Understanding the Factors Affecting COVID-19 Mortality in Italy: Does a Relationship Exist With a Sharp Increase in Intensive Care Unit Admissions?

OBJECTIVE: The present study aims to explore whether a relationship exists between the immediate sharp increase in intensive care unit (ICU) admissions and the mortality rates in Italy. METHODS: Official epidemiological data on coronavirus disease (COVID-19) were employed. The forward lagged (0, 3, 7, 14 days) daily variations in the number of deaths according to the number of days after the outbreak started and the daily increases in ICU admissions were estimated. RESULTS: A direct relationship between the sharp increase of ICU admissions and mortality rates has been shown. Furthermore, the analysis of the forward lagged daily variations in the number of deaths showed that an increase in the daily number of ICU admissions resulted in significantly higher mortality after 3, 7, and 14 days. The most pronounced effect was detected after 7 days, with 250 deaths (95% CI: 108.1-392.8) for the highest increase in the ICU admissions, from 100 to 200. CONCLUSIONS: These results would serve as a warning for the scientific community and the health care decision-makers to prevent a quick and out-of-control saturation of the ICU beds in case of a relapse of the COVID-19 outbreak.

Suicide spectrum among young people during the COVID-19 pandemic: A systematic review and meta-analysis.

Background: There are concerns that suicidal behaviors are arising among adolescents. The COVID-19 pandemic could have worsened the picture, however, studies on this topic reported contrasting results. This work aimed to summarise findings from the worldwide emerging literature on the rates of suicidality among young people during the COVID-19 pandemic. Methods: A systematic review and meta-analysis were performed, searching five electronic databases for studies published from January 1, 2020 until July 27, 2022. Studies reporting rates for each of the three considered outcomes (suicide, suicidal behaviors, and suicidal ideation) among young people under 19 years old during the COVID-19 pandemic were included. Random-effects meta-analyses were conducted, and the intra-study risk of bias was assessed. When pre-COVID-19 data were available, incidence rate ratio (IRR) and prevalence ratio (PR) estimates were calculated between the two periods. All the analyses were performed according to the setting explored: general population, emergency department (ED), and psychiatric services. The review protocol was registered on PROSPERO (CRD42022308014). Findings: Forty-seven observational studies were selected for more than 65 million subjects. The results of the meta-analysis showed a pooled annual incidence rate of suicides of 4.9 cases/100,000 during 2020, accounting for a non-statistically significant increase of 10% compared to 2019 (IRR 1.10, 95% CI: 0.94-1.29). The suicidal behaviors pooled prevalence during the COVID-19 pandemic was higher in the psychiatric setting (25%; 95% CI: 17-36%) than in the general population (3%; 1-13%) and ED (1%; 0-9%). The pooled rate of suicidal ideation was 17% in the general population (11-25%), 36% in psychiatric setting (20-56%) and 2% in ED (0-12%). The heterogeneity level was over 97% for both outcomes in all settings considered. The comparison between before and during COVID-19 periods highlighted a non-statistically significant upward trend in suicidal behaviors among the general population and in ED setting. The only significant increase was found for suicidal ideation in psychiatric setting among studies conducted in 2021 (PR 1.15; 95% CI: 1.04-1.27), not observed exploring 2020 alone. Interpretation: During the pandemic, suicide spectrum issues seemed to follow the known pattern described in previous studies, with higher rates of suicidal ideation than of suicidal behaviors and suicide events. Governments and other stakeholders should be mindful that youth may have unique risks at the outset of large disasters like the COVID-19 pandemic and proactive steps are necessary to address the needs of youth to mitigate those risks. Funding: The present study was funded by the University of Torino (CHAL_RILO_21_01).

COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies.

Introduction: An excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered. Methods: An inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies. Results: This study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations. Discussion: This study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.

A topic trend analysis on COVID-19 literature

Objective In the past 2 years, the number of scientific publications has grown exponentially. The COVID-19 outbreak hugely contributed to this dramatic increase in the volume of published research. Currently, text mining of the volume of SARS-CoV-2 and COVID-19 publications is limited to the first months of the outbreak. We aim to identify the major topics in COVID-19 literature collected from several citational sources and analyze the temporal trend from November 2019 to December 2021. Methods We performed an extensive literature search on SARS-Cov-2 and COVID-19 publications on PubMed, Scopus, and Web of Science (WoS) and a structural topic modelling on the retrieved s. The temporal trend of the recognized topics was analyzed. Furthermore, a comparison between our corpus and the COVID-19 Open Research Dataset (CORD-19) repository was performed. Results We collected 269,186 publications and identified 10 topics. The most popular topic was related to the clinical pictures of the COVID-19 outbreak, which has a constant trend, and the least popular includes studies on COVID-19 literature and databases. “Telemedicine”, “Vaccine development”, and “Epidemiology” were popular topics in the early phase of the pandemic;increasing topics in the last period are “COVID-19 impact on mental health”, “Forecasting”, and “Molecular Biology”. “Education” was the second most popular topic, which emerged in September 2020. Conclusions We identified 10 topics for classifying COVID-19 research publications and estimated a nonlinear temporal trend that gives an overview of their unfolding over time. Several citational databases must be searched to retrieve a complete set of studies despite the efforts to build repositories for COVID-19 literature. Our collected data can help build a more focused literature search between November 2019 and December 2021 when carrying out systematic and rapid reviews and our findings can give a complete picture on the topic.

Suicide spectrum among young people during the COVID-19 pandemic: A systematic review and meta-analysis

Background There are concerns that suicidal behaviors are arising among adolescents. The COVID-19 pandemic could have worsened the picture, however, studies on this topic reported contrasting results. This work aimed to summarise findings from the worldwide emerging literature on the rates of suicidality among young people during the COVID-19 pandemic. Methods A systematic review and meta-analysis were performed, searching five electronic databases for studies published from January 1, 2020 until July 27, 2022. Studies reporting rates for each of the three considered outcomes (suicide, suicidal behaviors, and suicidal ideation) among young people under 19 years old during the COVID-19 pandemic were included. Random-effects meta-analyses were conducted, and the intra-study risk of bias was assessed. When pre-COVID-19 data were available, incidence rate ratio (IRR) and prevalence ratio (PR) estimates were calculated between the two periods. All the analyses were performed according to the setting explored: general population, emergency department (ED), and psychiatric services. The review protocol was registered on PROSPERO (CRD42022308014). Findings Forty-seven observational studies were selected for more than 65 million subjects. The results of the meta-analysis showed a pooled annual incidence rate of suicides of 4.9 cases/100,000 during 2020, accounting for a non-statistically significant increase of 10% compared to 2019 (IRR 1.10, 95% CI: 0.94–1.29). The suicidal behaviors pooled prevalence during the COVID-19 pandemic was higher in the psychiatric setting (25%;95% CI: 17–36%) than in the general population (3%;1–13%) and ED (1%;0–9%). The pooled rate of suicidal ideation was 17% in the general population (11–25%), 36% in psychiatric setting (20–56%) and 2% in ED (0–12%). The heterogeneity level was over 97% for both outcomes in all settings considered. The comparison between before and during COVID-19 periods highlighted a non-statistically significant upward trend in suicidal behaviors among the general population and in ED setting. The only significant increase was found for suicidal ideation in psychiatric setting among studies conducted in 2021 (PR 1.15;95% CI: 1.04–1.27), not observed exploring 2020 alone. Interpretation During the pandemic, suicide spectrum issues seemed to follow the known pattern described in previous studies, with higher rates of suicidal ideation than of suicidal behaviors and suicide events. Governments and other stakeholders should be mindful that youth may have unique risks at the outset of large disasters like the COVID-19 pandemic and proactive steps are necessary to address the needs of youth to mitigate those risks. Funding The present study was funded by the 10.13039/501100006692University of Torino (CHAL_RILO_21_01).

Level of Diffusion and Training of Lung Ultrasound during the COVID-19 Pandemic - A National Online Italian Survey (ITALUS) from the Lung Ultrasound Working Group of the Italian Society of Anesthesia, Analgesia, Resuscitation, and Intensive Care (SIAARTI).

PURPOSE: The goal of this survey was to describe the use and diffusion of lung ultrasound (LUS), the level of training received before and during the COVID-19 pandemic, and the clinical impact LUS has had on COVID-19 cases in intensive care units (ICU) from February 2020 to May 2020. MATERIALS AND METHODS: The Italian Lung Ultrasound Survey (ITALUS) was a nationwide online survey proposed to Italian anesthesiologists and intensive care physicians carried out after the first wave of the COVID-19 pandemic. It consisted of 27 questions, both quantitative and qualitative. RESULTS: 807 responded to the survey. The median previous LUS experience was 3 years (IQR 1.0-6.0). 473 (60.9 %) reported having attended at least one training course on LUS before the COVID-19 pandemic. 519 (73.9 %) reported knowing how to use the LUS score. 404 (52 %) reported being able to use LUS without any supervision. 479 (68.2 %) said that LUS influenced their clinical decision-making, mostly with respect to patient monitoring. During the pandemic, the median of patients daily evaluated with LUS increased 3-fold (p < 0.001), daily use of general LUS increased from 10.4 % to 28.9 % (p < 0.001), and the daily use of LUS score in particular increased from 1.6 % to 9.0 % (p < 0.001). CONCLUSION: This survey showed that LUS was already extensively used during the first wave of the COVID-19 pandemic by anesthesiologists and intensive care physicians in Italy, and then its adoption increased further. Residency programs are already progressively implementing LUS teaching. However, 76.7 % of the sample did not undertake any LUS certification.

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy.

BACKGROUND: Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information. METHODS: We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)2. We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model. FINDINGS: Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags. INTERPRETATION: ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)2, getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.

Impact of the COVID-19 lockdown on psychological health and nutritional habits in Italy: results from the #PRESTOinsieme study.

OBJECTIVES: The present work aims to present the results of the 'PRESTOinsieme' (which is 'we will be together soon' in English). The web-based survey (www.prestoinsieme.com) describes changes in lifestyle habits and symptoms of psychological discomfort in the Italian population during the COVID-19 lockdown. DESIGN: Cross-sectional online survey disseminated by messaging apps (ie, WhatsApp and Telegram) and social networks (ie, Instagram, Facebook and LinkedIn). SETTING: Italy. PARTICIPANTS: Italian population older than 16 years of age. EXPOSURE: COVID-19 lockdown. MAIN OUTCOMES AND MEASURES: Survey respondents filled out a set of validated questionnaires aimed at assessing lifestyle habits and psychological health, that is, the General Health Questionnaire (GHQ-12) to screen for psychological distress, the Impact of Event Scale-Revised (IES-R) to screen for post-traumatic stress and the Center for Epidemiologic Studies Depression Scale (CES-D). RESULTS: Survey respondents totalled 5008. Moderate or severe psychological distress was reported in 25.5% and 22% of survey respondents, respectively. Lower age, female gender, being unemployed (OR 1.57, 95% CI 1.22 to 2.02) or being a student (OR 1.73, 95% CI 1.31 to 2.28) were predictors of more severe depressive symptoms. CONCLUSIONS: The present study is one of the largest population-based surveys conducted in Italy during the first COVID-19 lockdown, providing valuable data about the Italian population's psychological health. Further studies should be conducted to understand whether psychological distress persists after the end of the lockdown.

A Web-Based Application to Monitor and Inform about the COVID-19 Outbreak in Italy: The {COVID-19ita} Initiative.

The pandemic outbreak of COVID-19 has posed several questions about public health emergency risk communication. Due to the effort required for the population to adopt appropriate behaviors in response to the emergency, it is essential to inform the public of the epidemic situation with transparent data sources. The COVID-19ita project aimed to develop a public open-source tool to provide timely, updated information on the pandemic's evolution in Italy. It is a web-based application, the front end for the eponymously named R package freely available on GitHub, deployed both in English and Italian. The web application pulls the data from the official repository of the Italian COVID-19 outbreak at the national, regional, and provincial levels. The app allows the user to select information to visualize data in an interactive environment and compare epidemic situations over time and across different Italian regions. At the same time, it provides insights about the outbreak that are explained and commented upon to yield reasoned, focused, timely, and updated information about the outbreak evolution.

Exploring the Use and Usefulness of Educational Resources Among Nurses During the First Wave of the COVID-19 Pandemic: A Cross-Sectional Study.

Nurses needed to learn rapidly how to care for patients with coronavirus disease 2019 (COVID-19). This cross-sectional study identified the educational resources RNs used to learn how to care for these patients, measured the perceived usefulness of the content of these resources, and explored the most relevant educational content sought during the first wave of the pandemic. A total of 799 RNs completed an online survey. Significant differences were found in the educational resources used between RNs who cared for patients with COVID-19 and RNs who did not, as well as for RNs who changed units following the pandemic. The educational resources most often used were also ranked as most useful. The most relevant educational content sought was related to respiratory care techniques and infection containment. These findings provide insight into the educational resources available during the first wave of the COVID-19 pandemic. They could help in developing educational programs relevant to nurses' needs during future pandemics. [J Contin Educ Nurs. 2022;53(2):63-69.].

Non-Invasive Measurement of Exercise-Induced Oxidative Stress in Response to Physical Activity. A Systematic Review and Meta-Analysis.

Physical activity may benefit health by modulating oxidative stress and inflammation. However, the selection of suitable exercise-induced oxidative stress biomarkers is still challenging. This study aimed at systematically summarizing the available evidence on exercise-induced oxidative stress measured in urine and/or saliva. Two meta-analyses including the most frequently quantified biomarkers of oxidative stress, namely, urinary isoprostane and DNA oxidation products, were performed. Three electronic databases (PubMed, EMBASE and Cochrane CENTRAL) were interrogated. Among 4479 records, 43 original articles were included in the systematic review and 11 articles were included in meta-analysis I and II, respectively. We observed a pooled trend of increase of urinary isoprostanes in response to physical activity (+0.95, 95% CI: -0.18; 2.09). In comparison with aerobic exercise, anaerobic training determined a greater induction of isoprostanes (+5.21, 95% CI: 2.76; 7.66, p < 0.0001), which were markedly increased after vigorous physical activity (+6.01, 95% CI: 1.18; 10.84, p < 0.001) and slightly decreased in response to exercise interventions protracted over time (e.g., months) (-1.19, 95% CI: -2.25; -0.12, p < 0.001). We recommend the most integrative approach of oxidative stress multi-marker panels in response to physical activity instead of selecting one preferential biomarker to quantify physical activity-induced oxidative stress in humans.

Psychosocial factors associated with complicated grief in caregivers during COVID-19: Results from a preliminary cross-sectional study.

The COVID-19 pandemic has changed how end-of-life ceremonies are performed, affecting grief processing and bereavement experiences. In this study, caregivers of patients who died with COVID-19 during the first wave of the pandemic were asked to complete an online survey designed to detect psychosocial factors associated with the presence of complicated grief (CG). The results show CG present in 48.4% of caregivers. The marital and cohabitant status during lockdown, the perceived sense of guilt and depression levels were significantly associated with the presence of CG, whereas attendance at the funeral and social support were found to be significant protective factors.

The Impact of the COVID-19 Pandemic on Nursing Care: A Cross-Sectional Survey-Based Study.

The COVID-19 pandemic has had a severe impact on nursing care. This cross-sectional survey-based study compared aspects of nursing care and nurses' satisfaction with care provided before and during the first wave of the COVID-19 pandemic. A total of 936 registered nurses (RNs) rated the frequency with which they performed fundamental care, nursing techniques, patient education, symptom management, and nurse-patient relationships before and during the pandemic. A recursive partitioning for ordered multivariate response in a conditional inference framework approach was applied. More frequent fundamental cares were associated with their frequency before the pandemic (p < 0.001), caring for COVID-19 patients (p < 0.001), and workplace reassignment (p = 0.004). Caring for COVID-19 patients (p < 0.001), workplace reassignment (p = 0.030), and caring for ≤7.4 COVID-19 patients (p = 0.014) increased nursing techniques. RNs in high-intensity COVID-19 units (p = 0.002) who educated patients before the pandemic, stopped this task. RNs caring for COVID-19 patients reported increased symptom management (p < 0.001), as did RNs caring for more non-COVID-19 patients (p = 0.037). Less frequent nurse-patient relationships before the pandemic and working in high-intensity COVID-19 units decreased nurse-patient relationships (p = 0.002). Despite enormous challenges, nurses continued to provide a high level of care. Ensuring the appropriate deployment and education of nurses is crucial to personalize care and to maintain nurses' satisfaction with the care provided.

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol.

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.

To Swab or Not to Swab? The Lesson Learned in Italy in the Early Stage of the COVID-19 Pandemic

Testing for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this paper is to assess the impact of the extensive testing of symptomatic individuals and their contacts on the number of hospitalizations against a more stringent testing strategy limited to suspected cases with severe respiratory illness and an epidemiological link to a COVID-19 case. A Poisson regression modelling approach was adopted. In the first model developed, the cumulative daily number of positive cases and a temporal trend were considered as explanatory variables. In the second, the cumulative daily number of swabs was further added. The explanatory variable, given by the number of swabs over time, explained most of the observed differences in the number of hospitalizations between the two strategies. The percentage of the expected error dropped from 70% of the first, simpler model to 15%. Increasing testing to detect and isolate infected individuals in the early phase of an outbreak improves the capability to reduce the spread of serious infections, lessening the burden of hospitals.

Excess of all-cause mortality is only partially explained by COVID-19 in Veneto (Italy) during spring outbreak.

BACKGROUND: Italy has been the first European country to be affected by the COVID-19 epidemic which started out at the end of February. In this report, we focus our attention on the Veneto Region, in the North-East of Italy, which is one of the areas that were first affected by the rapid spread of SARS-CoV-2. We aim to evaluate the trend of all-cause mortality and to give a description of the characteristics of the studied population. METHODS: Data used in the analyses were released by the majority of municipalities and cover the 93% of the total population living in the Veneto Region. We evaluated the trend of overall mortality from Jan.01 to Jun.30. 2020. Moreover we compared the COVID-19-related deaths to the overall deaths. RESULTS: From March 2020, the overall mortality rate increased exponentially, affecting males and people aged > 76 the most. The confirmed COVID-19-related death rate in the Veneto region between Mar.01 and Apr.302020 is 30 per 100,000 inhabitants. In contrast, the all-cause mortality increase registered in the same months in the municipalities included in the study is 219 per 100,000 inhabitants. CONCLUSIONS: COVID-19 has a primary role in the increase in mortality but does not entirely explain such a high number of deaths. Strategies need to be developed to reduce this gap in case of future waves of the pandemic.

Regional Differences in Mortality Rates During the COVID-19 Epidemic in Italy.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) outbreak started in Italy on February 20, 2020, and has resulted in many deaths and intensive care unit (ICU) admissions. This study aimed to illustrate the epidemic COVID-19 growth pattern in Italy by considering the regional differences in disease diffusion during the first 3 mo of the epidemic. METHODS: Official COVID-19 data were obtained from the Italian Civil Protection Department of the Council of Ministers Presidency. The mortality and ICU admission rates per 100,000 inhabitants were calculated at the regional level and summarized by means of a Bayesian multilevel meta-analysis. Data were retrieved until April 21, 2020. RESULTS: The highest cumulative mortality rates per 100 000 inhabitants were observed in northern Italy, particularly in Lombardia (85.3; 95% credibility intervals [CI], 75.7-94.7). The difference in the mortality rates between northern and southern Italy increased over time, reaching a difference of 67.72 (95% CI, 66-67) cases on April 2, 2020. CONCLUSIONS: Northern Italy showed higher and increasing mortality rates during the first 3 mo of the epidemic. The uncontrolled virus circulation preceding the infection spreading in southern Italy had a considerable impact on system burnout. This experience demonstrates that preparedness against the pandemic is of crucial importance to contain its disruptive effects.

The COVID-19 Worsening Score (COWS)-a predictive bedside tool for critical illness.

OBJECTIVES: To evaluate the accuracy of a new COVID-19 prognostic score based on lung ultrasound (LUS) and previously validated variables in predicting critical illness. METHODS: We conducted a single-center retrospective cohort development and internal validation study of the COVID-19 Worsening Score (COWS), based on a combination of the previously validated COVID-GRAM score (GRAM) variables and LUS. Adult COVID-19 patients admitted to the emergency department (ED) were enrolled. Ten variables previously identified by GRAM, days from symptom onset, LUS findings, and peripheral oxygen saturation/fraction of inspired oxygen (P/F) ratio were analyzed. LUS score as a single predictor was assessed. We evaluated GRAM model's performance, the impact of adding LUS, and then developed a new model based on the most predictive variables. RESULTS: Among 274 COVID-19 patients enrolled, 174 developed critical illness. The GRAM score identified 51 patients at high risk of developing critical illness and 132 at low risk. LUS score over 15 (range 0 to 36) was associated with a higher risk ratio of critical illness (RR, 2.05; 95% confidence interval [CI], 1.52-2.77; area under the curve [AUC], 0.63; 95% CI 0.676-0.634). The newly developed COVID-19 Worsening Score relies on five variables to classify high- and low-risk patients with an overall accuracy of 80% and negative predictive value of 93% (95% CI, 87%-98%). Patients scoring more than 0.183 on COWS showed a RR of developing critical illness of 8.07 (95% CI, 4.97-11.1). CONCLUSIONS: COWS accurately identify patients who are unlikely to need intensive care unit (ICU) admission, preserving resources for the remaining high-risk patients.

Psychosocial factors associated with postpartum psychological distress during the Covid-19 pandemic: a cross-sectional study.

BACKGROUND: Trauma, natural and man-made catastrophic events can be predictors of postpartum psychological distress. In a public health response due to coronavirus disease 2019 outbreak, the Italian government imposed a lockdown from March 9 to May 3. This extraordinary situation may have been challenging for maternal psychological health. The aim of this study was to investigate the prevalence of depressive and post-traumatic stress symptoms in women giving birth during the Covid-19 pandemic and its associations with quarantine measures, obstetrical factors, and relational attachment style. METHODS: Women who gave birth in a high-volume obstetric/gynaecological medical centre located in an epidemic area during the Covid-19 pandemic (March 8 to June 15) were asked to complete an online survey about their childbirth experience and the perceived effect of the pandemic. The Edinburgh Postnatal Depression Scale (EPDS), the Impact of Event Scale-Revised (IES-R), and the Relationship Questionnaire (RQ) were administered to assess levels of postpartum depressive and post-traumatic stress symptoms (PTSS) and relational style of attachment, respectively. Multivariate analysis was applied to identify associations between quarantine measures, childbirth experience, attachment style, and EPDS and IES-R scores. RESULTS: The survey was completed by 163 women (response rate 60.8%). The prevalence of depressive symptoms was 44.2% (EPDS cut-off score ≥ 11) and the PTSS rate was 42.9% (IES-R cut-off score ≥ 24). Dismissive and fearful avoidant attachment styles were significantly associated with the risk of depression and PTSS, respectively. Perceived pain during birth was a risk factor for postpartum depression. Perceived support provided by healthcare staff was a protective factor against depression and PTSS. Another protective factor against PTSS was quiet on the ward due to the absence of hospital visitors. CONCLUSION: This study reports a high prevalence of postpartum depressive and PTSS in women who gave birth during the Covid-19 pandemic. Postnatal psychological distress seemed to be associated more with the prenatal experience and other individual factors than with the pandemic hospital restrictions. Early detection during pregnancy of an insecure attachment style is fundamental to provide targeted preventive and therapeutic psychological interventions.