irinagain/iglu

R package for Interpreting GLUcose data from CGMs (Continuous Glucose Monitors)

iglu's Issues

error in gri function

I tried to run the gri() function from iglu on my dataset (see below), but got the following error:
"Error:
! Failed to evaluate glue component {label}
Caused by error in vapply():
! values must be length 1,
but FUN(X[[1]]) result is length 0"

I suspect that it is because the data does not contain readings on neither hypoglycemic nor hyperglycemic range? But I might be wrong about it. Would it be possible for you to solve the issue? Thank you

gri_error_dataframe.xlsx

Possible bug?

Hello,

Thanks for the great library! :)

Can anyone clarify if this is a bug?

The if statement here https://github.com/irinagain/iglu/blob/master/R/mage_ma_single.R#L211 has the same instructions as the else statement

iglu/R/mage_ma_single.R

Line 214 in ccbfe50

} else {

Thanks in advance

hypoglycemic is wrong

Are you sure this is the equation
\sum(glucose) * n /30?
https://github.com/stevebroll/iglu/blob/master/R/hypo_index.R#L19

The real issue is that single raises to 2 power:
https://github.com/stevebroll/iglu/blob/master/R/hypo_index.R#L50
but multi is 1.1:
https://github.com/stevebroll/iglu/blob/master/R/hypo_index.R#L60

Another bug in MAGE algorithm

Hello again.

I'm reporting a new bug in the MAGE algorithm.

In both these lines:

iglu/R/mage_ma_single.R

Line 167 in baaf291

minmax <- append(minmax, min(.data$gl[s1:s2], na.rm = TRUE))

iglu/R/mage_ma_single.R

Line 171 in baaf291

minmax <- append(minmax, max(.data$gl[s1:s2], na.rm = TRUE))

You're assuming that in both of them (.data$gl[s1:s2], na.rm = TRUE) will always return at least one value that's not NA but in some datasets this will return an empty vector because all values in that range s1:s2 are NA and the min and max of an empty vector is always infinite, resulting in a NA result for the overall MAGE calculation.

I'm attaching a dataset where this occurs, I have a few more in case you need them. Just run the MAGE algorithm in this dataset and you'll see that the final result is NA due to the issue I pointed above.

Thanks for looking into this, I would submit a PR myself but I don't know what the algorithm should do when it encounters an edge case like this.
mage-error.csv

Where does this come from?

grade_hypo_single doesn't exist, where does this come from in
https://github.com/stevebroll/iglu/blob/master/R/grade_hypo.R#L77

Interpolate function fails on BGM data.

where data is the clean data and subj is created as follows:
subj = data[data$id == data$id[1,] #Get only the first subject
subj$time = as.POSIXct(subj$time) #Force time format
call:
iglu:::CGMS2DayByDay(subj)
Error message:
Error in rep(dt0, ndays * 24 * 60/dt0) : invalid 'times' argument

Backward compatibility of agp function

agp function, works properly in rendering t1 object, Only on using strings as factors=FALSE , on systems with R 3.6 (probably changed with R 4.0). At least this is the case with my system

Calculate metrics

tableStat = data.frame("Glucose statistics", "Value")

allow relevant metrics to be calculated in mmol/l and report in mmol/l

Hi Wondering if there is any plan to implement the ability to process data using glucose values in mmol/L and report on these in mmol/L for the relevant metrics? This would be useful for users in countries that use mmol/l and not mg. Metrics related to time in ranges would require the thresholds and the units to be specified. Thanks Mitch

[feature_request] personal glycemic state (PGS) metric

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467104/

This is a relatively new metric, that is very useful. Hence, it is increasingly popular. Dexcom, xdrip+ , nightscout et al already support it.
Unfortunately, iglu currently lacks support .

Differnece in claculcation

In https://github.com/stevebroll/iglu/blob/master/R/hbgi.R#L22, the power is grouped together (aka it's all to the power of the difference), but in
https://github.com/stevebroll/iglu/blob/master/R/hbgi.R#L49 it's not that calculation

Unable to calculate MAGE

Hi,
My current dataframe is for single participant and I am getting the following error. Not sure what could be the issue

> mage(glucose_data,
+      version = c("ma"),
+      sd_multiplier = 1,
+      short_ma = 5,
+      long_ma = 32,
+      return_type = "num",
+      dt0 = 15,
+      direction = "avg",
+      inter_gap = 45,
+      max_gap = 180,
+      plot = FALSE,
+      title = NA,
+      xlab = NA,
+      ylab = NA,
+      show_ma = FALSE,
+      show_excursions = TRUE
+ )
Error in `[.default`(tr, timeindex - 1) : 
  only 0's may be mixed with negative subscripts
> colnames(glucose_data)
[1] "time" "gl"   "id"  

Definition of hypoglycemia episode

Hi there,

The current definition of level 1 hypoglycemia as per the international consensus (https://pubmed.ncbi.nlm.nih.gov/29162583/) is any cgm reading of <70–54 mg/dL (3.9–3.0 mmol/L) for at least 15 min. If I use the episode_calculation function to calculate frequency of level 1 hypoglycemia with lvl1_hypo and lvl2_hypo threshold set at the aforementioned threshold, does the level 1 frequency calculated include all of the cgm reading below 70 mg/dL or only the ones within 70-54 mg/dL range? Thank you

Develop and extend Iglu for analyzing circadian patterns.

Dear Authors,

would it be possible to contribute to Iglu? I have some ideas for integrating more metrics. Best,
Nico

bug in episode_calculation

      # HYPO OR HYPER DURATION LENGTH CALCULATION
      hyp_result = hyp_episode[2:length(hyp_episode)] - hyp_episode[1:length(hyp_episode)-1]
      hyp_end_point = which(hyp_result %in% -1) + 1
      hyp_starting_point = which(hyp_result %in% 1) + 1

      if (is_hypo){
            if(gl_by_id_ip[length(gl_by_id_ip)] <= params[[2]]){
              hyp_end_point = c(hyp_end_point, length(hyp_result))    # wrong
              hyp_end_point= sort(hyp_end_point)
            }
            if(gl_by_id_ip[1] <= params[[2]] ) {
              hyp_starting_point = c(hyp_starting_point, 1)
              hyp_starting_point= sort(hyp_starting_point)
            }
      }else{
            if(gl_by_id_ip[length(gl_by_id_ip)] >= params[[3]]){
              hyp_end_point = c(hyp_end_point, length(hyp_result))
              hyp_end_point= sort(hyp_end_point)
            }
            if(gl_by_id_ip[1] >= params[[3]] ) {
              hyp_starting_point = c(hyp_starting_point, 1)
              hyp_starting_point= sort(hyp_starting_point)
            }
      }

      hyp_dur_length = hyp_end_point - hyp_starting_point