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1.
Swiss Med Wkly ; 150: w20277, 2020 05 04.
Article | MEDLINE | ID: covidwho-2217319

ABSTRACT

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.


Subject(s)
Coronavirus Infections , Forecasting/methods , Health Planning/methods , Hospital Bed Capacity , Intensive Care Units/supply & distribution , Pandemics , Pneumonia, Viral , Software , Ventilators, Mechanical/supply & distribution , COVID-19 , Coronavirus Infections/epidemiology , Decision Making, Computer-Assisted , Hospital Bed Capacity/statistics & numerical data , Hospitalization/statistics & numerical data , Hospitalization/trends , Humans , Intensive Care Units/statistics & numerical data , Models, Theoretical , Pandemics/statistics & numerical data , Patient Admission/statistics & numerical data , Pneumonia, Viral/epidemiology , Software/standards , Switzerland/epidemiology , Ventilators, Mechanical/statistics & numerical data
2.
PLoS One ; 17(3): e0263580, 2022.
Article in English | MEDLINE | ID: covidwho-1742000

ABSTRACT

BACKGROUND: Pulmonary embolisms are frequently and prognostically in individuals infected by coronavirus disease 2019 (COVID-19); the incidence of pulmonary embolisms is varied across numerous studies. This study aimed to assess the pooled incidence of pulmonary embolic events and the prognostic value of such events in intensive care unit (ICU) admissions of patients with COVID-19. METHODS: The Cochrane Library, PubMed, and EmBase were systematically searched for eligible studies published on or before October 20, 2021. The pooled incidence of pulmonary embolism was calculated using the random-effects model. Moreover, the prognostic value was assessed by measuring the sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). RESULTS: Thirty-six studies involving 10,367 COVID-19 patients were selected for the final meta-analysis. The cumulative incidence of pulmonary embolism in patients with COVID-19 was 21% (95% confidence interval [95%CI]: 18-24%; P<0.001), and the incidence of pulmonary embolism in ICU and non-ICU patients was 26% (95%CI: 22-31%; P<0.001) and 17% (95%CI: 14-20%; P<0.001), respectively. The predictive role of pulmonary embolism in ICU admission was also assessed, and the sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.31 (95%CI: 0.21-0.42), 0.84 (95%CI: 0.75-0.90), 1.88 (95%CI: 1.45-2.45), 0.83 (95%CI: 0.75-0.91), 2.25 (95%CI: 1.64-3.08), and 0.61 (95%CI: 0.57-0.65), respectively. CONCLUSION: This study found that the incidence of pulmonary embolism was relatively high in COVID-19 patients, and the incidence of pulmonary embolism in ICU patients was higher than that in non-ICU patients.


Subject(s)
COVID-19/epidemiology , Patient Admission/statistics & numerical data , Pulmonary Embolism/epidemiology , Humans , Incidence , Intensive Care Units , Prognosis , Sensitivity and Specificity
3.
Diabetes Metab Syndr ; 16(1): 102389, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1683070

ABSTRACT

BACKGROUND AND AIM: Describe the prevalence/outcomes of Diabetic Ketoacidosis (DKA) patients comparing pre- (March-April 2019) and pandemic (March-April 2020) periods. METHODS: Retrospective cohort of admitted pandemic DKA/COVID-19+ patients comparing prevalence/outcomes to pre-pandemic DKA patients that takes place in Eleven hospitals of New York City Health & Hospitals. Our included participants during the pandemic period were admitted COVID-19+ patients (>18 years) and during the pre-pandemic period were admissions (>18 years) selected through the medical record. We excluded transfers during both periods. The intervention was COVID-19+ by PCR testing. The main outcome measured was mortality during the index hospitalization and secondary outcomes were demographics, medical histories and triage vital signs, and laboratory tests. Definition of DKA: Beta-Hydroxybutyrate (BHBA) (>0.4 mmol/L) and bicarbonate (<15 mmol/L) or pH (<7.3). RESULTS: Demographics and past medical histories were similar during the pre-pandemic (n = 6938) vs. pandemic (n = 7962) periods. DKA prevalence was greater during pandemic (3.14%, 2.66-3.68) vs. pre-pandemic period (0.72%, 0.54-0.95) (p > 0.001). DKA/COVID-19+ mortality rates were greater (46.3% (38.4-54.3) vs. pre-pandemic period (18%, 8.6-31.4) (p < 0.001). Surviving vs. non-surviving DKA/COVID-19+ patients had more severe DKA with lower bicarbonates by 2.7 mmol/L (1.0-4.5) (p < 0.001) and higher both Anion Gaps by 3.0 mmol/L (0.2-6.3) and BHBA by 2.1 mmol/L (1.2-3.1) (p < 0.001). CONCLUSIONS: COVID-19 increased the prevalence of DKA with higher mortality rates secondary to COVID-19 severity, not DKA. We suggest DKA screening all COVID-19+ patients and prioritizing ICU DKA/COVID-19+ with low oxygen saturation, blood pressures, or renal insufficiency.


Subject(s)
COVID-19/epidemiology , Diabetic Ketoacidosis/epidemiology , Patient Admission/statistics & numerical data , COVID-19/complications , COVID-19/therapy , Cohort Studies , Diabetic Ketoacidosis/complications , Diabetic Ketoacidosis/therapy , Female , Humans , Male , Middle Aged , Pandemics , Prevalence , Retrospective Studies , SARS-CoV-2/physiology , United States/epidemiology
4.
Medicine (Baltimore) ; 101(2): e28567, 2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1625627

ABSTRACT

ABSTRACT: Gyeonggi-do (Gyeonggi province) has the second highest number of coronavirus disease (COVID-19) cases in the Republic of Korea after Seoul, with approximately 25% of the COVID-19 patients as of January 2021. Our center is a level I trauma center located in south Gyeonggi-do, and we aimed to evaluate whether the characteristics of trauma patients changed after the COVID-19 pandemic.We retrospectively reviewed the trauma patients registered with the Korea Trauma Database of the Center from February 2019 to January 2021. The patients were dichotomized into pre-coronavirus disease (pre-COVID) and coronavirus disease (COVID) groups, and their trauma volumes, injury characteristics, intentionality, and outcomes were compared.A total of 2628 and 2636 patients were included in the pre-COVID and COVID groups, respectively. During the COVID-19 period, motorcycle accidents, bicycle accidents, and penetrating injury cases increased, and pedestrian traffic accidents, slips, and injury by machines decreased. The average daily number of patients in the COVID group was lower in March (5.6 ±â€Š2.6/day vs 7.2 ±â€Š2.4/day, P = .014) and higher in September (9.9 ±â€Š3.2/day vs 7.7 ±â€Š2.0/day, P = .003) compared to the pre-COVID group. The COVID group also had a higher ratio of direct admissions (67.5% vs 57.2%, P < .001), proportion of suicidal patients (4.1% vs 2.7%, P = .005), and injury severity scores (14 [9-22] vs 12 [4-22], P < .001) than the pre-COVID group. The overall mortality (4.7% vs 4.9%, P = .670) and intensive care unit length of stay (2 [0-3] days vs 2 [0-4] days, P = .153) was not different between the 2 groups.Although the total number of patients did not change, the COVID-19 pandemic affected the number of monthly admissions and the injury mechanisms changed. More severely injured patients were admitted directly to the trauma center.


Subject(s)
COVID-19 , Patient Admission/statistics & numerical data , Trauma Centers/statistics & numerical data , Wounds and Injuries/epidemiology , Adult , COVID-19/epidemiology , Female , Humans , Male , Middle Aged , Pandemics/prevention & control , Republic of Korea/epidemiology , Retrospective Studies , SARS-CoV-2 , Wounds and Injuries/diagnosis , Wounds and Injuries/therapy
5.
PLoS One ; 16(12): e0261358, 2021.
Article in English | MEDLINE | ID: covidwho-1623654

ABSTRACT

INTRODUCTION: Colchicine may inhibit inflammasome signaling and reduce proinflammatory cytokines, a purported mechanism of COVID-19 pneumonia. The aim of this systematic review and meta-analysis is to report on the state of the current literature on the use of colchicine in COVID-19 and to investigate the reported clinical outcomes in COVID-19 patients by colchicine usage. METHODS: The literature was searched from January 2019 through January 28, 2021. References were screened to identify studies that reported the effect of colchicine usage on COVID-19 outcomes including mortality, intensive care unit (ICU) admissions, or mechanical ventilation. Studies were meta-analyzed for mortality by the subgroup of trial design (RCT vs observational) and ICU status. Studies reporting an risk ratio (RR), odds ratio (OR) and hazard ratio (HR) were analyzed separately. RESULTS: Eight studies, reporting on 16,248 patients, were included in this review. The Recovery trial reported equivalent mortality between colchicine and non-colchicine users. Across the other studies, patients who received colchicine had a lower risk of mortality-HR of 0.25 (95% CI: 0.09, 0.66) and OR of 0.22 (95% CI: 0.09, 0.57). There was no statistical difference in risk of ICU admissions between patients with COVID-19 who received colchicine and those who did not-OR of 0.26 (95% CI: 0.06, 1.09). CONCLUSION: Colchicine may reduce the risk of mortality in individuals with COVID-19. Further prospective investigation may further determine the efficacy of colchicine as treatment in COVID-19 patients in various care settings of the disease, including post-hospitalization and long-term care.


Subject(s)
Antiviral Agents/therapeutic use , COVID-19/drug therapy , Colchicine/therapeutic use , SARS-CoV-2/genetics , Adult , Aged , Aged, 80 and over , COVID-19/mortality , Female , Humans , Intensive Care Units , Male , Middle Aged , Patient Admission/statistics & numerical data , Polymerase Chain Reaction , Respiration, Artificial , Risk , Treatment Outcome
6.
Proc Natl Acad Sci U S A ; 119(3)2022 01 18.
Article in English | MEDLINE | ID: covidwho-1617035

ABSTRACT

COVID-19 remains a stark health threat worldwide, in part because of minimal levels of targeted vaccination outside high-income countries and highly transmissible variants causing infection in vaccinated individuals. Decades of theoretical and experimental data suggest that nonspecific effects of non-COVID-19 vaccines may help bolster population immunological resilience to new pathogens. These routine vaccinations can stimulate heterologous cross-protective effects, which modulate nontargeted infections. For example, immunization with Bacillus Calmette-Guérin, inactivated influenza vaccine, oral polio vaccine, and other vaccines have been associated with some protection from SARS-CoV-2 infection and amelioration of COVID-19 disease. If heterologous vaccine interventions (HVIs) are to be seriously considered by policy makers as bridging or boosting interventions in pandemic settings to augment nonpharmaceutical interventions and specific vaccination efforts, evidence is needed to determine their optimal implementation. Using the COVID-19 International Modeling Consortium mathematical model, we show that logistically realistic HVIs with low (5 to 15%) effectiveness could have reduced COVID-19 cases, hospitalization, and mortality in the United States fall/winter 2020 wave. Similar to other mass drug administration campaigns (e.g., for malaria), HVI impact is highly dependent on both age targeting and intervention timing in relation to incidence, with maximal benefit accruing from implementation across the widest age cohort when the pandemic reproduction number is >1.0. Optimal HVI logistics therefore differ from optimal rollout parameters for specific COVID-19 immunizations. These results may be generalizable beyond COVID-19 and the US to indicate how even minimally effective heterologous immunization campaigns could reduce the burden of future viral pandemics.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/immunology , Models, Theoretical , SARS-CoV-2/immunology , Seasons , Vaccination/methods , Algorithms , BCG Vaccine/administration & dosage , BCG Vaccine/immunology , COVID-19/epidemiology , COVID-19/virology , COVID-19 Vaccines/administration & dosage , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Pandemics/prevention & control , Patient Admission/statistics & numerical data , SARS-CoV-2/physiology , Survival Rate , United States/epidemiology , Vaccination/statistics & numerical data
7.
Sci Rep ; 11(1): 24171, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1593554

ABSTRACT

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.


Subject(s)
Algorithms , COVID-19/transmission , Cell Phone/statistics & numerical data , Hospitalization/statistics & numerical data , Models, Theoretical , Patient Admission/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Disease Transmission, Infectious/statistics & numerical data , Forecasting/methods , Geography , Hospitalization/trends , Humans , Pandemics/prevention & control , Patient Admission/trends , Retrospective Studies , SARS-CoV-2/physiology , Sweden/epidemiology , Travel/statistics & numerical data
8.
PLoS One ; 16(12): e0260006, 2021.
Article in English | MEDLINE | ID: covidwho-1581786

ABSTRACT

BACKGROUND: During the early COVID-19 pandemic travel in Uganda was tightly restricted which affected demand for and access to care for pregnant women and small and sick newborns. In this study we describe changes to neonatal outcomes in one rural central Ugandan newborn unit before and during the early phase of the COVID-19 pandemic. METHODS: We report outcomes from admissions captured in an electronic dataset of a well-established newborn unit before (September 2019 to March 2020) and during the early COVID-19 period (April-September 2020) as well as two seasonally matched periods one year prior. We report excess mortality as the percent change in mortality over what was expected based on seasonal trends. FINDINGS: The study included 2,494 patients, 567 of whom were admitted during the early COVID-19 period. During the pandemic admissions decreased by 14%. Patients born outside the facility were older on admission than previously (median 1 day of age vs. admission on the day of birth). There was an increase in admissions with birth asphyxia (22% vs. 15% of patients). Mortality was higher during COVID-19 than previously [16% vs. 11%, p = 0.017]. Patients born outside the facility had a relative increase of 55% above seasonal expected mortality (21% vs. 14%, p = 0.028). During this period patients had decreased antenatal care, restricted transport and difficulty with expenses and support. The hospital had difficulty with maternity staffing and supplies. There was significant community and staff fear of COVID-19. INTERPRETATION: Increased newborn mortality during the early COVID-19 pandemic at this facility was likely attributed to disruptions affecting maternal and newborn demand for, access to and quality of perinatal healthcare. Lockdown conditions and restrictions to public transit were significant barriers to maternal and newborn wellbeing, and require further focus by national and regional health officials.


Subject(s)
COVID-19/epidemiology , Hospitals, Rural/statistics & numerical data , Infant Mortality , Adult , Continuous Positive Airway Pressure/methods , Female , Hospitals, Rural/organization & administration , Humans , Infant , Infant, Newborn , Intensive Care Units, Neonatal/organization & administration , Intensive Care Units, Neonatal/statistics & numerical data , Maternal Age , Patient Admission/statistics & numerical data , Pregnancy , Retrospective Studies , Rural Health/statistics & numerical data , Uganda/epidemiology , Young Adult
10.
Nutrients ; 13(11)2021 Nov 16.
Article in English | MEDLINE | ID: covidwho-1574758

ABSTRACT

Hospital length of stay (LOS) is an important clinical and economic outcome and knowing its predictors could lead to better planning of resources needed during hospitalization. This analysis sought to identify structure, patient, and nutrition-related predictors of LOS available at the time of admission in the global nutritionDay dataset and to analyze variations by country for countries with n > 750. Data from 2006-2015 (n = 155,524) was utilized for descriptive and multivariable cause-specific Cox proportional hazards competing-risks analyses of total LOS from admission. Time to event analysis on 90,480 complete cases included: discharged (n = 65,509), transferred (n = 11,553), or in-hospital death (n = 3199). The median LOS was 6 days (25th and 75th percentile: 4-12). There is robust evidence that LOS is predicted by patient characteristics such as age, affected organs, and comorbidities in all three outcomes. Having lost weight in the last three months led to a longer time to discharge (Hazard Ratio (HR) 0.89; 99.9% Confidence Interval (CI) 0.85-0.93), shorter time to transfer (HR 1.40; 99.9% CI 1.24-1.57) or death (HR 2.34; 99.9% CI 1.86-2.94). The impact of having a dietician and screening patients at admission varied by country. Despite country variability in outcomes and LOS, the factors that predict LOS at admission are consistent globally.


Subject(s)
Diagnostic Tests, Routine/statistics & numerical data , Length of Stay/statistics & numerical data , Nutrition Assessment , Patient Admission/statistics & numerical data , Risk Assessment/methods , Adolescent , Adult , Aged , Aged, 80 and over , Diagnostic Tests, Routine/methods , Female , Hospital Mortality , Humans , Male , Middle Aged , Nutritional Status , Predictive Value of Tests , Proportional Hazards Models , Time Factors , Young Adult
13.
PLoS One ; 16(11): e0260169, 2021.
Article in English | MEDLINE | ID: covidwho-1526694

ABSTRACT

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide, and several sociodemographic variables, comorbidities and care variables have been associated with complications and mortality. OBJECTIVE: To identify the factors associated with admission to intensive care units (ICUs) and mortality in patients with COVID-19 from 4 clinics in Colombia. METHODS: This was a follow-up study of a cohort of patients diagnosed with COVID-19 between March and August 2020. Sociodemographic, clinical (Charlson comorbidity index and NEWS 2 score) and pharmacological variables were identified. Multivariate analyses were performed to identify variables associated with the risk of admission to the ICU and death (p<0.05). RESULTS: A total of 780 patients were analyzed, with a median age of 57.0 years; 61.2% were male. On admission, 54.9% were classified as severely ill, 65.3% were diagnosed with acute respiratory distress syndrome, 32.4% were admitted to the ICU, and 26.0% died. The factors associated with a greater likelihood of ICU admission were severe pneumonia (OR: 9.86; 95%CI:5.99-16.23), each 1-point increase in the NEWS 2 score (OR:1.09; 95%CI:1.002-1.19), history of ischemic heart disease (OR:3.24; 95%CI:1.16-9.00), and chronic obstructive pulmonary disease (OR:2.07; 95%CI:1.09-3.90). The risk of dying increased in those older than 65 years (OR:3.08; 95%CI:1.66-5.71), in patients with acute renal failure (OR:6.96; 95%CI:4.41-11.78), admitted to the ICU (OR:6.31; 95%CI:3.63-10.95), and for each 1-point increase in the Charlson comorbidity index (OR:1.16; 95%CI:1.002-1.35). CONCLUSIONS: Factors related to increasing the probability of requiring ICU care or dying in patients with COVID-19 were identified, facilitating the development of anticipatory intervention measures that favor comprehensive care and improve patient prognosis.


Subject(s)
COVID-19/epidemiology , Hospital Mortality/trends , Intensive Care Units/statistics & numerical data , Patient Admission/statistics & numerical data , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/therapy , Colombia , Comorbidity , Female , Humans , Male , Middle Aged , Myocardial Ischemia/epidemiology , Pulmonary Disease, Chronic Obstructive/epidemiology , Renal Insufficiency/epidemiology , Sex Factors
14.
Crit Care ; 25(1): 399, 2021 11 17.
Article in English | MEDLINE | ID: covidwho-1523316

ABSTRACT

BACKGROUND: The coronavirus disease-19 (COVID-19) pandemic had a relatively minimal direct impact on critical illness in children compared to adults. However, children and paediatric intensive care units (PICUs) were affected indirectly. We analysed the impact of the pandemic on PICU admission patterns and patient characteristics in the UK and Ireland. METHODS: We performed a retrospective cohort study of all admissions to PICUs in children < 18 years during Jan-Dec 2020, using data collected from 32 PICUs via a central database (PICANet). Admission patterns, case-mix, resource use, and outcomes were compared with the four preceding years (2016-2019) based on the date of admission. RESULTS: There were 16,941 admissions in 2020 compared to an annual average of 20,643 (range 20,340-20,868) from 2016 to 2019. During 2020, there was a reduction in all PICU admissions (18%), unplanned admissions (20%), planned admissions (15%), and bed days (25%). There was a 41% reduction in respiratory admissions, and a 60% reduction in children admitted with bronchiolitis but an 84% increase in admissions for diabetic ketoacidosis during 2020 compared to the previous years. There were 420 admissions (2.4%) with either PIMS-TS or COVID-19 during 2020. Age and sex adjusted prevalence of unplanned PICU admission reduced from 79.7 (2016-2019) to 63.1 per 100,000 in 2020. Median probability of death [1.2 (0.5-3.4) vs. 1.2 (0.5-3.4) %], length of stay [2.3 (1.0-5.5) vs. 2.4 (1.0-5.7) days] and mortality rates [3.4 vs. 3.6%, (risk-adjusted OR 1.00 [0.91-1.11, p = 0.93])] were similar between 2016-2019 and 2020. There were 106 fewer in-PICU deaths in 2020 (n = 605) compared with 2016-2019 (n = 711). CONCLUSIONS: The use of a high-quality international database allowed robust comparisons between admission data prior to and during the COVID-19 pandemic. A significant reduction in prevalence of unplanned admissions, respiratory diseases, and fewer child deaths in PICU observed may be related to the targeted COVID-19 public health interventions during the pandemic. However, analysis of wider and longer-term societal impact of the pandemic and public health interventions on physical and mental health of children is required.


Subject(s)
COVID-19/epidemiology , Intensive Care Units, Pediatric/statistics & numerical data , Pandemics , Patient Admission/statistics & numerical data , Child , Humans , Ireland/epidemiology , Retrospective Studies , United Kingdom/epidemiology
16.
Crit Care Med ; 49(12): 2033-2041, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1522364

ABSTRACT

OBJECTIVES: To characterize the impact of public health interventions on the volume and characteristics of admissions to the PICU. DESIGN: Multicenter retrospective cohort study. SETTING: Six U.S. referral PICUs during February 15, 2020-May 14, 2020, compared with the same months during 2017-2019 (baseline). PATIENTS: PICU admissions excluding admissions for illnesses due to severe acute respiratory syndrome coronavirus 2 and readmissions during the same hospitalization. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome was admission volumes during the period of stay-at-home orders (March 15, 2020-May 14, 2020) compared with baseline. Secondary outcomes were hospitalization characteristics including advanced support (e.g., invasive mechanical ventilation), PICU and hospital lengths of stay, and mortality. We used generalized linear mixed modeling to compare patient and admission characteristics during the stay-at-home orders period to baseline. We evaluated 7,960 admissions including 1,327 during March 15, 2020-May 14, 2020. Daily admissions and patients days were lower during the period of stay-at-home orders compared with baseline: median admissions 21 (interquartile range, 17-25) versus 36 (interquartile range, 30-42) (p < 0.001) and median patient days 93.0 (interquartile range, 55.9-136.7) versus 143.6 (interquartile range, 108.5-189.2) (p < 0.001). Admissions during the period of stay-at-home orders were less common in young children and for respiratory and infectious illnesses and more common for poisonings, endocrinopathies and for children with race/ethnicity categorized as other/unspecified. There were no differences in hospitalization characteristics except fewer patients received noninvasive ventilation during the period of stay-at-home orders. CONCLUSIONS: Reductions in PICU admissions suggest that much of pediatric critical illness in younger children and for respiratory and infectious illnesses may be preventable through targeted public health strategies.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/statistics & numerical data , Intensive Care Units, Pediatric/statistics & numerical data , Patient Admission/statistics & numerical data , Adolescent , Age Factors , Child , Child, Preschool , Female , Humans , Infant , Length of Stay , Male , Pandemics , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Socioeconomic Factors , Young Adult
17.
Crit Care Med ; 49(12): 2042-2057, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1522362

ABSTRACT

OBJECTIVE: Coronavirus disease 2019 is a heterogeneous disease most frequently causing respiratory tract infection, which can induce respiratory failure and multiple organ dysfunction syndrome in its severe forms. The prevalence of coronavirus disease 2019-related sepsis is still unclear; we aimed to describe this in a systematic review. DATA SOURCES: MEDLINE (PubMed), Cochrane, and Google Scholar databases were searched based on a prespecified protocol (International Prospective Register for Systematic Reviews: CRD42020202018). STUDY SELECTION: Studies reporting on patients with confirmed coronavirus disease 2019 diagnosed with sepsis according to sepsis-3 or according to the presence of infection-related organ dysfunctions necessitating organ support/replacement were included in the analysis. The primary end point was prevalence of coronavirus disease 2019-related sepsis among adults hospitalized in the ICU and the general ward. Among secondary end points were the need for ICU admission among patients initially hospitalized in the general ward and the prevalence of new onset of organ dysfunction in the ICU. Outcomes were expressed as proportions with respective 95% CI. DATA EXTRACTION: Two reviewers independently screened and reviewed existing literature and assessed study quality with the Newcastle-Ottawa Scale and the Methodological index for nonrandomized studies. DATA SYNTHESIS: Of 3,825 articles, 151 were analyzed, only five of which directly reported sepsis prevalence. Noting the high heterogeneity observed, coronavirus disease 2019-related sepsis prevalence was 77.9% (95% CI, 75.9-79.8; I2 = 91%; 57 studies) in the ICU, and 33.3% (95% CI, 30.3-36.4; I2 = 99%; 86 studies) in the general ward. ICU admission was required for 17.7% (95% CI, 12.9-23.6; I2 = 100%) of ward patients. Acute respiratory distress syndrome was the most common organ dysfunction in the ICU (87.5%; 95% CI, 83.3-90.7; I2 = 98%). CONCLUSIONS: The majority of coronavirus disease 2019 patients hospitalized in the ICU meet Sepsis-3 criteria and present infection-associated organ dysfunction. The medical and scientific community should be aware and systematically report viral sepsis for prognostic and treatment implications.


Subject(s)
COVID-19/complications , Hospitalization/statistics & numerical data , Sepsis/etiology , Sepsis/virology , Humans , Intensive Care Units/statistics & numerical data , Multiple Organ Failure/etiology , Patient Admission/statistics & numerical data , SARS-CoV-2 , Sepsis/mortality , Severity of Illness Index
19.
Clin Res Cardiol ; 111(3): 343-354, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1516853

ABSTRACT

BACKGROUND: COVID-19 has been associated with a high prevalence of myocardial injury and increased cardiovascular morbidity. Copeptin, a marker of vasopressin release, has been previously established as a risk marker in both infectious and cardiovascular disease. METHODS: This prospective, observational study of patients with laboratory-confirmed COVID-19 infection was conducted from June 6th to November 26th, 2020 in a tertiary care hospital. Copeptin and high-sensitive cardiac troponin I (hs-cTnI) levels on admission were collected and tested for their association with the primary composite endpoint of ICU admission or 28-day mortality. RESULTS: A total of 213 eligible patients with COVID-19 were included of whom 55 (25.8%) reached the primary endpoint. Median levels of copeptin and hs-cTnI at admission were significantly higher in patients with an adverse outcome (Copeptin 29.6 pmol/L, [IQR, 16.2-77.8] vs 17.2 pmol/L [IQR, 7.4-41.0] and hs-cTnI 22.8 ng/L [IQR, 11.5-97.5] vs 10.2 ng/L [5.5-23.1], P < 0.001 respectively). ROC analysis demonstrated an optimal cut-off of 19.3 pmol/L for copeptin and 16.8 ng/L for hs-cTnI and an increase of either biomarker was significantly associated with the primary endpoint. The combination of raised hs-cTnI and copeptin yielded a superior prognostic value to individual measurement of biomarkers and was a strong prognostic marker upon multivariable logistic regression analysis (OR 4.274 [95% CI, 1.995-9.154], P < 0.001). Addition of copeptin and hs-cTnI to established risk models improved C-statistics and net reclassification indices. CONCLUSION: The combination of raised copeptin and hs-cTnI upon admission is an independent predictor of ICU admission or 28-day mortality in hospitalized patients with COVID-19.


Subject(s)
COVID-19/blood , COVID-19/mortality , Glycopeptides/blood , Patient Admission/statistics & numerical data , Troponin I/blood , Aged , Biomarkers/blood , Female , Hospital Mortality , Humans , Intensive Care Units , Male , Middle Aged , Predictive Value of Tests , Prognosis , Prospective Studies , ROC Curve , SARS-CoV-2
20.
Lancet ; 399(10320): 152-160, 2022 01 08.
Article in English | MEDLINE | ID: covidwho-1506422

ABSTRACT

BACKGROUND: In the USA, COVID-19 vaccines became available in mid-December, 2020, with adults aged 65 years and older among the first groups prioritised for vaccination. We estimated the national-level impact of the initial phases of the US COVID-19 vaccination programme on COVID-19 cases, emergency department visits, hospital admissions, and deaths among adults aged 65 years and older. METHODS: We analysed population-based data reported to US federal agencies on COVID-19 cases, emergency department visits, hospital admissions, and deaths among adults aged 50 years and older during the period Nov 1, 2020, to April 10, 2021. We calculated the relative change in incidence among older age groups compared with a younger reference group for pre-vaccination and post-vaccination periods, defined by the week when vaccination coverage in a given age group first exceeded coverage in the reference age group by at least 1%; time lags for immune response and time to outcome were incorporated. We assessed whether the ratio of these relative changes differed when comparing the pre-vaccination and post-vaccination periods. FINDINGS: The ratio of relative changes comparing the change in the COVID-19 case incidence ratio over the post-vaccine versus pre-vaccine periods showed relative decreases of 53% (95% CI 50 to 55) and 62% (59 to 64) among adults aged 65 to 74 years and 75 years and older, respectively, compared with those aged 50 to 64 years. We found similar results for emergency department visits with relative decreases of 61% (52 to 68) for adults aged 65 to 74 years and 77% (71 to 78) for those aged 75 years and older compared with adults aged 50 to 64 years. Hospital admissions declined by 39% (29 to 48) among those aged 60 to 69 years, 60% (54 to 66) among those aged 70 to 79 years, and 68% (62 to 73), among those aged 80 years and older, compared with adults aged 50 to 59 years. COVID-19 deaths also declined (by 41%, 95% CI -14 to 69 among adults aged 65-74 years and by 30%, -47 to 66 among those aged ≥75 years, compared with adults aged 50 to 64 years), but the magnitude of the impact of vaccination roll-out on deaths was unclear. INTERPRETATION: The initial roll-out of the US COVID-19 vaccination programme was associated with reductions in COVID-19 cases, emergency department visits, and hospital admissions among older adults. FUNDING: None.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/epidemiology , Emergency Service, Hospital/statistics & numerical data , Mortality/trends , Patient Admission/statistics & numerical data , Aged , Aged, 80 and over , Female , Hospitals , Humans , Incidence , Male , United States/epidemiology , Vaccination/statistics & numerical data
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