scholarly journals The Utility of Outpatient Commitment: II. Mortality Risk and Protecting Health, Safety, and Quality of Life

2017 ◽  
Vol 68 (12) ◽  
pp. 1255-1261 ◽  
Author(s):  
Steven P. Segal ◽  
Stephania L. Hayes ◽  
Lachlan Rimes
Keyword(s):  
Quality Of Life ◽  
Mortality Risk ◽  
Outpatient Commitment ◽  
Health Safety

The Quality of Life and Mortality Risk of Elderly People in Rural China

2013 ◽  
Vol 27 (2) ◽  
pp. NP2232-NP2245 ◽  
Author(s):  
Huijun Liu ◽  
Qunying Xiao ◽  
Yanzhi Cai ◽  
Shuzhuo Li
Keyword(s):  
Quality Of Life ◽  
Elderly People ◽  
Mortality Risk ◽  
Rural China

scholarly journals Quality of Life and mortality in the general population: a systematic review and meta-analysis

2020 ◽  
Author(s):  
Aung Zaw Zaw Phyo ◽  
Rosanne Freak-Poli ◽  
Heather Craig ◽  
Danijela Gasevic ◽  
Nigel Stocks ◽  
...  
Keyword(s):  
Quality Of Life ◽  
General Population ◽  
Mortality Risk ◽  
Population Characteristics ◽  
Lower Mortality ◽  
Physical Component Score ◽  
Component Score ◽  
Meta Analyses ◽  
Lower Mortality Risk

Abstract Background: Quality of life (QoL) is multi-dimensional concept of an individual’ general well-being status in relation to their value, environment, cultural and social context in which they live. This study aimed to quantitatively synthesise available evidence on the association between QoL and mortality in the general population. Methods: An electronic search was conducted using three bibliographic databases, MEDLINE, EMBASE and PsycINFO. Inclusion criteria were studies that assessed QoL using standardized tools and examined mortality risk in a non-patient population. Qualitative data synthesis and meta-analyses using a random-effects model were performed. Results: Of 4,184 articles identified, 47 were eligible for inclusion, involving approximately 1,200,000 participants. Studies were highly heterogeneous in terms of QoL measures, population characteristics and data analysis. In total, 43 studies (91.5%) reported that better QoL was associated with lower mortality risk. The results of four meta-analyses indicated that higher health-related QoL (HRQoL) is associated with lower mortality risk, which was consistent for overall HRQoL (HR 0.633, 95% CI: 0.514 to 0.780), physical function (HR 0.987, 95% CI: 0.982 to 0.992), physical component score (OR 0.950, 95% CI: 0.935 to 0.965), and mental component score (OR 0.980, 95% CI: 0.969 to 0.992). Conclusion: These findings provide evidence that better QoL/HRQoL was associated with lower mortality risk. The utility of these measures in predicting mortality risk indicates that they should be considered further as potential screening tools in general clinical practice, beyond the traditional objective measures such as body mass index and the results of laboratory tests.

Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1510-1510
Author(s):  
Ravi Bharat Parikh ◽  
Jill Schnall ◽  
Manqing Liu ◽  
Peter Edward Gabriel ◽  
Corey Chivers ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Mortality Risk ◽  
Patient Reported Outcomes ◽  
Machine Learning Algorithms ◽  
Measurement Information ◽  
Patients With Cancer ◽  
Patient Reported ◽  
Mortality Table

1510 Background: Machine learning (ML) algorithms based on electronic health record (EHR) data have been shown to accurately predict mortality risk among patients with cancer, with areas under the curve (AUC) generally greater than 0.80. While patient-reported outcomes (PROs) may also predict mortality among patients with cancer, it is unclear whether routinely-collected PROs improve the predictive performance of EHR-based ML algorithms. Methods: This cohort study included 8600 patients with cancer who had an outpatient encounter at one of 18 medical oncology practices in a large academic health system between July 1st, 2019 and January 1st, 2020. 4692 (54.9%) patients completed assessments of symptoms, performance status, and quality of life from the PRO version of the Common Terminology Criteria for Adverse Events and the Patient-Reported Outcomes Measurement Information System Global v.1.2 scales. We hypothesized that ML models predicting 180-day all-cause mortality based on EHR + PRO data would improve AUC compared to ML models based on EHR data alone. We assessed univariate and adjusted associations between each PRO and 180-day mortality. To train the EHR-only model, we fit a Least Absolute Shrinkage and Selection Operator (LASSO) regression using 192 EHR demographic, comorbidity, and laboratory variables. To train the EHR + PRO model, we used a two-phase approach to fit a model using EHR data for all patients and PRO data for those who completed assessments. To test our hypothesis, we compared the bootstrapped AUC, area under the precision-recall curve (AUPRC), and sensitivity at a 20% risk threshold for both models. Results: 464 (5.4%) patients died within 180 days of the encounter. Decreased quality of life, functional status, and appetite were associated with greater 180-day mortality (Table). Compared to the EHR-only model, the EHR + PRO model significantly improved AUC (0.86 [95% CI 0.85-0.86] vs. 0.80 [95% CI 0.80-0.81]), AUPRC (0.40 [95% CI 0.37-0.42] vs. 0.30 [95% CI 0.28-0.32]), and sensitivity (0.45 [95% CI 0.42-0.48] vs. 0.33 [95% CI 0.30-0.35]). Conclusions: Routinely collected PROs augment EHR-based ML mortality risk algorithms. ML algorithms based on EHR and PRO data may facilitate earlier supportive care for patients with cancer. Association of PROs with 180-day mortality.[Table: see text]

Systematic review of the effectiveness of home versus hospital or satellite unit hemodialysis for people with end-stage renal failure

2004 ◽  
Vol 20 (3) ◽  
pp. 258-268 ◽  
Author(s):  
Graham Mowatt ◽  
Luke Vale ◽  
Alison MacLeod
Keyword(s):  
Quality Of Life ◽  
Renal Failure ◽  
Mortality Risk ◽  
Lower Mortality ◽  
Home Hemodialysis ◽  
End Stage Renal Failure ◽  
End Stage ◽  
Lower Mortality Risk ◽  
Satellite Unit

Background:Home hemodialysis offers potential advantages over hospital hemodialysis, including the opportunity for more frequent and/or longer dialysis sessions. Expanding home hemodialysis services may help cope with the increasing numbers of people requiring hemodialysis.Methods:We sought comparative studies or systematic reviews of home versus hospital/satellite unit hemodialysis for people with end-stage renal failure (ESRF). Outcomes included quality of life and survival. We searched MEDLINE, EMBASE, HealthSTAR, CINAHL, PREMEDLINE, and BIOSIS. Two reviewers independently extracted data and assessed the quality of the studies included.Results:Twenty-seven studies of variable quality were included. People on home hemodialysis generally experienced a better quality of life and lived longer than those on hospital hemodialysis. Their partners, however, found home hemodialysis more stressful. Four studies using a Cox proportional hazards model to compare home with hospital hemodialysis reported a lower mortality risk for home hemodialysis. Of two studies using a Cox model to compare home with satellite unit hemodialysis, one reported a similar mortality risk, whereas the other reported a lower mortality risk for home hemodialysis.Conclusions:Home hemodialysis was generally associated with better outcomes than hospital hemodialysis and (more modestly so) satellite unit hemodialysis, in terms of quality of life, survival, and other measures of effectiveness. People on home hemodialysis, however, are a highly selected group. Home hemodialysis also provides the opportunity for more frequent and/or longer dialysis sessions than would otherwise be possible. It is difficult to disentangle the true effects of home hemodialysis from such influencing factors.

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