Catheter-Associated Urinary Tract Infection: Utility of the ICD-10 Metric as a Surrogate for the National Healthcare Safety Network (NHSN) Surveillance Metric

Perspectives on research needs in healthcare epidemiology, infection prevention, and antimicrobial stewardship: what's on the horizon-Part II

In this overview, we articulate research needs and opportunities in the field of infection prevention that have been identified from insights gained during operative infection prevention work, our own research in healthcare epidemiology, and from reviewing the literature. The 10 areas of research need are: 1) Transmissions and interruptions, 2) personal protective equipment and other safety issues in occupational health, 3) climate change and other crises, 4) device, diagnostic, and antimicrobial stewardship, 5) implementation and deimplementation, 6) healthcare outside the acute care hospital, 7) low- and middle-income countries, 8) networking with the "neighbors," 9) novel research methodologies, and 10) the future state of surveillance. An introduction and chapters 1-5 are presented in part I of the article and chapters 6-10 and the discussion in part II. There are many barriers to advancing the field, such as finding and motivating the future IP workforce including professionals interested in conducting research, a constant confrontation with challenges and crises, the difficulty of performing studies in a complex environment, the relative lack of adequate incentives and funding streams, and how to disseminate and validate the often very local quality improvement projects. Addressing research gaps now (i.e., in the post-pandemic phase) will make healthcare systems more resilient when facing future crises.

Descriptive Epidemiology and Outcomes of Hospitalizations With Complicated Urinary Tract Infections in the United States, 2018

Background

Hospitalizations with complicated urinary tract infection (cUTI) in the United States have increased. Though most often studied as a subset of cUTI, catheter-associated UTI (CAUTI) afflicts a different population of patients and carries outcomes distinct from non-CA cUTI (nCAcUTI). We examined the epidemiology and outcomes of hospitalizations in these groups.

Methods

We conducted a cross-sectional multicenter study within the 2018 National Inpatient Sample (NIS) database, a 20% stratified sample of discharges from US community hospitals, to explore characteristics and outcomes of patients discharged with a UTI diagnosis. We divided cUTI into mutually exclusive categories of nCAcUTI and CAUTI. We applied survey methods to develop national estimates.

Results

Among 2 837 385 discharges with a UTI code, 500 400 (17.6%, 19.8% principal diagnosis [PD]) were nCAcUTI and 126 120 (4.4%, 63.8% PD) were CAUTI. Though similar in age (CAUTI, 70.1 years; and nCAcUTI, 69.7 years), patients with nCAcUTI had lower comorbidity (mean Charlson, 4.3) than those with CAUTI (mean Charlson, 4.6). Median (interquartile range [IQR]) length of stay (LOS) was 5 (3–8) days in nCAcUTI and 5 (3–9) days in CAUTI. Overall median (IQR) hospital costs were similar in nCAcUTI ($9713 [$5923–$17 423]) and CAUTI ($9711 [$5969–$17 420]). Though low in both groups, hospital mortality was lower in nCAcUTI (2.8%) than in CAUTI (3.4%). Routine discharges home were higher in nCAcUTI (41.5%) than CAUTI (22.1%).

Conclusions

There are >626 000 hospital admissions with a cUTI, comprising ~1.8% of all annual admissions in the United States; 4/5 are nCAcUTI. Because CAUTI is frequently the reason for admission, preventive efforts are needed beyond the acute care setting.

The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients

BACKGROUND

Surveillance for healthcare-associated infections such as healthcare-associated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resource-intensive and subject to bias.

AIM

To develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data.

METHODS

Five algorithms were developed using EHR data from 2979 admissions at Karolinska University Hospital from 2010 to 2011: (1) positive urine culture (UCx); (2) positive UCx + UTI codes (International Statistical Classification of Diseases and Related Health Problems, 10^th revision); (3) positive UCx + UTI-specific antibiotics; (4) positive UCx + fever and/or UTI symptoms; (5) algorithm 4 with negation for fever without UTI symptoms. Natural language processing (NLP) was used for processing free-text medical notes. The algorithms were validated in 1258 potential UTI episodes from January to March 2012 and results extrapolated to all UTI episodes within this period (N = 16,712). The reference standard for HA-UTIs was manual record review according to the European Centre for Disease Prevention and Control (and US Centers for Disease Control and Prevention) definitions by trained healthcare personnel.

FINDINGS

Of the 1258 UTI episodes, 163 fulfilled the ECDC HA-UTI definition and the algorithms classified 391, 150, 189, 194, and 153 UTI episodes, respectively, as HA-UTI. Algorithms 1, 2, and 3 had insufficient performances. Algorithm 4 achieved better performance and algorithm 5 performed best for surveillance purposes with sensitivity 0.667 (95% confidence interval: 0.594-0.733), specificity 0.997 (0.996-0.998), positive predictive value 0.719 (0.624-0.807) and negative predictive value 0.997 (0.996-0.997).

CONCLUSION

A fully automated surveillance algorithm based on NLP to find UTI symptoms in free-text had acceptable performance to detect HA-UTI compared to manual record review. Algorithms based on administrative and microbiology data only were not sufficient.

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Background

Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.

Objectives

To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.

Methods

In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.

Results

A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.

Conclusions

Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.