Influence of Various Nanomaterials on the Rheology and Hydration Kinetics of Oil Well Cement.

Nanomaterials have great potential to influence the properties of cement-based materials due to their small particle size and large specific surface area. The influences of Nano-SiO2 (NS), gamma-nano-Al2O3 (GNA), alpha-nano-Al2O3 (ANA), and nano-TiO2 (NT) on the rheology and hydration kinetics of class G cement at 30 °C were investigated in this study. The nanomaterials were added in dry powder form at dosages of 1, 2, 3, 5, and 7% by weight of cement (bwoc), and their dispersion was accomplished using polycarboxylate superplasticizer (PCE) at a dosage of 1.6% bwoc. PCE provides a uniform dispersion of nanoparticles in the cement matrix, enhancing the efficiency of nanomaterials. The w/c ratio varied between 0.718 and 0.78 to form a constant-density slurry of 1.65 g/cm3. Our test results showed that NS and GNA caused significant increases in the rheology of the cement slurry, with this effect increasing with dosage, while ANA and NT tended to reduce the rheology of the slurry. Compared to a well-suspended and well-dispersed cement slurry generated by the use of PCE and diutan gum, all nanomaterials can accelerate early hydration by reducing the induction time, with GNA having the strongest influence, while NS was the only nanomaterial that further increased the long-term hydration heat release at 7 days. The stronger effect of NS and GNA on the cement slurry properties can be attributed to their higher chemical reactivity. The dosage effect on total hydration extent was relatively strong for ANA, NT, and NS from 3% to 5% but weak for GNA in the range from 3% to 7%.

COVID Oximetry @home: evaluation of patient outcomes

BackgroundCOVID-19 has placed unprecedented demands on hospitals. A clinical service, COVID Oximetry @home (CO@h) was launched in November 2020 to support remote monitoring of COVID-19 patients in the community. Remote monitoring through CO@h aims to identify early patient deterioration and provide timely escalation for cases of silent hypoxia, while reducing the burden on secondary care. MethodsWe conducted a retrospective service evaluation of COVID-19 patients onboarded to CO@h from November 2020 to March 2021 in the North Hampshire (UK) community led service (a collaboration of 15 GP practices covering 230,000 people). We have compared outcomes for patients admitted to Basingstoke & North Hampshire Hospital who were CO@h patients (COVID-19 patients with home monitoring of SpO2 (n=115)), with non-CO@h patients (those directly admitted without being monitored by CO@h (n=633)). Crude and adjusted odds ratio analysis was performed to evaluate the effects of CO@h on patient outcomes of 30-day mortality, ICU admission and hospital length of stay greater than 3, 7, 14, and 28 days. ResultsAdjusted odds ratios for CO@h show an association with a reduction for several adverse patient outcome: 30-day hospital mortality (p<0.001 OR 0.21 95% CI 0.08-0.47), hospital length of stay larger than 3 days (p<0.05, OR 0.62 95% CI 0.39-1.00), 7 days (p<0.001 OR 0.35 95% CI 0.22-0.54), 14 days (p<0.001 OR 0.22 95% CI 0.11-0.41), and 28 days (p<0.05 OR 0.21 95% CI 0.05-0.59). No significant reduction ICU admission was observed (p>0.05 OR 0.43 95% CI 0.15-1.04). Within 30 days of hospital admission, there were no hospital readmissions for those on the CO@h service as opposed to 8.7% readmissions for those not on the service. ConclusionsWe have demonstrated a significant association between CO@h and better patient outcomes; most notably a reduction in the odds of hospital lengths of stays longer than 7, 14 and 28 days and 30-day hospital mortality.

Emergency department admissions during COVID-19: explainable machine learning to characterise data drift and detect emergent health risks

Supervised machine learning algorithms deployed in acute healthcare settings use data describing historical episodes to predict clinical outcomes. Clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (a phenomenon known as data drift), and so can the relationship between episode characteristics and associated clinical outcomes (so-called, concept drift). We demonstrate how explainable machine learning can be used to monitor data drift in a predictive model deployed within a hospital emergency department. We use the COVID-19 pandemic as an exemplar cause of data drift, which has brought a severe change in operational circumstances. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission to hospital during an emergency department attendance. We evaluate our models performance on attendances occurring pre-pandemic (AUROC 0.856 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC 0.826 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a features SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.