was due to decreasing the crystal size below an optimum value.

Noble metal decorating and other alcohol sacrificial agents

A significant increase in the H2 production rate was observed when decorating the TiO2 photocatalyst with Au or Pt (Figures 8a andb). The catalytic activity of the optimized 1D TiO2 sample synthesized at 126 o C, 15 M NaOH and 49 g•L -1 TiO2 was significantly enhanced by adding Au and Pt (Au/optimum or Pt/optimum) when compared to commercial TiO2 nanoparticles decorated with Au or Pt (Au/P25 or Pt/P25). The catalytic activity of the optimized 1D TiO2 samples decorated with 2 wt% Au (Au/optimum) and Pt (Pt/optimum) were 1146±26 μmol•h -1 (ε = 48.7±1.1%) and 1823±35 μmol•h -1 (ε = 77.5±1.5%), respectively. These rates were greater than those for other TiO2 substrates decorated with 2 wt% of each metal. The Pt/optimum 1D TiO2 activity was greater when compared to the activity for the Au/optimum 1D TiO2 photocatalyst. The H2 production rate for Pt/optimum was approximately 5-fold greater when compared to the H2 production rate (348 μmol•h -1 ) for TiO2 nanotubes decorated with Pt nanoparticles [START_REF] Kuo | A highly active bi-crystalline photocatalyst consisting of TiO2 (B) nanotube and anatase particle for producing H2 gas from neat ethanol[END_REF]. Photocatalytic H2 production studies were performed using the Pt/optimized 1D TiO2 catalyst and different EDs (Figure 8c). The H2 production rates were 1499±34 μmol•h -1 (ε = 63.7±1.4%), 1288±37 μmol•h -1 (ε = 54.8±1.5%) and 710±15 μmol•h -1 (ε = 30.2±0.6%) for methanol, formic acid and 1,2,3 propanetriol, respectively. In addition, under solar light conditions, the low H2 production rate for the Pt/optimum 1D TiO2 photocatalyst of 179±3 μmol•h -1 (ε = 7.6±0.1%) (Figure 8d) was due to the low irradiation energy and the longer visible light wavelength when compared to only UV light. This 1D TiO2 sample with an optimum mean crystal size was able to maximize the H2 production rate. The 1D TiO2 with biphasic anatase-rutile and anatase-TiO2-B structures was observed with decreasing photocatalytic H2 production rate. A significant increase in the H2 production rate was observed for the optimized 1D TiO2 photocatalysts decorated with Pt or Au.

This study demonstrated that 1D TiO2 containing a controllable phase structure, and crystal size was able to produce a photocatalyst with a H2 production rate greater than reported values.

Acknowledgements

  Figure 1: 1D TiO2 field emission scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM) images (a) FESEM image for sample #1 synthesized at 120 o C, 5 M NaOH and 43 g•L -1 ; (b) FESEM image for sample #4 synthesized at 190 o C, 15 M NaOH and 43 g•L -1 ; (c) FESEM image for sample #8 synthesized at 190 o C, 10 M and 100 g•L -1 ; (d). FESEM image for sample #9 synthesized at 150 o C, 5 M NaOH and 14 g•L -1 ; (e) HRTEM image sample #1 synthesized at 120 o C, 5 M NaOH and 43 g•L -1 and (f) HRTEM image for sample #8 synthesized at 190 o C, 10 M and 100 g•L -1 .

Figure 2 :

 2 Figure 2: (a) H2 production (b) High resolution transmission electron microscopy (HRTEM) image for sample #3 and TiO2 precursor using 300-nm UV light.

  A relatively low temperature at 120 o C and a high NaOH concentration at 15 M were closely associated with producing a pure anatase catalyst with a high H2 production rate. Increasing temperatures or decreasing NaOH concentrations were correlated with decreasing H2 production rates. The biphasic anatase-rutile structure, synthesized at 120 o C and 5 M NaOH, was linked to a lower H2 production rate. In addition, a significant decrease in the H2 production rate was observed for the biphasic anatase-TiO2-B structure which was synthesized at 190 o C and with ≥10 M NaOH. These observations indicate the rutile and TiO2-B phases are closely correlated with decreasing photocatalytic H2 production. Interaction between temperature and TiO2 concentration (Figure3c and d) and interaction between NaOH concentration and TiO2 concentration (Figure3e and f) confirmed decreasing the synthesis temperature and increasing the NaOH concentration were closely associated with increasing the H2 production rate. In addition, the surface and contour plots indicate the optimum H2 production rate was obtained with a TiO2 concentration of approximately 50 g•L -1 .

Figure 4 :

 4 Figure 4: Accuracy evaluation of the response surface model. (a) Scatter plot of the H2 produciton rate (µmol•h -1 ) versus experiment order (45 experiments). (b) Anderson-Darling normality plot of residuals. Notes: N = the number of experiments conducted in this study; P-Value = level of confidence; AD = Anderson-Darling statistic.

Figure 5 :

 5 Figure 5: Validation study of the response surface model for the varied factors under consideration. (a) H2 production rate versus temperature at experimental conditions set at 5 M NaOH and 43 g•L -1 TiO2; (b) H2 production rate value versus NaOH concentration at experimental conditions set at 150 o C and 43 g•L -1 TiO2; (c) H2 production rate versus TiO2 concentration at experimental conditions set at 150 o C and 5 M NaOH. 3.6. Phase structure, crystal size and bandgap energies

  the effect of reaction temperature and NaOH concentration on the phase structure and H2 production rate. The low NaOH concentration (5 M) and reaction temperature (120 o C) were correlated with the biphasic anatase-rutile structure. Increasing NaOH concentration and reaction temperature were related to converting rutile into anatase. In addition, higher NaOH concentrations greater than 10 M coupled with a temperature at 190 o C were associated with the conversion of anatase to TiO2-B. Variation of the phase structures had a main effect on the H2 production rate. The pure anatase (sample #2, #3, #5, #7, #9, #11-15 and the optimized sample synthesized at 126 o C, 15 M NaOH and 49 g•L -1 TiO2) were closely linked with increasing the H2 production rate, while the biphasic anatase-rutile (sample #1) and anatase-TiO2-B structures (sample #4, #6 and #8) were associated with decreasing H2 production rate. The pure anatase samples were correlated with high bandgaps ranging from 3.258±0.003 to 3.298±0.005 eV.However, the biphasic anatase-rutile sample #1 showed the lowest bandgap of 3.067±0.006 eV.The lowest H2 production rate was detected for the biphasic anatase-TiO2-B structure (sample #4, 6 and 8). The biphasic anatase-TiO2-B samples showed decreased bandgaps ranging from 3.210±0.007 to 3.229±0.009 eV when compared to the pure anatase samples. The I-t curves for sample #1 (biphasic anatase-rutile), #8 (biphasic anatase-TiO2-B), optimized 1D TiO2 (anatase) and P25 with an on-off UV-light irradiation (300 nm) source are shown in FigureS7. The greatest photocurrent density was observed for the optimized 1D TiO2 containing anatase. This observation suggests the optimized 1D TiO2 is characterized by an enhanced charge carrier transportation and separation. The photocurrent densities for sample #1 and P25 were comparable to each other. The lowest photocurrent density caused by the fast recombination of electrons and holes was detected for the sample (#8) containing in the TiO2-B phase.

  20.1±0.2 nm with BET SSA of 140±3 m 2 •g -1 . Smaller crystal size was closely linked to a larger BET SSA for 1D TiO2 containing anatase. The BET SSA (m 2 g -1 ) as a function of the mean crystal size is shown as equation (Eq. 10). S = 1518 × b 0.c. (16 ≤ L ≤ 56 and f = 95.6% )

Figure 8 :

 8 Figure 8: H2 production rate for (a) TiO2 decorated with 2% Au under UV light condition, (b) TiO2 decorated with 2% Pt under UV light condition, (c) Optimized 1D TiO2 decorated with 2% Pt photocatalysts using methanol, formic acid and 1,2,3 propanetriol under UV light condition, and (d) TiO2 decorated with 2% Pt using solar simulators. Note: a Au/P25, Au/#1, Au/optimum and Au/#8 represent TiO2 nanoparticles (P25), sample #1, optimized 1D TiO2 and sample #8 decorated with 2% Au cocatalysts. b Pt/P25, Pt/#1, Pt/optimum and Pt/#8 represent P25, sample #1, optimized 1D TiO2 and sample #8 decorated with Pt cocatalysts. c Optimum. represent optimized 1D TiO2 synthesized at 126 o C, 15 M NaOH and 49 g•L -1 TiO2.

Financial

  support for this work was provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant # 261797-2009), the Ontario Trillium scholarship program and the University of Windsor (grant #13320).

  Photocatalyst1D TiO 2 (Crystal phase, crystal size, bandgap energy)

Table 1 :

 1 Three selected factors and three level.

	Level		Factors
		Temperature ( o C)	NaOH concentration (M)	TiO2 concentration (g•L -1 )
	-1	120	5	14
	0	150	10	43
	1	190	15	100
	Note: the reaction mixture volume is 70 mL.

Table 2 :

 2 Design matrix for experimental factors and response (H2 production rate, mean crystal size and BET SSA) at different factor levels.

			Factors a			Response
		T ( o C)	NaOH (M)	TiO2 (g•L -1 )	H2 production	Mean crystal	BET SSA
	Expt. #	(X1)	(X2)	(X3)	rate (µmol•h -1 )	size L (nm)	(m 2 •g -1 )
	1	120	5	43	193±7	9.5±0.1	182 ± 5
	2	190	5	43	298±10	21.5±0.2	125 ± 5
	3	120	15	43	463±14	19.9±0.1	138 ± 3
	4	190	15	43	72±4	57.2±0.4	37 ± 3
	5	120	10	14	271±8	18.2±0.1	146 ± 5
	6	190	10	14	95±4	46.4±0.3	73 ± 3
	7	120	10	100	224±8	17.6±0.2	149 ± 5
	8	190	10	100	103±5	48.2±0.3	39 ± 3
	9	150	5	14	204±8	16.9±0.2	174 ± 5
	10	150	15	14	284±10	58.3±0.3	61 ± 3
	11	150	5	100	395±13	21.2±0.2	140 ± 4
	12	150	15	100	283±10	18.8±0.2	137 ± 5
	13	150	10	43	393±13	19.5±0.2	151 ± 4
	14	150	10	43	389±12	19.8±0.2	152 ± 5
	15	150	10	43	383±8	20.5±0.2	148± 5
	Note: a T, NaOH and TiO2 represent temperature, NaOH and TiO2 concentration. b the reaction mixture
	volume is 70 mL.

Table 3 .

 3 Phase structures, bandgap energies, mean crystal size, BET SSA and H2 production rate for all BBD samples.

						H2
	Phase	Sample #	Bandgap energy (eV)	Mean crystal size (nm)	BET SSA (m 2 •g -1 )	production rate
						(µmol•h-1)
	Anatase and rutile	1	3.067±0.006	9.5±0.1	182±5	193±7
	Anatase	2, 3, 5, 7, 9, 10, 11, 12, 13, 14 and 15	3.258±0.003 to 3.298±0.005	16.9±0.2 to 57.2±0.4	61±3 to 174±5	204±8 to 463±14
	Anatase and TiO2-B	4, 6, 8	3.210±0.007 to 3.229±0.009	46.4±0.3 to 58.3±0.3


MD,PhD Pierre Lantelme
email: pierre.lantelme@chu-lyon.fr

MD,PhD c , Hélène Eltchaninoff

MD Muriel Rabilloud

Geraud Souteyrand

MD Marion Dupré

MD Marco Spaziano

MD Clément Becle

PhD Benjamin Riche

MD,PhD c , Eric Durand

MD f , Erik Bouvier

MD,PhD Géraud Souteyrand

MD a , Marc Bonnet

MD,PhD Jean-Nicolas Dacher

MD,PhD Pierre-Yves Courand

MD,PhD Lucie Cassagnes

PhD Eduardo E Dávila Serrano

MD,PhD Pascal Motreff

MD,PhD Loic Boussel

MD f Thierry Lefevre

MD,MSc Brahim Harbaoui

Development of a Risk Score Based on Aortic Calcification to Predict 1-Year Mortality After Transcatheter Aortic Valve Replacement

Keywords: TAVI, aortic stiffness, risk stratification, outcome, mortality Aortic stenosis AUC: Area under the ROC curve Chronic obstructive pulmonary disease CT: Computed tomography GFR: Glomerular filtration rate estimated by the Cockcroft formula HF: Heart failure IDI: Integrated discrimination improvement HR: Hazard ratio LVEF: Left ventricular ejection fraction ROC: Receiver-operating characteristic TAC: Thoracic aortic calcification TAVI: Transcatheter aortic valve implantation

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Transcatheter aortic valve implantation (TAVI) represents the standard of care for relieving aortic stenosis (AS) in high risk patients; it is also preferred in symptomatic intermediate risk patients according to the latest European Society guidelines [START_REF] Baumgartner | ESC/EACTS Guidelines for the management of valvular heart disease[END_REF]. Although TAVI efficiently normalizes the gradient across the aortic valve, approximately 25% of high-risk patients die within the first year following the procedure (2). Because of the competing risk of noncardiovascular mortality or heart failure (HF) the true benefit of TAVI is difficult to estimate.

Whereas perioperative mortality accounts for a minor part of overall deaths and is diminishing with technical refinement (3) most of this residual risk remains high. This points to a need to better identify potentially poor TAVI responders who would not benefit from the procedure.

Lack of benefit may be defined as death and/or absence of functional improvement during a relatively short-term period after the procedure (6 months to 1 year) (4). Within this time frame, it is likely that adverse outcomes are mainly driven by factors present before the TAVI procedure.

Among various candidates (4), predictors of left ventricular function improvement are probably highly important but remain insufficiently characterized. Vascular after-load deserves scrutiny as it becomes preeminent after AS relief (5): our group recently established the prognostic significance of aortic calcifications assessed manually by computed tomography (CT), on the outcomes after TAVI. In particular, ascending aortic calcification was predictive of HF (6) and total aortic calcification burden was predictive of cardiac mortality (7). This prognostic implication may also concern non cardiovascular mortality (8,9).

The C 4 CAPRI study (4 Cities for assessing CAlcification PRognostic Impact, NCT02935491) aimed at developing a score based on aortic calcification burden combined with classical predictors, to predict one-year cardiovascular and all-cause mortality after TAVI.

Methods

This was a multicenter study performed in 4 high volume French centers. Two different cohorts were considered: a "training cohort" used to develop the risk scores and a "test cohort" in which the predictive value of the model was tested.

PATIENTS

All patients undergoing TAVI for severe AS during the study period were part of the FRANCE 2 (2,10) and of the FRANCE TAVI registries [START_REF] Auffret | Temporal Trends in Transcatheter Aortic Valve Replacement in France: FRANCE 2 to FRANCE TAVI[END_REF]. The training cohort encompassed all patients treated by TAVI for severe AS at Clermont-Ferrand University Hospital, Lyon Croix-Rousse University Hospital, Institut Cardiovasculaire Paris Sud, and Rouen University Hospital between January 2010 and December 2014. The test cohort comprised all patients implanted from January to December 2015 at the Lyon and Paris centers, reflecting the most recent practices available.

Patients were included in the analysis if a pre-operative CT-scan was available and thoracic aortic calcification (TAC) was assessed.

The C 4 CAPRI study was approved by the Ethical Committee (Comité de Protection des Personnes SUD-EST IV, L16-56) and by the Commission Informatique et Liberté (CNIL N° . All patients provided written informed consent to anonymous processing of their data.

CALCIFICATION ANALYSIS

CT acquisition was performed on CT scanners with at least a 4cm z-coverage: Brilliance 64 and iCT (Philips, Best, Netherlands); Discovery CT750 HD (GEMS, Waukesha, USA). For each examination, the whole thoracic aorta was studied. CTs were performed after an intra-venous injection of iodine-based contrast agent, with electrocardiogram gating, tube voltage ranging from 100 to 140 kV and adapted mAs. Reconstruction parameters for axial slices ranged from 0.625 to 0.8. All images were anonymized, transferred to a core lab and analyzed by 3 operators, blinded to outcomes data.

Calcifications were extracted using a semi-automated dedicated software based on an open source environment available at https://www.creatis.insa-lyon.fr/site7/fr/creatools_accueil. The rater first delineates the ascending, horizontal, and descending thoracic aorta by placing at least 3 points (Figure 1). The main axis of the aorta was calculated using a third-order b-spline. An adjustable tube embedding the whole aorta was created and an initial threshold set at 550 UH was applied in the tube in order to detect calcifications. This threshold could be adapted to improve the performance of the algorithm. The results of the segmentation were visually asserted and each calcification was manually adjusted (by addition or subtraction) and validated by the user. A connectivity algorithm, based on graph theory, was subsequently applied in order to segment each calcification. The algorithm is a simple recursive function initialized in the center of each calcification after thresholding. For each pixel, the 28 neighboring pixels were checked and the algorithm was re-run for each pixel if their value was above the threshold. For each patient, TAC was calculated from the aortic sinus to the aortic hiatus as previously described (7); valvular calcifications were excluded.

Inter-and intra-observer reproducibility, assessed in a subset of randomly selected patients from the training cohort (N= 50 and 75, respectively), was high with intra-class correlation coefficients estimated respectively at 0.997 (95% confidence interval (CI): [0.994 -0.998]) and 0.997 (95% CI: [0.996 -0.998]).

PROCEDURES

Two TAVI systems were mainly used: a self-expandable prosthesis (Medtronic CoreValve ReValving System, Medtronic, Minneapolis, Minnesota) and a balloon expandable prosthesis (Edwards SAPIEN valve, Edwards Lifesciences, Irvine, California); various routes (transfemoral, transapical, subclavian) and types of anesthesia were used in the different centers.

The variables collected have been described elsewhere (6,10) and encompass demographics, cardiovascular history, biological variables, and echocardiographic variables.

OUTCOMES

The primary end point was cardiovascular death at 1 year. Secondary endpoint was all-cause mortality at 1 year. Vital status was obtained by telephone contact with patients, their relatives, carers or physicians, and by on-site planned visits. Follow-up was censored at 1 year following TAVI. Cardiovascular deaths were adjudicated by 2 experienced cardiologists blinded to patient characteristics and TAC. Cardiovascular deaths were defined according to the VARC-2 criteria [START_REF] Kappetein | Updated standardized endpoint definitions for transcatheter aortic valve implantation: the Valve Academic Research Consortium-2 consensus document[END_REF]. Data collection was performed through dedicated web-based case report forms in each center, which were merged for analysis. Range checks to identify extreme values and assessments of internal consistency were applied during upload.

CONSTRUCTION OF CAPRI RISK SCORE

The characteristics of the cohorts were described using the absolute and relative frequencies for the qualitative characteristics and the mean and the standard deviation for the quantitative characteristics. TAC was considered as a three-class variable defined according to the tertiles for the analysis using the Kaplan Meïer method, and as a continuous variable in the model used to build the risk score. In addition to the continuous TAC, the risk score was constructed from known prognostic mortality factors according to the literature (2,10,[START_REF] Ludman | Transcatheter aortic valve implantation in the United Kingdom: temporal trends, predictors of outcome, and 6-year follow-up: a report from the UK Transcatheter Aortic Valve Implantation (TAVI) Registry, 2007 to 2012[END_REF][START_REF] Iung | Predictive factors of early mortality after transcatheter aortic valve implantation: individual risk assessment using a simple score[END_REF][START_REF] Arnold | Prediction of Poor Outcome After Transcatheter Aortic Valve Replacement[END_REF] and expert opinion, split into 3 groups: 1) demographic and comorbidities, 2) atherosclerotic disease, 3) cardiac function (see Online Methods for details). Non redundant factors were selected in each group after estimating correlations between factors by Spearman coefficient, tetrachoric or polychoric coefficients, according to the nature of the factors. Cox regression models were used to quantify the effects of the retained factors on mortality hazard. The prediction improvement linked to the added factors was tested using the likelihood ratio test as recommended [START_REF] Pepe | Testing for improvement in prediction model performance[END_REF], and quantified using the integrated discrimination improvement (IDI) index [START_REF] Uno | A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data[END_REF]. The calibration of the final model was estimated by plotting observed survival probabilities against deciles of prediction and its discriminative performance, by using the cumulative/dynamic area under the ROC curve (AUC). The bootstrap method was used to quantify and correct the optimism of the estimated AUCs. The risk scores of mortality were computed using the linear combination of the factors included in the Cox model weighted by the regression coefficients. The same strategy of analysis was applied to cardiovascular and all-cause mortality.

The predictive performance of the risk scores was evaluated on the independent test cohort by assessing its calibration and estimating the AUC.

To illustrate the impact of the risk scores on the identification of potentially futile TAVI, futility thresholds corresponding to the expected 1-year mortality (between 15% and 25% for cardiovascular mortality and between 25% and 35% for all-cause mortality) of a medically treated population [START_REF] Gonzalez-Saldivar | Comparison of 1-Year Outcome in Patients With Severe Aorta Stenosis Treated Conservatively or by Aortic Valve Replacement or by Percutaneous Transcatheter Aortic Valve Implantation (Data from a Multicenter Spanish Registry)[END_REF], were estimated in the training cohort. These thresholds were used in the test cohort to give an estimate of potentially futile TAVI, i.e., those patients in whom the mortality probability after TAVI would be equal to or greater than that without intervention (see Online Methods for details).

The analysis was performed using the statistical software SAS ® version 9.3, and the R software, version 3.3.1.

Results

CHARACTERISTICS OF PATIENTS AND PROCEDURES

The flow of patients is shown in Figure 2. The characteristics of the training and of the test cohorts were similar concerning risk profile, route for TAVI, type of prosthesis, echocardiographic parameters, and TAC (Table 1, Online Table 1). There were no substantial differences to the excluded patients (Online Table 2). The rate of procedural success was high and a marked decrease of transaortic gradient accompanied by a small increase of left ventricular ejection fraction (LVEF) was observed (Online Table 1).

In the training cohort, 237 deaths were observed during the first year following TAVI, 170 of which were from cardiovascular causes. The one-year cardiovascular and all-cause mortalities in the training cohort were estimated at 13.0% (95% CI: [11.2; 14.8]) and 17.9% (95% CI: [15.9;

19.1]), respectively. In the test cohort, 30 deaths, 21 from cardiovascular causes, were observed.

Cardiovascular and all-cause mortalities in this group were thus 8.2% (95% CI: [4.7; 11.6]) and 11.8% (95% CI: [7.7; 15.7]).

CORRELATION BETWEEN FACTORS BELONGING TO THE SAME GROUP AND TAC

For all three groups of risk factors, the correlations with TAC were low (Figure 3; Online Figure 1). The highest correlation was between TAC and peripheral vascular disease or coronary disease, with coefficients estimated at 0.17.

PREDICTIVE POWER OF TAC FOR CARDIOVASCULAR AND ALL-CAUSE MORTALITY

The survival curves of the 3 classes of TAC were significantly different for cardiovascular and all-cause mortality, respectively (Online figure 2), with a survival much lower for the patients with a TAC above the second tertile in comparison with the two other classes of TAC.

Among demographic variables and comorbidities, age, gender, glomerular filtration rate (GFR) and chronic obstructive pulmonary disease (COPD) were included in the Cox model. The inclusion significantly improved the prediction of cardiovascular and all-cause mortality compared with the model without covariates (p=0.002, p<0.0001 respectively). Inclusion of the atherosclerotic disease factors (coronary artery disease, history of stroke or transient ischemic attack, or history of peripheral vascular disease) did not significantly improve the predictive power of the model (p=0.75 for cardiovascular and p=0.16 for all-cause mortality). Addition of factors linked to cardiac function (LVEF, pulmonary pressure, mean gradient, dyspnea (NYHA), mitral regurgitation) to the 2 previous groups improved significantly the prediction of cardiovascular and all-cause mortality (p=0.0001, p<0.00001 respectively).

Adding TAC to the other factors improved significantly the prediction of cardiovascular and allcause mortality (p<0.01 and p=0.04 respectively). An increase of 1 cm 3 , was associated with a 6% increase in cardiovascular mortality (hazard ratio (HR): 1.06, 95% CI: [1.01; 1.10]) (Table 2) and a 4% increase in all-cause mortality (HR: 1.04, 95% CI: [1.00; 1.08]) (Table 3).

CALIBRATION AND DISCRIMINATION OF THE FINAL MODEL

The calibration of the model including the three groups of factors and the TAC was good for cardiovascular (Online Figure 3A) and all-cause mortality (Online Figure 3B). Slopes were 0.90 for cardiovascular and 0.95 for all-cause mortality, respectively. The IDI index measuring the improvement of the discrimination ability of the model by adding the TAC was significant, estimated at 0.006 (95% CI: [0.000; 0.025]) for cardiovascular mortality and at 0.004 (95% CI: [0.000; 0.018]) for all-cause mortality (Online Figure 4A and4B). The AUC for cardiovascular mortality was 68% (95% CI: [64; 72]); that for all-cause mortality was 68% (95% CI: [64; 72]).

The optimism of the AUC of the final models was low, estimated at 0.04% for cardiovascular mortality and 0.07% for all-cause mortality.

In comparison, the Euroscore performed less well for cardiovascular and all-cause mortality, with AUCs estimated at 56% (95% CI [52; 60]) and 57% (95% CI: [53; 61]), respectively. Interestingly, adding TAC to the Euroscore was also able to improve its discrimination ability with an estimated IDI at 0.007 (95% CI: [0.001; 0.021]) for cardiovascular mortality and at 0.006 (95% CI: [0; 0.018]) for all-cause mortality (Online Figure 4C and4D). The AUCs of TAC combined with Euroscore were estimated at 59% (95% CI [55; 62]) and 58% (95% CI [54; 62]), respectively.

RISK SCORES AND THRESHOLDS

Tables 2 and3 present the coefficients of the risk scores constructed on the training cohort for predicting cardiovascular and all-cause mortality, respectively. Figure 4 presents the components of the risk scores and the correspondence between risk scores and predicted mortality in the training cohort. The threshold of risk score corresponding to a predicted cardiovascular mortality of 20% was estimated at 0.7 (Online Table 3). The associated sensitivity and specificity were estimated at 33.4% and 87.5% respectively. The threshold of risk score corresponding to a predicted all-cause mortality of 30% was estimated at 0.78. The associated sensitivity and specificity were estimated at 27.8% and 90.8% respectively.

PREDICTIVE PERFORMANCE OF THE RISK SCORES ON THE TEST COHORT

The calibration of the risk scores on the test cohort was good (Online Figure 3C and3D) with slopes estimated at 0.92 and 0.95 for cardiovascular and all-cause mortality, respectively. The AUCs were estimated at 66% (95% CI: [65; 67]) and 67% (95% CI: [65; 67]) for cardiovascular and all-cause mortality, respectively. For patients with a risk score above the 20% mortality threshold, cardiovascular mortality probability at one year was estimated at 29.6%, compared with 6% for patients with a risk score below or equal to the threshold. For all-cause mortality, the one-year probability of death in patients with a risk score above the 30% threshold was estimated at 50%, vs. 8.5% in patients with a risk score under or equal to the threshold. This corresponds to 112 and 77 out of 1000 patients respectively in whom TAVI would potentially be futile. The stringency of the criteria may be varied as illustrated in Figure 5.

Discussion

The CAPRI score is a newly developed, dedicated score encompassing an original and meaningful variable -TACin addition to classical variables representative of comorbidities, atherosclerotic and cardiac factors. A 1 cm 3 increase in TAC predicted a 6% increase in cardiovascular mortality and a 4% increase in all-cause mortality. The CAPRI score has a good discriminative ability for one-year cardiovascular and all-cause mortality after TAVI.

THE CAPRI SCORE COMPONENTS AND PERFORMANCE

The identification of patients in whom non-modifiable factors will predispose to adverse outcomes post-TAVI is of utmost importance, both for ethical and economical reasons. The CAPRI score performs well when predicting cardiovascular and all-cause mortality. The validation process strictly followed recommendations [START_REF] Steyerberg | Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating[END_REF] and represents the most ambitious attempt so far to develop a risk model for TAVI candidates. Since prediction models tend to perform better on the data from which they were constructed than on new data, strategies of bootstrapping were used for internal validation in order to limit the optimism bias. External validation was also used as a confirmatory step to assess the predictive performance of the scores. The CAPRI score included 3 groups of meaningful factors: demographic and comorbidities, atherosclerotic disease, and cardiac function (Figure 4). The components of these 3 groups have been largely found to have an important prognostic value among patients undergoing TAVI albeit differences exist according to different settings (4). Although comorbidities and cardiac function were mostly strongly associated with mortality, the inclusion of other factors makes the scores less dependent on the training cohort and more extendable to other settings [START_REF] Steyerberg | Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating[END_REF]. The model is based on preprocedural factors only and can therefore be used prospectively to inform clinical decision-making. Importantly, the score is based on contemporary practice and not on cohorts from earlier trials [START_REF] Arnold | Prediction of Poor Outcome After Transcatheter Aortic Valve Replacement[END_REF][START_REF] Arnold | Predictors of poor outcomes after transcatheter aortic valve replacement: results from the PARTNER (Placement of Aortic Transcatheter Valve) trial[END_REF], which is relevant with respect to the continuing extension of indications for TAVI.

Based on our previous analyses (6,7), we added TAC to the predictive variables. We used continuous TAC because categorization of a continuous variable is usually associated with a loss of power and inaccurate estimation of the effect of the covariate [START_REF] Royston | Dichotomizing continuous predictors in multiple regression: a bad idea[END_REF][START_REF] Bennette | Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents[END_REF]. TAC increased significantly the predictive ability of the model as shown by the likelihood test ( 16) and further highlighted by the IDI index [START_REF] Uno | A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data[END_REF]. TAC appeared poorly correlated with other markers of atherosclerosis or risk factors in keeping with the Multi-Ethnic Study of Atherosclerosis [START_REF] Ong | The relationship between insulin resistance and vascular calcification in coronary arteries, and the thoracic and abdominal aorta: the Multi-Ethnic Study of Atherosclerosis[END_REF]. This suggests that TAC, a surrogate of aortic stiffening and vascular aging, may predict future HF after TAVI, in keeping with our previous reports (6,7). Only thoracic aortic calcification was assessed, as our previous study showed that most of the prognostic information was embedded there (7), and also because not all centers performed whole aorta CT-scan. TAC is precisely computable, highly reproducible, and may also encompass some aspect of frailty including fragility fracture (8), muscle loss [START_REF] Idoate | Noncoronary Vascular Calcification, Bone Mineral Density, and Muscle Mass in Institutionalized Frail Nonagenarians[END_REF] and cerebrovascular events (9). As frailty is complex to assess with no unequivocal definition [START_REF] Afilalo | Frailty assessment in the cardiovascular care of older adults[END_REF], this additional facet of TAC may be interesting.

The score was well calibrated for cardiovascular and all-cause mortality in both cohorts and its prognostic discriminative performance was also good. A score with a good calibration means that the model has a good ability to predict risk of poor outcome with the intervention.

The CAPRI score appears highly relevant for objective decision making in individual patients.

While no formal decision threshold can be proposed, we tried to estimate a meaningful threshold corresponding to the probability of one-year death with medical treatment. In a cohort comparable to C 4 CAPRI, the probability of dying was 23.7% which could increase to >35 % in the presence of symptoms (18). As in our study, most deaths were attributable to cardiovascular causes. In our cohort, CAPRI scores predicted a one-year cardiovascular and all-cause mortality risk after TAVI similar to or greater than that without the intervention in a substantial number of patients (Figure 5). For example, CAPRI scores above 0.70 and 0.78 for cardiovascular and allcause mortality, respectively, indicate a 50% risk of all-cause death, and a 30% risk of cardiovascular death within the first year after TAVI, markedly higher than the estimated risk without intervention. On top of other markers (frailty or cognitive function), such a level of risk may represent an important argument against further proceeding with TAVI. Thus, the CAPRI score represents an objective way for the Heart Team to improve patient selection, and for the patient to make an informed decision.

OTHER RISK SCORES

Many scores are dedicated to peri-operative mortality prediction, e.g. the surgical scores Euroscore, Euroscore II, or the STS score. Their discriminative ability for one-year mortality is poor [START_REF] Ludman | Transcatheter aortic valve implantation in the United Kingdom: temporal trends, predictors of outcome, and 6-year follow-up: a report from the UK Transcatheter Aortic Valve Implantation (TAVI) Registry, 2007 to 2012[END_REF] in keeping with our study. The same applies to the France 2 score which has also been proposed to predict early post-TAVI mortality [START_REF] Iung | Predictive factors of early mortality after transcatheter aortic valve implantation: individual risk assessment using a simple score[END_REF]. The PARTNER and the TARIS scores have been proposed to predict mid-term all-cause deaths [START_REF] Seiffert | circles represent the direction and the strength of the correlations. BMI, body mass index; COPD, chronic obstructive pulmonary disease; GFR, glomerular filtration rate; LVEF, left ventricular ejection fraction; TAC, thoracic aortic calcification. Online Figure 2: Survival curves according to TAC stratified into tertiles Online Figure 3: Calibration of the final Cox models including the 3 groups of factors and the thoracic aortic calcifications (TAC) for predicting cardiovascular mortality and all-cause mortality on the training cohort (Panels A and B) and on the test cohort (Panels C and D)[END_REF]. These scores mainly represent various combinations of the same variables collected in registries or study cohorts. Despite the addition of some specific variables of functional and cognitive capacity, the discrimination of the PARTNER score remains moderate [START_REF] Arnold | Predictors of poor outcomes after transcatheter aortic valve replacement: results from the PARTNER (Placement of Aortic Transcatheter Valve) trial[END_REF], much lower than the CAPRI score. The addition of "frailty syndrome" to the other risk factors appears not to improve 1-year predictions of the score [START_REF] Arnold | Prediction of Poor Outcome After Transcatheter Aortic Valve Replacement[END_REF]. Importantly, these previous scores are based on extreme-risk and high-risk patients for standard aortic valve surgery; thus, their performance in lower-risk patients remains unknown. In this respect, the CAPRI score is currently the only score which has been validated in lower risk patients (test cohort).

LIMITATIONS

The training cohort comprised patients treated between 2010 and 2014 and may not be representative of today's populations and technologies. It is conceivable that technological refinement and inclusion of patients with lower risk profiles may improve outcome further in the future. In this respect it is notable that the mortality rate in the test cohort, treated in 2015, was dramatically lower than in the training cohort. However, the CAPRI scores had very similar performance (AUC) in these more recently treated patients. The outcomes assessed in the C 4 CAPRI multicenter study were limited to cardiovascular and all-cause mortality; its usefulness in predicting other relevant outcomes such as residual HF and quality of life is untested. The score would need to be prospectively evaluated for its power to predict functional improvement and/or persisting HF.

The model might have been improved by the inclusion of frailty-associated variables, e.g. daily life activities. However, such markers were not available in the C 4 CAPRI cohorts and their prognostic impact remains to be determined. To illustrate the impact of the risk scores in identifying potentially futile TAVR, futility thresholds corresponding to the expected 1-year mortality of the same population treated medically (between 15% and 25% for cardiovascular mortality and between 25% and 35% for all-cause mortality) (10), were estimated in the training cohort by cross validation using a bootstrap method with thresholds corresponding to the mean of the estimations obtained on 1000 bootstrap samples. The sensitivities and specificities associated with these thresholds were also estimated. The thresholds estimated for the training cohort were used on the test cohort in order to quantify the mortality probability of patients with a prediction score below the thresholds using the Kaplan Meier method, and the proportion of patients with a prediction score above the thresholds. This allows for estimating potentially futile TAVR, i.e.

CONCLUSIONS

those patients in whom the mortality probability after TAVR would be equal to or greater than that without intervention.

Online Results

Online

  Objective, evidence-based decision-making is critical to avoid futile interventions to make optimal use of finite medical resources. The current study proposes a new specific TAVI-risk score based on pathophysiological variables. The inclusion of TAC, an independent unbiased variable, markedly increased the predictive power of the score. The implementation of a TACbased score into daily practice is facilitated by the fact that CT-scans are systematically performed before TAVI. This study also emphasizes the critical role of aortic biomechanics in determining outcomes after TAVI. PERSPECTIVES COMPETENCY IN MEDICAL KNOWLEDGE: A substantial number of patients exhibit a poor outcome despite TAVI; they should be identified. Thoracic aortic calcification (TAC) is associated with mortality after TAVI. COMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: The CAPRI score, which encompasses TAC combined with classical prognostic factors, has a good ability to predict 1-year cardiovascular and all-cause mortality. Calculation of CAPRI scores should be part of the initial work-up for a more personalized evaluation of the patients who are candidates for a TAVI procedure.TRANSLATIONAL OUTLOOK: The impact of aortic biomechanics and vascular aging on the outcome after TAVI are important but overlooked. Additional research is warranted in this field.

Figure legends Figure 1 :

 legends1 Figure legends

Figure 2 :Figure 3 :Figure 4 :

 234 Figure 2: Study Flowchart

Figure 5 :

 5 Figure5: Impact of CAPRI score to identify potentially futile TAVI. Mortality estimations for patients of the test cohort with a CAPRI score below or above a threshold corresponding to a predicted mortality of 15%, 20% or 25% for the cardiovascular mortality (Panel A) and of 20%, 25% or 30% for the all-cause mortality (Panel B), and expected numbers of avoided transcatheter aortic valve replacement (TAVI) per 1000 patients. The thresholds correspond to the expected mortality in a similar population treated medically (i.e not receiving TAVI), based on the literature.

3 )

 3 factors linked to cardiac function (history of acute pulmonary edema, dyspnea (NYHA), right or left cardiac failure, AF, permanent pacemaker, LVEF, pulmonary pressure, mean gradient, mitral regurgitation.In order to select meaningful non-redundant factors in each group, within-group correlation between factors as well as correlation between factors and TAC was estimated using the Spearman coefficient for quantitative factors, and tetrachoric or polychoric coefficients for binary and ordinal factors, respectively. Non-redundant factors with <5% of missing data were included, except for pulmonary pressure. For patients in whom the measurement of pulmonary pressure was technically not possible, a category of unknown pulmonary pressure was added to the model.Cox regression models were used to quantify the effects of the retained factors on mortality hazard. Four Cox models were constructed, the first including the retained demographic factors and comorbidities, the second including the first model and retained factors linked to atherosclerotic disease, the third including the second model and the retained factors linked to cardiac function, and the fourth including the third model and TAC. The likelihood ratio test was used to compare the embedded models and to test the prediction improvement linked to the added factors as recommended(6). To further highlight the discrimination improvement related to the addition of TAC in the model of prediction, the integrated discrimination improvement (IDI) index was calculated. The IDI index quantifies the mean distance between the risk difference due to the addition of TAC in patients with the event and the risk difference in patients without the event(7). The calibration of the final model was assessed by plotting the observed survival probability for groups of patients with similar predicted survival according to the deciles of prediction. The discriminative performance of the final model was quantified using the cumulative/dynamic area under the ROC curve (AUC) estimated with the semi-parametric method proposed by Song and Zhou(8). The bootstrap method (9) was used to quantify and correct the optimism of the estimated AUCs. The risk scores of mortality were computed using a linear combination of the factors included in the Cox model (i.e. those of the retained factors of the three groups and TAC) weighted by the regression coefficients. The same analysis was applied to cardiovascular and all-cause mortality. The predictive performance of the risk scores was assessed for the independent test cohort by plotting observed survival probabilities against deciles of prediction and by estimating the AUC and 95% CI for discrimination assessment.

Table 1 .

 1 Description of demographic characteristics, comorbidities and disease characteristics of the patients included in the C 4 CAPRI training and test cohorts

	Characteristics	N	Training Cohort	N	Test Cohort
	Age (years)	1425 83.4 ± 7.1	311	83.3 ± 7.4
	Male n (%)	1425 730 (51.2)	311	152 (48.9)
	BMI (kg/m²)*	1402 31.5 ± 7.8	304	32.0 ± 9.0
	Logistic Euroscore (%)	1407 17.5 ± 10.4	297	17.0 ± 11.6
	NYHA class III/IV n (%)	1387 868 (62.6)	294	174 (59.2)
	GFR (ml/min/1.73 m 2 )*	1415 50.5 ± 22.9	306	52.1 ± 23.8
	Clinical History -n (%)
	Previous CABG	1193 144 (12.1)	298	24 (8.1)
	Previous PTCA	1406 379 (27)	398	74 (23.8)
	Peripheral vascular disease	1421 316 (22.2)	305	63 (20.7)
	Stroke or TIA	1421 129 (9.1)	305	27 (8.9)
	COPD	1420 267 (18.8)	305	50 (16.4)
	Diabetes	1425 366 (25.7)	306	86 (28.1)
	Atrial fibrillation	1407 454 (32.3)	297	76 (25.6)
	Permanent pacemaker	1198 155 (12.9)	305	46 (15.1)
	Echocardiographic findings
	Mean gradient (mmHg)*	1396 47.1 ± 16.3	287	48.3 ± 16.5
	LVEF (%)*	1412 56 ± 14	286	54.9 ± 14.9
	Moderate/severe MR n (%)	1371 17 (1.2)	278	7 (2.5)

Table 3 .

 3 Hazard ratios and corresponding coefficients of the risk score estimated in the training cohort using a Cox model for predicting one year all-cause mortality

		Hazard ratio	95% CI	p-value	Coefficient
	Demographic factors and comorbidities
	Age (years)	1.03	1.002; 1.05	0.03	0.026
	Male	1.12	0.84; 1.50	0.44	0.114
	GFR level*	1.40	1.16; 1.70	<0.01	0.333
	COPD	1.21	0.87; 1.68	0.26	0.190
	Atherosclerotic disease factors
	Coronary artery disease	1.07	0.81; 1.41	0.65	0.064
	Peripheral vascular disease	1.31	0.96; 1.79	0.09	0.269
	Previous Stroke or TIA	1.14	0.73; 1.79	0.55	0.135
	Cardiac function factors
	LVEF (%)	0.99	0.98; 1.00	0.06	-0.010
	Pulmonary pressure	1.15	1.05; 1.26	<0.01	0.140
	(mmHg/10)
	Unknown pulmonary pressure 2.36	1.22; 4.55	0.01	0.858
	Mean gradient (mmHg)	0.99	0.98; 1.002	0.11	-0.007
	Dyspnea**	1.54	1.23; 1.94	<0.01	0.435
	Mitral regurgitation level***	0.93	0.78; 1.12	0.43	-0.076
	TAC (cm 3 )	1.04	1.002; 1.08	0.04	0.040

Table 1 .

 1 Procedural and performance characteristics of TAVR in the C4CAPRI training and test cohorts

	Characteristics	N	Training cohort	N	Test cohort
	TAVR route n (%)	1421		305
	Transfemoral		1005 (71)		249 (82)
	Apical		325 (23)		30 (10)
	Other		91 (6)		26 (8)
	Devices
	Implanted valve type n (%)	1223		309
	Edwards		891 (73)		211 (68)
	Corevalve		326 (26.5)		93 (30)
	Other		6 (0.5)		5 (2)

Table 2 .

 2 Demographic characteristics, comorbidities and disease characteristics of the training cohort and of patients excluded from the training cohort, respectively mass index; GFR, glomerular filtration rate; CABG, coronary artery bypass graft; PTCA, percutaneous transluminal coronary angioplasty; COPD, chronic obstructive pulmonary disease; LVEF, left ventricular ejection fraction; MR, mitral regurgitation; TAC, thoracic aortic calcifications. *mean ± sd Online Table3: Cross-validated risk scores thresholds corresponding to several predicted mortalities at one year and their associated sensitivity and specificity in the training cohort

	Characteristics	N	Included patients	N	Excluded patients
	Age (years)	1425	83.4 ± 7.1	472	83.7 ± 6.7
	Male sex n (%)	1425	730 (51.2)	472	215 (45.6)
	BMI (kg/m²)*	1402	31.5 ± 7.8	463	31.8 ± 6.1
	Logistic Euroscore (%)	1407	17.5 ± 10.4	461	18.7 ± 11.2
	NYHA class III/IV n (%)	1387	868 (62.6)	456	336 (73.7)
	GFR (mL/min/1.73 m 2 )*	1415	50.5 ± 22.9	466	47.3 ± 21.5
	Clinical History n° (%)
	Previous CABG	1193	144 (12.1)	430	49 (11.4)
	Previous PTCA	1406	379 (27)	466	129 (27.7)
	Peripheral vascular disease	1421	316 (22.2)	469	110 (23.5)
	Stroke or TIA	1421	129 (9.1)	469	46 (9.8)
	COPD	1420	267 (18.8)	469	115 (24.5)
	Diabetes	1425	366 (25.7)	472	144 (30.5)
	Atrial fibrillation	1407	454 (32.3)	459	140 (30.5)
	Permanent pacemaker	1198	155 (12.9)	427	63 (14.8)
	Echocardiographic findings
	Mean gradient (mmHg)*	1396	47.1 ± 16.3	454	46.1 ± 15.4

Online Figure 4: Distribution of difference in predicted risk between the models with and without thoracic aortic calcification (TAC) in patients who experienced an event (thick line) and in patients without the event (thin line). Red areas correspond to predictive gain and blue areas to predictive loss. The integrated discrimination improvement index (IDI) represents the difference between the red and blue areas. Discrimination improvement with TAC is shown for the CAPRI score on A) cardiovascular mortality and B) all-cause death. Discrimination improvement with TAC for the Euroscore is shown for C) cardiovascular mortality and D) all-cause death.

A) B) C) D)