Improving pharmaceutical practice in diabetes care using point-of-care glycated haemoglobin testing in the community pharmacy.

OBJECTIVES: To evaluate the use of point-of-care testing to detect new cases of diabetes mellitus at a Brazilian public community pharmacy. METHODS: This cross-sectional study included individuals without a previous diagnosis of diabetes mellitus who met the criteria for screening according to the Brazilian Diabetes Society, which were identified during their presence at a Brazilian public community pharmacy. The measurements of HbA1c were performed using a Cobas b101 device (Roche Diagnostics) and were categorized according to the following classification established by the Brazilian Society of Diabetes: HbA1c <5.7%, normal; HbA1c between 5.7% and 6.4%, pre-diabetes; and HbA1c >6.4%, new diagnosis of T2DM. KEY FINDINGS: One hundred and eight users met the inclusion criteria. The patients' mean age was 54.4 (± 15.4) years old, ranging from 22 to 80 years old. Eighty (74.1%) participants presented with glycated haemoglobin levels over the standard threshold, of which 58 (72.5%) were in the pre-diabetes range (glycated haemoglobin levels between 5.7% and 6.4%), and 22 (27.5%) had glycated haemoglobin levels >6.4%, which corresponds to a new diagnosis of type 2 diabetes mellitus. CONCLUSIONS: The use of point-of-care glycated haemoglobin testing allowed community pharmacists at a Brazilian public community pharmacy to identify health system users with glycated haemoglobin alterations that corresponded to the pre-diabetes state or a new diagnosis of type 2 diabetes mellitus. This presented a good opportunity to refer these users to diabetes diagnosis and treatment services.

Predição de choque séptico e hipovolêmico em pacientes de unidade de terapia intensiva com o uso de machine learning / Prediction of septic and hypovolemic shock in intensive care unit patients using machine learning

RESUMO Objetivo: Criar e validar um modelo de predição de choque séptico ou hipovolêmico a partir de variáveis de fácil obtenção coletadas na admissão de pacientes internados em uma unidade de terapia intensiva. Métodos: Estudo de modelagem preditiva com dados de coorte concorrente realizada em um hospital do interior do nordeste brasileiro. Foram incluídos pacientes com 18 anos ou mais sem uso de droga vasoativa no dia da admissão e que foram internados entre novembro de 2020 e julho de 2021. Foram testados os algoritmos de classificação do tipo Decision Tree, Random Forest, AdaBoost, Gradient Boosting e XGBoost para a construção do modelo. O método de validação utilizado foi o k-fold cross validation. As métricas de avaliação utilizadas foram recall, precisão e área sob a curva Receiver Operating Characteristic. Resultados: Foram utilizados 720 pacientes para criação e validação do modelo. Os modelos apresentaram alta capacidade preditiva com área sob a curva Receiver Operating Characteristic de 0,979; 0,999; 0,980; 0,998 e 1,00 para os algoritmos de Decision Tree, Random Forest, AdaBoost, Gradient Boosting e XGBoost, respectivamente. Conclusão: O modelo preditivo criado e validado apresentou elevada capacidade de predição do choque séptico e hipovolêmico desde o momento da admissão de pacientes na unidade de terapia intensiva.

ABSTRACT Objective: To create and validate a model for predicting septic or hypovolemic shock from easily obtainable variables collected from patients at admission to an intensive care unit. Methods: A predictive modeling study with concurrent cohort data was conducted in a hospital in the interior of northeastern Brazil. Patients aged 18 years or older who were not using vasoactive drugs on the day of admission and were hospitalized from November 2020 to July 2021 were included. The Decision Tree, Random Forest, AdaBoost, Gradient Boosting and XGBoost classification algorithms were tested for use in building the model. The validation method used was k-fold cross validation. The evaluation metrics used were recall, precision and area under the Receiver Operating Characteristic curve. Results: A total of 720 patients were used to create and validate the model. The models showed high predictive capacity with areas under the Receiver Operating Characteristic curve of 0.979; 0.999; 0.980; 0.998 and 1.00 for the Decision Tree, Random Forest, AdaBoost, Gradient Boosting and XGBoost algorithms, respectively. Conclusion: The predictive model created and validated showed a high ability to predict septic and hypovolemic shock from the time of admission of patients to the intensive care unit.

Prediction of septic and hypovolemic shock in intensive care unit patients using machine learning. / Predição de choque séptico e hipovolêmico em pacientes de unidade de terapia intensiva com o uso de machine learning.

OBJECTIVE: To create and validate a model for predicting septic or hypovolemic shock from easily obtainable variables collected from patients at admission to an intensive care unit. METHODS: A predictive modeling study with concurrent cohort data was conducted in a hospital in the interior of northeastern Brazil. Patients aged 18 years or older who were not using vasoactive drugs on the day of admission and were hospitalized from November 2020 to July 2021 were included. The Decision Tree, Random Forest, AdaBoost, Gradient Boosting and XGBoost classification algorithms were tested for use in building the model. The validation method used was k-fold cross validation. The evaluation metrics used were recall, precision and area under the Receiver Operating Characteristic curve. RESULTS: A total of 720 patients were used to create and validate the model. The models showed high predictive capacity with areas under the Receiver Operating Characteristic curve of 0.979; 0.999; 0.980; 0.998 and 1.00 for the Decision Tree, Random Forest, AdaBoost, Gradient Boosting and XGBoost algorithms, respectively. CONCLUSION: The predictive model created and validated showed a high ability to predict septic and hypovolemic shock from the time of admission of patients to the intensive care unit.

OBJETIVO: Criar e validar um modelo de predição de choque séptico ou hipovolêmico a partir de variáveis de fácil obtenção coletadas na admissão de pacientes internados em uma unidade de terapia intensiva. MÉTODOS: Estudo de modelagem preditiva com dados de coorte concorrente realizada em um hospital do interior do nordeste brasileiro. Foram incluídos pacientes com 18 anos ou mais sem uso de droga vasoativa no dia da admissão e que foram internados entre novembro de 2020 e julho de 2021. Foram testados os algoritmos de classificação do tipo Decision Tree, Random Forest, AdaBoost, Gradient Boosting e XGBoost para a construção do modelo. O método de validação utilizado foi o k-fold cross validation. As métricas de avaliação utilizadas foram recall, precisão e área sob a curva Receiver Operating Characteristic. RESULTADOS: Foram utilizados 720 pacientes para criação e validação do modelo. Os modelos apresentaram alta capacidade preditiva com área sob a curva Receiver Operating Characteristic de 0,979; 0,999; 0,980; 0,998 e 1,00 para os algoritmos de Decision Tree, Random Forest, AdaBoost, Gradient Boosting e XGBoost, respectivamente. CONCLUSÃO: O modelo preditivo criado e validado apresentou elevada capacidade de predição do choque séptico e hipovolêmico desde o momento da admissão de pacientes na unidade de terapia intensiva.

Improving Access to the Glycated Hemoglobin Test in Rural Communities With Point-of-Care Devices: An Application Study.

Background: Living in a rural or remote area is frequently associated with impaired access to health services, which directly affects the possibility of early diagnosis and appropriate monitoring of diseases, mainly non-communicable ones, because of their asymptomatic onset and evolution. Point-of-care devices have emerged as useful technologies for improving access to several laboratory tests closely patients' beds or homes, which makes it possible to eliminate the distance barrier. Objective: To evaluate the application of point-of-care technology for glycated hemoglobin (HbA1c) estimation in the assessment of glycemic control and identification of new diagnoses of diabetes in primary care among rural communities in a Brazilian municipality. Materials and Methods: We included individuals aged 18 years or older among rural communities in a Brazilian municipality. From September 2019 to February 2020, participants were assessed for anthropometrics, blood pressure, and capillary glycemia during routine primary care team activities at health fairs and in patient groups. Participants previously diagnosed with diabetes but without recent HbA1c test results or those without a previous diagnosis but with random capillary glycemia higher than 140 mg/dL were considered positive and were tested for HbA1c by using a point-of-care device. Results: At the end of the study, 913 individuals were accessed. Of these, 600 (65.7%) had no previous diagnosis of diabetes, 58/600 (9.7%) refused capillary glycemia screening and 542/600 (90.7%) were tested. Among tested individuals, 73/542 (13.5%) cases without a previous diagnosis of diabetes, were positive for capillary glycemia. Among positives, 31/73 (42.5%) had HbA1c levels that were considered indicative of prediabetes and 16/73 (21.9%) were newly diagnosed with diabetes. Among the participants, 313/913 (34.3%) were previously diagnosed with diabetes. Recent HbA1c results were unavailable for 210/313 (67.1%). These individuals were tested using point-of-care devices. Among them, 143/210 (68.1%) had HbA1c levels higher than target levels (>7% and >8% for adults and elderly individuals, respectively. Conclusion: The application of point-of-care devices for HbA1c level measurement improved the access to this test for people living in rural or remote areas. Thus, it was possible to include this technology in the routine activities of primary health care teams, which increased the rates of new diagnoses and identification of patients with uncontrolled glycemia.

Estimated glomerular filtration rate and drug dose adjustment in patients in an intensive care unit in Brazil

Objectives. This study sought to compare the estimated glomerular filtration rate and the indication of dose adjustment of antimicrobials when using Cockcroft-Gault or Modification of Diet in Renal Disease. Methods. A cross-sectional study was performed with patients admitted to the intensive care unit of a Brazilian general hospital. The glomerular filtration rate was calculated for patients on all days using the Cockcroft-Gault and Modification of Diet in Renal Disease equations. The difference in estimated glomerular filtration and the dose adjustment indication of antimicrobials were assessed. Results. A total of 631 patients were included in this study. The median estimated glomerular filtration was significantly higher when estimated using Modification of Diet in Renal Disease (100.3 mL/ min/1.73 m2) than the estimation by Cockcroft-Gault (83.2 mL/min) [p<0.001]. Greater differences in estimations produced by the two formulae were observed in patients at extremes of weight and age, and a different dose adjustment was indicated for all antimicrobials assessed. Conclusions. These results demonstrate a significant difference in estimated glomerular filtration rate values when calculated using either Cockcroft-Gault or Modification of Diet in Renal Disease as well as in the indication of dose adjustment in an intensive care unit

Association between increased mortality rate and antibiotic dose adjustment in intensive care unit patients with renal impairment.

PURPOSE: Adjusting the antibiotic dose based on an estimation of the glomerular filtration rate (eGFR) may result in subdosing, which may actually be significantly more problematic for intensive care unit (ICU) patients than not adjusting the dose. The aim of this study was to assess the outcomes of antibiotic dose adjustment in ICU patients with renal impairment. METHODS: A retrospective cohort study was conducted in adult patients admitted to an ICU of a Brazilian hospital from January 2014 to December 2015. The eGFR was determined using Cockcroft-Gault and Modified Diet in Renal Disease equations for each day of hospitalization. Treatment failure was defined based on the clinical, laboratory, and radiological criteria. RESULTS: A total of 126 patients were assessed to meet the inclusion criteria and subsequently enrolled in the study (19.9% of patients admitted to the ICU during the study period). Of the 168 opportunities for dose adjustment, 99 (58.9%) adjustments were made. The mean eGFR in the group with dose adjustment was lower than that in the group without dose adjustment (38.5 vs. 40.7 mL/min/1.73 m2, respectively). The treatment failure rate among patients with dose adjustment and those treated with the usual dose was 59.3 and 38.9%, respectively (p = 0.023), and the mortality rates in the respective groups were 74.1 and 55.5% (p = 0.033). An association between dose adjustment and treatment failure/mortality rates was also observed in the multivariate analysis including the prognostic score. CONCLUSIONS: In ICU patients with renal impairment, adjustments in antibiotic dose based on eGFR, significantly increased the risk of treatment failure and death.