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4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases

1. Identify the most important elements in managing post-operative pain. 2. Identify the most informative procedure or population-based targets to focus collection of additional, labor-intense detail surrounding adequacy of pain control (i.e., Patient Reported Outcome Measures (PROMs)). METHODS/STUD...

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Autores principales: Ingram, Martha-Conley, Tian, Yao, Mehrotra, Sanjay, Apley, Dan, Raval, Mehul V
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823073/
http://dx.doi.org/10.1017/cts.2020.407
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author Ingram, Martha-Conley
Tian, Yao
Mehrotra, Sanjay
Apley, Dan
Raval, Mehul V
author_facet Ingram, Martha-Conley
Tian, Yao
Mehrotra, Sanjay
Apley, Dan
Raval, Mehul V
author_sort Ingram, Martha-Conley
collection PubMed
description 1. Identify the most important elements in managing post-operative pain. 2. Identify the most informative procedure or population-based targets to focus collection of additional, labor-intense detail surrounding adequacy of pain control (i.e., Patient Reported Outcome Measures (PROMs)). METHODS/STUDY POPULATION: Our study population includes all children, ages 1-18 years, captured in the National Surgical Quality Improvement Project-Pediatric (NSQIP-P) from 2019 to 2021. We plan to apply statistical (regression modeling) and DSD methods to accomplish the aims listed above. RESULTS/ANTICIPATED RESULTS: For Aim 1, we expect to identify patient, procedure, and perioperative pain management practices that influence postoperative pain. For Aim 2, we will focus on outcomes such as PROMs that are challenging to obtain. By applying DSD methods, we will identify specific procedure and/or population-based cohorts to capture PROMs and decrease data collection burdens, while maintaining power, as the project is scaled nationally to all of NSQIP-P. DISCUSSION/SIGNIFICANCE OF IMPACT: Data from this study will inform expansion of NSQIP-P to collect novel outcomes of clinical and societal importance without prohibitively increasing data collection burden.
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spelling pubmed-88230732022-02-18 4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases Ingram, Martha-Conley Tian, Yao Mehrotra, Sanjay Apley, Dan Raval, Mehul V J Clin Transl Sci Translational Science, Policy, & Health Outcomes Science 1. Identify the most important elements in managing post-operative pain. 2. Identify the most informative procedure or population-based targets to focus collection of additional, labor-intense detail surrounding adequacy of pain control (i.e., Patient Reported Outcome Measures (PROMs)). METHODS/STUDY POPULATION: Our study population includes all children, ages 1-18 years, captured in the National Surgical Quality Improvement Project-Pediatric (NSQIP-P) from 2019 to 2021. We plan to apply statistical (regression modeling) and DSD methods to accomplish the aims listed above. RESULTS/ANTICIPATED RESULTS: For Aim 1, we expect to identify patient, procedure, and perioperative pain management practices that influence postoperative pain. For Aim 2, we will focus on outcomes such as PROMs that are challenging to obtain. By applying DSD methods, we will identify specific procedure and/or population-based cohorts to capture PROMs and decrease data collection burdens, while maintaining power, as the project is scaled nationally to all of NSQIP-P. DISCUSSION/SIGNIFICANCE OF IMPACT: Data from this study will inform expansion of NSQIP-P to collect novel outcomes of clinical and societal importance without prohibitively increasing data collection burden. Cambridge University Press 2020-07-29 /pmc/articles/PMC8823073/ http://dx.doi.org/10.1017/cts.2020.407 Text en © The Association for Clinical and Translational Science 2020 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Translational Science, Policy, & Health Outcomes Science
Ingram, Martha-Conley
Tian, Yao
Mehrotra, Sanjay
Apley, Dan
Raval, Mehul V
4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
title 4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
title_full 4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
title_fullStr 4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
title_full_unstemmed 4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
title_short 4162 Improving Data Capacity and Predictive Capability of NSQIP-P Using Designed Sampling from Databases
title_sort 4162 improving data capacity and predictive capability of nsqip-p using designed sampling from databases
topic Translational Science, Policy, & Health Outcomes Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823073/
http://dx.doi.org/10.1017/cts.2020.407
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