Practical prediction model for the risk of 2-year mortality of individuals in the general population

2016 ◽  
Vol 64 (4) ◽  
pp. 848-853 ◽  
Author(s):  
Alexander Goldfarb-Rumyantzev ◽  
Shiva Gautam ◽  
Robert S Brown
Keyword(s):  
General Population ◽  
Risk Stratification ◽  
Prediction Model ◽  
Mortality Rates ◽  
Risk Indicator ◽  
Intermediate Risk ◽  
Data Set ◽  
Health And Nutrition ◽  
Risk Stratification Tool ◽  
Potential Use

This study proposed to validate a prediction model and risk-stratification tool of 2-year mortality rates of individuals in the general population suitable for office practice use. A risk indicator (R) derived from data in the literature was based on only 6 variables: to calculate R for an individual, starting with 0, for each year of age above 60, add 0.14; for a male, add 0.9; for diabetes mellitus, add 0.7; for albuminuria >30 mg/g of creatinine, add 0.7; for stage ≥3 chronic kidney disease (CKD), add 0.9; for cardiovascular disease (CVD), add 1.4; or for both CKD and CVD, add 1.7. We developed a univariate logistic regression model predicting 2-year individual mortality rates. The National Health and Nutrition Examination Survey (NHANES) data set (1999–2004 with deaths through 2006) was used as the target for validation. These 12,515 subjects had a mean age of 48.9±18.1 years, 48% males, 9.5% diabetes, 11.7% albuminuria, 6.8% CVD, 5.4% CKD, and 2.8% both CKD and CVD. Using the risk indicator R alone to predict mortality demonstrated good performance with area under the receiver operating characteristic (ROC) curve of 0.84. Dividing subjects into low-risk (R=0–1.0), low intermediate risk (R>1.0–3.0), high intermediate risk (R>3.0–5.0) or high-risk (R>5.0) categories predicted 2-year mortality rates of 0.52%, 1.44%, 5.19% and 15.24%, respectively, by the prediction model compared with actual mortality rates of 0.29%, 2.48%, 5.13% and 13.40%, respectively. We have validated a model of risk stratification using easily identified clinical characteristics to predict 2-year mortality rates of individuals in the general population. The model demonstrated performance adequate for its potential use for clinical practice and research decisions.

scholarly journals Development and presentation of an objective risk stratification tool for healthcare workers when dealing with the COVID-19 pandemic in the UK: risk modelling based on hospitalisation and mortality statistics compared with epidemiological data

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e042225
Author(s):  
W David Strain ◽  
Janusz Jankowski ◽  
Angharad P Davies ◽  
Peter English ◽  
Ellis Friedman ◽  
...  
Keyword(s):  
General Population ◽  
Risk Stratification ◽  
Healthcare Workers ◽  
Epidemiological Data ◽  
Assessment Process ◽  
White Female ◽  
Increased Risk ◽  
Objective Risk ◽  
Risk Stratification Tool ◽  
Line Manager

ObjectivesHealthcare workers have greater exposure to SARS-CoV-2 and an estimated 2.5-fold increased risk of contracting COVID-19 than the general population. We wished to explore the predictive role of basic demographics to establish a simple tool that could help risk stratify healthcare workers.SettingWe undertook a review of the published literature (including multiple search strategies in MEDLINE with PubMed interface) and critically assessed early reports on preprint servers. We explored the relative risk of mortality from readily available demographics to identify the population at the highest risk.ResultsThe published studies specifically assessing the risk of healthcare workers had limited demographics available; therefore, we explored the general population in the literature. Clinician demographics: Mortality increased with increasing age from 50 years onwards. Male sex at birth, and people of black and minority ethnicity groups had higher susceptibility to both hospitalisation and mortality. Comorbid disease. Vascular disease, renal disease, diabetes and chronic pulmonary disease further increased risk. Risk stratification tool: A risk stratification tool was compiled using a white female aged <50 years with no comorbidities as a reference. A point allocated to risk factors was associated with an approximate doubling in risk. This tool provides numerical support for healthcare workers when determining which team members should be allocated to patient facing clinical duties compared with remote supportive roles.ConclusionsWe generated a tool that provides a framework for objective risk stratification of doctors and healthcare professionals during the COVID-19 pandemic, without requiring disclosure of information that an individual may not wish to share with their direct line manager during the risk assessment process. This tool has been made freely available through the British Medical Association website and is widely used in the National Health Service and other external organisations.

Venous Thromboembolism (VTE) in Patients (pts.) with Monoclonal Gammopathy of Undetermined Significance (MGUS)

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 5340-5340
Author(s):  
Alaa A Muslimani ◽  
Fadi Bailony ◽  
Madappa Kundranda ◽  
Timothy Spiro ◽  
Asif Chaudhry ◽  
...  
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Risk Stratification ◽  
Cleveland Clinic ◽  
Risk Groups ◽  
Cancer Center ◽  
Intermediate Risk ◽  
Risk Stratification Model ◽  
Significant Difference

Abstract Introduction: MGUS is considered to be a pre-malignant condition, and previous studies have reported VTE as a marker for a subsequent malignancy. We conducted a retrospective study to evaluate the incidence of VTE among MGUS patients (pts) and to correlate this incidence with different risk groups for developing malignancy in MGUS pts. Methods: The complete medical records of all MGUS pts at Cleveland Clinic Cancer Center at Fairview hospital from Jun/2005–Jun/2008 were retrospectively reviewed. Of 237 pts diagnosed with MGUS, 112 pts (65 males, 47 females) were eligible for our study. These pts were divided into 2 risk groups: low risk (LR)/low-intermediate risk (LIR) group (78 pts.) and high intermediate risk (HIR)/high risk (HR) group (34 pts) based on the Risk Stratification Model using three adverse risk factors; serum M-protein level ≥ 3 gm/dL, non-IgG MGUS, and an abnormal kappa/lambda free light chain ratio. Only pts with ≥ 12 months follow up were included. Exclusion criteria included a personal history of inherited thrombophilia, previous episode of VTE or anticoagulant treatment, thrombocytosis, malignancy, and renal impairment. Risk factors (RF) for VTE were identified in each pt and categorized into four groups: no RF, 0; one RF, 1; two RF, 2; and &gt; 2 RF, &gt;2. RF included &gt; 48 hours of immobilization, surgery in the past 3 months, current hospitalization at the time of VTE occurrence, oral contraceptive use, and congestive heart failure. Objectives: To compare the proportion of pts with MGUS who developed VTE to the proportion of pts in the general population who developed VTE. To compare VTE incidence between the two risk groups. Results: During the study period, 9 pts with MGUS experienced VTE. In the general population, the incidence of VTE is 117/100,000 persons/year (from literature). Therefore, the proportion of pts in the general population over 3 years was 117/100000 × 3 =0.0035. The proportion of VTE in MGUS pts, adjusted for 3 years, of 0.080 is significantly higher than that for the general population (p&lt;0.001). Comparison of VTE incidence between the two risk groups, while adjusting for the number of risk factors, showed no difference (Cox Proportional Model, p=0.38). There is no significant difference in the risk of VTE among different levels of risk factors (p=0.96). The Kaplan-Meier estimates of the proportions of pts free of VTE at 24 months are 0.96 and 0.93 for the LR/LIR and HIR/HR groups, respectively. Conclusions: MGUS is associated with a significantly higher rate of VTE compared to the general population. Despite many studies indicating VTE as a marker for subsequent malignancy, we did not find a difference in the incidence of VTE among the various risk factor groups. Any suggestive signs of VTE in pts with MGUS should be promptly evaluated and treatment initiated as soon as possible. Since the number of pts is small and the period of follow-up relatively short, a prospective cohort study is needed to verify our results. Table?: Comparison of event rate: VTE Po p-value Total number of pts Risk stratification model (pts) Groups (pts) VTE Proportion Note: Po is the VTE proportion for the general population over a 3-year time period. 112 LR (38) LR/LIR (78) (5) LIR (40) 0.080 0.0035 &lt;0.001 HIR (26) HIR/HR (34) (4) HR (8)

scholarly journals Prediction Model and Risk Stratification Tool for Survival in Patients With CKD

2018 ◽  
Vol 3 (2) ◽  
pp. 417-425 ◽  
Author(s):  
Alexander S. Goldfarb-Rumyantzev ◽  
Shiva Gautam ◽  
Ning Dong ◽  
Robert S. Brown
Keyword(s):  
Risk Stratification ◽  
Prediction Model ◽  
Risk Stratification Tool

scholarly journals Combination of NCCN Risk Stratification System and Pre-Transplant Minimal Residual Disease Levels for the Prognosis of Acute Myeloid Leukemia after Allogeneic Hematopoietic Stem Cell Transplantation

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3434-3434
Author(s):  
Yahan Li ◽  
Xue Sun ◽  
Xin Wang ◽  
Xiaosheng Fang
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Poor Prognosis ◽  
Residual Disease ◽  
Risk Groups ◽  
High Risk Group ◽  
Intermediate Risk ◽  
Hematopoietic Stem ◽  
Risk Stratification Tool ◽  
Prognosis Group

Abstract Background Numerous studies have confirmed that National Comprehensive Cancer Network (NCCN) risk stratification or pre-transplant minimal residual disease (MRD) levels can predict the risk of recurrence and survival after transplantation. But it is unclear whether combining these two parameters can more accurately predict prognosis. Methods We retrospectively analyzed 85 patients with acute myeloid leukemia (AML) who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) and constructed a new risk stratification tool combining NCCN risk stratification and pre-MRD levels. All patients were grouped by NCCN risk stratification (favorable/intermediate prognosis and poor prognosis group), pre-MRD levels (MRD (-) group (&lt;0.1%) and MRD (+) group (≥0.1%)) and a combination of the above (low, intermediate and high risk groups), and graft-versus-host disease (GVHD) and prognosis were compared between groups. Results Relative to the favorable/intermediate prognosis group, OS and RFS were poorer in the poor prognosis group (71% vs 82%, P= .156; 60% vs 74%, P= .101) and CIR (29% vs 20%, P= .229) and NRM (23% vs 14%, P= .200) were better. The incidence of aGVHD and cGVHD was slightly lower in the favorable/intermediate prognosis group than in the poor prognosis group (38% vs 46%, P= .415; 10% vs 11%, P=. 572). Relative to the MRD (+) group, the MRD (-) group had significantly better OS and RFS (89% vs 59%, P= .002; 79% vs 50%, P= .003), lower CIR and NRM (15.1% vs 37.5%, P= .011; 11.3% vs 28%, P= .040), and a lower incidence of cGVHD (6% vs 19%, P= .022). The new risk stratification tool stratified patients into low, intermediate and high risk groups. Patients in the high-risk group had the highest incidence of aGVHD and cGVHD (42% vs 35% vs 53%, P= .606; 6% vs 11% vs 20%, P= .157). The difference in cGVHD between the low- and high-risk groups was significant (P= .038). Three-year OS was 93.9%, 70% and 60% (P= .011) and RFS was 85%, 62% and 46.7% (P= .009) for low-, intermediate- and high-risk patients, respectively. The differences in OS and RFS between the low- and intermediate-risk groups were statistically significant (P= .010; P= .025), as were the differences in OS and RFS between the low- and high-risk groups (P= .001; P= .001). Patients in the high-risk group had the highest CIR and NRM relative to those in the low- and intermediate-risk groups (9% vs 32% vs 33.3%, P= .027; 6% vs 24.3% vs 26.7%; P= .059). The differences in CIR (P= .012) and NRM (P= .028) were statistically significant in both the low-risk and intermediate-risk groups, as well as in the low- and high-risk groups (CIR: P= .028; NRM: P= .021). Multivariate analysis indicated that time to ANC recovery, time from diagnosis to transplantation, and novel risk stratification were independent prognostic factors. Conclusions Both pre-MRD levels and NCCN risk stratification predict AML prognosis after allo-HSCT, and combining the two can more accurately predict post-transplant prognosis. Disclosures No relevant conflicts of interest to declare.

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

The Torino Pericarditis Score: a new-risk stratification tool to predict complicated pericarditis

2021 ◽  
Author(s):  
Massimo Imazio ◽  
Alessandro Andreis ◽  
Marta Lubian ◽  
George Lazaros ◽  
Emilia Lazarou ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Risk Stratification Tool

scholarly journals The Potential Use of Vitamin C to Prevent Kidney Injury in Patients with COVID-19

Diseases ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 46
Author(s):  
Feng Xu ◽  
Yawei Wen ◽  
Xinge Hu ◽  
Tiannan Wang ◽  
Guoxun Chen
Keyword(s):  
Vitamin C ◽  
Distress Syndrome ◽  
Kidney Injury ◽  
Mortality Rates ◽  
Protective Effects ◽  
Global Public Health ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Renal Functions ◽  
Potential Use

The newly found SARS-CoV-2 has led to the pandemic of COVID-19, which has caused respiratory distress syndrome and even death worldwide. This has become a global public health crisis. Unfortunately, elders and subjects with comorbidities have high mortality rates. One main feature of COVID-19 is the cytokine storm, which can cause damage in cells and tissues including the kidneys. Here, we reviewed the current literature on renal impairments in patients with COVID-19 and analyzed the possible etiology and mechanisms. In addition, we investigated the potential use of vitamin C for the prevention of renal injury in those patients. It appears that vitamin C could be helpful to improve the outcomes of patients with COVID-19. Lastly, we discussed the possible protective effects of vitamin C on renal functions in COVID-19 patients with existing kidney conditions.

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