Getting it right: automated surveillance of healthcare-associated infections

A review on adverse drug reaction related to medication in health sector: an account of what we have discovered and implemented-pharmacovigilance

Despite the extensive research on medication-related adverse events (MRAEs) in healthcare, the assessment of the present scenario is made more difficult by the high degree of variability in study results. This study's primary goal was to create a current picture of what is currently known about the prevalence, risk factors, and surveillance of MRAEs in healthcare and overview of pharmacovigilance in preventing MRAEs. In order to find specific research on the prevalence, risk factors, economic effects, and monitoring techniques of medication-related adverse events, a comprehensive search was conducted using relevant search terms across electronic databases. Only research/review published after 2015 were considered in this analysis in order to provide the most current picture of the scenario. Patients who are elderly and have reduced liver or renal function, polypharmacy, or have several other comorbidities are more likely to experience medication-related side effects. Nevertheless, the use of high-risk medications and specific care settings also significantly raises the risk of MRAEs. Computerized techniques may open up new opportunities for event forecasting across all MRAE subtypes when paired with machine learning. Supporting collaborative research between computer science and medicine should be a top priority for pharmacovigilance research and patient safety initiatives in the future in order to provide prospects for the creation of clever preventative work strategies. However, the creation of effective real-time detection techniques may lead to significant advancements in predicting and incident avoidance in the future.

Development and validation of selection algorithms for a non-ventilator hospital-acquired pneumonia semi-automated surveillance system

OBJECTIVES

Semi-automated surveillance systems save time compared with traditional manual methods, particularly for non-ventilator hospital-acquired pneumonia (nvHAP), a nosocomial infection which can affect all non-intubated patients. In semi-automated surveillance, a computerized algorithm selects patients with high probability (i.e. "at risk") for subsequent manual confirmation. This study aimed to evaluate the performance of several single indicators and algorithms to preselect patients at risk for nvHAP.

METHODS

Single nvHAP indicators, identified based on literature, expert opinion and data availability, were combined to simple and complex algorithms. Both single indicators and algorithms were applied on a patient cohort of 157 902 patients, including 947 patients with nvHAP according to our reference standard, i.e. validated semi-automated nvHAP surveillance system plus the manual surveillance of patients with hospital-acquired pneumonia discharge diagnostic codes. Performance characteristics like sensitivity, workload reduction, and number of patients needed to be screened to detect one case of nvHAP were assessed.

RESULTS

Compared with the reference standard, single indicators had a sensitivity ranging from 35.1% (332/947) (oxygen desaturation) to 99.7% (944/947) (radiologic procedure). The workload reduction varied from 57.3% (90 505/157 902) (length of hospital stay >5 days) to 98.4% (155 453/157 902) (ICD-10 discharge diagnostic code). The highest workload reduction was found in complex algorithms, e.g. the combination "radiologic procedure including full text AND temporally related abnormal white blood count or fever AND antimicrobials AND C-reactive protein AND decreased oxygenation AND hospital stay ≥5 days AND no intubation" which reduced the number of patients who have to undergo manual review by 96.2% (151 867/157 902), while maintaining a sensitivity of 92% (871/947). The number needed to screen applying this algorithm was 6.4 patients.

DISCUSSION

Several single indicators and algorithms showed a high workload reduction and a sensitivity above the defined threshold of 90%. Our results could assist hospitals or stakeholders of surveillance initiatives in developing algorithms customized to their local conditions.

How effective are monthly departmental tracer surveys? A 5-year retrospective study of 138 surveys in 96 departments

BACKGROUND

Repeat department-wide surveys are commonly employed for infection control. There remains debate concerning their cost-effectivess. The aim of the study was to measure the impact of repeat department-wide surveys in major in-patient departments (IPDs) and ambulatory facilities (AFs) in a tertiary care hospital. This was a retrospective study of 138 surveys condcuted in 96 departments over a 5-year period.

METHODS

Two itemized questionnaires were designed to assess the most frequently inadequately adhered to infection control measures: one for IPD (with 21 items) and the other for AF (with 17 items).

RESULTS

A total of 72 surveys were conducted in 49 IPDs, of which 39 (54%) were repeat surveys, and 66 surveys in 47 AFs, of which 33 (50%) were repeat surveys. The baseline rate of adherence/department was 71% ± 14 for the IPD, with an increase from the first to the last survey to 82% ± 13 (P = .037). In 15/21 measured infection control items, adherence improved. Adherence to infection control items was lower at baseline in the AFs than in the IPDs (63 ± 27), with an increase to 76 ± 20 (non significant). Although adherence improved for 9 items, it deteriorated in another 8, producing an overall statistically unchanged outcome.

CONCLUSION

Repeat whole-department surveys contribute moderately to increased adherence to infection control guidelines. AFs demonstrate lower rates of adherence to infection control guidelines and are less receptive to educational measures.

Automated surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP): a systematic literature review

BACKGROUND

Hospital-acquired pneumonia (HAP) and its specific subset, non-ventilator hospital-acquired pneumonia (nvHAP) are significant contributors to patient morbidity and mortality. Automated surveillance systems for these healthcare-associated infections have emerged as a potentially beneficial replacement for manual surveillance. This systematic review aims to synthesise the existing literature on the characteristics and performance of automated nvHAP and HAP surveillance systems.

METHODS

We conducted a systematic search of publications describing automated surveillance of nvHAP and HAP. Our inclusion criteria covered articles that described fully and semi-automated systems without limitations on patient demographics or healthcare settings. We detailed the algorithms in each study and reported the performance characteristics of automated systems that were validated against specific reference methods. Two published metrics were employed to assess the quality of the included studies.

RESULTS

Our review identified 12 eligible studies that collectively describe 24 distinct candidate definitions, 23 for fully automated systems and one for a semi-automated system. These systems were employed exclusively in high-income countries and the majority were published after 2018. The algorithms commonly included radiology, leukocyte counts, temperature, antibiotic administration, and microbiology results. Validated surveillance systems' performance varied, with sensitivities for fully automated systems ranging from 40 to 99%, specificities from 58 and 98%, and positive predictive values from 8 to 71%. Validation was often carried out on small, pre-selected patient populations.

CONCLUSIONS

Recent years have seen a steep increase in publications on automated surveillance systems for nvHAP and HAP, which increase efficiency and reduce manual workload. However, the performance of fully automated surveillance remains moderate when compared to manual surveillance. The considerable heterogeneity in candidate surveillance definitions and reference standards, as well as validation on small or pre-selected samples, limits the generalisability of the findings. Further research, involving larger and broader patient populations is required to better understand the performance and applicability of automated nvHAP surveillance.

Concerns and frustrations about the public reporting of device-related healthcare-associated infections: Perspectives of hospital leaders and staff

BACKGROUND

Public reporting of healthcare-associated infections (HAIs) aims to incentivize improvement in infection prevention. The motivation and mechanisms of public reporting have raised concerns about the reliability of this data, but little is known about the specific concerns of hospital leaders and staff. This study sought to better understand perspectives of individuals in these roles regarding the identification and public reporting of HAIs.

METHODS

We conducted interviews with 471 participants including hospitals leaders (eg, administrative and clinical leaders) and hospital staff (eg, physicians and nurses) between 2017 and 2019 across 18 US hospitals. A semistructured interview guide was used to explore perspectives about the use of HAI data within the context of management strategies used to support infection prevention.

RESULTS

Interviewees described concerns about public reporting of HAI data, including a lack of trust in the data and inadvertent consequences of its public reporting, as well as specific frustrations related to the identification and accountability for publicly-reported HAIs.

CONCLUSION

Concerns and frustrations related to public reporting of HAI data highlight the need for improved guidelines, transparency, and incentives. Efforts to build trust in publicly-reported HAI data can help ensure this information is used effectively to improve infection prevention practices.