Un modelo de programación por metas para la elaboración del contrato-programa de un hospital público

[ES] Proponemos un modelo de programación por metas para la estimación del plan de producción (case-mix) que debe reflejarse en el Contrato–Programa que suscriben anualmente los Hospitales Públicos y la Administración. Las variables de decisión son los volúmenes de actividad de cada servicio médico del hospital y los atributos son los indicadores básicos que se manejan al elaborar el Contrato-Programa: fi nanciación, número de altas, estancia media y peso de complejidad. Para resolver nuestro modelo empleamos la herramienta SOLVER de la hoja de cálculo EXCEL. La utilización de esta herramienta permite simular varios escenarios de una manera ágil, lo que es de gran ayuda para el estudio y discusión de las cantidades a contratar entre el Hospital y la Administración. El artículo finaliza con una breve presentación de los resultados obtenidos al aplicar nuestro modelo a un hospital de tamaño medio (118 camas) del Servicio Vasco de Salud.

Carbapenem-resistant and carbapenem-susceptible isogenic isolates of Klebsiella pneumoniae ST101 causing infection in a tertiary hospital

Background: In this study we describe the clinical and molecular characteristics of an outbreak due to carbapenem-resistant Klebsiella pneumoniae (CR-KP) producing CTX-M-15 and OXA-48 carbapenemase. Isogenic strains, carbapenem-susceptible K. pneumoniae (CS-KP) producing CTX-M-15, were also involved in the outbreak. Results: From October 2010 to December 2012 a total of 62 CR-KP and 23 CS-KP were isolated from clinical samples of 42 patients (22 had resistant isolates, 14 had susceptible isolates, and 6 had both CR and CS isolates). All patients had underlying diseases and 17 of them (14 patients with CR-KP and 3 with CS-KP) had received carbapenems previously. The range of carbapenem MICs for total isolates were: imipenem: 2 to >32 mu g/ml vs. <2 mu g/ml; meropenem: 4 to >32 mu g/ml vs. <2 mu g/ml; and ertapenem: 8 to >32 mu g/ml vs. <2 mu g/ml. All the isolates were also resistant to gentamicin, ciprofloxacin, and cotrimoxazole. Both types of isolates shared a common PFGE pattern associated with the multilocus sequence type 101 (ST101). The bla(CTX-M-15) gene was detected in all the isolates, whereas the bla(OXA-48) gene was only detected in CR-KP isolates on a 70 kb plasmid. Conclusions: The clonal spread of K. pneumoniae ST101 expressing the OXA-48 and CTX-M-15 beta-lactamases was the cause of an outbreak of CR-KP infections. CTX-M-15-producing isolates lacking the blaOXA-48 gene coexisted during the outbreak.

Predictive score for mortality in patients with COPD exacerbations attending hospital emergency departments

Background: Limited information is available about predictors of short-term outcomes in patients with exacerbation of chronic obstructive pulmonary disease (eCOPD) attending an emergency department (ED). Such information could help stratify these patients and guide medical decision-making. The aim of this study was to develop a clinical prediction rule for short-term mortality during hospital admission or within a week after the index ED visit. Methods: This was a prospective cohort study of patients with eCOPD attending the EDs of 16 participating hospitals. Recruitment started in June 2008 and ended in September 2010. Information on possible predictor variables was recorded during the time the patient was evaluated in the ED, at the time a decision was made to admit the patient to the hospital or discharge home, and during follow-up. Main short-term outcomes were death during hospital admission or within 1 week of discharge to home from the ED, as well as at death within 1 month of the index ED visit. Multivariate logistic regression models were developed in a derivation sample and validated in a validation sample. The score was compared with other published prediction rules for patients with stable COPD. Results: In total, 2,487 patients were included in the study. Predictors of death during hospital admission, or within 1 week of discharge to home from the ED were patient age, baseline dyspnea, previous need for long-term home oxygen therapy or non-invasive mechanical ventilation, altered mental status, and use of inspiratory accessory muscles or paradoxical breathing upon ED arrival (area under the curve (AUC) = 0.85). Addition of arterial blood gas parameters (oxygen and carbon dioxide partial pressures (PO2 and PCO2)) and pH) did not improve the model. The same variables were predictors of death at 1 month (AUC = 0.85). Compared with other commonly used tools for predicting the severity of COPD in stable patients, our rule was significantly better. Conclusions: Five clinical predictors easily available in the ED, and also in the primary care setting, can be used to create a simple and easily obtained score that allows clinicians to stratify patients with eCOPD upon ED arrival and guide the medical decision-making process.