Clinical Risk Factors for Orthostatic Hypotension: Results Among Elderly Fallers in Long-Term Care

2016 ◽  
Vol 16 (3) ◽  
pp. e143-e147 ◽  
Author(s):  
Deanna Gray-Miceli ◽  
Sarah J. Ratcliffe ◽  
Arwin Thomasson ◽  
Patricia Quigley ◽  
Kang Li ◽  
...  
2020 ◽  
Author(s):  
Kyoung Ja Moon ◽  
Chang-Sik Son ◽  
Jong-Ha Lee ◽  
Mina Park

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none


2021 ◽  
Vol 36 (3) ◽  
pp. 287-298
Author(s):  
Jonathan Bergman ◽  
Marcel Ballin ◽  
Anna Nordström ◽  
Peter Nordström

AbstractWe conducted a nationwide, registry-based study to investigate the importance of 34 potential risk factors for coronavirus disease 2019 (COVID-19) diagnosis, hospitalization (with or without intensive care unit [ICU] admission), and subsequent all-cause mortality. The study population comprised all COVID-19 cases confirmed in Sweden by mid-September 2020 (68,575 non-hospitalized, 2494 ICU hospitalized, and 13,589 non-ICU hospitalized) and 434,081 randomly sampled general-population controls. Older age was the strongest risk factor for hospitalization, although the odds of ICU hospitalization decreased after 60–69 years and, after controlling for other risk factors, the odds of non-ICU hospitalization showed no trend after 40–49 years. Residence in a long-term care facility was associated with non-ICU hospitalization. Male sex and the presence of at least one investigated comorbidity or prescription medication were associated with both ICU and non-ICU hospitalization. Three comorbidities associated with both ICU and non-ICU hospitalization were asthma, hypertension, and Down syndrome. History of cancer was not associated with COVID-19 hospitalization, but cancer in the past year was associated with non-ICU hospitalization, after controlling for other risk factors. Cardiovascular disease was weakly associated with non-ICU hospitalization for COVID-19, but not with ICU hospitalization, after adjustment for other risk factors. Excess mortality was observed in both hospitalized and non-hospitalized COVID-19 cases. These results confirm that severe COVID-19 is related to age, sex, and comorbidity in general. The study provides new evidence that hypertension, asthma, Down syndrome, and residence in a long-term care facility are associated with severe COVID-19.


Angiology ◽  
2021 ◽  
pp. 000331972110280
Author(s):  
Sukru Arslan ◽  
Ahmet Yildiz ◽  
Okay Abaci ◽  
Urfan Jafarov ◽  
Servet Batit ◽  
...  

The data with respect to stable coronary artery disease (SCAD) are mainly confined to main vessel disease. However, there is a lack of information and long-term outcomes regarding isolated side branch disease. This study aimed to evaluate long-term major adverse cardiac and cerebrovascular events (MACCEs) in patients with isolated side branch coronary artery disease (CAD). A total of 437 patients with isolated side branch SCAD were included. After a median follow-up of 38 months, the overall MACCE and all-cause mortality rates were 14.6% and 5.9%, respectively. Among angiographic features, 68.2% of patients had diagonal artery and 82.2% had ostial lesions. In 28.8% of patients, the vessel diameter was ≥2.75 mm. According to the American College of Cardiology lesion classification, 84.2% of patients had either class B or C lesions. Age, ostial lesions, glycated hemoglobin A1c, and neutrophil levels were independent predictors of MACCE. On the other hand, side branch location, vessel diameter, and lesion complexity did not affect outcomes. Clinical risk factors seem to have a greater impact on MACCE rather than lesion morphology. Therefore, the treatment of clinical risk factors is of paramount importance in these patients.


2011 ◽  
Vol 18 (3) ◽  
pp. 572-577 ◽  
Author(s):  
Buichi Tanaka ◽  
Mio Sakuma ◽  
Masae Ohtani ◽  
Jinichi Toshiro ◽  
Tadashi Matsumura ◽  
...  

2018 ◽  
Vol 42 (3) ◽  
pp. 224-237 ◽  
Author(s):  
Rebecca Chau ◽  
David W. Kissane ◽  
Tanya E. Davison

2018 ◽  
Vol 24 (9) ◽  
pp. 769-772 ◽  
Author(s):  
Hideharu Hagiya ◽  
Norihisa Yamamoto ◽  
Ryuji Kawahara ◽  
Yukihiro Akeda ◽  
Rathina Kumar Shanmugakani ◽  
...  

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Patience Moyo ◽  
Andrew R. Zullo ◽  
Kevin W. McConeghy ◽  
Elliott Bosco ◽  
Robertus van Aalst ◽  
...  

Author(s):  
Jeffrey Poss ◽  
Chi-Ling Sinn ◽  
Galina Grinchenko ◽  
Lialoma Salam-White ◽  
John Hirdes

ABSTRACTLong-stay home care clients mostly reside in private homes or retirement homes, and the type of residence may influence risk factors for long-term care placement. This multi-state analytic study uses RAI-Home Care and administrative data from the Hamilton Niagara Haldimand Brant Local Health Integration Network to model conceptualized states of risk at baseline through a 13-month follow-up period. Modifiable risk factors in these states were client loneliness or depressive symptoms, and caregiver distress. A higher adjusted likelihood of being discharged deceased was found for the lowest-risk clients in retirement homes. Adjusting for client, service, and caregiver characteristics, retirement home residency was associated with higher likelihood of placement in a long-term care home; reduced caregiver distress; and increased client loneliness/depression. As an alternative to private home settings as the location for aging in place among these long-stay home care clients, retirement home residency represents some trade-offs between client and informal caregiver.


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