Predictive model of readmission to internal medicine wards

Improvement of scoring system used before discharge to predict 30-day all-cause unplanned readmission in geriatric population: a prospective cohort study

BACKGROUND

Data taken from tertiary referral hospitals in Indonesia suggested readmission rate in older population ranging between 18.1 and 36.3%. Thus, it is crucial to identify high risk patients who were readmitted. Our previous study found several important predictors, despite unsatisfactory discrimination value.

METHODS

We aimed to investigate whether comprehensive geriatric assessment (CGA) -based modification to the published seven-point scoring system may increase the discrimination value. We conducted a prospective cohort study in July-September 2022 and recruited patients aged 60 years and older admitted to the non-surgical ward and intensive coronary care unit. The ROC curve was made based on the four variables included in the prior study. We conducted bivariate and multivariate analyses, and derived a new scoring system with its discrimination value.

RESULTS

Of 235 subjects, the incidence of readmission was 32.3% (95% CI 26-38%). We established a new scoring system consisting of 4 components. The scoring system had maximum score of 21 and incorporated malignancy (6 points), delirium (4 points), length of stay ≥ 10 days (4 points), and being at risk of malnutrition or malnourished (7 points), with a good calibration test. The C-statistic value was 0.835 (95% CI 0.781-0.880). The optimal cut-off point was ≥ 8 with a sensitivity of 90.8% and a specificity of 54.7%.

CONCLUSIONS

Malignancy, delirium, length of stay ≥ 10 days, and being at risk of malnutrition or malnourished are predictors for 30-day all-cause unplanned readmission. The sensitive scoring system is a strong model to identify whether an individual is at higher risk for readmission. The new CGA-based scoring system had higher discrimination value than that of the previous seven-point scoring system.

Postdischarge Video Visits for Adherence to Hospital Discharge Recommendations: A Randomized Clinical Trial

Objective

To determine whether a postdischarge video visit with patients, conducted by hospital medicine advanced practice providers, improves adherence to hospital discharge recommendations.

Patients and Methods

We conducted a single-institution 2-site randomized clinical trial with 1:1 assignment to intervention vs control, with enrollment from August 10, 2020, to June 23, 2022. Hospital medicine patients discharged home or to an assisted living facility were randomized to a video visit 2-5 days postdischarge in addition to usual care (intervention) vs usual care (control). During the video visit, advanced practice providers reviewed discharge recommendations. Both intervention and control groups received telephone follow-up 3-6 days postdischarge to ascertain the primary outcome of adherence to all discharge recommendations for new and chronic medication management, self-management and action plan, and home support.

Results

Among 1190 participants (594 intervention; 596 control), the primary outcome was ascertained in 768 participants (314 intervention; 454 control). In intervention vs control, there was no difference in the proportion of participants with the primary outcome (76.7% vs 72.5%; P=.19) or in the individual domains of the primary outcome: new and chronic medication management (94.1% vs 92.8%; P=.50), self-management and action plan (76.5% vs 71.5%; P=.18), and home support (94.1% vs 94.3%; P=.94). Women receiving intervention vs control had higher adherence to recommendations (odds ratio, 1.77; 95% CI, 1.08-2.91).

Conclusion

In hospital medicine patients, a postdischarge video visit did not improve adherence to discharge recommendations. Potential gender differences in adherence require further investigation.Clinicaltrials.gov number, NCT04547803.

RECALMIN IV. Evolution in the activity of internal medicine units of the National Health System (2008-2021)

AIMS

This work aims to analyze the structure, activity, and outcomes of internal medicine units and departments (IMU) of the Spanish National Health System (SNHS) and to analyze the challenges for the specialty and propose policies for improvement. It also aims to compare the results from the 2021 RECALMIN survey with IMU surveys from previous years (2008, 2015, 2017, 2019).

METHODS

This work is a cross-sectional, descriptive study of IMUs in acute care general hospitals of the SNHS that compares data from 2020 with previous studies. The study variables were collected through an ad hoc questionnaire.

RESULTS

Between 2014 and 2020, hospital occupancy and discharges by IMU increased (annual mean of 4% and 3.8%, respectively), as did hospital cross-consultation and initial consultation rates (2.1% in both cases). E-consultations increased notably in 2020. Risk-adjusted mortality and length of hospital stay did not show significant changes from 2013-2020. Progress in the implementation of good practices and systematic care for complex chronic patients was limited. A consistent finding in RECALMIN surveys was the variability among IMUs in terms of resources and activity, though no statistically significant differences were found in regard to outcomes.

CONCLUSIONS

There is considerable room for improvement in the operation of IMUs. The reduction in unjustified variability in clinical practice and inequities in health outcomes are a challenge for IMU managers and the Spanish Society of Internal Medicine.

Risk Factors for Early Hospital Readmission in Geriatric Patients: A Systematic Review

The number of older patients is constantly growing, and early hospital readmissions in this population represent a major problem from a health, social and economic point of view. Furthermore, the early readmission rate is often used as an indicator of the quality of care. We performed a systematic review of the literature to better understand the risk factors of early readmission (30 and 90 days) in the geriatric population and to update the existing evidence on this subject. The search was carried out on the MEDLINE, EMBASE and PsycINFO databases. Three independent reviewers assessed the potential inclusion of the studies, and then each study was independently assessed by two reviewers using Joanna Briggs Institute critical appraisal tools; any discrepancies were resolved by the third reviewer. Studies that included inpatients in surgical wards were excluded. Twenty-nine studies were included in the review. Risk factors of early readmission can be classified into socio-economic factors, factors relating to the patient's health characteristics, factors related to the use of the healthcare system and clinical factors. Among these risk factors, those linked to patient frailty play an important role, in particular malnutrition, reduced mobility, risk of falls, fatigue and functional dependence. The early identification of patients at higher risk of early readmission may allow for targeted interventions in view of discharge.

Relevant vs non-relevant subspecialist for patients hospitalised in internal medicine at a local hospital: which is better? A retrospective cohort study

Background

Studies of the treatment of patients in-hospital with a specific diagnosis show that physicians with a subspecialisation relevant to this diagnosis can provide a better quality of care. However, studies including patients with a range of diagnoses show a more negligible effect of being attended by a relevant subspecialist. This project aimed to study a more extensive set of patients and diagnoses in an environment where the subspecialist present could be controlled. Thus, this study investigated whether being attended by a physician with a subspeciality relevant to the patient’s primary diagnosis was prospectively associated with readmission, in-hospital mortality, or length of stay compared to a physician with a subspeciality not relevant to the patient’s primary diagnosis.

Methods

We have conducted a retrospective register-based study of 11,059 hospital admissions across 9 years at a local hospital in south-eastern Norway, where it was possible to identify the physician attending the patients at the beginning of the stay. The outcomes studied were emergency readmissions to the same ward within 30 days, any in-hospital mortality and the total length of stay. The patients admitted were matched with the consultant(s) responsible for their treatment. Then, the admissions were divided into two groups according to their primary diagnosis. Was their diagnosis within the subspeciality of the attending consultant (relevant subspecialist) or not (non-relevant subspecialist). The two groups were then compared using bivariable and multivariable models adjusted for patient characteristics, comorbidities, diagnostic group and physician sex.

Results

A relevant subspecialist was present during the first 3 days in 8058 (73%) of the 11,059 patient cases. Patients attended to by a relevant subspecialist had an odds ratio (OR) of 0.91 (95% confidence interval 0.76 to 1.09) for being readmitted and 0.71 (0.48 to 1.04) for dying in the hospital and had a length of stay that was 0.18 (− 0.07 to 0.42) days longer than for those attended to by a non-relevant subspecialist.

Conclusions

This study found that patients attended by a relevant subspecialist did not have a significantly different outcome to those attended by a non-relevant subspecialist.

Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study

BACKGROUND

Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).

OBJECTIVE

We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Adult patients discharged from a CCHS hospital April 2017-September 2020.

MAIN MEASURES

Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.

RESULTS

The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.

CONCLUSIONS

The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Older patients acutely admitted and readmitted to the same geriatric department: a descriptive cohort study of primary diagnoses and health characteristics

PURPOSE

Advancing age is associated with increased risk for acute admissions and readmissions. The societal challenges of ageing populations have made the prevention of readmissions come into focus. Readmission may be perceived as the result of inadequate treatment during index admission but may also be caused by the onset of new disease following a generally impaired health of geriatric patients. We aimed at comparing the diagnoses at index and readmission to illuminate this issue.

METHODS

This is a descriptive, retrospective cohort study of patients acutely admitted and readmitted (within 30 days from discharge) to the same geriatric ward (November 1, 2017-April 30, 2018). Electronic medical records were scrutinised manually for discharge diagnoses and patient characteristics.

RESULTS

Readmission rate was 10.7% (98 of 918 unique admissions). Mean age was 85.6 (men 56%). About 75% were readmitted with a new acute disease unrelated to index admission, most commonly pneumonia (27%), other infections (22%), and dehydration (14%). The health characteristics were long index length-of-stay (median 7; IQR 5-11), high Charlson Comorbidity Index (CCI ≥ 3, n = 49 (50%), polypharmacy (≥ 5 prescriptions) (94%), and hospitalisations 12 months prior to index admission (57%).

KEY CONCLUSIONS

The majority of readmitted geriatric patients have contracted a new acute condition. Although being characterised by several adverse health characteristics, prospective studies comparing readmitted and non-readmitted geriatric patients are needed. Still, increasing the awareness of early recognition of acute disease onset in geriatric patients is warranted.

External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients

BACKGROUND

Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort.

METHODS

A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression.

RESULTS

The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575-0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33-5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort.

CONCLUSION

This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.

Factors predicting readmission in patients with COVID-19

OBJECTIVE

COVID-19 has been introduced by the World Health Organization as a health emergency worldwide. Up to 9% of the patients with COVID-19 may be readmitted by 2 months after discharge. This study aimed to estimate the readmission rate and identify main risk factors for readmission in these patients. In this prospective study, 416 discharged COVID patients followed up with a minimum 1 month and the readmission rate was recorded. Evaluated characteristics included time of readmission, age and sex, main symptoms of disease, result of computed tomography scan, reverse transcription polymerase chain reaction test and treatment modalities.

RESULTS

Regarding readmission, 51 patients of 416 discharged patients, was readmitted during the study period. The rate of readmission for 30 and 60 days after discharge was 7.6% and 8.1%, respectively. The median age of the readmitted patients was 67 years (IQR: 53-78). About 65% of readmitted patients had underlying disease. The most significant factor in readmission rate was related to the site of lung involvement (OR > 4). Age over 60 years, underlying disease especially diabetes (OR = 3.43), high creatinine level (≥ to 1.2 mg/dl) (OR = 2.15) were the most important predictors of readmission.

Targeted Clinical Interventions for Reducing Pediatric Readmissions

BACKGROUND

In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions.

METHODS

A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children's hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions.

RESULTS

The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%-12.8%) and 11.1% (95% CI: 10.8%-11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431-$2 735 364).

CONCLUSIONS

A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.

Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers

Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model’s prediction of hospital readmission.

Published models that predict hospital readmission: a critical appraisal

INTRODUCTION

The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness.

OBJECTIVE

To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation.

METHODS

We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors.

RESULTS

We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%).

CONCLUSIONS

The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

A simple index predicting 30-day readmissions in acutely hospitalized patients

BACKGROUND

There are various models attempting to predict 30-day readmissions of acutely admitted internal medicine patients. However, it is uncertain how to create a parsimonious index that has equivalent predictive ability and can be extrapolated to other settings.

METHODS

We developed a regression equation to predict 30-day readmissions from all acute hospitalizations in internal medicine departments in a regional hospital in 2015-2016 and validated the model in 2019. The independent (predictor) variables were age, past hospitalizations, admission laboratory test results, length of stay in hospital and discharge diagnoses. We compared the predictive value of a logistic regression model and index that included discharge diagnoses and admission laboratory test results with one that included only age, past hospitalizations, and hospital length of stay.

RESULTS

Readmission rates were associated with age, time since last hospitalization, number of previous hospitalizations, and length of stay, as well as with a diagnosis of chronic obstructive lung disease and congestive heart failure and several laboratory data. Logistic regressions of the independent variables for 30-day readmission rates were similar in 2015-2016 and 2019. An index was derived from number of previous admissions to hospitals, time since last admission, age, and length of stay. In 2019, for every unit of the index, the odds of readmission increased by 1.33 (95% CI- 1.30-1.37), and ranged from 2.1% to 37.1%. Addition of discharge diagnoses and laboratory variables did not significantly improve the risk differentiation of the index. The c-statistic for the final parsimonious model was 0.704.

CONCLUSIONS

An index derived from the number of previous hospital admissions, days since last admission, age, and length of stay in days differentiated between the risks of readmission within 30 days without the need for discharge diagnosis and laboratory variables.

Frequency, risk factors, and outcomes of hospital readmissions of COVID-19 patients

To determine the proportion of patients with COVID-19 who were readmitted to the hospital and the most common causes and the factors associated with readmission. Multicenter nationwide cohort study in Spain. Patients included in the study were admitted to 147 hospitals from March 1 to April 30, 2020. Readmission was defined as a new hospital admission during the 30 days after discharge. Emergency department visits after discharge were not considered readmission. During the study period 8392 patients were admitted to hospitals participating in the SEMI-COVID-19 network. 298 patients (4.2%) out of 7137 patients were readmitted after being discharged. 1541 (17.7%) died during the index admission and 35 died during hospital readmission (11.7%, p = 0.007). The median time from discharge to readmission was 7 days (IQR 3–15 days). The most frequent causes of hospital readmission were worsening of previous pneumonia (54%), bacterial infection (13%), venous thromboembolism (5%), and heart failure (5%). Age [odds ratio (OR): 1.02; 95% confident interval (95% CI): 1.01–1.03], age-adjusted Charlson comorbidity index score (OR: 1.13; 95% CI: 1.06–1.21), chronic obstructive pulmonary disease (OR: 1.84; 95% CI: 1.26–2.69), asthma (OR: 1.52; 95% CI: 1.04–2.22), hemoglobin level at admission (OR: 0.92; 95% CI: 0.86–0.99), ground-glass opacification at admission (OR: 0.86; 95% CI:0.76–0.98) and glucocorticoid treatment (OR: 1.29; 95% CI: 1.00–1.66) were independently associated with hospital readmission. The rate of readmission after hospital discharge for COVID-19 was low. Advanced age and comorbidity were associated with increased risk of readmission.

Skin Ulcers are Predictors of 30-Day Hospital Readmission, But are Under-represented in the DRG Coding: A Retrospective Case-Control Study From an Italian Internal Medicine Unit

This study retrospectively analyzes all consecutive patients who underwent during a year hospital readmissions, defined as an admission to a hospital within 30 days of discharge, to an Italian Internal Medicine ward. All these data were compared with those from patients who underwent only 1 hospital admission in the same period. The aim of this study was to identify potential novel risk factors for hospital readmissions. In 2018, a total of 3012 patients were hospitalized. Among these, 14.1% (n = 426; mean age, 79.7 ± 11.9; range, 23-100) were defined as readmissions; data were compared with controls (n = 420; 13.9%; mean age, 75.9 ± 14.7; range, 22-99) who had only 1 hospitalization. Cases showed a significantly higher prevalence than controls regarding cerebrovascular disease (77.2% vs 48.1%), cognitive impairment (51.8% vs 26.9%), congestive heart failure (47.6% vs 20.2%), chronic kidney disease (31.7% vs 13.1%), and chronic obstructive pulmonary disease (23.0% vs 14.5%). Skin ulcers were significantly more prevalent among cases (45.1% vs 17.6%). Diagnosis-related group (DRG) analysis showed a higher proportion of "infectious disease" (24.4% vs 15.0%) among the cases than in controls. Despite skin ulcers were very frequent among cases and controls (45.1% vs 17.6%), they were codified as "skin wound" DRG only in 1.4% and 0.2%, respectively. At the DRGs analysis, sepsis (31.6% vs 19.1%), pneumonia (17.1% vs 7.6%), and kidney failure (9.6% vs 3.8%) represented the main significant cause of death in cases compared to controls. Our study confirms that readmissions to Internal Medicine departments are related to the severity of chronic diseases affecting patients. Skin ulcers are present in about half of patients who will be early readmitted within 30 days, but they are almost never reported in DRGs, so more accurate coding is needed. Key challenges for the future are sepsis prevention measures and investing resources in chronic disease assistance, including skin ulcer chronic management.

Evaluation of early unplanned readmissions and predisposing factors in an oncology clinic

BACKGROUND

Unplanned readmission in the first 30 days after discharge is an important medical problem, although the data on cancer patients is limited. So we planned to evaluate the rates and causes of early readmissions and the predisposing factors.

METHODS

Patients hospitalized in Hacettepe University Oncology services between August 2018 and July 2019 were included. The demographic features, tumor stages, regular drugs, last laboratory parameters before discharge, and readmissions in the first 30 days after discharge were recorded. The predisposing features were evaluated with univariate and multivariate analyses.

RESULTS

A total of 562 hospitalizations were included. The mean age of the patients was 58.5 ± 14.5 years. Almost 2/3 of the hospitalizations were due to symptom palliation and infections. Eighty-three percent of the patients had advanced disease, and over 60% had an ECOG score of 2 and above. In the first 30 days after discharge, 127 patients were readmitted (22.6%). Advanced stage disease, presence of polypharmacy (5 or more regular drugs), hospitalization setting (emergency department (ED) vs. outpatient clinic), and hypoalbuminemia (< 3 gr/dL) were associated with a statistically significant increase in the risk of readmission. Among these factors, advanced-stage disease (HR: 2.847, 95% CI: 1.375-5.895), hospitalization from ED (HR: 1.832, 95% CI: 1.208-2.777), and polypharmacy (HR: 1.782, 95% CI: 1.173-2.706) remained significant in multivariate analyses.

CONCLUSIONS

In this study, 22% of cancer patients had early readmissions. The readmission risk increased in patients with advanced disease, hospitalization from ED, and polypharmacy. The optimal post-discharge plan may reduce readmissions in all oncology patients, with priority for these patient groups.

Hospital Readmissions of Discharged Patients with COVID-19

OBJECTIVE

To analyse the rate of occurrence and the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.

SETTING

University hospital in Madrid (Spain).

PARTICIPANTS

Sixty-one patients (74% male) who presented COVID-19 were readmitted during the 3 weeks after discharge from hospital.

INTERVENTIONS

Nested case-control study paired (1:1 ratio) by age, sex and period of admission.

OUTCOME MEASURES

Rate of readmission rate of patients discharged after suffering COVID-19 and identification of the clinical variables associated with it.

RESULTS

Out of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted. Immunocompromised patients (N=10.2%) were at increased risk for readmission (p=0.04). There was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07). Cases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge. There were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups. Neutrophil-to-lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06). Neither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate. Patients who were readmitted due to a thrombotic event (8 patients, 13.1%) presented a higher level of D-dimer at discharge of initial admission.

CONCLUSION

The rate of readmission after discharge from hospital for COVID-19 was low. Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge were at greater risk of readmission to hospital.

What Are They Worth? Six 30-Day Readmission Risk Scores for Medical Inpatients Externally Validated in a Swiss Cohort

BACKGROUND

Several clinical risk scores for unplanned 30-day readmission have been published, but there is a lack of external validation and head-to-head comparison.

OBJECTIVE

Retrospective replication of six clinical risk scores (LACE, HOSPITAL, SEMI, RRS, PARA, Tsui et al.)f DESIGN: Models were fitted with the original intercept and beta coefficients as reported. Otherwise, a logistic model was refitted (SEMI and Tsui et al). We performed subgroup analyses on main admission specialty. This report adheres to the TRIPOD statement for reporting of prediction models.

PARTICIPANTS

We used our prospective cohort of 15,639 medical patients from a Swiss tertiary care institution from 2016 through 2018.

MAIN MEASURES

Thirty-day readmission rate and area under the curve (AUC < 0.50 worse than chance, > 0.70 acceptable, > 0.80 excellent) CONCLUSIONS: Among several readmission risk scores, HOSPITAL, PARA, and the score from Tsui et al. showed the best predictive abilities and have high potential to improve patient care. Interventional research is now needed to understand the effects of these scores when used in clinical routine.

KEY RESULTS

Among the six risk scores externally validated, calibration of the models was overall poor with overprediction of events, except for the HOSPITAL and the PARA scores. Discriminative abilities (AUC) were as follows: LACE 0.53 (95% CI 0.50-0.56), HOSPITAL 0.73 (95% CI 0.72-0.74), SEMI 0.47 (95% CI 0.46-0.49), RRS 0.64 (95% CI 0.62-0.66), PARA 0.72 (95% CI 0.72-0.74), and the score from Tsui et al. 0.73 (95% CI 0.72-0.75). Performance in subgroups did not differ from the overall performance, except for oncology patients in the PARA score (0.57, 95% CI 0.54-0.60), and nephrology patients in the SEMI index (0.25, 95% CI 0.18-0.31), respectively.

Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients

BACKGROUND

Identifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions.

OBJECTIVE

Derive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication.

DESIGN/SETTING/PARTICIPANTS

Retrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Comparison between patients readmitted within 30 days and non-readmitted patients, as identified using a specially designed algorithm. Univariate and multivariate regression analyses of demographic data, clinical diagnoses, laboratory results, and the medication data of patients admitted during the first period (2011-2013), to identify factors associated with PAR. Using these, derive a predictive score with a regression coefficient-based scoring method. Subsequently, validate this score with a second cohort of patients admitted in 2013-2014. Variables were identified at hospital discharge.

RESULTS

The derivation cohort included 7,317 hospital stays. Multivariate logistic regressions found significant associations with PAR for: [adjusted OR (95% CI)] hospital length of stay > 4 days [1.3 (1.1-1.7)], admission in previous 6 months [2.3 (1.9-2.8)], heart failure [1.3 (1.0-1.7)], chronic ischemic heart disease [1.7 (1.2-2.3)], diabetes with organ damage [2.2 (1.3-3.8)], cancer [1.4 (1.0-1.9)], metastatic carcinoma [1.9 (1.3-3.0)], anemia [1.2 (1.0-1.5)], hypertension [1.3 (1.1-1.7)], arrhythmia [1.3 (1.0-1.6)], hyperkalemia [1.4 (1.0-1.7)], opioid drug prescription [1.3 (1.1-1.6)], and acute myocardial infarction [0.6 (0.4-0.9)]. The PAR-Risk Score, derived from these results, demonstrated fair discriminatory and calibration power (C-statistic = 0.699; Brier Score = 0.069). The results for the validation cohort's operating characteristics were similar (C-statistic = 0.687; Brier Score = 0.064).

CONCLUSION

This study identified routinely-available factors that were significantly associated with PAR. A predictive score was derived and internally validated.

Profiling the hospital-dependent patient in a large academic hospital: Observational study

BACKGROUND

In older patients with acute illness, a condition of "hospital-dependence" may arise: patients get adapted to the hospital care and, once discharged, may experience health status decline, requiring repeated readmissions despite appropriate treatments.

AIMS

The objective of this case-series study was to describe the characteristics of 118 patients (72 F) aged ≥75 (mean 83.7 ± 4.9) who were urgently admitted to our institution at least 4 times in 2015.

METHODS

For each patient and admission, data on multimorbidity (Cumulative Illness Rating Scale Comorbidity Score and Severity Index), frailty (Rockwood Clinical Frailty Scale), functional dependence, functional status, polypharmacy, length of stay and interval between admissions were extrapolated from clinical records. Mortality during the years 2015 and 2016 was assessed on the institutional database.

RESULTS

At the first admission, patients had a high burden of polypharmacy (median number of drugs 8.5, IQR 6-11) and multimorbidity (Comorbidity Score 15.8 ± 4.1, Severity Index 2.9 ± 1.1). However, most (55.5%) were fit or pre-frail according to Clinical Frailty Scale (score 1-4). At multivariate models, Severity Index was significantly correlated with the length of stay (β ± SE 2.23 ± 0.89, p = .01) and readmission interval (β ± SE -22.49 ± 9.27, p = .02). Significantly increasing trends of multimorbidity and disability occurred across admissions. By the end of 2016, 66% of patients had died. Frailty (RR 2.005, 95%CI 1.054-3.814, p = .007) and cancer were the only predictors of mortality.

CONCLUSIONS

Hospital-dependent patients had severe multimorbidity, but exhibited an unexpectedly low prevalence of frailty/disability at baseline, though increasing across admissions. Trends of frailty and multimorbidity are paramount for profiling the hospital-dependence risk.

Predictive models for hospital readmission risk: A systematic review of methods

OBJECTIVES

Hospital readmission risk prediction facilitates the identification of patients potentially at high risk so that resources can be used more efficiently in terms of cost-benefit. In this context, several models for readmission risk prediction have been proposed in recent years. The goal of this review is to give an overview of prediction models for hospital readmission, describe the data analysis methods and algorithms used for building the models, and synthesize their results.

METHODS

Studies that reported the predictive performance of a model for hospital readmission risk were included. We defined the scope of the review and accordingly built a search query to select the candidate papers. This query string was used as input for the chosen search engines, namely PubMed and Google Scholar. For each study, we recorded the population, feature selection method, classification algorithm, sample size, readmission threshold, readmission rate and predictive performance of the model.

RESULTS

We identified 77 studies that met the inclusion criteria, out of 265 citations. In 68% of the studies (n = 52) logistic regression or other regression techniques were utilized as the main method. Ten (13%) studies used survival analysis for model construction, while 14 (18%) used machine learning techniques for classification, of which decision tree-based methods and SVM were the most utilized algorithms. Among these, only four studies reported the use of any class imbalance addressing technique, of which resampling is the most frequent (75%). The performance of the models varied significantly among studies, with Area Under the ROC Curve (AUC) values in the ranges between 0.54 and 0.92.

CONCLUSION

Logistic regression and survival analysis have been traditionally the most widely used techniques for model building. Nevertheless, machine learning techniques are becoming increasingly popular in recent years. Recent comparative studies suggest that machine learning techniques can improve prediction ability over traditional statistical approaches. Regardless, the lack of an appropriate benchmark dataset of hospital readmissions makes a comparison of models' performance across different studies difficult.

Care Complexity Individual Factors Associated With Hospital Readmission: A Retrospective Cohort Study

PURPOSE

To determine the frequency of care complexity individual factors documented in the nursing assessment and to identify the risk factors associated with hospital readmission within 30 days of hospital discharge.

DESIGN

Observational analysis of a retrospective cohort at a 700-bed university hospital in Barcelona, Spain. A total of 16,925 adult patient admissions to a ward or intermediate care units were evaluated from January to December 2016. Most patients were admitted due to cardiocirculatory and respiratory disorders (29.3%), musculoskeletal and nervous system disorders (21.8%), digestive and hepatobiliary conditions (17.9%), and kidney or urinary disorders (11.2%).

METHODS

Readmission was defined as rehospitalization for any reason within 30 days of discharge. Patients who required hospital readmission were compared with those who did not. The individual factors of care complexity included five domains (developmental, mental-cognitive, psycho-emotional, sociocultural, and comorbidity or complications) and were reviewed using the electronic nursing assessment records. Multivariate logistic analysis was performed to determine factors associated with readmission.

FINDINGS

A total of 1,052 patients (6.4%) were readmitted within 30 days of hospital discharge. Care complexity individual factors from the comorbidity or complications domain were found to be the most frequently e-charted (88.3%). Care complexity individual factors from developmental (33.2%), psycho-emotional (13.2%), mental-cognitive (7.2%), and sociocultural (0.7%) domains were less frequently documented. Independent factors associated with hospital readmission were old age (≥75 years), duration of first hospitalization, admission to a nonsurgical ward, major chronic disease, hemodynamic instability, immunosuppression, and relative weight of diagnosis-related group.

CONCLUSIONS

A substantial number of patients required readmission within 30 days after discharge. The most frequent care complexity individual factors recorded in the nursing assessment at index admission were related to comorbidity or complications, developmental, and psycho-emotional domains. Strategies related to transition of care that include clinical characteristics and comorbidity or complications factors should be a priority at hospital discharge and after leaving hospital, but other factors related to developmental and psycho-emotional domains could have an important effect on the use of healthcare resources.

CLINICAL RELEVANCE

Nurses should identify patients with comorbidity or complications, developmental, and psycho-emotional complexity factors during the index admission in order to be able to implement an effective discharge process of care.

Scalable and accurate deep learning with electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart.

Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments

OBJECTIVES

We developed a predictive model for the hospital readmission of patients with diabetes. The objective was to identify the frail population that requires additional strategies to prevent readmissions at 90 days.

METHODS

Using data collected from 1977 patients in 3 studies on the national prevalence of diabetes (2015-2017), we developed and validated a predictive model of readmission at 90 days for patients with diabetes.

RESULTS

A total of 704 (36%) readmissions were recorded. There were no differences in the readmission rates over the course of the 3 studies. The hospitals with more than 500 beds showed significantly (p=.02) higher readmission rates than those with fewer beds. The main reasons for readmission were infectious diseases (29%), cardiovascular diseases (24) and respiratory diseases (14%). Readmissions directly related to diabetic decompensations accounted for only 2% of all readmissions. The independent variables associated with hospital readmission were patient's age, degree of comorbidity, estimated glomerular filtration rate, degree of disability, presence of previous episodes of hypoglycaemia, use of insulin in treating diabetes and the use of systemic glucocorticoids. The predictive model showed an area under the ROC curve (AUC) of 0.676 (95% confidence interval [95% CI] 0.642-0.709; p=.001) in the referral cohort. In the validation cohort, the model showed an AUC of 0.661 (95% CI 0.612-0.710; p=.001).

CONCLUSION

The model we developed for predicting readmissions for hospitalised patients with type 2 diabetes helps identify a subgroup of frail patients with a high risk of readmission.

The association of discharge decisions after deceased donor kidney transplantation with the risk of early readmission: Results from the deceased donor study

BACKGROUND

Kidney transplant (KT) recipients experience high rates of early (≤30 days) hospital readmission (EHR) after KT, and existing studies provide limited data on modifiable discharge factors that may mitigate EHR risk.

METHODS

We performed a retrospective cohort study of 468 adult deceased donor KT recipients transplanted between 4/2010 and 11/2013 at 5 United States transplant centers. We fit multivariable mixed effects models to assess the association of two potentially modifiable discharge factors with the probability of EHR after KT: (i) weekend discharge and (ii) days to first scheduled follow-up.

RESULTS

Among 468 KT recipients, 38% (n = 178) experienced EHR after KT. In fully adjusted analyses, compared to weekday discharges, KT recipients discharged on the weekend had a 29% lower risk of EHR (adjusted odds ratio [aOR] 0.71, 95% confidence interval [CI] 0.41-0.94). Compared to follow-up within 2 days of discharge, KT recipients with follow-up within 3 to 6 days had a 28% higher probability of EHR (aOR 1.28, 95% CI 1.13-1.45).

CONCLUSIONS

These findings suggest that clinical decisions related to the timing of discharge and follow-up modify EHR risk after KT, independent of traditional risk factors.

Readmission to hospital of medical patients - A cohort study

INTRODUCTION

The incidence of acute readmissions is higher among elderly medical patients than in the general population. Risk factor identification is needed in order to prevent readmissions.

OBJECTIVE

To estimate the incidence of acute readmissions among medical patients ≥65years discharged from departments of internal medicine and to identify risk factors associated with readmissions.

MATERIAL AND METHODS

We included patients discharged between 1st of January 2011 and 1st of December 2014 and collected data regarding primary diagnosis and comorbidities. The primary outcome was acute readmission within 30days of discharge. We determined risk factors using a multivariable Cox proportional hazards model.

RESULTS

Out of 21,634 discharged patients, 3432 (15.9%) patients had an acute readmission. Risk factors were: age per decade (HR: 1.06, 95%CI: 1.02-1.11), male sex (HR: 1.07, 95%CI: 1.00-1.15), receiving home care service (personal care) (HR: 1.33, 95%CI: 1.15-1.55), nursing home residency (HR: 1.30, 95%CI: 1.14-1.48), a previous admission within six months (HR: 1.59, 95%CI: 1.48-1.72), increased length of index admission (HR: 1.14, 95%CI: 1.11-1.17), and moderate or high level of comorbidities (HR: 1.22, 95%CI: 1.13-1.32, HR: 1.52, 95%CI: 1.38-1.67, respectively).

CONCLUSION

Around one in six patients had an acute readmission and we identified several risk factors. The risk factors a previous hospital admission within six months, a long or very long length of index admission and a high level of comorbidities were strong risk factors for an acute readmission.

Emergency department visits in older people: pattern of use, contributing factors, geographical differences and outcomes

AIMS

To assess the pattern of use of Emergency Departments (EDs), factors contributing to the visits, geographical distribution and outcomes in people aged 65 years or more living in the Italian Lombardy Region in 2012.

METHODS

Based on an administrative database the study population was divided into groups according to the number of ED visits. A multinomial logistic regression model was performed to compare the characteristics of each group. The Getis-Ord's G statistic was used to evaluate the clusters of high and low visit prevalence odd ratios (OR) at district level. To estimate the severity of the disease leading to ED attendance, visits were stratified based on the level of emergency and outcome.

RESULTS

About 2 million older people were included in the analyses: 78 % had no ED visit, 15 % only 1, 7 % 2 or more. Male sex, age 85 years or more, high number of drugs, ED visits and hospital admissions in the previous year and the location of an ED within 10 km from the patient's place were all factors associated with a higher risk to have more ED visits. Clusters of high and low prevalence of visits were found for occasional users. Overall, 83 % of ED visit with a low emergency triage code at admission had as visit outcome discharge at home.

CONCLUSIONS

In older people several variables were associated with an increased risk to have a high number of ED visits. Most of the visits were done for non-urgent problems and significant geographic differences were observed for occasional users.

Risk assessment of comorbidities on 30-day avoidable hospital readmissions among internal medicine patients

RATIONALE, AIMS AND OBJECTIVES

Reducing the rate of hospital readmissions, particularly avoidable ones, has significant implications on patient outcomes, cost containment, and quality of care. Given that the reason of readmission may differ from the patient's main diagnosis in the index admission, this study aims to assess the influence of index comorbidities on the primary readmission diagnoses and explore the risk of deemed avoidable readmission because of prior comorbidities.

METHODS

A retrospective review of 3962 discharges was conducted at a 527-bed teaching hospital in Jordan, utilizing data related to 2025 internal medicine patients.

RESULTS

Among all discharges, 29% were followed by a 30-day readmission, of which 13% were identified as potentially avoidable. Of all readmissions, 36% of patients were readmitted because of one of the comorbidities that had been identified at index admission. In addition, 47% of the potentially avoidable readmissions had a main diagnosis that was one of the index comorbidities. The results also showed an association between readmission for one of the index stay's comorbidities and being avoidable, with an adjusted odds ratio of 2.12 (95% confidence interval, 1.65-2.72). Overall, the presence of certain diseases, being identified as one of the preceding comorbidities, was found to have a substantial influence on the risk of potentially avoidable readmission. These diseases included digestive, circulatory, respiratory, genitourinary systems, and infectious and parasitic diseases (adjusted relative risks = 1.57, 1.49, 1.36, 1.30, and 2.30, respectively).

CONCLUSION

To help reduce the rates of readmission, potential gains seem available if hospitals adopt clinical practices that support the patient's care during the post-discharge transition. This implies that health care providers need to pay more attention to the comorbidities of high-risk patients to be closely monitored after discharge.

[Hospital readmission rate and associated factors among health services enrollees in Colombia]

Hospital readmissions are common and expensive, and there is little information on the problem in Colombia. The objective was to determine the frequency of 30-day all-cause hospital readmissions and associated factors. This was a retrospective analytical cohort study of 64,969 hospitalizations from January 2008 to January 2009 in 47 Colombian cities. 6,573 hospital readmissions, prevalence: 10.1% (men 10.9%, women 9.5%), 44.7% > 65 years of age. Hospital readmissions was associated with higher mortality (5.8% vs. 1.8%). There was an increase in the Hospital readmissions rate in patients with diseases of the circulatory system. Hospital readmissions was more likely in hematological diseases and neoplasms. Mean length of stay during the first readmission was 7 days in patients that were readmitted and 4.5 in those without readmission. Greater total cost of hospital readmissions (USA 21,998,275): 15.8% of the total cost of hospitalizations. Higher prevalence rates in referred patients (18.8%) and patients from the outpatient clinic (13.7%). Hospital readmissions is common and is associated with longer length of hospital stay and higher mortality and cost. Increased risk of hospital readmissions in men > 65 years, patients referred from other institutions, and in hematological diseases and neoplasms.

Integrating the Principles of Evidence Based Medicine and Evidence Based Public Health: Impact on the Quality of Patient Care and Hospital Readmission Rates in Jordan

INTRODUCTION

Hospital readmissions impose not only an extra burden on health care systems but impact patient health outcomes. Identifying modifiable behavioural risk factors that are possible causes of potentially avoidable readmissions can lower readmission rates and healthcare costs.

METHODS

Using the core principles of evidence based medicine and public health, the purpose of this study was to develop a heuristic guide that could identify what behavioural risk factors influence hospital readmissions through adopting various methods of analysis including regression models, t-tests, data mining, and logistic regression. This study was a retrospective cohort review of internal medicine patients admitted between December 1, 2012 and December 31, 2013 at King Abdullah University Hospital, in Jordan.

RESULTS

29% of all hospitalized patients were readmitted during the study period. Among all readmissions, 44% were identified as potentially avoidable. Behavioural factors including smoking, unclear follow-up and discharge planning, and being non-compliant with treatment regimen as well as discharge against medical advice were all associated with increased risk of avoidable readmissions.

CONCLUSION

Implementing evidence based health programs that focus on modifiable behavioural risk factors for both patients and clinicians would yield a higher response in terms of reducing potentially avoidable readmissions, and could reduce direct medical costs.

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review

OBJECTIVE

To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.

DESIGN

Systematic review.

SETTING/DATA SOURCE

CINAHL, Embase, MEDLINE from 2011 to 2015.

PARTICIPANTS

All studies of 28-day and 30-day readmission predictive model.

OUTCOME MEASURES

Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.

RESULTS

Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21-0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables 'comorbidities', 'length of stay' and 'previous admissions' were frequently cited across 73 models. The variables 'laboratory tests' and 'medication' had more weight in the models for cardiovascular disease and medical condition-related readmissions.

CONCLUSIONS

The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.

[RECALMIN. Patient care in the internal medicine units of the Spanish national health system]

OBJECTIVES

To perform a situation analysis of the care provided by internal medicine units (IMUs) in Spain and to develop, based on this analysis, proposals for improving the quality of care in these units.

MATERIAL AND METHODS

A descriptive, cross-sectional study of the IMUs of general acute care hospitals of the Spanish National Health System (SNHS), with data referring to 2013. The study variables were collected via an ad hoc questionnaire.

RESULTS

Of the total 260hospitals identified in the SNHS, 142responses were obtained from 139hospitals throughout Spain, which represents 53.5% of the IMUs in the SNHS. The mean number of internists per IMU was 14±8, with a mean rate of 7.2±3.3 internists per 100,000 inhabitants. In 2013, the average number of hospital discharges from the IMU was 2,987±2,066, and those discharged by internists was 232±107. Sixty-one percent of the IMUs had implemented an interconsultation unit, and 41% had implemented a systematic care program for complex chronic patients. Thirty-three percent of the IMUs conducted multidisciplinary rounds, and 60% of these IMUs planned the discharge.

CONCLUSIONS

The 2013 RECALMIN survey revealed a number of important aspects of the organisation, structure and management of IMUs. The remarkable variability in the indicators of structure, activity and management probably reflect significant differences in efficiency and productivity, which therefore provide significant room for improvement.

Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data

INTRODUCTION

Unplanned 30-day hospital readmission account for roughly $17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most researchers have used insurance claims or administrative data to train and operationalize their Readmission Risk Prediction Models (RRPMs). Some RRPM developers have also used electronic health records data; however, using health informatics exchange data has been uncommon among such predictive models and can be beneficial in its ability to provide real-time alerts to providers at the point of care.

METHODS

We conducted a semi-systematic review of readmission predictive factors published prior to March 2013. Then, we extracted and merged all significant variables listed in those articles for RRPMs. Finally, we matched these variables with common HL7 messages transmitted by a sample of health information exchange organizations (HIO).

RESULTS

The semi-systematic review resulted in identification of 32 articles and 297 predictive variables. The mapping of these variables with common HL7 segments resulted in an 89.2% total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4%. The PID (patient identification) and OBX (observation results) segments cover 13.9% and 9.1% of the variables. Evaluating the same coverage in three sample HIOs showed data incompleteness.

DISCUSSION

HIOs can utilize HL7 messages to develop unique RRPMs for their stakeholders; however, data completeness of exchanged messages should meet certain thresholds. If data quality standards are met by stakeholders, HIOs would be able to provide real-time RRPMs that not only predict intra-hospital readmissions but also inter-hospital cases.

CONCLUSION

A RRPM derived using HIO data exchanged through may prove to be a useful method to prevent unplanned hospital readmissions. In order for the RRPM derived from HIO data to be effective, hospitals must actively exchange clinical information through the HIO and develop actionable methods that integrate into the workflow of providers to ensure that patients at high-risk for readmission receive the care they need.

Characteristics and temporal pattern of the readmissions of patients with multiple hospital admissions in the medical departments of a general hospital

INTRODUCTION

Patients with multiple hospital admissions represent a small percentage of total hospitalizations but result in a considerable proportion of the healthcare expenditure. There are no studies that have analyzed their long-term clinical evolution.

OBJECTIVES

To study the characteristics, temporal patterns of readmissions and clinical evolution of patients with multiple hospital admission in the long term.

METHODS

A retrospective study was conducted of all hospital admissions in the medical area of the Hospital of Lugo (Spain) between January 1, 2000 and December 31, 2012, based on data from the center's minimum basic data set.

RESULTS

A total of 139,249 hospital admissions for 62,515 patients were studied. Six hospital admissions were recorded for 6.4% of the patients. The overall mortality rate was 16% (9982 patients). The readmissions rate steadily increased with each new admission, from 48% after the first event to 74.6% after the fifth. The rate of hospital readmission before 30days increased from 18.3% in the second admission to 36.3% in the sixth. The number of chronic diseases increased from 3.1 (SD, 2) in the first hospital admission up to 4.9 (2.8) in the sixth. The Department of Internal Medicine treated a third of all hospital admissions. In the sixth hospitalization, conditions associated with admission in Internal Medicine were CIRS score, age, heart failure, COPD, dementia, diabetes, atrial fibrillation and anemia.

CONCLUSIONS

Patients with multiple hospital admissions are complex patients whose temporal pattern of readmissions changes with time, such that each hospital admission constitutes a factor facilitating the next.

Emergency Department Non-Urgent Visits and Hospital Readmissions Are Associated with Different Socio-Economic Variables in Italy

Objective

The aim of this paper was to evaluate socio-economic factors associated to poor primary care utilization by studying two specific subjects: the hospital readmission rate, and the use of the Emergency Department (ED) for non-urgent visits.

Methods

The study was carried out by the analysis of administrative database for hospital readmission and with a specific survey for non-urgent ED use.

Results

Among the 416,698 sampled admissions, 6.39% (95% CI, 6.32–6.47) of re-admissions have been registered; the distribution shows a high frequency of events in the age 65–84 years group, and in the intermediate care hospitals (51.97%; 95%CI 51.37–52.57). The regression model has shown the significant role played by age, type of structure (geriatric acute care), and deprivation index of the area of residence on the readmission, however, after adjusting for the intensity of primary care, the role of deprivation was no more significant. Non-urgent ED visits accounted for the 12.10%, (95%CI 9.38–15.27) of the total number of respondents to the questionnaire (N = 504). The likelihood of performing a non-urgent ED visit was higher among patients aged <65 years (OR 3.2, 95%CI 1.3–7.8 p = 0.008), while it was lower among those perceiving as urgent their health problem (OR 0.50, 95%CI 0.30–0.90).

Conclusions

In the Italian context repeated readmissions and ED utilization are linked to different trajectories, besides the increasing age and comorbidity of patients are the factors that are related to repeated admissions, the self-perceived trust in diagnostic technologies is an important risk factor in determining ED visits. Better use of public national health care service is mandatory, since its correct utilization is associated to increasing equity and better health care utilization.

Unscheduled-return-visits after an emergency department (ED) attendance and clinical link between both visits in patients aged 75 years and over: a prospective observational study

Background

Predictors of unscheduled return visits (URV), best time-frame to evaluate URV rate and clinical relationship between both visits have not yet been determined for the elderly following an ED visit.

Methods

We conducted a prospective-observational study including 11,521 patients aged ≥75-years and discharged from ED (5,368 patients (53.5%)) or hospitalized after ED visit (6,153 patients). Logistic Regression and time-to-failure analyses including Cox proportional model were performed.

Results

Mean time to URV was 17 days; 72-hour, 30-day and 90-day URV rates were 1.8%, 6.1% and 10% respectively. Multivariate analysis indicates that care-pathway and final disposition decisions were significantly associated with a 30-day URV. Thus, we evaluated predictors of 30-day URV rates among non-admitted and hospitalized patient groups. By using the Cox model we found that, for non-admitted patients, triage acuity and diagnostic category and, for hospitalized patients, that visit time (day, night) and diagnostic categories were significant predictors (p<0.001). For URV, we found that 25% were due to closely related-clinical conditions. Time lapses between both visits constituted the strongest predictor of closely related-clinical conditions.

Conclusion

Our study shows that a decision of non-admission in emergency departments is linked with an accrued risk of URV, and that some diagnostic categories are also related for non-admitted and hospitalized subjects alike. Our study also demonstrates that the best time frame to evaluate the URV rate after an ED visit is 30 days, because this is the time period during which most URVs and cases with close clinical relationships between two visits are concentrated. Our results suggest that URV can be used as an indicator or quality.

Multimorbidity in risk stratification tools to predict negative outcomes in adult population

INTRODUCTION

Risk stratification tools were developed to assess risk of negative health outcomes. These tools assess a variety of variables and clinical factors and they can be used to identify targets of potential interventions and to develop care plans. The role of multimorbidity in these tools has never been assessed.

OBJECTIVES

To summarize validated risk stratification tools for predicting negative outcomes, with a specific focus on multimorbidity.

METHODS

MEDLINE, Cochrane Central Register of Controlled Trials and PubMed database were interrogated for studies concerning risk prediction models in medical populations. Review was conducted to identify prediction models tested with patients in both derivation and validation cohorts. A qualitative synthesis was performed focusing particularly on how multimorbidity is assessed by each algorithm and how much this weighs in the ability of discrimination.

RESULTS

Of 3674 citations reviewed, 36 articles met criteria. Of these, 29 had as outcome hospital admission/readmission. The most common multimorbidity measure employed in the models was the Charlson Comorbidity Index (12 articles). C-statistics ranged between 0.5 and 0.85 in predicting hospital admission/ readmission. The highest c-statistics was 0.83 in models with disability as outcome. For healthcare cost, models which used ACG-PM case mix explained better the variability of total costs.

CONCLUSIONS

This review suggests that predictive risk models which employ multimorbidity as predictor variable are more accurate; CHF, cerebro-vascular disease, COPD and diabetes were strong predictors in some of the reviewed models. However, the variability in the risk factors used in these models does not allow making assumptions.

Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada

Objective

A number of predictive models have been developed to identify patients at risk of hospital readmission. Most of these have focused on readmission within 30 days of discharge. We used population-based health administrative data to develop a predictive model for hospital readmission within 12 months of discharge in Winnipeg, Canada.

Methods

This was a retrospective cohort study with derivation and validation data sets. Multivariable logistic regression analyses were performed and factors significantly associated with readmission were selected to construct a risk scoring tool.

Results

Several variables were identified that predicted readmission (i.e. older age, male, at least one hospital admission in the previous two years, an emergent (index) hospital admission, Charlson comorbidity score >0 and length of stay). Discrimination power was acceptable (C statistic =0.701). At a median risk score threshold, the sensitivity, specificity, positive and negative predictive values were 45.5%, 79%, 68.8% and 58.6%.

Conclusions

This predictive model demonstrated that hospital readmission within 12 months of discharge can be reasonably well predicted based on administrative data. It will help health care providers target interventions to prevent unnecessary hospital readmissions.

Risk factors of 30-days re-hospitalization after Hospital at Home in a cohort of patients treated with parenteral therapy

BACKGROUND

Hospital at Home provides acute medical treatment in the patient's home. To prevent re-hospitalization in these outpatients, it is decisive to identify high-risk groups. This study aimed to identify patient characteristics for increased risk of 30 days re-hospitalization, after referral to Hospital at Home.

METHODS

A registry-based study was conducted using information from a hospital and a team providing Hospital at Home treatment. A total of 379 patients above the age of 18, who had received parenteral therapy by the team, in the period 01.03.2011 to 31.12.2012, were identified. Comorbid conditions were defined using Charlson's Comorbidity Index and divided into groups of score (0, 1 and ≥ 2). Cox regression analysis was used to estimate a risk of 30 days re-hospitalization by computing the hazard ratios (HR) with 95% confidence interval (95% CI).

RESULTS

Within 30 days after referral to Hospital at Home 32.5% of the population was re-hospitalized. An increased risk of readmission was found in patients with a comorbidity score ≥ 2 (HR 2.06, 95% CI 1.33-3.18) and in patients referred to Hospital at Home from a hospital department (HR 1.56, 95% CI 1.02-2.38). Primary care tended to reduce the risk of readmission although not significant (HR 0.65, 95% CI 0.41-1.04).

CONCLUSIONS

This study suggests that, patients with comorbidities and/or patients referred by the hospital are in a greater risk of readmission. Further readmission may be prevented in patients receiving primary care.

Multiple hospitalizations at the Department of Internal Medicine of a tertiary hospital

BACKGROUND AND OBJECTIVES

Patient who require multiple hospitalizations result in a considerable consumption of healthcare resources. In this study, we analyzed the factors associated with the multiple hospitalizations of a cohort of patients treated at a department of internal medicine.

PATIENTS AND METHODS

A total of 613 consecutive hospitalizations were analyzed. A multiple-hospitalization patient was defined as one who at the time of admission had been hospitalized 3 or more times in the past year. We analyzed the relationship between demographic, clinical and societal factors on one hand and having been hospitalized on multiple occasions on the other. We also analyzed readmissions in the 6 months after discharge, as well as mortality during the hospitalization and in the 6 and 12 months after discharge.

RESULTS

When compared with patients who have not been hospitalized on multiple occasions, multiple-hospitalization patients are more likely to be male, younger and to have greater comorbidity, greater consumption of medicines and higher Katz Index scores. The main cause for admission for multiple-hospitalizations patients was chronic disease decompensation (87.3%). The diseases that were most obviously associated with multiple hospitalizations were heart failure, diabetes mellitus and chronic obstructive pulmonary disease. In the first 6 months after discharge, multiple-hospitalization patients had a greater number of readmissions. During the study period, 40.4% of the multiple-hospitalization patients died, and 28.8% of the nonmultiple-hospitalization patients died.

CONCLUSIONS

Multiple-hospitalization patients have a greater clinical complexity than nonmultiple-hospitalization patients, and multiple hospitalizations are associated with chronic diseases, polypharmacy, functional impairment and high mortality rates.