Dataset Information

The Psychometric Properties of a Self-Administered, Open-Source Module for Valuing Metastatic Epidural Spinal Cord Compression Utilities.

ABSTRACT:

Introduction

Web surveys are often used for utility valuation. Typically, custom utility valuation tools that have not undergone psychometric evaluation are used.

Objectives

This study aimed to determine the psychometric properties of a metastatic epidural spinal cord compression (MESCC) module run on a customizable open-source, internet-based, self-directed utility valuation platform (Self-directed Online Assessment of Preferences [SOAP]).

Methods

Individuals accompanying patients to the emergency department waiting room in Ottawa, Canada, were recruited. Participants made SOAP MESCC health state valuations in the waiting room and 48 h later at home. Validity, agreement reliability, and responsiveness were measured by logical consistency of responses, smallest detectable change, the interclass correlation coefficient, and Guyatt's responsiveness index, respectively.

Results

Of 285 participants who completed utility valuations, only 113 (39.6%) completed the re-test. Of these 113 participants, 92 (81.4%) provided valid responses on the first test and 75 (66.4%) provided valid responses on the test and re-test. Agreement for all groups of health states was adequate, since their smallest detectable change was less than the minimal clinically important difference. The mean interclass correlation coefficients for all health states were?>?0.8, indicating at least substantial reliability. Guyatt's responsiveness indices all exceeded 0.80, indicating a high level of responsiveness.

Conclusions

To our knowledge, this is the first validated open-source, web-based, self-directed utility valuation module. We have demonstrated the SOAP MESCC module is valid, reproducible, and responsive for obtaining ex ante utilities. Considering the successful psychometric validation of the SOAP MESCC module, other investigators can consider developing modules for other diseases where direct utility valuation is needed.

SUBMITTER: Pahuta M

PROVIDER: S-EPMC6533369 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

The Psychometric Properties of a Self-Administered, Open-Source Module for Valuing Metastatic Epidural Spinal Cord Compression Utilities.

Pahuta Markian M Frombach Aaron A Hashem Emile E Spence Stewart S Sun Christina C Wai Eugene K EK Werier Joel J van Walraven Carl C Coyle Doug D

PharmacoEconomics - open 20190601 2

<h4>Introduction</h4>Web surveys are often used for utility valuation. Typically, custom utility valuation tools that have not undergone psychometric evaluation are used.<h4>Objectives</h4>This study aimed to determine the psychometric properties of a metastatic epidural spinal cord compression (MESCC) module run on a customizable open-source, internet-based, self-directed utility valuation platform (Self-directed Online Assessment of Preferences [SOAP]).<h4>Methods</h4>Individuals accompanying ...[more]

PMID: 30178420

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Project description:BackgroundMetastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.PurposeTo develop a DL model for automated classification of MESCC on MRI.Materials and methodsPatients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet's kappa) and sensitivity/specificity were calculated.ResultsOverall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92-0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94-0.95, p < 0.001) compared to the reference standard.ConclusionA DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Project description:Simple Summary Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy, and early diagnosis is important to prevent irreversible neurological injury. MRI is the mainstay of diagnosis for MESCC, but it is expensive, and routine screening of asymptomatic patients is not feasible. Staging CT studies are performed routinely as part of the cancer diagnosis and represent an opportunity for earlier diagnosis and treatment planning. In this study, we trained deep learning models for automatic MESCC classification on staging CT studies using spine MRI and manual radiologist labels as the reference standard. On a test set, the DL models showed almost-perfect interobserver agreement for the classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two radiologists, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Abstract Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2–7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.

Project description:Background:Patient-reported outcomes (PRO) represent an important measure of cancer therapy effect. For patients with metastatic epidural spinal cord compression (MESCC), hybrid therapy using separation surgery and stereotactic radiosurgery preserves neurologic function and provides tumor control. There is currently a paucity of data reporting PRO after such combined modality therapy for MESCC. Delineation of hybrid surgery-radiosurgery therapy effect on PRO validates the hybrid approach as an effective therapy resulting in meaningful symptom relief. Patients and Methods:Brief Pain Inventory (BPI) and MD Anderson Symptom Inventory-Spine Tumor (MDASI-SP), PROs validated in the cancer population, were prospectively collected. Patients with MESCC who underwent separation surgery followed by stereotactic radiosurgery were included. Separation surgery included a posterolateral approach without extensive cytoreductive tumor excision. A median postoperative radiosurgery dose of 2700 cGy was delivered. The change in PRO 3 months after the hybrid therapy represented the primary study outcome. Preoperative and postoperative evaluations were analyzed using the Wilcoxon signed-rank test for matched pairs. Results:One hundred eleven patients were included. Hybrid therapy resulted in a significant reduction in the BPI items "worst" and "right now" pain (P < .0001), and in all BPI constructs (severity, interference with daily activities, and pain experience, P < .001). The MDASI-SP demonstrated reduction in spine-specific pain severity and interference with general activity (P < .001), along with decreased symptom interference (P < .001). Conclusions:Validated PRO instruments showed that in patients with MESCC, hybrid therapy with separation surgery and radiosurgery results in a significant decrease in pain severity and symptom interference. These prospective data confirm the benefit of hybrid therapy for treatment of MESCC and should facilitate referral of patients with MESCC for surgical evaluation.

Project description:PurposeMetastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced malignancy, which can result in neurologic complications and significant deterioration in overall function and quality of life. Most patients are not candidates for optimal surgical decompression and as a result, receive urgent 3D conformal radiotherapy (3DCRT) to prevent or attempt to reverse neurologic progression. Multiple trials indicate that response and ambulatory rates after 3DCRT are inferior to surgery. The advent of stereotactic body radiation therapy (SBRT) has created a method with which a "radiosurgical decompression" boost may facilitate improve outcomes for MESCC patients.MethodsWe are conducting a pilot study to investigate SBRT boost after urgent 3D CRT for patients with MESCC. The aim of the study is to establish feasibility of this two-phase treatment regimen, and secondarily to characterize post-treatment ambulation status, motor response, pain control, quality of life and survival.DiscussionWe describe the study protocol and present a case report of one patient. A quality assurance review was conducted after the first seven patients, and resultant dose-constraints were revised to improve safety and feasibility of planning through more conservative organ at risk constraints. There have been no severe adverse events (grade 3-5) to date. We have illustrated clinical and dosimetric data of an example case, where a patient regained full strength and ambulatory capacity.ConclusionsOur study aims to determine if SBRT is a feasible option in addition to standard 3DCRT for MESCC patients, with the goal to consider future randomized trials if successful. Having a robust quality assurance process in this study ensures translatability going forward if future trials with multicenter and increased patient representation are to be considered.Trial registrationclinicaltrials.gov; registration no. NCT03529708; https://clinicaltrials.gov/ct2/show/NCT03529708 ; First posted May 18, 2018.

Dataset Information

The Psychometric Properties of a Self-Administered, Open-Source Module for Valuing Metastatic Epidural Spinal Cord Compression Utilities.

Introduction

Objectives

Methods

Results

Conclusions

Publications

The Psychometric Properties of a Self-Administered, Open-Source Module for Valuing Metastatic Epidural Spinal Cord Compression Utilities.

Similar Datasets

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