The data reported in the present paper provide preliminary evidence of the reliability and validity of the ISSQoL questionnaire for the measurement of HRQoL in HIV-infected people. The direct involvement of HIV-positive people in all the phases of the project was a key aspect of our work.
We assessed the safety and efficacy of reconstructive therapy with facial fillers for the treatment of HIV-associated facial lipoatrophy (FLA) through a randomized, controlled, open-label single-center study. A total of 134 HIV-infected patients with severe FLA were randomly assigned to receive immediate (67 patients) or delayed (67 patients) facial injections of poly-l-lactic acid (PLA) or polyacrylamide gel (PAIG). Outcome measures included changes in physician and patient FLA severity scale, adverse events, and changes in health-related quality of life (HRQoL) and anxiety using validated measures. The mean average study follow-up was 27 weeks for the immediate and 25 weeks for the delayed subjects. Adverse events were mild and resolved after a mean of 4 days. Compared to patients randomized to the delayed treatment group, patients assigned to the immediate treatment group had significantly lower physician-rated (0.0 versus -3.0; p < 0.0001) and patient-rated (0.1 versus -1.8; p < 0.0001) FLA severity scores. By contrast, measures exploring HRQoL and anxiety did not show any significant difference between patients randomized to the immediate and deferred groups. Reconstructive therapy with facial fillers was effective and safe and led to significant improvements in FLA severity. However, no significant gains in HRQoL, relational and psychological consequences of body changes, and anxiety-related concerns were observed. Studies should be performed to identify patients who could maximally benefit from filling interventions for FLA.
The health-related quality of life (HRQoL) outcomes in HIV-infected, treatment-naive patients starting different HAART regimens in a 3-year, randomized, multinational trial were compared. HRQoL was measured in a subgroup of patients enrolled in the INITIO study (153/911), using a modified version of the MOS-HIV questionnaire. The regimens compared in the INITIO trial were composed by two NRTIs (didanosine + stavudine) plus either an NNRTI (efavirenz) or a PI (nelfinavir), or both (efavirenz + nelfinavir). Primary HRQoL outcomes were Physical and Mental Health Summary scores (PHS and MHS, respectively). During follow-up, an increase of PHS score was observed in all treatment arms. The MHS score remained substantially unchanged with the four-drug combination and showed with both NNRTI- and PI-based three-drug regimens a marked trend toward improvement, which became statistically significant when a multiple imputation method was used to adjust for missing data. Overall, starting all the combination regimens compared in the INITIO study was associated with a maintained or slightly improved HRQOL status, consistently with the positive immunological and virological changes observed in the main study. The observed differences in the MHS indicate a possible HRQoL benefit associated to the use of three-drug, two-class regimens and no additional benefit for the use of four-drug, three-class regimens, confirming that three-drug, two-class regimens that include two NRTIs plus either an NNRTI or a PI should be preferred as initial treatment of HIV infection.
Although a three-drug antiretroviral therapy regimen was superior in terms of short term virologic/immunologic response, the two-drug regimen was better in terms of quality of life. In general, triple therapy remains the most effective treatment option. However, quality of life assessments can yield results that may be discordant with and complementary to those obtained using conventional endpoints. Comparative trials should collect a comprehensive range of outcome measures, including patient-reported quality of life, in order to provide clinicians and patients with additional information that may influence treatment decisions.
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Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.
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