Inter-hospital transfer and patient outcomes: a retrospective cohort study

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Abstract

Background

Inter-hospital transfer (IHT, the transfer of patients between hospitals) occurs regularly and exposes patients to risks of discontinuity of care, though outcomes of transferred patients remains largely understudied.

Objective

To evaluate the association between IHT and healthcare utilisation and clinical outcomes.

Design

Retrospective cohort.

Setting

CMS 2013 100 % Master Beneficiary Summary and Inpatient claims files merged with 2013 American Hospital Association data.

Participants

Beneficiaries≥age 65 enrolled in Medicare A and B, with an acute care hospitalisation claim in 2013 and 1 of 15 top disease categories.

Main outcome measures

Cost of hospitalisation, length of stay (LOS) (of entire hospitalisation), discharge home, 3 -day and 30- day mortality, in transferred vs non-transferred patients.

Results

The final cohort consisted of 53 420 transferred patients and 53 420 propensity-score matched non-transferred patients. Across all 15 disease categories, IHT was associated with significantly higher costs, longer LOS and lower odds of discharge home. Additionally, IHT was associated with lower propensity-matched odds of 3-day and/or 30- day mortality for some disease categories (acute myocardial infarction, stroke, sepsis, respiratory disease) and higher propensity-matched odds of mortality for other disease categories (oesophageal/gastrointestinal disease, renal failure, congestive heart failure, pneumonia, renal failure, chronic obstructivepulmonary disease, hip fracture/dislocation, urinary tract infection and metabolic disease).

Conclusions

In this nationally representative study of Medicare beneficiaries, IHT was associated with higher costs, longer LOS and lower odds of discharge home, but was differentially associated with odds of early death and 30 -day mortality depending on patients’ disease category. These findings demonstrate heterogeneity among transferred patients depending on the diagnosis, presenting a nuanced assessment of this complex care transition.

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Most cited references 45

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M. Alan Brookhart, Sebastian Schneeweiss, Kenneth J. Rothman … (2006)

Despite the growing popularity of propensity score (PS) methods in epidemiology, relatively little has been written in the epidemiologic literature about the problem of variable selection for PS models. The authors present the results of two simulation studies designed to help epidemiologists gain insight into the variable selection problem in a PS analysis. The simulation studies illustrate how the choice of variables that are included in a PS model can affect the bias, variance, and mean squared error of an estimated exposure effect. The results suggest that variables that are unrelated to the exposure but related to the outcome should always be included in a PS model. The inclusion of these variables will decrease the variance of an estimated exposure effect without increasing bias. In contrast, including variables that are related to the exposure but not to the outcome will increase the variance of the estimated exposure effect without decreasing bias. In very small studies, the inclusion of variables that are strongly related to the exposure but only weakly related to the outcome can be detrimental to an estimate in a mean squared error sense. The addition of these variables removes only a small amount of bias but can increase the variance of the estimated exposure effect. These simulation studies and other analytical results suggest that standard model-building tools designed to create good predictive models of the exposure will not always lead to optimal PS models, particularly in small studies.

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The incidence and severity of adverse events affecting patients after discharge from the hospital.

Alan J Forster, Harvey Murff, Josh F Peterson … (2003)

Studies of hospitalized patients identify safety as a significant problem, but few data are available regarding injuries occurring after discharge. Patients may be vulnerable during this transition period. To describe the incidence, severity, preventability, and "ameliorability" of adverse events affecting patients after discharge from the hospital and to develop strategies for improving patient safety during this interval. Prospective cohort study. A tertiary care academic hospital. 400 consecutive patients discharged home from the general medical service. The three main outcomes were adverse events, defined as injuries occurring as a result of medical management; preventable adverse events, defined as adverse events judged to have been caused by an error; and ameliorable adverse events, defined as adverse events whose severity could have been decreased. Posthospital course was determined by performing a medical record review and a structured telephone interview approximately 3 weeks after each patient's discharge. Outcomes were determined by independent physician reviews. Seventy-six patients had adverse events after discharge (19% [95% CI, 15% to 23%]). Of these, 23 had preventable adverse events (6% [CI, 4% to 9%]) and 24 had ameliorable adverse events (6% [CI, 4% to 9%]). Three percent of injuries were serious laboratory abnormalities, 65% were symptoms, 30% were symptoms associated with a nonpermanent disability, and 3% were permanent disabilities. Adverse drug events were the most common type of adverse event (66% [CI, 55% to 76%]), followed by procedure-related injuries (17% [CI, 8% to 26%]). Of the 25 adverse events resulting in at least a nonpermanent disability, 12 were preventable (48% [CI, 28% to 68%]) and 6 were ameliorable (24% [CI, 7% to 41%]). Adverse events occurred frequently in the peridischarge period, and many could potentially have been prevented or ameliorated with simple strategies.

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Estimating causal effects from large data sets using propensity scores.

Donald B Rubin (1997)

The aim of many analyses of large databases is to draw causal inferences about the effects of actions, treatments, or interventions. Examples include the effects of various options available to a physician for treating a particular patient, the relative efficacies of various health care providers, and the consequences of implementing a new national health care policy. A complication of using large databases to achieve such aims is that their data are almost always observational rather than experimental. That is, the data in most large data sets are not based on the results of carefully conducted randomized clinical trials, but rather represent data collected through the observation of systems as they operate in normal practice without any interventions implemented by randomized assignment rules. Such data are relatively inexpensive to obtain, however, and often do represent the spectrum of medical practice better than the settings of randomized experiments. Consequently, it is sensible to try to estimate the effects of treatments from such large data sets, even if only to help design a new randomized experiment or shed light on the generalizability of results from existing randomized experiments. However, standard methods of analysis using available statistical software (such as linear or logistic regression) can be deceptive for these objectives because they provide no warnings about their propriety. Propensity score methods are more reliable tools for addressing such objectives because the assumptions needed to make their answers appropriate are more assessable and transparent to the investigator.

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Author and article information

Journal

Title: BMJ Quality & Safety

Abbreviated Title: BMJ Qual Saf

Publisher: BMJ

ISSN (Print): 2044-5415

ISSN (Electronic): 2044-5423

Publication date Created: October 18 2019

Publication date Created: November 2019

Publication date (Print): November 2019

Publication date (Electronic): September 26 2018

Volume: 28

Issue: 11

Page: e1

Article

DOI: 10.1136/bmjqs-2018-008087

PubMed ID: 30257883

SO-VID: cd8c6c5d-0124-4d95-953d-455c7ed40d6a

History

ScienceOpen disciplines: Quantitative & Systems biology,Biophysics

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ScienceOpen disciplines: Quantitative & Systems biology, Biophysics

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Inter-hospital transfer and patient outcomes: a retrospective cohort study

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Abstract

Background

Objective

Design

Setting

Participants

Main outcome measures

Results

Conclusions

Related collections

iGEM

Most cited references 45

Variable selection for propensity score models.

The incidence and severity of adverse events affecting patients after discharge from the hospital.

Estimating causal effects from large data sets using propensity scores.

Author and article information

Journal

Article

History

Comments

Comment on this article

Similar content 257

Cited by 25

Most referenced authors 536