NSRR staff
Boston, MA
0000-0002-0506-8368
Top Topics
Recent Topics
Here's some more information on the ECG data present in the SHHS dataset:
Baseline ECGs were performed by the parent cohorts prior to the PSG. All Field Sites performed a standard resting 12-lead ECG with the participant supine. Ten seconds of data were acquired simultaneously from each lead (I, II, III, aVR, aVL, aVF, V1-V6) to a Marquette MAC PC or MAC II system. At each site, a paper copy of the ECG is produced and filed. Minnesota coding of the ECG data is performed on all participants in ARIC, CHS, and SHS. Minnesota coding may be performed for other sites as well. Currently, NYCC ECG data were interpreted by a clinical cardiologist, and FHS ECG data were interpreted by the clinic physician. Minnesota codes provided by the Field Sites were then used to derive the 34 variables in the ECG dataset.
My guess is that the parent cohorts tracked additional details (e.g. ongoing presence, treatment) about each participant's AF, though these details did not trickle down into the SHHS datasets since these were secondary to the original outcomes of SHHS.
PS. It's Mike, not Mark!
Note that #2 and #3 take into account the "afib" variable as part of their derivations. Then for the Tung analysis she obtained additional parent cohort data that extended from the end of SHHS Visit 2 up until 2006.
These AF variables are difficult to interpret, in my opinion. For #1 you need to consider the ecgdate variable, given that the ECGs of interest were often not completed at the SHHS exam. The ECG data are "B Variables".
Furthermore, as you'll see from the missingness, not all cohorts had ECG data to share and/or may not have contributed supplemental data for the Tung AF analysis.
Directly to your question, I think the "afib" (#1) variable is the best representation of the presence of AF nearest to the two SHHS exams.
We did create ahi_a0h3a at a later date. Prior to that, a variable like rdi3p was commonly used as the primary AHI indicator in SHHS, though this variable required that apneas also have an associated >=3% desaturation (like the hypopneas). The meaning of "RDI" has also shifted a bit over time, so the variable name and its approach for including apneas had become a bit antiquated.
Just to give you a sense of how the scoring program works and how the different AHIs are computed, here's another description. The study is scored once. You cited the rules about hypopneas requiring an associated >=2% desaturation. The scoring program (Profusion) would then give us different variables ("components" for AHI calculations) representing counts of specific event types at specific desaturation/arousal criteria. For instance, we might get something like the following in an individual sleep study.
Thus, if I want a "total AHI" (all hypopneas) I might use #1, if I want ahi_a0h3 I am going to use #2 (for the hypopnea component), and if I want ahi_a0h4 I am going to include #3.
Joachim,
I'm not aware of any projects/publications that looked to re-score parts of the SHHS dataset. The ahi_X variables, like ahi_a0h3a, were derived from the counts of apneas and hypopneas from the original scoring. These were created to mimic AHIs from other cohorts where we normally use "all apneas" and "hypopneas with >=X% oxygen desaturation" as the AHI numerator. The scoring software output counts of respiratory events that met different desaturation thresholds and with/without arousals factored in. You can see this by looking at the variables that comprise the ahi_a0h3a calculation.
Mike
Do you mean variables like the following? https://sleepdata.org/datasets/shhs/variables?search=shift
I opened the dataset and it looks like hstg2t1p is equal to stg2t1p/(time_bed/60), so it looks like time in bed is the denominator.
Could you shed more light on how many subjects you have found that are affected with the misalignment issue? Did you do a test across all 1,800+ subjects with overlapping PSG/actigraphy, or did you identify the problematic subjects above in a more piecemeal fashion? Originally you said you were "noticing many subjects with temporal misalignment".
Thanks!
Thanks again. This is still on our radar - I am trying to see if someone else on our team can take a closer look to gain a better understanding of the issue.
Thanks! How about these IDs? These subjects all used the same actigraphy device and the studies below occurred in chronological order, with some other subjects in between who did not have accompanying PSG data. Between Groups 1 and 2 I believe the actigraphy device may have gone to the manufacturer for maintenance/repairs. Between Groups 2 and 3 there was a daylight saving time shift. I am curious to see if there is a point in time where the watch shifts from being aligned to misaligned, so to speak.
Group 1
Group 2
Group 3
jongguri,
Could you check for this issue and post your plots for MESA ID 5303? This subject used the same actigraphy device as your 6318 example. 5303 was completed a week before 6318.
We will take a look at the examples you provided in more detail and report back.