Risk factors for hospital readmission in older adults within 30 days of discharge - a comparative retrospective study

An Early Supported Discharge (ESD) Model of Care for Older Adults Admitted to Hospital: A Descriptive Cohort Study

Background

Early supported discharge (ESD) facilitates early discharge from acute hospitals with continued rehabilitation in the home environment from a multi-disciplinary team at the same intensity as would be received in the inpatient setting. Emerging evidence suggests it can have a positive impact on the care of older adults on discharge from the acute hospital setting to home. This study aims to characterize an inreach model of ESD for older adults discharged from four hospitals in the Mid-West of Ireland and describe its impact on clinical and process outcomes at 30 and 180 days.

Methods

Consecutive older adults referred for ESD from four hospitals were recruited over six-months. Baseline assessments were carried out on initial review, and patients were followed up at 30 and 180 days by an independent outcome assessor. Outcomes measured include functional status, frailty, health related quality of life, mortality, and healthcare utilization.

Results

One hundred and thirty older adults (mean age 76.62 years, SD 9.81 years) were recruited, 44 for surgical complaints and 86 for medical complaints. The ESD service was provided over a median of 31 (medical) - 44 (surgical) days, primarily by physiotherapy and occupational therapy. The incidence of functional decline was 16.41% at 30 days and 27.5% at 180 days. There was a significant improvement in the self-reported function from index visit 72.94 (19.50) mean standard deviation (SD) to 30 days 84.05 (21.08) mean (SD) which was maintained at 180 days 80.53 (30.93) mean (SD). Frailty was independently associated with incidence of functional decline at 30 days (OR 2.06, 95% CI 1.39 to 3.06) and 180 days (OR 1.7, 95% CI 1.29 to 2.24).

Conclusion

An ESD model of care can have significant effects on patient outcomes for older adults admitted to hospital at 30 and 180 days, without increasing the risk of unscheduled Emergency Department re-presentation. Future research should explore the impact of an ESD model of care on specific older adult cohorts.

Association of neighborhood socioeconomic status with echocardiographic parameters and re-admission following transcatheter aortic valve replacement

BACKGROUND

Data on predictors of poor hemodynamic presentation and rehospitalizations following transcatheter aortic valve replacement (TAVR) are limited. We evaluate the association between neighborhood socioeconomic status (NSES) on echocardiographic presentation and post-TAVR readmission at a high-volume institution.

METHODS

All patients undergoing TAVR at a single institution between 2012 and 2022 were included. Patient addresses, baseline variables including Society of Thoracic Surgeons (STS) preoperative risk of mortality and frailty, and post-procedural outcomes were extracted from electronic health records. Using a validated US Census Bureau Index, the NSES of each patient (1-100) was tabulated, with lower values correlating to increased social deprivation. Patients were separated into four ranked groups based on NSES (rank 1: 1-25, rank 4: 76-100). Multivariable regression was performed to determine variables associated with number of days hospitalized in one-year following index TAVR procedure.

RESULTS

A total of 2031 patients were included. The median NSES was 68 (IQR: 53-80). There was a total of 232 (11.4%) readmissions. The median number of days hospitalized in one year following TAVR was 4 (interquartile range [IQR]: 2-7) After adjusting for baseline variables including STS risk score and patient frailty, compared to patients in the lowest ranked socioeconomic group, patients of higher NSES were associated with lower aortic valve gradients at baselines (Exp[β]=0.997, 95% CI: 0.993-0.999, P=0.049). Additionally, compared to patients in the lowest ranked socioeconomic group, patients of NSES were associated with shorter duration of readmission after risk-factor adjustments (Exp[β]=0.996, 95% CI: 0.992-0.999, P=0.032).

CONCLUSIONS

Patients of lower socioeconomic status are associated with higher aortic valve gradient at baseline and more days hospitalized in the first year after their index TAVR procedure after adjusting for other risk factors. As TAVR volume continues to expand, physicians and health systems must consider this independent factor when determining patient prognosis and readmission policies.

Improved functional status from people with disabilities discharged from day medical care homes from 2017-2023 in Poland

This study aimed to assess changes in the functional status of 956 patients with disabilities covered by day care in Day Medical Care Homes in Poland between 2017 and 2023. Medical records (admission reports and discharge reports) of patients admitted to seven Day Medical Care Homes managed by the Medical and Diagnostic Centre (Poland) were analyzed. The functional status of the patients was assessed using the Barthel Index and the Instrumental Activities of Daily Living (IADL) tool. Out of 956 patients, 77.4% were females, and the mean age was 74.4 (SD=8.6) years. The average Barthel Index score was 59.4 (SD=5.8; 40-65) on admission and 72.6 (SD=10.2; 25-100) on discharge (p<0.001). The average IADL score was 19.2 (SD=3.2; 8-24) on admission and 20.6 (SD=3.0; 8-24) on discharge (p<0.001). Significant improvement (p<0.001) in functional status defined with both the Barthel Index and IADL scale was observed in all demographic groups and facilities.

An integrative systematic review of nurses' involvement in medication deprescription in long-term healthcare settings for older people

Background

Deprescription of medications for older people in long-term care settings is crucial to enhance medication safety by reducing polypharmacy and minimizing related adverse events. Nurses as the member of the multidisciplinary healthcare team can support deprescription initiatives, but there is a gap in comprehensive knowledge about their roles.

Objectives

To investigate the role and contribution of nurses in deprescribing medications within the multidisciplinary pharmaceutical care context of long-term healthcare for older people.

Design

A systematic review utilizing an integrative approach was performed.

Methods

Multiple databases were searched, including PubMed (covering MEDLINE), Scopus, CINAHL, ProQuest and Embase, focusing on studies published in English from 2014 to 2024. The preliminary search yielded 4872 studies, which were then refined to 32 qualitative and quantitative studies chosen for data analysis and narrative synthesis. Thematic comparisons and analysis led to the creation of meaningful categories integrating the studies' findings to meet the review's objective.

Results

The review findings were classified into categories: 'necessity and benefits of deprescribing', 'multidisciplinary collaboration for deprescribing', 'nurse role in deprescribing', 'identified challenges to deprescribing', 'involvement of older people and families in deprescribing'. They illustrated and exemplified various aspects of nurses' roles and contributions in deprescription initiatives within the multidisciplinary pharmaceutical care team, such as support for reducing doses, discontinuing medications or transitioning to safer alternatives, as well as factors influencing this process.

Conclusion

The main dimensions of nurses' roles and contributions in deprescription initiatives encompass monitoring, communicating and educating. Challenges to nurses' active participation in deprescribing, such as the need for increased knowledge, confidence and inclusion in team discussions, should be addressed through education, training and changing attitudes. These steps are essential for improving the safety of medication deprescribing in long-term care settings.

Trial registration

The review was registered under PROSPERO ID: CRD42023486484, and can be accessed at crd.york.ac.uk/PROSPERO/display_record.php?RecordID=486484.

Risk Factors for 90-Day Readmission Among Patients with Metastatic Spine Tumors in South Korea: A Nationwide Population-Based Study

BACKGROUND

Population-based studies on the cause of readmission within 90 days after surgery or radiotherapy for metastatic spine tumors are scarce. We aimed to investigate the risk factors for readmission within 90 days after initial surgical or radiation treatment for metastatic spine tumors.

METHODS

Patients who were diagnosed with metastatic spine tumors between 2012 and 2019 and underwent spinal magnetic resonance imaging within 1 year were classified according to treatment (surgical or radiotherapy groups), and the causes of the 90-day readmission and patient characteristics were compared.

RESULTS

Overall, data from 15,815 patients (surgical group, 13,974 patients; radiotherapy group, 1841 patients) were evaluated. Radiotherapy was preferred in younger and male patients with a high Charlson Comorbidity Index, whereas surgery was mainly performed in patients with lumbar metastasis. Radiotherapy, age of 30-69 years, male sex, and Charlson Comorbidity Index >1 increased the risk of 90-day readmission in patients with metastatic spine tumors. The main causes of 90-day readmission among patients with metastatic spine tumors who received radiotherapy included tumor recurrence, chemotherapy, radiotherapy, and treatment of other organ metastases with radiotherapy.

CONCLUSIONS

These study findings offer a better understanding of the causes of readmission following radiotherapy or surgical treatment in patients with metastatic spine tumors, and these results can help reduce postoperative morbidity and medical costs among these patients.

Discharge Follow-Up of Patients in Primary Care Does Not Meet Their Care Needs: Results of a Longitudinal Multicentre Study

Adequate coordination between healthcare levels has been proven to improve clinical indicators, care costs, and user satisfaction. This is more relevant to complex or vulnerable patients, who often require increased care. This study aims to evaluate the differences between hospital discharge follow-up indicators, including number of general practitioners' (GPs) and community nurses' (CNs) consultations, presentiality of consultations, type of first post-discharge consultation, and time between hospital discharge and first consultation. Vulnerable and non-vulnerable patients were compared. A longitudinal retrospective study was carried out in the north of Tenerife on the post-discharge care of patients discharged from the Canary Islands University Hospital (Spanish acronym HUC) between 1 January 2018 and 31 December 2022. The results obtained show deficiencies in the care provided to patients by primary care (PC) after being discharged from the hospital, including delayed first visits, low presentiality of those visits that were less frequent even with increased patient complexity, scarce first home visits to functionally impaired patients and delays in such visits, and a lack of priority visits to patients with increased follow-up needs. Addressing these deficiencies could help those most in need of care to receive PC, thus reducing inequalities and granting equal access to healthcare services in Spain.

Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data

The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.

The impact of discharge letter content on unplanned hospital readmissions within 30 and 90 days in older adults with chronic illness - a mixed methods study

BACKGROUND

Care transitions are high-risk processes, especially for people with complex or chronic illness. Discharge letters are an opportunity to provide written information to improve patients' self-management after discharge. The aim of this study is to determine the impact of discharge letter content on unplanned hospital readmissions and self-rated quality of care transitions among patients 60 years of age or older with chronic illness.

METHODS

The study had a convergent mixed methods design. Patients with chronic obstructive pulmonary disease or congestive heart failure were recruited from two hospitals in Region Stockholm if they were living at home and Swedish-speaking. Patients with dementia or cognitive impairment, or a "do not resuscitate" statement in their medical record were excluded. Discharge letters from 136 patients recruited to a randomised controlled trial were coded using an assessment matrix and deductive content analysis. The assessment matrix was based on a literature review performed to identify key elements in discharge letters that facilitate a safe care transition to home. The coded key elements were transformed into a quantitative variable of "SAFE-D score". Bivariate correlations between SAFE-D score and quality of care transition as well as unplanned readmissions within 30 and 90 days were calculated. Lastly, a multivariable Cox proportional hazards model was used to investigate associations between SAFE-D score and time to readmission.

RESULTS

All discharge letters contained at least five of eleven key elements. In less than two per cent of the discharge letters, all eleven key elements were present. Neither SAFE-D score, nor single key elements correlated with 30-day or 90-day readmission rate. SAFE-D score was not associated with time to readmission when adjusted for a range of patient characteristics and self-rated quality of care transitions.

CONCLUSIONS

While written summaries play a role, they may not be sufficient on their own to ensure safe care transitions and effective self-care management post-discharge.

TRIAL REGISTRATION

Clinical Trials. giv, NCT02823795, 01/09/2016.

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population

BACKGROUND

Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments.

METHODS

This was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision.

RESULTS

This study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors.

CONCLUSIONS

Leveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.

Quantitative Analysis of Characteristics Associated with Patient-Directed Discharges, Representations, and Readmissions: a Safety-Net Hospital Experience

BACKGROUND

No clinical tools currently exist to stratify patients' risks of patient-directed discharge (PDD).

OBJECTIVE

This study aims to identify trends and factors associated with PDD, representation, and readmission.

DESIGN

This was an IRB-approved, single-centered, retrospective study.

PARTICIPANTS

Patients aged > 18, admitted to medicine service, were included from January 1st through December 31st, 2019. Patients admitted to ICU or surgical services were excluded.

MAIN MEASURES

Demographics, insurance information, medical history, social history, rates of events occurrences, and discharge disposition were obtained.

KEY RESULTS

Of the 16,889 encounters, there were 776 (4.6%) PDDs, 4312 (25.5%) representations, and 2924 (17.3%) readmissions. Of those who completed PDDs, 42.1% represented and 26.4% were readmitted. Male sex, age ≤ 45, insurance type, homelessness, and substance use disorders had higher rates of PDD (OR = 2.0; 4.2; 4.5; 6.2; 5.2; p < 0.0001, respectively). Patients with homelessness, substance use disorders, mental health disorders, or prior history of PDD were more likely to represent (OR = 3.6; 2.0; 2.0; 1.5; p < 0.0001, respectively) and be readmitted (OR = 2.2; 1.6; 1.9; 1.5; p < 0.0001, respectively). Patients aged 30-35 had the highest PDD rate at 16%, but this was not associated with representations or readmissions. Between July and September, the PDD rate peaked at 5.5% and similarly representation and readmission rates followed. The rates of subsequent readmissions after PDDs were nearly two-fold compared to non-PDD patients in later half of the year. 51% of all subsequent readmissions occur within 7 days of PDD, compared to 34% in the non-PDD group (OR = 2.0; p < 0.0001). Patients with primary diagnosis of abscess had 16% PDDs.

CONCLUSIONS

Factors associated with PDD include male, younger age, insurance type, substance use, homelessness, and primary diagnosis of abscess. Factors associated with representation and readmission are homelessness, substance use disorders, mental health disorders, and prior history of PDD. Further research is needed to develop a risk stratification tool to identify at-risk patients.

Entangled in complexity: An ethnographic study of organizational adaptability and safe care transitions for patients with complex care needs

AIM

The aim of this study was to visualize vulnerabilities and explore the dynamics of inter-professional collaboration and organizational adaptability in the context of care transitions for patients with complex care needs.

DESIGN

An ethnographic design using multiple convergent data collection techniques.

METHODS

Data collection involved document review, participant observations and interviews with healthcare and social care professionals (HSCPs). Narrative analysis was employed to construct two illustrative patient scenarios, which were then examined using the Functional Resonance Analysis Method (FRAM). Thematic analysis was subsequently applied to synthesize the findings.

RESULTS

Inconsistencies in timing and precision during care transitions pose risks for patients with complex care needs as they force healthcare systems to prioritize structural constraints over individualized care, especially during unforeseen events outside regular hours. Such systemic inflexibility can compromise patient safety, increase the workload for HSCPs and strain resources. Organizational adaptability is crucial to managing the inherent variability of patient needs. Our proposed 'safe care transition pathway' addresses these issues, providing proactive strategies such as sharing knowledge and increasing patient participation, and strengthening the capacity of professionals to meet dynamic care needs, promoting safer care transitions.

CONCLUSION

To promote patient safety in care transitions, strategies must go beyond inter-professional collaboration, incorporating adaptability and flexible resource planning. The implementation of standardized safe care transition pathways, coupled with the active participation of patients and families, is crucial. These measures aim to create a resilient, person-centred approach that may effectively manage the complexities in care transitions.

IMPLICATIONS

The recommendations of this study span the spectrum from policy-level changes aimed at strategic resource allocation and fostering inter-professional collaboration to practical measures like effective communication, information technology integration, patient participation and family involvement. Together, the recommendations offer a holistic approach to enhance care transitions and, ultimately, patient outcomes.

REPORTING METHOD

Findings are reported per the Consolidated Criteria for Reporting Qualitative research (COREQ).

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Prescribing Benzodiazepines and Opioids and Clinical Characteristics Associated With 30-Day Hospital Return in Patients Aged ≥75 Years: Secondary Data Analysis

PURPOSE

The current study compared prevalence of opioid or benzodiazepine (BZD) prescription and co-prescription of opioids and BZD at discharge and return to a community hospital within 30 days, as well as identified clinical characteristics associated with hospital return in patients aged ≥75 years.

METHOD

A secondary analysis of a database created during implementation of the Safe Transitions for At Risk Patients program at a 400-bed community teaching hospital in south Florida was conducted. Multivariable logistic regression analyses were performed to identify significant demographic and clinical characteristics associated with return to the hospital within 30 days of discharge.

RESULTS

A total of 24,262 participants (52.6% women) with a mean age of 85.3 (SD = 6.42) years were included. More than 20% in each central nervous system prescription group (i.e., opioids only, BZD only, opioids and BZD) returned to the hospital within 30 days of discharge. Demographic and chronic conditions (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes) and poly-pharmacy were significant factors of a 30-day return to the hospital.

CONCLUSION

Findings highlight the importance of hospital nurses' role in identifying high-risk patients, educating patients and caregivers, monitoring them closely, communicating with primary care physicians and specialists, and conducting intensive follow up via telephone to avoid 30-day rehospitalization. [Journal of Gerontological Nursing, 50(4), 25-33.].

Predictors of hospital readmission rate in geriatric patients

BACKGROUND

Hospital readmissions among older adults are associated with progressive functional worsening, increased institutionalization and mortality.

AIM

Identify the main predictors of readmission in older adults.

METHODS

We examined readmission predictors in 777 hospitalized subjects (mean age 84.40 ± 6.77 years) assessed with Comprehensive Geriatric Assessment (CGA), clinical, anthropometric and biochemical evaluations. Comorbidity burden was estimated by Charlson Comorbidity Index (CCI). Median follow-up was 365 days.

RESULTS

358 patients (46.1%) had a second admission within 365 days of discharge. Estimated probability of having a second admission was 0.119 (95%C.I. 0.095-0.141), 0.158 (95%C.I. 0.131-0.183), and 0.496 (95%C.I. 0.458-0.532) at 21, 30 and 356 days, respectively. Main predictors of readmission at 1 year were length of stay (LOS) > 14 days (p < 0.001), albumin level < 30 g/l (p 0.018), values of glomerular filtration rate (eGFR) < 40 ml/min (p < 0.001), systolic blood pressure < 115 mmHg (p < 0.001), CCI ≥ 6 (p < 0.001), and cardiovascular diagnoses. When the joint effects of selected prognostic variables were accounted for, LOS > 14 days, worse renal function, systolic blood pressure < 115 mmHg, higher comorbidity burden remained independently associated with higher readmission risk.

DISCUSSION

Selected predictors are associated with higher readmission risk, and the relationship evolves with time.

CONCLUSIONS

This study highlights the importance of performing an accurate CGA, since defined domains and variables contained in the CGA (i.e., LOS, lower albumin and systolic blood pressure, poor renal function, and greater comorbidity burden), when combined altogether, may offer a valid tool to identify the most fragile patients with clinical and functional impairment enhancing their risk of unplanned early and late readmission.

Community health navigator-assisted transition of care from hospital to community: protocol for a randomised controlled trial

INTRODUCTION

The objective of this parallel group, randomised controlled trial is to evaluate a community health navigator (CHN) intervention provided to patients aged over 40 years and living with chronic health conditions to transition from hospital inpatient care to their homes. Unplanned hospital readmissions are costly for the health system and negatively impact patients.

METHODS AND ANALYSIS

Patients are randomised post hospital discharge to the CHN intervention or usual care. A comparison of outcomes between intervention and control groups will use multivariate regression techniques that adjust for age, sex and any independent variables that are significantly different between the two groups, using multiple imputation for missing values. Time-to-event analysis will examine the relationship between seeing a CHN following discharge from the index hospitalisation and reduced rehospitalisations in the subsequent 60 days and 6 months. Secondary outcomes include medication adherence, health literacy, quality of life, experience of healthcare and health service use (including the cost of care). We will also conduct a qualitative assessment of the implementation of the navigator role from the viewpoint of stakeholders including patients, health professionals and the navigators themselves.

ETHICS APPROVAL

Ethics approval was obtained from the Research Ethics and Governance Office, Sydney Local Health District, on 21 January 2022 (Protocol no. X21-0438 and 2021/ETH12171). The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations. Data will be deposited in an institutional data repository at the end of the trial. This is subject to Ethics Committee approval, and the metadata will be made available on request.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry (ACTRN 12622000659707).

ARTICLE SUMMARY

The objective of this trial is to evaluate a CHN intervention provided to patients aged over 40 years and living with chronic health conditions to transition from hospital inpatient care to their homes.

Social Services Post-discharge and Their Association With Readmission in a 2016 Swedish Geriatric Cohort

OBJECTIVES

To describe the social services received by a 2016 Swedish cohort after discharge from inpatient geriatric care and to analyze the association between level of social services post-discharge and 30-day readmission.

DESIGN

Observational, closed-cohort study.

SETTING AND PARTICIPANTS

All patients admitted to 1 of 3 regionally operated inpatient geriatric care settings in Region Stockholm, Sweden, in 2016 (n = 7453).

METHODS

Individual-level data from medical records and population registries were linked using unique personal identification numbers. Descriptive statistics were reported for 4 levels of municipal social services post-discharge: long-term care, 1 to 50 home help hours per month, >50 home help hours per month, and no home help. Multinomial logistic regression was performed to analyze the association between level of social services post-discharge and 3 outcomes within 30 days: readmission, death without readmission, or neither readmission nor death.

RESULTS

Results show that almost 11% of patients were discharged to long-term care and 54% received municipal home help services. Individuals with no municipal home help or with 1 to 50 hours per month were more likely to be readmitted within 30 days compared with those in long-term care. Living with more than 50 hours of help was not associated with an increased likelihood of 30-day readmission.

CONCLUSIONS AND IMPLICATIONS

Patients who received inpatient geriatric care are significant users of municipal social services post-discharge. Living in long-term care or with extensive home help appears to be a protective factor in preventing readmission compared with more limited or no home help services. Care transitions for this frail patient group require careful social care planning. Supporting individuals discharged with fewer social service hours may help reduce readmissions.

Are Primary Health Care Visits Associated With Reduced Risk of Hospital Readmissions After Discharge From Geriatric Inpatient Departments? Evidence From Stockholm County

INTRODUCTION/OBJECTIVES

Primary health care visits post-discharge could potentially play an important role in efforts of reducing hospital readmission. Focusing on a single or a particular type of visit obscures nuances in types of primary care contacts over time and fails to quantify the intensity of primary health care visits during the follow-up period. The aim of this study was to explore associations between the number and type of primary health care visits post-discharge and the risk of hospital readmission within 30 days.

METHODS

A register-based closed cohort study. The study population of 6135 individuals were residents of Stockholm who were discharged home from any of the 3 geriatric inpatient departments, excluding those who were readmitted within the next 24 h. The dependent variable was hospital readmission within 30 days of discharge. The key independent variable was the number and type of primary health care visits in 30 days post-discharge. Cox-regression with time-varying covariates was employed for data analyses.

RESULTS

Approximately, 12% of the participants were readmitted to hospital within 30 days. There was no statistically significant association between number of primary care visits post-discharge and readmission (HR 1.00; 95% CI 1.00-1.01). Compared to no primary health care visit, no statistically significant association were found for administrative care related visits (HR 0.33, 95%CI 0.08-1.33), clinic visits (HR 0.93, 95%CI 0.71-1.21), home visits (HR 1.03, 95%CI 0.84-1.27), or team visits (HR 0.76, 95%CI 0.54-1.07).

CONCLUSIONS

There were no associations between primary health care visits post-discharge and hospital readmission after geriatric inpatient care. Further studies using survey or qualitative approaches can provide insights into the factors that are relevant to post-discharge care but are unavailable in this type of register data studies.

Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review

BACKGROUND

Older adults have been disproportionately affected by the COVID-19 pandemic. This scoping review aimed to summarize the current evidence of artificial intelligence (AI) use in the screening/monitoring, diagnosis, and/or treatment of COVID-19 among older adults.

METHOD

The review followed the Joanna Briggs Institute and Arksey and O'Malley frameworks. An information specialist performed a comprehensive search from the date of inception until May 2021, in six bibliographic databases. The selected studies considered all populations, and all AI interventions that had been used in COVID-19-related geriatric care. We focused on patient, healthcare provider, and healthcare system-related outcomes. The studies were restricted to peer-reviewed English publications. Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought.

RESULTS

Six databases were searched , yielding 3,228 articles, of which 10 were included. The majority of articles used a single AI model to assess the association between patients' comorbidities and COVID-19 outcomes. Articles were mainly conducted in high-income countries, with limited representation of females in study participants, and insufficient reporting of participants' race and ethnicity.

DISCUSSION

This review highlighted how the COVID-19 pandemic has accelerated the application of AI to protect older populations, with most interventions in the pilot testing stage. Further work is required to measure effectiveness of these technologies in a larger scale, use more representative datasets for training of AI models, and expand AI applications to low-income countries.

Rural caregivers' preparedness for detecting and responding to the signs of worsening health conditions in recently hospitalised patients at risk for readmission: a qualitative descriptive study

OBJECTIVES

This study aimed to explore informal rural caregivers' perceived preparedness to detect and respond to the signs of worsening health conditions in patients recently discharged from hospital and at risk for readmission.

DESIGN

A qualitative descriptive design and semistructured interviews were used. Data were thematically analysed.

SETTING

Data collection occurred in 2018 and 2019 in rural communities in Southwestern and Northeastern Ontario, Canada.

PARTICIPANTS

The study included sixteen informal caregivers who were all family members of a relative discharged from hospital at high risk for readmission following hospitalisation mostly for a medical illness (63%). Participants were mostly women (87.5%), living with their relative (62.5%) who was most often a parent (56.3%).

RESULTS

Three themes were identified: (1) warning signs and rural communities, (2) perceived preparedness, and (3) improving preparedness. The first theme elucidates informal caregivers' view that they needed to be prepared because they were taking over care previously provided by hospital healthcare professionals yet lacked accessible medical help in rural communities. The second theme captures informal caregivers' perceptions that they lacked knowledge of how to detect warning signs and how to respond to them appropriately. The last theme illuminates informal caregivers' suggestions for improving preparation related to warning signs.

CONCLUSIONS

Informal caregivers in rural communities were largely unprepared for detecting and responding to the signs of worsening health conditions for patients at high risk for hospital readmission. Healthcare professionals can anticipate that informal caregivers, particularly those whose relatives live far from medical help, need information on how to detect and respond to warning signs, and may prioritise their time to this aspect of postdischarge care for these caregivers.

How Might We Have Known? Using Administrative Data to Predict 30-Day Hospital Readmission in Clients Receiving Home Care Services from 2018 to 2021

Background

Reducing hospital readmissions can improve individual health outcomes and lower system-level costs. This study aimed to understand the characteristics of home care Personal Support clients who experienced a hospital admission (ie, hospital hold) and to identify factors that predict hospital readmission within 30 days of resuming home care Personal Support services.

Methods

We conducted a retrospective cohort study using client administrative data from a home healthcare provider organization (2018-2021). The sample included clients (⩾18 years) who received publicly funded Personal Support services and experienced a hospital hold. Descriptive statistics and a binary logistic regression model analyzed the relationship between demographics, hospital service utilization, home care service utilization, and contextual factors on the outcome of 30-day hospital readmission.

Results

Approximately 17% (n = 662) of all clients with a hospital hold (n = 3992) were readmitted to hospital within 30 days. Compared with non-readmitted clients, those with greater home care Personal Support service intensity after the index hospital hold were less likely to experience a hospital 30-day readmission. In contrast, those with greater acuity, higher assessed care needs, more hospital holds overall, more extended hospital stays (⩾2 weeks), and lower social support had a higher likelihood of 30-day hospital readmission.

Conclusion

The findings from this study provide a greater understanding of factors associated with home care clients' risk of hospital readmission within 30 days and can be used to inform targeted, evidence-based support to reduce home care clients' hospital readmissions.

Unique characteristics of end-of-life hospitalizations in Parkinson disease

Introduction

Persons with Parkinson disease (PD) are hospitalized at higher rates, have longer lengths of stay, and are more likely to die in the hospital than age-matched peers. Although prior studies have compared inpatient outcomes between persons with and without PD, little is known about inpatient outcomes across the PD trajectory, or whether hospitalizations occurring in the last 6 months of life differ from earlier hospitalizations.

Methods

This cross-sectional study compared Medicare Part A and B beneficiaries aged 65 and older with a qualifying PD diagnosis who were hospitalized in 2017: decedents who died between 7/1/2017 and 12/31/2017 from all causes and were hospitalized at least once in their last 6 months of life, and non-decedents who were hospitalized between 1/1/2017 and 6/30/2017 and lived 6 or more months after discharge. End-of-life (EoL) hospitalizations were defined as those occurring in the last 6 months of life. Descriptive analyses compared patient-level variables (e.g., demographics, comorbidities, treatment intensity) and encounter-level variables (e.g., length of stay, total charges) between groups. Multivariable logistic regression models also compared rates of intensive care unit (ICU) admission and 30-day readmission between hospitalized decedents and hospitalized non-decedents, adjusting for age, sex, race/ethnicity, rural residence, and Charlson Comorbidity Index Score.

Results

Of 26,492 Medicare decedents with PD, 16,187 (61.1%) were hospitalized in their last 6 months of life. Of 347,512 non-decedents with PD, 62,851 (18.1%) were hospitalized in a 6-month period. Hospitalized decedents were slightly older than hospitalized non-decedents (82.3 [SD 7.40] vs. 79.5 [SD 7.54] years) and had significantly more comorbidities. Compared to non-EoL hospitalizations, EoL hospitalizations were slightly longer (5 [IQR 3-9] vs. 4 [IQR 3-7] days) and more expensive based on total charges per admission ($36,323 [IQR 20,091-69,048] vs. $32,309 [IQR 18,789-57,756]). In covariate-adjusted regression models using hospitalized non-decedents as the reference group, hospitalized decedents were more likely to experience an ICU admission (AOR 2.36; CI 2.28-2.45) and 30-day readmission (AOR 2.43; CI 2.34-2.54).

Discussion

Hospitalizations occurring in the last 6 months of life among persons with PD in the United States are longer, more costly, and more resource intensive than earlier hospitalizations and may stem from medical comorbidities. Once hospitalized, ICU admission and 30-day readmission may aid in prognostication and serve as markers of transition to the EoL period.

Identifying older adults at increased risk of medication-related readmission to hospital within 30 days of discharge: development and validation of a risk assessment tool

OBJECTIVE

Developing and validating a risk assessment tool aiming to identify older adults (≥65 years) at increased risk of possibly medication-related readmission to hospital within 30 days of discharge.

DESIGN

Retrospective cohort study.

SETTING

The risk score was developed using data from a hospital in southern Sweden and validated using data from four hospitals in the mid-eastern part of Sweden.

PARTICIPANTS

The development cohort (n=720) was admitted to hospital during 2017, whereas the validation cohort (n=892) was admitted during 2017-2018.

MEASURES

The risk assessment tool aims to predict possibly medication-related readmission to hospital within 30 days of discharge. Variables known at first admission and individually associated with possibly medication-related readmission were used in development. The included variables were assigned points, and Youden's index was used to decide a threshold score. The risk score was calculated for all individuals in both cohorts. Area under the receiver operating characteristic (ROC) curve (c-index) was used to measure the discrimination of the developed risk score. Sensitivity, specificity and positive and negative predictive values were calculated using cross-tabulation.

RESULTS

The developed risk assessment tool, the Hospitalisations, Own home, Medications, and Emergency admission (HOME) Score, had a c-index of 0.69 in the development cohort and 0.65 in the validation cohort. It showed sensitivity 76%, specificity 54%, positive predictive value 29% and negative predictive value 90% at the threshold score in the development cohort.

CONCLUSION

The HOME Score can be used to identify older adults at increased risk of possibly medication-related readmission within 30 days of discharge. The tool is easy to use and includes variables available in electronic health records at admission, thus making it possible to implement risk-reducing activities during the hospital stay as well as at discharge and in transitions of care. Further studies are needed to investigate the clinical usefulness of the HOME Score as well as the benefits of implemented activities.

Predictors of unplanned hospital readmission after non-cardiac surgery in Singapore: a 2-year retrospective review

INTRODUCTION

Unplanned hospital readmissions after surgery contribute significantly to healthcare costs and potential complications. Identifying predictors of readmission is inherently complex and involves an intricate interplay between medical factors, healthcare system factors and sociocultural factors. Therefore, the aim of this study was to elucidate the predictors of readmissions in an Asian surgical patient population.

METHODS

A two-year single-institution retrospective cohort study of 2744 patients was performed in a university-affiliated tertiary hospital in Singapore, including patients aged 45 and above undergoing intermediate or high-risk non-cardiac surgery. Unadjusted analysis was first performed, followed by multivariable logistic regression.

RESULTS

Two hundred forty-nine patients (9.1%) had unplanned 30-day readmissions. Significant predictors identified from multivariable analysis include: American Society of Anaesthesiologists (ASA) Classification grades 3 to 5 (adjusted OR 1.51, 95% CI 1.10-2.08, p = 0.01), obesity (adjusted OR 1.66, 95% CI 1.18-2.34, p = 0.04), asthma (OR 1.70, 95% CI 1.03-2.81, p = 0.04), renal disease (OR 2.03, 95% CI 1.41-2.92, p < 0.001), malignancy (OR 1.68, 95% CI 1.29-2.37, p < 0.001), chronic obstructive pulmonary disease (OR 2.46, 95% CI 1.19-5.11, p = 0.02), cerebrovascular disease (OR 1.73, 95% CI 1.17-2.58, p < 0.001) and anaemia (OR 1.45, 95% CI 1.07-1.96, p = 0.02).

CONCLUSION

Several significant predictors of unplanned readmissions identified in this Asian surgical population corroborate well with findings from Western studies. Further research will require future prospective studies and development of predictive risk modelling to further address and mitigate this phenomenon.

Postoperative rehospitalization in older surgical patients: an age-stratified analysis

BACKGROUND

Older adults comprise 40% of surgical inpatients and are at increased risk of postoperative rehospitalization. A decade ago, 30-day rehospitalizations for Medicare patients were reported as 15%, and more than 70% was attributed to medical causes. In the interim, there have been several large-scale efforts to establish best practice for older patients through surgical quality programs and national initiatives by Medicare and the National Health Service. To understand the current state of rehospitalization in the USA, we sought to report the incidence and cause of 30-day rehospitalization across surgical types by age.

STUDY DESIGN

We performed a retrospective study utilizing the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) dataset from 2015 to 2019. Our primary exposure of interest was age. Patients were categorized into four groups: 18-49, 50-64, 65-74, and 75 + years old. Reasons for rehospitalization were evaluated using NSQIP defined causes and reported International Classification of Disease (ICD)-9 and ICD-10 codes. Our primary outcome was the incidence of unplanned 30-day rehospitalization and secondary outcome the cause for rehospitalization. Variables were summarized by age group through relative (%) and absolute (n) frequencies; chi-square tests were used to compare proportions. Since rehospitalization is a time-to-event outcome in which death is a competing event, the cumulative incidence of rehospitalization at 30 days was estimated using the procedure proposed by Gray. The same strategy was used for estimating the cumulative incidence for unplanned rehospitalizations.

RESULTS

A total of 2,798,486 patients met inclusion criteria; 198,542 had unplanned rehospitalization (overall 7.09%). Rehospitalization by age category was 6.12, 6.99, 7.50, and 9.50% for ages 18-49, 50-64, 65-74, and 75 + , respectively. Complications related to the digestive system were the single most common cause of rehospitalization across age groups. Surgical site infection was the second most common cause, with the relative frequency decreasing with age as follows: 21.74%, 19.08%, 15.09%, and 9.44% (p < .0001). Medical causes such as circulatory or respiratory complications were more common with increasing age (2.10%, 4.43%, 6.27%, 8.86% and 3.27, 4.51, 6.07, 8.11%, respectively).

CONCLUSION

We observed a decrease in overall rehospitalization for older surgical patients compared to studies a decade ago. The oldest (≥ 75) surgical patients had the highest 30-day rehospitalization rates (9.50%). The single most common reason for rehospitalization was the same across age groups and likely attributed to surgery (ileus). However, the aggregate of medical causes of rehospitalization was more common in older patients; surgical and respiratory reasons were twice as common in this group. Rehospitalization increased by age for some surgery types, e.g., lower extremity bypass, more than others, e.g., ventral hernia repair. Future investigations should focus on interventions to reduce medical complications and further decrease postoperative rehospitalization for older surgical patients undergoing high-risk procedures.

Sense of Coherence at End of Life in Older People: An Interpretive Description

As people age, losses accumulate (ie, the death of family and friends, the loss of agility, and the loss of independence). Such losses have an impact on one's Sense of Coherence, that is, one's ability to see the world as comprehensible, manageable, and meaningful. Antonovsky deemed Sense of Coherence as a mostly stable state by the age of 30 years. Until now, there has not been an investigation into how serial loss of resources affects older people as they near the end of life. Sense of Coherence was used as the theoretical framework for this study to answer the question of how older people maintain or regain a Sense of Coherence in the presence of serious illness as they near death. Data were gathered using semistructured interviews and guided by interpretive description. This investigation found new concepts that contribute to Antonovsky's midlevel theory of salutogenesis and the construct of Sense of Coherence. Those are Incomprehensibility and Serial Loss of General Resistance Resources. The results indicate that the crux of a strong Sense of Coherence for this population is excellent communication and a coherent "big-picture" conversation.

The Impact of Potentially Inappropriate Medications and Polypharmacy on 3-Month Hospital Readmission among Older Patients: A Retrospective Cohort Study from Malaysia

INTRODUCTION

Potentially inappropriate medications (PIMs) use and polypharmacy are two issues that are commonly encountered among older people. They are associated with several negative outcomes including adverse drug reactions and medication-related hospitalization. There are insufficient studies regarding the impact of both PIMs and polypharmacy on hospital readmission, especially in Malaysia.

AIM

To investigate the possible association between polypharmacy and prescribing PIMs at discharge and 3-month hospital readmission among older patients.

MATERIALS AND METHOD

A retrospective cohort study involved 600 patients ≥60 years discharged from the general medical wards in a Malaysian teaching hospital. The patients were divided into two equal groups: patients with or without PIMs. The main outcome was any readmission during the 3-month follow-up. The discharged medications were assessed for polypharmacy (≥five medications) and PIMs (using 2019 Beers' criteria). Chi-square test, Mann-Whitney test, and a multiple logistic regression were conducted to study the impact of PIMs/polypharmacy on 3-month hospital readmission.

RESULTS

The median number for discharge medications were six and five for PIMs and non-PIMs patients, respectively. The most frequently prescribed PIMs was aspirin as primary prevention of cardiovascular diseases (33.43%) followed by tramadol (13.25%). The number of medications at discharge and polypharmacy status were significantly associated with PIMs use. Overall, 152 (25.3%) patients were re-admitted. Polypharmacy and PIMs at discharge did not significantly impact the hospital readmission. After applying the logistic regression, only male gender was a predictor for 3-month hospital readmission (OR: 2.07, 95% CI: 1.022-4.225).

CONCLUSION

About one-quarter of the patients were admitted again within three months of discharge. PIMs and polypharmacy were not significantly associated with 3-month hospital readmissions while male gender was found to be an independent risk factor for readmission.

Higher levels of multimorbidity are associated with increased risk of readmission for older people during post-acute transitional care

PURPOSE

Older patients are at high risk for poor outcomes after an acute hospital admission. The Transitional Aged Care Programme (TACP) was established by the Australian government to provide a short-term care service aiming to optimise functional independence following hospital discharge. We aim to investigate the association between multimorbidity and readmission amongst patients on TACP.

METHODS

Retrospective cohort study of all TACP patients over 12 months. Multimorbidity was defined using the Charlson Comorbidity Index (CCI), and prolonged TACP (pTACP) as TACP ≥ 8 weeks.

RESULTS

Amongst 227 TACP patients, the mean age was 83.3 ± 8.0 years, and 142 (62.6%) were females. The median length-of-stay on TACP was 8 weeks (IQR 5-9.67), and median CCI 7 (IQR 6-8). 21.6% were readmitted to hospital. Amongst the remainder, 26.9% remained at home independently, 49.3% remained home with supports; < 1% were transferred to a residential facility (0.9%) or died (0.9%). Hospital readmission rates increased with multimorbidity (OR 1.37 per unit increase in CCI, 95% CI 1.18-1.60, p < 0.001). On multivariable logistic regression analysis, including polypharmacy, CCI, and living alone, CCI remained independently associated with 30-day readmission (aOR 1.43, 95% CI 1.22-1.68, p < 0.001).

CONCLUSIONS

CCI is independently associated with a 30-day hospital readmission in TACP cohort. Identifying vulnerability to readmission, such as multimorbidity, may allow future exploration of targeted interventions.

The prognostic impacts of frailty on clinical and patient-reported outcomes in patients undergoing coronary artery or valvular surgeries/procedures: A systematic review and meta-analysis

BACKGROUND

Frailty is emerging as an important prognostic indicator for patients undergoing cardiac surgeries/procedures. We sought to evaluate the prognostic and differential impacts of frailty on patients undergoing coronary artery or valvular surgical procedures of different levels of invasiveness, and to explore the differential predictability of various frailty measurement models.

METHODS

Eight databases were searched for prospective cohort studies that have adopted validated measure(s) of frailty and reported clinical, healthcare service utilization, or patient-reported outcomes in patients undergoing coronary artery or valvular surgeries/procedures.

RESULTS

Sixty-two articles were included (N = 16,679). Frailty significantly predicted mortality (short-term [≤ 30 days]: odds ratio [OR]: 2.33, 95% confidence interval [CI]: 1.28-4.26; midterm [6 months to 1 year]: OR: 3.93, 95%CI: 2.65-5.83; long-term [>1 year]: HR: 2.23, 95%CI: 1.60-3.11), postoperative complications (ORs: 2.54-3.57), discharge to care facilities (OR: 5.52, 95%CI: 3.84-7.94), hospital readmission (OR: 2.00, 95%CI: 1.15-3.50), and reduced health-related quality of life (HRQoL; standardized mean difference: -0.74, 95%CI: -1.30 to -0.18). Subgroup analyses showed that frailty exerted a greater impact on short-term mortality in patients undergoing open-heart surgeries than those receiving transcatheter procedures. Multidimensional and physical-aspect-focused frailty measurements performed equally in predicting mortality, but multidimensional measurements were more predictive of hospital readmission than physical-aspect-focused measurements.

CONCLUSION

Frailty was predictive of postoperative mortality, complications, increased healthcare service utilization, and reduced HRQoL. The impact of frailty on short-term mortality was more prominent in patients undergoing open-heart surgeries than those receiving transcatheter procedures. Multidimensional measures of frailty enhanced prognostic risk estimation, especially for hospital readmission.

Use of a Recovery Messaging Application in Outpatient Total Joint Replacement

Total joint replacement (TJR) is performed in an outpatient setting for cost containment and reimbursement changes. However, outpatient readmission to the hospital within 30-90 days postoperatively can be costly. Increases in readmission to the hospital less than 30 days postoperatively increase healthcare expenditure and can leave surgical centers without reimbursement. The purpose of the integrative review was to examine 30-day readmission rates for adults post-outpatient TJR within a 12- to 14-week time frame using the recovery messaging application following discharge. A literature search was conducted, and articles were included if they were peer-reviewed academic journals written in English between 2017 and 2022. Fifteen articles were included and evaluated using the John Hopkins evidence summary table to assess the evidence level. The literature analysis identified three themes after using the smartphone application: (a) reducing hospital 30- day readmissions, (b) increasing patient engagement and early mobility, and (c) improving patient outcomes and satisfaction. The findings of this integrative review indicate that using an evidence-based intervention, such as the smartphone application for recovery messaging, can reduce less than 30-day hospital readmissions after outpatient TJR, thus reducing healthcare costs. Future studies should evaluate specific smartphone applications after other general surgical procedures.

Clinical value of Hospital Admission Risk Profile (HARP) and the Identification of Seniors at Risk (ISAR) scales to predict hospital-associated functional decline in an acute geriatric unit in Colombia

Background

Older adults admitted to a hospital for acute illness are at higher risk of hospital-associated functional decline during stays and after discharge.

Objective

This study aimed to assess the calibration and discriminative abilities of the Hospital Admission Risk Profile (HARP) and the Identification of Seniors at Risk (ISAR) scales as predictors of hospital-associated functional decline at discharge in a cohort of patients older than age 65 receiving management in an acute geriatric care unit in Colombia.

Methods

This study is an external validation of ISAR and HARP prediction models in a cohort of patients over 65 years managed in an acute geriatric care unit. The study included patients with Barthel index measured at admission and discharge. The evaluation discriminate ability and calibration, two fundamental aspects of the scales.

Results

Of 833 patients evaluated, 363 (43.6%) presented hospital-associated functional decline at discharge. The HARP underestimated the risk of hospital-associated functional decline for patients in low- and intermediate-risk categories (relation between observed/expected events (ROE) 1.82 and 1.51, respectively). The HARP overestimated the risk of hospital-associated functional decline for patients in the high-risk category (ROE 0.91). The ISAR underestimated the risk of hospital-associated functional decline for patients in low- and high-risk categories (ROE 1.59 and 1.11). Both scales showed poor discriminative ability, with an area under the curve (AUC) between 0.55 and 0.60.

Conclusions

This study found that HARP and ISAR scales have limited discriminative ability to predict HAFD at discharge. The HARP and ISAR scales should be used cautiously in the Colombian population since they underestimate the risk of hospital-associated functional decline and have low discriminative ability.

Roles of CRP and Neutrophil-to-Lymphocyte Ratio in the Prediction of Readmission of COVID-19 Patients Discharged From the ED

Background

Patient admissions beyond the capacity of emergency departments (EDs) have been reported since the coronavirus disease (COVID-19) pandemic. Thus, laboratory parameters to predict the readmission of patients discharged from the ED are needed. For this purpose, we investigated whether C-reactive protein (CRP) level and neutrophil-to-lymphocyte ratio (NLR) could predict the readmission of patients with COVID-19.

Methods

Patients aged >18 years who visited the ED in October 2020 and had positive polymerase chain reaction test results were evaluated. Among these patients, those who were not hospitalized and were discharged from the ED on the same day were included in the study. The patients' readmission status within 14 days after discharge, age, sex, complaint on admission, comorbidity, systolic blood pressure, diastolic blood pressure, fever, pulse, oxygen saturation level, CRP level, blood urea nitrogen level, creatinine level, neutrophil count, lymphocyte count, and NLR were recorded. Data were compared between the groups.

Results

Of the 779 patients who were included in the study, 359 (46.1%) were male. The median age was 41 years (range, 31-53 years). Among these patients, those who were not hospitalized and were discharged from the ED on logistic regression analysis, age, CRP level, NLR, loss of smell and taste, and hypertension had odds ratios of 2.494, 2.207, 1.803, 0.341, and 1.879, respectively.

Conclusions

The strongest independent predictor of readmission within 14 days after same-day ED discharge was age > 50 years. In addition, CRP level and NLR were the laboratory parameters identified as independent predictors of ED readmission.

Two factors that can increase the length of hospital stay of patients with dementia

OBJECTIVES

Patients with dementia are at greater risk of a long hospital stay and this is associated with adverse outcomes. The aim of this service evaluation was to identify variables most predictive of increased length of hospital stay amongst patients with dementia.

METHODS/DESIGN

We conducted a retrospective analysis on a cross-sectional hospital dataset for the period January-December 2016. Excluding length of stay less than 24h and readmissions, the sample comprised of 1133 patients who had a dementia diagnosis on record.

RESULTS

The highest incidence rate ratio for length of stay in the dementia sample was: (a) discharge to a care home (IRR: 2.443, 95% CI 1.778-3.357), (b) falls without harm (IRR: 2.486, 95% CI 2.029-3.045).

CONCLUSIONS

Based on this dataset, we conclude that improvements made to falls prevention strategies in hospitals and discharge planning procedures can help to reduce the length of stay for patients with dementia.

The number of beds occupied is an independent risk factor for discharge of trauma patients

Reducing the burden of limited capacity on medical practitioners and public health systems requires a time-dependent characterization of hospitalization rates, such that inferences can be drawn about the underlying causes for hospitalization and patient discharge. The aim of this study was to analyze non-medical risk factors that lead to the discharge of trauma patients. This retrospective cohort study includes trauma patients who were treated in Switzerland between 2011 and 2018. The national Swiss database for quality assurance in surgery (AQC) was reviewed for trauma diagnoses according to the ICD-10 code. Non-medical risk factors include seasonal changes, daily changes, holidays, and number of beds occupied by trauma patients across Switzerland. Individual patient information was aggregated into counts per day of total patients, as well as counts per day of levels of each categorical variable of interest. The ARIMA-modeling was utilized to model the number of discharges per day as a function of auto aggressive function of all previously mentioned risk factors. This study includes 226,708 patients, 118,059 male (age 48.18, standard deviation (SD) 22.34 years) and 108,649 female (age 62.57, SD 22.89 years) trauma patients. The mean length of stay was 7.16 (SD 14.84) days and most patients were discharged home (n = 168,582, 74.8%). A weekly and yearly seasonality trend can be observed in admission trends. The mean number of occupied trauma beds ranges from 3700 to 4000 per day. The number of occupied beds increases on weekdays and decreases on holidays. The number of occupied beds is a positive, independent risk factor for discharge in trauma patients; as the number of occupied beds increases at any given time, so does the risk for discharge. The number of beds occupied represents an independent non-medical risk factor for discharge. Capacity determines triage of hospitalized patients and therefore might increase the risk of premature discharge.

Palliative care symptoms, concerns and well-being of older people with frailty and complex care needs upon hospital discharge: a cross-sectional study

Background

Little is known about the nature and intensity of palliative care needs of hospitalised older people. We aimed to describe the palliative care symptoms, concerns, and well-being of older people with frailty and complex care needs upon discharge from hospital to home, and to examine the relationship between palliative care symptoms and concerns, and well-being.

Methods

Cross-sectional study using baseline survey data of a pilot randomised controlled trial. Hospital staff identified patients (≥ 70 years) about to be discharged home, with a clinical frailty score of 5 to 7 and complex needs based on physician-assessment. Patients completed structured interviews, using the Integrated Palliative Care Outcome Scale (IPOS), ICEpop CAPability measure for supportive care (ICECAP-SCM) and IPOS Views on Care quality of life item. We calculated descriptive statistics.

Results

We assessed 37 older people with complex needs (49% women, mean age 84, standard deviation 6.1). Symptoms rated as causing severe problems were weakness (46%) and poor mobility (40%); 75% reported that their family felt anxious at least occasionally. Of the 17 IPOS items, 41% of patients rated five or more symptoms as causing severe problems, while 14% reported that they were not severely affected by any symptom. 87% expressed feeling supported. There was a negative correlation between symptoms (IPOS) and well-being (ICECAP); r = -0.41.

Conclusion

We identified a large variety of symptoms experienced by older people identified as having frailty and complex needs upon hospital discharge. Many were severely affected by multiple needs. This population should be considered for palliative care follow-up at home.

Readmission rates and predictors of avoidable readmissions in older adults in a tertiary care centre

Context

Thirty-day readmissions are used to gauge health care accountability, which occurs as part of the natural course of the illness or due to avoidable fallacies during the index admission. The utility of this metric is unknown in older adults from developing countries.

Aim

To ascertain the unplanned 30-day readmission rate and enumerate predictors of avoidable hospital readmission among early (0-7 days) and late (8-30 days) readmissions.

Settings and Design

A retrospective chart audit of 140 older adults who were readmitted to a premier tertiary care teaching hospital under Geriatrics from the neighboring states of Tamil Nadu, Andhra Pradesh, and Kerala were undertaken.

Methods and Materials

Data from health records were collected from the hospital electronic database from May 2015 to May 2020. The data was reviewed to determine the 30-day readmission rate and to ascertain the predictors of avoidable readmissions among both early and late readmissions.

Results

Out of 2698 older adults admitted to the geriatric wards from the catchment areas, the calculated 30-day hospital readmission rate was 5.18%, and 41.4% of these readmissions were potentially avoidable. The median duration from discharge to the first readmission was ten days (Interquartile range: 5-18 days). Patients had to spend INR 44,000 (approximately 602 USD) towards avoidable readmission. The most common causes for readmission included an exacerbation, reactivation, or progression of a previously existing disease (55.7%), followed by the emergence of a new disease unrelated to index admission (43.2%). Fifty-eight patients (41.4%) were readmitted within seven days following discharge. Early readmissions were seen in patients with malignancies [8 (13.5%) vs. 4 (4.8%); P = 0.017], on insulin (P = 0.04) or on antidepressants (P = 0.01). Advanced age was found to be an independent predictor of avoidable early readmission (OR 2.99 95%CI 1.34-6.62, P = 0.007), and admission to a general ward (as compared to those admitted in a private ward) was an independent predictor of early readmissions (OR 2.99 95%CI 1.34-6.62, P = 0.007).

Conclusion

The 30-day readmission rate in a geriatric unit in a tertiary care hospital was 5.2%. Advanced age was considered to be an independent predictor of avoidable early readmission. Future prospective research on avoidable readmissions should be undertaken to delineate factors affecting 30-day avoidable hospital readmissions in developing nations.

Frailty Is Superior to Age for Predicting Readmission, Prolonged Length of Stay, and Wound Infection in Elective Otology Procedures

OBJECTIVE

To determine the predictive ability of the 5-point modified frailty index relative to age in elective otology patients.

STUDY DESIGN

Retrospective database analysis.

SETTING

Multicenter, national database of surgical patients.

PATIENTS

We selected all elective surgical patients who received tympanoplasty, tympanomastoidectomy, mastoidectomy, revision mastoidectomy, and cochlear implant procedures from 2016 to 2019 from the National Surgical Quality Improvement database.

INTERVENTIONS

Therapeutic.

MAIN OUTCOME MEASURES

Readmission rates, discharge disposition, reoperation rates, and extended length of hospital stay.

RESULTS

Utilizing receiver operating characteristics with area under the curve (AUC) analysis, nonrobust status was determined to be a superior predictor relative to age of readmission (AUC = 0.628 [p < 0.001] versus AUC = 0.567 [p = 0.047], respectively) and open wound infection relative to age (AUC = 0.636 [p = 0.024] versus AUC = 0.619 [p = 0.048], respectively). Nonrobust otology patients were more likely to have dyspnea at rest and an American Society of Anesthesiology score higher than 2 before surgery (odds ratios, 13.304 [95% confidence interval, 2.947-60.056; p < 0.001] and 7.841 [95% confidence interval, 7.064-8.704; p < 0.001], respectively).

CONCLUSION

Nonrobust status was found to be a useful predictor of readmission and prolonged length of stay in patients undergoing elective otology procedures, which generally have low complication rate. Given the aging population and corresponding increase in otology disease, it is important to use age-independent risk stratification measures. Frailty may provide a useful risk stratification tool to select surgical candidates within the aging population.

Evaluating the Hospital Standardized Home-Transition Ratios for Cerebral Infarction in Japan: A Retrospective Observational Study from 2016 through 2020

Discharge to home is considered appropriate as a treatment goal for diseases that often leave disabilities such as cerebral infarction. Previous studies showed differences in risk-adjusted in-hospital mortality and readmission rates; however, studies assessing the rate of hospital-to-home transition are limited. We developed and calculated the hospital standardized home-transition ratio (HSHR) using Japanese administrative claims data from 2016–2020 to measure the quality of in-hospital care for cerebral infarction. Overall, 24,529 inpatients at 35 hospitals were included. All variables used in the analyses were associated with transition to another hospital or facility for inpatients, and evaluation of the HSHR model showed good predictive ability with c-statistics (area under curve, 0.73 standard deviation; 95% confidence interval, 0.72–0.73). All HSHRs of each consecutive year were significantly correlated. HSHRs for cerebral infarction can be calculated using Japanese administrative claims data. It was found that there is a need for support for low HSHR hospitals because hospitals with high/low HSHR were likely to produce the same results in the following year. HSHRs can be used as a new quality indicator of in-hospital care and may contribute to assessing and improving the quality of care.

Re-admission following discharge from a Geriatric Evaluation and Management Unit: identification of risk factors

ObjectiveTo establish independent factors that influence the likelihood of re-admission within 30 days of discharge from a Geriatric Evaluation and Management Unit.MethodsAn observational prospective cohort design using clinical data extracted from the medical charts of eligible patients discharged from a tertiary public hospital Geriatric Evaluation and Management Unit between July 2017 and April 2019. Binary logistic regression was undertaken to determine variables that increased the likelihood of hospital re-admission (dependent variable).ResultsA total of 367 patients were eligible for inclusion, with 69 patients re-admitted within 30 days of discharge. Univariate analysis demonstrated significant differences between groups (re-admission vs non-re-admission) with respect to Charlson Comorbidity Index (CCI) (7.4 [2.4] vs 6.3 [2.2], P = 0.001), Clinical Frailty Scale (CFS) (5.6 [1.1] vs 5.2 [1.34], P = 0.02), and documented malnourishment (36.2% vs 23.6%, P = 0.04). All three variables remained significant when entered into the regression model (X2 = 25.095, P < 0.001). A higher score for the CFS (OR 1.3; 95% CI 1.03-1.64; P = 0.03) and CCI (OR 1.2; 95% CI 1.06-1.33; P = 0.004), and documented malnourishment (OR 1.92; 95% CI 1.06-3.47; P = 0.03) were all independent factors that increased the likelihood of patient re-admission within 30 days of discharge.ConclusionsThis study supports the formal inclusion of the CCI and CFS into routine practice in Geriatric Evaluation and Management Units. The inclusion of the measures can help inform future discharge planning practices. Clinicians should use malnourishment status, CCI and CFS to identify at risk patients and target discharge planning interventions accordingly.

Balancing standardisation and individualisation in transitional care pathways: a meta-ethnography of the perspectives of older patients, informal caregivers and healthcare professionals

BACKGROUND

Transitional care implies the transfer of patients within or across care settings in a seamless and safe way. For frail, older patients with complex health issues, high-quality transitions are especially important as these patients typically move more frequently within healthcare settings, requiring treatment from different providers. As transitions of care for frail people are considered risky, securing the quality and safety of these transitions is of great international interest. Nevertheless, despite efforts to improve quality in transitional care, research indicates that there is a lack of clear guidance to deal with practical challenges that may arise. The aim of this article is to synthesise older patients, informal caregivers and healthcare professionals' experiences of challenges to achieving high-quality transitional care.

METHODS

We used the seven-step method for meta-ethnography originally developed by Noblit and Hare. In four different but connected qualitative projects, the authors investigated the challenges to transitional care for older people in the Norwegian healthcare system from the perspectives of older patients, informal caregivers and healthcare professionals. In this paper, we highlight and discuss the cruciality of these challenging issues by synthesising the results from twelve articles.

RESULTS

The analysis resulted in four themes: i) balancing person-centred versus efficient care, ii) balancing everyday patient life versus the treatment of illness, iii) balancing user choice versus "What Matters to You", and iv) balancing relational versus practical care. These expressed challenges represent tensions at the system, organisation and individual levels based on partial competing assumptions on person-centred-care-inspired individualisation endeavours and standardisation requirements in transitional care.

CONCLUSIONS

There is an urgent need for a clearer understanding of the tension between standardisation and individualisation in transitional care pathways for older patients to ensure better healthcare quality for patients and more realistic working environments for healthcare professionals. Incorporating a certain professional flexibility within the wider boundary of standardisation may give healthcare professionals room for negotiation to meet patients' individual needs, while at the same time ensuring patient flow, equity and evidence-based practice.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

BACKGROUND

 Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.

OBJECTIVES

 To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.

METHODS

 In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.

RESULTS

 While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64].

CONCLUSION

 PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare

Healthcare costs due to unplanned readmissions are high and negatively affect health and wellness of patients. Hospital readmission is an undesirable outcome for elderly patients. Here, we present readmission risk prediction using five machine learning approaches for predicting 30-day unplanned readmission for elderly patients (age ≥ 50 years). We use a comprehensive and curated set of variables that include frailty, comorbidities, high-risk medications, demographics, hospital, and insurance utilization to build these models. We conduct a large-scale study with electronic health record (her) data with over 145,000 observations from 76,000 patients. Findings indicate that the category boost (CatBoost) model outperforms other models with a mean area under the curve (AUC) of 0.79. We find that prior readmissions, discharge to a rehabilitation facility, length of stay, comorbidities, and frailty indicators were all strong predictors of 30-day readmission. We present in-depth insights using Shapley additive explanations (SHAP), the state of the art in machine learning explainability.

[Postoperative multiprofessional comprehensive treatment]

BACKGROUND

In orthogeriatric centers, postoperative, multiprofessional comprehensive treatment has proven to be an effective component in the convalescence of geriatric patients. The aim of the treatment is to minimize the perioperative risk and safely achieve individual rehabilitation goals in the acute inpatient stay. To meet the needs of geriatric patients, in addition to changes in the spatial division and design, primarily adjustments to the team composition and the procedural processes are required.

THERAPEUTIC STRATEGIES

An interdisciplinary and multiprofessional team (orthopedics/traumatology, geriatrics, nursing, physiotherapy, occupational therapy, social services, psychology, speech therapy, …) uses geriatric assessments in regular team meetings to collect and analyze the current rehabilitation status of patients; ICF-based goals are formulated and the therapy is adapted to individual needs. Here, too, the focus is on recording the individual risk (comorbidities, mental status, polypharmacy, malnutrition, fragility) and avoiding preventable complications. Multiprofessional strategies for avoiding or treating postoperative delirium are particularly important. In addition, maintaining patients' autonomy is the top priority, so that they can be released from the acute inpatient stay strengthened for follow-up treatment or their home environment. The establishment of orthogeriatric comanagement in acute inpatient facilities is an important component in the process chain, from which many geriatric patients benefit in the context of postoperative recovery.

Control in the Hospital by Extensive Clinical rules for Unplanned hospitalizations in older Patients (CHECkUP); study design of a multicentre randomized study

Background

Due to ageing of the population the incidence of multimorbidity and polypharmacy is rising. Polypharmacy is a risk factor for medication-related (re)admission and therefore places a significant burden on the healthcare system. The reported incidence of medication-related (re)admissions varies widely due to the lack of a clear definition. Some medications are known to increase the risk for medication-related admission and are therefore published in the triggerlist of the Dutch guideline for Polypharmacy in older patients. Different interventions to support medication optimization have been studied to reduce medication-related (re)admissions. However, the optimal template of medication optimization is still unknown, which contributes to the large heterogeneity of their effect on hospital readmissions. Therefore, we implemented a clinical decision support system (CDSS) to optimize medication lists and investigate whether continuous use of a CDSS reduces the number of hospital readmissions in older patients, who previously have had an unplanned probably medication-related hospitalization.

Methods

The CHECkUP study is a multicentre randomized study in older (≥60 years) patients with an unplanned hospitalization, polypharmacy (≥5 medications) and using at least two medications from the triggerlist, from Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. Patients will be randomized. The intervention consists of continuous (weekly) use of a CDSS, which generates a Medication Optimization Profile, which will be sent to the patient’s general practitioner and pharmacist. The control group will receive standard care. The primary outcome is hospital readmission within 1 year after study inclusion. Secondary outcomes are one-year mortality, number of emergency department visits, nursing home admissions, time to hospital readmissions and we will evaluate the quality of life and socio-economic status.

Discussion

This study is expected to add evidence on the knowledge of medication optimization and whether use of a continuous CDSS ameliorates the risk of adverse outcomes in older patients, already at an increased risk of medication-related (re)admission. To our knowledge, this is the first large study, providing one-year follow-up data and reporting not only on quality of care indicators, but also on quality-of-life.

Trial registration

The trial was registered in the Netherlands Trial Register on October 14, 2018, identifier: NL7449 (NTR7691). https://www.trialregister.nl/trial/7449.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02723-8.

Direct phone communication to primary care physician to plan discharge from hospital: feasibility and benefits

BACKGROUND

The discharge summary is the main vector of communication at the time of hospital discharge, but it is known to be insufficient. Direct phone contact between hospitalist and primary care physician (PCP) at discharge could ensure rapid transmission of information, improve patient safety and promote interprofessional collaboration. The objective of this study was to evaluate the feasibility and benefit of a phone call from hospitalist to PCP to plan discharge.

METHODS

This study was a prospective, single-center, cross-sectional observational study. It took place in an acute medicine unit of a French university hospital. The hospitalist had to contact the PCP by telephone within 72 h prior discharge, making a maximum of 3 call attempts. The primary endpoint was the proportion of patients whose primary care physician could be reached by telephone at the time of discharge. The other criteria were the physicians' opinions on the benefits of this contact and its effect on readmission rates.

RESULTS

275 patients were eligible. 8 hospitalists and 130 PCPs gave their opinion. Calls attempts were made for 71% of eligible patients. Call attempts resulted in successful contact with the PCP 157 times, representing 80% of call attempts and 57% of eligible patients. The average call completion rate was 47%. The telephone contact was perceived by hospitalist as useful and providing security. The PCPs were satisfied and wanted this intervention to become systematic. Telephone contact did not reduce the readmission rate.

CONCLUSIONS

Despite the implementation of a standardized process, the feasibility of the intervention was modest. The main obstacle was hospitalists lacking time and facing difficulties in reaching the PCPs. However, physicians showed desire to communicate directly by telephone at the time of discharge.

TRIAL REGISTRATION

French C.N.I.L. registration number 2108852. Registration date October 12, 2017.

Factors associated with 14-day hospital readmission in frail older patients: A case-control study

Frailty is a key predictor of readmission among older patients. However, studies on the factors associated with readmission of frail older patients are lacking. This study aims to examine factors associated with 14-day hospital readmission in frail older patients. A retrospective case-control study was conducted. Patients were eligible for inclusion if they were age 65 and over and if their Clinical Frailty Scale (CFS) score was above 4. A total of 210 frail older patients were included. Patients who had partners, experienced a fall within 6 months before hospitalization, had pressure injuries, received surgery or chemotherapy, and received rehabilitation therapy from a physical therapist during hospitalization had increased odds of being readmitted to the hospital within 14 days. Moreover, patients receiving comprehensive geriatric assessment (CGA) services during hospitalization showed a significantly reduced risk of readmission. Adapting CGA and developing continuity care plans from hospitals to the community are crucial.

Determinants of long-term unplanned readmission and mortality following self-inflicted and non-self-inflicted major injury: a retrospective cohort study

PURPOSE

To describe the characteristics of major injury and identify determinants of long-term unplanned readmission and mortality after self-inflicted and non-self-inflicted injury to inform potential readmission screening.

METHOD

A retrospective cohort study of 11,269 individuals aged ≥ 15 years hospitalised for a major injury during 2013-2017 in New South Wales, Australia. Unplanned readmission and mortality up to 27-month post-injury were examined. Logistic regression was used to examine predictors of unplanned readmission.

RESULTS

During the 27-month follow-up, 2700 (24.8%) individuals with non-self-inflicted and 98 (26.1%) with self-inflicted injuries had an unplanned readmission. Individuals with an anxiety-related disorder and a non-self-inflicted injury who were discharged home were three times more likely (OR: 3.27; 95%CI 2.28-4.69) or if they were discharged to a psychiatric facility were four times more likely (OR: 4.11; 95%CI 1.07-15.80) to be readmitted. Compared to individuals aged 15-24 years, individuals aged ≥ 65 years were 3 times more likely to be readmitted (OR 3.12; 95%CI 2.62-3.70). Individuals with one (OR 1.60; 95%CI 1.39-1.84) or ≥ 2 (OR 1.88; 95%CI 1.52-2.32) comorbidities, or who had a drug-related dependence (OR 1.88; 95%CI 1.52-2.31) were more likely to be readmitted. The post-discharge age-adjusted mortality rate following a self-inflicted injury (35.6%; 95%CI 29.9-41.8) was higher than for individuals with a non-self-inflicted injury (11.0%; 95%CI 10.4-11.8).

CONCLUSIONS

Unplanned readmission after injury is associated with injury intent, age, and comorbid health. Screening for anxiety and drug-related dependence after major injury, accompanied by service referrals and post-discharge follow-up, has potential to prevent readmission.

Polypharmacy Is Significantly and Positively Associated with the Frailty Status Assessed Using the 5-Item FRAIL Scale, Cardiovascular Health Phenotypic Classification of Frailty Index, and Study of Osteoporotic Fractures Scale

The aim of this study was to investigate the association between frailty and polypharmacy using three different frailty screening tools. This was a cross-sectional study of people aged ≥65 years. Participants were included and interviewed using questionnaires. Polypharmacy was defined as the daily use of eight or more pills. Frailty was assessed using a screening tool, including (1) the Fatigue, Resistance, Ambulation, Illness and Loss of Weight Index (5-item FRAIL scale), (2) the Cardiovascular Health Phenotypic Classification of Frailty (CHS_PCF) index (Fried’s Frailty Phenotype), and (3) the Study of Osteoporotic Fracture (SOF) scale. A total of 205 participants (mean age: 71.1 years; 53.7% female) fulfilled our inclusion criteria. The proportion of patients with polypharmacy was 14.1%. After adjustments were made for comorbidity or potential confounders, polypharmacy was associated with frailty on the 5-item FRAIL scale (adjusted odds ratio [aOR]: 9.12; 95% confidence interval [CI]: 3.6–23.16), CHS_PCF index (aOR: 8.98; 95% CI: 2.51–32.11), and SOF scale (aOR: 6.10; 95% CI: 1.47–25.3). Polypharmacy was associated with frailty using three frailty screening tools. Future research is required to further enhance our understanding of the risk of frailty among older adults.

A prospective cohort study of two predictor models for 30-day emergency readmission in older patients

AIM

To undertake a prospective study of the accuracy of two models (LACE and BOOST) in predicting unplanned hospital readmission in older patients (>75 years).

METHODS

Data were collected from a single centre prospectively on 110 patients over 75 years old admitted to the acute medical unit. Follow-up was conducted at 30 days. The primary outcome was the c-statistic for both models.

RESULTS

The readmission rate was 32.7% and median age 82 years, and both BOOST and LACE scores were significantly higher in those readmitted compared with those who were not. C-statistics were calculated for both tools with BOOST score 0.667 (95% CI 0.559-0.775, P = .005) and LACE index 0.685 (95% CI 0.579-0.792, P = .002).

CONCLUSION

In this prospective study, both the BOOST and LACE scores were found to be significant yet poor, predictive models of hospital readmission. Recent hospitalisation (within the previous 6 months) was found to be the most significant contributing factor.

Current Trends in Readmission Prediction: An Overview of Approaches

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.