Lung ultrasound (LUS) has recently been advocated as an accurate tool to diagnose coronavirus disease 2019 (COVID-19) pneumonia. However, reports on its use are based mainly on hypothesis studies, case reports or small retrospective case series, while the prognostic role of LUS in COVID-19 patients has not yet been established. We conducted a prospective study aimed at assessing the ability of LUS to predict mortality and intensive care unit admission of COVID-19 patients evaluated in a tertiary level emergency department. Patients in our sample had a median of 6 lung areas with pathologic findings (inter-quartile range [IQR]: 6, range: 0–14), defined as a score different from 0. The median rate of lung areas involved was 71% (IQR: 64%, range: 0–100), while the median average score was 1.14 (IQR: 0.93, range: 0–3). A higher rate of pathologic lung areas and a higher average score were significantly associated with death, with an estimated difference of 40.5% (95% confidence interval [CI]: 4%–68%, p = 0.01) and of 0.47 (95% CI: 0.06–0.93, p = 0.02), respectively. Similarly, the same parameters were associated with a significantly higher risk of intensive care unit admission with estimated differences of 29% (95% CI: 8%–50%, p = 0.008) and 0.47 (95% CI: 0.05–0.93, p = 0.02), respectively. Our study indicates that LUS is able to detect COVID-19 pneumonia and to predict, during the first evaluation in the emergency department, patients at risk for intensive care unit admission and death.

Biological organisms have intrinsic control systems that act in response to internal and external stimuli maintaining homeostasis. Human heart rate is not regular and varies in time and such variability, also known as heart rate variability (HRV), is not random. HRV depends upon organism's physiologic and/or pathologic state. Physicians are always interested in predicting patient's risk of developing major and life-threatening complications. Understanding biological signals behavior helps to characterize patient's state and might represent a step toward a better care. The main advantage of signals such as HRV indexes is that it can be calculated in real time in noninvasive manner, while all current biomarkers used in clinical practice are discrete and imply blood sample analysis. In this paper HRV linear and nonlinear indexes are reviewed and data from real patients are provided to show how these indexes might be used in clinical practice.

BackgroundThe prognostic value of evoked potentials (EPs) in multiple sclerosis (MS) has not been fully established. The correlations between the Expanded Disability Status Scale (EDSS) at First Neurological Evaluation (FNE) and the duration of the disease, as well as between EDSS and EPs, have influenced the outcome of most previous studies. To overcome this confounding relations, we propose to test the prognostic value of EPs within an appropriate patient population which should be based on patients with low EDSS at FNE and short disease duration.MethodsWe retrospectively selected a sample of 143 early relapsing remitting MS (RRMS) patients with an EDSS < 3.5 from a larger database spanning 20 years. By means of bivariate logistic regressions, the best predictors of worsening were selected among several demographic and clinical variables. The best multivariate logistic model was statistically validated and prospectively applied to 50 patients examined during 2009–2011.ResultsThe Evoked Potentials score (EP score) and the Time to EDSS 2.0 (TT2) were the best predictors of worsening in our sample (Odds Ratio 1.10 and 0.82 respectively, p=0.001). Low EP score (below 15–20 points), short TT2 (lower than 3–5 years) and their interaction resulted to be the most useful for the identification of worsening patterns. Moreover, in patients with an EP score at FNE below 6 points and a TT2 greater than 3 years the probability of worsening was 10% after 4–5 years and rapidly decreased thereafter.ConclusionsIn an appropriate population of early RRMS patients, the EP score at FNE is a good predictor of disability at low values as well as in combination with a rapid buildup of disability. Interestingly, an EP score at FNE under the median together with a clinical stability lasting more than 3 years turned out to be a protective pattern. This finding may contribute to an early identification of benign patients, well before the term required to diagnose Benign MS (BMS).

To devise a multivariate parametric model for short-term prediction of disability using the Expanded Disability Status Scale (EDSS) and multimodal sensory EP (mEP). A total of 221 multiple sclerosis (MS) patients who underwent repeated mEP and EDSS assessments at variable time intervals over a 20-year period were retrospectively analyzed. Published criteria were used to compute a cumulative score (mEPS) of abnormalities for each of 908 individual tests. Data of a statistically balanced sample of 58 patients were fed to a parametrical regression analysis using time-lagged EDSS and mEPS along with other clinical variables to estimate future EDSS scores at 1 year. Whole sample cross-sectional mEPS were moderately correlated with EDSS, whereas longitudinal mEPS were not. Using the regression model, lagged mEPS and lagged EDSS along with clinical variables provided better future EDSS estimates. The R (2) measure of fit was significant and 72% of EDSS estimates showed an error value of ±0.5. A parametrical regression model combining EDSS and mEPS accurately predicts short-term disability in MS patients and could be used to optimize decisions concerning treatment.