Bolus intravenous 0.9% saline leads to interstitial permeability pulmonary edema in healthy volunteers.

PURPOSE: Bolus intravenous administration of 0.9% saline has been associated with the development of pulmonary edema, and increased mortality. An animal model has previously demonstrated that rapid intravenous administration of 0.9% saline was associated with non-hydrostatic lung injury with increased lung lavage protein. We hypothesized that this non-hydrostatic effect would also occur in human volunteers. METHODS: In a randomized, cross-over study of 14 healthy male subjects, the lung lavage protein concentration and cardiorespiratory effects of an intervention with rapid intravenous administration of 30 mL/kg of 0.9% saline were compared with sham intervention. Bronchoalveolar lavage (BAL) was performed after fluid administration. Doppler echocardiography, lung ultrasound, pulmonary function tests, and blood sampling were performed before and after each intervention. RESULTS: The BAL total protein concentration was greater after 0.9% saline administration than with sham (196.1 µg/mL (SD 87.6) versus 129.8 µg/mL (SD 55.4), respectively (p = 0.020). Plasma angiopoietin-2 concentration was also increased to 2.26 ng/mL (SD 0.87) after 0.9% saline administration compared with sham 1.53 ng/mL (SD 0.69) (p < 0.001). There were small increases in stroke volume (from 58 mL (IQR 51-74) to 66 mL (IQR 58-74), p = 0.045) and Doppler echocardiography left ventricle E/e' ratio (from 5.0 (IQR 4.5-6.0) to 5.7 (IQR 5.3-6.3), p = 0.007), but no changes to right ventricular function. CONCLUSION: Rapid intravenous administration of 0.9% saline leads to interstitial permeability pulmonary edema in healthy human volunteers. Further research is now warranted to understand these effects in critically ill patients.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Robust estimation of SARS-CoV-2 epidemic in US counties.

The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by [Formula: see text] of the infectious period of SARS-CoV-2 can reduce around [Formula: see text] (or 78 K, [Formula: see text] CI: [66 K , 89 K ]) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.

Robust estimation of SARS-CoV-2 epidemic in US counties.

The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by $5\%$ of the infectious period of SARS-CoV-2 can reduce around $39\%$ (or $78$K, $95\%$ CI: $[66$K $, 89$K $]$) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.