Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review

Predicting Outcomes in Frail Older Community-Dwellers in Western Australia: Results from the Community Assessment of Risk Screening and Treatment Strategies (CARTS) Programme

Understanding risk factors for frailty, functional decline and incidence of adverse healthcare outcomes amongst community-dwelling older adults is important to plan population-level health and social care services. We examined variables associated with one-year risk of institutionalisation, hospitalisation and death among patients assessed in their own home by a community-based Aged Care Assessment Team (ACAT) in Western Australia. Frailty and risk were measured using the Clinical Frailty Scale (CFS) and Risk Instrument for Screening in the Community (RISC), respectively. Predictive accuracy was measured from the area under the curve (AUC). Data from 417 patients, median 82 ± 10 years, were included. At 12-month follow-up, 22.5% (n = 94) were institutionalised, 44.6% (n = 186) were hospitalised at least once and 9.8% (n = 41) had died. Frailty was common, median CFS score 6/9 ± 1, and significantly associated with institutionalisation (p = 0.001), hospitalisation (p = 0.007) and death (p < 0.001). Impaired activities of daily living (ADL) measured on the RISC had moderate correlation with admission to long-term care (r = 0.51) and significantly predicted institutionalisation (p < 0.001) and death (p = 0.01). The RISC had the highest accuracy for institutionalisation (AUC 0.76). The CFS and RISC had fair to good accuracy for mortality (AUC of 0.69 and 0.74, respectively), but neither accurately predicted hospitalisation. Home assessment of community-dwelling older patients by an ACAT in Western Australia revealed high levels of frailty, ADL impairment and incident adverse outcomes, suggesting that anticipatory care planning is imperative for these patients.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data

BACKGROUND

Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.

METHODS

Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.

RESULTS

We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.

CONCLUSIONS

Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Around the EQUATOR with Clin-STAR: Prediction modeling opportunities and challenges in aging research

The 2015 Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement was published to improve reporting transparency for prediction modeling studies. The objective of this review is to highlight methodologic challenges that aging-focused researchers will encounter when designing and reporting studies involving prediction models for older adults and provide guidance for addressing these challenges. In following the 22-item TRIPOD checklist, researchers must consider the representativeness of cohorts used (e.g., whether older adults with frailty, cognitive impairment, and social isolation were included), strategies for incorporating common geriatric predictors (e.g., age, comorbidities, functional status, and frailty), methods for handling missing data and competing risk of death, and assessment of model performance heterogeneity across important subgroups (e.g., age, sex, race, and ethnicity). We provide guidance to help aging-focused researchers develop, validate, and report models that can inform and improve patient care, which we label "TRIPOD-65."

From bedside-to-model: Designing clinical prediction rules for implementation

This editorial comments on the article by Herasevich et al.

Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models

INTRODUCTION

Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults.

METHODS

We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up. We extracted and narratively synthesised data on study design, model characteristics, and model discrimination and calibration (accuracy of predictions). We assessed risk of bias and applicability using Prediction model Risk Of Bias Assessment Tool.

RESULTS

Five studies reporting validations of nine unique models were included. Model applicability was fair but risk of bias was mostly high due to not reporting model calibration. Morbidities were used as predictors in four models, most commonly neurological or psychiatric diseases. Physical function was also included in four models. For 1-year prediction, three of the six models had acceptable discrimination (area under the receiver operating characteristic curve (AUC)/c statistic 0.70-0.79) and the remaining three had poor discrimination (AUC < 0.70). No model accounted for competing mortality risk. The only study examining model calibration (but ignoring competing mortality) concluded that it was excellent.

CONCLUSIONS

The reporting of models was incomplete. Model discrimination was at best acceptable, and calibration was rarely examined (and ignored competing mortality risk when examined). There is a need to derive better models that account for competing mortality risk and report calibration as well as discrimination.

Performance of models for predicting 1-year to 3-year mortality in older adults: a systematic review of externally validated models

Mortality prediction models support identifying older adults with short life expectancy for whom clinical care might need modifications. We systematically reviewed external validations of mortality prediction models in older adults (ie, aged 65 years and older) with up to 3 years of follow-up. In March, 2023, we conducted a literature search resulting in 36 studies reporting 74 validations of 64 unique models. Model applicability was fair but validation risk of bias was mostly high, with 50 (68%) of 74 validations not reporting calibration. Morbidities (most commonly cardiovascular diseases) were used as predictors by 45 (70%) of 64 of models. For 1-year prediction, 31 (67%) of 46 models had acceptable discrimination, but only one had excellent performance. Models with more than 20 predictors were more likely to have acceptable discrimination (risk ratio [RR] vs <10 predictors 1·68, 95% CI 1·06-2·66), as were models including sex (RR 1·75, 95% CI 1·12-2·73) or predicting risk during comprehensive geriatric assessment (RR 1·86, 95% CI 1·12-3·07). Development and validation of better-performing mortality prediction models in older people are needed.

Reasons for hospitalisation and cumulative mortality in people, 75 years or older, at high risk of hospital admission: a prospective study

BACKGROUND

A small proportion of the older population accounts for a high proportion of healthcare use. For effective use of limited healthcare resources, it is important to identify the group with greatest needs. The aim of this study was to explore frequency and reason for hospitalisation and cumulative mortality, in an older population at predicted high risk of hospital admission, and to assess if a prediction model can be used to identify individuals with the greatest healthcare needs. Furthermore, discharge diagnoses were explored to investigate if they can be used as basis for specific interventions in the high-risk group.

METHODS

All residents, 75 years or older, living in Östergötland, Sweden, on January 1st, 2017, were included. Healthcare data from 2016 was gathered and used by a validated prediction model to create risk scores for hospital admission. The population was then divided into groups by percentiles of risk. Using healthcare data from 2017-2018, two-year cumulative incidence of hospitalisation was analysed using Gray´s test. Cumulative mortality was analysed with the Kaplan-Meier method and primary discharge diagnoses were analysed with standardised residuals.

RESULTS

Forty thousand six hundred eighteen individuals were identified (mean age 82 years, 57.8% women). The cumulative incidence of hospitalisation increased with increasing risk of hospital admission (24% for percentiles < 60 to 66% for percentiles 95-100). The cumulative mortality also increased with increasing risk (7% for percentiles < 60 to 43% for percentiles 95-100). The most frequent primary discharge diagnoses for the population were heart diseases, respiratory infections, and hip injuries. The incidence was significantly higher for heart diseases and respiratory infections and significantly lower for hip injuries, for the population with the highest risk of hospital admission (percentiles 85-100).

CONCLUSIONS

Individuals 75 years or older, with high risk of hospital admission, were demonstrated to have considerable higher cumulative mortality as well as incidence of hospitalisation. The results support the use of the prediction model to direct resources towards individuals with highest risk scores, and thus, likely the greatest care needs. There were only small differences in discharge diagnoses between the risk groups, indicating that interventions to reduce hospitalisations should be personalised.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.

Predictors for unplanned hospital admissions in community dwelling adults: A dynamic cohort study

BACKGROUND

Polypharmacy and inappropriate medication use are associated with unplanned hospital admissions. Targeted interventions might reduce the hospitalization risk. Yet, it remains unclear which patient profiles derive the largest benefit from such interventions.

OBJECTIVE

The aim of this study was to determine independent risk factors, among which polypharmacy, for unplanned hospital admissions in a cohort of community dwelling adults.

METHODS

A retrospective study was performed using a large general practice registry and an insurance database in Flanders, Belgium. Community dwelling adults aged 40 years or older with data for 2013-2015 were included. The index date was the last general practitioner contact in 2014. Determinants were collected during the preceding year. Unplanned hospital admissions were determined during the year after the index date. Univariable logistic regression models were fitted on each risk factor for an unplanned hospital admission as the primary outcome. Two multivariable models were derived.

RESULTS

In total, 40411 patients were included and 2126 (5.26%) experienced an unplanned hospital admission. Mean age was 58.3 (±12.3) years. The two models identified the following determinants for an unplanned hospital admission: excessive polypharmacy, older age, male sex, number of comorbidities, atrial fibrillation, chronic obstructive pulmonary disease or stroke, low hemoglobin, use of hypnotics, antipsychotics, antidepressants or antiepileptics and prior hospital and general practitioner visits. Prior hospital visits was the largest determinant.

CONCLUSIONS

In our study we identified and confirmed the presence of known determinants for unplanned hospital admissions in community dwelling adults, most of which align with a geriatric phenotype. Our findings can inform the allocation of interventions aiming to reduce unplanned hospital admissions.

Structured decision support to prevent hospitalisations of community-dwelling older adults in Denmark (PATINA): an open-label, stepped-wedge, cluster-randomised controlled trial

BACKGROUND

Ageing populations and health-care staff shortages encourage efforts in primary care to recognise and prevent health deterioration and acute hospitalisation in community-dwelling older adults. The PATINA algorithm and decision-support tool alerts home-based-care nurses to older adults at risk of hospitalisation. The study aim was to test whether use of the PATINA tool was associated with changes in health-care use.

METHODS

An open-label, stepped-wedge, cluster-randomised controlled trial was done in three Danish municipalities, covering 20 area teams providing home-based care to around 7000 recipients. During a period of 12 months, area teams were randomly assigned to an intervention crossover for older adults (aged 65 years or older) who received care at home. The primary outcome was hospitalisation within 30 days of identification by the algorithm as being at risk of hospitalisation. Secondary outcomes were hospital readmission and other hospital contacts, outpatient contacts, contact with primary care physicians (PCPs), temporary care, and death, within 30 days of identification. This study was registered at ClinicalTrials.gov (NTC04398797).

FINDINGS

In total, 2464 older adults participated in the study: 1216 (49·4%) in the control phase and 1248 (50·6%) in the intervention phase. In the control phase, 102 individuals were hospitalised within 30 days during 33 943 days of risk (incidence 0·09 per 30 days), compared with 118 individuals within 34 843 days of risk (0·10 per 30 days) during the intervention phase. The intervention was not associated with a reduction in the number of first hospitalisations within 30 days (incidence rate ratio [IRR] 1·10 [90% CI 0·90-1·40]; p=0·28). Furthermore it was not associated with reduced rates of other hospital contacts (IRR 1·10 [95% CI 0·90-1·40]; p=0·28), outpatient contacts (1·10 [0·88-1·40]; p=0·42), or mortality (0·82 [0·58-1·20]; p=0·25). The intervention was associated with a 59% reduction in readmissions within 30 days of hospital discharge (IRR 0·41 [95% CI 0·24-0·68]; p=0·0007), a 140% increase in contacts with PCPs (2·40 [1·18-3·20]; p<0·0001), and a 150% increase in use of temporary care (2·50 [1·40-4·70]; p=0·0027).

INTERPRETATION

Despite having no effect on the primary outcome, the PATINA tool showed other benefits for older adults receiving home-based care. Such algorithms have the potential to shift health-care use from secondary to primary care but need to be tested in other home-based care settings. Implementation of algorithms in clinical practice should be informed by analysis of cost-effectiveness and potential harms as well as the benefits.

FUNDING

Innovation Fund Denmark and Region of Southern Denmark.

TRANSLATIONS

For the Danish, French and German translations of the abstract see Supplementary Materials section.