scholarly journals Performing risk stratification for COVID-19 when individual level data is not available – the experience of a large healthcare organization

2020 ◽  
Author(s):  
Noam Barda ◽  
Dan Riesel ◽  
Amichay Akriv ◽  
Joseph Levi ◽  
Uriah Finkel ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Prediction Models ◽  
Case Fatality ◽  
Healthcare Organization ◽  
Operating Characteristics ◽  
Good Discrimination ◽  
Hybrid Strategy ◽  
Individual Level ◽  
Level Data

AbstractWith the global coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need for risk stratification tools to support prevention and treatment decisions. The Centers for Disease Control and Prevention (CDC) listed several criteria that define high-risk individuals, but multivariable prediction models may allow for a more accurate and granular risk evaluation. In the early days of the pandemic, when individual level data required for training prediction models was not available, a large healthcare organization developed a prediction model for supporting its COVID-19 policy using a hybrid strategy. The model was constructed on a baseline predictor to rank patients according to their risk for severe respiratory infection or sepsis (trained using over one-million patient records) and was then post-processed to calibrate the predictions to reported COVID-19 case fatality rates. Since its deployment in mid-March, this predictor was integrated into many decision-processes in the organization that involved allocating limited resources. With the accumulation of enough COVID-19 patients, the predictor was validated for its accuracy in predicting COVID-19 mortality among all COVID-19 cases in the organization (3,176, 3.1% death rate). The predictor was found to have good discrimination, with an area under the receiver-operating characteristics curve of 0.942. Calibration was also good, with a marked improvement compared to the calibration of the baseline model when evaluated for the COVID-19 mortality outcome. While the CDC criteria identify 41% of the population as high-risk with a resulting sensitivity of 97%, a 5% absolute risk cutoff by the model tags only 14% to be at high-risk while still achieving a sensitivity of 90%. To summarize, we found that even in the midst of a pandemic, shrouded in epidemiologic “fog of war” and with no individual level data, it was possible to provide a useful predictor with good discrimination and calibration.

scholarly journals Developing a COVID-19 mortality risk prediction model when individual-level data are not available

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Noam Barda ◽  
Dan Riesel ◽  
Amichay Akriv ◽  
Joseph Levy ◽  
Uriah Finkel ◽  
...  
Keyword(s):  
Case Fatality ◽  
Operating Characteristics ◽  
Hybrid Strategy ◽  
Individual Level ◽  
Level Data ◽  
Receiver Operating Characteristics Curve ◽  
Severe Respiratory Infection ◽  
Baseline Predictor ◽  
Risk Predictor ◽  
Risk Predictors

Abstract At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.

Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

2017 ◽  
Vol 35 (15) ◽  
pp. 1660-1667 ◽  
Author(s):  
Tuomo J. Meretoja ◽  
Kenneth Geving Andersen ◽  
Julie Bruce ◽  
Lassi Haasio ◽  
Reetta Sipilä ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Models ◽  
Persistent Pain ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Outcome Variable ◽  
Axillary Lymph ◽  
Operating Characteristics

Purpose Persistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort. Results Moderate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area ( P < .001), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.

scholarly journals Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

2021 ◽  
Author(s):  
Mohammad A. Dabbah ◽  
Angus B. Reed ◽  
Adam T.C. Booth ◽  
Arrash Yassaee ◽  
Alex Despotovic ◽  
...  
Keyword(s):  
High Risk ◽  
Mortality Risk ◽  
Prediction Models ◽  
Vital Signs ◽  
Classification Model ◽  
Risk Modeling ◽  
Uk Biobank ◽  
Individual Level ◽  
Random Forest Classification ◽  
The Uk

Abstract The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

scholarly journals Estimates of COVID-19 case-fatality risk from individual-level data

2020 ◽  
Author(s):  
Simona Bignami-Van Assche ◽  
Daniela Ghio ◽  
Ari Van Assche
Keyword(s):  
Case Fatality ◽  
Event History ◽  
Canadian Province ◽  
Fatality Risk ◽  
Individual Level ◽  
Level Data ◽  
The World ◽  
Simple Ratio ◽  
Case Fatality Risk ◽  
True Measure

ABSTRACTWhen calculated from aggregate data on confirmed cases and deaths, the case-fatality risk (CFR) is a simple ratio between the former and the latter, which is prone to numerous biases. With individual-level data, the CFR can be estimated as a true measure of risk as the proportion of incidence for the disease. We present the first estimates of the CFR for COVID-19 by age and sex based on event history modelling of the risk of dying among confirmed positive individuals in the Canadian province of Ontario, which maintains one of the few individual-level datasets on COVID-19 in the world.

scholarly journals Development and Validation of a Multivariable Prediction Model for Missed HIV Healthcare Provider Visits in a Large United States Clinical Cohort

2021 ◽  
Author(s):  
April C Pettit ◽  
Aihua Bian ◽  
Cassandra O Schember ◽  
Peter F Rebeiro ◽  
Jeanne C Keruly ◽  
...  
Keyword(s):  
Prediction Models ◽  
Community Level ◽  
Research Network ◽  
Hiv Care ◽  
People Living With Hiv ◽  
Individual Level ◽  
Visit Adherence ◽  
Level Data ◽  
Missed Visits ◽  
The Individual

Abstract Background Identifying individuals at high risk of missing HIV care provider visits could support proactive intervention. Previous prediction models for missed visits have not incorporated data beyond the individual-level. Methods We developed prediction models for missed visits among people living with HIV (PLWH) with ≥1 follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems from 2010-2016. Individual-level (medical record data and patient-reported outcomes), community-level (American Community Survey), HIV care site-level (standardized clinic leadership survey), and structural-level (HIV criminalization laws, Medicaid expansion, and state AIDS Drug Assistance Program budget) predictors were included. Models were developed using random forests with 10-fold cross-validation; candidate models with highest area under the curve (AUC) were identified. Results Data from 382,432 visits among 20,807 PLWH followed for a median of 3.8 years were included; median age was 44 years, 81% were male, 37% were Black, 15% reported injection drug use, and 57% reported male-to-male sexual contact. The highest AUC was 0.76 and strongest predictors were at the individual-level (prior visit adherence, age, CD4+ count) and community-level (proportion living in poverty, unemployed, and of Black race). A simplified model, including readily accessible variables available in a web-based calculator, had a slightly lower AUC of 0.700. Conclusions Prediction models validated using multi-level data had a similar AUC to previous models developed using only individual-level data. Strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive. Absent additional data, PLWH with previous missed visits should be prioritized by interventions to improve visit adherence.

scholarly journals COVID-19 Fatality and Comorbidity Risk Factors among Diagnosed Patients in Mexico

2020 ◽  
Author(s):  
Patricio Solís ◽  
Hiram Carreño
Keyword(s):  
Case Fatality Rate ◽  
Case Fatality ◽  
Fatality Rate ◽  
Cross Sectional ◽  
Fatality Risk ◽  
Individual Level ◽  
Level Data ◽  
Younger Age ◽  
High Prevalence ◽  
Right Censorship

AbstractAs of April 18, 2020, 2.16 million patients in the world had been tested positive with Coronavirus (COVID-19) and 146,088 had died, which accounts for a case fatality rate of 6.76%. In Mexico, according to official statistics (April 18), 7,497 cases have been confirmed with 650 deaths, for a case fatality rate of 8.67%. These estimates, however, may not reflect the final fatality risk among COVID-19 confirmed patients, because they are based on cross-sectional counts of diagnosed and deceased patients, and therefore are not adjusted by time of exposure and right-censorship. In this paper we estimate fatality risks based on survival analysis methods, calculated from individual-level data on symptomatic patients confirmed with COVID-19 recently released by the Mexican Ministry of Health. The estimated fatality risk after 35 days of onset of symptoms is 12.38% (95% CI: 11.37-13.47). Fatality risks sharply rise with age, and significantly increase for males (59%) and individuals with comorbidities (38%-168%, depending on the disease). Two reasons may explain the high COVID-19 related fatality risk observed in Mexico, despite its younger age structure: the high selectivity and self-selectivity in testing and the high prevalence of chronic-degenerative diseases.

scholarly journals Improved genetic prediction of complex traits from individual-level data or summary statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qianqian Zhang ◽  
Florian Privé ◽  
Bjarni Vilhjálmsson ◽  
Doug Speed
Keyword(s):  
Complex Traits ◽  
Prediction Models ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Genetic Prediction ◽  
Individual Level ◽  
Prediction Tools ◽  
Data Prediction ◽  
Level Data ◽  
Variance Explained

AbstractMost existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.

scholarly journals Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad A. Dabbah ◽  
Angus B. Reed ◽  
Adam T. C. Booth ◽  
Arrash Yassaee ◽  
Aleksa Despotovic ◽  
...  
Keyword(s):  
High Risk ◽  
Mortality Risk ◽  
Prediction Models ◽  
Vital Signs ◽  
Classification Model ◽  
Risk Modeling ◽  
Uk Biobank ◽  
Individual Level ◽  
Random Forest Classification ◽  
The Uk

AbstractThe COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

scholarly journals Higher Expression of WT1 With Lower CD58 Expression may be Biomarkers for Risk Stratification of Patients With Cytogenetically Normal Acute Myeloid Leukemia

2021 ◽  
Vol 20 ◽  
pp. 153303382110521
Author(s):  
Cunte Chen ◽  
Zhuowen Chen ◽  
Chi Leong Chio ◽  
Ying Zhao ◽  
Yongsheng Li ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
High Risk ◽  
Risk Stratification ◽  
Myeloid Leukemia ◽  
Wilms Tumor ◽  
Low Risk ◽  
Operating Characteristics ◽  
Potential Biomarker ◽  
Risk Patients ◽  
Acute Myeloid

Background: Cytogenetics at diagnosis is the most important prognostic factor for adult acute myeloid leukemia (AML), but nearly 50% of AML patients who exhibit cytogenetically normal AML (CN-AML) do not undergo effective risk stratification. Therefore, the development of potential biomarkers to further define risk stratification for CN-AML patients is worth exploring. Methods: Transcriptome data from 163 cases in the GSE12417-GPL96 dataset and 104 CN-AML patient cases in the GSE71014-GPL10558 dataset were downloaded from the Gene Expression Omnibus database for overall survival (OS) analysis and validation. Results: The combination of Wilms tumor 1 ( WT1) and cluster of diffraction 58 ( CD58) can predict the prognosis of CN-AML patients. High expression of WT1 and low expression of CD58 were associated with poor OS in CN-AML. Notably, when WT1 and CD58 were used to concurrently predict OS, CN-AML patients were divided into three groups: low risk, WT1low CD58high; intermediate risk, WT1high CD58high or WT1low CD58low; and high risk, WT1high CD58low. Compared with low-risk patients, intermediate- and high-risk patients had shorter survival time and worse OS. Furthermore, a nomogram model constructed with WT1 and CD58 may personalize and reveal the 1-, 2-, 3-, 4-, and 5-year OS rate of CN-AML patients. Both time-dependent receiver operating characteristics and calibration curves suggested that the nomogram model demonstrated good performance. Conclusion: Higher expression of WT1 with lower CD58 expression may be a potential biomarker for risk stratification of CN-AML patients. Moreover, a nomogram model constructed with WT1 and CD58 may personalize and reveal the 1-, 2-, 3-, 4-, and 5-year OS rates of CN-AML patients.

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