Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

Background:

Urgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We reviewed urgent readmissions to determine which were potentially avoidable and compared rates of all-cause and avoidable readmissions.

Methods:

In a multicentre, prospective cohort study, we reviewed all urgent readmissions that occurred within six months among patients discharged to the community from 11 teaching and community hospitals between October 2002 and July 2006. Summaries of the readmissions were reviewed by at least four practising physicians using standardized methods to judge whether the readmission was an adverse event (poor clinical outcome due to medical care) and whether the adverse event could have been avoided. We used a latent class model to determine whether the probability that each readmission was truly avoidable exceeded 50%.

Results:

Of the 4812 patients included in the study, 649 (13.5%, 95% confidence interval [CI] 12.5%–14.5%) had an urgent readmission within six months after discharge. We considered 104 of them (16.0% of those readmitted, 95% CI 13.3%–19.1%; 2.2% of those discharged, 95% CI 1.8%–2.6%) to have had a potentially avoidable readmission. The proportion of patients who had an urgent readmission varied significantly by hospital (range 7.5%–22.5%; χ² = 92.9, p < 0.001); the proportion of readmissions deemed avoidable did not show significant variation by hospital (range 1.2%–3.7%; χ² = 12.5, p < 0.25). We found no association between the proportion of patients who had an urgent readmission and the proportion of patients who had an avoidable readmission (Pearson correlation 0.294; p = 0.38). In addition, we found no association between hospital rankings by proportion of patients readmitted and rankings by proportion of patients with an avoidable readmission (Spearman correlation coefficient 0.28, p = 0.41).

Interpretation:

Urgent readmissions deemed potentially avoidable were relatively uncommon, comprising less than 20% of all urgent readmissions following hospital discharge. Hospital-specific proportions of patients who were readmitted were not related to proportions with a potentially avoidable readmission.Urgent, unplanned hospital readmissions are increasingly being used to measure institutional or regional quality of care.^1–4 The public reporting of readmissions and their use in considerations for funding suggest a belief that readmissions indicate the quality of care provided by particular institutions. However, urgent readmissions are an informative metric only if we know what proportion of them are avoidable. If they are rarely avoidable, they would be a poor gauge of the quality of patient care.Current estimates of the proportion of urgent readmissions that are avoidable are unreliable. In a systematic review of 34 studies that reviewed how many readmissions were avoidable, 3 of the studies relied solely on combinations of administrative diagnostic codes, and most used undefined or subjective criteria.⁵ In addition, most of the studies were conducted at a single centre and used only one reviewer. The proportion of readmissions deemed avoidable varied widely, from 5.1%⁶ to 78.9%,⁷ which reflected in part the lack of standardized and reliable methods to identify avoidable readmissions.We conducted a multicentre prospective cohort study to elicit judgments from multiple practising physicians who used standard implicit review methods to determine whether urgent readmissions were potentially avoidable. We analyzed these judgments using a latent class analysis. We also measured the proportion of readmissions deemed avoidable and compared hospital-specific proportions of all-cause and avoidable readmissions.     

Proportion of hospital readmissions deemed avoidable: a systematic review

Background

Readmissions to hospital are increasingly being used as an indicator of quality of care. However, this approach is valid only when we know what proportion of readmissions are avoidable. We conducted a systematic review of studies that measured the proportion of readmissions deemed avoidable. We examined how such readmissions were measured and estimated their prevalence.

Methods

We searched the MEDLINE and EMBASE databases to identify all studies published from 1966 to July 2010 that reviewed hospital readmissions and that specified how many were classified as avoidable.

Results

Our search strategy identified 34 studies. Three of the studies used combinations of administrative diagnostic codes to determine whether readmissions were avoidable. Criteria used in the remaining studies were subjective. Most of the studies were conducted at single teaching hospitals, did not consider information from the community or treating physicians, and used only one reviewer to decide whether readmissions were avoidable. The median proportion of readmissions deemed avoidable was 27.1% but varied from 5% to 79%. Three study-level factors (teaching status of hospital, whether all diagnoses or only some were considered, and length of follow-up) were significantly associated with the proportion of admissions deemed to be avoidable and explained some, but not all, of the heterogeneity between the studies.

Interpretation

All but three of the studies used subjective criteria to determine whether readmissions were avoidable. Study methods had notable deficits and varied extensively, as did the proportion of readmissions deemed avoidable. The true proportion of hospital readmissions that are potentially avoidable remains unclear.In most instances, unplanned readmissions to hospital indicate bad health outcomes for patients. Sometimes they are due to a medical error or the provision of suboptimal patient care. Other times, they are unavoidable because they are due to the development of new conditions or the deterioration of refractory, severe chronic conditions.Hospital readmissions are frequently used to gauge patient care. Many organizations use them as a metric for institutional or regional quality of care.¹ The widespread public reporting of hospital readmissions and their use in considerations for funding implicitly suggest a belief that readmissions indicate the quality of care provided by particular physicians and institutions.The validity of hospital readmissions as an indicator of quality of care depends on the extent that readmissions are avoidable. As the proportion of readmissions deemed to be avoidable decreases, the effort and expense required to avoid one readmission will increase. This decrease in avoidable admissions will also dilute the relation between the overall readmission rate and quality of care. Therefore, it is important to know the proportion of hospital readmissions that are avoidable.We conducted a systematic review of studies that measured the proportion of readmissions that were avoidable. We examined how such readmissions were measured and estimated their prevalence.     

Readmission rates of patients discharged against medical advice: a matched cohort study

Objective

We compared the readmission rates and the pattern of readmission among patients discharged against medical advice (AMA) to control patients discharged with approval over a one-year follow-up period.

Methods

A retrospective matched-cohort study of 656 patients(328 were discharged AMA) who were followed for one year after their initial hospitalization at an urban university-affiliated teaching hospital in Vancouver, Canada that serves a population with high prevalence of addiction and psychiatric disorders. Multivariate conditional logistic regression was used to examine the independent association of discharge AMA on 14-day related diagnosis hospital readmission. We fit a multivariate conditional negative binomial regression model to examine the readmission frequency ratio between the AMA and non-AMA group.

Principal Findings

AMA patients were more likely to be homeless (32.3% vs. 11%) and have co-morbid conditions such as psychiatric illnesses, injection drug use, HIV, hepatitis C and previous gastrointestinal bleeding. Patients discharged AMA were more likely to be readmitted: 25.6% vs. 3.4%, p<0.001 by day 14. The AMA group were more likely to be readmitted within 14 days with a related diagnosis than the non-AMA group (Adjusted Odds Ratio 12.0; 95% Confidence Interval [CI]: 3.7–38.9). Patients who left AMA were more likely to be readmitted multiple times at one year compared to the non-AMA group (adjusted frequency ratio 1.6; 95% CI: 1.3–2.0). There was also higher all-cause in-hospital mortality during the 12-month follow-up in the AMA group compared to non-AMA group (6.7% vs. 2.4%, p = 0.01).

Conclusions

Patients discharged AMA were more likely to be homeless and have multiple co-morbid conditions. At one year follow-up, the AMA group had higher readmission rates, were predisposed to multiple readmissions and had a higher in-hospital mortality. Interventions to reduce discharges AMA in high-risk groups need to be developed and tested.     

Do Post Discharge Phone Calls Improve Care Transitions? A Cluster-Randomized Trial

Importance

The transition from hospital to home can expose patients to adverse events during the post discharge period. Post discharge care including phone calls may provide support for patients returning home but the impact on care transitions is unknown.

Objective

To examine the effect of a 72-hour post discharge phone call on the patient''s transition of care experience.

Design

Cluster-randomized control trial.

Setting

Urban, academic medical center.

Participants

General medical patients age 18 and older discharged home after hospitalization.

Main Outcomes and Measures

Primary outcome measure was the Care Transition Measure (CTM-3) score, a validated measure of the quality of care transitions. Secondary measures included self-reported adherence to medication and follow up plans, and 30-day composite of emergency department (ED) visits and hospital readmission.

Results

328 patients were included in the study over an 6-month period. 114 (69%) received a post discharge phone call, and 214 of all patients in the study completed the follow outcome survey (65% response rate). A small difference in CTM-3 scores was observed between the intervention and control groups (1.87 points, 95% CI 0.47–3.27, p = 0.01). Self-reported adherence to treatment plans, ED visits, and emergency readmission rates were similar between the two groups (odds ratio 0.57, 95% CI 0.13–2.45, 1.20, 95% CI 0.61–2.37, and 1.18, 95% CI 0.53–2.61, respectively).

Conclusions and Relevance

A single post discharge phone call had a small impact on the quality of care transitions and no effect on hospital utilization. Higher intensity post discharge support may be required to improve the patient experience upon returning home.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01580774","term_id":"NCT01580774"}}NCT01580774     

Risk for hospital readmission following bariatric surgery

Background and Objectives

Complications resulting in hospital readmission are important concerns for those considering bariatric surgery, yet present understanding of the risk for these events is limited to a small number of patient factors. We sought to identify demographic characteristics, concomitant morbidities, and perioperative factors associated with hospital readmission following bariatric surgery.

Methods

We report on a prospective observational study of 24,662 patients undergoing primary RYGB and 26,002 patients undergoing primary AGB at 249 and 317 Bariatric Surgery Centers of Excellence (BSCOE), respectively, in the United States from January 2007 to August 2009.Data were collected using standardized assessments of demographic factors and comorbidities, as well as longitudinal records of hospital readmissions, complications, and mortality.

Results

The readmission rate was 5.8% for RYGB and 1.2% for AGB patients 30 days after discharge. The greatest predictors for readmission following RYGB were prolonged length of stay (adjusted odds ratio [OR], 2.3; 95% confidence interval [CI], 2.0–2.7), open surgery (OR, 1.8; CI, 1.4–2.2), and pseudotumor cerebri (OR, 1.6; CI, 1.1–2.4). Prolonged length of stay (OR, 2.3; CI, 1.6–3.3), history of deep venous thrombosis or pulmonary embolism (OR, 2.1; CI, 1.3–3.3), asthma (OR, 1.5; CI, 1.1–2.1), and obstructive sleep apnea (OR, 1.5; CI, 1.1–1.9) were associated with the greatest increases in readmission risk for AGB. The 30-day mortality rate was 0.14% for RYGB and 0.02% for AGB.

Conclusion

Readmission rates are low and mortality is very rare following bariatric surgery, but risk for both is significantly higher after RYGB. Predictors of readmission were disparate for the two procedures. Results do not support excluding patients with certain comorbidities since any reductions in overall readmission rates would be very small on the absolute risk scale. Future research should evaluate the efficacy of post-surgical managed care plans for patients at higher risk for readmission and adverse events.     

Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study

Background

Several studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis.

Methods

We construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system.

Findings

For the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs.

Conclusions

Classifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.     

Healthcare Resource Utilization for Recurrent Clostridium difficile Infection in a Large University Hospital in Houston,Texas

Background

There are limited data examining healthcare resource utilization in patients with recurrent Clostridium difficile infection (CDI).

Methods

Patients with CDI at a tertiary-care hospital in Houston, TX, were prospectively enrolled into an observational cohort study. Recurrence was assessed via follow-up phone calls. Patients with one or more recurrence were included in this study. The location at which healthcare was obtained by patients with recurrent CDI was identified along with hospital length of stay. CDI-attributable readmissions, defined as a positive toxin test within 48 hours of admission and a primary CDI diagnosis, were also assessed.

Results

372 primary cases of CDI were identified of whom 64 (17.2%) experienced at least one CDI recurrence. Twelve of 64 patients experienced 18 further episodes of CDI recurrence. Of these 64 patients, 33 (50.8%) patients with recurrent CDI were readmitted of which 6 (18.2%) required ICU care, 29 (45.3%) had outpatient care only, and 2 (3.1%) had an ED visit. Nineteen (55.9%) readmissions were defined as CDI-attributable. For patients with CDI-attributable readmission, the average length of stay was 6±6 days.

Conclusion

Recurrent CDI leads to significant healthcare resource utilization. Methods of reducing the burden of recurrent CDI should be further studied.     

Readmissions and Death after ICU Discharge: Development and Validation of Two Predictive Models

Introduction

Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI) data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS) to assist ICU discharge planning.

Methods

Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011. Exclusion criteria: DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1) to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC) in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge.

Results

469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission) differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups.

Conclusions

Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although prospective validation is warranted, we speculate that these models may have value in assisting clinicians with ICU discharge planning.     

Impact of physician continuity on death or urgent readmission after discharge among patients with heart failure

Background:

Early physician follow-up after discharge is associated with lower rates of death and readmission among patients with heart failure. We explored whether physician continuity further influences outcomes after discharge.

Methods:

We used data from linked administrative databases for all adults aged 20 years or more in the province of Alberta who were discharged alive from hospital between January 1999 and June 2009 with a first-time diagnosis of heart failure. We used Cox proportional hazard models with time-dependent covariates to analyze the effect of follow-up with a familiar physician within the first month after discharge on the primary outcome of death or urgent all-cause readmission over 6 months. A familiar physician was defined as one who had seen the patient at least twice in the year before the index admission or once during the index admission.

Results:

In the first month after discharge, 5336 (21.9%) of the 24 373 identified patients had no follow-up visits, 16 855 (69.2%) saw a familiar physician, and 2182 (9.0%) saw unfamiliar physician(s) exclusively. The risk of death or unplanned readmission during the 6-month observation period was lower among patients who saw a familiar physician (43.6%; adjusted hazard ratio [HR] 0.87, 95% confidence interval [CI] 0.83–0.91) or an unfamiliar physician (43.6%; adjusted HR 0.90, 95% CI 0.83–0.97) for early follow-up visits, as compared with patients who had no follow-up visits (62.9%). Taking into account all follow-up visits over the 6-month period, we found that the risk of death or urgent readmission was lower among patients who had all of their visits with a familiar physician than among those followed by unfamiliar physicians (adjusted HR 0.91, 95% CI 0.85–0.98).

Interpretation:

Early physician follow-up after discharge and physician continuity were both associated with better outcomes among patients with heart failure. Research is needed to explore whether physician continuity is important for other conditions and in settings other than recent hospital discharge.Hospital care accounts for almost one-third of health care spending, and unplanned readmissions within 30 days after discharge cost more than $20 billion each year in the United States and Canada.¹ Heart failure is one of the most common reasons for admission to hospital and is associated with a high risk of readmission.¹ Although the prognosis for patients with heart failure has improved over the past decade, the risk of early death or readmission after discharge is still high and is increasing.² Prompt follow-up of patients with heart failure has been associated with lower rates of death and readmission,^3,4 and 30-day follow-up has been included as a quality-of-care indicator in Canada.⁵It is unclear, however, whether the postdischarge visits should be with the physician who previously saw the patient or with any physician. Results of studies exploring the association between provider continuity and postdischarge outcomes have been inconclusive and the studies have included few patients with heart failure.^6–9 Intuitively, one might consider physician continuity important for patients with heart failure discharged from hospital, given their age, high comorbidity burdens and complex treatment regimens. However, a robust evidence base and multiple guidelines with consistent messaging on key management principles have made physician continuity potentially less important.We designed this study to determine whether physician continuity influenced postdischarge outcomes among patients with heart failure beyond the influence of early physician follow-up.     

Methicillin-resistant Staphylococcus aureus infection and hospitalization in high-risk patients in the year following detection

Background

Many studies have evaluated methicillin-resistant Staphylococcus aureus (MRSA) infections during single hospitalizations and subsequent readmissions to the same institution. None have assessed the comprehensive burden of MRSA infection in the period after hospital discharge while accounting for healthcare utilization across institutions.

Methodology/Principal Findings

We conducted a retrospective cohort study of adult patients insured by Harvard Pilgrim Health Care who were newly-detected to harbor MRSA between January 1991 and December 2003 at a tertiary care medical center. We evaluated all MRSA-attributable infections associated with hospitalization in the year following new detection, regardless of hospital location. Data were collected on comorbidities, healthcare utilization, mortality and MRSA outcomes. Of 591 newly-detected MRSA carriers, 23% were colonized and 77% were infected upon detection. In the year following detection, 196 (33%) patients developed 317 discrete and unrelated MRSA infections. The most common infections were pneumonia (34%), soft tissue (27%), and primary bloodstream (18%) infections. Infections occurred a median of 56 days post-detection. Of all infections, 26% involved bacteremia, and 17% caused MRSA-attributable death. During the admission where MRSA was newly-detected, 14% (82/576) developed subsequent infection. Of those surviving to discharge, 24% (114/482) developed post-discharge infections in the year following detection. Half (99/185, 54%) of post-discharge infections caused readmission, and most (104/185, 55%) occurred over 90 days post-discharge.

Conclusions/Significance

In high-risk tertiary care patients, newly-detected MRSA carriage confers large risks of infection and substantial attributable mortality in the year following acquisition. Most infections occur post-discharge, and 18% of infections associated with readmission occurred in hospitals other than the one where MRSA was newly-detected. Despite gains in reducing MRSA infections during hospitalization, the risk of MRSA infection among critically and chronically ill carriers persists after discharge and warrants targeted prevention strategies.     

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community

Background

Readmissions to hospital are common, costly and often preventable. An easy-to-use index to quantify the risk of readmission or death after discharge from hospital would help clinicians identify patients who might benefit from more intensive post-discharge care. We sought to derive and validate an index to predict the risk of death or unplanned readmission within 30 days after discharge from hospital to the community.

Methods

In a prospective cohort study, 48 patient-level and admission-level variables were collected for 4812 medical and surgical patients who were discharged to the community from 11 hospitals in Ontario. We used a split-sample design to derive and validate an index to predict the risk of death or nonelective readmission within 30 days after discharge. This index was externally validated using administrative data in a random selection of 1 000 000 Ontarians discharged from hospital between 2004 and 2008.

Results

Of the 4812 participating patients, 385 (8.0%) died or were readmitted on an unplanned basis within 30 days after discharge. Variables independently associated with this outcome (from which we derived the nmemonic “LACE”) included length of stay (“L”); acuity of the admission (“A”); comorbidity of the patient (measured with the Charlson comorbidity index score) (“C”); and emergency department use (measured as the number of visits in the six months before admission) (“E”). Scores using the LACE index ranged from 0 (2.0% expected risk of death or urgent readmission within 30 days) to 19 (43.7% expected risk). The LACE index was discriminative (C statistic 0.684) and very accurate (Hosmer–Lemeshow goodness-of-fit statistic 14.1, p = 0.59) at predicting outcome risk.

Interpretation

The LACE index can be used to quantify risk of death or unplanned readmission within 30 days after discharge from hospital. This index can be used with both primary and administrative data. Further research is required to determine whether such quantification changes patient care or outcomes.Readmission to hospital and death are adverse patient outcomes that are serious, common and costly.^1,2 Several studies suggest that focused care after discharge can improve post-discharge outcomes.^3–7 Being able to accurately predict the risk of poor outcomes after hospital discharge would allow health care workers to focus post-discharge interventions on patients who are at highest risk of poor post-discharge outcomes. Further, policy-makers have expressed interest in either penalizing hospitals with relatively high rates of readmission or rewarding hospitals with relatively low expected rates.⁸ To implement this approach, a validated method of standardizing readmission rates is needed.⁹Two validated models for predicting risk of readmission after hospital discharge have been published.^10,11 However, these models are impractical to clinicians. Both require area-level information (e.g., neighbourhood socio-economic status and community-specific rates of admission) that is not readily available. Getting this information requires access to detailed tables, thereby making the model impractical. Second, both models are so complex that risk estimates cannot be attained from them without the aid of special software. Although these models have been used by health-system planners in the United Kingdom, we are unaware of any clinicians who use them when preparing patients for hospital discharge.Our primary objective was to derive and validate a clinically useful index to quantify the risk of early death or unplanned readmission among patients discharged from hospital to the community.     

Are AMI Patients with Comorbid Mental Illness More Likely to be Admitted to Hospitals with Lower Quality of AMI Care?

Objective

Older patients with comorbid mental illness are shown to receive less appropriate care for their medical conditions. This study analyzed Medicare patients hospitalized for acute myocardial infarction (AMI) and determined whether those with comorbid mental illness were more likely to present to hospitals with lower quality of AMI care.

Methods

Retrospective analyses of Medicare claims in 2008. Hospital quality was measured using the five “Hospital Compare” process indicators (aspirin at admission/discharge, beta-blocker at admission/discharge, and angiotension-converting enzyme inhibitor or angiotension receptor blocker for left ventricular dysfunction). Multinomial logit model determined the association of mental illness with admission to low-quality hospitals (rank of the composite process score <10^th percentile) or high-quality hospitals (rank>90^th percentile), compared to admissions to other hospitals with medium quality. Multivariate analyses further determined the effects of hospital type and mental diagnosis on outcomes.

Results

Among all AMI admissions to 2,845 hospitals, 41,044 out of 287,881 patients were diagnosed with mental illness. Mental illness predicted a higher likelihood of admission to low-quality hospitals (unadjusted rate 2.9% vs. 2.0%; adjusted odds ratio [OR]1.25, 95% confidence interval [CI] 1.17–1.34, p<0.01), and an equal likelihood to high-quality hospitals (unadjusted rate 9.8% vs. 10.3%; adjusted OR 0.97, 95% CI 0.93–1.01, p = 0.11). Both lower hospital quality and mental diagnosis predicted higher rates of 30-day readmission, 30-day mortality, and 1-year mortality.

Conclusions

Among Medicare myocardial infarction patients, comorbid mental illness was associated with an increased risk for admission to lower-quality hospitals. Both lower hospital quality and mental illness predicted worse post-AMI outcomes.     

Thirty-Day Readmission Rates in Orthopedics: A Systematic Review and Meta-Analysis

Background

Hospital readmission rates are being used to evaluate performance. A survey of the present rates is needed before policies can be developed to decrease incidence of readmission. We address three questions: What is the present rate of 30-day readmission in orthopedics? How do factors such as orthopedic specialty, data source, patient insurance, and time of data collection affect the 30-day readmission rate? What are the causes and risk factors for 30-day readmissions?

Methods/Findings

A review was first registered with Prospero (CRD42014010293, 6/17/2014) and a meta-analysis was performed to assess the current 30-day readmission rate in orthopedics. Studies published after 2006 were retrieved, and 24 studies met the inclusion criteria. The 30-day readmission rate was extrapolated from each study along with the orthopedic subspecialty, data source, patient insurance, time of collection, patient demographics, and cause of readmission. A sensitivity analysis was completed on the stratified groups. The overall 30-day readmission rate across all orthopedics was 5.4 percent (95% confidence interval: 4.8,6.0). There was no significant difference between subspecialties. Studies that retrieved data from a multicenter registry had a lower 30-day readmission rate than those reporting data from a single hospital or a large national database. Patient populations that only included Medicare patients had a higher 30-day readmission rate than populations of all insurance. The 30-day readmission rate has decreased in the past ten years. Age, length of stay, discharge to skilled nursing facility, increased BMI, ASA score greater than 3, and Medicare/Medicaid insurance showed statistically positive correlation with increased 30-day readmissions in greater than 75 percent of studies. Surgical site complications accounted for 46 percent of 30-day readmissions.

Conclusions

This meta-analysis shows the present rate of 30-day readmissions in orthopedics. Demonstrable heterogeneity between studies underlines the importance of uniform collection and reporting of readmission rates for hospital evaluation and reimbursement.     

Risk Factors for Hospital Readmission after Radical Gastrectomy for Gastric Cancer: A Prospective Study

Background

Hospital readmission is gathering increasing attention as a measure of health care quality and a potential cost-saving target. The purpose of this prospective study was to determine risk factors for readmission within 30 days of discharge after gastrectomy for patients with gastric cancer.

Methods

We conducted a prospective study of patients undergoing radical gastrectomy for gastric cancer from October 2013 to November 2014 in our institution. The incidence, cause and risk factors for 30-day readmission were determined.

Results

A total of 376 patients were included in our analysis without loss in follow-up. The 30-day readmission rate after radical gastrectomy for gastric cancer was 7.2% (27of 376). The most common cause for readmission included gastrointestinal complications and postoperative infections. On the basis of multivariate logistic regression analysis, preoperative nutritional risk screening 2002 score ≥ 3 was an independent risk factor for 30-day readmission. Factors not associated with a higher readmission rate included a history of a major postoperative complication during the index hospitalization, prolonged primary length of hospital stay after surgery, a history of previous abdominal surgery, advanced age, body mass index, pre-existing cardiopulmonary comorbidities, American Society of Anesthesiology grade, type of resection, extent of node dissection and discharge disposition.

Conclusions

Readmission within 30 days of discharge after radical gastrectomy for gastric cancer is common. Patients with nutritional risk preoperatively are at high risk for 30-day readmission. Preoperative optimization of nutritional status of patients at nutritional risk may effectively decrease readmission rates.     

Tobacco Stained Fingers and Its Association with Death and Hospital Admission: A Retrospective Cohort Study

Background

Among smokers, the presence of tobacco stains on fingers has recently been associated with a high prevalence of tobacco related conditions and alcohol abuse.

Objective

we aimed to explore tobacco stains as a marker of death and hospital readmission.

Method

Seventy-three smokers presenting tobacco-tar staining on their fingers and 70 control smokers were followed during a median of 5.5 years in a retrospective cohort study. We used the Kaplan-Meier survival analysis and the log-rank test to compare mortality and hospital readmission rates among smokers with and smokers without tobacco stains. Multivariable Cox models were used to adjust for confounding factors: age, gender, pack-year unit smoked, cancer, harmful alcohol use and diabetes. The number of hospital admissions was compared through a negative binomial regression and adjusted for the follow-up time, diabetes, and alcohol use.

Results

Forty-three patients with tobacco-stained fingers died compared to 26 control smokers (HR 1.6; 95%CI: 1.0 to 2.7; p 0.048). The association was not statistically significant after adjustment. Patients with tobacco-stained fingers needed a readmission earlier than smokers without stains (HR 2.1; 95%CI: 1.4 to 3.1; p<0.001), and more often (incidence rate ratio (IRR) 1.6; 95%CI: 1.1 to 2.1). Associations between stains and the first hospital readmission (HR 1.6; 95%CI: 1.0 to 2.5), and number of readmissions (IRR 1.5; 95%CI: 1.1 to 2.1) persisted after adjustment for confounding factors.

Conclusions

Compared to other smokers, those presenting tobacco-stained fingers have a high unadjusted mortality rate and need early and frequent hospital readmission even when controlling for confounders.     

The effects of centralised and specialised intervention in the early course of severe unipolar depressive disorder: a randomised clinical trial

Background

Little is known on whether centralised and specialised combined pharmacological and psychological intervention in the early phase of severe unipolar depression improve prognosis. The aim of the present study was to assess the benefits and harms of centralised and specialised secondary care intervention in the early course of severe unipolar depression.

Methods

A randomised multicentre trial with central randomisation and blinding in relation to the primary outcome comparing a centralised and specialised outpatient intervention program with standard decentralised psychiatric treatment. The interventions were offered at discharge from first, second, or third hospitalisation due to a single depressive episode or recurrent depressive disorder. The primary outcome was time to readmission to psychiatric hospital. The data on re-hospitalisation was obtained from the Danish Psychiatric Central Register. The secondary and tertiary outcomes were severity of depressive symptoms according to the Major Depression Inventory, adherence to medical treatment, and satisfaction with treatment according to the total score on the Verona Service Satisfaction Scale-Affective Disorder (VSSS-A). These outcomes were assessed using questionnaires one year after discharge from hospital.

Results

A total of 268 patients with unipolar depression were included. There was no significant difference in the time to readmission (unadjusted hazard ratio 0.89, 95% confidence interval 0.60 to 1.32; log rank: χ² = 0.3, d.f. = 1, p = 0.6); severity of depressive symptoms (mood disorder clinic: median 21.6, quartiles 9.7–31.2 versus standard treatment: median 20.2, quartiles 10.0–29.8; p = 0.7); or the prevalence of patients in antidepressant treatment (73.9% versus 80.0%, p = 0.2). Centralised and specialised secondary care intervention resulted in significantly higher satisfaction with treatment (131 (SD 31.8) versus 107 (SD 25.6); p<0.001).

Conclusions

Centralised and specialised secondary care intervention in the early course of severe unipolar depression resulted in no significant effects on time to rehospitalisation, severity of symptoms, or use of antidepressants, but increased patient satisfaction.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00253071","term_id":"NCT00253071"}}NCT00253071     

Identifying acute coronary syndrome patients approaching end-of-life

Background

Acute coronary syndrome (ACS) is common in patients approaching the end-of-life (EoL), but these patients rarely receive palliative care. We compared the utility of a palliative care prognostic tool (Gold Standards Framework (GSF)) and the Global Registry of Acute Coronary Events (GRACE) score, to help identify patients approaching EoL.

Methods and Findings

172 unselected consecutive patients with confirmed ACS admitted over an eight-week period were assessed using prognostic tools and followed up for 12 months. GSF criteria identified 40 (23%) patients suitable for EoL care while GRACE identified 32 (19%) patients with ≥10% risk of death within 6 months. Patients meeting GSF criteria were older (p = 0.006), had more comorbidities (1.6±0.7 vs. 1.2±0.9, p = 0.007), more frequent hospitalisations before (p = 0.001) and after (0.0001) their index admission, and were more likely to die during follow-up (GSF+ 20% vs GSF- 7%, p = 0.03). GRACE score was predictive of 12-month mortality (C-statistic 0.75) and this was improved by the addition of previous hospital admissions and previous history of stroke (C-statistic 0.88).

Conclusions

This study has highlighted a potentially large number of ACS patients eligible for EoL care. GSF or GRACE could be used in the hospital setting to help identify these patients. GSF identifies ACS patients with more comorbidity and at increased risk of hospital readmission.     

Insurance Status Is Associated with Treatment Allocation and Outcomes after Subarachnoid Hemorrhage

Objective

Subarachnoid hemorrhage (SAH) is a particularly devastating type of stroke which is responsible for one third of all stroke-related years of potential life lost before age 65. Surgical treatment has been shown to decrease both morbidity and mortality after subarachnoid hemorrhage. We hypothesized that payer status other than private insurance is associated with lower allocation to surgical treatment for patients with SAH and worse outcomes.

Design

We examined the association between insurance type and surgical treatment allocation and outcomes for patients with SAH while adjusting for a wide range of patient and hospital factors. We analyzed the Nationwide Inpatient Sample hospital discharge database using survey procedures to produce weighted estimates representative of the United States population.

Patients

We studied 21047 discharges, representing a weighted estimate of 102595 patients age 18 and above with a discharge diagnosis of SAH between 2003 and 2008.

Measurements

Multivariable logistic and generalized linear regression analyses were used to assess for any associations between insurance status and surgery allocation and outcomes.

Main Results

Despite the benefits of surgery 66% of SAH patients did not undergo surgical treatment to prevent rebleeding. Mortality was more than twice as likely for patients with no surgical treatment compared to those who received surgery. Medicare patients were significantly less likely to receive surgical treatment.

Conclusions

Nearly two thirds of patients with SAH don''t receive operative care, and Medicare patients were significantly less likely to receive surgical treatment than other patients. Bias against the elderly and those with chronic illness and disability may play a part in these findings. A system of regionalized care for patients presenting with SAH may reduce disparities and improve appropriate allocation to surgical care and deserves prospective study.     

Prognostic Implication of Physical Signs of Congestion in Acute Heart Failure Patients and Its Association with Steady-State Biomarker Levels

Background

Congestive physical findings such as pulmonary rales and third heart sound (S3) are hallmarks of acute heart failure (AHF). However, their role in outcome prediction remains unclear. We sought to investigate the association between congestive physical findings upon admission, steady-state biomarkers at the time of discharge, and long-term outcomes in AHF patients.

Methods

We analyzed the data of 133 consecutive AHF patients with an established diagnosis of ischemic or non-ischemic (dilated or hypertrophic) cardiomyopathy, admitted to a single-center university hospital between 2006 and 2010. The treating physician prospectively recorded major symptoms and congestive physical findings of AHF: paroxysmal nocturnal dyspnea, orthopnea, pulmonary rales, jugular venous distension (JVD), S3, and edema. The primary endpoint was defined as rehospitalization for HF.

Results

Majority (63.9%) of the patients had non-ischemic etiology and, at the time of admission, S3 was seen in 69.9% of the patients, JVD in 54.1%, and pulmonary rales in 43.6%. The mean follow-up period was 726 ± 31days. Patients with pulmonary rales (p < 0.001) and S3 (p  =  0.011) had worse readmission rates than those without these findings; the presence of these findings was also associated with elevated troponin T (TnT) levels at the time of discharge (odds ratio [OR] 2.8; p  =  0.02 and OR 2.6; p  =  0.05, respectively).

Conclusion

Pulmonary rales and S3 were associated with inferior readmission rates and elevated TnT levels on discharge. The worsening of the readmission rate owing to congestive physical findings may be a consequence of on-going myocardial injury.