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1.
Am J Crit Care ; : e1-e9, 2022 Aug 12.
Article in English | MEDLINE | ID: covidwho-1994279

ABSTRACT

BACKGROUND: Tracheostomies are highly aerosolizing procedures yet are often indicated in patients with COVID-19 who require prolonged intubation. Robust investigations of the safety of tracheostomy protocols and provider adherence and evaluations are limited. OBJECTIVES: To determine the rate of COVID-19 infection of health care personnel involved in COVID-19 tracheostomies under a multidisciplinary safety protocol and to investigate health care personnel's attitudes and suggested areas for improvement concerning the protocol. METHODS: All health care personnel involved in tracheostomies in COVID-19-positive patients from April 9 through July 11, 2020, were sent a 22-item electronic survey. RESULTS: Among 107 health care personnel (80.5%) who responded to the survey, 5 reported a positive COVID-19 test result (n = 2) or symptoms of COVID-19 (n = 3) within 21 days of the tracheostomy. Respondents reported 100% adherence to use of adequate personal protective equipment. Most (91%) were familiar with the tracheostomy protocol and felt safe (92%) while performing tracheostomy. Suggested improvements included creating dedicated tracheostomy teams and increasing provider choices surrounding personal protective equipment. CONCLUSIONS: Multidisciplinary engagement in the development and implementation of a COVID-19 tracheostomy protocol is associated with acceptable safety for all members of the care team.

3.
J Thorac Dis ; 13(7): 4137-4145, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1344631

ABSTRACT

BACKGROUND: Whereas data from the pre-pandemic era have demonstrated that tracheostomy can accelerate liberation from the ventilator, reduce need for sedation, and facilitate rehabilitation, concerns for healthcare worker safety have led to disagreement on tracheostomy placement in COVID-19 patients. Data on COVID-19 patients undergoing tracheostomy may inform best practices. Thus, we report a retrospective institutional cohort experience with tracheostomy in ventilated patients with COVID-19, examining associations between time to tracheostomy and duration of mechanical ventilation in relation to patient characteristics, clinical course, and survival. METHODS: Clinical data were extracted for all COVID-19 tracheostomies performed at a quaternary referral center from April-July 2020. Outcomes studied included mortality, adverse events, duration of mechanical ventilation, and time to decannulation. RESULTS: Among 64 COVID-19 tracheostomies (13% of COVID-19 hospitalizations), patients were 64% male and 42% African American, with a median age of 54 (range, 20-89). Median time to tracheostomy was 22 (range, 7-60) days and median duration of mechanical ventilation was 39.4 (range, 20-113) days. Earlier tracheostomy was associated with shortened mechanical ventilation (R2=0.4, P<0.01). Median decannulation time was 35.3 (range, 7-79) days. There was 19% mortality and adverse events in 45%, mostly from bleeding in therapeutically anticoagulated patients. CONCLUSIONS: Tracheostomy was associated with swifter liberation from the ventilator and acceptable safety for physicians in this series of critically ill COVID-19 patients. Patient mortality was not increased relative to historical data on acute respiratory distress syndrome (ARDS). Future studies are required to establish conclusions of causality regarding tracheostomy timing with mechanical ventilation, complications, or mortality in COVID-19 patients.

4.
JMIR Med Inform ; 9(4): e25066, 2021 Apr 21.
Article in English | MEDLINE | ID: covidwho-1200031

ABSTRACT

BACKGROUND: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. OBJECTIVE: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. METHODS: The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. RESULTS: In non-COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95% CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95% CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale). CONCLUSIONS: The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.

5.
JMIR Med Inform ; 9(4): e25066, 2021 Apr 21.
Article in English | MEDLINE | ID: covidwho-1170048

ABSTRACT

BACKGROUND: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. OBJECTIVE: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. METHODS: The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. RESULTS: In non-COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95% CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95% CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale). CONCLUSIONS: The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.

7.
J Vasc Surg Venous Lymphat Disord ; 9(1): 23-35, 2021 01.
Article in English | MEDLINE | ID: covidwho-988707

ABSTRACT

OBJECTIVE: Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus confers a risk of significant coagulopathy, with the resulting development of venous thromboembolism (VTE), potentially contributing to the morbidity and mortality. The purpose of the present review was to evaluate the potential mechanisms that contribute to this increased risk of coagulopathy and the role of anticoagulants in treatment. METHODS: A literature review of coronavirus disease 2019 (COVID-19) and/or SARS-CoV-2 and cell-mediated inflammation, clinical coagulation abnormalities, hypercoagulability, pulmonary intravascular coagulopathy, and anticoagulation was performed. The National Clinical Trials database was queried for ongoing studies of anticoagulation and/or antithrombotic treatment or the incidence or prevalence of thrombotic events in patients with SARS-CoV-2 infection. RESULTS: The reported rate of VTE among critically ill patients infected with SARS-CoV-2 has been 21% to 69%. The phenomenon of breakthrough VTE, or the acute development of VTE despite adequate chemoprophylaxis or treatment dose anticoagulation, has been shown to occur with severe infection. The pathophysiology of overt hypercoagulability and the development of VTE is likely multifactorial, with evidence supporting the role of significant cell-mediated responses, including neutrophils and monocytes/macrophages, endothelialitis, cytokine release syndrome, and dysregulation of fibrinolysis. Collectively, this inflammatory process contributes to the severe pulmonary pathology experienced by patients with COVID-19. As the infection worsens, extreme D-dimer elevations, significant thrombocytopenia, decreasing fibrinogen, and prolongation of prothrombin time and partial thromboplastin time occur, often associated with deep vein thrombosis, in situ pulmonary thrombi, and/or pulmonary embolism. A new phenomenon, termed pulmonary intravascular coagulopathy, has been associated with morbidity in patients with severe infection. Heparin, both unfractionated heparin and low-molecular-weight heparin, have emerged as agents that can address the viral infection, inflammation, and thrombosis in this syndrome. CONCLUSIONS: The overwhelming inflammatory response in patients with SARS-CoV-2 infection can lead to a hypercoagulable state, microthrombosis, large vessel thrombosis, and, ultimately, death. Early VTE prophylaxis should be provided to all admitted patients. Therapeutic anticoagulation therapy might be beneficial for critically ill patients and is the focus of 39 ongoing trials. Close monitoring for thrombotic complications is imperative, and, if confirmed, early transition from prophylactic to therapeutic anticoagulation should be instituted. The interplay between inflammation and thrombosis has been shown to be a hallmark of the SARS-CoV-2 viral infection.


Subject(s)
COVID-19/complications , Venous Thromboembolism/virology , Anticoagulants/therapeutic use , COVID-19/physiopathology , Clinical Trials as Topic , Humans , Inflammation/physiopathology , Inflammation/virology , Venous Thromboembolism/drug therapy , Venous Thromboembolism/epidemiology , Venous Thromboembolism/physiopathology
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