Hospital readmission performance and patterns of readmission: retrospective cohort study of Medicare admissions

Predicting reoperation and readmission for head and neck free flap patients using machine learning

BACKGROUND

To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery.

METHODS

Data were extracted from the 2012-2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30-day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated.

RESULTS

Four-hundred and sixty-six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine-hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58.

CONCLUSION

XGBoost was used to predict 30-day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision-making and improve data collection in future database iterations.

Quality indicators for evaluating the 30-day postoperative outcome in pediatric brain tumor surgery: a 10-year single-center study and systematic review of the literature

OBJECTIVE

Surgery is the cornerstone in the management of pediatric brain tumors. To provide safe and effective health services, quantifying and evaluating quality of care are important. To do this, there is a need for universal measures in the form of indicators reflecting quality of the delivered care. The objective of this study was to analyze currently applied quality indicators in pediatric brain tumor surgery and identify factors associated with poor outcome at a tertiary neurosurgical referral center in western Norway.

METHODS

All patients younger than 18 years of age who underwent surgery for an intracranial tumor at the Department of Neurosurgery at Haukeland University Hospital in Bergen, Norway, between 2009 and 2020 were included. The primary outcomes of interest were classic quality indicators: 30-day readmission, 30-day reoperation, 30-day mortality, 30-day nosocomial infection, and 30-day surgical site infection (SSI) rates; and length of stay. The secondary aim was the identification of risk factors related to unfavorable outcome. The authors also conducted a systematic literature review. Articles concerning pediatric brain tumor surgery reporting at least two quality indicators were of interest.

RESULTS

The authors included 82 patients aged 0-17 years. The 30-day outcomes for unplanned reoperation, unplanned remission, mortality, nosocomial infection, and SSI were 9.8%, 14.6%, 0%, 6.1%, and 3.7%, respectively. Unplanned reoperation was associated with eloquent localization (p = 0.009), primary emergency surgery (p = 0.003), and CSF diversion procedures (p = 0.002). Greater tumor volume was associated with unplanned readmission (p = 0.008), nosocomial infection (p = 0.004), and CSF leakage (p = 0.005). In the systematic review, after full-text screening, 16 articles were included and provided outcome data for 1856 procedures. Overall, the 30-day mortality rate was low, varying from 0% to 9.3%. The 30-day reoperation rate varied from 1.5% to 12%. The SSI rate ranged between 0% and 3.9%, and 0% to 17.4% of patients developed CSF leakage. Four studies reported infratentorial tumor location as a risk factor for postoperative CSF leakage.

CONCLUSIONS

The 30-day outcomes in the authors' department were comparable to published outcomes. The most relevant factors related to unfavorable outcomes are tumor volume and location, both of which are not modifiable by the surgeon. This highlights the importance of risk adjustment. This evaluation of quality indicators reveals concerns related to the unclear and nonstandardized definitions of outcomes. Standardized outcome definitions and documentation in a large and multicentric database are needed in the future for further evaluation of quality indicators.

[Predictors for Early Unplanned Readmissions]

Predictors for Early Unplanned Readmissions Abstract. Unplanned rehospitalizations represent a major burden for patients, their relatives and the healthcare system. Since the introduction of the SwissDRG in 2012, financial incentives for hospitals have been promoted to forestall readmissions. Not every patient is at risk for rehospitalization. Affected patients can be identified by predictors from various areas in order to implement adequate interventions and avoid readmissions. Predictors can be directly related to patients as in the case of polypharmacy, multiple comorbidities or related to gender, but also provider-related and system-related. Early follow-up visits or a pre-discharge medication review are cited as effective interventions.

Can Currently Suggested Quality Indicators Be Transferred to Meningioma Surgery?-A Single-Center Pilot Study

BACKGROUND

 Risk stratification based on standardized quality measures has become crucial in neurosurgery. Contemporary quality indicators have often been developed for a wide range of neurosurgical procedures collectively. The accuracy of tumor-inherent characteristics of patients diagnosed with meningioma remains questionable. The objective of this study was the analysis of currently applied quality indicators in meningioma surgery and the identification of potential new measures.

METHODS

 Data of 133 patients who were operated on due to intracranial meningiomas were subjected to a retrospective analysis. The primary outcomes of interest were classical quality indicators such as the 30-day readmission, 30-day reoperation, 30-day mortality, 30-day nosocomial infection, and the 30-day surgical site infection rate. Uni- and multivariate analyses were performed. The occurrence of a new postoperative neurologic deficit was analyzed as a potential new quality indicator.

RESULTS

 The overall unplanned readmission rate was 3.8%; 13 patients were reoperated within 30 days (9.8%). The 30-day nosocomial infection and surgical site infection rates were 6.8 and 1.5%, respectively. A postoperative new neurologic deficit or neurologic deterioration as a currently assessed quality feature was observed in 12 patients (9.2%). The edema volume on preoperative scans proved to have a significant impact on the occurrence of a new postoperative neurologic deficit (p = 0.023).

CONCLUSIONS

 Classical quality indicators in neurosurgery have proved to correlate with considerable deterioration of the patient's health in meningioma surgery and thus should be taken into consideration for application in meningioma patients. The occurrence of a new postoperative neurologic deficit is common and procedure specific. Thus, this should be elucidated for application as a complementary quality indicator in meningioma surgery.

Influencia de la pluripatología y comorbilidad en el ingreso hospitalario en una cohorte de pacientes de una unidad de continuidad asistencial Primaria-Interna

Objetivo: estimar la frecuencia de pluripatología y comorbilidad en pacientes seguidos en una unidad de continuidad asistencial Primaria-Interna, así como conocer sus características clínicas y la relación entre pluripatología y comorbilidad con el ingreso hospitalario. Diseño: estudio observacional retrospectivo de casos y controles. Emplazamiento: Unidad de Continuidad Asistencial de Atención Primaria y Medicina Interna (UCAPI) del Complejo Hospitalario Universitario de Albacete. Participantes: Se analizaron 1.591 pacientes atendidos en una unidad de continuidad asistencial Primaria-Interna (292 con algún ingreso hospitalario en el último año y 1.299 sin ingresos). Mediciones principales: edad, sexo, problemas de salud, índice de Charlson, consumo de medicamentos y utilización de servicios sanitarios los 3 años previos. Resultados: del total de la muestra, un 18,4% (IC 95%:16,4-20,3) eran casos con algún ingreso. Presentaba pluripatología un 23,3% (IC 95%: 21,1-25,4) y comorbilidad (índice de Charlson ≥ 2) un 32,6% (IC 95%: 30,2-34,9). Fueron variables asociadas de forma independiente a ingreso hospitalario la pluripatología (OR: 2,51; IC 95%: 1,64-3,83; p < 0,001), comorbilidad (índice de Charlson ≥ 2) (OR: 1,81; IC 95%:1,18-2,78; p = 0,006), tener más de tres problemas de salud (OR: 1,49; IC 95%: 1,07-2,07; p = 0,017), contar con mayor número de consultas de Atención Primaria (AP)  (OR: 1,01; IC 95%: 1,00-1,02; p = 0,005), de hospital (1,03; IC 95%: 1,01-1,05), p < 0,001) y realizar más visitas a urgencias hospitalarias (OR: 1,12, IC 95%: 1,07-1,17). Conclusiones: casi una cuarta parte de los pacientes seguidos en una unidad de continuidad asistencial entre Medicina interna y de Familia presenta pluripatología y un tercio, comorbilidad. La presencia de pluripatología y comorbilidad son características relacionadas con el ingreso hospitalario, junto con la mayor utilización de servicios sanitarios. Palabras clave: multimorbilidad, comorbilidad, hospitalización, utilización de servicios de salud.

Influencia de la pluripatología y comorbilidad en el ingreso hospitalario en una cohorte de pacientes de una unidad de continuidad asistencial Primaria-Interna

Objetivo: estimar la frecuencia de pluripatología y comorbilidad en pacientes seguidos en una unidad de continuidad asistencial Primaria-Interna, así como conocer sus características clínicas y la relación entre pluripatología y comorbilidad con el ingreso hospitalario. Diseño: estudio observacional retrospectivo de casos y controles. Emplazamiento: Unidad de Continuidad Asistencial de Atención Primaria y Medicina Interna (UCAPI) del Complejo Hospitalario Universitario de Albacete. Participantes: Se analizaron 1.591 pacientes atendidos en una unidad de continuidad asistencial Primaria-Interna (292 con algún ingreso hospitalario en el último año y 1.299 sin ingresos). Mediciones principales: edad, sexo, problemas de salud, índice de Charlson, consumo de medicamentos y utilización de servicios sanitarios los 3 años previos. Resultados: del total de la muestra, un 18,4% (IC 95%:16,4-20,3) eran casos con algún ingreso. Presentaba pluripatología un 23,3% (IC 95%: 21,1-25,4) y comorbilidad (índice de Charlson ≥ 2) un 32,6% (IC 95%: 30,2-34,9). Fueron variables asociadas de forma independiente a ingreso hospitalario la pluripatología (OR: 2,51; IC 95%: 1,64-3,83; p < 0,001), comorbilidad (índice de Charlson ≥ 2) (OR: 1,81; IC 95%:1,18-2,78; p = 0,006), tener más de tres problemas de salud (OR: 1,49; IC 95%: 1,07-2,07; p = 0,017), contar con mayor número de consultas de Atención Primaria (AP)  (OR: 1,01; IC 95%: 1,00-1,02; p = 0,005), de hospital (1,03; IC 95%: 1,01-1,05), p < 0,001) y realizar más visitas a urgencias hospitalarias (OR: 1,12, IC 95%: 1,07-1,17). Conclusiones: casi una cuarta parte de los pacientes seguidos en una unidad de continuidad asistencial entre Medicina interna y de Familia presenta pluripatología y un tercio, comorbilidad. La presencia de pluripatología y comorbilidad son características relacionadas con el ingreso hospitalario, junto con la mayor utilización de servicios sanitarios. Palabras clave: multimorbilidad, comorbilidad, hospitalización, utilización de servicios de salud.

Factors Related to Pediatric Readmissions of Four Major Diagnostic Categories in Hawai`i

Readmissions are a key quality measure for health care decision making and understanding variables associated with readmissions has become a crucial research area. This study identified patient-level factors that might be associated with pediatric readmissions using a database that included inpatient data from 2008 to 2017 from Hawai`i. Four major diagnostic categories with the most pediatric readmissions in the state were identified: respiratory, digestive, mental, and nervous system diseases and disorders. The associations between readmission and patient-level variables, such as age, sex, race/ethnicity, insurance status, and Charlson Comorbidity Index (CCI), were determined for each diagnosis and for overall readmissions. CCI and insurance were the strongest predictors when all diagnoses were combined. However, for some diagnoses, there was weak or no association between CCI, insurance, and readmission. This suggests that diagnosis-specific analysis of predictors of readmission may be more useful than looking at predictors of readmission for all diagnoses combined. While this study focused on patient variables, future studies should also incorporate how hospital variables may also be related to diagnosis.

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

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

Causes and Predictors of 30-Day Readmission in Elderly Patients With Delirium

Objective

To study 30-day readmission (30-DR) rate and predictors for readmission among elderly patients with delirium.

Methods

This was a retrospective observational cohort study of patients aged ≥65 years with discharge diagnosis of delirium identified from the Nationwide Readmission Database using common International Classification of Diseases, Ninth Revision, and Clinical Modification codes linked to delirium diagnosis. Multivariate logistic regression analyses were performed adjusting for stratified cluster design to identify patient/system-specific factors associated with 30-DR.

Results

Overall, the 30-DR rate was 17% (7,140 of 42,655 weighted index admissions). The common causes of readmission were systemic diseases (43%), infections (27%), and neurologic diseases (18%). Compared with initial hospitalization, readmission costs were higher ($11,442 vs $10,350, p < 0.0001) with a longer length of stay (6.6 vs 6.1 days, p < 0.0001). Independent predictors of readmission included discharge against medical advice (odds ratio [OR] 1.8, p < 0.0034), length of stay (OR 1.3, p < 0.0001), and chronic systemic diseases (anemia, OR 2.4, p < 0.0001, chronic renal failure OR 1.4, p < 0.0001, congestive heart failure OR 1.3, p < 0.0001, lung disease OR 1.2, p < 0.0004, and liver disease OR 1.2, p < 0.03). Private insurance was associated with a lower risk of readmission (OR 0.78, p < 0.02).

Conclusions

The main predictors of readmission were chronic systemic diseases and discharge against medical advice. These data may help design directed clinical care pathways to optimize medical management and postdischarge care to reduce readmission rates.

What Can We Learn from the Past by Means of Very Long-Term Follow-Up after Aortic Valve Replacement?

Background: Studies on very long-term outcomes after aortic valve replacement are sparse. Methods: In this retrospective cohort study, long-term outcomes during 25.1 ± 2.8 years of follow-up were determined in 673 patients who underwent aortic valve replacement with or without concomitant coronary artery bypass surgery for severe aortic stenosis and/or regurgitation. Independent predictors of decreased long-term survival were determined. Cumulative incidence rates of major adverse events in patients with a mechanical versus those with a biologic prosthesis were assessed, as well as of major bleeding events in patients with a mechanical prosthesis under the age of 60 versus those above the age of 60. Results: Impaired left ventricular function, severe prosthesis–patient mismatch, and increased aortic cross-clamp time were independent predictors of decreased long-term survival. Left ventricular hypertrophy, a mechanical or biologic prosthesis, increased cardiopulmonary bypass time, new-onset postoperative atrial fibrillation, and the presence of symptoms did not independently predict decreased long-term survival. The risk of major bleeding events was higher in patients with a mechanical in comparison with those with a biologic prosthesis. Younger age (under 60 years) did not protect patients with a mechanical prosthesis against major bleeding events. Conclusions: Very long-term outcome data are invaluable for careful decision-making on aortic valve replacement.

Risk of 30-day hospital readmission associated with medical conditions and drug regimens of polymedicated, older inpatients discharged home: a registry-based cohort study

OBJECTIVES

The present study analysed 4 years of a hospital register (2015-2018) to determine the risk of 30-day hospital readmission associated with the medical conditions and drug regimens of polymedicated, older inpatients discharged home.

DESIGN

Registry-based cohort study.

SETTING

Valais Hospital-a public general hospital centre in the French-speaking part of Switzerland.

PARTICIPANTS

We explored the electronic records of 20 422 inpatient stays by polymedicated, home-dwelling older adults held in the hospital's patient register. We identified 13 802 hospital readmissions involving 8878 separate patients over 64 years old.

OUTCOME MEASURES

Sociodemographic characteristics, medical conditions and drug regimen data associated with risk of readmission within 30 days of discharge.

RESULTS

The overall 30-day hospital readmission rate was 7.8%. Adjusted multivariate analyses revealed increased risk of hospital readmission for patients with longer hospital length of stay (OR=1.014 per additional day; 95% CI 1.006 to 1.021), impaired mobility (OR=1.218; 95% CI 1.039 to 1.427), multimorbidity (OR=1.419 per additional International Classification of Diseases, 10th Revision condition; 95% CI 1.282 to 1.572), tumorous disease (OR=2.538; 95% CI 2.089 to 3.082), polypharmacy (OR=1.043 per additional drug prescribed; 95% CI 1.028 to 1.058), and certain specific drugs, including antiemetics and antinauseants (OR=3.216 per additional drug unit taken; 95% CI 1.842 to 5.617), antihypertensives (OR=1.771; 95% CI 1.287 to 2.438), drugs for functional gastrointestinal disorders (OR=1.424; 95% CI 1.166 to 1.739), systemic hormonal preparations (OR=1.207; 95% CI 1.052 to 1.385) and vitamins (OR=1.201; 95% CI 1.049 to 1.374), as well as concurrent use of beta-blocking agents and drugs for acid-related disorders (OR=1.367; 95% CI 1.046 to 1.788).

CONCLUSIONS

Thirty-day hospital readmission risk was associated with longer hospital length of stay, health disorders, polypharmacy and drug regimens. The drug regimen patterns increasing the risk of hospital readmission were very heterogeneous. Further research is needed to explore hospital readmissions caused solely by specific drugs and drug-drug interactions.

Perceived Versus Actual Risks of 30-Day Readmission in Patients With Cardiovascular Disease

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of hospitalization in the United States, and patients with CVD are at a high risk of readmission after discharge. We examined whether patients' perceived risk of readmission at discharge was associated with actual 30-day readmissions in patients hospitalized with CVD.

METHODS

We recruited 730 patients from the Duke Heart Center who were admitted for treatment of CVD between January 1, 2015, and August 31, 2017. A standardized survey was linked with electronic health records to ascertain patients' perceived risk of readmission, and other sociodemographic, psychosocial, behavioral, and clinical data before discharge. All-cause readmission within 30 days after discharge was examined.

RESULTS

Nearly 1-in-3 patients perceived a high risk of readmission at index admission and those who perceived a high risk had significantly more readmissions within 30 days than patients who perceived low risks of readmission (23.6% versus 15.8%, P=0.016). Among those who perceived a high risk of readmission, non-White patients (odds ratio [OR], 2.07 [95% CI, 1.28-3.36]), those with poor self-rated health (OR, 2.30 [95% CI, 1.38-3.85]), difficulty accessing care (OR, 2.72 [95% CI, 1.24-6.00]), and prior hospitalizations in the past year (OR, 2.13 [95% CI, 1.21-3.74]) were more likely to be readmitted. Among those who perceived a low risk of readmission, patients who were widowed (OR, 2.69 [95% CI, 1.60-4.51]) and reported difficulty accessing care (OR, 1.89 [95% CI, 1.07-3.33]) were more likely to be readmitted.

CONCLUSIONS

Patients who perceived a high risk of readmission had a higher rate of 30-day readmission than patients who perceived a low risk. These findings have important implications for identifying CVD patients at a high risk of 30-day readmission and targeting the factors associated with perceived and actual risks of readmission.

Endovascular and Surgical Treatment Is Predictive of Readmission Risk After Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

Aneurysmal subarachnoid hemorrhage (aSAH) is a debilitating disease process accounting for 5% of strokes. Although improvements in care have reduced the case-fatality rates, patients have an increased risk of neurological and medical complications after discharge. Additionally, the readmission rates have been increasingly used as a metric for patient care quality.

METHODS

In the present study, we reviewed the medical records of 206 patients who had been treated for aSAH at the University of Virginia from 2011 to 2018 to identify the causes and predictors of readmission.

RESULTS

The all-cause readmission rate was 9.8%, 15.3%, and 21.3% within 30, 60, and 180 days, respectively. The readmission rate for neurologic causes was 7.7%, 12.6%, and 18.0% within 30, 60, and 180 days, respectively. The neurologic causes of readmission included aneurysm retreatment, cranioplasty, a fall, hydrocephalus, stroke symptoms, and syncope. Surgical treatment (odds ratio [OR], 4.11-6.30) and endovascular treatment (OR, 3.79-8.33) of vasospasm were associated with an increased risk of all-cause readmission. Endovascular aneurysm treatment (OR, 0.22) was associated with a decreased risk of all-cause readmission. The average interval to the first follow-up appointment at our institution was 55.3 ± 63.3 days. Of the patients who had been readmitted from the emergency room, 65% had not had follow-up contact with physicians at our institution until their readmission.

CONCLUSIONS

To the best of our knowledge, the present study is the first to have examined the readmission rates for subarachnoid hemorrhage >90 days after treatment. Our results have suggested that the readmission rates >90 days after treatment could still be predicted by the hospital and treatment course during admission and that follow-up appointments with patients earlier in the clinic could identify those patients with a greater risk of readmission.

Reasons for readmission after hospital discharge in patients with chronic diseases-Information from an international dataset

Background

Chronic diseases are increasingly prevalent in Western countries. Once hospitalised, the chance for another hospitalisation increases sharply with large impact on well-being of patients and costs. The pattern of readmissions is very complex, but poorly understood for multiple chronic diseases.

Methods

This cohort study of administrative discharge data between 2009–2014 from 21 tertiary hospitals (eight USA, five UK, four Australia, four continental Europe) investigated rates and reasons of readmissions to the same hospital within 30 days after unplanned admission with one of the following chronic conditions; heart failure; atrial fibrillation; myocardial infarction; hypertension; stroke; chronic obstructive pulmonary disease (COPD); bacterial pneumonia; diabetes mellitus; chronic renal disease; anaemia; arthritis and other cardiovascular disease. Proportions of readmissions with similar versus different diseases were analysed.

Results

Of 4,901,584 admissions, 866,502 (17.7%) were due to the 12 chronic conditions. In-hospital, 43,573 (5.0%) patients died, leaving 822,929 for readmission analysis. Of those, 87,452 (10.6%) had an emergency 30-day readmission, rates ranged from 2.8% for arthritis to 18.4% for COPD. One third were readmitted with the same condition, ranging from 53% for anaemia to 11% for arthritis. Reasons for readmission were due to another chronic condition in 10% to 35% of the cases, leaving 30% to 70% due to reasons other than the original 12 conditions (most commonly, treatment related complications and infections). The chance of being readmitted with the same cause was lower in the USA, for female patients, with increasing age, more co-morbidities, during study period and with longer initial length of stay.

Conclusion

Readmission in chronic conditions is very common and often caused by diseases other than the index hospitalisation. Interventions to reduce readmissions should therefore focus not only on the primary condition but on a holistic consideration of all the patient’s comorbidities.

Readmissions after nonoperative trauma: Increased mortality and costs with delayed intervention

BACKGROUND

We sought to examine patterns of readmission after nonoperative trauma, including rates of delayed operative intervention and mortality.

METHODS

The Nationwide Readmissions Database (2013-2014) was queried for all adult trauma admissions and 30-day readmissions. Index admissions were classified as operative (OI) or nonoperative (NOI), and readmissions examined for major operative intervention (MOR). Multivariable regression modeling was used to evaluate risk for readmission requiring MOR and in-hospital mortality.

RESULTS

Of 2,244,570 trauma admissions, there were 59,573 readmissions: 66% after NOI, and 35% after OI. Readmission rate was higher after NOI compared with OI (3.6% vs. 1.7% p < 0.001). Readmitted NOI patients were older, with a higher proportion of Injury Severity Score ≥15 and were readmitted earlier (NOI median 8 days vs. OI 11 days). Thirty-one percent of readmitted NOI patients required MOR and experienced higher overall mortality compared with OI patients with operative readmission (NOI 2.9% vs. OI 2%, p = 0.02). Intracranial hemorrhage was an independent risk factor for NOI readmission requiring MOR in both the overall (hazard ratio, 1.11; 95% confidence interval [CI], 1.01-1.22) and Injury Severity Score of 15 or greater cohorts (hazard ratio, 1.46; 95% CI, 1.24-1.7), with a predominance of nonspine neurosurgical procedures (20.3% and 55.1%, respectively). Operative readmission after NOI cost a median of $17,364 (interquartile range, US $11,481 to US $27,816) and carried a total annual cost of US $147 million (95% CI, US $141 million to $154 million).

CONCLUSIONS

Nonoperative trauma patients have a higher readmission rate than operative index patients and nearly one third require operative intervention during readmission. Operative readmission carries a higher overall mortality rate in NOI patients and together accounts for nearly US $150 million in annual costs.

LEVEL OF EVIDENCE

Epidemiological, level III.

Top-tier emergency general surgery hospitals: Good at one operation, good at them all

BACKGROUND

There is a longstanding interest in the field of management science to study high performance organizations. Applied to medicine, research on hospital performance indicates that some hospitals are high performing, while others are not. The objective of this study was to identify a cluster of high-performing emergency general surgery (EGS) hospitals and assess whether high performance at one EGS operation was associated with high performance on all EGS operations.

METHODS

Adult patients who underwent one of eight EGS operations were identified in the California State Inpatient Database (2010-2011), which we linked to the American Hospital Association database. Beta regression was used to estimate a hospital's risk-adjusted mortality, accounting for patient- and hospital-level factors. Centroid cluster analysis grouped hospitals by patterns of mortality rates across the eight EGS operations using z scores. Multinomial logistic regression compared hospital characteristics by cluster.

RESULTS

A total of 220 acute care hospitals were included. Three distinct clusters of hospitals were defined based on assessment of mortality for each operation type: high-performing hospitals (n = 66), average performing (n = 99), and low performing (n = 55). The mortality by individual operation type at the high-performing cluster was consistently at least 1.5 standard deviations better than the low-performing cluster (p < 0.001). Within-cluster variation was minimal at high-performing hospitals compared with wide variation at low-performing hospitals. A hospital's high performance in one EGS operation type predicted high performance on all EGS operation types.

CONCLUSION

High-performing EGS hospitals attain excellence across all types of EGS operations, with minimal variability in mortality. Poor-performing hospitals are persistently below average, even for low-risk operations. These findings suggest that top-performing EGS hospitals are highly reliable, with systems of care in place to achieve consistently superior results. Further investigation and collaboration are needed to identify the factors associated with high performance.

LEVEL OF EVIDENCE

Prognostic, level III.

Relationship among Activities of Daily Living, Nutritional Status, and 90 Day Readmission in Elderly Patients with Heart Failure

Purpose: This investigation aimed to examine the relationship among activities of daily living (ADL), nutritional status and 90-day hospital readmission in elderly heart failure (HF) patients. Methods: Participants were selected from 634 HF patients consecutively hospitalized at one institution. We investigated patient characteristics, ADL (motor and cognitive items of Functional Independence Measure (FIM)) and nutritional status (Geriatric Nutritional Risk Index (GNRI)). Data were analyzed using unpaired t-test, χ² test, Cox proportional hazard model, and Kaplan-Meier method. Results: The 169 participants that met inclusion criteria were divided into two groups based on hospital readmission within 90 days of discharge. Body mass index (BMI) (p = 0.03), hemoglobin (p = 0.047), GNRI (p = 0.02) and motor-FIM (p = 0.007) were significantly different between the readmission (n = 31) and non-readmission (n = 138) groups. After Cox proportional hazard model analysis, GNRI (HR: 0.96; p = 0.048) and motor-FIM (HR: 0.97; p = 0.03) scores remained statistically significant. Participants were then classified into four groups based on a previous study’s cut-off values of prognosis for GNRI and motor-FIM. Readmission avoidance rate was significantly lower (p = 0.002) in the group with GNRI <92 and motor FIM <75. Conclusions: This study showed that motor-FIM and GNRI scores for hospitalized elderly HF patients were predictors of readmission within 90 days of discharge.

Readmissions After Acute Myocardial Infarction: How Often Do Patients Return to the Discharging Hospital?

Background

When patients require readmission after a recent myocardial infarction (MI), returning to the discharging (index) hospital may be associated with better outcomes as a result of greater continuity in care. However, little evidence exists to answer this frequent patient question.

Methods and Results

Among Medicare patients aged ≥65 years discharged home alive post‐MI from 491 US hospitals in the ACTION (Acute Coronary Treatment Intervention Outcomes Network) Registry, we compared reason for readmission, duration of rehospitalization, and 30‐day mortality between patients readmitted to the index versus nonindex hospital within 30 days of index MI discharge. Among 53 471 MI patients, 7715 (14%) were readmitted within 30 days, and most readmitted patients (73%) returned to the discharging hospital. Reason for readmission was not significantly associated with location of readmission. In multivariable modeling, the strongest factors associated with readmission to a nonindex hospital were distance from the discharging hospital, transfer‐in during the index MI hospitalization, and frequency of nonindex hospital admissions in the year preceding to the index MI. Duration of rehospitalization did not differ significantly between patients readmitted to the index versus nonindex hospital (median, 4 versus 3 days; P=0.17). Mortality risk was also not significantly different between patients readmitted to the index versus nonindex hospital overall (7.4 versus 7.7%; adjusted odds ratio, 0.89; 95% CI, 0.73–1.10) and when stratified by reason for readmission (P for interaction=0.61).

Conclusions

Post‐MI readmissions did not differ in reason for readmission, duration of rehospitalization, or associated mortality when compared between patients who returned to the discharging hospital and those who sought care elsewhere.

Continuity of care interventions for preventing hospital readmission of older people with chronic diseases: A meta-analysis

BACKGROUND

Hospital readmission after discharge is a frequent, burdensome and costly event, particularly frequent in older people with multiple chronic conditions. Few literature reviews have analysed studies of continuity of care interventions to reduce readmissions of older inpatients discharged home over the short and long term.

OBJECTIVE

To evaluate the effectiveness of continuity of care interventions in older people with chronic diseases in reducing short and long term hospital readmission after hospital discharge.

DESIGN

Meta-analysis of randomized controlled trials.

DATA SOURCES

A comprehensive literature search on the databases PubMed, Medline, CINAHL and EMBASE was performed on 27 January 2019 with no language and time limits.

REVIEW METHODS

RCTs on continuity of care interventions on older people discharged from hospital having hospital readmission as outcome, were included. Two reviewers independently screened the studies and assessed methodological quality using the Cochrane Risk of Bias tool. Selected outcome data were combined and pooled using a Mantel-Haenszel random-effects model.

RESULTS

Thirty RCTs, representing 8920 patients were included. Results were stratified by time of readmissions. At 1 month from discharge, the continuity interventions were associated with lower readmission rates in 207/1595 patients in the experimental group (12.9%), versus 264/1645 patients in the control group (16%) (Relative Risk [RR], 0.84 [95% CI, 0.71-0.99]). From 1 to 3 months, readmission rates were lower in 325/1480 patients in the experimental group (21.9%), versus 455/1523 patients in the control group (29.8%) (RR 0.74 [95% CI, 0.65-0.84]). A subgroup analysis showed that this positive effect was stronger when the interventions addressed all of the continuity dimensions. After 3 months this impact became inconclusive with moderate/high statistical heterogeneity.

CONCLUSIONS

Continuity of care interventions prevent short term hospital readmission in older people with chronic diseases. However, there is inconclusive evidence about the effectiveness of continuity interventions aiming to reduce long term readmission, and it is suggested that stronger focus on it is needed.

High-performance acute care hospitals: Excelling across multiple emergency general surgery operations in the geriatric patient

BACKGROUND

As the geriatric population grows, the need for hospitals performing high quality emergency general surgery (EGS) on older patients will increase. Identifying clusters of high-performing geriatric emergency general surgery hospitals would substantiate the need for in-depth analyses of hospital-specific structures and practices that benefit older EGS patients. The objectives of this study were therefore to identify clusters of hospitals based on mortality performance for geriatric patients undergoing common EGS operations and to determine if hospital performance was similar for all operation types.

METHODS

Hospitals in the California State Inpatient Database were included if they performed a range of eight common EGS operations in patients 65 years or older, with a minimum requirement of three of each operation performed over 2 years. Multivariable beta regression models were created to define hospital-level risk-adjusted mortality. Centroid cluster analysis was used to identify groups of hospitals based on mortality and to determine if mortality-performance differed by operation.

RESULTS

One hundred seven hospitals were included, performing a total of 24,279 operations in older patients. Hospitals separated into three distinct clusters: high, average, and low performers. The high-performing hospitals had survival rates 1 to 2 standard deviations better than the low-performers (p < 0.001). For each cluster, high performance in any one EGS operation consistently translated into high performance across all EGS operations.

CONCLUSION

Hospitals conducting EGS operations in the geriatric patient population cluster into three distinct groups based on their survival performance. High-performing hospitals significantly outperform the average and low performers across every operation. The high-performers achieve reliable, high-quality results regardless of operation type. Further qualitative research is needed to investigate the perioperative drivers of hospital performance in the geriatric EGS population.

LEVEL OF EVIDENCE

Study Type Prognostic, level III.

A quality improvement approach to reducing hospital readmissions in patients with cancer and heart failure

BACKGROUND

The management of patients with cancer and concurrent heart failure (HF) is challenging. The increased complexity of treatment and the occurrence of multiple overlapping symptoms may lead to frequent hospital admissions, which may result in cancer treatment delays, a diminished quality of life, and an increased financial burden for the patient's family. To provide holistic care to oncology patients with HF, we implemented the Heart Success Program (HSP), a patient-centered, interprofessional collaborative practice, which decreased the 30-day hospital readmission rate for HF diagnosis from 40 to 27%. However, this rate remains higher than that reported for Medicare beneficiaries.

AIM

To identify the factors contributing to frequent readmissions, the HSP committee participated in the institution's Clinical Safety and Effectiveness and utilize quality improvement methodologies and tools to decrease hospital readmission for HF.

METHODS

The DMAIC (Define, Measure, Analyze, Improve and Control) method was used to guide this quality improvement. Areas considered as having high impact and requiring low effort to address were patient education barriers, lack of documentation clarity, and care provider knowledge gaps about the HSP. We implemented workflow changes, improved clarity with documentation of HF diagnosis, and increase provider knowledge about the HSP.

FINDINGS

After 6 months of implementing quality improvement techniques, the 30-day hospital readmission rate for HF patients fell by 23.43% (from 31.7% for the baseline period to 8.27%), exceeding the target project goal of 10%. Our quality improvement method may also be effective in improving the management of patients with cancer and other comorbid conditions.

Factors Associated with Readmission of Pediatric Patients in a Developing Nation

OBJECTIVES

To determine the incidence of readmission in pediatric patients in a tertiary care hospital in a developing nation and to ascertain factors precipitating readmissions.

METHODS

A prospective study was conducted from February 2016 through January 2017 at a tertiary care hospital. Children between 1 mo to 15 y of age were included if they were readmitted within 60 d of discharge. The risk factors for readmission were determined on the basis of medical record review and a structured questionnaire and the ascribed cause of readmission was grouped into three categories: Patient specific factors, Hospital specific factors and Unrelated/ New illness.

RESULTS

The readmission rate was 3%, out of which 80.66% were found to be causally related to the index admission. Several sociodemographic characteristics i.e. lack of health information like television, lower socioeconomic status, absence of adequate breastfeeding, lower age, migrants were found to be significantly associated with readmission along with other patient specific factors like presence of cardiac disease, presence of comorbid conditions like anemia, malnutrition, and global developmental delay. The most important cause for readmission was determined as patient specific (48.66%) followed by hospital specific (38%) and unknown/unrelated factors (13.33%).

CONCLUSIONS

The progression of the primary illness and social determinants of pediatric readmissions are important contributing risk factors for readmission in developing countries in pediatric patients. Multicentric studies are needed from this region of the world to include different hospital readmissions rate and to address the issue of potential preventability of pediatric readmissions.

Thirty-day readmissions in aneurysmal subarachnoid hemorrhage: A good metric for hospital quality?

Thirty-day readmission rates has been increasingly used by clinicians, hospital administrators, and policy makers as a metric for the quality of care. However, the 30-day readmission rates may be affected by other factors intrinsic to the patient and may not be a good measure of the quality of care provided by the hospital. In this review, we examined the quality of the 30-day readmissions rate as a quality metric for the quality of care provided to patients with aneurysmal subarachnoid hemorrhage (SAH). It has been shown that in this patient population, 30-day readmission rate primarily captures values, such as the number of comorbidities, disease severity, and discharge dispositions. There is little association between SAH 30-day readmission rates and mortality. However, 30-day readmissions may be reduced by increasing early discharge surveillance, providing readmission reduction programs to patients discharged to medical facilities as well as to home, and identifying patients most at risk for readmission.

Risk-adjusted outcomes of inpatient medicare medical admissions

It is important that actual outcomes of care and not surrogate markers, such as process measures, be used to evaluate the quality of inpatient care. Because of the heterogenous composition of patients, risk-adjustment is essential for the objective evaluation of outcomes following inpatient care. Comparative evaluation of risk-adjusted outcomes can be used to identify suboptimal performance and can provide direction for care improvement initiatives.We studied the risk-adjusted outcomes of 6 medical conditions during the inpatient and 90-day post-discharge period to identify the opportunities for care improvement. The Medicare Limited Dataset for 2012 to 2014 was used to identify acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), pneumonia (PNEU), cerebrovascular accidents (CVA), and gastrointestinal hemorrhage (GIH). Stepwise logistic predictive models were developed for the adverse outcomes (AOs) of inpatient deaths, 3-sigma prolonged length-of-stay outliers, 90-day post-discharge deaths, and 90-day readmissions after unrelated events were excluded. Observed and predicted AOs were determined for each hospital with ≥75 cases for each of the 6 medical conditions. Z-scores and risk-adjusted AO rates for each hospital permitted comparative analysis of outcomes after adjusting for covariance among the medical conditions.There were a total of 1,811,749 patients from 973 acute care hospitals with the 6 medical conditions. A total of 41% of all patients had ≥1 AO events. One or more readmissions were identified in 29.8% of patients. A total of 64 hospitals (6.4%) were 2 standard deviations better than the mean for risk-adjusted outcomes, and 72 (7.4%) were 2 standard deviations poorer. The best performing decile of hospitals had mean AO rates of 35.1% (odds ratio = 0.766; 95% confidence interval (CI) CI: 0.762-0.771) and the poorest performing decile a mean AO rate of 48.5% (odds ratio = 1.357; 95% CI: 1.346-1.369). Volume of qualifying cases ranged from 670 to 9314; no association was identified for increased volume of patients (P < .40).Risk-adjusted AO rates demonstrated nearly a 14% opportunity for care improvement between top and suboptimal performing hospitals. Hospitals must be able to benchmark objective measurement of outcomes to inform quality initiatives.

Factors associated with hospital emergency readmission and mortality rates in patients with heart failure or chronic obstructive pulmonary disease: a national observational study

Background

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) lead to unplanned hospital activity, but our understanding of what drives this is incomplete.

Objectives

To model patient, primary care and hospital factors associated with readmission and mortality for patients with HF and COPD, to assess the statistical performance of post-discharge emergency department (ED) attendance compared with readmission metrics and to compare all the results for the two conditions.

Design

Observational study.

Setting

English NHS.

Participants

All patients admitted to acute non-specialist hospitals as an emergency for HF or COPD.

Interventions

None.

Main outcome measures

One-year mortality and 30-day emergency readmission following the patient’s first unplanned admission (‘index admission’) for HF or COPD.

Data sources

Patient-level data from Hospital Episodes Statistics were combined with publicly available practice- and hospital-level data on performance, patient and staff experience and rehabilitation programme website information.

Results

One-year mortality rates were 39.6% for HF and 24.1% for COPD and 30-day readmission rates were 19.8% for HF and 16.5% for COPD. Most patients were elderly with multiple comorbidities. Patient factors predicting mortality included older age, male sex, white ethnicity, prior missed outpatient appointments, (long) index length of hospital stay (LOS) and several comorbidities. Older age, missed appointments, (short) LOS and comorbidities also predicted readmission. Of the practice and hospital factors we considered, only more doctors per 10 beds [odds ratio (OR) 0.95 per doctor;p < 0.001] was significant for both cohorts for mortality, with staff recommending to friends and family (OR 0.80 per unit increase;p < 0.001) and number of general practitioners (GPs) per 1000 patients (OR 0.89 per extra GP;p = 0.004) important for COPD. For readmission, only hospital size [OR per 100 beds = 2.16, 95% confidence interval (CI) 1.34 to 3.48 for HF, and 2.27, 95% CI 1.40 to 3.66 for COPD] and doctors per 10 beds (OR 0.98;p < 0.001) were significantly associated. Some factors, such as comorbidities, varied in importance depending on the readmission diagnosis. ED visits were common after the index discharge, with 75% resulting in admission. Many predictors of admission at this visit were as for readmission minus comorbidities and plus attendance outside the day shift and numbers of admissions that hour. Hospital-level rates for ED attendance varied much more than those for readmission, but the omega statistics favoured them as a performance indicator.

Limitations

Data lacked direct information on disease severity and ED attendance reasons; NHS surveys were not specific to HF or COPD patients; and some data sets were aggregated.

Conclusions

Following an index admission for HF or COPD, older age, prior missed outpatient appointments, LOS and many comorbidities predict both mortality and readmission. Of the aggregated practice and hospital information, only doctors per bed and numbers of hospital beds were strongly associated with either outcome (both negatively). The 30-day ED visits and diagnosis-specific readmission rates seem to be useful performance indicators.

Future work

Hospital variations in ED visits could be investigated using existing data despite coding limitations. Primary care management could be explored using individual-level linked databases.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Unplanned early hospital readmission among critical care survivors: a mixed methods study of patients and carers

BACKGROUND

Many intensive care (ICU) survivors experience early unplanned hospital readmission, but the reasons and potential prevention strategies are poorly understood. We aimed to understand contributors to readmissions from the patient/carer perspective.

METHODS

This is a mixed methods study with qualitative data taking precedence. Fifty-eight ICU survivors and carers who experienced early unplanned rehospitalisation were interviewed. Thematic analysis was used to identify factors contributing to readmissions, and supplemented with questionnaire data measuring patient comorbidity and carer strain, and importance rating scales for factors that contribute to readmissions in other patient groups. Data were integrated iteratively to identify patterns, which were discussed in five focus groups with different patients/carers who also experienced readmissions. Major patterns and contexts in which unplanned early rehospitalisation occurred in ICU survivors were described.

RESULTS

Interviews suggested 10 themes comprising patient-level and system-level issues. Integration with questionnaire data, pattern exploration and discussion at focus groups suggested two major readmission contexts. A 'complex health and psychosocial needs' context occurred in patients with multimorbidity and polypharmacy, who frequently also had significant psychological problems, mobility issues, problems with specialist aids/equipment and fragile social support. These patients typically described inadequate preparation for hospital discharge, poor communication between secondary/primary care, and inadequate support with psychological care, medications and goal setting. This complex multidimensional situation contrasted markedly with the alternative 'medically unavoidable' readmission context. In these patients medical issues/complications primarily resulted in hospital readmission, and the other issues were absent or not considered important.

CONCLUSIONS

Although some readmissions are medically unavoidable, for many ICU survivors complex health and psychosocial issues contribute concurrently to early rehospitalisation. Care pathways that anticipate and institute anticipatory multifaceted support for these patients merit further development and evaluation.

Transitional care services: a quality and safety process improvement program in neurosurgery

OBJECTIVE

Readmissions increasingly serve as a metric of hospital performance, inviting quality improvement initiatives in both medicine and surgery. However, few readmission reduction programs have targeted surgical patient populations. The objective of this study was to establish a transitional care program (TCP) with the goal of decreasing length of stay (LOS), improving discharge efficiency, and reducing readmissions of neurosurgical patients by optimizing patient education and postdischarge surveillance.

METHODS

Patients undergoing elective cranial or spinal neurosurgery performed by one of 5 participating surgeons at a quaternary care hospital were enrolled into a multifaceted intervention. A preadmission overview and establishment of an anticipated discharge date were both intended to set patient expectations for a shorter hospitalization. At discharge, in-hospital prescription filling was provided to facilitate medication compliance. Extended discharge appointments with a neurosurgery TCP-trained nurse emphasized postoperative activity, medications, incisional care, nutrition, signs that merit return to medical attention, and follow-up appointments. Finally, patients received a surveillance phone call 48 hours after discharge. Eligible patients omitted due to staff limitations were selected as controls. Patients were matched by sex, age, and operation type—key confounding variables—with control patients, who were eligible patients treated at the same time period but not enrolled in the TCP due to staff limitation. Multivariable logistic regression evaluated the association of TCP enrollment with discharge time and readmission, and linear regression with LOS. Covariates included matching criteria and Charlson Comorbidity Index scores.

RESULTS

Between 2013 and 2015, 416 patients were enrolled in the program and matched to a control. The median patient age was 55 years (interquartile range 44.5–65 years); 58.4% were male. The majority of enrolled patients underwent spine surgery (59.4%, compared with 40.6% undergoing cranial surgery). Hospitalizations averaged 62.1 hours for TCP patients versus 79.6 hours for controls (a 16.40% reduction, 95% CI 9.30%–23.49%; p < 0.001). The intervention was associated with a higher proportion of morning discharges, which was intended to free beds for afternoon admissions and improve patient flow (OR 3.13, 95% CI 2.27–4.30; p < 0.001), and decreased 30-day readmissions (2.5% vs 5.8%; OR 2.43, 95% CI 1.14–5.27; p = 0.02).

CONCLUSIONS

This neurosurgical TCP was associated with a significantly shorter LOS, earlier discharge, and reduced 30-day readmission after elective neurosurgery. These results underscore the importance of patient education and surveillance after hospital discharge.

Consumer segmentation and time interval between types of hospital admission: a clinical linkage database study

Background

Healthcare policies target unplanned hospital admissions and 30-day re-admission as key measures of efficiency, but do not focus on factors that influence trajectories of different types of admissions in the same patient over time.

Objectives

To investigate the influence of consumer segmentation and patient factors on the time intervals between different types of hospital admission.

Research design, subjects and measures

A cohort design was applied to an anonymised linkage database for adults aged 40 years and over (N = 58 857). Measures included Mosaic segmentation, multimorbidity defined on six chronic condition registers and hospital admissions over a 27-month time period.

Results

The shortest mean time intervals between two consecutive planned admissions were: 90 years and over (160 days (95% confidence interval (CI): 146-175)), Mosaic groups 'Twilight subsistence' (171 days (164-179)) or 'Welfare borderline' and 'Municipal dependency' (177 days (172-182)) compared to the reference Mosaic groups (186 days (180-193)), and multimorbidity count of four or more (137 days (130-145)). Mosaic group 'Twilight subsistence' (rate ratio (RR) 1.22 (95% CI: 1.08-1.36)) or 'Welfare borderline' and 'Municipal dependency' RR 1.20 (1.10-1.31) were significantly associated with higher rate to an unplanned admission following a planned event. However, associations between patient factors and unplanned admissions were diminished by adjustment for planned admissions.

Conclusion

Specific consumer segmentation and patient factors were associated with shorter time intervals between different types of admissions. The findings support innovation in public health approaches to prevent by a focus on long-term trajectories of hospital admissions, which include planned activity.

Reoperation and readmission after clipping of an unruptured intracranial aneurysm: a National Surgical Quality Improvement Program analysis

OBJECTIVE

Although reoperation and readmission have been used as quality metrics, there are limited data evaluating the rate of, reasons for, and predictors of reoperation and readmission after microsurgical clipping of unruptured aneurysms.

METHODS

Adult patients who underwent craniotomy for clipping of an unruptured aneurysm electively were extracted from the prospective National Surgical Quality Improvement Program registry (2011–2014). Multivariable logistic regression and recursive partitioning analysis evaluated the independent predictors of nonroutine hospital discharge, unplanned 30-day reoperation, and readmission. Predictors screened included patient age, sex, comorbidities, American Society of Anesthesiologists (ASA) classification, functional status, aneurysm location, preoperative laboratory values, operative time, and postoperative complications.

RESULTS

Among the 460 patients evaluated, 4.2% underwent any reoperation at a median of 7 days (interquartile range [IQR] 2–17 days) postoperatively, and 1.1% required a cranial reoperation. The most common reoperation was ventricular shunt placement (23.5%); other reoperations were tracheostomy, craniotomy for hematoma evacuation, and decompressive hemicraniectomy. Independent predictors of any unplanned reoperation were age greater than 51 years and longer operative time (p ≤ 0.04). Readmission occurred in 6.3% of patients at a median of 6 days (IQR 5–13 days) after discharge from the surgical hospitalization; 59.1% of patients were readmitted within 1 week and 86.4% within 2 weeks of discharge. The most common reason for readmission was seizure (26.7%); other causes of readmission included hydrocephalus, cerebrovascular accidents, and headache. Unplanned readmission was independently associated with age greater than 65 years, Class II or III obesity (body mass index > 35 kg/m2), preoperative hyponatremia, and preoperative anemia (p ≤ 0.04). Readmission was not associated with operative time, complications during the surgical hospitalization, length of stay, or discharge disposition. Recursive partitioning analysis identified the same 4 variables, as well as ASA classification, as associated with unplanned readmission. The most potent predictors of nonroutine hospital discharge (16.7%) were postoperative neurological and cardiopulmonary complications; other predictors were age greater than 51 years, preoperative hyponatremia, African American and Asian race, and a complex vertebrobasilar circulation aneurysm.

CONCLUSIONS

In this national analysis, patient age greater than 65 years, Class II or III obesity, preoperative hyponatremia, and anemia were associated with adverse events, highlighting patients who may be at risk for complications after clipping of unruptured cerebral aneurysms. The preponderance of early readmissions highlights the importance of early surveillance and follow-up after discharge; the frequency of readmission for seizure emphasizes the need for additional data evaluating the utility and duration of postcraniotomy seizure prophylaxis. Moreover, readmission was primarily associated with preoperative characteristics rather than metrics of perioperative care, suggesting that readmission may be a suboptimal indicator of the quality of care received during the surgical hospitalization in this patient population.

Thirty-day Readmission Rates in an HIV-infected Cohort From Rio de Janeiro, Brazil

BACKGROUND

The 30-day readmission rate is an indicator of the quality of hospital care and transition to the outpatient setting. Recent studies suggest HIV infection might increase the risk of readmission although estimates of 30-day readmission rates are unavailable among HIV-infected individuals living in middle/low-income settings. Additionally, factors that may increase readmission risk in HIV-infected populations are poorly understood.

METHODS

Thirty-day readmission rates were estimated for HIV-infected adults from the Instituto Nacional de Infectologia Evandro Chagas/Fiocruz cohort in Rio de Janeiro, Brazil, from January 2007 to December 2013. Cox regression models were used to evaluate factors associated with the risk of 30-day readmission.

RESULTS

Between January 2007 and December 2013, 3991 patients were followed and 1861 hospitalizations were observed. The estimated 30-day readmission rate was 14% (95% confidence interval: 12.3 to 15.9). Attending a medical visit within 30 days after discharge (adjusted hazard ratio [aHR] = 0.73, P = 0.048) and being hospitalized in more recent calendar years (aHR = 0.89, P = 0.002) reduced the risk of 30-day readmission. In contrast, low CD4 counts (51-200 cells/mm³: aHR = 1.70, P = 0.024 and ≤ 50 cells/mm³: aHR = 2.05, P = 0.003), time since HIV infection diagnosis ≥10 years (aHR = 1.58, P = 0.058), and leaving hospital against medical advice (aHR = 2.67, P = 0.004) increased the risk of 30-day readmission.

CONCLUSIONS

Patients with advanced HIV/AIDS are most at risk of readmission and should be targeted with prevention strategies to reduce this risk. Efforts to reduce discharge against medical advice and to promote early postdischarge medical visit would likely reduce 30-day readmission rates in our population.

Risk Associated With Complications and Mortality After Urgent Surgery vs Elective and Emergency Surgery: Implications for Defining "Quality" and Reporting Outcomes for Urgent Surgery

Importance

Given the current climate of outcomes-driven quality reporting, it is critical to appropriately risk stratify patients using standardized metrics.

Objective

To elucidate the risk associated with urgent surgery on complications and mortality after general surgical procedures.

Design, Setting, and Participants

This retrospective review used the American College of Surgeons National Surgery Quality Improvement Program database to capture all general surgery cases performed at 435 hospitals nationwide between January 1, 2013, and December 31, 2013. Data analysis was performed from November 11, 2015, to February 16, 2017.

Exposures

Any operations coded as both nonelective and nonemergency were designated into a novel category titled urgent.

Main Outcomes and Measures

The primary outcome was 30-day mortality; secondary outcomes included 30-day rates of complications, reoperation, and readmission in urgent cases compared with both elective and emergency cases.

Results

Of 173 643 patients undergoing general surgery (101 632 females and 72 011 males), 130 235 (75.0%) were categorized as elective, 22 592 (13.0%) as emergency, and 20 816 (12.0%) as nonelective and nonemergency. When controlling for standard American College of Surgeons National Surgery Quality Improvement Program preoperative risk factors, with elective surgery as the reference value, the 3 groups had significantly distinct odds ratios (ORs) of experiencing any complication (urgent surgery: OR, 1.38; 95% CI, 1.30-1.45; P < .001; and emergency surgery: OR, 1.65; 95% CI, 1.55-1.76; P < .001) and of mortality (urgent surgery: OR, 2.32; 95% CI, 2.00-2.68; P < .001; and emergency surgery: OR, 2.91; 95% CI, 2.48-3.41; P < .001). Surgical procedures performed urgently had a 12.3% rate of morbidity (n = 2560) and a 2.3% rate of mortality (n = 471).

Conclusions and Relevance

This study highlights the need for improved risk stratification on the basis of urgency because operations performed urgently have distinct rates of morbidity and mortality compared with procedures performed either electively or emergently. Because we tie quality outcomes to reimbursement, such a category should improve predictive models and more accurately reflect the quality and value of care provided by surgeons who do not have traditional elective practices.

Seasonality and Readmission after Heart Failure, Myocardial Infarction, and Pneumonia

OBJECTIVE

To investigate whether hospital readmission after admission for heart failure (HF), myocardial infarction (MI), and pneumonia varies by season.

DATA SOURCES

All patients in 2005-2009 Healthcare Cost and Utilization Project State Inpatient Databases for New York and California hospitalized for HF, MI, or pneumonia.

STUDY DESIGN

The relationship between discharge season and unplanned readmission within 30 days was evaluated using multivariate modified Poisson regression.

PRINCIPAL FINDINGS

Cohorts included 869,512 patients with HF, 448,945 patients with MI, and 813,593 patients with pneumonia. While admissions varied widely by season, readmission rates only ranged from 25.0 percent (spring) to 25.6 percent (winter) for HF (p > .05), 18.9 percent (summer) to 20.0 percent (winter) for MI (p < .001), and 19.4 percent (spring) to 20.3 percent (summer) for pneumonia (p < .001). In adjusted models, in New York, there was lower readmission in spring and fall (RR: 0.98, 95% CI: 0.96-0.99 for both) after admission for HF and higher readmission in spring (RR: 1.04, 95% CI: 1.01-1.07) after MI. In California, there was lower readmission in spring and winter (RR: 0.95, 95% CI: 0.93-0.96 and RR: 0.96, 95% CI: 0.94-0.98, respectively) after pneumonia.

CONCLUSIONS

Given marked seasonality in incidence and mortality of HF, MI, and pneumonia, the modest seasonality in readmissions suggests that readmissions may be more related to non-seasonally dependent factors than to the seasonal nature of these diseases.

Readmission After Aneurysmal Subarachnoid Hemorrhage: A Nationwide Readmission Database Analysis

BACKGROUND AND PURPOSE

The goal of this nationwide study is to evaluate the suitability of readmission as a quality indicator in the aneurysmal subarachnoid hemorrhage (SAH) population.

METHODS

Patients with aneurysmal SAH were extracted from the Nationwide Readmission Database (2013). Multivariable Cox proportional hazard regression was used to evaluate predictors of a 30-day readmission, and multivariable linear regression was used to analyze the association of hospital readmission rates with hospital mortality rates. Predictors screened included patient demographics, comorbidities, severity of SAH, complications from the SAH hospitalization, and hospital characteristics.

RESULTS

The 30-day readmission rate was 10.2% (n=346) among the 3387 patients evaluated, and the most common reasons for readmission were neurological, hydrocephalus, infectious, and venous thromboembolic complications. Greater number of comorbidities, increased severity of SAH, and discharge disposition other than to home were independent predictors of readmission (P≤0.03). Although hydrocephalus during the SAH hospitalization was associated with readmission for the same diagnosis, other readmissions were not associated with having sustained the same complication during the SAH hospitalization. Hospital mortality rate was inversely associated with hospital SAH volume (P=0.03) but not significantly associated with hospital readmission rate; hospital SAH volume was also not associated with SAH readmissions.

CONCLUSIONS

In this national analysis, readmission was primarily attributable to new medical complications in patients with greater comorbidities and severity of SAH rather than exacerbation of complications from the SAH hospitalization. Additionally, hospital readmission rates did not correlate with other established quality metrics. Therefore, readmission may be a suboptimal quality indicator in the SAH population.

[Profile and evolution of chronic complex patients in a subacute unit]

Objetivo

Para mejorar el manejo de pacientes pluripatológicos, en Cataluña se ha promovido la identificación como paciente crónico complejo (PCC) o con enfermedad crónica avanzada (MACA). Ante descompensaciones se promueve el ingreso de estos pacientes en unidades de subagudos (SG) ubicadas en hospitales de atención intermedia y especializadas en geriatría, como alternativa al hospital de agudos. Queremos evaluar los resultados del ingreso de PCC/MACA en SG.

Diseño

Estudio cuantitativo descriptivo-comparativo, transversal.

Emplazamiento

Unidad de subagudos de un hospital de atención intermedia.

Participantes

Pacientes ingresados consecutivamente en SG durante 6 meses.

Mediciones principales

Comparamos características basales (datos demográficos, clínicos y de valoración geriátrica integral), resultados al alta y a 30 días post-alta entre pacientes identificados como PCC/MACA vs otros pacientes.

Resultados

De 244 pacientes (promedio edad ± DE = 85,6 ± 7,5; 65,6% mujeres), 91 (37,3%) eran PCC/MACA (PCC = 79,1%, MACA = 20,9%). Estos, comparado con los no identificados, presentaban mayor comorbilidad (Charlson = 3,2 ± 1,8 vs 2,0; p = 0,001) y polifarmacia (9,5 ± 3,7 fármacos vs 8,1 ± 3,8, p = 0,009). Al alta, el retorno al domicilio habitual y la mortalidad fueron comparables. PCC/MACA tuvieron mayor mortalidad sumando los 30 días post-alta (15,4% vs 8%; p = 0,010); en un análisis multivariable, la identificación PCC/MACA (p = 0,006) y demencia (p = 0,004) se asociaba a mayor mortalidad. A pesar de que PCC/MACA reingresaban más a 30 días (18,7% vs 10,5%; p = 0,014), en el análisis multivariable las únicas variables asociadas independientemente a reingresos fueron sexo masculino, polifarmacia e insuficiencia cardiaca.

Conclusiones

A pesar de mayor comorbilidad y polifarmacia, los resultados de PCC/MACA al alta de SG fueron comparables con los otros pacientes, aunque experimentaron más reingresos a 30 días, posiblemente por su comorbilidad y polimedicación.

Financial impact of ivabradine on reducing heart failure penalties under the Hospital Readmission Reduction Program

OBJECTIVE

The introduction of the Hospital Readmission Reduction Program (HRRP) has led to renewed interest in developing strategies to reduce 30 day readmissions among patients with heart failure (HF). In this study, a model was developed to investigate whether the addition of ivabradine to a standard-of-care (SoC) treatment regimen for patients with HF would reduce HRRP penalties incurred by a hypothetical hospital with excess 30 day readmissions.

RESEARCH DESIGN

A model using a Monte Carlo simulation framework was developed. Model inputs included national hospital characteristics, hospital-specific characteristics, and the ivabradine treatment effect as quantified by a post hoc analysis of the Systolic Heart failure treatment with the I_f inhibitor ivabradine Trial (SHIFT).

RESULTS

The model computed an 83% reduction in HF readmission penalty payments in a hypothetical hospital with a readmission rate of 22.95% (excess readmission ratio = 1.056 over the national average readmission rate of 21.73%), translating into net savings of $44,016. A sensitivity analysis indicated that the readmission penalty is affected by the specific characteristics of the hospital, including the readmission rate, size of the ivabradine-eligible population, and ivabradine utilization.

CONCLUSIONS

The results of this study indicate that the addition of ivabradine to an SoC treatment regimen for patients with HF may lead to a reduction in the penalties incurred by hospitals under the HRRP. This highlights the role ivabradine can play as part of a wider effort to optimize the care of patients with HF.

Readmission After Craniotomy for Tumor: A National Surgical Quality Improvement Program Analysis

BACKGROUND: Although readmission has become a common quality indicator, few national studies have examined this metric in patients undergoing cranial surgery.

OBJECTIVE: To utilize the prospective National Surgical Quality Improvement Program 2011-2013 registry to evaluate the predictors of unplanned 30-d readmission and postdischarge mortality after cranial tumor resection.

METHODS: Multivariable logistic regression was applied to screen predictors, which included patient age, sex, tumor location and histology, American Society of Anesthesiologists class, functional status, comorbidities, and complications from the index hospitalization.

RESULTS: Of the 9565 patients included, 10.7% (n = 1026) had an unplanned readmission. Independent predictors of unplanned readmission were male sex, infratentorial location, American Society of Anesthesiologists class 3 designation, dependent functional status, a bleeding disorder, and morbid obesity (all P ≤ .03). Readmission was not associated with operative time, length of hospitalization, discharge disposition, or complications from the index admission. The most common reasons for readmission were surgical site infections (17.0%), infectious complications (11.0%), venous thromboembolism (10.0%), and seizures (9.4%). The 30-d mortality rate was 3.2% (n = 367), of which the majority (69.7%, n = 223) occurred postdischarge. Independent predictors of postdischarge mortality were greater age, metastatic histology, dependent functional status, hypertension, discharge to institutional care, and postdischarge neurological or cardiopulmonary complications (all P &lt; .05).

CONCLUSION: Readmissions were common after cranial tumor resection and often attributable to new postdischarge complications rather than exacerbations of complications from the initial hospitalization. Moreover, the majority of 30-d deaths occurred after discharge from the index hospitalization. The preponderance of postdischarge mortality and complications requiring readmission highlights the importance of posthospitalization management.

Trajectories of Risk for Specific Readmission Diagnoses after Hospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia

BACKGROUND

The risk of rehospitalization is elevated in the immediate post-discharge period and declines over time. It is not known if the extent and timing of risk vary across readmission diagnoses, suggesting that recovery and vulnerability after discharge differ by physiologic system.

OBJECTIVE

We compared risk trajectories for major readmission diagnoses in the year after discharge among all Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008-2010.

METHODS

We estimated the daily risk of rehospitalization for 12 major readmission diagnostic categories after accounting for the competing risk of death after discharge. For each diagnostic category, we identified (1) the time required for readmission risk to peak and then decline 50% from maximum values after discharge; (2) the time required for readmission risk to approach plateau periods of minimal day-to-day change; and (3) the extent to which hospitalization risks are higher among patients recently discharged from the hospital compared with the general elderly population.

RESULTS

Among >3,000,000 hospitalizations, the yearly rate of rehospitalization was 67.0%, 49.5%, and 55.3% after hospitalization for HF, AMI, and pneumonia, respectively. The extent and timing of risk varied by readmission diagnosis and initial admitting condition. Risk of readmission for gastrointestinal bleeding/anemia peaked particularly late after hospital discharge, occurring 10, 6, and 7 days after hospitalization for HF, AMI, and pneumonia, respectively. Risk of readmission for trauma/injury declined particularly slowly, requiring 38, 20, and 38 days to decline by 50% after hospitalization for HF, AMI, and pneumonia, respectively.

CONCLUSIONS

Patterns of vulnerability to different conditions that cause rehospitalization vary by time after hospital discharge. This finding suggests that recovery of various physiologic systems occurs at different rates and that post-discharge interventions to minimize vulnerability to specific conditions should be tailored to their underlying risks.

Differences in Hospital Readmission Risk across All Payer Groups in South Carolina

OBJECTIVE

To evaluate differences in hospital readmission risk across all payers in South Carolina (SC).

DATA SOURCES/STUDY SETTING

South Carolina Revenue and Fiscal Affairs Office (SCRFA) statewide all payer claims database including 2,476,431 hospitalizations in SC acute care hospitals between 2008 and 2014.

STUDY DESIGN

We compared the odds of unplanned all-cause 30-day readmission for private insurance, Medicare, Medicaid, uninsured, and other payers and examined interaction effects between payer and index admission characteristics using generalized estimating equations.

DATA COLLECTION

SCRFA receives claims and administrative health care data from all SC health care facilities in accordance with SC state law.

PRINCIPAL FINDINGS

Odds of readmission were lower for females compared to males in private, Medicare, and Medicaid payers. African Americans had higher odds of readmission compared to whites across private insurance, Medicare, and Medicaid, but they had lower odds among the uninsured. Longer length of stay had the strongest association with readmission for private and other payers, whereas an increased number of comorbidities related to the highest readmission odds within Medicaid.

CONCLUSIONS

Associations between index admission characteristics and readmission likelihood varied significantly with payer. Findings should guide the development of payer-specific quality improvement programs.

Less with More: Hospitalization Cost and Event Rates with Quadripolar versus Bipolar CRT-D System

BACKGROUND

This is a comparative effectiveness study for cardiac resynchronization therapy defibrillator (CRT-D) therapy enabled by quadripolar (QUAD) versus bipolar (BIP) left ventricular (LV) leads.  Heart failure (HF) hospitalization (HFH) rates, associated costs, and 30-day readmissions after index HFH were compared.

METHODS

Patients with de novo LV leads implanted as part of a CRT-D system between January 2011 and August 2013 with ≥1-year follow-up were included. Medical history, dates, and locations of HFH were collected thereafter. Patients were divided based on LV lead model: QUAD or BIP. Universal billing records (UB-04) for each HFH and ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnoses/procedure codes were used to classify hospitalizations as HF or non-HF and calculate concurrent U.S. national-average medicare reimbursement. Rates, associated payer costs, and 30-day readmissions were then compared using nonparametric bootstrapping.

RESULTS

Baseline characteristics (N = 69 QUAD and N = 56 BIP) were similar. The inpatient HFH for the QUAD group (0.20/patient-year) was lower than the BIP group (0.31/patient-year, incidence rate ratio [IRR] = 0.62, P = 0.036). The overall HFH rate for the inpatient or outpatient setting for QUAD (0.29/patient-year) was lower than the BIP group (0.42/patient-year, IRR = 0.69, P = 0.055). Average cost of HFH in QUAD ($4,428/patient-year) was lower than BIP ($7,354/patient-year), a 39.8% cost reduction (P = 0.026). The 30-day readmission rate was also lower in QUAD compared to BIP (19% vs 28%, IRR = 0.68, P = 0.18).

CONCLUSION

This U.S. economic comparative study demonstrated that QUAD exhibited lower postimplant inpatient HFH rates and reduced healthcare utilization compared to BIP systems.

Who comes back with what: a retrospective database study on reasons for emergency readmission to hospital in children and young people in England

OBJECTIVE

To determine the proportion of children and young people (CYP) in England who are readmitted for the same condition.

DESIGN

Retrospective cohort study.

SETTING

National administrative hospital data (Hospital Episode Statistics).

PARTICIPANTS

CYP (0-year-olds to 24-year-olds) discharged after an emergency admission to the National Health Service in England in 2009/2010.

MAIN OUTCOME MEASURES

Coded primary diagnosis classified in six broad groups indicating reason for admission (infection, chronic condition, injury, perinatal related or pregnancy related, sign or symptom or other). We grouped readmissions as ≤30 days or between 31 days and 2 years after the index discharge. We used multivariable logistic regression to determine factors at the index admission that were predictive of readmission within 30 days.

RESULTS

9% of CYP were readmitted within 30 days. Half of the 30-day readmissions and 40% of the recurrent admissions between 30 days and 2 years had the same primary diagnosis group as the original admission. These proportions were consistent across age, sex and diagnostic groups, except for infants and young women with pregnancy-related problems (15-24 years) who were more likely to be readmitted for the same primary diagnostic group. CYP with underlying chronic conditions were readmitted within 30 days twice as often (OR: 1.93, 95% CI 1.89 to 1.99) compared with CYP without chronic conditions.

CONCLUSIONS

Financial penalties for readmission are expected to incentivise more effective care of the original problem, thereby avoiding readmission. Our findings, that half of children come back with different problems, do not support this presumption.

Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison

BACKGROUND

Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions.

OBJECTIVE

To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay).

DESIGN

Observational cohort study.

SUBJECTS

All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites.

MEASURES

Thirty-day nonelective readmissions were ascertained from 75 regional hospitals.

RESULTS

Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficile infection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06).

CONCLUSIONS

Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016;11:473-480. © 2016 Society of Hospital Medicine.