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To derive and validate a model for risk of resistance to first-line community-acquired pneumonia (CAP) therapy.
Design:
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.
Setting:
This study was conducted using data from 177 Premier Healthcare database hospitals and 11 Cleveland Clinic hospitals.
Participants:
Adults hospitalized for CAP.
Exposure:
Risk factors for resistant infection.
Results:
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.
Conclusions:
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.
Clostridioides difficile infection (CDI) is the most common cause of gastroenteritis, and community-acquired pneumonia (CAP) is the most common infection treated in hospitals. American Thoracic Society (ATS)/Infectious Diseases Society of America (IDSA) CAP guidelines recommend empiric therapy with a respiratory fluoroquinolone or cephalosporin plus macrolide combination, but the CDI risk of these regimens is unknown. We examined the association between each antibiotic regimen and the development of hospital-onset CDI.
Methods:
We conducted a retrospective cohort study using data from 638 US hospitals contributing administrative including 177 also contributing microbiologic data to Premier, Inc. We included adults admitted with pneumonia and discharged from July 2010 through June 2015 with a pneumonia diagnosis code who received ≥3 days of either empiric regimen. Hospital-onset CDI was defined by a diagnosis code not present on admission and positive laboratory test on day 4 or later or readmission for CDI. Mixed propensity-weighted multiple logistic regression was used to estimate the associations of CDI with antibiotic regimens.
Results:
Our sample included 58,060 patients treated with either cephalosporin plus macrolide (36,796 patients) or a fluoroquinolone alone (21,264 patients) and with microbiological data; 127 (0.35%) patients who received cephalosporin plus macrolide and 65 (0.31%) who received a fluoroquinolone developed CDI. After adjustment for patient demographics, comorbidities, risk factors for antimicrobial resistance, and hospital characteristics, CDI risks were similar for fluoroquinolones versus cephalosporin plus macrolide (odds ratio [OR], 0.98; 95% confidence interval [CI], 0.70–1.38).
Conclusion:
Among patients with CAP at US hospitals, CDI was uncommon, occurring in ∼0.33% of patients. We did not detect a significant association between the choice of empiric guideline recommended antibiotic therapy and the development of CDI.
Evidence from pandemics suggests that influenza is often associated with bacterial coinfection. Among patients hospitalized for influenza pneumonia, we report the rate of coinfection and distribution of pathogens, and we compare outcomes of patients with and without bacterial coinfection.
Methods:
We included adults admitted with community-acquired pneumonia (CAP) and tested for influenza from 2010 to 2015 at 179 US hospitals participating in the Premier database. Pneumonia was identified using an International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) algorithm. We used multiple logistic and gamma-generalized linear mixed models to assess the relationships between coinfection and inpatient mortality, intensive care unit (ICU) admission, length of stay, and cost.
Results:
Among 38,665 patients hospitalized with CAP and tested for influenza, 4,313 (11.2%) were positive. In the first 3 hospital days, patients with influenza were less likely than those without to have a positive culture (10.3% vs 16.2%; P < .001), and cultures were more likely to contain Staphylococcus aureus (34.2% vs 28.2%; P = .007) and less likely to contain Streptococcus pneumoniae (24.9% vs 31.0%; P = .008). Of S. aureus isolates, 42.8% were methicillin resistant among influenza patients versus 53.2% among those without influenza (P = .01). After hospital day 3, pathogens for both groups were similar. Bacterial coinfection was associated with increased odds of in-hospital mortality (aOR, 3.00; 95% CI, 2.17–4.16), late ICU transfer (aOR, 2.83; 95% CI, 1.98–4.04), and higher cost (risk-adjusted mean multiplier, 1.77; 95% CI, 1.59–1.96).
Conclusions:
In a large US inpatient sample hospitalized with influenza and CAP, S. aureus was the most frequent cause of bacterial coinfection. Coinfection was associated with worse outcomes and higher costs.
Viruses are more common than bacteria in patients hospitalized with community-acquired pneumonia. Little is known, however, about the frequency of respiratory viral testing and its associations with antimicrobial utilization.
Design:
Retrospective cohort study.
Setting:
The study included 179 US hospitals.
Patients:
Adults admitted with pneumonia between July 2010 and June 2015.
Methods:
We assessed the frequency of respiratory virus testing and compared antimicrobial utilization, mortality, length of stay, and costs between tested versus untested patients, and between virus-positive versus virus-negative patients.
Results:
Among 166,273 patients with pneumonia on admission, 40,787 patients (24.5%) were tested for respiratory viruses, 94.8% were tested for influenza, and 20.7% were tested for other viruses. Viral assays were positive in 5,133 of 40,787 tested patients (12.6%), typically for influenza and rhinovirus. Tested patients were younger and had fewer comorbidities than untested patients, but patients with positive viral assays were older and had more comorbidities than those with negative assays. Blood cultures were positive for bacterial pathogens in 2.7% of patients with positive viral assays versus 5.3% of patients with negative viral tests (P < .001). Antibacterial courses were shorter for virus-positive versus -negative patients overall (mean 5.5 vs 6.4 days; P < .001) but varied by bacterial testing: 8.1 versus 8.0 days (P = .60) if bacterial tests were positive; 5.3 versus 6.1 days (P < .001) if bacterial tests were negative; and 3.3 versus 5.2 days (P < .001) if bacterial tests were not obtained (interaction P < .001).
Conclusions:
A minority of patients hospitalized with pneumonia were tested for respiratory viruses; only a fraction of potential viral pathogens were assayed; and patients with positive viral tests often received long antibacterial courses.
The Food and Drug Administration (FDA) has offered guidance on using health related quality of life (HRQoL) measures to support labeling claims, and the definition of HRQoL has become more systematized. HRQoL measures look at patients' reports of their perceived health in either very general or very particular terms. Utility assessment is an increasingly active area of research in multiple sclerosis (MS). HRQoL data are used for three general purposes: to classify or group patients by levels of disease severity, predict the health of subjects at a future point in time, and as outcome variables. MS-specific HRQoL measures have been included as endpoints in many clinical studies, including some randomized controlled clinical trials. Selection of the most appropriate disease-specific measures by investigators should be based on available validity and reliability data for those measures and the specific questions that the researcher hopes to answer.