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

Hybrid model of care for older persons for improvement of frailty index-a community-based interventional study in an urban setting

BACKGROUND

An older person undergoes a 'disablement' process with aging. A comprehensive geriatric assessment centered around the functional status informs the healthcare provider of their frailty status, based on which tailored interventions may be designed to help prevent/reverse frailty. This study was conducted to assess the improvement in frailty index by training older persons for self-care practices using a multi-domain behavioural intervention, assisted by their caregivers.

METHODS

It is a community-based interventional trial among older persons aged ≥ 60 years and their primary caregivers conducted in an urban community for a period of 15 months. A hybrid model, which exploits the advantages of every indigenous geriatric model of care, in providing a holistic care to old persons, was developed and adopted. Intervention was designed to incorporate all domains of frailty assessed, based upon self-efficacy and social interdependence theory. Frail-VIG scale and SPPB scores were used to measure the outcomes.

RESULTS

128 older persons and their primary caregivers were recruited. Median age was 70 and 67 years in the intervention and control group respectively, with majority being males. The median frailty index at baseline was 0.36 in both the groups, with improvement in intervention group (0.20) and worsening in control group (0.44) at end-line. From the DID analysis, a reduction of 0.19 points of frailty index was observed (even after adjustment for co-variates) in the intervention group, as compared to the control group. Also, it was observed that age and gender of the old person, their per capita income and the family support played an interactive effect in improvement of the frailty index. There was a significant difference in SPPB scores as well, between the groups [5 (1) in CG vs. 7 (2) in IG, p < 0.001].

CONCLUSION

Frailty could be reversed with appropriate interventions designed on the pillars of self-efficacy, and social interdependence among family members. The hybrid model of care delineates the role of caregivers, who reinforce the old persons to follow prescribed interventions.

Association between the frailty index and readmission risk in hospitalised elderly Chinese patients: a retrospective cohort study

OBJECTIVES

Frailty is a common and important concern of the ageing population. This study examined the association between the frailty index and negative outcomes of hospitalised elderly Chinese patients.

DESIGN

Retrospective cohort study.

SETTING

Geriatrics Department of Peking University First Hospital.

PARTICIPANTS

470 hospitalised elderly patients.

MAIN OUTCOMES AND MEASURES

Frailty was measured using a 30-item deficit-accumulation frailty index. The outcomes were the hospitalisation duration and readmission.

RESULTS

The frailty index was available for 470 patients: 72 (15.32%) were categorised as robust, 272 (57.87%) as prefrail and 126 (26.81%) as frail. The frail group had a longer hospital stay than the robust and prefrail groups. After adjustment for age, sex and cause of hospitalisation at baseline, frailty remained a strong independent risk factor for all-cause readmission and cardiocerebrovascular disease readmission (HR 2.41, 95% CI 1.49 to 3.91, p<0.001; HR 4.92, 95% CI 1.47 to 6.31, p<0.001, respectively).

CONCLUSIONS

The frailty index predicted a longer length of stay and higher all-cause and cardiocerebrovascular disease readmission risk in hospitalised elderly patients.

Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size

BACKGROUND

Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence about the performance of machine learning techniques compared to conventional regression is mixed. It is also unclear what methodological and database factors are associated with performance.

OBJECTIVE

This study aimed to compare the mortality prediction accuracy of various machine learning classifiers for identifying frail older adults in different scenarios.

METHODS

We used deidentified data collected from older adults (65 years of age and older) assessed with interRAI-Home Care instrument in New Zealand between January 1, 2012, and December 31, 2016. A total of 138 interRAI assessment items were used to predict 6-month and 12-month mortality, using 3 machine learning classifiers (random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) and regularized logistic regression. We conducted a simulation study comparing the performance of machine learning models with logistic regression and interRAI Home Care Frailty Scale and examined the effects of sample sizes, the number of features, and train-test split ratios.

RESULTS

A total of 95,042 older adults (median age 82.66 years, IQR 77.92-88.76; n=37,462, 39.42% male) receiving home care were analyzed. The average area under the curve (AUC) and sensitivities of 6-month mortality prediction showed that machine learning classifiers did not outperform regularized logistic regressions. In terms of AUC, regularized logistic regression had better performance than XGBoost, MLP, and RF when the number of features was ≤80 and the sample size ≤16,000; MLP outperformed regularized logistic regression in terms of sensitivities when the number of features was ≥40 and the sample size ≥4000. Conversely, RF and XGBoost demonstrated higher specificities than regularized logistic regression in all scenarios.

CONCLUSIONS

The study revealed that machine learning models exhibited significant variation in prediction performance when evaluated using different metrics. Regularized logistic regression was an effective model for identifying frail older adults receiving home care, as indicated by the AUC, particularly when the number of features and sample sizes were not excessively large. Conversely, MLP displayed superior sensitivity, while RF exhibited superior specificity when the number of features and sample sizes were large.

Factors associated with entry to residential care in frail older inpatients

OBJECTIVE

To examine factors that may influence the risk of discharge to a residential aged care facility (RACF) in a population of frail older inpatients.

METHODS

We analysed data from 5846 inpatients aged over 60 years from 27 hospitals in Queensland, Australia, admitted from independent living and referred for geriatric consultation. Patients underwent an interRAI Acute Care Comprehensive Geriatric Assessment by trained nurses. Frailty was assessed using a 52-item frailty index (FI). Risk/protective factors were determined a priori. Logistic regression assessed the relationship between factors and discharge destination, adjusted for FI, age, sex and hospital. Frailty × risk/protective factor interactions were performed.

RESULTS

Patients had a mean (SD) age of 79.7 (8.2) years and a mean (SD) FI of 0.44 (0.14). Twenty-nine per cent (n = 1678) of patients were discharged to an RACF. Each 0.1 increment in FI increased the risk of discharge to an RACF by 54% (OR 1.54, 95% CI 1.40-1.68, p < 0.01). Being married or in a de facto relationship had protective effects up to an FI of 0.7, whereas behavioural and psychological symptoms of dementia (BPSD) increased the risk of RACF discharge up to an FI of 0.7. Female sex, faecal incontinence and living alone did not influence the relationship between frailty and discharge destination.

CONCLUSIONS

The association between frailty and discharge to RACF has previously been recognised but here we found that risk and protective factors can influence this association. Whether early identification and management of mutable factors can reduce discharge to RACF should be addressed in future studies.

Comparison of a multidomain frailty index from routine health data with the hospital frailty risk score in older patients in an Australian hospital

BACKGROUND

Frailty is an important determinant of health-care needs and outcomes for people in hospital.

OBJECTIVES

To compare characteristics and predictive ability of a multidomain frailty index derived from routine health data (electronic frailty index-acute hospital; eFI-AH) with the hospital frailty risk score (HFRS).

METHODS

This retrospective study included 6771 patients aged ≥75 years admitted to an Australian metropolitan tertiary referral hospital between October 2019 and September 2020. The eFI-AH and the HFRS were calculated for each patient and compared with respect to characteristics, agreement, association with age and ability to predict outcomes.

RESULTS

Median eFI-AH was 0.17 (range 0-0.66) whilst median HFRS was 3.2 (range 0-42.9). Moderate agreement was shown between the tools (Pearson's r 0.61). After adjusting for age and gender, both models had associations with long hospital stay, in-hospital mortality, unplanned all-cause readmission and fall-related readmission. Specifically, the eFI-AH had the strongest association with in-hospital mortality (adjusted odds ratio (aOR) 2.81, 95% confidence intervals (CI) 2.49-3.17), whilst the HFRS was most strongly associated with long hospital stay (aOR 1.20, 95% CI 1.18-1.21). Both tools predicted hospital stay >10 days with good discrimination and calibration.

CONCLUSIONS

Although the eFI-AH and the HFRS did not consistently identify the same inpatients as frail, both were associated with adverse outcomes and they had comparable predictive ability for prolonged hospitalisation. These two constructs of frailty may have different implications for clinical practice and health service provision and planning.

Preserving the discreteness of deficits during coding leads to a lower frailty index in individuals living in long-term care

This study assesses two coding approaches on the frailty index (FI). Two FI were calculated using 43 variables from 29,758 older adults (84.6 ± 8 years old; 64 % female) in long-term care. Scores were coded as 0, 0.5, or 1 regardless of the number of levels (grouped), or preserved (e.g., a 4 level variable was coded as 0, 0.33, 0.67, or 1; discrete). Grouped and discrete FI were compared with each ordinal variable removed but all other ordinal variables included. This was repeated until 28 unique (14 grouped, 14 discrete) FI had been constructed each with one ordinal variable removed per FI. FI was correlated to age and mortality separated by sex. The median grouped (0.302 (0.221-0.372)) was higher relative to the discrete (0.237 (0.170-0.307)) FI. The discrete (r = 0.91, r = 0.87) and grouped (r = 0.93, r = 0.87) FI showed similar relationships to age and mortality. Removal of any ordinal variable reduced grouped FI by 0.004 or 0.016, whereas removal led to both increases (range: 0.003-0.001) and reductions (range: 0.002-0.008) for discrete FI. A grouped approach inflates FI. A discrete approach provides a more accurate measure of frailty.

Post-discharge consequences of protein-energy malnutrition, sarcopenia, and frailty in older adults admitted to rehabilitation: A systematic review

BACKGROUND & AIMS

Malnutrition, sarcopenia, and frailty are three prevalent wasting conditions among older rehabilitation patients that lead to multiple health-related negative outcomes. This systematic review and meta-analysis aimed to determine the post-discharge consequences of malnutrition, sarcopenia, and frailty in older adults admitted to inpatient rehabilitation.

METHODS

MEDLINE, Embase, Web of Science, and CINAHL databases were searched on 20 April, 2021 for longitudinal studies in older adults (≥65 years) admitted for inpatient rehabilitation. This systematic review included and synthesised studies that 1) measured malnutrition, sarcopenia, and/or frailty using a validated assessment tool or guideline; and 2) reported the association with post-discharge mortality, physical function, quality of life, or discharge location. The Academy of Nutrition & Dietetics Quality Criteria Checklist and GRADE criteria were used to assess risk of bias and evidence certainty. Where possible, data were pooled using Revman.

RESULTS

Twenty-six observational studies (n = 9709 participants in total) with similarly aged populations were included. Eight, seven, and eleven studies assessed malnutrition, sarcopenia, and frailty, respectively. Follow-up periods ranged from immediate to 7 years post-rehabilitation. Malnutrition was associated with discharge to a higher level of care (GRADE: very low), and worse quality of life (GRADE: very low) and physical function (GRADE: very low). Sarcopenia was associated with worse physical function (GRADE: very low) and lower rate of home discharge (OR: 0.14; 95%CI: 0.09-0.20; I²:30%; GRADE: low). Frailty was associated with increased mortality (GRADE: very low), hospital readmission (GRADE: very low), and decreased home discharge (GRADE: very low).

CONCLUSION

Wasting conditions in older adults during rehabilitation admission may be associated with poorer quality of life, lower rates of home discharge, and higher rates of health service use, physical dysfunction, and mortality following discharge. Further research is needed to investigate the comparative and combined impacts, as well as the overlap of malnutrition, sarcopenia, and frailty during and after rehabilitation to guide priority screening and intervention.

Multistate Competing Risk Analysis of Transition Back to the Community Among Long-Term Care Home (LTC) Destined Patients: A Brief Report

OBJECTIVE

The demand for long-term care in community and facilitybased settings in Canada is expected to increase with population growth. The Toronto Grace Health Center piloted an intervention program that aims to support return to the community of acute hospital patients designated for LTC placement. We investigated whether this program was effective in transitioning the program patients back to their homes in the community and the factors associated with transitioning patients to different destinations.

METHOD

We performed a competing risk multi-state analysis of 111 patients enrolled into the Harbour Light (HL) transitional unit program between January 2020 and June 2023.

RESULTS

At the time of the study, 92 enrolled patients had been discharged and of those these, 48.9% (45) were successfully transitioned back to their private home in the community. The remaining 51.1% (46) were discharged to other destinations. Being a female was the only positive predictor of transitioning back home. Higher CPS scores (HR 0.53, 95% CI 0.31-0.88), PADDRS scale of 1+, and higher ADL Hierarchy scale, strongly predicted lower odds of transitioning back to the community.

CONCLUSION

Within the context of rising LTC bed demand and lengthy waiting time in Canada, with appropriate measures, this program successfully transitioned LTC home bound persons back to their homes. If replicable on a large scale, this could provide short and long-term solution to LTC bed demand in Canada.

Development and validation of a frailty index compatible with three interRAI assessment instruments

BACKGROUND

a Frailty Index (FI) calculated by the accumulation of deficits is often used to quantify the extent of frailty in individuals in specific settings. This study aimed to derive a FI that can be applied across three standardised international Residential Assessment Instrument assessments (interRAI), used at different stages of ageing and the corresponding increase in support needs.

METHODS

deficit items common to the interRAI Contact Assessment (CA), Home Care (HC) or Long-Term Care Facilities assessment (LTCF) were identified and recoded to form a cumulative deficit FI. The index was validated using a large dataset of needs assessments of older people in New Zealand against mortality prediction using Kaplan Meier curves and logistic regression models. The index was further validated by comparing its performance with a previously validated index in the HC cohort.

RESULTS

the index comprised 15 questions across seven domains. The assessment cohort and their mean frailty (SD) were: 89,506 CA with 0.26 (0.15), 151,270 HC with 0.36 (0.15) and 83,473 LTCF with 0.41 (0.17). The index predicted 1-year mortality for each of the CA, HC and LTCF, cohorts with area under the receiver operating characteristic curves (AUCs) of 0.741 (95% confidence interval, CI: 0.718-0.762), 0.687 (95%CI: 0.684-0.690) and 0.674 (95%CI: 0.670-0.678), respectively.

CONCLUSIONS

the results for this multi-instrument FI are congruent with the differences in frailty expected for people in the target settings for these instruments and appropriately associated with mortality at each stage of the journey of progressive ageing.

An interRAI derived frailty index predicts acute hospitalizations in older adults residing in retirement villages: A prospective cohort study

Objectives

The 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.

Design

Prospective cohort study.

Setting and participants

34 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%).

Methods

interRAI-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).

Results

Over 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 implications

The 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.

Feasibility of Measuring Frailty and Patient-Reported Outcomes During and After Post-Acute Skilled Nursing Facility Rehabilitation

Functional status and quality of life are not routinely assessed after skilled nursing facility (SNF) discharge. We determined feasibility of measuring frailty among adults ≥65 years admitted to SNF after hospitalization, and post-discharge outcomes. We calculated a frailty index (non-frail [≤0.25], mild frailty [0.26-0.35], moderate [0.36-0.45], and severe [>0.45]). After SNF discharge, we conducted serial telephone interviews measuring ability to perform functional activities and Patient Reported Outcome Measurement Information System (PROMIS) scores. Overall of 68 screened patients, 42 were eligible, and 24 (57.1%) eligible patients were enrolled. Of these, 5 (20.8%) were admitted after elective hospitalizations, 17 (70.8%) were female, and 11 (45.8%) had moderate-to-severe frailty. Frailty was measured in all participants in a mean 32.1 minutes. At 90 days, a total of three participants died, and two were lost to follow-up. Post-discharge functional status varied by frailty, with moderate-to-severe frailty having persistent impairment and lower PROMIS scores (worse quality of life) compared to those with no or mild frailty (38.2 [13.7] vs. 47.3 [8.1] p = .04). Measuring frailty and quality of life in older patients admitted to SNF is feasible. Furthermore, measuring frailty may help identify those at particularly high risk of poor recovery and lower quality of life after discharge.

Web-based software applications for frailty assessment in older adults: a scoping review of current status with insights into future development

Background

A crucial aspect of continued senior care is the early detection and management of frailty. Developing reliable and secure electronic frailty assessment tools can benefit virtual appointments, a need especially relevant in the context of the COVID-19 pandemic. An emerging effort has targeted web-based software applications to improve accessibility and usage. The objectives of this scoping review are to identify and evaluate web-based frailty assessment tools currently available and to identify challenges and opportunities for future development.

Methods

We conducted a review with literature (e.g., using MEDLINE databases) and Google searches (last updated on October 10, 2021). Each of the identified web applications were assessed based on eight featured categories and assigned a rating score accordingly.

Results

Twelve web-based frailty assessment applications were found, chiefly provided by the USA (50%) or European countries (41%) and focused on frailty grading and outcome prediction for specific patient groups (59%). Categories that scored well among the applications included the User Interface (2.8/3) and the Cost (2.7/3). Other categories had a mean score of 1.6/3 or lower. The least developed feature was Data Saving.

Conclusions

Web-based applications represent a viable option for remote frailty assessments and multidisciplinary integrated care of older adults. Despite the available web-based frailty assessments on the Internet, many missed certain needed features for professional use in healthcare settings. This situation calls for fully comprehensive web-based applications, taking into consideration a number of key functions linking graphical user interface and functionalities, and paying special attention to secure data management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02660-6.

Mortality prediction in intensive care units including premorbid functional status improved performance and internal validity

OBJECTIVE

Prognostic models are key for benchmarking intensive care units (ICUs). They require up-to-date predictors and should report transportability properties for reliable predictions. We developed and validated an in-hospital mortality risk prediction model to facilitate benchmarking, quality assurance, and health economics evaluation.

STUDY DESIGN AND SETTING

We retrieved data from the database of an international (Finland, Estonia, Switzerland) multicenter ICU cohort study from 2015 to 2017. We used a hierarchical logistic regression model that included age, a modified Simplified Acute Physiology Score-II, admission type, premorbid functional status, and diagnosis as grouping variable. We used pooled and meta-analytic cross-validation approaches to assess temporal and geographical transportability.

RESULTS

We included 61,224 patients treated in the ICU (hospital mortality 10.6%). The developed prediction model had an area under the receiver operating characteristic curve 0.886, 95% confidence interval (CI) 0.882-0.890; a calibration slope 1.01, 95% CI (0.99-1.03); a mean calibration -0.004, 95% CI (-0.035 to 0.027). Although the model showed very good internal validity and geographic discrimination transportability, we found substantial heterogeneity of performance measures between ICUs (I-squared: 53.4-84.7%).

CONCLUSION

A novel framework evaluating the performance of our prediction model provided key information to judge the validity of our model and its adaptation for future use.

Ability of 3 Frailty Measures to Predict Short-Term Outcomes in Older Patients Admitted for Post-Acute Inpatient Rehabilitation

OBJECTIVES

To evaluate the ability of 3 commonly used frailty measures to predict short-term clinical outcomes in older patients admitted for post-acute inpatient rehabilitation.

DESIGN

Observational cohort study.

SETTING AND PARTICIPANTS

Consecutive patients (n = 207) admitted to a geriatric inpatient rehabilitation facility.

METHODS

Frailty on admission was assessed using a frailty index, the physical frailty phenotype, and the Clinical Frailty Scale (CFS). Predictive capacity of the frailty instruments was analyzed for (1) nonhome discharge, (2) readmission to acute care, (3) functional decline, and (4) prolonged length of stay, using multivariate logistic regression models and receiver operating characteristic (ROC) curves.

RESULTS

The number of patients classified as frail was 91 (44.0%) with the frailty index, 134 (64.7%) using the frailty phenotype, and 151 (73.0%) with the CFS. The 3 frailty measures revealed acceptable discriminatory accuracy for nonhome discharge (area under the curve ≥ 0.7) but differed in their predictive ability: the adjusted odds ratio (OR) for nonhome discharge was highest for the CFS [6.2, 95% confidence interval (CI) 1.8-21.1], compared to the frailty index (4.1, 95% CI 2.0-8.4) and the frailty phenotype (OR 2.9, 95% CI 1.2-6.6). For the other outcomes, discriminatory accuracy based on ROC tended to be lower and predictive ability varied according to frailty measure. Readmission to acute care from inpatient rehabilitation was predicted by all instruments, most pronounced by the frailty phenotype (OR 5.4, 95% CI 1.6-18.8) and the frailty index (OR 2.5, 95% CI 1.1-5.6), and less so by the CFS (OR 1.4, 95% CI 0.5-3.8).

CONCLUSIONS AND IMPLICATIONS

Frailty measures may contribute to improved prediction of outcomes in geriatric inpatient rehabilitation. The choice of the instrument may depend on the individual outcome of interest and the corresponding discriminatory ability of the frailty measure.

The Role of Frailty in Predicting 3 and 6 Months Functional Decline in Hospitalized Older Adults: Findings from a Secondary Analysis

Frailty represents one of the most relevant geriatric syndromes in the 21st century and is a predictor of adverse outcomes in hospitalized older adult, such as, functional decline (FD). This study aimed to examine if frailty, evaluated with the Frailty Index (FI), can predict FD during and after hospitalization (3 and 6 months). Secondary data analysis of a prospective cohort study of 101 hospitalized older adults was performed. The primary outcome was FD at discharge, 3 and 6 months. The FI was created from an original database using 40 health deficits. Functional decline models for each time-point were examined using a binary logistic regression. The prevalence of frailty was 57.4% with an average score of 0.25 (±0.11). Frail patients had significant and higher values for functional decline and social support for all time periods and more hospital readmission in the 3 month period. Multivariable regression analysis showed that FI was a predictor of functional decline at discharge (OR = 1.07, 95% CI = 1.02–1.14) and 3-month (OR = 1.05, 95% CI = 1.01–1.09) but not 6-month (OR = 1.03, 95% CI = 0.99–1.09) follow-up. Findings suggest that frailty at admission of hospitalized older adults can predict functional decline at discharge and 3 months post-discharge.

Differential Impact of Advanced Age on Clinical Outcomes After Vestibular Schwannoma Resection in the Very Elderly: Cohort Study

BACKGROUND

Vestibular schwannomas (VS) have a peak incidence in the sixth and seventh decades of life. Stereotactic radiosurgery is often the preferred treatment for VS among patients of advanced age. The fraction of elderly patients potentially requiring consideration for surgical treatment is anticipated to expand, mandating an update to management paradigms in this population.

OBJECTIVE

To describe our experience with surgical management of VS in patients aged 75 yr and older.

METHODS

Cohort study of all patients aged ≥75 yr with sporadic VS requiring surgical treatment at our institution between 1999 and 2020. Data included preoperative baseline characteristics and outcome data including extent of resection, facial nerve and hearing status, functional outcome, length of stay, and complications.

RESULTS

A total of 24 patients were included, spanning an age range of 75 to 90 yr. Average tumor size was 2.76 ± 1.04 cm, and average baseline Modified 5-item Frailty Index (mFI-5) score was 1.08 ± 0.93. Extent of resection was gross total in 5 (20.8%), near total in 3 (12.5%), and sub-total resection in the remaining 16 (66.7%). One patient died in the postoperative period because of an acute sub-dural hematoma. Favorable facial nerve function (HB1-2) was preserved in 12 patients (75%) between 75 and 79 yr and 2 patients (28.6%) aged ≥ 80 yr. No cerebrospinal fluid leak or surgical site infection was observed; 3 patients developed hydrocephalus requiring ventriculo-peritoneal shunt placement. Nine patients required out-of-home disposition; all patients eventually returned to independent living.

CONCLUSION

Microsurgical resection of VS can be safely undertaken in patients greater than 75 y/o but may carry an increased risk of poor facial function.

Risk factors of readmission after geriatric hospital care: An interRAI-based cohort study in Finland

PURPOSE

To identify risk factors for readmission after geriatric hospital care.

METHODS

A retrospective cohort study of 1,167 community-dwelling patients aged ≥70 years who were hospitalised in two geriatric hospitals and discharged to their homes over a three-year period. We combined the results of the interRAI-post acute care instrument (interRAI-PAC) with hospital discharge records. Factors associated with readmissions within 90 days following discharge were analysed using logistic regression analysis.

RESULTS

The patients' mean age was 84.5 (SD 6.2) years, and 71% (n = 827) were women. The 90-day readmission rate was 29.5%. The risk factors associated with readmission in the univariate analysis were as follows: age, admission from home vs. acute care hospital, Alzheimer's disease, unsteady gait, fatigue, unstable conditions, Activities of Daily Living Hierarchy Scale (ADLH) score, Cognitive Performance Scale (CPS) score, body mass index (BMI), frailty index, bowel incontinence, hearing difficulties, and poor self-rated health. In the multivariable analysis, age of ≥90 years, ADLH ≥1, unsteady gait, BMI <25 or ≥30 kg/m 2 , and frailty remained as risk factors for readmission. Surgical operation during the treatment period was associated with a lower readmission risk.

CONCLUSIONS AND IMPLICATIONS

InterRAI-PAC performed upon admission to geriatric hospitals revealed patient-related risk factors for readmission. Based on the identified risk factors, we recommend that the patient's functional ability, activities of daily living (ADL) needs, and individual factors underlying ADL disability, as well as nutritional and mobility problems should be carefully addressed and managed during hospitalization to diminish the risk for readmission.

SARC-F Predicts Mortality Risk of Older Adults during Hospitalization

OBJECTIVES

To determine the association between SARC-F scores and the in-hospital mortality risk among older patients admitted to acute care hospitals.

DESIGN

Single-center retrospective study.

SETTING

A university hospital.

PARTICIPANTS

All consecutive patients aged older than 65 were admitted and discharged from the study hospital between July 2019 and September 2019.

MEASUREMENTS

Relevant patient data included age, sex, body mass index, nutritional status, fat-free mass, disease, activities of daily living (ADL), duration of hospital stay, SARC-F, and occurrence of death within 30 days of hospitalization. The diseases that caused hospitalization and comorbidities (Charlson Comorbidity Index; CCI) were obtained from medical records. The Eastern Cooperative Oncology Group-performance status (PS) was used to determine ADL, and the in-hospital mortality rate within 30 days of hospitalization as the outcome.

RESULTS

We analyzed 2,424 patients. The mean age was 75.9±6.9 and 55.5% were male. Fifty-three in-hospital mortalities occurred among the participants within the first 30 days of hospitalization. Patients who died in-hospital were older, had poorer nutritional status and severer PS scores, and more comorbidities than those who did not. A SARC-F score of ≥4 predicted a higher mortality risk within those 30 days with the following precision: sensitivity 0.792 and specificity 0.805. There were significantly more deaths in Kaplan-Meier curves regarding a score of SARC-F≥4 than a score of SARC-F<4 (p<0.001). Cox proportional hazard analysis was used to identify the clinical indicators most associated with in-hospital mortality. SARC-F≥4 (Hazard Ratio: HR 5.65, p<0.001), CCI scores (HR1.11, p=0.004), and infectious and parasitic diseases (HR3.13, p=0.031) were associated with in-hospital mortality. The SARC-F items with significant in-hospital mortality effects were assistance with walking (HR 2.55, p<0.001) and climbing stairs (HR 2.46, p=0.002).

CONCLUSION

The SARC-F questionnaire is a useful prognostic indicator for older adults because a SARC-F ≥4 score during admission to an acute care hospital predicts in-hospital mortality within 30 days of hospitalization.

Development and Testing of Compatible Diagnosis Code Lists for the Functional Comorbidity Index: International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, 10th Revision, Clinical Modification

BACKGROUND

The Functional Comorbidity Index (FCI) was developed for community-based adult populations, with function as the outcome. The original FCI was a survey tool, but several International Classification of Diseases (ICD) code lists-for calculating the FCI using administrative data-have been published. However, compatible International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM versions have not been available.

OBJECTIVE

We developed ICD-9-CM and ICD-10-CM diagnosis code lists to optimize FCI concordance across ICD lexicons.

RESEARCH DESIGN

We assessed concordance and frequency distributions across ICD lexicons for the FCI and individual comorbidities. We used length of stay and discharge disposition to assess continuity of FCI criterion validity across lexicons.

SUBJECTS

State Inpatient Databases from Arizona, Colorado, Michigan, New Jersey, New York, Utah, and Washington State (calendar year 2015) were obtained from the Healthcare Cost and Utilization Project. State Inpatient Databases contained ICD-9-CM diagnoses for the first 3 calendar quarters of 2015 and ICD-10-CM diagnoses for the fourth quarter of 2015. Inpatients under 18 years old were excluded.

MEASURES

Length of stay and discharge disposition outcomes were assessed in separate regression models. Covariates included age, sex, state, ICD lexicon, and FCI/lexicon interaction.

RESULTS

The FCI demonstrated stability across lexicons, despite small discrepancies in prevalence for individual comorbidities. Under ICD-9-CM, each additional comorbidity was associated with an 8.9% increase in mean length of stay and an 18.5% decrease in the odds of a routine discharge, compared with an 8.4% increase and 17.4% decrease, respectively, under ICD-10-CM.

CONCLUSION

This study provides compatible ICD-9-CM and ICD-10-CM diagnosis code lists for the FCI.

An interRAI-derived frailty index is associated with prior hospitalisations in older adults residing in retirement villages

OBJECTIVES

To develop and validate a frailty index (FI) from interRAI-Community Health Assessments (CHA) on older adults in retirement villages (RVs).

METHODS

This is a cross-sectional analysis of a current RV research study. A FI was generated using the cumulative deficit model. Health-care utilisation measures were acute, and all, hospitalisations 12 months before baseline assessment. Associations between FI and hospitalisations were explored using multivariable logistic regression to estimate odds ratio (OR).

RESULTS

Of 577 included residents, mean (SD) age was 81 (7) and 419 (73%) were female. Mean (SD) FI was 0.16 (0.09); 260 (45%) were mildly frail, and 108 (19%) moderate-severely frail. In multivariate-adjusted analysis, odds of acute hospitalisation for mild (OR = 3.3, P < .001) and moderate-severely frail (OR = 6.4, P < .001) were significantly higher than fit residents. Higher odds were also observed for all hospitalisations.

CONCLUSION

A considerable proportion of RV residents were moderately-severely frail. FI was associated with acute and all hospitalisations.