Publications by MT

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14.12.2023

Characteristic N Overall, N = 1831 0, N = 1501 1, N = 331 p-value2 baseline_lvef 183 60 (54, 65) 60 (53, 65) 60 (57, 65) 0.3 x30day_lvef 166 58 (53, 63) 58 (53, 62) 58 (54, 63) 0.6     Unknown 17 13 4 x1year_lvef 119 58 (53, 63) 58 (54, 63) 59 (50, 62) >0.9     Unknown 64 53 11 ppi_company 183 0.003     boston scientific 1 (0.5%)...

3 sym 1 tbl

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14.12.2023

Characteristic N Overall, N = 961 0, N = 571 1, N = 391 p-value2 tavr_valve.x 96 0.3     9600tfx29a 29mm 1 (1.0%) 1 (1.8%) 0 (0%)     corevalve evolut 34-us 4 (4.2%) 3 (5.3%) 1 (2.6%)     corevalve evolute 23-us 1 (1.0%) 1 (1.8%) 0 (0%)     corevalve evolute 26-us 1 (1.0%) 0 (0%) 1 (2.6%)     corevalve evolute 29-us 1...

3 sym 1 tbl

HTML

14.12.2023

Characteristic N Overall, N = 961 0, N = 571 1, N = 391 p-value2 tavr_valve.x 96 0.3     9600TFX29A 29mm 1 (1.0%) 1 (1.8%) 0 (0%)     Corevalve Evolut 34-US 4 (4.2%) 3 (5.3%) 1 (2.6%)     CoreValve Evolute 23-us 1 (1.0%) 1 (1.8%) 0 (0%)     CoreValve Evolute 26-us 1 (1.0%) 0 (0%) 1 (2.6%)     CoreValve Evolute 29-us 1...

3 sym 1 tbl

Document

13.11.2023

Best predictors of end point using random forest Best predictors using univariate logistic regression term estimate std.error statistic p.value eqco2_rest -2.330254e-01 8.947979e-02 -2.604224e+00 0.009208264 vd_vt_at -1.222322e+01 4.705899e+00 -2.597425e+00 0.009392559 vd_vt_maxload -1.324462e+01 5.542342e+00 -2.389716e+00 0.016861420 physical_a...

114 sym 1 img 1 tbl

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29.10.2023

Best predictors of end point using random forest Best predictors using univariate logistic regression term estimate std.error statistic p.value eqco2_rest -2.330254e-01 8.947979e-02 -2.604224e+00 0.009208264 vd_vt_at -1.222322e+01 4.705899e+00 -2.597425e+00 0.009392559 vd_vt_maxload -1.324462e+01 5.542342e+00 -2.389716e+00 0.016861420 physical_a...

114 sym 1 img 1 tbl

Document

11.10.2023

## mapping: y = ~number/2 + c(0, cumsum(number)[-length(number)]), label = ~paste0(round(percentage, 1), "%") ## geom_text: parse = FALSE, check_overlap = FALSE, na.rm = FALSE ## stat_identity: na.rm = FALSE ## position_identity...

5 sym 1 img

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11.10.2023

## mapping: y = ~number/2 + c(0, cumsum(number)[-length(number)]), label = ~paste0(round(percentage, 1), "%") ## geom_text: parse = FALSE, check_overlap = FALSE, na.rm = FALSE ## stat_identity: na.rm = FALSE ## position_identity...

5 sym 1 img

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11.10.2023

## mapping: y = ~number/2 + c(0, cumsum(number)[-length(number)]), label = ~paste0(round(percentage, 1), "%") ## geom_text: parse = FALSE, check_overlap = FALSE, na.rm = FALSE ## stat_identity: na.rm = FALSE ## position_identity...

5 sym 1 img

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11.10.2023

# Create pie chart dataset_complication <- dataset_complication %>% mutate(percentage = number / sum(number) * 100) dataset_complication$side_effect_group <- factor(dataset_complication$side_effect_group, levels = c("no_side_effect", "ischemic_complication", "bleeding_complication")) new_labels <- c("No complication", "Ischemia", "Bleeding...

7 sym 1 img

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11.10.2023

# Create pie chart dataset_complication <- dataset_complication %>% mutate(percentage = number / sum(number) * 100) dataset_complication$side_effect_group <- factor(dataset_complication$side_effect_group, levels = c("no_side_effect", "ischemic_complication", "bleeding_complication")) new_labels <- c("No complication", "Ischemia", "Bleeding...

7 sym 1 img