Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations

Evaluación del impacto de una intervención de enfermería en la prevención y tratamiento de las úlceras por presión

Objetivo: Evaluar el impacto de una intervención de enfermería en la prevención y tratamiento de úlceras por presión (UPP). Metodología: Diseño cuasi experimental pre- y postintervención. Resultados: La prevalencia global de UPP no varía tras la intervención (χ² = 1,059, p = 0,589). No obstante, descienden hasta 0 las UPP de grado III y IV, la prevalencia en la Unidad de Onco-Hematología, los pacientes con riesgo bajo que desarrollaron UPP y mejora el registro del riesgo de UPP al ingreso. Conclusiones: A pesar de estas mejoras, se continuará haciendo estudios de prevalencia para conocer la situación y poder implementar mejoras que disminuyan la tasa de prevalencia

Estudio de prevalencia de úlceras por presión en la Clínica Universidad de Navarra

El objetivo del estudio es el conocimiento de la prevalencia de úlceras por presión en un centro hospitalario. Se realizó un estudio transversal utilizando un muestreo de conveniencia. La recogida de datos se realizó mediante un cuestionario elaborado y pilotado. En el análisis de los datos se empleó estadística descriptiva e inferencial. Este estudio nos ha permitido conocer la situación actual sobre la que partimos e implantar acciones de mejora como la difusión de los datos obtenidos, formación a los profesionales, unificación de la escala de valoración del riesgo y mejora de los registros y reevaluación de úlceras por presión

Image_1_Divergent SARS-CoV-2-specific T cell responses in intensive care unit workers following mRNA COVID-19 vaccination.tif

The cellular immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in response to full mRNA COVID-19 vaccination could be variable among healthy individuals. Studies based only in specific antibody levels could show an erroneous immune protection at long times. For that, we analyze the antibody levels specific to the S protein and the presence of SARS-CoV-2-specific T cells by ELISpot and AIM assays in intensive care unit (ICU) workers with no antecedents of COVID-19 and vaccinated with two doses of mRNA COVID-19 vaccines. All individuals were seronegative for the SARS-CoV-2 protein S before vaccination (Pre-v), but 34.1% (14/41) of them showed pre-existing T lymphocytes specific for some viral proteins (S, M and N). One month after receiving two doses of COVID-19 mRNA vaccine (Post-v1), all cases showed seroconversion with high levels of total and neutralizing antibodies to the spike protein, but six of them (14.6%) had no T cells reactive to the S protein. Specifically, they lack of specific CD8+ T cells, but maintain the contribution of CD4+ T cells. Analysis of the immune response against SARS-CoV-2 at 10 months after full vaccination (Post-v10), exhibited a significant reduction in the antibody levels (p+ T cells after vaccination, that may condition the susceptibility to further viral infections with SARS-CoV-2. By contrast, around 77% of individuals developed strong humoral and cellular immune responses to SARS-CoV-2 that persisted even after 10 months. Analysis of the cellular immune response is highly recommended for providing exact information about immune protection against SARS-CoV-2.</p

Image_3_Divergent SARS-CoV-2-specific T cell responses in intensive care unit workers following mRNA COVID-19 vaccination.tif

The cellular immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in response to full mRNA COVID-19 vaccination could be variable among healthy individuals. Studies based only in specific antibody levels could show an erroneous immune protection at long times. For that, we analyze the antibody levels specific to the S protein and the presence of SARS-CoV-2-specific T cells by ELISpot and AIM assays in intensive care unit (ICU) workers with no antecedents of COVID-19 and vaccinated with two doses of mRNA COVID-19 vaccines. All individuals were seronegative for the SARS-CoV-2 protein S before vaccination (Pre-v), but 34.1% (14/41) of them showed pre-existing T lymphocytes specific for some viral proteins (S, M and N). One month after receiving two doses of COVID-19 mRNA vaccine (Post-v1), all cases showed seroconversion with high levels of total and neutralizing antibodies to the spike protein, but six of them (14.6%) had no T cells reactive to the S protein. Specifically, they lack of specific CD8+ T cells, but maintain the contribution of CD4+ T cells. Analysis of the immune response against SARS-CoV-2 at 10 months after full vaccination (Post-v10), exhibited a significant reduction in the antibody levels (p+ T cells after vaccination, that may condition the susceptibility to further viral infections with SARS-CoV-2. By contrast, around 77% of individuals developed strong humoral and cellular immune responses to SARS-CoV-2 that persisted even after 10 months. Analysis of the cellular immune response is highly recommended for providing exact information about immune protection against SARS-CoV-2.</p

Image_2_Divergent SARS-CoV-2-specific T cell responses in intensive care unit workers following mRNA COVID-19 vaccination.tif

The cellular immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in response to full mRNA COVID-19 vaccination could be variable among healthy individuals. Studies based only in specific antibody levels could show an erroneous immune protection at long times. For that, we analyze the antibody levels specific to the S protein and the presence of SARS-CoV-2-specific T cells by ELISpot and AIM assays in intensive care unit (ICU) workers with no antecedents of COVID-19 and vaccinated with two doses of mRNA COVID-19 vaccines. All individuals were seronegative for the SARS-CoV-2 protein S before vaccination (Pre-v), but 34.1% (14/41) of them showed pre-existing T lymphocytes specific for some viral proteins (S, M and N). One month after receiving two doses of COVID-19 mRNA vaccine (Post-v1), all cases showed seroconversion with high levels of total and neutralizing antibodies to the spike protein, but six of them (14.6%) had no T cells reactive to the S protein. Specifically, they lack of specific CD8+ T cells, but maintain the contribution of CD4+ T cells. Analysis of the immune response against SARS-CoV-2 at 10 months after full vaccination (Post-v10), exhibited a significant reduction in the antibody levels (p+ T cells after vaccination, that may condition the susceptibility to further viral infections with SARS-CoV-2. By contrast, around 77% of individuals developed strong humoral and cellular immune responses to SARS-CoV-2 that persisted even after 10 months. Analysis of the cellular immune response is highly recommended for providing exact information about immune protection against SARS-CoV-2.</p

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations