>> Okay, let's look at Mr. Peabody, who is age 47.
He was admitted to ICU.
He has a triple lumen central line.
He also has a urinary catheter.
He had a fever on the 1st of February.
He had the catheter removed,
the subclavian central line catheter removed,
replaced with a PICC.
The fever continues.
The urine culture was collected.
It's E.coli.
We have a blood culture collected,
and it was E coli and Enterobacter.
And he also has a blood pressure of 77 over 62.
So, could this be a sign, be considered secondary to a UTI,
was your first question.
And then you were supposed to figure
out if this is a primary CLABSI, identify the IWP
and DOE and RIT and an SBAP.
So, this is showcasing the HAI POA worksheet generator.
If you haven't had a chance to work with that,
I would encourage you to do that.
So, yours should look similar to mine,
where the 2/3 urine culture
of the E.coli sets the IWP from 1/31 to 2/6.
The 2/1 fever is used to meet the SUTI.
So, the date of event is 2/1.
So, this is an HAI.
The catheter was in place greater
than two days on the date of event.
Therefore, this meets a SUTI 1a: CAUTI.
So, the SUTI RIT is from 2/1 to 2/14.
And the SBAP is from 1/31 to 2/14.
The 2/3 matching blood pathogen occurs
within the secondary BSI attribution period.
Therefore, it is considered secondary.
So, our teaching point here is that this is a SUTI 1a,
sets that RIT in an SBAP.
And when you get these slides all within a few weeks,
you will have this explanation to go
with your slides when they're posted.
Okay, so, I'm going to give you a bonus question here.
What if that urine culture
that was collected was only 80,000 CFUs per ml?
Same scenario, but could the BSI be secondary to a UTI?
Ooh, we've got lots of conversation going on.
[ Inaudible ]
So, for the purpose of NHSN,
in order for that bloodstream infection
to be determined secondary to another site of infection,
the following requirements must be met.
At least one organism from the blood specimen must match the
organism identified from the site specific infection
that is used as an element
to meet the NHSN site specific infection criterion,
and the blood specimen is collected
in the secondary BSI attribution period.
This urine culture of 80,000 does not meet that criteria
to be used as an element to meet the NHSN site specific
infection criterion.
So, no infection identified, no IWP, no SBAP.
That blood sample must be investigated as a primary BSI
as an element, or to meet another site specific infection.
Yeah, we've got a lot of conversation on that,
so think about that one.
Okay, let's look at Case 2.
So, we have Mr. Urea, who is age 77,
who has a positive urine culture on 1/28 with greater
than 100,000 proteus mirabilis and a fever.
Fever continues on 1/30.
On February 2nd, the Foley catheter is inserted by urology
to gross hematuria and clots.
We have a fever on 2/3.
We have some dementia going on.
And we have a positive urine culture E.coli that happens
to be ESBL of greater than 100,000.
So, what is Mr. Urea's determination?
So, IWP, DOE, RIT, SBAP.
So, this is what you should have, except on your diagram,
you don't have this little pop up, of course.
The reason why you will get this pop up,
should you use the POA HAI, calculator,
is you have selected a calendar day that occurs
in a POA time frame period defined as two days before
and one day after in patient admission.
For the purposes of NHSN surveillance
and the determination of a repeat infection time frame,
if the date of the event is determined to be either
of the two days prior to or the admit date, then the date
of event will be hospital day one.
And this is one of your learning points.
Likewise, the first day of the RIT will be hospital day one.
So, we have 1/26, dysuria with 1/28,
positive urine culture meeting the SUTI 1b: Non CAUTI.
The DOE is 1/27, which is POA.
RIT is set 1/27 to 2/9.
The SBAP is 1/25 to 2/9.
The 2/8 fever and a positive urine culture occur
within the RIT.
Therefore, this is not a new event.
Additional pathogen is considered
as part of the POA event.
Even though the catheter is in place greater than two days
on 2/8, it does not become a CAUTI.
So, our teaching points.
We have a SUTI 1b: POA on a patient who is greater
than 65 years of age by using this additional symptoms
besides fever.
When symptoms occur prior to admission, if documented
in the medical record and the symptom occurs within the IWP,
the DOE becomes day one of admission.
Do not change the device association during the RIT.
Add a new, do add a new pathogen.
So, let's say that this person didn't have this fever on 2/8.
What if there was no temp on 2/8?
There would be no SUTI met.
So, think about that.
And I'm going to hand you off to Agasha.
>> Thank you, Bonnie.
Hi, everyone.
My name is Agasha Katabarwa.
I will be doing the analyzing catheter associated urinary
tract infections group exercise.
It is the next page from the exercises you just did
right now.
And I will give you 10 minutes to do it.
But here's the scenario.
So, Sarah Savvy, a new infection preventionist
for freestanding LTACH called Safe Hospital would
like to know how her facility's CAUTI SIR is calculated.
After gathering her data for the annual survey,
she tallies up 10,920 annual patient days,
and 329 annual admissions.
Safe Hospital has two adult wards,
E4 and W5, and one ICU unit.
Safe Hospital observed five infections in E4 ward
and 980 Foley days in this unit in 2018.
So, here is the table that you can also find in the SIR guide.
And this is the model for CAUTI long term acute care hospitals.
So, you should have this also in your booklet.
So, it includes your intercept, the variables included
in the model, and the parameter estimates, the standard error,
the p value, letting you know
that these are statistically significant predictors.
And at the bottom, it is important to note
that the average length of stay is taken
from the annual LTACH survey.
It is calculated as total number of patient days divided
by total number of admission days.
This LTACH is setting its --- and the asterisk
at the bottom also says LTACH setting, free standing
versus hospital versus within a hospital is taken
from the annual LTACH survey.
And, whoops, so, I'll give you seven minutes
to work out the questions.
I'm going to walk around.
And if you have any questions, you can ask me,
or anyone that's walking around from the MAT team.
[ Inaudible ]
All right, I hope you guys have had enough time
to look through everything.
But if you don't, we'll just keep on going ahead.
And I want to encourage you that we are going to go
through these models
and equations a couple of times this week.
So, if you didn't get it, do not lose heart.
We're going to go over them multiple times
in different presentations.
And hopefully at the end of the week,
you would have a good grasp of what the equations are
and how to utilize them.
So, the first one is calculate Safe's average length of stay.
And that is total annual patient days divided
by total annual admissions.
And in Safe's Hospital, we have 10,920 patient days,
divide that by 329, you should have got 33.19.
So, the second question was,
calculate Safe Hospital's predicted number
of infections for E4 ward.
And so this is the same equation
that Prachi had just an hour ago.
This is a negative binomial equation.
So, I'm going to try to go through it quickly
since we don't have enough time.
But I will, after this, I will go back into the audience.
If you have any questions, just let me know.
So, this is basically the same thing as a Y is equal
to MX plus B equation in grade school.
So, your logit theta is Y. Your intercept,
that squirrelly thing, is,
the alpha is what would have been the Y intercept.
Your betas are slopes.
And your Xs are what the variables that you want
to put into the model.
So, this is the model that we have.
So, Safe Hospital had greater
than 27.52 average length of stay.
So, that is a that's something to note.
We wanted to calculate the number
of predicted days for E4 ward.
So, you can look at the bottom of the table.
You would be looking at the referent category.
And Safe Hospital was, I believe,
a freestanding LTACH hospital.
So, take note of the number 0.1941.
So, here is our equation with all the numbers filled in.
So, our Y intercept was negative 6,
which is the alpha, was negative 6.6068.
Add that to our parameter estimate of the average length
of stay, which is greater than 27.52.
And then add that to what our result would be
of beta 2 times the second variable,
which is a freestanding hospital.
And then it was a ward, the LTACH was the E4 is a ward,
so we use the parameter estimate 0.3135.
And at the end, you take the exponent of all that,
multiply it by your catheter days,
which was 980 catheter days.
After doing all this, you should get 1.976 predicted infections.
If you didn't get, if you didn't get this number
of predicted infections and you really, really want to know why,
just call me and I'll come over
and we will go through it together.
And also notice on this slide, the dividing,
the division signs were gone.
So, let's just pretend that after observed infections,
there was a divide sign divided by predicted infections.
So, we had 5 observed infections divided
by 1.976 predicted infections.
And that should give us an SIR score of 2.530.
And the third question is, if the p value
of this SIR score is greater than 0.05,
how can the results be interpreted?
So, the answer to this is Safe Hospital's observed catheter,
observed catheter associated urinary tract infection are not
statistically significantly more than the predicted number
of infections, based on the 2015 national aggregate data.
Question four, if the number of catheter days is the is
in the same, is the same in the ICU as in the ward,
would the number of predicted infections be higher, lower,
or the same as the ward?
So, you go ahead and do the exact same equation all
over again.
And you should get 4.035 predicted infections.
If the number of catheter days was the same as in the ICU
in the ward, the number
of predicted infections would be higher.
So, this question is really put in to say like, you know,
if my location has changed in my LTACH, you know,
if I change from the ICU or ward,
I would not get the exact same predicted number of infections.
And if you divide 5 by 4.035,
you would get a smaller SIR score.
I hope you all got that, but I'll be in the audience
if you guys want to talk to me and ask me questions.
Thank you.
[ Applause ]