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1.
Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.
Acta Anaesthesiol Scand
; 66(1): 65-75, 2022 01.
Artículo
en Inglés
| MEDLINE | ID: mdl-34622441
2.
Predictors for extubation failure in COVID-19 patients using a machine learning approach.
Crit Care
; 25(1): 448, 2021 12 27.
Artículo
en Inglés
| MEDLINE | ID: mdl-34961537
3.
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.
Crit Care
; 25(1): 304, 2021 08 23.
Artículo
en Inglés
| MEDLINE | ID: mdl-34425864
4.
Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.
Ann Intensive Care
; 12(1): 99, 2022 Oct 20.
Artículo
en Inglés
| MEDLINE | ID: mdl-36264358
5.
Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.
Int J Med Inform
; 167: 104863, 2022 11.
Artículo
en Inglés
| MEDLINE | ID: mdl-36162166
6.
INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.
Shock
; 58(5): 358-365, 2022 11 01.
Artículo
en Inglés
| MEDLINE | ID: mdl-36155964
7.
Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.
Crit Care Explor
; 3(10): e0555, 2021 Oct.
Artículo
en Inglés
| MEDLINE | ID: mdl-34671747
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