Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05099627 |
| Other study ID # |
21SM7167 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 31, 2021 |
| Est. completion date |
December 31, 2022 |
Study information
| Verified date |
October 2021 |
| Source |
Imperial College Healthcare NHS Trust |
| Contact |
Salvatore Russo, FRCS |
| Phone |
+44 7883088997 |
| Email |
salvatore.russo[@]nhs.net |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
DESIGN: Ambispective cohort study (retrospective + prospective). AIMS: The overall aim of
this research study is to create two predictive machine learning models that are based on
radiological, clinical and biochemical variables, which allows spine surgeons to diagnose CSM
earlier and more accurately, as well as allowing them to give patients highly individualised
and accurate predictive information regarding treatment outcomes.
OUTCOME MEASURES: For the prospective arm of the study the independent variables will be
patient characteristics, clinical, radiological and biochemical markers. Dependent variables
are mJOA and JOACMEQ scores. For the retrospective arm of the study the independent variables
will be patient characteristics, co-morbidities and symptomology, outcome variable will be
radiological confirmation on cervical myelopathy.
POPULATION: Patients with cervical myelopathy over 18 years old (19 years and above
included), treated at Imperial College NHS Healthcare Trust for CM with full capacity to
consent and assessed for cervical myelopathy symptoms at Community MSK Hounslow and Richmond
Community Healthcare NHS Trust (catchment area of ICHT neurosurgery).
ELIGIBILITY: Over 18 years old, with full capacity to consent. TREATMENT: Cervical myelopathy
diagnosis and/or conservative and/or surgical management of disease DURATION: 18 months
Description:
1. INTRODUCTION
1.1 BACKGROUND
Cervical spondylotic myelopathy (CSM) is a pathology of the spinal cord that culminates
in progressive compression of the cervical spinal cord. It is one of the most common
causes of spinal cord pathology globally and can result in significant detrimental
effects to quality of life of patients (Merali et al., 2019). The current gold standard
treatment for CSM is surgical decompression, which restores function and improves
quality of life; however surgical decompression does not benefit all patients. Given
this dichotomy, it is evident that it is pivotal to correctly identify patients suitable
for surgery, to spare unsuitable patients from unnecessary risks of surgery. Spine
surgeons must analyse vast amounts of patient data, including radiological and clinical
parameters, to correctly stratify patients. However, it remains elusive how each
parameter should be weighted and whether all predictive parameters have been identified.
Furthermore, it is pivotal for the diagnosis of CSM to be made as early as possible, as
surgical outcomes have been reported to be significantly improved if diagnosed at an
early disease stage compared to a late stage. performed in earlier stages during the
course of CSM was reported to be more successful when compared with later stage (Baron &
Young, 2007). Radiological parameters that can aid in early detection have been
identified, including diffusion tensor imaging and increased density of MRI T2-weighted
images, however these parameters have been shown to have low sensitivities (15-65%) and
have been yielded with conventional, non-deep-learning statistical methods, which are
highly likely to have missed salient parameters due to inherent statistical limitations.
Machine learning is the current statistical gold standard for data modelling and
analysis. It combines computer science and statistics to yield maximal predictive
accuracy. Recently, machine learning has been increasingly and successfully applied to
medical and surgical research to predict disease and treatment outcomes for various
condition, yielding superior results to conventional statistical methods. To continue,
besides radiological signs, some clinical signs such as Hoffman's sign have been
identified to aid in early diagnosis of cervical myelopathy (Denno & Meadows, 1991),
however the findings on clinical predictors are sparse and the question remains how they
compare to radiological and biochemical biomarkers and whether there are unidentified
clinical predictors. Merali et al. used a machine learning model to combine clinical and
biochemical parameters to create a holistic predictive model, but they failed to include
radiological parameters, as well as primarily focusing on treatment outcome rather than
early diagnosis. Hence, this study aims to fill the gap in the literature and analyse
all pivotal patient data, namely radiological, biochemical and clinical, to detect novel
and weigh existing predictors relevant for early cervical myelopathy diagnosis and
treatment outcome, as well as disease progress stratification.
Three recent studies have looked at machine learning in the context of CSM. Firstly,
Merali et al. (2019) investigated the use of machine learning in predicting surgical
outcomes in patients with CSM. Their model performed well, however it did not consider
any radiological imaging, which is a significant limitation to this study, as imaging
gives pivotal clues particularly for early diagnosis of CSM. Moreover, while creating a
machine learning model, the authors failed to create a tool for clinicians to use which
is based on the machine learning model. The second study, also by Merali et al. (2021)
applied machine learning to radiological imaging for detection of CSM pathology. The
model again performed reasonably well, however only confirmed CSM patients were
included, which is an immense limitation. It is pivotal to include early-stage CSM
patients to analyse how the machine learning model performs compared to conventional
methods in terms of prediction, and whether it can aid in early detection. Moreover,
both studies were retrospective in nature, meaning that no comparison between machine
learning model and conventional methods would have been possible. Hopkins et al. (2019)
analysed the use of machine learning in the diagnosis, rather than prediction, of CSM.
However, an important limitation of this study is that he only used radiological
imaging, completely neglecting clinical and biochemical parameters. In fact, Merali et
al. (2019) also completely neglected biochemical parameters in their analysis.
Potentially, this is an important unexplored variable in the pathogenesis and diagnosis
of CSM which must be explored. To conclude, studies have looked at machine learning to
diagnose and make prediction in CSM, however they have severe limitations only looking
at fragments of the diagnostic pathway. To that end, given that the diagnosis, and
predictions regarding CSM disease progression and treatment outcome share a multitude of
the exact same variables, it is logical, and necessary to analyse them together, rather
than in separate studies. Moreover, it is necessary to include radiological, but also
clinical and biochemical variables to achieve a near-optimal machine learning model, as
these are the tools a clinician would also have access to base his diagnoses and
predictions. Hence, this study will examine radiological-clinical-biochemical variables
with machine learning in the context of diagnosis, as well as predictions on disease
progression and treatment outcomes. Next, all studies failed to examine the use of
machine learning for early diagnosis. As early diagnosis is pivotal to enhance treatment
outcome, this is a huge gap in the literature, which this research study aims to fill,
by focusing on variables that aid early detection of CSM via inclusion of mild CSM
cases. Ultimately, none of the studies have used patient-based QOL assessment (i.e.
JOACMEQ) as outcome variable which might be more important than mJOA.
In summary The limitations of studies on the topic of CSM and prediction of treatment
outcome so far have been that they only used mJOA as outcome variable, instead of using
the patient-based JOACMEQ. This is a huge limitation, as ultimately only the patient can
subjectively assess and evaluate outcome of the treatment. As to the topic of early
diagnosis of CSM, there have been no studies who explored this topic whatsoever.
1.2 RATIONALE FOR CURRENT STUDY
AIMS
The overall aim of this research study is to create two predictive machine learning
models that are based on radiological, clinical and biochemical variables, which allows
spine surgeons to diagnose CSM earlier and more accurately, as well as allowing them to
give patients highly individualised and accurate predictive information regarding
treatment outcomes.
HYPOTHESES
There are unidentified patient characteristics, biochemical, radiological and clinical
parameters that could aid in early diagnosis and treatment prediction of cervical
myelopathy when combined with already known biomarkers. These patterns can be identified
and weighted with supervised and unsupervised machine learning, as well as multivariate
linear regression. Subsequently, the machine learning model can be trained with nested
cross-validation to achieve optimal fit, while avoiding overfitting, to predict cervical
myelopathy diagnosis.. Ultimately, a point-of-care tool can be developed for GPs and
spine surgeons to aid in early detection of cervical myelopathy, and treatment outcome
prediction of patients with cervical myelopathy. The machine learning predictions
regarding treatment outcome and disease progression are hypothesised to be significantly
more accurate than clinicians' current predictive ability.
RISKS AND BENEFITS
Risks: Potential discomfort during examination. This will be mitigated by asking for
pain prior to examination and using gentle examination techniques. Potential
inconvenience will be mitigated by finding a time that is convenient for the patient.
Benefit: In case of recurrence of their symptoms after surgery, our machine learning
model and algorithm can help their treating neurosurgeons to diagnose recurrence of
cervical myelopathy at an early stage and predict more accurately if they will benefit
from surgery again, or if they would benefit from conservative management instead.
Patients will not receive any payments, reimbursement of expenses or any other benefits
or incentives for taking part in this research, not any personal payment over and above
normal salary, or any other benefits or incentives, and the Chief Investigator or any
other investigator/collaborator does not have any direct personal involvement (e.g.
financial, share holding, personal relationship etc.) in the organisations sponsoring or
funding the research that may give rise to a possible conflict of interest.
Research participants will not receive any payments, reimbursement of expenses or any
other benefits or incentives for taking part in this research.
2. STUDY OBJECTIVES
OBJECTIVES
Early diagnosis of CSM
1. To identify early clinical predictors of cervical myelopathy diagnosis based on
individual patient-reported symptoms by using a retrospective questionnaire, and
possibly retrospective analysis of biochemical, radiological and clinical data by means
of pattern detection with machine learning, or alternatively multivariate linear
regression.
2. To convert the findings of machine learning model or multivariate regression analysis
into a scoring system or machine learning tool that can be used clinically to score
patients into non-likely, moderately likely or high-likely to have CSM groups to aid
early clinical diagnosis and prevent unnecessary use of imaging.
Treatment outcome prediction of CSM
1. To prospectively follow a cohort of CSM patients collecting as many relevant clinical,
biochemical and radiological independent variables as possible, as well as using the
JOACMEQ questionnaire and mJOA before, as outcome variables. The relationship of
independent and outcome variables will be analysed by means of conventional methods and
machine learning.
2. To build and train a machine learning model to accurately predict treatment outcome in
diagnosed patients.
3. To use the machine learning model as base to design a scoring tool or point-of-care
algorithm that can be used clinically to score patients into non-likely, moderately
likely or high-likely to be CSM surgery responders to aid appropriate management of CSM
patients.
3. STUDY DESIGN
Type of study: Non-randomised prospective and retrospective cohort study Duration:
Prospective: Observations at pre-treatment, post-treatment at 3 months and 6 months (minimum
18 months).
Retrospective: Minimum last 2 years. Patient number: Approximately 300 (100 prospective, 200
retrospective patients).
This sample was determined based on estimations of cervical myelopathy patients at Imperial
College NHS Healthcare Trust in the past for prospective study. 100 cervical myelopathy
patient treated at Imperial College NHS Healthcare Trust in the following 18 months minimum
for prospective arm, as well as 100 patients treated in last 2 years minimum in ICHT, and 100
patients with negative diagnosis from a single Community MSK team from Hounslow and Richmond
Community Healthcare NHS Trust, which is in the catchment area of Imperial College Healthcare
NHS Trust Neurosurgery. Given the machine learning approach, this sample size may seem too
small to retain accuracy (as comprehensively explained here:
https://www.fharrell.com/post/ml-sample-size/ ) , however appropriate measures have been
taken to ensure high accuracy while avoiding overfitting. For machine learning it has been
shown that nested cross-validation (NCV) is not affected by bias due to small sample sizes,
in fact it does not linearly positively or negatively respond to sample size changes,
remaining very accurate and robust (Vabalas et al., 2019). Hence, we will be using NCV.
Additionally, the sample size of n=100, and n=200 respectively, is further strengthened by
the high number of characteristic features (250+ for the prospective study, 10-50 for the
[depending on CMSK data cleanliness] see both questionnaires), which is significantly
positively influential on model accuracy (Vabalas et al. 2019) Data metrics: Information
gathered through clinical examination and questionnaires performed retrospectively over the
phone, pre-operation and at 3- and 6-months post-operation. The questionnaire "Cervical
myelopathy treatment outcome questionnaire" attached records information relating to
co-morbidities and symptomology. The examination will be a thorough neurological examination
in addition to a focused cardiovascular examination. Biochemical blood markers from existing
blood markers or from GP records, which have been or would have been performed regardless of
this trial. Radiological findings will be taken from routine MRI and cervical spine X-rays
performed as part of the CSM diagnostic work-up The retrospective questionnaire "CSM early
diagnosis questionnaire" contains all the questions which will be collected retrospectively
via telephone. Furthermore patients from the community MSK team, those diagnosed with CM
eventually and those examined for CM query, their clinical, biochemical and radiological data
from the local SystemOne Databases will be collected for analysis and comparison.
model will be converted into a web-based point-of-care treatment outcome prediction tool
using the Shiny packet in R. Data and all appropriate documentation will be stored as per
Trust policy, including the follow-up period.
3.1 STUDY OUTCOME MEASURES The mJOA score and JOACMEQ score are the outcome measures for the
prospective arm of the study. Good response to surgery is a 1-point improvement in mJOA score
at 3 and 6 months. Radiological confirmation of cervical myelopathy is the outcome measure
for the retrospective arm.
