Longevity/Mortality Risk Modeling and Securities Pricing

2010 ◽  
Author(s):  
Patrick L. Brockett ◽  
Richard D. MacMinn ◽  
Yinglu Deng
Keyword(s):  
Mortality Risk ◽  
Risk Modeling ◽  
Securities Pricing

Longevity/Mortality Risk Modeling and Securities Pricing

2012 ◽  
Vol 79 (3) ◽  
pp. 697-721 ◽  
Author(s):  
Yinglu Deng ◽  
Patrick L. Brockett ◽  
Richard D. MacMinn
Keyword(s):  
Mortality Risk ◽  
Risk Modeling ◽  
Securities Pricing

scholarly journals Combined Use of Serum Uromodulin and eGFR to Estimate Mortality Risk

2021 ◽  
Vol 8 ◽  
Author(s):  
Babak Yazdani ◽  
Graciela E. Delgado ◽  
Hubert Scharnagl ◽  
Bernhard K. Krämer ◽  
Heinz Drexel ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Cardiovascular Health ◽  
Prediction Models ◽  
Driving Forces ◽  
Health Study ◽  
Cardiovascular Health Study ◽  
Inverse Association ◽  
Risk Modeling ◽  
Metabolic Disturbances ◽  
Improve Risk Stratification

Serum uromodulin (sUmod) shows a strong direct correlation with eGFR in patients with impaired kidney function and an inverse association with mortality. However, there are patients in whom only one of both markers is decreased. Therefore, we aimed to investigate the effect of marker discordance on mortality risk. sUmod and eGFR were available in 3,057 participants of the Ludwigshafen Risk and Cardiovascular Health study and 529 participants of the VIVIT study. Both studies are monocentric prospective studies of patients that had been referred for coronary angiography. Participants were categorized into four groups according to the median values of sUmod (LURIC: 146 ng/ml, VIVIT: 156) and eGFR (LURIC: 84 ml/min/1.73 m2, VIVIT: 87). In 945 LURIC participants both markers were high (UHGH), in 935 both were low (ULGL), in 589 only eGFR (UHGL), and in 582 only sUmod (ULGH) was low. After balancing the groups for cardiovascular risk factors, hazard ratios (95%CI) for all-cause mortality as compared to UHGH were 2.03 (1.63–2.52), 1.43 (1.13–1.81), and 1.32 (1.03–1.69) for ULGL, UHGL, and ULGH, respectively. In VIVIT, HRs were 3.12 (1.38–7.08), 2.38 (1.01–5.61), and 2.06 (0.81–5.22). Adding uromodulin to risk prediction models that already included eGFR as a covariate slightly increased the Harrell's C and significantly improved the AUC in LURIC. In UHGL patients, hypertension, heart failure and upregulation of the renin-angiotensin-aldosterone-system seem to be the driving forces of disease development, whereas in ULGH patients metabolic disturbances might be key drivers of increased mortality. In conclusion, SUmod/eGFR subgroups mirror distinct metabolic and clinical patterns. Assessing sUmod additionally to creatinine or cystatin C has the potential to allow a more precise risk modeling and might improve risk stratification.

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 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 Heart Failure Supportive Care: Integrating Mortality Risk Modeling Into Heart Failure Care to Impact Outcomes

2015 ◽  
Vol 21 (8) ◽  
pp. S123
Author(s):  
Mitchell T. Saltzberg ◽  
Roshni Guerry ◽  
Kelly A. Whitmarsh ◽  
Carolyn Moffa ◽  
Lisa Keichline
Keyword(s):  
Heart Failure ◽  
Supportive Care ◽  
Mortality Risk ◽  
Risk Modeling ◽  
Heart Failure Care

Mortality risk modeling: Applications to insurance securitization

2010 ◽  
Vol 46 (1) ◽  
pp. 242-253 ◽  
Author(s):  
Samuel H. Cox ◽  
Yijia Lin ◽  
Hal Pedersen
Keyword(s):  
Mortality Risk ◽  
Risk Modeling

486 Clinical composite endpoint classification of REVIVE II corresponds to all-cause mortality risk

2007 ◽  
Vol 6 (1) ◽  
pp. 106-107
Author(s):  
J TEERLINK ◽  
L DELGADOHERRERA ◽  
R THAKKAR ◽  
B HUANG ◽  
R PADLEY
Keyword(s):  
Mortality Risk ◽  
Composite Endpoint ◽  
All Cause Mortality

Discharge to Institution Tied to Mortality Risk

2010 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
DAMIAN McNAMARA
Keyword(s):  
Mortality Risk
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