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- Castelnuovo, Gianluca, et al.
(author)
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What Is the Role of the Placebo Effect for Pain Relief in Neurorehabilitation? : Clinical Implications From the Italian Consensus Conference on Pain in Neurorehabilitation
- 2018
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In: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 9
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Research review (peer-reviewed)abstract
- Background: It is increasingly acknowledged that the outcomes of medical treatments are influenced by the context of the clinical encounter through the mechanisms of the placebo effect. The phenomenon of placebo analgesia might be exploited to maximize the efficacy of neurorehabilitation treatments. Since its intensity varies across neurological disorders, the Italian Consensus Conference on Pain in Neurorehabilitation (ICCP) summarized the studies on this field to provide guidance on its use.Methods: A review of the existing reviews and meta-analyses was performed to assess the magnitude of the placebo effect in disorders that may undergo neurorehabilitation treatment. The search was performed on Pubmed using placebo, pain, and the names of neurological disorders as keywords. Methodological quality was assessed using a pre-existing checklist. Data about the magnitude of the placebo effect were extracted from the included reviews and were commented in a narrative form.Results: 11 articles were included in this review. Placebo treatments showed weak effects in central neuropathic pain (pain reduction from 0.44 to 0.66 on a 0-10 scale) and moderate effects in postherpetic neuralgia (1.16), in diabetic peripheral neuropathy (1.45), and in pain associated to HIV (1.82). Moderate effects were also found on pain due to fibromyalgia and migraine; only weak short-term effects were found in complex regional pain syndrome. Confounding variables might have influenced these results.Clinical implications: These estimates should be interpreted with caution, but underscore that the placebo effect can be exploited in neurorehabilitation programs. It is not necessary to conceal its use from the patient. Knowledge of placebo mechanisms can be used to shape the doctor-patient relationship, to reduce the use of analgesic drugs and to train the patient to become an active agent of the therapy.
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| 2. |
- Adigun, Jubril Gbolahan, et al.
(author)
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Collaborative Artificial Intelligence Needs Stronger Assurances Driven by Risks
- 2022
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In: Computer. - : IEEE Computer Society. - 0018-9162 .- 1558-0814. ; 55:3, s. 52-63
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Journal article (peer-reviewed)abstract
- Collaborative artificial intelligence systems (CAISs) aim to work with humans in a shared space to achieve a common goal, but this can pose hazards that could harm human beings. We identify emerging problems in this context and report our vision of and progress toward a risk-driven assurance process for CAISs.
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| 3. |
- Mazzarone, Tessa, et al.
(author)
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Predicting In-Hospital Acute Heart Failure Worsening in the Oldest Old : Insights from Point-of-Care Ultrasound
- 2023
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In: Journal of Clinical Medicine. - 2077-0383. ; 12:23
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Journal article (peer-reviewed)abstract
- The decompensation trajectory check is a basic step to assess the clinical course and to plan future therapy in hospitalized patients with acute decompensated heart failure (ADHF). Due to the atypical presentation and clinical complexity, trajectory checks can be challenging in older patients with acute HF. Point-of-care ultrasound (POCUS) has proved to be helpful in the clinical decision-making of patients with dyspnea; however, to date, no study has attempted to verify its role in predicting determinants of ADHF in-hospital worsening. In this single-center, cross-sectional study, we consecutively enrolled patients aged 75 or older hospitalized with ADHF in a tertiary care hospital. All of the patients underwent a complete clinical examination, blood tests, and POCUS, including Lung Ultrasound and Focused Cardiac Ultrasound. Out of 184 patients hospitalized with ADHF, 60 experienced ADHF in-hospital worsening. By multivariable logistic analysis, total Pleural Effusion Score (PEFs) [aO.R.: 1.15 (CI95% 1.02-1.33), p = 0.043] and IVC collapsibility [aO.R.: 0.90 (CI95% 0.83-0.95), p = 0.039] emerged as independent predictors of acute HF worsening after extensive adjustment for potential confounders. In conclusion, POCUS holds promise for enhancing risk assessment, tailoring diuretic treatment, and optimizing discharge timing for older patients with ADHF.
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| 4. |
- Okoye, Chukwuma, et al.
(author)
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Predicting mortality and re-hospitalization for heart failure : a machine-learning and cluster analysis on frailty and comorbidity
- 2023
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In: Aging Clinical and Experimental Research. - 1594-0667 .- 1720-8319. ; 35, s. 2919-2928
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Journal article (peer-reviewed)abstract
- BackgroundMachine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital.MethodsIn this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By k-means and hierarchical clustering, those predictors were utilized to phenomapping the cohort in four different clusters.ResultsA total of 571 patients were included in the study. Cluster analysis identified four different clusters according to frailty, burden of comorbidities and BNP. As compared with Cluster 4, we found an increased 6-month risk of poor outcomes patients in Cluster 1 (very frail and comorbid; HR 3.53 [95% CI 2.30-5.39]), Cluster 2 (pre-frail with low levels of BNP; HR 2.59 [95% CI 1.66-4.07], and in Cluster 3 (pre-frail and comorbid with high levels of BNP; HR 3.75 [95% CI 2.25-6.27])).ConclusionsIn older patients discharged for ADHF, the cluster analysis identified four distinct phenotypes according to frailty degree, comorbidity, and BNP levels. Further studies are warranted to validate these phenogroups and to guide an appropriate selection of personalized, model of care.
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