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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.

BackgroundWe aimed to detect SARS-CoV-2 serum antibodies in the general population of the Netherlands and identify risk factors for seropositivity amidst the first COVID-19 epidemic wave.MethodsParticipants (n=3207, aged 2–90 years), enrolled from a previously established nationwide serosurveillance study, provided a self-collected fingerstick blood sample and completed a questionnaire (median inclusion date 3 April 2020). IgG antibodies targeted against the spike S1-protein of SARS-CoV-2 were quantified using a validated multiplex-immunoassay. Seroprevalence was estimated controlling for survey design, individual pre-pandemic concentration, and test performance. Random-effects logistic regression identified risk factors for seropositivity.ResultsOverall seroprevalence in the Netherlands was 2.8% (95% CI 2.1 to 3.7), with no differences between sexes or ethnic background, and regionally ranging between 1.3 and 4.0%. Estimates were highest among 18–39 year-olds (4.9%), and lowest in children 2–17 years (1.7%). Multivariable analysis revealed that persons taking immunosuppressants and those from the Orthodox-Reformed Protestant community had over four times higher odds of being seropositive compared to others. Anosmia/ageusia was the most discriminative symptom between seropositive (53%) and seronegative persons (4%, p<0.0001). Antibody concentrations in seropositive persons were significantly higher in those with fever or dyspnoea in contrast to those without (p=0.01 and p=0.04, respectively).ConclusionsIn the midst of the first epidemic wave, 2.8% of the Dutch population was estimated to be infected with SARS-CoV-2, that is, 30 times higher than reported. This study identified independent groups with increased odds for seropositivity that may require specific surveillance measures to guide future protective interventions internationally, including vaccination once available.

Households are important sites for transmission of SARS-CoV-2 and preventive measures are recommended. This study aimed to 1) investigate the impact of living with a person infected with SARS-CoV-2; 2) understand how household members implemented infection control recommendations in their home; and 3) identify the information and support needs of household members. For this observational mixed-methods study, households with a person with confirmed SARS-CoV-2 infection were recruited via drive-through testing sites of Municipal Health Services, healthcare worker screening or hospital emergency visits in the University Medical Centre Utrecht, the Netherlands and via primary care physicians, hospital emergency visits or preoperative screening in the University Hospital of Antwerp, Belgium. We recorded household characteristics, including characteristics of all household members, together with their views on prevention measures. In a subset of households one adult household member was asked to participate in an interview investigating their views on preventive measures. Survey data were analysed using descriptive statistics and interview data by rapid framework analysis. A triangulation protocol was used to integrate findings. Thirty-four households (120 household members) were included in the quantitative survey. Twenty-two households were invited to be interviewed, of which 18 completed an interview (response 81.8%). Survey data showed that almost all households implemented some preventive measures, the use of face masks being least frequently reported. Measures taken depended on what was physically possible, the perceived severity of illness of the index patient and to what extent household members were willing to limit social interaction. Respondents did not believe in the effectiveness of wearing face masks within the house, and from the interviews this was explained by media coverage of face masks, impracticality and the stigma associated with wearing masks. Interviewees reported that quarantine had a high emotional burden and wished to have more information about the exact duration of quarantine, their own COVID-19 status, symptoms and when to seek medical help. People were willing to implement prevention measures, however actual adherence depended on perceived severity of illness and the perceived risk of becoming infected. Homes are social environments and recommendations for infection prevention should account for this context. Incorporating our findings into policy making could provide households with more relevant and actionable advice.

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.

Standard operating procedures (SOPs) contain general instructions and principles to standardize care, to improve effective and safe healthcare. Developing new, or updating current, SOPs is, however, challenging in fields where high-level evidence is limited. Still, SOPs alone have been shown to result in less complications. In this narrative review, we describe the process of creating a consensus-based SOP that is pragmatic for clinical practice since it can be created regardless of the current level of evidence. Through live audience engagement platforms, a group of experts will be able to both anonymously respond to a created questionnaire, and (subsequently) discuss the results within the same meeting. This modified Digital Delphi method as described here can be used as a tool toward consensus-based healthcare.

Household transmission studies are useful to quantify SARS-CoV-2 transmission dynamics. We conducted a remote prospective household study to quantify transmission, and the effects of subject characteristics, household characteristics, and implemented infection control measures on transmission. Households with a laboratory-confirmed SARS-CoV-2 index case were enrolled < 48 h following test result. Follow-up included digitally daily symptom recording, regular nose-throat self-sampling and paired dried blood spots from all household members. Samples were tested for virus detection and SARS-CoV-2 antibodies. Secondary attack rates (SARs) and associated factors were estimated using logistic regression. In 276 households with 920 participants (276 index cases and 644 household members) daily symptom diaries and questionnaires were completed by 95%, and > 85% completed sample collection. 200 secondary SARS-CoV-2 infections were detected, yielding a household SAR of 45.7% (95% CI 39.7–51.7%) and per-person SAR of 32.6% (95% CI: 28.1-37.4%). 126 (63%) secondary cases were detected at enrollment. Mild (aRR = 0.57) and asymptomatic index cases (aRR=0.29) were less likely to transmit SARS-CoV-2, compared to index cases with an acute respiratory illness (p=0.03 for trend), and child index cases (< 12 years aRR=0.60 and 12-18 years aRR = 0.85) compared to adults (p=0.03 for trend). Infection control interventions in households had no significant effect on transmission. We found high SARs with the majority of transmissions occuring early after SARS-CoV-2 introduction into the household. This may explain the futile effect of implemented household measures. Age and symptom status of the index case influence secondary transmission. Remote, digitally-supported study designs with self-sampling are feasible for studying transmission under pandemic restrictions.

Background During the COVID-19 pandemic hospitals reorganized their resources and delivery of care, which may have affected the number of healthcare-associated infections (HAIs). We aimed to quantify changes in trends in the number of HAIs in Dutch hospitals during the COVID-19 pandemic. Methods National surveillance data from 2016 to 2020 on the prevalence of HAIs measured by point prevalence surveys, and the incidence of surgical site infections (SSIs) and catheter-related bloodstream infections (CRBSIs) were used to compare rates between the pre-pandemic (2016–February 2020) and pandemic (March 2020–December 2020) period. Results The total HAI prevalence among hospitalised patients was higher during the pandemic period (7.4%) compared to pre-pandemic period (6.4%), mainly because of an increase in ventilator-associated pneumonia (VAP), gastro-intestinal infections (GIs) and central nervous system (CNS) infections. No differences in SSI rates were observed during the pandemic, except for a decrease after colorectal surgeries (6.3% (95%-CI 6.0–6.6%) pre-pandemic versus 4.4% (95%-CI 3.9–5.0%) pandemic). The observed CRBSI incidence in the pandemic period (4.0/1,000 CVC days (95%-CI 3.2–4.9)) was significantly higher than predicted based on pre-pandemic trends (1.4/1000 (95%-CI 1.0–2.1)), and was increased in both COVID-19 patients and non-COVID-19 patients at the intensive care unit (ICU). Conclusions Rates of CRBSIs, VAPs, GIs and CNS infections among hospitalised patients increased during the first year of the pandemic. Higher CRBSI rates were observed in both COVID-19 and non-COVID-19 ICU population. The full scope and influencing factors of the pandemic on HAIs needs to be studied in further detail.

Surveillance of healthcare-associated infections is often performed by manual chart review. Semiautomated surveillance may substantially reduce workload and subjective data interpretation. We assessed the validity of a previously published algorithm for semiautomated surveillance of deep surgical site infections (SSIs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) in Dutch hospitals. In addition, we explored the ability of a hospital to automatically select the patients under surveillance. Multicenter retrospective cohort study. Hospitals identified patients who underwent THA or TKA either by procedure codes or by conventional surveillance. For these patients, routine care data regarding microbiology results, antibiotics, (re)admissions, and surgeries within 120 days following THA or TKA were extracted from electronic health records. Patient selection was compared with conventional surveillance and patients were retrospectively classified as low or high probability of having developed deep SSI by the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction were calculated and compared to conventional surveillance. Of 9,554 extracted THA and TKA surgeries, 1,175 (12.3%) were revisions, and 8,378 primary surgeries remained for algorithm validation (95 deep SSIs, 1.1%). Sensitivity ranged from 93.6% to 100% and PPV ranged from 55.8% to 72.2%. Workload was reduced by ≥98%. Also, 2 SSIs (2.1%) missed by the algorithm were explained by flaws in data selection. This algorithm reliably detects patients with a high probability of having developed deep SSI after THA or TKA in Dutch hospitals. Our results provide essential information for successful implementation of semiautomated surveillance for deep SSIs after THA or TKA.