Modelo 5MPB-Toledo para predecir bacteriemia en los pacientes atendidos por infección en el servicio de urgencias / Predicting bacteremia in patients attended for infections in an emergency department: the 5MPB-Toledo model

Objetivo: Diseñar un modelo sencillo de riesgo para predecir bacteriemia en los pacientes atendidos por un episodio de infección en el servicio de urgencias hospitalario (SUH). Métodos: Estudio observacional, de cohortes retrospectivo, de todos los hemocultivos (SUH) extraídos en un SU en los pacientes adultos ($18 años) atendidos por infección desde el 1 de julio de 2018 hasta el 31 de marzo de 2019. Se analizaron 38 variables independientes (demográficas, comorbilidad, funcionales, clínicas y analíticas) que pudieran predecir la existencia de bacteriemia. Se realizó un estudio univariado y multivariable, mediante regresión logística, y después se construyó una escala de puntuación de riesgo. Resultados: Se incluyeron 2.181 episodios de HC extraídos. De ellos se consideraron como bacteriemias verdaderas 262 (12%) y como HC negativos 1.919 (88%). Entre los negativos, 1.755 (80,5%) no tuvieron crecimiento y 164 (7,5%) se consideraron contaminados. Se definió un modelo predictivo de bacteriemia con 5 variables (5MPB-Toledo). El modelo incluyó la temperatura > 38,3°C (1 punto), un índice de Charlson $ 3 (1 punto), la frecuencia respiratoria $ 22 respiraciones por minuto (1 punto), leucocitos > 12.000/mm3 (1 punto) y procalcitonina $ 0,51 ng/ml (4 puntos). Se categorizó a los pacientes en bajo (0-2 puntos), moderado (3-5 puntos) y alto (6-8 puntos) riesgo, con una probabilidad de bacteriemia de 1,1%, 10,5% y 77%, respectivamente. El ABC-COR del modelo tras remuestreo fue de 0,946 (IC 95%: 0,922-0,969). Conclusiones: El Modelo 5MPB-Toledo podría ser de utilidad para predecir bacteriemia en los pacientes atendidos por un episodio de infección en el SUH

Objectives: To develop a simple risk score to predict bacteremia in patients in our hospital emergency department for infection. Methods: Retrospective observational short study of all blood cultures ordered in the emergency department for adults (aged 18 or older) from July 1, 2018, to March 31, 2019. We gathered data on 38 independent variables (demographic, comorbidity, functional status, and laboratory findings) that might predict bacteremia. Univariate and multiple logistic regression analyses were applied to the data and a risk scale was developed. Results: A total of 2181 blood samples were cultured. True cases of bacteremia were confirmed in 262 (12%). The remaining 1919 cultures (88%) were negative. No growth was observed in 1755 (80.5%) of the negative cultures, and 164 (7.5%) were judged to be contaminated. The 5MPB-Toledo model identified 5 predictors of bacteremia: temperature higher than 38.3°C (1 point), a Charlson comorbidity index of 3 or more (1 point), respiratory frequency of at least 22 breaths/min (1 point), leukocyte count greater than 12 000/mm3 (1 point), and procalcitonin concentration of 0.51 ng/mL or higher (4 points). Low risk for bacteremia was indicated by a score of 0 to 2 points, intermediate risk by 3 to 5 points, and high risk by 6 to 8 points. Bacteremia in these 3 risk groups was predicted for 1.1%, 10.5%, and 77%, respectively. The model's area under the receiver operating characteristic curve was 0.946 (95% CI, 0.922-0.969). Conclusion: The 5MPB-Toledo score could be useful for predicting bacteremia in patients attended in hospital emergency departments for infection

Predicting bacteremia in patients attended for infections in an emergency department: the 5MPB-Toledo model. / Modelo 5MPB-Toledo para predecir bacteriemia en los pacientes atendidos por infección en el servicio de urgencias.

OBJECTIVES: To develop a simple risk score to predict bacteremia in patients in our hospital emergency department for infection. MATERIAL AND METHODS: Retrospective observational cohort study of all blood cultures ordered in the emergency department for adults (aged 18 or older) from July 1, 2018, to March 31, 2019. We gathered data on 38 independent variables (demographic, comorbidity, functional status, and laboratory findings) that might predict bacteremia. Univariate and multiple logistic regression analyses were applied to the data and a risk scale was developed. RESULTS: A total of 2181 blood samples were cultured. True cases of bacteremia were confirmed in 262 (12%). The remaining 1919 cultures (88%) were negative. No growth was observed in 1755 (80.5%) of the negative cultures, and 164 (7.5%) were judged to be contaminated. The 5MPB-Toledo model identified 5 predictors of bacteremia: temperature higher than 38.3°C (1 point), a Charlson comorbidity index of 3 or more (1 point), respiratory frequency of at least 22 breaths/min (1 point), leukocyte count greater than 12 000/mm3 (1 point), and procalcitonin concentration of 0.51 ng/mL or higher (4 points). Low risk for bacteremia was indicated by a score of 0 to 2 points, intermediate risk by 3 to 5 points, and high risk by 6 to 8 points. Bacteremia in these 3 risk groups was predicted for 1.1%, 10.5%, and 77%, respectively. The model's area under the receiver operating characteristic curve was 0.946 (95% CI, 0.922-0.969). CONCLUSION: The 5MPB-Toledo score could be useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

OBJETIVO: Diseñar un modelo sencillo de riesgo para predecir bacteriemia en los pacientes atendidos por un episodio de infección en el servicio de urgencias hospitalario (SUH). METODO: Estudio observacional, de cohortes retrospectivo, de todos los hemocultivos (HC) extraídos en un SUH en los pacientes adultos ($ 18 años) atendidos por infección desde el 1 de julio de 2018 hasta el 31 de marzo de 2019. Se analizaron 38 variables independientes (demográficas, comorbilidad, funcionales, clínicas y analíticas) que pudieran predecir la existencia de bacteriemia. Se realizó un estudio univariado y multivariable, mediante regresión logística, y después se construyó una escala de puntuación de riesgo. RESULTADOS: Se incluyeron 2.181 episodios de HC extraídos. De ellos se consideraron como bacteriemias verdaderas 262 (12%) y como HC negativos 1.919 (88%). Entre los negativos, 1.755 (80,5%) no tuvieron crecimiento y 164 (7,5%) se consideraron contaminados. Se definió un modelo predictivo de bacteriemia con 5 variables (5MPBToledo). El modelo incluyó la temperatura > 38,3°C (1 punto), un índice de Charlson $ 3 (1 punto), la frecuencia respiratoria $ 22 respiraciones por minuto (1 punto), leucocitos > 12.000/mm3 (1 punto) y procalcitonina $ 0,51 ng/ ml (4 puntos). Se categorizó a los pacientes en bajo (0-2 puntos), moderado (3-5 puntos) y alto (6-8 puntos) riesgo, con una probabilidad de bacteriemia de 1,1%, 10,5% y 77%, respectivamente. El ABC-COR del modelo tras remuestreo fue de 0,946 (IC 95%: 0,922-0,969). CONCLUSIONES: El Modelo 5MPB-Toledo podría ser de utilidad para predecir bacteriemia en los pacientes atendidos por un episodio de infección en los SUH.

Out-of-hospital cardiac arrest (OHCA) attended by mobile emergency teams with a physician on board. Results of the Spanish OHCA Registry (OSHCAR).

Most survival outcomes in out-of-hospital cardiac arrest (OHCA) are provided by emergency medical services (EMS) without a doctor on board. Our objective was to determine such outcomes in a whole country with public physician-led EMS. METHODS: We analyzed data from a nationwide prospective registry of OHCA cases attended by 19 public EMS in Spain, covering the period from 1-October 2013 to 30-October 2014. RESULTS: Advanced life support (ALS) was initiated in 9347 cases (incidence 18.6 cases/105 inhabitants per year). Resuscitation was considered futile in 558 cases (5.9%), and ALS was continued in 8789 cases (94.1%); mean age 63.5±17 years, 72.1% men. Initial rhythm was shockable in 22.1% of cases. Basic life support (BLS) was provided by bystanders in 1602 (24%) cases (635 of them with telephone assistance from the dispatch center). Of 8789 patients receiving ALS, 72.1% men, 2669 (30.4%) patients had return of spontaneous circulation on hospital arrival, 50.6% when the initial rhythm was shockable. Hospital discharge with good neurological status (CPC1-2) was found in 11.1% of the study population and in 27.6% when considering the Utstein comparator group of patients. A total of 216 (2.5%) patients arrived at the hospital with ongoing resuscitation, of whom only one survived with CPC1-2, and 165 (1.9%) patients were included in non-heart-beating donation programs. CONCLUSIONS: In Spain with physician-led EMS, OHCA survival with CPC1-2 reached a reasonable percentage despite only a modest contribution of bystander BLS. Ongoing resuscitation strategy seems to be futile except when considering non-heart beating donation programs.