13 research outputs found

    Flowchart of participants.

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    Diabetes and poor glycemic control are significant predictors of severity and death in the COVID-19 disease. The perception of this risk in individuals with type 2 diabetes (T2D) could modify coping styles, leading to behaviors associated with better self-care and metabolic control. Theoretically, active coping is associated with better glycemic control in patients with T2D. Nonetheless, information during extreme risk like the COVID-19 pandemic is still limited. Our objective was to evaluate the association between coping styles and risk perception in the COVID-19 pandemic and the change in metabolic parameters. This is a prospective study that included individuals with T2D treated in a tertiary care center during the COVID-19 outbreak who returned to follow-up one year later. We assessed coping styles and risk perception with the Extreme Risk Coping Scale and the risk perception questionnaire. Clinical characteristics and metabolic parameters were registered in both visits. Groups were compared using Kruskal Wallis tests, and changes in metabolic parameters were assessed with Wilcoxon rank sum tests. Our sample included 177 participants at baseline, and 118 concluded the study. Passive coping was more frequent in women. Low-risk perception was associated with higher age, lower psychiatric comorbidities, and lower frequency of psychiatric treatment compared with other risk perception groups. Patients with active coping plus high-risk perception did not have a change in metabolic parameters at follow-up, whereas patients with other coping styles and lower risk perception had an increase in total cholesterol, LDL-cholesterol, and triglycerides. There were no differences by coping group or by risk perception in glycemic control.</div

    Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

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    Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications

    Mechanical ventilation in patients with cardiogenic pulmonary edema : a sub-analysis of the LUNG SAFE study

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    Patients with acute respiratory failure caused by cardiogenic pulmonary edema (CPE) may require mechanical ventilation that can cause further lung damage. Our aim was to determine the impact of ventilatory settings on CPE mortality. Patients from the LUNG SAFE cohort, a multicenter prospective cohort study of patients undergoing mechanical ventilation, were studied. Relationships between ventilatory parameters and outcomes (ICU discharge/hospital mortality) were assessed using latent mixture analysis and a marginal structural model. From 4499 patients, 391 meeting CPE criteria (median age 70 [interquartile range 59-78], 40% female) were included. ICU and hospital mortality were 34% and 40%, respectively. ICU survivors were younger (67 [57-77] vs 74 [64-80] years, p < 0.001) and had lower driving (12 [8-16] vs 15 [11-17] cmHO, p < 0.001), plateau (20 [15-23] vs 22 [19-26] cmHO, p < 0.001) and peak (21 [17-27] vs 26 [20-32] cmHO, p < 0.001) pressures. Latent mixture analysis of patients receiving invasive mechanical ventilation on ICU day 1 revealed a subgroup ventilated with high pressures with lower probability of being discharged alive from the ICU (hazard ratio [HR] 0.79 [95% confidence interval 0.60-1.05], p = 0.103) and increased hospital mortality (HR 1.65 [1.16-2.36], p = 0.005). In a marginal structural model, driving pressures in the first week (HR 1.12 [1.06-1.18], p < 0.001) and tidal volume after day 7 (HR 0.69 [0.52-0.93], p = 0.015) were related to survival. Higher airway pressures in invasively ventilated patients with CPE are related to mortality. These patients may be exposed to an increased risk of ventilator-induced lung injury. Trial registration Clinicaltrials.gov NCT02010073

    Mechanical ventilation in patients with cardiogenic pulmonary edema: a sub-analysis of the LUNG SAFE study

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    International audienceBackground: Patients with acute respiratory failure caused by cardiogenic pulmonary edema (CPE) may require mechanical ventilation that can cause further lung damage. Our aim was to determine the impact of ventilatory settings on CPE mortality. Methods: Patients from the LUNG SAFE cohort, a multicenter prospective cohort study of patients undergoing mechanical ventilation, were studied. Relationships between ventilatory parameters and outcomes (ICU discharge/ hospital mortality) were assessed using latent mixture analysis and a marginal structural model. Results: From 4499 patients, 391 meeting CPE criteria (median age 70 [interquartile range 59-78], 40% female) were included. ICU and hospital mortality were 34% and 40%, respectively. ICU survivors were younger (67 [57-77] vs 74 [64-80] years, p < 0.001) and had lower driving (12 [8-16] vs 15 [11-17] cmH 2 O, p < 0.001), plateau (20 [15-23] vs 22 [19-26] cmH 2 O, p < 0.001) and peak (21 [17-27] vs 26 [20-32] cmH 2 O, p < 0.001) pressures. Latent mixture analysis of patients receiving invasive mechanical ventilation on ICU day 1 revealed a subgroup ventilated with high pressures with lower probability of being discharged alive from the ICU (hazard ratio [HR] 0.79 [95% confidence interval 0.60-1.05], p = 0.103) and increased hospital mortality (HR 1.65 [1.16-2.36], p = 0.005). In a marginal structural model, driving pressures in the first week (HR 1.12 [1.06-1.18], p < 0.001) and tidal volume after day 7 (HR 0.69 [0.52-0.93], p = 0.015) were related to survival. Conclusions: Higher airway pressures in invasively ventilated patients with CPE are related to mortality. These patients may be exposed to an increased risk of ventilator-induced lung injury
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