Lengths in The Neonatal Intensive Care Unit (NICU) at the U - PowerPoint Presentation

Slide1

Lengths in The Neonatal Intensive Care Unit (NICU) at the UICH

Presented by: Sathia Veeramoothoo Fan YangSlide2

Introduction

Measurements of growth are a good indication of overall well being and outcomes in infants. Length is a non-invasive measure of skeletal growth. Accurate measures of length are important for monitoring growth in infants transitioning to home, for high risk and primary care provider follow up, and infant nutrition programs.Slide3

Kirsten’s Goals

Increased NP knowledge, confidence, and evidence based techniques for obtaining lengths. Increased documentation of discharge lengths in EPIC growth chart.Increased number of lengths in children at risk for growth failure.

Increased reliability, precision and accuracy of lengths measures. Slide4

Main Goal

Problem: Measurement of infant lengths using paper tape measures is inaccurate and unreliable. Purpose: To increase the accuracy, reliability and precision of length measurements in infants in newborn and intensive care units cared for and discharged from UICH. Slide5

Data Collection

Design: For each infant a length measurement will be performed four times, twice each by two experienced Nurse Practitioners. Procedure: 1. NP1- Using tape measure in the envelope, obtain a length using standard procedure. 2. NP1- Reposition the child and obtain a second measure of the child’s length using an unmarked tape.

3. Give the envelope to another nurse practitioner to obtain repeated length within 24 hours. 4. NP 2- Using tape measure, obtain a length using standard procedure. 5. NP 2 - Reposition the child and obtain a second measure of the child’s length using an unmarked tape. Slide6

Overview of Original DataSlide7

Data Re-format using SAS

/*Reformat data for SAS model fitting.*/data babiesNew; set babies;nurse=NP_1;y=NP1_L1;treatment="standard";output;nurse=NP_1;y=NP1_L2;

treatment="unmarked";output;nurse=NP_2;y=NP2_L1;treatment="standard";output;nurse=NP_2;y=NP2_L2;treatment="unmarked";output;keep ID GA BW DOL AGA y nurse treatment;run;Slide8

Modified DataSlide9

Modeling

Rosenberg et al. (1992) essentially performed separate reliability analyses for each method being compared (e.g. paper tape vs. Prematometer).Using this same tactic for Kirsten’s data, we can model the variability in lengths within each method (marked vs. unmarked) as being caused by one of three sources:

1. baby-to-baby variability 2. nurse-to-nurse variability (inter-rater variability) 3. random noiseThe resulting two reliability measures would then be compared to see if one method was more reliable than the other.Slide10

Modeling Challenges

Pure within nurse or intra-rater variability:Nurses did not repeatedly measure the same baby under the exact same conditions (i.e. with the same type of tape).Intra-baby variability:We do have two measurements from the same nurse on the same baby, but they were under

different conditions (specially, one was done on a marked tape and one was done on an unmarked tape).Confounding: the difference in these two measurements could be due to a difference in the methods (marked vs. unmarked) or due

to intra-rater variability.Slide11

Intra-class Correlation and Reliability

With the previously-mentioned three sources of reliability, we can compare the reliability of these two methods of measuring length by comparing the value of their intra-class correlation (ICC). ICC is used as

a measure of how reliable the method is for measuring length is, and it essentially relates the variability between nurses to the variability between babies. For example, if nurses tend to give the same measurement for a baby, then the ICC will be close to 1.Slide12

SAS Code

data standard;set babiesNewer;where treatment = “marked";

run;proc mixed data=marked;class ID nurse;model y = ;random nurse ID;run

Covariance Parameter Estimates

Cov

Parm

Estimate

nurse 0.01643

ID 10.97

37

Residual 0.4204

Covariance

Parameter

Estimates

Cov

Parm

Estimate

nurse

0.8301

ID

10.2593

Residual

0.7103

data unmarked;

set

babiesNewer

;

where treatment = "unmarked";

run;

proc

mixed data=unmarked;

class ID nurse;

model y = ;

random nurse ID;

run;

0.9617

0.8695Slide13

ICC Values and Interpretation

This suggests the nurses were in better alignment when using the marked tapes.

Limitation: We haven't tested to see if the ICC values are actually statistically significantly different.Baby-to-baby variability in these two analysis were essentially

identical (as would be expected because the same babies were used for both), and it was the difference in the nurse-to-nurse variability across the methods that was the source of the differing ICC values.Slide14

More on Reliability

Lack of data: Kirsten has not yet collected data on length boards.Recommendations for future data collection: For intra- and inter-rater reliability:Take two (or more) measurements on each baby with the same nurse AND the same type of measurement instrumentGet these same measurements

by a second nurseFor comparing intra-rater reliability for length boards compared to tapes:Take the above four measurements under each method (length board vs. paper tape)Slide15

Kirsten’s Survey and AnalysisSlide16

Survey – Technique SummarySlide17

Survey - continuedSlide18

Point Data Analysis – Overview of LengthSlide19

Point Data Analysis – Baby ExposureSlide20

Point Data Analysis - Distribution of RFSlide21

Point Data Analysis – Exposure by CLDSlide22

Point Data Analysis – Exposure by >=1RFSlide23

Point Data Analysis – Other StatisticsSlide24

Discharge Data Analysis - OverviewSlide25

Discharge Analysis - ExposureSlide26

Comparison of Lengths by Chart

Number of lengths

Discharge chartGrowth chart

N=012.86%21

60%

N=1

34

97.14%

14

40%

Discharge Length in Discharge chart

Discharge Length in Growth chart

N=0

N=1

N=0

0

21

N=1

1

13

Discharge chart

Growth chart

MEAN

0.97

0.4Slide27

Discharge – Correlation with RFSlide28

Summary and Conclusion

More training recommendedNew data collection Better documentationPositive correlation: number of measurements With: length of time spent at the UICH With: presence of at least 1 risk factor

Positive correlation: GA and BWNo correlation: number of measurements & presence of CLD No significant differences between bays 4 and 5. Next steps: paired t-tests on the marked and blank tapesStatistically: Experience and position do not impact on the accuracy of the first three survey questionsSlide29

Thank you.