Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy

Khan, Omar; Badhiwala, Jetan H.; Witiw, Christopher D.; Wilson, Jefferson R.; Fehlings, Michael G.

doi:10.1016/j.spinee.2020.02.003

Cited by 50 publications

(49 citation statements)

References 36 publications

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“…A recent study showed that machine learning algorithms might become useful to identify patients with mild DCM that will benefit from surgery. 37 The phenotype of mild DCM needs to be acknowledged, and a recent study reported that neck pain, motor symptoms, and female gender were associated with greater impairment of quality of life and greater response to surgery. 38 Additional observational studies or clinical trials should be encouraged to clarify the natural course of the disease and evaluate surgery and structured rehabilitation for patients with mild DCM.…”

Section: Discussionmentioning

confidence: 99%

Surgery for Degenerative Cervical Myelopathy: A Nationwide Registry-Based Observational Study With Patient-Reported Outcomes

Gulati¹,

Vangen-Lønne²,

Nygaard³

et al. 2021

Neurosurg.

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BACKGROUND Indications and optimal timing for surgical treatment of degenerative cervical myelopathy (DCM) remain unclear, and data from daily clinical practice are warranted. OBJECTIVE To investigate clinical outcomes following decompressive surgery for DCM. METHODS Data were obtained from the Norwegian Registry for Spine Surgery. The primary outcome was change in the neck disability index (NDI) 1 yr after surgery. Secondary endpoints were the European myelopathy score (EMS), quality of life (EuroQoL 5D [EQ-5D]), numeric rating scales (NRS) for headache, neck pain, and arm pain, complications, and perceived benefit of surgery assessed by the Global Perceived Effect (GPE) scale. RESULTS We included 905 patients operated between January 2012 and June 2018. There were significant improvements in all patient-reported outcome measures (PROMs) including NDI (mean −10.0, 95% CI −11.5 to −8.4, P < .001), EMS (mean 1.0, 95% CI 0.8-1.1, P < .001), EQ-5D index score (mean 0.16, 95% CI 0.13-0.19, P < .001), EQ-5D visual analogue scale (mean 13.8, 95% CI 11.7-15.9, P < .001), headache NRS (mean −1.1, 95% CI −1.4 to −0.8, P < .001), neck pain NRS (mean −1.8, 95% CI −2.0 to −1.5, P < .001), and arm pain NRS (mean −1.7, 95% CI −1.9 to −1.4, P < .001). According to GPE scale assessments, 229/513 patients (44.6%) experienced “complete recovery” or felt “much better” at 1 yr. There were significant improvements in all PROMs for both mild and moderate-to-severe DCM. A total of 251 patients (27.7%) experienced adverse effects within 3 mo. CONCLUSION Surgery for DCM is associated with significant and clinically meaningful improvement across a wide range of PROMs.

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Section: Discussionmentioning

confidence: 99%

Surgery for Degenerative Cervical Myelopathy: A Nationwide Registry-Based Observational Study With Patient-Reported Outcomes

Gulati¹,

Vangen-Lønne²,

Nygaard³

et al. 2021

Neurosurg.

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“…The suggested advantage of these techniques appears not to be determined [26]. Reported AUC values or c-indexes with these analytic methods range from 0.59 to 0.90 [8,12,21,22]. Probably the more critical aspect of outcome prediction, and possibilities of improving precision, is related to addition of more predictor variables.…”

Section: Discussionmentioning

confidence: 99%

“…org Patient-centred outcome prediction is a growing focus in spine research, producing several reports annually. The majority discuss prediction in terms of Patient-Reported Outcome Measures (PROMs) [5][6][7][8][9][10][11][12][13][14][15], a few deals with adverse events [16,17], length of stay [18], revision surgery [19] or return to work [20]. Among the PROM analyses, the outcome measure is usually dichotomized.…”

Section: Introductionmentioning

confidence: 99%

Prediction of outcome after spinal surgery—using The Dialogue Support based on the Swedish national quality register

2021

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Purpose To evaluate the predictive precision of the Dialogue Support, a tool for additional help in shared decision-making before surgery of the degenerative spine. Methods Data in Swespine (Swedish national quality registry) of patients operated between 2007 and 2019 found the development of prediction algorithms based on logistic regression analyses, where socio-demographic and baseline variables were included. The algorithms were tested in four diagnostic groups: lumbar disc herniation, lumbar spinal stenosis, degenerative disc disease and cervical radiculopathy. By random selection, 80% of the study population was used for the prediction of outcome and then tested against the actual outcome of the remaining 20%. Outcome measures were global assessment of pain (GA), and satisfaction with outcome. Results Calibration plots demonstrated a high degree of concordance on a group level. On an individual level, ROC curves showed moderate predictive capacity with AUC (area under the curve) values 0.67–0.68 for global assessment and 0.6–0.67 for satisfaction. Conclusion The Dialogue Support can serve as an aid to both patient and surgeon when discussing and deciding on surgical treatment of degenerative conditions in the lumbar and cervical spine. Level of evidence I.

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“…A shining future application in diagnosis, prognosis and decision-making process is desirable and will soon become an essential spine physician tool. Khan et al introduced the application in the clinal management of cervical myelopathy and nontraumatic spinal cord injury to predict the risk of neurological impairment at one year [39]. These tools allowed the physicians to predict individual patient outcome after surgery for degenerative cervical myelopathy [40] and to apply preventive strategies as targeted physiotherapy and timing of psychological counselling.…”

Section: Discussionmentioning

confidence: 99%

The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach

Langella

Ventriglia

Compagnone

et al. 2021

Preprint

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Aims To create, using a machine learning (ML) approach, a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome. Materials and Methods A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineReg) was performed. The inclusion criteria were age > 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a "good early outcome". A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an "excellent early outcome". The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). Results A total of 1243 patients were included in this study. The model for predicting ODI at 6 months follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and a ROC AUC = 0.808. Conclusion The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

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Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy

Cited by 50 publications

References 36 publications

Surgery for Degenerative Cervical Myelopathy: A Nationwide Registry-Based Observational Study With Patient-Reported Outcomes

Surgery for Degenerative Cervical Myelopathy: A Nationwide Registry-Based Observational Study With Patient-Reported Outcomes

Prediction of outcome after spinal surgery—using The Dialogue Support based on the Swedish national quality register

The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach

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