Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Surgery ; 171(4): 1092-1099, 2022 04.
Article in English | MEDLINE | ID: covidwho-1401876


OBJECTIVES: We evaluated rotational thromboelastometry tracings in 44 critically ill coronavirus disease 2019 patients, to determine whether there is a viscoelastic fingerprint and to test the hypothesis that the diagnosis and prediction of venous thromboembolism would be enhanced by the addition of rotational thromboelastometry testing. RESULTS: Rotational thromboelastometry values reflected an increase in clot strength for the EXTEM, INTEM, and FIBTEM assays beyond the reference range. No hyperfibrinolysis was noted. Fibrinolysis shutdown was present but did not correlate with thrombosis; 32% (14/44) of patients experienced a thrombotic episode. For every 1 mm increase of FIBTEM maximum clot formation, the odds of developing thrombosis increased 20% (95% confidence interval, 0-40%, P = .043), whereas for every 1,000 ng/mL increase in D-dimer, the odds of thrombosis increased by 70% (95% confidence interval, 20%-150%, P = .004), after adjustment for age and sex (AUC 0.96, 95% confidence interval, 0.90-1.00). There was a slight but significant improvement in model performance after adding FIBTEM maximum clot formation and EXTEM clot formation time to D-dimer in a multivariable model (P = .04). CONCLUSIONS: D-dimer concentrations were more predictive of thrombosis in our patient population than any other parameter. Rotational thromboelastometry confirmed the hypercoagulable state of coronavirus disease 2019 intensive care unit patients. FIBTEM maximum clot formation and EXTEM clot formation time increased the predictability for thrombosis compared with only using D-dimer. Rotational thromboelastometry analysis is most useful in augmenting the information provided by the D-dimer concentration for venous thromboembolism risk assessment when the D-dimer concentration is between 1,625 and 6,900 ng/dL, but the enhancement is modest. Fibrinolysis shutdown did not correlate with thrombosis.

COVID-19 , Respiratory Distress Syndrome , Thrombophilia , Thrombosis , COVID-19/complications , COVID-19/diagnosis , Humans , Thrombelastography , Thrombophilia/diagnosis , Thrombophilia/etiology , Thrombosis/diagnosis , Thrombosis/etiology
MMWR Morb Mortal Wkly Rep ; 69(28): 918-922, 2020 Jul 17.
Article in English | MEDLINE | ID: covidwho-1389847


To limit introduction of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), the United States restricted travel from China on February 2, 2020, and from Europe on March 13. To determine whether local transmission of SARS-CoV-2 could be detected, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) conducted deidentified sentinel surveillance at six NYC hospital emergency departments (EDs) during March 1-20. On March 8, while testing availability for SARS-CoV-2 was still limited, DOHMH announced sustained community transmission of SARS-CoV-2 (1). At this time, twenty-six NYC residents had confirmed COVID-19, and ED visits for influenza-like illness* increased, despite decreased influenza virus circulation.† The following week, on March 15, when only seven of the 56 (13%) patients with known exposure histories had exposure outside of NYC, the level of community SARS-CoV-2 transmission status was elevated from sustained community transmission to widespread community transmission (2). Through sentinel surveillance during March 1-20, DOHMH collected 544 specimens from patients with influenza-like symptoms (ILS)§ who had negative test results for influenza and, in some instances, other respiratory pathogens.¶ All 544 specimens were tested for SARS-CoV-2 at CDC; 36 (6.6%) tested positive. Using genetic sequencing, CDC determined that the sequences of most SARS-CoV-2-positive specimens resembled those circulating in Europe, suggesting probable introductions of SARS-CoV-2 from Europe, from other U.S. locations, and local introductions from within New York. These findings demonstrate that partnering with health care facilities and developing the systems needed for rapid implementation of sentinel surveillance, coupled with capacity for genetic sequencing before an outbreak, can help inform timely containment and mitigation strategies.

Betacoronavirus/genetics , Betacoronavirus/isolation & purification , Community-Acquired Infections/diagnosis , Community-Acquired Infections/virology , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , Sentinel Surveillance , Adolescent , Adult , Aged , COVID-19 , Child , Child, Preschool , Community-Acquired Infections/epidemiology , Coronavirus Infections/epidemiology , Emergency Service, Hospital , Female , Humans , Infant , Male , Middle Aged , New York City/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Sequence Analysis , Travel-Related Illness , Young Adult
Clin Chem ; 66(11): 1396-1404, 2020 11 01.
Article in English | MEDLINE | ID: covidwho-727045


BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Coronavirus Infections/diagnosis , Hematologic Tests , Machine Learning , Pneumonia, Viral/diagnosis , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Female , Humans , Laboratories , Male , Middle Aged , Models, Theoretical , Pandemics , ROC Curve , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , Young Adult