Cardiovascular and Autonomic Research Design.

card

Cardiovascular and Autonomic Research Design

The goal of card is to create functions and analytical approaches in neurocardiology, with a focus on electrocardiogram data and epidemiological concepts. An important engine within this ecosystem are the circadian analysis tools. Its developed to fit a tidy approach to statistical analysis.

The areas of focus of this package are the following:

• cardiovascular hemodynamics
• circadian rhythms
• electrocardiography (heart rate variability and morphology)
• signal processing

Installation

Once published, you can install the released version of card from CRAN with:

#install.packages("card")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("asshah4/card")

Usage

This package is intended for analyzing cardiovascular signals and autonomic physiology. The current collection of functions are built practically around this paradigm. The current focus, in card v0.1.0 is the development of a flexible and tidy analytical approach to circadian rhythms.

library(card)

Datasets

It comes with several useful datasets:

• Cardiovascular disease outcomes with recurrent events
• Vectorcardiography data in a high-risk cohort
• Continuous electrocardiography recordings in a clinical cohort with quantitative stress data

# ECG data
data("twins")
head(twins)
#> # A tibble: 6 x 23
#> # Groups:   patid, hour [6]
#>   patid   age   bmi race  smoking hptn  dm    chf   prevchd med_beta_blocke…
#>   <dbl> <dbl> <dbl> <fct> <fct>   <fct> <fct> <fct> <fct>   <fct>           
#> 1     1    49  27.4 0     1       1     0     0     0       0               
#> 2     1    49  27.4 0     1       1     0     0     0       0               
#> 3     1    49  27.4 0     1       1     0     0     0       0               
#> 4     1    49  27.4 0     1       1     0     0     0       0               
#> 5     1    49  27.4 0     1       1     0     0     0       0               
#> 6     1    49  27.4 0     1       1     0     0     0       0               
#> # … with 13 more variables: med_antidepr <fct>, beck_total <dbl>,
#> #   sad_bin <fct>, sad_cat <fct>, PETdiff_2 <fct>, dyxtime <dttm>, date <date>,
#> #   hour <dbl>, rDYX <dbl>, sDYX <dbl>, HR <dbl>, CP <dbl>, zip <chr>

# Outcomes data
data("mims")
head(mims)
#> # A tibble: 6 x 11
#>   patid first_visit_dat… ldka       mi_date_1  mi_date_2  mi_date_3  CHF_DATE_1
#>   <dbl> <date>           <date>     <date>     <date>     <date>     <date>    
#> 1     1 2012-07-27       2017-10-23 NA         NA         NA         NA        
#> 2     2 2012-08-15       2017-07-17 NA         NA         NA         NA        
#> 3     3 2012-08-27       2012-08-29 NA         NA         NA         NA        
#> 4     4 2012-08-29       2017-07-10 NA         NA         NA         NA        
#> 5     5 2012-09-04       2017-07-20 NA         NA         NA         NA        
#> 6     6 2012-09-21       2017-07-10 NA         NA         NA         NA        
#> # … with 4 more variables: CHF_DATE_2 <date>, CHF_DATE_3 <date>,
#> #   DEATH_CV_YN <dbl>, RDR_ISCHEMIA_M_YN_bl <dbl>

# VCG data
data("geh")
head(geh)
#> # A tibble: 6 x 70
#>      pid hhp_id   age sex   age_cat systolic_bp_fir… systolic_bp_sec…
#>    <dbl>  <dbl> <dbl> <fct> <fct>              <dbl>            <dbl>
#> 1 200481 220946    42 0     0                    162              165
#> 2 200489 224643    62 1     1                    133              128
#> 3 200495 224845    84 0     2                    100               99
#> 4 200052 222917    65 0     2                    139              141
#> 5 200517 220638    64 1     1                    166              163
#> 6 200623 224862    57 0     1                    151              144
#> # … with 63 more variables: systolic_bp_third <dbl>, diastolic_bp_first <dbl>,
#> #   diastolic_bp_second <dbl>, diastolic_bp_third <dbl>,
#> #   pulse_rate_first <dbl>, pulse_rate_second <dbl>, height_cm <dbl>,
#> #   weight_kg <dbl>, waist_cm <dbl>, dia_trt_allopdrug <hvn_lbll>,
#> #   hbp_trt_allopdrug <hvn_lbll>, hyp_trt_allopdrug <hvn_lbll>,
#> #   lab_hba1c <dbl>, lab_fasting_bg <dbl>, lab_fasting_insulin <dbl>,
#> #   lab_tchol <dbl>, lab_ldlchol <dbl>, lab_hdlchol <dbl>, lab_triglyc <dbl>,
#> #   lab_ser_urea <dbl>, lab_ser_creatinine <dbl>, lab_urin_malbumin <dbl>,
#> #   pd_heart <hvn_lbll>, bmi <dbl>, bmi_cat <dbl>, obese <fct>,
#> #   obese_asian <fct>, sbp_mean <dbl>, dbp_mean <dbl>, pulse_mean <dbl>,
#> #   htn <fct>, cad <fct>, drugs_dm <fct>, dm <fct>, dm_lab <fct>,
#> #   dm_control <fct>, dm_pre <fct>, homa <dbl>, high_waist <fct>,
#> #   high_tchol <fct>, high_ldl <fct>, low_hdl <fct>, high_triglyc <fct>,
#> #   met_syn_num <dbl>, met_syn <fct>, pr_interval <dbl>, p_duration <dbl>,
#> #   p_amp <dbl>, qrs_duration <dbl>, qt_interval <dbl>, cornell_voltage <fct>,
#> #   nhanes_score <dbl>, svg_mag <dbl>, az_svg <dbl>, az_svg_m <dbl>,
#> #   el_svg <dbl>, el_svg_m <dbl>, qrs_tang <dbl>, auc_vm_qt <dbl>, wvg <dbl>,
#> #   log_svg <dbl>, log_auc_qt <dbl>, log_wvg <dbl>

Cosinor Analysis

This modeling algorithm requires only base R, which allows internal flexibility for modeling heuristics and improved efficiency. For the user, it also allows standard modeling tools/approachs, and a flexible user interface that accounts for individual/population analysis and single/multiple component analysis.

m <- cosinor(rDYX ~ hour, twins, tau = c(24, 12))
summary(m)
#> Individual Cosinor Model 
#> ------------------------------------------
#> Call: 
#> cosinor(formula = rDYX ~ M + A1 * cos(2*pi*hour/24 + phi1) + A2 * cos(2*pi*hour/12 + phi2) 
#> 
#> Period(s): 24, 12 
#> 
#> Residuals: 
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#> -3.00770 -0.52024 -0.03247  0.00000  0.48753  4.88552 
#> 
#> Coefficients: 
#>         Estimate  Std. Error
#> mesor  2.8586510 0.006062639
#> amp1   0.2964114 0.008702368
#> amp2   0.1302012 0.008542526
#> phi1  -2.6542757 0.028911445
#> phi2  -3.6636921 0.065235427
ggcosinor(m)
#> This is a harmonic multiple-component cosinor object. The orthophase, bathyphase, and global amplitude were calculated.
#> Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
#> 'x' values
#> `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

head(augment(m))
#> # A tibble: 6 x 8
#>       y     t     x1        x2        z1        z2 .fitted .resid
#>   <dbl> <dbl>  <dbl>     <dbl>     <dbl>     <dbl>   <dbl>  <dbl>
#> 1  2.63     8 -0.500 -5.00e- 1  8.66e- 1 -8.66e- 1    3.22 -0.592
#> 2  2.42     9 -0.707 -1.84e-16  7.07e- 1 -1.00e+ 0    3.21 -0.787
#> 3  1.81    10 -0.866  5.00e- 1  5.00e- 1 -8.66e- 1    3.15 -1.34 
#> 4  2.01    11 -0.966  8.66e- 1  2.59e- 1 -5.00e- 1    3.08 -1.07 
#> 5  1.63    12 -1      1.00e+ 0  1.22e-16 -2.45e-16    3.01 -1.38 
#> 6  1.95    13 -0.966  8.66e- 1 -2.59e- 1  5.00e- 1    2.95 -0.996