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Frailty Index and functional level upon admission predict hospital outcomes: an interRAI-based cohort study of older patients in post-acute care hospitals.

ABSTRACT: BACKGROUND:Geriatric assessment upon admission may reveal factors that contribute to adverse outcomes in hospitalized older patients. The purposes of this study were to derive a Frailty Index (FI-PAC) from the interRAI Post-Acute Care instrument (interRAI-PAC) and to analyse the predictive ability of the FI-PAC and interRAI scales for hospital outcomes. METHODS:This retrospective cohort study was conducted by combining patient data from interRAI-PAC with discharge records from two post-acute care hospitals. The FI-PAC was derived from 57 variables that fulfilled the Frailty Index criteria. Associations of the FI-PAC and interRAI-PAC scales (ADLH for activities of daily living, CPS for cognition, DRS for mood, and CHESS for stability of health status) with hospital outcomes (prolonged hospital stay ≥90 days, emergency department admission during the stay, and in-hospital mortality) were analysed using logistic regression and ROC curves. RESULTS:The cohort included 2188 patients (mean age (SD) 84.7 (6.3) years) who were hospitalized in two post-acute care hospitals. Most patients (n = 1691, 77%) were discharged and sent home. Their median length of stay was 35 days (interquartile range 18-87 days), and 409 patients (24%) had a prolonged hospital stay. During their stay, 204 patients (9%) were admitted to the emergency department and 231 patients (11%) died. The FI-PAC was normally distributed (mean (SD) 0.34 (0.15)). Each increase of 0.1 point in the FI-PAC increased the likelihood of prolonged hospital stay (odds ratio [95% CI] 1.91 [1.73─2.09]), emergency admission (1.24 [1.11─1.37]), and in-hospital death (1.82 [1.63─2.03]). The best instruments for predicting prolonged hospital stay and in-hospital mortality were the FI-PAC and the ADLH scale (AUC 0.75 vs 0.72 and 0.73 vs 0.73, respectively). There were no differences in the predictive abilities of interRAI scales and the FI-PAC for emergency department admission. CONCLUSIONS:The Frailty Index derived from interRAI-PAC predicts adverse hospital outcomes. Its predictive ability was similar to that of the ADLH scale, whereas other interRAI-PAC scales had less predictive value. In clinical practice, assessment of functional ability is a simple way to assess a patient's prognosis.

SUBMITTER: Kerminen H

PROVIDER: S-EPMC7201739 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Frailty Index and functional level upon admission predict hospital outcomes: an interRAI-based cohort study of older patients in post-acute care hospitals.

Kerminen Hanna H Huhtala Heini H Jäntti Pirkko P Valvanne Jaakko J Jämsen Esa E

BMC geriatrics 20200505 1

<h4>Background</h4>Geriatric assessment upon admission may reveal factors that contribute to adverse outcomes in hospitalized older patients. The purposes of this study were to derive a Frailty Index (FI-PAC) from the interRAI Post-Acute Care instrument (interRAI-PAC) and to analyse the predictive ability of the FI-PAC and interRAI scales for hospital outcomes.<h4>Methods</h4>This retrospective cohort study was conducted by combining patient data from interRAI-PAC with discharge records from two ...[more]

PMID: 32370740

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Project description:ImportanceFalls have been associated with morbidity and mortality in elderly patients. Assessment of frailty at hospital admission may help health care professionals evaluate fall risk in patients with trauma-related injury.ObjectiveTo determine whether frailty assessed using the Canadian Study of Health and Aging Clinical Frailty Scale is associated with readmission for falls after index admission for trauma-related injury in patients aged 50 years and older.Design, setting, and participantsThis retrospective cohort study reviewed the medical records of 804 patients aged 50 years and older with trauma-related injury who were admitted to the University of Iowa Hospitals and Clinics between July 1, 2010, and June 30, 2015. Records were reviewed from May 30 to August 1, 2017, and patient demographics, admission data, injury severity scores, history of falls, and postindex readmission data for ground-level falls were recorded. Frailty scores were calculated using the Canadian Study of Health and Aging Clinical Frailty Scale. Patients with a score of 5 or higher were classified as frail.Main outcomes and measuresFrailty assessed using the Canadian Study of Health and Aging Clinical Frailty Scale and readmission for falls after index admission for trauma-related injury.ResultsA total of 804 patients with trauma-related injury were included in the study. The mean (SD) age was 70 (13.4) years; 744 patients (93.4%) were white, and 380 (47.3%) were men. Among the total population, the mortality rate was 3.7%; 255 patients (31.7%) were classified as frail and 549 (68.3%) as nonfrail. The mean (SD) injury severity score was 9.8 (7.9), and the score was similar between frail and nonfrail patients. Of 255 frail patients, 179 (70.2%) were women, and frail patients were significantly older than nonfrail patients (mean [SD], 79.2 [12.1] years vs 66.2 [11.9] years, respectively; P < .001). The percentages of frail patients presenting to the hospital with a history of falls and readmitted for falls after index admission were higher than those of nonfrail patients (63 [24.8%] vs 53 [9.6%] and 55 [21.6%] vs 58 [10.6%], respectively; both P < .001). Frailty was associated with discharge to the home with health care (odds ratio [OR], 4.82; 95% CI, 2.10-11.01; P < .001), to a skilled nursing facility (OR, 5.47; 95% CI, 3.40-8.80; P < .001), and to a hospice care facility (OR, 8.47; 95% CI, 2.09-34.42; P = .003) compared with discharge to the home with self-care. Frailty was also associated with readmission for falls after index admission (OR, 2.26; 95% CI, 1.39-3.66; P = .001) and the number of falls within 1 year after index admission (OR, 1.32; 95% CI, 1.04-1.67; P = .02) compared with nonfrailty. The frailty analysis was controlled for age, body mass index, sex, and falls at index admission.Conclusions and relevanceMeasurement of frailty at hospital admission may be an effective tool to assess fall risk and discharge disposition among patients with trauma-related injury aged 50 years and older.

Project description:ObjectivesThe development of frailty tools from electronically recorded healthcare data allows frailty assessments to be routinely generated, potentially beneficial for individuals and healthcare providers. We wished to assess the predictive validity of a frailty index (FI) derived from interRAI Community Health Assessment (CHA) for outcomes in older adults residing in retirement villages (RVs), elsewhere called continuing care retirement communities.DesignProspective cohort study.Setting and participants34 RVs across two district health boards in Auckland, Aotearoa New Zealand (NZ). 577 participants, mean age 81 years; 419 (73%) female; 410 (71%) NZ European, 147 (25%) other European, 8 Asian (1%), 7 Māori (1%), 1 Pasifika (<1%), 4 other (<1%).MethodsinterRAI-CHA FI tool was used to stratify participants into fit (0-0.12), mild (>0.12-0.24), moderate (>0.24-0.36) and severe (>0.36) frail groups at baseline (the latter two grouped due to low numbers of severely frail). Primary outcome was acute hospitalization; secondary outcomes included long-term care (LTC) entry and mortality. The relationship between frailty and outcomes were explored with multivariable Cox regression, estimating hazard ratios (HRs) and 95% confidence intervals (95%CIs).ResultsOver mean follow-up of 2.5 years, 33% (69/209) of fit, 58% (152/260) mildly frail and 79% (85/108) moderate-severely frail participants at baseline had at least one acute hospitalization. Compared to the fit group, significantly increased risk of acute hospitalization were identified in mildly frail (adjusted HR = 1.88, 95%CI = 1.41-2.51, p<0.001) and moderate-severely frail (adjusted HR = 3.52, 95%CI = 2.53-4.90, p<0.001) groups. Similar increased risk in moderate-severely frail participants was seen in LTC entry (adjusted HR = 5.60 95%CI = 2.47-12.72, p<0.001) and mortality (adjusted HR = 5.06, 95%CI = 1.71-15.02, p = 0.003).Conclusions and implicationsThe FI derived from interRAI-CHA has robust predictive validity for acute hospitalization, LTC entry and mortality. This adds to the growing literature of use of interRAI tools in this way and may assist healthcare providers with rapid identification of frailty.

Project description:Background and aimPatients with chronic kidney disease (CKD) are susceptible to SARS-CoV-2 infection and more prone to develop severe disease. It is important to know predictors of poor outcomes to optimize the strategies of care.Methods93 patients with CKD and 93 age-sex matched patients without CKD were included in the study. Data on demographic, clinical features, hematological indices and outcomes were noted and compared between the groups. Neutrophile to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII) (platelet counts×neutrophil counts/lymphocyte counts) and lymphocyte-to-CRP ratio (LCR) were calculated on admission and the association of these markers with disease mortality in CKD patients was identified.ResultsCKD patients had higher risk of severe disease, and mortality compared to non-CKD patients (72% vs 50.5%, p=0.003, 36.6% vs 10.8%, p<0.001, respectively) and were more likely to have higher values of immuno-inflammatory indices (leukocyte count, neutrophil, NLR, SII and C-reactive protein, etc.) and lower level of lymphocyte and LCR. Also, higher levels of NLR, SII, PLR and lower level of LCR were seen in CKD patients who died compared to those recovered. In a receiver operating characteristic curve analysis, NLR, SII, PLR and LCR area under the curve for in-hospital mortality of CKD patients were 0.830, 0.811, 0.664 and 0.712, respectively. Among all parameters, NLR and SII gave us the best ability to distinguish patients with higher risk of death. Based on the cut-off value of 1180.5, the sensitivity and specificity of the SII for predicting in-hospital mortality were found to be 67.5% and 79.6%, respectively. The corresponding sensitivity and specificity of the NLR were 85.2% and 66.1%, respectively, at the cut-off value of 5.1. Forward stepwise logistic regression analysis showed that NLR (≥5.1), SII (≥1180.5) and LCR (≤9) were predictors for in-hospital mortality.ConclusionWe report for the first time that SII is able to distinguish COVID-19 infected CKD patients of worse survival and it is as powerful as NLR in this regard. As SII is easily quantified from blood sample data, it may assist for early identification and timely management of CKD patients with worse survival.

Project description:Background and aimPatients with chronic kidney disease (CKD) are susceptible to SARS-CoV-2 infection and more prone to develop severe disease. It is important to know predictors of poor outcomes to optimize the strategies of care.Methods93 patients with CKD and 93 age-sex matched patients without CKD were included in the study. Data on demographic, clinical features, hematological indices and outcomes were noted and compared between the groups. Neutrophile to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII) (platelet counts × neutrophil counts/lymphocyte counts) and lymphocyte-to-CRP ratio (LCR) were calculated on admission and the association of these markers with disease mortality in CKD patients was identified.ResultsCKD patients had higher risk of severe disease, and mortality compared to non-CKD patients (72% vs 50.5%, p = 0.003, 36.6% vs 10.8%, p < 0.001, respectively) and were more likely to have higher values of immuno-inflammatory indices (leukocyte count, neutrophil, NLR, SII and C-reactive protein, etc.) and lower level of lymphocyte and LCR. Also, higher levels of NLR, SII, PLR and lower level of LCR were seen in CKD patients who died compared to those recovered. In a receiver operating characteristic curve analysis, NLR, SII, PLR and LCR area under the curve for in-hospital mortality of CKD patients were 0.830, 0.811, 0.664 and 0.712, respectively. Among all parameters, NLR and SII gave us the best ability to distinguish patients with higher risk of death. Based on the cut-off value of 1180.5, the sensitivity and specificity of the SII for predicting in-hospital mortality were found to be 67.5% and 79.6%, respectively. The corresponding sensitivity and specificity of the NLR were 85.2% and 66.1%, respectively, at the cut-off value of 5.1. Forward stepwise logistic regression analysis showed that NLR (≥5.1), SII (≥1180.5) and LCR (≤9) were predictors for in-hospital mortality.ConclusionWe report for the first time that SII is able to distinguish COVID-19 infected CKD patients of worse survival and it is as powerful as NLR in this regard. As SII is easily quantified from blood sample data, it may assist for early identification and timely management of CKD patients with worse survival.

Project description:Mortality rates among hospitalized children in many government hospitals in sub-Saharan Africa are high. Pediatric emergency services in these hospitals are often sub-optimal. Timely recognition of critically ill children on arrival is key to improving service delivery. We present a simple risk score to predict inpatient mortality among hospitalized children. Between April 2010 and June 2011, the Uganda Malaria Surveillance Project (UMSP), in collaboration with the National Malaria Control Program (NMCP), set up an enhanced sentinel site malaria surveillance program for children hospitalized at four public hospitals in different districts: Tororo, Apac, Jinja and Mubende. Clinical data collected through March 2013, representing 50249 admissions were used to develop a mortality risk score (derivation data set). One year of data collected subsequently from the same hospitals, representing 20406 admissions, were used to prospectively validate the performance of the risk score (validation data set). Using a backward selection approach, 13 out of 25 clinical parameters recognizable on initial presentation, were selected for inclusion in a final logistic regression prediction model. The presence of individual parameters was awarded a score of either 1 or 2 based on regression coefficients. For each individual patient, a composite risk score was generated. The risk score was further categorized into three categories; low, medium, and high. Patient characteristics were comparable in both data sets. Measures of performance for the risk score included the receiver operating characteristics curves and the area under the curve (AUC), both demonstrating good and comparable ability to predict deathusing both the derivation (AUC =0.76) and validation dataset (AUC =0.74). Using the derivation and validation datasets, the mortality rates in each risk category were as follows: low risk (0.8% vs. 0.7%), moderate risk (3.5% vs. 3.2%), and high risk (16.5% vs. 12.6%), respectively. Our analysis resulted in development of a risk score that ably predicted mortality risk among hospitalized children. While validation studies are needed, this approach could be used to improve existing triage systems.

Dataset Information

Frailty Index and functional level upon admission predict hospital outcomes: an interRAI-based cohort study of older patients in post-acute care hospitals.

Publications

Frailty Index and functional level upon admission predict hospital outcomes: an interRAI-based cohort study of older patients in post-acute care hospitals.

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