Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan

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There are many factors that influence people with HTN to develop cardiovascular disease, such as lifestyle, genetics, economics, overall health, and medications; the authors did not find any previous study employing Multi-Attribute Utility Theory (MAUT) analysis using machine learning. There is a sense of urgency to understand the characteristics of HTN patients in Pakistan; by using MAUT to analyze and evaluate different factors related to developing HTN, we can gain valuable insights into the characteristics of HTN and cardiovascular HTN (HTN-CVD) patients in Pakistan. Methods and results This study utilized a cross-sectional survey with 98 participants diagnosed with HTN and HTN-CVD. The questionnaire included sociodemographic items, symptoms, heredity factors, and dietary habits. The study was approved by the Research Ethical Committees of Pir Mehr Ali Shah Arid Agriculture University Rawalpindi (PMAS-AAUR), Pakistan. The result shows that there is a wide difference between HTN and HTN-CVD patients’ scores for symptoms (37% vs. 63%) and economic status (39% vs. 61%) (respectively). Other than these, the attribute proportions of scores for HTN and HTN-CVD are almost the same. The highest MAUT scores were higher for patients with HTN-CVD (373) than for those with HTN alone (270), and the mean age of HTN patients is higher than HTN-CVD; almost all attributes’ groups of the 10 highest MAUT scores of HTN-CVD higher than HTN patients except for attribute of medication status. Conclusion Patients with HTN-CVD complications have higher MAUT scores for lifestyle, social status, health status, and economic status. This emphasizes the importance of educating patients (and indeed the general public) about risks, symptoms, and adopting healthy behaviors. 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F1000Research 2024, 13 :766 ( https://doi.org/10.12688/f1000research.151569.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan [version 1; peer review: awaiting peer review] Sania Rauf 1,2 , Desy Nuryunarsih https://orcid.org/0000-0002-5306-0467 3 , Heni Pujiastuti 4 , [...] Mahija Zaidan 5 , Lucky Herawati 6 , Okatiranti Okatiranti 7 , Abida Arshad 1 , Syed Shakeel Raza Rizvi 1 Sania Rauf 1,2 , Desy Nuryunarsih https://orcid.org/0000-0002-5306-0467 3 , [...] Heni Pujiastuti 4 , Mahija Zaidan 5 , Lucky Herawati 6 , Okatiranti Okatiranti 7 , Abida Arshad 1 , Syed Shakeel Raza Rizvi 1 PUBLISHED 08 Jul 2024 Author details Author details 1 Department Zoology, Wildlife, and Fisheries, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Rawalpindi, Rawalpindi, Pakistan 2 Department of Biosciences, University of Wah, Wah Cantt., Punjab, Pakistan 3 Faculty of Medical Sciences, Newcastle University Population Health Sciences Institute, Newcastle upon Tyne, England, UK 4 Department of Midwifery, Health Polytechnic, Ministry of Health, Yogyakarta, Yogyakarta, Indonesia 5 School of Law and Social Science, University of Nottingham, Nottingham, England, UK 6 Nursing Department, Health Polytechnic, Ministry of Health, Yogyakarta, Yogyakarta, Indonesia 7 School of Medicines, University of Nottingham, Nottingham, England, UK Sania Rauf Roles: Data Curation, Methodology, Project Administration, Writing – Original Draft Preparation Desy Nuryunarsih Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Heni Pujiastuti Roles: Data Curation, Methodology, Project Administration, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Mahija Zaidan Roles: Data Curation, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Lucky Herawati Roles: Conceptualization, Formal Analysis, Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Okatiranti Okatiranti Roles: Supervision, Writing – Review & Editing Abida Arshad Roles: Supervision, Writing – Review & Editing Syed Shakeel Raza Rizvi Roles: Supervision, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Abstract Background and aim Hypertension (HTN) is a significant public health concern worldwide, affecting approximately a third of people during their lifetimes. There are many factors that influence people with HTN to develop cardiovascular disease, such as lifestyle, genetics, economics, overall health, and medications; the authors did not find any previous study employing Multi-Attribute Utility Theory (MAUT) analysis using machine learning. There is a sense of urgency to understand the characteristics of HTN patients in Pakistan; by using MAUT to analyze and evaluate different factors related to developing HTN, we can gain valuable insights into the characteristics of HTN and cardiovascular HTN (HTN-CVD) patients in Pakistan. Methods and results This study utilized a cross-sectional survey with 98 participants diagnosed with HTN and HTN-CVD. The questionnaire included sociodemographic items, symptoms, heredity factors, and dietary habits. The study was approved by the Research Ethical Committees of Pir Mehr Ali Shah Arid Agriculture University Rawalpindi (PMAS-AAUR), Pakistan. The result shows that there is a wide difference between HTN and HTN-CVD patients’ scores for symptoms (37% vs. 63%) and economic status (39% vs. 61%) (respectively). Other than these, the attribute proportions of scores for HTN and HTN-CVD are almost the same. The highest MAUT scores were higher for patients with HTN-CVD (373) than for those with HTN alone (270), and the mean age of HTN patients is higher than HTN-CVD; almost all attributes’ groups of the 10 highest MAUT scores of HTN-CVD higher than HTN patients except for attribute of medication status. Conclusion Patients with HTN-CVD complications have higher MAUT scores for lifestyle, social status, health status, and economic status. This emphasizes the importance of educating patients (and indeed the general public) about risks, symptoms, and adopting healthy behaviors. READ ALL READ LESS Keywords Multi-attribute utility theory, hypertension, cardiovascular, Pakistan, machine learning. Corresponding Author(s) Desy Nuryunarsih ( [email protected] ) Close Corresponding author: Desy Nuryunarsih Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Rauf S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Rauf S, Nuryunarsih D, Pujiastuti H et al. Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :766 ( https://doi.org/10.12688/f1000research.151569.1 ) First published: 08 Jul 2024, 13 :766 ( https://doi.org/10.12688/f1000research.151569.1 ) Latest published: 08 Jul 2024, 13 :766 ( https://doi.org/10.12688/f1000research.151569.1 ) Introduction Hypertension (HTN) Hypertension (HTN) is a significant public health concern worldwide, affecting approximately a third of people during their lifetimes and diagnosed in an estimated 1.13 billion people as of 2021, according to the World Health Organization (WHO). 1 In 2000, about 26.4% of the adult population had HTN, with 26.6% of men and 26.1% of women affected; by 2025, it is projected that 29.2% of the population will have HTN, with 29.0% of men and 29.5% of women being affected. 2 HTN in Pakistan Developing countries exhibit inevitably increasing HTN prevalence, and earlier ages of cardiovascular disease and HTN onset and diagnosis. 3 The second National Diabetes Survey of Pakistan (NDSP) reported the urgency of the alarmingly high occurrence of HTN in the country in 2016, with age-adjusted weighted prevalence of HTN of 46.2%, of whom 24.9% had longstanding diagnoses, while 21.3% were newly diagnosed. 4 Perhaps counterintuitively, HTN prevalence was slightly higher in rural areas (46.8%) compared to urban areas (44.3%), despite common assumptions of healthier and more active lifestyles offsetting HTN in the former. The highest regional prevalence was in the Punjab province (49.2%), followed by Sindh (46.3%), Baluchistan (40.9%), and Khyber Pakhtunkhwa (33.3%). It is important to note that cardiovascular disease is continuously evolving and is projected to be the leading cause of death by 2030. HTN is often associated with cardiovascular disease, and various behavioral and socio-demographic factors are connected to both conditions. 5 – 7 In this context, studies of particular context can offer insights for healthcare professionals and public health systems to address widespread and systemic issues contributing to local and global care burdens. HTN and lifestyle HTN poses a major global health challenge, and innumerable studies have been conducted over many decades to understand HTN causes. Recent studies concur that lifestyle factors fundamentally influence HTN, 6 – 8 encompassing obesity, excessive fat and alcohol consumption, physical inactivity, smoking, and inadequate nutritional intake (e.g., fruits, vegetables, whole grains, and pulses). 8 – 10 HTN and socioeconomic problems Socioeconomic challenges are intertwined with HTN; some socioeconomic factors are associated with HTN etiology, while HTN itself can result in socioeconomic issues (e.g., affecting employment status) and downstream health impacts for individuals, families, and communities. To help address health disparities globally and locally, the WHO-commissioned Centre for Urban Health created a document called “The Solid Facts” to raise awareness, encourage discussion, and inspire action on the social determinants of health. It identifies ten factors: social gradient, stress, early life experiences, social exclusion, employment, unemployment, social support, addiction, access to nutritious food, and transportation. 11 HTN and genetics The genetic and environmental factors that make someone more likely to develop HTN are often shared among family members; thus, the family history of HTN is used as a predictor of individual risk, alongside other risk factors. A study conducted in Sri Lanka involving 5000 adults revealed that HTN prevalence was significantly higher in individuals with a family history (29.3%, n = 572 out of 1951) compared to those without (24.4%, n = 616 out of 2530) (p < 0.001). A family history significantly increases the risk of HTN (OR: 1.29; 95% CI: 1.13-1.47). 12 HTN and medication (MS) Lowering blood pressure (BP) is effective in reducing the risks of cardiovascular diseases. It is crucial to maintain a systolic blood pressure (SBP) below 130 mm Hg to prevent complications in patients with heart failure, diabetes, coronary artery disease, stroke, and other cardiovascular conditions. Therefore, the selection of appropriate antihypertensive medications is vital. Some common BP medicines include ACE inhibitors, angiotensin-2 receptor blockers, calcium channel blockers, diuretics, beta-blockers, alpha-blockers, and other diuretics. 13 , 14 HTN crisis, emergency and symptoms (SS) HTN can cause fatigue, headaches, a heavy feeling in the neck, dizziness, blurred vision, palpitations, ringing in the ears, and nosebleeds. Patients with HTN who have poor lifestyle habits and coping skills are more likely to experience emotional disturbances, leading to feelings of tension, anxious thoughts, and physical changes such as increased BP and its associated impacts. 15 , 16 Hypertensive emergencies and crises are common medical emergencies, accounting for over 25% of all emergency cases. 17 , 18 Due to their high prevalence, all physicians need to be well-prepared in identifying and distinguishing between urgent and emergent HTN. A hypertensive crisis refers to a sudden increase in BP (≥ 180/120 mmHg) accompanied by symptoms that can range from mild, such as headache and dizziness, to severe, such as dyspnea, chest pain, coma, and even death. If the symptoms are mild and there is no acute target organ damage, it is considered a hypertensive crisis. However, if the symptoms pose a life-threatening risk to the patient and there is acute target organ damage, it is classified as a hypertensive emergency. 17 HTN and health status HTN and its treatment with medication have been found to significantly impact reported health status. HTN is defined as having a SBP (≥140/90 mm Hg or ≥130/80 mm Hg); such elevated SBP has significant impacts, and both systolic and diastolic HTN independently influence the risk of adverse cardiovascular events, regardless of the HTN definition. 19 Literature gap and study rationale Based on the factors mentioned above that could influence people with HTN to develop cardiovascular disease, such as lifestyle, genetics, economics, overall health, and medications, the authors did not find any previous study employing Multi-Attribute Utility Theory (MAUT) with machine learning. The objective of this therefore to analyze data for HTN and HTN-CVD patients MAUT analysis, identifying attributes of lifestyle, symptoms, health status, genetic factors, social status, and medication status attributes that frequently appear and risk factors. By understanding these patterns and associations, we aim to enhance our knowledge of the relationship between risk groups and the presence of HTN, providing valuable insights for patient care. Objective Based on the above information, there is a sense of urgency to understand the characteristics of HTN patients in Pakistan; by using MAUT to analyze and evaluate different factors related to developing HTN, we can gain valuable insights into the characteristics of HTN patients in Pakistan. This analysis can help policymakers and healthcare professionals make evidence-based decisions on which policies and interventions would benefit HTN patients in Pakistan the most. This provides a systematic approach to understanding the complex interplay of various factors and can guide the development of targeted interventions and strategies. Methods Quantitative questionnaire This study utilized a cross-sectional survey with 98 participants diagnosed with HTN and cardiovascular HTN (HTN-CVD). The variables of interest were participants’ characteristics and HTN scores. The characteristics of respondents comprised the attributes of lifestyle, socioeconomic factors, symptoms, genetics, medication, and health status. The questionnaire thus included sociodemographic items such as age, job, and income, and asked about health status, symptoms, hereditary factors, dietary habits, and age range (participants were aged from 19 to 60 years). Data analysis was undertaken using MAUT . The patients were briefed about the purpose and protocol of the study, and their written consent (in English/Urdu) was taken for inclusion in the study. The patients examined at Military Hospital (MH) Rawalpindi, Pakistan, and Fauji Foundation Hospital (FFH), Rawalpindi, Pakistan were selected for the current investigation. Inclusion criteria The male and female cardiovascular disease patients visiting MH and FFH Rawalpindi, Pakistan, with symptoms of high BP like headache, dizziness, blurred vision, chest pain, shortness of breath, nausea, sleep apnea was included in the present study. Exclusion criteria Patients with neurological diseases, chronic renal impairment, known psychological illnesses, asthma and pregnancy, alcoholics, advanced hepatic and renal insufficiency, and suffering from any other endocrinological disorder were excluded from the study. Data preparation The BP of hypertensive cardiovascular patients was measured by using a sphygmomanometer. Hypertensive patients were identified on the basis of three measured values of SBP higher than 140 mmHg and three measured values of diastolic blood pressure (DBP) higher than 90 mmHg, or those patients taking hypertensive medicines. The height of hypertensive cardiovascular patients and normal subjects was measured using a Harpenden Stadiometer with a precision of 0.1 cm. The weight of the boys was determined through a digital weight scale as close as 0.1 Kg. The determination of BMI was carried out through Cole et al.’s equation 20 : BMI = Weight ( kg ) Height ( m 2 ) Data analysis Multi-Attribute Utility Theory (MAUT) The MAUT approach involves assigning a weight to each dimension of an object and combining it with a relevant value to determine the overall evaluation; this evaluation is commonly referred to as the utility value. MAUT helps convert multiple interests into a numerical value on a scale of 0 to 1, whereby 0 represents the worst choice, and 1 represents the best. This allows for a direct comparison of different options. Ultimately, MAUT provides a ranking order of alternative evaluations that reflects the decision-maker’s choices. 21 MAUT procedure This analysis model consists of several steps: first, we identify relevant attributes for the analysis, such as lifestyle, economic factors, social status, genetics, symptoms of patients, and health status attributes, and assign a point of the score for each of these attribute’s variables based on several references from previous studies looking into the correlation between those attributes to HTN and the HTN-cardiovascular problem with a maximum of score one for the most support reasonable lifestyle/less risk genetic/most advantage socio-economic factors/no symptoms/good health status and minimum of score zero for the opposite condition. We then conducted a multi-variable regression analysis, Shapley additive explanation (SHAP), and feature selection technique for comparison, and assigned weights for those groups of attribute variables based on their contribution to or correlation with HTN and cardiovascular symptoms, with a maximum weight of 3 indicating the greatest correlation. Several attributes are not statistically significant, thus we hand pick those attributes based on discussion with an expert in HTN and HTN-cardiovascular patients. The total attribute scores and weight scores were calculated with the formula: V ( x ) = ∑ i = 1 n Wj . Xij … … … … . . Where V(χ) is the overall value of each of the HTN or HTN cardiovascular patients, Wj is the weight of the criteria, and Xi is the value of the attribute score. 22 We calculated the overall score by multiplying each attribute score by its corresponding weight and summing it up to get each case’s overall score. This enables comparison and ranking of cases based on their scores. Finally, we interpreted the results by analyzing the scores and identifying patterns, trends, or relationships between the attributes and the presence of HTN or cardiovascular disease. This helps understand the factors that contribute to these conditions. Figure 1 displays a summary the MAUT procedure used in this study. Figure 1. MAUT analysis procedure in this study. Analytical tools The MAUT analysis was developed using Python 3.7 (Python Software Foundation, Wilmington, DE, USA). We used libraries “pandas”, “numpy”, “statsmodels” and”shap”. The calculation was performed, and the data was visualized using the “seaborn" and “matplotlib” libraries. Statistical analysis In the statistical analysis, we presented demographic data as counts (percentages). Age data had two missing values and two outliers. We filled the missing values with median values and addressed outliers using the normal data range. Python version 3.7 was utilized for data preparation and statistical analysis. In our study for the MAUT analysis, the sample size of participants allows us to assess their suitability by considering the complexity of our model and the attributes we are analyzing. It is crucial to focus on the quality and relevance of these attributes rather than just the number of participants. Additionally, we employed feature selection techniques to enhance the robustness of our results with this sample size. Ethical permission The study was approved by the institutional board of Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Ethics Committee (No. PMAS-AAUR/1406) Results Table 1 shows that 98 patients with HTN and HTN-CVD participated in the study, ranging in age from 19 to 60. The youngest participant diagnosed with HTN was 13 years old, and the oldest was 56. In the CVD group, the youngest was 22, and the oldest was 60. The mean ages for each category were 51, 41, and 47 years old for general, HTN, and HTN-CVD, respectively. Table 1. Sociodemographic and clinical characteristics of participants. Description Age Age of HTN Age of CVD Count 98 11 87 mean 51.13 41.09 46.95 std 9.44 17.3 9.14 min 19.00 13.00 22.00 25% 19.00 24.00 40.25 50% 47.00 37.00 50.00 75% 59.00 47.50 54.00 max 60.00 56.00 60.00 Tables 2 show the weight and scores of each attribute determined through the multivariate regression analysis. The data indicates no strong correlation between the attributes and the diagnosis of HTN and HTN-CVD. The p-value is not significant for attributes such as pulses, vegetable, income, blurred vision, sleep apnea, chest pain, ingestion, feeling weak, family having CVD, direct relationship family having CVD and SBP. Tables 2. The weight and scores of each attribute. No Attribute weight Correlation score Score p-value Lifestyle 1. Having Diet plan 0.108 2 2.889879e-02* No 1 Yes 0 2. Meal/day 0.036 1 7.241356e-03* No calory restriction 1 Having calory restriction 0 3. Having a fat diet daily 0.221 3 2.877233e-02* No 1 Yes 0 4. Having meat daily 0.051 1 6.157459e-03* Less than 50 g daily or more than 100 g less than 50–100 g (one to two servings) per day of unprocessed red meat 1 or recommendation of zero to less than 50 g (one serving) per day of processed red meat to reduce the risk of HTN and CVD 23 0 5. Having chicken daily 0.148 2 1.450373e-02* Non-daily consumption 1 Daily 0 6. Having Fish (mackerel, sardine, mussels, salmon) 0.152 2 1.340766e-02* Non-daily consumption 1 consuming daily 0 7. Having pulses daily 8 0.036 1 7.241356e-02 Non-daily 0 Daily 1 8. Vegetable 0.036 1 7.241356e-02 Non-daily 1 Daily 0 9. Fruits 0.071 1 8.242655e-03* Non-daily 1 Daily 0 10. Physical activity 0.265 3 4.877168e-02* No 1 Yes, more than 30 minutes daily 0 11. Smoking or snuffing 0.165 2 1.039092e-02* Yes or less than 5 years stop 1 No 0 Symptoms 1. Dizziness 0.055 1 5.876944e-03* Yes 1 No 0 2. Blurred vision 0.006 1 9.534218e-02* Yes 1 No 0 3. Nausea 0.012 1 2.740167e-02* Yes 1 No 0 4. Sleep apnea 0.039 1 6.997248e-02 Yes 1 No 0 5. Chest pain 0.023 1 8.220504e-02 Yes 1 No 0 6. Breath 0.040 2 2.448521e-02* Yes 1 No 0 7. Indigestion 0.061 1 5.506504e-02* Yes 1 No 0 8. Palpitations 0.032 1 7.550983e-03* Yes 1 No 0 9. Pain/Discomfort (neck,jaw,back) 0.05 1 6.204327e-03* Yes 1 No 0 10. Feeling weak 0.004 1 6.644674e-02* Yes 1 No 0 Genes 1. Family Having hypertension. 0.062 1 5.470787e-03* Yes 1 No 0 2. Direct relationship to the hypertension person (Mother, father, brother, sister) 0.062 1 5.470787e-03* Yes 1 No 0 3. Family Having cardiovascular. 0.019 1 8.552685e-02 Yes 1 No 0 4. Direct relationship to the hypertension-cardiovascular person (Mother, father, brother, sister) 0.019 1 8.552685e-02 Yes 1 No 0 Medications 1. Treatment 0.163 2 1.087632e-02* No 1 Yes 0 2. Combination treatment 0.163 2 1.087632e-02* No 1 Yes 0 Health status 1. Health status 0.106 2 2.988307e-02* Not healthy 1 Healthy 0 2. BMI 0.119 3 9.689823e-03* Underweight or overweight less than 18.5 and more than 24.9 1 Normal 0 3. Blood pressure at the time checking 0.112 2 9.062792e-03* more than 120 1 SBP Normal 0 4. Blood pressure at the time checking 0.119 2 2.448521e-02* more than 80 0 DBP Normal 1 Tables 3 and 4 show the participants diagnosed with HTN who obtained the highest total MAUT scores. These participants are numbered 1, 72, 87, 5, 94, 97, 30, 51, 95, and 23, with total scores ranging from 20 to 32. The scores are broken down into scores for lifestyle (ranging from 4 to 14), symptom (ranging from 2 to 11), genetics (ranging from 0 to 4), medication (ranging from 0 to 4), health status (ranging from 1 to 5), and economic status (ranging from 0 to 3). Table 3. Ten participants with the highest MAUT score (HTN patients). No Participant Number Rank Age Gender Score 1. 1 1 28 M 32 2. 72 2 52 M 32 3. 87 3 60 M 32 4. 5 4 40 M 31 5. 94 5 37 M 28 6. 97 6 28 M 26 7. 30 7 47 M 25 8. 51 8 53 M 22 9. 95 9 35 M 22 10. 23 10 50 M 20 Table 4. Comparison of participants with highest MAUT scores based on their attributes (HTN patients). No Participant Number Lifestyle score Symptoms score Genetic score Medication score Health status score Economy status MAUT total 1. 1 9 11 4 0 5 3 32 2. 72 11 11 4 4 1 1 32 3. 87 13 11 0 0 5 3 32 4. 5 7 10 4 4 5 1 31 5. 94 14 7 2 0 3 2 28 6. 97 12 5 2 0 4 3 26 7. 30 4 10 2 4 5 0 25 8. 51 14 4 0 0 2 2 22 9. 95 12 2 4 0 4 0 22 10. 23 5 9 0 0 4 2 20 Figure 2 show MAUT scores for each attribute patient hypertension. Figure 2. MAUT scores for each attribute patient hypertension. MAUT_LS: MAUT score participants based of lifestyle factor, MAUT_SS: MAUT score participants based on their symptoms status, MAUT_GS: MAUT score participants based on their genes factors, MAUT_HS: MAUT score participant based on their health status, MAUT_ES: MAUT score participants based on their economic status. On the other hand, Tables 5 and 6 present the ten participants with HTN-CVD who achieved the highest MAUT scores. These participants are numbered 84, 76, 6, 82, 78, 83, 77, 33, 89, and 80, with total scores ranging from 36 to 39. The scores are further categorized into scores for lifestyle (ranging from 10 to 15), economic status (ranging from 12 to 14), genetics (ranging from 0 to 4), medication (ranging from 0 to 4), health status (ranging from 2 to 6), and economic status (ranging from 2 to 3). Table 5. Ten participants with the highest MAUT score, HTN and cardiovascular disease (HTN-CVD). No Participant Number Rank Age Gender Score 1. 84 1 59 M 39 2. 76 2 51 M 39 3. 6 3 38 M 38 4. 82 4 60 M 38 5. 78 5 60 M 38 6. 83 6 60 M 37 7. 77 7 51 M 36 8. 33 8 56 M 36 9. 89 9 60 M 36 10. 80 10 60 M 36 Table 6. Comparison of participants with highest MAUT scores based on lifestyle and symptoms (HTN-CVD. No Patient Number Life style Symptoms Genetic status Medication status Health status Economic status MAUT_total 1. 77 11 14 4 4 4 2 39 2. 85 12 14 0 4 6 3 39 3. 7 13 14 4 0 5 2 38 4. 79 11 14 4 0 6 3 38 5. 83 12 14 4 0 5 3 38 6. 84 12 14 4 0 5 2 37 7. 34 12 12 0 4 5 3 36 8. 59 15 12 4 0 2 3 36 9. 78 11 14 4 0 5 2 36 10. 81 10 14 4 0 5 3 36 Figure 3 show MAUT scores for each attribute patient hypertension and cardiovascular. Figure 3. MAUT scores for each attribute patient hypertension cardiovascular. MAUT_LS: MAUT score participants based of lifestyle factor, MAUT_SS: MAUT score participants based on their symptoms status, MAUT_GS: MAUT score participants based on their genes factors, MAUT_HS: MAUT score participant based on their health status, MAUT_ES: MAUT score participants based on their economic status. Table 7 shows the MAUT scores for HTN-CVD patients. It can be seen that higher scores are associated with older age. Looking at the specific attributes, HTN-CVD patients have higher scores for lifestyle, social status, health status, and economic status. The total MAUT score for HTN-CVD is 101 compared to 119 for lifestyle, 80 compared to 136 for social status, 22 compared to 32 for genetic factors, 38 compared to 48 for health status, 17 compared to 26 for economic status, and a total of 270 compared to 373 overall. However, the medication status has the same score of 12. The average age for the highest MAUT score patients, CVD has a higher average of 55.5, and HTN only has 43. All of those 20 highest score both MAUT for HTN and HTN-CVD are male. Table 7. The ten highest-scoring attributes are associated with HTN and HTN-CVD. No Patient Number Age mean Life style Social status Genetic status Medication status Health status Economic status MAUT total 1. HTN 43 101 80 22 12 38 17 270 2. HTN-CVD 55.5 119 136 32 12 48 26 373 Figure 4 shows a swarm plot illustrating participants’ highest MAUT scores for HTN across lifestyle, symptoms, genetics, medication, and health status. It also highlights the range of MAUT scores for HTN-CVD, showing both the lowest and highest scores. Figure 4. Comparison of patient MAUT scores by diagnosis. A paired T-test analysis between HTN and HTN-CVD participant’s MAUT score shows that a relatively high p-value (0.441), indicating that the observed difference is insignificant. The confidence interval (-0.5833, 2.4726) gives us a range of values within which we can be confident that the actual mean difference between the two groups lies. In this case, the confidence interval is relatively wide, indicating much uncertainty in estimating the actual mean difference. Based on these results, we can conclude that no substantial evidence suggests a significant difference between the two groups regarding the total MAUT score. Figure 5 displays the distribution of MAUT scores for the top 10 participants with HTN and HTN-CVD. It reveals that the HTN participants have a wider range of scores, while the HTN-CVD scores are more tightly clustered, and are generally higher. Figure 5. Distribution of ten highest MAUT scores (HTN and HTN-CVD). Figure 6 displays the percentage distributions of each attribute for HTN and HTN-CVD. As we can see, CVD-HTN has higher percentages for all attributes than HTN. Additionally, HTN participants have fewer symptoms, economic status and genetic scores compared to HTN-CVD. Figure 6. Comparison of score distribution between HTN and HTN-CVD patients. Regarding score distribution, there is a wide difference between HTN and HTN-CVD patients’ scores for symptoms (37% vs. 63%) and economic status (39% vs. 61%) (respectively). Other than these, the attribute proportions of scores for HTN and HTN-CVD are almost the same. Discussion In this study, we invited 98 patients of HTN and HTN-CVD from MH and FFH Rawalpindi Hospital, Pakistan. There were 11 patients with HTN and 86 patients with HTN-CVD. When we conducted a multi-variable regression analysis, the p-value was not significant for attributes such as having pulses daily, eating vegetable daily, sleep apnea, chest pain, family having CVD, direct relationship to HTN-HTN-CVD. The small sample size could be why the p-values are only significant for specific attributes in the multi-variable regression analysis. With a smaller sample size, there may be limited statistical power to detect significant associations between variables. However, we have seen that those attributes as mentioned above have a strong relationship with the outcome of studies on HTN patients, 6 , 8 , 10 , 23 – 32 which served as our basis to continue the data analysis using MAUT. Participants in this study were aged from 19 to 60 years’ old, with the youngest and oldest participants being 13 and 56 in the HTN group; and 22 and 60 in the HTN-CVD group (respectively). The mean ages for each category were 51, 41, and 47 years’ old for general, HTN, and HTN-CVD, respectively. The average age difference between patients’ HTN is younger than the HTN-CVD 41 and 47; as people get older, stroke and heart disease can come with various complications that arise from prolonged exposure to high BP. This could be due to the organs becoming more resistant, or even slight increases in BP that have been present for a long time 33 ; if people can prevent BP from increasing as they age, it would significantly reduce the typical vascular problems associated with aging. People who develop high BP later in life are not at a higher risk for cardiovascular disease earlier in life. 34 In this study, we found several attributes that showed a stronger correlation with HTN and HTN-CVD. These included having a diet plan, consuming a high-fat diet daily, eating chicken and fish daily, engaging in physical activity for more than 30 minutes, smoking or using snuff per day, having a physically demanding job, experiencing difficulty breathing, receiving treatment, receiving a combination of treatments, overall health status, BMI, and BP (SBP and DBP) at the time of checking. These attributes were given higher weights of 2-3 points in our analysis. When we analyze the MAUT scores for HTN-CVD patients, we see that higher scores are associated with older age. Looking at the specific attributes, HTN-CVD patients have higher scores in lifestyle, social status, health status, and economic status. The total MAUT score for HTN-CVD is 101 compared to 119 for lifestyle, 80 compared to 136 for social status, 22 compared to 32 for genetic factors, 38 compared to 48 for health status, 17 compared to 26 for economic status, and a total of 270 compared to 373 overall. However, the medication status has the same score of 12. In terms of average age for the highest MAUT score patients, CVD has a higher average of 55.5 and HTN only 43. Implications of the study Studies have shown that patients with complications of HTN cardiovascular have higher MAUT scores in lifestyle, social status, health status, and economic status. This indicates that individuals with CVD tend to have higher scores in these areas. These findings emphasize the importance of educating individuals about the risks, symptoms, and the significance of adopting healthy behaviors. It also highlights the need to pay more attention to individuals who may be disadvantaged by socioeconomic factors. Early detection and management of HTN and CVD can be facilitated through BP monitoring, while access to affordable and quality healthcare services is crucial. 11 Our study revealed that cardiovascular disease can begin as early as 13 years old, and genetic factors play a significant role in HTN risk. 35 Lifestyle modifications, such as promoting healthy choices through public health initiatives, encourage individuals to follow a balanced diet, engage in regular physical activity, and avoid or quit tobacco consumption. These modifications can help prevent and manage HTN and CVD. 36 In terms of policy, the findings from this study suggest the importance of implementing strategies that address the identified attributes with stronger correlations to HTN and HTN-CVD. Policies could focus on promoting healthy eating habits, providing resources for physical activity, implementing smoking cessation programs, considering the physical demands of specific jobs, and ensuring access to appropriate treatment and healthcare services. 37 By incorporating these measures into policies, we can work towards reducing the prevalence and impact of HTN and HTN-CVD in the population. Summary The MAUT scores were higher for patients with HTN-CVD (373) than for those with HTN alone (270), and there is mean age of HTN patients is higher than HTN-CVD, almost all attributes of 10 highest MAUT score of HTN-CVD higher than HTN patients except for attribute of medication status. Patients with HTN cardiovascular complications have higher MAUT scores for lifestyle, social status, health status, and economic status. This emphasizes the importance of educating patients (and indeed the general public) about risks, symptoms, and adopting healthy behaviors. Early detection and management are facilitated through BP monitoring and access to quality healthcare. Lifestyle changes like promoting healthy choices can prevent and manage HTN and CVD. Health policies should focus on promoting healthy eating, physical activity, smoking cessation, and ensuring access to treatment and healthcare services. Author contribution All authors contributed to the manuscript. SR collected data, curated data, wrote, reviewed, and edited. DN curated data, conducted analysis, interpreted data, wrote results, reviewed, and edited. HW curated data, wrote drafts, interpreted data, reviewed, and edited. MZ: handled project administration, curated data, and edited manuscripts. LH: wrote, supervised, reviewed, and edited. OO, AA, and SSR also wrote, supervised, reviewed, and edited. All authors read and approved the final version of the manuscript. Ethical permission The study was approved by the institutional board of Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Ethics Committee (No. PMAS-AAUR/1406) for the use of human subjects, and informed consent was obtained from all individual participants. Consent to participant The patients were briefed about the study's purpose and protocol, and their written consent (in English/Urdu) was obtained for inclusion in the study. Data availability These data are confidential and can only be accessed by internal study members for analysis. External researchers can request access to specific projects through the MH and FFH Rawalpindi Hospital. Interested parties should contact the corresponding author for further discussion at her email [email protected] . 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PubMed Abstract | Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 08 Jul 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department Zoology, Wildlife, and Fisheries, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Rawalpindi, Rawalpindi, Pakistan 2 Department of Biosciences, University of Wah, Wah Cantt., Punjab, Pakistan 3 Faculty of Medical Sciences, Newcastle University Population Health Sciences Institute, Newcastle upon Tyne, England, UK 4 Department of Midwifery, Health Polytechnic, Ministry of Health, Yogyakarta, Yogyakarta, Indonesia 5 School of Law and Social Science, University of Nottingham, Nottingham, England, UK 6 Nursing Department, Health Polytechnic, Ministry of Health, Yogyakarta, Yogyakarta, Indonesia 7 School of Medicines, University of Nottingham, Nottingham, England, UK Sania Rauf Roles: Data Curation, Methodology, Project Administration, Writing – Original Draft Preparation Desy Nuryunarsih Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Heni Pujiastuti Roles: Data Curation, Methodology, Project Administration, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Mahija Zaidan Roles: Data Curation, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Lucky Herawati Roles: Conceptualization, Formal Analysis, Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Okatiranti Okatiranti Roles: Supervision, Writing – Review & Editing Abida Arshad Roles: Supervision, Writing – Review & Editing Syed Shakeel Raza Rizvi Roles: Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 08 Jul 2024, 13:766 https://doi.org/10.12688/f1000research.151569.1 Copyright © 2024 Rauf S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Rauf S, Nuryunarsih D, Pujiastuti H et al. Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :766 ( https://doi.org/10.12688/f1000research.151569.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: AWAITING PEER REVIEW AWAITING PEER REVIEW ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 08 Jul 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status AWAITING PEER REVIEW Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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