Risk factors for prediction of delirium at hospital admittance

Cano‐Escalera, Guillermo; Graña, Manuel; Irazusta, Jon; Labayen, Idoia; Besga, Ariadna

doi:10.1111/exsy.12698

Cited by 6 publications

(7 citation statements)

References 45 publications

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“…The incidence of computer vision syndrome was determined to be 67% in the current investigation, which was done on medical students at a university. In comparison to our study, a study done on Chennai medical students found a high prevalence of 78.6% (12). All symptoms, even those that were fleeting, were deemed to fit the criteria for CVS in this analysis, but in our investigation, only symptoms that lasted at least one month were taken into account.…”

Section: Discussioncontrasting

confidence: 61%

Risk Factors of Computer Vision Syndrome and its Prevention

Imran¹,

Khan²,

Zaib³

et al. 2023

PJMHS

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Background: The 21st century is thought to need these gadgets. They are employed in households, as well as in recreational settings and educational organizations, in addition to workplaces and businesses. The term "video display terminal" is used to refer to a computer screen (VDT). It includes computers, tablets, e-readers, smartphones, and other electronic gadgets. According to estimates, 45 million employees use computers directly by gazing at VDTs nonstop for long periods. Aim: The goal of this study was to count how many CMT (college of Medical Technology) students have computer vision syndrome and to identify the risk factors that go along with it. Methods: This cross-sectional research was conducted on 198 CMT students at Pakistan institute of Medical Sciences, Islamabad, Pakistan, from 1st April 2022, to 30th September, 2022. The research covered all students between the ages of 17 and 25 who had used a computer in the month before the programs start date. Those students who were using eye medications, those with underlying systemic conditions like diabetes or hypertension, those with pre-existing eye problems, and those who did not provide informed written permission were excluded from the study. To demonstrate relationships between categorical data, the chi-square test was performed. The SPSS version 23 was used to analyze the data. Results: Age of 20.16 ± 3.81 years on average. Out of 198 respondents, 133 (67.2%) said they had at least one of the following symptoms of computer vision syndrome: headache, eye tiredness, burning, eye irritation, neck discomfort, and shoulder pain. Computer users' eye complaints included eye irritability (48%), burning (33%), and eye weariness (15%). Extraocular symptoms range from headaches (38%) to neck and shoulder discomfort (21.8%). Computer use time (240 minutes/four hours) was substantially correlated with eye tiredness and headache. Practical Implication: The benefit of this study is to identify the risk variables linked with computer vision syndrome and its incidence among undergraduate medical students. It will aid in raising public awareness of the health risks associated with computers and other digital electronic devices, as well as assisting public health experts and other stakeholders in taking action to address this public health problem Conclusions: The ailment known as "Computer Vision Syndrome" affects a lot of young students who use computer regularly Keywords: Computer Vision Syndrome, Risk, Prevention, households, irritability, pain, eye tiredness, .

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Section: Discussioncontrasting

confidence: 61%

Risk Factors of Computer Vision Syndrome and its Prevention

Imran¹,

Khan²,

Zaib³

et al. 2023

PJMHS

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“…Most frequent precipitating factors in community-dwelling older individuals are infectious diseases, followed by drugs and hydro-electrolytic disorders [ 9 ]. Machine learning tools have been proposed for the identification of delirium onset risk factors [ 10 , 11 ]. The duration of delirium is variable, persisting for weeks or months in 20% of patients with dementia [ 12 ], which is a high risk factor for delirium in older patients [ 13 ], while delirium onset often leads to cognitive deterioration and dementia [ 3 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Survival of Frail Elderly with Delirium

Cano‐Escalera

Graña

Irazusta

et al. 2022

IJERPH

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This study aims to determine when frailty increases the risks of delirium mortality. Hospital patients falling into the elderly frail or pre-frail category were recruited, some without delirium, some with delirium at admission, and some who developed delirium during admission. We screened for frailty, cognitive status, and co-morbidities whenever possible and extracted drug information and mortality data from electronic health records. Kaplan–Meier estimates of survival probability functions were computed at four times, comparing delirium versus non delirium patients. Differences in survival were assessed by a log-rank test. Independent Cox’s regression was carried out to identify significant hazard risks (HR) at 1 month, 6 months, 1 year, and 2 years. Delirium predicted mortality (log-rank test, p < 0.0001) at all four censoring points. Variables with significant HRs were frailty indicators, comorbidities, polypharmacy, and the use of specific drugs. For the delirium cohort, variables with the most significant 2-year hazard risks (HR(95%CI)) were: male gender (0.43 20 (0.26,0.69)), weight loss (0.45 (0.26,0.74)), sit and stand up test (0.67 (0.49,0.92)), readmission within 30 days of discharge (0.50 (0.30,0.80)), cerebrovascular disease (0.45 (0.27,0.76)), head trauma (0.54 22 (0.29,0.98)), number of prescribed drugs (1.10 (1.03,1.18)), and the use of diuretics (0.57 (0.34,0.96)). These results suggest that polypharmacy and the use of diuretics increase mortality in frail elderly patients with delirium.

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“…The search strategy identified a total of 921 records; after duplicate removal and title and abstract screening, 114 full-text studies were retrieved, of which 3916–54 met the selection criteria for inclusion in the final analysis (figure 2).…”

Section: Resultsmentioning

confidence: 99%

“…Study characteristics are summarised in online supplemental table 1. Studies originated from the USA (n=12),17 19–23 25 41 43 50 51 54 Austria (n=9),24 28–31 33 39 47 48 China (n=6),26 32 35 49 52 53 Germany (n=3),37 45 46 South Korea (n=3),27 40 44 Canada (n=3),30 36 38 Brazil (n=1),16 Japan (n=1),34 Spain (n=1)18 and one study was labelled as international 42. Over the 6-year distribution of publications to June 2022, most studies were published in 2021 (n=10) and the first half of 2022 (n=12), indicating considerable growth in research in this area since the publication of previous reviews of studies published up to 2019 11 12.…”

Section: Resultsmentioning

confidence: 99%

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Strating

Hanjani

Tornvall

et al. 2023

BMJ Health Care Inform

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ObjectivesEarly identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.MethodsWe searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.ResultsAmong 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.DiscussionML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.ConclusionThis review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

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Risk factors for prediction of delirium at hospital admittance

Cited by 6 publications

References 45 publications

Risk Factors of Computer Vision Syndrome and its Prevention

Risk Factors of Computer Vision Syndrome and its Prevention

Survival of Frail Elderly with Delirium

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

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