Dataset Information

Can patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen Decision Tool for Chronic Low Back Pain.

ABSTRACT:

Introduction

Chronic Low Back Pain (CLBP) is a heterogeneous condition with lack of diagnostic clarity. Therapeutic interventions show small effects. To improve outcomes by targeting interventions it is recommended to develop a triage system to surgical and non-surgical treatments based on treatment outcomes. The objective of the current study was to develop and internally validate prognostic models based on pre-treatment patient-reported profiles that identify patients who either respond or do not respond to two frequently performed treatments (lumbar spine surgery and multidisciplinary pain management program).

Methods

A consecutive cohort study in a secondary referral spine center was performed. The study followed the recommendations of the PROGRESS framework and was registered in the Dutch Trial Register (NTR5946). Data of forty-seven potential pre-consultation (baseline) indicators predicting 'response' or 'non-response' at one-year follow-up for the two treatments were obtained to develop and validate four multivariable logistic regression models. The source population consisted of 3,410 referred CLBP-patients. Two treatment cohorts were defined: elective 'spine surgery' (n = 217 [6.4%]) and multidisciplinary bio-psychosocial 'pain management program' (n = 171 [5.0%]). Main inclusion criteria were age ?18, CLBP (?6 months), and not responding to primary care treatment. The primary outcome was functional ability: 'response' (Oswestry Disability Index [ODI] ?22) and 'non-response' (ODI ?41).

Results

Baseline indicators predictive of treatment outcome were: degree of disability (all models), ?2 previous spine surgeries, psychosocial complaints, age (onset <20 or >50), and patient expectations of treatment outcomes. The explained variances were low for the models predicting response and non-response to pain management program (R2 respectively 23% and 26%) and modest for surgery (R2 30% and 39%). The overall performance was acceptable (c-index; 0.72-0.83), the model predicting non-response to surgery performed best (R2 = 39%; c-index = 0.83).

Conclusion

This study was the first to identify different patient-reported profiles that predict response to different treatments for CLBP. The model predicting 'non-response' to elective lumbar spine surgery performed remarkably well, suggesting that referrals of these patients to a spine surgeon could be avoided. After external validation, the patient-reported profiles could potentially enhance timely patient triage to the right secondary care specialist and improve decision-making between clinican and patient. This could lead to improved treatment outcomes, which results in a more efficient use of healthcare resources.

SUBMITTER: van Hooff ML

PROVIDER: S-EPMC6145570 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Can patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen Decision Tool for Chronic Low Back Pain.

van Hooff Miranda L ML van Dongen Johanna M JM Coupé Veerle M VM Spruit Maarten M Ostelo Raymond W J G RWJG de Kleuver Marinus M

PloS one 20180919 9

<h4>Introduction</h4>Chronic Low Back Pain (CLBP) is a heterogeneous condition with lack of diagnostic clarity. Therapeutic interventions show small effects. To improve outcomes by targeting interventions it is recommended to develop a triage system to surgical and non-surgical treatments based on treatment outcomes. The objective of the current study was to develop and internally validate prognostic models based on pre-treatment patient-reported profiles that identify patients who either respon ...[more]

PMID: 30231051

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Project description:BACKGROUND:In Western Europe, low back pain has the greatest burden of all diseases. When back pain persists, different medical specialists are involved and a lack of consensus exists among these specialists for medical decision-making in Chronic Low Back Pain (CLBP). OBJECTIVE:To develop a decision tool for secondary or tertiary spine care specialists to decide which patients with CLBP should be seen by a spine surgeon or by other non-surgical medical specialists. METHODS:A Delphi study was performed to identify indicators predicting the outcome of interventions. In the preparatory stage evidence from international guidelines and literature were summarized. Eligible studies were reviews and longitudinal studies. Inclusion criteria: surgical or non-surgical interventions and persistence of complaints, CLBP-patients aged 18-65 years, reported baseline measures of predictive indicators, and one or more reported outcomes had to assess functional status, quality of life, pain intensity, employment status or a composite score. Subsequently, a three-round Delphi procedure, to reach consensus on candidate indicators, was performed among a multidisciplinary panel of 29 CLBP-professionals (>five years CLBP-experience). The pre-set threshold for general agreement was ?70%. The final indicator set was used to develop a clinical decision tool. RESULTS:A draft list with 53 candidate indicators (38 with conclusive evidence and 15 with inconclusive evidence) was included for the Delphi study. Consensus was reached to include 47 indicators. A first version of the decision tool was developed, consisting of a web-based screening questionnaire and a provisional decision algorithm. CONCLUSIONS:This is the first clinical decision tool based on current scientific evidence and formal multidisciplinary consensus that helps referring the patient for consultation to a spine surgeon or a non-surgical spine care specialist. We expect that this tool considerably helps in clinical decision-making spine care, thereby improving efficient use of scarce sources and the outcomes of spinal interventions.

Project description:BackgroundOccupation-related noise-induced hearing loss (NIHL) has both negative economic and quality of life implications. The risk spine surgeons undertake in regards to NIHL during operative intervention is unknown. Governing bodies, including the National Institute for Occupational Safety and Health, have recommended exposure limits not to exceed 85 decibels (dB) over 8 hours. The purpose of this study is to characterize noise exposure to spine surgeons in the operating room (OR).MethodsProspective collection of intraoperative recordings of spinal surgeries (cervical and thoracic/lumbar) was undertaken. Data gathered included procedure, operative duration, presence of background music, and noise information. Noise information included maximum decibel level (MDL), Peak level (LCPeak), Equivalent continuous sound pressure level, time weighted average (TWA), dose, and projected dose. Noise measurements were compared with baseline controls with and without music (empty ORs).ResultsTwo hundred seven noise recordings were analyzed. One hundred eighteen of those being spinal surgeries, 49 baseline recordings without music, and 40 with music. Maximum decibel level reached a maximum value of 111.5 dBA, with an average amongst surgical recordings of 103 dBA. Maximum decibel level exceeded 85 dBA in 100% of cases and was greater than 100 dBA in 78%. The maximum LCPeak recorded was 132.9 dBC with an average of 120 dBC. Furthermore, the average dose was 7.8% with an average projected dose of 26.5%. The highest dose occurred during a laminectomy at 72.9% of daily allowable noise. Maximum projected dose yielded 156% during a 3-level anterior cervical discectomy and fusion.ConclusionsSpine surgeons are routinely exposed to damaging noise levels (>85 dBA) during operative intervention. With spine surgeons often performing multiple surgeries a day, the cumulative risk of noise exposure cannot be ignored. The synergistic effects of continuous and impact noise places spine surgeons at risk for the development of occupation-related NIHL.

Project description:Background:Traditionally trained spine surgeons may want to transition from open spinal surgeries to endoscopic decompressions. The aspiring endoscopic spine surgeon may have to overcome multiple hurdles to master a learning curve without readily available training. Replacing traditional time-proven open spinal surgeries with endoscopic decompression may put the surgeons' reputation at risk and have an additional negative impact on his or her practice due to reduced revenue. The authors report on the utility of the mentor- and proctorship concepts to facilitate the transition from traditional open to endoscopic outpatient spine surgeries. Methods:The study population (learning curve groups) was provided by two traditionally trained "apprentice" surgeons who have been in practice for 12 and 28 years, respectively. They trained with the remaining two authors under mentorship and proctorship arrangements. A VAS and Macnab outcomes analysis was performed by one surgeon laminectomy versus endoscopy in relationship to the case log representative of the initial learning curve. The second surgeon performed a postoperative narcotic utilization analysis as a representative way of favorable clinical outcomes in relation to his increasing case log with spinal endoscopy. Results:The learning curve study by the first author (NA Ransom-under the proctorship program) consisted of 40 patients with 20 patients each divided into the traditional laminectomy control group and 20 patients in the endoscopic group. There were 22 females and 18 males with an average age of 57.38 years and a mean follow-up of 38.58 months. The preoperative VAS for patients in both groups was 7.95 compared to the postoperative VAS at final follow-up of 4.01 with a statistically significant postoperative VAS reduction (P<0.001) but without any significant difference between open laminectomy control- and endoscopic decompression groups. The endoscopic learning curve group outcomes improved significantly after 15 cases (P<0.048). The second author (S Gollogly-under mentorship program) performed a similar review of his surgical cases log and noted a significant reduction of postoperative narcotic utilization as a result of improved outcomes after an initial learning curve of 15 cases. Clinical outcomes for both authors showed improved Macnab outcomes in the majority of patients (NA Ransom =65%; S Gollogly =57%) with a slightly higher success rate in the laminectomy group (70%) versus the endoscopy group (65%) at a statistical significant level (P=0.036). Conclusions:The mentorship and proctorship approach is useful in helping traditionally trained spine surgeons to integrate spinal endoscopy into their well-established spine practices. Under the close guidance of an endoscopic master spine surgeon, the endoscopic learning curve may be comprehended by the experienced traditionally trained spine surgeon in approximately 15 lumbar decompression cases. During this initial 15-case learning curve, clinical outcomes with endoscopy may be slightly inferior to open laminectomy but may ultimately improve to equivalent levels.

Project description:ObjectivesDue to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies.MethodsThis retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network-derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity).ResultsFour hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18-85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8-89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2).ConclusionThe investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies.Key points• Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

Dataset Information

Can patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen Decision Tool for Chronic Low Back Pain.

Introduction

Methods

Results

Conclusion

Publications

Can patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen Decision Tool for Chronic Low Back Pain.

Similar Datasets

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