How do medical students value health on the EQ-5D? Evaluation of hypothetical health states compared to the general population

<p>Abstract</p> <p>Background</p> <p>Medical students gain a particular perspective on health problems during their medical education. This article describes how medical students value 10 hypothetical health states using the EQ-5D compared to the general population.</p> <p>Methods</p> <p>Based on a sample of 161 medical students (male: 41%) we compared valuations of 10 hypothetical EQ-5D health states collected in face to face interviews with the valuations of the general population. Self-reported health on the EQ-5D was also collected.</p> <p>Results</p> <p>Every third health state was valuated higher by the medical students compared to data of the general population. The differences were independent of the severity of the hypothetical health state. Concerning the self-reported health, the majority of the students (66%) reported no problems in the five EQ-5D domains (EQ-5D VAS M = 87.3 ± 9.6 SD). However, when compared to an age-matched sample the medical students show significantly more problems in the area of pain/discomfort and anxiety/depression.</p> <p>Conclusion</p> <p>Medical students have a tendency to value health states higher than the general public. Medical professionals should be continuously aware that their assessment of the patients health state can differ from the valuations of the general population.</p

Do neurooncological patients and their significant others agree on quality of life ratings?

<p>Abstract</p> <p>Introduction</p> <p>Patients suffering from brain tumours often experience a wide range of cognitive impairments that impair their ability to report on their quality of life and symptom burden. The use of proxy ratings by significant others may be a promising alternative to gain information for medical decision making or research purposes, if self-ratings are not obtainable. Our study investigated the agreement of quality of life and symptom ratings by the patient him/herself or by a significant other.</p> <p>Methods</p> <p>Patients with primary brain tumours were recruited at the neurooncological outpatient unit of Innsbruck Medical University. Quality of life self- and proxy-ratings were collected using the EORTC QLQ-C30 and its brain cancer module, the QLQ-BN20.</p> <p>Results</p> <p>Between May 2005 and August 2007, 42 pairs consisting of a patient and his/her significant other were included in the study. Most of the employed quality of life scales showed fairly good agreement between patient- and proxy-ratings (median correlation 0.46). This was especially true for Physical Functioning, Sleeping Disturbances, Appetite Loss, Constipation, Taste Alterations, Visual Disorders, Motor Dysfunction, Communication Deficits, Hair Loss, Itchy Skin, Motor Dysfunction and Hair Loss. Worse rater agreement was found for Social Functioning, Emotional Functioning, Cognitive Functioning, Fatigue, Pain, Dyspnoea and Seizures.</p> <p>Conclusion</p> <p>The assessment of quality of life in brain cancer patients through ratings from their significant others seems to be a feasible strategy to gain information about certain aspects of patient's quality of life and symptom burden, if the patient is not able to provide information himself.</p

Pathological Internet Use—An Important Comorbidity in Child and Adolescent Psychiatry: Prevalence and Correlation Patterns in a Naturalistic Sample of Adolescent Inpatients

Background. Few studies have examined the prevalence of problematic internet use (PIU) in young people undergoing inpatient treatment in child and adolescent psychiatry centers. The aims of our study were thus (a) to assess the frequency of comorbid PIU in a sample of adolescent psychiatric inpatients and compare it with a control group of nonreferred adolescents and (b) to gain insights into correlations between PIU and psychiatric comorbidities. Methods. 111 child and adolescent psychiatry inpatients (CAP-IP, mean age 15.1±1.4 years; female : male 72.4% : 27.6%) undergoing routine psychodiagnostics were screened for the presence of PIU. The widely used Compulsive Internet Use Scale (CIUS) was chosen for this purpose. Prevalence rates of PIU were then compared to matched nonreferred control subjects from a school sample. Additionally, comorbidities of inpatients with PIU were compared to inpatients without PIU. Results. Our inpatient sample showed a much higher prevalence of PIU than that found in previous populational samples of young people. Compared with a matched school sample, addictive internet use was 7.8 times higher and problematic internet use 3.3 times higher among our adolescent sample. PIU was significantly associated with characteristic patterns of psychopathology, that is, suicidality, difficulties in establishing stable and consolidated identity, and peer victimization. Conclusion. PIU among adolescents undergoing inpatient psychiatric treatment is much more frequent than among their peers in the general population and is associated with specific patterns of psychopathology

Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures

A common way to treat hip, knee or foot injuries is by conducting a corresponding physician-guided rehab over several weeks or even months. While health professionals are often able to estimate the treatment success beforehand to a certain extent based on their experience, it is scientifically still not clear to what extent relevant factors and circumstances explain or predict rehab outcomes. To this end, we apply modern machine learning techniques to a real-life dataset consisting of data from more than a thousand rehab patients (N = 1,047) and build models that are able to predict the rehab success for a patient upon treatment start. By utilizing clinical and patient-reported outcome measures (PROMs) from questionnaires, we compute patient-related clinical measurements (CROMs) for different targets like the range of motion of a knee, and subsequently use those indicators to learn prediction models. While we at first apply regression algorithms to estimate the rehab success in terms of percental admission and discharge value differences, we finally also utilize classification models to make predictions based on a three-classed grading scheme. Extensive evaluations for different treatment groups and targets show promising results with F-scores exceeding 65% that are able to substantially outperform baselines (by up to 40%) and thus show that machine learning can indeed be applied for better medical controlling and optimized treatment paths in rehab praxis. Future developments should include further relevant critical success criteria in the rehabilitation routine to further optimize the prognosis models for clinical practice

Clinical Outcome Assessment in Cancer Rehabilitation and the Central Role of Patient-Reported Outcomes

The aim of cancer rehabilitation is to help patients regain functioning and social participation. In order to evaluate and optimize rehabilitation, it is important to measure its outcomes in a structured way. In this article, we review the different types of clinical outcome assessments (COAs), including Clinician-Reported Outcomes (ClinROs), Observer-Reported Outcomes (ObsROs), Performance Outcomes (PerfOs), and Patient-Reported Outcomes (PROs). A special focus is placed on PROs, which are commonly defined as any direct report from the patient about their health condition without any interpretation by a third party. We provide a narrative review of available PRO measures (PROMs) for relevant outcomes, discuss the current state of PRO implementation in cancer rehabilitation, and highlight trends that use PROs to benchmark value-based care. Furthermore, we provide examples of PRO usage, highlight the benefits of electronic PRO (ePRO) collection, and offer advice on how to select, implement, and integrate PROs into the cancer rehabilitation setting to maximize efficiency