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Background: It remains controversial as to whether delayed gastric emptying in functional dyspepsia is associated with a specific symptom pattern, and it is unknown if gastric emptying in functional dyspepsia is a driver of impaired health related quality of life (HRQOL). We aimed to evaluate the relationship between functional dyspepsia symptoms, gastric emptying, and HRQOL. Methods: US patients (n = 864; mean age 44 years (range 18–82); 74% female) with functional dyspepsia, as defined by Rome II criteria, were enrolled into one of four clinical trials. All patients had a baseline scintigraphic assessment of gastric emptying of an egg substitute meal, and the trials were stratified on this assessment. Delayed gastric emptying was defined as having at least 6.3% residual volume at four hours. A total of 290 (34%) patients had delayed gastric emptying. HRQOL was assessed by the SF 36 and Nepean dyspepsia index (NDI). Results: Postprandial fullness was independently associated with delayed gastric emptying but the association was weak (odds ratio (OR) 1.98 (95% confidence interval (CI) 1.02, 3.86); p = 0.04). No independent association was seen with epigastric pain, early satiety, nausea, or bloating. Mean SF 36 physical composite score (PCS) was 42.3 (95% CI 41.6, 43.0) and the mean SF 36 mental composite score (MCS) was 46.8 (95% CI 46.0, 47.5); both mean scores were significantly lower than age and sex adjusted national norms of 50 (p<.0001). Female sex, increasing age, and higher symptom scores for fullness, epigastric pain, and nausea were each independently associated with decreased PCS scores (all p<0.05). Higher baseline nausea symptom score, lower gastric emptying rates at one hour, and lower body mass index were associated with decreased MCS (all p<0.05). Female sex, epigastric pain, and nausea, but not gastric emptying, were associated with an impaired score on the NDI. However, the magnitude of the significant associations were all small. Conclusions: In patients with functional dyspepsia selected for a clinical trial programme, gastric emptying did not usefully stratify them symptomatically. Quality of life of patients with functional dyspepsia enrolled in this clinical trial programme was significantly impaired but this was not explained by delayed gastric emptying.

Background Health-related quality of life (HRQOL) is related to body composition, which is also related to resting metabolic rate (RMR). RMR can be increased by exercise and diet interventions that are not dependent on changes in body composition, so a link between RMR and HRQOL may provide interventions that directly improve HRQOL in women. Methods One hundred twenty women (median age 63.5 [IQR: 53.0–71.0] years) completed one-time measurement of body composition (multi-frequency bioelectrical impedance), RMR (handheld calorimetry), and HRQOL (RAND-36). Physical (PCS) and mental (MCS) composite scores were calculated for the RAND-36. Pearson correlations were used to identify relationships between RMR, body composition, and HRQOL. Variables at the p  < .01 level were entered into multiple regression models. Results Median body mass index was 26.1 [IQR: 23.2–30.9] kg/m 2 and median lean mass index was 16.1 [IQR: 14.6–17.3] kg/m 2 . Body composition consisted of fat mass (median 27.2 [IQR: 20.3–34.7] kg) and lean mass (median 42.7 [IQR: 38.2–46.9] kg). Median RMR was 1165.0 [IQR: 1022.5–1380.0] kcal/day. Median HRQOL scores were PCS (84.0 [IQR: 74.0–93.0]) and MCS (85.0 [IQR: 74.3–90.0]). RMR was not directly related to PCS, but was directly and negatively related to MCS ( p  = .002). RMR was significantly and positively related to body composition (lean mass: p  < .001; fat mass: p  < .001), body mass index ( p  = .005), and lean mass index ( p  < .001); but only fat mass (PCS: p  < .001; MCS: p  < .001) and body mass index (PCS: p  < .001; MCS: p  < .001) were related to HRQOL, although the relationship was negative. In addition, age was found to be significantly negatively related to RMR ( p  < .001) and PCS ( p  = .003). Regression models confirmed the moderating influence of age and body composition on the relationship between RMR and HRQOL. RMR, age, fat mass, and body mass index explained 24% ( p  < .001) of variance in PCS; and RMR, fat mass, and body mass index explained 15% ( p  < .001) of variance in MCS. Conclusion In women, the relationship between RMR and HRQOL is moderated by age and body composition. Understanding these pathways will allow clinicians and researchers to direct interventions more effectively.

Background Living donor liver transplantation (LDLT) involves healthy individuals undergoing voluntary major hepatic resection. LDLT program only started in 2012 in Pakistan and its impact on donor’s quality of life (QOL) post resection is not known. The objective of this study was to determine health-related QOL in donors who underwent hepatectomy in country’s first liver transplant program. Methods A total of 60 donors who underwent hepatectomy between 2012 and 2014 with a minimum follow-up of 6 months were included in the study. Short form (SF-36) and Profile of mood states (POMS-65) was used to assess QOL. In addition scores were compared between patients who did and did not develop complications. Result Mean time duration between hepatectomy and administration of questionnaire was 15 ± 5.1 months. Median age was 28 (19–45) years. Mean BMI was 24.4 ± 3.7. A total of 7 (11.6 %) Grade 3 and above complications were observed in donors. Donors exceeded a score of 90 in 6 out of 8 evaluated categories on SF-36. The highest mean score was recorded for emotional role limitation 95.5 ± 17.1 and lowest for energy 84.8 ± 17.5. The mean score for anger was 6.6 ± 7.5. Donors also did well on the POMS vigor score with a mean of 22.7 ± 5. No significant difference in scores was observed between donors with and without complications for any of the categories except tension. Donors who developed complications post-operatively had a significantly low mean tension score of 1.5 versus 3.8 for donors without complications. Conclusion Acceptable post donation QOL was achieved and surgical complications did not adversely affect SF-36 and POMS scores.

Background: Health related quality of life (HRQOL) is increasingly recognised as an important outcome in epilepsy. However, interpretation of HRQOL data is difficult because there is no agreement on what constitutes a clinically important change in the scores of the various instruments. Objectives: To determine the minimum clinically important change, and small, medium, and large changes, in broadly used epilepsy specific and generic HRQOL instruments. Methods: Patients with difficult to control focal epilepsy (n = 136) completed the QOLIE-89, QOLIE-31, SF-36, and HUI-III questionnaires twice, six months apart. Patient centred estimates of minimum important change, and of small, medium, and large change, were assessed on self administered 15 point global rating scales. Using regression analysis, the change in each HRQOL instrument that corresponded to the various categories of change determined by patients was obtained. The results were validated in a subgroup of patients tested at baseline and at nine months. Results: The minimum important change was 10.1 for QOLIE-89, 11.8 for QOLIE-31, 4.6 for SF-36 MCS, 3.0 for SF-36 physical composite score, and 0.15 for HUI-III. All instruments differentiated between no change and minimum important change with precision, and QOLIE-89 and QOLIE-31 also distinguished accurately between minimum important change and medium or large change. Baseline HRQOL scores and the type of treatment (surgical or medical) had no impact on any of the estimates, and the results were replicated in the validation sample. Conclusions: These estimates of minimum important change, and small, medium, and large changes, in four HRQOL instruments in patients with epilepsy are robust and can distinguish accurately among different levels of change. The estimates allow for categorisation of patients into various levels of change in HRQOL, and will be of use in assessing the effect of interventions in individual patients.

Today, the 10-year cardiovascular risk largely relies on conventional cardiovascular risk factors (CCVRFs) and suffers from the effect of atherosclerotic wall changes. In this study, we present a novel risk calculator AtheroEdge Composite Risk Score (AECRS1.0), designed by fusing CCVRF with ultrasound image-based phenotypes. Ten-year risk was computed using the Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score. AECRS1.0 was computed by measuring the 10-year five carotid phenotypes such as IMT (ave., max., min.), IMT variability, and total plaque area (TPA) by fusing eight CCVRFs and then compositing them. AECRS1.0 was then benchmarked against the five conventional cardiovascular risk calculators by computing the receiver operating characteristics (ROC) and area under curve (AUC) values with a 95% CI. Two hundred four IRB-approved Japanese patients’ left/right common carotid arteries (407 ultrasound scans) were collected with a mean age of 69 ± 11 years. The calculators gave the following AUC: FRS, 0.615; UKPDS56, 0.576; UKPDS60, 0.580; RRS, 0.590; PCRS, 0.613; and AECRS1.0, 0.990. When fusing CCVRF, TPA reported the highest AUC of 0.81. The patients were risk-stratified into low, moderate, and high risk using the standardized thresholds. The AECRS1.0 demonstrated the best performance on a Japanese diabetes cohort when compared with five conventional calculators.

Objectives: Cognitive problems in multiple sclerosis are common but any possible benefits of treatment remain uncertain. The aim of the study was to evaluate the benefits of providing a psychology service, including cognitive assessment and intervention, to patients with multiple sclerosis. Method: The study was a single blind randomised controlled trial. A total of 240 patients with clinically definite, laboratory supported, or clinically probable multiple sclerosis were recruited from an multiple sclerosis management clinic and assessed on a brief screening battery. They were randomised into three groups. The control group received no further intervention. The assessment group received a detailed cognitive assessment, the result of which was fed back to staff involved in the patients' care. The treatment group received the same detailed cognitive assessment and a treatment programme designed to help reduce the impact of their cognitive problems. Patients were followed up 4 and 8 months later on the general health questionnaire (GHQ-28), extended activities of daily living scale, SF-36, everyday memory questionnaire, dysexecutive syndrome questionnaire, and memory aids questionnaire. Results: The three groups were compared on the outcome measures at 4 and 8 months after recruitment. There were few significant differences between the groups and those that occurred favoured the control group. Overall, the results showed no effect of the interventions on mood, quality of life, subjective cognitive impairment or independence. Conclusions: The study failed to detect any significant effects of cognitive assessment or cognitive intervention in this cohort of people with multiple sclerosis.

This paper reflects on some practical implications of the excellent treatment of sum scoring and classical test theory (CTT) by Sijtsma et al. (Psychometrika 89(1):84–117, 2024). I have no major disagreements about the content they present and found it to be an informative clarification of the properties and possible extensions of CTT. In this paper, I focus on whether sum scores—despite their mathematical justification—are positioned to improve psychometric practice in empirical studies in psychology, education, and adjacent areas. First, I summarize recent reviews of psychometric practice in empirical studies, subsequent calls for greater psychometric transparency and validity, and how sum scores may or may not be positioned to adhere to such calls. Second, I consider limitations of sum scores for prediction, especially in the presence of common features like ordinal or Likert response scales, multidimensional constructs, and moderated or heterogeneous associations. Third, I review previous research outlining potential limitations of using sum scores as outcomes in subsequent analyses where rank ordering is not always sufficient to successfully characterize group differences or change over time. Fourth, I cover potential challenges for providing validity evidence for whether sum scores represent a single construct, particularly if one wishes to maintain minimal CTT assumptions. I conclude with thoughts about whether sum scores—even if mathematically justified—are positioned to improve psychometric practice in empirical studies.

Background: Healthy lifestyles are inversely associated with the risk of noncommunicable diseases, which are leading causes of death. However, few studies have used longitudinal data to assess the impact of changing lifestyle behaviours on all-cause and cancer mortality. Methods: Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, lifestyle profiles of 308,497 cancer-free adults (71% female) aged 35u201370 years at recruitment across nine countries were assessed with baseline and follow-up questionnaires administered on average of 7 years apart. A healthy lifestyle index (HLI), assessed at two time points, combined information on smoking status, alcohol intake, body mass index, and physical activity, and ranged from 0 to 16 units. A change score was calculated as the difference between HLI at baseline and follow-up. Associations between HLI change and all-cause and cancer mortality were modelled with Cox regression, and the impact of changing HLI on accelerating mortality rate was estimated by rate advancement periods (RAP, in years). Results: After the follow-up questionnaire, participants were followed for an average of 9.9 years, with 21,696 deaths (8407 cancer deaths) documented. Compared to participants whose HLIs remained stable (within one unit), improving HLI by more than one unit was inversely associated with all-cause and cancer mortality (hazard ratio [HR]: 0.84; 95% confidence interval [CI]: 0.81, 0.88; and HR: 0.87; 95% CI: 0.82, 0.92; respectively), while worsening HLI by more than one unit was associated with an increase in mortality (all-cause mortality HR: 1.26; 95% CI: 1.20, 1.33; cancer mortality HR: 1.19; 95% CI: 1.09, 1.29). Participants who worsened HLI by more than one advanced their risk of death by 1.62 (1.44, 1.96) years, while participants who improved HLI by the same amount delayed their risk of death by 1.19 (0.65, 2.32) years, compared to those with stable HLI. Conclusions: Making healthier lifestyle changes during adulthood was inversely associated with all-cause and cancer mortality and delayed risk of death. Conversely, making unhealthier lifestyle changes was positively associated with mortality and an accelerated risk of death.

This study aims to compare methods of constructing a composite score for the Flare-OA-16 self-reported questionnaire. Participants with knee and hip osteoarthritis (OA) completed a validated 16-item questionnaire assessing five domains of flare. Three estimation methods were compared: (i) second-order confirmatory factor analysis (CFA); (ii) logistic regression, according to the participant's self-report of flare (yes/no); and (iii) Rasch method, with weighted scores in each dimension. The distribution (floor effect [FF] and ceiling effect [CF]) were described and the known-group validity (by self-reported flare) tested by Wilcoxon rank-sum test. Similarity between the scores was analyzed by intraclass correlation coefficient (ICC) and their performance against self-report compared by areas under ROC curves (AUC). Intrascore test-retest reliability at 14 days was assessed by ICC. In a sample of 381 participants, 247 reported having a flare. CFA showed fit indices (comparative fit index [CFI] = 0.95; root mean square error of approximation [RMSEA] = 0.08) and estimated composite mean score = 4.33(SD = 2.85) (FF = 14.9%, CF = 0%). For the logistic regression estimation, the mean composite score was 6.48 (SD = 3.13) (FF = 0%; CF = 0%). With Rasch model, the mean composite score was 4.35 (SD = 2.60) (FF = 14.9%; CF = 0%). Similarity analysis indicated a greater concordance between CFA and Rasch scores (ICC = 0.98) than between logistic regression score and the two others (ICC = 0.88 with Rasch score and 0.90 with CFA score). The AUC indicated similar performance of all methods: logistic model (AUC = 0.89 [0.85–0.92]), CFA, and Rasch model (AUC = 0.86 [0.82–0.90]). The difference between groups was significant (P < .05) for scores estimated by CFA (3.98), Rasch model (4.95), and logistic regression (4.30). The reproducibility was ICC = 0.84 (0.75–0.90) for Rasch and CFA scores and ICC = 0.78(0.66–86) for logistic model. Three alternatives explored to build a composite score showed similar construct validity. Some metric superiority (better score distribution and reproducibility) of the Rasch model is promising for the detection of occurrence and assessment of severity of a flare in OA. [Display omitted] •A score to assess the occurrence and severity of flares of knee or hip osteoarthritis (OA) would help guide interventions.•Three estimation methods to obtain a composite score for the Flare-OA-16 self-reported questionnaire were compared: second-order confirmatory factor analysis, logistic regression, and Rasch model.•The three methods showed similar performance in predicting self-reported flare but a better scale distribution was in favor of the Rasch model.

Influenza vaccination plays a crucial role in reducing morbidity and mortality from influenza. However, its effectiveness varies due to multiple factors. Reliable point systems combining age, sex, BMI, vaccination history, and other baseline characteristics could aid in making evidence-based decisions regarding influenza vaccination. Using human vaccination cohort data from the University of Georgia (UGA) over multiple influenza seasons, we developed two point systems: the Simple-Test score (STS) and the No-Test score (NTS). These scores predict vaccine-elicited antibody responses measured by hemagglutination inhibition (HAI) titers. Data from four influenza seasons (2016–2017 to 2019–2020) were used for model development and validation. The STS and NTS demonstrated good performance in discriminating between predicted lower-, moderate-, and higher-response groups. The AUC values for the STS were 0.943 for derivation and 0.841, 0.936, and 0.796 from the validation cohorts for 2016–2017, 2018–2019, and 2019–2020, respectively. Age, race, BMI, baseline HAI titers, and vaccination history significantly influenced the point system's performance. The point system showed robustness across age groups (teenagers, adults, and elderly). The AUC values for the NTS were 0.913 for derivation and 0.658 to 0.875 for validation datasets. We successfully developed and validated two practical point systems to predict individual-level influenza vaccine-elicited antibody responses. These systems could facilitate personalized vaccination recommendations, policymaking, and resource allocation in influenza vaccination programs. The proposed point system is also a valuable tool for targeting populations that are likely to benefit most from influenza vaccination.