Empiric antibiotics for community-acquired pneumonia in adult patients: a systematic review and a network meta-analysis.

OBJECTIVE: The main aim of this network meta-analysis is to identify the empiric antibiotic (Em-ATB) with the highest probability of being the best (HPBB) in terms of (1) cure rate and (2) mortality rate in hospitalised patients with community acquired pneumonia (CAP) . METHOD: Inclusion criteria: (1) adult patients (>16 years old) diagnosed with CAP that required hospitalisation; (2) randomised to at least two different Em-ATBs, (3) that report cure rate and (4) are written in English or Spanish. EXCLUSION CRITERIA: (1) ambiguous antibiotics protocol and (2) published exclusively in abstract or letter format. DATA SOURCES: Medline, Embase, Cochrane and citation reviews from 1 January 2000 to 31 December 2018. Risk of bias: Cochrane's tool. Quality of the systematic review (SR): A MeaSurement Tool to Assess systematic Reviews-2. Certainity of the evidence: Grading of Recommendations Assessment, Development and Evaluation. STATISTICAL ANALYSES: frequentist method performed with the 'netmeta' library, R package. RESULTS: 27 randomised controlled trials (RCTs) from the initial 41 307 screened citations were included. Regarding the risk of bias, more than one quarter of the studies presented low risk and no study presented high risk in all domains. The SR quality is moderate. For cure, two networks were constructed. Thus, two Em-ATBs have the HPBB: cetaroline 600 mg (two times a day) and piperacillin 2000 mg (two times a day). For mortality, three networks were constructed. Thus, three Em-ATBs have the HPBB: ceftriaxone 2000 mg (once a day) plus levofloxacin 500 (two times a day), ertapenem 1000 mg (two times a day) and amikacin 250 mg (two times a day) plus clarithromycin 500 mg (two times a day). The certainity of evidence for each results is moderate. CONCLUSION: For cure rate, ceftaroline and piperaciline are the options with the HPBB. However, for mortality rate, the options are ceftriaxone plus levofloxacin, ertapenem and amikacin plus clarithromycin. It seems necessary to conduct an RCT that compares treatments with the HPBB for each event (cure or mortality) (CRD42017060692).

Desarrollo de un modelo predictivo para mortalidad y otro para reingreso hospitalario en una cohorte de paciente con infección que requieren hospitalización / Development of a predictive model for hospital mortality and re-admission in a cohort of infected patients that require hospitalization

INTRODUCCIÓN: Los objetivos del estudio fueron: identificar variables asociadas a mortalidad intrahospitalaria y reingreso hospitalario a 3 meses; identificar el impacto de la demora en el inicio de la antibioticoterapia en la mortalidad y reportar la tasa de antibioticoterapia inapropiada. MATERIAL Y MÉTODOS: Estudio observacional de cohortes retrospectivo realizado en el Hospital Universitario HM Sanchinarro en Madrid. Los criterios de inclusión fueron: edad>18 años de edad, hospitalización desde urgencias durante el periodo 1 de septiembre 2012 al 31 de marzo del 2013 con diagnóstico de infección bacteriana. Los criterios de exclusión fueron: sospecha de infección viral y cultivos bacteriológicos negativos, expectativa de vida inferior a 6 meses, falta de información clínica, asistencia exclusivamente por el servicio de urgencias traumatológicas. Se realizaron dos modelos logísticos (mortalidad y reingreso hospitalarios). RESULTADOS: Se incluyeron 517 pacientes. Variables asociadas a mortalidad (30 fallecidos): frecuencia respiratoria (OR 1,12; IC95% 1,02; 1,22), saturación de oxígeno (OR 0,92; IC95% 0,87; 0,98), creatinina (OR 2,33; IC95% 1,62; 3,36), EPOC (OR 3,02; IC95% 1,06; 8,21), cáncer OR 3,34; IC95% 1,07; 9,98) y quimioterapia en los últimos 3 meses (OR 4,83; IC95% 1,54; 16,41). Variables asociadas a reingreso hospitalario (28 fallecidos): hepatopatía, GPT, antecedente de ictus e hipertensión arterial. Ambos modelos se destacan por su elevada especificidad y capacidad discriminativa pero baja sensibilidad. La demora en el inicio de la antibioticoterapia no influyo en la mortalidad ni reingreso. En 56 pacientes se identificó el microorganismo causal y el tratamiento antibiótico fue inapropiado en 11. CONCLUSIONES: Se registro un 5,8% de mortalidad hospitalaria y un 5,7% de reingresos. Las variables asociadas a la mortalidad intrahospitalaria difieren de las asociadas al reingreso. La demora en el inicio de la antibioticoterapia no se asoció a un efecto deletéreo. La antibioticoterapia inadecuada fue de casi el 20%

INTRODUCTION: The aims of the study were: to develop a predictive model for hospital mortality and another for hospital re-admission, to identify the impact of antibiotic delay in the mortality rate and, to report the rate of inappropriate antibiotic therapy. MATERIAL AND METHODS: A cohort and retrospective study was conducted at the HM Sanchinarro University Hospital during the period September 1st, 2012 to March 31th, 2013. The inclusion criteria were: age> 18 years, hospital admission from the ED with a diagnosis of bacterial infection. The exclusion criteria were: suspected viral infection, negative bacteriological cultures, life expectancy less than 6 months, lack of clinical information, assistance exclusively by the trauma emergency department. Two logistic models were made (hospital mortality and hospital re-admission). RESULTS: A total of 517 patients were included. The final mortality model (30 deaths) include the following variables: respiratory rate (OR 1.12; IC95% 1.02; 1.22), oxygen saturation (OR 0.92; IC95% 0.87; 0.98), creatinine (OR 2.33; IC95% 1.62; 3.36), COPD (OR 3.02; IC95% 1.06; 8.21), cancer (OR 3.34; IC95% 1.07; 9.98) and chemotherapy in the last 3 months (OR 4.83; IC95% 1.54; 16.41). The final model for hospital re-admission (28 re-admissions) include the following variables: hepatopathy (OR 5.51; IC95% 1.57; 16.88), GPT (OR 1.005; IC95% 1.003; 1.008), history of stroke (OR 5.06; IC95% 1.04; 18.80) and arterial hypertension (OR 3.15; IC95% 1.38; 7.56). The antibiotic therapy delays not influenced the mortality or re-admission rate. In 24.3% the causative microorganism was identified and antibiotic treatment was inappropriate 19.6%. CONCLUSION: Hospital mortality rate was 5.8% and readmission rate was 5.7%. Variables associated with mortality differ from those associated with re-admission. The delay in the antibiotic initiation was not associated with a deleterious effect. Antibiotic therapy was inadequate in almost 20% of patients

What have we learned from network meta-analyses applied to critical care?

It is widely accepted in modern medicine that medical decisions must be supported by scientific evidence. Identifying the best intervention when several options are available constitute a great challenge for every clinician. Traditional meta-analysis (TMA) allows summarizing evidence from studies that compare the same two interventions for one event (head to head studies or direct comparisons). Network meta-analysis (NMA) is a relatively new procedure that allows to compare multiple interventions for one event, even when non-head to head studies have been conducted (indirect evidence). Other advantages of NMA include increasing the accuracy of the results and ranking all the interventions according to their effectiveness. These features are of paramount importance as: 1) they summarize information from events (e.g. diseases or outcomes) that has more than two possible interventions (e.g. treatments or procedures); 2) they strengthen the level of guideline recommendations; and 3) they identify new hypotheses based on indirect comparison. As this is a narrative review, all manuscripts have been selected from PubMed according to our best knowledge with the aim to illustrate different features, options or applications of NMA in critical care. First, we provide a description of the usefulness, interpretation, assumptions and main plots related to NMAs. Second, we analyzed some examples of NMAs related to critical care medicine. Third, we include a pragmatic approach about how results from NMAs can improve the clinical practice as well an R script with a database to conduct an NMAs and reproduce figures and tables that have been shown here. As a conclusion, NMA is an established, robust, objective and reproducible statistic technique that has been applied to several critical care areas. Clinical practice guidelines have started to include NMA evidence to support their recommendations. In future years, it seems highly probable that this technique will increase it applicability in almost all areas of critical care medicine.