Clinical trial NCT03330496: Assessment of Pain in Newborns and Older Infants (Infant Pain Assessment Study =
Official title: Using Machine-learning Algorithms to Assess Acute Pain in Nonverbal Infants
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Pain, Acute
Keywords: infant-newborn
infant-premature
pain
acute pain
Lead sponsor: Stanford University
Sponsor class: OTHER
Collaborators: name: Autonomous Healthcare, Inc.
First posted: 2017-11-06
Enrollment: 43
Sex: ALL
Minimum age (years): 1
Maximum age (years): 6
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: We will collect data from 30 study subjects in each of the following age groups:

* Preterm infants (34-37 weeks, postnatal age 3-30 days)
* Term newborns (37-42 weeks gestation, less than 1 month of age)
* Infants from 1-3 months age, and
* Older infants from 3-6 months age. A total of 120 subjects will be recruited before study closure.

Brief summary: Pain assessments in non-verbal, critically ill infants represent an important clinical challenge. Older children or adults can easily express their pain, but infants lack that capability. They frequently experience repetitive acute pain during routine ICU care, but their analgesic management flounders on the horns of a dilemma: (a) failure to treat infant pain leads to immediate clinical instability and potentially long-term physical, behavioral, and cognitive sequelae, vs. (b) strong analgesics may increase risks for medical complications and/or impaired brain growth. Bedside nurses currently assess pain using pain scores, before taking action to ameliorate pain. Pain scores increase nursing workload and provide subjective assessments, rather than objective data for evaluating infant pain. Consequently, infants exposed to skin-breaking procedures, surgery, or other painful conditions often receive variable and inconsistent pain management in the ICU. The investigators aim to develop a multimodal pain assessment system, using sensor fusion and novel machine learning algorithms to provide an objective measure of pain that is context-dependent and rater-independent. This will enhance the quality of pain management in ICUs and allow continuous pain monitoring in real-time.


Eligibility criteria: Inclusion Criteria:

* 1. All infants less than 6 months of corrected chronological age. 2. Infants admitted to the participating ICUs at LPCH (NICU, PICU, CVICU). 3. Require an acute painful procedure for routine clinical care.

Exclusion Criteria:

1. Newborns with birth trauma, intrapartum asphyxia (5-minute Apgar Score \<4 or cord pH \< 7.01), fetal growth restriction (birth weight \< 5th percentile for gestation), congenital anomalies or metabolic disorders, or any kind of brain injury.
2. Newborns born to mothers with a history of heavy smoking or drug abuse (alcohol, cocaine, ketamine, heroin/other opiates) or psychiatric drugs used during this pregnancy.
3. Newborns and infants requiring positive pressure ventilation using a face mask (BiPAP) or endotracheal tube (conventional or high-frequency ventilators).
4. Newborns and infants receiving continuous infusions of opioid drugs (morphine, fentanyl, others) and nerve blocks or neuraxial analgesia affecting the site of the invasive procedure during the 24 hours prior to study entry. (Note: Infants receiving intermittent opioids or non-opioid analgesics (acetaminophen, ibuprofen, ketorolac, others) WILL NOT be excluded).
5. Newborns and infants with facial anomalies, injuries or other pathologies affecting the facial area.

Primary outcomes:
measure: Pain Measurement (all infants)
timeFrame: 10 minutes
Secondary outcomes:
measure: Pain Measurement
timeFrame: 30 minutes
measure: Pain Measurement (newborns 0-30 days)
timeFrame: 5 minutes
measure: Pain Measurement (newborns 0-30 days)
timeFrame: 8 minutes
measure: Pain Measurement (infants 1-6 months)
timeFrame: 3 minutes
Other outcomes:
Locations:
city: Palo Alto
country: United States
facility: Lucile Packard Children's Hospital Stanford
state: California
Study officials:
affiliation: Prof.
name: Kanwaljeet S Anand, MBBS, D.Phil.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Roue JM, Morag I, Haddad WM, Gholami B, Anand KJS. Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol. BMJ Open. 2021 Jan 6;11(1):e039292. doi: 10.1136/bmjopen-2020-039292.