A predictive model of symptoms for pain in independently living frail older people in palliative care

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In older people, pain assessment can be challenging due to underreporting and atypical pain manifestations by other distressing symptoms. Anxiety, fatigue, loss of appetite, insomnia, dyspnoea, and bowel problems correlate with pain in palliative care patients. Insight into these symptoms as predictors may help to identify the underlying presence of pain. This study aimed to develop a prediction model for pain in independently living frail older people in palliative care. Methods: In this cross-sectional observational study, community-care nurses from multiple organizations across the Netherlands included eligible patients (life expectancy < 1 year, aged 65+, independently living and frail). The outcome pain and symptoms were assessed by means of the Utrecht Symptom Diary. Also, demographic and illness information, including relevant covariates age, sex and living situation, was collected. Multivariable logistic regression and minimum Akaike Information Criterion(AIC) were used for model development and Receiver Operating Characteristics(ROC)-analysis for model performance. Additionally, predicted probability of pain are given for groups differing in age and sex. Results: A total of 157 patients were included. The final model consisted of insomnia(Odds Ratio[OR]=2.13, 95% Confidence Interval[CI]=1.013-1.300), fatigue(OR=3.47, 95% CI=1.107-1.431), sex(female)(OR=3.83, 95% CI=2.111-9.806) and age(OR=-1.59, 95% CI=0.922-1.008) as predicting variables. There is an overall decreasing trend for age, older persons suffer less from pain and females have a higher probability of experiencing pain. Model performance was indicated as fair with a sensitivity of 0.74(95% CI=0.64-0.83) and a positive predictive value of 0.80(95% CI=0.70-0.88). Conclusion: Insomnia and fatigue are predicting symptoms for pain, especially in women and younger patients. The use of a symptom diary in primary care can support the identification of pain. palliative care frail elderly pain symptom assessment pain management clinical decision rules Figures Figure 1 Figure 2 Introduction Unrelieved pain is a common problem in palliative care patients [ 1 ]. Worldwide approximately 25 million people die in pain each year [ 2 ]. Pain is consistently found as an important symptom for one-third of older people in palliative care [ 3 ]. In the concept of total pain, the central belief is that pain emerges from both physical and nonphysical sources [ 4 ]. The concept recognizes that palliative care patients will also suffer from distressing symptoms other than pain. Therefore it is necessary to assess and manage all potential sources of additional distress [ 5 ]. Pain rarely occurs in isolation and co-occurring symptoms appear to have synergistic associations with patients’ treatment outcomes, prognosis, functional status, and quality of life [ 6 ]. Cohen and Mount note a bidirectional relationship: not only does pain affect all aspects of the person, but all aspects of the person can contribute to the perception of pain [ 7 ]. According to the World Health Organization (WHO), evidence has shown that older people suffer unnecessarily because of widespread underassessment and undertreatment of health-related problems [ 3 ]. Pain is often underreported in older people partly due to their beliefs that pain is a normal consequence of ageing [ 8 ]. It is not apparent whether these “age normative” beliefs are influential on other distressing symptoms in the last stages of life. Effective pain assessment in older people includes challenges such as the underreporting of pain on the part of the patients, proper assessment of pain, and atypical manifestations of pain (through experiencing other distressing symptoms)[ 9 ]. The atypical manifestations and the bidirectional relationship of symptoms with pain can contribute to the perception of experiencing pain in older people. Predicting underlying pain by determining other symptoms as predictors may help to identify pain and challenge underreporting and consequently undertreatment of pain in older people. A variety of symptoms are correlated with pain in palliative care patients; nausea [ 10 ], anxiety [ 11 ], fatigue [ 12 ], loss of appetite [ 11 ], insomnia [ 13 ], dyspnoea [ 14 ], and bowel problems [ 15 ]. Little is known about these univariate associations with older palliative care patients with a single exception [ 11 ]. The associations found could differ from the population of independently living frail older people in palliative care due to age-related decline, comorbidities, and high mortality. A lack of evidence exists regarding predicting underlying experiences of pain in independently living frail older people in palliative care. In clinical practice, a prediction model can help to identify pain and open the possibility of discussing adequate pain management with the patient and/or relatives. The aim of this study is to develop a prediction model for pain in independently living frail older people in palliative care and to develop a prediction model. Methods This cross-sectional observational study assessed the predicting variables (symptoms and covariates) and the outcome variable (pain) simultaneously for each patient. The study took place from February 2021 to September 2023. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Checklist for prediction model development was used to facilitate reporting of the study [ 16 ]. Sample Data were collected from independently living frail older people in palliative care from thirteen community-care organizations across the Netherlands. These community-care organizations provide long-term care or specialized care at home. For this study, a convenience sampling method was used to select eligible patients. The eligibility criteria were: having a life expectancy of less than one year, aged 65 years or older, living at home, receiving assistance from home-care nursing, screened as frail (score = 4–15) based on the Groningen Frailty Indicator (GFI)-questionnaire [ 17 ], and able to self-assess and communicate their symptoms. Estimated life expectancy was determined by community-care nurses based on the valid and reliable ‘surprise question’: “Would I be surprised if this patient were to die in the next twelve months?” [ 18 ], which is highly effective in predicting patients in high need of palliative care [ 18 ]. Patients with a diagnosis of dementia or mild cognitive impairment were excluded to ensure the reliability of symptom intensity scores. The sample size was computed with G*power version 3.1.9.4. using a power 0.90 and significance level 0.05, resulting in an estimated sample size of 168 patients [ 19 ]. The effect size was calculated using linear multiple regression with Cohen’s f2 of 0.114 based on the correlations of the symptoms known in palliative care patients [ 19 ]. Data collection The Utrecht Symptom Diary (USD) was used to assess the intensity of predicting symptoms as well as outcome variable pain [ 20 ]. The USD is a validated Dutch version of the Edmonton Symptom Assessment System (ESAS) to self-assess the eleven most prevalent symptoms in cancer patients [ 20 – 21 ]. Patients self-assessing their symptoms is considered the “gold standard” for symptom assessment [ 21 ]. The severity of symptoms at the time of assessment was rated from zero to ten on a Numerical Rating Scale (NRS), in which zero means the least and ten is the most possible symptom severity [ 21 ]. Covariates such as sex, age, GFI-score and primary diagnosis, were collected from the Case Report Form (CRF). The availability of informal care was asked and entails someone who provides unpaid help to a relative needing support. Procedures Multiple community-care nurses from different organizations assisted in the data collection process. Through networking efforts, they voluntarily helped with data collection for this study, and new nurses were continuously approached and trained in eligibility criteria and data collection. Each participating community-care nurse checked the eligibility of their patients with the use of the study protocol or in collaboration with the main researcher (SvV). Eligible patients were invited to participate and provided with study information. Before data collection began they signed the informed consent form. Data was collected on the hard-copy questionnaires CRF and USD. The community-care nurses completed the CRF in consultation with the patients, while patients completed the USD themselves. When patients were unable to write down their answers but able to read the USD and verbally communicate their answers, the nurse assisted in administering the answers. Collected data per patient was sent to the main researcher by secure email. Before entering collected data into the database, a respondent identification number was assigned by the main researcher (SvV) for each case. Data analysis Descriptive analysis was used for patient characteristics (mean and SD or N(%)). The prevalence of the selected symptoms defined as USD scores larger or equal to one is given to describe the sample. The outcome-variable pain was dichotomized into PresPain indicating the presence or absence of pain according to scores larger or equal to one. For descriptive purposes of the sample, the frequencies of symptom scores equal to and greater than three are reported as they are seen as clinically relevant [ 20 ]. The proportion of missing values for the variables involved was computed. Missing values of the variables with less than 5 percent missing were imputed by the variable means or random categories. A multivariable logistic regression model was created with the dichotomized USD pain score as the outcome variable and continuous symptom scores of anxiety, fatigue, loss of appetite, insomnia, nausea, dyspnoea, and bowel problems as predictor variables. Covariates (age, sex, and living situation) were selected based on known relevance [ 22 – 24 ]. The predicting symptoms and covariates were selected according to the minimum Akaike Information Criterion(AIC), which results in the best predictive model [ 25 ]. The Odds Ratios (OR) and effect plots give the effects of the selected variables on the presence of pain (PresPain). Additionally, several figures will be given predicting the underlying presence of pain for groups differing in age and sex with prediction lines and confidence bands [ 26 ]. To determine the predictive accuracy of the final model, the Receiver Operating Characteristics (ROC)-analysis was used to determine the Area Under the ROC Curve (AUC). An AUC of ≥ 0.80 indicates good and an AUC between 0.70 and 0.80 fair accuracy [ 27 ]. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and their confidence intervals are reported [ 28 ]. Statistical significance (two-sided) was set at p ≤ 0.05. All analyses were conducted using Statistical Package for the Social Sciences (SPSS) version 23.0 and R version 2.0.60[ 29 – 30 ]. Ethics Patients provided written consent to use their collected data for scientific purposes. Regulations of the General Data Protection were followed [ 31 ]. The study was conducted according to the Declaration of Helsinki (the latest version WMA General Assembly 2013) [ 32 ]. A non-WMO (the Medical Research Involving Human Subject Act) statement was granted by the Ethics Committee of the University Medical Center Groningen [registration number: 202100021] [ 33 ]. Results Respondents In total, 157 patients were enrolled in this study. Of these patients, 38.2% were male and the mean age was 83.4 (SD ±8.4) of whom 60.5% lived alone and 35.6% lived with a partner or relatives. The primary informal caregiver was in 28% living with the patient and 58% non-residential informal caregiver. The primary diagnosis was predominantly cardiovascular diseases (33.1%), followed by cancer (22.9%) and diseases of the nervous- and sensory system (15.3%). The mean frailty score was 7.5 (SD ±2.5). All patient characteristics are presented in Table 1. Prevalence and intensity of selected symptoms The prevalence of pain was 61.8% with a mean intensity score of 3.4 (SD ±3.3). Most uncommon symptom was nausea: 19.1% with a score of one or higher, and 10.8% with a score of ≥ 3. Most prevalent symptom was fatigue (84.7%) and had the highest mean intensity score (5.1±3.0). The prevalence of USD symptoms was larger than 50% for six out of eight in total. Model development and specifications Of all data points, 0.64% were missing. Of the explanatory variables the missing data was imputed so that all available cases could be included for further analysis (N=157). The model was developed using multivariable logistic regression and minimum AIC model selection, revealing insomnia (OR=2.13, 95% Confidence Interval(CI)=1.013-1.300) and fatigue (OR=3.47, 95% CI=1.107-1.431) were independent predicting symptoms for pain, after correction for age and sex. Sex (female)(OR=3.83, 95% CI=2.111-9.806) and age (OR=-1.59, 95% CI=0.922-1.008) were the predicting covariates. Specifications of the final model after minimum AIC are presented in Table 3. The effects of Age and Sex on the probability of the person experiencing pain is visualized with prediction lines and confidence bands in Figure 1. It can be observed that there is a slight decrease in pain experience with increasing Age. Persons with a probability larger than the cut-off of 0.599 are best predicted to have pain by the model. How the probability of pain depends on Age, Sex, Insomnia and Fatigue is detailed in Figure 2. There is an overall decreasing trend for Age, older persons suffer less from pain. For low USD values, both females and males are below the cut-off value of 0.599, whereas for large USD values both are above. For USD values equal to 3 females younger than 85 years of age are above the cut-off value. The figure illustrates the difference in the effect of low versus high USD levels on the probability of pain. In particular, the effect for males is more drastic than for females. Model performance The final model resulted in 42 true negative, 25 false negative, 18 false positive and 72 true positive predictions causing the overall percentage of correct predictions equal to 80%. The estimated sensitivity is 0.74 (95% CI=0.64-0.83), the specificity 0.70 (95% CI=0.57-0.81), the positive predictive value 0.80 (95% CI=0.70-0.88) and the negative predictive value 0.63 (95% CI=0.50-0.74). The AUC was calculated at 0.768 (95% CI=0.692-0.843). Discussion Summary of the results This study aimed to determine whether the seven symptoms anxiety, fatigue, loss of appetite, insomnia, nausea, dyspnoea, and bowel problems are predictors for experiencing pain in independently living frail older people in palliative care. This study found that insomnia and fatigue are statistically significantly independent predicting symptoms for pain after correcting for sex and age. The analysis gave age as a predicting covariate, although not significant did give an effect in the final model (see Fig. 1 ). The AUC showed fair predictive accuracy. The estimated OR of insomnia indicated that persons experiencing insomnia one score higher have a 2.13 times higher chance of experiencing pain. For fatigue, the OR is 3.47 times higher. The OR of sex indicated that females have a 3.83 times higher chance of experiencing pain than males. Although not statistically significant, the overall decreasing trend for age, where older persons suffer less from pain, is seen in the OR of -1.59. Reflection on the study results Almost two-thirds of the sample in this study recorded the presence of pain (61.8%). This was similar to other studies of pain experienced in older people with a life expectancy of less than one year with a prevalence of 66.3% [ 34 ] and a prevalence range of 57–88% [ 35 ]. Insomnia and fatigue were found to be significant and relevant predictors for the presence of pain. Pain processing happens in the insula and the somatosensory cortex of the brain. The increased involvement of the anterior insula was negatively associated with insomnia [ 36 ] and is involved in the experiencing of fatigue [ 37 ]. The insula integrates sensory with emotional and cognitive processes and is involved in aversive motivational salience [ 38 ]. Salience processing is often associated with the extension of certain sensory inputs, such as the interoceptive stimuli of pain [ 38 ]. The processing of these symptoms within the insula might explain the predictability of pain. The above effect of sex indicated that women are at a higher risk for common pain conditions in comparison to men [ 39 ]. Hormonal factors are thought to explain sex differences in pain perception as these regulate the cortical processing of pain-related stimuli [ 39 ]. The decreasing effect of age indicated that older people suffer less from pain. This might be explained by a change of the peripheral nerves and receptors with ageing resulting in different pain sensitivity [ 40 ] and, the generational differences in pain beliefs and attitudes that may occur across age groups [ 41 ]. Strengths and limitations A strength of this study was that the analysis was run on a near-complete dataset. Less than one percent of all data points (20 of 3140 data points, 0.64%) were missing and of the explanatory variables less than one percent of data points (nine of 1570 data points, 0.57%) were missing. These data points were considered to be missing at random. Another strength is that the developed prediction model had fair to high sensitivity (0.74) and specificity (0.70) resulting in a high proportion of positives correctly identified (0.80) as experiencing pain. This study also had some limitations. Part of the inclusion criteria was to assess the palliative phase based on the surprise question. Some community-care nurses gave the feedback that they found the palliative phase (life expectancy < 6 months), terminally ill (life expectancy < 2 weeks) or end-stage diseases easier to identify. This may have introduced some selection bias due to underestimation resulting in a more frail group than intended. However, the descriptive statistics of the sample do not substantiate this bias. The sample size was 157 instead of the 168 required by the sample size calculation, as a result, this study might be underpowered. It may be noted that the USD was validated among cancer patients [ 20 ]. This instrument is widely used in the Netherlands as the multidimensional symptom assessment instrument within palliative care. Moreover, the current population overlaps with the cancer patients, and it seems reasonable to assume that the results of the validation study are generalizable to the current population. The best fit model was selected based on the minimum AIC and the model performance was tested on the same dataset, therefore the diagnostics of the performance may have resulted in some overestimation. Recommendations for practice The use of the USD or any other symptom-burden measurement instrument is generally not part of standard practice. In combination with the underreporting of pain makes correct identification of the presence of pain challenging. The use of a symptom diary in primary care can support the identification of pain. Insomnia and fatigue are predicting symptoms for pain, especially in women and in younger patients. The risk groups identified give a high predictive accuracy of the underlying experience of pain. The results of this model are based on the population of independently living frail older people with a life expectancy of less than one year. In this final stage in life, it is, for the most part, still possible to communicate with patients and/or relatives about advance care planning and their desired pain management. Identifying the underlying presence of pain is therefore an essential part of care. Therefore, symptom assessment, especially for the risk groups identified, can help the primary care professional in the management of pain. Conclusions This study showed insomnia and fatigue as statistically significant independent predictors for the presence of pain in independently living frail older people with a life expectancy of less than one year. The final prediction model presented has sex and years of age as additional effects on the presence of pain. The predictive accuracy of the model for the presence of pain was fair with a high positive predictive value of 0.80. The current study identified several subgroups which are highly at risk for underlying presence of pain. Abbreviations AIC Akaike Information Criterion AUC Area Under the Curve CI Confidence interval CRF Case report form ESAS Edmonton symptom assessment system GFI Groningen Frailty Indicator N number of proportion NPV Negative predictive value NRS Numerical rating scale OR Odds ratio PPV Positive predictive value ROC Receiver Operating Characteristics SD Standard deviation SPSS Statistical package for social sciences TRIPOD Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis USD Utrecht Symptom Diary WHO World Health Organization WMO Wet medisch-wetenschappelijk onderzoek met mensen [Dutch Medical research involving Human Subject Act] Declarations Supplementary files: Not applicable. Ethics approval and consent to participate: A non-WMO (the Medical Research Involving Human Subject Act) statement was granted by the Ethics Committee of the University Medical Center Groningen [registration number: 202100021] Funding: This study had no funding. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. References Klint Å, Bondesson E, Rasmussen BH, Fürst CJ, Schelin MEC: Dying With Unrelieved Pain—Prescription of Opioids Is Not Enough. Journal of pain and symptom management 2019, 58(5):784-791.e1. Bhatnagar S, Gupta M: Integrated pain and palliative medicine model. Annals of Palliative Medicine 2016, 5(3):196-208. World Health Organisation Europe. Better Palliative Care for Older People. In: Davies E, Higginson IJ, editors. 2004:1-40. 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Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Palliative Care → Version 1 posted Editorial decision: Revision requested 22 Feb, 2025 Reviews received at journal 21 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviews received at journal 31 Jul, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers agreed at journal 19 May, 2024 Reviewers invited by journal 16 May, 2024 Editor invited by journal 21 Mar, 2024 Submission checks completed at journal 21 Mar, 2024 Editor assigned by journal 21 Mar, 2024 First submitted to journal 20 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4137378","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283175271,"identity":"f27a79fe-7a6d-4588-8c72-f1c726c1feef","order_by":0,"name":"Suzan van Veen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYHADxsYHH6CsA0RqYT5sOAPKJFYLW5o0DzFa5Bt4DD8XMByW45fuMZC2bdsmL+/AewCvFoMDPMbSMxgOG0vOOWNgnNt223DjAb4E/FoY2BKA7klL3HAjxyAZqCXBsIHHgIDD2JJ/A7XU7wdqOWxJjBaGA8zHgLbYJBhIpCU2MwK1yDMQ0GJwmPmYNY+BjeGMG8mHGXvO3TbcwEzIYe2Nzbd5KiTk+Wcktv/4UXZbXr69x/ABXocxg+1CsReveqz2NpCsZRSMglEwCoY5AAD2TUVqe8acHAAAAABJRU5ErkJggg==","orcid":"","institution":"University Medical Center Groningen","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Suzan","middleName":"van","lastName":"Veen","suffix":""},{"id":283175272,"identity":"8e335545-13cd-4da5-9f20-c6cdbda62062","order_by":1,"name":"Hans Drenth","email":"","orcid":"","institution":"ZuidOostZorg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Drenth","suffix":""},{"id":283175273,"identity":"0c238351-42aa-44ba-8f15-aba90e15d7ff","order_by":2,"name":"Hans Hobbelen","email":"","orcid":"","institution":"Hanze University of Applied Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Hobbelen","suffix":""},{"id":283175274,"identity":"3be84747-28f2-45b4-8512-336b6a857cd7","order_by":3,"name":"Wim Krijnen","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wim","middleName":"","lastName":"Krijnen","suffix":""},{"id":283175275,"identity":"55ad70ee-e448-4d22-93e2-cfb311a798e9","order_by":4,"name":"Everlien de Graaf","email":"","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Everlien","middleName":"","lastName":"de Graaf","suffix":""},{"id":283175276,"identity":"ade705e9-cfb5-460d-9774-decd4b63b7fb","order_by":5,"name":"Evelyn Finnema","email":"","orcid":"","institution":"University Medical Center Groningen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Evelyn","middleName":"","lastName":"Finnema","suffix":""}],"badges":[],"createdAt":"2024-03-20 13:07:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4137378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4137378/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12904-025-01888-y","type":"published","date":"2025-10-14T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53500374,"identity":"6723e75b-1622-4575-8905-8b7473313897","added_by":"auto","created_at":"2024-03-26 18:11:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEffects from logistic regression of Age, Sex, Insomnia and Fatigue on predicted probability of pain (vertically).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4137378/v1/94bf8afdfe5be17bcebe03f2.png"},{"id":53500373,"identity":"db7f5508-25af-4a7c-8148-c3f2b6416e6e","added_by":"auto","created_at":"2024-03-26 18:11:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted probability of pain (vertically) from logistic regression depending on Age and Insomnia and Fatigue across Sex.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4137378/v1/2fda04285a995fde8c7a2b57.png"},{"id":93956732,"identity":"c38f1fb6-b396-49b2-8f85-4cdbd594e7d0","added_by":"auto","created_at":"2025-10-20 16:12:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":737458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4137378/v1/91cae086-b25c-47d8-bddf-3fabe0520722.pdf"},{"id":53500372,"identity":"6a0843e0-61eb-490a-9e7c-b6fa470bfa6c","added_by":"auto","created_at":"2024-03-26 18:11:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":61592,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4137378/v1/6d3a686e2e941871c9842319.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A predictive model of symptoms for pain in independently living frail older people in palliative care","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnrelieved pain is a common problem in palliative care patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Worldwide approximately 25\u0026nbsp;million people die in pain each year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pain is consistently found as an important symptom for one-third of older people in palliative care [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the concept of total pain, the central belief is that pain emerges from both physical and nonphysical sources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The concept recognizes that palliative care patients will also suffer from distressing symptoms other than pain. Therefore it is necessary to assess and manage all potential sources of additional distress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Pain rarely occurs in isolation and co-occurring symptoms appear to have synergistic associations with patients\u0026rsquo; treatment outcomes, prognosis, functional status, and quality of life [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cohen and Mount note a bidirectional relationship: not only does pain affect all aspects of the person, but all aspects of the person can contribute to the perception of pain [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the World Health Organization (WHO), evidence has shown that older people suffer unnecessarily because of widespread underassessment and undertreatment of health-related problems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Pain is often underreported in older people partly due to their beliefs that pain is a normal consequence of ageing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is not apparent whether these \u0026ldquo;age normative\u0026rdquo; beliefs are influential on other distressing symptoms in the last stages of life. Effective pain assessment in older people includes challenges such as the underreporting of pain on the part of the patients, proper assessment of pain, and atypical manifestations of pain (through experiencing other distressing symptoms)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe atypical manifestations and the bidirectional relationship of symptoms with pain can contribute to the perception of experiencing pain in older people. Predicting underlying pain by determining other symptoms as predictors may help to identify pain and challenge underreporting and consequently undertreatment of pain in older people. A variety of symptoms are correlated with pain in palliative care patients; nausea [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], anxiety [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], fatigue [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], loss of appetite [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], insomnia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], dyspnoea [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and bowel problems [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLittle is known about these univariate associations with older palliative care patients with a single exception [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The associations found could differ from the population of independently living frail older people in palliative care due to age-related decline, comorbidities, and high mortality.\u003c/p\u003e \u003cp\u003eA lack of evidence exists regarding predicting underlying experiences of pain in independently living frail older people in palliative care. In clinical practice, a prediction model can help to identify pain and open the possibility of discussing adequate pain management with the patient and/or relatives.\u003c/p\u003e \u003cp\u003eThe aim of this study is to develop a prediction model for pain in independently living frail older people in palliative care and to develop a prediction model.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis cross-sectional observational study assessed the predicting variables (symptoms and covariates) and the outcome variable (pain) simultaneously for each patient. The study took place from February 2021 to September 2023. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Checklist for prediction model development was used to facilitate reporting of the study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003eData were collected from independently living frail older people in palliative care from thirteen community-care organizations across the Netherlands. These community-care organizations provide long-term care or specialized care at home. For this study, a convenience sampling method was used to select eligible patients. The eligibility criteria were: having a life expectancy of less than one year, aged 65 years or older, living at home, receiving assistance from home-care nursing, screened as frail (score\u0026thinsp;=\u0026thinsp;4\u0026ndash;15) based on the Groningen Frailty Indicator (GFI)-questionnaire [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and able to self-assess and communicate their symptoms. Estimated life expectancy was determined by community-care nurses based on the valid and reliable \u0026lsquo;surprise question\u0026rsquo;: \u0026ldquo;Would I be surprised if this patient were to die in the next twelve months?\u0026rdquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which is highly effective in predicting patients in high need of palliative care [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Patients with a diagnosis of dementia or mild cognitive impairment were excluded to ensure the reliability of symptom intensity scores. The sample size was computed with G*power version 3.1.9.4. using a power 0.90 and significance level 0.05, resulting in an estimated sample size of 168 patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The effect size was calculated using linear multiple regression with Cohen\u0026rsquo;s f2 of 0.114 based on the correlations of the symptoms known in palliative care patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe Utrecht Symptom Diary (USD) was used to assess the intensity of predicting symptoms as well as outcome variable pain [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The USD is a validated Dutch version of the Edmonton Symptom Assessment System (ESAS) to self-assess the eleven most prevalent symptoms in cancer patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Patients self-assessing their symptoms is considered the \u0026ldquo;gold standard\u0026rdquo; for symptom assessment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The severity of symptoms at the time of assessment was rated from zero to ten on a Numerical Rating Scale (NRS), in which zero means the least and ten is the most possible symptom severity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Covariates such as sex, age, GFI-score and primary diagnosis, were collected from the Case Report Form (CRF). The availability of informal care was asked and entails someone who provides unpaid help to a relative needing support.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003eMultiple community-care nurses from different organizations assisted in the data collection process. Through networking efforts, they voluntarily helped with data collection for this study, and new nurses were continuously approached and trained in eligibility criteria and data collection. Each participating community-care nurse checked the eligibility of their patients with the use of the study protocol or in collaboration with the main researcher (SvV). Eligible patients were invited to participate and provided with study information. Before data collection began they signed the informed consent form. Data was collected on the hard-copy questionnaires CRF and USD. The community-care nurses completed the CRF in consultation with the patients, while patients completed the USD themselves. When patients were unable to write down their answers but able to read the USD and verbally communicate their answers, the nurse assisted in administering the answers. Collected data per patient was sent to the main researcher by secure email. Before entering collected data into the database, a respondent identification number was assigned by the main researcher (SvV) for each case.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDescriptive analysis was used for patient characteristics (mean and SD or N(%)). The prevalence of the selected symptoms defined as USD scores larger or equal to one is given to describe the sample. The outcome-variable pain was dichotomized into PresPain indicating the presence or absence of pain according to scores larger or equal to one.\u003c/p\u003e \u003cp\u003eFor descriptive purposes of the sample, the frequencies of symptom scores equal to and greater than three are reported as they are seen as clinically relevant [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The proportion of missing values for the variables involved was computed. Missing values of the variables with less than 5 percent missing were imputed by the variable means or random categories.\u003c/p\u003e \u003cp\u003eA multivariable logistic regression model was created with the dichotomized USD pain score as the outcome variable and continuous symptom scores of anxiety, fatigue, loss of appetite, insomnia, nausea, dyspnoea, and bowel problems as predictor variables. Covariates (age, sex, and living situation) were selected based on known relevance [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The predicting symptoms and covariates were selected according to the minimum Akaike Information Criterion(AIC), which results in the best predictive model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Odds Ratios (OR) and effect plots give the effects of the selected variables on the presence of pain (PresPain). Additionally, several figures will be given predicting the underlying presence of pain for groups differing in age and sex with prediction lines and confidence bands [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo determine the predictive accuracy of the final model, the Receiver Operating Characteristics (ROC)-analysis was used to determine the Area Under the ROC Curve (AUC). An AUC of \u0026ge;\u0026thinsp;0.80 indicates good and an AUC between 0.70 and 0.80 fair accuracy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and their confidence intervals are reported [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Statistical significance (two-sided) was set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05. All analyses were conducted using Statistical Package for the Social Sciences (SPSS) version 23.0 and R version 2.0.60[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003ePatients provided written consent to use their collected data for scientific purposes. Regulations of the General Data Protection were followed [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The study was conducted according to the Declaration of Helsinki (the latest version WMA General Assembly 2013) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A non-WMO (the Medical Research Involving Human Subject Act) statement was granted by the Ethics Committee of the University Medical Center Groningen [registration number: 202100021] [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eRespondents\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 157 patients were enrolled in this study. Of these patients, 38.2% were male and the mean age was 83.4 (SD \u0026plusmn;8.4) of whom 60.5% lived alone and 35.6% lived with a partner or relatives. The primary informal caregiver was in 28% living with the patient and 58% non-residential informal caregiver. The primary diagnosis was predominantly cardiovascular diseases (33.1%), followed by cancer (22.9%) and diseases of the nervous- and sensory system (15.3%). The mean frailty score was 7.5 (SD \u0026plusmn;2.5). All patient characteristics are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrevalence and intensity of selected symptoms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of pain was 61.8% with a mean intensity score of 3.4 (SD \u0026plusmn;3.3). Most uncommon symptom was nausea: 19.1% with a score of one or higher, and 10.8% with a score of \u0026ge; 3. Most prevalent symptom was fatigue (84.7%) and had the highest mean intensity score (5.1\u0026plusmn;3.0). The prevalence of USD symptoms was larger than 50% for six out of eight in total.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModel development and specifications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf all data points, 0.64% were missing. Of the explanatory variables the missing data was imputed so that all available cases could be included for further analysis (N=157). The model was developed using multivariable logistic regression and minimum AIC model selection, revealing insomnia (OR=2.13, 95% Confidence Interval(CI)=1.013-1.300) and fatigue (OR=3.47, 95% CI=1.107-1.431) were independent predicting symptoms for pain, after correction for age and sex. Sex (female)(OR=3.83, 95% CI=2.111-9.806) and age (OR=-1.59, 95% CI=0.922-1.008) were the predicting covariates. Specifications of the final model after minimum AIC are presented in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effects of Age and Sex on the probability of the person experiencing pain is visualized with prediction lines and confidence bands in Figure 1. It can be observed that there is a slight decrease in pain experience with increasing Age. Persons with a probability larger than the cut-off of 0.599 are best predicted to have pain by the model. How the probability of pain depends on Age, Sex, Insomnia and Fatigue is detailed in Figure 2. There is an overall decreasing trend for Age, older persons suffer less from pain. For low USD values, both females and males are below the cut-off value of 0.599, whereas for large USD values both are above. For USD values equal to 3 females younger than 85 years of age are above the cut-off value. The figure illustrates the difference in the effect of low versus high USD levels on the probability of pain. In particular, the effect for males is more drastic than for females.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModel performance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe final model resulted in 42 true negative, 25 false negative, 18 false positive and 72 true positive predictions causing the overall percentage of correct predictions equal to 80%. The estimated sensitivity is 0.74 (95% CI=0.64-0.83), the specificity 0.70 (95% CI=0.57-0.81), the positive predictive value 0.80 (95% CI=0.70-0.88) and the negative predictive value 0.63 (95% CI=0.50-0.74). The AUC was calculated at 0.768 (95% CI=0.692-0.843).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSummary of the results\u003c/h2\u003e \u003cp\u003eThis study aimed to determine whether the seven symptoms anxiety, fatigue, loss of appetite, insomnia, nausea, dyspnoea, and bowel problems are predictors for experiencing pain in independently living frail older people in palliative care. This study found that insomnia and fatigue are statistically significantly independent predicting symptoms for pain after correcting for sex and age. The analysis gave age as a predicting covariate, although not significant did give an effect in the final model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe AUC showed fair predictive accuracy. The estimated OR of insomnia indicated that persons experiencing insomnia one score higher have a 2.13 times higher chance of experiencing pain. For fatigue, the OR is 3.47 times higher. The OR of sex indicated that females have a 3.83 times higher chance of experiencing pain than males. Although not statistically significant, the overall decreasing trend for age, where older persons suffer less from pain, is seen in the OR of -1.59.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReflection on the study results\u003c/h2\u003e \u003cp\u003eAlmost two-thirds of the sample in this study recorded the presence of pain (61.8%). This was similar to other studies of pain experienced in older people with a life expectancy of less than one year with a prevalence of 66.3% [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and a prevalence range of 57\u0026ndash;88% [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsomnia and fatigue were found to be significant and relevant predictors for the presence of pain. Pain processing happens in the insula and the somatosensory cortex of the brain. The increased involvement of the anterior insula was negatively associated with insomnia [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and is involved in the experiencing of fatigue [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The insula integrates sensory with emotional and cognitive processes and is involved in aversive motivational salience [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Salience processing is often associated with the extension of certain sensory inputs, such as the interoceptive stimuli of pain [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The processing of these symptoms within the insula might explain the predictability of pain.\u003c/p\u003e \u003cp\u003eThe above effect of sex indicated that women are at a higher risk for common pain conditions in comparison to men [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Hormonal factors are thought to explain sex differences in pain perception as these regulate the cortical processing of pain-related stimuli [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The decreasing effect of age indicated that older people suffer less from pain. This might be explained by a change of the peripheral nerves and receptors with ageing resulting in different pain sensitivity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and, the generational differences in pain beliefs and attitudes that may occur across age groups [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA strength of this study was that the analysis was run on a near-complete dataset. Less than one percent of all data points (20 of 3140 data points, 0.64%) were missing and of the explanatory variables less than one percent of data points (nine of 1570 data points, 0.57%) were missing. These data points were considered to be missing at random. Another strength is that the developed prediction model had fair to high sensitivity (0.74) and specificity (0.70) resulting in a high proportion of positives correctly identified (0.80) as experiencing pain.\u003c/p\u003e \u003cp\u003eThis study also had some limitations. Part of the inclusion criteria was to assess the palliative phase based on the surprise question. Some community-care nurses gave the feedback that they found the palliative phase (life expectancy\u0026thinsp;\u0026lt;\u0026thinsp;6 months), terminally ill (life expectancy\u0026thinsp;\u0026lt;\u0026thinsp;2 weeks) or end-stage diseases easier to identify. This may have introduced some selection bias due to underestimation resulting in a more frail group than intended. However, the descriptive statistics of the sample do not substantiate this bias. The sample size was 157 instead of the 168 required by the sample size calculation, as a result, this study might be underpowered.\u003c/p\u003e \u003cp\u003eIt may be noted that the USD was validated among cancer patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This instrument is widely used in the Netherlands as the multidimensional symptom assessment instrument within palliative care. Moreover, the current population overlaps with the cancer patients, and it seems reasonable to assume that the results of the validation study are generalizable to the current population. The best fit model was selected based on the minimum AIC and the model performance was tested on the same dataset, therefore the diagnostics of the performance may have resulted in some overestimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations for practice\u003c/h2\u003e \u003cp\u003eThe use of the USD or any other symptom-burden measurement instrument is generally not part of standard practice. In combination with the underreporting of pain makes correct identification of the presence of pain challenging. The use of a symptom diary in primary care can support the identification of pain. Insomnia and fatigue are predicting symptoms for pain, especially in women and in younger patients. The risk groups identified give a high predictive accuracy of the underlying experience of pain.\u003c/p\u003e \u003cp\u003eThe results of this model are based on the population of independently living frail older people with a life expectancy of less than one year. In this final stage in life, it is, for the most part, still possible to communicate with patients and/or relatives about advance care planning and their desired pain management. Identifying the underlying presence of pain is therefore an essential part of care. Therefore, symptom assessment, especially for the risk groups identified, can help the primary care professional in the management of pain.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study showed insomnia and fatigue as statistically significant independent predictors for the presence of pain in independently living frail older people with a life expectancy of less than one year. The final prediction model presented has sex and years of age as additional effects on the presence of pain. The predictive accuracy of the model for the presence of pain was fair with a high positive predictive value of 0.80. The current study identified several subgroups which are highly at risk for underlying presence of pain.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCase report form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEdmonton symptom assessment system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGroningen Frailty Indicator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enumber of proportion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumerical rating scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical package for social sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRIPOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUtrecht Symptom Diary\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWet medisch-wetenschappelijk onderzoek met mensen [Dutch Medical research involving Human Subject Act]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary files:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eA non-WMO (the Medical Research Involving Human Subject Act) statement was granted by the Ethics Committee of the University Medical Center Groningen [registration number: 202100021]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study had no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKlint \u0026Aring;, Bondesson E, Rasmussen BH, F\u0026uuml;rst CJ, Schelin MEC: Dying With Unrelieved Pain\u0026mdash;Prescription of Opioids Is Not Enough. Journal of pain and symptom management 2019, 58(5):784-791.e1.\u003c/li\u003e\n\u003cli\u003eBhatnagar S, Gupta M: Integrated pain and palliative medicine model. Annals of Palliative Medicine 2016, 5(3):196-208.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation Europe. Better Palliative Care for Older People. In: Davies E, Higginson IJ, editors. 2004:1-40.\u003c/li\u003e\n\u003cli\u003eMcPherson CJ, Hadjistavropoulos T, Lobchuk MM, Kilgour KN: Cancer-related pain in older adults receiving palliative care: Patient and family caregiver perspectives on the experience of pain. Pain Res Manag 2013, 18(6):293-300.\u003c/li\u003e\n\u003cli\u003ePlatt M: Pain Challenges at the End of Life - Pain and Palliative Care Collaboration. Rev Pain 2010, 4(2):18-23.\u003c/li\u003e\n\u003cli\u003eDong ST, Butow PN, Costa DSJ, Lovell MR, Agar M: Symptom Clusters in Patients With Advanced Cancer: A Systematic Review of Observational Studies. Journal of pain and symptom management 2014, 48(3):411-450.\u003c/li\u003e\n\u003cli\u003eCohen SR, Mount BM: Pain with Life-Threatening Illness: Its Perception and Control Are Inextricably Linked with Quality of Life. Pain research \u0026amp; management 2000, 5(4):271-275.\u003c/li\u003e\n\u003cli\u003eHofland SL: Elder beliefs: blocks to pain management. J Gerontol Nurs 1992, 18(6):19-23.\u003c/li\u003e\n\u003cli\u003eCavalieri TA: Management of pain in older adults. J Am Osteopath Assoc 2005, 105(3 Suppl 1):12.\u003c/li\u003e\n\u003cli\u003eWilson KG, Chochinov HM, Allard P, Chary S, Gagnon PR, Macmillan K, De Luca M, O\u0026apos;Shea F, Kuhl D, Fainsinger RL: Prevalence and Correlates of Pain in the Canadian National Palliative Care Survey. Pain research \u0026amp; management 2009, 14(5):365-370.\u003c/li\u003e\n\u003cli\u003eBlack B, Herr K, Fine P, Sanders S, Tang X, Bergen-Jackson K, Titler M, Forcucci C: The Relationships among Pain, Nonpain Symptoms, and Quality of Life Measures in Older Adults with Cancer Receiving Hospice Care. Pain Med 2011, 12(6):880-889.\u003c/li\u003e\n\u003cli\u003eStone P, Hardy J, Broadley K, Tookman AJ, Kurowska A, A\u0026apos;Hern R: Fatigue in advanced cancer: a prospective controlled cross-sectional study. British Journal of Cancer 1999, 79(9-10):1479-1486.\u003c/li\u003e\n\u003cli\u003eDelgado-Guay M, Yennurajalingam S, Parsons H, Palmer JL, Bruera E: Association Between Self-Reported Sleep Disturbance and Other Symptoms in Patients with Advanced Cancer. Journal of pain and symptom management 2011, 41(5):819-827.\u003c/li\u003e\n\u003cli\u003eTanaka K, Akechi T, Okuyama T, Nishiwaki Y, Uchitomi Y: Factors correlated with dyspnea in advanced lung cancer patients: organic causes and what else? J Pain Symptom Manage 2002, 23(6):490-500.\u003c/li\u003e\n\u003cli\u003eClark, Katherine, MB BS, MMed, FRACP,FAChPM, Smith JM,BPsych, Currow, David C., BMed MPH,FRACP: The Prevalence of Bowel Problems Reported in a Palliative Care Population. Journal of Pain and Symptom Management 2012, 43(6):993-1000.\u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015, 350:g7594.\u003c/li\u003e\n\u003cli\u003eSlaets J. Groningen Frailty Indicator: Instructie ontleend aan werkwijzen screening op kwetsbaarheid. [Internet] Available from: https://www.pallialine.nl/uploaded/docs/Kwaliteitskader_pz/Meetinstrument_GFI.pdf?u=1PpZQ+. [Accessed Nov 6, 2020].\u003c/li\u003e\n\u003cli\u003eVeldhoven CMM, Nutma N, De Graaf W, Schers H, Verhagen, C. a. H. H. V. M., Vissers KCP, Engels Y: Screening with the double surprise question to predict deterioration and death: an explorative study. BMC Palliat Care 2019, 18(1):118.\u003c/li\u003e\n\u003cli\u003eFaul F, Erdfelder E, Lang A, Buchner A: G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007, 39(2):175-191.\u003c/li\u003e\n\u003cli\u003eBaan FH, Koldenhof JJ, Nijs EJ, Echteld MA, Zweers D, Hesselmann GM, Vervoort SC, Vos JB, Graaf E, Witteveen PO, Suijkerbuijk KP, Graeff A, Teunissen SC: Validation of the Dutch version of the Edmonton Symptom Assessment System. Cancer medicine (Malden, MA) 2020, 9(17):6111-6121.\u003c/li\u003e\n\u003cli\u003eBruera E, Kuehn N, Miller MJ, Selmser P, Macmillan K: The Edmonton Symptom Assessment System (ESAS): a simple method for the assessment of palliative care patients. J Palliat Care 1991, 7(2):6-9.\u003c/li\u003e\n\u003cli\u003ede Graaf E, Zweers D, de Graeff A, Daggelders G, Theunissen S: Does Age Influence Symptom Prevalence and Intensity in Hospice Patients, or Not? A Retrospective Cohort Study. Journal of Geriatrics and Palliative Care 2014, 7(S(1)).\u003c/li\u003e\n\u003cli\u003eCheung WY, Le LW, Gagliese L, Zimmermann C: Age and gender differences in symptom intensity and symptom clusters among patients with metastatic cancer. Support Care Cancer 2011, 19(3):417-423.\u003c/li\u003e\n\u003cli\u003eJohnson MH: How does distraction work in the management of pain? Curr Pain Headache Rep 2005, 9(2):90-95.\u003c/li\u003e\n\u003cli\u003eKonishi S, Kitagawa G: Information Criteria and Statistical Modeling: Springer Science \u0026amp; Business Media; 2008.\u003c/li\u003e\n\u003cli\u003eBreheny P, Burchett W: Visualization of Regression Models Using visreg. The R Journal 2017, 9(2):56-71.\u003c/li\u003e\n\u003cli\u003eMandrekar JN: Receiver Operating Characteristic Curve in Diagnostic Test Assessment. Journal of thoracic oncology 2010, 5(9):1315-1316.\u003c/li\u003e\n\u003cli\u003eStevenson M, Sergeant E: epiR: Tools for the Analysis of Epidemiological Data. R package version 2.0.60. 2023.\u003c/li\u003e\n\u003cli\u003eIBM: IBM SPSS Statistics 23. 2016, 23: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-23. Accessed Nov 7, 2020.\u003c/li\u003e\n\u003cli\u003eR Core Team: R: A language and environment for statistical computing. 2021, Version 2.0.60.\u003c/li\u003e\n\u003cli\u003eZaken MvA. Voldoen aan de Algemene verordening gegevensbescherming (AVG) - Privacy en persoonsgegevens - Rijksoverheid.nl. 2017. [Internet] Available at: https://www.rijksoverheid.nl/onderwerpen/privacy-en-persoonsgegevens/voldoen-aan-de-avg. Accessed Nov 7, 2020.\u003c/li\u003e\n\u003cli\u003eWMA - The World Medical Association-: Declaration of Helsinki (latest version 2013).\u003c/li\u003e\n\u003cli\u003eKoninkrijksrelaties MvBZe. Wet medisch-wetenschappelijk onderzoek met mensen. [Internet] Available at: https://wetten.overheid.nl/BWBR0009408/2020-01-01. Accessed Nov 7, 2020.\u003c/li\u003e\n\u003cli\u003evan Lancker A, Velghe A, van Hecke A, Verbrugghe M, van den Noortgate N, Grypdonck M, Verhaeghe S: Prevalence of Symptoms in Older Cancer Patients Receiving Palliative Care: A Systematic Review and Meta-Analysis - ScienceDirect. Journal of Pain and Symptom Management 2014, 47(1):90-104.\u003c/li\u003e\n\u003cli\u003eHelme RD, Gibson SJ: The epidemiology of pain in elderly people. Clin Geriatr Med 2001, 17(3):417-431.\u003c/li\u003e\n\u003cli\u003eChen MC, Chang C, Glover GH, Gotlib IH: Increased insula coactivation with salience networks in insomnia. Biol Psychol 2014, 97:1-8.\u003c/li\u003e\n\u003cli\u003eDobryakova E, DeLuca J, Genova HM, Wylie GR: Neural correlates of cognitive fatigue: cortico-striatal circuitry and effort-reward imbalance. J Int Neuropsychol Soc 2013, 19(8):849-853.\u003c/li\u003e\n\u003cli\u003eLabrakakis C: The Role of the Insular Cortex in Pain. Int J Mol Sci 2023, 24(6):5736.\u003c/li\u003e\n\u003cli\u003ePieretti S, Di Giannuario A, Di Giovannandrea R, Marzoli F, Piccaro G, Minosi P, Aloisi AM: Gender differences in pain and its relief. Ann Ist Super Sanita 2016, 52(2):184-189.\u003c/li\u003e\n\u003cli\u003eTinnirello A, Mazzoleni S, Santi C: Chronic Pain in the Elderly: Mechanisms and Distinctive Features. Biomolecules 2021, 11(8):1256.\u003c/li\u003e\n\u003cli\u003eZimney KJ, Louw A, Roosa C, Maiers N, Sumner K, Cox T: Cross-sectional analysis of generational differences in pain attitudes and beliefs of patients receiving physical therapy care in outpatient clinics. Musculoskelet Sci Pract 2022, 62:102682.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-palliative-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcar","sideBox":"Learn more about [BMC Palliative Care](http://bmcpalliatcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pcar/default.aspx","title":"BMC Palliative Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"palliative care, frail elderly, pain, symptom assessment, pain management, clinical decision rules","lastPublishedDoi":"10.21203/rs.3.rs-4137378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4137378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePain assessment is a necessary step in pain management in older people in palliative care. In older people, pain assessment can be challenging due to underreporting and atypical pain manifestations by other distressing symptoms. Anxiety, fatigue, loss of appetite, insomnia, dyspnoea, and bowel problems correlate with pain in palliative care patients. Insight into these symptoms as predictors may help to identify the underlying presence of pain. This study aimed to develop a prediction model for pain in independently living frail older people in palliative care.\u003cbr\u003e\n\u003cstrong\u003eMethods:\u003c/strong\u003e In this cross-sectional observational study, community-care nurses from multiple organizations across the Netherlands included eligible patients (life expectancy \u0026lt; 1 year, aged 65+, independently living and frail). The outcome pain and symptoms were assessed by means of the Utrecht Symptom Diary. Also, demographic and illness information, including relevant covariates age, sex and living situation, was collected. Multivariable logistic regression and minimum Akaike Information Criterion(AIC) were used for model development and Receiver Operating Characteristics(ROC)-analysis for model performance. Additionally, predicted probability of pain are given for groups differing in age and sex.\u003cbr\u003e\n\u003cstrong\u003eResults: \u003c/strong\u003eA total of 157 patients were included. The final model consisted of insomnia(Odds Ratio[OR]=2.13, 95% Confidence Interval[CI]=1.013-1.300), fatigue(OR=3.47, 95% CI=1.107-1.431), sex(female)(OR=3.83, 95% CI=2.111-9.806) and age(OR=-1.59, 95% CI=0.922-1.008) as predicting variables. There is an overall decreasing trend for age, older persons suffer less from pain and females have a higher probability of experiencing pain. Model performance was indicated as fair with a sensitivity of 0.74(95% CI=0.64-0.83) and a positive predictive value of 0.80(95% CI=0.70-0.88).\u003cbr\u003e\n\u003cstrong\u003eConclusion:\u003c/strong\u003e Insomnia and fatigue are predicting symptoms for pain, especially in women and younger patients. The use of a symptom diary in primary care can support the identification of pain.\u003c/p\u003e","manuscriptTitle":"A predictive model of symptoms for pain in independently living frail older people in palliative care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 18:11:26","doi":"10.21203/rs.3.rs-4137378/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-23T02:03:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-21T17:45:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-02-17T05:51:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T12:25:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4270556882693513077886500109155869075","date":"2024-06-10T06:01:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336459126669151804440525366901303754984","date":"2024-05-29T10:12:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316138038655178726808816972483283870760","date":"2024-05-27T16:37:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18894841083866347450637050608714580667","date":"2024-05-19T18:27:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-16T08:40:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-21T08:38:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-21T07:31:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-21T07:31:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Palliative Care","date":"2024-03-20T13:06:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-palliative-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcar","sideBox":"Learn more about [BMC Palliative Care](http://bmcpalliatcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pcar/default.aspx","title":"BMC Palliative Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79fee9ac-38ba-4bdb-a80b-341ec0c1952c","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:08:42+00:00","versionOfRecord":{"articleIdentity":"rs-4137378","link":"https://doi.org/10.1186/s12904-025-01888-y","journal":{"identity":"bmc-palliative-care","isVorOnly":false,"title":"BMC Palliative Care"},"publishedOn":"2025-10-14 15:57:32","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2024-03-26 18:11:26","video":"","vorDoi":"10.1186/s12904-025-01888-y","vorDoiUrl":"https://doi.org/10.1186/s12904-025-01888-y","workflowStages":[]},"version":"v1","identity":"rs-4137378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4137378","identity":"rs-4137378","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}