mrueschman

Thanks for inquiring and for using the NSRR site.

I believe the NEPSY variable you want is nep10b_nepsy. The means appear to follow the patterns shown in Table 2 from the NEJM article. The Ns are slightly different due to limiting the sample in the manuscript, but the magnitude of change is consistent when looking by treatment arm.

I'm not sure stroke or hypertension included in the CVD Outcomes definition. From the CVD Outcomes Protocol:

It should align with the component variables (e.g. MI, PTCA, CABG, etc.), though I cannot guarantee 100% agreement. This work was done by the SHHS Coordinating Center before finalizing the datasets for BioLINCC and NSRR.

One option might be to create your own "new" sort of "any CVD" variable.

Thanks for checking out the site.

Everything from the Health Interview is self-report. There was no confirmation, diagnostic testing, nor medical chart review done to compare with what the participant marked on the questionnaire.

#1 - I'm guessing "COPD" is a term less people had discussed with their doctors. SHHS1 was almost 25 years ago now so I'm not sure how these pulmonary diseases were conceptualized and presented to patients back then.

#2 - I think you'll want "asthma15"/"asth1215" for SHHS1 and "hi201d"/"hi201e" for SHHS2. In SHHS1 we only know if they ever received a diagnosis and whether they had an asthma attack in the last 12 months (asth1215), which you might use as a proxy for "having asthma at the time of the PSG".

#3 - The Health Interview questionnaires should have been done around the time of the actual PSGs in the SHHS visits. So, I expect it's on the order of days. (Comparing "stdatep" and "formdate_hi" could be informative; at a glance they look to match as I expect.)

#4 - Unfortunately all we have is self-report. I expect the parent cohorts have more definitive information about testing, diagnoses, and ongoing presence/treatment.

Here are the parent cohort sites where I would start:

• Atherosclerosis Risk in Communities (ARIC): https://sites.cscc.unc.edu/aric/
• Cardiovascular Health Study (CHS): https://chs-nhlbi.org/
• Framingham Heart Study (FHS): https://www.framinghamheartstudy.org/

The NSRR does not have ongoing relationships directly with these cohorts. I expect each one has different policies and procedures regarding access to supplemental data.

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.

1. 50 total hypopneas (>=2% desaturation criteria as based on scoring manual)
2. 40 hypopneas with >=3% desaturation (subset of #1)
3. 25 hypopneas with >=4% desaturation (subset of #2)

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!