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To derive and validate a model for risk of resistance to first-line community-acquired pneumonia (CAP) therapy.
We developed a logistic regression prediction model from a large multihospital discharge database and validated it versus the Drug Resistance in Pneumonia (DRIP) score in a holdout sample and another hospital system outside that database. Resistance to first-line CAP therapy (quinolone or third generation cephalosporin plus macrolide) was based on blood or respiratory cultures.
This study was conducted using data from 177 Premier Healthcare database hospitals and 11 Cleveland Clinic hospitals.
Adults hospitalized for CAP.
Risk factors for resistant infection.
Among 138,762 eligible patients in the Premier database, 12,181 (8.8%) had positive cultures and 5,200 (3.8%) had organisms resistant to CAP therapy. Infection with a resistant organism in the previous year was the strongest predictor of resistance; markers of acute illness (eg, receipt of mechanical ventilation or vasopressors) and chronic illness (eg, pressure ulcer, paralysis) were also associated with resistant infections. Our model outperformed the DRIP score with a C-statistic of 0.71 versus 0.63 for the DRIP score (P < .001) in the Premier holdout sample, and 0.65 versus 0.58 (P < .001) in Cleveland Clinic hospitals. Clinicians at Premier facilities used broad-spectrum antibiotics for 20%–30% of patients. In discriminating between patients with and without resistant infections, physician judgment slightly outperformed the DRIP instrument but not our model.
Our model predicting infection with a resistant pathogen outperformed both the DRIP score and physician practice in an external validation set. Its integration into practice could reduce unnecessary use of broad-spectrum antibiotics.
Critical insights into the etiology of insulin resistance have been gained by the use of animal models where insulin action has been modulated by strictly controlled dietary interventions not possible in human studies. Overall, the literature has moved from a focus on macronutrient proportions to understanding the unique effects of individual subtypes of fats, carbohydrates and proteins. Substantial evidence has now accumulated for a major role of dietary fat subtypes in insulin action. Intake of saturated fats is strongly linked to development of obesity and insulin resistance, while that of polyunsaturated fats (PUFAs) is not. This is consistent with observations that saturated fats are poorly oxidized for energy and thus readily stored, are poorly mobilized by lipolytic stimuli, impair membrane function, and increase the expression of genes associated with adipocyte profileration (making their own home). PUFAs have contrasting effects in each instance. It is therefore not surprising that increased PUFA intake in animal models is associated with improved insulin action and reduced adiposity. Less information is available for carbohydrate subtypes. Early work clearly demonstrated that diets high in simple sugars (in particular fructose) led to insulin resistance. However, again attention has rightly shifted to the very interesting issue of subtypes of complex carbohydrates. While no differences in insulin action have yet been shown, differences in substrate flux suggest there could be long-term beneficial effects on the fat balance of diets enhanced in slowly digested/resistant starches. A new area of major interest is in protein subtypes. Recent results have shown that rats fed high-fat diets where the protein component was from casein or soy were insulin-resistant, but when the protein source was from cod they were not. These are exciting times in our growing understanding of dietary factors and insulin action. While it has been clear for some time that ‘oils ain't oils’, the same is now proving true for carbohydrates and proteins.
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