scholarly journals Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003–2006

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 4
Author(s):  
Lucia Tabacu ◽  
Mark Ledbetter ◽  
Andrew Leroux ◽  
Ciprian Crainiceanu ◽  
Ekaterina Smirnova
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Time Horizons ◽  
All Cause Mortality ◽  
Mortality Prediction Models ◽  
Activity Measures ◽  
Traditional Risk Factors ◽  
Wearable Accelerometers

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.

scholarly journals All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease

2021 ◽  
Author(s):  
Massimiliano Copetti ◽  
Edoardo Biancalana ◽  
Andrea Fontana ◽  
Federico Parolini ◽  
Monia Garofolo ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Early Stage ◽  
Mortality Prediction ◽  
Stage Of Disease ◽  
All Cause Mortality ◽  
Mortality Prediction Models

scholarly journals Quantifying the Predictive Performance of Objectively Measured Physical Activity on Mortality in the UK Biobank

2020 ◽  
Author(s):  
Andrew Leroux ◽  
Shiyao Xu ◽  
Prosenjit Kundu ◽  
John Muschelli ◽  
Ekaterina Smirnova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Cox Regression ◽  
Predictive Performance ◽  
Prediction Performance ◽  
Objective Measures ◽  
Uk Biobank ◽  
All Cause Mortality ◽  
Traditional Risk Factors ◽  
The Uk

Abstract Background Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank. Method A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82 304 individuals over the age of 50 (439 707 person-years of follow-up, 1959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality. Results In univariate Cox regression, age was the best predictor of all-cause mortality (C = 0.681) followed by 12 PA predictors, led by minutes of moderate-to-vigorous PA (C = 0.661) and total acceleration (C = 0.661). Overall, 16 of the top 20 predictors were objective PA measures (C = 0.578–0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value < .001). Conclusions Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).

scholarly journals High mortality rate of obstetric critically ill women in Rwanda and its predictability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amyl Lucille Cassidy ◽  
Théogène Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Mortality Prediction ◽  
Routine Practice ◽  
Icu Mortality ◽  
Resource Limited ◽  
Obstetric Haemorrhage ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission to intensive care units (ICUs) for obstetric patients vary from one setting to another. Outcomes from ICU and prediction models are not well explored in Rwanda owing to lack of appropriate scores. This study aimed to assess reasons for admission and accuracy of prediction models for mortality of obstetric patients admitted to ICUs of two public tertiary hospitals in Rwanda. Methods We prospectively collected data from all obstetric patients admitted to the ICUs of the two public tertiary hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admission, demographic and clinical characteristics, outcome including death and its predictability by both the Modified Early Obstetric Warning Score (MEOWS) and quick Sequential Organ Failure Assessment (qSOFA). We analysed the accuracy of mortality prediction models by MEOWS or qSOFA by using logistic regression adjusting for factors associated with mortality. Area under the Receiver Operating characteristic (AUROC) curves is used to show the predicting capacity for each individual tool. Results Obstetric patients (n = 94) represented 12.8 % of all 747 ICU admissions which is 1.8 % of all 4.999 admitted women for pregnancy or labor. Sepsis (n = 30; 31.9 %) and obstetric haemorrhage (n = 24; 25.5 %) were the two commonest reasons for ICU admission. Overall ICU mortality for obstetric patients was 54.3 % (n = 51) with average length of stay of 6.6 ± 7.525 days. MEOWS score was an independent predictor of mortality (adjusted (a)OR 1.25; 95 % CI 1.07–1.46) and so was qSOFA score (aOR 2.81; 95 % CI 1.25–6.30) with an adjusted AUROC of 0.773 (95 % CI 0.67–0.88) and 0.764 (95 % CI 0.65–0.87), indicating fair accuracy for ICU mortality prediction in these settings of both MEOWS and qSOFA scores. Conclusions Sepsis and obstetric haemorrhage were the commonest reasons for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA scores could accurately predict ICU mortality of obstetric patients in resource-limited settings, but larger studies are needed before a recommendation for their use in routine practice in similar settings.

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

scholarly journals Equitably Allocating Resources During Crises: Racial Differences in Mortality Prediction Models

2021 ◽  
Author(s):  
Deepshikha Charan Ashana ◽  
George L Anesi ◽  
Vincent X Liu ◽  
Gabriel J Escobar ◽  
Christopher Chesley ◽  
...  
Keyword(s):  
Racial Differences ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Mortality Prediction Models

scholarly journals High mortality rate of obstetric critically ill patients in Rwanda and its predictability

2020 ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amy Lucille Cassidy ◽  
Theogene Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Roc Curves ◽  
Mortality Prediction ◽  
Icu Mortality ◽  
Average Length Of Stay ◽  
Referral Hospitals ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission at the intensive care units (ICU) for obstetric patients vary from a setting to another. Outcomes from ICU and its prediction models are not well explored in Rwanda because of lack of appropriate scores. This study intended to assess profile and accuracy of predictive models for obstetric patients admitted in ICU in the two public tertiary hospitals in Rwanda.Methods We prospectively collected data from all obstetric patients admitted in the ICU of public referral hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admissions and factors for prognosis. We analysed the accuracy of mortality prediction models including the quick Sequential Organ Failure Assessment (qSOFA) and Modified Early Obstetric Warning Score (MEOWS) by using the Logistic Regression and adjusted Receiver Operating characteristic (ROC) curves. Results Obstetric patients represented 12.8% of all ICU admissions and 1.8% of all deliveries. Sepsis (31.9%) and haemorrhage (25.5%) were the two commonest reasons for ICU admission in our study participants. The overall ICU mortality for our obstetric patients was 54.3% while the average length of stay was 6.6 days. MEOWS score was an independent predictor to mortality (adjusted OR=1.25[1.07-1.46]; p=0.005) and so was the qSOFA score (adjusted OR=2.81[1.25-6.30]; p=0.012). The adjusted Area Under the ROC (AUROC) for MEOWS was 0.773[0.666-0.880] and that of the qSOFA was 0.764[0.654-0.873] signing fair accuracies for ICU mortality prediction in these settings for both models.Conclusion Sepsis is the commonest reason for admissions to ICU for obstetric patients in Rwanda. Simple models comprising MEOWS and qSOFA could accurately predict the mortality for those patients but further larger studies are needed before generalization.

Abstract 2656: Copeptin Predicts Cardiovascular Death in STEMI Independent of Clinical Factors and NT-proBNP

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Nihar R Desai ◽  
David A Morrow ◽  
Songtao Jiang ◽  
Christoph Bode ◽  
Nader Rifai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Death ◽  
Mortality Prediction ◽  
Clinical Factors ◽  
Killip Class ◽  
C Statistic ◽  
St Elevation ◽  
Baseline Characteristics ◽  
Traditional Risk Factors ◽  
Circulating Levels

Background : Elevated levels of copeptin, the c-terminal portion of provasopressin, add significantly to natriuretic peptides for mortality prediction in heart failure. We hypothesized that elevated levels would predict mortality in ST-elevation myocardial infarction (STEMI). Methods : Circulating copeptin levels were measured at baseline in a case-cohort of 535 STEMI patients undergoing fibrinolysis in CLARITY-TIMI 28. Patients were stratified into quartiles by baseline copeptin. Multivariable logistic regression was used to examine the association between copeptin and 30-d cardiovascular (CV) mortality independent of clinical factors and NT-proBNP. Results : The median level of copeptin was 615 pg/ml (IQR 333–728 pg/ml). Baseline levels of copeptin tended to be higher in patients who were older, had a prior MI, were treated sooner after sx onset, and were in Killip class II-IV. For each 1-SD increase in log-transformed copeptin, the OR for CV death was 1.45 (p=0.01). After adjusting for differences in baseline characteristics patients in the highest copeptin quartile were at a significantly higher risk of CV death compared with patients in quartiles 1–3 (OR 1.99 [1.05–3.78]). Copeptin was not significantly correlated with NT-proBNP (r=0.09). In a multivariable model, copeptin and NT-proBNP were each significant independent predictors of CV death (Figure ); the c-statistic went from 0.75 to 0.81 with their addition to a model containing clinical risk factors. Conclusion : In a multimarker model, circulating levels of copeptin and NT-proBNP at presentation were powerful and complementary predictors of CV death beyond traditional risk factors in patients with STEMI.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

Sign in / Sign up

Export Citation Format

Share Document