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Association Between Multimorbidity and Quality of Life After

1. JOURNAL READING Pembimbing : dr . Hj.Sherliyanah,M.Si.Med ., Sp.An ASSOCIATION BETWEEN MULTIMORBIDITY AND QUALITY OF LIFE AFTER HIP REPLACEMENT SURGERY: ANALYSIS OF ROUTINELY COLLECTED PATIENT-REPORTED OUTCOMES I GUSTI BAGUS TANAYA KASHIBARA ARYAWANGSA 019.06.0036 )

2. ABSTRACT Total hip replacement surgery is performed to improve quality of life (QoL). We explored the association between multimorbidity and change in QoL after total hip replacement. Background Analysis of patients included in the NHS England hip replacement Patient Reported Outcome Measures (PROMs) database with complete preoperative from 3 to 6 months postoperative EQ-5D QoL data from April 2013 to March 2018. Multimorbidity was defined as two or more chronic diseases excluding arthritis. The primary outcome measure was change in QoL using the Pareto Classification of Health Change. We compared QoL change for patients with and without multimorbidity and those with no multimorbidity using multivariable modelling. Data are presented as odds ratio (OR) with 95% confidence interval or n (%). Methods

3. ABSTRAK Hip replacement surgery  improves QoL. Patients with multimorbidity  are less likely to experience these benefits. Poor QoL outcomes  became more frequent as the number of comorbid diseases increased. These data should inform  shared decision-making  conversations around joint replacement surgery. Conclusions Total patients included : 216,191 Patients with complete data : 178,129 (82.4%) Age distribution :70-79 years: 63,327 patients (35.6%) Gender distribution : Women: 98,513 patients (55.3%) Multimorbidity :Present in 38,384 patients (21.6%) Quality of Life (QoL) after surgery : Improved: 149,774 patients (84.1%) Unchanged: 10,219 patients (5.7%) Worse: 7,289 patients (4.1%) Mixed QoL change (at least one domain improved and one deteriorated): 10,847 patients (6.1%) Poor QoL outcomes  (unchanged/mixed/worse) were more likely in patients with multimorbidity - Odds Ratio (OR): 1.53 [1.49–1.58] Results

4. Introduction Degenerative joint diseases , such as osteoarthritis, significantly impair QoL due to: Pain Disability Reduced range of motion Joint replacement surgery  is a common and effective treatment that: Improves QoL for most patients Over one million hip replacements are performed annually worldwide However, joint replacement surgery carries  risks , particularly for: High-risk patients Potential complications Diminished QoL Risk of death postoperatively

5. Introduction Aging population  leads to an increase in the prevalence of: Chronic diseases Multimorbidity (coexistence of two or more chronic conditions) This increase raises concerns about the  impact on surgical outcomes . The relationship between  multimorbidity  and  QoL after hip replacement  remains unclear, which: Limits predictive ability Impacts shared decision-making The study  hypothesizes  that: Greater multimorbidity reduces QoL improvement It explores associated risk factors

6. Study Design The study utilized  routinely collected data  from the: NHS England Patient Reported Outcomes Measures (PROMs) database The study design was a  prospectively planned observational analysis . Ethical approval  was not required because: The data was anonymized The data was publicly available The study adhered to  STROBE guidelines  and followed a: Predefined statistical analysis plan Methods Setting The PROMs database collects preoperative and postoperative patient-reported outcomes related to the quality of care and surgical sequelae for elective surgeries funded by NHS England.

7. Methods Exposure Variables Key exposure variables  included: Age, Sex 12 chronic diseases Surgery type (primary/revision) Preoperative PROMs, such as: Oxford Hip Score (OHS) Social factors, such as: Assistance, Symptom duration, Daily activity performance Disability The  Oxford Hip Score (OHS) : A validated 12-item measure Scores disease severity from 0 to 48 Comorbidities  were self-reported, with: Multimorbidity  defined as two or more chronic diseases (excluding arthritis, the primary reason for hip replacement) Participants This analysis included all patients undergoing total hip replacement surgery recorded in the PROMs database between April 1, 2013, and March 31, 2018, with completed questionnaires and linked hospital episode statistics.

8. Methods Primary Outcome measure The primary outcome was QoL improvement, measured using the Pareto Classification of Health Change (PCHC), which compares preoperative and postoperative EQ-5D scores. The PCHC categorizes outcomes as: Better (improvement in at least one EQ-5D dimension with no worsening in others) Unchanged (no change in any dimension) Mixed (improvement in some dimensions but worsening in others), or Worse (worsening in at least one dimension with no improvement in others). The EQ-5D assesses five dimensions—mobility, self-care, usual activities, pain/discomfort, and anxiety/depression—on three severity levels. It also includes the EQ-VAS, a 0–100 scale reflecting overall health, with changes calculated as the difference between preoperative and postoperative scores.

9. Methods Bias and Missing data Main source of bias : Missing data, as outcomes depend on the return of questionnaires. Exclusion of patients  who: Died (likely with the highest disease burden), making this an analysis of survivors. The study included only patients with: Complete EQ-5D data for all domains Complete EQ-VAS data Characteristics of records with missing EQ-5D data  are reported. Suppressed age bands  were treated as a separate category. Diseases with  unreported/missing values  were assumed absent.

10. Statistical Analysis A statistical analysis plan was created prior to analysis, with datasets downloaded electronically. Patients with missing EQ-VAS or EQ-Index Profile data were excluded. All independent variables were categorical and presented as : n (%) The study calculated the frequency of disease combination pairs and triads. The primary outcome was QoL change using PCHC, which was: Stratified by patient-reported data Analyzed using multivariable logistic regression Independent variables were chosen based on: Biological plausibility (age, sex, multimorbidity, OHS category, symptom period) EQ-VAS change was modeled with: Multivariable linear regression P < 0.05 considered statistically significant Results are presented as: Odds ratios (OR) with 95% confidence intervals (CI) n (%) Statistical analysis was conducted using R. An exploratory analysis was done by excluding data on a per-domain basis.

11. Participants Total hip replacement patients identified : 216,191 (61.5% of the total cohort) Mortality rate : 0.24% Patients excluded : 38,062 (17.6%) due to missing EQ-Index Profile or EQ-VAS data No significant differences between complete and incomplete datasets (P > 0.05) Final analysis  included: 178,129 patients (82.4%)Summarized in  Figure 1 Results

12. Fig 1. Study population flowchart. PROMs, Patient Reported Outcome Measures; THR, total hip replacement; VAS, visual analogue scale.

13. Characteristics of the cohort Age distribution : 70-79 years: 35.6% of patients Gender distribution : Female: 59.5% of patients Most common chronic conditions : Arthritis: 72.1% Hypertension: 38.5% Diabetes mellitus: 9.2% Heart disease: 8.9% Lung disease: 8.2% Multimorbidity  was present in 21.6% of the cohort . Results Network graph findings : Hypertension  had significant associations with other comorbidities, including: Lung disease Heart disease Diabetes Most common disease pairs : Hypertension ↔ Diabetes mellitus Heart disease ↔ Hypertension Hypertension ↔ Lung disease Common disease triads : Heart disease ↔ Hypertension ↔ Diabetes mellitus Heart disease ↔ Hypertension ↔ Lung disease

14. Fig 2. Network graph. The network plot shows interactions of all chronic diseases bar arthritis and the strength of the relationship between them. The light grey lines represent a prevalence association of less than 1%, purple lines representing a prevalence association of more than 1%, with the thicker the line showing a stronger association. The size of the comorbidity dot is the prevalence of the disease in the cohort.

15. Outcomes Results Most patients (84.1%) showed overall improvement in QoL, while 5.7% had no change, 6.1% had mixed outcomes, and 4.1% reported worse QoL. Patients with severe OHS pre-surgery had better PCHC outcomes (86.5%), while those with satisfactory OHS had worse outcomes (48.9%). Shorter symptom periods (<1 year) were linked to better QoL outcomes (85.8%), compared to those with longer symptom periods (>10 years, 79.3%). Over time, PCHC outcomes gradually improved, with a median change of -10 in EQ-VAS, and 66.4% of patients had improved postoperative EQ-VAS scores. Each EQ-5D domain showed overall improvement.

16. Fig 3. Waffle plot and age histogram. A waffle plot showing the proportion of patients per Pareto Classification of Health Change outcome cohorted into the number of comorbidities (excluding arthritis) with an age histogram.

17. Fig 4. Heatmap. With the outcome ‘not better’ according to the Pareto Classification of Health Change (PCHC). ‘Not better’ is defined as the PCHC as unchanged, mixed, or worse. As the age increases, and as the number of comorbidities increases, the higher the percentage of patients with a ‘not better’ outcome.

18. Multimorbidity and quality of life gain Prevalence of Multimorbidity: 21.6% of patients had multimorbidity. Quality of Life (QoL) Outcomes: Patients with multimorbidity had lower rates of better QoL outcomes (80.2%) compared to those without multimorbidity (84.5%). Worse outcomes were more frequent in patients with multimorbidity (5.2% vs. 3.8%). QoL Gains and Disease Count: QoL gains decreased as the number of comorbidities increased. Patients with six or more comorbidities had the lowest gains (median EQ-VAS change: -6; 57.9% gain). Impact of Age and Comorbidities on QoL Gains: Older age and higher comorbidity counts were associated with reduced QoL gains. Patients over 90 years showed a median EQ-VAS change of 0 and a gain of 45.1%. Results

19. Adjusted association between multimorbidity and quality of life gain The multivariable modelling looks at the adjusted association between the presence of multimorbidity and PCHC outcome of unchanged, mixed, or worse ( summarised in Table 2). All associations were statistically significant. Patients with multi?morbidity had 53% higher odds of having a poorer outcome (OR 1.53 [1.49-1.58]). Results Exploratory analysis When we repeated the core analysis excluding missing data on a per-domain basis, with this larger dataset (192 449 patients), the adjusted association between multimorbidity and poorer outcome was unchanged (OR 1.53 [1.46e1.55]).

20. Table 2 Multivariable model.

21. DISCUSSIONS

22. Multimorbidity, chronic disease burden, and age  significantly reduce QoL improvements after hip replacement. While most patients benefit,  one in six does not . Patients with severe hip disease : See greater gains in QoL. Longer symptom duration and multimorbidity  result in: Reduced QoL improvements. These findings highlight the need for: Shared decision-making Patient awareness of potential outcomes. Despite baseline differences and multimorbidity patterns,  most patients  experience: Improved QoL after hip replacement. Outcomes are influenced by : Perioperative factors Patient factors Psychosocial factors Patients with lower preoperative PROMs  and those for whom  nonoperative management has failed : Show greater improvements in pain, function, and QoL post-surgery. DISCUSSION

23. Study Focus: Examined how patient and preoperative factors impact changes in QoL after hip replacement surgery. Prediction of Postoperative Outcomes: PROMs moderately predict outcomes such as the need for future surgeries. Complications (e.g., joint infections, fractures, or dislocations) significantly worsen QoL. Anesthesia and Pain Management: Local (infiltration) anesthesia is more effective than spinal (neuraxial) anesthesia. Multimodal analgesia improves recovery and outcomes more than unimodal approaches. DISCUSSION

24. Association with Osteoarthritis: Osteoarthritis is strongly linked to multimorbidity, affecting 50% of individuals over 65 with significant cardiac, pulmonary, or mental health conditions. Impact of Aging Population: As the population ages, multimorbid patients, despite poorer QoL, still demonstrate post-surgery QoL gains, though smaller . Challenges for Multimorbid Patients: These patients face longer wait times and are less frequently offered surgery, leading to worsened outcomes as waiting times increase. Healthcare Policy Implications: Identifying patients unlikely to fully benefit from surgery enables alternative treatments and supports better-informed shared decision-making. Emphasizes the need for policy and practice adjustments to address disparities in care and improve outcomes for multimorbid patients. DISCUSSION

25. Other factors influencing outcomes  include: Hospital location BMI Limited impact  from: Age Marital status Education Income BMI  (both low and high) is linked to: Poorer QoL Increased complications Likely due to underlying health issues The study could not capture  all variables  affecting outcomes, so: While  multimorbidity  is associated with poorer QoL, it cannot be confirmed as a  direct causal factor . DISCUSSION

26. The data showed a  small improvement in outcomes over time , consistent with previous studies.This improvement is likely due to advances in  technology , such as: Computer navigation Minimally invasive approaches Robotic assistance Patient-specific tools The implementation of  novel pathways , such as: Fast-track surgical programs Focus on multifaceted treatments, including: Preoperative rehabilitation Postoperative rehabilitation Nutrition These factors may have contributed to  better results . DISCUSSION

27. Strengths of the Study: Large Dataset: Analyzed data spanning multiple years of surgeries, capturing a wide range of variables such as age, complications, comorbidities, and QoL data. Standardized Data Collection: Ensured consistency and repeatability in collecting pre- and postoperative patient-reported QoL data. Prespecified Statistical Analysis Plan: Followed a predefined approach, ensuring reliability and consistency in the analysis. Comprehensive Variable Inclusion: Considered key factors influencing outcomes, enhancing the study's robustness and relevance. DISCUSSION

28. Weaknesses of the Study: Missing Data: 17.6% of records were incomplete due to loss of follow-up. This may lead to overrepresentation of good outcomes, as patients with poor outcomes or those who died were less likely to be included. Unaccounted Factors: Surgical technique, chronic pain, socioeconomic status, and comorbidity severity were not included in the analysis, despite their potential impact on outcomes. Self-Reported Comorbidities: Self-reported data may be biased and fail to accurately capture disease severity, affecting result validity. Unmeasured Influences: Biological, psychosocial, and demographic factors were not included, limiting the study's comprehensiveness . DISCUSSION

29. Conclusions Hip replacement surgery generally improves QoL for most patients, but about one in six do not experience significant gains. Patients with multimorbidity show more modest improvements in QoL compared to those without. Future research should consider factors not included in the PROMs dataset, such as operative approach, analgesia use, BMI, socioeconomic status, and detailed perioperative complications. These factors could enhance the dataset and provide valuable insights for shared decision-making in joint replacement surgery.

30. CRITICAL APPRAISAL

31. Title: "Association between multimorbidity and quality of life after hip replacement surgery: analysis of routinely collected patient-reported outcomes" Authors: Nicola J. Vickery, Alexander J. Fowler, John Prowle, and Rupert Pearse Year of Publication: This journal was published in 2025 Journal Type: Retrospective cohort study with multivariate analysis. Journal Identity

32. PICO Patients who underwent total hip replacement surgery (hip joint replacement) in England, with complete data before and 3-6 months after surgery (178,129 patients). P The presence of multimorbidity, defined as two or more chronic diseases (excluding arthritis). I Patients without multimorbidity (or with fewer chronic diseases). C Changes in Quality of Life (QoL) after surgery, measured using the Pareto Classification of Health Change (PCHC) and the EuroQoL five-dimensional (EQ-5D) scale. O

33. The study uses a retrospective cohort method, which is valid for evaluating the relationship between multimorbidity and postoperative outcomes. However, due to its retrospective nature, there is potential for data bias. The data comes from the Patient-Reported Outcome Measures (PROMs) of the NHS in England, a large and reliable database. This strengthens the external validity of the study. Multivariate analysis was used to control for confounding factors, making the results more reliable. However, certain variables, such as BMI or perioperative complications, were not analyzed, which could influence the conclusions. VIA (Validity)

34. Is this study important? VIA (Importance) Yes, this study is important because it provides insights into the impact of multimorbidity on postoperative outcomes of hip replacement surgery, aids clinical decision-making, and supports healthcare service planning for an aging population at high risk.

35. VIA (Applicabillity) Can this study be applied? Yes, this study can be applied, particularly in clinical decision-making for patients with multimorbidity, helping doctors and patients understand postoperative risks and set realistic expectations.

36. STRENGTHS a. This journal uses data from NHS England with a large sample size (178,129 patients), making the results more statistically reliable. b. The study performs multivariable analysis to evaluate the relationship between multimorbidity and postoperative quality of life, providing deep insights into the factors influencing outcomes. c. The journal adheres to the STROBE guidelines for reporting observational studies and uses the validated EQ-5D tool to measure patient quality of life. d. The findings provide essential data-driven information for decision-making regarding hip replacement surgery, particularly for patients with multimorbidity.

37. WEAKNESSES a. Approximately 17.6% of data was missing due to the absence of follow-up surveys. This could lead to bias in the results, as patients with poor outcomes may be underrepresented. b. Most of the data on chronic diseases were self-reported by patients, which may be prone to reporting bias. c. Factors such as BMI, socioeconomic status, surgical techniques, and perioperative complications were not considered, meaning the findings may not account for all significant factors. d. As this is an observational study, it cannot conclude that multimorbidity is a direct cause of poor quality-of-life outcomes.

38. CONCLUSION

39. CONCLUSION This study highlights the relationship between multimorbidity and changes in quality of life (QoL) following hip replacement surgery using large-scale data from NHS England. Overall, the surgery improved QoL for the majority of patients, with 84.1% experiencing improvement. However, patients with multimorbidity were more likely to have poor QoL outcomes (unchanged, worsened, or mixed) compared to patients without multimorbidity, with an odds ratio of 1.53. This decline in QoL became more pronounced with an increasing number of chronic conditions. These findings emphasize the importance of considering multimorbidity factors in shared decision-making prior to surgery, particularly for older patients or those with multiple chronic diseases.

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