Among patients with occlusion of a large intracerebral vessel who had a clinical deficit that was disproportionately severe relative to the infarct volume, 90-day outcomes for disability were better ...with late thrombectomy plus standard care than with standard care alone.
Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating ...presurgical decision support. Machine learning may help automatically select and weigh many predictors to create models that maximize predictive power. However, these techniques are underused among studies of total joint arthroplasty (TJA) patients, particularly those exploring changes in postsurgical PROMs. QUESTION/PURPOSES: (1) To evaluate whether machine learning algorithms, applied to hospital registry data, could predict patients who would not achieve a minimally clinically important difference (MCID) in four PROMs 2 years after TJA; (2) to explore how predictive ability changes as more information is included in modeling; and (3) to identify which variables drive the predictive power of these models.
Data from a single, high-volume institution's TJA registry were used for this study. We identified 7239 hip and 6480 knee TJAs between 2007 and 2012, which, for at least one PROM, patients had completed both baseline and 2-year followup surveys (among 19,187 TJAs in our registry and 43,313 total TJAs). In all, 12,203 registry TJAs had valid SF-36 physical component scores (PCS) and mental component scores (MCS) at baseline and 2 years; 7085 and 6205 had valid Hip and Knee Disability and Osteoarthritis Outcome Scores for joint replacement (HOOS JR and KOOS JR scores), respectively. Supervised machine learning refers to a class of algorithms that links a mapping of inputs to an output based on many input-output examples. We trained three of the most popular such algorithms (logistic least absolute shrinkage and selection operator (LASSO), random forest, and linear support vector machine) to predict 2-year postsurgical MCIDs. We incrementally considered predictors available at four time points: (1) before the decision to have surgery, (2) before surgery, (3) before discharge, and (4) immediately after discharge. We evaluated the performance of each model using area under the receiver operating characteristic (AUROC) statistics on a validation sample composed of a random 20% subsample of TJAs excluded from modeling. We also considered abbreviated models that only used baseline PROMs and procedure as predictors (to isolate their predictive power). We further directly evaluated which variables were ranked by each model as most predictive of 2-year MCIDs.
The three machine learning algorithms performed in the poor-to-good range for predicting 2-year MCIDs, with AUROCs ranging from 0.60 to 0.89. They performed virtually identically for a given PROM and time point. AUROCs for the logistic LASSO models for predicting SF-36 PCS 2-year MCIDs at the four time points were: 0.69, 0.78, 0.78, and 0.78, respectively; for SF-36 MCS 2-year MCIDs, AUROCs were: 0.63, 0.89, 0.89, and 0.88; for HOOS JR 2-year MCIDs: 0.67, 0.78, 0.77, and 0.77; for KOOS JR 2-year MCIDs: 0.61, 0.75, 0.75, and 0.75. Before-surgery models performed in the fair-to-good range and consistently ranked the associated baseline PROM as among the most important predictors. Abbreviated LASSO models performed worse than the full before-surgery models, though they retained much of the predictive power of the full before-surgery models.
Machine learning has the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA. Applied to presurgical registry data, such models can predict, with fair-to-good ability, 2-year postsurgical MCIDs. Although we report all parameters of our best-performing models, they cannot simply be applied off-the-shelf without proper testing. Our analyses indicate that machine learning holds much promise for predicting orthopaedic outcomes. LEVEL OF EVIDENCE: Level III, diagnostic study.
Purpose: This systematic review examines research and practical applications of the World Health Organization Disability Assessment Schedule (WHODAS 2.0) as a basis for establishing specific criteria ...for evaluating relevant international scientific literature. The aims were to establish the extent of international dissemination and use of WHODAS 2.0 and analyze psychometric research on its various translations and adaptations. In particular, we wanted to highlight which psychometric features have been investigated, focusing on the factor structure, reliability, and validity of this instrument.
Method: Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, we conducted a search for publications focused on "whodas" using the ProQuest, PubMed, and Google Scholar electronic databases.
Results: We identified 810 studies from 94 countries published between 1999 and 2015. WHODAS 2.0 has been translated into 47 languages and dialects and used in 27 areas of research (40% in psychiatry).
Conclusions: The growing number of studies indicates increasing interest in the WHODAS 2.0 for assessing individual functioning and disability in different settings and individual health conditions. The WHODAS 2.0 shows strong correlations with several other measures of activity limitations; probably due to the fact that it shares the same disability latent variable with them.
Implications for Rehabilitation
WHODAS 2.0 seems to be a valid, reliable self-report instrument for the assessment of disability.
The increasing interest in use of the WHODAS 2.0 extends to rehabilitation and life sciences rather than being limited to psychiatry.
WHODAS 2.0 is suitable for assessing health status and disability in a variety of settings and populations.
A critical issue for rehabilitation is that a single "minimal clinically important .difference" score for the WHODAS 2.0 has not yet been established.
Purpose The content of and methods for collecting health information often vary across settings and challenge the comparability of health information across time, individuals or populations. The ...International Classification of Functioning, Disability and Health (ICF) contains an exhaustive set of categories of information which constitutes a unified and consistent language of human functioning suitable as a reference for comparing health information. Methods and results In two earlier papers, we have proposed rules for linking existing health information to the ICF. Further refinements to these existing ICF Linking Rules are presented in this paper to enhance the transparency of the linking process. The refinements involve preparing information for linking, perspectives from which information is collected and the categorization of response options. Issues regarding the linking of information not covered or unspecified within the ICF are also revisited in this paper. Conclusion: The ICF Linking Rules are valuable for enhancing comparability of health information to ensure that information is available in a consistent manner to serve as a foundation for evidence-based decision-making across all levels of health systems. The refinements presented in this paper enhance transparency in, and ultimately reliability of the process of, linking health information to the ICF.
Implications for Rehabilitation
The International Classification of Functioning, Disability and Health (ICF) constitutes a unified and consistent language of human functioning suitable as a reference for comparing health information.
Comparability of information is essential to ensure that the widest range of information is available in a consistent manner for any decision-maker at all levels of the health system.
The refined ICF Linking Rules presented in this article outline the method to establish comparability of health information based on the ICF.
AbstractObjectivesTo use data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) to estimate mortality and disability trends for the population aged ≥70 and evaluate ...patterns in causes of death, disability, and risk factors.DesignSystematic analysis.SettingParticipants were aged ≥70 from 204 countries and territories, 1990-2019.Main outcomes measuresYears of life lost, years lived with disability, disability adjusted life years, life expectancy at age 70 (LE-70), healthy life expectancy at age 70 (HALE-70), proportion of years in ill health at age 70 (PYIH-70), risk factors, and data coverage index were estimated based on standardised GBD methods.ResultsGlobally the population of older adults has increased since 1990 and all cause death rates have decreased for men and women. However, mortality rates due to falls increased between 1990 and 2019. The probability of death among people aged 70-90 decreased, mainly because of reductions in non-communicable diseases. Globally disability burden was largely driven by functional decline, vision and hearing loss, and symptoms of pain. LE-70 and HALE-70 showed continuous increases since 1990 globally, with certain regional disparities. Globally higher LE-70 resulted in higher HALE-70 and slightly increased PYIH-70. Sociodemographic and healthcare access and quality indices were positively correlated with HALE-70 and LE-70. For high exposure risk factors, data coverage was moderate, while limited data were available for various dietary, environmental or occupational, and metabolic risks.ConclusionsLife expectancy at age 70 has continued to rise globally, mostly because of decreases in chronic diseases. Adults aged ≥70 living in high income countries and regions with better healthcare access and quality were found to experience the highest life expectancy and healthy life expectancy. Disability burden, however, remained constant, suggesting the need to enhance public health and intervention programmes to improve wellbeing among older adults.
Objective:Attitudes toward marijuana are changing, the prevalence of DSM-IV cannabis use disorder has increased, and DSM-5 modified the cannabis use disorder criteria. Therefore, updated information ...is needed on the prevalence, demographic characteristics, psychiatric comorbidity, disability, and treatment for DSM-5 cannabis use disorder.Method:In 2012–2013, 36,309 participants ≥18 years old were interviewed in the National Epidemiologic Survey on Alcohol and Related Conditions–III. Psychiatric and substance use disorders were assessed with the Alcohol Use Disorders and Associated Disabilities Interview Schedule–5.Results:The prevalences of 12-month and lifetime cannabis use disorder were 2.5% and 6.3%. Among those with 12-month and lifetime diagnoses, the mean days of marijuana use per year were 225.3 (SE=5.7) and 274.2 (SE=3.8). The odds of 12-month and lifetime cannabis use disorder were higher for men, Native Americans, unmarried individuals, those with low incomes, and young adults (e.g., among those age 18–24 years versus ≥45: odds ratio for 12-month disorder, 7.2; 95% confidence interval, 5.5–9.5). Cannabis use disorder was associated with other substance use disorders, affective disorders, anxiety, and personality disorders. Twelve-month cannabis use disorder was associated with disability. As disorder severity increased, virtually all associations became stronger. Only 13.2% with lifetime cannabis use disorder participated in 12-step programs or professional treatment.Conclusions:DSM-5 cannabis use disorder is prevalent, associated with comorbidity and disability, and largely untreated. Findings suggest the need to improve prevention and educate the public, professionals, and policy makers about possible harms associated with cannabis use disorders and available interventions.
Hearing impairment negatively affects well-being and is a major contributor to years lived with disability. The World Health Organization (WHO) estimates that 466 million people were living with ...disabling hearing impairment in 2018 and this estimate is projected to rise to 630 million by 2030 and to over 900 million by 2050. However, these projections are based on a hearing impairment classification that does not fully reflect the provisions of the International classification of functioning, disability and health for assessing all forms of functional impairments. Here we make the case for a review of the concept of disabling hearing loss adopted by WHO after the recommendation of the Global Burden of Disease (GBD) Expert Group on Hearing Impairment in 2008. The need for an independent classification system for all impairments and disabilities as a complement to the well-established International statistical classification of diseases and related health problems, was first suggested in 1976 by the World Health Assembly. As a result, in 1980 WHO developed the International classification of impairments, disabilities and handicaps. One of the key features of this system was the use of qualifiers such as mild, moderate, severe and profound to distinguish various levels of observed or measured deviations outside of the range considered for normal functioning for any health condition. This categorization has been reinforced in the subsequent revisions to the system, such as the International Classification of Functioning, Disability and Health, and accompanied with descriptions of typical problems encountered in daily activities at various levels of severity. The classification, notably, does not use the term disabling, as it recognizes the needs of all persons with functional impairments for appropriate intervention.
Numerous studies have documented high rates of functional impairment among bipolar disorder (BD) patients, even during phases of remission. However, the majority of the available instruments used to ...assess functioning have focused on global measures of functional recovery rather than specific domains of psychosocial functioning. In this context, the Functioning Assessment Short Test (FAST) is a brief instrument designed to assess the main functioning problems experienced by psychiatric patients, particularly bipolar patients. It comprises 24 items that assess impairment or disability in six specific areas of functioning: autonomy, occupational functioning, cognitive functioning, financial issues, interpersonal relationships and leisure time.
101 patients with DSM-IV TR bipolar disorder and 61 healthy controls were assessed in the Bipolar Disorder Program, Hospital Clinic of Barcelona. The psychometric properties of FAST (feasibility, internal consistency, concurrent validity, discriminant validity (euthymic vs acute patients), factorial analyses, and test-retest reliability) were analysed.
The internal consistency obtained was very high with a Cronbach's alpha of 0.909. A highly significant negative correlation with GAF was obtained (r = -0.903; p < 0.001) pointing to a reasonable degree of concurrent validity. Test-retest reliability analysis showed a strong correlation between the two measures carried out one week apart (ICC = 0.98; p < 0.001). The total FAST scores were lower in euthymic (18.55 +/- 13.19; F = 35.43; p < 0.001) patients, as compared with manic (40.44 +/- 9.15) and depressive patients (43.21 +/- 13.34).
The FAST showed strong psychometrics properties and was able to detect differences between euthymic and acute BD patients. In addition, it is a short (6 minutes) simple interview-administered instrument, which is easy to apply and requires only a short period of time for its application.
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