Incidence of healthcare-associated infections in a tertiary care hospital: results from a three-year period of electronic surveillance

Establishing semi-automated infection surveillance in obstetrics and gynaecology

BACKGROUND

Surveillance is an acknowledged method to decrease nosocomial infections, such as surgical site infections (SSIs). Electronic healthcare records create the opportunity for automated surveillance. While approaches for different types of surgeries and indicators already exist, there are very few for obstetrics and gynaecology.

AIM

To analyse the sensitivity and workload reduction of semi-automated surveillance in obstetrics and gynaecology.

METHODS

In this retrospective, single-centre study at a 1438-bed tertiary care hospital in Germany, semi-automated SSI surveillance using the indicators 'antibiotic prescription', 'microbiological data' and 'administrative data' (diagnosis codes, readmission, post-hospitalization care) was compared with manual analysis and categorization of all patient files. Breast surgeries (BSs) conducted in 2018 and caesarean sections (CSs) that met the inclusion criteria between May 2013 and December 2019 were included. Indicators were analysed for sensitivity, number of analysed procedures needed to identify one case, and potential workload reduction in detecting SSIs in comparison with the control group.

FINDINGS

The reference standard showed nine SSIs in 416 BSs (2.2%). Sensitivities for the indicators 'antibiotic prescription', 'diagnosis code', 'microbiological sample taken', and the combination 'diagnosis code or microbiological sample' were 100%, 88.9%, 66.7% and 100%, respectively. The reference standard showed 54 SSIs in 3438 CSs (1.6%). Sensitivities for the indicators 'collection of microbiological samples', 'diagnosis codes', 'readmission/post-hospitalization care', and the combination of all indicators were 38.9%, 27.8%, 85.2% and 94.4%, respectively.

CONCLUSIONS

Semi-automated surveillance systems may reduce workload by maintaining high sensitivity depending on the type of surgery, local circumstances and thorough digitalization.

The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery

BACKGROUND

In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).

METHODS

Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.

RESULTS

From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.

CONCLUSIONS

The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.

Semiautomated surveillance of deep surgical site infections after colorectal surgeries: A multicenter external validation of two surveillance algorithms

Objective:

Automated surveillance methods increasingly replace or support conventional (manual) surveillance; the latter is labor intensive and vulnerable to subjective interpretation. We sought to validate 2 previously developed semiautomated surveillance algorithms to identify deep surgical site infections (SSIs) in patients undergoing colorectal surgeries in Dutch hospitals.

Design:

Multicenter retrospective cohort study.

Methods:

From 4 hospitals, we selected colorectal surgery patients between 2018 and 2019 based on procedure codes, and we extracted routine care data from electronic health records. Per hospital, a classification model and a regression model were applied independently to classify patients into low- or high probability of having developed deep SSI. High-probability patients need manual SSI confirmation; low-probability records are classified as no deep SSI. Sensitivity, positive predictive value (PPV), and workload reduction were calculated compared to conventional surveillance.

Results:

In total, 672 colorectal surgery patients were included, of whom 28 (4.1%) developed deep SSI. Both surveillance models achieved good performance. After adaptation to clinical practice, the classification model had 100% sensitivity and PPV ranged from 11.1% to 45.8% between hospitals. The regression model had 100% sensitivity and 9.0%–14.9% PPV. With both models, <25% of records needed review to confirm SSI. The regression model requires more complex data management skills, partly due to incomplete data.

Conclusions:

In this independent external validation, both surveillance models performed well. The classification model is preferred above the regression model because of source-data availability and less complex data-management requirements. The next step is implementation in infection prevention practices and workflow processes.

Direct hand hygiene observations and feedback increased hand hygiene compliance among nurses and doctors in medical and surgical wards - an eight-year observational study

BACKGROUND

The improvement of hand hygiene compliance (HHC) is critical to preventing healthcare-associated infections (HCAIs). The present study explored how direct observation and feedback influences HHC among nurses and doctors in surgical and medical wards, and whether these actions impact HCAI incidence.

METHODS

In this longitudinal observational study, HHC and the incidence of HCAIs were observed in six medical and seven surgical wards in a tertiary hospital in Finland from May 2013 to Dec 2020. Data of the observations of five hand hygiene (HH) moments were collected from the hospital HH and the HCAI monitoring registries. For statistical analyses a multivariable logistic regression analysis and a Poisson regression model were used.

FINDINGS

HH monitoring included 24 614 observations among nurses and 6 396 observations among doctors. In medical wards, HHC rates increased 10.8% - from 86.2% to 95.5%, and HCAI incidence decreased from 15.9 to 13.5 per 1000 patient days (p<0.0001). In surgical wards, HHC increased 32.7% - from 67.6% to 89.7%, and HCAI incidence decreased from 13.7 to 12.0 per 1000 patient days (p< 0.0001). The overall HHC increased significantly among nurses (17.8%) and doctors (65.8%). The HHC was better among nurses than doctors (in medical wards; OR 3.36; 95% CI 2.90-3.90, p<0.001 and in surgical wards; OR 9.85; 95% CI 8.97-10.8, p<0.001).

CONCLUSION

Direct observations and feedback of HH increased significantly HHC among nurses and doctors over an eight-year period. During the same period, the incidence of HCAIs significantly decreased in both medical and surgical wards.

PRAISE: providing a roadmap for automated infection surveillance in Europe

INTRODUCTION

Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care.

METHODS

The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts.

RESULTS

This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS.

CONCLUSIONS

Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists.

The current scope of healthcare-associated infection surveillance activities in hospitalized immunocompromised patients: a systematic review

BACKGROUND

Immunocompromised patients are at increased risk of acquiring healthcare-associated infections (HAIs) and often require specialized models of care. Surveillance of HAIs is essential for effective infection-prevention programmes. However, little is known regarding standardized or specific surveillance methods currently employed for high-risk hospitalized patients.

METHODS

A systematic review adopting a narrative synthesis approach of published material between 1 January 2000 and 31 March 2018 was conducted. Publications describing the application of traditional and/or electronic surveillance of HAIs in immunocompromised patient settings were identified from the Ovid MEDLINE®, Ovid Embase® and Elsevier Scopus® search engines [PROSPERO international prospective register of systematic reviews (registration ID: CRD42018093651)].

RESULTS

In total, 2708 studies were screened, of whom 17 fulfilled inclusion criteria. Inpatients diagnosed with haematological malignancies were the most-represented immunosuppressed population. The majority of studies described manual HAI surveillance utilizing internationally accepted definitions for infection. Chart review of diagnostic and pathology reports was most commonly employed for case ascertainment. Data linkage of disparate datasets was performed in two studies. The most frequently monitored infections were bloodstream infections and invasive fungal disease. No surveillance programmes applied risk adjustment for reporting surveillance outcomes.

CONCLUSIONS

Targeted, tailored monitoring of HAIs in high-risk immunocompromised settings is infrequently reported in current hospital surveillance programmes. Standardized surveillance frameworks, including risk adjustment and timely data dissemination, are required to adequately support infection-prevention programmes in these populations.

Hand-hygiene compliance by hospital staff and incidence of health-care-associated infections, Finland

Objective

To determine changes in hand-hygiene compliance after the introduction of direct observation of hand-hygiene practice for doctors and nurses, and evaluate the relationship between the changes and the incidence of health-care-associated infections.

Methods

We conducted an internal audit survey in a tertiary-care hospital in Finland from 2013 to 2018. Infection-control link nurses observed hand-hygiene practices based on the World Health Organization's strategy for hand hygiene. We calculated hand-hygiene compliance as the number of observations where necessary hand-hygiene was practised divided by the total number of observations where hand hygiene was needed. We determined the incidence of health-care-associated infections using a semi-automated electronic incidence surveillance programme. We calculated the Pearson correlation coefficient (r) to evaluate the relationship between the incidence of health-care-associated infections and compliance with hand hygiene.

Findings

The link nurses made 52 115 hand-hygiene observations between 2013 and 2018. Annual hand-hygiene compliance increased significantly from 76.4% (2762/3617) in 2013 to 88.5% (9034/10 211) in 2018 (P < 0.0001). Over the same time, the number of health-care-associated infections decreased from 2012 to 1831, and their incidence per 1000 patient-days fell from 14.0 to 11.7 (P < 0.0001). We found a weak but statistically significant negative correlation between the monthly incidence of health-care-associated infections and hand-hygiene compliance (r = -0.48; P < 0.001).

Conclusion

The compliance of doctors and nurses with hand-hygiene practices improved with direct observation and feedback, and this change was associated with a decrease in the incidence of health-care-associated infections. Further studies are needed to evaluate the contribution of hand hygiene to reducing health-care-associated infections.

Incidence of healthcare-associated infections in a tertiary hospital in Beijing, China: results from a real-time surveillance system

Background

To quantify the five year incidence trend of all healthcare-associated infections (HAI) using a real-time HAI electronic surveillance system in a tertiary hospital in Beijing, China.

Methods

The real-time surveillance system scans the hospital’s electronic databases related to HAI (e.g. microbiological reports and antibiotics administration) to identify HAI cases. We conducted retrospective secondary analyses of the data exported from the surveillance system for inpatients with all types of HAIs from January 1st 2013 to December 31st 2017. Incidence of HAI is defined as the number of HAIs per 1000 patient-days. We modeled the incidence data using negative binomial regression.

Results

In total, 23361 HAI cases were identified from 633990 patients, spanning 6242375 patient-days during the 5-year period. Overall, the adjusted five-year HAI incidence rate had a marginal reduction from 2013 (4.10 per 1000 patient days) to 2017 (3.62 per 1000 patient days). The incidence of respiratory tract infection decreased significantly. However, the incidence rate of bloodstream infections and surgical site infection increased significantly. Respiratory tract infection (43.80%) accounted for the most substantial proportion of HAIs, followed by bloodstream infections (15.74%), and urinary tract infection (12.69%). A summer peak in HAIs was detected among adult and elderly patients.

Conclusions

This study shows how continuous electronic incidence surveillance based on existing hospital electronic databases can provide a practical means of measuring hospital-wide HAI incidence. The estimated incidence trends demonstrate the necessity for improved infection control measures related to bloodstream infections, ventilator-associated pneumonia, non-intensive care patients, and non-device-associated HAIs, especially during summer months.

Electronic supplementary material

The online version of this article (10.1186/s13756-019-0582-7) contains supplementary material, which is available to authorized users.

A complete and multifaceted overview of antibiotic use and infection diagnosis in the intensive care unit: results from a prospective four-year registration

BACKGROUND

Preparing an antibiotic stewardship program requires detailed information on overall antibiotic use, prescription indication and ecology. However, longitudinal data of this kind are scarce. Computerization of the patient chart has offered the potential to collect complete data of high resolution. To gain insight in our global antibiotic use, we aimed to explore antibiotic prescription in our intensive care unit (ICU) from various angles over a prolonged time period.

METHODS

We studied all adult patients admitted to Ghent University Hospital ICU from 1 January 2013 until 31 December 2016. Antibiotic prescription data were prospectively merged with diagnostic (suspected focus, severity and probability of infection at the time of prescription, or prophylaxis) and microbiology data by ICU physicians during daily workflow through dedicated software. Definite focus of infection and probability of infection (classified as high/moderate/low) were reassessed by dedicated ICU physicians at patient discharge.

RESULTS

During the study period, 8763 patients were admitted and overall antibiotic consumption amounted to 1232 days of therapy (DOT)/1000 patient days. Antibacterial DOT (84% of total DOT) were linked with infection in 80%; the predominant foci were the respiratory tract (49%) and the abdomen (19%). A microbial cause was identified in 56% (3169/5686). Moderate/low probability infections accounted for 42% of antibacterial DOT prescribed for respiratory tract infections; for abdominal infections, this figure was 15%. The median treatment duration of moderate/low probability respiratory infections was 4 days (IQR 3-7). Antifungal DOT (16% of total DOT) were linked with infection in 47% of total antifungal DOT. Antifungal prophylaxis was primarily administered in the surgical ICU (76%), with a median duration of 4 DOT (IQR 2-9).

CONCLUSIONS

By prospectively combining antibiotic, microbiology and clinical data we were able to construct a longitudinal, multifaceted dataset on antibiotic use and infection diagnosis. A complete overview of this kind may allow the identification of antibiotic prescription patterns that require future antibiotic stewardship attention.

Effectiveness of a hospital-wide infection control programme on the incidence of healthcare-associated infections and associated severe sepsis and septic shock: a prospective interventional study

OBJECTIVES

To evaluate whether a hospital-wide infection control programme (ICP) is effective at reducing the burden of healthcare-associated infections (HAIs) and associated severe sepsis/septic shock or death (severe HAIs).

METHODS

Prospective, quasi-experimental study with two surveillance periods (September 2011 to August 2012; May 2013 to August 2014). Starting October 2012, the ICP included hand hygiene promotion and bundle implementation for common HAIs. We applied segmented mixed-effects Poisson regression and multi-state models. We reported adjusted incidence rate ratios (aIRR) and adjusted hazard ratios (aHR) with 95% confidence intervals (CI).

RESULTS

Overall, 62 154 patients were under surveillance, with 1568 HAIs identified in 1170 patients (4.3 per 100 admissions) in the first and 2336 HAIs identified in 1711 patients (4.9 per 100 admissions) in the second surveillance period. No differences were found in the overall HAI incidence rates between the periods in the general wards (aIRR 1.29, 95% CI 0.78-2.15) and intensive care units (ICUs) (aIRR 0.59, 95% CI 0.27-1.31). However, the HAI incidence rate was declining in the ICUs after starting the ICP (aIRR 0.98, 95% CI 0.97-1.00 per 1-week increment), in contrast to general wards (aIRR 1.01, 95% CI 1.00-1.02). A reduction in severe HAIs (aIRR 0.13, 95% CI 0.05-0.32) and a lower probability of HAI-associated in-hospital deaths (aHR 0.56, 95% CI 0.31-0.99) were observed in the second period in the ICUs.

CONCLUSIONS

There was no overall reduction in HAIs after implementation of the ICP. However, there was a significant reduction in severe HAIs in ICUs. Whether this difference was a consequence of the ICP or improvement in HAI case management is not clear.

Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error

OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January-March 2015 (development cohort) and January-March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance-automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826-833.

Electronic surveillance and using administrative data to identify healthcare associated infections

PURPOSE OF REVIEW

Traditional surveillance of healthcare associated infections (HCAI) is time consuming and error-prone. We have analysed literature of the past year to look at new developments in this field. It is divided into three parts: new algorithms for electronic surveillance, the use of administrative data for surveillance of HCAI, and the definition of new endpoints of surveillance, in accordance with an automatic surveillance approach.

RECENT FINDINGS

Most studies investigating electronic surveillance of HCAI have concentrated on bloodstream infection or surgical site infection. However, the lack of important parameters in hospital databases can lead to misleading results. The accuracy of administrative coding data was poor at identifying HCAI. New endpoints should be defined for automatic detection, with the most crucial step being to win clinicians' acceptance.

SUMMARY

Electronic surveillance with conventional endpoints is a successful method when hospital information systems implemented key changes and enhancements. One requirement is the access to systems for hospital administration and clinical databases.Although the primary source of data for HCAI surveillance is not administrative coding data, these are important components of a hospital-wide programme of automated surveillance. The implementation of new endpoints for surveillance is an approach which needs to be discussed further.

Epidemiology and economic impact of health care-associated infections and cost-effectiveness of infection control measures at a Thai university hospital

BACKGROUND

Data on clinical and economic impact of health care-associated infections (HAIs) from resource limited countries are limited. We aimed to determine epidemiology and economic impact of HAIs and cost-effectiveness of infection prevention and control measures in a resource-limited setting.

METHODS

A retrospective cohort study was conducted among hospitalized patients at Siriraj Hospital, Thailand. Results from the cohort were subsequently used to conduct cost-effective analysis (CEA) to compare the comprehensive implementation of individualized bundling infection control measures (IBICMs) with regular infection control care.

RESULTS

From February-May 2013, there were 515 hospitalizations (497 patients) with 7,848 hospitalization days. Cumulative incidence of HAIs was 23.30%, and the incidence rate of HAIs was 18.66 ± 44.19 per 1,000 hospitalization days. Hospital mortality among those with and without HAIs was 33.33% and 20.00%, respectively (P < .001). The adjusted cost attributable to HAIs was $704.72 ± $226.73 (P < .001). CEA identified IBICMs as a non-dominated strategy, with an incremental cost-effectiveness ratio of -$20,444.62 per life saved.

CONCLUSIONS

HAI is significantly related with higher hospital mortality, longer length of stay, and higher hospitalization costs. IBICMs were confirmed to be cost-effective at Siriraj Hospital. Implementing this intervention could improve care quality and save costs.