scholarly journals TOWARD A COVID-19 SCORE-RISK ASSESSMENTS AND REGISTRY

2020 ◽  
Author(s):  
M Cristina Vazquez Guillamet ◽  
Rodrigo Vazquez Guillamet ◽  
Andrew A. Kramer ◽  
Paula M. Maurer ◽  
Gregory A. Menke ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hospital Mortality ◽  
Predictive Value ◽  
Prediction Models ◽  
Rapid Development ◽  
External Validation ◽  
Validation Dataset ◽  
Assisted Mechanical Ventilation ◽  
Icu Admission ◽  
Starting Point

ABSTRACTImportanceCritical care resources like ventilators, used to manage the current COVID-19 pandemic, are potentially inadequate. Established triage standards and guidelines may not contain the most appropriate severity assessment and outcome prediction models.ObjectivesDevelop a draft pandemic specific triage assessment score for the current COVID-19 pandemic. Design a website where initial Toward a COVID-19 Scores (TACS) can be quickly calculated and used to compare various treatment strategies. Create a TACS Registry where data and outcomes for suspected and confirmed COVID-19 patients can be recorded. Use the TACS Registry to develop an influenza epidemic specific database and score for use in future respiratory based epidemics.Design, Setting, ParticipantsRetrospective analysis of 3,301 ICU admissions with respiratory failure admitted to 41 U.S. Intensive Care Units from 2015-19. Independent external validation on 1,175 similar ICU Admissions using identical entry criteria from Barnes Jewish Hospital (BJH), Washington University from 2016-2019.Main OutcomesTACS was created with 16 readily available predictive variables for risk assessment of hospital mortality 24 hours after ICU Admission and the need for prolonged assisted mechanical ventilation (PAMV) (> 96 hours) at 24- and 48-hours post ICU admission.ResultsTACS achieved an Area Under the Curve (AUC) for hospital mortality after 24 hours of 0.80 in the development dataset; 0.81 in the internal validation dataset. At a probability of 50% hospital mortality, positive predictive value (PPV) was 0.55, negative predictive value (NPV) 0.89; sensitivity 22%, specificity 97%.For PAMV after 24 hours, the AUC was 0.84 in the development dataset, 0.81 in the validation dataset. For PAMV after 48 hours, the AUC was 0.82 in the development dataset, 0.78 in the validation dataset.In the external validation the AUC for TACS was 0.76 +/- 0.024. We launched a website that is scaled for mobile device use (https://covid19score.azurewebsites.net/) that provides open access to a user-friendly TACS Calculator for all predictions. We also designed a voluntary TACS Registry for collection of data and outcomes on ICU Admissions with COVID-19.Conclusions and RelevanceToward a COVID-19 score is a starting point for an epidemic specific triage assessment that could be used to evaluate various approaches to treatment. The TACS Registry provides the ability to establish a respiratory specific outcomes database that can be used to create a triage approach for future such pandemics.Key PointsQuestionCan a rapid epidemic specific risk assessment severity score and data and outcome repository be constructed in the midst of a pandemic.FindingsUsing development and validation datasets with ICU admissions similar to those developing COVID-19, developed an initial Toward a COVID-19 Score that could be used to compare various treatment approaches. Also launched an online facilitated data collection and outcome assessment registry for collection of a pandemic specific database so a new triage score could be created for use in the next pandemic.MeaningIn the midst of a pandemic rapid development of an epidemic specific triage score and a data registry for the creation of a new score for use in future pandemics appears feasible.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

scholarly journals Improvement of APACHE II score system for disease severity based on XGBoost algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yan Luo ◽  
Zhiyu Wang ◽  
Cong Wang
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Patient Outcomes ◽  
External Validation ◽  
Apache Ii ◽  
Validation Dataset ◽  
Apache Ii Score ◽  
Severity Scores ◽  
The Impact ◽  
Improve Patient

Abstract Background Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system. Methods We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model. Results We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good. Conclusions As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

Abstract 210: Effects of Indication for Veno-Arterial Extracorporeal Membrane Oxygenation on Predictive Value of Save Score

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Marinos Kosmopoulos ◽  
Jason A Bartos ◽  
Demetris Yannopoulos
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Roc Curve ◽  
Predictive Value ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Extracorporeal Cardiopulmonary Resuscitation ◽  
Membrane Oxygenation ◽  
Survival Estimation ◽  
Va Ecmo

Introduction: Veno-Arterial Extracorporeal Membrane Oxygenation (VA ECMO) has emerged as a prominent tool for management of patients with Inability to Wean Off Cardiopulmonary Bypass (IWOCB), extracorporeal cardiopulmonary resuscitation (eCPR) or refractory cardiogenic shock (RCS). The high mortality that is still associated with these diseases urges for the development of reliable prediction models for mortality after cannulation. Survival After VA ECMO (SAVE) Score consists one of the most widely used prediction tools and the only model with external validation. However, its predictive value is still under debate. Hypothesis: Whether VA ECMO indication affects the predictive value of SAVE Score. Methods: 317 patients treated with VA ECMO in a quaternary center (n= 52 for IWOCB, n=179 for eCPR and n=86 for RCS) were retrospectively assessed for differences in SAVE Score and their primary outcomes. The Receiver Operating Characteristic (ROC) curve for SAVE Score and mortality was calculated separately for each VA ECMO indication. Results: The three groups had significant differences in SAVE Score (p<0.01) without significant differences in mortality (p=0.176). ROC Curve calculation indicated significant differences in predictive value of SAVE Score for survival among its different indications. (Area Under the Curve= 81.69% for IWOCB, 53.79% for eCPR and 69.46% for RCS). Conclusion: VA ECMO indication markedly affects the predictive value of SAVE Score. Prediction of primary outcome in IWOCB patients was reliable. On the contrary, routine application for survival estimation in eCPR patients is not supported from our results.

scholarly journals Preoperative Risk Assessment: A Poor Predictor of Outcome in Critically ill Elderly with Sepsis After Abdominal Surgery

2020 ◽  
Vol 44 (12) ◽  
pp. 4060-4069
Author(s):  
Anne C. M. Cuijpers ◽  
Marielle M. E. Coolsen ◽  
Ronny M. Schnabel ◽  
Susanne van Santen ◽  
Steven W. M. Olde Damink ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Abdominal Surgery ◽  
Hospital Mortality ◽  
Emergency Surgery ◽  
Outcome Prediction ◽  
Elective Surgery ◽  
Perioperative Risk ◽  
Physical Status ◽  
Icu Admission ◽  
Apache Iv

Abstract Background Postoperative outcome prediction in elderly is based on preoperative physical status but its predictive value is uncertain. The goal was to evaluate the value of risk assessment performed perioperatively in predicting outcome in case of admission to an intensive care unit (ICU). Methods A total of 108 postsurgical patients were retrospectively selected from a prospectively recorded database of 144 elderly septic patients (>70 years) admitted to the ICU department after elective or emergency abdominal surgery between 2012 and 2017. Perioperative risk assessment scores including Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality (P-POSSUM) and American Society of Anaesthesiologists Physical Status classification (ASA) were determined. Acute Physiology and Chronic Health Evaluation IV (APACHE IV) was obtained at ICU admission. Results In-hospital mortality was 48.9% in elderly requiring ICU admission after elective surgery (n = 45), compared to 49.2% after emergency surgery (n = 63). APACHE IV significantly predicted in-hospital mortality after complicated elective surgery [area under the curve 0.935 (p < 0.001)] where outpatient ASA physical status and P-POSSUM did not. In contrast, P-POSSUM and APACHE IV significantly predicted in-hospital mortality when based on current physical state in elderly requiring emergency surgery (AUC 0.769 (p = 0.002) and 0.736 (p = 0.006), respectively). Conclusions Perioperative risk assessment reflecting premorbid physical status of elderly loses its value when complications occur requiring unplanned ICU admission. Risks in elderly should be re-assessed based on current clinical condition prior to ICU admission, because outcome prediction is more reliable then.

scholarly journals DEVELOPING AND VALIDATING COVID-19 ADVERSE OUTCOME RISK PREDICTION MODELS FROM A BI-NATIONAL EUROPEAN COHORT OF 5594 PATIENTS

2020 ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Organ Dysfunction ◽  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Manuscript Preparation ◽  
Design Data ◽  
Prognostic Features ◽  
Icu Admission ◽  
Risk Of Death

ABSTRACTBackgroundPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that Machine Learning (ML) models could be used to predict risks at different stages of management (at diagnosis, hospital admission and ICU admission) and thereby provide insights into drivers and prognostic markers of disease progression and death.MethodsFrom a cohort of approx. 2.6 million citizens in the two regions of Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. A cohort of SARS- CoV-2 positive cases from the United Kingdom Biobank was used for external validation.FindingsThe ML models predicted the risk of death (Receiver Operation Characteristics – Area Under the Curve, ROC-AUC) of 0.904 at diagnosis, 0.818, at hospital admission and 0.723 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. We identified some common risk factors, including age, body mass index (BMI) and hypertension as driving factors, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission.InterpretationML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. Prognostic features included age, BMI and hypertension, although markers of shock and organ dysfunction became more important in more severe cases.We provide access to an online risk calculator based on these findings.FundingThe study was funded by grants from the Novo Nordisk Foundation to MS (#NNF20SA0062879 and #NNF19OC0055183) and MN (#NNF20SA0062879). The foundation took no part in project design, data handling and manuscript preparation.

scholarly journals Predicting asthma-related crisis events using routine electronic healthcare data

2021 ◽  
pp. BJGP.2020.1042
Author(s):  
Michael Noble ◽  
Annie Burden ◽  
Susan Stirling ◽  
Allan Clark ◽  
Stanley Musgrave ◽  
...  
Keyword(s):  
High Risk ◽  
Predictive Value ◽  
External Validation ◽  
Predictive Ability ◽  
Prediction Algorithm ◽  
Validation Dataset ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Healthcare Data ◽  
Crisis Events

Background: There is no published algorithm predicting asthma crisis events (Accident and Emergency (A&E) attendance, hospitalisation or death) using routinely available electronic health record (EHR) data. Aim: To develop an algorithm to identify individuals at high risk of an asthma crisis event. Design and Setting: Database analysis from primary care EHRs. Method: Multivariable logistic regression was applied to a dataset of 61,861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage databank of 174,240 patients from Wales. Outcomes were one or more hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance or death (validation dataset) within a 12-month period. Results: Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a Receiver Operating Characteristic (ROC) of 0.71 (0.70, 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI 5.3 – 6.1) and a negative predictive value of 98.9% (98.9 – 99.0), with sensitivity of 28.5% (26.7 – 30.3) and specificity of 93.3% (93.2 – 93.4); they had an event risk of 6.0% compared 1.1% for the remaining population. Eighteen people would be “needed to follow” to identify one admission. Conclusions: This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding individuals not at high risk.

scholarly journals Development and validation of circulating CA125 prediction models in postmenopausal women

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Naoko Sasamoto ◽  
Ana Babic ◽  
Bernard A. Rosner ◽  
Renée T. Fortner ◽  
Allison F. Vitonis ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Prediction Model ◽  
Postmenopausal Women ◽  
Prediction Models ◽  
Smoking Status ◽  
Large Population ◽  
External Validation ◽  
Validation Dataset ◽  
Ovarian Cancer Screening

Abstract Background Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. Methods We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses’ Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. Result The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. Conclusions The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.

scholarly journals The comparison of two risk prediction models specific for COVID-19: The Brescia-COVID Respiratory Severity Scale versus the Quick COVID-19 Severity Index

2021 ◽  
pp. 1-17
Author(s):  
Rohat AK ◽  
Erdem KURT ◽  
Suphi BAHADIRLI
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Scoring Systems ◽  
Severity Index ◽  
Severity Scale ◽  
Risk Prediction Models ◽  
Icu Admission ◽  
The Mean

Abstract Objective: This study compared the prognostic performances of the Brescia-COVID Respiratory Severity Scale (BCRSS) and the Quick COVID-19 Severity Index (qCSI) scores in hospitalized patients diagnosed with COVID-19. Methods: The data of all adult patients (over 18 years of age) who were admitted into a state hospital with confirmed COVID-19 between May 1, 2020 and October 31, 2020 were retrospectively examined. The area under the receiver operating characteristic (ROC) curve, known as the area under the curve (AUC), was used to assess the BCRSS prediction rule and the qCSI score to assess the discriminatory power in predicting in-hospital mortality and intensive care unit (ICU) admission. Results: There were 341 patients included in this study. The mean age of the patients was 58.2 ± 17.2, of which 165 were men and 176 were women, and 61.3% of patients had at least one comorbidity. The most common comorbidity was hypertension. The predictive power scores of BCRSS and qCSI were found as very good in terms of in-hospital mortality (AUC 0.804 and 0.847, respectively) and likewise in terms of ICU admission (AUC 0.842 and 0.851, respectively). Conclusion: Both BCRSS and qCSI scoring systems were found to be successful in predicting in-hospital mortality and ICU admission in our patient population.

scholarly journals Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246640
Author(s):  
Tomohisa Seki ◽  
Yoshimasa Kawazoe ◽  
Kazuhiko Ohe
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Test Data ◽  
Random Forests ◽  
Prediction Models ◽  
Laboratory Data ◽  
Data Set ◽  
Prediction Capability

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient’s severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization.

scholarly journals Comparison of in-hospital mortality risk prediction models from COVID-19

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244629
Author(s):  
Ali A. El-Solh ◽  
Yolanda Lawson ◽  
Michael Carter ◽  
Daniel A. El-Solh ◽  
Kari A. Mergenhagen
Keyword(s):  
Hospital Mortality ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
External Validation ◽  
Prognostic Models ◽  
Study Cohort ◽  
Goodness Of Fit Test ◽  
Risk Prediction Models

Objective Our objective is to compare the predictive accuracy of four recently established outcome models of patients hospitalized with coronavirus disease 2019 (COVID-19) published between January 1st and May 1st 2020. Methods We used data obtained from the Veterans Affairs Corporate Data Warehouse (CDW) between January 1st, 2020, and May 1st 2020 as an external validation cohort. The outcome measure was hospital mortality. Areas under the ROC (AUC) curves were used to evaluate discrimination of the four predictive models. The Hosmer–Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases. Results During the study period, 1634 unique patients were identified. The mean age of the study cohort was 68.8±13.4 years. Hypertension, hyperlipidemia, and heart disease were the most common comorbidities. The crude hospital mortality was 29% (95% confidence interval [CI] 0.27–0.31). Evaluation of the predictive models showed an AUC range from 0.63 (95% CI 0.60–0.66) to 0.72 (95% CI 0.69–0.74) indicating fair to poor discrimination across all models. There were no significant differences among the AUC values of the four prognostic systems. All models calibrated poorly by either overestimated or underestimated hospital mortality. Conclusions All the four prognostic models examined in this study portend high-risk bias. The performance of these scores needs to be interpreted with caution in hospitalized patients with COVID-19.

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