Designing a Virtual Hospital-at-Home Intervention for Patients with Infectious Diseases: A Data-Driven Approach.

BACKGROUND: Virtual hospital-at-home care might be an alternative to standard hospital care for patients with infectious diseases. In this study, we explore the potential for virtual hospital-at-home care and a potential design for this population. METHODS: This was a retrospective cohort study of internal medicine patients suspected of infectious diseases, admitted between 1 January and 31 December 2019. We collected information on delivered care during emergency department visits, the first 24 h, between 24 and 72 h, and after 72 h of admission. Care components that could be delivered at home were combined into care packages, and the potential number of eligible patients per package was described. The most feasible package was described in detail. RESULTS: 763 patients were included, mostly referred for general internal medicine (35%), and the most common diagnosis was lower respiratory tract infection (27%). The most frequently administered care components were laboratory tests, non-oral medication, and intercollegiate consultation. With a combination of telemonitoring, video consultation, non-oral medication administration, laboratory tests, oxygen therapy, and radiological diagnostics, 48% of patients were eligible for hospital-at-home care, with 35% already eligible directly after emergency department visits. CONCLUSION: While the potential for virtual hospital-at-home care is high, it depends greatly on which care can be arranged.

Point-of-care neutrophil and monocyte surface markers differentiate bacterial from viral infections at the emergency department within 30†min.

Rapid discrimination between viral and bacterial infections in a point-of-care setting will improve clinical outcome. Expression of CD64 on neutrophils (neuCD64) increases during bacterial infections, whereas expression of CD169 on classical monocytes (cmCD169) increases during viral infections. The diagnostic value of automated point-of-care neuCD64 and cmCD169 analysis was assessed for detecting bacterial and viral infections at the emergency department. Additionally, their value as input for machine learning models was studied. A prospective observational cohort study in patients suspected of infection was performed at an emergency department. A fully automated point-of-care flow cytometer measured neuCD64, cmCD169, and additional leukocyte surface markers. Flow cytometry data were gated using the FlowSOM algorithm. Bacterial and viral infections were assessed in standardized clinical care. The sole and combined diagnostic value of the markers was investigated. Clustering based on unsupervised machine learning identified unique patient clusters. Eighty-six patients were included. Thirty-five had a bacterial infection, 30 had a viral infection, and 21 had no infection. neuCD64 was increased in bacterial infections (P < 0.001), with an area under the receiver operating characteristic curve (AUROC) of 0.73. cmCD169 was higher in virally infected patients (P < 0.001; AUROC 0.79). Multivariate analyses incorporating additional markers increased the AUROC for bacterial and viral infections to 0.86 and 0.93, respectively. The additional clustering identified 4 distinctive patient clusters based on infection type and outcome. Automated neuCD64 and cmCD169 determination can discriminate between bacterial and viral infections. These markers can be determined within 30 min, allowing fast infection diagnostics in the acute clinical setting.

A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these "silver" labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research.

Axial light loss of monocytes as a readily available prognostic biomarker in patients with suspected infection at the emergency department.

OBJECTIVES: To evaluate the prognostic value of the coefficient of variance of axial light loss of monocytes (cv-ALL of monocytes) for adverse clinical outcomes in patients suspected of infection in the emergency department (ED). METHODS: We performed an observational, retrospective monocenter study including all medical patients ≥18 years admitted to the ED between September 2016 and June 2019 with suspected infection. Adverse clinical outcomes included 30-day mortality and ICU/MCU admission <3 days after presentation. We determined the additional value of monocyte cv-ALL and compared to frequently used clinical prediction scores (SIRS, qSOFA, MEWS). Next, we developed a clinical model with routinely available parameters at the ED, including cv-ALL of monocytes. RESULTS: A total of 3526 of patients were included. The OR for cv-ALL of monocytes alone was 2.21 (1.98-2.47) for 30-day mortality and 2.07 (1.86-2.29) for ICU/MCU admission <3 days after ED presentation. When cv-ALL of monocytes was combined with a clinical score, the prognostic accuracy increased significantly for all tested scores (SIRS, qSOFA, MEWS). The maximum AUC for a model with routinely available parameters at the ED was 0.81 to predict 30-day mortality and 0.81 for ICU/MCU admission. CONCLUSIONS: Cv-ALL of monocytes is a readily available biomarker that is useful as prognostic marker to predict 30-day mortality. Furthermore, it can be used to improve routine prediction of adverse clinical outcomes at the ED. CLINICAL TRIAL REGISTRATION: Registered in the Dutch Trial Register (NTR) und number 6916.

Pancreatic Stone Protein as a Biomarker for Sepsis at the Emergency Department of a Large Tertiary Hospital.

Early recognition of sepsis is essential for improving outcomes and preventing complications such as organ failure, depression, and neurocognitive impairment. The emergency department (ED) plays a key role in the early identification of sepsis, but clinicians lack diagnostic tools. Potentially, biomarkers could be helpful in assisting clinicians in the ED, but no marker has yet been successfully implemented in daily practice with good clinical performance. Pancreatic stone protein (PSP) is a promising biomarker in the context of sepsis, but little is known about the diagnostic performance of PSP in the ED. We prospectively investigated the diagnostic value of PSP in such a population for patients suspected of infection. PSP was compared with currently used biomarkers, including white blood cell count (WBC) and C-reactive protein (CRP). Of the 156 patients included in this study, 74 (47.4%) were diagnosed with uncomplicated infection and 26 (16.7%) patients with sepsis, while 56 (35.9%) eventually had no infection. PSP was significantly higher for sepsis patients compared to patients with no sepsis. In multivariate regression, PSP was a significant predictor for sepsis, with an area under the curve (AUC) of 0.69. Positive and negative predictive values for this model were 100% and 84.4%, respectively. Altogether, these findings show that PSP, measured at the ED of a tertiary hospital, is associated with sepsis but lacks the diagnostic performance to be used as single marker.

Hematological Ratios Are Associated with Acute Kidney Injury and Mortality in Patients That Present with Suspected Infection at the Emergency Department.

The early recognition of acute kidney injury (AKI) is essential to improve outcomes and prevent complications such as chronic kidney disease, the need for renal-replacement therapy, and an increased length of hospital stay. Increasing evidence shows that inflammation plays an important role in the pathophysiology of AKI and mortality. Several inflammatory hematological ratios can be used to measure systemic inflammation. Therefore, the association between these ratios and outcomes (AKI and mortality) in patients suspected of having an infection at the emergency department was investigated. Data from the SPACE cohort were used. Cox regression was performed to investigate the association between seven hematological ratios and outcomes. A total of 1889 patients were included, of which 160 (8.5%) patients developed AKI and 102 (5.4%) died in <30 days. The Cox proportional-hazards model revealed that the neutrophil-to-lymphocyte ratio (NLR), segmented-neutrophil-to-monocyte ratio (SMR), and neutrophil-lymphocyte-platelet ratio (NLPR) are independently associated with AKI <30 days after emergency-department presentation. Additionally, the NLR, SMR and NLPR were associated with 30-day all-cause mortality. These findings are an important step forward for the early recognition of AKI. The use of these markers might enable emergency-department physicians to recognize and treat AKI in an early phase to potentially prevent complications.

Remote Hospital Care for Recovering COVID-19 Patients Using Telemedicine: A Randomised Controlled Trial.

BACKGROUND: To ensure availability of hospital beds and improve COVID-19 patients' well-being during the ongoing pandemic, hospital care could be offered at home. Retrospective studies show promising results of deploying remote hospital care to reduce the number of days spent in the hospital, but the beneficial effect has yet to be established. METHODS: We conducted a single centre, randomised trial from January to June 2021, including hospitalised COVID-19 patients who were in the recovery stage of the disease. Hospital care for the intervention group was transitioned to the patient's home, including oxygen therapy, medication and remote monitoring. The control group received in-hospital care as usual. The primary endpoint was the number of hospital-free days during the 30 days following randomisation. Secondary endpoints included health care consumption during the follow-up period and mortality. RESULTS: A total of 62 patients were randomised (31 control, 31 intervention). The mean difference in hospital-free days was 1.7 (26.7 control vs. 28.4 intervention, 95% CI of difference -0.5 to 4.2, p = 0.112). In the intervention group, the index hospital length of stay was 1.6 days shorter (95% CI -2.4 to -0.8, p < 0.001), but the total duration of care under hospital responsibility was 4.1 days longer (95% CI 0.5 to 7.7, p = 0.028). CONCLUSION: Remote hospital care for recovering COVID-19 patients is feasible. However, we could not demonstrate an increase in hospital-free days in the 30 days following randomisation. Optimising the intervention, timing, and identification of patients who will benefit most from remote hospital care could improve the impact of this intervention.

Code status documentation at admission in COVID-19 patients: a descriptive cohort study.

OBJECTIVES: The COVID-19 pandemic pressurised healthcare with increased shortage of care. This resulted in an increase of awareness for code status documentation (ie, whether limitations to specific life-sustaining treatments are in place), both in the medical field and in public media. However, it is unknown whether the increased awareness changed the prevalence and content of code status documentation for COVID-19 patients. We aim to describe differences in code status documentation between infectious patients before the pandemic and COVID-19 patients. SETTING: University Medical Centre of Utrecht, a tertiary care teaching academic hospital in the Netherlands. PARTICIPANTS: A total of 1715 patients were included, 129 in the COVID-19 cohort (a cohort of COVID-19 patients, admitted from March 2020 to June 2020) and 1586 in the pre-COVID-19 cohort (a cohort of patients with (suspected) infections admitted between September 2016 to September 2018). PRIMARY AND SECONDARY OUTCOME MEASURES: We described frequency of code status documentation, frequency of discussion of this code status with patient and/or family, and content of code status. RESULTS: Frequencies of code status documentation (69.8% vs 72.7%, respectively) and discussion (75.6% vs 73.3%, respectively) were similar in both cohorts. More patients in the COVID-19 cohort than in the before COVID-19 cohort had any treatment limitation as opposed to full code (40% vs 25%). Within the treatment limitations, 'no intensive care admission' (81% vs 51%) and 'no intubation' (69% vs 40%) were more frequently documented in the COVID-19 cohort. A smaller difference was seen in 'other limitation' (17% vs 9%), while 'no resuscitation' (96% vs 92%) was comparable between both periods. CONCLUSION: We observed no difference in the frequency of code status documentation or discussion in COVID-19 patients opposed to a pre-COVID-19 cohort. However, treatment limitations were more prevalent in patients with COVID-19, especially 'no intubation' and 'no intensive care admission'.

Signaling by the inhibitory receptor CD200R is rewired by type I interferon.

CD200 receptor 1 (CD200R) is an inhibitory immunoreceptor that suppresses Toll-like receptor (TLR)­induced cytokine production through the adaptor protein Dok2 and the GTPase activating protein (GAP) p120-RasGAP, which can be cleaved during mild cellular stress. We found that in the presence of cleaved p120-RasGAP, CD200R lost its capacity to inhibit phosphorylation of ribosomal S6 protein (rpS6), suggesting the reduced activity of mammalian target of rapamycin complex 1 (mTORC1). Furthermore, treatment of human peripheral blood mononuclear cells (PBMC) with interferon-α (IFN-α) resulted in increased amounts of cleaved p120-RasGAP. Upon pretreatment of cells with increasing concentrations of IFN-α, CD200R switched from inhibiting to potentiating the TLR7- and TLR8-induced expression of the gene encoding IFN-γ, a cytokine that is important for innate and adaptive immunity and is implicated in systemic lupus erythematosus (SLE) pathogenesis. PBMC from patients with SLE, a prototypic type I IFN disease, had an increased abundance of cleaved p120-RasGAP compared to that in cells from healthy controls. In a subset of SLE patients, CD200R stopped functioning as an inhibitory receptor or potentiated TLR-induced IFNG mRNA expression. Thus, our data suggest that type I IFN rewires CD200R signaling to be proinflammatory, which could contribute to the perpetuation of inflammation in patients with SLE.

The number of T cell allo-epitopes associates with CD4+ and CD8+ T-cell infiltration in pediatric cutaneous GVHD.

Risk factors for graft-versus-host disease (GVHD) following allogeneic hematopoietic stem-cell transplantation (HCST) include: HLA mismatches, sex-mismatch, and stem-cell source. We retrospectively analyzed if HLA- and sex-mismatching quantitatively affects the composition of GVHD-induced T-cell infiltrates. We quantified absolute numbers of CD4+ and CD8+ T cells present in tissue sections from skin biopsies of 23 pediatric HSCT-recipients with GVHD. HSCT with a sex-mismatched unrelated donor was associated with an increased number of CD4+ T cells when compared to a sex-matched unrelated donor (p=0.01). The absolute numbers of skin-infiltrating T cells were increased in patients expressing T-cell epitopes derived from the recipient's mismatched HLA, so called predicted indirectly recognizable HLA epitopes (PIRCHE). The combined expression of PIRCHE with a sex-mismatch resulted in the highest number of skin-infiltrating T cells. Our results indicate that an increased number of recipient-specific T-cell epitopes is associated with accumulation of CD4+ and CD8+ T cells in the skin.