Abstract
Background:
Chronic pain is a significant clinical problem in the world. There is still no quite effective treatment for this pain due to its complex nature. Timely retrieval of accurate and comprehensive information through organized clinical and epidemiological studies is an essential prerequisite for providing high quality clinical care and more accurate health planning. We aimed to determine minimum set of data needed as a first step in design and development of a chronic pain registry system.
Materials and Methods:
This descriptive-applied study was carried out in three phases; identifying necessary minimum data, preparing a primary minimum dataset, and surveying expertsby questionnaire.
Result:
The literature review revealed that, theprimary minimum dataset consisted of 51 elements, which were reduced to 41 after applying the experts’ opinion. This dataset covered six areas:demographic information(8 elements), initial pain assessment(12 elements), medical history (8 elements), mental health and well-being(6 elements), diagnostic measures(3elements), and diagnosis and treatment plan (4 elements).
Conclusion:
Determining minimum set of chronic pain data will be an effective step towards integrating and improving information management of patients with chronic pain. It will also allow for proper storage and retrieval of information related to these patients.
Keywords: Chronicpain, Registry, Minimum dataset, Pain management
Introduction
Pain is an unpleasant emotional and sensory experience caused by potential or actual tissue damage, which mankind has been trying to control since the beginning of history (1,2). Chronic pain is a pain that lasts longer than expected and may persist for many years even if the underlying cause is removed.
Chronic pain in the long term can lead to economic, psychological and behavioural problems by reducinga person’s abilities to perform personal and social activities such as daily, recreational, and occupational activities (3-5). For this reason, physiciansdo not view chronic pain as a symptom or even a disease, but as a complex biological, psychological, and social phenomenon (6, 7).
While most diseases, such as diabetes and hypertension, have a well-defined and proven treatment plan, unfortunately, chronic pain does not have any definite and validated treatment plan. Also, specialists in this field are required to use evidence-based activities that include analysis of therapeutic models and patient’s responses to each model in order to ensure the quality of care provided to patients. In the face of such challenges, the complex role of information management through the creation of electronic registry systems is increasingly apparent(8). The ability of registrysystems to deliver high-quality and accurate reports, assist in better clinical decision-making, and facilitate research projects depends of the entry of data with predetermined standards (9-11). These predetermined standards are in fact, the minimum datasets in the registry systems that enable proper communication between care providers and health managers by providing the necessary minimum variables (12-14). The use of standard elements along with the same definitions as the minimum dataset results in increased comparability of data, and facilitatesthe decision-making process. Thus, the minimum dataset, in addition to beingconsidered a standard, can be a valuable resource for continuous evaluation of patient progress and performance (15, 16).
Considering the benefits of designing and determining the necessary minimum dataseton diseases and other health domains, and also taking into account the importance of chronic pain control and monitoring, the main objective of this study was to identify and create the minimum dataset needed for the registration, control and monitoring of patients with chronic pains. Creation of this dataset can be the first step in establishing a chronic pain registry system in Iran (17-18).
Methods and methods
This descriptive-applied study was carried out in three structured phases. In the first phase, a literature review was carried out by conducting a search in databases such as PubMed, Science direct, Clinical key and Google scholar search engine using defined keywords. The keywords used in this search includedchronic pain, registries, pain management, and minimum dataset.
In the second phase, a list ofnecessaryminimum datasets was prepared considering the similarities and differencesin the datasets. Then, based on theprimary list obtained from the literature review phase, a questionnaire was designed by the experts. Validity and reliability of the questionnaire were confirmed in the pain clinic of RasoulAkram Hospital. This clinical settingwas selected because it wasa teaching and research environment with the presence ofleading expertswho had over 10 years of experience in the field of pain. At first, the face validity of the questionnaire was approved by experts and then, its content validity was assessed by using the proposed method of Lawshe and calculating content validity ratio (CVR) and content validity index (CVI). Reliabilityof the questionnaire was also determined and accepted by calculating the percentage of experts’ agreement.
Results
After searching the valid databases, 530 articles on pain registries and guidelineswere found worldwide of which the duplicate and inaccessible articles were removed. Finally, 27 sources including valid articles and guidelines related to the topic were selected as the main sources of study. The sources were mainly from the United States, Canada, England, Australia, Sweden, Denmark, Norway and Scotland. The highest number of sources with 6 national guidelines in the field of pain relief and management and 3 chronic pain registry systems were related to the United States of America. It is worth mentioning that, the World Health Organization (WHO) has also published an international guideline on the management of non-cancer chronic pain in adults in recent years to illustrate the importance of chronic pain treatment and management.
In the next phase, a total of 51 data elements were identified as the primary elements of the descriptive phase which are listed in tables 1 to 4, presenting frequency of each data elements (19-45).
Table 1.
List of demographic and social data elements
|
Elements
Reference |
N(%) | WHO 2008 (19) | SIGN 2013 (20) | Burce et al ., 2017(21) | Chou et al ., 2016(22) | Giummarra et al ., 2014 (23) | Practice parameter 2010(24) | Rosenquist et al., 2018(25) | Nyberg et al., 2011(26) | Choiniere et al., 2017(27) | Zaslansky et al., 2014(28) | Vaegter et al ., 2017(29) | Health care guideline 2009(30) | NICE 2018(31) | Paice et al ., 2016(32) | MCRH 2020(33) | Mackey et al., 2015(34) | Lacasse et al., 2017(35) | Griffiths et al ., 2003(36) | Granan et al., 2019(37) | Caraceni et al., 2002(38) | Jamison et al., 2012(39) | Love et al., 2012(40) | Breivik et al., 2008(41) | Hansen et al., 2010(42) | Milton et al., 2013(43) | Hawker et al., 2011(44) | Dressler et al., 2019(45) |
| Gender | 17(62.96) | - | - | - | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - |
| Age | 23(85.18) | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - |
| Nationality | 8(29.62) | ✓ | - | - | ✓ | ✓ | - | - | - | - | ✓ | - | ✓ | - | - | - | - | ✓ | - | - | - | ✓ | ✓ | - | - | - | - | - |
| Education level | 12(44.44) | ✓ | - | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | - | - | - | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | - | - | - | - | - | - |
| Religion | 9(33.33) | - | - | - | ✓ | - | - | - | ✓ | - | - | - | ✓ | - | ✓ | - | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| Income status | 12(44.44) | ✓ | - | - | - | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | - | - | ✓ | ✓ | - | ✓ | - | ✓ | - | - | - | - | - | ✓ |
| Marital status | 7(25.92) | - | - | - | - | - | - | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | - | - | - | - | - | ✓ | - | - | ✓ | - | - | - | - | - |
| Type of insurance | 5(18.51) | - | - | - | - | - | - | - | - | - | - | ✓ | - | ✓ | - | - | - | - | - | ✓ | ✓ | ✓ | - | - | - | - | - | - |
| Email/post code | 7(25.92) | - | - | - | - | - | - | - | ✓ | ✓ | ✓ | ✓ | - | - | - | - | ✓ | - | ✓ | ✓ | - | - | - | - | - | - | - | - |
| Weight | 5(18.51) | - | - | - | - | ✓ | - | - | - | - | ✓ | ✓ | - | - | - | - | - | ✓ | - | - | - | - | ✓ | - | - | - | - | - |
| Height | 3(11.11) | - | - | - | - | - | - | - | - | - | - | ✓ | - | - | - | - | - | ✓ | - | - | - | - | ✓ | - | - | - | - | - |
| Address &Contact number | 3(11.11) | - | - | - | - | - | - | - | ✓ | ✓ | - | ✓ | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Table 4.
List of psychological and activities of daily living data elements
|
Elements
Reference |
N(%) | WHO 2008(19) | SIGN 2013(20) | Burce et al ., 2017(21) | Chou et al ., 2016(22) | Giummarra et al ., 2014(23) | Practice parameter 2010(24) | Rosenquist et al., 2018(25) | Nyberg et al., 2011(26) | Choiniere et al., 2017(27) | Zaslansky et al., 2014(28) | Vaegter et al ., 2017(29) | Health care guideline 2009(30) | NICE 2018 (31) | Paice et al ., 2016(32) | MCRH 2020(33) | Mackey et al., 2015(34) | Lacasse et al., 2017(35) | Griffiths et al ., 2003(36) | Granan et al., 2019(37) | Caraceni et al., 2002(38) | Jamison et al., 2012(39) | Love et al., 2012(40) | Breivik et al., 2008(41) | Hansen et al., 2010(42) | Milton et al., 2013(43) | Hawker et al., 2011(44) | Dressler et al., 2019(45) |
| Tendency to catastrophize in the face of pain | 18(66.66) | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - | - | - |
| Pain interference on daily activities | 26(96.29) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ |
| Impact of pain on occupational tasks | 22(81.48) | ✓ | ✓ | - | - | ✓ | ✓ | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ |
| Impact of pain on ability to participate in social activities | 25(92.59) | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ |
| Impact of pain on sleep | 20(74.04) | ✓ | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | - | - | - |
| Impact of pain on mood, anxiety depression,…. | 24(88.88) | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - |
| Patient satisfaction with treatment | 15(55.55) | - | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| Risk of opioid abuse/misuse | 7(25.92) | ✓ | ✓ | - | ✓ | - | - | - | - | - | - | - | ✓ | - | ✓ | ✓ | - | - | - | - | - | ✓ | - | - | - | - | - | - |
Table 2.
List of medical history and examination data elements
|
Elements
Reference |
N(%) | WHO 2008 (19) | SIGN 2013(20) | Burce et al ., 2017(21) | Chou et al ., 2016(22) | Giummarra et al ., 2014(23) | Practice parameter 2010(24) | Rosenquist et al., 2018(25) | Nyberg et al., 2011(26) | Choiniere et al., 2017(27) | Zaslansky et al., 2014(28) | Vaegter et al ., 2017(29) | Health care guideline 2009(30) | NICE 2018 (31) | Paice et al ., 2016(23) | MCRH 2020(33) | Mackey et al., 2015(34) | Lacasse et al., 2017(35) | Griffiths et al ., 2003(36) | Granan et al., 2019(37) | Caraceni et al., 2002(38) | Jamison et al., 2012(39) | Love et al., 2012(40) | Breivik et al., 2008(41) | Hansen et al., 2010(42) | Milton et al., 2013(43) | Hawker et al., 2011(44) | Dressler et al., 2019(45) |
| Consumption habit (opioid, cigarettes, alcohol) | 18(66.66) | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | - | - | - | - | - |
| Comorbidities | 20(74.04) | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| Any history of allergy | 14(51.85) | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | - | - | - | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| History of mental disorder | 20(74.04) | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | ✓ | ✓ |
| Family history of chronic pain | 5(18.51) | - | - | - | - | - | ✓ | ✓ | - | ✓ | - | - | - | - | - | - | - | - | - | - | - | ✓ | - | ✓ | - | - | - | - |
| Analgesics | 17(62.96) | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | - | - | - | - |
| Adverse/side effect | 19(70.37) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| Past treatments & interventions | 21(77.77) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - |
| Current treatments & interventions | 21(77.77) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - |
| Assessment of impact of treatment/ interventions | 19(70.37) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - |
| Physical examination | 14(51.85) | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | - | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - | ✓ | ✓ | - | - | - | - | - | ✓ |
| Assessment result of laboratory & imaging tests | 9(33.33) | - | - | - | - | - | ✓ | ✓ | - | ✓ | - | - | ✓ | - | ✓ | ✓ | - | - | - | - | ✓ | ✓ | - | - | - | - | - | ✓ |
Table 3.
List of Pain data elements
|
Elements
Reference |
N(%) | WHO 2008(19) | SIGN 2013(20) | Burce et al ., 2017(21) | Chou et al ., 2016(22) | Giummarra et al ., 2014(23) | Practice parameter 2010(24) | Rosenquist et al., 2018(25) | Nyberg et al., 2011(26) | Choiniere et al., 2017(27) | Zaslansky et al., 2014(28) | Vaegter et al ., 2017(29) | Health care guideline 2009(30) | NICE 2018 (31) | Paice et al ., 2016(32) | MCRH 2020(33) | Mackey et al., 2015(34) | Lacasse et al., 2017(35) | Griffiths et al ., 2003(36) | Granan et al., 2019(37) | Caraceni et al., 2002(38) | Jamison et al., 2012(39) | Love et al., 2012(40) | Breivik et al., 2008(41) | Hansen et al., 2010(42) | Milton et al., 2013(43) | Hawker et al., 2011(44) | Dressler et al., 2019(45) |
| Location | 22(81.48) | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | ✓ |
| Pain intensity | 23(85.18) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | ✓ |
| quality of pain | 11(40.74) | - | - | ✓ | ✓ | - | - | - | - | - | - | - | ✓ | - | ✓ | - | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - |
| Onset of pain | 23(85.18) | - | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | - | - | ✓ | ✓ | - | - | - | - | ✓ |
| Pain duration | 13(48.14) | ✓ | ✓ | ✓ | - | - | ✓ | - | - | - | - | - | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| Kind of pain | 12(44.44) | ✓ | ✓ | - | - | - | - | ✓ | - | ✓ | - | - | ✓ | - | - | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | ✓ | - | - | - | - |
| The spread pain to other organs | 9(33.33) | - | - | - | - | - | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ | - | ✓ | ✓ | - | - | - | - | - | - | - | - | - | - | - |
| Suspected etiology | 12(44.44) | ✓ | - | ✓ | - | ✓ | - | - | - | ✓ | - | - | - | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | ✓ | ✓ | ✓ | - | - | - | - | - |
| Assessment of pain with VRS.NRS,… | 13(48.14) | ✓ | ✓ | - | ✓ | - | - | - | - | - | - | - | ✓ | - | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - |
| Frequency in the past 6 months | 5(18.51) | - | - | ✓ | - | - | - | ✓ | - | ✓ | - | ✓ | - | - | - | - | - | ✓ | - | - | - | - | - | - | - | - | - | - |
| Frequency in the past 7 day | 6(22.22) | - | - | ✓ | - | - | - | ✓ | - | ✓ | - | - | - | - | - | - | - | ✓ | - | ✓ | ✓ | - | - | - | - | - | - | - |
| Pain relievers & exacerbating factors | 12(44.44) | - | - | ✓ | ✓ | - | ✓ | ✓ | - | ✓ | - | - | ✓ | - | ✓ | ✓ | ✓ | - | - | ✓ | ✓ | - | ✓ | - | - | - | - | - |
| Intensity of pain during movement | 8(29.62) | ✓ | - | ✓ | - | - | - | - | - | - | - | - | - | ✓ | - | ✓ | - | ✓ | ✓ | - | - | ✓ | - | - | - | ✓ | - | |
| Diagnosis | 18(66.66) | - | ✓ | - | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - |
| Treatment plans & interventions | 22(81.48) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - |
| Next visit date | 7(25.92) | - | - | - | - | - | - | ✓ | ✓ | ✓ | - | - | - | - | ✓ | - | ✓ | - | - | ✓ | - | - | ✓ | - | - | - | - | - |
| Total health status | 11(40.74) | - | - | - | - | - | - | - | ✓ | ✓ | ✓ | ✓ | - | - | - | - | ✓ | - | ✓ | ✓ | - | - | ✓ | ✓ | - | ✓ | ✓ | - |
Then, based on the classification ofworld’s famous registries such as Quebec’s Pain Registry, EU’s Pain Out System, Swedish Quality Registryfor Pain and Canadian Chronic Pain System, the researcher summarized and classifiedthe range of data elements in six domains, including demographic information, initial pain assessment, pain history, mental health and wellbeing, diagnostic measures, and diagnosis and treatment plan. Early corrections were approved by two pain specialists, two information management specialists and one epidemiologist. After the confirmation of face validity by experts, the questionnaire was given to 14 pain specialists who were selected through convenience and snowball sampling to confirm its content validity using Lawshe method. Although the minimum number of experts was calculated to be five based on Lawshe method, more than fiveexperts were selected to compensate forthe possiblesample drop or non-compilation ofthe questionnaire in order to increase its reliability. Finally, out of 14 questionnaires distributed among the specialists, 8 were returned to the research team. The specialists involved in this phase were mostly men (87.5%) with the age range of 40-49 years (50%). Also, 62.5% of the specialists were faculty members and 75% of them were full professors. In terms of medical practice, half of them(50%) had more than 20 years of experience in practice and more than 10 years of experience in painmanagement and control. The specifications of the expert panel are listed in table 5.
Table 5.
Demographic information about respondents on the dataset of chronic pain registry
| Profile | frequency | percentage | |
| gender | female male total |
1 7 8 |
12.5 % 87.5 % 100 % |
| age | 30-39 40-49 >50 total |
1 4 3 8 |
12.5 % 50 % 37.5 % 100 % |
| Being Faculty Member | Yes no Total |
5 3 8 |
62.5 % 37.5 % 100 % |
| Science ranking | Professor Fellowship Assistant total |
6 2 8 |
75 % 25 % 100 % |
| Years of Experience | <10 10-20 >20 Total |
1 3 4 8 |
12.5 % 37.5 % 50 % 100 % |
| History of pain medicine | <5 5-9 >10 Total |
3 1 4 8 |
37.5 % 12.5 % 50 % 100 % |
By analyzing the results of questionnaires, calculating the CVR by following formula, and considering the mean scores given to each information element, only items with the CVR of 0.75and the mean of 2 orhigherwere considered(46-49).
In this formula, “Ne“represents the number of people who found the selected item necessary, and “n”refers to the total number of experts who completed the questionnaire(46-49).
According to the results of table 6 (Additional file), out of 51 surveyed information elements, 41 elements with the CVR of above 0.75 had the necessary conditions to remain in the questionnaire. Also, 10 data elements with the CVR of less than 0.75 were removed from the minimum dataset, which included religion, nationality, income status, type of insurance, email, height, frequency of pain in the past 6 months, assessment of patient’s willingness to use addictive drugs, assessment of patient’s attitude towards pain, and assessment of patient’s overall health. Then, the total content validity index was calculated bythe following formula with an acceptable value of 0.9(46, 47).
Table 6.
CVR values, Mean Judgments and Results of Acceptance or Rejection of data elements from the Primary Questionnaire of Chronic Pain Registry
| Row | Data elements | CVR | Numerical Mean of Judgments | Accept/Reject |
| 1 | gender | 1 | 3 | accept |
| 2 | age | 0.75 | 2.87 | accept |
| 3 | National code | 0.75 | 2.87 | accept |
| 4 | Educational level | 0.75 | 2.87 | accept |
| 5 | Employment status | 0.75 | 2.87 | accept |
| 6 | Religion | 0 | 2.37 | reject |
| 7 | Income status | 0.25 | 2.62 | reject |
| 8 | Nationality/Race | 0.25 | 2.62 | reject |
| 9 | Marital status | 0.75 | 2.75 | accept |
| 10 | Type of insurance | 0.25- | 2 | reject |
| 11 | 0.75- | 1.62 | reject | |
| 12 | weight | 0.75 | 2.87 | accept |
| 13 | Height | 0.25 | 2.62 | reject |
| 14 | Address & contact number | 0.75 | 2.87 | accept |
| 15 | Pain location | 1 | 3 | accept |
| 16 | Pain intensity | 1 | 3 | accept |
| 17 | Quality of pain | 1 | 3 | accept |
| 18 | assessment of pain with NRS و VAS و VRS | 1 | 3 | accept |
| 19 | Onset of pain | 1 | 3 | accept |
| 20 | Pain duration | 1 | 3 | accept |
| 21 | Kind of pain(neuropathic, …) | 1 | 3 | accept |
| 22 | The spread pain to other organs | 1 | 3 | accept |
| 23 | Suspected etiology | 1 | 3 | accept |
| 24 | Exacerbating & relievers factors | 1 | 3 | accept |
| 25 | Intensity of pain during activity & resting | 1 | 3 | accept |
| 26 | Frequency in the past 6 months | 0.5 | 2.62 | reject |
| 27 | Frequency in the past 7 days | 1 | 3 | accept |
| 28 | Physical examination | 1 | 3 | accept |
| 29 | Assessment result of laboratory & imaging tests | 1 | 3 | accept |
| 30 | Consumption habits(cigarettes, alcohol, illicit drugs) | 1 | 3 | accept |
| 31 | comorbidities | 1 | 3 | accept |
| 32 | Any history of allergy | 0.75 | 2.87 | accept |
| 33 | History of mental disorder | 1 | 3 | accept |
| 34 | Is it degree family history of chronic pain | 0.75 | 2.87 | accept |
| 35 | analgesics | 1 | 3 | accept |
| 36 | Adverse/side effect | 1 | 3 | accept |
| 37 | Risk of opioid abuse/misuse | 0.5 | 2.75 | reject |
| 38 | Past and current treatments/interventions | 1 | 3 | accept |
| 39 | Assessment of impact of treatment interventions | 0.75 | 2.87 | accept |
| 40 | Patient satisfaction with treatment | 0.75 | 2.87 | accept |
| 41 | Tendency to catastrophize in the face of pain | 0.5 | 2.75 | reject |
| 42 | Pain interference on daily activities | 0.75 | 2.87 | accept |
| 43 | Impact of pain on occupational tasks | 0.75 | 2.87 | accept |
| 44 | Impact of pain on ability to participate in social activities | 0.75 | 2.87 | accept |
| 45 | Impact of pain on sleep | 1 | 3 | accept |
| 46 | Impact of pain on mood, anxiety, depression | 0.75 | 2.87 | accept |
| 47 | Total health status | 0.5 | 2.75 | reject |
| 48 | Differential diagnosis | 1 | 3 | accept |
| 49 | Diagnosis | 1 | 3 | accept |
| 50 | Treatment plans | 1 | 3 | accept |
| 51 | The next visit date | 0.75 | 2.87 | accept |
The reliability of the questionnaire was determined by calculating the percentage of experts’ agreement, which is called: “Expert Percent Agreement (EPA)”(50). For this purpose, the questionnaire was distributed among pain specialists who were divided into two groups of specialists with more than 5 years of experience and those with less than 5 years of experience. Then, the percentages of their agreement were calculated by examining the agreement and disagreement of the two groups in regard to adding an information element in the questionnaire. The EPA in regard to demographic dataset, pain assessment data, health and well-being dataset, diagnostic measures dataset, and diagnosis and treatment plan dataset was 100% and in regard to the medical history dataset, it was 95% that indicated a high level of agreement.
Finally, the minimum dataset needed to control chronic pain were grouped into six main groups as shown in Figure 1, with details of each group being compared.
Figure 1.
Minimum dataset for the chronic pain registry
Discussion
Systematic collection and analysis of chronic pain control and management data through the establishment of a registry system can have positive effects on health planning, treatment and prevention. Since the success of registry systems depends on the quality of stored data, identifying and determining the necessary minimum data elements to meet the needs of registry users are among the first steps in designing and creating a chronic pain registry system. The necessary minimum dataset is, in fact, a standard that facilitates data exchange and comparability at a different individual (patient), organizational (care provider) and national and international levels (society).
In 1995, the Swedish Rehabilitation Association was first to set up and developed a national qualitative registry system with the aim of rehabilitating patients with chronic musculoskeletal pain and assessing the impact of rehabilitation programs through the study of patient-reported outcomes. The system assessed the impact of rehabilitation programs and compared patients in different rehabilitation clinics through a standard set of data collected before the rehabilitation, a few days after the rehabilitation, and one year later. The minimum required data in this set were divided into two main groups of demographic information (age, sex, income status, education level, type of insurance coverage) and clinical information (severity of pain, mental and behavioural status, physical disability, and life satisfaction). The classification systems of the International Classification of Functioning, Disability and Health (ICF)were also used to provide diagnostic codes for disability and pain, respectively(26). Collection of chronic pain data in Sweden, Norway, Denmark, Canada and the United States aimedat establishing national and international chronic pain registry systems to provide quality and accurate care to patients, evaluate the quality of services provided, and facilitateclinical projects.
Quebec Pain Registry System in Canada is one of the most successful chronic pain registries in the world due to its rich and high-quality data. It also has been able to meet all three management, clinical and research goals of registry systemssimultaneously. The main feature of this system is that it pays considerable attention to the data it collects to the extent that, it provides researchers with a very clear understanding of chronic pain and thus contributes to the high quality of research. Also in 2007, the system was introduced as a strategic initiative in the area of chronic pain management. An example of the quality of its data can be the recordingof the code for chronic pain diagnosis and intervention based on a unique network(27). Most chronic pain registry systems in the world (except for the Oslo chronic pain registry system) use ICD versions 9 and 10, while they do not provide appropriate diagnostic code for chronic pain syndrome as a disease. On the other hand, the codes presented by the International Association for the Study ofPain (IASP)are so complex that they are practically non-functional (27, 37).
The Danish multicenter clinical pain registry system, which was developed in 2015 by the Danish University of North and the University of Odense, has similar minimum dataset to that identified in this study, but with little difference in classification. This dataset also consists of 6 main sections, including demographic data, social data, pain characteristics, psychological factors, daily living activities, and other information elements(29).
In the United States, the chronic pain registry system was developedin 2016 by the medical department of Stanford University and the clinical informatics centrein collaboration with the national health organization to monitor the progress of chronic pain status and the impact of interventions. This registry system emphasizes on the follow-up of patient information as the key components of clinical practice. This system’s minimum dataset is similar to the one identifiedin this studywith a difference that, it includes the data of children with chronic pain. When the patient who has chronic pain is a child, the system collects data from both the child and his/her caregiver or care provider(34).
Comparing the findings of this study with the chronic pain registries of other countries shows a significant similarity in the minimumdatasets of these registries. So it seems that the management and evaluation of chronic pain are similar in all countries, and allknown chronic pain registries consider approximately the same datasetsin order to provide high-quality care. Therefore, we can conclude that, since the minimum dataset of ourregistry system is based on data obtained from a comprehensive review of clinical literature and guidelines, it highly corresponds with the datasets of chronic pain registry systems in the leading countries.
Conclusion
Due to the complex nature of pain, patients with chronic pain often suffer for many years, and in many cases, they are faced with many psychological, behavioural, social and economic problems that challenge not only them but also those around them(5). Thus, long-term management of chronic pain information through the creation of integrated and comprehensive dataset is inevitablein managing and improving the quality of treatment. The findings of the present study showed that determining a minimum set of chronic pain data by experts in this field is an effective step towards integrating the information of these patients in Iran and improving the quality of care for patients with chronic pain. It is hoped that by defining the minimum set of data and designing a chronic pain registry system suitable for Iranian social and health contexts, it would be possible to store and retrieve accurate and standard data related to these patients and thus, improve services delivered to them.
Acknowledgment:
The authors would like to thank the Rasool Akram Medical Complex Clinical Research Development Center (RCRDC) for its technical and editorial assist.
Conflicts of interest:
Each author declares that he or she has no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangement etc.) that might pose a conflict of interest in connection with the submitted article.
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