Interaction effects of multimorbidity and frailty on adverse health outcomes in elderly hospitalised patients

Lujic, Sanja; Randall, Deborah; Simpson, Judy M.; Falster, Michael O.; Jorm, Louisa

doi:10.1038/s41598-022-18346-x

Cited by 23 publications

(8 citation statements)

References 31 publications

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“…We screened 14,006 abstracts and 2287 full texts, from which 45 cohorts (n = 39,041,266 admissions, 49 publications) 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 were eligible for inclusion ( Fig. 1 ).…”

Section: Resultsmentioning

confidence: 99%

Prevalence and outcomes of frailty in unplanned hospital admissions: a systematic review and meta-analysis of hospital-wide and general (internal) medicine cohorts

Boucher¹,

Gan²,

Rothwell³

et al. 2023

eClinicalMedicine

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Section: Resultsmentioning

confidence: 99%

Prevalence and outcomes of frailty in unplanned hospital admissions: a systematic review and meta-analysis of hospital-wide and general (internal) medicine cohorts

Boucher¹,

Gan²,

Rothwell³

et al. 2023

eClinicalMedicine

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“…These finding are similar to reports from a recent meta-analysis of frailty in COVID-19.30 In our study, readmission was also higher in frail patients, especially in those with mild-moderate frailty. Previous studies have noted a higher risk of readmission in frail individuals with multiple chronic diseases after surgical or medical admission [ 33 ]. In our study the patients with severe frailty did not have a higher risk of readmission.…”

Section: Discussionmentioning

confidence: 99%

Association between Clinical Frailty Scale (CFS) and clinical presentation and outcomes in older inpatients with COVID-19

et al. 2023

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Background Frailty is a physiological condition characterized by a decreased reserve to stressors. In patients with COVID-19, frailty is a risk factor for in-hospital mortality. The aim of this study was to assess the relationship between clinical presentation, analytical and radiological parameters at admission, and clinical outcomes according to frailty, as defined by the Clinical Frailty Scale (CFS), in old people hospitalized with COVID-19. Materials and methods This retrospective cohort study included people aged 65 years and older and admitted with community-acquired COVID-19 from 3 March 2020 to 31 April 2021. Patients were categorized using the CFS. Primary outcomes were symptoms of COVID-19 prior to admission, mortality, readmission, admission in intensive care unit (ICU), and need for invasive mechanical ventilation. Analysis of clinical symptoms, clinical outcomes, and CFS was performed using multivariable logistic regression, and results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Results Of the 785 included patients, 326 (41.5%, 95% CI 38.1%–45.0%) were defined as frail (CFS ≥ 5 points): 208 (26.5%, 95% CI 23.5%–29.7%) presented mild-moderate frailty (CFS 5–6 points) and 118 (15.0%, 95% CI 12.7%–17.7%), severe frailty (7–9 points). After adjusting for epidemiological variables (age, gender, residence in a nursing home, and Charlson comorbidity index), frail patients were significantly less likely to present dry cough (OR 0.58, 95% CI 0.40–0.83), myalgia-arthralgia (OR 0.46, 95% CI 0.29–0.75), and anosmia-dysgeusia (OR 0.46, 95% CI 0.23-0.94). Confusion was more common in severely frail patients (OR 3.14; 95% CI 1.64-5.97). After adjusting for epidemiological variables, the risk of in-hospital mortality was higher in frail patients (OR 2.79, 95% CI 1.79-4.25), including both those with mild-moderate frailty (OR 1.98, 95% CI 1.23-3.19) and severe frailty (OR 5.44, 95% CI 3.14-9.42). Readmission was higher in frail patients (OR 2.11, 95% CI 1.07–4.16), but only in mild-moderate frailty (OR 2.35, 95% CI 1.17–4.75).. Conclusion Frail patients presented atypical symptoms (less dry cough, myalgia-arthralgia, and anosmia-dysgeusia, and more confusion). Frailty was an independent predictor for death, regardless of severity, and mild-moderate frailty was associated with readmission.

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“…Record linkage techniques [ 89 ] such as probabilistic matching [ 90 ] bridge this gap by linking disparate data records, offering a more comprehensive view of a patient’s health. Nevertheless, our synthetic data sets, despite their potential, carry limitations such as the absence of a master linkage key [ 91 ], thereby reducing their applicability in university courses for data management and curation. Having such linked data sets are also great for health data science students to test hypotheses on the effects of comorbidities.…”

Section: Discussionmentioning

confidence: 99%

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

Kuo,

Perez-Concha,

Hanly

et al. 2024

JMIR Med Educ

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Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym’s synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

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Interaction effects of multimorbidity and frailty on adverse health outcomes in elderly hospitalised patients

Cited by 23 publications

References 31 publications

Prevalence and outcomes of frailty in unplanned hospital admissions: a systematic review and meta-analysis of hospital-wide and general (internal) medicine cohorts

Prevalence and outcomes of frailty in unplanned hospital admissions: a systematic review and meta-analysis of hospital-wide and general (internal) medicine cohorts

Association between Clinical Frailty Scale (CFS) and clinical presentation and outcomes in older inpatients with COVID-19

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

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