Assignment: Early Integration of Palliative Care PICOT
Background Questions and PICOT Question
The topic I am interested in researching involves the integration of palliative care and early conversations with patients who have been diagnosed with terminal illness. My background questions are:
(1) When should palliative care discussions be initiated?
(2) Does early initiation of palliative care conversations promote better quality of life?
(3) What conversations are essential for patients to have the knowledge they need to make informed decisions about advanced care planning?
My PICOT question is as follows:
P (population): patients diagnosed with a terminal illness
I (intervention): early integration of the palliative care team and palliative care conversations by the interdisciplinary team
C (comparison): early palliative care discussions and advanced care planning vs. minimal conversation coupled with aggressive treatment options
O (outcomes): opting out of aggressive treatment and improved quality at the end of life
T (time): the last 6 month of life
In patients with terminal illness, what is the effect of early palliative care conversations by the interdisciplinary team compared to minimal conversation coupled with aggressive treatment options on the quality of the last 6 months of life?
Barriers to the Implementation of Evidence Based Practice. Assignment: Early Integration of Palliative Care PICOT.
CONTEXT: Pediatric palliative care (PPC) is intended to promote children’s quality of life by using a family-centered approach. However, the measurement of this multidimensional outcome remains challenging.
OBJECTIVE: To review the instruments used to assess the impact of PPC interventions.
DATA SOURCES: Five databases (Embase, Scopus, The Cochrane Library, PsychInfo, Medline) were searched.
STUDY SELECTION: Inclusion criteria were as follows: definition of PPC used; patients aged 0 to 18 years; diseases listed in the directory of life-limiting diseases; results based on empirical data; and combined descriptions of a PPC intervention, its outcomes, and a measurement instrument.
DATA EXTRACTION: Full-text articles were assessed and data were extracted by 2 independent researchers, and each discrepancy was resolved through consensus. The quality of the studies was assessed by using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers From a Variety of Fields checklist.
RESULTS: Nineteen of 2150 articles met the eligibility criteria. Researchers in 15 used quantitative methods, and 9 were of moderate quality. Multidimensional outcomes included health-related quality of life, spiritual well-being, satisfaction with care and/or communication, perceived social support, and family involvement in treatment or place-of-care preferences. PPC interventions ranged from home-based to hospital and respite care. Only 15 instruments (of 23 reported) revealed some psychometric properties, and only 5 included patient-reported (child) outcome measures.
LIMITATIONS: We had no access to the developmental process of the instruments used to present the underlying concepts that were underpinning the constructs.
CONCLUSIONS: Data on the psychometric properties of instruments used to assess the impact of PPC interventions were scarce. Children are not systematically involved in reporting outcomes.
- APCA c-POS —
- African Palliative Care Association Children’s Palliative Outcome Scale
- CI —
- confidence interval
- HADS —
- Hospital Anxiety and Depression Scale
- PedsQL 4.0 —
- Pediatric Quality of Life Inventory 4.0
- PPC —
- pediatric palliative care
- PRISMA —
- Preferred Reporting Items for Systematic Reviews and Meta-analyses
- PROM —
- patient-reported outcome measure
- QoL —
- quality of life
- QOLLTI-F —
- Quality of Life in Life-Threatening Illness–Family Carer Version
- SCCC —
- Survey About Caring for Children With Cancer
- SEM —
- standard error of measurement
The purpose of pediatric palliative care (PPC) is to enhance the quality of life (QoL) of children and their families when facing life-limiting or life-threatening illnesses. According to the World Health Organization, palliative care for children is the active total care of a child’s body, mind, and spirit, and it also involves giving support to the family. It begins when illness is diagnosed and continues regardless of whether a child receives treatment directed at the disease.1 It is estimated that although ∼21 million children worldwide would benefit from a palliative care approach, 8 million children are in need of specialized palliative care.
Measuring outcomes in PPC is considered to be essential to improve clinical care, evaluate the quality of services, and secure funding for programs, and it has been identified as a priority on the research agenda, especially in PPC.3–8 However, there is currently little evidence of the effectiveness of PPC because it is difficult to define appropriate outcome measures in this field.9–11 Several obstacles have been identified regarding outcomes research in PPC: small sample sizes, the difficulty of identifying a relevant comparison group, and the broad heterogeneity in children’s diseases and ages. Moreover, the relevance of assessing QoL in children within a palliative context has been criticized by some consequentialist authors, who argue that in the face of inevitable death, measuring the impact on QoL would no longer be a priority.12 These assumptions are often linked to false representations of PPC that are largely restricted to the end-of-life moments.
A systematic review conducted by Coombes et al13 revealed that there is currently no ideal outcome assessment measure available yet for use in PPC. This finding is in agreement with the conclusions of Knapp and Madden12 and Huang et al,14 who found none of the generic QoL measurement instruments to be valid for use in a PPC context.
Measuring QoL has become a growing interest and an end point in many clinical settings. However, in studies of palliative care, QoL may become the principal or only end point of consideration.15 QoL outcomes are now also commonly called patient-reported or person-reported outcomes to more clearly reflect the broad spectrum of dimensions that are measured (such as pain, fatigue, depression, and observable physical symptoms, such as nausea and vomiting), which are included in the assessment. Measuring children’s QoL in a PPC context remains challenging for at least 3 reasons. First, additional dimensions have been suggested by some researchers, such as the ability to cope with illness, the spiritual dimension, and satisfaction with life.16 Second, depending on the nature of the disease and age of the child, proxy assessments by a relative or other close observer are often used. Third, the diversity of diseases and contexts dealt with by those in PPC often make it difficult to disentangle the impact of disease severity and treatment from the impact of PPC interventions.
QoL is seldom measured directly but rather is explored through a combined assessment of several aspects, which are labeled as dimensions, and although there is disagreement about the aspects that should be included, there is a consensus that QoL should be considered to be a multidimensional construct. Thus, regardless of the instrument used, items of different natures used to target different dimensions will be included.
Commonly, instruments used to measure health-related outcomes rely on 1 of 2 theoretical frameworks depending on the relationship between the items and the construct to be measured.17 The distinction between reflective and formative models in the field of QoL was introduced by Fayers and Machin.15
In a reflective model, the construct manifests itself, and the items are effective direct indicators of that construct (eg, on a scale intended to measure anxiety, all items will reflect a certain degree of anxiousness). In a formative model, the items form or build the construct and are called causal items (eg, in an instrument intended to measure stress, the amount of stress is measured by assessing many items that all contain stress-evoking events). In the field of QoL measurements, many instruments used in PPC settings have a hybrid nature and contain reflective and causal items. They most often are conceptualized as multidimensional scales and contain direct observable, self-reported, and proxy-reported items. Thus, it is challenging to assess their psychometric characteristics.
In this systematic review, we aim to identify and describe the instruments that have been used to assess the impact of PPC interventions and assess their psychometric properties.
This study is in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.18 The methods were prespecified and documented in a protocol.
We conducted primary systematic literature searches using a combination of keywords, including “child,” “infant,” “pediatric,” “adolescent,” “young adult,” “palliative care,” “comfort care,” “supportive care,” “bereavement care,” “respite care,” “organization,” “standards,” “planning,” “outcomes,” “results,” and “effects” (see Supplemental Table 6 for the full search strings). We used broad keywords and Medical Subject Headings terms to maximize inclusiveness and searched 5 databases (Embase, Scopus, The Cochrane Library, PsychInfo and PubMed [Medline]) for studies published from January 1, 2006, to June 1, 2016.
The year 2006 for starting the inclusion of studies was chosen because the first International Meeting for Palliative Care in Children took place that same year in Trento, Italy, leading to the first publication of standards for PPC in Europe in 2007.19
All types of PPC interventions and programs were eligible for inclusion, such as supportive care, respite care, and bereavement care.
Furthermore, all types of outcomes (not restricted to QoL but focused on the child, siblings, or parents) were taken into account.
A study was included if all of the following criteria were fulfilled: (1) the full text was written in English, French, German, Italian, or Dutch; (2) the study sample included a clear description of infants, children, and/or adolescents ranging in age from 0 to 18 years; (3) children’s diseases were included in the directory of life-limiting diseases20 or were labeled as life-limiting or life-limited diseases or complex chronic conditions; (4) the study included empirical data; (5) the study presented a combined description of a PPC intervention, outcome, and measure instrument; and (6) a minimal definition of PPC was presented in the study.
In contrast, a study was excluded if any of the following criteria were fulfilled: (1) sickle cell disease, diabetes, obesity, perinatal death, or chronic pain were included; (2) patient age was >18 years; and (3) being restricted to a specific molecule or treatment assessment, to pain as the single outcome, or to an economic assessment.
Children with sickle cell diseases are rarely referred to PPC teams. The management of pain alone was not a criterion of inclusion. Perinatal deaths were also excluded because they concern a specific population with particular PPC needs for which the literature would need to be searched independently.
Data Extraction and Analysis
After retrieving all records, the duplicates were removed. All studies were initially screened on the basis of titles and abstracts and then on the basis of the full text. Three authors (M.F., I.A., and J.M.D.) independently assessed the eligibility of the studies. Any discrepancy was discussed and resolved by consensus. The quality of studies included was assessed by using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers From a Variety of Fields checklist.21 Supplemental Table 7 includes detailed information on the data extraction and analysis.
The Reliability of the Measurement Instruments
There are several ways to interpret a reliability coefficient of a given value depending on the type of characteristic measured and the method of obtaining the estimate of reliability. In classic test theory, a reliability coefficient can be interpreted as the proportion of the observed variance that is “true” rather than the “error variance.” However, 1 difficulty with expressing the reliability coefficient as a dimensionless ratio of variances is that it is difficult to interpret in terms of an individual score.22 The standard error of measurement (SEM) is defined in terms of the SD (σ) and reliability as SEM = σ × √1−reliability. Knowledge of the SEM makes it possible to construct a 95% confidence interval (CI) (95% CI = ± 1.96 × SEM) around a person’s observed score so that the amount of measurement error around that score can be quantified in a meaningful way.17 Thus, we decided to compute the SEM whenever possible using either the reported reliability index or any other reliability figures available in the literature.
Our electronic search was performed on June 4, 2016, and we identified 2150 studies. Figure 1 includes the PRISMA flowchart.
After duplicates were removed, 2111 studies were screened on the basis of titles and abstracts, and 44 studies were screened on the basis of the full text. With this procedure, we identified 19 studies that met all the inclusion criteria.
Table 1 includes an overview of the main characteristics of the included studies.
A total of 19 studies were included in this systematic review, and researchers in 15 used quantitative methods, those in 1 used qualitative methods, and 3 were based on mixed methods (qualitative and quantitative). Regarding the design of the studies, 9 were retrospective, 6 were prospective, and 4 consisted of randomized controlled trials. Among all the included studies (n = 19), researchers in 14 used instruments (n = 23 different instruments). Three studies were based exclusively on a chart review, and researchers in 2 studies used interviews or focus groups to measure outcomes. Researchers in the included studies covered the observational period from 1990 to 2014, a time frame of 24 years, excluding 2 studies in which researchers did not specify the time period. The patient population included patients with cancer (n = 10), children facing various life-limiting or life-threatening conditions (n = 8), or children with a “serious illness” (n = 1). Of the studies, 8 were conducted in the United States, 7 were conducted in Europe, 2 were conducted in Australia, 1 was conducted in Canada, and 1 was conducted in Lebanon. Researchers in half of the studies collected data exclusively among parents (n = 9). Children and parents were both interviewed in 5 studies. Researchers in 1 study collected data among parents and health professionals, and those in another study combined the children’s, parents’, and health care professionals’ perspectives. Sample sizes varied from 11 families to 134 families.
Quality Appraisal of Articles
Classification of the quality of the studies revealed that 5 studies were of high quality (>8 out of 10), 5 were of good quality (6–8 out of 10), and 9 were of moderate quality (4–6 out of 10). None of the studies were labeled as being of poor quality.
Types of Interventions
Interventions or programs presented in the studies varied from home care (n = 6) to hospital care (n = 5), hospice care (n = 2), and respite care (n = 3) or a combination of home, community-based, and hospital care (n = 3).
Flexibility was found in the individualized approach in PPC interventions, in which the focus is placed on the personal desires and priorities expressed by the children and their families. Table 2 includes the types of interventions offered, the outcomes expected versus achieved, and the definition of PPC.
Types of Outcomes
The outcomes addressed in the studies, when focused on children, were multidimensional and included physical (pain, fatigue, dyspnea, and appetite), psychological (anxiety and depression), social (relationships), and spiritual dimensions. QoL, when explicitly explored, was defined as “having fun or experiencing events that added meaning to life.”23,26,27 Other outcomes were related to satisfaction with care and communication with health care professionals, often in parallel with the opportunity given to the children or adolescents to express their wishes, treatment preferences, or place of care. Finally, the length of hospitalization or the place of death were outcomes searched for through chart review and interpreted by authors as quality indicators of services.