Case-mix Models Research Articles

331. CASE-MIX ADJUSTMENT IN BETWEEN-HOSPITAL COMPARISONS ON CLINICAL OUTCOMES IN ESOPHAGEAL AND GASTRIC CANCER SURGERY: A NATIONWIDE POPULATION-BASED COHORT STUDY

Abstract Background Clinical outcomes of esophageal and gastric cancer surgery are used for internal and external benchmarking in clinical auditing. For true hospital comparisons, proper case-mix adjustment is required. This study aimed to develop distinct models that allow for case-mix adjusted quality assessment of esophageal and gastric cancer surgery separately and assessed the impact on between-hospital comparisons. Methods This study included all patients undergoing esophagogastric cancer surgery in the Netherlands between 2017-2022 registered in the Dutch Upper Gastrointestinal Cancer Audit. We developed distinct case-mix adjustment models for ten quality indicators (QIs) using backward selection. Model performance of each individual model was evaluated with area-under-the-receiving-operator-curve (AUC) statistics, representing the impact of case-mix on the QI scores. The impact on between-hospital comparisons was quantified using unadjusted and adjusted O/E ratios. Results A total of 4,354 esophageal cancer and 2,109 gastric cancer patients were included. The most frequently selected case-mix variables in the models for all QIs for esophageal cancer surgery were ASA-score, salvage surgery, peripheral vascular disease/aortic aneurysm, chronic lung disease, and tumor histology, whereas for gastric cancer these were age, preoperative weight loss, tumor location, and clinical M stage. The case-mix adjustment models for esophageal and gastric cancer surgery showed low to moderate performance, with internally validated AUCs of 0.58-0.73 and 0.58-0.74, respectively. Case-mix adjustment had low impact on between-hospital comparisons. For both types of cancer, case-mix adjustment had most pronounced impact on between-hospital comparisons for QIs 30-day/in-hospital mortality and failure-to-rescue. Conclusion This study showed that correct quality assessment requires different case-mix models per quality indicator for clinical outcomes of esophageal and gastric cancer surgery. The case-mix models had low to moderate performance scores, suggesting that case-mix has limited impact in between-hospital comparisons of oesopagogastric cancer surgery.

Association of multimorbidity with mortality after stroke stratified by age, severity, etiology, and prior disability.

Multimorbidity is common in patients with stroke and is associated with increased medium- to long-term mortality, but its value for clinical decision-making and case-mix adjustment will depend on other factors, such as age, stroke severity, etiological subtype, prior disability, and vascular risk factors. In the absence of previous studies, we related multimorbidity to long-term post-stroke mortality with stratification by these factors. In patients ascertained in a population-based stroke incidence study (Oxford Vascular Study; 2002-2017), we related pre-stroke multimorbidity (weighted/unweighted Charlson comorbidity index (CCI)) to all-cause/vascular/non-vascular mortality (1/5/10 years) using regression models adjusted/stratified by age, sex, predicted early outcome (THRIVE score), stroke severity (NIH stroke scale (NIHSS)), etiology (Trial of Org 10172 in Acute Stroke Treatment (TOAST)), premorbid disability (modified Rankin Scale (mRS)), and non-CCI risk factors (hypertension, hyperlipidemia, atrial fibrillation, smoking, deprivation, anxiety/depression). Among 2454 stroke patients (M/SD age: 74.1/13.9 years; 48.9% male; M/SD NIHSS: 5.7/7.0), 1375/56.0% had ⩾ 1 CCI comorbidity and 685/27.9% had ⩾ 2. After age/sex adjustment, multimorbidity (unweighted CCI ⩾ 2 vs 0) predicted (all ps < 0.001) mortality at 1 year (aHR = 1.57, 95% CI = 1.38-1.78), 5 years (aHR = 1.73, 95% CI = 1.53-1.96), and 10 years (aHR = 1.79, 95% CI = 1.58-2.03). Although multimorbidity was independently associated with premorbid disability (mRS > 2: aOR = 2.76, 2.13-3.60) and non-CCI risk factors (hypertension: 1.56, 1.25-1.95; hyperlipidemia: 2.58, 2.03-3.28; atrial fibrillation: 2.31; 1.78-2.98; smoking: 1.37, 1.01-1.86), it predicted death after adjustment for all measured confounders (10-year-aHR = 1.56, 1.37-1.78, p < 0.001), driven mainly by non-vascular death (aHR = 1.89, 1.55-2.29). Predictive value for 10-year all-cause death was greatest in patients with lower expected early mortality: lower THRIVE score (pint < 0.001), age < 75 years (aHR = 2.27, 1.71-3.00), NIHSS < 5 (1.84, 1.53-2.21), and lacunar stroke (3.56, 2.14-5.91). Results were similar using the weighted CCI. Pre-stroke multimorbidity is highly prevalent and is an independent predictor of death after stroke, supporting its inclusion in case-mix adjustment models and in informing decision-making by patients, families, and carers. Prediction in younger patients and after minor stroke, particularly for non-vascular death, suggests potential clinical utility in targeting interventions that require survival for 5-10 years to achieve a favorable risk/benefit ratio. Data requests will be considered by the Oxford Vascular Study (OXVASC) Study Director (P.M.R.-peter.rothwell@ndcn.ox.ac.uk).

Temporal Changes in Quality Indicators in a Regional System of Care After Surgical and Transcatheter Aortic Valve Replacement

BackgroundHistorically, quality-of-care monitoring was performed separately for transcatheter and surgical aortic valve replacement (TAVR, SAVR). Using consensus indicators, we provide a global report on the quality of care for treatment of aortic stenosis across the highest-volume treatments: transfemoral (TF) TAVR, isolated SAVR, and SAVR combined with coronary artery bypass graft. MethodsRetrospective observational cohort study of consecutive patients in a regional system of care. Primary endpoint was 30-day and 1-year mortality (2015-2019). Secondary endpoints included rate of new pacemaker, rate of readmission, and length of stay (2012-2019). Following multivariable logistic regressions, we developed mortality case-mix adjustment models to report risk estimates. ResultsThe proportion of patients receiving TAVR grew from 32% to 53% (2015-2019). Those receiving TF TAVR were significantly older, with higher rates of comorbidities. Observed 30-day and 1-year all-cause mortality after TF TAVR decreased from 3.1% to 0.6% (P = 0.03), and 13.6% to 6.6% (P = 0.09), respectively; surgical mortality rates for isolated SAVR and SAVR combined with coronary artery bypass graft were low and did not change significantly over time, ranging from 0.3% to 1.4% and from 0.9% to 3.4%, respectively at 30 days, and from 0.9% to 3.4% and from 4.7% to 6.7 at 1 year. In the TF TAVR cohort, the observed vs expected ratio for 30-day and 1-year mortality decreased significantly from 1.9 (95% confidence interval [CI] 0.9, 3.5) to 0.3 (95% CI 0.1, 0.8), and from 1.3 (95% CI 0.9, 1.7) to 0.7 (95% CI 0.5, 0.99), respectively; no change occurred in risk-adjusted surgical mortality. ConclusionsConsensus quality indicators provide unique insights on the quality of care for patients receiving treatment for aortic stenosis.

Case-mix adjustment of patient-reported experience measures in National Regional Center for Pediatric.

The aim of the study was to identify case-mix adjusters for the Chinese version of the Child Hospital Consumer Assessment of Healthcare Providers and Systems (Child-HCAHPS) and assess the impact of case-mix adjustment on patient experience measures in China. This study analyzed data collected from six National Regional Center for Pediatric across China retrospectively. Participants were children aged ≤17 years and their guardians who completed the survey. The Chinese Child-HCAHPS was used to measure pediatric inpatient care experience. Candidate case-mix adjusters were assessed using a summary measure of explanatory power. Changes in scores and rankings of the six centers were quantified to assess the impact of adjustment. A total of 2708 respondents completed the survey from January to March 2021, with a response rate of 7-15%. The child's global health status and the respondent being the child's mother were identified as case-mix adjusters, and case-mix adjustment models for 18 patient experience items were constructed. Kendall's τ correlation of hospital rankings before and after adjustment ranged from 0.73 to 1.00. Although the impact of case-mix adjustment may appear modest in our sample, it demonstrated the feasibility, necessity, and methodology for further development of case-mix adjustment models in pediatric healthcare facilities in China. Case-mix adjustment models adjust for factors that are unamendable by healthcare providers that may affect patient experience ratings, thereby improving the comparability of institutional-level ratings. Standardized case-mix adjustment protocols for quality measures need to be modified in different settings. This is the first study to identify adjustment variables and the possible impact of case-mix adjustment on pediatric inpatients' experience measures in a Chinese population. This study provided evidence on the feasibility and necessity for further development of case-mix adjustment models for pediatric healthcare facilities in China.

Racial Disparity: The Adult Congenital Heart Disease Surgery Perspective.

The influence of race and ethnicity on clinical outcomes in medicine are widely acknowledged. However, the effect of race on adult congenital heart disease (ACHD) surgery is not known. We sought to evaluate the possible association between race and outcomes following ACHD operations. Discharge records for patients who underwent ACHD surgery between 2005 and 2014, were isolated from an all-payer voluntary database in the United States. Hierarchical case-mix regression models and sensitivity analyses examined any complication, in-hospital mortality, and discharge disposition (home/non-home) by race (white-WP, black-BP, non-white non-black-NWNB). Of the 174,370 patients (WP: 80.8%, BP: 5.8%, NWNB: 13.4%), black patients were youngest to undergo surgery (WP: 57.9 ± 15.8years, BP: 50.2 ± 16.1years, NWNB: 51.6 ± 16.9years, P < 0.0001), the most likely to have a comorbidity (WP: 70.3%, BP: 74.3%, NWNB: 68.6%, P < 0.0001), and most likely to have had a post-operative cardiac complication (WP: 9.4%, BP: 15.3%, NWNB: 10.9%, P < 0.0001). BP had similar odds of having any complication (AOR = 0.99, 95%CI = 0.94-1.04), while NWNB had significantly decreased odds of a major complication (AOR = 0.90, 95%CI = 0.87-0.93). BP had equivalent in-hospital mortality compared to WP (AOR = 1.03, 95%CI = 0.91-1.18), while NWNB had significantly increased odds of in-hospital mortality (AOR = 1.29, 95%CI = 1.18-1.41). Among survivors, BP were less likely to discharge home (AOR = 0.88, 95%CI = 0.82-0.94), and NWNB were more likely to discharge home than WP (AOR = 1.26, 95%CI = 1.19-1.33). Race and clinical outcomes are associated among patients undergoing surgery for ACHD. Understanding why and how these factors are impactful will help improve care for this complex population.

Identifying client characteristics to predict homecare use more accurately: a Delphi-study involving nurses and homecare purchasing specialists

BackgroundCase-mix based prospective payment of homecare is being implemented in several countries to work towards more efficient and client-centred homecare. However, existing models can only explain a limited part of variance in homecare use, due to their reliance on health- and function-related client data. It is unclear which predictors could improve predictive power of existing case-mix models. The aim of this study was therefore to identify relevant predictors of homecare use by utilizing the expertise of district nurses and health insurers.MethodsWe conducted a two-round Delphi-study according to the RAND/UCLA Appropriateness Method. In the first round, participants assessed the relevance of eleven client characteristics that are commonly included in existing case-mix models for predicting homecare use, using a 9-Point Likert scale. Furthermore, participants were also allowed to suggest missing characteristics that they considered relevant. These items were grouped and a selection of the most relevant items was made. In the second round, after an expert panel meeting, participants re-assessed relevance of pre-existing characteristics that were assessed uncertain and of eleven suggested client characteristics. In both rounds, median and inter-quartile ranges were calculated to determine relevance.ResultsTwenty-two participants (16 district nurses and 6 insurers) suggested 53 unique client characteristics (grouped from 142 characteristics initially). In the second round, relevance of the client characteristics was assessed by 12 nurses and 5 health insurers. Of a total of 22 characteristics, 10 client characteristics were assessed as being relevant and 12 as uncertain. None was found irrelevant for predicting homecare use. Most of the client characteristics from the category ‘Daily functioning’ were assessed as uncertain. Client characteristics in other categories – i.e. ‘Physical health status’, ‘Mental health status and behaviour’, ‘Health literacy’, ‘Social environment and network’, and ‘Other’ – were more frequently considered relevant.ConclusionAccording to district nurses and health insurers, homecare use could be predicted better by including other more holistic predictors in case-mix classification, such as on mental functioning and social network. The challenge remains, however, to operationalize the new characteristics and keep stakeholders on board when developing and implementing case-mix classification for homecare prospective payment.

Musculoskeletal case-mix adjustment in a UK primary/community care cohort: Testing musculoskeletal models to make recommendations in this setting

Benchmarking musculoskeletal (MSK) services is limited by the need to adjust for differences in patient characteristics/case-mix. Without this providers and services cannot be usefully compared. This paper investigates the predictive ability of case-mix adjustment models in a primary/community care cohort. ObjectivesTo investigate the predictive ability of two existing MSK case-mix adjustment models and compare to the predictive ability of an evidence informed and statistically informed model. MethodA secondary analysis of the ‘Subgrouping for Targeted Treatment in Musculoskeletal Conditions’ cluster randomised controlled trial data (n = 1211). Stepwise linear regression models were built and compared using available baseline variables. The MSK-HQ was used as the primary functional status outcome. ResultsTwo existing models were compared (UK National PROMs Model, US FOTO Model) using available variables. Of these models the modified US FOTO model showed the best predictive ability in this cohort predicting 44% of the variation in MSK-HQ outcome, the modified UK National PROMs model predicted 41%. A newly developed evidence informed model (Keele Model 1) performed no better than the existing models, and a statistically informed model (Keele Model 2) gave only an additional 2% increase in model power compared to the modified US FOTO model. ConclusionAll models showed strong predictive ability. The modified US FOTO model looks to be best suited to the UK primary/community care cohort of the existing models. This model performed so well that we recommend that this model is used in a UK setting moving forwards rather than development of an alternative UK model.

Dietary Potassium Intake and Mortality in a Prospective Hemodialysis Cohort

Among hemodialysis patients, clinical practice guidelines recommend dietary potassium restriction given concerns about potential hyperkalemia leading to malignant arrhythmias and mortality. However, there are sparse data informing recommendations for dietary potassium intake in this population. We thus sought to examine the relationship between dietary potassium intake and death risk in a prospective cohort of hemodialysis patients. Among 415 hemodialysis patients from the prospective "Malnutrition, Diet, and Racial Disparities in Chronic Kidney Disease" cohort recruited across 16 outpatient dialysis clinics, information regarding dietary potassium intake was obtained using Food Frequency Questionnaires administered over October 2011 to March 2015. We first examined associations of baseline dietary potassium intake categorized as tertiles with mortality risk using Cox regression. We then examined clinical characteristics associated with low dietary potassium intake (defined as the lowest tertile) using logistic regression. In expanded case-mix Cox analyses, patients whose dietary potassium intake was in the lowest tertile had higher mortality (ref: highest tertile) (adjusted hazard ratio 1.74, 95% confidence interval 1.14-2.66). These associations had even greater magnitude of risk following adjustment for laboratory and nutritional covariates (adjusted hazard ratio 2.65, 95% confidence interval 1.40-5.04). In expanded case-mix restricted cubic spline analyses, there was a monotonic increase in mortality risk with incrementally lower dietary potassium intake. In expanded case-mix logistic regression models, female sex; higher serum bicarbonate; and lower dietary energy, protein, and fiber intake were associated with low dietary potassium intake. In a prospective cohort of hemodialysis patients, lower dietary potassium intake was associated with higher mortality risk. These findings suggest that excessive dietary potassium restriction may be deleterious in hemodialysis patients, and further studies are needed to determine the optimal dietary potassium intake in this population.

The Australian National Aged Care Classification (AN-ACC): a new casemix classification for residential aged care.

To develop a casemix classification to underpin a new funding model for residential aged care in Australia. Cross-sectional study of resident characteristics in thirty non-government residential aged care facilities in Melbourne, the Hunter region of New South Wales, and northern Queensland, March 2018 - June 2018. 1877 aged care residents and 1600 residential aged care staff. The Australian National Aged Care Classification (AN-ACC), a casemix classification for residential aged care based on the attributes of aged care residents that best predict their need for care: frailty, mobility, motor function, cognition, behaviour, and technical nursing needs. The AN-ACC comprises 13 aged care resident classes reflecting differences in resource use. Apart from the class that included palliative care patients, the primary branches were defined by the capacity for mobility; further classification is based on physical capacity, cognitive function, mental health problems, and behaviour. The statistical performance of the AN-ACC was good, as measured by the reduction in variation statistic (RIV; 0.52) and class-specific coefficients of variation. The statistical performance and clinical acceptability of AN-ACC compare favourably with overseas casemix models, and it is better than the current Australian aged care funding model, the Aged Care Funding Instrument (64 classes; RIV, 0.20). The care burden associated with frailty, mobility, function, cognition, behaviour and technical nursing needs drives residential aged care resource use. The AN-ACC is sufficiently robust for estimating the funding and staffing requirements of residential aged care facilities in Australia.

Value-based healthcare in ischemic stroke care: case-mix adjustment models for clinical and patient-reported outcomes

BackgroundPatient-Reported Outcome Measures (PROMs) have been proposed for benchmarking health care quality across hospitals, which requires extensive case-mix adjustment. The current study’s aim was to develop and compare case-mix models for mortality, a functional outcome, and a patient-reported outcome measure (PROM) in ischemic stroke care.MethodsData from ischemic stroke patients, admitted to four stroke centers in the Netherlands between 2014 and 2016 with available outcome information (N = 1022), was analyzed. Case-mix adjustment models were developed for mortality, modified Rankin Scale (mRS) scores and EQ-5D index scores with respectively binary logistic, proportional odds and linear regression models with stepwise backward selection. Predictive ability of these models was determined with R-squared (R2) and area-under-the-receiver-operating-characteristic-curve (AUC) statistics.ResultsAge, NIHSS score on admission, and heart failure were the only common predictors across all three case-mix adjustment models. Specific predictors for the EQ-5D index score were sex (β = 0.041), socio-economic status (β = − 0.019) and nationality (β = − 0.074). R2-values for the regression models for mortality (5 predictors), mRS score (9 predictors) and EQ-5D utility score (12 predictors), were respectively R2 = 0.44, R2 = 0.42 and R2 = 0.37.ConclusionsThe set of case-mix adjustment variables for the EQ-5D at three months differed considerably from the set for clinical outcomes in stroke care. The case-mix adjustment variables that were specific to this PROM were sex, socio-economic status and nationality. These variables should be considered in future attempts to risk-adjust for PROMs during benchmarking of hospitals.

Development of a model based on case-mix analysis to predict 6-month patient survival after liver transplantation: a multicenter Italian study

Short-term patient survival (PS) after liver transplantation (LT) is predicted by MELD score. This SITO-AISF study aimed to identify, besides the MELD score, factors predicting 6- and 12-month PS after LT, in order to develop case-mix models. Primary endpoint was 6-month PS; secondary end-points were 6-month graft survival and 12 month PS. LT was considered futile if associated with 5 years PS < 50% and/or 6 months PS < 60%.

Profiling Hospital Performance Based on Mortality After Transcatheter Aortic Valve Replacement in Ontario, Canada.

Public reporting of hospital-level outcomes is increasingly common as a means to target quality improvement strategies to ensure the delivery of optimal care. Despite the rapid dissemination of transcatheter aortic valve replacement (TAVR), there is a paucity of reliable case-mix adjustment models for hospital profiling in TAVR. Our objective was to develop and evaluate different models for calculating risk-standardized all-cause mortality rates (RSMRs) post-TAVR. In this population-based study in Ontario, Canada, we identified all patients who underwent a TAVR procedure between April 1, 2012, and March 31, 2016. For each hospital, we calculated 30-day and 1-year RSMR, using 2-level hierarchical logistic regression models that accounted for patient-specific demographic and clinical characteristics, as well as the clustering of patients within the same hospital using a hospital-specific random effects. We classified each hospital into one of 3 groups: performing worse than expected, better than expected, or performing as expected, based on whether the 95% CI of the RSMR was above, below, or included the provincial average mortality rate, respectively. Our cohort consisted of 2129 TAVR procedures performed at 10 hospitals. The observed mortality was 7.0% at 30 days and 16.4% at 1 year, with a range of 4% to 10% and 8% to 22%, respectively, across hospitals. We developed case-mix adjustment models using 28 clinically relevant variables. Using 30-day and 1-year RSMR to profile each hospital, we found that all hospitals performed as expected, with 95% CI that included the provincial average. We found no significant interhospital variation in RSMR among hospitals, suggesting that quality improvement efforts should be directed at aspects other than the variation in observed mortality.

Association of thyroid status prior to transition to end-stage renal disease with early dialysis mortality.

Advanced chronic kidney disease (CKD) patients, including those receiving dialysis, have a high prevalence of thyroid dysfunction. Although hypothyroidism is associated with higher death risk in end-stage renal disease (ESRD) patients, no studies have examined whether thyroid status in the pre-ESRD period impacts mortality after dialysis initiation. Among US veterans with CKD identified from the national Veterans Affairs database that transitioned to dialysis over the period from October 2007 to September 2011, we examined the association of pre-ESRD serum thyrotropin (TSH) levels averaged over the 1-year pre-dialysis ('prelude') period with all-cause mortality in the first year following dialysis initiation. Among 15 335 patients in the 1-year prelude cohort, TSH levels >5.0 mIU/L were associated with higher mortality in expanded case-mix Cox models (reference: TSH 0.5-5.0 mIU/L): adjusted hazard ratio (aHR) [95% confidence interval (CI) 1.20 (1.07-1.33). Similar findings were observed for TSH >5.0 mIU/L and mortality in the 2- and 5-year cohorts: aHRs (95% CI) 1.11 (1.02-1.21) and 1.15 (1.07-1.24), respectively. Analyses of finer gradations of TSH in the 1-year prelude cohort demonstrated that incrementally higher levels >5.0 mIU/L were associated with increasingly higher mortality in expanded case-mix models (reference: TSH 0.5-3.0 mIU/L): aHRs (95% CI) 1.18 (1.04-1.33) and 1.28 (1.03-1.59) for TSH levels >5.0-10.0 mIU/L and >10.0 mIU/L, respectively. In the 2- and 5-year cohorts, mortality associations persisted most strongly for those with TSH >10.0 mIU/L, particularly after laboratory covariate adjustment. Among new ESRD patients, there is a dose-dependent relationship between higher pre-ESRD TSH levels >5.0 mIU/L and post-ESRD mortality. Further studies are needed to determine the impact of TSH reduction with thyroid hormone supplementation in this population.

PROFILING HOSPITAL PERFORMANCE BASED ON MORTALITY AFTER TRANS-CATHETER AORTIC VALVE REPLACEMENT IN ONTARIO, CANADA

Public reporting of hospital-level outcomes is increasingly common as a means to target quality improvement strategies to ensure the delivery of optimal care. Despite the rapid dissemination of transcatheter aortic valve replacement (TAVR), there is a paucity of reliable case-mix adjustment models for hospital profiling in TAVR. Our objective was to develop models for calculating risk-standardized all-cause mortality rates (RSMR) post-TAVR. In this population-based study in Ontario, Canada, we identified all patients that underwent a TAVR procedure between April 1, 2012 and March 31, 2016. For each hospital, we calculated 30-day and 1-year RSMR, using 2-level hierarchical logistic regression models that accounted for patient-specific demographic and clinical characteristics as well as the clustering of patients within the same hospital using a hospital-specific random effects. We classified each hospital into one of three groups: performing worse than expected, better than expected, or performing as expected, based on whether the 95% confidence interval (CI) of the RSMR was above, below or included the provincial average mortality rate respectively. Our cohort consisted of 2,129 TAVR procedures performed at 10 hospitals. The observed mortality was 7.0% at 30-days and 16.4% at one-year with a range of 4%-10% and 8%-22% respectively across hospitals. We developed case-mix adjustment models using 28 clinically relevant, available variables. Using 30-days and one-year RSMR to profile each hospital, we found that all hospitals performed as expected, with 95% CI that included the provincial average. We found no significant inter-hospital variation in RSMR among hospitals, suggesting that quality improvement efforts should be directed at aspects other than the variation in observed mortality.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Case-Mix for Performance Management: A Risk Algorithm Based on ICD-10-CM.

Accurate risk adjustment is the key to a reliable comparison of cost and quality performance among providers and hospitals. However, the existing case-mix algorithms based on age, sex, and diagnoses can only explain up to 50% of the cost variation. More accurate risk adjustment is desired for provider performance assessment and improvement. To develop a case-mix algorithm that hospitals and payers can use to measure and compare cost and quality performance of their providers. All 6,048,895 patients with valid diagnoses and cost recorded in the US Veterans health care system in fiscal year 2016 were included in this study. The dependent variable was total cost at the patient level, and the explanatory variables were age, sex, and comorbidities represented by 762 clinically homogeneous groups, which were created by expanding the 283 categories from Clinical Classifications Software based on ICD-10-CM codes. The split-sample method was used to assess model overfitting and coefficient stability. The predictive power of the algorithms was ascertained by comparing the R, mean absolute percentage error, root mean square error, predictive ratios, and c-statistics. The expansion of the Clinical Classifications Software categories resulted in higher predictive power. The R reached 0.72 and 0.52 for the transformed and raw scale cost, respectively. The case-mix algorithm we developed based on age, sex, and diagnoses outperformed the existing case-mix models reported in the literature. The method developed in this study can be used by other health systems to produce tailored risk models for their specific purpose.

Increments in serum high-density lipoprotein cholesterol over time are not associated with improved outcomes in incident hemodialysis patients

Elevated serum high-density lipoprotein cholesterol (HDL-C) has not been associated with better cardiovascular (CV) and all-cause mortality in hemodialysis patients. However, the association between change in HDL over time and mortality has not been fully examined. In a nationally representative cohort of incident hemodialysis patients who had available HDL data at baseline and 6months after dialysis initiation, we studied the association of change in HDL-C during the first 6months of dialysis with all-cause and CV mortality. Associations between HDL-C change and mortality were determined in Cox proportional hazard regression models with adjustment for multiple variables. In case-mix models, there was a J-shaped association between change in HDL-C and mortality, such that quartiles 1 (<-5mg/dL) and 4 (≥7mg/dL) were each associated with higher all-cause (hazard ratio, 1.32 [95% confidence interval, 1.21-1.45] and 1.09 [1.01-1.18]) and CV (1.28 [1.06-1.55] and 1.23 [1.04-1.45]) death risk, respectively. In fully adjusted models that included indices of malnutrition and inflammation, the higher death risk observed in the lowest quartile was attenuated, whereas the highest quartile continued to demonstrate significantly higher all-cause (1.11 [1.02-1.20]) and CV mortality (1.15 [1.00-1.32]). These associations persisted across various subgroups. Although malnutrition and inflammation may explain the increased risk for mortality in patients with decreasing serum HDL-C concentrations over time, these indices do not mitigate the elevated risk in patients with rising serum HDL-C. We found that increasing serum HDL-C over time is paradoxically associated with worse outcomes in incident hemodialysis patients.

Case-Mix Adjustment of the Bereaved Family Survey.

Surveys of bereaved family members are increasingly being used to evaluate end-of-life (EOL) care and to measure organizational performance in EOL care quality. The Bereaved Family Survey (BFS) is used to monitor EOL care quality and benchmark performance in the Veterans Affairs (VA) health-care system. The objective of this study was to develop a case-mix adjustment model for the BFS and to examine changes in facility-level scores following adjustment, in order to provide fair comparisons across facilities. We conducted a cross-sectional secondary analysis of medical record and survey data from veterans and their family members across 146 VA medical centers. Following adjustment using model-based propensity weighting, the mean change in the BFS-Performance Measure score across facilities was -0.6 with a range of -2.6 to 0.6. Fifty-five (38%) facilities changed within ±0.5 percentage points of their unadjusted score. On average, facilities that benefited most from adjustment cared for patients with greater comorbidity burden and were located in urban areas in the Northwest and Midwestern regions of the country. Case-mix adjustment results in minor changes to facility-level BFS scores but allows for fairer comparisons of EOL care quality. Case-mix adjustment of the BFS positions this National Quality Forum-endorsed measure for use in public reporting and internal quality dashboards for VA leadership and may inform the development and refinement of case-mix adjustment models for other surveys of bereaved family members.

Association of Growth Differentiation Factor 15 with Mortality in a Prospective Hemodialysis Cohort

Background/Aims: Cardiovascular disease and protein-energy wasting are among the strongest predictors of the high mortality of dialysis patients. In the general population, the novel cardiovascular and wasting biomarker, growth differentiation factor 15 (GDF15), is associated with decreased survival. However, little is known about GDF15 in dialysis patients. Methods: Among prevalent hemodialysis patients participating in a prospective study (October 2011 to August 2015), we examined the association of baseline GDF15 levels with all-cause mortality using unadjusted and case mix-adjusted death hazard ratios (HRs) that controlled for age, sex, race, ethnicity, diabetes, and dialysis vintage. Results: The mean age ± SD of the 203 patients included in the study was 53.2 ± 14.5 years, and the cohort included 41% females, 34% African-Americans, and 48% Hispanics. GDF15 levels (mean ± SD 5.94 ± 3.90 ng/mL; range 1.58-39.8 ng/mL) were higher among older patients and were inversely associated with serum creatinine concentrations as a surrogate for muscle mass. Each 1.0 ng/mL increase in GDF15 was associated with an approximately 17-18% higher mortality risk in the unadjusted and case mix models (p < 0.05). Increments of about 1 SD (a 4.0 ng/mL increase in GDF15) were associated with a nearly 2-fold higher death risk. The highest GDF15 tertile was associated with higher mortality risk (reference: lowest tertile): the HRs (95% CI) were 3.19 (1.35-7.55) and 2.45 (1.00-6.00) in the unadjusted and the case mix-adjusted model, respectively. These incremental death trends were confirmed in cubic spline models. Conclusion: Higher circulating GDF15 levels are associated with higher mortality risk in hemodialysis patients. Future studies are needed to determine whether GDF15 may represent a novel therapeutic target for cardiovascular disease, wasting, and death in this population.

Serum sodium and mortality in a national peritoneal dialysis cohort.

Sodium disarrays are common in peritoneal dialysis (PD) patients, and may be associated with adverse outcomes in this population. However, few studies of limited sample size have examined the association of serum sodium with mortality in PD patients, with inconsistent results. We hypothesized that both hypo- and hypernatremia are associated with higher death risk in a nationally representative cohort of US PD patients. We sought to examine the association of serum sodium over time and mortality among 4687 adult incident PD patients from a large US dialysis organization who underwent one or more serum sodium measurements within the first 3 months of dialysis over January 2007 to December 2011. We examined the association of time-dependent and baseline sodium with all-cause mortality as a proxy of short- and long-term sodium-mortality associations, respectively. Hazard ratios were estimated using Cox models with three adjustment levels: minimally adjusted, case-mix adjusted, and case-mix + laboratory adjusted. In time-dependent analyses, sodium levels <140 mEq/L were associated with incrementally higher death risk in case-mix models (ref: 140 to <142 mEq/L); following laboratory covariate adjustment, associations between lower sodium and higher mortality remained significant for levels <136 mEq/L. In analyses using baseline values, sodium levels <140 mEq/L were associated with higher mortality risk across all models (ref: 140 to <142 mEq/L). In PD patients, lower time-dependent and baseline sodium levels were independently associated with higher death risk. Further studies are needed to determine whether correction of dysnatremia improves longevity in this population.

Patient-reported outcome measures in arthroplasty registries

— The International Society of Arthroplasty Registries (ISAR) Patient-Reported Outcome Measures (PROMs) Working Group have evaluated and recommended best practices in the selection, administration, and interpretation of PROMs for hip and knee arthroplasty registries. The 2 generic PROMs in common use are the Short Form health surveys (SF-36 or SF-12) and EuroQol 5-dimension (EQ-5D). The Working Group recommends that registries should choose specific PROMs that have been appropriately developed with good measurement properties for arthroplasty patients. The Working Group recommend the use of a 1-item pain question (“During the past 4 weeks, how would you describe the pain you usually have in your [right/left] [hip/knee]?”; response: none, very mild, mild, moderate, or severe) and a single-item satisfaction outcome (“How satisfied are you with your [right/left] [hip/knee] replacement?”; response: very unsatisfied, dissatisfied, neutral, satisfied, or very satisfied). Survey logistics include patient instructions, paper- and electronic-based data collection, reminders for follow-up, centralized as opposed to hospital-based follow-up, sample size, patient- or joint-specific evaluation, collection intervals, frequency of response, missing values, and factors in establishing a PROMs registry program. The Working Group recommends including age, sex, diagnosis at joint, general health status preoperatively, and joint pain and function score in case-mix adjustment models. Interpretation and statistical analysis should consider the absolute level of pain, function, and general health status as well as improvement, missing data, approaches to analysis and case-mix adjustment, minimal clinically important difference, and minimal detectable change. The Working Group recommends data collection immediately before and 1 year after surgery, a threshold of 60% for acceptable frequency of response, documentation of non-responders, and documentation of incomplete or missing data.