Control hospitalario de la diabetes y de la hiperglucemia: una auditoría en hospitales españoles / Inpatient management of diabetes and hyperglycemia: an audit of Spanish hospitals

Objetivos Evaluación de la calidad de la asistencia a los pacientes con diabetes mellitus ingresados en España. Métodos Estudio transversal que incluyó a 1.193 (26,7%) pacientes con diabetes tipo 2 o hiperglucemia de un total de 4.468 pacientes ingresados en los servicios de medicina interna de 53 hospitales (España). Se recogieron datos demográficos, adecuación de la monitorización de la glucemia capilar, tratamiento administrado durante el ingreso y terapia recomendada al alta. Resultados La edad mediana fue de 80 años (74-87), 561 (47%) pacientes eran mujeres, con un índice de Charlson de 4 (2-6) puntos, siendo clasificados frágiles 742 (65%). La mediana de glucemia al ingreso fue de 155 (119-213) mg/dL. Al tercer día de ingreso el número de glucemias capilares en objetivo (80-180mg/dL) fue de 792/1.126 (70,3%) en el predesayuno, 601/1.083 (55,4%) en la precomida, 591/1.073 (55,0%) en la precena y 317/529 (59,9%) durante la noche. Se observó hipoglucemia en 35 (0,9%) pacientes. El tratamiento durante el ingreso fue realizado con insulina en escala móvil en 352 (40,5%) pacientes, insulina basal y análogos de insulina rápida en 434 (50%) y dieta exclusivamente en 101 (9,1%). Un total de 735 (61,6%) pacientes disponían de un valor reciente de HbA1c. En el alta se incrementó el uso de iSGLT2 (30,1 vs. 21,6%; p<0,001) y el uso de insulina basal (25,3 vs. 10,1%; p<0,001). Conclusiones Existe un excesivo uso de insulina en escala móvil, una deficiente información de los valores de HbA1c y una prescripción aún deficiente de tratamientos con beneficio cardiovascular al alta (AU)

Objectives Evaluation of the quality of care for patients with diabetes mellitus admitted to hospitals in Spain. Methods Cross-sectional study in one day that included 1193 (26.7%) patients with type 2 diabetes or hyperglycemia out of 4468 patients admitted to the internal medicine departments of 53 hospitals in Spain. We collected demographic data, adequacy of capillary glycemic monitoring, treatment administered during admission, and recommended therapy at discharge. Results The median age of the patients was 80 years (74-87), of which 561 (47%) were women, with a Charlson index of 4 points (2-6), and 742 (65%) were fragile. Median blood glucose on admission was 155mg/dL (119-213). On the third day, the number of capillary blood glucose levels in target (80-180mg/dL) was pre-breakfast 792/1126 (70.3%), pre-lunch 601/1083 (55.4%), pre-dinner 591/1073 (55.0%) and night 317/529 (59.9%). A total of 35 patients (0.9%) were suffering from hypoglycemia. Treatment during hospitalization was performed with sliding scale insulin in 352 (40.5%) patients, with basal insulin and rapid insulin analogs in 434 (50%), or with diet exclusively in 101 (9.1%). A total of 735 (61.6%) patients had a recent HbA1c value. At the time of discharge, the use of iSGLT2 increased significantly (30.1% vs. 21.6%; p<0.001), as well as the use of basal insulin (25.3% vs. 10.1%; p<0.001). Conclusions There is an excessive use of insulin on a sliding scale as well as deficient information on HbA1c values and an even deficient prescription at the discharge of treatments with cardiovascular benefit (AU)

Inpatient management of diabetes and hyperglycaemia: an audit of Spanish hospitals.

OBJECTIVES: Evaluation of quality of care for patients with diabetes mellitus admitted to hospitals in Spain. METHODS: Cross-sectional study in one day that included 1193 (26.7%) patients with type 2 diabetes or hyperglycaemia out of a total of 4468 patients admitted to the internal medicine departments of 53 hospitals in Spain. We collected demographic data, adequacy of capillary glycaemic monitoring, treatment administered during admission, and recommended therapy at discharge. RESULTS: The median age of the patients was 80 years [74-87], of which 561 (47%) were women, with a Charlson index of 4 points [2-6], and 742 (65%) were fragile. Median blood glucose on admission was 155 mg/dl [119-213]. On the third day, the number of capillary blood glucose levels in target (80-180 mg/dl) at pre-breakfast was 792/1126 (70.3%), pre-lunch 601/1083 (55.4%), pre-dinner 591/1073 (55.0%), and at night 317/529 (59.9%). A total of 35 patients (0.9%) were suffering from hypoglycemia. Treatment during hospitalization was performed with sliding scale insulin in 352 (40.5%) patients, with basal insulin and rapid insulin analogues in 434 (50%), or with diet exclusively in 101 (9.1%). A total of 735 (61.6%) patients had a recent HbA1c value. At discharge, the use of SGLT2i increased significantly (30.1% vs. 21.6%; p < 0.001), as did the use of basal insulin (25.3% vs. 10.1%; p < 0.001). CONCLUSIONS: There is an excessive use of sliding scale insulin as well as insufficient information on HbA1c values and prescription upon discharge of treatments with cardiovascular benefit.

Derivación y validación de un modelo predictivo de reingreso en pacientes con diabetes mellitus atendidos en servicios de medicina interna / Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments

Objetivos: Hemos desarrollado un modelo predictivo de reingreso hospitalario en pacientes con diabetes. El objetivo es identificar aquella población frágil que requiera estrategias adicionales para evitar reingresos a 90 días. Métodos: Utilizando datos recogidos en 3 estudios de prevalencia nacionales (2015-2017) que incluyeron un total de 1.977 pacientes hemos desarrollado y validado un modelo predictivo de reingreso a 90 días en pacientes con diabetes. Resultados: Se registraron un total de 704 (36%) reingresos. No hubo diferencias en la tasa de reingreso a lo largo de los 3 periodos de estudio. Los hospitales de más de 500 camas mostraron de forma estadísticamente significativa (p=0,02) mayores tasas de reingreso que los de menor tamaño. Los motivos principales de reingreso fueron enfermedades infecciosas (29%), enfermedades cardiovasculares (24%) y enfermedades respiratorias (14%). Los reingresos directamente relacionados con descompensaciones diabéticas fueron solo un 2%. Las variables independientes asociadas con reingresos hospitalarios fueron la edad del paciente, el grado de cormobilidad, el filtrado glomerular estimado, el grado de discapacidad, la presencia de episodios previos de hipoglucemia, el uso de insulina en el tratamiento de la diabetes y el uso de glucocorticoides sistémicos. El modelo predictivo mostró en la cohorte de derivación un área bajo de curva ROC: 0,676 (intervalo de confianza al 95% [IC 95%]: 0,642-0,709; p=0,001). En la cohorte de validación el modelo mostró un área bajo la curva: 0,661 (IC 95%: 0,612-0,710; p=0,001). Conclusión: El modelo de predicción de reingresos para pacientes con diabetes tipo 2 hospitalizados que hemos desarrollado permite identificar un subgrupo de pacientes frágiles con alto riesgo de reingreso

Objectives: We developed a predictive model for the hospital readmission of patients with diabetes. The objective was to identify the frail population that requires additional strategies to prevent readmissions at 90 days. Methods: Using data collected from 1977 patients in 3 studies on the national prevalence of diabetes (2015-2017), we developed and validated a predictive model of readmission at 90 days for patients with diabetes. Results: A total of 704 (36%) readmissions were recorded. There were no differences in the readmission rates over the course of the 3 studies. The hospitals with more than 500 beds showed significantly (p=.02) higher readmission rates than those with fewer beds. The main reasons for readmission were infectious diseases (29%), cardiovascular diseases (24) and respiratory diseases (14%). Readmissions directly related to diabetic decompensations accounted for only 2% of all readmissions. The independent variables associated with hospital readmission were patient's age, degree of comorbidity, estimated glomerular filtration rate, degree of disability, presence of previous episodes of hypoglycaemia, use of insulin in treating diabetes and the use of systemic glucocorticoids. The predictive model showed an area under the ROC curve (AUC) of 0.676 (95% confidence interval [95% CI] 0.642-0.709; p=.001) in the referral cohort. In the validation cohort, the model showed an AUC of 0.661 (95% CI 0.612-0.710; p=.001). Conclusion: The model we developed for predicting readmissions for hospitalised patients with type 2 diabetes helps identify a subgroup of frail patients with a high risk of readmission

Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments. / Derivación y validación de un modelo predictivo de reingreso en pacientes con diabetes mellitus atendidos en servicios de medicina interna.

OBJECTIVES: We developed a predictive model for the hospital readmission of patients with diabetes. The objective was to identify the frail population that requires additional strategies to prevent readmissions at 90 days. METHODS: Using data collected from 1977 patients in 3 studies on the national prevalence of diabetes (2015-2017), we developed and validated a predictive model of readmission at 90 days for patients with diabetes. RESULTS: A total of 704 (36%) readmissions were recorded. There were no differences in the readmission rates over the course of the 3 studies. The hospitals with more than 500 beds showed significantly (p=.02) higher readmission rates than those with fewer beds. The main reasons for readmission were infectious diseases (29%), cardiovascular diseases (24) and respiratory diseases (14%). Readmissions directly related to diabetic decompensations accounted for only 2% of all readmissions. The independent variables associated with hospital readmission were patient's age, degree of comorbidity, estimated glomerular filtration rate, degree of disability, presence of previous episodes of hypoglycaemia, use of insulin in treating diabetes and the use of systemic glucocorticoids. The predictive model showed an area under the ROC curve (AUC) of 0.676 (95% confidence interval [95% CI] 0.642-0.709; p=.001) in the referral cohort. In the validation cohort, the model showed an AUC of 0.661 (95% CI 0.612-0.710; p=.001). CONCLUSION: The model we developed for predicting readmissions for hospitalised patients with type 2 diabetes helps identify a subgroup of frail patients with a high risk of readmission.

Efecto de una intervención sobre indicadores de calidad para mejorar el tratamiento de la hiperglucemia en pacientes hospitalizados en áreas no críticas / Effect of an intervention on quality indicators for improving the treatment of hyperglycemia in patients hospitalized in noncritical areas

Objetivos. Evaluamos el efecto de una intervención sobre determinados indicadores de calidad utilizados para mejorar el tratamiento de la hiperglucemia hospitalaria. Material y métodos. Estudio transversal multicéntrico de pacientes con hiperglucemia ingresados en servicios de Medicina Interna de 44 hospitales evaluados en 2 periodos: 2014 (periodo basal) y 2015 (periodo postintervención). La intervención consistió en la difusión de los indicadores obtenidos en el año 2014 y de los objetivos de mejora. Como indicadores se evaluó la frecuencia de monitorización de la glucosa adaptada a la ingesta o la medicación del paciente, el uso de insulina en régimen basal-bolo o basal-bolo-corrección como método de control de la hiperglucemia y la disponibilidad reciente de HbA1c previa al alta hospitalaria. Resultados. En el año 2014 se evaluó a 506 pacientes y en el 2015 a 562. Los resultados de los indicadores en el periodo basal y postintervención fueron los siguientes: monitorización de la glucemia adaptada a la ingesta o la medicación (71,5 frente a 74,1%; p=0,33), uso de insulina en régimen basal-corrección (32 frente a 32,6%; p=0,61) o basal-bolo-corrección (20,7 frente a 24%; p=0,20) y valor reciente de HbA1c (54,1 frente a 66,3%; p<0,001). Los valores medios de glucosa en las 24h previos al estudio fueron similares en los 2 periodos. El porcentaje de hipoglucemias también fue similar en ambos periodos (3,3 vs. 2,3%; p=0,31). Conclusiones. Es necesario implementar intervenciones multimodales para mejorar el tratamiento de la hiperglucemia en pacientes hospitalizados en áreas no críticas (AU)

Objectives. We evaluated the effect of an intervention on certain quality indicators employed for improving the treatment of hospital hyperglycemia. Material and methods. A multicenter cross-sectional study was conducted on patients with hyperglycemia hospitalized in the internal medicine departments of 44 hospitals evaluated in 2 time periods: 2014 (baseline period) and 2015 (postintervention period). The intervention consisted of the dissemination of the indicators obtained in 2014 and the objectives for improvement. As indicators, we assessed the frequency of glucose monitoring adapted to the patient's dietary intake or medication, the use of basal-bolus or basal-bolus-correction insulin therapy as the preferred control method of hyperglycemia and the recent availability of HbA1c prior to hospital discharge. Results. A total of 506 and 562 patients were assessed in 2014 and 2015, respectively. The results of the indicators in the baseline and postintervention periods were as follows: blood glucose monitoring adapted to the dietary intake or the medication (71.5 vs. 74.1%, P=.33), use of insulin in basal-correction regimen (32 vs. 32.6%, P=.61) or basal-bolo-correction (20.7 vs. 24, P=.20) and recent HbA1c value (54.1 vs. 66.3%, P<.001). The mean glucose values in the 24h prior to the study were similar in the 2 periods. The rate of hypoglycemia was also similar in both periods (3.3 vs. 2.3%, P=.31). Conclusions. There is a need to implement multimodal interventions to improve the treatment of hyperglycemia in patients hospitalized in noncritical areas (AU)

The use of ertapenem for the treatment of community-acquired pneumonia in routine hospital practice: a matched cohort study.

OBJECTIVE: The clinical response to ertapenem in community-acquired pneumonia (CAP) at the setting of routine hospital practice has been scarcely evaluated. METHODS: We retrospectively compared CAP cases treated with ertapenem or with other standard antimicrobials (controls) at a tertiary 1,434-bed center from 2005 to 2014. RESULTS: Out of 6,145 patients hospitalized with CAP, 64 (1%) ertapenem-treated and 128 controls were studied (PSI IV-V 72%, mean age 73 years.). A significant higher proportion of bedridden patients (41% vs. 21%), residence in nursing homes (19% vs. 7%), previous use of antibiotics (39% vs. 29%) and necrotizing (13% vs. 1%) or complicated (36% vs. 19%) pneumonia, was observed in the ertapenem vs. non-ertapenem patients. Initial treatment with ertapenem was independently associated with an earlier resolution of signs of infection. In patients aged 65 or older the independent risks factors for mortality were: PSI score (7.0, 95%CI 1.8-27.7), bedridden status (4.6, 95%CI 1.1-20.9) and Health Care Associated Pneumonia (HCAP) (4.6, 95%CI 1.3-16.5). First-line treatment with ertapenem was an independent protector factor in this subgroup of patients (0.1, 95%CI 0.1-0.7). CONCLUSIONS: Ertapenem showed a superior clinical response in frail elderly patients with complicated community-acquired pneumonia, and it may be considered as a first-line therapeutic regimen in this setting.

Effect of an intervention on quality indicators for improving the treatment of hyperglycemia in patients hospitalized in noncritical areas. / Efecto de una intervención sobre indicadores de calidad para mejorar el tratamiento de la hiperglucemia en pacientes hospitalizados en áreas no críticas.

OBJECTIVES: We evaluated the effect of an intervention on certain quality indicators employed for improving the treatment of hospital hyperglycemia. MATERIAL AND METHODS: A multicenter cross-sectional study was conducted on patients with hyperglycemia hospitalized in the internal medicine departments of 44 hospitals evaluated in 2 time periods: 2014 (baseline period) and 2015 (postintervention period). The intervention consisted of the dissemination of the indicators obtained in 2014 and the objectives for improvement. As indicators, we assessed the frequency of glucose monitoring adapted to the patient's dietary intake or medication, the use of basal-bolus or basal-bolus-correction insulin therapy as the preferred control method of hyperglycemia and the recent availability of HbA1c prior to hospital discharge. RESULTS: A total of 506 and 562 patients were assessed in 2014 and 2015, respectively. The results of the indicators in the baseline and postintervention periods were as follows: blood glucose monitoring adapted to the dietary intake or the medication (71.5 vs. 74.1%, P=.33), use of insulin in basal-correction regimen (32 vs. 32.6%, P=.61) or basal-bolo-correction (20.7 vs. 24, P=.20) and recent HbA1c value (54.1 vs. 66.3%, P<.001). The mean glucose values in the 24h prior to the study were similar in the 2 periods. The rate of hypoglycemia was also similar in both periods (3.3 vs. 2.3%, P=.31). CONCLUSIONS: There is a need to implement multimodal interventions to improve the treatment of hyperglycemia in patients hospitalized in noncritical areas.