Patient and disease profile of emergency medical readmissions to an Irish teaching hospital

A novel risk score predicting 30-day hospital re-admission of patients with acute stroke by machine learning model

BACKGROUND

The 30-day hospital re-admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re-admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30-day hospital re-admissions after discharge of AS patients and define an early re-admission risk score (RRS).

METHODS

This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re-admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis.

RESULTS

Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re-admitted within 30 days from discharge. After identifying the predictors of early re-admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0-1), medium (RRS = 2-3) and high (RRS >3) with re-admission rates of 5%, 8% and 14%, respectively.

CONCLUSIONS

The identification of risk factors for early re-admission after AS and the elaboration of a score to stratify at discharge time the risk of re-admission can provide a tool for clinicians to plan a personalized follow-up and contain healthcare costs.

Priorities in integrating primary and secondary care: a multimethod study of GPs

Purpose

Throughout the world, healthcare policy has committed to delivering integrated models of care. The interface between primary–secondary care has been identified as a particularly challenging area in this regard. To that end, this study aimed to examine the issue of integrated care from general practitioners’ (GPs) perspectives in Ireland.

Design/methodology/approach

This multimethod study involved a cross-sectional survey and semi-structured interviews with GPs in the Ireland East region. A total of 1,274 GPs were identified from publicly available data as practising in the region, of whom the study team were able to identify 430 GPs with email addresses. An email invite was sent to 430 potential participants asking them to complete a 34-item online questionnaire and, for those who were willing, an in-depth interview was conducted with a member of the study team.

Findings

In total, 116 GPs completed the survey. Most GPs felt that enhancing integration between primary and secondary care in Ireland was a priority (n = 109, 93.9%). Five themes concerning the state of integrated care and initiatives to improve matters were identified from semi-structured interviews with 12 GPs.

Originality/value

The uniqueness of this study is that it uses a multimethod approach to provide insight into current GP views on the state of integrated care in Ireland, as well as their perspectives on how to improve integration within the Irish healthcare system.

The dynamics of the emergency medical readmission - The underlying fundamentals

BACKGROUND

Hospital readmissions are a perennial problem. We reviewed readmissions to one institution (2002-2015) and investigated their dynamics.

METHODS

96,474 emergency admissions (in 50,701 patients) to an Irish hospital over a 15-year period were studied, and patterns surrounding early (<28days) and late (any other) readmissions determined. Univariate and logistic or truncated Poisson regression methods were employed.

RESULTS

Early readmission rate averaged 9.6% (95% CI: 9.4, 9.8) with a low/high of 8.4% (95% CI: 7.8, 9.1) and 10.3% (95% CI: 9.6, 11.0) respectively with no overall time trend. Early readmissions represented 20.1% (95% CI: 19.8, 20.5) of emergency medical readmissions. Median time to first readmission was 55weeks (95% CI: 13, 159), time to second was 35weeks (95% CI: 9, 98); by the 7th/8th readmissions, intervals were 13weeks (95% CI: 4, 36) and 11weeks (95% CI: 4, 30). Readmissions were older 67.1years (95% CI: 48.3, 79.2) vs. single admissions 53.9years (34.3, 72.4) and stayed longer - 5.8days (2.7, 10.6) vs. 3.9days (1.5, 8.0). Readmissions had more Acute Illness Severity, Charlson Co-Morbidity and Chronic Disabling Disease. Between 2002 and 2015 the logistic adjusted model of 30-day in-hospital mortality reduced from 6.1% (95% CI: 5.7, 6.5) to 4.4% (95% CI: 4.1, 4.7) (RRR 30.4%).

CONCLUSION

Early hospital readmission rate did not change over 15years despite improvements in hospital mortality outcomes. Readmissions have a consistent pattern related to patient illness and social characteristics; the fundamentals are driven by disease progression over time.

Which young people in England are most at risk of an alcohol-related revolving-door readmission career?

BACKGROUND

This research investigated what Hospital Episode Statistics (HES) records could reveal about the development of problematic drinking careers among young people in England.

METHODS

A cohort of 7286 young people (aged 12-18) who had an index alcohol-related emergency admission between April 2003 and March 2004 were investigated for subsequent alcohol-related readmission. Regressions of patient and visit characteristics were performed against measures of readmission.

RESULTS

A total of 677 patients (9.3% of the cohort) were readmitted during the following 3.75 years, and this group had on average 1.52 readmissions following their index admission. Predictors of having a first readmission included living in a deprived area at index admission (B = -.081, OR = .923, 95% CI = .894 to .952, df = 1, p < .001); having another substance use diagnosis (B = .302, OR = 1.352, 95% CI = 1.017 to 1.798, df = 1, p < .05), or a comorbid mental health diagnosis (B = .441, OR = 1.555, 95% CI = 1.147 to 2.108, df = 1, p < .01), or a diagnosis of self-harm (B = .316, OR = 1.371, 95% CI = 1.082 to 1.738, df = 1, p < .01) at index admission. These last three results were also associated with the readmission rate being higher for young women than young men (B = -.250, OR = .779, 95% CI = .656 to .925, df = 1, p < .01). Patients who had an injury diagnosis alongside their alcohol diagnosis were less likely to be readmitted in the future (B = -.439, OR = .645, 95% CI = .475 to .876, df = 1, p < .01) On average, each subsequent admission featured a longer hospital stay; was progressively more likely to occur on a non-traditional drinking day; and occurred after a progressively smaller number of days since previous admission.

CONCLUSIONS

This study illustrates ways in which problematic drinking careers can be analysed using routinely collected health information, and the results from this analysis may be useful in informing the process of hospital screening and treatment referral. The effects of poverty and comorbid conditions on the initiation of a drinking career are suggested by these results.

The crucial factor of hospital readmissions: a retrospective cohort study of patients evaluated in the emergency department and admitted to the department of medicine of a general hospital in Italy

Background

Early hospital readmissions, defined as rehospitalization within 30 days from a previous discharge, represent an economic and social burden for public health management. As data about early readmission in Italy are scarce, we aimed to relate the phenomenon of 30-day readmission to factors identified at the time of emergency department (ED) visits in subjects admitted to medical wards of a general hospital in Italy.

Methods

We performed a retrospective 30-month observational study, evaluating all patients admitted to the Department of Medicine of the Hospital of Ferrara, Italy. Our study compared early and late readmission: patients were evaluated on the basis of the ED admission diagnosis and classified differently on the basis of a concordant or discordant readmission diagnosis in respect to the diagnosis of a first hospitalization.

Results

Out of 13,237 patients admitted during the study period, 3,631 (27.4%) were readmitted; of those, 656 were 30-day rehospitalizations (5% of total admissions). Early rehospitalization occurred 12 days (median) later than previous discharge. The most frequent causes of rehospitalization were cardiovascular disease (CVD) in 29.3% and pulmonary disease (PD) in 29.7% of cases. Patients admitted with the same diagnosis were younger, had lower length of stay (LOS) and higher prevalence of CVD, PD and cancer. Age, CVD and PD were independently associated with 30-day readmission with concordant diagnosis and kidney disease with 30-day rehospitalization with a discordant diagnosis.

Conclusions

Comorbid patients are at higher risk for 30-day readmission. Reduction of LOS, especially in elderly subjects, could increase early rehospitalization rates.

Emergency readmissions are substantially determined by acute illness severity and chronic debilitating illness: a single centre cohort study

BACKGROUND

The factors influencing hospital readmissions are debated. We assessed whether readmissions could be predicted using routinely collected hospital data.

METHODS

All emergency admissions to a single institution over 12years (2002-2013) were included. The predictor variables, of acute illness severity, Manchester Triage Category, chronic disabling disease and Charlson co-morbidity scores, were studied univariably and entered into a multivariable logistic regression model to predict the bivariate of any readmission or none. A zero truncated Poisson regression model assessed the predictors against the readmission count and incidence rate ratios were calculated. Factors reflecting the clinical load on the emergency department were examined.

RESULTS

66,933 admissions were recorded in 36,271 patients. The readmission rates at 1, 3, 6 and 9years were 29.5%, 38.9%, 42.9% and 44.1%. Early readmissions represented 14.1%. In the multivariable model, an admission in the previous 6months was the strongest predictor of readmission, OR of 5.02 (95% CI: 4.86, 5.18). Acute illness severity - OR of 2.68 (95% CI: 2.33, 3.09) for group VI vs group I, and chronic disabling score - OR of 2.08 (95% CI: 1.87, 2.32) for a score of 4+ vs 0 were significant predictors of readmission in the multivariable model. Both of these predictors demonstrated a linear relationship. Illness severity was the strongest predictor of an early readmission within 4weeks.

CONCLUSION

Readmissions increase as a function of time; illness severity, chronic disabling disease score and a recent admission are the strongest predictors of readmission.

Risk factors for potentially avoidable readmissions due to end-of-life care issues

BACKGROUND

Repeated hospitalizations are frequent toward the end of life, where each admission should be an opportunity to initiate advance-care planning to high-risk patients.

OBJECTIVE

To identify the risk factors for having a 30-day potentially avoidable readmission due to end-of-life care issues among all medical patients.

DESIGN

Nested case-control study.

SETTING/PATIENTS

All 10,275 consecutive discharges from any medical service of an academic tertiary medical center in Boston, Massachusetts between July 1, 2009 and June 30, 2010.

MEASUREMENTS

A random sample of all the potentially avoidable 30-day readmissions was independently reviewed by 9 trained physicians to identify the ones due to end-of-life issues.

RESULTS

Among 534, 30-day potentially avoidable readmission cases reviewed, 80 (15%) were due to an end-of-life care issue. In multivariable analysis, the following risk factors were significantly associated with a 30-day potentially avoidable readmission due to end-of-life care issues: number of admissions in the previous 12 months (odds ratio [OR]: 1.10 per admission, 95% confidence interval [CI]: 1.02-1.20), neoplasm (OR: 5.60, 95% CI: 2.85-10.98), opiate medications at discharge (OR: 2.29, 95% CI: 1.29-4.07), Elixhauser comorbidity index (OR: 1.16 per 5-point increase, 95% CI: 1.10-1.22). The discrimination of the model (C statistic) was 0.85.

CONCLUSIONS

In a medical population, we identified 4 main risk factors that were significantly associated with 30-day potentially avoidable readmission due to end-of-life care issues, producing a model with very good to excellent discrimination. Patients with these risk factors might benefit from palliative care consultation prior to discharge in order to improve end-of-life care and possibly reduce unnecessary rehospitalizations.

Hospital readmission among elderly patients

This study investigates the incidence and determinants of hospital readmissions among elderly patients in Norway. The analyses are based on registered data on inpatient admissions to public hospitals from 1999 to 2006. During this period, mean length of stay in hospital decreased, while readmission rates increased. Probit and instrumental variable regression models are applied for the analyses. The results indicate that longer length of stay in the hospital is associated with lower probability of readmission. A patient's age, comorbidities, and complexity of the treatment procedure are positively associated with readmissions, while higher number of diagnostic procedures negatively affects hospital readmission. Finally, patients discharged to institutions are more likely to be readmitted to the hospital.

The effect of activity-based financing on hospital length of stay for elderly patients suffering from heart diseases in Norway

Background

Whether activity-based financing of hospitals creates incentives to treat more patients and to reduce the length of each hospital stay is an empirical question that needs investigation. This paper examines how the level of the activity-based component in the financing system of Norwegian hospitals influences the average length of hospital stays for elderly patients suffering from ischemic heart diseases. During the study period, the activity-based component changed several times due to political decisions at the national level.

Methods

The repeated cross-section data were extracted from the Norwegian Patient Register in the period from 2000 to 2007, and included patients with angina pectoris, congestive heart failure, and myocardial infarction. Data were analysed with a log-linear regression model at the individual level.

Results

The results show a significant, negative association between the level of activity-based financing and length of hospital stays for elderly patients who were suffering from ischemic heart diseases. The effect is small, but an increase of 10 percentage points in the activity-based component reduced the average length of each hospital stay by 1.28%.

Conclusions

In a combined financing system such as the one prevailing in Norway, hospitals appear to respond to economic incentives, but the effect of their responses on inpatient cost is relatively meagre. Our results indicate that hospitals still need to discuss guidelines for reducing hospitalisation costs and for increasing hospital activity in terms of number of patients and efficiency.

A person-centred lifestyle change intervention model: working with older people experiencing chronic illness

A person-centred health promotion model of care to improve self-care and lifestyle changes for older people with chronic illnesses is conceptualized in this paper. The model supports effective interpersonal communication with nurses and health-care consumers and is developed to concept stage. Older people with chronic illnesses who experience stress, anxiety or social isolation are more likely to be admitted and re-admitted to acute hospitals. Interventions to decrease the risk factors are frequently unsuccessful in this patient group. Programmes, led by nurses, aimed at reducing stress, anxiety and social isolation while supporting older people postdischarge from hospital might be successful. The model integrates research from synthesized case studies and a critical literature review. The practices of interrelating four key elements-'construct', 'context', 'process' and 'outcome'-are proposed for nurses to assist patients advancing self-care and lifestyle change. The model is designed for implementation in outpatient, clinic or community settings.

Discharging older people from hospital to home: what do we know?

Aims and objectives.  The aim of this literature review was to examine empirical research to date on hospital discharge and illuminate areas in need of further exploration. Background.  Discharging older people from hospital has long been associated with difficulties and complications. With a steady increase in the ageing population in Ireland and their use of acute hospital services, the successful management of hospital discharge is now a central concern to health service management. Methods.  Using a seven-step approach by Stolz et al. [Scandinavian Journal of Caring Sciences (2004) Vol. 18, 111-119] a keyword database search was conducted which revealed 44 studies that matched the author's inclusion criteria. Results.  These studies uncover aspects of the discharge process that are in need of further research, and particularly suggest more rigour in the measurement of hospital discharge outcomes. Relevance to clinical practice.  This review focuses on a very important clinical issue given the predicted increase in the use of acute services by older people and the known vulnerabilities of the interface between hospital and community settings.

Emergency medical readmission: long-term trends and impact on mortality

There is increasing emphasis on prevention of emergency medical readmissions. The broad pattern of acute medical readmissions was studied over a seven-year period and the impact of any readmission on 30-day mortality was recorded. Significant predictors of outcome, including co-morbidity and illness severity score, were entered into a multivariate regression model, adjusting the univariate estimates of the readmission status on mortality. In total, 23,114 consecutive acute medical patients were admitted between 2002-8; the overall readmission rate was 27%. Readmission independently predicted an increased 30-day mortality; the odds ratio, was 1.12 (95% confidence interval (CI) 1.09 to 1.14). This fell to 1.05 (95% CI 1.02 to 1.08) when adjusted for outcome predictors including acute illness severity. The trend for readmissions was to progressively increase over time; the median times between consecutive admissions formed an exponential time series. Efforts to reduce or avoid readmissions may depend on an ability to modify the underlying chronic disease.

Frequency and risk factors associated with emergency medical readmissions in Galway University Hospitals

BACKGROUND

Unplanned readmissions of medical hospital patients have been increasing in recent years. We examined the frequency and associates of emergency medical readmissions to Galway University Hospitals (GUH).

METHOD

Readmissions during the calendar year 2006 were examined using hospital in-patient enquiry data. Associations with clinical and demographic factors were determined using univariate and multivariate analyses.

RESULTS

The medical emergency readmission rate to GUH, after correction for death during the index admission, was 19.5%. Age 65 years or more, male gender, length of stay more than 7 days and primary diagnoses of chronic obstructive pulmonary disease, myocardial infarction, alcohol-related disease and heart failure during the index admission were significantly associated with readmission in univariate and multivariate analyses.

CONCLUSION

The medical emergency readmission rate in GUH is comparable to other acute hospitals in Ireland and Britain. Further evaluation is needed to estimate the proportion of readmissions that are potentially avoidable.

Emergency department census of patients awaiting admission following reorganisation of an admissions process

OBJECTIVES

To determine the impact of reorganisation of an acute admissions process on numbers of people in the emergency department (ED) awaiting admission to a hospital bed in a major teaching hospital.

METHODS

We studied all emergency medical patients admitted to St James' Hospital, Dublin, between 1 January 2002 and 31 December 2004. In 2002, patients were admitted to a variety of wards from the ED when a hospital bed became available. In 2003, two centrally located wards were reconfigured to function as an acute medical admissions unit (AMAU) (bed capacity 59), and all emergency patients were admitted directly to this unit from the ED (average 15 admissions per day). The maximum permitted length of stay on the AMAU was 5 days. We recorded the number of patients in the ED, who were awaiting the availability of a hospital bed, at 0700 and 1700 on the days of recording during the 36 month study period.

RESULTS

The impact of the AMAU reduced overall hospital length of stay from 7 days in 2002 to 5 days in 2003 and 2004 (p<0.0001). The median number of patients waiting in the ED for a hospital bed reduced from 14 in 2002 to 9 in 2003 and 8 in 2004 (p<0.0001). While age and sex of patients did not differ over the years, the factors that independently contributed to the number of patients awaiting admission were the day of the week, the month of the year, and and the extent of the comorbidity index on the previous day's intake (p<0.0001).

CONCLUSIONS

This study found that reorganisation of a system for acute medical admissions can significantly impact on the number of patients awaiting admission to a hospital bed, and allow an ED to operate efficiently and at a level of risk acceptable to patients.

Factors influencing the costs of emergency medical admissions to an Irish teaching hospital

This study examined whether there is a relationship between coded diseases at the time of hospital discharge and costs of hospital re-admission. We carried out a systematic review of data relating to emergency medical patients admitted to St. James' Hospital in Dublin between 1 January 2002 and 31 October 2004. Data on discharges from hospital were analyzed as recorded in the hospital in-patient enquiry (HIPE) system. Of 15,876 episodes recorded among 11,201 patients admitted the number of re-admissions numbered up to 43. Age, year of admission, and frequency of admission were factors associated with increased hospital costs. HIPE coding at first discharge predicted increased costs: codes related to heart failure, pneumonia, stroke, diabetes, malignancy, psychiatric, and anaemia-related codes. Clinical coding using the HIPE database thus strongly predicted hospital costs.

Do consultants differ? Inferences drawn from hospital in-patient enquiry (HIPE) discharge coding at an Irish teaching hospital

OBJECTIVE

To find out if there was a difference between hospital consultants, all trained in acute general medicine, in length of stay (LOS), re-admission rates, resource utilisation, and diagnostic coding, among patients admitted as emergencies to St James' Hospital (SJH) Dublin.

METHODS

A retrospective analysis was performed of data on discharges from hospital, recorded in the hospital in-patient enquiry (HIPE) system, relating to 9204 episodes among 6968 emergency medical patients admitted to SJH between 1 January 2002 and 31 October 2003. For comparative analysis, four physician groups were defined consisting of gastroenterology (GI, n = 4), respiratory (n = 3), general internal medicine (GIM, n = 2), or specialty (n = 5).

RESULTS

GIM consultants had the shortest LOS (median 5 days); GIM and respiratory consultants were less likely to have long stay patients (> 30 days, p<0.0001). Patients re-admitted under the same consultant had a longer LOS than those re-admitted under a different consultant (p<0.0001). Endoscopy and GI radiology investigations were used most by GI consultants, computed tomography of the thorax by respiratory, ECHO by respiratory and specialty, and computed tomography of brain by GIM and specialty consultants. GI diagnostic codings were more frequent with GI consultants (p<0.0001), respiratory diagnoses and malignancy with respiratory (p<0.0001 for both), diabetes and hypertension with specialty (p = 0.0017), and heart failure more with GIM consultants (p = 0.001).

CONCLUSIONS

This study found that the HIPE database was very powerful in predicting differences between hospital consultants in LOS, re-admission rates, resource utilisation, and disease coding. It would be of interest to examine the extent to which protocols and guidelines could reduce such variations.