Predictors associated with unplanned hospital readmission of medical and surgical intensive care unit survivors within 30 days of discharge

Abstract Background Reducing the 30-day unplanned hospital readmission rate is a goal for physicians and policymakers in order to improve quality of care. However, data on the readmission rate of critically ill patients in Japan and knowledge of the predictors associated with readmission are lacking. We investigated predictors associated with 30-day rehospitalization for medical and surgical adult patients separately. Methods Patient data from 502 acute care hospitals with intensive care unit (ICU) facilities in Japan were retrospectively extracted from the Japanese Diagnosis Procedure Combination (DPC) database between April 2012 and February 2014. Factors associated with unplanned hospital readmission within 30 days of hospital discharge among medical and surgical ICU survivors were identified using multivariable logistic regression analysis. Results Of 486,651 ICU survivors, we identified 5583 unplanned hospital readmissions within 30 days of discharge following 147,423 medical hospitalizations (3.8% readmitted) and 11,142 unplanned readmissions after 339,228 surgical hospitalizations (3.3% readmitted). The majority of unplanned hospital readmissions, 60.9% of medical and 63.1% of surgical case readmissions, occurred within 15 days of discharge. For both medical and surgical patients, the Charlson comorbidity index score; category of primary diagnosis during the index admission (respiratory, gastrointestinal, and metabolic and renal); hospital length of stay; discharge to skilled nursing facilities; and having received a packed red blood cell transfusion, low-dose steroids, or renal replacement therapy were significantly associated with higher unplanned hospital readmission rates. Conclusions From patient data extracted from a large Japanese national database, the 30-day unplanned hospital readmission rate after ICU stay was 3.4%. Further studies are required to improve readmission prediction models and to develop targeted interventions for high-risk patients

Observational Research Using Propensity Scores

In most observational studies, treatments or other "exposures" (in an epidemiologic sense) do not occur at random. Instead, treatments or other such interventions depend on several patient-related and patient-independent characteristics. Such factors, associated with the receipt vs nonreceipt of treatment, may also be-independently-associated with outcomes. Thus, confounding exists making it difficult to ascertain the true association between treatments and outcomes. Propensity scores (PS) represent an intuitive set of approaches to reduce the influence of such "confounding" factors. PS is a computed probability of treatment, a value that is estimated for each patient in an observational study and then applied (in a variety of ways such as matching, stratification, weighting, etc.) to reduce distortion in the true nature of the association between treatment (or any similar exposure) and outcomes. Despite several advantages, PS-based methods cannot account for unmeasured confounding, ie, for factors that are not being included in the computation of PS

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2016 (J-SSCG 2016)

Background and purposeThe Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2016 (J-SSCG 2016), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in February 2017 and published in the Journal of JSICM, [2017; Volume 24 (supplement 2)] https://doi.org/10.3918/jsicm.24S0001 and Journal of Japanese Association for Acute Medicine [2017; Volume 28, (supplement 1)] http://onlinelibrary.wiley.com/doi/10.1002/jja2.2017.28.issue-S1/issuetoc.This abridged English edition of the J-SSCG 2016 was produced with permission from the Japanese Association of Acute Medicine and the Japanese Society for Intensive Care Medicine.MethodsMembers of the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine were selected and organized into 19 committee members and 52 working group members. The guidelines were prepared in accordance with the Medical Information Network Distribution Service (Minds) creation procedures. The Academic Guidelines Promotion Team was organized to oversee and provide academic support to the respective activities allocated to each Guideline Creation Team. To improve quality assurance and workflow transparency, a mutual peer review system was established, and discussions within each team were open to the public. Public comments were collected once after the initial formulation of a clinical question (CQ) and twice during the review of the final draft. Recommendations were determined to have been adopted after obtaining support from a two-thirds (> 66.6%) majority vote of each of the 19 committee members.ResultsA total of 87 CQs were selected among 19 clinical areas, including pediatric topics and several other important areas not covered in the first edition of the Japanese guidelines (J-SSCG 2012). The approval rate obtained through committee voting, in addition to ratings of the strengths of the recommendation, and its supporting evidence were also added to each recommendation statement. We conducted meta-analyses for 29 CQs. Thirty-seven CQs contained recommendations in the form of an expert consensus due to insufficient evidence. No recommendations were provided for five CQs.ConclusionsBased on the evidence gathered, we were able to formulate Japanese-specific clinical practice guidelines that are tailored to the Japanese context in a highly transparent manner. These guidelines can easily be used not only by specialists, but also by non-specialists, general clinicians, nurses, pharmacists, clinical engineers, and other healthcare professionals

Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review

<div><p>Objectives</p><p>To systematically review AKI outcome prediction models and their external validation studies, to describe the discrepancy of reported accuracy between the results of internal and external validations, and to identify variables frequently included in the prediction models.</p><p>Methods</p><p>We searched the MEDLINE and Web of Science electronic databases (until January 2016). Studies were eligible if they derived a model to predict mortality of AKI patients or externally validated at least one of the prediction models, and presented area under the receiver-operator characteristic curves (AUROC) to assess model discrimination. Studies were excluded if they described only results of logistic regression without reporting a scoring system, or if a prediction model was generated from a specific cohort.</p><p>Results</p><p>A total of 2204 potentially relevant articles were found and screened, of which 12 articles reporting original prediction models for hospital mortality in AKI patients and nine articles assessing external validation were selected. Among the 21 studies for AKI prediction models and their external validation, 12 were single-center (57%), and only three included more than 1,000 patients (14%). The definition of AKI was not uniform and none used recently published consensus criteria for AKI. Although good performance was reported in their internal validation, most of the prediction models had poor discrimination with an AUROC below 0.7 in the external validation studies. There were 10 common non-renal variables that were reported in more than three prediction models: mechanical ventilation, age, gender, hypotension, liver failure, oliguria, sepsis/septic shock, low albumin, consciousness and low platelet count.</p><p>Conclusions</p><p>Information in this systematic review should be useful for future prediction model derivation by providing potential candidate predictors, and for future external validation by listing up the published prediction models.</p></div

Selection of articles by PRISMA flow diagram

<p>Selection of articles by PRISMA flow diagram</p

Variables included in more than one prediction model and their odds ratios / p values.

<p>Variables included in more than one prediction model and their odds ratios / p values.</p

AKI definitions, exclusion criteria and follow-up of articles reporting outcome prediction models for acute kidney injury.

<p>AKI definitions, exclusion criteria and follow-up of articles reporting outcome prediction models for acute kidney injury.</p

Area under the receiver operating characteristic curves (AUROC) for hospital mortality reported in the original articles and external validation studies.

<p>Black horizontal bars: AUROC in original studies, gray columns: AUROC in external validation studies.</p

Characteristics of articles reporting outcome prediction models for acute kidney injury.

<p>Characteristics of articles reporting outcome prediction models for acute kidney injury.</p

Characteristics of external validation studies for acute kidney injury outcome prediction models.

<p>Characteristics of external validation studies for acute kidney injury outcome prediction models.</p