<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04422288</url>
  </required_header>
  <id_info>
    <org_study_id>NUVA.MSK1901</org_study_id>
    <nct_id>NCT04422288</nct_id>
  </id_info>
  <brief_title>MSK Validation Study</brief_title>
  <acronym>MSK</acronym>
  <official_title>Prediction of Postoperative Global Sagittal Alignment Using Musculoskeletal Modeling - Validation Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>NuVasive</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>NuVasive</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Spinal posture and imbalance are known to be related to increased muscle expenditure, with&#xD;
      narrow &quot;cone of economy&quot; of muscle effort defining the most comfortable postures. Therefore,&#xD;
      it is hypothesized that predicting the posture of the lowest muscle effort available for a&#xD;
      patient with a given spinal alignment and body properties will correspond to the posture the&#xD;
      patient will most likely assume. Based on established musculoskeletal models, a model&#xD;
      application was configured to allow prediction of this optimal posture. This study aims to&#xD;
      assess the validity of this approach and the value of using biomechanical modeling for&#xD;
      pre-operative planning.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The objective of this study is to validate a novel method of post-operative posture&#xD;
      prediction - a full-body biomechanical model based on an established technology and&#xD;
      physiological reasoning. Specifically, the model ability to predict postoperative global&#xD;
      sagittal alignment, including compensatory and reciprocal changes, from pre-operative&#xD;
      radiographic imaging and the information about planned posture correction will be evaluated.&#xD;
      This will be realized by comparing model-predicted radiographic measures and overall balance&#xD;
      to follow-up patient radiographs.&#xD;
&#xD;
      Having demonstrated model validity to predict postoperative posture will allow to use this&#xD;
      method for simulating various &quot;what-if&quot; scenarios to empower surgical planning by predicting&#xD;
      expected outcomes. This can be used to optimizing preoperative planning, which has a&#xD;
      potential to substantially improved surgery predictability and patient outcomes.&#xD;
&#xD;
      Furthermore, validated model will allow scientific investigation of the principles governing&#xD;
      human posture and biomechanics of the pathological spine. Generated scientific knowledge of&#xD;
      biomechanical factors influencing sagittal posture and surgery outcomes (e.g. number of&#xD;
      levels fused, amount and distribution of posture correction, etc.) can lead to improvements&#xD;
      in clinical management of spinal disorders.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Active, not recruiting</overall_status>
  <start_date type="Actual">May 15, 2020</start_date>
  <completion_date type="Anticipated">December 31, 2022</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Difference between model-predicted and observed postural measures - Thoracic Kyphosis (TK)</measure>
    <time_frame>3 months</time_frame>
    <description>The simulation-predicted posture will be compared against the posture observed at follow-up, using the thoracic kyphosis (TK) angle.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Difference between model-predicted and observed postural measures - Lumbar Lordosis (LL)</measure>
    <time_frame>3 Months</time_frame>
    <description>The simulation-predicted posture will be compared against the posture observed at follow-up, using the LL - lumbar lordosis (LL) angle.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Difference between model-predicted and observed postural measures - T1 Pelvic Angle (TPA)</measure>
    <time_frame>3 Months</time_frame>
    <description>The simulation-predicted posture will be compared against the posture observed at follow-up, using the T1 pelvic angle (TPA).</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Difference between model-predicted and observed postural measures - Pelvic Incidence-Lumbar Lordosis Mismatch (∆PILL)</measure>
    <time_frame>3 Months</time_frame>
    <description>The simulation-predicted posture will be compared against the posture observed at follow-up, using the pelvic incidence-lumbar lordosis mismatch (∆PILL).</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Model sensitivity and specificity in predicting posture imbalance</measure>
    <time_frame>3 months</time_frame>
    <description>A McNemar's test (a paired Chi-squared test) will be used to test the null hypothesis that the balance prediction is due to chance, allowing to assess if the model predictive power is better than random.</description>
  </secondary_outcome>
  <enrollment type="Anticipated">200</enrollment>
  <condition>Proximal Junctional Kyphosis</condition>
  <condition>Spino-pelvic Alignment</condition>
  <condition>Thoracolumbar Spinal Fusion</condition>
  <eligibility>
    <study_pop>
      <textblock>
        At least 186 patients will be enrolled in this multicenter combined observational / in&#xD;
        silico study, recruited into 3 groups (at least 62 cases each) according to the number of&#xD;
        levels instrumented:&#xD;
&#xD;
          1. local fusion, 1-2 segments;&#xD;
&#xD;
          2. short fusion, 3-4 segments;&#xD;
&#xD;
          3. long fusion, 5+ segments.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Male or female patients&#xD;
&#xD;
          2. Any ethnicity&#xD;
&#xD;
          3. At least 18 years of age&#xD;
&#xD;
          4. Has undergone a thoracolumbar spinal fusion procedure&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Patient has had a prior spinal surgery in the thoracic and/or lumbar spine;&#xD;
&#xD;
          2. Patient presents scoliosis greater or equal to 20° T4-T12 Cobb angle;&#xD;
&#xD;
          3. Patient has been diagnosed with idiopathic adolescent scoliosis (treated or&#xD;
             untreated);&#xD;
&#xD;
          4. Patient has other implants that obstruct the spine and/or pelvis in the lateral view;&#xD;
&#xD;
          5. Patient presents any of the following complications: pseudoarthrosis, instrumentation&#xD;
             failure, instrumentation pull-out and/or requires a revision surgery at any time&#xD;
             following the primary surgery and prior to 3 months post-op;&#xD;
&#xD;
          6. Patient is a prisoner.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Kyle Malone, MS</last_name>
    <role>Study Director</role>
    <affiliation>NuVasive</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University of Colorado</name>
      <address>
        <city>Aurora</city>
        <state>Colorado</state>
        <zip>80045</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Univerisity of Pittsburgh Medical Center</name>
      <address>
        <city>Pittsburgh</city>
        <state>Pennsylvania</state>
        <zip>15213</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>September 2022</verification_date>
  <study_first_submitted>June 2, 2020</study_first_submitted>
  <study_first_submitted_qc>June 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 9, 2020</study_first_posted>
  <last_update_submitted>September 30, 2022</last_update_submitted>
  <last_update_submitted_qc>September 30, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">October 4, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Kyphosis</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04422288
org study id: NUVA.MSK1901
nct id: NCT04422288
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Spinal posture and imbalance are known to be related to increased muscle expenditure, with
      narrow "cone of economy" of muscle effort defining the most comfortable postures. Therefore,
      it is hypothesized that predicting the posture of the lowest muscle effort available for a
      patient with a given spinal alignment and body properties will correspond to the posture the
      patient will most likely assume. Based on established musculoskeletal models, a model
      application was configured to allow prediction of this optimal posture. This study aims to
      assess the validity of this approach and the value of using biomechanical modeling for
      pre-operative planning.
The objective of this study is to validate a novel method of post-operative posture
      prediction - a full-body biomechanical model based on an established technology and
      physiological reasoning. Specifically, the model ability to predict postoperative global
      sagittal alignment, including compensatory and reciprocal changes, from pre-operative
      radiographic imaging and the information about planned posture correction will be evaluated.
      This will be realized by comparing model-predicted radiographic measures and overall balance
      to follow-up patient radiographs.

      Having demonstrated model validity to predict postoperative posture will allow to use this
      method for simulating various "what-if" scenarios to empower surgical planning by predicting
      expected outcomes. This can be used to optimizing preoperative planning, which has a
      potential to substantially improved surgery predictability and patient outcomes.

      Furthermore, validated model will allow scientific investigation of the principles governing
      human posture and biomechanics of the pathological spine. Generated scientific knowledge of
      biomechanical factors influencing sagittal posture and surgery outcomes (e.g. number of
      levels fused, amount and distribution of posture correction, etc.) can lead to improvements
      in clinical management of spinal disorders.
observational model: Cohort
time perspective: Retrospective
measure: Difference between model-predicted and observed postural measures - Thoracic Kyphosis (TK)
time frame: 3 months
description: The simulation-predicted posture will be compared against the posture observed at follow-up, using the thoracic kyphosis (TK) angle.
measure: Difference between model-predicted and observed postural measures - Lumbar Lordosis (LL)
time frame: 3 Months
description: The simulation-predicted posture will be compared against the posture observed at follow-up, using the LL - lumbar lordosis (LL) angle.
measure: Difference between model-predicted and observed postural measures - T1 Pelvic Angle (TPA)
time frame: 3 Months
description: The simulation-predicted posture will be compared against the posture observed at follow-up, using the T1 pelvic angle (TPA).
measure: Difference between model-predicted and observed postural measures - Pelvic Incidence-Lumbar Lordosis Mismatch (∆PILL)
time frame: 3 Months
description: The simulation-predicted posture will be compared against the posture observed at follow-up, using the pelvic incidence-lumbar lordosis mismatch (∆PILL).
measure: Model sensitivity and specificity in predicting posture imbalance
time frame: 3 months
description: A McNemar's test (a paired Chi-squared test) will be used to test the null hypothesis that the balance prediction is due to chance, allowing to assess if the model predictive power is better than random.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Kyle Malone, MS
role: Study Director
affiliation: NuVasive
facility: 
facility: 
country: United States
responsible party type: Sponsor
mesh term: Kyphosis