Measures Of Discrimination Research Articles

Social Maltreatment and Symptomatology: Validating the Social Discrimination and Maltreatment Scale-Short Form in a Diverse Online Sample.

There are few psychometrically valid measures of exposure to social maltreatment that simultaneously assess sexism, racism, and anti-LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and other nonheteronormative) behavior, despite the commonness of these phenomena. The Social Discrimination and Maltreatment Scale (SDMS) meets this requirement but is, as a result, somewhat lengthy (36 items). This article introduces a short form of the SDMS containing only half the number of items but generally retaining the psychometric qualities of the original measure. The 18-item Social Discrimination and Maltreatment Scale-Short Form (SDMS-SF) consists of six SDMS stem items (e.g., I have been disrespected, People made cruel or demeaning jokes about me) each of which is rated according to how often it had happened "because of my sex," "because of my race," and "because of my sexual orientation or gender identity." In the SDMS online sample (N = 528), SDMS-SF Sexism, Racism, and Cisheterosexism subscales were validated by confirmatory factor analysis and were internally consistent (α = .91-.95) and highly correlated with the original SDMS subscales (r = .94 in all cases). All SDMS-SF subscales correlated with self-reported anxiety, depression, and posttraumatic stress (mean r = .29), corresponding to a medium effect size. In all but one instance, related SDMS and SDMS-SF subscales did not differ significantly in the strength of their association with symptomatology. Together, these results suggest that the SDMS-SF is a reliable and valid measure of social discrimination, generally equivalent to the SDMS despite containing only half as many items.

Location Matters: Everyday Gender Discrimination in Remote and On-site Work

Remote work has dramatically transformed professional environments, sparking considerable scholarly interest in its impact on employees and organizations. Contributing to this burgeoning literature, we investigate how remote versus on-site work affects women’s experiences of gender discrimination. Given that work location can alter the gendered nature of interactions, we focus on everyday gender discrimination—slights and offenses that occur in interactions and are perceived by recipients as reflecting gender bias. Integrating gender frame theory and scholarship on virtual work, we argue that the gender frame tends to be less salient in remote settings. Thus, we predict that women will experience less everyday gender discrimination when working remotely than on-site. Moreover, because the gender frame is likely to be more salient during on-site work for younger women and those who work with mostly men, we expect that these women will experience a particularly pronounced reduction in everyday gender discrimination when working remotely. To test these predictions, we developed a new measure of everyday gender discrimination and conducted an original survey of 1,091 professional women who work in the same job both remotely and on-site. We find that women consistently report less everyday gender discrimination in remote versus on-site work. This effect is particularly pronounced for younger women and those who interact mainly with men. Overall, this study advances research on how work location shapes workers’ outcomes and experiences, enriches the literature on the trade-offs women face in virtual and on-site settings, and extends scholarship on the contextual factors shaping workplace discrimination.

Investigating Eating Symptoms in the LGBTQIA+ Population: Do Discrimination and Adverse Conditions Matter?

Eating disorders (EDs) have traditionally been studied among heterosexual cisgender women, but recent research highlights a higher prevalence in LGBTQIA+ individuals. This study aims (1) to investigate the association between different groups based on gender identity and sexual orientation (GISO) and experiencing eating symptoms, and (2) to explore the extent to which self-perceived discrimination and adverse conditions explain this association. We administered an online survey to assess eating symptoms using the Eating Disorder Examination Questionnaire (EDE-Q 6.0) and measures of self-perceived discrimination and adverse conditions. Multistep logistic regressions were employed to analyze the associations between GISO and eating symptoms, initially unadjusted, then adjusted for sociodemographic variables, and finally adjusted for self-perceived discrimination and/or adverse conditions. A total of 560 adults aged 30 ± 10.9 years old were included. After adjusting for socio-demographics all groups were more likely to experience eating symptoms compared to heterosexual men, with odds ratios (ORs) of 5.7 [95% CI: 1.3, 24.3] for cisgender heterosexual women, 6.7 [95% CI: 1.5, 29.8] for cisgender non-heterosexual women, and 9.3 [95% CI: 1.8, 47.5] for non-cisgender individuals. After adjusting for self-perceived discrimination and adverse conditions, the associations for women were attenuated, while the associations for non-cisgender individuals were no longer significant. This study (1) confirms that sexual and gender minorities may be at higher risk for EDs, and (2) suggests that discrimination and adverse conditions may contribute to the higher prevalence of eating symptoms in this population. Additional research is needed to investigate these minority stressors as they may represent targets for effective interventions to prevent eating symptoms in the LGBTQIA+ community.

Clinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis

BackgroundThe limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC. We assessed models’ characteristics and methodologies, as well as performance, risk of bias (RoB), and applicability.MethodsLiterature searches were conducted in MEDLINE (OVID), Embase (OVID) and IEEE databases from January 2005 to November 2023. The search algorithm used comprehensive text word and index term combinations without language or publication type restrictions. Independent reviewers screened titles and abstracts using a predefined Population, Index, Comparator, Outcomes, Timing and Setting (PICOTS) framework. We included externally validated (EV) multivariable models, with at least one clinical predictor, that provided recurrence or survival predictions. The SR-MA followed PRISMA reporting guidelines, and PROBAST framework for RoB assessment. Model discrimination was assessed using C-index/AUC, and was presented for all models using forest plots. MA was only performed for models that were externally validated in two or more cohorts, using random-effects model. The main outcomes were model discrimination and calibration measures for survival (OS) and/or local recurrence (LR) prediction. All measures and assessments were preplanned prior to data collection.ResultsThe SR-MA identified 11 models, reported in 16 studies. Seven models for OS showed good discrimination on development, with only one excelling (C-index >0.9), and three had weak or poor discrimination. Inclusion of a radiomics score as a model parameter achieved relatively better performance. Most models had poor generalisability, demonstrated by worse discrimination performance on EV, but they still outperformed the TNM system. Only two models met the criteria for MA, with pooled EV AUCs 0.73 (95% CI 0.71-0.76) and 0.67 (95% CI 0.6-0.74). RoB was high for all models, particularly in the analysis domain.ConclusionsThis review highlighted the shortcomings of currently available models, while emphasizing the need for rigorous independent evaluations. Despite the proliferation of models, most exhibited methodological limitations and bias. Currently, no models can confidently be recommended for routine clinical use.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021248762, identifier CRD42021248762.

Assessing the Accuracy of Cardiovascular Disease Prediction Using Female-Specific Risk Factors in Women Aged 45 to 69 Years in the UK Biobank Study.

Cardiovascular disease (CVD) is the leading cause of mortality in women. We aimed to assess whether adding female-specific risk factors to traditional factors could improve CVD risk prediction. We used a cohort of women from the UK Biobank Study aged 45 to 69 years, free of CVD at baseline (2006-2010) followed until the end of 2019. We developed Cox proportional hazards models using the risk factors included in 3 contemporary CVD risk calculators: Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT. We added each of the following female-specific risk factors, individually and all together, to determine if these improved measures of discrimination and calibration for predicting CVD: early menarche (<11 years), endometriosis, excessive, frequent or irregular menstruation, miscarriage, number of miscarriages, number of stillbirths, infertility, preeclampsia or eclampsia, gestational diabetes (without subsequent type 2 diabetes), premature menopause (<40 years), early menopause (<45 years), and natural or surgical early menopause (menopause <45 years or timing of menopause reported as unknown and oophorectomy reported at age <45). In the model of 135 142 women (mean age, 57.5 years; SD, 6.8) using risk factors from Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, CVD incidence was 5.3 per 1000 person-years. The c-indices for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models were 0.710, 0.713, and 0.718, respectively. Adding each of the female-specific risk factors did not improve the c-index, the net reclassification index, the integrated discrimination index, the slope of the regression line for predicted versus observed events, and the Brier score or plots of calibration. Adding all female-specific risk factors simultaneously increased the c-index for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models to 0.712, 0.715, and 0.720, respectively. Although several female-specific factors have been shown to be early indicators of CVD risk, these factors should not be used to reclassify risk in women aged 45 to 69 years when considering whether to commence a blood pressure or lipid-lowering medication.

Systematic review of prognostic models for predicting recurrence and survival in patients with treated oropharyngeal cancer

ObjectivesThis systematic review aims to evaluate externally validated models for individualised prediction of recurrence or survival in adults treated with curative intent for oropharyngeal cancer.DesignSystematic review.SettingHospital care.MethodsSystematic searches were conducted...

Assessing EFL learners’ attitudes on Generative Artificial Intelligence: Development and validation of Generative Artificial Intelligence attitude scale for EFL learners (GenAIAS)

Generative Artificial Intelligence (GenAI) has emerged as a transformative force in education, particularly in the context of English as a Foreign Language (EFL) instruction. This study aims to develop and validate the Generative Artificial Intelligence Attitude Scale (GenAIAS) to assess EFL learners’ attitudes toward GenAI. The research involved two independent samples of university students in Türkiye, with data collected through scales and analyzed using exploratory and confirmatory factor analyses. The final scale, comprising 33 items, was categorized into four factors: Learning/Utility, Enjoyment, Usefulness, and Interest. The scale demonstrated good psychometric properties, including satisfactory convergent and discriminant validity, internal consistency, and measurement invariance across genders. The findings indicate no significant gender differences in attitudes toward GenAI, suggesting a general acceptance of GenAI technologies among EFL learners. Additionally, a positive correlation was observed between daily internet usage and attitudes toward GenAI. This study contributes to the growing body of knowledge on GenAI in language education and offers practical implications for integrating GenAI into language learning, highlighting the importance of fostering positive attitudes for successful technology adoption. Future research should further validate the scale with diverse populations to enhance its reliability and applicability.

Purpose in life moderates the relationship between perceived discrimination and cognitive functioning in midlife and older adults

Objectives Discrimination is associated with worse cognitive outcomes, but research is urgently needed to identify modifiable psychosocial resources that may buffer the impact of discrimination on cognition. Purpose in life is one such resource associated with positive health outcomes. However, it is unclear whether purpose in life may buffer the relationship between discrimination and cognition among older adults. Method We drew a sample of 22,369 adults aged 51 and older in the 2006 to 2020 waves of the nationally-representative Health and Retirement Study (HRS). Multilevel mixed models assessed whether (1) measures of everyday discrimination and purpose in life and (2) a purpose in life x discrimination interaction term were associated with cognition. Results Discrimination was associated with poorer cognitive functioning, both on its own and when controlling for purpose in life. Likewise, purpose in life was significantly associated with better cognitive functioning, with and without controlling for discrimination. Results also indicated that purpose in life significantly moderated the association between discrimination and cognitive functioning (b = 0.05, p = 0.01). Conclusion Findings underscore the importance of psychosocial resources that may serve as stress-buffering mechanisms and protect older adults’ cognitive health. Future research is needed to clarify the biobehavioral mechanisms that may underlie this association.

Assessment of the psychometric properties of the eHealth Literacy Scale for Brazilian adolescents.

With the increase in digitalization of adolescents and health services, the population must demonstrate digital health literacy skills to be able to navigate online health information, as well as identify, evaluate, and apply relevant information. The present study aimed to investigate the psychometric properties of the adapted version of the eHealth Literacy Scale (eHEALS-BrA) for Brazilian adolescents. This study was conducted between September 2022 and May 2023, involving a total of 260 adolescents aged 13 to 19, with a mean age of 15.64 years (SD = 1.84), all of whom were enrolled in public schools in Brazil. Structural validity was evaluated by confirmatory factor analysis (CFA). The reliability of the instrument was assessed using Cronbach's alpha (α) and McDonald's omega (ω), and test-retest reliability. Questionnaires about sociodemographic aspects, health-related characteristics, and internet use were administered and used as discriminant validation measures. Convergent Validity was determined by correction with the domains of the World Health Organization Quality of Life-Bref (WHOQOL-Bref) instrument. For test-retest reliability, 67 participants completed the eHEALS-BrA a fortnight later. The average eHEALS-BrA score was 28.05 points (SD = 5.05). CFA revealed that the model with the best fit had a single factor (χ2 = 49.884 [df = 20, p = 0.0002], CFI = 0.934, TLI = 0.908, RMSEA = 0.076(CI:0.05-0.10), and SRMR = 0.045). The instrument demonstrated good reliability, with satisfactory internal consistency (α and ω = 0.71) and stability (ICC = 0.82, 95% CI:0.70-0.89). The eHEALS-BrA was correlated with the physical, psychological, and environmental domains of the WHOQOL-Bref (p = 0.001) (convergent validity). Higher scores were found among male adolescents, individuals who practiced physical activity, those with better self-rated health, those who considered the internet useful for searching for health information (discriminant validity), and those with better self-rated skills related to using the internet (convergent validity) (p < 0.05). The eHEALS-BrA demonstrated adequate psychometric properties for measuring digital health literacy in Brazilian adolescents.

Psychometric Evaluation of the Heterosexist Harassment, Rejection, and Discrimination Scale: Identification of a Unidimensional 11-Item Scale.

Despite the Heterosexist Harassment, Rejection, and Discrimination Scale (HHRDS) being a routinely used measure of discrimination, there is no current consensus regarding its fundamental psychometric properties. This study sought to: (a) test the dimensionality of the scale using a classical test theory approach, and (b), if lacking a clear multidimensional structure, use an item response theory approach to develop a concise unidimensional measure and examine differential item and test functioning across various identity factors. In a sample of sexual minority adults (N = 1,243), evaluation of the HHRDS supported a reduced 11-item unidimensional measure. Assessment of differential item functioning suggested differences for some items between cisgender men and cisgender women, cisgender men and gender-diverse individuals, gay/lesbian and bi+ individuals, and white/non-Latine individuals and people of color. However, differential item functioning had minimal impact on total test scores, supporting the use of the revised 11-item HHRDS across groups.

Rehabilitation effect of manual lymphatic drainage on pain threshold and tolerance, tactile sensation, and strength.

This study evaluates the acute therapeutic effect of manual lymphatic drainage (MLD). Eighty-two individuals (164 upper limbs) participating in the study were divided into two groups: MLD and sham. Before and after treatment, measurements of pressure pain threshold (PPT), pain tolerance, muscle strength (using a hand dynamometer and pinchmeter), and two-point discrimination (2PD) with an esthesiometer were conducted. Age, height, weight, body mass index, gender, and dominant extremity of the participants showed similar characteristics (p > 0.05). There was a difference hypothenar PPT (p = 0.038) and pain tolerance (p = 0.009), thenar PPT (p = 0.021) and pain tolerance (p = 0.001), mid-ulnar PPT (p = 0.028), biceps PPT (p < 0.001), pain tolerance (p < 0.001), and grip strength (p = 0.030) between the groups after the therapy. When comparison was made between the groups at baseline and after the treatment all were found to differ (p < 0.05). MLD reduced PPR, pain tolerance, hand grip, and pinch strength in young adults. However, in this population, the distance felt in 2PD evaluation with MLD decreased.

Prediction models for ischemic stroke and bleeding in dialysis patients: a systematic review and meta-analysis

Abstract Background Patients with kidney failure on maintenance dialysis have a high stroke and bleeding risk. Multivariable prediction models can be used to estimate the risk of ischemic stroke and bleeding. A systematic review and meta-analysis was performed to determine the performance of the existing models in patients on dialysis. Methods MEDLINE and Embase databases were searched, from inception through January 12, 2024, for studies of prediction models for stroke or bleeding, derived or validated in dialysis cohorts. Discrimination measures for models with c-statistic data from ≥ 3 cohorts were pooled by random effects meta-analysis and a 95% prediction interval (PI) was calculated. Risk of bias was assessed using PROBAST. The review was conducted according to the PRISMA statement and the CHARMS checklist. Results Eight studies were included in this systematic review. All the included studies validated pre-existing models that were derived in cohorts from the general population. None of the identified studies reported the development of a new dialysis specific prediction model for stroke, while dialysis specific risk scores for bleeding were proposed by two studies. In meta-analysis of c-statistics, the CHA2DS2-VASc, CHADS2, ATRIA, HEMORR(2)HAGES and HAS-BLED scores showed very poor discriminative ability in the dialysis population. Six of the eight included studies were at low or unclear risk of bias and certainty of evidence was moderate. Conclusions The existing prediction models for stroke and bleeding have very poor performance in the dialysis population. New dialysis-specific risk scores should be developed to guide clinical decision making in these patients.

Environmental dependence of geometrical efficiency for the scintillation cell in radon and thoron measurement.

222Rn is recognized as a matter of international concern for human health risk. Because 220Rn as well as 222Rn coexist in the natural environment, thoron sometimes influences the experiment for radon measurement. It is important to measure radon and thoron separately to evaluate the risk of the exposure to 222Rn. As a discriminative measurement method for 222Rn and 220Rn, a simple technique with a single scintillation cell is well known. However, in recent years, the influence of atmospheric environment on the geometrical efficiency of the scintillation cell has not yet been investigated. In this study, environmental dependence of geometrical efficiency for the scintillation cell in 222Rn and 220Rn measurement was investigated using the Lucas type scintillation cell and Monte Carlo particle simulation. It was found that the influence of temperature and pressure on the geometrical efficiencies were larger than that of relative humidity.

Real-time experiences of racism and stress in association with postpartum weight retention: A longitudinal ecological momentary assessment study.

Racial inequities in postpartum weight have been documented with limited studies on the influences of racism and other forms of discrimination. In a prospective longitudinal study applying ecological momentary assessment (EMA) and ambulatory assessment of weight, we measured the association between discrimination, stress and postpartum weight change. The Postpartum Mothers Mobile Study (PMOMS) is a cohort of 313 pregnant and birthing individuals who were followed during their second and third trimester through 1 year postpartum. They were recruited in clinical settings between 2017-2020 in a major city center in Pennsylvania. Measures of racism and gender-based discrimination were collected via random EMA surveys throughout pregnancy and postpartum; and weight was collected via blue-tooth enabled scales at least weekly. Among Black participants, a 10% increase in the number of days a participant experienced racial discrimination in the past month was associated with retaining 0.3 more pounds; 10% increase in EMA gender discrimination was associated with retaining 0.4 more pounds; and EMA stress reported in the past month was associated with decreased weight retention. Chronic experiences of racial and gender discrimination may contribute to weight retention immediately after pregnancy and beyond.

Comparing predictive validity of Youth Level of Service/Case Management Inventory scores in Indigenous and non-Indigenous Canadian youth.

There is an increasing recognition of the necessity to establish the predictive validity of risk assessment scores within specific population subgroups, particularly those (including Indigenous peoples) who are overrepresented in the criminal justice system. I compared measures of discrimination and calibration of the Youth Level of Service/Case Management Inventory (YLS/CMI) in Indigenous and non-Indigenous youth probationers in Ontario, Canada. Compared with non-Indigenous youth, Indigenous youth would have higher risk scores and reoffense rates. The YLS/CMI would predict reoffending and time to reoffense significantly and comparably for Indigenous and non-Indigenous youth, but there would be group difference discrimination (sensitivity, specificity) and calibration (positive predictive value, negative predictive value). Justice ministry-supplied data on 400 Indigenous and non-Indigenous youth (330 male, 70 female) individually matched on key background variables were analyzed to provide measures of discrimination and calibration of the YLS/CMI, with 3-year recidivism as the primary outcome. Indigenous youth were assessed at significantly higher risk than non-Indigenous youth (d = .60); 70% of Indigenous youth and 46% of non-Indigenous youth reoffended (ϕ = .24). Overall measures of discrimination (area under the curve) and calibration (logistic regression) were significant and did not differ across groups. Cross-area under the curve results indicated that the YLS/CMI discriminated Indigenous recidivists from non-Indigenous nonrecidivists but differentiated Indigenous nonrecidivists from non-Indigenous recidivists at chance level. In addition, recidivism was underestimated for low-risk Indigenous youth compared with non-Indigenous youth, but specificity was also low; only 28% of Indigenous youth who did not reoffend were assessed as low risk. Results were largely consistent across male and female youth. Examining subgroup predictive validity using multiple indices provides important information that should inform policy and practice discussions regarding fair use of risk assessment tools. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Predicting post-COVID-19 condition in children and young people up to 24 months after a positive SARS-CoV-2 PCR-test: the CLoCk study

BackgroundPredicting which children and young people (CYP) are at the highest risk of developing post-COVID-19 condition (PCC) could improve care pathways. We aim to develop and validate prediction models for persistent PCC up to 24 months post-infection in CYP.MethodsCYP who were PCR-positive between September 2020 and March 2021, with follow-up data up to 24-months post-infection, were analysed. Persistent PCC was defined in two ways, as PCC at (a) 3, 6, 12 and 24 months post-infection (N = 943) or (b) 6, 12 and 24 months post-infection (N = 2373). Prediction models were developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping; the final model was adjusted for overfitting.ResultsWhile 24.7% (233/943) of CYP met the PCC definition 3 months post-infection, only 7.2% (68/943) continued to meet the PCC definition at all three subsequent timepoints, i.e. at 6, 12 and 24 months. The final models predicting risk of persistent PCC (at 3, 6, 12 and 24 months and at 6, 12 and 24 months) contained sex (female), history of asthma, allergy problems, learning difficulties at school and family history of ongoing COVID-19 problems, with additional variables (e.g. older age at infection and region of residence) in the model predicting PCC at 6, 12 and 24 months. Internal validation showed minimal overfitting of models with good calibration and discrimination measures (optimism-adjusted calibration slope: 1.064–1.142; C-statistic: 0.724–0.755).ConclusionsTo our knowledge, these are the only prediction models estimating the risk of CYP persistently meeting the PCC definition up to 24 months post-infection. The models could be used to triage CYP after infection. CYP with factors predicting longer-term symptomology, may benefit from earlier support.

Spatial-temporal attention for video-based assessment of intraoperative surgical skill

Accurate, unbiased, and reproducible assessment of skill is a vital resource for surgeons throughout their career. The objective in this research is to develop and validate algorithms for video-based assessment of intraoperative surgical skill. Algorithms to classify surgical video into expert or novice categories provide a summative assessment of skill, which is useful for evaluating surgeons at discrete time points in their training or certification of surgeons. Using a spatial-temporal neural network architecture, we tested the hypothesis that explicit supervision of spatial attention supervised by instrument tip locations improves the algorithm’s generalizability to unseen dataset. The best performing model had an area under the receiver operating characteristic curve (AUC) of 0.88. Augmenting the network with supervision of spatial attention improved specificity of its predictions (with small changes in sensitivity and AUC) and led to improved measures of discrimination when tested with unseen dataset. Our findings show that explicit supervision of attention learned from images using instrument tip locations can improve performance of algorithms for objective video-based assessment of surgical skill.

‘I am fine, but my group is not': exploring the meanings of the personal/group discrimination discrepancy among minority and majority populations

ABSTRACT Prior literature has highlighted that individuals are more likely to report discrimination against their group rather than towards themselves, a phenomenon known as the personal-group discrimination discrepancy (PGD). Drawing on a recent, large-sample representative survey from France, Trajectories and Origins 2 (TeO2), we offer a novel empirical approach for measuring the discrepancy and investigate three channels through which it operates: identity affirmation, awareness of discrimination, and spatial proximity to discrimination. We show that the discrepancy – reporting group discrimination but no personal experience of discrimination – and its determinants vary substantially across minority and majority populations. Among ethnoracial minorities, awareness of discrimination through higher education, as well as spatial proximity to racially-motivated crimes, increases the likelihood of exclusively reporting group discrimination. Among the majority, the discrepancy is more prominent among those with a salient ethnoracial identity and far-right political orientation, but less so among the higher educated, pointing to politics of racial grievance among disadvantaged majority members. The conclusion highlights the theoretical and methodological implications of different discrimination measurements as well as their potential consequences for individual outcomes.

Multi-Institutional Model to Predict Intensive Care Unit Length of Stay after Cardiac Surgery

Intensive care unit length of stay (ICU LOS) accounts for a large percent of inpatient cost following cardiac surgery. The Society of Thoracic Surgeons (STS) risk calculator predicts total LOS but does not discriminate between ICU and non-ICU time. We sought to develop a predictive model of prolonged ICU LOS. Adult patients undergoing STS index operations within a regional collaborative (2014-2021) were included. Prolonged ICU LOS was defined as ICU care for ≥72 hours postoperatively. A logistic regression model was utilized to develop a prediction model for the prolonged ICU LOS with pre-specified risk factors identified from our previous single center study. Internal prediction model validation was determined by bootstrapping resampling method. The prediction model performance was assessed by measures of discrimination and calibration. We identified 37,519 patients that met inclusion criteria with 11,801 (31.5%) patients experiencing prolonged ICU stay. From the logistic regression model, there were significant associations between prolonged ICU LOS and all pre-specified factors except sleep apnea (all p<0.05). MELD, preoperative intra-aortic balloon pump use, and procedure types were the most significant predictors of prolonged ICU LOS (all p<0.0001). Our prediction model had not only a good discrimination power (bootstrapped-corrected C-index=0.71) but also excellent calibration (bootstrapped-corrected mean absolute error=0.005). Prolonged ICU stay following cardiac surgery can be predicted with good predictive accuracy utilizing preoperative data and may aid in patient counseling and resource allocation. Through use of a state-wide database, the application of this model may extend to other practices.

Prediction models for outcomes in people with low back pain receiving conservative treatment: a systematic review

To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment. In this systematic review, literature searches were conducted in Embase, Medline, CINAHL from their inception until February/2024. Studies containing follow-up assessment (e.g., prospective cohort studies, registry-based studies) investigating prediction models of outcomes (e.g., pain intensity and disability) for people with LBP receiving conservative treatment were included. Two independent reviewers performed the study selection, the data extraction using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and risk of bias assessment using the Prediction model Risk of Bias Assessment (PROBAST). Findings of individual studies were reported narratively taking into account the discrimination and calibration measures of the prediction models. Seventy-five studies developing or investigating the validity of 216 models were included in this review. Most prediction models investigated people receiving physiotherapy treatment and most models included socio-demographic variables, clinical features, and self-reported measures as predictors. The discriminatory capacity of the internal validity of the 27 prediction models for pain intensity varied greatly showing a c-statistic ranging from 0.48 to 0.94. Similarly, the discriminatory capacity for 31 models for disability had the same pattern showing a c-statistic ranging from 0.48 to 0.86. The calibration measures of the internal validity of the prediction models predicting pain intensity and disability showed to be adequate. Only one of three studies testing the external validity of models to predict pain intensity and disability and reported both discrimination and calibration measures, which showed to be inadequate. The prediction models predicting the secondary outcomes (e.g., self-reported recovery, quality of life, return to work) showed varied performance measures for internal validity, and only two studies tested the external validity of models although they did not provide performance the performance measures. Several prediction models have been developed for people with LBP receiving conservative treatment, however most show inadequate discriminatory validity. A few studies externally validated the prediction models and future studies should focus on testing this before implementing in clinical practice.