Variability in the utility of predictive models in predicting patient-reported outcomes following spine surgery for degenerative conditions: A systematic review

Nicholas Dietz, Mayur Sharma, Ahmad Alhourani, Beatrice Ugiliweneza, Dengzhi Wang, Miriam A Nuno, Doniel Drazin, Maxwell Boakye

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

OBJECTIVE There is increasing emphasis on patient-reported outcomes (PROs) to quantitatively evaluate quality outcomes from degenerative spine surgery. However, accurate prediction of PROs is challenging due to heterogeneity in outcome measures, patient characteristics, treatment characteristics, and methodological characteristics. The purpose of this study was to evaluate the current landscape of independently validated predictive models for PROs in elective degenerative spinal surgery with respect to study design and model generation, training, accuracy, reliability, variance, and utility. METHODS The authors analyzed the current predictive models in PROs by performing a search of the PubMed and Ovid databases using PRISMA guidelines and a PICOS (participants, intervention, comparison, outcomes, study design) model. They assessed the common outcomes and variables used across models as well as the study design and internal validation methods. RESULTS A total of 7 articles met the inclusion criteria, including a total of 17 validated predictive models of PROs after adult degenerative spine surgery. National registry databases were used in 4 of the studies. Validation cohorts were used in 2 studies for model verification and 5 studies used other methods, including random sample bootstrapping techniques. Reported c-index values ranged from 0.47 to 0.79. Two studies report the area under the curve (0.71-0.83) and one reports a misclassification rate (9.9%). Several positive predictors, including high baseline pain intensity and disability, demonstrated high likelihood of favorable PROs. CONCLUSIONS A limited but effective cohort of validated predictive models of spine surgical outcomes had proven good predictability for PROs. Instruments with predictive accuracy can enhance shared decision-making, improve rehabilitation, and inform best practices in the setting of heterogeneous patient characteristics and surgical factors.

Original languageEnglish (US)
Article numberE10
JournalNeurosurgical Focus
Volume45
Issue number5
DOIs
StatePublished - Nov 1 2018

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Spine
Outcome Assessment (Health Care)
Databases
Anatomic Models
Patient Reported Outcome Measures
Practice Guidelines
PubMed
Area Under Curve
Registries
Decision Making
Rehabilitation
Guidelines
Pain

Keywords

  • Degeneration
  • Patient reported outcomes
  • Predictive models
  • Spine surgery

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology

Cite this

Variability in the utility of predictive models in predicting patient-reported outcomes following spine surgery for degenerative conditions : A systematic review. / Dietz, Nicholas; Sharma, Mayur; Alhourani, Ahmad; Ugiliweneza, Beatrice; Wang, Dengzhi; Nuno, Miriam A; Drazin, Doniel; Boakye, Maxwell.

In: Neurosurgical Focus, Vol. 45, No. 5, E10, 01.11.2018.

Research output: Contribution to journalArticle

Dietz, Nicholas ; Sharma, Mayur ; Alhourani, Ahmad ; Ugiliweneza, Beatrice ; Wang, Dengzhi ; Nuno, Miriam A ; Drazin, Doniel ; Boakye, Maxwell. / Variability in the utility of predictive models in predicting patient-reported outcomes following spine surgery for degenerative conditions : A systematic review. In: Neurosurgical Focus. 2018 ; Vol. 45, No. 5.
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abstract = "OBJECTIVE There is increasing emphasis on patient-reported outcomes (PROs) to quantitatively evaluate quality outcomes from degenerative spine surgery. However, accurate prediction of PROs is challenging due to heterogeneity in outcome measures, patient characteristics, treatment characteristics, and methodological characteristics. The purpose of this study was to evaluate the current landscape of independently validated predictive models for PROs in elective degenerative spinal surgery with respect to study design and model generation, training, accuracy, reliability, variance, and utility. METHODS The authors analyzed the current predictive models in PROs by performing a search of the PubMed and Ovid databases using PRISMA guidelines and a PICOS (participants, intervention, comparison, outcomes, study design) model. They assessed the common outcomes and variables used across models as well as the study design and internal validation methods. RESULTS A total of 7 articles met the inclusion criteria, including a total of 17 validated predictive models of PROs after adult degenerative spine surgery. National registry databases were used in 4 of the studies. Validation cohorts were used in 2 studies for model verification and 5 studies used other methods, including random sample bootstrapping techniques. Reported c-index values ranged from 0.47 to 0.79. Two studies report the area under the curve (0.71-0.83) and one reports a misclassification rate (9.9{\%}). Several positive predictors, including high baseline pain intensity and disability, demonstrated high likelihood of favorable PROs. CONCLUSIONS A limited but effective cohort of validated predictive models of spine surgical outcomes had proven good predictability for PROs. Instruments with predictive accuracy can enhance shared decision-making, improve rehabilitation, and inform best practices in the setting of heterogeneous patient characteristics and surgical factors.",
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