scholarly journals Using an electronic frailty index to predict adverse outcomes in geriatric COVID-19 patients: data from the Stockholm GeroCovid study

2021 ◽  
Author(s):  
Jonathan K. L. Mak ◽  
Maria Eriksdotter ◽  
Martin Annetorp ◽  
Ralf Kuja-Halkola ◽  
Laura Kananen ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Mortality ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Strong Predictor ◽  
Characteristic Curve ◽  
Frailty Index ◽  
Predictive Ability ◽  
Adverse Outcomes ◽  
Clinical Frailty Scale

ABSTRACTBackgroundThe Clinical Frailty Scale (CFS) is a strong predictor for worse outcomes in geriatric COVID-19 patients, but it is less clear whether an electronic frailty index (eFI) constructed from routinely collected electronic health records (EHRs) provides similar predictive value. This study aimed to investigate the predictive ability of an eFI in comparison to other frailty and comorbidity measures, using mortality, readmission, and the length of stay as outcomes in geriatric COVID-19 patients.MethodsWe conducted a retrospective cohort study using EHRs from nine geriatric clinics in Stockholm, Sweden, comprising 3,405 COVID-19 patients (mean age 81.9 years) between 1/3/2020 and 31/10/2021. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the CFS, and Hospital Frailty Risk Score (HFRS). Comorbidity was measured using the Charlson Comorbidity Index (CCI). We analyzed in-hospital mortality and 30-day readmission using logistic regression and area under receiver operating characteristic curve (AUC). 30-day and 6-month mortality were modelled by Cox regression, and the length of stay by linear regression.ResultsControlling for age and sex, a 10% increase in the eFI was associated with higher risks of in-hospital mortality (odds ratio [OR]=2.84; 95% confidence interval=2.31-3.51), 30-day mortality (hazard ratio [HR]=2.30; 1.99-2.65), 6-month mortality (HR=2.33; 2.07-2.62), 30-day readmission (OR=1.34; 1.06-1.68), and longer length of stay (β=2.28; 1.90-2.66).The CFS, HFRS and CCI similarly predicted these outcomes, but the eFI had the best predictive accuracy for in-hospital mortality (AUC=0.775).ConclusionsAn eFI based on routinely collected EHRs can be applied in identifying high-risk geriatric COVID-19 patients.

scholarly journals Development of an electronic frailty index for hospitalized geriatric patients in Sweden

2021 ◽  
Author(s):  
Jonathan K. L. Mak ◽  
Sara Hagg ◽  
Maria Eriksdotter ◽  
Martin Annetorp ◽  
Ralf Kuja-Halkola ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Mortality ◽  
Geriatric Patients ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Frailty Index ◽  
Laboratory Data ◽  
Adverse Outcomes ◽  
Clinical Frailty Scale

Background: Frailty assessment in the Swedish health system relies on the Clinical Frailty Scale (CFS), but it requires training, in-person evaluation, and is often missing in medical records. We aimed to develop an electronic frailty index (eFI) from routinely collected electronic health records (EHRs) and assess its predictive ability for adverse outcomes in geriatric patients. Methods: EHRs were extracted for 18,225 geriatric patients with unplanned admissions between 1/3/2020 and 17/6/2021 from nine geriatric clinics in Stockholm, Sweden. A 48-item eFI was constructed using diagnostic codes, functioning and other health indicators, and laboratory data. The CFS, Hospital Frailty Risk Score, and Charlson Comorbidity Index were used for comparative assessment of the eFI. We modelled in-hospital mortality and 30-day readmission using logistic regression; 30-day and 6-month mortality using Cox regression; and length of stay using linear regression. Results: 13,188 patients were included in analyses (mean age 83.1 years). A 10% increment in the eFI was associated with higher risks of in-hospital (odds ratio: 5.34; 95% confidence interval: 4.20-6.82), 30-day (hazard ratio [HR]: 3.28; 2.91-3.69), and 6-month mortality (HR: 2.70; 2.52-2.90) adjusted for age and sex. Of the frailty and comorbidity measures, the eFI had the best predictive accuracy for in-hospital mortality, yielding an area under receiver operating characteristic curve of 0.813. Higher eFI also predicted a longer length of stay, but had a rather poor discrimination for 30-day readmission. Conclusions: An EHR-based eFI has good predictive accuracy for adverse outcomes, suggesting that it can be used in risk stratification in geriatric patients.

scholarly journals Management of Frailty and its Challenges in Geriatric Patients on General Abdominal Anesthesia.

2021 ◽  
Vol 5 (1) ◽  
pp. 785-790
Author(s):  
Majlinda Naço ◽  
Haxhire Gani ◽  
Monika Belba ◽  
Suzana Mukaj ◽  
Nertila Kodra ◽  
...  
Keyword(s):  
Geriatric Patients ◽  
Strong Predictor ◽  
Frailty Index ◽  
Early Mobilization ◽  
Adverse Outcomes ◽  
Clinical Frailty Scale ◽  
Operative Morbidity ◽  
Operative Care ◽  
Patient Will ◽  
Organ Specific

Introduction: In that material, we are doing to informed for frailty, how we can measure it, surgical outcome and its management from the anesthesiologist. Frailty is a condition of decreased physiological reserves that often increases with increasing age and decided in adverse outcomes. Frailty in elderly surgical patients may be varied from 25,5 -56.1% and is a strong predictor for surgical outcomes. The anesthetist needs to predict how a geriatric patient will tolerate the stress of surgery and to do what is necessary to protect and save elderly lives. In Albania, there are almost 439 000 elderly people in 2021 and suspect to 626 000 in 2051. According to the WHO, 1 in 25 persons performed surgery, so the number of geriatric patients that done surgery will be very high. According to deficits’ in function, mobility, cognition, chronic diseases, and geriatric syndromes we can use the clinical frailty scale, the Edmonton Frailty Scale, or frailty index for calculation of frailty. We need frailty patients to evaluated preoperative risk-classification, intra-operative care, management of general anesthesia, early immobilization as well as treatment of postoperative delirium because frailty increased intra-operative morbidity, increased postoperative complications especially delirium, extends hospitalization, non – home discharge, and mortality. Anesthesiologists would be always aware to prepare the geriatric patients for surgery, to maintain the intra-operative functional reserve of frailty patients, to manage perfect anesthesia, to realized early mobilization, and discharge back home. Conclusion: Many geriatric patients have multi-organ problems. Frailty is a practical, unifying concept in the care of these older people that directs attention away from organ-specific diagnoses towards a more holistic viewpoint of the patient and their medical medicament. All geriatric patients need to screen for frailty.

scholarly journals Analysis of genome-wide mutation profile and establishment of risk signature for prognosis of bladder cancer

2020 ◽  
Author(s):  
Yanyun Zhao ◽  
Rong Ma ◽  
Fangxiao Liu ◽  
Liwen Zhang ◽  
Xuemei Lv ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Time Dependent ◽  
Net Benefit ◽  
Data Set ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Mutation Profile

Abstract Background: Emerging studies have shown that a variety of gene mutations occur in development and progression of cancer and highly mutation genes could play oncogenic or tumor suppressive roles in cancer. Therefore, our aim is to explore mutation genes which affect the prognosis of bladder.Methods: Mutation profile was obtained and analyzed from TCGA data set. A mutation-based signature was established by multivariable Cox regression analysis. Kaplan-Meier was performed to assess the prognostic power of signature. Time-dependent ROC was conducted to evaluate predictive accuracy of signature for bladder cancer patients.Results: There are 20177 genes have alteration in 403 bladder patients and 662 of them were frequently variation (mutation frequency > 5%). In this study, we assessed the prognostic predictive ability of 662 highly mutated genes and identified a mutation signature as an independent indicator for predicting the prognosis of bladder. The time-dependent ROC showed that AUC were 0.893, 0.896, 0.916 and 0.965 at 1, 3, 5 and 10 year, respectively. Stratified analysis and Multivariate Cox analysis showed that this mutation signature was reliable and independent biomarker. Furthermore, the nomogram predictive model can be used to effectively predict clinical prognosis of bladder patients. The decision analysis curve showed patients with risk threshold of 0.03-0.92 potentially yielded clinical net benefit. Finally, we identified several signaling pathways that associated with risk score by GSEA and KEGG analysis including PI3K-Akt signaling pathway and so on.Conclusions: In general, this study provide an optimal mutation signature as potential prognosis biomarker for bladder patients.

Assessing Dynamic Violence Risk in a High-Risk Treated Sample of Violent Offenders

Assessment ◽  
2018 ◽  
Vol 27 (8) ◽  
pp. 1886-1900 ◽  
Author(s):  
Richard B. A. Coupland ◽  
Mark E. Olver
Keyword(s):  
Risk Assessment ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Violent Offenders ◽  
Violence Risk ◽  
Violence Risk Assessment ◽  
Change Scores ◽  
Assessment And Treatment ◽  
General Community

The present study featured an investigation of the predictive properties of risk and change scores of two violence risk assessment and treatment planning tools—the Violence Risk Scale (VRS) and the Historical, Clinical, Risk–20, Version 2 (HCR-20)—in sample of 178 treated adult male violent offenders who attended a high-intensity violence reduction program. The cases were rated on the VRS and HCR-20 using archival information sources and followed up nearly 10 years postrelease. Associations of HCR-20 and VRS risk and change scores with postprogram institutional and community recidivism were examined. VRS and HCR-20 scores converged in conceptually meaningful ways, supporting the construct validity of the tools for violence risk. Receiver operating characteristic curve analyses demonstrated moderate- to high-predictive accuracy of VRS and HCR-20 scores for violent and general community recidivism, but weaker accuracy for postprogram institutional recidivism. Cox regression survival analyses demonstrated that positive pretreatment and posttreatment changes, as assessed via the HCR-20 and VRS, were each significantly associated with reductions in violent and general community recidivism, as well as serious institutional misconducts, after controlling for baseline pretreatment score. Implications for use of the HCR-20 and VRS for dynamic violence risk assessment and management are discussed.

In-hospital outcomes and prevalence of comorbidities in patients with infective endocarditis with and without heart blocks: Insight from the National Inpatient Sample

2020 ◽  
pp. jim-2020-001501
Author(s):  
Shakeel M Jamal ◽  
Asim Kichloo ◽  
Michael Albosta ◽  
Beth Bailey ◽  
Jagmeet Singh ◽  
...  
Keyword(s):  
Infective Endocarditis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Heart Block ◽  
Kidney Injury ◽  
Primary Diagnosis ◽  
Cost Of Care ◽  
Adverse Outcomes ◽  
Cardiac Resynchronization ◽  
National Inpatient Sample

Infective endocarditis (IE) complicated by heart block can have adverse outcomes and usually requires immediate surgical and cardiac interventions. Data on outcomes and trends in patients with IE with concurrent heart block are lacking. Patients with a primary diagnosis of IE with or without heart block were identified by querying the Healthcare Cost and Utilization Project database, specifically the National Inpatient Sample for the years 2013 and 2014, based on International Classification of Diseases Clinical Modification Ninth Revision codes. During 2013 and 2014, a total of 18,733 patients were admitted with a primary diagnosis of IE, including 867 with concurrent heart blocks. Increased in-hospital mortality (13% vs 10.3%), length of stay (19 vs 14 days), and cost of care ($282,573 vs $223,559) were found for patients with IE complicated by heart block. Additionally, these patients were more likely to develop cardiogenic shock (8.9% vs 3.2%), acute kidney injury (40.1% vs 32.6%), and hematologic complications (19.3% vs 15.2%), and require placement of a pacemaker (30.6% vs 0.9%). IE and concurrent heart block resulted in increased requirement for aortic (25.7% vs 6.1%) and mitral (17.3% vs 4.2%) valvular replacements. Conclusion was made that IE with concurrent heart block worsens in-hospital mortality, length of stay, and cost for patients. Our analysis demonstrates an increase in cardiac procedures, specifically aortic and/or mitral valve replacements, and Implantable Cardiovascular Defibrillator/Cardiac Resynchronization Therapy/ Permanent Pacemaker (ICD/CRT/PPM) placement in IE with concurrent heart block. A close telemonitoring system and prompt interventions may represent a significant mitigation strategy to avoid the adverse outcomes observed in this study.

scholarly journals Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

2020 ◽  
Vol 13 (5) ◽  
pp. 92
Author(s):  
Katarina Valaskova ◽  
Pavol Durana ◽  
Peter Adamko ◽  
Jaroslav Jaros
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Early Warning Systems ◽  
Emerging Countries ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Prediction Ability

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

scholarly journals The natriuretic peptide MR-proANP predicts all-cause mortality and adverse outcome in community patients: a 10-year follow-up study

2017 ◽  
Vol 55 (9) ◽  
Author(s):  
Jonas Odermatt ◽  
Lara Hersberger ◽  
Rebekka Bolliger ◽  
Lena Graedel ◽  
Mirjam Christ-Crain ◽  
...  
Keyword(s):  
Natriuretic Peptide ◽  
Cox Regression ◽  
Adverse Outcome ◽  
Characteristic Curve ◽  
Physiological Role ◽  
Adverse Outcomes ◽  
Community Dwelling ◽  
Blood Levels ◽  
All Cause Mortality

AbstractBackground:The precursor peptide of atrial natriuretic peptide (MR-proANP) has a physiological role in fluid homeostasis and is associated with mortality and adverse clinical outcomes in heart failure patients. Little is known about the prognostic potential of this peptide for long-term mortality prediction in community-dwelling patients. We evaluated associations of MR-proANP levels with 10-year all-cause mortality in patients visiting their general practitioner for a respiratory tract infection.Methods:In this post-hoc analysis including 359 patients (78.5%) of the original trial, we calculated cox regression models and area under the receiver operating characteristic curve (AUC) to assess associations of MR-proANP blood levels with mortality and adverse outcome including death, pulmonary embolism, and major adverse cardiac or cerebrovascular events.Results:After a median follow-up of 10.0 years, 9.8% of included patients died. Median admission MR-proANP levels were significantly elevated in non-survivors compared to survivors (80.5 pmol/L, IQR 58.6–126.0; vs. 45.6 pmol/L, IQR 34.2–68.3; p<0.001) and associated with 10-year all-cause mortality (age-adjusted HR 2.0 [95% CI 1.3–3.1, p=0.002]; AUC 0.79). Results were similar for day 7 blood levels and also for the prediction of other adverse outcomes.Conclusions:Increased MR-proANP levels were associated with 10-year all-cause mortality and adverse clinical outcome in a sample of community-dwelling patients. If diagnosis-specific cut-offs are confirmed in future studies, this marker may help to direct preventive measures in primary care.

scholarly journals A Novel Predictive Model Associated With Osteosarcoma Metastasis

2021 ◽  
Author(s):  
Han Zhang ◽  
Guanhong Chen ◽  
Xiajie Lyu ◽  
Tao Li ◽  
Rong Chun ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Cox Regression ◽  
High Reliability ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Metastasis Model ◽  
Non Coding Rnas ◽  
Novel Model

Abstract Background: Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational aspects, whereas its role in the metastasis of osteosarcoma (OS) is unclear.Method: Expression and clinical data were downloaded from TARGET datasets. The OS metastasis model was established by seven lncRNAs screened by univariate cox regression, lasso regression and multivariate cox regression analysis. The area under receiver operating characteristic curve (AUC) values were used to evaluate the models.Results: The predictive ability of this model is extraordinary (1 year: AUC = 0.92, 95% Cl = 0.83–1.01; 3 years: AUC = 0.87, 95% Cl = 0.79–0.96; 5 years: AUC = 0.86, 95% Cl = 0.76–0.96). Patients in high group had poor survival compared to low group (p < 0.0001). “NOTCH_SIGNALING”, and “WNT_BETA_CATENIN_SIGNALING” were enriched via the GSEA analysis and dendritic cells resting were associated with the AL512422.1, AL357507.1 and AC006033.2 (p < 0.05).Conclusion: We constructed a novel model with high reliability and accuracy to predict the metastasis of OS patients based on seven prognosis-related lncRNAs.

scholarly journals Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chunyu Zhang ◽  
Haitao Liu ◽  
Pengfei Xu ◽  
Yinqiu Tan ◽  
Yang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Prognostic Indicator ◽  
High Risk Group ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Who Grade ◽  
Cox Analysis

Abstract Background To accurately predict the prognosis of glioma patients. Methods A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. Results By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. Conclusion We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

scholarly journals Temporal Trend of Comorbidity and Increasing Impacts on Mortality, Length of Stay, and Hospital Costs of First Stroke in Tianjin, North of China

2021 ◽  
Author(s):  
Ruixiao Hao ◽  
Xuemei Qi ◽  
Xiaoshuang Xia ◽  
Lin Wang ◽  
Xin Li
Keyword(s):  
Length Of Stay ◽  
Hospital Mortality ◽  
Cox Regression ◽  
Hospital Costs ◽  
Hazard Ratio ◽  
Adjusted Hazard Ratio ◽  
Kaplan Meier ◽  
Comorbidity Burden ◽  
Hospitalization Costs ◽  
Severe Comorbidity

Abstract Purpose: Stroke patients have a high incidence of comorbidity. Our study aimed to explore the trend of comorbidity among patients with first stroke from 2010 to 2020, and the influence of comorbidity on admission mortality, length of stay and hospitalization costs.5988 eligible patients were enrolled in our study, and divided into 4 comorbidity burden groups according to Charlson comorbidity index (CCI): none, moderate, severe, very severe. Survival analysis was expressed by Kaplan - Meier curve. Cox regression model was used to analyze the effect of comorbidity on 7-day and in-hospital mortality. Generalized linear model (GLM) was used to analyze the association between comorbidity and hospitalization days and cost. Results: Compared to patients without comorbidity, those with very severe comorbidity were more likely to be male (342, 57.7%), suffer from ischemic stroke (565, 95.3%), afford higher expense (Midian, 19339.3RMB, IQR13020.7-27485.9RMB), and have a higher in-hospital mortality (60, 10.1%). From 2010 to 2020, proportion of patients with severe and very severe comorbidity increased 12.9%. The heaviest comorbidity burden increased the risk of 7-day mortality (adjusted hazard ratio, 3.51, 95% CI, 2.22-5.53) and in-hospital mortality (adjusted hazard ratio, 3.83, 95% CI, 2.70-5.45). Patients with very severe comorbidity had a 12% longer LOS and extra 27% expense than those without comorbidity.Conclusion: Comorbidity burden showed an increasing trend year in past eleven years. The heavy comorbidity burden increased in-hospital mortality, LOS, and hospitalization cost, especially in patients aged 55 years or more.

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