Desdolarizando la economía peruana: Un enfoque de portafolio

La visión de consenso es que la dolarización financiera complica el diseño de la política económica, al abrir la puerta a un mayor grado de vulnerabilidad financiera. A pesar de esto, en la última década no hubo un esfuerzo explícito por reducir el nivel de dolarización junto con el programa de estabilización. Además de presentar un conjunto de indicadores para cuantificar cada tipo de dolarización, así como sus resultados para la economía peruana, en este estudio se evalúan distintas medidas para combatir el fenómeno de dolarización financiera. Para esto, y atendiendo a los resultados obtenidos, se privilegia el enfoque de portafolio y se analizan los efectos de modificar el grado de cobertura del seguro de depósitos y la varianza relativa de la inflación y la devaluación real. Los resultados permiten adelantar que una reducción en la cobertura de dicho seguro para depósitos en dólares no se traduciría en un menor ratio de dolarización, ya que propiciaría una transferencia de recursos hacia el exterior. La balanza se inclina hacia aquellas medidas orientadas a reducir la volatilidad de la inflación respecto a la de la devaluación real.

Effectiveness and Safety of the Switch from Remicade® to CT-P13 in Patients with Inflammatory Bowel Disease

[Background and Aims] To evaluate the clinical outcomes in patients with IBD after switching from Remicade® to CT-P13 in comparison with patients who maintain Remicade®.[Methods] Patients under Remicade® who were in clinical remission with standard dosage at study entry were included. The ‘switch cohort’ [SC] comprised patients who made the switch from Remicade® to CT-P13, and the ‘non-switch’ cohort [NC] patients remained under Remicade®.[Results] A total of 476 patients were included: 199 [42%] in the SC and 277 [58%] in the NC. The median follow-up was 18 months in the SC and 23 months in the NC [p < 0.01]. Twenty-four out of 277 patients relapsed in the NC; the incidence of relapse was 5% per patient-year. The cumulative incidence of relapse was 2% at 6 months and 10% at 24 months in this group. Thirty-eight out of 199 patients relapsed in the SC; the incidence rate of relapse was 14% per patient-year. The cumulative incidence of relapse was 5% at 6 months and 28% at 24 months. In the multivariate analysis, the switch to CT-P13 was associated with a higher risk of relapse (HR = 3.5, 95% confidence interval [CI] = 2–6). Thirteen percent of patients had adverse events in the NC, compared with 6% in the SC [p < 0.05].[Conclusions] Switching from Remicade® to CT-P13 might be associated with a higher risk of clinical relapse, although this fact was not supported in our study by an increase in objective markers of inflammation. The nocebo effect might have influenced this result. Switching from Remicade® to CT-P13 was safe.This research has been funded by grants from the Instituto de Salud Carlos III [PI13/00041 and FI17/00143]

Long-Term Real-World Effectiveness and Safety of Ustekinumab in Crohn’s Disease Patients: The SUSTAIN Study

Background Large real-world-evidence studies are required to confirm the durability of response, effectiveness, and safety of ustekinumab in Crohn’s disease (CD) patients in real-world clinical practice. Methods A retrospective, multicentre study was conducted in Spain in patients with active CD who had received ≥1 intravenous dose of ustekinumab for ≥6 months. Primary outcome was ustekinumab retention rate; secondary outcomes were to identify predictive factors for drug retention, short-term remission (week 16), loss of response and predictive factors for short-term efficacy and loss of response, and ustekinumab safety. Results A total of 463 patients were included. Mean baseline Harvey-Bradshaw Index was 8.4. A total of 447 (96.5%) patients had received prior biologic therapy, 141 (30.5%) of whom had received ≥3 agents. In addition, 35.2% received concomitant immunosuppressants, and 47.1% had ≥1 abdominal surgery. At week 16, 56% had remission, 70% had response, and 26.1% required dose escalation or intensification; of these, 24.8% did not subsequently reduce dose. After a median follow-up of 15 months, 356 (77%) patients continued treatment. The incidence rate of ustekinumab discontinuation was 18% per patient-year of follow-up. Previous intestinal surgery and concomitant steroid treatment were associated with higher risk of ustekinumab discontinuation, while a maintenance schedule every 12 weeks had a lower risk; neither concomitant immunosuppressants nor the number of previous biologics were associated with ustekinumab discontinuation risk. Fifty adverse events were reported in 39 (8.4%) patients; 4 of them were severe (2 infections, 1 malignancy, and 1 fever). Conclusions Ustekinumab is effective and safe as short- and long-term treatment in a refractory cohort of CD patients in real-world clinical practice

Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

Ustekinumab has shown efficacy in Crohn's Disease (CD) patients. To identify patient profiles of those who benefit the most from this treatment would help to position this drug in the therapeutic paradigm of CD and generate hypotheses for future trials. The objective of this analysis was to determine whether baseline patient characteristics are predictive of remission and the drug durability of ustekinumab, and whether its positioning with respect to prior use of biologics has a significant effect after correcting for disease severity and phenotype at baseline using interpretable machine learning. Patients' data from SUSTAIN, a retrospective multicenter single-arm cohort study, were used. Disease phenotype, baseline laboratory data, and prior treatment characteristics were documented. Clinical remission was defined as the Harvey Bradshaw Index <= 4 and was tracked longitudinally. Drug durability was defined as the time until a patient discontinued treatment. A total of 439 participants from 60 centers were included and a total of 20 baseline covariates considered. Less exposure to previous biologics had a positive effect on remission, even after controlling for baseline disease severity using a non-linear, additive, multivariable model. Additionally, age, body mass index, and fecal calprotectin at baseline were found to be statistically significant as independent negative risk factors for both remission and drug survival, with further risk factors identified for remission

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing