title stringlengths 1 1.19k | keywords stringlengths 0 668 | concept stringlengths 0 909 | paragraph stringlengths 0 61.8k | PMID stringlengths 10 11 |
|---|---|---|---|---|
2. Materials and Methods | PMC10608521 | |||
2.1. Study Design and Subjects | obesity, Obesity | OBESITY, PEDIATRIC OBESITY, OBESITY, DISEASE | We conducted a cross-sectional comparative study that was a branch of a randomized clinical trial where the main objective was to evaluate several physical activity interventions in pediatric patients with obesity who were included in a multidisciplinary lifestyle change intervention program.Children and adolescents aged 8 to 16 years who had not been previously treated and were sent to the Pediatric Obesity Clinic at the Child Welfare Unit in the General Hospital of Mexico were eligible. Obesity was defined using the Centers for Disease Control and Prevention criteria (body mass index (BMI ≥ 95th percentile according to age and sex)), and only Class 1 obesity participants were included (BMI < 120% over 95th percentile according to age and sex) [The study was approved by the Hospital’s Institutional Research, Ethics, and Biosafety for Human Research Committees (Number DI/17/311/03/028) and registered in ClinicalTrials.gov (NCT03552367). Parents and children provided written informed consent and assent, respectively. This trial was conducted in accordance with the 1975 and 2013 Declarations of Helsinki and adhered to the Good Clinical Practice Guidelines issued by the International Conference of Harmonization. All patient data were protected in compliance with the Health Insurance Portability and Accountability Act (HIPAA). | PMC10608521 |
2.2. Measurements | PMC10608521 | |||
2.2.1. Anthropometry | CREST | The measurements were performed by trained pediatricians and nutritionists after a standardization procedure. Total body weight was measured relative to participants dressed in light clothes, and a mechanical column scale was used (to the nearest 0.1 kg); standing height was measured using a standard stadiometer board mounted to the wall (to the nearest 0.1 cm). Waist circumference was measured at the midway point between the last costal cartilage and the anterosuperior iliac crest with a non-stretchable fiberglass measuring tape at the end of a breath. Body composition was determined using Body Composition Analyzer Model IOI 353 (to 0.1 kg) (Jawon Medical Co, Gyeonggi-do, Republic of Korea) after a 12 h fasting period. We registered blood pressure using a digital sphygmomanometer, following a fifteen-minute rest and using an appropriate cuff size for the children and adolescents’ upper arms. Pubertal assessments according to mammary, genital, and pubic statuses for both boys and girls and based on Marshall and Tanner staging were explored and defined by a pediatric endocrinologist [ | PMC10608521 | |
2.2.2. Biochemical Evaluation | INSULIN SENSITIVITY | After 12 h of fasting, a venous blood sample was obtained to measure alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-Chol), low-density lipoprotein cholesterol (LDL-Chol), and triglycerides (Tg) using available commercial kits. To calculate Matsuda ISI, we performed an oral glucose tolerance test using 1.75 g/kg of the body weight of anhydrous glucose (to a maximum of 75 g). Samples were taken at 0, 30, 60, 90, and 120 min, and glucose and insulin were determined. The Matsuda-ISI (Insulin Sensitivity Index) was calculated as follows: 10,000/√ (fasting glucose [mg/dL] × fasting insulin [mU/L]) × (mean glucose × mean insulin) [Tg, TC, and HDL-Chol were measured using standardized enzymatic methods, and LDL-Chol was calculated using Friedewald’s method. | PMC10608521 | |
2.2.3. Evaluation of Liver Steatosis and Fibrosis | steatosis, Liver steatosis, hepatic abnormalities, fibrosis | STEATOSIS, LIVER STEATOSIS, FIBROSIS | Liver steatosis and fibrosis were estimated using transient elastography (Fibros canThree groups were categorized according to hepatic abnormalities evaluated using Fibroscan: non-MASLD (children without steatosis and fibrosis), MASLD (children with steatosis and without fibrosis), and MASLD + fibrosis (children with steatosis and fibrosis). | PMC10608521 |
2.2.4. Targeted Metabolomic Determinations | AC18OH | A targeted metabolome analysis was carried out, including glucose, free carnitine, acylcarnitines (AC2, AC3, AC4, AC5, AC5:1, AC6, AC8, AC8:1, AC10, AC10:1, AC10:2, AC12, AC12:1, AC14, AC14:1, AC14:2, AC14OH, AC16, AC16:1, AC16:1OH, AC16OH, AC18, AC18:1, AC18:1OH, AC18:2, and AC18OH), arginosuccinate (ASA), and 12 L-amino acids (alanine, glycine, arginine, methionine, proline, arginine, valine, leucine, phenylalanine, tyrosine, citrulline, and ornithine). The above was achieved by using a Quattro Micro API (MicroMass, Cary, NC, USA) tandem mass spectrometer (MS-MS). All procedures for sample preparation and MS-MS analysis were performed using a NeoBase Non-derivatized kit (PerkinElmer, Waltham, MA, USA) according to the manufacturer’s protocol. The serum was dried in filter papers, and single disks were punched from each spot using a 3 mm punch. One disk was used per well. Using a multichannel pipette, 190 μL of extraction solution containing a mixture of the respective stable isotope-labeled internal standards was added to each well. The plate was covered with aluminum foil, shaken at 650× | PMC10608521 | |
2.3. Statistical Analysis | fibrosis | REGRESSION, FIBROSIS | To describe the studied population, the mean and standard deviations (SD) were estimated for continuous variables, and proportions and frequencies were calculated for categorical variables. To evaluate the differences between groups (non-MASLD, MASLD, and MASLD + fibrosis), ANOVA and exact Fisher tests were performed, and Bonferroni post hoc tests were conducted to identify the differences between groups.Metabolite concentrations and biochemical data were median-normalized, square- or cube-root-transformed, and range-scaled. A partial least squares discriminant analysis (PLS-DA) was performed to identify the discrimination between study groups (non-MASLD vs. MASLD, non-MASLD vs. MASLD + fibrosis, and MASLD vs. MASLD + fibrosis). Variable importance projection (VIP) plots were graphed to visualize the metabolites that contributed the most to the discrimination of samples, and heatmaps based on the VIP scores were made to visualize the differences between the groups’ metabolite concentrations and biochemical data.Finally, logistic regression models were constructed considering the metabolites and biochemical data with VIP scores >1.5 as independent variables (different combinations of these variables were tested), and the MASLD groups (Non-MASLD vs. MASLD and Non-MASLD vs. MASLD + fibrosis) as dependent variables. To evaluate whether different sets of metabolites and biochemical data can identify MASLD and MASLD + fibrosis groups, receiver operating characteristic (ROC) curves were estimated. To define the metabolomic phenotype, we selected models with a reduced number of metabolites and an adequate ROC area.A | PMC10608521 |
3. Results | fibrosis | EARLY PUBERTY, FIBROSIS | A total of 79 children and adolescents were included in the study; of them, 52.7% were female with a mean age of 11.74 ± 2.52 years. For males, the mean age was 10.66 ± 1.71. Of the total sample, 39% were classified as prepubertal, 32.4% belonged to the early puberty group, and 28.4% belonged to advanced puberty. There were no significant differences in the Tanner stage between the study groups (non-MASLD, MASLD, and MASLD + fibrosis). No significant differences in sex distribution were found in the prepubertal group; nonetheless, significantly higher proportions of males within early puberty and females within advanced puberty were evident ( | PMC10608521 |
3.1. Metabolic Phenotypes According to the Progression of MASLD | PMC10608521 | |||
3.1.1. Non-MASLD and MASLD | The hierarchical heatmap ( | PMC10608521 | ||
3.1.2. Non-MASLD and MASLD + Fibrosis | The hierarchical heatmap ( | PMC10608521 | ||
3.1.3. MASLD and MASLD + Fibrosis | fibrosis | FIBROSIS | Once the phenotypes for MASLD and MASLD + fibrosis were established, we aimed to identify the differences between MASLD and MASLD + fibrosis. The heatmap showed that children with MASLD + fibrosis had higher concentrations of methionine, leucine, glycine, alanine, proline, arginine, phenylalanine, ornithine, citrulline, carnitine, AC5:1, medium-chain acylcarnitine (AC6, AC8, and AC10:2), AC18, ALT, AST, and GGT than children with only MASLD (The PLS-DA discriminated into two clusters ( | PMC10608521 |
4. Discussion | obesity, MASLD (hepatic steatosis, fibrosis, hepatic and muscle glucose, hepatic abnormalities, hepatic damage, steatosis | OBESITY, FIBROSIS, PATHOPHYSIOLOGY, REMISSION, HEPATIC STEATOSIS, HEPATIC DAMAGE, REGRESSION, HEPATIC FIBROSIS, STEATOSIS | In the present study, we described the metabolomic phenotypes associated with two stages of MASLD progression (MASLD (hepatic steatosis) and MASLD + fibrosis (hepatic steatosis + fibrosis)) in Mexican children with obesity compared to those with obesity but without MASLD. According to logistic regression models, MASLD was associated with a phenotype characterized by increased concentrations of ALT and decreased arginine, glycine, and AC5:1 (tiglylcarnitine). On the other hand, MASLD + fibrosis, a progression stage of MASLD, was associated with a phenotype characterized by increased concentrations of ALT, proline, and alanine and a decreased Matsuda Index. This metabolic signature also identified MASLD + fibrosis children from the MASLD group. A major metabolic change in children affected with MAFLD + fibrosis results in the increased availability of amino acids in circulation.The VIP analyses identified other metabolite differences between groups; however, we wanted to select a reduced set of metabolites and biochemical data capable of identifying MASLD and MASLD + fibrosis groups that might be useful in clinical practice.ALT is a marker of hepatic damage and is widely used to screen MASLD, SH, and hepatic fibrosis in adults and children [Although ALT elevation is part of MASLD and MASLD + fibrosis phenotypes, other metabolites acted as metabolic signatures in each group. The MASLD phenotype included decreased concentrations of arginine, glycine, and AC5:1. The ROC area of this model was below 0.70, and the VIP scores were lower than 2.0; therefore, the measurement of these metabolites was not enough to identify the MASLD phenotype. Despite the above, other studies have found alterations in some of these metabolites. Arginine is essential in the urea cycle and is a precursor of nitric oxide (NO) [The MASLD + fibrosis phenotype included, in addition to increased ALT, increased proline and alanine and a decreased Matsuda Index. Proline is essential in the structure and function of proteins and is a major component of collagen [Increased ALT, proline, and alanine and a decreased Matsuda Index were characteristics of children with MASLD + fibrosis, which discriminated this group from the non-MASLD and MASLD subjects, indicating that these variables reflect specific changes that could be a consequence of the progression of MASLD to fibrosis. Consistent with our findings, a study conducted in a population of Mexican adults with obesity [From a practical and clinical point of view, it seems imperative to generate diagnostic tools in order to detect hepatic abnormalities in children with obesity in a timely manner, since the few available long-term studies have shown that the remission of SH in children and adolescents who participated in an intervention program was 30% in the case in which they did not receive any supplementation or medication; in contrast, with supplementation, remission was 58% and, with medication, it was 41% [Some limitations must be considered in the present study. First, the sample size was small. However, it was sufficient for identifying metabolic phenotypes that are consistent with the findings of other studies and the pathophysiology of MASLD, even though these metabolites are not yet routinely used. On the other hand, a liver biopsy was not performed, which we know is the gold standard for diagnosing steatosis and hepatic fibrosis; nonetheless, FibroScanA strength of this study is that all patients belonged to Class 1 obesity (which provides internal validity), they had similar genetic–environmental backgrounds, and they presented similar durations with respect to obesity. Moreover, we evaluated glucose metabolism using the Matsuda index, which more accurately reflects hepatic and muscle glucose sensitivity compared to those that consider a single blood determination. Additionally, to our knowledge, this is the first study aimed at identifying a metabotype for MASLD stages in Mexican children living with obesity. | PMC10608521 |
5. Conclusions | obesity, metabolic alterations | OBESITY, PATHOLOGY, DYSFUNCTION | According to our findings, MASLD phenotype changes as this dysfunction progresses, involving a switch in amino acid use. The assessment of ALT, proline, alanine, and Matsuda Index could be considered as metabolic signatures of MASLD in children living with obesity. However, more studies are necessary to broaden the knowledge about metabolic alterations in MASLD in order to prevent and identify this pathology during childhood, as well as its comorbidities, in addition to improving clinical treatment. | PMC10608521 |
Author Contributions | N.G.-N.: Conceptualization, methodology, investigation, data curation, writing—original preparation, review and editing, visualization, project administration, and funding acquisition. K.P.-E.: Investigation, data curation, and project administration. I.O.-G.: Formal analysis, writing—original preparation, and review and editing. M.J.G.-H.: Investigation. E.V.-O.: Investigation. M.F.-T.: Investigation. J.L.P.-H.: Investigation. M.L.-H.: Investigation. E.L.-S.: Formal analysis. B.P.-G.: Formal analysis. J.C.L.-A.: Review and editing. M.L.-M.: Review and editing. F.V.-O.: Conceptualization, methodology, preparation, visualization, review and editing, supervision, and funding acquisition. All authors have read and agreed to the published version of the manuscript. | PMC10608521 | ||
Institutional Review Board Statement | The study was approved by the Hospital General de Mexico’s Research, Ethics and Biosafety for Human Research Committees (Number DI/17/311/03/028; Approval date: 19 April 2017), was conducted in accordance with the 1975 and 2013 Declaration of Helsinki, and adhered to the Good Clinical Practice Guidelines issued by the International Conference of Harmonization. All patient data were protected in compliance with the Health Insurance Portability and Accountability Act (HIPAA). | PMC10608521 | ||
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC10608521 | ||
Data Availability Statement | The data presented in this study are available upon request from the corresponding author. | PMC10608521 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10608521 | ||
References | fibrosis | REGRESSION, FIBROSIS | Serum metabolite and biochemical profile in children with MASLD. (Serum metabolite and biochemical profile in children with MASLD + fibrosis. (Serum metabolite and biochemical profile in children with MASLD. (Demographic, clinical, biochemical, and metabolomic characteristics of children enrolled in the study.The Evaluated logistic regression models to identify the phenotypes of MASLD. | PMC10608521 |
Key summary points | PMC10169200 | |||
Aim | Does a multicomponent agility training improves handgrip strength (maximum force and rate of force development) in healthy older adults and what is the link between handgrip strength dimensions and agility in healthy older adults? | PMC10169200 | ||
Findings | Neither maximum handgrip strength nor rate of force development of handgrip strength in healthy older adults is influenced by a 1-year multicomponent agility training. However, maximum handgrip strength and rate of force development are associated with agility performance measured via the agility challenge for the elderly. | PMC10169200 | ||
Message | Handgrip strength is not influenced by multicomponent agility training but could serve as an indicator for agility performance in older adults. | PMC10169200 | ||
Purpose | Handgrip strength is considered as important indicator for general fitness in older adults. However, it does not notably reflect adaptations from whole-body training but may reflect adaptions of multicomponent exercise training. These approaches seem to be more functional and related to relevant daily tasks. Effects of multicomponent agility training on handgrip strength are analysed. | PMC10169200 | ||
Methods | Healthy older adults ( | PMC10169200 | ||
Results | Neither maximum handgrip strength (F | PMC10169200 | ||
Conclusion | A 1-year multicomponent agility training does not affect handgrip strength in healthy older adults. However, handgrip strength (F | PMC10169200 | ||
Keywords | Open Access funding enabled and organized by Projekt DEAL. | PMC10169200 | ||
Introduction | sarcopenia | DISEASES, SARCOPENIA | Measuring maximum isometric handgrip strength is common in the field of gerontology as it is considered as an important vitality surrogate for general fitness, cognitive status, frailty and sarcopenia in older adults [The effect of general exercise training and physical activity on handgrip strength is merely small to moderate. A meta-analysis of our group revealed that handgrip strength does not notably reflect adaptations from whole-body resistance training in healthy older adults [Physical activities, even when not directly affecting handgrip strength, with low intensity, can lead to important health benefits and risk reductions of both non-communicable diseases and aging-associated diseases [The results from the previous meta-analysis of our working group [Thus, the present RCT aims at analysing the effects of a multicomponent agility training approach on handgrip strength in healthy older adults. Furthermore, it aims analysing the correlation between the measurements of handgrip strength and agility operationalized by the agility challenge for the elderly (ACE). The hypothesis is that the multicomponent agility training improves maximum force and rate of force development of handgrip strength in healthy older adults. In addition, it is hypothesised that a higher value of maximum force and rate of force development correlates with a faster time measured for completing ACE. | PMC10169200 |
Method | The present study is a two-armed randomized controlled intervention trial over 1 year. All participants were informed about the procedures and provided informed written consent. The study is in accordance with the Declaration of Helsinki, and received ethical approval of the German Sport University (no. 31/2018). Anthropometric data, maximum strength and rate of force development of handgrip strength and agility via the agility challenge for the elderly (ACE) were assessed before and after the intervention period. All testing session were conducted according to established standard operational procedures at the laboratory and were performed at the similar time of day for each participant. | PMC10169200 | ||
Intervention | IG took part in multicomponent agility training (strength, coordination, start-stop movements and change of directions, dual task and decision-making tasks) twice a week on non-consecutive days for 1 year. The total training volume was around 90 sessions and adherence of the participants ranged between 56 and 91% (mean 75%, standard deviation 10%). Participants were divided into three training groups of 13 participants to be able to individually assess everyone. Two trained study assistants (male and female) with experience in exercise training for older adults supervised all training sessions. The training was held at gyms at the German Sport University and were equipped with regular basic equipment as mats, balls, cones, ropes, hoops, etc. Every session lasted 60 min, where 10 min were agility specific warming-up, 45 min of agility training and 5 min of cool-down. The detailed description of the training program can be found elsewhere [ | PMC10169200 | ||
Measurements | PMC10169200 | |||
Handgrip strength (HGS) | Handgrip strength (FData were computed using the software IsoTest (version 2.0, meachTronic, Hamm, Germany). Measurements were conducted with a 100 Hz sample rate. Maximum handgrip strength measured in Newton is the highest force value participants could reach. Out of the three trials, the mean of the two best trials was used for further analysis. Rate of force development measured in Newton per seconds was analysed as the maximum slope in the force–time curve averaged over 150 ms (centered moving average). Again, the mean of the two best trials out of the three were used for further analysis. | PMC10169200 | ||
Agility challenge for the elderly (ACE) | As a functional and integrative test for neuromuscular and cardiocirculatory capacity, the ACE course was assessed. The detailed description of the course and its three segments (i) start-and-stop movements, (ii) change of direction and (iii) spatial orientation can be found elsewhere [ | PMC10169200 | ||
Statistical analysis | All data were checked for normal distribution using the Shapiro–Wilk-test. A mixed ANOVA (2 (group: IG vs. CG) × 2 (time: pre vs. post) was computed to analyse the time × group interaction effect of maximum handgrip strength and rate of force development. Post-hoc testing was done in case of a statistical significant interactions. To estimate the corresponding main or interaction effect sizes, partial eta squares ( | PMC10169200 | ||
Results | PMC10169200 | |||
Handgrip strength | PMC10169200 | |||
Rate of force development of handgrip strength | Repeated measurements ANOVA for handgrip strength RFD (see Table | PMC10169200 | ||
ACE course | Comparing pre (M = 50.7; SD = 6.8) and post (M = 46.3; SD = 5.0) measurement, participants were significantly faster in completing the course ( | PMC10169200 | ||
Correlation analysis | Spearman correlation for ACE and maximum handgrip strength (r(64) =− 0.367, Spearman correlation for time to complete ACE challenge (in seconds) and | PMC10169200 | ||
Author contributions | BKL performed parts of the measurement, did the statistical analysis and wrote the manuscript in consultation with LD. | PMC10169200 | ||
Funding | Open Access funding enabled and organized by Projekt DEAL. This research received no specific Grant from any funding agency in the public, commercial, or not-for-profit sectors. | PMC10169200 | ||
Data availability | All data analysed during this study are included in this article. Further enquiries can be directed to the corresponding author. | PMC10169200 | ||
Declarations | PMC10169200 | |||
Conflict of interest | All authors declare that they have no conflicts of interest. | PMC10169200 | ||
Ethical approval | All participants were informed about the procedures and provided written informed consent. The study is in accordance with the Declaration of Helsinki, and got an ethical approvement of the German Sport University (no. 31/2018). | PMC10169200 | ||
Informed consent | All participants were informed about the procedures and provided informed written consent. | PMC10169200 | ||
References | PMC10169200 | |||
Background | SKIN CONDITION, PERISTOMAL SKIN COMPLICATION | Peristomal skin complications (PSCs) pose a major challenge for people living with an ostomy. To avoid severe PSCs, it is important that people with an ostomy check their peristomal skin condition on a regular basis and seek professional help when needed. | PMC10734405 | |
Aim | To validate a new ostomy skin tool (OST 2.0) that will make regular assessment of the peristomal skin easier. | PMC10734405 | ||
Methods | Seventy subjects participating in a clinical trial were eligible for the analysis and data used for the validation. Item-level correlation with anchors, inter-item correlations, convergent validity of domains, test-retest reliability, anchor- and distribution-based methods for assessment of meaningful change were all part of the psychometric validation of the tool. | PMC10734405 | ||
Results | A final tool was established including six patient reported outcome items and automatic assessment of the discolored peristomal area. Follow-up with cognitive debriefing interviews assured that the concepts were considered relevant for people with an ostomy. | PMC10734405 | ||
Conclusion | COMPLICATIONS | The OST 2.0 demonstrated evidence supporting its reliability and validity as an outcome measure to capture both visible and non-visible peristomal skin complications. | PMC10734405 | |
Introduction | LEAKAGE, SKIN, COMPLICATION, SKIN CONDITION, LEAKAGE, COMPLICATIONS | A compromised skin barrier in the peristomal area can be detrimental to people living with an ostomy. Findings from a recent systematic literature review demonstrated that peristomal skin complications (PSCs) are the most frequent post-operative complication associated with creation of an ostomy (Leakage (ostomy output under the adhesive part of the appliance) is a major contributor to development of PSCs. The occurrence of leakage has been shown to significantly correlate with the incidence of PSCs (The Ostomy Skin Tool (OST) is a clinical reported outcome tool designed to assess the condition of peristomal skin in a standardized manner and is considered state-of-the-art approach for this purpose (The DET score has been widely used across various clinical studies for evaluation of peristomal skin conditions (Given the limitations of the DET score in the OST, the aim of the current study was to validate a new score for a patient-reported version of the OST. The new tool, referred to as OST 2.0 ( | PMC10734405 | |
Materials & Methods | PMC10734405 | |||
Study design | COMPLICATION | Data for the psychometric validation study was obtained from a randomized controlled, open-label, comparative, cross-over, multicenter investigation (Clinical Trial ID: NCT04101318). This investigation was carried out in four countries including United Kingdom (UK), Germany, Italy, and Norway. Subjects were eligible for enrolment if they had a colostomy or ileostomy for at least three months, were at least 18 years old, were able to use an electronic diary (questionnaire), had liquid fecal output, and an existing skin complication in the peristomal area. A total of 79 subjects were enrolled of which 72 completed the investigation. Of these, 70 subjects were eligible to be part of the psychometric analysis population. A small subset of the participants from UK were asked if they were willing to participate in an exit cognitive debriefing interview. Prior to commencing data collection, the investigation was approved by the local ethics committee in each country (UK: 20/LO/0220, Germany: 19-363 and 00012177, the Netherlands: NL71653.068.19, Italy: NP 3841, and Norway: 65025). All subjects provided written informed consent. | PMC10734405 | |
Patient reported outcome (PRO) questionnaire | itching,, pain | The new OST 2.0 comprises a PRO questionnaire consisting of six items designed to assess the severity of PSCs (The remaining three items (Q4–Q6) assess symptoms of itching, pain, and burning (sensation symptoms). For each symptom, the corresponding item asks the subject to rate the severity of the symptom at its worst since the last ostomy product change. These items utilize a 0–10 numerical rating scale ranging from 0 (No symptom) to 10 (Worst possible peristomal skin symptom).In an exit interview 12 subjects from the UK population participated in 30 min Cognitive Debriefing (CD) interviews conducted by phone.During the interviews, subjects were asked to discuss and evaluate item relevance, interpretation of items, item response options, and recall periods. Moreover, the subjects were asked whether they thought any important concepts were missing and whether any items should be removed. All interviews were audio-recorded and transcribed verbatim. Qualitative analysis of the verbatim transcripts, was conducted using the computer assisted qualitative analysis software program ATLAS.ti. ( | PMC10734405 | |
Peristomal skin image analysis | Image analysis techniques were applied to pictures of peristomal skin taken by the subjects to quantify the total area of discolored skin. Specifically, this was an automated assessment using an algorithm based on artificial intelligence ( | PMC10734405 | ||
Decision tree model scoring | SKIN CONDITION, COMPLICATIONS | The PRO questionnaire and image analysis data were combined in a Decision Tree model to provide an overall score between the score 0–3 representing the severity level of skin complications for each patient. A composite score of 0 represents no treatment required peristomal skin condition and the score of 3 is represents a severe peristomal skin condition. | PMC10734405 | |
Anchor measures | stoma, ’ | EROSION, COMPLICATIONS | For the psychometric evaluation, five anchor measures were included. After review of the literature for gold standard measures to use as anchor measures, it was deemed there were none that were appropriate for use. As such, new items were developed in line with US FDA guidance (For the PGIS anchor, subjects were initially asked whether they had “any skin complications around your stoma today” (Yes/No). If patients answered ‘Yes’, they were then asked to “describe the skin complications around your stoma today”, using a five-point Likert-type scale, with options of ‘very mild’, ‘mild’, ‘moderate’, ‘severe’, and ‘very severe’. These responses were coded from ‘1- very mild’ to ‘5- very severe’ (0 if ‘No’ to the first question). This was asked at both visits.For the PGIC anchor, subjects were asked “Compared to the beginning of this test period, how have any skin complications around your stoma changed”. Response options used a seven-point Likert-type scale ranging from ‘1 = very much improved, 2 = much improved, 3 = a little improved, 4 = no change, 5 = a little worse, 6 = much worse, 7 = very much worse’. This question was completed at Visit 2 only.For the CGIS anchor, three versions of the anchor were included. These questions asked about the subject’s overall PSCs, erosion, and discoloration. Firstly, “Does the subject have any PSCs on the peristomal skin today?” (Yes/No). Secondly, “If yes, overall, how would you describe the severity of the subject’s PSCs on the peristomal skin today?” (very severe, severe, moderate, mild, very mild). The responses were coded from ‘1- very mild’ to ‘5- very severe’ (0 if ‘No’ to the first question). This was asked at both visits.Similarly, there were three CGIC questions asking about changes in the subject’s PSCs. Response options used a seven-point Likert scale ranging from ‘1 = very much improved, 2 = much improved, 3 = a little improved, 4 = no change, 5 = a little worse, 6 = much worse, and 7 = very much worse’. This was asked at Visit 2 only.The DET score as an anchor measure was calculated by summing all scores given, which resulted in a range of scores from 0 to 15, where higher scores represented more severe symptoms. | PMC10734405 |
Psychometric validation | Data for the psychometric validation was derived from 70 eligible subjects participating in the clinical investigation (Clinical Trial ID: NCT04101318). Although the study was a cross-over design, only data from the first test period was used (Visit 1 and Visit 2) with exception of the subpopulation eligible for the test-retest evaluation. A detailed overview of the clinical trial is outlined in | PMC10734405 | ||
Analysis | All analyses were pre-defined in a statistical analysis plan prior to conducting psychometric evaluation and conducted using SAS software (SAS Institute Inc. Cary, NC, USA). The psychometric evaluation was conducted in accordance with European Medicines Agency and US Food & Drug Administration (FDA) best practice guidelines ( | PMC10734405 | ||
Item-level correlations with anchors | To evaluate the properties of the individual items, the relationships with anchor measures was explored. Specifically, correlations with the PGIS anchor were explored, and correlations were calculated using data collected at Visit 2, where the PRO data used was from the closest assessment to Visit 2 (provided this was within four days) in the psychometric analysis population. For item 1–3, the point-biserial correlation coefficient was determined due to the use of a dichotomous scale’. For item 4–6, the polyserial correlation coefficient was determined for these severity items. For all correlation coefficients, the following interpretation cut-offs were applied: ‘weak correlation’: | PMC10734405 | ||
Inter-item correlations | Inter-item correlations were used to explore the relationships among the PRO items. Inter-item correlations were determined using correlation coefficients appropriate for the variables in question between each pair of items at Visit 1. Due to the complexity and variety of the data of interest, using a single type of correlation coefficient would not have been appropriate for all calculations. For item 1–3 (dichotomous scale), the appropriate correlation coefficient was simple matching coefficient, while Pearson’s correlation coefficient was used for the inter-item correlations of item 4–6. Items correlating very highly with one another ( | PMC10734405 | ||
Convergent validity of domains | The convergent validity method was applied to evaluate the construct validity and correlation between the different measures ( | PMC10734405 | ||
Test-retest reliability | bleeding, ulcer, ’ | BLEEDING, ULCER | Test-retest reliability was used to evaluate the stability of the PIB score and the Decision Tree score in relation to the PGIS, PGIC, CGIS, and CGIC anchor. Moreover, the stability of the weeping, bleeding, and ulcer items were evaluated using the same four anchors. The test-retest reliability measured the degree to which the given score was similar at different points in time in a subset of ‘stable’ patients. A stable subject was defined as a subject with no change in PGIS and CGIS scores from Visit 1 to Visit 2 and similarly no change for the PGIC and CGIC scores from Visit 1 to Visit 2.The test-retest reliability was determined by calculating the intraclass correlation coefficient (ICC). Specifically, an ICC based on a single measurement, absolute agreement, two-way mixed effects model was used which has been specifically recommended for use in test-retest reliability analyses ( | PMC10734405 |
Known-groups analysis | stoma, ’ | COMPLICATIONS | The PIB score and the Decision Tree score were evaluated in patients who differed on variables hypothesized to influence the construct of interest. The magnitude of differences in scores characterized the degree to which the PIB score/Decision Tree score could distinguish among groups hypothesized a priori to be clinically distinct. Known-groups comparisons were assessed using data from the measurement period associated with Visit 2 in the psychometric analysis population. The known-groups were defined for the PGIS anchor by asking the following question: ‘Do you have any complications around your stoma today? If yes, overall, how would you describe the skin complications around your stoma today’. This led to three defined groups: ‘Group 1- no (reference)’, ‘Group 2- very mild or mild’, and ‘Group 3- moderate, severe, or very severe’.The magnitude of the differences was evaluated using between-group effect size estimates, calculated using the pooled standard deviation (SD) as the denominator, and based against the reference group as defined. The following cut-offs were used to interpret the magnitude of each effect size (ES): small change (ES = 0.20), moderate change (ES = 0.50), and large change (ES = 0.80) ( | PMC10734405 |
Ability to detect change | The ability of a score to detect change over time was assessed using data from the measurement periods associated with Visit 1 and Visit 2 in the psychometric analysis population. To investigate the ability of the PIB score to detect change, subjects were grouped according to the PGIC anchor and categorized into ‘Improved’, ‘Stable’, and ‘Worsened’ groups as follows: ‘Improved’ (very much improved, much improved, or a little improved at Visit 2), ‘Stable’ (no change at Visit 2), and ‘Worsened’ (a little worse, much worse, or very much worse at Visit 2). For the Decision Tree score, the same groups were defined using the CGIS anchor instead. For both domains, the frequency and percentage of subjects in each category were summarized, and the mean change scores for each group from Visit 1 to Visit 2 were listed alongside the SD. The mean change scores were compared between the three groups, and one-way ANOVA | PMC10734405 | ||
Anchor-based methods for assessing meaningful change | COMPLICATIONS | Anchor-based methods were conducted to establish the level of change which could be considered meaningful for the domains. For this analysis, both PIB weekly mean and PIB weekly maximum scores were assessed alongside the Decision Tree score. The anchor-based analyses were performed in the psychometric analysis population using data from Visit 1 and Visit 2. The suitability of proposed anchors was tested using a polyserial correlation coefficient to establish the relationship between the anchor categories and change in domain scores. Anchors with correlations of For PIB weekly mean and PIB weekly maximum, PGIC was the only anchor demonstrating a sufficient polyserial correlation coefficient. Thus, the PGIC anchor was used to define groups of patients who had experienced improvement or no change. For the Decision Tree score, the CGIS anchor was used instead due to a sufficient polyserial correlation coefficient, and patient groups were again defined as experiencing either improvement or no change. Subjects with worsened skin complications were excluded from this analysis. The groupings based on the PGIC/CGIS anchor were as follows: ‘Improved’ (very much improved, much improved, or a little improved at Visit 2) and ‘Stable’ (no change at Visit 2).The within-group mean change scores evaluated the minimal important change (MIC) within groups. The mean change in domain score was calculated for patients classified according to the PGIC anchor (PIB weekly mean and PIB weekly maximum) and the CGIS anchor (Decision Tree score). The MIC estimate was derived using each groups’ mean change scores.The between-group differences in mean change scores evaluated the minimal important difference (MID) between groups. This analysis informed between-group MID estimates, and the mean change in domain scores was calculated for patients classified as above according to the PGIC anchor (PIB weekly mean and PIB weekly maximum) and the CGIS anchor (Decision Tree score). The MID estimate was defined as the difference in mean change score between these groups. | PMC10734405 | |
Distribution-based methods for assessing meaningful change | A distribution-based approach was employed, and these methods consisted of computing the SD and the standard error of measurement (SEm) ( | PMC10734405 | ||
Results | PMC10734405 | |||
Sociodemographic profile | The psychometric analysis sample was comprised of a total of 70 subjects living with an ostomy. There was an even distribution between females (51%) and males (49%), and the population had a mean age of 55.3 years ( | PMC10734405 | ||
Sociodemographic profile of subjects. | The psychometric analysis population was comprised of 70 subjects living with an ostomy. Data shows distribution of samples according to gender, age, and type of ostomy. | PMC10734405 | ||
Item-level correlations with anchors | The severity items were correlated with the PGIS anchor. | PMC10734405 | ||
Item-level correlations. | bleeding | BLEEDING | The correlations of the six items were determined by calculating the relevant correlation coefficient based on the PGIS anchor (Based on the applied cut-off values, five out of six items demonstrated a moderate or strong correlation with the PGIS anchor. The item regarding bleeding (item 1) showed a 0.266 correlation coefficient, which was therefore classified as a weak correlation with the given anchor. | PMC10734405 |
Inter-item correlations | itching, pain | To explore how the items could be grouped into domains, the inter-item correlations were examined among the items assessing itching severity, pain severity, and burning severity (item 4, 5, and 6). As depicted in | PMC10734405 | |
Inter-item correlations for severity items. | bleeding, itching, pain | BLEEDING | The Pearson’s correlation coefficient was determined for the itching severity, pain severity, and burning severity items. The weeping, bleeding, and ulcer/sore items were also subject to inter-item correlation analysis. All correlation among those items were poor; thus, the weeping, bleeding, and ulcer/sore items were not combined into a domain score but kept as single items ( | PMC10734405 |
Convergent validity of domains | In addition to the composite outcome score of the OST 2.0, namely the Decision Tree score, the PIB domain was also taken through for further validation at the domain level. The PGIS and DET score were the two anchors used for assessing convergent validity of the two domains. When determining the polyserial correlation coefficient, it was evident that the PIB score correlated moderately with the PGIS anchor ( | PMC10734405 | ||
Convergent validity of domains. | The polyserial correlation coefficient was determined for correlation of the PIB score (weekly mean) and the PGIS anchor ( | PMC10734405 | ||
Test-retest reliability | The ICC can be interpreted as the correlation between repeatedly measured scores within subjects, where higher values indicate greater stability in scores. The test-retest reliability was investigated for the PIB score (weekly mean) and the Decision Tree score. The PIB score demonstrated good reliability when using the CGIS anchor (ICC = 0.871) and the PGIC anchor (ICC = 0.785) ( | PMC10734405 | ||
Test-retest reliability of weekly mean domain scores between the two visits. | bleeding | BLEEDING | The test-retest reliability of the PIB score (weekly mean) and Decision Tree score were evaluated by calculating the intraclass correlation coefficient (ICC). Data is listed with 95% confidence intervals displayed in brackets. For the number of subjects, data is displayed as n (PIB score)/n (Decision Tree score). The following cut-offs were applied: ICC < 0.5 indicated poor reliability, ICC values between 0.5 and 0.75 indicated moderate reliability, ICC values between 0.75 and 0.9 indicated good reliability, and ICC values greater than 0.90 indicated excellent reliability.When evaluating the bleeding item, strong ICC scores when stable patients were defined using the PGIS, PGIC, and CGIC anchors (ICC range: 0.758–0.804) were demonstrated, whereas for the CGIS anchor test-retest results were poor (ICC = 0.314) ( | PMC10734405 |
Test-retest reliability of bleeding, weeping, and ulcers/sores items. | bleeding | BLEEDING | The test-retest reliability the bleeding, weeping, and ulcers/sores items were evaluated by calculating the intraclass correlation coefficient (ICC). Data is listed with 95% confidence intervals displayed in brackets. The number of subjects used for the analysis is displayed (n). The following cut-offs were applied: ICC < 0.5 indicated poor reliability, ICC values between 0.5 and 0.75 indicated moderate reliability, ICC values between 0.75 and 0.9 indicated good reliability, and ICC values greater than 0.90 indicated excellent reliability. | PMC10734405 |
Known-groups analysis | The known-groups analysis of the PIB score and the Decision Tree score was evaluated by comparing groups defined based on the PGIS anchor. When evaluating the differences in PIB mean scores between the three groups, Group 1 (reference) showed a mean score of 1.5, while group 2 and 3 demonstrated a mean score of 1.9 and 3.6, respectively ( | PMC10734405 | ||
Known-groups analysis of the domain scores. | COMPLICATIONS | Known-groups analysis was investigated for the PIB score (weekly mean) and for the Decision Tree score. Subjects were divided into three groups depending on presence and severity of peristomal skin complications. Using the PGIS anchor, the between group effect sizes (ES) were estimated using the pooled standard deviation (SD) based on the reference group (Group 1). The following cut-offs were applied: small change (ES = 0.20), moderate change (ES = 0.50), and large change (ES = 0.80). The | PMC10734405 | |
Ability to detect change | The ability of the PIB score to detect change was investigated by using the PGIC anchor to define change groups, while the ability of the Decision Tree score to detect change was evaluated by comparison with the CGIS anchor. The mean change score was assessed for the three groups of subjects. For the PIB score, the change score was negative (indicating an improvement in score) in the improved group (mean change score = −1.6) with a larger change compared to the stable population (mean change score = −0.3) ( | PMC10734405 | ||
Ability to detect change of domain scores. | The ability of the PIB score (weekly mean) to detect change was evaluated by use of the PGIC anchor, while the ability of the Decision Tree score to detect change was investigated by comparison with the CGIS anchor. Subjects were divided into three groups depending on their progression from Visit 1 to Visit 2. These groups included ‘Improved’ subjects (very much improved, much improved, or a little improved at Visit 2), ‘Stable’ subjects (no change at Visit 2), and ‘Worsened’ subjects (a little worse, much worse or Very much worse at Visit 2). The mean change score was determined. One-way ANOVA F-test was used to calculate potential statistical significance of differences in change scores between groups.For the Decision Tree score, a larger negative change in mean score was shown for the improved group (mean change score = −0.4) compared to the stable one (mean change score = −0.1). Moreover, the worsened group demonstrated a positive change in mean score (mean score = 0.1) compared to the stable group (mean change score = −0.1) ( | PMC10734405 | ||
Anchor-based methods of score interpretation | To establish an estimate for a meaningful change in domain score, a correlation between the anchor and the change in domain scores of | PMC10734405 | ||
Meaningful change estimates for domain scores. | Meaningful change estimates for the PIB weekly mean and PIB weekly maximum domains were calculated using the PGIC anchor. For the Decision Tree score, the CGIS anchor was used instead. The correlation between the anchor and the change in domain score was determined by calculating polyserial correlation coefficient. Subjects were divided into groups based on their progression from Visit 1 to Visit 2. According to the anchor point used, the groups were defined as ‘Improved’ (very much improved, much improved, or a little improved at Visit 2) and ‘Stable’ (no change at Visit 2). Meaningful change estimates were determined within subjects (minimal important change) and between groups (minimal important difference). Data is displayed as the mean change score / mean difference score with the 95% confidence interval being displayed in brackets for each mean value.
minimal important changeminimal important difference | PMC10734405 | ||
Distribution-based methods of score interpretation | In addition to the anchor-based methods, distribution-based methods were also used to determine a meaningful change for the domain scores. These methods aimed to identify the smallest amount of change which exceeded measurement errors. Thus, the distribution-based estimates, in the form of 0.5 SD and the SEm, were calculated for the domain scores. For PIB weekly mean, the distribution-based methods suggested a point reduction exceeding 1.13 to be meaningful ( | PMC10734405 | ||
Distribution-based estimates for PIB weekly mean and PIB weekly maximum. |
The distribution-based estimates were determined for the PIB weekly mean and PIB weekly maximum domain. The estimates were 0.5 of the SD and the SEm.
standard deviationstandard error of measurementintraclass correlation coefficient | PMC10734405 | ||
Discussion | bleeding, itching, pain | BLEEDING | The OST 2.0 was designed to evaluate the severity of PSCs within the ostomy population, and the Decision Tree score offers a simple and evidence-based categorization of PSC severity (Despite the continuous development of improved ostomy devices, people living with an ostomy continue to experience challenges with PSCs (A review by To be fit for purpose, an instrument should demonstrate psychometric properties including validity, reliability, and responsiveness to change (Overall, the OST 2.0 instrument demonstrated good correlations with the anchor measures at item level, and inter-item correlations were therefore subsequently evaluated; revealing that pain, itching, and burning severity items could be mapped together. Thus, generating the possibility of using the PIB score as a second composite score in addition to the Decision Tree score, which currently is the outcome score of the OST 2.0.Concept elicitation work performed during development of the OST 2.0 (When evaluating convergent validity of the PIB domain, a moderate correlation with the PGIS anchor was found, while its correlation with the DET score was weak. These data underlined that there was conformity in what the PIB score measures and what people with an ostomy were experiencing. The weak correlation with the DET score was expected as it further supports the difference between what the DET score measures and how people with an ostomy experience sensation symptoms in the peristomal area. The Decision Tree score demonstrated a strong correlation with the DET score, which could partially be due to the incorporation of peristomal image analysis and subsequent quantification of the discolored area in this domain. Moreover, this correlation could also reflect that the visible signs of PSCs (weeping, bleeding, and ulcer/sores) are an integrated part of the Decision Tree score. As the discoloration domain is strongly impacting the outcome of the DET score (The OST 2.0 demonstrated good stability based on the test-retest reliability assessment. This evaluation was conducted to evaluate the degree to which the PIB (weekly mean) score and the Decision Tree score were similar over time in a subset of subjects (defined as having stable peristomal skin according to anchor points). In general, test-retest reliability findings should be interpreted in consideration of the ability to detect change findings, as good test-retest reliability can be the artefact of a score being unable to detect change. If an instrument like the OST 2.0 is intended to measure a change in patients over time, it is crucial that the tool is responsive to change (The ability to detect change is an inevitable prerequisite to subsequently determine the meaningful change of a score. Positioning the magnitude of a given clinical change into a meaningful context can often be challenging and a statistical analysis for interpreting the outcome of a clinical score should not stand alone ( | PMC10734405 |
Limitations | Despite the fact that the psychometric analysis sample was broad and representative of the end user population, the study did encompass some limitations. Specifically, the sample size for (70 subjects for the psychometric validation) could have been larger although similar sample sizes have been used for other tools PGI/CGI items were developed specifically for use as anchor measures in the psychometric evaluation of the OST 2.0 due to lack of existing measures that would be appropriate for these analyses. However, the PGI/CGI items were qualitatively tested prior to use to ensure patients understood the items as intended, and the items were developed in line with FDA guidance. Additionally, comparisons of the DET and OST 2.0 scores were drawn to confirm that the new OST 2.0 measures the same concepts as the DET score but with the aim of being more sensitive.Finally, different types of correlations were used in the analyses based on the type of data included. Although this follows guidelines it may be harder to draw comparisons across correlations. Factor analysis was not performed to evaluate dimensionality due to sample size limitations and the complexity of the instrument. | PMC10734405 | ||
Conclusions | discolored peristomal skin | SKIN CONDITION | This study presents the psychometric validation of the OST 2.0 instrument. The evidence provided support that OST 2.0 is reliable and valid for assessing severity of PSCs. Unlike the OST, this new tool enables close monitoring and captures subjects with PSC even in the absence of discolored peristomal skin. The Decision Tree score and PIB score both have great potential as a primary endpoint in clinical investigations. However, the meaningful change estimates should be interpreted with caution due to the sample size and the SD intervals of the estimates. Collectively, the OST 2.0 instrument provides a standardized, objective, sensitive, and easy-to-use approach for closely assessing changes in peristomal skin conditions over time, which can capture both visual and non-visual symptoms of PSC. | PMC10734405 |
Supplemental Information | PMC10734405 | |||
Patient questionnaire | Click here for additional data file. | PMC10734405 | ||
Study design | Click here for additional data file. | PMC10734405 | ||
Skin area visit 3 | Click here for additional data file. | PMC10734405 | ||
Skin Discolouration score | Click here for additional data file. | PMC10734405 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.