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1.
Trials ; 22(1): 127, 2021 Feb 10.
Article in English | MEDLINE | ID: covidwho-1629960

ABSTRACT

OBJECTIVES: The objective of the study is to measure the efficacy of ionic-iodine polymer complex [1] for clinical and radiological improvement in coronavirus disease 2019 (COVID-19) patients. TRIAL DESIGN: The trial will be closed label, randomized and placebo-controlled with a 1:1:1:1 allocation ratio and superiority framework. PARTICIPANTS: All PCR confirmed COVID-19 adult patients including non-pregnant females, with mild to moderate disease, will be enrolled from Shaikh Zayed Post-Graduate Medical Complex, Ali Clinic and Doctors Lounge in Lahore (Pakistan). Patients with any pre-existing chronic illness will be excluded from the study. INTERVENTION AND COMPARATOR: In this multi-armed study ionic-iodine polymer complex with 200 mg of elemental iodine will be given using three formulations to evaluate efficacy. Patients will be receiving either encapsulated iodine complex of 200 mg (arm A), iodine complex syrup form 40 ml (arm B), iodine complex throat spray of 2 puffs (arm C) or empty capsule (arm D) as placebo; all three times a day. All the 4 arms will be receiving standard care as per version 3.0 of the clinical management guidelines for COVID-19 established by the Ministry of National Health Services of Pakistan. MAIN OUTCOMES: Primary outcomes will be viral clearance with radiological and clinical improvement. SARS-CoV-2 RT-PCR and HRCT chest scans will be done on the admission day and then after every fourth day for 12 days or till the symptoms are resolved. RT-PCR will only be shown as positive or negative while HRCT chest scoring will be done depending on the area and severity of lung involvement [2]. Time taken for the alleviation of symptoms will be calculated by the number of days the patient remained symptomatic. 30-day mortality will be considered as a secondary outcome. RANDOMISATION: Stratification for initial COVID-19 status (or days from initial symptoms as a proxy), age groups, gender, baseline severity of symptoms and co-morbidities will be used to ensure that the study arms remain balanced in size for the 1:1:1:1 allocation ratio. Randomization will be done using the lottery method. As patients are being admitted at different times, they will be recruited after obtaining their voluntary written informed consent following all standard protocols of the infection, control and disinfection. BLINDING (MASKING): This is a quadruple (participants, care providers, investigators and outcomes assessors) blinded study where only the study's Primary Investigator will have information about the arms and their interventions. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): 200 patients will be randomized into four groups with three experimental and one placebo arm. TRIAL STATUS: Protocol Version Number is 2.3 and it is approved from IRB Shaikh Zayed Hospital with ID SZMC/IRB/Internal0056/2020 on July 14th, 2020. The recruitment is in progress. It was started on July 30, 2020, and the estimated end date for the trial is August 15, 2021. TRIAL REGISTRATION: Clinical Trial has been retrospectively registered on www.clinicaltrials.gov with registration ID NCT04473261 dated July 16, 2020. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). With the intention of expediting dissemination of this trial, the conventional formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines.


Subject(s)
COVID-19/drug therapy , Iodine Compounds/administration & dosage , Polymers/administration & dosage , SARS-CoV-2/genetics , Severity of Illness Index , Adult , COVID-19/epidemiology , COVID-19/mortality , Capsules , Female , Humans , Male , Oral Sprays , Pakistan/epidemiology , Patient Admission , Randomized Controlled Trials as Topic , Reverse Transcriptase Polymerase Chain Reaction , Treatment Outcome
2.
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
3.
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
4.
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
5.
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
6.
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
7.
Ann Med ; 53(1): 337-344, 2021 12.
Article in English | MEDLINE | ID: covidwho-1575678

ABSTRACT

BACKGROUND: To minimise the risk of COVID-19 transmission, an ambulant screening protocol for COVID-19 in patients before admission to the hospital was implemented, combining the SARS CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR) on a nasopharyngeal swab, a chest computed tomography (CT) and assessment of clinical symptoms. The aim of this study was to evaluatethe diagnostic yield and the proportionality of this pre-procedural screeningprotocol. METHODS: In this mono-centre, prospective, cross-sectional study, all patients admitted to the hospital between 22nd April 2020 until 14th May 2020 for semi-urgent surgery, haematological or oncological treatment, or electrophysiological investigationunderwent a COVID-19 screening 2 days before their procedure. At a 2-week follow-up, the presence of clinical symptoms was evaluated by telephone as a post-hoc evaluation of the screening approach.Combined positive RT-PCR assay and/or positive chest CT was used as gold standard. Post-procedural outcomes of all patients diagnosed positive for COVID-19 were assessed. RESULTS: In total,528 patients were included of which 20 (3.8%) were diagnosed as COVID-19 positive and 508 (96.2%) as COVID-19 negative. 11 (55.0%) of COVID-19 positive patients had only a positive RT-PCR assay, 3 (15.0%) had only a positive chest CT and 6 (30%) had both a positive RT-PCR assay and chest CT. 10 out of 20 (50.0%) COVID-19 positive patients reported no single clinical symptom at the screening. At 2 week follow-up, 50% of these patients were still asymptomatic. 37.5% of all COVID-19 negative patients were symptomatic at screening. In the COVID-19 negative group without symptoms at screening, 78 (29.3%) patients developed clinical symptoms at a 2-week follow-up. CONCLUSION: This study suggests that routine chest CT and assessment of self-reported symptoms have limited value in the preprocedural COVID-19 screening due to low sensitivity and/or specificity.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Mass Screening/methods , Patient Admission , Adult , Aged , COVID-19/epidemiology , Cross-Sectional Studies , Female , Follow-Up Studies , Humans , Male , Middle Aged , Prospective Studies , Reverse Transcriptase Polymerase Chain Reaction , Sensitivity and Specificity , Tomography, X-Ray Computed
9.
J Med Internet Res ; 23(2): e24246, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1573886

ABSTRACT

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.


Subject(s)
COVID-19/physiopathology , Hospitalization , Intubation, Intratracheal/statistics & numerical data , Machine Learning , Respiration, Artificial/statistics & numerical data , Respiratory Insufficiency/epidemiology , Aged , COVID-19/complications , Clinical Decision Rules , Early Warning Score , Emergency Service, Hospital , Female , Hospitals , Humans , Logistic Models , Male , Middle Aged , Patient Admission , ROC Curve , Respiratory Insufficiency/etiology , Retrospective Studies , SARS-CoV-2 , Triage
11.
Lancet Diabetes Endocrinol ; 9(10): 671-680, 2021 10.
Article in English | MEDLINE | ID: covidwho-1531932

ABSTRACT

BACKGROUND: Diabetic ketoacidosis (DKA) has been reported to be increasing in frequency during the COVID-19 pandemic. We aimed to examine the rates of DKA hospital admissions and the patient demographics associated with DKA during the pandemic compared with in prepandemic years. METHODS: Using a comprehensive, multiethnic, national dataset, the Secondary Uses Service repository, we extracted all emergency hospital admissions in England coded with DKA from March 1 to June 30, 2020 (first wave of the pandemic), July 1 to Oct 31, 2020 (post-first wave), and Nov 1, 2020, to Feb 28, 2021 (second wave), and compared these with DKA admissions in the equivalent periods in 2017-20. We also examined baseline characteristics, mortality, and trends in patients who were admitted with DKA. FINDINGS: There were 8553 admissions coded with DKA during the first wave, 8729 during the post-first wave, and 10 235 during the second wave. Compared with preceding years, DKA admissions were 6% (95% CI 4-9; p<0·0001) higher in the first wave of the pandemic (from n=8048), 6% (3-8; p<0·0001) higher in the post-first wave (from n=8260), and 7% (4-9; p<0·0001) higher in the second wave (from n=9610). In the first wave, DKA admissions reduced by 19% (95% CI 16-21) in those with pre-existing type 1 diabetes (from n=4965 to n=4041), increased by 41% (35-47) in those with pre-existing type 2 diabetes (from n=2010 to n=2831), and increased by 57% (48-66) in those with newly diagnosed diabetes (from n=1072 to n=1681). Compared with prepandemic, type 2 diabetes DKA admissions were similarly common in older individuals and men but were higher in those of non-White ethnicities during the first wave. The increase in newly diagnosed DKA admissions occurred across all age groups and these were significantly increased in men and people of non-White ethnicities. In the post-first wave, DKA admissions did not return to the baseline level of previous years; DKA admissions were 14% (11-17) lower in patients with type 1 diabetes (from n=5208 prepandemic to n=4491), 30% (24-36) higher in patients with type 2 diabetes (from n=2011 to n=2613), and 56% (47-66) higher in patients with newly diagnosed diabetes (from n=1041 to n=1625). During the second wave, DKA admissions were 25% (22-27) lower in patients with type 1 diabetes (from n=5769 prepandemic to n=4337), 50% (44-56) higher in patients with type 2 diabetes (from n=2608 to n=3912), and 61% (52-70) higher in patients with newly diagnosed diabetes (from n=1234 to n=1986). INTERPRETATION: Our results provide evidence for differences in the numbers and characteristics of people presenting with DKA during the COVID-19 pandemic compared with in the preceding 3 years. Greater awareness of risk factors for DKA in type 2 diabetes and vigilance for newly diagnosed diabetes presenting with DKA during the COVID-19 pandemic might help mitigate the increased impact of DKA. FUNDING: None.


Subject(s)
COVID-19/epidemiology , Diabetes Mellitus, Type 2/epidemiology , Diabetic Ketoacidosis/epidemiology , Emergency Service, Hospital/trends , Patient Admission/trends , Population Surveillance , Adolescent , Adult , Aged , COVID-19/prevention & control , Databases, Factual/trends , Diabetes Mellitus, Type 2/therapy , Diabetic Ketoacidosis/therapy , England/epidemiology , Female , Humans , Male , Middle Aged , Population Surveillance/methods , Time Factors , Young Adult
13.
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
15.
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
16.
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
17.
Sci Rep ; 11(1): 21807, 2021 11 08.
Article in English | MEDLINE | ID: covidwho-1506761

ABSTRACT

In this study, we compare the predictive value of clinical scoring systems that are already in use in patients with Coronavirus disease 2019 (COVID-19), including the Brescia-COVID Respiratory Severity Scale (BCRSS), Quick SOFA (qSOFA), Sequential Organ Failure Assessment (SOFA), Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension, and Age (MuLBSTA) and scoring system for reactive hemophagocytic syndrome (HScore), for determining the severity of the disease. Our aim in this study is to determine which scoring system is most useful in determining disease severity and to guide clinicians. We classified the patients into two groups according to the stage of the disease (severe and non-severe) and adopted interim guidance of the World Health Organization. Severe cases were divided into a group of surviving patients and a deceased group according to the prognosis. According to admission values, the BCRSS, qSOFA, SOFA, MuLBSTA, and HScore were evaluated at admission using the worst parameters available in the first 24 h. Of the 417 patients included in our study, 46 (11%) were in the severe group, while 371 (89%) were in the non-severe group. Of these 417 patients, 230 (55.2%) were men. The median (IQR) age of all patients was 44 (25) years. In multivariate logistic regression analyses, BRCSS in the highest tertile (HR 6.1, 95% CI 2.105-17.674, p = 0.001) was determined as an independent predictor of severe disease in cases of COVID-19. In multivariate analyses, qSOFA was also found to be an independent predictor of severe COVID-19 (HR 4.757, 95% CI 1.438-15.730, p = 0.011). The area under the curve (AUC) of the BRCSS, qSOFA, SOFA, MuLBSTA, and HScore was 0.977, 0.961, 0.958, 0.860, and 0.698, respectively. Calculation of the BRCSS and qSOFA at the time of hospital admission can predict critical clinical outcomes in patients with COVID-19, and their predictive value is superior to that of HScore, MuLBSTA, and SOFA. Our prediction is that early interventions for high-risk patients, with early identification of high-risk group using BRCSS and qSOFA, may improve clinical outcomes in COVID-19.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/immunology , Adult , Aged , Area Under Curve , Coinfection/diagnosis , Female , Hospital Mortality , Humans , Intensive Care Units , Lymphocytosis , Male , Middle Aged , Observer Variation , Organ Dysfunction Scores , Patient Admission , Predictive Value of Tests , Prognosis , Regression Analysis , Respiration , Respiration Disorders , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Smoking , Treatment Outcome
18.
Clin Appl Thromb Hemost ; 27: 10760296211051712, 2021.
Article in English | MEDLINE | ID: covidwho-1495925

ABSTRACT

BACKGROUND: Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) pandemic, there have been many reports of increased incidence of venous thromboembolism and arterial events as a complication. OBJECTIVE: To determine the incidence of symptomatic thrombotic events (TEs) in patients hospitalized for SARS-CoV2 disease (coronavirus 19 [Covid-19]). METHODS: A retrospective single-center cohort study with adult patients with a positive reverse transcriptase-polymerase chain reaction (rt-PCR) for SARS-CoV2, included from the date of diagnosis of Covid-19 and followed for 90 days or until death. RESULTS: A total of 1621 patients were included in this study. The median age was 73 years (interquartile range25th-75th [IQR] 53-87 years) and 57% (913) were female. Overall mortality was 21.6% (348). The overall incidence of symptomatic TEs within 90 days of diagnosis was 1.8% (30 of 1621) occurring in 28 patients, including an incidence of pulmonary embolism of 0.9% (15, 95% confidence interval [CI] 0.60%-1.6%), deep venous thrombosis of 0.61% (10, 95% CI 0.2%-1%), ischemic stroke of 0.25% (4, 95% CI 0.09%-0.65%), and ischemic arterial events of 0.06% (1, 95% CI 0.008%-0.43%). No acute coronary syndrome events were recorded. The incidence of symptomatic TEs was significantly lower in the general ward than in intensive care units (1.2% vs 5.7%; p < .001). The median time since positive rt-PCR for SARS-CoV2 to symptomatic TE was 22.5 days (IQR 19-43 days). There was no significant difference in the proportion of patients receiving (53.6%) and not receiving thromboprophylaxis (66.5%) and the development of TEs. CONCLUSION: The overall incidence of symptomatic TEs among these patients was lower than the incidence previously reported.


Subject(s)
Arterial Occlusive Diseases/epidemiology , COVID-19/epidemiology , Pulmonary Embolism/epidemiology , Thromboembolism/epidemiology , Venous Thrombosis/epidemiology , Aged , Aged, 80 and over , Argentina/epidemiology , Arterial Occlusive Diseases/blood , Arterial Occlusive Diseases/diagnosis , COVID-19/blood , COVID-19/diagnosis , Female , Humans , Incidence , Ischemic Stroke/blood , Ischemic Stroke/diagnosis , Ischemic Stroke/epidemiology , Male , Middle Aged , Patient Admission , Pulmonary Embolism/blood , Pulmonary Embolism/diagnosis , Retrospective Studies , Thromboembolism/blood , Thromboembolism/diagnosis , Time Factors , Venous Thrombosis/blood , Venous Thrombosis/diagnosis
19.
Emerg Med J ; 38(12): 901-905, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1495501

ABSTRACT

OBJECTIVE: Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation. METHODS: Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation. RESULTS: In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively. CONCLUSION: In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.


Subject(s)
COVID-19/diagnosis , Critical Illness , Early Warning Score , Adult , Aged , Aged, 80 and over , COVID-19/classification , Female , Humans , Intensive Care Units , Male , Middle Aged , Patient Admission , Predictive Value of Tests , ROC Curve , Triage
20.
Lancet ; 398(10313): 1825-1835, 2021 11 13.
Article in English | MEDLINE | ID: covidwho-1492790

ABSTRACT

BACKGROUND: England's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories. METHODS: This mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions. FINDINGS: The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69-83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500-5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700-1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness. INTERPRETATION: Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures. FUNDING: National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control/organization & administration , SARS-CoV-2 , Vaccination Coverage/organization & administration , COVID-19/epidemiology , COVID-19/mortality , England/epidemiology , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Humans , Models, Theoretical , Patient Admission/statistics & numerical data
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