Several frailty rating scales have been developed to detect and screen for the level of frailty. It is uncertain what diagnostic value screening of frailty level have in the emergency department.
To ...assess the accuracy of the screening tools used in the emergency department to detect frailty in patients≥65years by their ability to identify the risk of adverse outcomes.
An extensive medical literature search of Embase and PubMed was conducted, to identify studies using frailty screening scales in the emergency department. Data was subsequently extracted and evaluated from the results of the included studies.
Four studies met the exact inclusion criteria. Four different frailty screening scales: Clinical Frailty Scale, Deficit Accumulation Index, Identification of Seniors At Risk and The Study of Osteoporotic Fracture frailty index used in the emergency department were described and compared. Predictive values for various outcomes are represented and discussed.
The results suggest that frailty successfully predicts increased risk of hospitalization, nursing home admission, mortality and prolonged length of stay after an initial emergency department visit. Frailty does however not predict increased risk of 30day emergency department revisit. Further research highlighting the value of screening for frailty level in elderly emergency department patients is needed.
Although frail elders in need of further geriatric assessment should be identified as soon as possible, this systematic review only identified four cohort studies of frailty assessment in emergency departments.
Although frailty screening appeared to predict the risk of mortality and of admission to hospital/nursing home, these four studies did not show that it could predict return visits to emergency departments within 30days.
Randomized clinical trials of frailty screening tools compared to usual care or other methods of assessment are clearly needed.
•Frail elders in need of further geriatric assessment should be identified in the ED.•Frailty Screening predicts risk of mortality and admission to hospital/nursing home.•Increased risk of 30day ED revisit is not predicted by frailty screening.•To assess validity of frailty screening in EDs randomized clinical trials are needed.
Vital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to ...clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear.
To evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness.
PubMed, Embase, Cochrane Library and CINAHL were searched in December 2017.
Studies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest.
Performed separately by two authors using a preformed extraction sheet.
Of 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated.
This review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.
We validate the Clinical Frailty Scale by examining its independent predictive validity for 30-day mortality, ICU admission, and hospitalization and by determining its reliability. We also determine ...frailty prevalence in our emergency department (ED) as measured with the Clinical Frailty Scale.
This was a prospective observational study including consecutive ED patients aged 65 years or older, from a single tertiary care center during a 9-week period. To examine predictive validity, association with mortality was investigated through a Cox proportional hazards regression; hospitalization and ICU transfer were investigated through multivariable logistic regression. We assessed reliability by calculating Cohen's weighted κ for agreement of experts who independently assigned Clinical Frailty Scale levels, compared with trained study assistants. Frailty was defined as a Clinical Frailty Scale score of 5 and higher.
A total of 2,393 patients were analyzed in this study, of whom 128 died. Higher frailty levels were associated with higher hazards for death independent of age, sex, and condition (medical versus surgical). The area under the curve for 30-day mortality prediction was 0.81 (95% confidence interval CI 0.77 to 0.85), for hospitalization 0.72 (95% CI 0.70 to 0.74), and for ICU admission 0.69 (95% CI 0.66 to 0.73). Interrater reliability between the reference standard and the study team was good (weighted Cohen’s κ was 0.74; 95% CI 0.64 to 0.85). Frailty prevalence was 36.8% (n=880).
The Clinical Frailty Scale appears to be a valid and reliable instrument to identify frailty in the ED. It might provide ED clinicians with useful information for decisionmaking in regard to triage, disposition, and treatment.
Emergency patients with hypoalbuminemia are known to have increased mortality. No previous studies have, however, assessed the predictive value of low albumin on mortality in unselected acutely ...admitted medical patients. We aimed at assessing the predictive power of hypoalbuminemia on 30-day all-cause mortality in a cohort of acutely admitted medical patients.
We included all acutely admitted adult medical patients from the medical admission unit at a regional teaching hospital in Denmark. Data on mortality was extracted from the Danish Civil Register to ensure complete follow-up. Patients were divided into three groups according to their plasma albumin levels (0-34, 35-44 and ≥45 g/L) and mortality was identified for each group using Kaplan-Meier survival plot. Discriminatory power (ability to discriminate patients at increased risk of mortality) and calibration (precision of predictions) for hypoalbuminemia was determined.
We included 5,894 patients and albumin was available in 5,451 (92.5%). A total of 332 (5.6%) patients died within 30 days of admission. Median plasma albumin was 40 g/L (IQR 37-43). Crude 30-day mortality in patients with low albumin was 16.3% compared to 4.3% among patients with normal albumin (p<0.0001). Patients with low albumin were older and admitted for a longer period of time than patients with a normal albumin, while patients with high albumin had a lower 30-day mortality, were younger and were admitted for a shorter period. Multivariable logistic regression analyses confirmed the association of hypoalbuminemia with mortality (OR: 1.95 (95% CI: 1.31-2.90)). Discriminatory power was good (AUROC 0.73 (95% CI, 0.70-0.77)) and calibration acceptable.
We found hypoalbuminemia to be associated with 30-day all-cause mortality in acutely admitted medical patients. Used as predictive tool for mortality, plasma albumin had acceptable discriminatory power and good calibration.
There exist several risk stratification systems for predicting mortality of emergency patients. However, some are complex in clinical use and others have been developed using suboptimal methodology. ...The objective was to evaluate the capability of the staff at a medical admission unit (MAU) to use clinical intuition to predict in-hospital mortality of acutely admitted patients.
This is an observational prospective cohort study of adult patients (15 years or older) admitted to a MAU at a regional teaching hospital. The nursing staff and physicians predicted in-hospital mortality upon the patients' arrival. We calculated discriminatory power as the area under the receiver-operating-characteristic curve (AUROC) and accuracy of prediction (calibration) by Hosmer-Lemeshow goodness-of-fit test.
We had a total of 2,848 admissions (2,463 patients). 89 (3.1%) died while admitted. The nursing staff assessed 2,404 admissions and predicted mortality in 1,820 (63.9%). AUROC was 0.823 (95% CI: 0.762-0.884) and calibration poor. Physicians assessed 738 admissions and predicted mortality in 734 (25.8% of all admissions). AUROC was 0.761 (95% CI: 0.657-0.864) and calibration poor. AUROC and calibration increased with experience. When nursing staff and physicians were in agreement (±5%), discriminatory power was very high, 0.898 (95% CI: 0.773-1.000), and calibration almost perfect. Combining an objective risk prediction score with staff predictions added very little.
Using only clinical intuition, staff in a medical admission unit has a good ability to identify patients at increased risk of dying while admitted. When nursing staff and physicians agreed on their prediction, discriminatory power and calibration were excellent.
Objective
To validate the Clinical Frailty Scale (CFS) for prediction of 1‐year all‐cause mortality in the emergency department (ED) and compare its performance to the Emergency Severity Index (ESI).
...Methods
Prospective cohort study at the ED of a tertiary care center in Northwestern Switzerland. All patients aged ≥65 years were included from March 18 to May 20, 2019, after informed consent. Frailty status was assessed using CFS, excluding level 9 (palliative). Acuity level was assessed using ESI. Both CFS and ESI were adjusted for age, sex and presenting condition in multivariable logistic regression. Prognostic performance was assessed for discrimination and calibration separately. Estimates were internally validated by Bootstrapping. Restricted mean survival time (RMST) was determined for all levels of CFS.
Results
In the final study population of 2191 patients, 1‐year all‐cause mortality was 17% (n = 372). RMST values ranged from 219 days for CFS 8 to 365 days for CFS 1. The adjusted CFS model had an area under receiver operating characteristic of 0.767 (95% confidence interval CI: 0.741–0.793), compared to 0.703 (95% CI: 0.673–0.732) for the adjusted ESI model.
Conclusion
The CFS predicts 1‐year all‐cause mortality for older ED patients and predicts survival time in a graded manner. The CFS is superior to the ESI when adjusted for age, sex, and presenting condition.
Patients that initially appear stable on arrival to the hospital often have less intensive monitoring of their vital signs, possibly leading to excess mortality. The aim was to describe risk factors ...for deterioration in vital signs and the related prognosis among patients with normal vital signs at arrival to a medical emergency department (MED).
Single-centre, retrospective cohort study of all patients admitted to the MED from September 2010-August 2011.
Patients were included when their vital signs (systolic blood pressure, pulse rate, respiratory rate, Glasgow Coma Scale, oxygen saturation and temperature) were within the normal range at arrival. Deterioration was defined as a deviation from the defined normal range 2-24 hours after arrival.
4292 of the 6257 (68.6%) admitted to the MED had a full set of vital signs at first presentation, 1440/4292 (33.6%) had all normal vital signs and were included in study, 44.0% were male, median age 64 years (5th/95th percentile: 21-90 years) and 446/1440 (31.0%) deteriorated within 24 hours. Independent risk factors for deterioration included age 65-84 years odds ratio (OR): 1.79 (95% confidence interval CI: 1.27-2.52), 85+ years OR 1.67 (95% CI: 1.10-2.55), Do-not-attempt-to-resuscitate order OR 3.76 (95% CI: 1.37-10.31) and admission from the open general ED OR 1.35 (95% CI: 1.07-1.71). Thirty-day mortality was 7.9% (95% CI: 5.5-10.7%) among deteriorating patients and 1.9% (95% CI: 1.2-3.0%) among the non-deteriorating, hazard ratio 4.11 (95% CI: 2.38-7.10).
Among acutely admitted medical patients who arrive with normal vital signs, 31.0% showed signs of deterioration within 24 hours. Risk factors included old age, Do-not-attempt-to-resuscitate order, admission from the open general ED. Thirty-day mortality among patients with deterioration was four times higher than among non-deteriorating patients. Further research is needed to determine whether intensified monitoring of vital signs can help to prevent deterioration or mortality among medical emergency patients.
Focus on frailty status has become increasingly important when determining care plans within and across health care sectors. A standardized frailty measure applicable for both primary and secondary ...health care sectors is needed to provide a common reference point. The aim of this study was to translate the Clinical Frailty Scale (CFS) into Danish (CFS-DK) and test inter-rater reliability for key health care professionals in the primary and secondary sectors using the CFS-DK.
The Clinical Frailty Scale was translated into Danish using the ISPOR principles for translation and cultural adaptation that included forward and back translation, review by the original developer, and cognitive debriefing. For the validation exercise, 40 participants were asked to rate 15 clinical case vignettes using the CFS-DK. The raters were distributed across several health care professions: primary care physicians (n = 10), community nurses (n = 10), hospital doctors from internal medicine (n = 10) and intensive care (n = 10). Inter-rater reliability was assessed using intraclass correlation coefficients (ICC), and sensitivity analysis was performed using multilevel random effects linear regression.
The Clinical Frailty Scale was translated and culturally adapted into Danish and is presented in this paper in its final form. Inter-rater reliability in the four professional groups ranged from ICC 0.81 to 0.90. Sensitivity analysis showed no significant impact of professional group or length of clinical experience. The health care professionals considered the CFS-DK to be relevant for their own area of work and for cross-sectoral collaboration.
The Clinical Frailty Scale was translated and culturally adapted into Danish. The inter-rater reliability was high in all four groups of health care professionals involved in cross-sectoral collaborations. However, the use of case vignettes may reduce the generalizability of the reliability findings to real-life settings. The CFS has the potential to serve as a common reference tool when treating and rehabilitating older patients.
ObjectiveSepsis is a condition associated with high mortality and morbidity, and survivors often experience physical and psychological decline. Previous research has primarily focused on sepsis ...survivors discharged from the intensive care unit (ICU). We aimed to explore and understand the consequences of sepsis experienced by sepsis survivors in general.DesignA qualitative study inspired by a phenomenological hermeneutical approach was conducted. Data were analysed using systematic text condensation.SettingPatients with sepsis were identified on admission to the emergency department and invited to an interview 3 months after discharge.ParticipantsSixteen sepsis survivors were purposively sampled and interviewed. Among these survivors, one patient was admitted to the ICU.ResultsThree main themes were derived from the analysis: new roles in life, cognitive impairment and anxiety. Although many survivors described a physical decline, they experienced psychological and cognitive impairments after sepsis as the most influential factors in daily life. The survivors frequently experienced fatigue, withdrawals from social activities and anxiety.ConclusionSepsis survivors’ experiences appeared to overlap regardless of ICU admission or treatment at the general ward. Identifying patients with sepsis-related decline is important to understand and support overall patient processes and necessary in meeting specific needs of these patients after hospital discharge.
Most existing risk stratification systems predicting mortality in emergency departments or admission units are complex in clinical use or have not been validated to a level where use is considered ...appropriate. We aimed to develop and validate a simple system that predicts seven-day mortality of acutely admitted medical patients using routinely collected variables obtained within the first minutes after arrival.
This observational prospective cohort study used three independent cohorts at the medical admission units at a regional teaching hospital and a tertiary university hospital and included all adult (≥ 15 years) patients. Multivariable logistic regression analysis was used to identify the clinical variables that best predicted the endpoint. From this, we developed a simplified model that can be calculated without specialized tools or loss of predictive ability. The outcome was defined as seven-day all-cause mortality. 76 patients (2.5%) met the endpoint in the development cohort, 57 (2.0%) in the first validation cohort, and 111 (4.3%) in the second. Systolic blood Pressure, Age, Respiratory rate, loss of Independence, and peripheral oxygen Saturation were associated with the endpoint (full model). Based on this, we developed a simple score (range 0-5), ie, the PARIS score, by dichotomizing the variables. The ability to identify patients at increased risk (discriminatory power and calibration) was excellent for all three cohorts using both models. For patients with a PARIS score ≥ 3, sensitivity was 62.5-74.0%, specificity 85.9-91.1%, positive predictive value 11.2-17.5%, and negative predictive value 98.3-99.3%. Patients with a score ≤ 1 had a low mortality (≤ 1%); with 2, intermediate mortality (2-5%); and ≥ 3, high mortality (≥ 10%).
Seven-day mortality can be predicted upon admission with high sensitivity and specificity and excellent negative predictive values.
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