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1.
BMC Endocr Disord ; 22(1): 13, 2022 Jan 06.
Article in English | MEDLINE | ID: covidwho-1613234

ABSTRACT

BACKGROUND: Research regarding the association between severe obesity and in-hospital mortality is inconsistent. We evaluated the impact of body mass index (BMI) levels on mortality in the medical wards. The analysis was performed separately before and during the COVID-19 pandemic. METHODS: We retrospectively retrieved data of adult patients admitted to the medical wards at the Mount Sinai Health System in New York City. The study was conducted between January 1, 2011, to March 23, 2021. Patients were divided into two sub-cohorts: pre-COVID-19 and during-COVID-19. Patients were then clustered into groups based on BMI ranges. A multivariate logistic regression analysis compared the mortality rate among the BMI groups, before and during the pandemic. RESULTS: Overall, 179,288 patients were admitted to the medical wards and had a recorded BMI measurement. 149,098 were admitted before the COVID-19 pandemic and 30,190 during the pandemic. Pre-pandemic, multivariate analysis showed a "J curve" between BMI and mortality. Severe obesity (BMI > 40) had an aOR of 0.8 (95% CI:0.7-1.0, p = 0.018) compared to the normal BMI group. In contrast, during the pandemic, the analysis showed a "U curve" between BMI and mortality. Severe obesity had an aOR of 1.7 (95% CI:1.3-2.4, p < 0.001) compared to the normal BMI group. CONCLUSIONS: Medical ward patients with severe obesity have a lower risk for mortality compared to patients with normal BMI. However, this does not apply during COVID-19, where obesity was a leading risk factor for mortality in the medical wards. It is important for the internal medicine physician to understand the intricacies of the association between obesity and medical ward mortality.


Subject(s)
Body Mass Index , COVID-19/mortality , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Obesity/physiopathology , SARS-CoV-2/isolation & purification , Aged , COVID-19/epidemiology , COVID-19/pathology , COVID-19/virology , Case-Control Studies , Female , Humans , Male , Middle Aged , New York City/epidemiology , Prognosis , Retrospective Studies , Risk Factors , Survival Rate
2.
Crit Care ; 25(1): 328, 2021 09 08.
Article in English | MEDLINE | ID: covidwho-1582035

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. METHODS: Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. RESULTS: SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. CONCLUSIONS: An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Machine Learning/standards , Severity of Illness Index , COVID-19/epidemiology , Cohort Studies , Female , Humans , Male , Prognosis , Respiration, Artificial/statistics & numerical data , Risk Assessment/methods , Risk Factors
3.
Sci Rep ; 11(1): 23874, 2021 12 13.
Article in English | MEDLINE | ID: covidwho-1569277

ABSTRACT

The worsening progress of coronavirus disease 2019 (COVID-19) is attributed to the proinflammatory state, leading to increased mortality. Statin works with its anti-inflammatory effects and may attenuate the worsening of COVID-19. COVID-19 patients were retrospectively enrolled from two academic hospitals in Wuhan, China, from 01/26/2020 to 03/26/2020. Adjusted in-hospital mortality was compared between the statin and the non-statin group by CHD status using multivariable Cox regression model after propensity score matching. Our study included 3133 COVID-19 patients (median age: 62y, female: 49.8%), and 404 (12.9%) received statin. Compared with the non-statin group, the statin group was older, more likely to have comorbidities but with a lower level of inflammatory markers. The Statin group also had a lower adjusted mortality risk (6.44% vs. 10.88%; adjusted hazard ratio [HR] 0.47; 95% CI, 0.29-0.77). Subgroup analysis of CHD patients showed a similar result. Propensity score matching showed an overall 87% (HR, 0.13; 95% CI, 0.05-0.36) lower risk of in-hospital mortality for statin users than nonusers. Such survival benefit of statin was obvious both among CHD and non-CHD patients (HR = 0.30 [0.09-0.98]; HR = 0.23 [0.1-0.49], respectively). Statin use was associated with reduced in-hospital mortality in COVID-19. The benefit of statin was both prominent among CHD and non-CHD patients. These findings may further reemphasize the continuation of statins in patients with CHD during the COVID-19 era.


Subject(s)
COVID-19/drug therapy , Coronary Disease/drug therapy , Hydroxymethylglutaryl-CoA Reductase Inhibitors/administration & dosage , Inpatients/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/mortality , China/epidemiology , Comorbidity , Coronary Disease/mortality , Female , Hospital Mortality/trends , Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use , Male , Middle Aged , Retrospective Studies , Treatment Outcome
4.
PLoS One ; 16(10): e0258918, 2021.
Article in English | MEDLINE | ID: covidwho-1496517

ABSTRACT

The objective was to describe the clinical characteristics and outcomes of hospitalized COVID-19 patients during the two different epidemic periods. Prospective, observational, cohort study of hospitalized COVID-19. A total of 421 consecutive patients were included, 188 during the first period (March-May 2020) and 233 in the second wave (July-December 2020). Clinical, epidemiological, prognostic and therapeutic data were compared. Patients of the first outbreak were older and more comorbid, presented worse PaO2/FiO2 ratio and an increased creatinine and D-dimer levels at hospital admission. The hospital stay was shorter (14.5[8;29] vs 8[6;14] days, p<0.001), ICU admissions (31.9% vs 13.3%, p<0.001) and the number of patients who required mechanical ventilation (OR = 0.12 [0.05-10.26]; p<0.001) were reduced. There were no significant differences in hospital and 30-day after discharge mortality (adjusted HR = 1.56; p = 0.1056) or hospital readmissions. New treatments and clinical strategies appear to improve hospital length, ICU admissions and the requirement for mechanical ventilation. However, we did not observe differences in mortality or readmissions.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , COVID-19/therapy , Adult , Aged , Aged, 80 and over , Cohort Studies , Epidemics/statistics & numerical data , Female , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Hospitalization/trends , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Prognosis , Prospective Studies , Respiration, Artificial/mortality , Risk Factors , SARS-CoV-2/pathogenicity , Spain/epidemiology , Treatment Outcome
5.
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
7.
Crit Care Med ; 49(11): 1895-1900, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1467429

ABSTRACT

OBJECTIVES: To determine whether the previously described trend of improving mortality in people with coronavirus disease 2019 in critical care during the first wave was maintained, plateaued, or reversed during the second wave in United Kingdom, when B117 became the dominant strain. DESIGN: National retrospective cohort study. SETTING: All English hospital trusts (i.e., groups of hospitals functioning as single operational units), reporting critical care admissions (high dependency unit and ICU) to the Coronavirus Disease 2019 Hospitalization in England Surveillance System. PATIENTS: A total of 49,862 (34,336 high dependency unit and 15,526 ICU) patients admitted between March 1, 2020, and January 31, 2021 (inclusive). INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: The primary outcome was inhospital 28-day mortality by calendar month of admission, from March 2020 to January 2021. Unadjusted mortality was estimated, and Cox proportional hazard models were used to estimate adjusted mortality, controlling for age, sex, ethnicity, major comorbidities, social deprivation, geographic location, and operational strain (using bed occupancy as a proxy). Mortality fell to trough levels in June 2020 (ICU: 22.5% [95% CI, 18.2-27.4], high dependency unit: 8.0% [95% CI, 6.4-9.6]) but then subsequently increased up to January 2021: (ICU: 30.6% [95% CI, 29.0-32.2] and high dependency unit, 16.2% [95% CI, 15.3-17.1]). Comparing patients admitted during June-September 2020 with those admitted during December 2020-January 2021, the adjusted mortality was 59% (CI range, 39-82) higher in high dependency unit and 88% (CI range, 62-118) higher in ICU for the later period. This increased mortality was seen in all subgroups including those under 65. CONCLUSIONS: There was a marked deterioration in outcomes for patients admitted to critical care at the peak of the second wave of coronavirus disease 2019 in United Kingdom (December 2020-January 2021), compared with the post-first-wave period (June 2020-September 2020). The deterioration was independent of recorded patient characteristics and occupancy levels. Further research is required to determine to what extent this deterioration reflects the impact of the B117 variant of concern.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Bed Occupancy , Comorbidity , Critical Care , Female , Humans , Length of Stay , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , United Kingdom/epidemiology , Young Adult
8.
Sci Rep ; 11(1): 20289, 2021 10 13.
Article in English | MEDLINE | ID: covidwho-1467125

ABSTRACT

Chagas disease (CD) continues to be a major public health burden in Latina America. Information on the interplay between COVID-19 and CD is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Consecutive patients with confirmed COVID-19 were included from March to September 2020. Genetic matching for sex, age, hypertension, diabetes mellitus and hospital was performed in a 4:1 ratio. Of the 7018 patients who had confirmed COVID-19, 31 patients with CD and 124 matched controls were included (median age 72 (64-80) years-old, 44.5% were male). At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (p < 0.05). C-reactive protein levels were lower in CD patients compared with the controls (55.5 [35.7, 85.0] vs. 94.3 [50.7, 167.5] mg/dL). In-hospital management, outcomes and complications were similar between the groups. In this large Brazilian COVID-19 Registry, CD patients had a higher prevalence of atrial fibrillation and chronic heart failure compared with non-CD controls, with no differences in-hospital outcomes. The lower C-reactive protein levels in CD patients require further investigation.


Subject(s)
COVID-19/complications , Chagas Disease/pathology , Hospitalization/trends , Aged , Atrial Fibrillation , Brazil , C-Reactive Protein/analysis , COVID-19/pathology , Chagas Disease/complications , Chagas Disease/virology , Coinfection , Diabetes Mellitus , Female , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Hospitals , Humans , Hypertension , Male , Middle Aged , Retrospective Studies , Risk Factors , SARS-CoV-2/pathogenicity
9.
Medicine (Baltimore) ; 100(40): e27422, 2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1462561

ABSTRACT

ABSTRACT: As severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model.Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient's age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital's COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively.We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Age Factors , Aged , Aged, 80 and over , Body Mass Index , Comorbidity , Creatinine/blood , Female , Health Behavior , Humans , Logistic Models , Male , Michigan/epidemiology , Middle Aged , Oximetry , Prognosis , ROC Curve , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Sex Factors , Socioeconomic Factors
10.
Sci Rep ; 11(1): 19450, 2021 09 30.
Article in English | MEDLINE | ID: covidwho-1447321

ABSTRACT

Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF < 50% occurred in 3.4%, abnormal LV global longitudinal strain (< 16%) in 24%, and diastolic dysfunction in 20%. Right ventricular systolic dysfunction (RV free wall strain < 20%) was noted in 18%. Moderate and large pericardial effusion were uncommon with an incidence of 0.4% for each category. Forty patients received ECMO support, and 79 died (10.9%). A stepwise increase in AUC was observed with addition of vital signs and laboratory measurements to baseline clinical characteristics, and a further significant increase (AUC 0.91) was observed when echocardiographic measurements were added. The performance of an optimized prediction model was similar to the model including baseline characteristics + vital signs and laboratory results + echocardiographic measurements.


Subject(s)
COVID-19/complications , Heart Diseases/etiology , Heart Diseases/mortality , Hospitalization/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/diagnosis , COVID-19/mortality , COVID-19/therapy , Clinical Decision Rules , Echocardiography , Extracorporeal Membrane Oxygenation , Female , Heart Diseases/diagnostic imaging , Hospital Mortality/trends , Humans , Machine Learning , Male , Middle Aged , Models, Theoretical , Prognosis , ROC Curve , Retrospective Studies , Young Adult
11.
PLoS One ; 16(9): e0258018, 2021.
Article in English | MEDLINE | ID: covidwho-1443853

ABSTRACT

BACKGROUND: Data of critically ill COVID-19 patients are being evaluated worldwide, not only to understand the various aspects of the disease and to refine treatment strategies but also to improve clinical decision-making. For clinical decision-making in particular, prognostic factors of a lethal course of the disease would be highly relevant. METHODS: In this retrospective cohort study, we analyzed the first 59 adult critically ill Covid-19 patients treated in one of the intensive care units of the University Medical Center Regensburg, Germany. Using uni- and multivariable regression models, we extracted a set of parameters that allowed for prognosing in-hospital mortality. RESULTS: Within the cohort, 19 patients died (mortality 32.2%). Blood pH value, mean arterial pressure, base excess, troponin, and procalcitonin were identified as highly significant prognostic factors of in-hospital mortality. However, no significant differences were found for other parameters expected to be relevant prognostic factors, like low arterial partial pressure of oxygen or high lactate levels. In the multivariable logistic regression analysis, the pH value and the mean arterial pressure turned out to be the most influential prognostic factors for a lethal course.


Subject(s)
COVID-19/blood , COVID-19/mortality , Adult , Aged , Arterial Pressure/physiology , Blood Physiological Phenomena , Blood Pressure/physiology , Cohort Studies , Critical Illness/mortality , Female , Germany/epidemiology , Hospital Mortality/trends , Humans , Hydrogen-Ion Concentration , Intensive Care Units/trends , Male , Middle Aged , Mortality/trends , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2/pathogenicity
12.
Lancet ; 398(10307): 1230-1238, 2021 10 02.
Article in English | MEDLINE | ID: covidwho-1440421

ABSTRACT

BACKGROUND: Over the course of the COVID-19 pandemic, the care of patients with COVID-19 has changed and the use of extracorporeal membrane oxygenation (ECMO) has increased. We aimed to examine patient selection, treatments, outcomes, and ECMO centre characteristics over the course of the pandemic to date. METHODS: We retrospectively analysed the Extracorporeal Life Support Organization Registry and COVID-19 Addendum to compare three groups of ECMO-supported patients with COVID-19 (aged ≥16 years). At early-adopting centres-ie, those using ECMO support for COVID-19 throughout 2020-we compared patients who started ECMO on or before May 1, 2020 (group A1), and between May 2 and Dec 31, 2020 (group A2). Late-adopting centres were those that provided ECMO for COVID-19 only after May 1, 2020 (group B). The primary outcome was in-hospital mortality in a time-to-event analysis assessed 90 days after ECMO initiation. A Cox proportional hazards model was fit to compare the patient and centre-level adjusted relative risk of mortality among the groups. FINDINGS: In 2020, 4812 patients with COVID-19 received ECMO across 349 centres within 41 countries. For early-adopting centres, the cumulative incidence of in-hospital mortality 90 days after ECMO initiation was 36·9% (95% CI 34·1-39·7) in patients who started ECMO on or before May 1 (group A1) versus 51·9% (50·0-53·8) after May 1 (group A2); at late-adopting centres (group B), it was 58·9% (55·4-62·3). Relative to patients in group A2, group A1 patients had a lower adjusted relative risk of in-hospital mortality 90 days after ECMO (hazard ratio 0·82 [0·70-0·96]), whereas group B patients had a higher adjusted relative risk (1·42 [1·17-1·73]). INTERPRETATION: Mortality after ECMO for patients with COVID-19 worsened during 2020. These findings inform the role of ECMO in COVID-19 for patients, clinicians, and policy makers. FUNDING: None.


Subject(s)
COVID-19/therapy , Extracorporeal Membrane Oxygenation/methods , Hospital Mortality/trends , Respiratory Distress Syndrome/therapy , Adult , COVID-19/mortality , Duration of Therapy , Extracorporeal Membrane Oxygenation/trends , Female , Humans , Male , Middle Aged , Patient Selection , Practice Guidelines as Topic , Registries , Respiratory Distress Syndrome/mortality , SARS-CoV-2
13.
Crit Care Med ; 49(9): 1439-1450, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1434523

ABSTRACT

OBJECTIVES: To evaluate the impact of ICU surge on mortality and to explore clinical and sociodemographic predictors of mortality. DESIGN: Retrospective cohort analysis. SETTING: NYC Health + Hospitals ICUs. PATIENTS: Adult ICU patients with coronavirus disease 2019 admitted between March 24, and May 12, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hospitals reported surge levels daily. Uni- and multivariable analyses were conducted to assess factors impacting in-hospital mortality. Mortality in Hispanic patients was higher for high/very high surge compared with low/medium surge (69.6% vs 56.4%; p = 0.0011). Patients 65 years old and older had similar mortality across surge levels. Mortality decreased from high/very high surge to low/medium surge in, patients 18-44 years old and 45-64 (18-44 yr: 46.4% vs 27.3%; p = 0.0017 and 45-64 yr: 64.9% vs 53.2%; p = 0.002), and for medium, high, and very high poverty neighborhoods (medium: 69.5% vs 60.7%; p = 0.019 and high: 71.2% vs 59.7%; p = 0.0078 and very high: 66.6% vs 50.7%; p = 0.0003). In the multivariable model high surge (high/very high vs low/medium odds ratio, 1.4; 95% CI, 1.2-1.8), race/ethnicity (Black vs White odds ratio, 1.5; 95% CI, 1.1-2.0 and Asian vs White odds ratio 1.5; 95% CI, 1.0-2.3; other vs White odds ratio 1.5, 95% CI, 1.0-2.3), age (45-64 vs 18-44 odds ratio, 2.0; 95% CI, 1.6-2.5 and 65-74 vs 18-44 odds ratio, 5.1; 95% CI, 3.3-8.0 and 75+ vs 18-44 odds ratio, 6.8; 95% CI, 4.7-10.1), payer type (uninsured vs commercial/other odds ratio, 1.7; 95% CI, 1.2-2.3; medicaid vs commercial/other odds ratio, 1.3; 95% CI, 1.1-1.5), neighborhood poverty (medium vs low odds ratio 1.6, 95% CI, 1.0-2.4 and high vs low odds ratio, 1.8; 95% CI, 1.3-2.5), comorbidities (diabetes odds ratio, 1.6; 95% CI, 1.2-2.0 and asthma odds ratio, 1.4; 95% CI, 1.1-1.8 and heart disease odds ratio, 2.5; 95% CI, 2.0-3.3), and interventions (mechanical ventilation odds ratio, 8.8; 95% CI, 6.1-12.9 and dialysis odds ratio, 3.0; 95% CI, 1.9-4.7) were significant predictors for mortality. CONCLUSIONS: Patients admitted to ICUs with higher surge scores were at greater risk of death. Impact of surge levels on mortality varied across sociodemographic groups.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Adolescent , Adult , Aged , Analysis of Variance , Female , Hospital Mortality/ethnology , Hospitals, Public/statistics & numerical data , Humans , Intensive Care Units , Male , Middle Aged , New York City/epidemiology , Odds Ratio , Patient Transfer/statistics & numerical data , Retrospective Studies , Young Adult
14.
Crit Care ; 25(1): 331, 2021 09 13.
Article in English | MEDLINE | ID: covidwho-1413915

ABSTRACT

BACKGROUND: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. METHODS: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. RESULTS: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). CONCLUSIONS: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation.


Subject(s)
COVID-19/therapy , Respiration, Artificial/methods , Respiratory Distress Syndrome/therapy , Ventilation-Perfusion Ratio/physiology , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/physiopathology , Cohort Studies , Critical Care/methods , Critical Care/trends , Female , Hospital Mortality/trends , Humans , Intensive Care Units/trends , Male , Middle Aged , Prognosis , Prospective Studies , Pulmonary Ventilation/physiology , Respiration, Artificial/trends , Respiratory Distress Syndrome/epidemiology , Respiratory Distress Syndrome/physiopathology , Retrospective Studies , Spain/epidemiology
15.
J Med Virol ; 94(1): 318-326, 2022 01.
Article in English | MEDLINE | ID: covidwho-1404586

ABSTRACT

When hospitals first encountered coronavirus disease 2019 (COVID-19), there was a dearth of therapeutic options and nearly 1 in 3 patients died from the disease. By the summer of 2020, as deaths from the disease declined nationally, multiple single-center studies began to report declining mortality of patients with COVID-19. To evaluate the effect of COVID-19 on hospital-based mortality, we searched the Vizient Clinical Data Base for outcomes data from approximately 600 participating hospitals, including 130 academic medical centers, from January 2017 through December 2020. More than 32 million hospital admissions were included in the analysis. After an initial spike, mortality from COVID-19 declined in all regions of the country to under 10% by June 2020 and remained constant for the remainder of the year. Despite this, inpatient, all-cause mortality has increased since the beginning of the pandemic, even those without respiratory failure. Inpatient mortality has particularly increased in elderly patients and in those requiring intubation for respiratory failure. Since June 2020, COVID-19 kills one in every 10 patients admitted to the hospital with this diagnosis. The addition of this new disease has raised overall hospital mortality especially those who require intubation for respiratory failure.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Respiratory Insufficiency/mortality , Hospitalization/statistics & numerical data , Humans , Inpatients/statistics & numerical data , Intubation/statistics & numerical data , Respiration, Artificial/mortality , SARS-CoV-2
17.
Head Neck ; 42(7): 1392-1396, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-1384168

ABSTRACT

The severe acute respiratory syndrome (SARS)-CoV-2 pandemic continues to produce a large number of patients with chronic respiratory failure and ventilator dependence. As such, surgeons will be called upon to perform tracheotomy for a subset of these chronically intubated patients. As seen during the SARS and the SARS-CoV-2 outbreaks, aerosol-generating procedures (AGP) have been associated with higher rates of infection of medical personnel and potential acceleration of viral dissemination throughout the medical center. Therefore, a thoughtful approach to tracheotomy (and other AGPs) is imperative and maintaining traditional management norms may be unsuitable or even potentially harmful. We sought to review the existing evidence informing best practices and then develop straightforward guidelines for tracheotomy during the SARS-CoV-2 pandemic. This communication is the product of those efforts and is based on national and international experience with the current SARS-CoV-2 pandemic and the SARS epidemic of 2002/2003.


Subject(s)
Clinical Decision-Making , Coronavirus Infections/epidemiology , Hospital Mortality/trends , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Severe Acute Respiratory Syndrome/therapy , Tracheotomy/methods , COVID-19 , Coronavirus Infections/prevention & control , Critical Care/methods , Elective Surgical Procedures/methods , Elective Surgical Procedures/statistics & numerical data , Emergencies , Female , Follow-Up Studies , Humans , Intensive Care Units/statistics & numerical data , Internationality , Intubation, Intratracheal , Male , Occupational Health , Pandemics/prevention & control , Patient Safety , Pneumonia, Viral/prevention & control , Respiration, Artificial/methods , Risk Assessment , SARS Virus/pathogenicity , Survival Rate , Time Factors , Treatment Outcome , United States/epidemiology , Ventilator Weaning/methods
18.
JAMA Netw Open ; 4(4): e216556, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1384068

ABSTRACT

Importance: Mortality is an important measure of the severity of a pandemic. This study aimed to understand how mortality by age of hospitalized patients who were tested for SARS-CoV-2 has changed over time. Objective: To evaluate trends in in-hospital mortality among patients who tested positive for SARS-CoV-2. Design, Setting, and Participants: This retrospective cohort study included patients who were hospitalized for at least 1 day at 1 of 209 US acute care hospitals of variable size, in urban and rural areas, between March 1 and November 21, 2020. Eligible patients had a SARS-CoV-2 polymerase chain reaction (PCR) or antigen test within 7 days of admission or during hospitalization, and a record of discharge or in-hospital death. Exposure: SARS-CoV-2 positivity. Main Outcomes and Measures: SARS-CoV-2 infection was defined as a positive SARS-CoV-2 PCR or antigen test within 7 days before admission or during hospitalization. Mortality was extracted from electronically available data. Results: Among 503 409 admitted patients, 42 604 (8.5%) had SARS-CoV-2-positive tests. Of those with SARS-CoV-2-positive tests, 21 592 (50.7%) were male patients. Hospital admissions among patients with SARS-CoV-2-positive tests were highest in the group aged 65 years or older (19 929 [46.8%]), followed by those aged 50 to 64 years (11 602 [27.2%]) and 18 to 49 years (10 619 [24.9%]). Hospital admissions among patients 18 to 49 years of age increased from 1099 of 5319 (20.7%) in April to 1266 of 4184 (30.3%) in June and 2156 of 7280 (29.6%) in July, briefly exceeding those in the group 50 to 64 years of age (June: 1194 of 4184 [28.5%]; 2039 of 7280 [28.0%]). Patients with SARS-CoV-2-positive tests had higher in-hospital mortality than patients with SARS-CoV-2-negative tests (4705 [11.0%] vs 11 707 of 460 805 [2.5%]; P < .001). In-hospital mortality rates increased with increasing age for both patients with SARS-CoV-2-negative tests and SARS-CoV-2-positive tests. In patients with SARS-CoV-2-negative tests, mortality increased from 45 of 11 255 (0.4%) in those younger than 18 years to 4812 of 107 394 (4.5%) in those older than 75 years. In patients with SARS-CoV-2-positive tests, mortality increased from 1 of 454 (0.2%) of those younger than 18 years to 2149 of 10 287 (20.9%) in those older than 75 years. In-hospital mortality rates among patients with SARS-CoV-2-negative tests were similar for male and female patients (6273 of 209 086 [3.0%] vs 5538 of 251 719 [2.2%]) but higher mortality was observed among male patients with SARS-CoV-2-positive tests (2700 of 21 592 [12.5%]) compared with female patients with SARS-CoV-2-positive tests (2016 of 21 012 [9.60%]). Overall, in-hospital mortality increased from March to April (63 of 597 [10.6%] to 1047 of 5319 [19.7%]), then decreased significantly to November (499 of 5350 [9.3%]; P = .04), with significant decreases in the oldest age groups (50-64 years: 197 of 1542 [12.8%] to 73 of 1341 [5.4%]; P = .02; 65-75 years: 269 of 1182 [22.8%] to 137 of 1332 [10.3%]; P = .006; >75 years: 535 of 1479 [36.2%] to 262 of 1505 [17.4%]; P = .03). Conclusions and Relevance: This nationally representative study supported the findings of smaller, regional studies and found that in-hospital mortality declined across all age groups during the period evaluated. Reductions were unlikely because of a higher proportion of younger patients with lower in-hospital mortality in the later period.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , SARS-CoV-2 , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Retrospective Studies , United States/epidemiology , Young Adult
19.
JAMA Netw Open ; 4(9): e2123374, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1380357

ABSTRACT

Importance: In the absence of a national strategy in response to the COVID-19 pandemic, many public health decisions fell to local elected officials and agencies. Outcomes of such policies depend on a complex combination of local epidemic conditions and demographic features as well as the intensity and timing of such policies and are therefore unclear. Objective: To use a decision analytical model of the COVID-19 epidemic to investigate potential outcomes if actual policies enacted in March 2020 (during the first wave of the epidemic) in the St Louis region of Missouri had been delayed. Design, Setting, and Participants: A previously developed, publicly available, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1) designed to enable localized COVID-19 epidemic projections was used. The compartmental epidemic model is programmed in R and Stan, uses bayesian inference, and accepts user-supplied demographic, epidemiologic, and policy inputs. Hospital census data for 1.3 million people from St Louis City and County from March 14, 2020, through July 15, 2020, were used to calibrate the model. Exposures: Hypothetical delays in actual social distancing policies (which began on March 13, 2020) by 1, 2, or 4 weeks. Sensitivity analyses were conducted that explored plausible spontaneous behavior change in the absence of social distancing policies. Main Outcomes and Measures: Hospitalizations and deaths. Results: A model of 1.3 million residents of the greater St Louis, Missouri, area found an initial reproductive number (indicating transmissibility of an infectious agent) of 3.9 (95% credible interval [CrI], 3.1-4.5) in the St Louis region before March 15, 2020, which fell to 0.93 (95% CrI, 0.88-0.98) after social distancing policies were implemented between March 15 and March 21, 2020. By June 15, a 1-week delay in policies would have increased cumulative hospitalizations from an observed actual number of 2246 hospitalizations to 8005 hospitalizations (75% CrI: 3973-15 236 hospitalizations) and increased deaths from an observed actual number of 482 deaths to a projected 1304 deaths (75% CrI, 656-2428 deaths). By June 15, a 2-week delay would have yielded 3292 deaths (75% CrI, 2104-4905 deaths)-an additional 2810 deaths or a 583% increase beyond what was actually observed. Sensitivity analyses incorporating a range of spontaneous behavior changes did not avert severe epidemic projections. Conclusions and Relevance: The results of this decision analytical model study suggest that, in the St Louis region, timely social distancing policies were associated with improved population health outcomes, and small delays may likely have led to a COVID-19 epidemic similar to the most heavily affected areas in the US. These findings indicate that an open-source modeling platform designed to accept user-supplied local and regional data may provide projections tailored to, and more relevant for, local settings.


Subject(s)
COVID-19/mortality , Health Policy , Hospitalization/statistics & numerical data , Physical Distancing , Bayes Theorem , Female , Hospital Mortality/trends , Humans , Male , Missouri , Pandemics , SARS-CoV-2
20.
Lancet Glob Health ; 9(9): e1216-e1225, 2021 09.
Article in English | MEDLINE | ID: covidwho-1368858

ABSTRACT

BACKGROUND: The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves. METHODS: In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression. FINDINGS: Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240·4 cases per 100 000 people vs 136·0 cases per 100 000 people; admissions, 27·9 admissions per 100 000 people vs 16·1 admissions per 100 000 people; deaths, 8·3 deaths per 100 000 people vs 3·6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1·19, 95% CI 1·18-1·20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1·22, 95% CI 1·14-1·31), and older than 65 years (aOR 1·38, 1·25-1·52), compared with younger than 40 years; of Mixed race (aOR 1·21, 1·06-1·38) compared with White race; and admitted in the public sector (aOR 1·65, 1·41-1·92); and less likely to be Black (aOR 0·53, 0·47-0·60) and Indian (aOR 0·77, 0·66-0·91), compared with White; and have a comorbid condition (aOR 0·60, 0·55-0·67). For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1·31, 95% CI 1·28-1·35). In-hospital case-fatality risk increased from 17·7% in weeks of low admission (<3500 admissions) to 26·9% in weeks of very high admission (>8000 admissions; aOR 1·24, 1·17-1·32). INTERPRETATION: In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage. FUNDING: DATCOV as a national surveillance system is funded by the National Institute for Communicable Diseases and the South African National Government.


Subject(s)
COVID-19/mortality , COVID-19/therapy , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Adult , Aged , COVID-19/epidemiology , Female , Humans , Male , Middle Aged , Prospective Studies , Risk Factors , South Africa/epidemiology
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