scholarly journals Predicting cardiovascular intensive care unit readmission after cardiac surgery: derivation and validation of the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) cardiovascular intensive care unit clinical prediction model from a registry cohort of 10,799 surgical cases

Critical Care ◽  
2014 ◽  
Vol 18 (6) ◽  
Author(s):  
Sean van Diepen ◽  
Michelle M Graham ◽  
Jayan Nagendran ◽  
Colleen M Norris
Keyword(s):  
Intensive Care Unit ◽  
Coronary Heart Disease ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Heart Disease ◽  
Prediction Model ◽  
Outcomes Assessment ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Cardiovascular Intensive Care

scholarly journals NEUROlogical Prognosis After Cardiac Arrest in Kids (NEUROPACK) study: protocol for a prospective multicentre clinical prediction model derivation and validation study in children after cardiac arrest

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e037517
Author(s):  
Barnaby Robert Scholefield ◽  
James Martin ◽  
Kate Penny-Thomas ◽  
Sarah Evans ◽  
Mirjam Kool ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
Prediction Model ◽  
Neurodevelopmental Outcome ◽  
Paediatric Intensive Care ◽  
Ethical Review ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Paediatric Cardiac Arrest

IntroductionCurrently, we are unable to accurately predict mortality or neurological morbidity following resuscitation after paediatric out of hospital (OHCA) or in-hospital (IHCA) cardiac arrest. A clinical prediction model may improve communication with parents and families and risk stratification of patients for appropriate postcardiac arrest care. This study aims to the derive and validate a clinical prediction model to predict, within 1 hour of admission to the paediatric intensive care unit (PICU), neurodevelopmental outcome at 3 months after paediatric cardiac arrest.Methods and analysisA prospective study of children (age: >24 hours and <16 years), admitted to 1 of the 24 participating PICUs in the UK and Ireland, following an OHCA or IHCA. Patients are included if requiring more than 1 min of cardiopulmonary resuscitation and mechanical ventilation at PICU admission Children who had cardiac arrests in PICU or neonatal intensive care unit will be excluded. Candidate variables will be identified from data submitted to the Paediatric Intensive Care Audit Network registry. Primary outcome is neurodevelopmental status, assessed at 3 months by telephone interview using the Vineland Adaptive Behavioural Score II questionnaire. A clinical prediction model will be derived using logistic regression with model performance and accuracy assessment. External validation will be performed using the Therapeutic Hypothermia After Paediatric Cardiac Arrest trial dataset. We aim to identify 370 patients, with successful consent and follow-up of 150 patients. Patient inclusion started 1 January 2018 and inclusion will continue over 18 months.Ethics and disseminationEthical review of this protocol was completed by 27 September 2017 at the Wales Research Ethics Committee 5, 17/WA/0306. The results of this study will be published in peer-reviewed journals and presented in conferences.Trial registration numberNCT03574025.

scholarly journals Validation of a Clinical Prediction Model for Early Admission to the Intensive Care Unit of Patients With Pneumonia

2012 ◽  
Vol 19 (9) ◽  
pp. 993-1003 ◽  
Author(s):  
José Labarère ◽  
Philipp Schuetz ◽  
Bertrand Renaud ◽  
Yann-Erick Claessens ◽  
Werner Albrich ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Early Admission

scholarly journals Development and external validation of an online clinical prediction model for augmented renal clearance in adult mixed critically ill patients: the ARC predictor

2020 ◽  
Author(s):  
Matthias Gijsen ◽  
Chao-yuan Huang ◽  
Marine Flechet ◽  
Ruth Van Daele ◽  
Peter Declercq ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Model ◽  
Renal Clearance ◽  
Tertiary Care ◽  
External Validation ◽  
Clinical Information ◽  
Clinical Prediction ◽  
Augmented Renal Clearance ◽  
Clinical Prediction Model ◽  
Icu Stay

Abstract Background Augmented renal clearance (ARC) might lead to subtherapeutic plasma levels of drugs with predominant renal clearance. Early identification of ARC remains challenging for the intensive care unit (ICU) physician. We developed and validated the ARC predictor, a clinical prediction model for ARC on the next day during ICU stay, and made it available via an online calculator. Its predictive performance was compared with that of two existing models for ARC. Methods A large multicenter database including medical, surgical and cardiac surgery ICU patients (n = 33258 ICU days) from three Belgian tertiary care academic hospitals was used for the development of the prediction model. Development was based on clinical information available during ICU stay. We assessed performance by measuring discrimination, calibration and net benefit. The final model was externally validated (n = 10259 ICU days) in a single-center population. Results ARC was found on 19.6% of all ICU days in the development cohort. Six clinical variables were retained in the ARC predictor: day from ICU admission, age, sex, serum creatinine, trauma and cardiac surgery. External validation confirmed good performance with an area under the curve of 0.88 (95% CI 0.87 – 0.88), and a sensitivity and specificity of 84.1 (95% CI 82.5 – 85.7) and 76.3 (95% CI 75.4 – 77.2) at the default threshold probability of 0.2, respectively. Conclusion ARC on the next day can be predicted with good performance during ICU stay, using routinely collected clinical information that is readily available at bedside. The ARC predictor is available at www.arcpredictor.com .

scholarly journals Investigation on Risk Factors of Ventilator-Associated Pneumonia in Acute Cerebral Hemorrhage Patients in Intensive Care Unit

2017 ◽  
Vol 2017 ◽  
pp. 1-4 ◽  
Author(s):  
Li Chang ◽  
Yun Dong ◽  
Ping Zhou
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Coronary Heart Disease ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Heart Disease ◽  
Hospital Stay ◽  
Cerebral Hemorrhage ◽  
Chronic Obstructive ◽  
Ventilator Associated Pneumonia

Ventilator-associated pneumonia (VAP) is a predominant factor of pulmonary infection. We analyzed the risk factors of VAP with acute cerebral hemorrhage in intensive care unit (ICU) by univariate and multivariate logistic regression analyses. After comparison of 197 cases of the VAP and non-VAP patients, we found that age > 65 years (P=0.003), smoke (P=0.003), coronary heart disease (P=0.005), diabetes (P=0.001), chronic obstructive pulmonary disease (COPD) (P=0.002), ICU and hospital stay (P=0.01), and days on mechanical ventilation (P=0.01) were significantly different, indicating that they are risk factors of VAP. All the age > 65 years (OR = 3.350, 95% CI = 1.936–5.796, P≤0.001), smoke (OR = 3.206, 95% CI = 1.909–5.385, P≤0.001), coronary heart disease (OR = 3.179, 95% CI = 1.015–4.130, P=0.017), diabetes (OR = 5.042, 95% CI = 3.518–7.342, P≤0.001), COPD (OR = 1.942, 95% CI = 1.258–2.843, P=0.012), ICU and hospital stay (OR = 2.34, 95% CI = 1.145–3.892, P=0.038), and days on mechanical ventilation (OR = 1.992, 95% CI = 1.107–3.287, P=0.007) are independent risk factors of VAP. After observation of patients with 6 months of follow-up, the BI score was significantly lower in VAP than that in non-VAP, and the rebleeding rate and mortality rate were significantly higher in VAP than those in non-VAP. Thus, the prognosis of the patients with acute cerebral hemorrhage and VAP in ICU is poor.

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