Development of prediction models for clinically meaningful improvement in PROMIS scores after lumbar decompression

The ability to preoperatively predict which patients will achieve a minimal clinically important difference (MCID) after lumbar spine decompression surgery can help determine the appropriateness and timing of surgery. Patient-Reported Outcome Measurement Information System (PROMIS) scores are an inc...

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Veröffentlicht in:The spine journal 2021-03, Vol.21 (3), p.397-404
Hauptverfasser: Karhade, Aditya V., Fogel, Harold A., Cha, Thomas D., Hershman, Stuart H., Doorly, Terence P., Kang, James D., Bono, Christopher M., Harris, Mitchel B., Schwab, Joseph H., Tobert, Daniel G.
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container_end_page 404
container_issue 3
container_start_page 397
container_title The spine journal
container_volume 21
creator Karhade, Aditya V.
Fogel, Harold A.
Cha, Thomas D.
Hershman, Stuart H.
Doorly, Terence P.
Kang, James D.
Bono, Christopher M.
Harris, Mitchel B.
Schwab, Joseph H.
Tobert, Daniel G.
description The ability to preoperatively predict which patients will achieve a minimal clinically important difference (MCID) after lumbar spine decompression surgery can help determine the appropriateness and timing of surgery. Patient-Reported Outcome Measurement Information System (PROMIS) scores are an increasingly popular outcome instrument. The purpose of this study was to develop algorithms predictive of achieving MCID after primary lumbar decompression surgery. This was a retrospective study at two academic medical centers and three community medical centers including adult patients 18 years or older undergoing one or two level posterior decompression for lumbar disc herniation or lumbar spinal stenosis between January 1, 2016 and April 1, 2019. The primary outcome, MCID, was defined using distribution-based methods as one half the standard deviation of postoperative patient-reported outcomes (PROMIS physical function, pain interference, pain intensity). Five machine learning algorithms were developed to predict MCID on these surveys and assessed by discrimination, calibration, Brier score, and decision curve analysis. The final model was incorporated into an open access digital application. Overall, 906 patients completed at least one PROMs survey in the 90 days before surgery and at least one PROMs survey in the year after surgery. Attainment of MCID during the study period by PROMIS instrument was 74.3% for physical function, 75.8% for pain interference, and 79.2% for pain intensity. Factors identified for preoperative prediction of MCID attainment on these outcomes included preoperative PROs, percent unemployment in neighborhood of residence, comorbidities, body mass index, private insurance, preoperative opioid use, surgery for disc herniation, and federal poverty level in neighborhood of residence. The discrimination (c-statistic) of the final algorithms for these outcomes was 0.79 for physical function, 0.74 for pain interference, and 0.69 for pain intensity with good calibration. The open access digital application for these algorithms can be found here: https://sorg-apps.shinyapps.io/promis_pld_mcid/ Lower preoperative PROMIS scores, fewer comorbidities, and certain sociodemographic factors increase the likelihood of achieving MCID for PROMIS after lumbar spine decompression.
doi_str_mv 10.1016/j.spinee.2020.10.026
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Patient-Reported Outcome Measurement Information System (PROMIS) scores are an increasingly popular outcome instrument. The purpose of this study was to develop algorithms predictive of achieving MCID after primary lumbar decompression surgery. This was a retrospective study at two academic medical centers and three community medical centers including adult patients 18 years or older undergoing one or two level posterior decompression for lumbar disc herniation or lumbar spinal stenosis between January 1, 2016 and April 1, 2019. The primary outcome, MCID, was defined using distribution-based methods as one half the standard deviation of postoperative patient-reported outcomes (PROMIS physical function, pain interference, pain intensity). Five machine learning algorithms were developed to predict MCID on these surveys and assessed by discrimination, calibration, Brier score, and decision curve analysis. The final model was incorporated into an open access digital application. Overall, 906 patients completed at least one PROMs survey in the 90 days before surgery and at least one PROMs survey in the year after surgery. Attainment of MCID during the study period by PROMIS instrument was 74.3% for physical function, 75.8% for pain interference, and 79.2% for pain intensity. Factors identified for preoperative prediction of MCID attainment on these outcomes included preoperative PROs, percent unemployment in neighborhood of residence, comorbidities, body mass index, private insurance, preoperative opioid use, surgery for disc herniation, and federal poverty level in neighborhood of residence. The discrimination (c-statistic) of the final algorithms for these outcomes was 0.79 for physical function, 0.74 for pain interference, and 0.69 for pain intensity with good calibration. The open access digital application for these algorithms can be found here: https://sorg-apps.shinyapps.io/promis_pld_mcid/ Lower preoperative PROMIS scores, fewer comorbidities, and certain sociodemographic factors increase the likelihood of achieving MCID for PROMIS after lumbar spine decompression.</description><identifier>ISSN: 1529-9430</identifier><identifier>EISSN: 1878-1632</identifier><identifier>DOI: 10.1016/j.spinee.2020.10.026</identifier><identifier>PMID: 33130302</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Lumbar decompression ; MCID ; Minimal clinically important difference ; Patient-reported outcomes ; Physical function ; Prediction ; PROMIS</subject><ispartof>The spine journal, 2021-03, Vol.21 (3), p.397-404</ispartof><rights>2020 Elsevier Inc.</rights><rights>Copyright © 2020 Elsevier Inc. 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Patient-Reported Outcome Measurement Information System (PROMIS) scores are an increasingly popular outcome instrument. The purpose of this study was to develop algorithms predictive of achieving MCID after primary lumbar decompression surgery. This was a retrospective study at two academic medical centers and three community medical centers including adult patients 18 years or older undergoing one or two level posterior decompression for lumbar disc herniation or lumbar spinal stenosis between January 1, 2016 and April 1, 2019. The primary outcome, MCID, was defined using distribution-based methods as one half the standard deviation of postoperative patient-reported outcomes (PROMIS physical function, pain interference, pain intensity). Five machine learning algorithms were developed to predict MCID on these surveys and assessed by discrimination, calibration, Brier score, and decision curve analysis. The final model was incorporated into an open access digital application. Overall, 906 patients completed at least one PROMs survey in the 90 days before surgery and at least one PROMs survey in the year after surgery. Attainment of MCID during the study period by PROMIS instrument was 74.3% for physical function, 75.8% for pain interference, and 79.2% for pain intensity. Factors identified for preoperative prediction of MCID attainment on these outcomes included preoperative PROs, percent unemployment in neighborhood of residence, comorbidities, body mass index, private insurance, preoperative opioid use, surgery for disc herniation, and federal poverty level in neighborhood of residence. The discrimination (c-statistic) of the final algorithms for these outcomes was 0.79 for physical function, 0.74 for pain interference, and 0.69 for pain intensity with good calibration. 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subjects Lumbar decompression
MCID
Minimal clinically important difference
Patient-reported outcomes
Physical function
Prediction
PROMIS
title Development of prediction models for clinically meaningful improvement in PROMIS scores after lumbar decompression
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