Useless for survivor recovery. I don't want a prediction on my lack of recovery. I WANT PROTOCOLS THAT LEAD DIRECTLY TO RECOVERY
PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke
Cathy M. Stinear,
1
,
2
Winston D. Byblow,
2
,
3
Suzanne J. Ackerley,
1
,
2
Marie‐Claire Smith,
1
,
2
Victor M. Borges,
1
,
2
and P. Alan Barber
1
,
2
,
4
1
,
2
Winston D. Byblow,
2
,
3
Suzanne J. Ackerley,
1
,
2
Marie‐Claire Smith,
1
,
2
Victor M. Borges,
1
,
2
and P. Alan Barber
1
,
2
,
4
This article has been cited by other articles in PMC.
Abstract
Objective
Recovery
of motor function is important for regaining independence after stroke,
but difficult to predict for individual patients. Our aim was to
develop an efficient, accurate, and accessible algorithm for use in
clinical settings. Clinical, neurophysiological, and neuroimaging
biomarkers of corticospinal integrity obtained within days of stroke
were combined to predict likely upper limb motor outcomes 3 months after
stroke.
Methods
Data
from 207 patients recruited within 3 days of stroke [103 females (50%),
median age 72 (range 18–98) years] were included in a Classification
and Regression Tree analysis to predict upper limb function 3 months
poststroke.
Results
The
analysis produced an algorithm that sequentially combined a measure of
upper limb impairment; age; the presence or absence of upper limb motor
evoked potentials elicited with transcranial magnetic stimulation; and
stroke lesion load obtained from MRI or stroke severity assessed with the NIHSS
score. The algorithm makes correct predictions for 75% of patients. A
key biomarker obtained with transcranial magnetic stimulation is
required for one third of patients. This biomarker combined with NIHSS score can be used in place of more costly magnetic resonance imaging, with no loss of prediction accuracy.
Interpretation
The
new algorithm is more accurate, efficient, and accessible than its
predecessors, which may support its use in clinical practice. While
further work is needed to potentially incorporate sensory and cognitive
factors, the algorithm can be used within days of stroke to provide
accurate predictions of upper limb functional outcomes at 3 months after
stroke. www.presto.auckland.ac.nz
Introduction
Recovery of upper limb motor function is important for regaining independence after stroke.1, 2 In general, greater initial impairment is associated with worse motor outcomes.2, 3 However, experienced clinicians find it difficult to accurately predict functional outcomes for individual patients.4
Being able to predict motor outcomes soon after stroke could support
realistic discharge planning, rehabilitation, goal setting, and
appropriate allocation of time and resources by clinicians and patients.5
There is growing interest in using biomarkers to predict patients’ motor recovery and outcomes.6, 7
Patients in whom transcranial magnetic stimulation elicits a motor
evoked potential in muscles of the paretic limb typically experience
greater motor recovery and better outcomes than patients without motor
evoked potentials.3, 8, 9 MRI can also be used to derive biomarkers of the motor system after stroke.10 Worse upper limb motor recovery and outcomes are predicted by greater stroke lesion load on descending corticomotor pathways,11 and greater asymmetry in fractional anisotropy along the corticospinal tracts.12, 13, 14, 15
To date no single clinical measure or neurological biomarker has been
able to accurately predict motor recovery or outcome for all patients,
and therefore approaches using combinations of measures and biomarkers
are needed.6
We developed the Predict Recovery Potential (PREP)
algorithm which combines clinical measures and neurological biomarkers
in the initial days after stroke to predict upper limb functional
outcomes at 3 months.15
The algorithm is unique in its sequential nature, which begins with a
simple clinical test and then uses biomarkers as required to resolve
uncertainty. The algorithm has been validated in a sample of 192
patients including those with previous stroke.5
Using PREP in clinical practice increased therapist confidence,
modified therapy content, and was associated with a 1 week reduction in
length of stay, with no detrimental effects on patient outcomes.5
While using PREP can increase rehabilitation efficiency, not all
clinical settings have access to transcranial magnetic stimulation and
the ability to derive quantitative biomarkers from diffusion‐weighted
MRI.
The purpose of this study was to
develop a new algorithm that would be more efficient, accurate, and
accessible to practising clinicians. We sought to determine if TMS could
be used in fewer patients than originally proposed or even eliminated,
and whether the diffusion‐weighted MRI biomarker could be replaced with a
simpler measure of stroke lesion load obtained from T1‐weighted images
alone.11, 16
Methods
Patients
A
retrospective analysis of data from two previous studies of 207
patients [103 females (50%); median age 72 (range 18–98) years] was
carried out.5, 15 Patient clinical characteristics are summarized in Table 1.
The 2014 study provided data from 50 patients with first‐ever
monohemispheric ischemic stroke. The 2017 study provided data from an
independent cohort of 157 patients with previous or first‐ever ischemic
stroke or intracerebral hemorrhage. For both studies, people were
excluded if they had cerebellar stroke, cognitive or communication
impairments precluding informed consent, or if they resided out of area
precluding follow‐up. The primary outcome for both studies was Action
Research Arm Test (ARAT) score at 3 months poststroke (mean = 92 days,
SD = 9 days), which was obtained by a trained clinical assessor blinded
to algorithm prognosis and not involved in patient care. Upper limb
therapy dose in minutes was recorded for each session by treating
physical and occupational therapists during inpatient rehabilitation,
and the total number of upper limb therapy minutes was calculated for
subsequent analysis.
Table 1
Participant characteristics
| N = 207 | |
|---|---|
| Demographic characteristics | |
| Age (years) | |
| Median age (range) | 72 (18–98) |
| *lt;80 years | 139 (67%) |
| Sex | |
| Male | 104 (50%) |
| Female | 103 (50%) |
| Ethnicity | |
| European | 131 (63%) |
| Maori | 10 (5%) |
| Pacific | 30 (15%) |
| Asian | 36 (17%) |
| Stroke risk factors | |
| Hypertension | 133 (64%) |
| Dyslipidemia | 66 (32%) |
| Previous cardiac history | 56 (27%) |
| Atrial fibrillation | 47 (23%) |
| Diabetes mellitus | 43 (21%) |
| Ex‐smoker | 35 (17%) |
| Smoker | 17 (8%) |
| Stroke characteristics | |
| First stroke | |
| yes | 181 (87%) |
| no | 26 (13%) |
| Stroke type (Oxfordshire classification) | |
| Total anterior circulation infarct | 12 (6%) |
| Partial anterior circulation infarct | 74 (36%) |
| Lacunar infarct | 84 (40%) |
| Posterior circulation infarct (excluding cerebellar) | 16 (8%) |
| Intracerebral hemorrhage | 21 (10%) |
| Hemisphere | |
| Right | 108 (52%) |
| Hand | |
| Dominant | 95 (46%) |
| Intravenous thrombolysis | |
| yes | 19 (9%) |
| Endovascular thrombectomy | |
| yes | 3 (1%) |
| Stroke Severity | |
| Mild (NIHSS score 0 – 4) | 112 (54%) |
| Moderate (NIHSS score 5 – 15) | 85 (41%) |
| Severe (NIHSS score ≥ 16) | 10 (5%) |
| Paretic upper limb measures | |
| Baseline SAFE score | |
| Excellent outcome median (range) | 8 (0 – 9) |
| Good outcome median (range) | 6 (0 – 9) |
| Limited outcome median (range) | 1 (0 – 5) |
| Poor outcome median (range) | 0 (0 – 3) |
| Baseline UE‐FM score | |
| Excellent outcome median (range) | 58 (16 – 65) |
| Good outcome median (range) | 43 (6 – 63) |
| Limited outcome median (range) | 13 (2 – 27) |
| Poor outcome median (range) | 7 (4 – 14) |
| 3‐month UE‐FM score | |
| Excellent outcome median (range) | 64 (47 – 66) |
| Good outcome median (range) | 54 (40 – 65) |
| Limited outcome median (range) | 32 (21 – 50) |
| Poor outcome median (range) | 9 (7 – 31) |
Paretic
upper limb measures are reported for actual (not predicted) outcome
categories, based on Action Research Arm Test score at 3 months (Table 2).
NIHSS, National Institutes of Health Stroke Scale; SAFE, Shoulder
Abduction, Finger Extension; UE‐FM, upper extremity Fugl‐Meyer.
Algorithm measures
Shoulder
abduction and finger extension strength were graded in the paretic
upper limb using the Medical Research Council (MRC) grades 3 days after
stroke symptom onset (median = 3 days, range = 1–4 days). The MRC grades
for each movement were summed to obtain a SAFE score out of 10. The
NIHSS and upper extremity Fugl‐Meyer (UE‐FM) scores were obtained at the
same time as the SAFE score. TMS and MRI biomarkers were obtained for
all patients in the smaller cohort (n = 50), and only as
required by the PREP algorithm for patients in the larger cohort. TMS
was used to determine the presence or absence of MEPs in the paretic
extensor carpi radialis and first dorsal interosseous muscles, 5 to
7 days poststroke. These muscles were chosen as impaired wrist extension
and index finger control often limit upper limb function after stroke.
Standard surface EMG techniques were used, and single pulse TMS was
delivered with a figure‐of‐eight coil connected to a MagStim 200
stimulator. The coil was oriented to produce posterior‐to‐anterior
current flow in the ipsilesional primary motor cortex. The patient was
considered MEP+ if MEPs of any amplitude were observed at a consistent
latency (±3 msec) on at least 50% of at least eight trials in either of
the recorded muscles.
MRI was used 10 to 14 days
poststroke to obtain three biomarkers, described below. T1‐weighted and
diffusion‐weighted images were acquired with a Siemens 1.5 T Avanto
scanner. Axial T1‐weighted images had 1.0 × 1.0 × 1.0 mm voxels, a
256 mm field of view, TR = 11 msec, and TE = 4.94 msec.
Diffusion‐weighted images had 1.8 × 1.8 × 3.0 mm voxels, a 230 mm field
of view, b = 2000 s.mm2, TR = 6700 msec, TE = 101 msec, 30
gradient directions, and two averages. The first biomarker was used in
both previous studies and involved calculating the mean fractional
anisotropy within the posterior limb of each internal capsule. A
template volume of interest for the posterior limb of each internal
capsule was warped to the patients’ images. The microstructural
characteristics of the internal capsules were quantified by calculating
an asymmetry index from the mean fractional anisotropy values: PLIC FAAI = (FAcontralesional – FAipsilesional)/(FAcontralesional + FAipsilesional).17
Two more biomarkers that could be
calculated from T1‐weighted images were developed in this study. These
were stroke lesion load on the ipsilesional corticospinal tract and
sensorimotor tracts. In preparation, template tracts were constructed
using probabilistic fiber tracking in the contralesional hemispheres of
85 patients. Diffusion‐weighted images were preprocessed with motion and
eddy current correction, skull stripping, estimation and fitting of
diffusion parameters, and modeling of crossing fibers.18
Seed masks were placed at the pyramid and the primary motor cortex
(M1), with a way point at the posterior limb of the internal capsule,
for the corticospinal tract template. Seed masks were placed at the
medial lemniscus near the inferior border of the pons and the primary
somatosensory cortex (S1), with a way point at the ventral nuclei of the
thalamus, for the sensory tract template. Tractography was conducted
with a curvature threshold of 0.2 and step‐length of 0.5. Tracts were
then nonlinearly transformed to MNI space and mirrored along the
mid‐sagittal axis as required so that all tracts were in the left
hemisphere. Tracts from all participants were then combined and
thresholded at 75% probability to ensure that only fibers at each
tract's core were used for subsequent analyses. Two template tracts were
generated; an M1 corticospinal tract, and a sensorimotor tract formed
by combining the S1 sensory tract with the M1 corticospinal tract. The
template corticospinal and sensorimotor tracts were then nonlinearly
registered to each patient's T1‐weighted image. A stroke lesion mask was
hand‐drawn on each patient's T1‐weighted image and the percentage of
tract voxels that overlapped the stroke lesion was calculated.16, 19
Analysis
A
hypothesis‐free cluster analysis of ARAT scores at 3 months poststroke
was carried out, to re‐evaluate the boundaries between the four outcome
categories of Excellent, Good, Limited, and Poor upper limb outcome.
These category labels replace the previous labels of Complete, Notable,
Limited, and None.15 Each patient was categorized into one of the four outcomes according to their ARAT score at 3 months.
A
classification and regression tree (CART) analysis was carried out with
IBM SPSS (version 24) to determine which factors best predict outcome
category. CART analysis produces a decision tree without the user
determining which variables to include, or their order, in the tree.
This approach substantially differed from that used in the development
of the PREP algorithm, and means that there were no a priori
assumptions made about the likely sequence or type of predictors that
were included in the resulting decision tree. The demographic and
clinical variables available to the CART analysis were sex, age
binarized at 80 years (<80, ≥80), hemisphere affected (left, right),
hand affected (dominant, nondominant), stroke classification (lacunar
infarct, partial anterior circulation infarct, total anterior
circulation infarct, posterior circulation infarct, intracerebral
hemorrhage), intravenous thrombolysis (yes, no), previous stroke (yes,
no), SAFE score, stroke severity (NIHSS score), upper limb impairment
(UE‐FM score), and upper limb therapy dose (minutes). The biomarkers
were MEP status (MEP+, MEP−), PLIC FAAI, corticospinal tract
lesion load (%), and sensorimotor tract lesion load (%). While therapy
dose is not known at the beginning of rehabilitation, and therefore is
not a predictor per se, it was included in the analysis as a potential modifier of outcome.
The
CART analysis had a maximum tree depth of 3, minimum terminal node size
of 10 cases, and automated pruning to avoid over‐fitting with a maximum
difference in risk of 1 standard error. “Gini” was used to optimize
homogeneity within terminal nodes. Alternative CART analyses were
carried out by removing either TMS or MRI data, or both. The results of
the CART analyses were reformatted and combined to produce the PREP2
algorithm.
Results
Cluster analysis
As in previous studies, the cluster analysis identified four nonoverlapping outcome categories (Table 2). The cluster boundaries were similar to those found previously15 with the lower boundary for Good dropping from 39 to 34 points.
Table 2
ARAT scores for functional outcome categories 3 months poststroke
| Outcome | Mean | Median | Minimum | Maximum | N |
|---|---|---|---|---|---|
| Excellent | 56 | 57 | 50 | 57 | 113 |
| Good | 43 | 42 | 34 | 48 | 55 |
| Limited | 22 | 22 | 13 | 31 | 16 |
| Poor | 2 | 3 | 0 | 9 | 23 |
CART analysis
The
CART analysis produced a decision tree with MEP status as the first
decision point, followed by sensorimotor tract and corticospinal tract
lesion load, and then NIHSS and SAFE score, with an overall prediction
accuracy of 73%. However, using TMS with every patient is impractical,
and unnecessary as all patients with a SAFE score of 5 or more were MEP+
(n = 141, 68%). Therefore, we first binarized the group
according to SAFE score (SAFE < 5, SAFE ≥ 5), and performed separate
CART analyses for each category.
For patients with a SAFE score of 5 or more, the overall prediction accuracy was 78% (Fig. 1, Table 3).
If patients were less than 80 years of age they were most likely to
have an Excellent upper limb outcome. If they were 80 years old or more,
and their UE‐FM score was less than 48 points, they were likely to have
a Good outcome; otherwise they were likely to have an Excellent
outcome. If UE‐FM scores were removed, the CART analysis predicted that
patients aged at least 80 years were likely to have a Good outcome if
their SAFE score was 5, 6, or 7, and an Excellent outcome if their SAFE
score was 8 or more. Given that prediction accuracy was similar (79% for
UE‐FM and 78% for SAFE), and the SAFE score is quicker and easier to
obtain, we elected to retain the SAFE score in the algorithm.
CART analysis for patients with a SAFE score ≥ 5 within 72 h poststroke. All of these patients are MEP+.
Table 3
PREP2 algorithm accuracy, positive and negative predictive values
| PPV (95% CI) | NPV (95% CI) | Accuracy for SAFE ≥ 5 | Accuracy for SAFE < 5 | |
|---|---|---|---|---|
| PREP2: Overall accuracy 75% | ||||
| Excellent N = 113 | 79% (73–84%) | 83% (75–89%) | 78% | 70% |
| Good N = 55 | 58% (46–68%) | 84% (79–88%) | ||
| Limited N = 16 | 86% (44–98%) | 95% (93–97%) | ||
| Poor N = 23 | 91% (73–98%) | 99% (96–100%) | ||
| With MRI: Overall accuracy 75% | ||||
| Excellent N = 113 | 79% (73–84%) | 83% (75–89%) | 78% | 70% |
| Good N = 55 | 58% (46–68%) | 84% (79–88%) | ||
| Limited N = 16 | 73% (46–89%) | 100% (97–100%) | ||
| Poor N = 23 | 100% | 92% (82–96%) | ||
| With no TMS, and no TMS or MRI: Overall accuracy 71% | ||||
| Excellent N = 113 | 79% (73–84%) | 83% (75–89%) | 78% | 55% |
| Good N = 55 | 53% (41–64%) | 82% (77–85%) | ||
| Limited N = 16 | No predictions | 92% | ||
| Poor N = 23 | 64% (50–75%) | 99% (96–100%) | ||
For patients with a SAFE score less than 5, the overall prediction accuracy was 70% (Fig. 2A, Table 3). Given the smaller number of patients in this analysis (n = 66)
the minimum terminal node size was reduced from 10 to 5 cases. The CART
analysis found that if patients were MEP+ they were most likely to have
a Good outcome. If patients were MEP− and had a sensorimotor tract
lesion load of 15% or less they were most likely to have a Limited
outcome. If patients were MEP− with a sensorimotor tract lesion load
more than 15% they had a Poor outcome. The accuracy of predictions for
MEP− patients was 90%. Note that fractional anisotropy asymmetry indices
for the posterior limbs of the internal capsules, as well as lesion
load on the corticospinal and sensorimotor tracts, were all entered into
the CART analysis. Only sensorimotor tract lesion load was selected by
the CART analysis, indicating that it was a better predictor than the
other MRI biomarkers.
CART analyses of patients with a SAFE score < 5 at 72 h poststroke. (A) Both TMS and MRI biomarkers available. The analysis selects sensorimotor tract (SMT) lesion load to differentiate between MEP− patients who will have a Limited versus Poor upper limb outcome. (B) TMS but no MRI biomarkers available. The analysis selects NIHSS score to differentiate between MEP− patients who will have a Limited versus Poor upper limb outcome.
Alternative
CART analyses were also carried out for patients with a SAFE score less
than 5. If MRI biomarkers were removed, the CART analysis selected
NIHSS score to predict outcome for MEP− patients (Fig. 2B).
Patients who were MEP− were most likely to have a Limited outcome if
their NIHSS score was less than 7, and a Poor outcome if their NIHSS
score was 7 or more. The accuracy of predictions for MEP− patients was
the same as when MRI biomarkers were available (90%). The overall
accuracy of predictions for patients with a SAFE score less than 5 was
also the same as when MRI biomarkers were available (70%), but with
different positive and negative predictive values (Table 3).
If
MEP status was removed, the CART analysis selected NIHSS score to
predict outcome for patients with a SAFE score less than 5. Patients
with an NIHSS score less than 9 were most likely to have a Good outcome,
while those with an NIHSS score of 10 or more were most likely to have a
Poor outcome. However, prediction accuracy dropped to 55% (Table 3).
MRI biomarkers were available but not selected as predictors by the
CART analysis because large overlaps in values meant they could not be
used as surrogates for MEP status. The fractional anisotropy asymmetry
index for the posterior limbs of the internal capsules ranged from −0.04
to 0.53 for MEP+ patients and from −0.09 to 0.55 for MEP− patients.
Corticospinal tract lesion load ranged from 0.4% to 51.1% for MEP+
patients and from 2.1% to 51.7% for MEP− patients. Sensorimotor tract
lesion load ranged from 0.2% to 43.5% for MEP+ patients and from 2.0% to
39.6% for MEP− patients. This indicates that these MRI biomarkers do
not distinguish between MEP+ and MEP− patients.
If both
TMS and MRI biomarkers were removed, the CART analysis again used NIHSS
score to predict outcome for patients with a SAFE score less than 5.
This produced the same decision tree as when TMS was removed.
The
potential predictors that the CART analyses did not select were sex,
hemisphere affected, hand affected, stroke classification, intravenous
thrombolysis, and previous stroke. Upper limb outcome was not predicted
by these factors, nor was it modified by upper limb therapy dose.
An algorithm for clinical use
The
decision trees produced by the CART analyses were reformatted into a
new algorithm (PREP2) suitable for use by clinicians, in order to make
predictions for individual patients (Fig. 3).
The new algorithm does not include MRI biomarkers, because the decision
trees produced with and without MRI biomarker information had
equivalent prediction accuracy (Table 3),
and the NIHSS score at 3 days poststroke is more accessible than an MRI
biomarker. The information that could be offered to patients in each
predicted outcome category is provided in Table 4.
The PREP2
algorithm predicts upper limb functional outcome at 3 months
poststroke. The four possible upper limb outcomes are color‐coded. The
colored dots depict the proportion of patients expected to achieve each
color‐coded outcome, depending on their pathway through the algorithm,
based on the results of the CART analysis. Patients who achieve a SAFE
score of five or more within 72 h of stroke symptom onset, and are less
than 80 years old, are most likely to have an Excellent upper limb
outcome. Patients who achieve a SAFE
score of five or more within 72 h of stroke symptom onset and are
80 years old or more, are most likely to have an Excellent upper limb
outcome provided their SAFE score is at least 8; otherwise they are likely to have a Good upper limb outcome. Patients whose SAFE score is less than 5 at 72 h after stroke symptom onset need TMS to determine MEP status in the paretic upper limb, a key biomarker of corticospinal tract integrity. If a MEP can be elicited (MEP+) approximately 5 days poststroke then the patient is likely to have at least a Good upper limb outcome. If a MEP cannot be elicited, the NIHSS
score obtained 3 days poststroke can be used to predict either a
Limited outcome if the score is less than 7, or a Poor outcome if the
score is 7 or more.
Table 4
Algorithm predictions
| Predicted outcome | Description | Rehabilitation focus |
|---|---|---|
| Excellent | Potential to make a complete, or near‐complete, recovery of hand and arm function within 3 months | Promote normal use of the affected hand and arm with task‐specific practice, while minimizing adaptation and compensation. |
| Good | Potential to be using the affected hand and arm for most activities of daily living within 3 months, though with some weakness, slowness, or clumsiness | Promote normal function of the affected hand and arm by improving strength, coordination, and fine motor control with repetitive and task‐specific practice. Minimize compensation with the other hand and arm, and the trunk. |
| Limited | Potential to regain movement in the affected hand and arm within 3 months, but daily activities are likely to require significant modification | Promote movement and reduce impairment by improving strengthand active range of motion. Promote adaptation in daily activities, incorporating the affected upper limb wherever safely possible. |
| Poor | Unlikely to regain useful movement of the hand and arm within 3 months | Prevent secondary complications such as pain, spasticity, and shoulder instability. Reduce disability by learning to complete daily activities with the stronger hand and arm. |
Note that the outcome category names replace those used in the original PREP algorithm (Complete, Notable, Limited, None).
Overall,
the new algorithm correctly predicted upper limb outcome for 156 of 207
patients (75%). Of the remaining 51 patients, the algorithm was too
optimistic for 35 (69%) and too pessimistic for 16 (31%). See Table 3
for positive and negative predictive values for each outcome. Most of
the patients for whom the algorithm was too optimistic were predicted to
have an Excellent outcome, but had a Good (n = 25) or Limited (n = 1)
outcome instead. Most of the patients for whom the algorithm was too
pessimistic were predicted to have a Good outcome, but had an Excellent
outcome instead (n = 14). This contributed to the relatively low positive predictive value for the Good outcome category.
The
new PREP2 algorithm correctly predicted the actual (rather than
minimum) level of function at 3 months for 156 of 207 patients (75%),
and is more accurate than the PREP algorithm which could predict actual
level of function for 132 of these patients (64%). PREP2 required TMS
for 66 of 207 patients (32%) in this sample, which is more efficient
than the PREP algorithm that would have required TMS for 116 of these
patients (56%). The new PREP2 algorithm eliminated the need for MRI for
the 30 of 207 patients (15%) who were MEP−, because NIHSS score 3 days
poststroke could be used with equivalent accuracy.
Discussion
PREP2
is an efficient, accessible, and accurate algorithm that may be useful
in clinical practice. If a patient achieves a SAFE score of 5 or more
within 72 h poststroke, knowing their age allows prediction of a Good or
Excellent upper limb outcome. If the SAFE score is less than 5 at 72 h
poststroke, the NIHSS score can be obtained at this time and a TMS
assessment scheduled within the next 3 days. These measures allow
prediction of a Good, Limited, or Poor outcome. While further work is
needed to potentially incorporate sensory and cognitive factors that may
affect upper limb outcomes, the PREP2 algorithm highlights the value of
sequentially combining clinical predictors and a key biomarker of
corticospinal tract integrity, MEP status, for predicting upper limb
function after stroke.
This study addresses some of the
limitations of previous work. Efficiency was improved by the finding
that patients with a SAFE score of 5 or more are MEP+ so that TMS is
only required for a third of patients using PREP2, instead of more than
half if using the PREP algorithm. Accessibility was improved by removing
the need for MRI scans. Provided MEP status information is available,
the NIHSS score can be used with equivalent prediction accuracy. Despite
these simplifications, accuracy increased with PREP2 correctly
predicting the actual upper limb functional outcome for 75% of patients,
which is an improvement on the 64% accuracy of PREP. Predictions were
too optimistic for most of the remaining 25% of patients. Erring on the
side of optimism is preferable to the alternative, to avoid reducing
patient and therapist motivation. These improvements in efficiency,
accessibility, and accuracy may support the testing and further
validation of PREP2 in a variety of clinical settings.
The
simple bedside assessment of shoulder abduction and finger extension
strength (SAFE score), combined with the patient's age, discriminated
with 78% accuracy between patients who had Excellent or Good upper limb
function 3 months poststroke. This 2‐min assessment is all that was
needed to provide a prediction for 68% of patients, indicating that
accurate predictions can be easily made for most patients. Age binarized
at 80 years is a new predictor identified by the CART analysis. The
finding that patients aged 80 years or more needed to be less impaired
(SAFE score ≥ 8) in order to achieve the same functional outcome as
their younger counterparts is in keeping with previous reports that age
is an independent predictor of stroke outcome.2, 20
TMS is only required for patients with a SAFE score less than 5. MEP+
patients are most likely to have Good upper limb function 3 months
poststroke. An MEP− patient will have Limited or Poor upper limb
function 3 months poststroke, and NIHSS score can be used to
discriminate between these two possibilities.
The
accuracy of predictions based on clinical assessment alone was 78% for
patients with a SAFE score of 5 or more, but only 55% for patients with a
SAFE score less than 5. Without MEP status, the CART analysis did not
select any of the MRI biomarkers employed here, and instead selected
NIHSS score to predict either a Good or Poor outcome. However, the
accuracy of these predictions was only marginally better than chance
(55%, Table 3).
The addition of TMS biomarker information increased prediction accuracy
to 70% for these patients, underlining the value of testing
corticospinal tract function in patients with more severe motor
impairment.21, 22
While PREP2 requires TMS for a smaller proportion of patients, this
does not eliminate barriers to using this technique in a clinical
setting. The major barrier is the cost of the TMS equipment but this
might be offset by a reduced average length of stay when algorithm
predictions are used in clinical practice.5 MEP status is a simple TMS measure, which can be obtained in approximately 20 min.5 Few patients (2%) have contraindications to TMS such as a history of epilepsy.5
Future studies could explore the possibility of replacing TMS with SAFE
scores obtained at later time points, as per previous work.23
The positive and negative predictive values for PREP2
ranged between 83% and 99%, with the exception of the positive
predictive value for the Good category which was only 58%. This was
partly because 27% of patients predicted to have a Good outcome exceeded
this expectation and had an Excellent outcome. In clinical practice,
this might mean that patients in this category could be informed that
they are likely to have a Good upper limb outcome, and there is a one in
four chance it could be Excellent. Predicting that patients will
achieve at least a Good upper limb outcome increases the positive
predictive value for this category from 58% (95% CI: 46–68%) to 85% (95%
CI: 73–92%), which is similar to the positive predictive values for the
other outcome categories. For MEP− patients, NIHSS score and
sensorimotor tract lesion load produced predictions with equivalent
accuracy (90%). However, the positive predictive value was higher for
the Limited category when NIHSS score was used, and higher for the Poor
category when sensorimotor tract lesion load was used. In clinical
practice, certainty of a Poor prognosis could be maximized using
sensorimotor tract lesion load rather than NIHSS score for MEP−
patients.
Previous studies have used clinical measures alone to predict upper limb outcomes.2, 3, 4
One study found that patients at 48 h poststroke with a Fugl‐Meyer
scale score of at least 1 point for paretic finger extension, and a
Motricity Index score of at least 9 points for shoulder abduction, had a
98% probability of having “manual dexterity” 6 months poststroke,
defined as an ARAT score of at least 10 points.23
If both scores were below these cut‐offs, the probability was only 25%.
Another study used two items from the ARAT to predict whether patients
would have a Fugl‐Meyer scale score of at least 32 points at 12 months
poststroke, as this was the minimum required to perform a drinking task
with the paretic upper limb.24
Patients at 3 days poststroke whose combined score on the “pour water
from glass to glass” and “place hand on top of head” items of the ARAT
was at least 2 points (out of 6) were predicted to achieve a Fugl‐Meyer
score of at least 32 points by 12 months poststroke, with 81% accuracy.24
While the predictions made by these studies are accurate, they are for
dichotomized outcomes that are not particularly useful. ARAT scores
between 10 and 57 points23
embrace such a wide range of functional outcomes that making this
prediction provides very little guidance for patients or therapists.
Predicting whether a patient will be able to perform a single drinking
task or not, based on their expected Fugl‐Meyer score,24
is also not particularly informative when planning rehabilitation.
Neither of these predictive models has yet been validated in independent
cohorts, and the effects of using them in clinical practice are yet to
be explored.
In contrast, PREP2 predicts one of four
functionally meaningful upper limb outcomes. The sequential nature of
the algorithm means that predictions can be made for 68% of patients
using only SAFE score and age, with 78% accuracy. TMS is only needed for
patients who have a SAFE score less than 5, and is essential for
identifying which of these patients are MEP+ and have the potential for a
Good outcome. When PREP predictions are available, therapists are more
confident they know what to expect for the patient's recovery and modify
their therapy content according to the suggested rehabilitation goals,
and patients experience a shorter length of stay with no detrimental
effects on outcomes or satisfaction.5
One of the limitations of this study is the small
number of MEP− patients relative to MEP+. Further work could usefully
explore other neuroimaging biomarkers that might provide important
prognostic information for MEP− patients. These may involve measures of
alternative descending motor pathways,25, 26, 27, 28 and of the wider ipsilesional and contralesional sensorimotor networks, including the corpus callosum.29, 30, 31, 32, 33
However, more sophisticated measures may also require expertise not
readily available in most clinical settings. Patients with previous
stroke or intracerebral hemorrhage were also relatively
under‐represented in this study. Other possible predictors of upper limb
outcome also need to be explored, such as impaired upper limb
somatosensation, vision, visuospatial attention, and cognition.3, 34, 35
It is possible that PREP2 predictions, which are based on motor system
measures, are less likely to be achieved when the patient's motor
performance is also affected by deficits in sensory and cognitive
domains.
PREP2 could be used for selection and
stratification of patients for upper limb rehabilitation trials
initiated early after stroke. Matching treatment and control groups on
baseline clinical measures alone runs the risk of the groups being
mismatched in terms of likely outcomes, particularly when patients with
moderate to severe initial impairment are included. Being able to match
treatment and control groups for their expected outcome may reduce noise
and increase the trial's sensitivity to treatment effects.
PREP2
is an efficient, accessible, and accurate algorithm that could be
useful in clinical practice. Its predecessor has been validated and
found to increase rehabilitation efficiency.5
This needs to be confirmed for PREP2, preferably in the context of a
multi‐site study with a larger sample of patients being rehabilitated in
a variety of clinical settings.
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