scholarly journals Conventional risk prediction models fail to accurately predict mortality risk among patients with coronavirus disease 2019 in intensive care units: a difficult time to assess clinical severity and quality of care

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hideki Endo ◽  
Hiroyuki Ohbe ◽  
Junji Kumasawa ◽  
Shigehiko Uchino ◽  
Satoru Hashimoto ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Risk Prediction ◽  
Prediction Models ◽  
Clinical Severity ◽  
Risk Prediction Models ◽  
Prediction Tools ◽  
Difficult Time ◽  
Care Database

AbstractSince the start of the coronavirus disease 2019 (COVID-19) pandemic, it has remained unknown whether conventional risk prediction tools used in intensive care units are applicable to patients with COVID-19. Therefore, we assessed the performance of established risk prediction models using the Japanese Intensive Care database. Discrimination and calibration of the models were poor. Revised risk prediction models are needed to assess the clinical severity of COVID-19 patients and monitor healthcare quality in ICUs overwhelmed by patients with COVID-19.

scholarly journals Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257768
Author(s):  
Wei Zhang ◽  
Yun Tang ◽  
Huan Liu ◽  
Li ping Yuan ◽  
Chu chu Wang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Icu Patients ◽  
Risk Prediction Models ◽  
Verification Methods

Background and objectives Intensive care unit-acquired weakness (ICU-AW) commonly occurs among intensive care unit (ICU) patients and seriously affects the survival rate and long-term quality of life for patients. In this systematic review, we synthesized the findings of previous studies in order to analyze predictors of ICU-AW and evaluate the discrimination and validity of ICU-AW risk prediction models for ICU patients. Methods We searched seven databases published in English and Chinese language to identify studies regarding ICU-AW risk prediction models. Two reviewers independently screened the literature, evaluated the quality of the included literature, extracted data, and performed a systematic review. Results Ultimately, 11 studies were considered for this review. For the verification of prediction models, internal verification methods had been used in three studies, and a combination of internal and external verification had been used in one study. The value for the area under the ROC curve for eight models was 0.7–0.923. The predictor most commonly included in the models were age and the administration of corticosteroids. All the models have good applicability, but most of the models are biased due to the lack of blindness, lack of reporting, insufficient sample size, missing data, and lack of performance evaluation and calibration of the models. Conclusions The efficacy of most models for the risk prediction of ICU-AW among high-risk groups is good, but there was a certain bias in the development and verification of the models. Thus, ICU medical staff should select existing models based on actual clinical conditions and verify them before applying them in clinical practice. In order to provide a reliable basis for the risk prediction of ICU-AW, it is necessary that large-sample, multi-center studies be conducted in the future, in which ICU-AW risk prediction models are verified.

scholarly journals Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Aziz Sheikh ◽  
Ulugbek Nurmatov ◽  
Huda Amer Al-Katheeri ◽  
Rasmeh Ali Al Huneiti
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Ethnically Diverse ◽  
Risk Of Bias ◽  
Low Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Eligibility Criteria ◽  
Risk Prediction Models ◽  
Ascvd Risk

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population. Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population. Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models. Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains. Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

scholarly journals Development and Evaluation of Models to Predict Readmission or Death After Discharge from Intensive Care 

2021 ◽  
Author(s):  
Jamie M Boyd ◽  
Matthew T James ◽  
Danny J Zuege ◽  
Henry Thomas Stelfox
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Local Data ◽  
Predictive Values ◽  
Risk Prediction Models ◽  
Icu Discharge

Abstract Background Patients being discharged from the intensive care unit (ICU) have variable risks of subsequent readmission or death; however, there is limited understanding of how to predict individual patient risk. We sought to derive risk prediction models for ICU readmission or death after ICU discharge to guide clinician decision-making. Methods Systematic review and meta-analysis to identify risk factors. Development and validation of risk prediction models using two retrospective cohorts of patients discharged alive from medical-surgical ICUs (n = 3 ICUs, n = 11,291 patients; n = 14 ICUs, n = 11,400 patients). Models were developed using literature and data-derived weighted coefficients. Results Sixteen variables identified from the systematic review were used to develop four risk prediction models. In the validation cohort there were 795 (7%) patients who were re-admitted to ICU and 703 (7%) patients who died after ICU discharge. The area under the curve (AUROC) for ICU readmission for the literature (0.615 [95%CI: 0.593, 0.637]) and data (0.652 [95%CI: 0.631, 0.674]) weighted models showed poor discrimination. The AUROC for death after ICU discharge for the literature (0.708 [95%CI: 0.687, 0.728]) and local data weighted (0.752 [95%CI: 0.733, 0.770]) models showed good discrimination. The negative predictive values for ICU readmission and death after ICU discharge ranged from 94%-98%. Conclusions Identifying risk factors and weighting coefficients using systematic review and meta-analysis to develop prediction models is feasible and can identify patients at low risk of ICU readmission or death after ICU discharge.

scholarly journals 702 Evaluation of Risk Prediction Models for Postoperative Pulmonary Complications in Adults Undergoing Major Abdominal Surgery: A Systematic Review and External Validation Study in The Respiratory Complications After Abdominal Surgery (RECON) Cohort

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Introduction Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Method A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland, and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records (112 full texts). These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUROC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUROC >0.7). Conclusions The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals V2 Evaluation of risk prediction models for postoperative pulmonary complications in adults undergoing major abdominal surgery: A systematic review and external validation study of the REspiratory COmplications after abdomiNal surgery (RECON) cohort

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Omar Kouli
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Background Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Methods A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records. These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUC >0.7). Conclusion The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

Sign in / Sign up

Export Citation Format

Share Document