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1.
Clin Infect Dis ; 73(11): e4141-e4151, 2021 12 06.
Article in English | MEDLINE | ID: covidwho-1561160

ABSTRACT

BACKGROUND: Coronavirus disease (COVID-19) can cause severe illness and death. Predictors of poor outcome collected on hospital admission may inform clinical and public health decisions. METHODS: We conducted a retrospective observational cohort investigation of 297 adults admitted to 8 academic and community hospitals in Georgia, United States, during March 2020. Using standardized medical record abstraction, we collected data on predictors including admission demographics, underlying medical conditions, outpatient antihypertensive medications, recorded symptoms, vital signs, radiographic findings, and laboratory values. We used random forest models to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for predictors of invasive mechanical ventilation (IMV) and death. RESULTS: Compared with age <45 years, ages 65-74 years and ≥75 years were predictors of IMV (aORs, 3.12 [95% CI, 1.47-6.60] and 2.79 [95% CI, 1.23-6.33], respectively) and the strongest predictors for death (aORs, 12.92 [95% CI, 3.26-51.25] and 18.06 [95% CI, 4.43-73.63], respectively). Comorbidities associated with death (aORs, 2.4-3.8; P < .05) included end-stage renal disease, coronary artery disease, and neurologic disorders, but not pulmonary disease, immunocompromise, or hypertension. Prehospital use vs nonuse of angiotensin receptor blockers (aOR, 2.02 [95% CI, 1.03-3.96]) and dihydropyridine calcium channel blockers (aOR, 1.91 [95% CI, 1.03-3.55]) were associated with death. CONCLUSIONS: After adjustment for patient and clinical characteristics, older age was the strongest predictor of death, exceeding comorbidities, abnormal vital signs, and laboratory test abnormalities. That coronary artery disease, but not chronic lung disease, was associated with death among hospitalized patients warrants further investigation, as do associations between certain antihypertensive medications and death.


Subject(s)
COVID-19 , Aged , Hospitalization , Humans , Middle Aged , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2 , United States
2.
PLoS One ; 16(9): e0257056, 2021.
Article in English | MEDLINE | ID: covidwho-1438346

ABSTRACT

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.


Subject(s)
COVID-19 , Machine Learning , Models, Biological , Respiratory Distress Syndrome , SARS-CoV-2/metabolism , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/blood , COVID-19/complications , COVID-19/diagnosis , COVID-19/physiopathology , Critical Illness , Female , Humans , Male , Medical Records Systems, Computerized , Middle Aged , Oxygen/blood , Respiratory Distress Syndrome/blood , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/etiology , Respiratory Distress Syndrome/physiopathology , Respiratory Rate , Risk Factors
3.
Crit Care Explor ; 3(5): e0402, 2021 May.
Article in English | MEDLINE | ID: covidwho-1254873

ABSTRACT

BACKGROUND: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes. OBJECTIVES: The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased. DERIVATION COHORT: All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699). VALIDATION COHORT: External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389). PREDICTION MODEL: All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score. RESULTS: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives. CONCLUSIONS: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.

4.
Emerg Infect Dis ; 27(4): 1164-1168, 2021.
Article in English | MEDLINE | ID: covidwho-1146202

ABSTRACT

We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.


Subject(s)
COVID-19 , Diabetes Mellitus , Glycated Hemoglobin/analysis , Hospitalization/statistics & numerical data , Hypertension , Obesity , Patient Care Management , Age Factors , COVID-19/epidemiology , COVID-19/psychology , COVID-19/therapy , Diabetes Mellitus/blood , Diabetes Mellitus/epidemiology , Disease Progression , Female , Georgia/epidemiology , Humans , Hypertension/drug therapy , Hypertension/epidemiology , Male , Middle Aged , Multimorbidity , Obesity/diagnosis , Obesity/epidemiology , Patient Acceptance of Health Care , Patient Care Management/methods , Patient Care Management/standards , Patient Care Management/statistics & numerical data , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2
5.
Open Forum Infect Dis ; 8(1): ofaa596, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-960578

ABSTRACT

BACKGROUND: The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. METHODS: This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower's dissimilarity matrix between each patient's clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. RESULTS: One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P < .01, all comparisons). CONCLUSIONS: Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.

6.
Crit Care Med ; 48(11): e1045-e1053, 2020 11.
Article in English | MEDLINE | ID: covidwho-720989

ABSTRACT

OBJECTIVES: Increasing time to mechanical ventilation and high-flow nasal cannula use may be associated with mortality in coronavirus disease 2019. We examined the impact of time to intubation and use of high-flow nasal cannula on clinical outcomes in patients with coronavirus disease 2019. DESIGN: Retrospective cohort study. SETTING: Six coronavirus disease 2019-specific ICUs across four university-affiliated hospitals in Atlanta, Georgia. PATIENTS: Adults with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection who received high-flow nasal cannula or mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 231 patients admitted to the ICU, 109 (47.2%) were treated with high-flow nasal cannula and 97 (42.0%) were intubated without preceding high-flow nasal cannula use. Of those managed with high-flow nasal cannula, 78 (71.6%) ultimately received mechanical ventilation. In total, 175 patients received mechanical ventilation; 44.6% were female, 66.3% were Black, and the median age was 66 years (interquartile range, 56-75 yr). Seventy-six patients (43.4%) were intubated within 8 hours of ICU admission, 57 (32.6%) between 8 and 24 hours of admission, and 42 (24.0%) greater than or equal to 24 hours after admission. Patients intubated within 8 hours were more likely to have diabetes, chronic comorbidities, and higher admission Sequential Organ Failure Assessment scores. Mortality did not differ by time to intubation (≤ 8 hr: 38.2%; 8-24 hr: 31.6%; ≥ 24 hr: 38.1%; p = 0.7), and there was no association between time to intubation and mortality in adjusted analysis. Similarly, there was no difference in initial static compliance, duration of mechanical ventilation, or ICU length of stay by timing of intubation. High-flow nasal cannula use prior to intubation was not associated with mortality. CONCLUSIONS: In this cohort of critically ill patients with coronavirus disease 2019, neither time from ICU admission to intubation nor high-flow nasal cannula use were associated with increased mortality. This study provides evidence that coronavirus disease 2019 respiratory failure can be managed similarly to hypoxic respiratory failure of other etiologies.


Subject(s)
Cannula/statistics & numerical data , Coronavirus Infections/therapy , Critical Illness/therapy , Intubation, Intratracheal/statistics & numerical data , Oxygen Inhalation Therapy/methods , Pneumonia, Viral/therapy , Aged , COVID-19 , Cannula/adverse effects , Coronavirus Infections/complications , Coronavirus Infections/mortality , Female , Humans , Intensive Care Units , Intubation, Intratracheal/adverse effects , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/mortality , Respiratory Insufficiency/therapy , Retrospective Studies
7.
Crit Care Med ; 48(9): e799-e804, 2020 09.
Article in English | MEDLINE | ID: covidwho-378160

ABSTRACT

OBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019. DESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020. SETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States. PATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report. Overall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge. Mortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy. CONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.


Subject(s)
Betacoronavirus , Coronavirus Infections/mortality , Pneumonia, Viral/mortality , Respiration, Artificial , Respiratory Distress Syndrome/mortality , Aged , COVID-19 , Cohort Studies , Comorbidity , Coronavirus Infections/complications , Coronavirus Infections/therapy , Critical Illness , Female , Georgia/epidemiology , Hospital Mortality , Humans , Intensive Care Units , Male , Middle Aged , Organ Dysfunction Scores , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/therapy , Respiratory Distress Syndrome/etiology , Respiratory Distress Syndrome/therapy , SARS-CoV-2 , Socioeconomic Factors
8.
MMWR Morb Mortal Wkly Rep ; 69(18): 545-550, 2020 May 08.
Article in English | MEDLINE | ID: covidwho-142205

ABSTRACT

SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in the United States during January 2020 (1). Since then, >980,000 cases have been reported in the United States, including >55,000 associated deaths as of April 28, 2020 (2). Detailed data on demographic characteristics, underlying medical conditions, and clinical outcomes for persons hospitalized with COVID-19 are needed to inform prevention strategies and community-specific intervention messages. For this report, CDC, the Georgia Department of Public Health, and eight Georgia hospitals (seven in metropolitan Atlanta and one in southern Georgia) summarized medical record-abstracted data for hospitalized adult patients with laboratory-confirmed* COVID-19 who were admitted during March 2020. Among 305 hospitalized patients with COVID-19, 61.6% were aged <65 years, 50.5% were female, and 83.2% with known race/ethnicity were non-Hispanic black (black). Over a quarter of patients (26.2%) did not have conditions thought to put them at higher risk for severe disease, including being aged ≥65 years. The proportion of hospitalized patients who were black was higher than expected based on overall hospital admissions. In an adjusted time-to-event analysis, black patients were not more likely than were nonblack patients to receive invasive mechanical ventilation† (IMV) or to die during hospitalization (hazard ratio [HR] = 0.63; 95% confidence interval [CI] = 0.35-1.13). Given the overrepresentation of black patients within this hospitalized cohort, it is important for public health officials to ensure that prevention activities prioritize communities and racial/ethnic groups most affected by COVID-19. Clinicians and public officials should be aware that all adults, regardless of underlying conditions or age, are at risk for serious illness from COVID-19.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Adolescent , Adult , Black or African American/statistics & numerical data , Aged , Aged, 80 and over , COVID-19 , Cohort Studies , Comorbidity , Coronavirus Infections/ethnology , Georgia/epidemiology , Hospitalization/statistics & numerical data , Humans , Middle Aged , Pandemics , Pneumonia, Viral/ethnology , Risk Factors , Treatment Outcome , Young Adult
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