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PRO-ACT - How to Use PRO-ACT
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Ethical statement for PRO-ACT
Basic information
How to use the data dictionary
Family and Medical History
Demographics
Subject ALS history
Symptoms and outcome measures (FVC, SVC, ALSFRS and
Survival)
Vital signs
Lab data
The Amyotrophic Lateral Sclerosis Functional Rating Scale
(ALSFRS)
Concomitant Medication Use
Adverse Events
Ethical statement for PRO-ACT
In all of the trials that generated the data included in this
database, study protocols were
approved by the participating medicalcenters and all

2. participating patients gave informed
consent. De-identified data from these trials were donated to the
PRO-ACT database for
research purposes only and under the explicit conditions that
Prize4Life and all users of the
data would maintain the anonymity of subjects and not attempt
to discover the identity of any
subject. In the rare cases where donated data was not already
completely anonymized,
donated data was further anonymized following the HIPAA de-
identification conventions for
personal health information: any potential patient initials and/or
dates of birth were removed,
new randomized subject numbers were created, and wherever
possible, trial-specific
information was removed in the merging of datasets, including
trial center identity and
location, trial dates, or other identifying informatio n.
Basic information
Amyotrophic lateral sclerosis, or ALS (also known in the US as
Lou Gehrig’s Disease and as
Motor Neuron Disease in the UK) is a disease that involves the
degeneration and death of the
nerve cells in the brain and spinal cord that control voluntary
muscle movement. Death
typically occurs within 3 - 5 years of diagnosis. Only about 25%
of patients survive for more
than 5 years after diagnosis.
PRO-ACT includes information from over 8500 ALS patients
who participated in industry
clinical trials. The data is de-identified to protect patient
privacy. Different types of information
may be available for different patients because multiple trials
were merged to create

3. PRO-ACT. Some patients received placebo treatments, while
others received experimental
treatments (medication), however the medications tested in
these specific trials were found to
be no better than placebo with respect to their effects on ALS
progression.
For every type of information available there is a data file and a
dictionary file. Each subject is
identified by a SubjectID and the specific assessment for this
subject is identified by a record
(each subject has multiple records). The assessments are
separated into different files
according to type:
ALSFRS(R)
Death Report
Demographics
Family History
Forced Vital Capacity
PRO-ACT - How to Use PRO-ACT
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Home Data How to Use PRO-ACT ALS Prize ALS Links
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4. PRO-ACT - How to Use PRO-ACT
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Case-Study 15
Amyotrophic Lateral Sclerosis (ALS)
Overview: This case-study examines the patterns, symmetries,
associations and causality in a rare but devastating disease,
amyotrophic lateral sclerosis (ALS). ALS demands conducting
clinical trials and collecting big, multi-source and
heterogeneous datasets that can be interrogated to derive
potential biomarkers. Overcoming many scientific, technical
and infrastructure barriers is required to establish complete,
efficient, and reproducible protocols (pipelines/workflows)
starting with acquiring raw data, preprocessing, aggregation,
harmonization, analysis, visualization and result interpretation.
The clinical data shows that the rate of ALS progression varies
significantly among patients. Majority of the patients die within
3 to 5 years after ALS onset, however, a few are able survive
for over 10 years. This heterogeneity of disease course hinders
demonstration of its biological mechanism and development of
effective treatment. We need to develop reliable predictive
models of ALS progression to understand the pathophysiology
of the disease.
Driving Challenges:
· What patient phenotypes can be automatically and reliably
determined?
· Predict the change of the ALSFRS slope change using the

5. holistic patient-specific data.
· Predict survival of patients at a given time-point (post
diagnosis).
Meta-Data
· There are 2 datasets:
· training (N1=2,223): ALS_TrainingData_2223.csv, and
· testing (N2=78): ALS_TestingData_78.csv
· Each dataset includes the following 131 variables:
ID; Age_mean; Albumin_max; Albumin_median; Albumin_min;
Albumin_range; ALSFRS_slope; ALSFRS_Total_max;
ALSFRS_Total_median; ALSFRS_Total_min;
ALSFRS_Total_range; ALT.SGPT._max; ALT.SGPT._median;
ALT.SGPT._min; ALT.SGPT._range; AST.SGOT._max;
AST.SGOT._median; AST.SGOT._min; AST.SGOT._range;
Basophils_max; Basophils_median; Basophils_min;
Basophils_range; Bicarbonate_max; Bicarbonate_median;
Bicarbonate_min; Bicarbonate_range; Bilirubin..total._max;
Bilirubin..total._median; Bilirubin..total._min;
Bilirubin..total._range; Blood.Urea.Nitrogen..BUN._max;
Blood.Urea.Nitrogen..BUN._median;
Blood.Urea.Nitrogen..BUN._min;
Blood.Urea.Nitrogen..BUN._range; BMI_max;
bp_diastolic_max; bp_diastolic_median; bp_diastolic_min;
bp_diastolic_range; bp_systolic_max; bp_systolic_median;
bp_systolic_min; bp_systolic_range; Calcium_max;
Calcium_median; Calcium_min; Calcium_range; Chloride_max;
Chloride_median; Chloride_min; Chloride_range;
Creatinine_max; Creatinine_median; Creatinine_min;
Creatinine_range; Eosinophils_max; Eosinophils_median;
Eosinophils_min; Eosinophils_range; Gender_mean;
Glucose_max; Glucose_median; Glucose_min; Glucose_range;
hands_max; hands_median; hands_min; hands_range;

6. Hematocrit_max; Hematocrit_median; Hematocrit_min;
Hematocrit_range; Hemoglobin_max; Hemoglobin_median;
Hemoglobin_min; Hemoglobin_range; leg_max; leg_median;
leg_min; leg_range; Lymphocytes_max; Lymphocytes_median;
Lymphocytes_min; Lymphocytes_range; Monocytes_max;
Monocytes_median; Monocytes_min; Monocytes_range;
mouth_max; mouth_median; mouth_min; mouth_range;
onset_delta_mean; onset_site_mean; Platelets_max;
Platelets_median; Platelets_min; Potassium_max;
Potassium_median; Potassium_min; Potassium_range;
pulse_max; pulse_median; pulse_min; pulse_range;
Red.Blood.Cells..RBC._max; Red.Blood.Cells..RBC._median;
Red.Blood.Cells..RBC._min; Red.Blood.Cells..RBC._range;
respiratory_max; respiratory_median; respiratory_min;
respiratory_range; Sodium_max; Sodium_median; Sodium_min;
Sodium_range; SubjectID; trunk_max; trunk_median;
trunk_min; trunk_range; Urine.Ph_max; Urine.Ph_median;
Urine.Ph_min; Urine.Ph_range; White.Blood.Cell..WBC._max;
White.Blood.Cell..WBC._median;
White.Blood.Cell..WBC._min; White.Blood.Cell..WBC._range
References:
· Tang, M., Gao, C, Goutman, SA, Kalinin, A, Mukherjee, B,
Guan, Y, and Dinov, ID. (2018) Model-Based and Model-Free
Techniques for Amyotrophic Lateral Sclerosis Diagnostic
Prediction and Patient Clustering, Neuroinformatics, 1-15, DOI:
10.1007/s12021-018-9406-9.
·
https://scholar.google.com/scholar?hl=en&as_sdt=1%2C23&q=
%22proact%22+%22als%22&btnG=