Impact of Empirical Antibiotic Therapy on Outcomes of Outpatient Urinary Tract Infection Due to Nonsusceptible Enterobacterales.

Resistance to oral antibiotics commonly used to treat outpatient urinary tract infections (UTIs) is increasing, but the implications of empirical treatment of resistant pathogens are not well described. Using an electronic records database, we reviewed the outcomes of patients >18 years of age who developed an outpatient UTI and had an outpatient urine culture result showing a member of the order Enterobacterales along with prescription data for an oral antibiotic filled on the day before, day of, or day after the culture was collected. Linear probability models were used to estimate partial effects of select clinical and demographic variables on the composite outcome. In all, 4,792 patients had 5,587 oral antibiotic prescriptions. Of 5,395 evaluable episodes, 22% of patients received an antibiotic to which the pathogen was resistant in vitro, and those patients were almost twice as likely to require a second prescription (34% versus 19%) or be hospitalized (15% versus 8%) within 28 days of the initial prescription fill compared to patients who received an antibiotic to which the pathogen was susceptible. Approximately 1% of Enterobacterales isolates were resistant to all commonly available classes of oral antibiotics. A greater risk of treatment failure was seen in patients over 60 years of age, patients with diabetes mellitus, men, and those treated with an antibiotic when prior culture identified an organism resistant to that class. The increasing resistance among members of Enterobacterales responsible for outpatient UTIs is limiting the effectiveness of empirical treatment with existing antibiotics, and consequently, outpatients with UTI are more likely to require additional courses of therapy or be hospitalized. IMPORTANCE Resistance rates for bacteria that cause urinary tract infections (UTIs) have increased dramatically. Regional rates of resistance to commonly prescribed antibiotics now exceed 20%, which is the threshold at which the Infectious Diseases Society of America recommends therapy be guided by culture. Our goals were to describe outcomes for outpatients with UTIs caused by bacteria resistant to empirically chosen antibiotics and to create a simple stratification schema for clinicians to identify UTI patients at increased risk of treatment failure due to antibiotic mismatch. These data are relevant to clinicians, given how common uncomplicated UTIs are, and highlight the need for clinicians to understand local resistance rates and the importance of culture-guided treatment, especially in vulnerable patients. These findings also showed that 1% of bacteria were resistant to all major classes of oral antibiotics, underscoring the need for new antibiotics to treat patients with UTIs due to resistant bacteria.

Data mining and infection control.

Patterns embedded in large volumes of clinical data may provide important insights into the characteristics of patients or care delivery processes, but may be difficult to identify by traditional means. Data mining offers methods that can recognize patterns in these large data sets and make them actionable. We present an example of this capability in which we successfully applied data mining to hospital infection control. The Data Mining Surveillance System (DMSS) uses data from the clinical laboratory and hospital information systems to create association rules linking patients, sample types, locations, organisms, and antibiotic susceptibilities. Changes in association strength over time signal epidemiologic patterns potentially appropriate for follow-up, and additional heuristic methods identify the most informative of these patterns for alerting.

The effects of laparoscopic cholecystectomy, hysterectomy, and appendectomy on nosocomial infection risks.

BACKGROUND: Recent reviews of the literature have concluded that additional, well-defined studies are required to clarify the superiority of laparoscopic or open surgery. This paper presents precise estimates of nosocomial infection risks associated with laparoscopic as compared to open surgery in three procedures: cholecystectomy, appendectomy, and hysterectomy. METHODS: A retrospective analysis was performed on 11,662 admissions from 22 hospitals that have a nosocomial infection monitoring system. The Nosocomial Infection Marker (NIMtrade mark, patent pending) was used to identify nosocomial infections during hospitalization and post discharge. The dataset was limited to admissions with laparoscopic or open cholecystectomy (32.7%), appendectomy (24.0%), or hysterectomy (43.3%) and was analyzed by source of infection: urinary tract, wounds, respiratory tract, bloodstream, and others. Single- and multivariable logistic regression analyses were performed to control for the following potentially confounding variables: gender, age, type of insurance, complexity of admission on presentation, admission through the emergency department, and hospital case mix index (CMI). RESULTS: Analyses were based on 399 NIMs in 337 patients. Laparoscopic cholecystectomy and hysterectomy each reduced the overall odds of acquiring nosocomial infections by more than 50% (p < 0.01) Laparoscopic cholecystectomy and hysterectomy also resulted in statistically significantly fewer readmissions with nosocomial infections (p < 0.01). Excluding appendectomy, the odds ratio for laparoscopic versus open NIM-associated readmission was 0.346 (p < 0.01). Laparoscopic appendectomy did not significantly change the odds of acquiring nosocomial infections. CONCLUSION: As compared to open surgery, laparoscopic cholecystectomy and hysterectomy are associated with statistically significantly lower risks for nosocomial infections. For appendectomy, when comparing open versus laparoscopic approaches, no differences in the rate of nosocomial infections were detected.

A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance?

Faced with expectations to improve patient safety and contain costs, the US health care system is under increasing pressure to comprehensively and objectively account for nosocomial infections. Widely accepted nosocomial infection surveillance methods, however, are limited in scope, not sensitive, and applied inconsistently. In 907 inpatient admissions to Evanston Northwestern Healthcare hospitals (Evanston, IL), nosocomial infection identification by the Nosocomial Infection Marker (MedMined, Birmingham, AL), an electronic, laboratory-based marker, was compared with hospital-wide nosocomial infection detection by medical records review and established nosocomial infection detection methods. The sensitivity and specificity of marker analysis were 0.86 (95% confidence interval [CI 95], 0.76-0.96) and 0.984 (CI 95, 0.976, 0.992). Marker analysis also identified 11 intensive care unit-associated nosocomial infections (sensitivity, 1.0; specificity, 0.986). Nosocomial Infection Marker analysis had a comparable sensitivity (P > .3) to and lower specificity (P < .001) than medical records review. It is important to note that marker analysis statistically outperformed widely accepted surveillance methods, including hospital-wide detection by Study on the Efficacy of Nosocomial Infection Control chart review and intensive care unit detection by National Nosocomial Infections Surveillance techniques.

Cost of bloodstream infections.

BACKGROUND: Hospital-acquired bloodstream infections (BSIs) are relatively rare but do not occur randomly. This suggests that unobserved confounding factors can bias estimates of BSI-associated incremental costs of care. Compared with previous studies, this analysis used a large sample size for greater precision, actual cost-accounting data, and case matching combined with bounding estimates to correct for bias. METHODS: Data from 1,355,647 admissions during 69 months in 55 hospitals were collected from a large population database. BSIs were identified by the Nosocomial Infection Marker, a well-validated, electronic, laboratory-based marker used for automatic infection surveillance. Costs were obtained by matching laboratory data with hospital accounting system calculations and converted to 2006 US dollars. RESULTS: Of 58,376 presumed nosocomial infections, 12,578 (21.6%) were identified as BSIs. More than 50% of BSIs occurred within the first week of hospitalization and 80% during the first 2 weeks. Various analyses resulted in the following estimates of BSI-associated incremental costs: basic regression analysis, $19,643 (P < .0001; 95% confidence interval [CI]: $9026-$30,260); excluding infections occurring after day 14, $19,427 (P < .001; 95% CI: $8867-$29,986); excluding infections occurring after day 7, $20,600 (P < .001; 95% CI: $10,123-$30,077); controlling for other nosocomial infections, $12,774 (P < .001; 95% CI: $6257-$19,290); and controlling for length of stay, $5534 (P < .012; 95% CI: $1282-$9785). CONCLUSION: Even when intentionally underestimated, BSI-associated increased costs are substantial. True costs of BSIs are likely to be between $10,000 and $20,000. More research is needed to explore how controlling BSI costs may affect the cost of inpatient care.

The costs of nosocomial infections.

BACKGROUND: Although nosocomial infections (NIs) are widely regarded as expensive complications of healthcare delivery, their costs have not been rigorously quantified in large-scale studies. Additionally, problems that can bias cost estimates have often gone unaddressed. For example, are NIs more likely to cause significant extra length of stay (LOS) and costs, or are they more likely to be relatively inexpensive and inevitable consequences of long and expensive hospitalizations? This study is the largest of its kind to provide a rigorous analysis of the costs of NIs. OBJECTIVE: To precisely bound the attributable costs of a NI using large-scale data and to determine the effects of endogeneity between NIs and LOS on cost estimates. DESIGN, SETTING AND PATIENTS: Discharge diagnoses, cost, LOS, and NI data were collected for 1,355,347 admissions from March 30, 2001 to January 31, 2006 in 55 hospitals. MAIN OUTCOME MEASURES: The cost effects of NIs (in 2007 $) were estimated using multivariable regression models. Restricted models were applied to determine how cost estimates are confounded by disease severity and LOS. RESULTS: NIs are associated with $12,197 (95% CI, $4862-$19,533, P < 0.001) in incremental cost. A lower bound estimate of infection cost, controlling for LOS, is $4644 (95% CI, $1266-$7391). CONCLUSIONS: NIs are associated with substantial increases in the costs of inpatient care, even when estimates are corrected for potential endogenous confounding.

Clinical correlation of the CLSI susceptibility breakpoint for piperacillin- tazobactam against extended-spectrum-beta-lactamase-producing Escherichia coli and Klebsiella species.

We assessed infections caused by extended-spectrum-beta-lactamase-producing Escherichia coli or Klebsiella spp. treated with piperacillin-tazobactam to determine if the susceptibility breakpoint predicts outcome. Treatment was successful in 10 of 11 nonurinary infections from susceptible strains and in 2 of 6 infections with MICs of >16/4 mug/ml. All six urinary infections responded to treatment regardless of susceptibility.