Risk Categories in COVID-19 Based on Degrees of Inflammation: Data on More Than 17,000 Patients from the Spanish SEMI-COVID-19 Registry.

(1) Background: The inflammation or cytokine storm that accompanies COVID-19 marks the prognosis. This study aimed to identify three risk categories based on inflammatory parameters on admission. (2) Methods: Retrospective cohort study of patients diagnosed with COVID-19, collected and followed-up from 1 March to 31 July 2020, from the nationwide Spanish SEMI-COVID-19 Registry. The three categories of low, intermediate, and high risk were determined by taking into consideration the terciles of the total lymphocyte count and the values of C-reactive protein, lactate dehydrogenase, ferritin, and D-dimer taken at the time of admission. (3) Results: A total of 17,122 patients were included in the study. The high-risk group was older (57.9 vs. 64.2 vs. 70.4 years; p < 0.001) and predominantly male (37.5% vs. 46.9% vs. 60.1%; p < 0.001). They had a higher degree of dependence in daily tasks prior to admission (moderate-severe dependency in 10.8% vs. 14.1% vs. 17%; p < 0.001), arterial hypertension (36.9% vs. 45.2% vs. 52.8%; p < 0.001), dyslipidemia (28.4% vs. 37% vs. 40.6%; p < 0.001), diabetes mellitus (11.9% vs. 17.1% vs. 20.5%; p < 0.001), ischemic heart disease (3.7% vs. 6.5% vs. 8.4%; p < 0.001), heart failure (3.4% vs. 5.2% vs. 7.6%; p < 0.001), liver disease (1.1% vs. 3% vs. 3.9%; p = 0.002), chronic renal failure (2.3% vs. 3.6% vs. 6.7%; p < 0.001), cancer (6.5% vs. 7.2% vs. 11.1%; p < 0.001), and chronic obstructive pulmonary disease (5.7% vs. 5.4% vs. 7.1%; p < 0.001). They presented more frequently with fever, dyspnea, and vomiting. These patients more frequently required high flow nasal cannula (3.1% vs. 4.4% vs. 9.7%; p < 0.001), non-invasive mechanical ventilation (0.9% vs. 3% vs. 6.3%; p < 0.001), invasive mechanical ventilation (0.6% vs. 2.7% vs. 8.7%; p < 0.001), and ICU admission (0.9% vs. 3.6% vs. 10.6%; p < 0.001), and had a higher percentage of in-hospital mortality (2.3% vs. 6.2% vs. 23.9%; p < 0.001). The three risk categories proved to be an independent risk factor in multivariate analyses. (4) Conclusion: The present study identifies three risk categories for the requirement of high flow nasal cannula, mechanical ventilation, ICU admission, and in-hospital mortality based on lymphopenia and inflammatory parameters.

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.

Ability of procalcitonin to differentiate true bacteraemia from contaminated blood cultures in an emergency department. / Capacidad de la procalcitonina para diferenciar bacteriemia verdadera de los hemocultivos contaminados en el servicio de urgencias.

OBJECTIVES: To analyse and compare the ability of procalcitonin (PCT), C-reactive protein (CRP) and leukocytes to differentiate true bacteraemia from contaminated blood cultures in patients seen in the emergency department (ED) for an episode of infectious disease. METHODS: Observational, retrospective and descriptive analytical study of all blood cultures with positive growth extracted in an ED in adult patients (≥18 years) during 2016 and 2017. The follow-up was carried out over a 30-day period to calculate the predictive power and the prognostic performance for true bacteraemia. RESULTS: A total of 266 blood cultures with positive growth were included in the study. Out of these, 154 (57.9%) were considered true bacteraemia and 112 (42.1%) were considered to be contaminated blood cultures. The area under the Receiver Operating Characteristic curve (AUC-ROC) for PCT to predict true bacteraemia was 0.983 (95% CI: 0.972-0.994; P<0.001) and, considering a cut-off value of≥0.43 ng/ml, PCT achieved 94% sensitivity, 91% specificity, positive predictive value of 94%, and negative predictive value of 92%. The AUC-ROC obtained for CRP was 0.639 (95% CI: 0.572-0.707, P<.001), for leukocytes of 0.693 (95% CI: 0.630-0.756, P<.001) and for immature leukocytes (>10% bands) of 0.614 (95% CI: 0.547-0.682, P<.001). The mean values for PCT were 3.44 (SD 6.30) ng/ml in true bacteraemia vs. 0.16 (SD 0.18) ng/ml in contaminated blood cultures (P<.001). CONCLUSIONS: In blood cultures with positive growth extracted in an ED, PCT achieves the best prognostic performance of true bacteraemia vs. contaminated blood cultures, better than CRP and leukocytes.