Using NHSN to Investigate Causes for Elevated SSI Rates

Debbra Wightman- MPH Student

Preceptor - Frank Myers

UC San Diego Health System- IPCE

November 2014

Objectives

Identifying 3 statistical test which can be used to evaluate a cause in a SSI outbreak

Identifying the variable that can be an value when analyzing SSI

The steps taken to extract the data to formulate the importance of the line listing for SSI

NHSN

Utilizing NHSN Analysis tools a). Be familiar with your reports b). Be familiar with your data & trends The benefits of NHSN a). It can help to you support the data b). Can help to rule out problems with SSI Outliers c). It can help provide surgeons a snapshot of their data

Make NSHN work for you!

Surgical Site Infections outliers are identified and presented at an Infection Control Committee (ICC)

Preliminary investigation is not done for months after the fact delaying implementing improvements.

Problems with processes

Range from time problem identified until data is shared with the surgeons 3 months to 1.5 years

Process had no standard report for analyzing potential causes for variation

Flow of Process SSI Outlier identified in analysis BEFORE Infection Control Committee (ICC) meeting

Data goes to Infection Control Committee (ICC)

Data given to Medical Director

Investigation into causes for high SSI rate is done before

next ICC meeting

Data discussed at ICC and decision to investigate further reached

Non-standardized investigations done using ICC

hypothesis

Data and identified issues shared with surgical group

If theories are incorrect, investigation results go

back to ICC (?)

Identify a surgical site outlier

Use SIR and p value assuming an alpha 0.05

Pick a least a 6 months to a year of data

Run line listing report to capture all variables which is a interest to you and the providers

Extracting Line Listing from NSHN

Selecting your variables from NSHN

Sorting the Variables

Use the filter icon in Excel to help you sort your variables Filter by infections / No infections 1) Gender 2) Avg. AGE 3) BMI Avg. 4) Scope 5) Average Surgery Time 6) ASA Score 7) Wound Class

Sample of a Line Listing

Line listing from NHSN

formula used

=(3*60)+K42

patID dob gender proceed Proc

Date

Proc

Code asa

BMI_

val

proc

Duration

Hr

Combo

of

hrs/mins

Proc

Duratio

n

Min

Anes. emerg scope

Sw

Clas

s

traum

a

Age

At

Proc

infection

12345678 1/1/00 F 12345678 1/1/00 colo 3 24.5 3 222 42 3 N Y 2 N 32 Y

Once the line listing is complete Sort out your variables

Female – Males

Infection – No infections

Scope – Not Scope

Avg Procedure time

BMI average

ASA score

Sorting the Line Listing

Identifying the variable that can be an value when analyzing SSI.

EXAMPLES: INFECTION NO INFECTIONS

GENDER Females (10) Males (9) = 19 Females (103) Males (117) =220

Avg. AGE 60 54

AVG SURG.TIME 4.7 hours 3.6 hours

SCOPE 9 Were scoped 129 Not scoped

BMI Average 27.4 25

ASA Score 2.7 2.9

88 % of COLO patients in 8 months had no infections

Built in NHSN test

http://www.openepi.com/SMR/SMR.htm

Statistical Test

The following test can help to identify different variables, which can help to recognize what is significantly expected or not expected.

Whitney-Mann Test

Fisher Exact Test T- test independent Sample Test (add this website)

Website: http://www.real-statistics.com

Whitney-Mann Test

Whitney- Mann U Test: is a nonparametric test of the null hypothesis that two populations are the same against an alternative hypothesis, especially that a particular population tends to have larger values than the other.

Example: ASA Score ( infection & No Infection)

Mann-Whitney U Test RAW DATA

ASA Infections ASA No Infection

3 3 3 3 3 3 2 3 2 3 3 4 3 3 2 3 2 3 3 4 2 3 2 3 3 3 3 3 3 2 3 2 3 3 3 3 2

2 3 2 2

3 3 3 1

3 4 3 3

3 3 2 3

3 3 5 3

3 4 2 3

3 3 3 2

3 3 2 2

3 3 2 2

3 2 2 2

1 3 2 3

3 2 2 3

3 3 3 3

4 3 3 3

2 3 3 3

4 4 4

Test via normal distribution

ASA infection ASA No Infection

count 17 64

median 3 3

rank sum 51 163

U 1190 3005

α 0.05

tails 1

U 1190

mean 544

variance 7434.7

Example: ASA Score ( infection & No Infection)

Whitney-Mann Test

KEY = CC 1

Mann-Whitney U Test CO 2

Raw Data D 3

U 4

Wound Class Infections

Wound Class No Infection

1 1 1 1 2

1 2 1 2 1

2 1 1 1 3

2 1 1 1 2

2 1 2 1 1

2 3 1 2 1

2 1 1 1 2

2 2 2 2 1

2 1 4 4 1

1 1 2 3 1 1 1 1 1 1 1 2 1 2 2 2 1 3 1 2 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 3 1 1 1 1 3 3 3 3 3 3

Whitney-Mann Test

Example: Wound Class (infection & No Infection)

Test via normal distribution

Wound Class

infection Wound Class No Infection

count 10 88

median 2 1

rank sum 16 138

U 919 4658

α 0.05

tails 1

U 919

mean 440

variance 7260

std dev 85.20563362

z-score 5.621694009

U-crit 299.3492045

p-value 0.999999991

sig no

r 0.567878651

Fisher Exact Test

Fisher Exact Test : is a statistical significance test used in the analysis of contingency tables. Although in practice it is employed when sample sizes are small, it is valid for all sample sizes.

86% of those patients were not scoped http://research.microsoft.com/en us/um/redmond/projects/mscompbio/fisherexacttest/

Fisher Exact Test

Scope No Scope

Infection a= 10 b= 9

No Infection c= 88 d=131

Total 98 140

Fisher Exact Test

Observed Values Expected Values

emergency no emergency Total emergency non emergency Total

infection 0 19 infection 0 19 0

non INFECTIONS 0 219 NON-EMERGENCY 0 219 0

Total 0 238 Total 0 238 0

α 0.05

df 1

χ2 #DIV/0!

p-value #DIV/0!

χ2-crit 3.841458821

sig #DIV/0!

Example: Emergency (infection & No Infection)

T-Test-Two Independent Sample Test

T-Test- two independent sample test: A t-test helps you compare whether two

groups have different average values (for example, whether men and women have different average heights).

Example: Age & BMI (infection/ no infections)

AGE SUMMARY Hyp Mean Diff 0

Groups Count Mean Variance Cohen d

Infection 10 61.8 134.8

No Infection 219 53.4 298.7

Pooled 292.2 0.49183

T TEST: Equal Variances Alpha 0.05

std err t-stat df p-value t-crit lower upper sig effect r

One Tail 5.527607 1.520967 227 0.06483 1.651594 no 0.10044

Two Tail 5.527607 1.520967 227 0.12966 1.97047 -2.48468 19.29928785 no 0.10044

T Test: Two Independent Samples for AGE

T Test: Two Independent Samples for BMI

BMI SUMMARY Hyp Mean

Diff 0

Groups Count Mean Variance Cohen d

Infection 12 27.4 118.3

No Infection 147 24.9 37.0

Pooled 159 42.68252 0.381046

T TEST: Equal Variances Alpha 0.05

std err t-stat df p-value t-crit lower upper sig effect r

One Tail 1.961437 1.269195 157 0.103125 1.654617 no 0.100777

Two Tail 1.961437 1.269195 157 0.20625 1.975189 -1.38476 6.363654 no 0.100777

Jan 2014 we started to capture BMI status and since then 12 patients had infections.

New Process

Turn around time is days after Infection Control Committee (as analysis is done before the meeting) The new process will help to rule out any problems (examples)

Surgeon performance Operative arena or discharge issues Time to onset and organisms Failure of the risk model to account for patient severity (gender, age risk

score)

Flow of New Process

SSI outlier identified in analysis before ICC meeting

Analysis done of likely causes for high rate

Data goes to Medical Director Data goes to ICC along with issues ruled out as causes

Data and identified issues shared with surgical group

Discussion at ICC about data, identified issues

QUESTIONS?

And

Thank you!