Mathematical Model To Quantify the Effects of Risk Factors on Carbapenem-Resistant Acinetobacter baumannii

Abstract

e Carbapenem-resistant Acinetobacter baumannii (CRAB) infections are increasing, and they are associated with an increased risk of mortality in hospitalized patients. Linear regression is commonly used to identify concurrent trends, but it cannot quantify the relationship between risk factors and resistance. We developed a model to quantify the impact of antibiotic consumption on the prevalence of CRAB over time. Data were collected from January 2007 to June 2013 from our institution. Quarterly antibiotic consumption was expressed as defined daily dose/1,000 inpatient days. Six-month prevalence of CRAB was expressed as a percentage of all nonrepeat A. baumannii isolates tested. Individual trends were identified using linear regression. T he prevalence of carbapenem-resistant Acinetobacter baumannii (CRAB) is escalating in all parts of the world (1, 2). Locally, carbapenem resistance is found in almost 50% of all blood isolates of Acinetobacter species within Singapore (2). CRAB is implicated in a wide range of nosocomial infections and is associated with many outbreaks within institutions (3). Many first-line antibiotics are ineffective for CRAB infections, resulting in increased risk of mortality and morbidity in hospitalized patients (4-6). With limited new therapeutic agents on the horizon, there is a greater emphasis on controlling risk factors associated with the rise in CRAB and reducing its transmission.The development of CRAB appears to be associated with multiple risk factors. These factors can be antibiotics, such as imipenem, 3rd-generation cephalosporins (7), or any prior antibiotic treatment (8, 9), all of which have been associated with CRAB. Nondrug risk factors, such as surgery (9), duration of hospitalization (8), and previous admission to an intensive care unit (7,8,10), have also been associated with CRAB.Risk factors associated with resistance are traditionally identified in case-control studies (11). Simple linear regression analyses are commonly used to explore individual trends of factors and of resistance over the same time frame, and significant associations between trends are then examined by correlational analysis (12-15). However, correlation between trends is limited in determining the quantitative relationship between risk factors and resistance, because the trends may be far from linear. Without a good understanding of the relative impact of risk factors on resistance over time, it is difficult to prioritize countermeasures and design long-term strategies. A more robust quantitative relationship would guide the use of finite resources for tackling resistance.In addition to correlation analysis, model structures such as time series and transfer function analysis have been suggested as an alternative with a more realistic description of the relationship between risk factors and resistance over time (11,16). The idea behind the combined autoregressive integrated moving average (ARIMA) time series/transfer function analysis is as follows: a model with random inputs is used to account f...

Factores asociados a infección por Acinetobacter baumannii multidrogorresistente en unidad de cuidados intensivos. Estudio multicéntrico

“…El sexo, no estuvo asociado a mayor frecuencia de Ab-MDR, tanto en los casos como en los controles el sexo masculino fue predominante (63.3% y 53.3%, respectivamente). Esto concuerda con la evidencia revisada, en que el sexo no estaría influenciando mayor o menor resistencia antimicrobiana en el contexto de una infección por A. baumannii (Tan et al, 2014;Cheon et al, 2016). Algunos investigadores han mostrado cierta diferencia en el riesgo de resistencia antimicrobiana, basada en la inmunidad diferencial entre hombres y mujeres, Pires S, y colaboradores, determinaron mayor susceptibilidad de neumonía por A. baumannii y mayor resistencia en ratones hembras que en machos, explicado por una diferencia en el aclaramiento de los macrófagos alveolares de cada tipo (Pires et al, 2020), sin embargo, debe ser estudiado con mayor profundidad.…”

Ecological competition and the incidence of Acinetobacter baumannii bloodstream infections in a teaching hospital in Southeastern Brazil

“…These factors include the severity of illness and the use of invasive devices and antimicrobials [10][11][12] . However, many of these risk factors are similar to those reported for other pathogens 13 .…”