Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets

Has multimorbidity and frailty in adult hospital admissions changed over the last 15 years? A retrospective study of 107 million admissions in England

BACKGROUND

Few studies have quantified multimorbidity and frailty trends within hospital settings, with even fewer reporting how much is attributable to the ageing population and individual patient factors. Studies to date have tended to focus on people over 65, rarely capturing older people or stratifying findings by planned and unplanned activity. As the UK's national health service (NHS) backlog worsens, and debates about productivity dominate, it is essential to understand these hospital trends so health services can meet them.

METHODS

Hospital Episode Statistics inpatient admission records were extracted for adults between 2006 and 2021. Multimorbidity and frailty was measured using Elixhauser Comorbidity Index and Soong Frailty Scores. Yearly proportions of people with Elixhauser conditions (0, 1, 2, 3 +) or frailty syndromes (0, 1, 2 +) were reported, and the prevalence between 2006 and 2021 compared. Logistic regression models measured how much patient factors impacted the likelihood of having three or more Elixhauser conditions or two or more frailty syndromes. Results were stratified by age groups (18-44, 45-64 and 65 +) and admission type (emergency or elective).

RESULTS

The study included 107 million adult inpatient hospital episodes. Overall, the proportion of admissions with one or more Elixhauser conditions rose for acute and elective admissions, with the trend becoming more prominent as age increased. This was most striking among acute admissions for people aged 65 and over, who saw a 35.2% absolute increase in the proportion of admissions who had three or more Elixhauser conditions. This means there were 915,221 extra hospital episodes in the last 12 months of the study, by people who had at least three Elixhauser conditions compared with 15 years ago. The findings were similar for people who had one or more frailty syndromes. Overall, year, age and socioeconomic deprivation were found to be strongly and positively associated with having three or more Elixhauser conditions or two or more frailty syndromes, with socioeconomic deprivation showing a strong dose-response relationship.

CONCLUSIONS

Overall, the proportion of hospital admissions with multiple conditions or frailty syndromes has risen over the last 15 years. This matches smaller-scale and anecdotal reports from hospitals and can inform how hospitals are reimbursed.

Inequalities in access to and outcomes of cardiac surgery in England: retrospective analysis of Hospital Episode Statistics (2010-2019)

BACKGROUND

We aimed to characterise the variation in access to and outcomes of cardiac surgery for people in England.

METHODS

We included people >18 years of age with hospital admission for ischaemic heart disease (IHD) and heart valve disease (HVD) between 2010 and 2019. Within these populations, we identified people who had coronary artery bypass graft (CABG) and/or valve surgery, respectively. We fitted logistic regression models to examine the effects of age, sex, ethnicity and socioeconomic deprivation on having access to surgery and in-hospital mortality, 1-year mortality and hospital readmission.

RESULTS

We included 292 140 people, of whom 28% were women, 11% were from an ethnic minority and 17% were from the most deprived areas. Across all types of surgery, one in five people are readmitted to hospital within 1 year, rising to almost one in four for valve surgery. Women, black people and people living in the most deprived areas were less likely to have access to surgery (CABG: 59%, 32% and 35% less likely; valve: 31%, 33% and 39% less likely, respectively) and more likely to die within 1 year of surgery (CABG: 24%, 85% and 18% more likely, respectively; valve: 19% (women) and 10% (people from most deprived areas) more likely).

CONCLUSIONS

Female sex, black ethnicity and economic deprivation are independently associated with limited access to cardiac surgery and higher post-surgery mortality. Actions are required to address these inequalities.

Predictive value of the National Early Warning Score 2 for hospitalised patients with viral respiratory illness is improved by the addition of inspired oxygen fraction as a weighted variable

OBJECTIVES

The National Early Warning Score 2 (NEWS2) is validated for predicting acute deterioration, however, the binary grading of inspired oxygen fraction (FiO2) may limit performance. We evaluated the incorporation of FiO2 as a weighted categorical variable on NEWS2 prediction of patient deterioration.

SETTING

Two hospitals at a single medical centre, Guy's and St Thomas' NHS Foundation Trust.

DESIGN

Retrospective cohort of all ward admissions, with a viral respiratory infection (SARS-CoV-2/influenza).

PARTICIPANTS

3704 adult ward admissions were analysed between 01 January 2017 and 31 December 2021.

METHODS

The NEWS-FiO2 score transformed FiO2 into a weighted categorical variable, from 0 to 3 points, substituting the original 0/2 points. The primary outcome was a composite of cardiac arrest, unplanned critical care admission or death within 24 hours of the observation. Sensitivity, positive predictive value (PPV), number needed to evaluate (NNE) and area under the receiver operating characteristic curve (AUROC) were calculated. Failure analysis for the time from trigger to outcome was compared by log-rank test.

RESULTS

The mean age was 60.4±19.4 years, 52.6% were men, with a median Charlson Comorbidity of 0 (IQR 3). The primary outcome occurred in 493 (13.3%) patients, and the weighted FiO2 score was strongly associated with the outcome (p=<0.001). In patients receiving supplemental oxygen, 78.5% of scores were reclassified correctly and the AUROC was 0.81 (95% CI 0.81 to 0.81) for NEWS-FiO2 versus 0.77 (95% CI 0.77 to 0.77) for NEWS2. This improvement persisted in the whole cohort with a significantly higher failure rate for NEWS-FiO2 (p=<0.001). At the 5-point threshold, the PPV increased by 22.0% (NNE 6.7) for only a 3.9% decrease in sensitivity.

CONCLUSION

Transforming FiO2 into a weighted categorical variable improved NEWS2 prediction for patient deterioration, significantly improving the PPV. Prospective external validation is required before institutional implementation.

An exploration of healthcare use in older people waiting for and receiving Australian community-based aged care services

AIM

Home care packages (HCPs) facilitate older individuals to remain at home, with longer HCP wait times associated with increased mortality risk. We analyze healthcare cost data pre- and post-HCP access to inform hypotheses around the effects of healthcare use and mortality risk.

METHODS

Regression models were used to assess the impact of delayed HCP access on healthcare costs and to compare costs whilst waiting and in the 6- and 12 month periods post-HCP access for 16 629 older adults.

RESULTS

Average wait time for a HCP was 89.7 days (SD = 125.6) during the study period. Wait-time length had no impact on any healthcare cost category or time period. However, total per day healthcare costs were higher in the 6 and 12 months post-receipt of a HCP (AU$61.5, AU$63, respectively) compared with those in the time waiting for a HCP (AU$48.1). Inpatient care accounted for a higher proportion of total healthcare costs post-HCP (AU$45.1, AU$46.3, respectively) compared with in the wait time (AU$30.6), whilst spending on medical services and pharmaceuticals reduced slightly in the 6 month (AU$7.1, AU$6.3) and 12 month (AU$7.2, AU$6.3) post-HCP periods compared with in the wait time (AU$7.9, AU$7.1).

CONCLUSIONS

Increased spending post-HCP on inpatient care or non-health support afforded by HCPs may offer protective effects for mortality and risk of admission to aged care. Further research should explore the association between delayed access to inpatient care for geriatric syndromes and mortality to inform recommendations on extensions to residential care outreach services into the community to improve the timely identification of the need for inpatient care. Geriatr Gerontol Int 2023; 23: 899-905.

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Background

Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.

Objective

This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.

Method

LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.

Results

Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.

Conclusion

To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

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

Background

Guidelines recommend routine frailty screening for all hospitalised older adults to inform care decisions, based mainly on studies in elective or speciality-specific settings. However, most hospital bed days are accounted for by acute non-elective admissions, in which the prevalence and prognostic value of frailty might differ, and uptake of screening is limited. We therefore did a systematic review and meta-analysis of frailty prevalence and outcomes in unplanned hospital admissions.

Methods

We searched MEDLINE, EMBASE and CINAHL up to 31/01/2023 and included observational studies using validated frailty measures in adult hospital-wide or general medicine admissions. Summary data on the prevalence of frailty and associated outcomes, measurement tools, study setting (hospital-wide vs general medicine), and design (prospective vs retrospective) were extracted and risk of bias assessed (modified Joanna Briggs Institute checklists). Unadjusted relative risks (RR; moderate/severe frailty vs no/mild) for mortality (within one year), length of stay (LOS), discharge destination and readmission were calculated and pooled, where appropriate, using random-effects models. PROSPERO CRD42021235663.

Findings

Among 45 cohorts (median/SD age = 80/5 years; n = 39,041,266 admissions, n = 22 measurement tools) moderate/severe frailty ranged from 14.3% to 79.6% overall (and in the 26 cohorts with low-moderate risk of bias) with considerable heterogeneity between studies (phet < 0.001) preventing pooling of results but with rates <25% in only 3 cohorts. Moderate/severe vs no/mild frailty was associated with increased mortality (n = 19 cohorts; RR range = 1.08-3.70), more consistently among cohorts using clinically administered tools (n = 11; RR range = 1.63-3.70; phet = 0.08; pooled RR = 2.53, 95% CI = 2.15-2.97) vs cohorts using (retrospective) administrative coding data (n = 8; RR range = 1.08-3.02; phet < 0.001). Clinically administered tools also predicted increasing mortality across the full range of frailty severity in each of the six cohorts that allowed ordinal analysis (all p < 0.05). Moderate/severe vs no/mild frailty was also associated with a LOS >8 days (RR range = 2.14-3.04; n = 6) and discharge to a location other than home (RR range = 1.97-2.82; n = 4) but was inconsistently related to 30-day readmission (RR range = 0.83-1.94; n = 12). Associations remained clinically significant after adjustment for age, sex and comorbidity where reported.

Interpretation

Frailty is common in older patients with acute, non-elective hospital admission and remains predictive of mortality, LOS and discharge home with more severe frailty associated with greater risk, justifying more widespread implementation of screening using clinically administered tools.

Funding

None.

Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data

BACKGROUND

Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes.

OBJECTIVE

We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms.

METHODS

This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy.

RESULTS

In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease.

CONCLUSIONS

The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

Data consistency in the English Hospital Episodes Statistics database

BACKGROUND

To gain maximum insight from large administrative healthcare datasets it is important to understand their data quality. Although a gold standard against which to assess criterion validity rarely exists for such datasets, internal consistency can be evaluated. We aimed to identify inconsistencies in the recording of mandatory International Statistical Classification of Diseases and Related Health Problems, tenth revision (ICD-10) codes within the Hospital Episodes Statistics dataset in England.

METHODS

Three exemplar medical conditions where recording is mandatory once diagnosed were chosen: autism, type II diabetes mellitus and Parkinson's disease dementia. We identified the first occurrence of the condition ICD-10 code for a patient during the period April 2013 to March 2021 and in subsequent hospital spells. We designed and trained random forest classifiers to identify variables strongly associated with recording inconsistencies.

RESULTS

For autism, diabetes and Parkinson's disease dementia respectively, 43.7%, 8.6% and 31.2% of subsequent spells had inconsistencies. Coding inconsistencies were highly correlated with non-coding of an underlying condition, a change in hospital trust and greater time between the spell with the first coded diagnosis and the subsequent spell. For patients with diabetes or Parkinson's disease dementia, the code recording for spells without an overnight stay were found to have a higher rate of inconsistencies.

CONCLUSIONS

Data inconsistencies are relatively common for the three conditions considered. Where these mandatory diagnoses are not recorded in administrative datasets, and where clinical decisions are made based on such data, there is potential for this to impact patient care.

Evaluation of the 2018-2019 vaccine effectiveness against medically attended influenza-like illness using medical records and claims data

BACKGROUND

Healthcare administrative databases are a rich source of information that could be leveraged to estimate real-world influenza vaccine effectiveness (VE). We aimed to evaluate the VE of standard egg-based influenza vaccines and determine if administrative healthcare data provide accurate VE estimates compared to the US CDC data.

METHODS

This retrospective cohort study was conducted during the 2018-2019 influenza season. Individuals who had at least one relevant record per year between 2015 and 2019 in their electronic medical record were included. Individuals were considered protected 14 days after receiving an influenza vaccine. The outcome was the occurrence of medically attended influenza-like illness (MA-ILI) defined by clinical diagnostic codes. Adjusted odds ratios (aORs) were derived from multivariate logistic regression and adjusted VE (aVEs) were calculated using 100 × (1-aORs).

RESULTS

A total of 5,066,980 individuals were included in the analysis with 1,307,702 (25.8%) considered vaccinated. Overall, the median age was 54 (IQR, 32-66) and 58.1% were female. Vaccine protection against MA-ILI was moderate in children and low in adults. All estimates were lower than VEs reported by the CDC for the 2018-2019 influenza season. Our results were robust to potential loss to follow up, but misclassification bias and residual confounding led to underestimation of the 2018-2019 aVE. When stratified by the number of primary care visits, aVE estimates and vaccination coverage increased with the number of primary care visits, reaching estimates similar to those obtained by the US CDC and US national vaccination coverage among those with ≥ 6 primary care visits, resulting in significant positive vaccine protection in frequent healthcare users.

CONCLUSIONS

Moderate and low aVEs were observed during the 2018-2019 season using administrative healthcare data, which was likely due to detection and misclassification biases, correlated with healthcare seeking behaviour, leading to an underestimation of the 2018-2019 influenza VE.

Development and validation of the electronic screening index of frailty

BACKGROUND

primary care screening for frailty status is recommended in clinical guidelines, but is impeded by doctor and nurse workloads and the lack of valid, easy-to-use and time-saving screening tools.

AIM

to develop and validate a new electronic tool (the electronic screening index of frailty, e-SIF) using routinely available electronic health data to automatically and massively identify frailty status in the population aged ≥65 years.

METHODS

the e-SIF was developed in three steps: selection of clinical conditions; establishment of ICD-10 codes, criteria and algorithms for their definition; and electronic tool design and data extraction, transformation and load processes. The validation phase included an observational cohort study with retrospective data collection from computerised primary care medical records. The study population included inhabitants aged ≥65 years corresponding to three primary care centres (n = 9,315). Evaluated was the relationship between baseline e-SIF categories and mortality, institutionalisation, hospitalisation and health resource consumption after 2 years.

RESULTS

according to the e-SIF, which includes 42 clinical conditions, frailty prevalence increases with age and is slightly greater in women. The 2-year adjusted hazard ratios for pre-frail, frail and very frail subjects, respectively, were as follows: 2.23 (95% CI: 1.74-2.85), 3.34 (2.44-4.56) and 6.49 (4.30-9.78) for mortality; 2.80 (2.39-3.27), 5.53 (4.59-6.65) and 9.14 (7.06-11.8) for hospitalisation; and 1.02 (0.70-1.49), 1.93 (1.21-3.08) and 2.69 (1.34-5.40) for institutionalisation.

CONCLUSIONS

the e-SIF shows good agreement with mortality, institutionalisation, hospitalisation and health resource consumption, indicating satisfactory validity. More studies in larger populations are needed to corroborate our findings.

Defining ICD-10 surrogate variables to estimate the modified frailty index: a Delphi-based approach

BACKGROUND

There are currently no validated globally and freely available tools to estimate the modified frailty index (mFI). The widely available and non-proprietary International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) coding could be used as a surrogate for the mFI. We aimed to establish an appropriate set of the ICD-10 codes for comorbidities to be used to estimate the eleven-variable mFI.

METHODS

A three-stage, web-based, Delphi consensus-building process among a panel of intensivists and geriatricians using iterative rounds of an online survey, was conducted between March and July 2021. The consensus was set a priori at 75% overall agreement. Additionally, we assessed if survey responses differed between intensivists and geriatricians. Finally, we ascertained the level of agreement.

RESULTS

A total of 21 clinicians participated in all 3 Delphi surveys. Most (86%, 18/21) had more than 5-years' experience as specialists. The agreement proportionately increased with every Delphi survey. After the third survey, the panel had reached 75% consensus in 87.5% (112/128) of ICD-10 codes. The initially included 128 ICD-10 variables were narrowed down to 54 at the end of the 3 surveys. The inter-rater agreements between intensivists and geriatricians were moderate for surveys 1 and 3 (κ = 0.728, κ = 0.780) respectively, and strong for survey 2 (κ = 0.811).

CONCLUSIONS

This quantitative Delphi survey of a panel of experienced intensivists and geriatricians achieved consensus for appropriate ICD-10 codes to estimate the mFI. Future studies should focus on validating the mFI estimated from these ICD-10 codes.

TRIAL REGISTRATION

Not applicable.

Development and validation of a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year hospital discharge data: The electronic frailty score

BACKGROUND

Most claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP).

METHODS

We used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score).

FINDINGS

In the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission.

INTERPRETATION

The eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults.

FUNDING

The study received no external funding.

Variation in coded frailty syndromes in secondary care administrative data: an international retrospective exploratory study

OBJECTIVES

Challenges with manual methodologies to identify frailty, have led to enthusiasm for utilising large-scale administrative data, particularly standardised diagnostic codes. However, concerns have been raised regarding coding reliability and variability. We aimed to quantify variation in coding frailty syndromes within standardised diagnostic code fields of an international dataset.

SETTING

Pooled data from 37 hospitals in 10 countries from 2010 to 2014.

PARTICIPANTS

Patients ≥75 years with admission of >24 hours (N=1 404 671 patient episodes).

PRIMARY AND SECONDARY OUTCOME MEASURES

Frailty syndrome groups were coded in all standardised diagnostic fields by creation of a binary flag if the relevant diagnosis was present in the 12 months leading to index admission. Volume and percentages of coded frailty syndrome groups by age, gender, year and country were tabulated, and trend analysis provided in line charts. Descriptive statistics including mean, range, and coefficient of variation (CV) were calculated. Relationship to in-hospital mortality, hospital readmission and length of stay were visualised as bar charts.

RESULTS

The top four contributors were UK, US, Norway and Australia, which accounted for 75.4% of the volume of admissions. There were 553 595 (39.4%) patient episodes with at least one frailty syndrome group coded. The two most frequently coded frailty syndrome groups were 'Falls and Fractures' (N=3 36 087; 23.9%) and 'Delirium and Dementia' (N=221 072; 15.7%), with the lowest CV. Trend analysis revealed some coding instability over the frailty syndrome groups from 2010 to 2014. The four countries with the lowest CV for coded frailty syndrome groups were Belgium, Australia, USA and UK. There was up to twofold, fourfold and twofold variation difference for outcomes of length of stay, 30-day readmission and inpatient mortality, respectively, across the countries.

CONCLUSIONS

Variation in coding frequency for frailty syndromes in standardised diagnostic fields are quantified and described. Recommendations are made to account for this variation when producing risk prediction models.

A review of literature on risk prediction tools for hospital readmissions in older adults

PURPOSE

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

DESIGN/METHODOLOGY/APPROACH

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

FINDINGS

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5-0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

RESEARCH LIMITATIONS/IMPLICATIONS

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

ORIGINALITY/VALUE

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Hospital frailty risk score and adverse health outcomes: evidence from longitudinal record linkage cardiac data

BACKGROUND

Despite recent evidence on the effect of frailty on health outcomes among those with heart failure, there is a dearth of knowledge on measuring frailty using administrative health data on a wide range of cardiovascular diseases (CVD).

METHODS

We conducted a retrospective record-linkage cohort study of patients with diverse CVD in Queensland, Australia. We investigated the relationship between the risk of frailty, defined using the hospital frailty risk score (HFRS), and 30-day mortality, 30-day unplanned readmission, non-home discharge, length of hospital stay (LOS) at an emergency department and inpatient units and costs of hospitalisation. Descriptive analysis, bivariate logistic regression and generalised linear models were used to estimate the association between HFRS and CVD outcomes. Smear adjustment was applied to hospital costs and the LOS for each frailty risk groups.

RESULTS

The proportion of low, medium and high risk of frailty was 24.6%, 34.5% and 40.9%, respectively. The odds of frail patients dying or being readmitted within 30 days of discharge was 1.73 and 1.18, respectively. Frail patients also faced higher odds of LOS, and non-home discharge at 3.1 and 2.25, respectively. Frail patients incurred higher hospital costs (by 42.7-55.3%) and stayed in the hospital longer (by 49%).

CONCLUSION

Using the HFRS on a large CVD cohort, this study confirms that frailty was associated with worse health outcomes and higher healthcare costs. Administrative data should be more accessible to research such that the HFRS can be applied to healthcare planning and patient care.

Increased time from physiological derangement to critical care admission associates with mortality

Background

Rapid response systems aim to achieve a timely response to the deteriorating patient; however, the existing literature varies on whether timing of escalation directly affects patient outcomes. Prior studies have been limited to using ‘decision to admit’ to critical care, or arrival in the emergency department as ‘time zero’, rather than the onset of physiological deterioration. The aim of this study is to establish if duration of abnormal physiology prior to critical care admission [‘Score to Door’ (STD) time] impacts on patient outcomes.

Methods

A retrospective cross-sectional analysis of data from pooled electronic medical records from a multi-site academic hospital was performed. All unplanned adult admissions to critical care from the ward with persistent physiological derangement [defined as sustained high National Early Warning Score (NEWS) > / = 7 that did not decrease below 5] were eligible for inclusion. The primary outcome was critical care mortality. Secondary outcomes were length of critical care admission and hospital mortality. The impact of STD time was adjusted for patient factors (demographics, sickness severity, frailty, and co-morbidity) and logistic factors (timing of high NEWS, and out of hours status) utilising logistic and linear regression models.

Results

Six hundred and thirty-two patients were included over the 4-year study period, 16.3% died in critical care. STD time demonstrated a small but significant association with critical care mortality [adjusted odds ratio of 1.02 (95% CI 1.0–1.04, p = 0.01)]. It was also associated with hospital mortality (adjusted OR 1.02, 95% CI 1.0–1.04, p = 0.026), and critical care length of stay. Each hour from onset of physiological derangement increased critical care length of stay by 1.2%. STD time was influenced by the initial NEWS, but not by logistic factors such as out-of-hours status, or pre-existing patient factors such as co-morbidity or frailty.

Conclusion

In a strictly defined population of high NEWS patients, the time from onset of sustained physiological derangement to critical care admission was associated with increased critical care and hospital mortality. If corroborated in further studies, this cohort definition could be utilised alongside the ‘Score to Door’ concept as a clinical indicator within rapid response systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03650-1.

Estimating Prognosis and Frailty in Persons Aged ≥75 Years in the Emergency Department: Further Validation of Dynamic Silver Code

OBJECTIVES

To assess concurrent validity of the Dynamic Silver Code (DSC), a tool based on administrative data that predicts prognosis in older adults accessing the emergency department (ED), in terms of association with markers of poor functional and cognitive status.

DESIGN

Cross-sectional.

SETTING AND PARTICIPANTS

Data were obtained in the AIDEA study, which enrolled a cohort of ≥75-year-old patients, accessing the ED of 2 hospitals in Florence, Italy.

METHODS

The DSC score and classes (I to IV, corresponding to an increasing risk of death) were obtained from administrative data. Information on health and functional status prior to ED access were collected from face-to-face, direct, or proxy interviews. The 4AT test was administered to screen for possible delirium. Bivariate comparisons of the prevalence of each functional and cognitive marker across 4 DSC classes were performed. Multinomial logistic regression was used to assess the multivariable risk of being in II, III, or IV DSC class vs I.

RESULTS

Among 3358 participants (mean age 83 years, men 44%), 32.9%, 30.3%, 19.5%, and 17.2% were in DSC class I, II, III, and IV. Preadmission abnormal functional and cognitive conditions, and delirium in the ED, were increasingly more common from DSC class I through IV (P < .001). In particular, the prevalence of total inability to walk increased from 2.9% (class I) to 23.4% (class IV). In multivariable analyses, this was the strongest predictor of being in progressively worse DSC classes, whereas feeling of exhaustion, reporting of serious falls, weight loss, and severe memory loss or diagnosis of dementia gave some contribution.

CONCLUSIONS AND IMPLICATIONS

The ability of the DSC to predict survival in older persons appears to rely on its prevailing association with markers of functional impairment. These results may support clinical use of the tool.

The ROX index has greater predictive validity than NEWS2 for deterioration in Covid-19

BACKGROUND

Patients admitted to hospital with Covid-19 are at risk of deterioration. The National Early Warning Score (NEWS2) is widely recommended, however it's validity in Covid-19 is not established and indices more specific for respiratory failure may be more appropriate. We aim to describe the physiological antecedents to deterioration, test the predictive validity of NEWS2 and compare this to the ROX index ([SpO2/FiO2]/respiratory rate).

METHOD

A single centre retrospective cohort study of adult patients who were admitted to a medical ward, between 1/3/20 and 30/5/20, with positive results for SARS-CoV-2 RNA. Physiological observations and the NEWS2 were extracted and analysed. The primary outcome was a composite of cardiac arrest, unplanned critical care admission or death within 24 hours. A generalized linear model was used to assess the association of physiological values, NEWS2 and ROX with the outcome.

FINDINGS

The primary outcome occurred in 186 patients (26%). In the preceding 24 hours, deterioration was most marked in respiratory parameters, specifically in escalating oxygen requirement; tachypnoea was a late sign, whilst cardiovascular observations remained stable. The area under the receiver operating curve was 0.815 (95% CI 0.804-0.826) for NEWS2 and 0.848 (95% CI 0.837-0.858) for ROX. Applying the optimal level of ROX, the majority of patients triggered four hours earlier than with NEWS2 of 5.

INTERPRETATION

NEWS2 may under-perform in Covid-19 due to intrinsic limitations of the design and the unique pathophysiology of the disease. A simple index utilising respiratory parameters can outperform NEWS2 in predicting the occurrence of adverse events.

Risk indices that predict in-hospital mortality of elderly patients

Background/aim

Mortality in the elderly population tends to be higher than in all other age groups; the risk factors that predict mortality among those in this age cohort are not fully understood. This large-scale clinical study aimed to identify effective risk factors that predict mortality in the elderly population with a particular focus on age and hospitalization status.

Material and methods

We retrospectively analyzed outcomes from patients with clinical follow-up between July 2015 and January 2020 at 29 Mayıs State Hospital, Ankara, Turkey. Patient records with missing or ambiguous data were excluded. Age, sex, length of hospital stay, comorbidities, consultation requests and diagnoses that include infectious diseases were evaluated for their role in predicting in-hospital mortality using binary logistic regression analysis. Primary outcomes focused on factors that had an impact on overall in-hospital mortality in the elderly population.

Results

Our study included 11,430 patients; of this group, 39.9% were elderly, which we defined as 65 years of age or older. Risk factors for in-hospital mortality in this cohort included consultation requests (AOR = 1.95, CI (1.53–2.49), P < 0.001) and length of hospital stay of ≥4 days (AOR = 2.49, CI (1.90–3.26), P < 0.001).

Conclusion

Elderly patients are at significantly higher risk for in-hospital mortality than are younger patients. Among the factors that may be used to predict the risk of in-hospital mortality in the elderly patient cohort, the most important factor is the length of hospital stay.