This systematic review is reported according to the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) checklist with the protocol published and registered at PROSPERO (Protocol #CRD42016037144).[6, 7]
We included retrospective and prospective studies that derived and validated a predictive model for the need for supportive services for adults (≥18 years of age) discharged from a non-elective general medical inpatient ward or medical sub-specialty ward. We defined supportive services as medical care or formal assistance with IADLS or ADLs at home or in an institutional environment (e.g., skilled nursing facility). We excluded studies of patients admitted to rehabilitation hospitals and studies where patients were discharged to a rehab hospital or long term acute care hostpial that was not the final discharge destination. We limited the review to validated models to avoid variables that are not causally related to our outcome of interest. We considered a model validated if they had at a minimum performed internal validation.
Literature Search and Information Sources
Our search strategy was designed in an iterative process with the assistance of a medical information specialist. We used medical subject headings (MeSH) terms and free text terms representing the included study types, population, and outcomes. To account for geographical variations in describing supportive services we included terms such as home care, skilled nursing facility, care home, and nursing home (see Additional File 1). Our search strategy formatted for MEDLINE can be found in Additional File 1. We searched the MEDLINE, CINAHL, EMBASE, and COCHRANE databases from inception to May 1st 2017 with no limitation based on language. We hand searched the reference lists of published systematic reviews, and eligible studies. Duplicates were removed prior to stage 1 screening.
Study Selection and Data Collection
The title and abstract of all references were screened for eligibility independently by two reviewers (DIM, JN, or BN). Studies written in a language other than English were translated using Google© Translate prior to screening. Full-text articles for all potentially eligible papers were obtained and reviewed in duplicate by two independent reviewers. Data extraction was performed independently and in duplicate with all eligibility and extraction disagreements resolved by consensus. Screening and data extraction were performed with Distiller SR® (Ottawa, Canada).
The data extraction was guided by the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Our data extraction form was pilot tested on for 2 studies, modified and then used for the remaining studies. All data extraction was performed in duplicate with disagreements resolved by consensus. We extracted study characteristics including setting, design, prognostic variable collection timeframe, and sample size for the derivation and validation cohorts. We collected patient characteristics including the mean age of participants, most common admitting diagnoses, and predictor variables used in model development. We collected model characteristics including variable selection method, method of screening variables for inclusion, and variables included in the final model, discrimination and calibration.
Synthesis of Results
Our primary analysis was a narrative description of models that predict the need for supportive services after discharge, and the predictor variables included in the models.
Risk of bias and Quality of Evidence
Two reviewers (DK and DIM) independently used the Quality In Prognosis Studies (QUIPS) tool to assess the methodological quality of each study with disagreements resolved by discussion and consensus.
The quality of evidence for each predictor variable was summarized using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool that has been adapted for use in narrative systematic reviews of prognostic studies. Predictor variables that were associated with the outcome in a single study were not included in the strength of evidence analysis.
Causal Pathway Creation
We constructed a causal pathway using the predictor variables identified in our review, informed by the concepts of directed acyclic graphs. Directed acyclic graphs are a graphical method for representing causal relationships. Predictor variables that were associated with the outcome only in bivariate analysis but not in multivariate analyses were assumed to be confounders or to be indirectly causing the outcome through a more direct effect mediator. The causal pathway construction was guided by the review results and by the expert knowledge of the authors in an iterative process.
Role of the Funding Source: No funding source.