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Background Context: A significant proportion of patients experience poorly controlled surgical pain and fail to achieve satisfactory clinical improvement after spine surgery. However, a direct association between these variables has not been previously demonstrated., Purpose: To investigate the association between poor postoperative pain control and patient-reported outcomes after spine surgery., Study Design: Ambispective cohort study., Patient Sample: Consecutive adult patients (≥18-years old) undergoing inpatient elective cervical or thoracolumbar spine surgery., Outcome Measure: Poor surgical outcome was defined as failure to achieve a minimal clinically important difference (MCID) of 30% improvement on the Oswestry Disability Index or Neck Disability Index at follow-up (3-months, 1-year, and 2-years)., Methods: Poor pain control was defined as a mean numeric rating scale score of >4 during the first 24-hours after surgery. Multivariable mixed-effects regression was used to investigate the relationship between poor pain control and changes in surgical outcomes while adjusting for known confounders. Secondarily, the Calgary Postoperative Pain After Spine Surgery (CAPPS) Score was investigated for its ability to predict poor surgical outcome., Results: Of 1294 patients, 47.8%, 37.3%, and 39.8% failed to achieve the MCID at 3-months, 1-year, and 2-years, respectively. The incidence of poor pain control was 56.9%. Multivariable analyses showed poor pain control after spine surgery was independently associated with failure to achieve the MCID (OR 2.35 [95% CI=1.59-3.46], p<.001) after adjusting for age (p=.18), female sex (p=.57), any nicotine products (p=.041), ASA physical status >2 (p<.001), ≥3 motion segment surgery (p=.008), revision surgery (p=.001), follow-up time (p<.001), and thoracolumbar surgery compared to cervical surgery (p=.004). The CAPPS score was also found to be independently predictive of poor surgical outcome., Conclusion: Poor pain control in the first 24-hours after elective spine surgery was an independent risk factor for poor surgical outcome. Perioperative treatment strategies to improve postoperative pain control may lead to improved patient-reported surgical outcomes., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Coronary artery disease (CAD) confers increased risks of premature mortality, non-fatal morbidity, and significant impairment in functional status and health-related quality of life. Routine administration of electronic patient-reported outcome measures (PROMs) and its real time delivery to care providers is known to have the potential to inform routine cardiac care and to improve quality of care and patient outcomes. This study describes a user-centered development and evaluation of the Alberta Provincial Project for Outcomes Assessment (APPROACH) electronic Patient Reported Outcomes Measurement (e-PROM) system. This e-PROM system is an electronic system for the administration of PROMs to patients with CAD and the delivery of the summarized information to their care providers to facilitate patient-physician communication and shared decision-making. This electronic platform was designed to be accessible via web-based and hand-held devices. Heuristic and user acceptance evaluation were conducted with patients and attending care providers., Results: The APPROACH e-PROM system was co-developed with patients and care providers, research investigators, informaticians and information technology experts. Five PROMs were selected for inclusion in the online platform after consultations with patient partners, care providers, and PROMs experts: the Seattle Angina Questionnaire, Patient Health Questionnaire, EuroQOL, and Medical Outcomes Study Social Support Survey, and Self-Care of Coronary Heart Disease Inventory. The heuristic evaluation was completed by four design experts who examined the usability of the prototype interfaces. User acceptance testing was completed with 13 patients and 10 cardiologists who evaluated prototype user interfaces of the e-PROM system., Conclusion: Both patients and physicians found the APPROACH e-PROM system to be easy to use, understandable, and acceptable. The APPROACH e-PROM system provides a user-informed electronic platform designed to incorporate PROMs into the delivery of individualized cardiac care for persons with CAD., (© 2024. The Author(s).)

Purpose: Unsupervised item-response theory (IRT) models such as polytomous IRT based on recursive partitioning (IRTrees) and mixture IRT (MixIRT) models can be used to assess differential item functioning (DIF) in patient-reported outcome measures (PROMs) when the covariates associated with DIF are unknown a priori. This study examines the consistency of results for IRTrees and MixIRT models., Methods: Data were from 4478 individuals in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry who received cardiac angiography in Alberta, Canada, and completed the Hospital Anxiety and Depression Scale (HADS) depression subscale items. The partial credit model (PCM) based on recursive partitioning (PCTree) and mixture PCM (MixPCM) were used to identify covariates associated with differential response patterns to HADS depression subscale items. Model covariates included demographic and clinical characteristics., Results: The median (interquartile range) age was 64.5(15.7) years, and 3522(78.5%) patients were male. The PCTree identified 4 terminal nodes (subgroups) defined by smoking status, age, and body mass index. A 3-class PCM fits the data well. The MixPCM latent classes were defined by age, disease indication, smoking status, comorbid diabetes, congestive heart failure, and chronic obstructive pulmonary disease., Conclusion: PCTree and MixPCM were not consistent in detecting covariates associated with differential interpretations of PROM items. Future research will use computer simulations to assess these models' Type I error and statistical power for identifying covariates associated with DIF., (© 2023. The Author(s).)

Purpose: The objective of this systematic review was to describe the prevalence and magnitude of response shift effects, for different response shift methods, populations, study designs, and patient-reported outcome measures (PROM)s., Methods: A literature search was performed in MEDLINE, PSYCINFO, CINAHL, EMBASE, Social Science Citation Index, and Dissertations & Theses Global to identify longitudinal quantitative studies that examined response shift using PROMs, published before 2021. The magnitude of each response shift effect (effect sizes, R-squared or percentage of respondents with response shift) was ascertained based on reported statistical information or as stated in the manuscript. Prevalence and magnitudes of response shift effects were summarized at two levels of analysis (study and effect levels), for recalibration and reprioritization/reconceptualization separately, and for different response shift methods, and population, study design, and PROM characteristics. Analyses were conducted twice: (a) including all studies and samples, and (b) including only unrelated studies and independent samples., Results: Of the 150 included studies, 130 (86.7%) detected response shift effects. Of the 4868 effects investigated, 793 (16.3%) revealed response shift. Effect sizes could be determined for 105 (70.0%) of the studies for a total of 1130 effects, of which 537 (47.5%) resulted in detection of response shift. Whereas effect sizes varied widely, most median recalibration effect sizes (Cohen's d) were between 0.20 and 0.30 and median reprioritization/reconceptualization effect sizes rarely exceeded 0.15, across the characteristics. Similar results were obtained from unrelated studies., Conclusion: The results draw attention to the need to focus on understanding variability in response shift results: Who experience response shifts, to what extent, and under which circumstances?, (© 2023. The Author(s).)

Purpose: Item non-response (i.e., missing data) may mask the detection of differential item functioning (DIF) in patient-reported outcome measures or result in biased DIF estimates. Non-response can be challenging to address in ordinal data. We investigated an unsupervised machine-learning method for ordinal item-level imputation and compared it with commonly-used item non-response methods when testing for DIF., Methods: Computer simulation and real-world data were used to assess several item non-response methods using the item response theory likelihood ratio test for DIF. The methods included: (a) list-wise deletion (LD), (b) half-mean imputation (HMI), (c) full information maximum likelihood (FIML), and (d) non-negative matrix factorization (NNMF), which adopts a machine-learning approach to impute missing values. Control of Type I error rates were evaluated using a liberal robustness criterion for α = 0.05 (i.e., 0.025-0.075). Statistical power was assessed with and without adoption of an item non-response method; differences > 10% were considered substantial., Results: Type I error rates for detecting DIF using LD, FIML and NNMF methods were controlled within the bounds of the robustness criterion for > 95% of simulation conditions, although the NNMF occasionally resulted in inflated rates. The HMI method always resulted in inflated error rates with 50% missing data. Differences in power to detect moderate DIF effects for LD, FIML and NNMF methods were substantial with 50% missing data and otherwise insubstantial., Conclusion: The NNMF method demonstrated comparable performance to commonly-used non-response methods. This computationally-efficient method represents a promising approach to address item-level non-response when testing for DIF., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objective: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics., Methods: Consecutive outpatients seen at the Calgary Comprehensive Epilepsy Program outpatient clinics with complete patient-reported outcome measures on quality of life, health state valuation, depression, and epilepsy severity and disability were studied. Data were acquired at each patient's first clinic visit. We used k-means++ to segregate the population into three unique clusters. We then used multinomial regression to determine factors that were statistically associated with patient assignment to each cluster., Results: We identified 462 consecutive patients with complete patient-reported outcome measure (PROM) data. Post hoc analysis of each cluster revealed one reporting elevated measures of psychosocial health on all five PROMs ("high psychosocial health" cluster), one with intermediate measures ("intermediate" cluster), and one with poor overall measures of psychosocial health ("poor psychosocial health" cluster). Failing to achieve at least 1 year of seizure freedom (relative risk [RR] = 4.34, 95% confidence interval [CI] = 2.13-9.09) predicted placement in the "intermediate" cluster relative to the "high" cluster. In addition, failing to achieve seizure freedom, social determinants of health, including the need for partially or completely subsidized income support (RR = 6.10, 95% CI = 2.79-13.31, P < .001) and inability to drive (RR = 4.03, 95% CI = 1.6-10.00, P = .003), and a history of a psychiatric disorder (RR = 3.16, 95% CI = 1.46-6.85, P = .003) were associated with the "poor" cluster relative to the "high" cluster., Significance: Seizure-related factors appear to drive placement in the "intermediate" cluster, with social determinants driving placement in the "poor" cluster, suggesting a threshold effect. Precision intervention based on cluster assignment, with an initial emphasis on improving social support and careful titration of medications for those reporting the worst psychosocial health, could help optimize health for patients with epilepsy., (© 2020 International League Against Epilepsy.)

Background: Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient's perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making. We compared the precision and bias of several missing data methods when estimating longitudinal change in PRO scores., Methods: This research conducted analyses of clinical registry data and simulated data. Registry data were from a population-based regional joint replacement registry for Manitoba, Canada; the study cohort consisted of 5631 patients having total knee arthroplasty between 2009 and 2015. PROs were measured using the 12-item Short Form Survey, version 2 (SF-12v2) at pre- and post-operative occasions. The simulation cohort was a subset of 3000 patients from the study cohort with complete PRO information at both pre- and post-operative occasions. Linear mixed-effects models based on complete case analysis (CCA), maximum likelihood (ML) and multiple imputation (MI) without and with an auxiliary variable (MI-Aux) were used to estimate longitudinal change in PRO scores. In the simulated data, bias, root mean squared error (RMSE), and 95% confidence interval (CI) coverage and width were estimated under varying amounts and types of missing data., Results: Three thousand two hundred thirty (57.4%) patients in the study cohort had complete data on the SF-12v2 at both occasions. In this cohort, mixed-effects models based on CCA resulted in substantially wider 95% CIs than models based on ML and MI methods. The latter two methods produced similar estimates and 95% CI widths. In the simulation cohort, when 50% of the data were missing, the MI-Aux method, in which a single hypothetical auxiliary variable was strongly correlated (i.e., 0.8) with the outcome, reduced the 95% CI width by up to 14% and bias and RMSE by up to 50 and 45%, respectively, when compared with the MI method., Conclusions: Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging.

Purpose: This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift., Methods: Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines., Synthesis: A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method., Conclusions: While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored.

Patient satisfaction with therapeutic interventions is an important outcome of care. Although generic measures of patient satisfaction exist, there is no validated scale for measuring patient satisfaction with epilepsy surgery. We aimed to systematically obtain patient-identified factors related to satisfaction with epilepsy surgery as a means of informing clinicians about the ways that patients evaluate outcomes of their treatment and as a conceptual basis for the future development of epilepsy surgery patient satisfaction scales. Focus group discussions with epilepsy surgery patients (n=9) were conducted to identify themes relevant to patient satisfaction with epilepsy surgery and to draft initial items of importance. Consensus methodology (Delphi technique) was used to obtain expert opinion (n=13) to refine the items. Member-checking with focus group participants was performed to ensure the identified items were relevant, clear, and inclusive. A list of 31 items embodied 12 themes related to patient-reported satisfaction with epilepsy surgery. These included adverse effects, medical care or rehabilitation, seizure control, post-operative recovery, anti-seizure medication, independence, seizure worry, ability to drive, social relationships, self-confidence, improved cognitive function, and improved physical health. This study used a systematic approach to identify factors that are important to patients when assessing satisfaction with epilepsy surgery. This knowledge can assist clinicians caring for these patients and is also a critical step towards the validation of a formal scale to assess satisfaction with epilepsy surgery.

Purpose: Patient-reported outcome measures (PROMs) are frequently used in heterogeneous patient populations. PROM scores may lead to biased inferences when sources of heterogeneity (e.g., gender, ethnicity, and social factors) are ignored. Latent variable mixture models (LVMMs) can be used to examine measurement invariance (MI) when sources of heterogeneity in the population are not known a priori. The goal of this article is to discuss the use of LVMMs to identify invariant items within the context of test construction., Methods: The Draper-Lindely-de Finetti (DLD) framework for the measurement of latent variables provides a theoretical context for the use of LVMMs to identify the most invariant items in test construction. In an expository analysis using 39 items measuring daily activities, LVMMs were conducted to compare 1- and 2-class item response theory models (IRT). If the 2-class model had better fit, item-level logistic regression differential item functioning (DIF) analyses were conducted to identify items that were not invariant. These items were removed and LVMMs and DIF testing repeated until all remaining items showed MI., Results: The 39 items had an essentially unidimensional measurement structure. However, a 1-class IRT model resulted in many statistically significant bivariate residuals, indicating suboptimal fit due to remaining local dependence. A 2-class LVMM had better fit. Through subsequent rounds of LVMMs and DIF testing, nine items were identified as being most invariant., Conclusions: The DLD framework and the use of LVMMs have significant potential for advancing theoretical developments and research on item selection and the development of PROMs for heterogeneous populations.

Background: Response shift (RS) has been defined as a change in the meaning of an individual's self-evaluation of his/her health status and quality of life. Several statistical model- and design-based methods have been developed to test for RS in longitudinal data. We reviewed the uptake of these methods in patient-reported outcomes (PRO) literature., Methods: CINHAHL, EMBASE, Medline, ProQuest, PsycINFO, and Web of Science were searched to identify English-language articles about RS published until 2016. Data on year and country of publication, PRO measure adopted, RS detection method, type of RS detected, and testing of underlying model assumptions were extracted from the included articles., Results: Of the 1032 articles identified, 101 (9.8%) articles were included in the study. While 54.5 of the articles reported on the Then-test, 30.7% of the articles reported on Oort's or Schmitt's structural equation modeling (SEM) procedure. Newer RS detection methods, such as relative importance analysis and random forest regression, have been used less frequently. Less than 25% reported on testing the assumptions underlying the adopted RS detection method(s)., Conclusions: Despite rapid methodological advancements in RS research, this review highlights the need for further research about RS detection methods for complex longitudinal data and standardized reporting guidelines.

Objectives: Patient‐reported outcome measures (PROMs) are used widely to elicit patient's self‐appraisal of their health status and quality of life. One fundamental assumption when measuring PROMs is that all individuals interpret questions about their health status in a consistent manner. However, subgroups of patients with a similar health condition may respond differently to PROM questions (ie, differential item functioning [DIF]), leading to biased estimates of group differences on PROM scores. Understanding these differences can help inform the clinical interpretation of PROMs. This study examined whether DIF affects 10‐item Quality of Life in Epilepsy (QOLIE10) scores reported by patients with epilepsy in outpatient clinics. Methods: Data were from the Calgary Comprehensive Epilepsy Program, a prospective registry of patients with epilepsy in Calgary, Alberta. Latent variable mixture models (LVMMs) based on standard two‐parameter graded response models with increasing numbers of latent classes were applied to QOLIE10 item data. Model fit was assessed using the Bayesian Information Criterion (BIC) and latent class model entropy. Ordinal logistic regression was used to identify QOLIE10 items that exhibited DIF. Results: In this cohort of 1143 patients, 567 (49.6%) were female and the median age was 37.0 (interquartile range [IQR] 27.0) years. A two‐class LVMM, which provided the best fit to the data, identified two subgroups of patients with different response patterns to QOLIE10 items, with class proportions of 0.62 and 0.38. The two subgroups differed with respect to antiseizure polytherapy, reported medication side effects, frequency of seizures, and psychiatric comorbidities. QOLIE10 items on the physical and psychological side effects of medication exhibited large DIF effects. Significance: Our study revealed two different response patterns to quality‐of‐life instruments, suggesting heterogeneity in how patients interpret some of the questions. Researchers and users of PROMs in epilepsy need to consider the differential interpretation of items for various instruments to ensure valid understanding and comparisons of PROM scores. [ABSTRACT FROM AUTHOR]

Purpose: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items.Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients’ characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression.Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status.PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics.Methods: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items.Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients’ characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression.Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status.PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics.Results: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items.Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients’ characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression.Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status.PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics.Conclusion: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items.Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients’ characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression.Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status.PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics. [ABSTRACT FROM AUTHOR]