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Ann Med ; 53(1): 151-159, 2021 12.
Article in English | MEDLINE | ID: covidwho-1574907


OBJECTIVE: To utilize publicly reported, state-level data to identify factors associated with the frequency of cases, tests, and mortality in the USA. MATERIALS AND METHODS: Retrospective study using publicly reported data collected included the number of COVID-19 cases, tests and mortality from March 14th through April 30th. Publicly available state-level data was collected which included: demographics comorbidities, state characteristics and environmental factors. Univariate and multivariate regression analyses were performed to identify the significantly associated factors with percent mortality, case and testing frequency. All analyses were state-level analyses and not patient-level analyses. RESULTS: A total of 1,090,500 COVID-19 cases were reported during the study period. The calculated case and testing frequency were 3332 and 19,193 per 1,000,000 patients. There were 63,642 deaths during this period which resulted in a mortality of 5.8%. Factors including to but not limited to population density (beta coefficient 7.5, p < .01), transportation volume (beta coefficient 0.1, p < .01), tourism index (beta coefficient -0.1, p = .02) and older age (beta coefficient 0.2, p = .01) are associated with case frequency and percent mortality. CONCLUSIONS: There were wide variations in testing and case frequencies of COVID-19 among different states in the US. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality. Key messages There were wide variations in testing and case frequencies of COVID-19 among different states in the USA. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality.

Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/mortality , Pneumonia, Viral/mortality , COVID-19 , COVID-19 Testing , Comorbidity , Coronavirus Infections/diagnosis , Female , Healthcare Disparities , Humans , Male , Pandemics , Pneumonia, Viral/diagnosis , United States/epidemiology
Epidemiol Infect ; 148: e168, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-1537262


This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.

Coronavirus Infections/mortality , Coronavirus Infections/therapy , Logistic Models , Machine Learning , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Adolescent , Adult , Aged , COVID-19 , China/epidemiology , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Prognosis , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment/methods , Young Adult
Can J Anaesth ; 67(9): 1217-1248, 2020 09.
Article in English | MEDLINE | ID: covidwho-1536371


PURPOSE: We conducted two World Health Organization-commissioned reviews to inform use of high-flow nasal cannula (HFNC) in patients with coronavirus disease (COVID-19). We synthesized the evidence regarding efficacy and safety (review 1), as well as risks of droplet dispersion, aerosol generation, and associated transmission (review 2) of viral products. SOURCE: Literature searches were performed in Ovid MEDLINE, Embase, Web of Science, Chinese databases, and medRxiv. Review 1: we synthesized results from randomized-controlled trials (RCTs) comparing HFNC to conventional oxygen therapy (COT) in critically ill patients with acute hypoxemic respiratory failure. Review 2: we narratively summarized findings from studies evaluating droplet dispersion, aerosol generation, or infection transmission associated with HFNC. For both reviews, paired reviewers independently conducted screening, data extraction, and risk of bias assessment. We evaluated certainty of evidence using GRADE methodology. PRINCIPAL FINDINGS: No eligible studies included COVID-19 patients. Review 1: 12 RCTs (n = 1,989 patients) provided low-certainty evidence that HFNC may reduce invasive ventilation (relative risk [RR], 0.85; 95% confidence interval [CI], 0.74 to 0.99) and escalation of oxygen therapy (RR, 0.71; 95% CI, 0.51 to 0.98) in patients with respiratory failure. Results provided no support for differences in mortality (moderate certainty), or in-hospital or intensive care length of stay (moderate and low certainty, respectively). Review 2: four studies evaluating droplet dispersion and three evaluating aerosol generation and dispersion provided very low certainty evidence. Two simulation studies and a crossover study showed mixed findings regarding the effect of HFNC on droplet dispersion. Although two simulation studies reported no associated increase in aerosol dispersion, one reported that higher flow rates were associated with increased regions of aerosol density. CONCLUSIONS: High-flow nasal cannula may reduce the need for invasive ventilation and escalation of therapy compared with COT in COVID-19 patients with acute hypoxemic respiratory failure. This benefit must be balanced against the unknown risk of airborne transmission.

Coronavirus Infections/therapy , Oxygen Inhalation Therapy/methods , Pneumonia, Viral/therapy , Respiratory Insufficiency/therapy , Aerosols , COVID-19 , Cannula , Coronavirus Infections/complications , Coronavirus Infections/mortality , Humans , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/mortality , Randomized Controlled Trials as Topic , Respiratory Insufficiency/physiopathology , Respiratory Insufficiency/virology
Int J Mol Sci ; 22(21)2021 Nov 01.
Article in English | MEDLINE | ID: covidwho-1488619


The COVID-19 pandemic caused by SARS-CoV-2 infection poses a serious threat to global public health and the economy. The enzymatic product of cholesterol 25-hydroxylase (CH25H), 25-Hydroxycholesterol (25-HC), was reported to have potent anti-SARS-CoV-2 activity. Here, we found that the combination of 25-HC with EK1 peptide, a pan-coronavirus (CoV) fusion inhibitor, showed a synergistic antiviral activity. We then used the method of 25-HC modification to design and synthesize a series of 25-HC-modified peptides and found that a 25-HC-modified EK1 peptide (EK1P4HC) was highly effective against infections caused by SARS-CoV-2, its variants of concern (VOCs), and other human CoVs, such as HCoV-OC43 and HCoV-229E. EK1P4HC could protect newborn mice from lethal HCoV-OC43 infection, suggesting that conjugation of 25-HC with a peptide-based viral inhibitor was a feasible and universal strategy to improve its antiviral activity.

Antiviral Agents/pharmacology , Hydroxycholesterols/chemistry , Lipopeptides/chemistry , SARS-CoV-2/drug effects , Amino Acid Sequence , Animals , Antiviral Agents/chemistry , Antiviral Agents/therapeutic use , Body Weight/drug effects , COVID-19/drug therapy , COVID-19/virology , Coronavirus 229E, Human/drug effects , Coronavirus 229E, Human/pathogenicity , Coronavirus Infections/drug therapy , Coronavirus Infections/mortality , Coronavirus Infections/virology , Coronavirus OC43, Human/drug effects , Coronavirus OC43, Human/pathogenicity , Disease Models, Animal , Drug Synergism , Humans , Hydroxycholesterols/pharmacology , Hydroxycholesterols/therapeutic use , Lipopeptides/pharmacology , Lipopeptides/therapeutic use , Mice , Mice, Inbred BALB C , Polyethylene Glycols/chemistry , SARS-CoV-2/isolation & purification , SARS-CoV-2/physiology , Survival Rate , Virus Internalization/drug effects
JAMA ; 323(16): 1574-1581, 2020 04 28.
Article in English | MEDLINE | ID: covidwho-1453471


Importance: In December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) emerged in China and has spread globally, creating a pandemic. Information about the clinical characteristics of infected patients who require intensive care is limited. Objective: To characterize patients with coronavirus disease 2019 (COVID-19) requiring treatment in an intensive care unit (ICU) in the Lombardy region of Italy. Design, Setting, and Participants: Retrospective case series of 1591 consecutive patients with laboratory-confirmed COVID-19 referred for ICU admission to the coordinator center (Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy) of the COVID-19 Lombardy ICU Network and treated at one of the ICUs of the 72 hospitals in this network between February 20 and March 18, 2020. Date of final follow-up was March 25, 2020. Exposures: SARS-CoV-2 infection confirmed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasal and pharyngeal swabs. Main Outcomes and Measures: Demographic and clinical data were collected, including data on clinical management, respiratory failure, and patient mortality. Data were recorded by the coordinator center on an electronic worksheet during telephone calls by the staff of the COVID-19 Lombardy ICU Network. Results: Of the 1591 patients included in the study, the median (IQR) age was 63 (56-70) years and 1304 (82%) were male. Of the 1043 patients with available data, 709 (68%) had at least 1 comorbidity and 509 (49%) had hypertension. Among 1300 patients with available respiratory support data, 1287 (99% [95% CI, 98%-99%]) needed respiratory support, including 1150 (88% [95% CI, 87%-90%]) who received mechanical ventilation and 137 (11% [95% CI, 9%-12%]) who received noninvasive ventilation. The median positive end-expiratory pressure (PEEP) was 14 (IQR, 12-16) cm H2O, and Fio2 was greater than 50% in 89% of patients. The median Pao2/Fio2 was 160 (IQR, 114-220). The median PEEP level was not different between younger patients (n = 503 aged ≤63 years) and older patients (n = 514 aged ≥64 years) (14 [IQR, 12-15] vs 14 [IQR, 12-16] cm H2O, respectively; median difference, 0 [95% CI, 0-0]; P = .94). Median Fio2 was lower in younger patients: 60% (IQR, 50%-80%) vs 70% (IQR, 50%-80%) (median difference, -10% [95% CI, -14% to 6%]; P = .006), and median Pao2/Fio2 was higher in younger patients: 163.5 (IQR, 120-230) vs 156 (IQR, 110-205) (median difference, 7 [95% CI, -8 to 22]; P = .02). Patients with hypertension (n = 509) were older than those without hypertension (n = 526) (median [IQR] age, 66 years [60-72] vs 62 years [54-68]; P < .001) and had lower Pao2/Fio2 (median [IQR], 146 [105-214] vs 173 [120-222]; median difference, -27 [95% CI, -42 to -12]; P = .005). Among the 1581 patients with ICU disposition data available as of March 25, 2020, 920 patients (58% [95% CI, 56%-61%]) were still in the ICU, 256 (16% [95% CI, 14%-18%]) were discharged from the ICU, and 405 (26% [95% CI, 23%-28%]) had died in the ICU. Older patients (n = 786; age ≥64 years) had higher mortality than younger patients (n = 795; age ≤63 years) (36% vs 15%; difference, 21% [95% CI, 17%-26%]; P < .001). Conclusions and Relevance: In this case series of critically ill patients with laboratory-confirmed COVID-19 admitted to ICUs in Lombardy, Italy, the majority were older men, a large proportion required mechanical ventilation and high levels of PEEP, and ICU mortality was 26%.

Betacoronavirus , Coronavirus Infections/epidemiology , Critical Care/statistics & numerical data , Hospital Mortality , Intensive Care Units/statistics & numerical data , Pneumonia, Viral/epidemiology , Positive-Pressure Respiration/statistics & numerical data , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , COVID-19 , Comorbidity , Coronavirus Infections/mortality , Coronavirus Infections/physiopathology , Coronavirus Infections/therapy , Critical Illness/therapy , Female , Hospitalization , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/physiopathology , Pneumonia, Viral/therapy , Respiration, Artificial , Retrospective Studies , SARS-CoV-2 , Sex Distribution , Young Adult