Site of hospital readmission and mortality: a population-based retrospective cohort study

Association between new insertion of a long-term enteral feeding tube and mortality in adults admitted to the hospital with aspiration: A retrospective cohort study

BACKGROUND

We aimed to describe the association between insertion of a new long-term enteral feeding tube during admission for aspiration and in-hospital mortality.

METHODS

This retrospective cohort study across 28 Canadian hospitals from 2015 to 2022 included consecutive patients who were admitted for aspiration. Patients were categorized based on new long-term enteral feeding tube insertion during hospital stay or not. The primary outcome was the time to death in hospital. Secondary outcomes included time to discharge alive and hospital readmission for aspiration within 90 days. We used propensity score weighting to balance covariates, and a competing risk model to describe in-hospital death and discharge.

RESULTS

Of 12,850 patients admitted for aspiration, 852 (6.6%) patients received a long-term enteral feeding tube. In the hospital, 184 (21.6%) and 2489 (20.8%) patients in the enteral feeding tube group and no enteral feeding tube group died, respectively. Within 90 days of discharge, 127 (14.9%) and 1148 (9.6%) patients in the enteral feeding tube and no enteral feeding tube group were readmitted for aspiration, respectively. After balancing covariates, an enteral feeding tube was associated with a similar in-hospital mortality risk (subdistribution hazard ratio [sHR] = 1.05, 95% CI = 0.89-1.23; P = 0.5800), longer time to discharge alive (sHR = 0.58, 95% CI = 0.54-0.63; P < 0.0001), and a higher risk of readmission (risk difference = 5.0%, 95% CI = 2.4%-7.6%; P = 0.0001).

CONCLUSION

Initiation of long-term enteral tube feeding was not uncommon after admission for aspiration and was not associated with an improvement in the probability of being discharged alive from the hospital or readmitted for aspiration.

Physician Financial Incentives to Reduce Unplanned Hospital Readmissions: A Propensity Score Weighted Cohort Study

BACKGROUND

Unplanned hospital readmissions are associated with adverse patient outcomes and substantial healthcare costs. It remains unknown whether physician financial incentives for enhanced discharge planning can reduce readmission risk.

METHODS

In 2012, policymakers in British Columbia, Canada, introduced a $75 fee-for-service physician payment to incentivize enhanced discharge planning (the "G78717" fee code). We used population-based administrative health data to compare outcomes among G78717-exposed and G78717-unexposed patients. We identified all nonelective hospitalizations potentially eligible for the incentive over a 5-year study interval. We examined the composite risk of unplanned readmission or death and total direct healthcare costs accrued within 30 days of discharge. Propensity score overlap weights and adjustment were used to account for differences between exposed and unexposed patients.

RESULTS

A total of 5262 of 24,787 G78717-exposed and 28,096 of 136,541 unexposed patients experienced subsequent unplanned readmission or death, suggesting exposure to the G78717 incentive did not reduce the risk of adverse outcomes after discharge (crude percent, 21.1% vs 20.6%; adjusted odds ratio, 0.97; 95% CI, 0.93-1.01; P = .23). Mean direct healthcare costs within 30 days of discharge were $3082 and $2993, respectively (adjusted cost ratio, 1.00; 95% CI, 0.95-1.05; P = .93).

CONCLUSIONS

A physician financial incentive that encouraged enhanced hospital discharge planning did not reduced the risk of readmission or death, and did not significantly decrease direct healthcare costs. Policymakers should consider the baseline prevalence and effectiveness of enhanced discharge planning, the magnitude and design of financial incentives, and whether auditing of incentivized activities is required when implementing similar incentives elsewhere.

TRIAL REGISTRATION

ClinicalTrials.gov ID, NCT03256734.

Anaerobic Antibiotic Coverage in Aspiration Pneumonia and the Associated Benefits and Harms: A Retrospective Cohort Study

BACKGROUND

Antibiotics with extended anaerobic coverage are used commonly to treat aspiration pneumonia, which is not recommended by current guidelines.

RESEARCH QUESTION

In patients admitted to hospital for community-acquired aspiration pneumonia, does a difference exist between antibiotic therapy with limited anaerobic coverage (LAC) vs antibiotic therapy with extended anaerobic coverage (EAC) in terms of in-hospital mortality and risk of Clostridioides difficile colitis?

STUDY DESIGN AND METHODS

We conducted a multicenter retrospective cohort study across 18 hospitals in Ontario, Canada, from January 1, 2015, to January 1, 2022. Patients were included if the physician diagnosed aspiration pneumonia and prescribed guideline-concordant first-line community-acquired pneumonia parenteral antibiotic therapy to the patient within 48 h of admission. Patients then were categorized into the LAC group if they received ceftriaxone, cefotaxime, or levofloxacin. Patients were categorized into the EAC group if they received amoxicillin-clavulanate, moxifloxacin, or any of ceftriaxone, cefotaxime, or levofloxacin in combination with clindamycin or metronidazole. The primary outcome was all-cause in-hospital mortality. Secondary outcomes included incident C difficile colitis occurring after admission. Overlap weighting of propensity scores was used to balance baseline prognostic factors.

RESULTS

The LAC and EAC groups included 2,683 and 1,316 patients, respectively. In hospital, 814 patients (30.3%) and 422 patients (32.1%) in the LAC and EAC groups died, respectively. C difficile colitis occurred in five or fewer patients (≤ 0.2%) and 11 to 15 patients (0.8%-1.1%) in the LAC and EAC groups, respectively. After overlap weighting of propensity scores, the adjusted risk difference of EAC minus LAC was 1.6% (95% CI, -1.7% to 4.9%) for in-hospital mortality and 1.0% (95% CI, 0.3%-1.7%) for C difficile colitis.

INTERPRETATION

We found that extended anaerobic coverage likely is unnecessary in aspiration pneumonia because it was associated with no additional mortality benefit, only an increased risk of C difficile colitis.

Ambulance use and subsequent fragmented hospital readmission among older adults

BACKGROUND

Interhospital care fragmentation, when a patient is readmitted to a different hospital than they were originally discharged from, occurs in 20%-25% of readmissions. Mode of transport to the hospital, specifically ambulance use, may be a risk factor for fragmented readmissions. Our study seeks to further understand the relationship between ambulance transport and fragmented readmissions in older adults, a population that is at increased risk for poor outcomes following fragmented readmissions.

METHODS

We analyzed inpatient claims from Medicare beneficiaries in 2018 who had a hospital admission for select Hospital Readmission Reduction Program Conditions (acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, pneumonia) as well as dehydration, syncope, urinary tract infection, or behavioral issues. We evaluated the associations between ambulance transport and a fragmented readmission using logistic regression models adjusted for demographic, clinical, and hospital characteristics.

RESULTS

The study included 1,186,600 30-day readmissions. Of these, 46.8% (n = 555,847) required ambulance transport. In fully adjusted models, taking an ambulance to the readmission hospital increased the odds of a fragmented readmission by 38% (95% CI 1.32, 1.44). When this association was examined by readmission major diagnostic category (MDC), the strongest associations were seen for Factors Influencing Health Status and Other Contacts with Health Services (i.e., rehabilitation, aftercare) (AOR 3.66, 95% CI 3.11, 4.32), Mental Diseases and Disorders (AOR 2.69, 95% CI 2.44, 2.97), and Multiple Significant Trauma (AOR 2.61, 95% CI 1.56, 4.35). When the model was stratified by patient origin, ambulance use remained associated with fragmented readmissions across all locations.

CONCLUSIONS

Ambulance use is associated with increased odds of a fragmented readmission, though the strength of the association varies by readmission diagnosis and origin. Patient-, hospital-, and system-level interventions should be developed, implemented, and evaluated to address this modifiable risk factor.

The Impact of Health Information Exchange on In-Hospital and Postdischarge Mortality in Older Adults with Alzheimer Disease Readmitted to a Different Hospital Within 30 Days of Discharge: Cohort Study of Medicare Beneficiaries

BACKGROUND

Although electronic health information sharing is expanding nationally, it is unclear whether electronic health information sharing improves patient outcomes, particularly for patients who are at the highest risk of communication challenges, such as older adults with Alzheimer disease.

OBJECTIVE

To determine the association between hospital-level health information exchange (HIE) participation and in-hospital or postdischarge mortality among Medicare beneficiaries with Alzheimer disease or 30-day readmissions to a different hospital following an admission for one of several common conditions.

METHODS

This was a cohort study of Medicare beneficiaries with Alzheimer disease who had one or more 30-day readmissions in 2018 following an initial admission for select Hospital Readmission Reduction Program conditions (acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, and pneumonia) or common reasons for hospitalization among older adults with Alzheimer disease (dehydration, syncope, urinary tract infection, or behavioral issues). Using unadjusted and adjusted logistic regression, we examined the association between electronic information sharing and in-hospital mortality during the readmission or mortality in the 30 days following the readmission.

RESULTS

A total of 28,946 admission-readmission pairs were included. Beneficiaries with same-hospital readmissions were older (aged 81.1, SD 8.6 years) than beneficiaries with readmissions to different hospitals (age range 79.8-80.3 years, P<.001). Compared to admissions and readmissions to the same hospital, beneficiaries who had a readmission to a different hospital that shared an HIE with the admission hospital had 39% lower odds of dying during the readmission (adjusted odds ratio [AOR] 0.61, 95% CI 0.39-0.95). There were no differences in in-hospital mortality observed for admission-readmission pairs to different hospitals that participated in different HIEs (AOR 1.02, 95% CI 0.82-1.28) or to different hospitals where one or both hospitals did not participate in HIE (AOR 1.25, 95% CI 0.93-1.68), and there was no association between information sharing and postdischarge mortality.

CONCLUSIONS

These results indicate that information sharing between unrelated hospitals via a shared HIE may be associated with lower in-hospital, but not postdischarge, mortality for older adults with Alzheimer disease. In-hospital mortality during a readmission to a different hospital was higher if the admission and readmission hospitals participated in different HIEs or if one or both hospitals did not participate in an HIE. Limitations of this analysis include that HIE participation was measured at the hospital level, rather than at the provider level. This study provides some evidence that HIEs can improve care for vulnerable populations receiving acute care from different hospitals.

Association of distance between hospitals and volume of shared admissions

Background

To assess whether decreasing distance between hospitals was associated with the number of shared patients (patients with an admission to one hospital and a readmission to another).

Methods

Data were from the Healthcare Cost and Utilization Project’s State Inpatient Databases (Florida, Georgia, Maryland, Utah [2017], New York, Vermont [2016]) and the American Hospital Association Annual Survey (2016 & 2017). This was a cross-sectional analysis of patients who had an index admission and subsequent readmission at different hospitals within the same year. We used unadjusted and adjusted linear regression to evaluate the association between the number of shared patients and the distance between admission-readmission hospital pairs.

Results

There were 691 hospitals in the sample (247 in Florida, 151 in Georgia, 50 in Maryland, 172 in New York, 58 in Utah, and 13 in Vermont), accounting for a total of 596,772 admission-readmission pairs. 32.6% of the admission-readmission pairs were shared between two hospitals. On average, a one-mile decrease in distance between two hospitals was associated with of 3.05 (95% CI, 3.02, 3.07) more shared admissions. However, variability between states was wide, with Utah having 0.37 (95% CI 0.35, 0.39) more shared admissions between hospitals per one-mile shorter distance, and Maryland having 4.98 (95% CI 4.87, 5.08) more.

Conclusions

We found that proximity between hospitals is associated with higher volumes of shared admissions.

Location and outcomes of rehospitalizations after critical illness in a single-payer healthcare system

PURPOSE

Unplanned rehospitalization at a hospital other than the initial hospital may contribute to poor outcomes. We examined the location of rehospitalizations and assessed outcomes following critical illness in a single-payer healthcare system.

MATERIALS AND METHODS

Population-based retrospective cohort study using linked datasets (2012-2017) from Ontario, Canada including adults (≥18 years) with an unplanned rehospitalization within 30-days after an index hospitalization that included an ICU stay with mechanical ventilation. Outcomes were the percentage of 30-day rehospitalizations at non-index hospitals, mortality and costs. We employed logistic regression and generalized linear models to assess associations.

RESULTS

There were 14,997 (16.4%) 30-day rehospitalizations. Of these 2765 (18.4%) occurred in a non-index hospital. Distance of home residence from the index hospital was the strongest predictor of a non-index rehospitalization (adjusted odds ratio (aOR) 8.40, 95%CI 7.05-10.01, highest vs. lowest distance quintile). Within 30-days of rehospitalization, deaths (aOR 0.91, 95%CI (0.80-1.04)) and total healthcare costs (adjusted relative risk 1.03 (1.00-1.06)), were similar for patients readmitted to the index or a non-index hospital.

CONCLUSION

Non-index rehospitalization within 30-days of initial discharge is common following critical illness. These rehospitalizations were not significantly associated with an increased risk of harm or higher costs in a single-payer healthcare system.

Outcomes in patients with and without disability admitted to hospital with COVID-19: a retrospective cohort study

Background:

Disability-related considerations have largely been absent from the COVID-19 response, despite evidence that people with disabilities are at elevated risk for acquiring COVID-19. We evaluated clinical outcomes in patients who were admitted to hospital with COVID-19 with a disability compared with patients without a disability.

Methods:

We conducted a retrospective cohort study that included adults with COVID-19 who were admitted to hospital and discharged between Jan. 1, 2020, and Nov. 30, 2020, at 7 hospitals in Ontario, Canada. We compared in-hospital death, admission to the intensive care unit (ICU), hospital length of stay and unplanned 30-day readmission among patients with and without a physical disability, hearing or vision impairment, traumatic brain injury, or intellectual or developmental disability, overall and stratified by age (≤ 64 and ≥ 65 yr) using multivariable regression, controlling for sex, residence in a long-term care facility and comorbidity.

Results:

Among 1279 admissions to hospital for COVID-19, 22.3% had a disability. We found that patients with a disability were more likely to die than those without a disability (28.1% v. 17.6%), had longer hospital stays (median 13.9 v. 7.8 d) and more readmissions (17.6% v. 7.9%), but had lower ICU admission rates (22.5% v. 28.3%). After adjustment, there were no statistically significant differences between those with and without disabilities for in-hospital death or admission to ICU. After adjustment, patients with a disability had longer hospital stays (rate ratio 1.36, 95% confidence interval [CI] 1.19–1.56) and greater risk of readmission (relative risk 1.77, 95% CI 1.14–2.75). In age-stratified analyses, we observed longer hospital stays among patients with a disability than in those without, in both younger and older subgroups; readmission risk was driven by younger patients with a disability.

Interpretation:

Patients with a disability who were admitted to hospital with COVID-19 had longer stays and elevated readmission risk than those without disabilities. Disability-related needs should be addressed to support these patients in hospital and after discharge.

Bedspacing and clinical outcomes in general internal medicine: A retrospective, multicenter cohort study

BACKGROUND

Admitting hospitalized patients to off-service wards ("bedspacing") is common and may affect quality of care and patient outcomes.

OBJECTIVE

To compare in-hospital mortality, 30-day readmission to general internal medicine (GIM), and hospital length-of-stay among GIM patients admitted to GIM wards or bedspaced to off-service wards.

DESIGN, PARTICIPANTS, AND MEASURES

Retrospective cohort study including all emergency department admissions to GIM between 2015 and 2017 at six hospitals in Ontario, Canada. We compared patients admitted to GIM wards with those who were bedspaced, using multivariable regression models and propensity score matching to control for patient and situational factors.

KEY RESULTS

Among 40,440 GIM admissions, 10,745 (26.6%) were bedspaced to non-GIM wards and 29,695 (73.4%) were assigned to GIM wards. After multivariable adjustment, bedspacing was associated with no significant difference in mortality (adjusted hazard ratio 0.95, 95% confidence interval [CI]: 0.86-1.05, p = .304), slightly shorter median hospital length-of-stay (-0.10 days, 95% CI:-0.20 to -0.001, p = .047) and lower 30-day readmission to GIM (adjusted OR 0.89, 95% CI: 0.83-0.95, p = .001). Results were consistent when examining each hospital individually and outcomes did not significantly differ between medical or surgical off-service wards. Sensitivity analyses focused on the highest risk patients did not exclude the possibility of harm associated with bedspacing, although adverse outcomes were not significantly greater.

CONCLUSIONS

Overall, bedspacing was associated with no significant difference in mortality, slightly shorter hospital length-of-stay, and fewer 30-day readmissions to GIM, although potential harms in high-risk patients remain uncertain. Given that hospital capacity issues are likely to persist, future research should aim to understand how bedspacing can be achieved safely at all hospitals, perhaps by strengthening the selection of low-risk patients.

External validation of the modified LACE+, LACE+, and LACE scores to predict readmission or death after hospital discharge

BACKGROUND

Unplanned hospital readmissions are common adverse events. The LACE+ score has been used to identify patients at the highest risk of unplanned readmission or death, yet the external validity of this score remains uncertain.

METHODS

We constructed a cohort of patients admitted to hospital between 1 October 2014 and 31 January 2017 using population-based data from British Columbia (Canada). The primary outcome was a composite of urgent hospital readmission or death within 30 days of index discharge. The primary analysis sought to optimize clinical utility and international generalizability by focusing on the modified LACE+ (mLACE+) score, a variation of the LACE+ score which excludes the Case Mix Group score. Predictive performance was assessed using model calibration and discrimination.

RESULTS

Among 368,154 hospitalized individuals, 31,961 (8.7%) were urgently readmitted and 5428 (1.5%) died within 30 days of index discharge (crude composite risk of readmission or death, 9.95%). The mLACE+ score exhibited excellent calibration (calibration-in-the-large and calibration slope no different than ideal) and adequate discrimination (c-statistic, 0.681; 95%CI, 0.678 to 0.684). Higher risk dichotomized mLACE+ scores were only modestly associated with the primary outcome (positive likelihood ratio 1.95, 95%CI 1.93 to 1.97). Predictive performance of the mLACE+ score was similar to that of the LACE+ and LACE scores.

CONCLUSION

The mLACE+, LACE+ and LACE scores predict hospital readmission with excellent calibration and adequate discrimination. These scores can be used to target interventions designed to prevent unplanned hospital readmission.

Physician Financial Incentives to Reduce Unplanned Hospital Readmissions: an Interrupted Time Series Analysis

BACKGROUND

In 2012, the Ministry of Health in British Columbia, Canada, introduced a $75 incentive payment that could be claimed by hospital physicians each time they produced a written post-discharge care plan for a complex patient at the time of hospital discharge.

OBJECTIVE

To examine whether physician financial payments incentivizing enhanced discharge planning reduce subsequent unplanned hospital readmissions.

DESIGN

Interrupted time series analysis of population-based hospitalization data.

PARTICIPANTS

Individuals with one or more eligible hospitalizations occurring in British Columbia between 2007 and 2017.

MAIN MEASURES

The proportion of index hospital discharges with subsequent unplanned hospital readmission within 30 days, as measured each month of the 11-year study interval. We used interrupted time series analysis to determine if readmission risk changed after introduction of the incentive payment policy.

KEY RESULTS

A total of 40,588 unplanned hospital readmissions occurred among 409,289 eligible index hospitalizations (crude 30-day readmission risk, 9.92%). Policy introduction was not associated with a significant step change (0.393%; 95CI, - 0.190 to 0.975%; p = 0.182) or change-in-trend (p = 0.317) in monthly readmission risk. Policy introduction was associated with significantly fewer prescription fills for potentially inappropriate medications among older patients, but no improvement in prescription fills for beta-blockers after cardiovascular hospitalization and no change in 30-day mortality. Incentive payment uptake was incomplete, rising from 6.4 to 23.5% of eligible hospitalizations between the first and last year of the post-policy interval.

CONCLUSION

The introduction of a physician incentive payment was not associated with meaningful changes in hospital readmission rate, perhaps in part because of incomplete uptake by physicians. Policymakers should consider these results when designing similar interventions elsewhere.

TRIAL REGISTRATION

ClinicalTrials.gov ID, NCT03256734.

Caractéristiques et issues des hospitalisations pour les cas de COVID-19 et d’influenza dans la région de Toronto

CONTEXTE:

Les caractéristiques des patients, les soins cliniques, l’utilisation des ressources et les issues cliniques des personnes atteintes de la maladie à coronavirus 2019 (COVID-19) hospitalisées au Canada ne sont pas bien connus.

MÉTHODES:

Nous avons recueilli des données sur tous les adultes hospitalisés atteints de la COVID-19 ou de l’influenza ayant obtenu leur congé d’unités médicales ou d’unités de soins intensifs médicaux et chirurgicaux entre le 1er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons comparé les issues cliniques des patients à l’aide de modèles de régression multivariée, en tenant compte des facteurs sociodémographiques et de l’intensité des comorbidités. Nous avons validé le degré d’exactitude de 7 scores de risque mis au point à l’externe pour déterminer leur capacité à prédire le risque de décès chez les patients atteints de la COVID-19.

RÉSULTATS:

Parmi les hospitalisations retenues, 1027 patients étaient atteints de la COVID-19 (âge médian de 65 ans, 59,1 % d’hommes) et 783 étaient atteints de l’influenza (âge médian de 68 ans, 50,8 % d’hommes). Les patients âgés de moins de 50 ans comptaient pour 21,2 % de toutes les hospitalisations dues à la COVID-19 et 24,0 % des séjours aux soins intensifs. Comparativement aux patients atteints de l’influenza, les patients atteints de la COVID-19 présentaient un taux de mortalité perhospitalière (mortalité non ajustée 19,9 % c. 6,1 %; risque relatif [RR] ajusté 3,46 %, intervalle de confiance [IC] à 95 % 2,56–4,68) et un taux d’utilisation des ressources des unités de soins intensifs (taux non ajusté 26,4 % c. 18,0 %; RR ajusté 1,50, IC à 95 % 1,25–1,80) significativement plus élevés, ainsi qu’une durée d’hospitalisation (durée médiane non ajustée 8,7 jours c. 4,8 jours; rapport des taux d’incidence ajusté 1,45; IC à 95 % 1,25–1,69) significativement plus longue. Le taux de réhospitalisation dans les 30 jours n’était pas significativement différent (taux non ajusté 9,3 % c. 9,6 %; RR ajusté 0,98 %, IC à 95 % 0,70–1,39). Trois scores de risque utilisant un pointage pour prédire la mortalité perhospitalière ont montré une bonne discrimination (aire sous la courbe [ASC] de la fonction d’efficacité du récepteur [ROC] 0,72–0,81) et une bonne calibration.

INTERPRÉTATION:

Durant la première vague de la pandémie, l’hospitalisation des patients atteints de la COVID-19 était associée à des taux de mortalité et d’utilisation des ressources des unités de soins intensifs et à une durée d’hospitalisation significativement plus importants que les hospitalisations des patients atteints de l’influenza. De simples scores de risque peuvent prédire avec une bonne exactitude le risque de mortalité perhospitalière des patients atteints de la COVID-19.

Morning Discharges and Patient Length of Stay in Inpatient General Internal Medicine

BACKGROUND

Many initiatives seek to increase the number of morning hospital discharges to improve patient flow, but little evidence supports this practice.

OBJECTIVE

To determine the association between the number of morning discharges and emergency department (ED) length of stay (LOS) and hospital LOS in general internal medicine (GIM).

DESIGN, SETTING, AND PARTICIPANTS

Multicenter retrospective cohort study involving all GIM patients discharged between April 1, 2010, and October 31, 2017, at seven hospitals in Ontario, Canada.

MAIN MEASURES

The primary outcomes were ED LOS and hospital LOS, and secondary outcomes were 30-day readmission and in-hospital mortality. The number of morning GIM discharges (defined as the number of patients discharged alive between 8:00 AM and 12:00 PM) on the day of each hospital admission was the primary exposure. Multivariable regression models were fit to control for patient characteristics and situational factors, including GIM census.

RESULTS

The sample included 189,781 patient admissions. In total, 36,043 (19.0%) discharges occurred between 8:00 AM and 12:00 PM. The average daily number of morning discharges and total discharges per hospital was 1.7 (SD, 1.4) and 8.4 (SD, 4.6), respectively. The median ED LOS was 14.5 hours (interquartile range [IQR], 10.0- 23.1), and the median hospital LOS was 4.6 days (IQR, 2.4-9.0). After multivariable adjustment, there was not a significant association between morning discharge and hospital LOS (adjusted rate ratio [aRR], 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471), or in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P = .183). The lack of association between morning discharge and LOS was generally consistent across all seven hospitals. At one hospital, morning discharge was associated with a 1.9% shorter ED LOS after multivariable adjustment (aRR, 0.981; 95% CI, 0.966-0.996; P = .013).

CONCLUSIONS

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS in GIM. Our findings suggest that increasing the number of morning discharges alone is unlikely to substantially improve patient throughput in GIM, but further research is needed to determine the effectiveness of specific interventions.

Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area

BACKGROUND

Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described.

METHODS

We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical-surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19.

RESULTS

There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56-4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25-1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25-1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70-1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration.

INTERPRETATION

During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy.

Clinical and Financial Implications of Consulting Physicians in the Management of Surgical Patients

BACKGROUND

The present study was designed to evaluate the immediate consequences that the number of consulting physicians has on length of stay (LOS), in-hospital mortality, 30-day readmission rates, direct health care costs, and contribution margins.

METHODS

A retrospective review of administrative databases for the years 2013 and 2014 was performed at the Florida Hospital Adventist Healthcare System.

RESULTS

11 274 patients were included in the analysis. Total and variable costs increased by $1347 and $592, respectively, with each consulting physician service per patient. The contribution margin decreased by $354 per patient/consulting physician. Each consulting physician increased LOS by .72 days and increased odds ratio of mortality and 30-day readmission by 5% and 3%, respectively.

CONCLUSIONS

Our research suggests that each consulting physician added to the care of an individual surgical patient negatively affected LOS, readmission rates, in-hospital mortality, and costs.

Effects of a standardized community health worker intervention on hospitalization among disadvantaged patients with multiple chronic conditions: A pooled analysis of three clinical trials

OBJECTIVE

To analyze the effects of a standardized community health worker (CHW) intervention on hospitalization.

DATA SOURCES/STUDY SETTING

Pooled data from three randomized clinical trials (n = 1340) conducted between 2011 and 2016.

STUDY DESIGN

The trials in this pooled analysis were conducted across diseases and settings, with a common study design, intervention, and outcome measures. Participants were patients living in high-poverty regions of Philadelphia and were predominantly Medicaid insured. They were randomly assigned to receive usual care versus IMPaCT, an intervention in which CHWs provide tailored social support, health behavior coaching, connection with resources, and health system navigation. Trial one (n = 446) tested two weeks of IMPaCT among hospitalized general medical patients. Trial two (n = 302) tested six months of IMPaCT among outpatients at two academic primary care clinics. Trial three (n = 592) tested six months of IMPaCT among outpatients at academic, Veterans Affairs (VA), and Federally Qualified Health Center primary care practices.

DATA COLLECTION/EXTRACTION METHODS

The primary outcome for this study was all-cause hospitalization, as measured by total number of hospital days per patient. Hospitalization data were collected from statewide or VA databases at 30 days postenrollment in Trial 1, twelve months postenrollment in Trial 2, and nine months postenrollment in Trial 3.

PRINCIPAL FINDINGS

Over 9398 observed patient months, the total number of hospital days per patient in the intervention group was 66 percent of the total in the control group (849 days for 674 intervention patients vs 1258 days for 660 control patients, incidence rate ratio (IRR) 0.66, P < .0001). This reduction was driven by fewer hospitalizations per patient (0.27 vs 0.34, P < .0001) and shorter mean length of stay (4.72 vs 5.57 days, P = .03). The intervention also decreased rates of hospitalization outside patients' primary health system (18.8 percent vs 34.8 percent, P = .0023).

CONCLUSIONS

Data from three randomized clinical trials across multiple settings show that a standardized CHW intervention reduced total hospital days and hospitalizations outside the primary health system. This is the largest analysis of randomized trials to demonstrate reductions in hospitalization with a health system-based social intervention.

Patient Outcomes Following Interhospital Care Fragmentation: A Systematic Review

INTRODUCTION

Interhospital fragmentation of care occurs when patients are admitted to different, disconnected hospitals. It has been hypothesized that this type of care fragmentation decreases the quality of care received and increases hospital costs and healthcare utilization. This systematic review aims to synthesize the existing literature exploring the association between interhospital fragmentation of care and patient outcomes.

METHODS

MEDLINE, the Cochrane Library, EMBASE, and the Science Citation Index were systematically searched for studies published up to April 30, 2018 reporting the association between interhospital fragmentation of care and patient outcomes. We included peer-reviewed observational studies conducted in adults that reported measures of association between interhospital care fragmentation and one or more of the following patient outcomes: mortality, hospital length of stay, cost, and subsequent hospital readmission.

RESULTS

Seventy-nine full texts were reviewed and 22 met inclusion criteria. Nearly all studies defined fragmentation of care as a readmission to a different hospital than the patient was previously discharged from. The strongest association reported was that between a fragmented readmission and in-hospital or short-term mortality (adjusted odds ratio range 0.95-3.62). Over half of the studies reporting length-of-stay showed longer length of stay in fragmented readmissions. All three studies that investigated healthcare utilization suggested an association between fragmented care and odds of subsequent readmission. The study populations and exposures were too heterogenous to perform a meta-analysis.

DISCUSSION

Our review suggests that fragmented hospital readmissions contribute to increased mortality, longer length-of-stay, and increased risk of readmission to the hospital.

Hospital Readmission and Mortality in a Brazilian Tertiary Public Hospital

BACKGROUND AND OBJECTIVES

Data on mortality associated with hospital readmission are imprecise and highly variable. This study aimed to describe the rate of nonelective 30-day readmission and associated hospital mortality of patients discharged from the Internal Medicine Unit of a Brazilian tertiary public hospital.

METHODS

This retrospective cohort study included all patients discharged from the Internal Medicine Unit of our institution between September and November 2017 who were nonelectively readmitted within 30 days.

RESULTS

A total of 1047 hospital discharges were analyzed. The rate of nonelective 30-day readmission was 13.7%. Of these, 41 (28.5%) were early readmissions (0-7 days) and 103 (71.5%) were late readmissions (8-30 days). The hospital mortality rate during readmission was 27.8%, being significantly higher during early readmissions (41.5% vs 22.3%; P = .035). Early (as compared with late) readmission was associated with mortality during readmission (relative risk [RR] 1.95; 95% confidence interval, 1.18-3.22; P = .002), regardless of age and Charlson comorbidity index.

CONCLUSION

The Readmission rate was 13.7%, with an associated mortality of 27.8%. Early readmission was an independent predictor of mortality (RR 1.95) in relation to late readmission. Larger studies are needed to better identify this group of patients with an aim to adopt preventive measures.

Physician-level variation in clinical outcomes and resource use in inpatient general internal medicine: an observational study

Background

Variations in inpatient medical care are typically attributed to system, hospital or patient factors. Little is known about variations at the physician level within hospitals. We described the physician-level variation in clinical outcomes and resource use in general internal medicine (GIM).

Methods

This was an observational study of all emergency admissions to GIM at seven hospitals in Ontario, Canada, over a 5-year period between 2010 and 2015. Physician-level variations in inpatient mortality, hospital length of stay, 30-day readmission and use of ‘advanced imaging’ (CT, MRI or ultrasound scans) were measured. Physicians were categorised into quartiles within each hospital for each outcome and then quartiles were pooled across all hospitals (eg, physicians in the highest quartile at each hospital were grouped together). We report absolute differences between physicians in the highest and lowest quartiles after matching admissions based on propensity scores to account for patient-level variation.

Results

The sample included 103 085 admissions to 135 attending physicians. After propensity score matching, the difference between physicians in the highest and lowest quartiles for in-hospital mortality was 2.4% (95% CI 0.6% to 4.3%, p<0.01); for readmission was 3.3% (95% CI 0.7% to 5.9%, p<0.01); for advanced imaging was 0.32 tests per admission (95% CI 0.12 to 0.52, p<0.01); and for hospital length of stay was 1.2 additional days per admission (95% CI 0.5 to 1.9, p<0.01). Physician-level differences in length of stay and imaging use were consistent across numerous sensitivity analyses and stable over time. Differences in mortality and readmission were consistent across most sensitivity analyses but were not stable over time and estimates were limited by sample size.

Conclusions

Patient outcomes and resource use in inpatient medical care varied substantially across physicians in this study. Physician-level variations in length of stay and imaging use were unlikely to be explained by patient factors whereas differences in mortality and readmission should be interpreted with caution and could be explained by unmeasured confounders. Physician-level variations may represent practice differences that highlight quality improvement opportunities.

Characteristics and clinical outcomes of index versus non-index hospital readmissions in Australian hospitals: a cohort study

Objective Risk factors and clinical outcomes of non-index hospital readmissions (readmissions to a hospital different from the previous admission) have not been studied in Australia. The present study compared characteristics and clinical outcomes between index and non-index hospital readmissions in the Australian healthcare setting. Methods This retrospective cohort study included medical admissions from 2012 to 2016 across all major public hospitals in South Australia. Readmissions within 30 day to all public hospitals were captured using electronic health information system. In-hospital mortality and readmission length of hospital stay (LOS) were compared, along with 30-day mortality and subsequent readmissions among patients readmitted to index or non-index hospitals. Results Of 114105 index admissions, there were 20539 (18.0%) readmissions. Of these, 17519 (85.3%) were index readmissions and 3020 (14.7%) were non-index readmissions. Compared with index readmissions, patients in the non-index readmissions group had a lower Charlson comorbidity index, shorter LOS and fewer complications during the index admission and were more likely to be readmitted with a different diagnosis to the index admission. No difference in in-hospital mortality was observed, but readmission LOS was shorter and 30-day mortality was higher among patients with non-index readmissions. Conclusion A substantial proportion of patients experienced non-index hospital readmissions. Non-index hospital readmitted patients had no immediate adverse outcomes, but experienced worse 30-day outcomes. What is known about the topic? A significant proportion of unplanned hospital readmissions occur to non-index hospitals. North American studies suggest that non-index hospital readmissions are associated with worse outcomes for patients due to discontinuity of care, medical reconciliation and delayed treatment. Limited studies have determined factors associated with non-index hospital readmissions in Australia, but whether such readmissions lead to adverse clinical outcomes is unknown. What does this paper add? In the Australian healthcare setting, 14.7% of patients were readmitted to non-index hospitals. Compared with index hospital readmissions, patients admitted to non-index hospitals had a lower Charlson comorbidity index, a shorter index LOS and fewer complications during the index admission. At the time of readmission there was no differences in discharge summary completion rates between the two groups. Unlike other studies, the present study found no immediate adverse outcomes for patients readmitted to non-index hospitals, but 30-day outcomes were worse than for patients who had an index hospital readmission. What are the implications for practitioners? Non-index hospital readmissions may not be totally preventable due to factors such as ambulance diversions stemming from emergency department overcrowding and prolonged emergency department waiting times. Patients should be advised to re-present to hospital in case they experience recurrence or relapse of a medical condition, and preferably should be readmitted to the same hospital to prevent discontinuity of care.

The association of continuity of care and cancer centre affiliation with outcomes among patients with cancer who require emergency department care

BACKGROUND

Patients with cancer have complex care requirements and frequently use the emergency department. The purpose of this study was to determine whether continuity of care, cancer expertise of an institution or both affect outcomes in patients with cancer in the emergency setting.

METHODS

We conducted a retrospective cohort study using administrative databases from Ontario, Canada, involving records of patients aged 20 years and older who received chemotherapy or radiation in the 30 days before a cancer-related visit to the emergency department between 2006 and 2011. Patients seen in an emergency department at an alternative hospital (not the site where cancer treatment was given) were matched based on propensity score to patients who visited their original hospital (site where cancer treatment was given). Next, patients seen at an alternative emergency department that was in a general hospital (i.e., not a cancer centre) were matched to patients who visited their original hospital or a cancer centre. Outcomes were admission to hospital at the index visit to the emergency department, 30-day mortality, having imaging with computed tomography and return visits to the emergency department.

RESULTS

We found 42 820 patients who were eligible for our study. Patients seen in the emergency departments at alternative hospitals were less likely to be admitted to hospital (odds ratio [OR] 0.78, 95% confidence interval [CI] 0.74-0.83) and had higher hazards of return visits to the emergency department than matched patients at original hospitals (hazard ratio [HR] 1.06, 95% CI 1.03-1.11). In comparison, patients at alternative general hospitals also had lower odds of admission to hospital (OR 0.83, 95% CI 0.79-0.88) and higher hazards of return visits to the emergency department (HR 1.07, 95% CI 1.03-1.11) compared with matched counterparts; however, these patients had higher 30-day mortality (OR 1.13, 95% CI 1.05-1.22) and lower odds of having CT imaging (OR 0.74, 95% CI 0.69-0.80).

INTERPRETATION

Cancer expertise of an institution rather than continuity of care may be an important predictor of outcomes following emergency treatment of patients with cancer.

The Care Transitions Measure-3 Is Only Weakly Associated with Post-discharge Outcomes: a Retrospective Cohort Study in 48,384 Albertans

BACKGROUND

The National Quality Forum endorsed a 3-item Care Transitions Measure (CTM-3), part of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, for evaluating hospital care transitions performance.

OBJECTIVE

To explore whether CTM-3 scores are a suitable proxy for quality of transitional care.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A random sample of 48,384 adults discharged from medical or surgical wards in all 113 acute care hospitals in Alberta, Canada, between April 2011 and March 2016.

MAIN MEASURES

CTM-3 scores and their associations with all-cause emergency department (ED) visits or non-elective readmissions at 30 days, 3 months, and 12 months anywhere in the province.

RESULTS

CTM-3 scores were significantly lower (all p < 0.01) for females, older patients, those discharged from medical wards or teaching hospitals, and those with longer length of stay, higher Charlson scores, prior ED visits/hospitalizations, or who did not return to independent living after discharge. CTM-3 scores were not significantly associated with outcomes at 30 days (mean score 77.5 in those who subsequently had an ED visit/readmission vs. 77.9 in those who did not, p = 0.13, aOR 0.99, 95% CI 0.99-0.99). Although CTM-3 scores were significantly lower in patients who subsequently had ED visit/readmission at 3 months (77.5 vs. 78.5) and 12 months (77.6 vs. 79.5), the magnitude of risk was small: for every 10 point decrease in the CTM-3 score, the risk of ED visit/readmission was 2.6% higher (aOR 1.03, 95% CI 1.01-1.05) at 3 months and 4.0% higher (aOR 1.04, 95% CI 1.01-1.08) at 12 months.

CONCLUSIONS

The CTM-3 score is influenced by baseline patient and hospital factors, is not associated with 30-day post-discharge outcomes, and is only weakly associated with 3- and 12-month outcomes. These findings suggest that the CTM-3 score is not a good performance measure for the quality of transitional care.

In-Hospital Mortality in Patients with Idiopathic Pulmonary Fibrosis: A US Cohort Study

Purpose

In patients with idiopathic pulmonary fibrosis (IPF), hospitalizations are associated with high mortality. We sought to determine in-hospital mortality rates and factors associated with in-hospital mortality in patients with IPF.

Methods

Patients with IPF were identified from the Premier Healthcare Database, a representative administrative dataset that includes > 20% of hospital discharges in the US, using an algorithm based on diagnostic codes and billing data. We used logistic regression to analyze associations between patient-, hospital-, and treatment-related characteristics and a composite primary outcome of death during the index visit, lung transplant during the index visit and > 1 day after admission, or death during a readmission within 90 days.

Results

The cohort comprised 6665 patients with IPF hospitalized between October 2011 and October 2014. A total of 963 (14.4%) met the primary outcome. Factors significantly associated with a higher risk of the primary outcome included mechanical ventilation [odds ratio 4.65 (95% CI 3.73, 5.80)], admission to the intensive care unit [1.83 (1.52, 2.21)], treatment with opioids (3.06 [2.57, 3.65]), and a diagnosis of pneumonia [1.44 (1.21, 1.71)]. Factors significantly associated with a lower risk included concurrent chronic obstructive pulmonary disease [0.65 (0.55, 0.77)] and female sex [0.67 (0.57, 0.79)].

Conclusions

Patients with IPF, particularly those receiving mechanical ventilation or intensive care, are at substantial risk of death or lung transplant during hospitalization or death during a readmission within 90 days.

Electronic supplementary material

The online version of this article (10.1007/s00408-019-00270-z) contains supplementary material, which is available to authorized users.

Association of the Trauma of Hospitalization With 30-Day Readmission or Emergency Department Visit

IMPORTANCE

Trauma of hospitalization refers to the depersonalizing and stressful experience of a hospital admission and is hypothesized to increase the risk of readmission after discharge.

OBJECTIVES

To characterize the trauma of hospitalization by measuring patient-reported disturbances in sleep, mobility, nutrition, and mood among medical inpatients, and to examine the association between these disturbances and the risk of unplanned return to hospital after discharge.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study enrolled participants between September 1, 2016, and September 1, 2017, at 2 academic hospitals in Toronto, Canada. Participants were adults admitted to the internal medicine ward for more than 48 hours. Participants were interviewed before discharge using a standardized questionnaire to assess sleep, mobility, nutrition, and mood. Responses for each domain were dichotomized as disturbance or no disturbance. Disturbance in 3 or 4 domains (the upper tertile) was considered high trauma of hospitalization, and disturbance in 0 to 2 domains (the lower 2 tertiles) was considered low trauma.

MAIN OUTCOME AND MEASURES

The primary outcome was readmission or emergency department visit within 30 days of discharge. The association between trauma of hospitalization and the primary outcome was examined using logistic regression, adjusted for age; sex; length of stay; Charlson Comorbidity Index Score; Laboratory-Based Acute Physiology Score; and baseline disturbances in sleep, mobility, nutrition, and mood.

RESULTS

A total of 207 patients participated, of whom 82 (39.6%) were women and 125 (60.4%) were men, with a mean (SD) age of 60.3 (16.8) years. Among the 207 participants, 75 (36.2%) reported sleep disturbance, 162 (78.3%) reported mobility disturbance, 114 (55.1%) reported nutrition disturbance, and 48 (23.2%) reported mood disturbance. Nearly all participants (192 [92.8%]) described a disturbance in at least 1 domain, and 61 participants (29.5%) had high trauma exposure. A statistically significant 15.8% greater absolute risk of readmission or emergency department visit was found in participants with high trauma (37.7%; 95% CI, 25.9%-51.1%) compared with those with low trauma (21.9%; 95% CI, 15.7%-29.7%), which remained statistically significant after adjusting for baseline characteristics (adjusted odds ratio, 2.52; 95% CI, 1.24-5.17; P = .01) and propensity score matching (odds ratio, 2.47; 95% CI, 1.11-5.73; P = .03).

CONCLUSIONS AND RELEVANCE

Disturbances in sleep, mobility, nutrition, and mood were common in medical inpatients; such trauma of hospitalization may be associated with a greater risk of 30-day readmission or emergency department visit after hospital discharge.

Preventability of 28-Day Hospital Readmissions in General Internal Medicine Patients: A Retrospective Analysis at a Quaternary Hospital

BACKGROUND

Unplanned hospital readmissions are associated with increased patient mortality and health care costs, yet only a fraction are likely to be preventable. This study's objective was to identify preventable hospital readmissions of general internal medicine patients, and their common causes.

METHODS

Patients who were discharged from the general internal medicine teaching service and readmitted to hospital within 28 days for 24 hours or more were recruited to the study; they were identified via the hospital electronic medical record system. Data were gathered via structured review of hospital charts/electronic medical records, along with standardized patient interviews. Unique to our study, a multidisciplinary panel of physicians, nurses, and hospital administrators adjudicated preventability and identified common causes of readmission.

RESULTS

Fifty-five hospital readmissions were identified; 53% were adjudicated to be preventable. There was no difference in any variable analyzed between preventable and nonpreventable readmissions. The most common causes of preventable readmissions were inadequate coordination of community services upon discharge, insufficient clinical postdischarge follow-up, and suboptimal end-of-life care.

CONCLUSION

This study identified a higher proportion of preventable 28-day hospital readmissions when compared with prior research. Increased involvement of palliative care during initial hospitalization for appropriate conditions and improvements in care after discharge may reduce preventable hospital readmissions.

Reducing excess hospital readmissions: Does destination matter?

Reducing excess hospital readmissions has become a high policy priority to lower health care spending and improve quality. The Affordable Care Act (ACA) penalizes hospitals with higher-than-expected readmission rates. This study tracks patient-level admissions and readmissions to Florida hospitals from 2006 to 2014 to examine whether the ACA has reduced readmission effectively. We compare not only the change in readmissions in targeted conditions to that in non-targeted conditions, but also changes in sites of readmission over time and differences in outcomes based on destination of readmission. We find that the drop in readmissions is largely owing to the decline in readmissions to the original hospital where they received operations or treatments (i.e., the index hospital). Patients readmitted into a different hospital experienced longer hospital stays. The results suggest that the reduction in readmission is likely achieved via both quality improvement and strategic admission behavior.

All-Cause Versus Complication-Specific Readmission Following Total Knee Arthroplasty

BACKGROUND

Unplanned readmissions have become an important quality indicator, particularly for reimbursement; thus, accurate assessment of readmission frequency and risk factors for readmission is critical. The purpose of this study was to determine (1) the frequency of and (2) risk factors for readmissions for all causes or procedure-specific complications within 30 days after total knee arthroplasty (TKA) as well as (3) the association between hospital volume and readmission rate.

METHODS

The Statewide Planning and Research Cooperative System (SPARCS) database from the New York State Department of Health was used to identify 377,705 patients who had undergone primary TKA in the period from 1997 to 2014. Preoperative diagnoses, comorbidities, and postoperative complications were determined using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Readmission was defined as all-cause, due to complications considered by the Centers for Medicare & Medicaid Services (CMS) to be TKA-specific, or due to an expanded list of TKA-specific complications based on expert opinion. Multivariable logistic regression analysis was utilized to determine the independent predictors of readmission within 30 days after surgery.

RESULTS

There were 22,076 all-cause readmissions-a rate of 5.8%, with a median rate of 3.9% (interquartile range [Q1, Q3] = 1.1%, 7.2%]) among the hospitals-within 30 days after discharge. Of these, only 11% (0.7% of all TKAs) were due to complications considered to be TKA-related by the CMS whereas 31% (1.8% of all TKAs) were due to TKA-specific complications on the expanded list based on expert opinion. Risk factors for TKA-specific readmissions based on the expanded list of criteria included an age of >85 years (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.15 to 1.52), male sex (OR = 1.41, 95% CI = 1.34 to 1.49), black race (OR = 1.24, 95% CI = 1.14 to 1.34), Medicaid coverage (OR = 1.40, 95% CI = 1.26 to 1.57), and comorbidities. Several comorbid conditions contributed to the all-cause but not the TKA-specific readmission risk. Very low hospital volume (<90 cases per year) was associated with a higher readmission risk.

CONCLUSIONS

The frequency of readmissions for TKA-specific complications was low relative to the frequency of all-cause readmissions. Reasons for hospital readmission are multifactorial and may not be amenable to simple interventions. Health-care-quality measurement of readmission rates should be calculated and risk-adjusted on the basis of procedure-specific criteria.

LEVEL OF EVIDENCE

Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Patients With Heart Failure Readmitted to the Original Hospital Have Better Outcomes Than Those Readmitted Elsewhere

Background

Up to one fifth of readmissions after a heart failure hospitalization occur at a different hospital. This negatively impacts information continuity, but whether site of readmission impacts subsequent outcomes is unclear.

Methods and Results

Retrospective cohort study of all patients discharged with a primary diagnosis of heart failure in Canada between April 2004 and December 2013. We compared patients readmitted within 30 days to the original hospital versus a different hospital. Of the 217 039 heart failure patients (mean age, 76.8 years, 50.1% male), 39 368 (18.1%) were readmitted within 30 days—32 771 (83.2%) to the original hospital and 6597 (16.8%) to a different hospital (increasing over time from 15.6% in 2004 to 18.5% by 2013; P for trend=0.001). Patients readmitted to different hospitals were younger and were more likely to be male, have a rural residence, a more‐recent discharge year, an index hospitalization at a teaching hospital, and to be brought in by ambulance at the time of the readmission. Readmissions to the original hospital were substantially shorter (mean, 10.4 days [95% CI, 10.3–10.6] versus 11.6 days [95% CI, 11.3–12.0]; adjusted means, 11.0 versus 12.0; P<0.0001) and had lower mortality (14.4% versus 15.0%; adjusted odds ratio, 0.89; 95% CI, 0.82–0.96) than readmissions to different hospitals.

Conclusions

Readmissions to a different hospital are becoming more frequent over time and are associated with longer stays and higher mortality rates than readmissions to the original hospital. Our findings provide further evidence that care fragmentation may be deleterious for patients with heart failure.

Understanding readmissions following operations of the thyroid and parathyroid glands

BACKGROUND

In anticipation of bundled-payment models for thyroid and parathyroid disease, a better understanding of resource utilization following surgery is required. We sought to characterize the use of hospital services following such operations using an analysis of readmissions.

METHODS

Patients age 18+years who underwent a thyroid or parathyroid operation in CA or NY (2008-2011) were classified by procedure type. Primary outcome was readmission within 90 days. Univariate and multivariable logistic regression were used to determine factors associated with readmission. Subset analysis was performed for thyroid cancer patients.

RESULTS

Among 59,427 patients, 34.2% had thyroid cancer. Eleven percent (n = 6462) were readmitted within 90 days, with 27% readmitted to a different hospital than the index. 66.2% of thyroid cancer patients were readmitted for a related condition.

CONCLUSION

Eleven percent of patients are admitted to the hospital within 90 days of an operation in the thyroid or parathyroid glands. Patient factors and diseases necessitate the use of hospital services. Bundled payments must consider the patients' needs for hospital-based services in calculating costs for surgically treated endocrine disorders.

Hospital Readmissions Following Physician Call System Change: A Comparison of Concentrated and Distributed Schedules

BACKGROUND

Physician call schedules are a critical element for medical practice and hospital efficiency. We compared readmission rates prior to and after a change in physician call system at Sunnybrook Health Sciences Centre.

METHODS

We studied patients discharged over a decade (2004 through 2013) and identified whether or not each patient was readmitted within the subsequent 28 days. We excluded patients discharged for a surgical, obstetrical, or psychiatric diagnosis. We used time-to-event analysis and time-series analysis to compare rates of readmission prior to and after the physician call system change (January 1, 2009).

RESULTS

A total of 89,697 patients were discharged, of whom 10,001 (11%) were subsequently readmitted and 4280 died. The risk of readmission was increased by about 26% following physician call system change (9.7% vs 12.2%, P <.001). Time-series analysis confirmed a 26% increase in the readmission rate after call system change (95% confidence interval, 22%-31%; P <.001). The increase in readmission rate after call system change persisted across patients with diverse ages, estimated readmission risks, and medical diagnoses. The net effect was equal to 7240 additional patient days in the hospital following call system change. A modest increase was observed at a nearby acute care hospital that did not change physician call system, and no increase in risk of death was observed with increased hospital readmissions.

CONCLUSION

We suggest that changes in physician call systems sometimes increase subsequent hospital readmission rates. Further reductions in readmissions may instead require additional resources or ingenuity.

Readmissions After Colon Cancer Surgery: Does It Matter Where Patients Are Readmitted?

PURPOSE

Readmissions to a different hospital may place patients at increased risk for poor outcomes and may increase their overall costs of care. We evaluated whether mortality and costs differ for patients with colon cancer on the basis of whether patients are readmitted to the index hospital or to a different hospital within 30 days of discharge.

METHODS

We conducted a retrospective analysis using SEER-Medicare linked claims data for patients with stage I to III colon cancer diagnosed between 2000 and2009 who were readmitted within 30 days (N = 3,399). Our primary outcome was all-cause mortality, which was modeled by using Cox proportional hazards. Secondary outcomes included colon cancer-specific mortality, 90-day mortality, and costs of care. We used subhazard ratios for colon cancer- specific mortality and generalized linear models for costs. For each model, we used a propensity score-weighted doubly robust approach to adjust for patient, physician, and hospital characteristics.

RESULTS

Approximately 23% (n = 769) of readmitted patients were readmitted to a different hospital than where they were initially discharged. After adjustment, there was no difference in all-cause mortality, colon cancer-specific mortality, or cost of care for patients readmitted to a different hospital. Patient readmitted to a different hospital did have a higher risk of short-term mortality (90-day all-cause mortality; adjusted hazard ratio, 1.18; 95% CI, 1.02 to 1.38).

CONCLUSION

Readmission to a different hospital after colon cancer surgery is associated with short-term mortality but not with long-term mortality nor with post-discharge costs of care. Additional investigation is needed to determine how to improve short-term mortality among patients readmitted to different hospitals.

Verbal autopsy and therapeutic itinerary of children who die before arrival in a paediatric centre in Yaoundé, Cameroon

BACKGROUND

In Cameroon the rate of infant-juvenile mortality remains high and most death occur in the community. Mortality statistics is usually based on hospital data which are generally insufficient and less reliable. In a context where legislation on death registration is not applied, and where conventional autopsy is not often done, verbal autopsy (VA) provides information on mortality. This study tried to experiment this method and also analyses the therapeutic pathway of a group of children who died before arrival at the emergency department of a pediatric hospital.

METHODS

A cross sectional descriptive study was carried out on children who died before arrival, at the Mother and Child Centre of the Chantal Biya Foundation in Yaounde, between October 2013 and April 2014. The addresses of parents or relatives of the deceased children were registered at the start of the study. Each respondent was interviewed 5 to 6 weeks later at the residence of the deceased child, with the aid of a VA questionnaire. Information obtained was on the socio-demographic characteristics of the families, past history of deceased, clinical presentation and the different health care services sought before the death.

RESULTS

In all, 40 children who died were included in the study. The majority of the deceased children were less than 5 years (82.5%) with 50.0% being less than 1 year of age. Almost half of them (47.5%) had been ill for more than 24 hours, 40% for more than 3 days. Up to 50.0% had not been taken to a health facility. Most of them had visited 2 or 3 other health facilities before dying on the way to our hospital. Auto medication was frequent (42.5%); parents initially recourse to drugs which were either bought or obtained from home. Some parents (25.0%) brought their children only after they had been to a private dispensary, or a traditional healer (15.0%). Only 7.5% benefited from consultation in a public health facility and 2.5% resorted to prayers and incantations. Whatever the kind of care sought, the choice was mostly guided by its proximity (32.5%), advice from a relative (27.5%) or its affordability.

CONCLUSIONS

It is of crucial importance that the government reinforces the measures to avoid the existence of clandestine health centres and check the competence of health care professionals. Improving referral/counter referral system will permit the limitation of fatal medical errors.

Predictors and outcomes of unplanned readmission to a different hospital

OBJECTIVES

To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital.

DESIGN

A population-based, secondary analysis using multilevel causal modeling.

SETTING

Acute care hospitals in California in the USA.

PARTICIPANTS

In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge.

INTERVENTION

No intervention.

MAIN OUTCOME MEASURE(S)

Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions.

RESULTS

Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group.

CONCLUSIONS

Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.

Remodeling a broken system through hospital-payer partnerships

One way to fix our broken system is to strengthen hospital-payer partnerships, which will help shift caregiver focus from volume to value.

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions

BACKGROUND

Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance.

OBJECTIVES

To study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection.

METHODS

Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence.

RESULTS

Predicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error.

CONCLUSION

Targeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.