The risk factors of recurrent falls among inpatients: A retrospective study (Preprint)

2021 ◽  
Author(s):  
Wen-Pei Chang ◽  
Hsiu-Ju Jen
Keyword(s):  
Risk Factors ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Lower Risk ◽  
The Other ◽  
Poor Vision ◽  
Recurrent Falls ◽  
Physical Independence ◽  
Event Notification ◽  
Highly Correlated

BACKGROUND Recurrent falls are more likely to cause injuries and disabilities than single falls. OBJECTIVE This study investigated the incidence and risk factors of recurrent falls among inpatients. METHODS Adopting a retrospective research design, we collected and analyzed inpatient fall data from the anomaly event notification database of a teaching hospital in Taiwan in coordination with electronic medical records in the four years from January 1, 2016 to December 31, 2019. RESULTS We collected data from 1,059 inpatients. Among these inpatients were 390 recurrent-fall inpatients (36.83%) who had a fallen more than once. After controlling the other variables, we found that inpatients in the orthopedics and neurology wards were at higher risk of recurrent falls within a year than those in surgical wards (OR=2.44, p< .001; OR=1.93, p= .002); inpatients who were completely physically dependent on others or required assistance before their falls were at higher risk of recurrent falls within a year than those who were physically independent (OR = 2.10, p= .014; OR=1.57, p= .015); inpatients with self-perceived poor vision were at lower risk of recurrent falls within a year than those without this issue (OR=0.61, p< .001), and inpatients who were using antidepressants were at higher risk of recurrent falls within a year than those who were not using antidepressants (OR=1.44, p= .042). CONCLUSIONS There are multiple aspects to the factors of recurrent falls among inpatients within a year and that the risk of recurrent falls is highly correlated with ward type (orthopedics and neurology), physical independence (completely dependent or requiring assistance), no poor vision, and use of antidepressants. CLINICALTRIAL NA

scholarly journals Comparing the Clinical Characteristics and Mortality of Residential and Non-Residential Older People with COVID-19: Retrospective Observational Study

2022 ◽  
Vol 19 (1) ◽  
pp. 483
Author(s):  
Francesc X. Marin-Gomez ◽  
Jacobo Mendioroz-Peña ◽  
Miguel-Angel Mayer ◽  
Leonardo Méndez-Boo ◽  
Núria Mora ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Clinical Characteristics ◽  
Fold Increase ◽  
The Elderly ◽  
Health Administration ◽  
Epidemic Outbreak ◽  
Risk Of Death ◽  
Using Data

Nursing homes have accounted for a significant part of SARS-CoV-2 mortality, causing great social alarm. Using data collected from electronic medical records of 1,319,839 institutionalised and non-institutionalised persons ≥ 65 years, the present study investigated the epidemiology and differential characteristics between these two population groups. Our results showed that the form of presentation of the epidemic outbreak, as well as some risk factors, are different among the elderly institutionalised population with respect to those who are not. In addition to a twenty-fold increase in the rate of adjusted mortality among institutionalised individuals, the peak incidence was delayed by approximately three weeks. Having dementia was shown to be a risk factor for death, and, unlike the non-institutionalised group, neither obesity nor age were shown to be significantly associated with the risk of death among the institutionalised. These differential characteristics should be able to guide the actions to be taken by the health administration in the event of a similar infectious situation among institutionalised elderly people.

scholarly journals Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach

2018 ◽  
Author(s):  
Thanat Chokwijitkul ◽  
Anthony Nguyen ◽  
Hamed Hassanzadeh ◽  
Siegfried Perez
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Learning Approach

Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing

Epilepsia ◽  
2019 ◽  
Author(s):  
Kristen Barbour ◽  
Dale C. Hesdorffer ◽  
Niu Tian ◽  
Elissa G. Yozawitz ◽  
Patricia E. McGoldrick ◽  
...  
Keyword(s):  
Risk Factors ◽  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records ◽  
Automated Detection ◽  
Sudden Unexpected Death ◽  
Unexpected Death

scholarly journals Phenotype analysis of early risk factors from electronic medical records improves image-derived diagnostic classifiers for optic nerve pathology

2017 ◽  
Author(s):  
Shikha Chaganti ◽  
Kunal P. Nabar ◽  
Katrina M. Nelson ◽  
Louise A. Mawn ◽  
Bennett A. Landman
Keyword(s):  
Risk Factors ◽  
Optic Nerve ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Early Risk ◽  
Early Risk Factors ◽  
Phenotype Analysis

scholarly journals Disease Risk Factors Identified Through Shared Genetic Architecture and Electronic Medical Records

2014 ◽  
Vol 6 (234) ◽  
pp. 234ra57-234ra57 ◽  
Author(s):  
L. Li ◽  
D. J. Ruau ◽  
C. J. Patel ◽  
S. C. Weber ◽  
R. Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Genetic Architecture ◽  
Disease Risk ◽  
Shared Genetic

scholarly journals Population prevalence and control of cardiovascular risk factors: What electronic medical records tell us

2014 ◽  
Vol 46 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Arantxa Catalán-Ramos ◽  
Jose M. Verdú ◽  
María Grau ◽  
Manuel Iglesias-Rodal ◽  
José L. del Val García ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Population Prevalence ◽  
And Control

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

2011 ◽  
pp. 1874-1899 ◽  
Author(s):  
Morgan Price
Keyword(s):  
Information Systems ◽  
Social Interactions ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Clinical Information ◽  
Hospital Information Systems ◽  
The Other ◽  
Clinical Information Systems ◽  
Complete Understanding

The purpose of this chapter is to provide the reader with an overview of several models and theories from the general HCI literature, highlighting models at three levels of focus: biomechanical interactions, individual-cognitive interactions, and social interactions. This chapter will also explore how these models were or could be applied to the design and evaluation of clinical information systems, such as electronic medical records and hospital information systems. Finally, it will conclude with how an understanding at each level compliments the other two in order to create a more complete understanding of the interactions of information systems in healthcare.

Abstract 16935: Cardiovascular Disease in Head Neck Squamous Cell Carcinoma Patients - A Case-Control Study Using Electronic Medical Records Data

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Amrita Mukherjee ◽  
Howard Wiener ◽  
Russell Griffin ◽  
Lisle M Nabell ◽  
Carrie G Lenneman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Cancer Site ◽  
Cancer Stage ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Related Factors ◽  
Traditional Risk Factors ◽  
Head Neck

Introduction: Cancer survivors have higher rates of cardiovascular disease (CVD) compared to age-adjusted general population. However, traditional CVD risk factors alone do not fully explain increased CVD risk in cancer patients. Cancer-related factors like cancer site, stage and chemotherapy may also contribute to CVD. Despite increase in head neck squamous cell carcinoma (HNSCC) cases in recent years, little is known about CVD risk in HNSCC patients. We aim to assess association of traditional risk factors and cancer-related factors with CVD in HNSCC patients. Methods: Electronic medical records data of 2391 HNSCC patients diagnosed between 2012-2018 at the UAB O’Neal Comprehensive Cancer Center, were included. ICD-9/10 codes were used to identify HNSCC patients, CVD cases and traditional risk factors. CVD cases were defined as those with composite events of ischemic heart disease and/ or heart failure; controls were without any CVD diagnosis. Cancer site, stage and treatment were included. Logistic regression [OR(95%CI)] was used to assess association of risk factors with CVD, adjusting for age, race, and gender. Results: HNSCC patients were mostly white (82.7%), male (74.7%) and had Stage III/IV cancer (46.6%). Oral cavity was the most common cancer site (32.9%), followed by oropharynx (31.7%); 55.4% patients had hypertension, 23.0% had dyslipidemia, and 16.1% had diabetes. CVD was diagnosed in 16.1% patients, who were more likely to be older [median age 67.0 vs 60.0 years, p<0.0001]. In the multivariable model, hypertension [2.08(1.54-2.80)], diabetes [2.03(1.52-2.70)] and dyslipidemia [2.36(1.82-3.07)] were associated with CVD. Smoking status was not associated. Association of cancer stage with CVD varied by cancer site. Stage III/IV oropharynx cancer patients had lower odds of CVD than stage I/II oropharynx patients [0.38(0.30-0.92)]. No association with cancer stage was observed in oral cavity patients. Compared to chemotherapy, surgery [0.85(0.63-1.14)] and other treatments {0.61(0.41-0.89)] had lower odds of CVD. Conclusions: Traditional CVD risk factors remain associated with CVD in HNSCC patients. In addition, cancer-related factors (oropharynx cancer, advanced cancer stage and chemotherapy) are also associated with CVD.

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