Development,Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange

ObjectivesIdentifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.MethodsOur objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients.ResultsA risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates.ConclusionsThe risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.     

Are readmissions avoidable?

OBJECTIVE--To examine the possible use of readmission rates as an outcome indicator of hospital inpatient care by investigating avoidability of unplanned readmissions within 28 days of discharge. DESIGN--Retrospective analysis of a stratified random sample of case notes of patients with an unplanned readmission between July 1987 and June 1988 by nine clinical assessors (263 assessments) and categorisation of the readmission as avoidable, unavoidable, or unclassifiable. SETTING--District in North East Thames region. 481 General medical, geriatric, and general surgical inpatients with a readmission at 0-6 days or 21-27 days after the first (index) discharge between July 1987 and June 1988 from whom 100 case notes were selected randomly and of which 74 were available for study. MAIN OUTCOME MEASURES--Assessment of readmissions as avoidable, unavoidable, unclassifiable, variability of assessment within cases and variability among assessors according to specialty and duration to readmission. RESULTS--General medical and geriatric readmissions and surgical readmissions at 0-6 days after discharge were more likely to be assessed as avoidable than those at 21-27 days (medical readmissions 32 v 6%, surgical admissions 49 v 19%). General surgical readmissions were significantly more frequently assessed as avoidable than general medical and geriatric readmissions. The extent of agreement between doctors varied, with general medical and geriatric readmissions at 21-27 days after first discharge causing the greatest variability of judgment. CONCLUSIONS--Differences were apparent in the extent of avoidability of readmissions in different groups of admissions. However, assessors rated only 49.3% of the group with the highest proportion of avoidable admissions (surgical readmissions at 0-6 days) as avoidable. The remainder were thought to be unavoidable except for 2%, which could not be classified. The use of readmission rates as an outcome indicator of hospital inpatient care should be avoided.     

Rates of readmission and death associated with leaving hospital against medical advice: a population-based study

Background:

Leaving hospital against medical advice may have adverse consequences. Previous studies have been limited by evaluating specific types of patients, small sample sizes and incomplete determination of outcomes. We hypothesized that leaving hospital against medical advice would be associated with increases in subsequent readmission and death.

Methods:

In a population-based analysis involving all adults admitted to hospital and discharged alive in Manitoba from Apr. 1, 1990, to Feb. 28, 2009, we evaluated all-cause 90-day mortality and 30-day hospital readmission. We used multivariable regression, adjusted for age, sex, socioeconomic status, year of hospital admission, patient comorbidities, hospital diagnosis, past frequency of admission to hospital, having previously left hospital against medical advice and data clustering (patients with multiple admissions). For readmission, we assessed both between-person and within-person effects of leaving hospital against medical advice.

Results:

Leaving against medical advice occurred in 21 417 of 1 916 104 index hospital admissions (1.1%), and was associated with higher adjusted rates of 90-day mortality (odds ratio [OR] 2.51, 95% confidence interval [CI] 2.18–2.89), and 30-day hospital readmission (within-person OR 2.10, CI 1.99–2.21; between-person OR 3.04, CI 2.79–3.30). In our additional analyses, elevated rates of readmission and death associated with leaving against medical advice were manifest within 1 week and persisted for at least 180 days after discharge.

Interpretation:

Adults who left the hospital against medical advice had higher rates of hospital readmission and death. The persistence of these effects suggests that they are not solely a result of incomplete treatment of acute illness. Interventions aimed at reducing these effects may need to include longitudinal interventions extending beyond admission to hospital.Patients leaving hospital against medical advice have been discussed in the medical literature for more than 50 years.¹ Reported to occur in 1%–2% of patients in general hospitals,^2,3 the numbers are large; in the United States, 368 000 patients left against medical advice in 2007,³ and rates higher than 10% have been documented in certain subgroups, including Canadian patients with HIV and predominantly poor residents of inner city areas.^4,5 The main concern over leaving hospital against medical advice is that it may increase morbidity or mortality. Previous studies attempting to assess this effect^2,4–13 have all been restricted to specific types of patients, and most studies were limited by small sample sizes and incomplete determination of outcomes. In this study, we used data that avoided these limitations to test the hypothesis that patients who leave hospital against medical advice have higher rates of hospital readmission and death.     

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.     

Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features

Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755–0.771) to 0.769 (95% CI: 0.761–0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.     

Hospital Readmission Prevalence and Analysis of Those Potentially Avoidable in Southern Italy

Background

One quality indicator of hospital care, which can be used to judge the process of care, is the prevalence of hospital readmission because it reflects the impact of hospital care on the patient’s condition after discharge. The purposes of the study were to measure the prevalence of hospital readmissions, to identify possible factors that influence such readmission and to measure the prevalence of readmissions potentially avoidable in Italy.

Methods

A sample of 2289 medical records of patients aged 18 and over admitted for medical or surgical illness at one 502-bed community non-teaching hospital were randomly selected.

Results

A total of 2252 patients were included in the final analysis, equaling a response rate of 98.4%. The overall hospital readmission prevalence within 30 days of discharge was 10.2%. Multivariate logistic regression analysis revealed that the proportion of patients readmitted within 30 days of discharge significantly increased regardless of Charlson et al. comorbidity score, among unemployed or retired patients, and in patients in general surgery. A total of 43.7% hospital readmissions were judged to be potentially avoidable. Multivariate logistic regression analysis showed that potentially avoidable readmissions were significantly higher in general surgery, in patients referred to hospital by an emergency department physician, and in those with a shortened time between discharge and readmission.

Conclusion

Additional research on intervention or bundle of interventions applicable to acute inpatient populations that aim to reduce potentially avoidable readmissions is strongly needed, and health care providers are urged to implement evidence-based programs for more cost-effective delivery of health care.     

Self-Administered Outpatient Antimicrobial Infusion by Uninsured Patients Discharged from a Safety-Net Hospital: A Propensity-Score-Balanced Retrospective Cohort Study

Background

Outpatient parenteral antimicrobial therapy (OPAT) is accepted as safe and effective for medically stable patients to complete intravenous (IV) antibiotics in an outpatient setting. Since, however, uninsured patients in the United States generally cannot afford OPAT, safety-net hospitals are often burdened with long hospitalizations purely to infuse antibiotics, occupying beds that could be used for patients requiring more intensive services. OPAT is generally delivered in one of four settings: infusion centers, nursing homes, at home with skilled nursing assistance, or at home with self-administered therapy. The first three—termed healthcare-administered OPAT (H-OPAT)—are most commonly used in the United States by patients with insurance funding. The fourth—self-administered OPAT (S-OPAT)—is relatively uncommon, with the few published studies having been conducted in the United Kingdom. With multidisciplinary planning, we established an S-OPAT clinic in 2009 to shift care of selected uninsured patients safely to self-administration of their IV antibiotics at home. We undertook this study to determine whether the low-income mostly non-English-speaking patients in our S-OPAT program could administer their own IV antimicrobials at home with outcomes as good as, or better than, those receiving H-OPAT.

Methods and Findings

Parkland Hospital is a safety-net hospital serving Dallas County, Texas. From 1 January 2009 to 14 October 2013, all uninsured patients meeting criteria were enrolled in S-OPAT, while insured patients were discharged to H-OPAT settings. The S-OPAT patients were trained through multilingual instruction to self-administer IV antimicrobials by gravity, tested for competency before discharge, and thereafter followed at designated intervals in the S-OPAT outpatient clinic for IV access care, laboratory monitoring, and physician follow-up. The primary outcome was 30-d all-cause readmission, and the secondary outcome was 1-y all-cause mortality. The study was adequately powered for readmission but not for mortality. Clinical, sociodemographic, and outcome data were collected from the Parkland Hospital electronic medical records and the US census, constituting a historical prospective cohort study. We used multivariable logistic regression to develop a propensity score predicting S-OPAT versus H-OPAT group membership from covariates. We then estimated the effect of S-OPAT versus H-OPAT on the two outcomes using multivariable proportional hazards regression, controlling for selection bias and confounding with the propensity score and covariates.Of the 1,168 patients discharged to receive OPAT, 944 (81%) were managed in the S-OPAT program and 224 (19%) by H-OPAT services. In multivariable proportional hazards regression models controlling for confounding and selection bias, the 30-d readmission rate was 47% lower in the S-OPAT group (adjusted hazard ratio [aHR], 0.53; 95% CI 0.35–0.81; p = 0.003), and the 1-y mortality rate did not differ significantly between the groups (aHR, 0.86; 95% CI 0.37–2.00; p = 0.73). The S-OPAT program shifted a median 26 d of inpatient infusion per patient to the outpatient setting, avoiding 27,666 inpatient days. The main limitation of this observational study—the potential bias from the difference in healthcare funding status of the groups—was addressed by propensity score modeling.

Conclusions

S-OPAT was associated with similar or better clinical outcomes than H-OPAT. S-OPAT may be an acceptable model of treatment for uninsured, medically stable patients to complete extended courses of IV antimicrobials at home.     

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.     

Clinical and Financial Outcomes Due to Methicillin Resistant Staphylococcus aureus Surgical Site Infection: A Multi-Center Matched Outcomes Study

Background

The clinical and financial outcomes of SSIs directly attributable to MRSA and methicillin-resistance are largely uncharacterized. Previously published data have provided conflicting conclusions.

Methodology

We conducted a multi-center matched outcomes study of 659 surgical patients. Patients with SSI due to MRSA were compared with two groups: matched uninfected control patients and patients with SSI due to MSSA. Four outcomes were analyzed for the 90-day period following diagnosis of the SSI: mortality, readmission, duration of hospitalization, and hospital charges. Attributable outcomes were determined by logistic and linear regression.

Principal Findings

In total, 150 patients with SSI due to MRSA were compared to 231 uninfected controls and 128 patients with SSI due to MSSA. SSI due to MRSA was independently predictive of readmission within 90 days (OR = 35.0, 95% CI 17.3–70.7), death within 90 days (OR = 7.27, 95% CI 2.83–18.7), and led to 23 days (95% CI 19.7–26.3) of additional hospitalization and $61,681 (95% 23,352–100,011) of additional charges compared with uninfected controls. Methicillin-resistance was not independently associated with increased mortality (OR = 1.72, 95% CI 0.70–4.20) nor likelihood of readmission (OR = 0.43, 95% CI 0.21–0.89) but was associated with 5.5 days (95% CI 1.97–9.11) of additional hospitalization and $24,113 (95% 4,521–43,704) of additional charges.

Conclusions/Significance

The attributable impact of S. aureus and methicillin-resistance on outcomes of surgical patients is substantial. Preventing a single case of SSI due to MRSA can save hospitals as much as $60,000.     

Time Trends in Ischemic Stroke among Type 2 Diabetic and Non-Diabetic Patients: Analysis of the Spanish National Hospital Discharge Data (2003-2012)

Background

Type 2 Diabetes (T2DM) is the most rapidly increasing risk factor for ischemic stroke. We aimed to compare trends in outcomes for ischemic stroke in people with or without diabetes in Spain between 2003 and 2012.

Methods

We selected all patients hospitalized for ischemic stroke using national hospital discharge data. We evaluated annual incident rates stratified by T2DM status. We analyzed trends in the use of diagnostic and therapeutic procedures, patient comorbidities, and in-hospital outcomes. We calculated in-hospital mortality (IHM), length of hospital stay (LOHS) and readmission rate in one month after discharge. Time trend on the incidence of hospitalization was estimated fitting Poisson regression models by sex and diabetes variables. In-hospital mortality was analyzed using logistic regression models separate for men and women. LOHS were compared with ANOVA or Kruskal-Wallis when necessary.

Results

We identified a total of 423,475 discharges of patients (221,418 men and 202,057 women) admitted with ischemic stroke as primary diagnosis. Patients with T2DM accounted for 30.9% of total. The estimated incidence rates of discharges increased significantly in all groups. The incidence of hospitalization due to stroke (with ICD9 codes for stroke as main diagnosis at discharge) was higher among those with than those without diabetes in all the years studied. T2DM was positively associated with ischemic stroke with an adjusted incidence rate ratio (IRR) of 2.27 (95% CI 2.24–2.29) for men and 2.15 (95%CI 2.13–2.17) for women. Over the 10 year period LOHS decreased significantly in men and women with and without diabetes. Readmission rate remained stable in diabetic and non diabetic men (around 5%) while slightly increased in women with and without diabetes. We observed a significant increase in the use of fibrinolysis from 2002–2013. IHM was positively associated with older age in all groups, with Charlson Comorbidity Index > 3 and atrial fibrillation as risk factors. The IHM did not change significantly over time among T2DM men and women ranging from 9.25% to 10.56% and from 13.21% to 14.86%, respectively; neither did among non-diabetic women. However, in men without T2DM IHM decreased significantly over time. Diabetes was associated to higher IHM only in women (OR 1.07; 95% CI, 1.05–1.11).

Conclusions

Our national data show that incidence rate of ischemic stroke hospitalization increased significantly during the period of study (2003–2012). People with T2DM have more than double the risk of ischemic stroke after adjusting for other risk factors. Women with T2DM had poorer outcomes- IHM and readmission rates- than diabetic men. Diabetes was an independent factor for IHM only in women.     

Fatal opioid overdoses during and shortly after hospital admissions in England: A case-crossover study

BackgroundHospital patients who use illicit opioids such as heroin may use drugs during an admission or leave the hospital in order to use drugs. There have been reports of patients found dead from drug poisoning on the hospital premises or shortly after leaving the hospital. This study examines whether hospital admission and discharge are associated with increased risk of opioid-related death.Methods and findingsWe conducted a case-crossover study of opioid-related deaths in England. Our study included 13,609 deaths between January 1, 2010 and December 31, 2019 among individuals aged 18 to 64. For each death, we sampled 5 control days from the period 730 to 28 days before death. We used data from the national Hospital Episode Statistics database to determine the time proximity of deaths and control days to hospital admissions. We estimated the association between hospital admission and opioid-related death using conditional logistic regression, with a reference category of time neither admitted to the hospital nor within 14 days of discharge. A total of 236/13,609 deaths (1.7%) occurred following drug use while admitted to the hospital. The risk during hospital admissions was similar or lower than periods neither admitted to the hospital nor recently discharged, with odds ratios 1.03 (95% CI 0.87 to 1.21; p = 0.75) for the first 14 days of an admission and 0.41 (95% CI 0.30 to 0.56; p < 0.001) for days 15 onwards. 1,088/13,609 deaths (8.0%) occurred in the 14 days after discharge. The risk of opioid-related death increased in this period, with odds ratios of 4.39 (95% CI 3.75 to 5.14; p < 0.001) on days 1 to 2 after discharge and 2.09 (95% CI 1.92 to 2.28; p < 0.001) on days 3 to 14. 11,629/13,609 deaths (85.5%) did not occur close to a hospital admission, and the remaining 656/13,609 deaths (4.8%) occurred in hospital following admission due to drug poisoning. Risk was greater for patients discharged from psychiatric admissions, those who left the hospital against medical advice, and those leaving the hospital after admissions of 7 days or more. The main limitation of the method is that it does not control for time-varying health or drug use within individuals; therefore, hospital admissions coinciding with high-risk periods may in part explain the results.ConclusionsDischarge from the hospital is associated with an acute increase in the risk of opioid-related death, and 1 in 14 opioid-related deaths in England happens in the 2 weeks after the hospital discharge. This supports interventions that prevent early discharge and improve linkage with community drug treatment and harm reduction services.

In a case-crossover study, Dan Lewer and coauthors investigate factors associated with fatal opioid overdoses during and shortly after hospital admissions in England.     

Hospital Admission following Acute Kidney Injury in Kidney Transplant Recipients Is Associated with a Negative Impact on Graft Function after 1-Year

The incidence and outcomes of acute kidney injury (AKI) in kidney transplantation are poorly known. Retrospective cohort analysis was performed on the data of all patients (≥3 months after transplantation and ≥16 years of age) admitted to the hospital due to medical or surgical complications from 2007 to 2010. We analyzed 458 kidney transplant recipients, 55.2% men, median age 49 (IQR, 36–58) years, median of 12.5 (IQR, 3–35) months after kidney transplantation; admitted to the hospital due to medical or surgical complications. Most of the patients received a kidney from a deceased donor (62.2%), the primary cause for hospital admission was infection (60.7%) and 57 (12.4%) individuals were diagnosed with acute rejection (AR). The incidence of AKI was 82.3%: 31.9% stage 1, 29.3% stage 2 and 21.2% stage 3. Intensive care unit (ICU) admission (OR 8.90, 95% CI: 1.77–44.56 p = 0.008), infection (OR 5.73, 95% CI: 2.61–12.56, p<0.001) and the use of contrast media (OR 9.34, 95% CI: 2.04–42.70, p = 0.004) were the independent risk factors for AKI development. The mortality rate was 2.1% and all patients who died were diagnosed with AKI. Even after the exclusion of AR cases, at the end of 12 months, the individuals with AKI exhibited higher percent changes in creatinine values when compared with individuals without AKI (9.1% vs. -4.3%; p<0.001). According to KDIGO system, we found a high incidence of AKI among the complications of renal transplantation. As in other scenarios, AKI was associated with renal function loss at 1-year after the hospital discharge.     

Hospital Readmissions of Patients with Heart Failure: The Impact of Hospital and Primary Care Organizational Factors in Northern Italy

BackgroundPrimary health care is essential for an appropriate management of heart failure (HF), a disease which is a major clinical and public health issue and a leading cause of hospitalization. The aim of this study was to evaluate the impact of different organizational factors on readmissions of patients with HF.MethodsThe study population included elderly resident in the Local Health Authority of Bologna (Northern Italy) and discharged with a diagnosis of HF from January to December 2010. Unplanned hospital readmissions were measured in four timeframes: 30 (short-term), 90 (medium-term), 180 (mid-long-term), and 365 days (long-term). Using multivariable multilevel Poisson regression analyses, we investigated the association between readmissions and organizational factors (discharge from a cardiology department, general practitioners’ monodisciplinary organizational arrangement, and implementation of a specific HF care pathway).ResultsThe 1873 study patients had a median age of 83 years (interquartile range 77–87) and 55.5% were females; 52.0% were readmitted to the hospital for any reason after a year, while 20.1% were readmitted for HF. The presence of a HF care pathway was the only factor significantly associated with a lower risk of readmission for HF in the short-, medium-, mid-long- and long-term period (short-term: IRR [incidence rate ratio]=0.57, 95%CI [confidence interval]=0.35–0.92; medium-term: IRR=0.70, 95%CI=0.51–0.96; mid-long-term: IRR=0.79, 95%CI=0.64–0.98; long-term: IRR=0.82, 95%CI=0.67–0.99), and with a lower risk of all-cause readmission in the short-term period (IRR=0.73, 95%CI=0.57–0.94).ConclusionOur study shows that the HF care specific pathway implemented at the primary care level was associated with lower readmission rate for HF in each timeframe, and also with lower readmission rate for all causes in the short-term period. Our results suggest that the engagement of primary care professionals starting from the early post-discharge period may be relevant in the management of patients with HF.     

Psychiatric illness in inpatients with neurological disorders: patients' views on discussion of emotional problems with neurologists.

The prevalence of psychiatric morbidity in inpatients with neurological disorders and the extent to which it is detected by neurologists were measured by using a two stage model of psychiatric assessment and from information recorded in the patients'' medical notes. The prevalence of psychiatric morbidity was estimated as 39%, of which 72% was unrecognised by the neurologists. Only a minority of patients with an uncertain physical diagnosis had a psychiatric illness, showing the error in assuming that a patient''s physical symptoms arise from a psychological disturbance if an organic aetiology cannot be determined. When the patients were interviewed on their discharge from hospital they were divided on whether they had wished to discuss their mood with neurologists while they were in hospital. The reasons that they gave suggested that interactions between patients and doctors and the lack of ward facilities for private consultations with doctors are important determinants of hidden psychiatric morbidity in medical inpatients.     

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.     

Living the Good Life? Mortality and Hospital Utilization Patterns in the Old Order Amish

Lifespan increases observed in the United States and elsewhere throughout the developed world, have been attributed in part to improvements in medical care access and technology and to healthier lifestyles. To differentiate the relative contributions of these two factors, we have compared lifespan in the Old Order Amish (OOA), a population with historically low use of medical care, with that of Caucasian participants from the Framingham Heart Study (FHS), focusing on individuals who have reached at least age 30 years.Analyses were based on 2,108 OOA individuals from the Lancaster County, PA community born between 1890 and 1921 and 5,079 FHS participants born approximately the same time. Vital status was ascertained on 96.9% of the OOA cohort through 2011 and through systematic follow-up of the FHS cohort. The lifespan part of the study included an enlargement of the Anabaptist Genealogy Database to 539,822 individuals, which will be of use in other studies of the Amish. Mortality comparisons revealed that OOA men experienced better longevity (p<0.001) and OOA women comparable longevity than their FHS counterparts.We further documented all OOA hospital discharges in Lancaster County, PA during 2002–2004 and compared OOA discharge rates to Caucasian national rates obtained from the National Hospital Discharge Survey for the same time period. Both OOA men and women experienced markedly lower rates of hospital discharges than their non-Amish counterparts, despite the increased lifespan.We speculate that lifestyle factors may predispose the OOA to greater longevity and perhaps to lesser hospital use. Identifying these factors, which might include behaviors such as lesser tobacco use, greater physical activity, and/or enhanced community assimilation, and assessing their transferability to non-Amish communities may produce significant gains to the public health.     

Associations between Anticholinergic Burden and Adverse Health Outcomes in Parkinson Disease

Background

Elderly adults should avoid medications with anticholinergic effects since they may increase the risk of adverse events, including falls, delirium, and cognitive impairment. However, data on anticholinergic burden are limited in subpopulations, such as individuals with Parkinson disease (PD). The objective of this study was to determine whether anticholinergic burden was associated with adverse outcomes in a PD inpatient population.

Methods

Using the Cerner Health Facts® database, we retrospectively examined anticholinergic medication use, diagnoses, and hospital revisits within a cohort of 16,302 PD inpatients admitted to a Cerner hospital between 2000 and 2011. Anticholinergic burden was computed using the Anticholinergic Risk Scale (ARS). Primary outcomes were associations between ARS score and diagnosis of fracture and delirium. Secondary outcomes included associations between ARS score and 30-day hospital revisits.

Results

Many individuals (57.8%) were prescribed non-PD medications with moderate to very strong anticholinergic potential. Individuals with the greatest ARS score (≥4) were more likely to be diagnosed with fractures (adjusted odds ratio (AOR): 1.56, 95% CI: 1.29–1.88) and delirium (AOR: 1.61, 95% CI: 1.08–2.40) relative to those with no anticholinergic burden. Similarly, inpatients with the greatest ARS score were more likely to visit the emergency department (adjusted hazard ratio (AHR): 1.32, 95% CI: 1.10–1.58) and be readmitted (AHR: 1.16, 95% CI: 1.01–1.33) within 30-days of discharge.

Conclusions

We found a positive association between increased anticholinergic burden and adverse outcomes among individuals with PD. Additional pharmacovigilance studies are needed to better understand risks associated with anticholinergic medication use in PD.     

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.     

Evaluating the Use of Blood Cultures in the Management of Children Hospitalized for Community-Acquired Pneumonia

BackgroundBlood cultures are often recommended for the evaluation of community-acquired pneumonia (CAP). However, institutions vary in their use of blood cultures, and blood cultures have unclear utility in CAP management in hospitalized children.ObjectiveTo identify clinical factors associated with obtaining blood cultures in children hospitalized with CAP, and to estimate the association between blood culture obtainment and hospital length of stay (LOS).MethodsWe performed a multicenter retrospective cohort study of children admitted with a diagnosis of CAP to any of four pediatric hospitals in the United States from January 1, 2011-December 31, 2012. Demographics, medical history, diagnostic testing, and clinical outcomes were abstracted via manual chart review. Multivariable logistic regression evaluated patient and clinical factors for associations with obtaining blood cultures. Propensity score-matched Kaplan-Meier analysis compared patients with and without blood cultures for hospital LOS.ResultsSix hundred fourteen charts met inclusion criteria; 390 children had blood cultures obtained. Of children with blood cultures, six (1.5%) were positive for a pathogen and nine (2.3%) grew a contaminant. Factors associated with blood culture obtainment included presenting with symptoms of systemic inflammatory response syndrome (OR 1.78, 95% CI 1.10–2.89), receiving intravenous hydration (OR 3.94, 95% CI 3.22–4.83), receiving antibiotics before admission (OR 1.49, 95% CI 1.17–1.89), hospital admission from the ED (OR 1.65, 95% CI 1.05–2.60), and having health insurance (OR 0.42, 95% CI 0.30–0.60). In propensity score-matched analysis, patients with blood cultures had median 0.8 days longer LOS (2.0 vs 1.2 days, P < .0001) without increased odds of readmission (OR 0.94, 95% CI 0.45–1.97) or death (P = .25).ConclusionsObtaining blood cultures in children hospitalized with CAP rarely identifies a causative pathogen and is associated with increased LOS. Our results highlight the need to refine the role of obtaining blood cultures in children hospitalized with CAP.