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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.

Background Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. Methods The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. Results Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. Conclusions The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery.

Infection with Human Immunodeficiency virus (HIV) is an important risk factor for Tuberculosis (TB). Anti-Retroviral Therapy (ART) has improved the prognosis of HIV and reduced the risk of TB infected patients. Isoniazid Preventive Therapy (IPT) aims to reduce the development of active TB in patients with latent TB. Systematically review and synthesize effect estimates of IPT for TB prevention in adult HIV patients. Secondary objectives were to assess the effect of IPT on HIV disease progression, all-cause mortality and adverse drug reaction (ADR). Electronic databases were searched to identify relevant articles in English available by September 11th 2015. Research articles comparing IPT to placebo or no treatment in HIV infected adults using randomized clinical trials. A qualitative review included study-level information on randomization and treatment allocation. Effect estimates were pooled using random-effects models to account for between-study heterogeneity. This review assessed ten randomized clinical trials that assigned 7619 HIV patients to IPT or placebo. An overall 35% of TB risk reduction (RR = 0.65, 95% CI (0.51, 0.84)) was found in all participants, however, larger benefit of IPT was observed in Tuberculin Skin Test (TST) positive participants, with pooled relative risk reduction of 52% [RR = 0.48; 95% CI (0.29, 0.82)] and with a prediction interval ranging from 0.13 to 1.81. There was no statistically significant effect of IPT on TB occurrence in TST negative or unknown participants. IPT also reduced the risk of HIV disease progression in all participants (RR = 0.69; 95% CI (0.48, 0.99)) despite no benefits observed in TST strata. All-cause mortality was not affected by IPT although participants who had 12 months of IPT tend to have a reduced risk (RR = 0.65; 95% CI(0.47, 0.90)). IPT had an elevated, yet statistically non-significant, risk of adverse drug reaction [RR = 1.20; 95% CI (1.20, 1.71)]. Only a single study assessed the effect of IPT in combination with ART in preventing TB and occurrence of multi-drug resistant tuberculosis. IPT use substantially contributes in preventing TB in persons with HIV in general and in TST positive individuals in particular. More evidence is needed to explain discrepancies in the protective effect of IPT in these individuals.

Surveillance and feedback of infection rates to clinicians and other stakeholders is a cornerstone of healthcare-associated infection (HAI) prevention programs. In addition, HAIs are increasingly included in public reporting and payment mandates. Conventional manual surveillance methods are resource intensive and lack standardization. Developments in information technology have propelled a movement toward the use of standardized and semiautomated methods.When developing automated surveillance systems, several strategies can be chosen with regard to the degree of automation and standardization and the definitions used. Yet, the advantages of highly standardized surveillance may come at the price of decreased clinical relevance and limited preventability. The choice among (automated) surveillance approaches, therefore, should be guided by the intended aim and scale of surveillance (eg, research, in-hospital quality improvement, national surveillance, or pay-for-performance mandates), as this choice dictates subsequent methods, important performance characteristics, and suitability of the data generated for the different applications.

Abstract Rationale Accurate surveillance of ventilator-associated pneumonia (VAP) is hampered by subjective diagnostic criteria. A novel surveillance paradigm for ventilator-associated events (VAEs) was introduced. Objectives To determine the validity of surveillance using the new VAE algorithm. Methods Prospective cohort study in two Dutch academic medical centers (2011–2012). VAE surveillance was electronically implemented and included assessment of (infection-related) ventilator-associated conditions (VAC, IVAC) and VAP. Concordance with ongoing prospective VAP surveillance was assessed, along with clinical diagnoses underlying VAEs and associated mortality of all conditions. Consequences of minor differences in electronic VAE implementation were evaluated. Measurements and Main Results The study included 2,080 patients with 2,296 admissions. Incidences of VAC, IVAC, VAE-VAP, and VAP according to prospective surveillance were 10.0, 4.2, 3.2, and 8.0 per 1000 ventilation days, respectively. The VAE algorithm detected at most 32% of the patients with VAP identified by prospective surveillance. VAC signals were most often caused by volume overload and infections, but not necessarily VAP. Subdistribution hazards for mortality were 3.9 (95% confidence interval, 2.9–5.3) for VAC, 2.5 (1.5–4.1) for IVAC, 2.0 (1.1–3.6) for VAE-VAP, and 7.2 (5.1–10.3) for VAP identified by prospective surveillance. In sensitivity analyses, mortality estimates varied considerably after minor differences in electronic algorithm implementation. Conclusions Concordance between the novel VAE algorithm and VAP was poor. Incidence and associated mortality of VAE were susceptible to small differences in electronic implementation. More studies are needed to characterize the clinical entities underlying VAE and to ensure comparability of rates from different institutions.

ObjectiveMeasuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.MethodsSystematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.Results57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.ConclusionsAdministrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

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. Multicenter retrospective cohort study. 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. 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. 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.

Background Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns. Methods In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species. Results All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan ( n  = 9) compared to CLAR ( n  = 319) and the P75 system ( n  = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms. Conclusions Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.

Hospital-onset bacteremia and fungemia (HOB) has been suggested as a suitable and automatable surveillance target to include in surveillance programs, however differences in definitions across studies limit interpretation and large-scale implementation. We aimed to apply an automated surveillance system for HOB in multiple hospitals using a consensus definition, and describe HOB rates. Retrospective cohort study in four Dutch hospitals: 1 tertiary hospital and 3 secondary hospitals. All patients admitted for at least one overnight stay between 2017 and 2021 were included, except patients in psychiatry wards. Data from the electronic health records and laboratory information system were used to identify HOBs based on the PRAISE consensus definition. HOB rates were calculated at ward and micro-organism-level. Hospital-wide HOB rates varied from 1.0 to 1.9, and ICU rates varied from of 8.2 to 12.5 episodes per 1000 patient days. The median time between admission and HOB was 8-13 days. HOBs were predominantly caused by Enterobacterales, Enterococci, and coagulase-negative staphylococci. Longitudinal HOB surveillance detected differences over time at ward and micro-organism level; for example increased HOB rates were observed during the COVID-19 pandemic. Sensitivity analyses demonstrated the impact of assumptions regarding the collection of confirmatory blood cultures for common commensals. Applying a fully automated definition for HOB surveillance was feasible in multiple centers with different data infrastructures, and enabled detection of differences over time at ward and micro-organism-level. HOB surveillance may lead to prevention initiatives in the future.

Automated surveillance of healthcare-associated infections reduces workload and improves standardization, but it has not yet been adopted widely. In this study, we assessed the performance and feasibility of an easy implementable framework to develop algorithms for semiautomated surveillance of deep incisional and organ-space surgical site infections (SSIs) after orthopedic, cardiac, and colon surgeries. Retrospective cohort study in multiple countries. European hospitals were recruited and selected based on the availability of manual SSI surveillance data from 2012 onward (reference standard) and on the ability to extract relevant data from electronic health records. A questionnaire on local manual surveillance and clinical practices was administered to participating hospitals, and the information collected was used to pre-emptively design semiautomated surveillance algorithms standardized for multiple hospitals and for center-specific application. Algorithm sensitivity, positive predictive value, and reduction of manual charts requiring review were calculated. Reasons for misclassification were explored using discrepancy analyses. The study included 3 hospitals, in the Netherlands, France, and Spain. Classification algorithms were developed to indicate procedures with a high probability of SSI. Components concerned microbiology, prolonged length of stay or readmission, and reinterventions. Antibiotics and radiology ordering were optional. In total, 4,770 orthopedic procedures, 5,047 cardiac procedures, and 3,906 colon procedures were analyzed. Across hospitals, standardized algorithm sensitivity ranged between 82% and 100% for orthopedic surgery, between 67% and 100% for cardiac surgery, and between 84% and 100% for colon surgery, with 72%-98% workload reduction. Center-specific algorithms had lower sensitivity. Using this framework, algorithms for semiautomated surveillance of SSI can be successfully developed. The high performance of standardized algorithms holds promise for large-scale standardization.