The risk adjusted facility level Standardized Transfusion Ratio (STrR) is specified for all adult dialysis patients. It is a ratio of the number of eligible red blood cell transfusion events observed in patients dialyzing at a facility, to the number of eligible transfusion events that would be expected under a national event rate, after accounting for the patient characteristics within each facility.
Since the previous endorsement cycle, we have made the following changes:
- Denominator: the measure now includes Medicare Advantage (MA) patients that had previously been excluded due to lack of claims data.
- Exclusion criteria: the measure no longer excludes patients with certain coagulation disorders, hereditary anemias, or malignancies since our analyses indicate that these patients receive erythrocyte stimulating agents as part of their anemia management strategy. The measure now risk adjusts for these comorbidities.
- Risk Adjustment: the measure now risk adjusts for certain coagulation disorders, hereditary anemias, or malignancies that had previously been excluded.
- Risk Adjustment: based on stakeholder feedback, the measure now risk adjusts for certain comorbidities that indicate a history of gastrointestinal bleeding.
Measure Specs
General Information
End Stage Renal Disease (ESRD) is associated with severe anemia, historically often requiring repeated blood transfusions. With the introduction of erythropoiesis stimulating agents (ESAs; e.g. erythropoietin alpha, darbepoetin, etc.) in the late 1980’s, transfusion events in ESRD patients declined dramatically. Responsibility for anemia management was assigned to the treating dialysis facility by CMS regulation and also by specific payment policy and has remained so since that time. Safety concerns arising from clinical trials of ESA treatment of anemia of chronic kidney disease (CKD) led to changes in FDA recommendations on ESA use in patients with CKD. In addition, changes in financial incentives for treatment of anemia following the implementation of the revised Medicare ESRD Prospective Payment System in 2011 further heightened concerns in the dialysis community that patients with CKD-related anemia would be denied adequate access to ESAs for prevention of red blood cell transfusion. This concern was confirmed by subsequent trends in anemia management in US chronic dialysis patients, demonstrating rapid declines in achieved hemoglobin from mid-2010, just prior to implementation of the Expanded ESRD Prospective Payment System in 2011 and increasing transfusion rates in the 2011-2014 time period. The FDA Prescribing Information for EPO and other ESAs state that blood transfusion avoidance is the primary goal of treatment with these agents. A specific achieved hemoglobin target is not recommended in the Prescribing Information.
Dialysis facilities, as the primary assigned anemia managers for this patient population are charged with the difficult task of optimizing ESRD patients’ anemia, striking a balance between avoidance of the risks of over-treatment against the risk of developing symptomatic anemia and requirement for blood transfusion. This clinical responsibility is shared with other institutional providers (e.g. acute care hospitals) when ESRD patients are admitted to the hospital for any reason and care coordination in such situations is ideal. In particular, patients undergoing invasive procedures, or those with acute infectious illnesses causing or contributing to the hospitalization, may require blood transfusion. Since the decision to transfuse blood is often made by the treating inpatient team, albeit using well-established criteria for blood transfusion, there is shared responsibility for the transfusion event. The anemia management preceding the acute illness or hospitalization contributes to the starting hemoglobin level as well as to the transfusion risk. Furthermore, the subsequent acute care and medical situation also contributes to this risk. Given the clear responsibility of overall anemia management assigned to the dialysis facility and the clear associations between facility anemia management with both achieved hemoglobin and transfusion risk, this measure is useful as an indicator of successful use of ESAs and HIF protease inhibitors as indicated by the FDA, at reducing the risk of unnecessary blood transfusion (and associated risks, including allosensitization to HLA antigens).
It has been postulated that a national trend toward increased use of transfusions in dialysis patients would adversely affect the supply of blood available for acute injuries and surgical procedures in other patients. Lastly, greater exposure to human leukocyte antigens, present in transfused blood, may increase anti-HLA antibodies in kidney transplant candidates, resulting in reduced access to kidney transplantation.
The inverse relationship between achieved hemoglobin and risk for blood transfusion has been reported previously. In addition, the dialysis facility’s anemia management processes also predict adverse outcomes associated with anemia management, including need for blood transfusion. These issues are discussed in the Evidence Form included as part of this submission.
Data are derived from the EQRS patient-specific clinical and administrative data, including ESRD patient list, CMS-2728 Medical Evidence Form, CMS-2746 Death Notification Form, and patient admission and discharge data, from all Medicare certified dialysis facilities, the Medicare Enrollment Database (EDB), and Medicare claims data.
In addition, the database includes transplant data from the Scientific Registry of Transplant Recipients (SRTR), data from the Nursing Home Minimum Dataset, and the provider and survey and certification data from the Internet Quality Improvement and Evaluation System (iQIES) data.
Information on transfusions is obtained from inpatient and outpatient claims of Standard Analysis Files (SAFs), and past-year comorbidity data are obtained from multiple claim types (inpatient, outpatient, home health, hospice (Part A only), skilled nursing facility claims, and physician supply).
Fee-for-service (FFS) Medicare Part A (inpatient) and Part B (outpatient and physician supply) claims for dialysis patients are included in the current database; additionally, the measure now incorporates Part C Medicare Advantage (MA) data for the MA enrollees. This ensures that hospital, outpatient dialysis, and other billable services under Medicare – whether FFS or MA – are captured.
Numerator
The numerator is the number of eligible observed red blood cell transfusion events. An event is defined as the transfusion of one or more units of red blood cells into a recipient’s blood stream (code set is provided in the numerator details) among patients dialyzing at the facility during the reporting period.
Transfusion events in the inpatient setting are counted in the following way. The event is identified by presence in a Medicare Part A inpatient claims and Part C inpatient encounter data of the appropriate ICD procedure codes (99.03, 99.04, 30230H1, 30230N1, 30230P1, 30233H1, 30233N1, 30233P1, 30240H1, 30240N1, 30240P1, 30243H1, 30243N1, 30243P1, 30250H1, 30250N1, 30250P1, 30253H1, 30253N1, 30253P1, 30260H1, 30260N1, 30260P1, 30263H1, 30263N1, 30263P1, 302A3H1, 302A3N1, 302A3P1 ), or revenue center codes (0380, 0381, 0382, 0389, 0390, 0391, 0392, 0399) or value code (37). We only count a single transfusion event for an inpatient claim regardless of the number of transfusion revenue center, procedure, and value codes reported so that the number of discrete events counted is the same whether the claim indicates one unit of blood or multiple units of blood. This results in a more conservative estimate of blood transfusions from inpatient claims.
Transfusion events are less common in the outpatient setting. Transfusion events in the outpatient setting are counted in the following way. Events derived from Medicare Part B outpatient claims and Part C outpatient encounter data are identified by claims with HCPCS code (P9010, P9011, P9016, P9021, P9022, P9038, P9039, P9040, P9051, P9054, P9056, P9057, P9058, 36430) with revenue center codes in (0380, 0381, 0382, 0389, 0390, 0391, 0392, 0399) or value code (37). One or more transfusion-related HCPCS codes with at least one transfusion-related revenue center codes, or one or more transfusion-related value codes listed on an outpatient claim are counted as a single transfusion event regardless of the number of units of blood recorded. In other words, three units of blood would be counted as a single transfusion event.
In addition, we combine the transfusion claims with overlapping periods into a single claim, and only count one transfusion event from each claim. The transfusion event for a particular claim is treated as having occurred on last day of that claim.
The detailed procedures to determine unique transfusion events at the claim level are presented in a flow chart. See the second page of the STrR_Flowchart_Final_Oct_2025_508 PDF, attached to 1.18a.
Denominator
The denominator is the number of eligible red blood cell transfusion events that would be expected among patients at a facility during the reporting period, given the patient mix at the facility.
Assignment of Patients to Facilities
The ESRD Quality Reporting System (EQRS), including CMS Medical Evidence Form (Form CMS-2728) and Death Notification Form (Form CMS-2746) is the primary basis for placing patients at dialysis facilities. Outpatient dialysis claims are used as an additional source when needed. We create a complete history of the status, location, and dialysis treatment modality of an ESRD patient from the date of the first ESRD service until the patient dies or the data collection cutoff date is reached. A new record is created each time a patient changes facilities or dialysis treatment modality; therefore, each record represents a time period associated with a specific modality and dialysis facility. Information regarding first ESRD service date, death and transplant is obtained from additional sources including the CMS Enrollment Database (EDB), transplant data from the Organ Procurement and Transplant Network (OPTN), and the Social Security Death Master File.
We detail patient inclusion criteria, facility assignment and how to count days at risk, all of which are required for the risk adjustment model. As patients can receive dialysis treatment at more than one facility in a given year, we assign each patient day to a facility (or no facility, in some cases) based on a set of conventions below.
General Inclusion Criteria for Dialysis Patients
Patients are included in the measure only after they have had ESRD for greater than 90 days. This minimum 90-day period assures that patients are eligible for Medicare, either as their primary or secondary insurer, and that follow-up is complete. Thus, the measure excludes transfusion events during the first 90 days of ESRD as well as patients who die or recover kidney function during that time period.
In order to exclude patients who only received temporary dialysis therapy, we assign patients to a particular facility only after they have been on chronic dialysis there for the past 60 days. This 60-day period is used both for patients who started ESRD for the first time and for those who returned to dialysis after a transplant. That is, transfusion events during the first 60 days of dialysis at a facility do not affect the STrR of that facility.
In order to assure completeness of information on transfusions for all patients included in the analysis, we restrict to Medicare patients who are either enrolled in Medicare Advantage or who reach a certain threshold of Medicare dialysis and inpatient claims. Specifically, months within a given dialysis patient-period are used for STrR calculation when the patient is enrolled in Medicare Advantage or meets the criterion of being within two months after a month with either: (a) $1200+ of Medicare-paid dialysis claims OR (b) at least one Medicare inpatient claim.
Identifying Facility Treatment Histories for Each Patient
For each patient, we identify the dialysis provider at each point in time. Starting with day 91 after onset of ESRD, we attribute patients to facilities according to the following rules. A patient is attributed to a facility once the patient has been treated there for the past 60 days. When a patient transfers from one facility to another, the patient continues to be attributed to the original facility for 60 days and then is attributed to the destination facility. In particular, a patient is attributed to his or her current facility on day 91 of ESRD if that facility had treated him or her for the past 60 days. If on day 91, the facility had not treated a patient for the past 60 days, we wait until the patient reaches day 60 of continuous treatment at that facility before attributing the patient to that facility. When a patient is not treated in a single facility for a span of 60 days (for instance, if there were two switches within 60 days of each other), we do not attribute that patient to any facility. Patients are removed from facilities three days prior to transplant in order to exclude the transplant hospitalization. Patients who withdrew from dialysis or recovered renal function remain assigned to their treatment facility for 60 days after withdrawal or recovery.
If a period of one year passes with neither paid dialysis claims nor EQRS information to indicate that a patient was receiving dialysis treatment, we consider the patient lost to follow-up and do not include that patient in the analysis. If dialysis claims or other evidence of dialysis reappears, the patient is entered into analysis after 60 days of continuous therapy at a single facility.
Days at Risk for Medicare Dialysis Patients
After patient treatment histories are defined as described above, periods of follow-up in time since ESRD onset are created for each patient. In order to adjust for duration of ESRD appropriately, we define six, time intervals with cut points at 6 months, 1 year, 2 years, 3 years and 5 years. A new time period begins each time the patient is determined to be at a different facility, or at the start of each calendar year or when crossing any of the above cut points.
The number of days at risk in each of these six, time intervals listed above is used to calculate the expected number of transfusions for the patient during that period. The STrR for a facility is the ratio of the total number of observed transfusions to the total number of expected transfusions during all time periods at the facility. Based on a risk adjustment model for the overall national transfusion rates, we compute the expected number of transfusions that would occur for each month that each patient is attributed to a given facility. The sum of all such expectations for patients and months yields the overall number of transfusions that would be expected given the specific patient mix. This forms the denominator of the measure.
The denominator of the STrR is derived from a proportional rates model [4] [5] [3]. This is the recurrent event analog of the well-known proportional hazards or Cox model [2] [3]. To accommodate large-scale data, we adopt a model with piecewise constant baseline rates [1] and the computational methodology developed for clustered recurrent event data [6].
References
[1] Cook, R. and Lawless, J. The Statistical Analysis of Recurrent Events. New York: Springer. 2007.
[2] Cox, D.R. (1972) Regression Models and Life Tables (with Discussion). J. Royal statistical Society, Series B, 34, 187-220.
[3] Kalbfleisch, J.D. and Prentice, R. L. The Statistical Analysis of Failure Time Data. Wiley, New York, 2002.
[4] Lawless, J. F. and Nadeau, C. Some simple and robust methods for the analysis of recurrent events, Technometrics, 37 1995, 355-364.
[5] Lin, D.Y., Wei, L.J., Yang, I. and Ying, Z. Semi parametric regression for the mean and rate functions of recurrent events, Journal of the Royal Statistical Society Series B, 62, 2000, 771-730
[6] Liu D, Schaubel DE, Kalbfleisch JD. Computationally efficient marginal models for clustered recurrent event data. Biometrics. 2012
Exclusions
Exclusions that are implicit in the denominator definition include:
- Time at risk while a patient has had ESRD for 90 days or less
- <18 years old
- Non-Medicare primary insurance
See Denominator Details, 1.15a above
Measure Calculation
The numerator is the observed number of transfusion events for a facility, and the denominator for the same facility is the expected number of transfusion events adjusted for patient mix. The measure for a given facility is calculated by dividing the numerator by the denominator. See STrR_Flowchart_Final_Oct_2025_508 PDF, attached to 1.18a, for further detail.
This measure wasn't stratified.
Public reporting of this measure on DFCC or in the ESRD QIP would be restricted to facilities with at least 10 patient years at risk for the measure to comply with restrictions on reporting of potentially patient identifiable information related to small cell size. We have applied this restriction to all the reliability and validity testing reported here.
Supplemental Attachment
Measure Record
Point of Contact
N/A
Wilfred Agbenyikey
Baltimore, MD
United States
Jonathan Segal
UM-KECC
Ann Arbor, MI
United States
Importance
Evidence
See STrR_2.2_Final_Oct 2025_508_revised PDF, attached in Section 7.1 Supplemental Attachment, for full response to this question.
Measure Impact
There is at least one study indicating that patients with kidney failure who require dialysis value an assessment of red blood cell transfusion risk associated with dialysis facilities. In a discreet choice experiment of 200 dialysis patients [1], participants were willing to accept a 6% medication-related risk of heart attack to avoid having two red blood cell transfusions per month. Overall, the study reported that patients on dialysis had clear preferences for anemia management to reduce symptoms and reduce the frequency of red blood cell transfusions.
References
[1] Hauber B, Caloyeras J, Posner J, Brommage D, Tzivelekis S, Pollock A. Hemodialysis patients' preferences for the management of anemia. BMC Nephrol. 2017 Jul 28;18(1):253. doi: 10.1186/s12882-017-0664-9. PMID: 28750609; PMCID: PMC5532766.
Performance Gap
Data are from the dataset described in section 1.25 for year 2023. The total number of dialysis facilities included in the performance scores was 7,268. The total number of patients included in the performance scores was 473,742.
See STrR_2.4 Table 1_Final_Oct 2025_508 PDF, attached to 2.4a, for Table 1 data and caption.
Care Gaps
Closing Care Gaps
Closing Care Gaps is not required for the Fall 2025 cycle.
Feasibility
Feasibility
All the data incorporated into our database come from structured data. Data collection for this measure is accomplished via data sources including EQRS, a web-based and electronic batch submission platform maintained and operated by CMS contractors, Medicare Claims, and other supplemental data sources (see Section 1.25 Data Source Details). Publicly reported measures like this one are reviewed on a regular basis by dialysis facility providers. Only rare instances of inaccurate or missing data are present (based on comments received during facility previews).
As the data required for this measure is already part of routine data collection, no additional costs or burden are anticipated.
Public reporting of this measure on DFCC or in the ESRD QIP would be restricted to facilities with at least 10 patient years at risk for the measure to comply with restrictions on reporting of potentially patient identifiable information related to small cell size.
No feasibility challenges have been identified that resulted in a change to the measure. Changes to the measure were made to broaden the number of eligible patients and facilities as a consequence of new administrative data sources becoming available AND internal analyses that identified different anemia management practices that challenged our original assumptions (from 2015) when the measure was first developed. These measure specification changes relate to elimination of some previous exclusions and do not require additional data submission by providers. The feasibility profile is not affected by the changes made.
Proprietary Information
Scientific Acceptability
Testing Data
Data from January – December 2023 was used to calculate STrR. Please refer to Section 1.25 Data Source Details for information on data sources.
January – December 2023
None
Number of facilities included for testing and analysis for the year 2023
Year: 2023
# of Facilities: 7,268
Mean Facility size (patients): 71.0
Mean Facility Patient Years: 48.8
See STrR_5.1.4_Final_Oct 2025_508 PDF, attached in Section 7.1 Supplemental Attachment, for full response to this question
Reliability
The reliability of the STrR was assessed using data among ESRD dialysis patients during 2023. If the measure were a simple average across individuals in the facility, the usual approach for determining measure reliability would be a one-way analysis of variance (ANOVA), in which the between and within facility variation in the measure is determined. The inter-unit reliability (IUR) measures the proportion of the measure variability that is attributable to the between-facility variance. The STrR, however, is not a simple average and we instead estimate the IUR using a bootstrap approach, which uses a resampling scheme to estimate the within facility variation that cannot be directly estimated by ANOVA. A small IUR (near 0) reveals that most of the variation of the measures between facilities is driven by random noise, indicating the measure would not be a good characterization of the differences among facilities, whereas a large IUR (near 1) indicates that most of the variation between facilities is due to the real difference between facilities.
Here we describe our approach to calculating IUR. Let T1,…,TN be the STrR for these facilities. Within each facility, select at random and with replacement B bootstrap samples. Our numerical experiments reveal that B=100 is sufficient. That is, if the ith facility has ni subjects, randomly draw with replacement ni subjects from those in the same facility, find their corresponding STrRi and repeat the process B (say, 100) times. Thus, for the ith facility, we have bootstrapped STrRs of T*1,…,T*100 …, Let Si*2 be the sample variance of this bootstrap sample. From this it can be seen that
St,w2= ΣNi=1[(ni-1) S i*2]/ ΣNi=1(ni-1)
is a bootstrap estimate of the within-facility variance in the STrR, namely σ t,w2 . Calling on formulas from the one-way analysis of variance, an estimate of the overall variance of Ti is
St2= ΣNi=1[ni (Ti -Ť)2]/ [n’(N-1)],
where
Ť = Sni Ti / Sni
is the weighted mean of the observed STrR and
n’ = (Sni - Sni2/Sni)/(N-1)
is approximately the average facility size (number of patients per facility). Note that St2 is an estimate of σ b2 + σ t,w2, where σ b2 is the between-facility variance, the true signal reflecting the differences across facilities. Thus, the IUR = σ b2 /( σ b2 + σ t,w2) can be estimated by (St2- St,w2)/St2.
The STrR calculation only included facilities with at least 10 patient years at risk.
The overall IUR for the sample dataset was 0.45. The IUR’s per deciles of patients ranged from 0.10 to 0.64. The overall IUR of 0.45 indicates 45% of variation in the overall measure can be attributed to between facility variations. Please see STrR_5.2.3a Table 2_IUR Reliability Results_Revised Nov 2025_508 in Section 5.2.3a for additional information but to summarize:
- Dialysis facilities are extremely small compared to other health care entities (e.g. hospitals, nursing homes) such that risk adjusted measures do not have a large enough facility size to achieve an IUR of 0.6
- Determining if a facility is “worse than expected” uses statistical hypothesis testing to mitigate the risk of inappropriately flagging small facilities. Specifically, smaller facilities need to have an STrR farther from the median to be flagged compared to larger facilities.
- Star Ratings for dialysis facilities combine information across multiple measures to reduce random noise so that even a measure with a low IUR can contribute to raising the overall reliability of the combined measure set.
- The Quality Incentive Program (QIP) uses a small-facility adjuster (generally applied to facilities with 25 or fewer eligible patients), which helps mitigate the low IURs that would otherwise contribute to payment reductions.
- The number of preventable events, even for facilities in the lower IUR decile groups, is generally >3, suggesting the potential for improvement at a given facility.
This value of IUR indicates a moderate degree of reliability. The results support an inference of reliability because the measure demonstrated moderate internal consistency. When stratified by facility size, we find that, as expected, larger facilities have greater IUR. To better understand the characteristics of entities with low and high reliabilities, the boxplot included in 5.2.3a (see STrR_5.2.3a Table 2_IUR Reliability Results_Revised Nov 2025_508 PDF) displays the distribution of STrRs across decile groups. In general, the STrR distributions are similar across decile groups, with the median STrR remaining close to 1 in all cases.
In addition, while smaller facilities naturally have measure values that exhibit greater uncertainty and variation, statistical hypothesis testing methods account for this variability and flag only providers with truly extreme results. To illustrate the benefit of including more facilities in the STrR evaluation, we also analyzed preventable events. These results (attached in 5.2.3a) suggest that even smaller facilities in low-reliability groups still have a high average burden of adverse events, supporting the case for broader inclusion in STrR-based quality improvement programs.
See STrR_5.2.3a Table 2_IUR Reliability Results_Revised Nov 2025_508 PDF, attached to Section 5.2.3a, for Table 2 data and caption.
Validity
Validity was assessed using Poisson regression models to measure the association between the facility level 2023 Standardized Mortality Ratio (SMR, NQF 0369) and 2023 Standardized Hospitalization Ratio (SHR, NQF 1463) and tertiles of 2023 STrR. Facility-level STrR were divided into tertiles (T1 to T3) and the relative risk (RR) of mortality was calculated for each tertile, using T1 as the reference group. Similar calculation was performed for hospitalization. Thus, a RR>1.0 would indicate a higher relative risk of mortality or hospitalization, compared to the highest performance tertile (T1) of STrR. We expect the risk of mortality and hospitalization, respectively, will be positively associated with higher tertiles of STrR.
Validity was also assessed using a Poisson regression model to measure the association between facility level STrR and tertiles of % of patients with Hgb < 10. Facility-level % of patients with Hgb < 10 in 2023 were divided into tertiles (T1 to T3) and relative risk (RR) of transfusions were calculated for each tertile, using T1 as the reference group. Thus, a RR > 1.0 would indicate a higher relative risk of transfusion, compared to the highest performance tertile (T1) of % of patients with Hgb < 10. We expect the risk of a transfusion event will be positively associated with higher tertiles of % of patients with Hgb < 10.
See STrR_5.3.4_Final_Oct 2025_508 PDF, attached to 5.3.4a
Results from the Poisson models indicated that the STrR was significantly associated with the risks of mortality and hospitalization.
For the 2023 SMR, the relative risk of mortality increased as the STrR tertiles increased from the reference group (tertile 1). For tertile 2, RR=1.05 (95% CI: 1.04, 1.06; p<0.001), and for tertile 3, RR=1.11 (95% CI: 1.11, 1.12; p<0.001).
Similarly, for the 2023 SHR, the relative risk of hospitalization increased as the STrR tertiles increased from the reference group (tertile 1) with the lowest risk in tertile 1. For tertile 2, RR=1.13 (95% CI: 1.12, 1.14; p<0.001), and for tertile 3, RR=1.26 (95% CI: 1.25, 1.27; p<0.001).
Results from the Poisson model indicated that the % of patient months with Hgb < 10g/dl was significantly associated with the risks of transfusion as expressed in facility-level Standardized Transfusion Ratio.
The facility average STrR increased as the tertiles of % of patients with Hgb < 10g/dl increased from the reference group (tertile 1). For tertile 2, RR=1.18 (95% CI: 1.16, 1.19; p<0.001), and for tertile 3, RR=1.37 (95% CI: 1.36, 1.39; p<0.001).
Risk Adjustment
Several approaches were involved in our exploration of potential risk adjustment. The current endorsed version of STrR includes some patient demographic, incident comorbidity, and clinical status risk adjustment covariates determined from internal analyses performed during prior submission work in 2016 and 2019. In addition, we re-evaluated several socio-demographic risk variables (sex, race, ethnicity, Medicare/Medicaid dual eligibility, and the effect of Area Deprivation Index) on risk of transfusion event. Most of these SDS risk variables had been considered for inclusion during our 2019 re-evaluation for NQF, but we chose NOT to include because of very limited effect of their inclusion on facility-level results. We chose to re-evaluate these factors since we are including another large group of patients (Medicare Advantage) that were excluded previously and we wanted to verify our prior analyses and check for interactions between the SDS variables and the new Medicare Advantage covariate.
Our updated literature review and several supplemental literature searches performed during this re-evaluation were used to identify factors (sex; race) for consideration. Female sex was identified in the pre-ESA era as being associated with lower achieved hemoglobin and increased transfusion risk, attributed largely to relative absence of testosterone. In the pre-ESA era, exogenous testosterone was often administered to both female and male patients with high transfusion requirements for its erythropoietic effect. In the ESA era of CKD anemia management, female sex has been associated with higher ESA requirements and sometimes with lower achieved hemoglobin. Black race has been variably associated with decreased achieved hemoglobin and/or increased ESA requirement in the literature, and was included in our analyses on that basis. Of note, there is some evidence and more speculation that some of the race effect could be related to hereditary anemias (e.g. sickle cell disease or trait, hgb C disease, beta-thalassemias, etc.)
Finally, public commenters during prior measure re-evaluation cycles and from other public comment opportunities for specific use cases (e.g. ESRD QIP) have suggested risk-adjustment for GI bleeding. During this re-evaluation, we developed a set of ICD10 diagnosis codes that defined GI/GU bleeding (acute or chronic) which was included in risk adjustment testing.
Potential Risk Adjustment Factors Tested
- Age groups
- Cause of ESRD
- Duration of ESRD
- Nursing Home Status (none; <90 d; >=90 d)
- BMI (at incidence; categorical)
- Incident Comorbidities (from Medicare Form 2728)
- Prevalent Comorbidity Groups (Previous Exclusion Categories for malignancies, hereditary anemias, coagulation disorders)
- h/o GI/GU Bleeding from Medicare Claims and Medicare Advantage Encounter Data
- active MA insurance variable
- Interaction tests
- Duration of ESRD with Diabetes as ESRD cause
- Age groups with DM as ESRD cause
- MA with Coagulation disorders
- MA with lymphoma
Sociodemographics
- Sex
- Race
- White
- Black
- Asian/PI
- Native Amer
- Hispanic Ethnicity
- Employment Status (Incident) (Employed; Unemployed)
- Medicare coverage
- Primary Traditional alone
- Dual eligible
- Medicare Advantage
- Other
- Area Deprivation Index
See STrR_5.4.3_Final_Oct 2025_508 PDF, attached to 5.4.3a
See STrR_5.4.4 and 5.4.4a_Final_Oct 2025_508 PDF, attached to 5.4.4a. The first 6 pages of the document address 5.4.4 and the 7th page addresses 5.4.4a
To assess model performance, we evaluated discrimination using the C-statistics. The C-statistics quantifies the model’s ability to discriminate between outcomes based on the included risk factors. Specifically, the STrR model is a recurrent event model, for which the C-statistics measures the concordance between the observed rates of recurrent events and the model-based predicted rates.
For the Fall 2025 maintenance submission, the C-statistic is 0.63, which indicates moderate model discrimination, reflecting the model’s ability to distinguish high-risk from low-risk subjects.
Note: this text is also uploaded as an attachment to 5.4.5a since that is a required field.
See STrR_5.4.6_Final_Oct 2025_508 PDF, attached to Section 7.1 Supplemental Attachment, for full response to this question
Use & Usability
Use
Dialysis Facility Care Compare helps patients find detailed information about Medicare-certified dialysis facilities. Patients can compare the services and the quality of care that facilities provide.
United States. All Medicare-certified dialysis facilities that are eligible for the measure and have at least 10 patient years at risk are included in the measure calculation for the program. For calendar year 2023, 7,268 U.S. dialysis facilities serving 473,742 patients would have had STrR results reported if the proposed version of the measure was in use.
Facility level, Dialysis Facilities
The Centers for Medicare & Medicaid Services (CMS) administers the End-Stage Renal Disease Quality Incentive Program (ESRD QIP) to promote high-quality services in renal dialysis facilities. The first of its kind in Medicare, this program changes the way CMS pays for the treatment of patients who receive dialysis by linking a portion of payment directly to facilities’ performance on quality of care measures. These types of programs are known as “pay-for-performance” or “value-based purchasing” (VBP) programs.
United States. All Medicare-certified dialysis facilities that are eligible for the measure and have at least 10 patient years at risk are included in the measure calculation for the program. For calendar year 2023, 7,268 U.S. dialysis facilities serving 473,742 patients would have had STrR results reported if the proposed version of the measure was in use.
Facility level, Dialysis Facilities
The Dialysis Facility Reports (DFRs) are provided as a resource for characterizing selected aspects of clinical experience at this facility relative to other caregivers in this state, End Stage Renal Disease (ESRD) Network, and across the United States. Since these data could be useful in quality improvement and assurance activities, each state’s surveying agency may utilize the DFRs as a resource during their survey and certification process. Measures included in the DFRs are updated annually and available to dialysis facilities to review and submit comments prior to their release to State Survey Agencies and Regional Offices in September of each year.
United States. All Medicare-certified dialysis facilities that are eligible for the measure and have at least 10 patient years at risk are included in the measure calculation for the program. For calendar year 2023, 7,268 U.S. dialysis facilities serving 473,742 patients would have had STrR results reported if the proposed version of the measure was in use.
Facility level, Dialysis Facilities
Usability
There are several actions that dialysis facilities can take to improve anemia management and avoid the need for red blood cell transfusion. These include:
- Use of an anemia manager and protocols to standardize use of ESA or HIF protease inhibitor administration and dosing.
- Optimize iron administration to aggressively treat iron deficiency
- Adjust frequency of monitoring hemoglobin (ie. Checking after hospitalization or other absence from facility; checking more frequently if ESA is placed on hold)
- Evaluate patients with poor response to ESA for infection / inflammation.
- Collaborate with inpatient providers to follow guidelines and exercise a judicious use of red blood cell transfusions.
For DFCC, feedback can be provided any time through contacting the dialysisdata.org helpdesk. Preview periods allow for specific times for facilities review and comment on measure calculations, and provide an opportunity to request data suppression.
For the ESRD QIP, feedback can be provided any time through contacting the QIP helpdesk. Preview periods allow for specific times for facilities review and comment on measure calculations. Comments can also be submitted in response to the Notice of Proposed Rulemaking for each QIP calendar year.
DFCC: Comments received during DFCC preview periods tend to be technical in nature, asking for clarification on how the STrR is calculated for particular facilities, including questions about patient assignment and application of exclusion criteria.
QIP: Since the STrR was first proposed for the ESRD QIP commenters raised issues related to additional risk adjustment for SDS factors or clinical factors, and question whether the outcome of the measure (transfusions) were attributable to the dialysis facility. Both of these issues are addressed in this and prior successful submissions to the CBE.
Based on the feedback received, we made the following changes:
- The measure has been modified from an approach that used a more extensive set of exclusion criteria for malignancies that might preclude the use of ESAs, to one of risk adjustment where these conditions have been added to the risk adjustment model and are no longer exclusion criteria.
- The measure has been modified to now adjust for GI bleeding. A panel of ICD-10 codes have been added to the risk adjustment model to account for GI bleeding, which can have a direct impact on transfusion risk and is generally believed to be outside the control of the dialysis facility.
- The denominator has been expanded to include patients with Medicare Advantage plans who had previously been excluded. Based on enrollment information from the Medicare Enrollment Database (EDB), the percentage of ESRD dialysis beneficiaries enrolled in Medicare Advantage (MA) has steadily increased over time. From 12% in 2010, the proportion rose to 22% by 2020. Prior to 2020, there was an annual increase of approximately 1%. However, since 2021, the annual increase has been more than 5% The growth in ESRD beneficiaries joining MA plans carries significant implications for the metrics used to assess dialysis facility performance. Contrary to the data from Fee-For-Service (FFS) Medicare beneficiaries, MA outpatient encounters and administrative records have not been readily available for the purposes of analyzing facility quality, except for internal CMS use in risk adjustment and performance assessment.
See STrR_6.2.4_Final_Oct 2025_508 PDF, attached to Section 7.1 Supplemental Attachment, for full response to this question
Patient denominator time at risk has fallen dramatically over the last several years, correlating with rapid increased uptake of Medicare Advantage insurance over the same interval. We saw these trends as negatively impacting both patients and facilities. For patients without Medicare FFS coverage, they were effectively excluded from the quality measure and consequently did not benefit from any postulated incentivization toward higher quality anemia management. Similarly, dialysis facilities were increasingly being judged on anemia management only for Medicare FFS patient outcomes. For those facilities in geographic areas with high MA uptake, results could have been biased by small sample size related to MA patient time at risk exclusion. The measure in this submission includes both FFS and ME time at risk and results in significantly larger number of patients, patient-years, and dialysis facilities included.
Comments
Staff Preliminary Assessment
CBE #2979 Staff Preliminary Assessment
Importance
Strengths
- A clear logic model is provided, depicting the relationships between inputs (e.g., anemia management protocols and guidelines, dialysis center staff and quality improvement team), activities (e.g., identification of high-risk patients for intervention, root cause analysis for low hemoglobin, education for patients and providers on transfusion avoidance strategies), and desired outcomes (e.g., improved proportion of patients with achieved hemoglobin, sustained decrease in transfusion frequency and optimized anemia management). This model demonstrates how the measure's implementation will lead to the anticipated outcomes.
The problem this measure addresses represents a significant issue, anemia management in end stage renal disease (ESRD) patients on chronic dialysis. By highlighting facility-level differences in transfusion rates, this measure helps ensure that issues such as avoidable transfusions and suboptimal anemia management are addressed, ultimately improving patient outcomes such as reduced transfusion-related allosensitization.
The measure is supported by a comprehensive literature review, including high-quality systematic reviews of anemia therapies in chronic kidney disease (CKD), clinical practice guidelines for anemia management, and multiple empirical studies (e.g., Molony, 2016 and Obrador & Macdougall, 2013) demonstrating a clear net benefit in terms of reducing transfusions and transfusion-related allosensitization for patients on chronic dialysis.
Data from 7,268 dialysis facilities in 2023 show a performance gap, with decile ranges from 0.33 to 2.01, indicating variation in measure performance across the target population.
Description of patient input supports the conclusion that the measured outcome is meaningful with at least moderate certainty. Evidence from one 2017 study, representing a survey of 200 dialysis patients, showed that patients strongly preferred anemia treatments and were willing to accept the risks associated with anemia medications to avoid red blood cell transfusions.
Limitations
- Evidence is predominately observational, showing associations between anemia management practices (e.g., hemoglobin levels, ESA/iron use) and transfusion events.
Evidence of patient meaningfulness relies on a single 2017 study; more recent and direct patient input would strengthen the submission.
Rationale
- This maintenance measure meets all criteria for 'Met' for importance due to its significant anticipated impact, its robust evidence base, a documented performance gap, and well-articulated logic model, making it essential for addressing avoidable red blood cell transfusions and suboptimal anemia management.
Closing Care Gaps
The developer did not address this optional domain.
Feasibility Assessment
Strengths
- All required data elements are routinely generated and available in structured fields within administrative data, claims data, or registries (e.g., transplant data).
The developer indicated there have been changes to the measure specification (e.g., inclusion of Medicare Advantage patients, removal of exclusions for patients with certain coagulation disorders, hereditary anemias, or malignancies, and updates to risk adjustment), but reported no challenges with data structure or availability resulting from these updates. Based on comments they received during facility previews, there are only rare instances of inaccurate or missing data.
The developer described that there are no additional costs and burden associated with data collection and data entry, validation, and analysis as the data are routinely collected.
The developer described how all required data elements can be collected without risk to patient confidentiality, including restricting public reporting to facilities with at least 10 patient years at risk to avoid small cell size issues.
There are no fees, licensing, or other requirements to use any aspect of the measure (e.g., value/code set, risk model, programming code, algorithm).
Limitations
- None identified.
Rationale
- This maintenance measure meets all criteria for 'Met' for feasibility due to its well-documented feasibility assessment, clear and implementable data collection strategy, clear description of adjustments made to the specifications, and transparent handling of patient confidentiality, burden, licensing, and fees. These factors collectively ensure that the measure can be implemented effectively and sustainably in a real-world health care setting.
Scientific Acceptability
Strengths
- The developer performed the required reliability testing for this maintenance measure, namely, they conducted accountable entity-level (“measure score”) reliability testing at the level for which the measure is specified. Data sources used for reliability analysis are adequately described and include data from over 450K ESRD dialysis patients at 7,268 facilities during the period of 1/2023-12/2023.
Limitations
- The developer conducted signal-to-noise reliability testing at the accountable entity-level. Less than 20% of accountable entities were above the expected threshold of 0.6. The developer did provide some interpretation and rationale for these results. They acknowledge that the low reliability is driven by the typical small size of dialysis facilities, but that a lower standard for reliability may be warranted in this situation due to the impactfulness of this measure. They state that the impact of small facility size is reduced by combination with other measures for Star Ratings and that QIP uses a small facility adjuster to limit effects on payment reductions.
Rationale
- This maintenance measure is rated as ‘Not Met’ for reliability because the reliability testing results significantly fall below the established thresholds, indicating major issues with the consistency and accuracy of the results across different settings and populations. The developer did provide an interpretation and some rationale for these results for consideration.
Strengths
- The developer performed the required validity testing for this maintenance measure, namely, they conducted accountable entity-level (“measure score”) validity testing at the level for which the measure is specified. Data sources used for validity analysis are adequately described and include End Stage Renal Disease Quality Reporting System (EQRS) data and Medicare claims data (Parts A, B, and C) during the period January-December 2023. Testing included data from 7,268 dialysis facilities serving a mean of 71 patients.
The developer conducted empirical validity testing at the accountable entity-level. They hypothesized that the measure score (STrR) would be positively associated with the 2023 Standardized Mortality Ratio (SMR, CBE 0369), the 2023 Standardized Hospitalization Ratio (SHR, CBE 1463), and the proportion of patients with hemoglobin (Hgb) < 10 g/dl, a measure of anemia. The validity analysis implicitly situated the measure within a causal pathway, i.e., from clinical indicator (Hgb level) to the measure focus (STrR) to material outcomes (SHR, SMR). The developer performed separate Poisson regressions of SMR and SHR on the STrR score in tertiles (with the lowest tertile [T1, mean score 0.52] as the reference group). The developer also regressed the STrR score on the facility-level proportion of patients with Hgb < 10 g/dl (in tertiles, with the lowest tertile [T1, mean rate 6.97%] as the reference).
The results showed significantly higher risk of SMR (T2, RR=1.05 [95% CI: 1.04, 1.06]; T3, RR=1.11 [95% CI: 1.11, 1.12]) and SHR (T2, RR=1.13 [95% CI: 1.12, 1.14]; T3, RR=1.26 [95% CI: 1.25, 1.27]) for facilities with higher STrR scores. Results also showed significantly higher STrR scores for facilities with higher rates of patients with low Hgb (T2, RR=1.18 [95% CI: 1.16, 1.19]; T3, RR=1.37 [95% CI: 1.36, 1.39; p<0.001]). Both sets of results were consistent with hypotheses.
A well-developed logic model and thorough literature review suggest adequate “ruling in” of mechanisms that can explain the measure focus.
The developer conducted statistical risk adjustment, based on a conceptual model, selecting risk factors that are present at the start of care and have a significant correlation with the outcome.
Limitations
- The developers did not report performing data element validity testing on this respecified measure. However, in the feasibility section, the developer indicated that the measure's respecification removed prior exclusions and does not require additional data submission.
The measure's low reliability does not support an inference of validity, because it may indicate that observed relationships are not real.
The variable distributions provided by the developer are presented in pooled form, therefore they do not demonstrate differences in facility-level prevalence of risk factors that would warrant adjustment for fair comparisons. The developer reported a c-statistic of 0.63, indicating moderate model discrimination, but did not provide performance metrics reflecting model calibration.
Rationale
- This maintenance measure is rated as ‘Not Met But Addressable’ for validity because the validity testing results partially support an inference of validity for the measure, suggesting that the measure somewhat accurately reflects performance on quality and can distinguish good from poor performance.
Specifically, while the risk adjustment methods used are appropriate and demonstrate that the risk factors contribute to unique variation in the outcome, and model discrimination is acceptable, the developer did not provide model calibration testing results or evidence of variation in the prevalence of risk factors across measured entities.
Use and Usability
Strengths
- The measure is currently used in several programs, including CMS Dialysis Facility Care Compare, CMS End Stage Renal Disease (ESRD) Quality Incentive Program (QIP), and CMS Dialysis Facility Reports (DFRs).
The developer provided a summary of how accountable entities can use the measure results to improve performance. Specifically, use of an anemia manager and protocols to standardize the use of erythropoiesis stimulating agent (ESA) dosing, optimizing iron therapy to promptly and aggressively correct iron deficiency, and collaborating with inpatient providers to follow guidelines and exercise a judicious use of red blood cell transfusions. These possible actions are reflected in the measure’s logic model.
Interested parties can submit ongoing feedback through online helpdesks. A preview period is also held to allow dialysis facilities to review, comment, and ask questions about measure calculations before public reporting. The developer noted that dialysis facilities ask technical questions related to patient assignment, application of exclusion criteria, and risk adjustment clinical factors.
The developer noted that updates to the measure specifications were made to address feedback received, which included expanding the denominator to include Medicare Advantage (MA) patients due to the growth in ESRD patients joining MA plans, shifting from exclusions to risk adjustment for malignancies that might preclude the use of ESAs, and updates to risk adjustment to account for gastrointestinal bleeding which can impact transfusion risk.
The developer reported changes in performance from calendar years 2010–2023. Compared to 2010, transfusion event rates were approximately 25% lower in 2019–2022 (excluding 2020 during COVID), indicating measurable improvement over time and supporting the usability of the measure.
The developer reported that patient denominator time at risk had declined significantly as more patients enrolled in MA, resulting in the exclusion of these patients from the measure and creating potential bias for facilities with few remaining Medicare Fee-For-Service (FFS) patients. The updates to the measure address this finding by including both FFS and MA patients, improving the measure’s completeness and the number of facilities represented.
Limitations
- Progress on improvement trend analyses are based on a version of the measure that includes Medicare FFS patients only, due to previous data limitations with Medicare outpatient encounter data.
Rationale
- This maintenance measure is rated ‘Met’ for use and usability because it is actively used in at least one accountability application, with a systematic feedback approach that allows for continuous updates based on stakeholder feedback. The measure also demonstrates a positive trend in performance results, affirming its ongoing usability. The developer reported declines in patient denominator time at risk, but explained the finding is addressed by including both FFS and MA patient time at risk, increasing the number of patients, patient-years, and facilities represented, and improving the measure’s completeness.
Committee Independent Review
Support
Importance
Very clear logical model. Appreciate the framing of the importance discussion and analysis/public comment informing changes to exclusions and the risk adjustment model.
Closing Care Gaps
Not required - no evidence presented
Feasibility Assessment
Feasibility supported by use of existing data sources and no required change in provider data submission requirements to support measure specification updates.
Scientific Acceptability
Appreciate the discussion of the lower reliability results for small facilities and steps to mitigate. Otherwise agree with staff assessment
Agree with staff assessment
Use and Usability
Actions providers can take to improve rates clear and feasible. Examples, if available, of successful QI initiatives/strategies would be valuable
Summary
Overall comprehensive and thoughtful submission
No additional comments
Importance
Agree with staff assessment
Closing Care Gaps
Agree with staff assessment
Feasibility Assessment
Agree with staff assessment
Scientific Acceptability
Agree with staff review
Agree with staff review
Use and Usability
Agree with staff assessment
Summary
No additional comments
Patient Partner review of 2979 STrR
Importance
As a patient partner, I believe this is met as the measure is evidence based (been in use since 2016), is important for making gains to limit avoidable transfusions, addressing suboptimal anemia management, and ultimately improving patient outcomes such as reduced transfusion-related allosensitization.
Closing Care Gaps
Not required
Feasibility Assessment
The data required for this measure is already part of routine data collection, no additional costs or burden are anticipated.
Scientific Acceptability
As a patient partner, I am not a technical expert in this area. As such, I agree with the staff assessment.
As a patient partner, I am not a technical expert in this area. As such, I agree with the staff assessment.
Use and Usability
The measure is currently used in Dialysis Facility Care Compare (provides patients detailed information), ESRD Quality Incentive Program (pay-for-performance), and Dialysis Facility Reports (quality improvement).
Summary
As a Patient Partner, I support this measure. A discussion of the American Society of Nephrology public comments is appreciated.
2979 Summary
Importance
agree with staff assessment - strong logic model, demonstrated performance gap. In addition, literature review has been updated and measure developers provide support for changes in specifications (exclusions and risk adjusters).
There is limited support (1 published paper) for the importance of this measure to individuals on dialysis - this should be looked at for future measure development.
The narrative suggests that this measure serves as a check on unanticipated consequences of payment models that incentivize reduced use of ESAs - interesting rationale for importance of measure - why not change payment incentives to align with best practice?
Closing Care Gaps
Optional
Feasibility Assessment
Data are routinely generated and available. Measure developers note there are no new issues associated with changes in measure specifications.
Scientific Acceptability
IUR low (.45) with same rationale about small facility size
American Society of Nephrology raises question about reliability of hospital transfusion data. Need to ask measure developers about this.
Measure significantly related to mortality and hospitalization rates in predicted direction. Need further explanation of staff rating of not met but addressable.
Use and Usability
Measure initially introduced in 2016; used in public reporting and quality payment programs.
Most of the interventions are based on observational data, rely on facility tracking common causes of low hemoglobin levels.
Summary
Overall measure of transfusion rates; appears to be important in terms of perverse incentives mediating against appropriate use of medications. Low reliability as with other measures in this set - apparently importance and issues re small facility size have driven recommendations to endorse in past
nothing more to add
Importance
met
Closing Care Gaps
Not met
Feasibility Assessment
believe this to be met
Scientific Acceptability
agree not met
not met
Use and Usability
agree, met
Summary
nothing more to add. Agree with staff findings.
need to discuss rate/ratio consideration
Importance
agree
Closing Care Gaps
agree
Feasibility Assessment
agree
Scientific Acceptability
agree
ASN comment re: using rate rather than O/E (?) ratio worthy of discussion
Use and Usability
Agree with ASN recommendation to set target rate rather than O/E ratio
Summary
need to discuss ASN concerns rate vs ratio
Nothing more to add
Importance
Agree with staff assessment.
Closing Care Gaps
Not required.
Feasibility Assessment
Agree with staff assessment.
Scientific Acceptability
Agree with staff assessment.
Agree with staff assessment.
Use and Usability
Agree with staff assessment.
Summary
Nothing more to add
Await committee discussion
Importance
The measure is important for making significant gains in health care quality or cost where there is variation in or overall less-than-optimal performance.
Closing Care Gaps
Developer did not address. The measure can identify and reduce gaps in care for ESRD patients
Feasibility Assessment
Data are readily available OR could be captured without undue burden AND can be implemented for performance measurement.
Scientific Acceptability
The measure, as specified, can produce consistent (reliable)results about the quality of care with implementation of better communication protocols between dialysis facilities and hospitals
The measure, as specified, ca produce credible (valid) results about the quality of care
Use and Usability
Measure results can be used for both accountability and performance improvement to achieve the goal of high-quality efficient health care for individuals or populations.
Summary
Await committee discussion
Support but questions remain
Importance
-
Closing Care Gaps
-
Feasibility Assessment
-
Scientific Acceptability
I am deferring to the staff assessment.
I am deferring to the staff assessment.
Use and Usability
-
Summary
In the supplemental attachment, 6.2.4 progress on improvement, it states: “After revision of the FDA Prescribing Information and almost simultaneously, implementation of the expanded Medicare ESRD Prospective Payment System (ESRD PPS), which included ESA payment in the dialysis facility capitated payment system for chronic dialysis, achieved hemoglobin began to fall precipitously (not shown here) and per capitata transfusion event rates began increasing.”
It also states that the trend is that transfusion rates are lower now than they were in 20210. Does the team believe the availability of blood has impacted results? How else might you explain this?
The inclusion of ESA in the capitation calculation is something that CKD advocates were very concerned about when ESA was added to the payment calculations for dialysis centers. From my own experience, I am concerned about the micro details of how anemia is managed in this setting.
Summary for #2979
Importance
Agree with staff prelim assessment; logic model appropriate and evidence of care/practice gap. Meaningfulness to patient population is not clear
Closing Care Gaps
Not addressed
Feasibility Assessment
Data for this metric is captured in a structured and standardized wayl there is no additional burden for facilities
Scientific Acceptability
Defer to staff assessment
The overall IUR of 0.45 does vary across deciles, however only one decile meets the 0.60 standard threshold.
defer to staff assessment
The developers tested associations between facility mortality ratio and hospitalization ratio with the transfusion ratio.
Use and Usability
Currently used in Care Compare
The developers list actions that facilities/providers can take if transfusion rates are not meeting benchmarks. Data provided by the developers does demonstrate improvement over time
Summary
I do support this measure. Again, the comments by the ASN are thoughtful and appropriate regarding the fact that dialysis patients may receive transfusions for reasons unrelated to dialysis. These comments should be considered
Improved measure-want to discuss ratio vs rate
Importance
Agree with staff assessment. An important measure of quality which is enhanced by including the MA population (now > 50% of the Medicare population and, likely, the population of Medicare beneficiaries on dialysis), inclusion of patients with hematologic disorders, and risk adjustment for this population.
Useful logic model though some activities are outputs, one output is an activity, and the rest of the listed outputs appear to be outcomes.
Closing Care Gaps
not submitted/not required
Feasibility Assessment
agree with staff assessment
Scientific Acceptability
agree with staff assessment.
agree with staff assessment.
However, staff assessment summary indicates this is a new measure, but it is a maintenance measure with revisions.
Use and Usability
Agree with staff assessment.
Summary
Important measure which reflects an important aspect of dialysis faciity quality. Revisions (expanded to MA population and fewer exclusions) strenthen the measure. Would like to discuss ASN comments about ratio vs rate. I think rate is more easily understood by the public than ratio. Would need to know more from developer about this issue.
More risk adjustments may be needed
Importance
I can see the intent and importance of this measure. Appreciate staff assessment.
Closing Care Gaps
Support staff assessment
Feasibility Assessment
Support
Scientific Acceptability
Support
Support
Use and Usability
Support
Summary
There may be numerous causes for the need for transfusion, and I am wondering if the current risk adjustments are sufficient.
Public Comments
ASN Comments on Standardized Transfusion Ratio
ASN appreciates the updates to the exclusion criteria and risk adjustments, including those for certain coagulation disorders, hereditary anemias, and comorbidities indicative of a history of gastrointestinal bleeding. However, it is important to recognize that the need for blood transfusions is often unrelated to kidney disease specifically and rather reflective of other complex conditions (i.e. oncologic diagnoses, anemias and gastrointestinal bleeding), which are not under the purview of the dialysis facility. In addition, available data on transfusions are of low reliability due to fragmented communication between hospitals and dialysis facilities, which compromises the accuracy and interpretability of the measure. The measure should also be modified to be a true risk-standardized rate as opposed to a ratio. This modification allows accurate assessment of improvement as well as accurate benchmarking, elements that are critical to the quality program and to individual dialysis facilities as they seek to improve the care provided to individual patients.