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- Andersson, Emelie, et al.
(author)
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Disease burden and unmet need for acute allergic reactions - A patient perspective
- 2024
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In: World Allergy Organization Journal. - : Elsevier. - 1939-4551. ; 17:4
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Journal article (peer-reviewed)abstract
- Background: Acute allergic reactions (AARs) occur shortly after exposure to an allergen, and the severity is on a continuum. Systemic corticosteroids (CS) are mainstay treatment of moderate to severe AARs, whereas those at risk of the most severe AARs (ie, anaphylaxis) are also recommended prescription of epinephrine autoinjectors. There is limited research on the impact of AARs not fulfilling the criteria for anaphylaxis. We have characterized a sample with a history of moderate to severe AARs and evaluated their self-reported disease burden (ie, daily life impact, anxiety, and treatment impediments).Methods: Survey study of adults with experience of AARs treated with CS. Participants recruited from a web-based panel and using social media were asked to complete a questionnaire related to their allergy and experience of AARs. The results were summarized for the whole sample and across subgroups with and without prescription of epinephrine.Results: The final study sample included 387 participants (80% women, mean age 41), of which 129 (33%) had at some point been prescribed epinephrine. The most common symptoms were respiratory (80%) and skin (78%) manifestations, and the mean (standard deviation, SD) self-rated severity score (scale from 0 [very mild] to 10 [very severe]) of the most recent AAR was 6.1 (2.0). More than 80% had experience of AARs interrupting daily activities and 50% of AARs that had limited work/studies or participation in leisure activities. Most of the respondents reported some degree of anxiety related to AARs and 43% had feared for their lives. Moreover, difficulties swallowing allergy medicine at an AAR was experienced by 26% and not having the medicine available when needed by 66%. Participants with prescription of epinephrine experienced more severe AARs than those without such prescription (mean [SD] severity 6.8 [2.1] vs 5.8 [1.8], p < 0.0001); however, also those without epinephrine prescription reported considerable anxiety and impact on daily life and to a similar degree as those with prescription.Conclusions: In this sample, subjects with experience of AARs treated with CS showed a considerable disease burden with anxiety and interruption on daily life, as well as problems related to access to, and swallowing of, medication. Although respondents with epinephrine prescription had more severe disease, a high disease burden was also evident among those without epinephrine. The study increases the knowledge of people with moderate to severe AARs, a patient population that has previously been underrepresented in the research literature.
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- Bergström, Göran, et al.
(author)
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Self-report tool for identification of individuals with coronary atherosclerosis : the Swedish cardiopulmonary bioimage study
- 2024
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In: Journal of the American Heart Association. - : John Wiley & Sons. - 2047-9980. ; 13:14
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Journal article (peer-reviewed)abstract
- BACKGROUND: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis.METHODS AND RESULTS: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly.CONCLUSIONS: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.
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- Bergström, Göran, et al.
(author)
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Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis : The Swedish CardioPulmonary BioImage Study
- 2024
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In: Journal of the American Heart Association. - : American Heart Association. - 2047-9980. ; 13:14
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Journal article (peer-reviewed)abstract
- BACKGROUND: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. METHODS AND RESULTS: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly. CONCLUSIONS: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.
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- Ericson, Oskar, et al.
(author)
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The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
- 2022
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In: Journal of health economics and outcomes research. - : The Journal of Health Economics and Outcomes Research. - 2326-697X .- 2327-2236. ; 9:1, s. 101-110
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Journal article (peer-reviewed)abstract
- Background: Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown.Objectives: The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting.Methods: A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY® Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice.Results: In Sweden, the total cost per patient amounts to €16 436 and €16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of €76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of €1009 per ICU patient. Stochastic scenario analysis showed that NAVOY® Sepsis was a dominant treatment option in most scenarios and well below an established threshold of €20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year.Conclusions: A sepsis prediction algorithm such as NAVOY® Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system.
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- Hjelmgren, Jonas, et al.
(author)
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Estimating the value of novel interventions for Parkinson's disease: An early decision-making model with application to dopamine cell replacement.
- 2006
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In: Parkinsonism & Related Disorders. - : Elsevier BV. - 1873-5126 .- 1353-8020. ; 12:7, s. 443-452
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Journal article (peer-reviewed)abstract
- A long-term cost-effectiveness model for early decision-making and estimation of outcomes of novel therapeutic procedures for Parkinson's disease (PD) was developed based on the Hoehn and Yahr (HY) stages of PD. Results provided support for model validity. Model application to a future dopamine cell replacement therapy indicated long-term cost offsets and gains in quality-adjusted life years (QALYs) in early onset PD (HY III-IV), as compared to standard drug therapy. The maximum price premium (i.e., profit or compensation for developmental costs) for the intervention to remain cost-effective was estimated to EURO12000-64000 according to cost-per-QALY thresholds of EURO38000-70000 and depending on whether all or only medical direct costs are considered. The study illustrates the value of early health economic modeling and the described model shows promise as a means to estimate outcomes and aid decision-making regarding novel interventions for PD. (c) 2006 Elsevier Ltd. All rights reserved.
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- Hjortsberg, Catharina, et al.
(author)
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Are Treatment Satisfaction, Quality of Life, and Self-assessed Disease Severity Relevant Parameters for Patient Registries? Experiences from Finnish and Swedish Patients with Psoriasis.
- 2011
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In: Acta Dermato-Venereologica. - : Medical Journals Sweden AB. - 1651-2057 .- 0001-5555. ; 91, s. 409-414
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Journal article (peer-reviewed)abstract
- Patient registries often lack indicators of the disease as experienced by patients, e.g. treatment satisfaction and self-assessed disease severity. There is scarce information about the relationship between these assessments and currently existing instruments used in treatment evaluation. Our objective was to explore the importance of these indicators among patients with psoriasis in Finland and Sweden, in relation to treatment patterns and current measures of health-related quality of life. Data were collected from a patient survey and a retrospective chart review for 273 patients over 12 months. To assess psoriasis treatment completely, it is necessary to consider the impact of the disease on the patient in terms of treatment satisfaction, disease severity and health-related quality of life. The individual disease burden on patients should play a central role in formulating treatment goals. Clinician- and patient-based perspectives of the overall impact of psoriasis can assist clinical decision-making and evaluations of treatments.
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