Accuracy of pulse pressure variations for fluid responsiveness prediction in mechanically ventilated patients with biphasic positive airway pressure mode.

The accuracy of pulse pressure variation (PPV) to predict fluid responsiveness using pressure-controlled (PC) instead of volume-controlled modes is under debate. To specifically address this issue, we designed a study to evaluate the accuracy of PPV to predict fluid responsiveness in severe septic patients who were mechanically ventilated with biphasic positive airway pressure (BIPAP) PC-ventilation mode. 45 patients with sepsis or septic shock and who were mechanically ventilated with BIPAP mode and a target tidal volume of 7-8 ml/kg were included. PPV was automatically assessed at baseline and after a standard fluid challenge (Ringer's lactate 500 ml). A 15% increase in stroke volume (SV) defined fluid responsiveness. The predictive value of PPV was evaluated through a receiver operating characteristic (ROC) curve analysis and "gray zone" statistical approach. 20 (44%) patients were considered fluid responders. We identified a significant relationship between PPV decrease after volume expansion and SV increase (spearman ρ = - 0.5, p < 0.001). The area under ROC curve for PPV was 0.71 (95%CI 0.56-0.87, p = 0.007). The best cut-off (based on Youden's index) was 8%, with a sensitivity of 80% and specificity of 60%. Using a gray zone approach, we identified that PPV values comprised between 5 and 15% do not allow a reliable fluid responsiveness prediction. In critically ill septic patients ventilated under BIPAP mode, PPV appears to be an accurate method for fluid responsiveness prediction. However, PPV values comprised between 5 and 15% constitute a gray zone that does not allow a reliable fluid responsiveness prediction.

Economic uncertainty before and during the COVID-19 pandemic.

We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based policy uncertainty, Twitter chatter about economic uncertainty, subjective uncertainty about business growth, forecaster disagreement about future GDP growth, and a model-based measure of macro uncertainty. Four results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly - from a 35% rise for the model-based measure of US economic uncertainty (relative to January 2020) to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting differences between Wall Street and Main Street uncertainty measures. Fourth, in Cholesky-identified VAR models fit to monthly U.S. data, a COVID-size uncertainty shock foreshadows peak drops in industrial production of 12-19%.

Acute liver failure following immune checkpoint inhibitors.

We report the case of a 64-year-old man admitted to intensive care unit for liver failure secondary to immune-mediated hepatitis. This patient suffered from a progressing laryngeal squamous cell carcinoma. A treatment was started with immune checkpoint inhibitors combining anti PD-L1 plus novel anti-TIGIT or placebo (ATEZOLIZUMAB plus TIRAGOLUMAB or placebo), as part of a clinical trial. The patient then developed immune-mediated hepatitis, proven by liver biopsy. Despite 14 days of corticosteroids at 2 mg/kg the condition of the patient worsened, with the development of liver failure. The patient was admitted to intensive care unit, treated with plasma exchange, and made a complete recovery from this life-threatening condition. To our knowledge this is the case of a successful use of plasma exchange to treat ATEZOLIZUMAB +/- TIRAGOLUMAB induced liver toxicity. INSIGHTS: Plasma exchange could be a potential lifesaving treatment to severe immune-mediated hepatitis.

Social distancing beliefs and human mobility: Evidence from Twitter.

We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing-at the state level-to capture social distancing beliefs by analyzing the number of tweets containing keywords such as "stay home", "stay safe", "wear mask", "wash hands" and "social distancing". We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID-19 cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others. Our findings suggest that policy makers can use geotagged Twitter data-in conjunction with mobility data-to better understand individual voluntary social distancing actions.