Impact of Blood Culture Contamination on Antibiotic Use, Resource Utilization, and Clinical Outcomes: A Retrospective Cohort Study in Dutch and US Hospitals

Background

Blood culture contamination (BCC) has been associated with prolonged antibiotic use (AU) and increased health care utilization; however, this has not been widely reevaluated in the era of increased attention to antibiotic stewardship. We evaluated the impact of BCC on AU, resource utilization, and length of stay in Dutch and US patients.

Methods

This retrospective observational study examined adults admitted to 2 hospitals in the Netherlands and 5 hospitals in the United States undergoing ≥2 blood culture (BC) sets. Exclusion criteria included neutropenia, no hospital admission, or death within 48 hours of hospitalization. The impact of BCC on clinical outcomes-overall inpatient days of antibiotic therapy, test utilization, length of stay, and mortality-was determined via a multivariable regression model.

Results

An overall 22 927 patient admissions were evaluated: 650 (4.1%) and 339 (4.8%) with BCC and 11 437 (71.8%) and 4648 (66.3%) with negative BC results from the Netherlands and the United States, respectively. Dutch and US patients with BCC had a mean ± SE 1.74 ± 0.27 (P < .001) and 1.58 ± 0.45 (P < .001) more days of antibiotic therapy than patients with negative BC results. They also had 0.6 ± 0.1 (P < .001) more BCs drawn. Dutch but not US patients with BCC had longer hospital stays (3.36 days; P < .001). There was no difference in mortality between groups in either cohort. AU remained higher in US but not Dutch patients with BCC in a subanalysis limited to BC obtained within the first 24 hours of admission.

Conclusions

BCC remains associated with higher inpatient AU and health care utilization as compared with patients with negative BC results, although the impact on these outcomes differs by country.

Detecting changes in the performance of a clinical machine learning tool over time

BACKGROUND

Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a machine learning model to predict BC outcomes and enhance diagnostic stewardship. While the model showed promising initial results, concerns over performance drift due to evolving patient demographics, clinical practices, and outcome rates warrant continual monitoring and evaluation of such models.

METHODS

A real-time evaluation of the model's performance was conducted between October 2021 and September 2022. The model was integrated into Amsterdam UMC's Electronic Health Record system, predicting BC outcomes for all adult patients with BC draws in real time. The model's performance was assessed monthly using metrics including the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and Brier scores. Statistical Process Control (SPC) charts were used to monitor variation over time.

FINDINGS

Across 3.035 unique adult patient visits, the model achieved an average AUC of 0.78, AUPRC of 0.41, and a Brier score of 0.10 for predicting the outcome of BCs drawn in the ED. While specific population characteristics changed over time, no statistical points outside the statistical control range were detected in the AUC, AUPRC, and Brier scores, indicating stable model performance. The average BC positivity rate during the study period was 13.4%.

INTERPRETATION

Despite significant changes in clinical practice, our BC stewardship tool exhibited stable performance, suggesting its robustness to changing environments. Using SPC charts for various metrics enables simple and effective monitoring of potential performance drift. The assessment of the variation of outcome rates and population changes may guide the specific interventions, such as intercept correction or recalibration, that may be needed to maintain a stable model performance over time. This study suggested no need to recalibrate or correct our BC stewardship tool.

FUNDING

No funding to disclose.

Age-related changes in plasma biomarkers and their association with mortality in COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19)-induced mortality occurs predominantly in older patients. Several immunomodulating therapies seem less beneficial in these patients. The biological substrate behind these observations is unknown. The aim of this study was to obtain insight into the association between ageing, the host response and mortality in patients with COVID-19.

METHODS

We determined 43 biomarkers reflective of alterations in four pathophysiological domains: endothelial cell and coagulation activation, inflammation and organ damage, and cytokine and chemokine release. We used mediation analysis to associate ageing-driven alterations in the host response with 30-day mortality. Biomarkers associated with both ageing and mortality were validated in an intensive care unit and external cohort.

RESULTS

464 general ward patients with COVID-19 were stratified according to age decades. Increasing age was an independent risk factor for 30-day mortality. Ageing was associated with alterations in each of the host response domains, characterised by greater activation of the endothelium and coagulation system and stronger elevation of inflammation and organ damage markers, which was independent of an increase in age-related comorbidities. Soluble tumour necrosis factor receptor 1, soluble triggering receptor expressed on myeloid cells 1 and soluble thrombomodulin showed the strongest correlation with ageing and explained part of the ageing-driven increase in 30-day mortality (proportion mediated: 13.0%, 12.9% and 12.6%, respectively).

CONCLUSIONS

Ageing is associated with a strong and broad modification of the host response to COVID-19, and specific immune changes likely contribute to increased mortality in older patients. These results may provide insight into potential age-specific immunomodulatory targets in COVID-19.

Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models

This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training data but from just a single cohort. Although this concept seems simple and obvious, no current prediction model development guidelines recommend such an approach.

Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P < .001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P < .001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow.

[Can artificial intelligence write a medical-scientific article of sufficient quality?]

In this article, we describe the process - from the first draft, through peer revision to a final manuscript - of writing a scientific article only using AI. We discuss the problems and questions that arise and make recommendations for how text-generative AI may be used in the medical-scientific world.

Host Response Biomarkers for Sepsis in the Emergency Room

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Factors influencing in-hospital prescribing errors: A systematic review

AIM

In-hospital prescribing errors (PEs) may result in patient harm, prolonged hospitalization and hospital (re)admission. These events are associated with pressure on healthcare services and significant healthcare costs. To develop targeted interventions to prevent or reduce in-hospital PEs, identification and understanding of facilitating and protective factors influencing in-hospital PEs in current daily practice is necessary, adopting a Safety-II perspective. The aim of this systematic review was to create an overview of all factors reported in the literature, both protective and facilitating, as influencing in-hospital PEs.

METHODS

PubMed, EMBASE.com and the Cochrane Library (via Wiley) were searched, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, for studies that identified factors influencing in-hospital PEs. Both qualitative and quantitative study designs were included.

RESULTS

Overall, 19 articles (6 qualitative and 13 quantitative studies) were included and 40 unique factors influencing in-hospital PEs were identified. These factors were categorized into five domains according to the Eindhoven classification ('organization-related', 'prescriber-related', 'prescription-related', 'technology-related' and 'unclassified') and visualized in an Ishikawa (Fishbone) diagram. Most of the identified factors (87.5%; n = 40) facilitated in-hospital PEs. The most frequently identified facilitating factor (39.6%; n = 19) was 'insufficient (drug) knowledge, prescribing skills and/or experience of prescribers'.

CONCLUSION

The findings of this review could be used to identify points of engagement for future intervention studies and help hospitals determine how to optimize prescribing. A multifaceted intervention, targeting multiple factors might help to circumvent the complex challenge of in-hospital PEs.

Biomedical heterogeneous data categorization and schema mapping toward data integration

Data integration is a well-motivated problem in the clinical data science domain. Availability of patient data, reference clinical cases, and datasets for research have the potential to advance the healthcare industry. However, the unstructured (text, audio, or video data) and heterogeneous nature of the data, the variety of data standards and formats, and patient privacy constraint make data interoperability and integration a challenge. The clinical text is further categorized into different semantic groups and may be stored in different files and formats. Even the same organization may store cases in different data structures, making data integration more challenging. With such inherent complexity, domain experts and domain knowledge are often necessary to perform data integration. However, expert human labor is time and cost prohibitive. To overcome the variability in the structure, format, and content of the different data sources, we map the text into common categories and compute similarity within those. In this paper, we present a method to categorize and merge clinical data by considering the underlying semantics behind the cases and use reference information about the cases to perform data integration. Evaluation shows that we were able to merge 88% of clinical data from five different sources.

Artificial Intelligence: its Future and Impact on Acute Medicine

This commentary explores the potential impact of artificial intelligence (AI) in acute medicine, considering its possibilities and challenges. With its ability to simulate human intelligence, AI holds the promise for supporting timely decision-making and interventions in acute care. While AI has significantly contributed to improvements in various sectors, its implementation in healthcare remains limited. The development of AI tools tailored to acute medicine can improve clinical decision-making, and AI's role in streamlining administrative tasks, exemplified by ChatGPT, may offer immediate benefits. However, challenges include uniform data collection, privacy, bias, and preserving the doctor-patient relationship. Collaboration among AI researchers, healthcare professionals, and policymakers is crucial to harness the potential of AI in acute medicine and create a future where advanced technologies synergistically enhance human expertise.

The impact of a sepsis performance improvement program in the emergency department: a before–after intervention study

Purpose

The latest Surviving Sepsis Campaign guidelines advocate that all hospitals use sepsis performance improvement programs. However, there is a limited evidence about how to structure such programs and what their potential impact is on sepsis management and outcomes in the emergency department (ED). In this study, we evaluated the implementation of a sepsis performance improvement program in the ED including a dedicated sepsis response team and analyzed the management and outcomes of sepsis patients before and after.

Methods

We conducted a before–after interventional study in the ED of the Amsterdam University Medical Centers, the Netherlands. The sepsis performance improvement program included regular educational meetings, daily audits and weekly feedback, a screening tool, and a dedicated multidisciplinary sepsis response team. We studied all adult patients who presented to the ED with a suspected infection and a Modified Early Warning Score (MEWS) ≥ 3 during their stay. In the postintervention phase, these patients were seen by the sepsis team. Process-related and patient-related outcomes were measured between November 2019 and February 2020 (preintervention) and December 2021–May 2022 (postintervention).

Results

A total of 265 patients were included in the primary study, 132 patients preintervention and 133 patients postintervention. The postintervention phase was associated with improvements in nearly all process-related outcomes, such as a shorter time to antibiotics (66 vs. 143 min; p < 0.001), increased number of lactate measurements (72.9 vs. 46.2%; p < 0.001), and improved completeness of documented MEWS scores (85.0 vs. 62.9%; p < 0.001). Except for an improvement in the number of immediate versus delayed ICU admissions (100% immediate vs. 64.3% immediate; p = 0.012), there was no improvement in the other patient-related outcomes such as 28 days mortality (14.3 vs. 9.1%; p = 0.261), during the postintervention phase.

Conclusion

Our program stimulated physicians to make timely decisions regarding diagnostics and treatment of sepsis in the ED. Implementing the sepsis performance improvement program was associated with significant improvements in most process-related outcomes but with minimal improvements in patient-related outcomes in our cohort.

Recent infection with HCoV-OC43 may be associated with protection against SARS-CoV-2 infection

Antibodies against seasonal human coronaviruses (HCoVs) are known to cross-react with SARS-CoV-2, but data on cross-protective effects of prior HCoV infections are conflicting. In a prospective cohort of healthcare workers (HCWs), we studied the association between seasonal HCoV (OC43, HKU1, 229E and NL63) nucleocapsid protein IgG and SARS-CoV-2 infection during the first pandemic wave in the Netherlands (March 2020 - June 2020), by 4-weekly serum sampling. HCW with HCoV-OC43 antibody levels in the highest quartile, were less likely to become SARS-CoV-2 seropositive when compared with those with lower levels (6/32, 18.8%, versus 42/97, 43.3%, respectively: p = 0.019; HR 0.37, 95% CI 0.16-0.88). We found no significant association with HCoV-OC43 spike protein IgG, or with antibodies against other HCoVs. Our results indicate that the high levels of HCoV-OC43-nucleocapsid antibodies, as an indicator of a recent infection, are associated with protection against SARS-CoV-2 infection; this supports and informs efforts to develop pancoronavirus vaccines.

Decreased Passive Immunity to Respiratory Viruses through Human Milk during the COVID-19 Pandemic

Infants may develop severe viral respiratory tract infections because their immune system is still developing in the first months after birth. Human milk provides passive humoral immunity during the first months of life. During the COVID-19 pandemic, circulation of common respiratory viruses was virtually absent due to the preventative measures resulting in reduced maternal exposure. Therefore, we hypothesized that this might result in lower antibody levels in human milk during the pandemic and, subsequently, decreased protection of infants against viral respiratory tract infections. We assessed antibody levels against respiratory syncytial virus (RSV), Influenza virus, and several seasonal coronaviruses in different periods of the COVID-19 pandemic in serum and human milk using a Luminex assay. IgG levels against RSV, Influenza, HCoV-OC43, HCoV-HKU1, and HCoV-NL63 in human milk were reduced with a factor of 1.7 (P < 0.001), 2.2 (P < 0.01), 2.6 (P < 0.05), 1.4 (P < 0.01), and 2.1 (P < 0.001), respectively, since the introduction of the COVID-19 restrictions. Furthermore, we observed that human milk of mothers that experienced COVID-19 contained increased levels of IgG and IgA binding to other respiratory viruses. Passive immunity via human milk against common respiratory viruses was reduced during the COVID-19 pandemic, which may have consequences for the protection of breastfed infants against respiratory infections. IMPORTANCE Passive immunity derived from antibodies in human milk is important for protecting young infants against invading viruses. During the COVID-19 pandemic, circulation of common respiratory viruses was virtually absent due to preventative measures. In this study, we observed a decrease in human milk antibody levels against common respiratory viruses several months into the COVID-19 pandemic. This waning of antibody levels might partially explain the previously observed surge of hospitalizations of infants, mostly due to RSV, when preventative hygiene measures were lifted. Knowledge of the association between preventative measures, antibody levels in human milk and subsequent passive immunity in infants might help predict infant hospital admissions and thereby enables anticipation to prevent capacity issues. Additionally, it is important in the consideration for strategies for future lockdowns to best prevent possible consequences for vulnerable infants.

Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool

Background

Overuse of blood cultures (BCs) in emergency departments (EDs) leads to low yields and high numbers of contaminated cultures, accompanied by increased diagnostics, antibiotic usage, prolonged hospitalization, and mortality. We aimed to simplify and validate a recently developed machine learning model to help safely withhold BC testing in low-risk patients.

Methods

We extracted data from the electronic health records (EHR) for 44.123 unique ED visits with BC sampling in the Amsterdam UMC (locations VUMC and AMC; the Netherlands), Zaans Medical Center (ZMC; the Netherlands), and Beth Israel Deaconess Medical Center (BIDMC; United States) in periods between 2011 and 2021. We trained a machine learning model on the VUMC data to predict blood culture outcomes and validated it in the AMC, ZMC, and BIDMC with subsequent real-time prospective evaluation in the VUMC.

Findings

The model had an Area Under the Receiver Operating Characteristics curve (AUROC) of 0.81 (95%-CI = 0.78–0.83) in the VUMC test set. The most important predictors were temperature, creatinine, and C-reactive protein. The AUROCs in the validation cohorts were 0.80 (AMC; 0.78–0.82), 0.76 (ZMC; 0.74–0.78), and 0.75 (BIDMC; 0.74–0.76). During real-time prospective evaluation in the EHR of the VUMC, it reached an AUROC of 0.76 (0.71–0.81) among 590 patients with BC draws in the ED. The prospective evaluation showed that the model can be used to safely withhold blood culture analyses in at least 30% of patients in the ED.

Interpretation

We developed a machine learning model to predict blood culture outcomes in the ED, which retained its performance during external validation and real-time prospective evaluation. Our model can identify patients at low risk of having a positive blood culture. Using the model in practice can significantly reduce the number of blood culture analyses and thus avoid the hidden costs of false-positive culture results.

Funding

This research project was funded by the Amsterdam Public Health – Quality of Care program and the Dutch “Doen of Laten” project (project number: 839205002).

Neurofilament light increases over time in severe COVID-19 and is associated with delirium

Neurological monitoring in sedated Intensive Care Unit patients is constrained by the lack of reliable blood-based biomarkers. Neurofilament light is a cross-disease biomarker for neuronal damage with potential clinical applicability for monitoring Intensive Care Unit patients. We studied the trajectory of neurofilament light over a month in Intensive Care Unit patients diagnosed with severe COVID-19 and explored its relation to clinical outcomes and pathophysiological predictors. Data were collected over a month in 31 Intensive Care Unit patients (166 plasma samples) diagnosed with severe COVID-19 at Amsterdam University Medical Centre, and in the first week after emergency department admission in 297 patients with COVID-19 (635 plasma samples) admitted to Massachusetts General hospital. We observed that Neurofilament light increased in a non-linear fashion in the first month of Intensive Care Unit admission and increases faster in the first week of Intensive Care Unit admission when compared with mild-moderate COVID-19 cases. We observed that baseline Neurofilament light did not predict mortality when corrected for age and renal function. Peak neurofilament light levels were associated with a longer duration of delirium after extubation in Intensive Care Unit patients. Disease severity, as measured by the sequential organ failure score, was associated to higher neurofilament light values, and tumour necrosis factor alpha levels at baseline were associated with higher levels of neurofilament light at baseline and a faster increase during admission. These data illustrate the dynamics of Neurofilament light in a critical care setting and show associations to delirium, disease severity and markers for inflammation. Our study contributes to determine the clinical utility and interpretation of neurofilament light levels in Intensive Care Unit patients.

The host response in different aetiologies of community-acquired pneumonia

BACKGROUND

Community-acquired pneumonia (CAP) can be caused by a variety of pathogens, of which Streptococcus pneumoniae, Influenza and currently SARS-CoV-2 are the most common. We sought to identify shared and pathogen-specific host response features by directly comparing different aetiologies of CAP.

METHODS

We measured 72 plasma biomarkers in a cohort of 265 patients hospitalized for CAP, all sampled within 48 hours of admission, and 28 age-and sex matched non-infectious controls. We stratified the biomarkers into several pathophysiological domains- antiviral response, vascular response and function, coagulation, systemic inflammation, and immune checkpoint markers. We directly compared CAP caused by SARS-CoV-2 (COVID-19, n=39), Streptococcus pneumoniae (CAP-strep, n=27), Influenza (CAP-flu, n=22) and other or unknown pathogens (CAP-other, n=177). We adjusted the comparisons for age, sex and disease severity scores.

FINDINGS

Biomarkers reflective of a stronger cell-mediated antiviral response clearly separated COVID-19 from other CAPs (most notably granzyme B). Biomarkers reflecting activation and function of the vasculature showed endothelial barrier integrity was least affected in COVID-19, while glycocalyx degradation and angiogenesis were enhanced relative to other CAPs. Notably, markers of coagulation activation, including D-dimer, were not different between the CAP groups. Ferritin was most increased in COVID-19, while other systemic inflammation biomarkers such as IL-6 and procalcitonin were highest in CAP-strep. Immune checkpoint markers showed distinctive patterns in viral and non-viral CAP, with highly elevated levels of Galectin-9 in COVID-19.

INTERPRETATION

Our investigation provides insight into shared and distinct pathophysiological mechanisms in different aetiologies of CAP, which may help guide new pathogen-specific therapeutic strategies.

FUNDING

This study was financially supported by the Dutch Research Council, the European Commission and the Netherlands Organization for Health Research and Development.

Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines in health care workers in the Netherlands: A prospective cohort study

BACKGROUND

Emerging and future SARS-CoV-2 variants may jeopardize the effectiveness of vaccination campaigns. Therefore, it is important to know how the different vaccines perform against diverse SARS-CoV-2 variants.

METHODS AND FINDINGS

In a prospective cohort of 165 SARS-CoV-2 naive health care workers in the Netherlands, vaccinated with either one of four vaccines (BNT162b2, mRNA-1273, AZD1222 or Ad26.COV2.S), we performed a head-to-head comparison of the ability of sera to recognize and neutralize SARS-CoV-2 variants of concern (VOCs; Alpha, Beta, Gamma, Delta and Omicron). Repeated serum sampling was performed 5 times during a year (from January 2021 till January 2022), including before and after booster vaccination with BNT162b2. Four weeks after completing the initial vaccination series, SARS-CoV-2 wild-type neutralizing antibody titers were highest in recipients of mRNA-1273, followed by recipients of BNT162b2 (geometric mean titers (GMT) of 358 [95% CI 231-556] and 214 [95% CI 153-299], respectively; p<0.05), and substantially lower in those vaccinated with the adenovirus vector-based vaccines AZD1222 and Ad26.COV2.S (GMT of 18 [95% CI 11-30] and 14 [95% CI 8-25] IU/ml, respectively; p<0.001). VOCs neutralization was reduced in all vaccine groups, with the greatest reduction in neutralization GMT observed against the Omicron variant (fold change 0.03 [95% CI 0.02-0.04], p<0.001). The booster BNT162b2 vaccination increased neutralizing antibody titers for all groups with substantial improvement against the VOCs including the Omicron variant. We used linear regression and linear mixed model analysis. All results were adjusted for possible confounding of age and sex. Study limitations include the lack of cellular immunity data.

CONCLUSIONS

Overall, this study shows that the mRNA vaccines appear superior to adenovirus vector-based vaccines in inducing neutralizing antibodies against VOCs four weeks after initial vaccination and after booster vaccination, which implies the use of mRNA vaccines for both initial and booster vaccination.

Comparing complaint-based triage scales and early warning scores for emergency department triage

Background

Emergency triage systems are used globally to prioritise care based on patients’ needs. These systems are commonly based on patient complaints, while the need for timely interventions on regular hospital wards is usually assessed with early warning scores (EWS). We aim to directly compare the ability of currently used triage scales and EWS scores to recognise patients in need of urgent care in the ED.

Methods

We performed a retrospective, single-centre study on all patients who presented to the ED of a Dutch Level 1 trauma centre, between 1 September 2018 and 24 June 2020 and for whom a Netherlands Triage System (NTS) score as well as a Modified Early Warning Score (MEWS) was recorded. The performance of these scores was assessed using surrogate markers for true urgency and presented using bar charts, cross tables and a paired area under the curve (AUC).

Results

We identified 12 317 unique patient visits where NTS and MEWS scores were documented during triage. A paired comparison of the AUC of these scores showed that the MEWS score had a significantly better AUC than the NTS for predicting the need for hospital admission (0.65 vs 0.60; p<0.001) or 30-day all-cause mortality (0.70 vs 0.60; p<0.001). Furthermore, when non-urgent MEWS scores co-occur with urgent NTS scores, the MEWS score seems to more accurately capture the urgency level that is warranted.

Conclusions

The results of this study suggest that EWSs could potentially be used to replace the current emergency triage systems.

Sepsis Performance Improvement Programs: From Evidence Toward Clinical Implementation

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901.

Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study

OBJECTIVES

To develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.

DESIGN

Retrospective observational study.

SETTING

ED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020.

PARTICIPANTS

Adult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits.

MAIN OUTCOME MEASURES

The primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED.

RESULTS

In 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%.

CONCLUSIONS

Both models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.

Anti-C5a antibody vilobelimab treatment and the effect on biomarkers of inflammation and coagulation in patients with severe COVID-19: a substudy of the phase 2 PANAMO trial

We recently reported in the phase 3 PANAMO trial that selectively blocking complement 5a (C5a) with vilobelimab led to improved survival in critically ill COVID-19 patients. C5a is an important contributor to the innate immune system and can also activate the coagulation system. High C5a levels have been reported in severely ill COVID-19 patients and correlate with disease severity and mortality. Previously, we assessed the potential benefit and safety of vilobelimab in severe COVID-19 patients. In the current substudy of the phase 2 PANAMO trial, we aim to explore the effects of vilobelimab on various biomarkers of inflammation and coagulation. Between March 31 and April 24, 2020, 17 patients with severe COVID-19 pneumonia were enrolled in an exploratory, open-label, randomised phase 2 trial. Blood markers of complement, endothelial activation, epithelial barrier disruption, inflammation, neutrophil activation, neutrophil extracellular trap (NET) formation and coagulopathy were measured using enzyme-linked immunosorbent assay (ELISA) or utilizing the Luminex platform. During the first 15 days after inclusion, change in biomarker concentrations between the two groups were modelled with linear mixed-effects models with spatial splines and compared. Eight patients were randomized to vilobelimab treatment plus best supportive care (BSC) and nine patients were randomized to BSC only. A significant decrease over time was seen in the vilobelimab plus BSC group for C5a compared to the BSC only group (p < 0.001). ADAMTS13 levels decreased over time in the BSC only group compared to the vilobelimab plus BSC group (p < 0.01) and interleukin-8 (IL-8) levels were statistically more suppressed in the vilobelimab plus BSC group compared to the BSC group (p = 0.03). Our preliminary results show that C5a inhibition decreases the inflammatory response and hypercoagulability, which likely explains the beneficial effect of vilobelimab in severe COVID-19 patients. Validation of these results in a larger sample size is warranted.

Guest Editorial - A personal journey in Acute Medicine

The Nobel-winning physicist Niels Bohr famously said that "prediction is very difficult, especially if it's about the future." Nevertheless, the prediction of rapid clinical deterioration has acquired its place in Acute Medicine. Time-urgent medical emergencies can benefit significantly from early detection when treatment delays increase the risk of death. Many Early Warning Systems (EWS) have thus been developed to stratify those at high risk of deterioration. It has been demonstrated that different types of EWS, including the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2, are clinically useful when identifying rapid deterioration. However, as we are inundated with risk stratification tools, it can be hard to decide which we should or should not use.

Indoleamine 2,3-dioxygenase (IDO)-1 and IDO-2 activity and severe course of COVID-19

COVID-19 is a pandemic with high morbidity and mortality. In an autopsy cohort of COVID-19 patients, we found extensive accumulation of the tryptophan degradation products 3-hydroxy anthranilic acid and quinolinic acid in lungs, heart, and brain. This was not related to the expression of the tryptophan-catabolizing indoleamine 2,3-dioxygenase (IDO)-1, but rather to that of its isoform IDO-2, which otherwise is expressed rarely. Bioavailability of tryptophan is an absolute requirement for proper cell functioning and synthesis of hormones, whereas its degradation products can cause cell death. Markers of apoptosis and severe cellular stress were associated with IDO-2 expression in large areas of lung and heart tissue, whereas affected areas in brain were more restricted. Analyses of tissue, cerebrospinal fluid, and sequential plasma samples indicate early initiation of the kynurenine/aryl-hydrocarbon receptor/IDO-2 axis as a positive feedback loop, potentially leading to severe COVID-19 pathology. This article is protected by copyright. All rights reserved.

Early warning scores to assess the probability of critical illness in patients with COVID-19

OBJECTIVE

Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation.

METHODS

Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation.

RESULTS

In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively.

CONCLUSION

In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.

Towards Understanding the Effective Use of Antibiotics for Sepsis

BACKGROUND

The benefits of early antibiotics for sepsis have recently been questioned. Evidence for this mainly comes from observational studies. The only randomized trial on this subject, the Prehospital Antibiotics Against Sepsis (PHANTASi) trial, did not find significant mortality benefits from early antibiotics. That subgroups of patients benefit from this practice is still plausible, given the heterogeneous nature of sepsis.

RESEARCH QUESTION

Do subgroups of sepsis patients experience 28-day mortality benefits from early administration of antibiotics in a prehospital setting? And what key traits drive these benefits?

STUDY DESIGN AND METHODS

We used machine learning to conduct exploratory partitioning cluster analysis to identify possible subgroups of sepsis patients who may benefit from early antibiotics. We further tested the influence of several traits within these subgroups, using a logistic regression model.

RESULTS

We found a significant interaction between age and benefits of early antibiotics (P = .03). When we adjusted for this interaction and several other confounders, there was a significant benefit of early antibiotic treatment (OR, 0.07; 95% CI, 0.01-0.79; P = .03).

INTERPRETATION

An interaction between age and benefits of early antibiotics for sepsis has not been reported before. When validated, it can have major implications for clinical practice. This new insight into benefits of early antibiotic treatment for younger sepsis patients may enable more effective care.

Time since SARS-CoV-2 infection and humoral immune response following BNT162b2 mRNA vaccination

BACKGROUND

To optimise the use of available SARS-CoV-2 vaccines, some advocate delaying second vaccination for individuals infected within six months. We studied whether post-vaccination immune response is equally potent in individuals infected over six months prior to vaccination.

METHODS

We tested serum IgG binding to SARS-CoV-2 spike protein and neutralising capacity in 110 healthcare workers, before and after both BNT162b2 messenger RNA (mRNA) vaccinations. We compared outcomes between participants with more recent infection (n = 18, median two months, IQR 2-3), with infection-vaccination interval over six months (n = 19, median nine months, IQR 9-10), and to those not previously infected (n = 73).

FINDINGS

Both recently and earlier infected participants showed comparable humoral immune responses after a single mRNA vaccination, while exceeding those of previously uninfected persons after two vaccinations with 2.5 fold (p = 0.003) and 3.4 fold (p < 0.001) for binding antibody levels, and 6.4 and 7.2 fold for neutralisation titres, respectively (both p < 0.001). The second vaccine dose yielded no further substantial improvement of the humoral response in the previously infected participants (0.97 fold, p = 0.92), while it was associated with a 4 fold increase in antibody binding levels and 18 fold increase in neutralisation titres in previously uninfected participants (both p < 0.001). Adjustment for potential confounding of sex and age did not affect these findings.

INTERPRETATION

Delaying the second vaccination in individuals infected up to ten months prior may constitute a more efficient use of limited vaccine supplies.

FUNDING

Netherlands Organization for Health Research and Development ZonMw; Corona Research Fund Amsterdam UMC; Bill & Melinda Gates Foundation.

Emerging SARS-CoV-2 variants of concern evade humoral immune responses from infection and vaccination

Emerging SARS-CoV-2 variants of concern (VOCs) pose a threat to human immunity induced by natural infection and vaccination. We assessed the recognition of three VOCs (B.1.1.7, B.1.351, and P.1) in cohorts of COVID-19 convalescent patients (n = 69) and Pfizer-BioNTech vaccine recipients (n = 50). Spike binding and neutralization against all three VOCs were substantially reduced in most individuals, with the largest four- to sevenfold reduction in neutralization being observed against B.1.351. While hospitalized patients with COVID-19 and vaccinees maintained sufficient neutralizing titers against all three VOCs, 39% of nonhospitalized patients exhibited no detectable neutralization against B.1.351. Moreover, monoclonal neutralizing antibodies show sharp reductions in their binding kinetics and neutralizing potential to B.1.351 and P.1 but not to B.1.1.7. These data have implications for the degree to which pre-existing immunity can protect against subsequent infection with VOCs and informs policy makers of susceptibility to globally circulating SARS-CoV-2 VOCs.

Integrated single-cell analysis unveils diverging immune features of COVID-19, influenza, and other community-acquired pneumonia

The exact immunopathophysiology of community-acquired pneumonia (CAP) caused by SARS-CoV-2 (COVID-19) remains clouded by a general lack of relevant disease controls. The scarcity of single-cell investigations in the broader population of patients with CAP renders it difficult to distinguish immune features unique to COVID-19 from the common characteristics of a dysregulated host response to pneumonia. We performed integrated single-cell transcriptomic and proteomic analyses in peripheral blood mononuclear cells from a matched cohort of eight patients with COVID-19, eight patients with CAP caused by Influenza A or other pathogens, and four non-infectious control subjects. Using this balanced, multi-omics approach, we describe shared and diverging transcriptional and phenotypic patterns—including increased levels of type I interferon-stimulated natural killer cells in COVID-19, cytotoxic CD8 T EMRA cells in both COVID-19 and influenza, and distinctive monocyte compositions between all groups—and thereby expand our understanding of the peripheral immune response in different etiologies of pneumonia.

Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort

OBJECTIVE

Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.

DESIGN

Retrospective cohort study.

SETTING

A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.

PARTICIPANTS

SARS-CoV-2 positive patients (age ≥18) admitted to the hospital.

MAIN OUTCOME MEASURES

21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.

RESULTS

2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).

CONCLUSION

Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.

Serologic Surveillance and Phylogenetic Analysis of SARS-CoV-2 Infection Among Hospital Health Care Workers

IMPORTANCE

It is unclear when, where, and by whom health care workers (HCWs) working in hospitals are infected with SARS-CoV-2.

OBJECTIVE

To determine how often and in what manner nosocomial SARS-CoV-2 infection occurs in HCW groups with varying exposure to patients with COVID-19.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study comprised 4 weekly measurements of SARS-CoV-2-specific antibodies and collection of questionnaires from March 23 to June 25, 2020, combined with phylogenetic and epidemiologic transmission analyses at 2 university hospitals in the Netherlands. Included individuals were HCWs working in patient care for those with COVID-19, HCWs working in patient care for those without COVID-19, and HCWs not working in patient care. Data were analyzed from August through December 2020.

EXPOSURES

Varying work-related exposure to patients infected with SARS-CoV-2.

MAIN OUTCOMES AND MEASURES

The cumulative incidence of and time to SARS-CoV-2 infection, defined as the presence of SARS-CoV-2-specific antibodies in blood samples, were measured.

RESULTS

Among 801 HCWs, there were 439 HCWs working in patient care for those with COVID-19, 164 HCWs working in patient care for those without COVID-19, and 198 HCWs not working in patient care. There were 580 (72.4%) women, and the median (interquartile range) age was 36 (29-50) years. The incidence of SARS-CoV-2 was increased among HCWs working in patient care for those with COVID-19 (54 HCWs [13.2%; 95% CI, 9.9%-16.4%]) compared with HCWs working in patient care for those without COVID-19 (11 HCWs [6.7%; 95% CI, 2.8%-10.5%]; hazard ratio [HR], 2.25; 95% CI, 1.17-4.30) and HCWs not working in patient care (7 HCWs [3.6%; 95% CI, 0.9%-6.1%]; HR, 3.92; 95% CI, 1.79-8.62). Among HCWs caring for patients with COVID-19, SARS-CoV-2 cumulative incidence was increased among HCWs working on COVID-19 wards (32 of 134 HCWs [25.7%; 95% CI, 17.6%-33.1%]) compared with HCWs working on intensive care units (13 of 186 HCWs [7.1%; 95% CI, 3.3%-10.7%]; HR, 3.64; 95% CI, 1.91-6.94), and HCWs working in emergency departments (7 of 102 HCWs [8.0%; 95% CI, 2.5%-13.1%]; HR, 3.29; 95% CI, 1.52-7.14). Epidemiologic data combined with phylogenetic analyses on COVID-19 wards identified 3 potential HCW-to-HCW transmission clusters. No patient-to-HCW transmission clusters could be identified in transmission analyses.

CONCLUSIONS AND RELEVANCE

This study found that HCWs working on COVID-19 wards were at increased risk for nosocomial SARS-CoV-2 infection with an important role for HCW-to-HCW transmission. These findings suggest that infection among HCWs deserves more consideration in infection prevention practice.

Emerging SARS-CoV-2 variants of concern evade humoral immune responses from infection and vaccination

Emerging SARS-CoV-2 variants pose a threat to human immunity induced by natural infection and vaccination. We assessed the recognition of three variants of concern (B.1.1.7, B.1.351 and P.1) in cohorts of COVID-19 patients ranging in disease severity (n = 69) and recipients of the Pfizer/BioNTech vaccine (n = 50). Spike binding and neutralization against all three VOC was substantially reduced in the majority of samples, with the largest 4-7-fold reduction in neutralization being observed against B.1.351. While hospitalized COVID-19 patients and vaccinees maintained sufficient neutralizing titers against all three VOC, 39% of non-hospitalized patients did not neutralize B.1.351. Moreover, monoclonal neutralizing antibodies (NAbs) show sharp reductions in their binding kinetics and neutralizing potential to B.1.351 and P.1, but not to B.1.1.7. These data have implications for the degree to which pre-existing immunity can protect against subsequent infection with VOC and informs policy makers of susceptibility to globally circulating SARS-CoV-2 VOC.

[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave]

OBJECTIVE

To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands.

DESIGN

Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported.

METHODS

Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission.

RESULTS

Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels.

CONCLUSION

Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

The Value of Artificial Intelligence in Laboratory Medicine

OBJECTIVES

As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI.

METHODS

We conducted a web-based survey on the use of AI with participants from Roche's Strategic Advisory Network that included key stakeholders in laboratory medicine.

RESULTS

In total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine.

CONCLUSIONS

This survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.

Short Keynote Paper: Mainstreaming Personalized Healthcare-Transforming Healthcare Through New Era of Artificial Intelligence

Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.

Timeliness of antibiotics for patients with sepsis and septic shock

For many years, sepsis guidelines have focused on early administration of antibiotics. While this practice may benefit some patients, for others it might have detrimental consequences. The increasingly shortened timeframes in which administration of antibiotics is recommended, have forced physicians to sacrifice diagnostic accuracy for speed, encouraging the overuse of antibiotics. The evidence supporting this practice is based on retrospective data, with all the limitations attached, while the only randomized trial on this subject does not show a mortality benefit from early administration of antibiotics in a population of patients with sepsis as often seen in the emergency department (ED). Physicians are challenged to treat patients suspected of having sepsis within a short period of time, while the real challenge should be to identify patients who would not be harmed by withholding treatment with antibiotics until the diagnosis of infection with a bacterial origin is confirmed and the appropriateness of a course of antibiotics can be evaluated more adequately. Therefore, in the general population of patients with sepsis, taking the time to gather additional data to confirm the diagnosis should be encouraged without a specific timeframe, although physicians should be encouraged to perform an adequate work-up as soon as possible. Patients with suspected sepsis and signs of shock should immediately be treated with antibiotics, as there is no margin for error.

Introducing Artificial Intelligence Training in Medical Education

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

Clinical applications of artificial intelligence in sepsis: A narrative review

Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify knowledge gaps and prioritize areas with high priority for further research. A literature search was conducted in PubMed from inception to February 2019. Search terms related to AI were combined with terms regarding sepsis. Articles were included when they reported an area under the receiver operator characteristics curve (AUROC) as outcome measure. Fifteen articles on diagnosis of sepsis with AI models were included. The best performing model reached an AUROC of 0.97. There were also seven articles on prognosis, predicting mortality over time with an AUROC of up to 0.895. Finally, there were three articles on assistance of treatment of sepsis, where the use of AI was associated with the lowest mortality rates. Of the articles, twenty-two were judged to be at high risk of bias or had major concerns regarding applicability. This was mostly because predictor variables in these models, such as blood pressure, were also part of the definition of sepsis, which led to overestimation of the performance. We conclude that AI models have great potential for improving early identification of patients who may benefit from administration of antibiotics. Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models. Until these problems have been resolved, a large gap remains between the creation of an AI algorithm and its implementation in clinical practice.

Incidence of seropositive myasthenia gravis in Cape Town and South Africa

BACKGROUND

Myasthenia gravis (MG) is a treatable autoimmune disease characterised by fatiguable weakness of skeletal muscles. More than 85% of MG patients have antibodies to the acetylcholine receptor (AChR) at the neuromuscular junction or are seropositive for MG (SPMG). In the developed world the incidence of MG has increased, particularly among older individuals, but no epidemiological studies have been done on SPMG in Africa.

OBJECTIVES

To determine the annual incidence rate (IR) of SPMG in the Cape Town (CT) municipality, and the crude annual IR of SPMG for the whole of South Africa (SA).

METHODS

Positive AChR antibody tests were identified between 1 January 2003 and 1 January 2005 for patients living in CT, and the age- and sex-specific incidences were calculated. To determine the national crude annual IR over the same period, positive assays were identified from the laboratories that process AChR assays for SA. National Census 2001 population statistics formed the denominators.

RESULTS

There were 65 positive assays in CT, and 230 nationwide. Based on these figures the annual IR for CT was 11.2 per million per year (95% confidence interval (CI) 8.7 - 14.3), and for South Africa 2.6 per million/year (95% CI 2.2 - 2.9). After a questionnaire response from CT neurologists regarding the routine use of the AChR antibody assay, the annual IR for CT was adjusted to 12.6 per million (95% CI 9.9 - 15.9) to incorporate those presumed to have SPMG without a confirmatory test. In CT, the IR in females was 15.3 per million/year (95% CI 11.2 - 20.4), and in males, 6.8 per million/year (95% CI 4.1 - 10.7). The CT IRs for blacks, coloureds and whites were not statistically different after adjusting for age and gender. The IR of SPMG in CT was 6 times greater in those presenting after the age of 50 years than in those with earlier disease onset (95% CI 3.7 - 9.7).

CONCLUSIONS

The annual IR of SPMG in CT is much the same as rates recorded recently in other developed countries, but the rest of SA has a much lower IR. A preponderance of MG starting after the age of 50 years reflects a worldwide trend, although the CT data showed a relatively lower-than-expected incidence for older males. IRs for SPMG vary widely in different regions in SA; this is likely to be related to differences in regional health care delivery, and underdiagnosis.

Latex allergy at Groote Schuur Hospital--prevalence, clinical features and outcome

BACKGROUND

The incidence of latex allergy is increasing worldwide but there is very little information available on the clinical outcome for affected individuals.

OBJECTIVE

To determine the prevalence of latex allergy at Groote Schuur Hospital, a large teaching hospital in Cape Town, and to study the outcome for affected individuals.

METHOD

Using a questionnaire, we screened 2,316 hospital workers for the presence of work-related symptoms. Workers who were symptomatic had Immunocap RAST (CAP RAST) or skin-prick tests to confirm latex sensitivity. Latex-avoidance measures were implemented in positive subjects. One hundred symptomatic, sensitised individuals were followed up 3 months after intervention to assess their clinical status. A further cohort of 25 individuals with ongoing nasal symptoms were studied in detail.

RESULTS

Latex sensitisation was confirmed in 182 of 717 symptomatic workers (25.3%). Sensitised symptomatic workers were significantly more likely to have had a previous history of urticaria (P = < 0.001), oral allergy syndrome (P = < 0.001), or allergic conjunctivitis (P = 0.001), but not hay fever, perennial rhinitis, eczema or insect allergies. Latex sensitisation occurred among all classes of health care workers. Ocular and cutaneous symptoms were significantly associated with positive latex sensitisation (P = < 0.001). After latex intervention, ocular symptoms (P = < 0.001), skin rashes (P = < 0.001) and wheezing (P = 0.001) reduced significantly. Nasal symptoms did not improve. Undiagnosed and untreated underlying allergies to common aero-allergens were present in the majority of latex-sensitised patients with ongoing nasal symptomatology.

CONCLUSION

The prevalence of symptomatic sensitisation to latex allergy at Groote Schuur Hospital is between 9.2 and 11.2%. Ocular and cutaneous symptoms are the most prevalent in sensitised workers, and unlike nasal symptoms are significantly reduced when latex-avoidance measures are introduced. Ongoing nasal symptoms after intervention is instituted are probably due to other allergic sensitivities in latex-sensitised health care workers.