There has been a remarkable improvement in patient and transplant allograft survival since the early days of organ transplantation.1-3 The practice of transplantation, however, continues to have many unique challenges, including the limited availability of organs, and the complex, long-term care that is needed posttransplant. Despite continued improvements in the technical aspects of transplantation,4-6 major problems remain for patients with end-stage organ failure, including lack of appropriate access to transplantation, premature transplant failure and death, and a reduced quality of life due to various complications. There remains an opportunity to improve transplantation care; however, the best approach for determining whether a transplant center is delivering effective, high-quality, and safe care remains unknown.7-9
Measuring quality in healthcare is challenging, and is often focused on examining easily accessible data, such as administrative databases, rather than what may be most meaningful to patients.10 Furthermore, current quality assessments and performance improvement efforts are limited by their focus on short-term outcomes, whereas long-term survival or patient-reported outcomes may provide a better indication of overall healthcare quality.11 Effectiveness outcomes, such as the 1-year allograft survival, are frequently used as quality measures in transplantation7; however, on their own, these types of metrics do not adequately capture all aspects of quality. Other components of quality, such as access and patient-centeredness, also need to be developed and validated to generate a comprehensive picture of transplantation care.12 In this review, we aimed to systematically identify, describe, and characterize the impact of healthcare quality metrics in the field of solid organ transplantation.
MATERIALS AND METHODS
This systematic review followed a prespecified published protocol13 and is registered with PROSPERO (CRD42016035353). This review is reported according to the recommendations from the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement.14
Data Sources and Searches
We searched Medline, Embase, and Cochrane Central Register for Controlled Trials from inception to February 1, 2017. The search strategy is available in Table S1, SDC ( http://links.lww.com/TP/B543). To avoid the inappropriate exclusion of relevant articles, we used broad inclusion criteria to capture all articles that discussed quality metrics related to transplantation. Eligibility was restricted to articles published in the English language.
Literature search results were compiled and duplicates were removed using an EndNote X7 database. References were sorted alphabetically by title and exported to Microsoft Word for screening, and 2 independent reviewers screened all titles/abstracts. Full text reports were obtained for all potentially eligible articles and further screened by 2 independent reviewers. Disagreements were resolved by consensus or a third party. The references of included studies were scanned for additional articles not identified by the search, but none were found to be relevant.
Articles were screened using the following predefined inclusion and exclusion criteria.
All relevant full text reports and conference abstracts for primary research articles, commentaries, narrative, and systematic reviews were included. We accepted all relevant article types as we anticipated there would be very few transplant-related quality metrics validated in primary research studies. We included articles if they reported using quality metrics, if they discussed quality metrics that are currently being used, if they proposed quality metrics for future use, or if they reported quality metrics that were being validated. Studies were included that examined solid organ transplantation (kidney, heart, liver, lung, pancreas, small bowel, and combinations of these organs) in humans of all ages.
Data Extraction and Quality Assessment
Data were extracted from each eligible study by 2 independent reviewers using Google Forms. Data were then downloaded into a Microsoft Excel sheet for comparison between reviewers. Discrepancies were resolved by consensus or a third-party reviewer. The extracted data included information pertaining to the study (author, year of publication, number and location of centers, funding, and journal), study design (type of study, sample size, eligibility criteria, and length of follow-up), aggregate patient characteristics (age, gender, organ(s) studied, length of time since transplant, comorbidities), and relevant quality metrics (name and definition of metric(s), type of metric, measurement time points, the comparator outcome, main findings, limitations of the metric, and the methodology for selecting the metrics).
As per our protocol,13 quality metrics are defined as “any objective measure that has been developed to support self-assessment and quality improvement at the provider, hospital and/or healthcare system.”15 However, we accepted an article reporting the metric in the transplantation setting as sufficient for the metric to be included in the study. Only metrics that were defined with sufficient detail such that they could be measured in clinical practice were included in the study. Metrics were excluded if it was not possible to infer what the metric aimed to measure from the information provided. Quality metrics were excluded if they were solely related to the deceased organ procurement process as organ donation is most often carried out by professionals and organizations that are separate from transplant programs. We did, however, include donor metrics that could be influenced by the practice of the transplant program (eg, organ acceptance rate). We intended to include living donor metrics, however, we did not find any in our search. Given that we included a wide variety of article types, we were not able to assess the risk of bias uniformly across studies.
Data Synthesis and Analysis
The evidence was synthesized in summary tables and text. Quality metrics that were similar across multiple articles were grouped together and discussed as 1 metric. For this process, we grouped metrics that were similar in terms of what they aimed to measure but may have slightly different names, definitions, or follow-up times. For example, for “length of stay” various definitions were used with subtle differences with respect to the timing of the measurement (eg, “from admission to discharge,” or “each stay past midnight counts as 1 day”); however, the essence of these definitions was the same, and thus they were grouped together as 1 metric. Metrics were not included in more than 1 grouping. The decision about which metrics to group together was at the discretion of KB and was reviewed by GK. The number of publications reporting a specific metric was used as a proxy to examine how frequently certain metrics were discussed in the literature. Quality metrics noted in 4 or more unique citations were considered as frequently reported.
Each quality metric was classified into a period of care where the indicator would be used clinically. The periods of care were as follows: referral and waitlisting (period that encompasses referral, acceptance for transplantation, and waitlisting); inpatient transplant surgery (hospitalization for the transplant procedure until time of discharge); short-term follow-up (up to 30 days postdischarge); long-term follow-up (after 30 days postdischarge); and program (metrics not specific to a period of care). Quality metrics were categorized by the framework developed by Donabedian16 as structure, process or outcome metrics, and further categorized into the 6 domains of quality: access, effectiveness, efficiency, equitable, patient-centered, and safety.12,17 Tables were constructed to summarize the metrics used to measure clinical care (eg, change in care following a quality improvement initiative), those metrics that were directly compared with patient or graft survival, and the metrics that described the process or methodology used to select the metrics included in their report.
Meta-analysis could not be performed due to significant heterogeneity across the metrics with regards to the study design, how the metric was measured, the organ(s) of interest, and the populations studied. As an alternative, we established criteria that we could use for selecting metrics for further development as potential transplantation quality metrics. Metrics were selected for further development if they were frequently reported, modifiable, not specific to only 1 organ, and met at least 2 of the following criteria: they have been used to measure clinical care; they were directly compared with patient or graft survival; or there is a description of their selection process.
The literature search identified 13 781 unique citations. After independently reviewing the title and abstracts, 324 articles were retrieved and reviewed in full text. Of these, 76 studies were found to meet inclusion criteria. The study selection process and reasons for exclusion at the full text screening level are outlined in a PRISMA flow diagram (Figure S1, SDC,http://links.lww.com/TP/B543). Of the 76 included articles, there were 53 full text publications and 23 conference abstracts. The following article types were included: observational study (n = 46), annual report (n = 7), report on quality improvement initiatives (n = 9), narrative review (n = 5), experimental study (quality improvement) (n = 3), study protocol (n = 1), program evaluation (n = 1), consensus report (n = 1), and editorial (n = 3). The characteristics of the included studies (eg, study design, inclusion criteria, sample size etc.) are outlined in Table S2, SDC (http://links.lww.com/TP/B543). Overall, there were 317 metrics identified in the included studies that we were able to condense into 140 unique quality metrics. Of these, only 114 metrics were defined with sufficient detail to be measured in clinical practice and are presented in Table 1. The remaining 26 metrics did not provide sufficient information (eg, proper definition) to be implemented in clinical practice and are listed separately (Table S3, SDC,http://links.lww.com/TP/B543). Only 5 articles reported the process or methodology for selecting the quality metrics included in their report8,23,30,34,45; the details of these processes are summarized in Table S4, SDC (http://links.lww.com/TP/B543). Only 1 article explicitly reported involving patients in the process for developing the metrics.23 Based on our selection criteria, we identified 8 metrics that should be considered for further development: efficiency of the evaluation and listing process; in-hospital mortality; length of stay; early hospital readmission (EHR); unplanned return to the operating room; graft survival; patient survival; and patient satisfaction (Table S5, SDC,http://links.lww.com/TP/B543).
Frequency of Metric Reporting
The 14 most frequently reported quality metrics are presented in Figure 1. Early hospital readmission was the most frequently reported quality metric and was noted in 26 separate publications.18,19,24,25,42,44-46,49,51,54-69 Patient survival was the next most frequently reported quality metric (n = 20 publications),19,20,24,25,27,28,30,31,33,35,38,42,51,52,71-76 followed by graft survival (n = 16 publications),19,20,24,27,28,31,33,38,39,42,51,72-76 transplant center volume (n = 9 publications),18,19,24,25,28,33,38,89,90 and length of stay (n = 9 publications).18,24,25,39,40,42,44-46 Unplanned return to the operating room18,24,25,30,36,39,70 was reported in 7 unique publications, and an additional 3 metrics (blood pressure, graft function, and hemoglobin concentration)77-83 were also reported in 7 publications, however, these were consecutive annual reports from the same agency rather than unique citations. The other commonly reported quality metrics included the following: waitlist mortality (n = 6 publications),19,24,27,31,33,35 patient satisfaction (n = 5 publications),24,30,35,47,48 the wait time from initial evaluation to final disposition (n = 5 publications),18-22 in-hospital mortality (n = 4 publications),45,46,51,52 acute graft rejection (n = 4 publications),39,42,51,52 and infections (n = 4 publications).24,25,42,51
Types of Quality Metrics
Of the 114 well-defined quality metrics, 59 (51.8%) were outcome measures, 26 (22.8%) were structure measures, 27 (23.7%) were process-of-care measures and 2 (1.7%) were economic measures. The period in which the different categories of metrics would be used is illustrated in Figure 2. For example, during the referral and waitlisting period, structure measures, such as the referral efficiency rate and the wait time from referral to initial evaluation, were the most common type of metric. In comparison, during the short- and long-term follow-up periods, outcome metrics, such as EHR and graft survival, were the most common type of metric.
Domain of Quality
Figure 3 shows the metrics by domain of quality. Figure 3A shows the overall frequency of the domains of quality. Of the 114 metrics, safety was the most common domain of quality applying to 42 (36.8%) different metrics. The other domains of quality appeared in the following frequency: effectiveness (n = 25, 21.9%), access (n = 24, 21.1%), efficiency (n = 13, 11.4%), patient-centered (n = 9, 7.9%), and equitable (n = 1, 0.9%). Of the 9 quality metrics classified as patient-centered, there were 4 patient-reported outcome measures (PROMs) and 4 patient-reported experience measures (PREMs) identified; the remaining metric, functional status, is patient-focused but measured by a healthcare provider.
Figure 3B demonstrates how the domains of quality vary across the different periods of care in transplantation. For example, during the referral and waitlisting period, access to care was the most frequent domain of quality. In contrast, the domains of safety and effectiveness were more frequently identified in metrics from the long-term follow-up period. The supplemental figure (Figure S2, SDC,http://links.lww.com/TP/B543) demonstrates how the number of articles reporting metrics within the different domains of quality changes over time. The overall trend suggests that the number of articles reporting quality metrics is increasing overtime, with the years 2013 and 2016 reporting the highest number of metrics.
With respect to organ type, of the 114 metrics, 83 metrics involved only 1 organ, while the other 31 metrics were used or discussed with respect to more than 1 organ. Kidney transplantation was the organ type with the most quality metrics (n = 65) followed by liver (n = 61), heart (n = 23), kidney/pancreas (n = 7), lung (n = 7), pancreas (n = 3), multivisceral (n = 2), and small bowel (n = 2) (total > 114 due to metrics involving ≥ 2 organs). The type of metric and domain of quality by organ type is shown in Figure S3, SDC (http://links.lww.com/TP/B543). Figure 4 summarizes the metrics by grouping them by domain of quality and period of care, and mapping them against the different organs to which they could be applied in future quality improvement initiatives.
Measuring Clinical Care Delivered
Twenty-four of the 76 included articles used the quality metrics to measure clinical care (Table 2). Evaluating a quality improvement initiative was the most common clinical use of the metrics (n = 12 articles). In addition, quality metrics were used to make comparisons between transplant centers in 10 articles, and to measure change in quality over time within a center in 2 articles. Only 2 of these articles provided their methodology for selecting the quality metrics.8,34 In addition, 1 article outlined the protocol for a quality improvement randomized controlled trial.23 The details of the interventions, comparator groups, metrics, and outcomes are outlined in Table 2.
Association Between Quality Metrics and Patient or Graft Survival
There were 16 articles that reported an association between a quality metric and patient/graft survival (Table 3). Ten articles examined the association between EHR and patient or graft survival. While most studies in kidney and liver transplantation found worse outcomes for those with early readmission, this was not seen in lung transplantation.67,69 Other metrics compared with patient or graft survival include center volume (n = 1 articles), length of stay (n = 2), patient satisfaction survey results (n = 2), unplanned return to the operating room (n = 1), empathetic care delivery (n = 1), intraoperative transfusion (n = 1), perioperative composite quality metric (n = 1), and failure to rescue (n = 1). Specific details of the studies, organ type, metrics and results are outlined in Table 3.
This analysis has identified 114 metrics used to measure healthcare quality in solid organ transplantation. The majority of metrics identified focused on the initial perioperative period of care with less focus on long-term follow-up. Transplantation is a resource-intensive surgical procedure that has a dramatic effect on survival and quality of life, characteristics that make it a high-impact condition optimally suited for quality measurement.93 Despite these characteristics, this review has highlighted the dearth of well-developed, validated quality metrics in the field of transplantation. The large number of heterogeneous metrics identified suggests there is lack of consensus on what constitutes an important quality measure in transplantation and that there is a need to select, refine, and validate candidate metrics. In fact, the National Quality Forum, an American organization that reviews and endorses healthcare performance measures, does not list any transplant-specific metrics on its website.94 In contrast, there are more than 20 quality metrics listed and endorsed for measuring the quality of care in heart failure.94
Many of the reports were descriptive in nature with only 5 describing the methodology used to select metrics (Table S3, SDC,http://links.lww.com/TP/B543)8,23,30,34,45 which may have contributed to the large number of quality metrics reported in the literature. Even within these 5 reports, there were 19 different metrics selected, none of which were repeated across the reports. Only 1 article addressed the reliability or validity of the quality metrics selected.30 This consensus report outlined the development, testing and implementation of a set of quality indicators for liver transplantation with clear definitions for each metric as well as the rationale, method of calculation, population, data sources, and the established standard.30 Although the exact methods varied, 4 other studies used an expert panel in the selection of quality indicators.8,23,30,34
Our analysis showed that less than a third of studies actually used the quality indicators in clinical care, most commonly in the evaluation of a quality improvement initiative. Six studies used a before-and-after design to analyze and evaluate a quality improvement initiative.21,39,52,66,68,88 In all 6 studies, the selection of the quality metric was at the investigator's discretion and the rationale for metric selection was not provided. Four of these studies examined only 1 metric (EHR, total complication rate, and wait time from initial evaluation to final disposition),21,66,68,88 whereas 1 study evaluated 4 quality metrics,52 and another study evaluated 12 different quality metrics.39 Notably, 4 of the reports showed a significant improvement in quality after implementing program changes: a 50% reduction in the 30-day readmission rate postliver transplantation,66 a significant decrease in the time to listing for kidney transplant,21 fewer complications postpercutaneous biopsy,88 and a shorter time to removal of foley catheters, central lines, and drains.39 An additional 6 studies described a quality improvement initiative, but did not provide a quantitative analysis of their findings limiting inferences that could be made from the reports.18,24,25,40,42,91 Two of the studies which compared quality metrics between centers did describe their process for selecting the metrics8,34; however, only 1 of the articles included a quantitative analysis of their results and found that documentation in the medical record of the liver transplant discussion was significantly more likely at the faculty practice clinic than the other 2 clinics.34 Another study found significant differences in 10 of the 12 quality metrics they measured when comparing high and low surgical volume hospitals.51
In addition to the studies that measured clinical care, a number of studies aimed to determine whether any association existed between quality metrics and patient/graft survival. For the most part, the quality indicators that were evaluated showed a significant association with patient and/or graft survival. Such a link to patient outcomes is an important step in the proper validation of quality indicators.95 Although the high proportion of positive associations noted in this review may reflect careful selection of metrics for analysis, publication bias may also be playing a role.
Our review has highlighted areas for further study in transplantation quality measurement. First, many of the metrics identified were focused on patient and graft survival. Although survival is clearly a major goal of transplantation, other quality indicators, such as side effects and complications, are known to be important to patients.96 Second, our review highlighted the lack of metrics in the quality domains of efficiency, patient centeredness, and equity of care. Although outcome measures were the most frequent type of metric, only 8 indicators were patient-reported (either a patient-reported outcome or experience measure). Patient-reported outcome measures include metrics assessing health, illness, or healthcare benefits from the patient’s perspective (eg, quality of life or symptom burden), whereas PREMs capture information about the healthcare experience as perceived by the patient (eg, cleanliness of facilities or communication of information).97 Although transplant programs may be hesitant to collect PROMs and PREMs because of the need for additional resources, evidence suggests that incorporating these indicators into practice may lead to improved clinical outcomes.59,98,99 Indeed, only 1 article explicitly reported involving patients in the process for developing quality metrics,23 suggesting that more patient involvement in metric selection may be necessary to incorporate more patient-reported measures in common practice. Although only 1 metric identified in this review was distinctly in the domain of equitable care, a number of the access metrics could be stratified by factors, such as sex, race, ethnicity, region, or socioeconomic status, to measure whether equitable care is being delivered. Finally, many of the quality metrics were lacking sufficient detail to determine if they were potentially useful or feasible in clinical practice. Standardized definitions would make it easier to compare results from the different studies as well as across transplant programs.
Despite the lack of evidence validating transplantation quality metrics, using our selection criteria, we identified 8 metrics that should be considered for further development (Table S5, SDC,http://links.lww.com/TP/B543). These metrics also cover different periods of care in the transplantation process, as well as 4 of the domains of quality: access, safety, effectiveness, and patient centeredness. In addition, 5 of the metrics have been listed as performance measures for other medical conditions on the National Quality Forum website,94 suggesting further investigation of these metrics in the context of transplantation may be worthwhile. However, before widespread introduction, patient input and opinion is needed to ensure that these metrics are measuring what is important to both patients and their caregivers.
To our knowledge, this is the first systematic review evaluating quality measures for use in solid organ transplantation. We used an open and comprehensive search strategy to ensure we captured all relevant articles. As a result, this analysis included “accepted” quality measures such as patient survival19,24,25,27,30,38,51,71 as well as recently developed metrics such as failure to rescue69,71,87 and the composite perioperative quality index.45 Limitations to our analysis should be noted. First, we did not restrict our search to primary research articles. Although this approach identified a number of metrics that were not validated, it was an intentional strategy to identify the widest range of transplantation quality measures that can be further developed and evaluated. Second, because of the diverse types of publications and study designs included, we were unable to appropriately assess the risk of bias of the included studies. Third, despite the use of an objective, prespecified protocol,14 there was a subjective component to determining which quality measures to include and how to classify them (eg, structural vs process, etc.) from the various publications. It is possible that some metrics were overlooked or misclassified but these risks were mitigated by the use of 2 independent reviewers for data abstraction. Finally, this review was restricted to articles published in the English language, and therefore, it is possible that some metrics were missed if they were published in another language.
In conclusion, this review has identified a number of quality measures for use in solid organ transplantation. The measures to date have focused on safety and effectiveness with very few addressing other domains of health quality such as equity of care and patient centeredness. Many of the reported metrics were poorly defined with little detail on how they were developed. There are limited data on the actual use of quality measures in transplantation, perhaps because of unclear definitions in the literature, but the published results have shown that practice change can lead to improvements in care. Future research will need to focus on transparent and objective metric development with proper testing, evaluation, and implementation in practice. Patients and caregivers will need to be involved in this process to ensure that transplantation quality metrics encompass the broad domains of healthcare quality and measure what is important to not only healthcare professionals but to patients and their families.
The authors would like to thank Risa Shorr for her guidance with the development of the search strategy, and Nicholas Fergusson for his help screening the abstracts.
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