Managing Acute Pain in Critically Ill Non-communicative Palliative Care Patients

The Multiple Principal Investigators (MPIs) are Dr. Deborah McGuire (VCU) and Dr. Carl
Shanholtz (University of Maryland. University of Maryland Medical Center (UMMC) delivers
palliative care to acutely ill persons across a range of ages, medical illnesses, and
traumatic injuries for whom death is not a foregone conclusion. Data will be collected on
eight inpatient acute care nursing units, which are grouped into services defined by medical
specialties and patients: Medical Intensive Care Unit, Medical Intermediate Care Unit (IMC),
Neuro-Trauma IMC Unit, Neuro-Trauma Critical Care Unit, Multi-trauma Critical Care Unit,
Select Trauma IMC Unit, Select Trauma Critical Care, and Surgical Intensive Care Unit.
Average lengths of stay are highly variable but are generally a week or less. This is a one
site, quasi-experimental cohort control group design, conducted in two sequential phases with
repeated measures in two different but comparable cohorts. The primary aim is to test whether
a pain algorithm that incorporates the Multi-dimensional Pain Assessment Tool (MOPAT) and an
analgesic order set improves pain severity and use of pharmacologic pain management
strategies in critically ill non-communicative palliative care patients who are hospitalized
on medical, surgical, and trauma intensive care units when compared to patients without the
algorithm. The secondary descriptive aims are to: (S1) compare pain severity and use of
pharmacologic pain management strategies in patients with and without concurrent pain-related
conditions, (S2) describe the pattern of patients' pain over time, and (S3) evaluate nurses'
perceptions of clinical utility of the pain algorithm. This study has two samples:
non-communicative palliative care patients who have acute pain and nurses who will use the
PAIN Algorithm and order set to manage their patients' pain. Patients will be 300 critically
ill adults who are non-communicative for a variety of reasons (intubation, neurological
impairment, etc.) and have conditions that are known to produce acute pain. Patient data
collection will be collected for 7 days (day 1 to 7) or until the patient dies, regains the
ability to self-report pain, is transferred to a non-participating unit, or is discharged,
whichever comes first. This timeframe is based on average lengths of stay for
non-communicative patients on the units, quality of care benchmarks for pain relief in
self-reporting patients, and practical considerations for research in acutely ill
individuals. Volunteer Staff Nurse data will be collected at baseline and monthly over 24-36
months. Study Phase 1 will occur before introduction of the pain algorithm which occurs in
Phase 2. Prior to starting this phase, nurses will be trained to use the MOPAT and it will be
incorporated into the pain standard of care and the electronic medical record (EMR),
permitting comparison of pain assessment data between Phases 1 and 2. The MOPAT will replace
the Checklist of Nonverbal Pain Indicators (CNPI), which is currently used to assess pain in
non-communicative patients but has multiple limitations. Following a 6 week run-in period in
which nurses' appropriate and universal use of the MOPAT is assessed and assured, data
collection will begin. Patients in this cohort will comprise the usual care group (UCG), or
control cohort. Nurses will also be enrolled from the participating inpatient units. Usual
care is defined as all pain assessment and management practices that nurses are currently
performing on the study units, including inconsistent use of existing algorithms and
protocols. Data will be collected on patients and nurses until we accrue 150 patients. Study
Phase 2 will begin following the completion of Phase 1. Prior to starting this phase, our
pain algorithm, christened the Pain Assessment and Intervention for the Non-communicative
(PAIN) Algorithm, will be introduced to nurses and physicians on all participating units and
they will be trained in its use. The PAIN Algorithm couples the MOPAT with an analgesic order
set. The final algorithm will have specific numerical cut points derived from a consensual
process. In addition, orders in the order set, and adaptations to accommodate patient
demographic and medical variables will be finalized through a detailed collaborative process.
After a 6 week run-in period to assure appropriate use of the PAIN Algorithm, data collection
will begin. Patients enrolled in phase 2 will be considered the intervention group (IG), also
called the experimental cohort. We will collect the same patient and nurse data as in Phase
1, with the addition of nurse perceptions of clinical utility of the PAIN Algorithm, and will
continue until we have accrued 150 patients. Considerations regarding potential risks to
patients are as follows. Because the algorithm includes opioids, and patients targeted in
this study will be critically ill with numerous pathological processes that could be
adversely affected by these drugs, there is concern about adverse opioid-related side effects
such as sedation and respiratory depression. To deal with this concern, the PAIN Algorithm
order set will include orders for routine monitoring of these side effects and interventions
to manage them, for example, titrating the opioid dose or using a reversing agent in the case
of severe respiratory depression. To ensure that these orders conform to standards for safe
practice, the interdisciplinary panel that develops the analgesic order set in conjunction
with the researchers will build in drug side effect assessment, treatment, and reassessment.
Another area of potential risk, however, remains of concern. Since the MOPAT instrument is a
relatively new pain assessment tool, and medication decisions will be made using MOPAT
Behavioral Dimension scores, there is the possibility that treatment decision may
under-medicate, or overmedicate a patient's pain. Thus, even with careful attention to drug
side effects, the study is probably greater than minimal risk. There are no alternative
treatments and procedures, since the PAIN Algorithm will be integrated into clinical practice
as the standard of care while the patient is on study. There are no anticipated potential
risks to the volunteer study nurses who consent to participate in the study. Since the PAIN
Algorithm with the analgesic order set will be incorporated into standard of care all nurses
on the inpatient units will use it whether or not they consent to participate in the study.
Those nurses who consent will provide demographic and practice data, and provide monthly
appraisals of usefulness of the MOPAT in Phase 1, and the PAIN Algorithm (including MOPAT) in
Phase 2 and will not be affected by the study intervention. Because there is always a risk of
opioid-induced side effects when opioids are used to manage pain, the PAIN Algorithm
analgesic order set includes provisions for monitoring, detecting, and managing such side
effects thus making the algorithm potentially safer than usual care which does not generally
include side effect monitoring. There are no physical or psychosocial risks to nurses who
consent to participate in the study. For both patient and nurse subjects, protection of
confidentiality is a concern. Therefore, all patient and nurse data collection forms will
contain only the unique study identification numbers, with the master list kept in a locked
file cabinet in a locked project office. When the study is completed, the list will be
destroyed and all data will be identified only by study identification numbers. Only the PI
and project manager will have access to these lists during the study. Standard operating
procedures for analysis activities, such as recruitment, data collection, data management,
data analysis, reporting of adverse events, and change in management are outlined in the
study manual of operating procedures. Source data include: electronic medical records,
patient's paper charts, data collection "teleforms" . The manual of operations also includes
standard operating procedures for data entry, transfer, and quality assurance. "Teleforms"
have a built in quality checks including predetermined rules for range and consistency with
other study data fields. The plan for statistical analysis is as follows.The primary aim
testing that there is a decrease in pain severity for the intervention group as compared to
the usual care group, will use the interaction F-test from 2 x 4 repeated measures analysis.
If test assumptions are not met, or if there is missing data, mixed linear modeling will be
used that does not require sphericity or complete data. Specific comparisons also will be
made between the groups at both 2 and 4 days using interaction contrasts. The number and
total dosage of pharmacologic interventions will be computed for each time period using the
American Pain Society equi-analgesic table.To test for an increase in the number of
pharmacologic agents used for pain and an increase in the total equi-analgesic dosage of
pharmacologic agents used for pain a 2 X 3 ANOVA or mixed linear modeling approach will be
used depending on whether assumptions are met. To address covariates and control for
unit-based effects, first a comparison of units to identify unit-level differences, such as
type of service or ambient environment, that might be related to patient outcomes and then
include them in a hierarchical linear model will be conducted. Patient demographic variables
such as age and gender and clinical variables such as pain-related conditions, type of pain
and co-analgesics, and Glasgow Coma Scale score will be examined for their relationship to
outcomes and incorporated as factors or covariates depending on the level of measure and
strength of bivariate relationship. These variables can be incorporated into a hierarchical
model or, if unit-level differences are not relevant, then into analysis of covariance or
multiple regression models. The secondary aims will be analyzed using descriptive approaches.
For (S1) patients will be categorized on the basis of whether they have pain-related
conditions using the Clinical Classification System and a 2 x 4 repeated measures analysis
will be used to compare these two groups on pain severity and a 2 x 3 repeated measures
analysis to compare the number and types of drug categories and total dosage of pharmacologic
agents. If there is a significant interaction effect, the groups will be compare at each time
point as post-hoc analyses. For (S2) a descriptive pattern of patients' pain over time will
be examined. Of interest is whether there are sub-groups of patients who demonstrate
different patterns. A graphic techniques (e.g., spaghetti plots) will be used to identify
sub-groups and attempt to describe them based on clinical and demographic variables. In
addition, we will compute the change in pain between the time periods and plot the cumulative
distribution function at each time point as an initial step in identifying potential cut
points to define a clinical important change. To evaluate nurse perceptions of the clinical
utility of the PAIN Algorithm and the MOPAT (S3), we will examine the frequency distribution
for responses to each item on the Clinical Utility Questionnaire (CUQ) and the overall
summated response. For CUQ items that are used in evaluating MOPAT utility in both phases,
the responses will be compared using t-tests. In addition, the percentage of nurses who agree
or strongly agree with each statement and make comparisons based on nurse demographic and
practice characteristics will be done.

Inclusion Criteria Patients:

- 18 years of age or older

- Diagnosed with potentially life-threatening conditions accompanied by acute pain

- With or without concurrent pain-related conditions

- Unable to self-report pain

- Receiving care on the participating units

Exclusion Criteria Patients:

- Receiving paralytic agents

- Sedated and with a Richmond Agitation Sedation Scale score of -5

- Able to communicate pain through any verbal or physical means such as nodding or
wiggling fingers

Inclusion Criteria Nurses:

- Assigned to a participating unit

- Working at least 36 hours/week

Exclusion Criteria Nurses:

- Routinely rotating between participating and non-participating units