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Table 4 Data extraction table

From: Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review

Study

Number of models in the study

Outcomes

Outcome measure (type of outcome, ICF classification)

Outcome at discharge? Yes/no

Predictors (number)

Timing of the measurement

Methods for features selection

Algorithm of the best performing model

Validation approach

Measures and methods used for the description of model performance

Almubark et al.

102

Upper extremity use at home

MAL ratio (dichotomous variable, d5d6)

N/R

Trunk compensation, ARAT (3)

N/R

N/A

RF after PCA

Leave-One-Subject-Out

Classification accuracy 93.33%

Upper extremity use at home

Accel ratio (dichotomous variable, b7)

KNN

Classification accuracy 86.66%

Bates et al.

1

Physical grade achievement

FIM (numeric variable, d2d3d4d5d7)

Yes

Anagrafic data, clinical data, comorbidities data, acute procedures (38)

N/R

Unadjusted bivariate logistic analyses _ features selected are with p < 0.2

LogR

60% -40% split

ROC area on the derivation set = 0.84

ROC area on the validation set = 0.83

 + Hosmer–Lemeshow test at p = 0.93 not significant on the derivation cohort

Berlowitz et al.

4

Functional outcome

FIM change (numeric variable, d2d3d4d5d7)

Yes

Age, gender (2)

N/R

N/A

LR

Bootstrap method (1000 samples)

R^2 = 0.75

Bland et al.

2

Walking ability

10 m walking speed (dichotomous variable, b7)

Yes

Motricity Index, somatosensation of the dorsum of the foot, Modified Ashworth Scale for plantar flexors, FIM walk item, Berg Balance Scale, 10-m walk speed, age, TPO (8)

Admission

Pearson product-moment correla_

tion

LogR

110 -159 samples split

Sensitivity (0.94), specificity (0.65), OR (32), positive and negative predictive values (0.70, 0.93)

10 m walking speed (numeric variable, b7)

LR

Sensitivity (0.94), specificity (0.65), OR (32), positive and negative predictive values (0.70, 0.93)

Cheng et al.

3

Recovery

MRS (dichotomous variable)

No, at 3 months

Gender, hypertension, heart disease, diabetes,

previous stroke with yes or no nodes, age, OTT, NIHSS (8)

N/R

N/A

NN

80%—20% split

ROC curve = 0.969,sensitivity = 0.9444,specificity = 0.9565,accuracy = 0.9512

De Marchis et al.

2

Unfavourable functional outcome

MRS (dichotomous variable, d2d4)

No, at 3 months

Age, NIHSS score, thrombolysis, log10-transformed copeptin levels (4)

Admission

Chosen variables that were

independently associated with 3-month functional outcome in the dev and val cohorts

LogR

Model trained on COSMOS dataset (319) and tenfold CV; Ex. validated on CoRisk dataset (783)

Brier score

 + AUC (0.819)

 + NRI = continuous net reclassification index (0.46)

De Ridder et al.

7

Functional outcome

MRS (dichotomous variable, d2d4)

No, at 3 months

Gender; age; NIHSS,

Diabetes, previous stroke

atrial fibrillation and hypertension (7)

N/R

Selected variables that were clinically relevant and/or previously reported to predict outcome in the literature

LR

Model trained on PAIS dataset (1227) and ex. validated on PASS dataset (2107)

AUC = 0.81

George et al.

6

Extent of motor recovery after constraint-induced movement therapy

WMFT (dichotomous variable, d2d4)

Yes

Side of motor impairment, motor predictors: each of the 15 WMFT natural-log-transformed item times; Sensory-motor predictors: BKT score, TM for the affected side (18)

N/R

All possible combinations of 18 inputs, a total of 262,125 combinations, were generated

NN

35 different splits at different random ratios

(RTT)

Accuracy = 100%

König et al.

1

Functional independence

BI (dichotomous variable, d2d4d5)

No, at 3 months

Single items as well as the

overall score of the NIHSS (16)

N/R

Systematic literature search

LogR

Model trained on original dataset (1754); ex. validated on VISTA dataset (5048)

AUC = 72.9%

Sonoda et al.

2

Stroke outcome

Motor FIM (numerical variable, d2d4d5)

Yes

Total cognitive subscore of the FIM, age, days from stroke onset to dmission, motor-FIM (4)

Admission

N/A

LR

87 -44 samples split

Correlation coefficients = 0.93

Kuceyeski et al.

7

Clinical performance

Motor FIM (numerical variable, d2d4d5)

N/R

Right inferior occipital and calcarine areas (N/R)

N/R

Jackknife CV

LR

Bootstrap

Akaike Information Criterion (AIC) and R^2 = 0.45 (0.08)

FIM (numerical variable, d2d3d4d5d7)

Akaike Information Criterion (AIC) and R^2 = 0.37 (0.08)

MI (numerical variable, b7)

Akaike Information Criterion (AIC) and R^2 = 0.54 (0.14)

Li et al.

2

Functional status

BI (numerical variable, d2d4d5)

Yes

Demographic information (age, sex and smoking habit), medical history (hypertension, diabetes mellitus, atrial fibrillation and hypercholesterolemia), evaluation at initial admission in the emergency department (blood glucose, blood pressure, laboratory data and the stroke severity) (N/R)

Admission

N/A

LR

CV (90–10% _ split)

R^2 adjusted = 0.573

Scrutinio, Lanzillo, et al.

2

Functional status

FIS (dichotomous variable, d2d4d5)

Yes

Age, sex, marital staus, employment status, hypertension,

diabetes mellitus,

COPD, coronary heart disease, atrial fibrillation, TPO, aetiology, side of impairment, aphasia, unilateral neglect, M-FIM, cognitive FIM,

blood urea nitrogen,

estimated glomerular filtration rate, hemoglobin (19)

Admission

Forward stepwise selection approach with P < 0.05

LogR

717–875

samples split

AUC (0.913), Hosmer–Lemeshow test ( 1.20 (P = 0.754)) and calibration plots

Motor FIM (dichotomous variable, d2d4d5)

AUC (0.883), Hosmer–Lemeshow test ( 4.12 (P = 0.249)) and calibration plots

Mostafavi et al.

12

Assessment of impairment

MAS (numerical variable, b7)

Yes

postural hand speed; reaction and its timing; initial movement direction error/ratio, hand speed ratio; number of speed peaks, speed ranges; movement time, hand path length, and maximum hand speed trial-to-trial variability of the active hand; contraction/expansion of the overall spatial area of the active hand relative to the passive hand; systematic shift between the passive and active hand (8)

During every session, they are instrumental attributes

N/A

PCI

tenfold CV, repeated 100 times + external valiudation

R-value, RMSE, NRMSE (0.054, 0.405, 31.2)

Masiero et al. [29]

1

Ambulation

FAC (dichotomous variable, d4)

Yes

Age, gender, arterial hypertension, hypolipoproteinaemia, diabetes,

event date and aetiology, paralysed side

length of hospital stay, up MI and low MI, TCT, FIM and mot FIM (12)

Admission

N/R

LogR

100–50 samples split

ROC curves (ROC area = 0.94, CI 95%: 0.86–0.96, p < 0.0001), with sensitivity of 86.5% (CI 95%: 77–96%) and specificity of 95.5%

(CI 95%: 75–95%))

Abdel Majeed et al.

8

Change in clinical outcomes

FM change (numerical variable, b2b7)

Yes

Demographic/physiological characteristics

descriptive statistics of movement (51)

Demogr. and physiol. at baseline, movement features

Random forests with 100 repeats of

fourfold CV

LR

CV

RMSE and R^2 < 2.24%

Scrutinio, Guida, et al.

1

Treatment failure

FIM-M (dichotomous variable, d2d4d5)

Yes

Age, sex, marital status, diabetes mellitus, TPO, stroke type, side of impairment, FIM-M and cognitive scores, neglect (10)

N/R

Backward

stepwise selection (P > 0.157 for exclusion)

LogR

Resampling 200 bootstrap replications

Hosmer–Lemeshow test (7.77 (PZ.456)) and AUC (0.834)

Mostafavi et al.

12

Assessment of impairment

FIM-M (numerical variable, d2d4d5)

Yes

postural hand speed; reaction and its timing; initial movement direction error/ratio, hand speed ratio; number of speed peaks, speed ranges; movement time, hand path length, and maximum hand speed trial-to-trial variability of the active hand; contraction/expansion of the overall spatial area of the active hand relative to the passive hand; systematic shift between the passive and active hand (8)

During every session, they are instrumental attributes

N/A

PCI

Tenfold CV, repeated 100 times

R-value, RMSE, NRMSE (0.562, 16.6, 21.7)

FIM (numerical variable, d2d3d4d5d7)

R-value, RMSE, NRMSE (0.596, 16.8, 20.5)

Purdue Pegboard score (numerical variable, d2d4)

R-value, RMSE, NRMSE (0.483, 4.1, 14.1)

Abdel Majeed et al.

8

Change in clinical outcomes

WMFT change (numerical variable, d2d4)

Yes

Demographic/physiological characteristics

descriptive statistics of movement (51)

Demogr. and physiol. at baseline, movement features

Random forests with 100 repeats of

fourfold CV

LR

CV

RMSE and R^2 < 4.68%

Sale et al.

9

Motor improvement

FIM-M (numerical variable, d2d4d5)

Yes

Age, gender, aetiology, first event,

recombinant tissue plasminogen activator, BI, FIM

motor impairment, dysphagia, tracheostomy, neuropsychological impairment, speech impairment, presence of nasogastric feeding tube, length of stay (14)

T0 = admission

T1 = discharge

Mutual Information (MI) criterion

SVM

20 rep. of hold-out approach with 70%—30% split

 + nested fivefold CV on the training set

Correlation, RMSE and MADP (0.76, 16.32, 26.79%)

FIM (numerical variable, d2d3d4d5d7)

Correlation, RMSE and MADP (0.79, 18.78, 18.88%)

BI (numerical variable, d2d4d5)

Correlation, RMSE and MADP (0.75, 22.6, 83.96%)

Zariffa et al.

2

Measure of upper-limb function

FMA (numerical variable, b2b7)

Yes

Mean velocity, peak velocity, RMS jerk, mean-rectified jerk,

number of peaks, path smoothness, speed smoothness,

SPARC, passive ROMs,

passive ROM Area,

Active ROMs, Active ROM Area (14)

During 76 assessments

Exhaustive search of all the combinations of the 14 features

LR

Leave-one-subject-out

R^2 = 0.4390, SRD = 1.4621

ARAT (numerical variable, b7)

R^2 = 0.4246, SRD = 2.6803

  1. A short description of the methods, predictor, outcomes, and the total number of models performed is presented
  2. N/R information should be specified but it is not reported in the paper, N/A information not applicable to the specific paper, ARAT Action Research Arm Test, BI Barthel Index, FAC Functional Ambulation Categories, FIM Functional Independence Measure, FIM-M Functional Independence Measure-Motor, FIS Fatigue Impact Scale, FM Fugl-Meyer, FMA Fugl-Meyer Assessment, MAL Motor Activity Log, MAS Modified Ashworth Scale, MI Motricity Index, MRS Modified Rankin Scale, SDMT Symbol Digit Modalities Test, WMFT Wolf Motor Function Test, ANN Artificial Neural Networks, FOS Fast Orthogonal Search, kNN k-Nearest Neighbours, LR Linear Regression, LogR Logistic Regression, PCI Parallel Cascade Identification, RF Random Forest, SVM Support Vector Machine, AUC Area Under the Curve, MADP Mean Absolute Deviation Percentage, NRI Net Reclassification Index, NRMSE Normalized Root Mean Square Error, RMSE Root Mean Square Error, SRD smallest real difference, CV cross-validation