Survival does not differ by annual center transplant volume-A Pediatric Heart Transplant Society Registry study.

BACKGROUND: There are conflicting data regarding the relationship between center volume and outcomes in pediatric heart transplantation. Previous studies have not fully accounted for differences in case mix, particularly in high-risk congenital heart disease (CHD) groups. We aimed to evaluate the relationship between center volume and outcomes using the Pediatric Heart Transplant Society (PHTS) Registry and explore how case mix may affect outcomes. METHODS: A retrospective cohort study of all pediatric patients in the PHTS Registry who received a heart transplant from 2009 to 2018 was performed. Centers were divided into 5 groups based on average yearly transplant volume. The primary outcome was time to death or graft loss and outcomes were compared using Kaplan-Meier analysis. RESULTS: There were 4583 cases among 55 centers included. There was no difference in time to death or graft loss by center volume in the entire cohort (p = .75), in patients with CHD (p = .79) or in patients with cardiomyopathy (p = .23). There was also no difference in time to death or graft loss by center size in patients undergoing transplant after Norwood, Glenn or Fontan (log rank p = .17, p = .31, and p = .10 respectively). There was a statistically significant difference in outcomes by center size in the positive crossmatch group (p < .0001), though no discernible pattern related to high or low center volume. CONCLUSIONS: Outcomes are similar among transplant centers of all sizes, including for high-risk patient groups with CHD. Future work is needed to understand how patient-specific risk factors may vary among centers of various sizes and whether this influences patient outcomes.

Impact of case-mix adjustment on observed variation in the healing of diabetic foot ulcers at 12 weeks using data from the National Diabetes Foot Care Audit of England and Wales: A cohort study.

AIM: This cohort study investigates the extent to which variation in ulcer healing between services can be explained by demographic and clinical characteristics. METHODS: The National Diabetes Foot Care Audit collated data on people with diabetic foot ulcers presenting to specialist services in England and Wales between July 2014 and March 2018. Logistic regression models were created to describe associations between risk factors and a person being alive and ulcer-free 12 weeks from presentation, and to investigate whether variation between 120 participating services persisted after risk factor adjustment. RESULTS: Of 27,030 people with valid outcome data, 12,925 (47.8%) were alive and ulcer-free at 12 weeks, 13,745 (50.9%) had an unhealed ulcer and 360 had died (1.3%). Factors associated with worse outcome were male sex, more severe ulcers, history of cardiac or renal disease and a longer time between first presentation to a non-specialist healthcare professional and first expert assessment. After adjustment for these factors, four services (3.3%) were more than 3SD above and seven services (5.8%) were more than 3SD below the national mean for proportions that were alive and ulcer-free at follow-up. CONCLUSIONS/INTERPRETATIONS: Variation in the healing of diabetic foot ulcers between specialist services in England and Wales persisted after adjusting for demographic characteristics, ulcer severity, smoking, body mass index and co-morbidities. We conclude that other factors contribute to variation in healing of diabetic foot ulcers and include the time to specialist assessment.

Resource Utilization Groups in transitional home care: validating the RUG-III/HC case-mix system in hospital-to-home care programs.

BACKGROUND: Transitional hospital-to-home care programs support safe and timely transition from acute care settings back into the community. Case-mix systems that classify transitional care clients into groups based on their resource utilization can assist with care planning, calculating reimbursement rates in bundled care funding models, and predicting health human resource needs. This study evaluated the fit and relevance of the Resource Utilization Groups version III for Home Care (RUG-III/HC) case-mix classification system in transitional care programs in Ontario, Canada. METHODS: We conducted a retrospective analysis of clinical assessment data and administrative billing records from a cohort of clients (n = 1,680 care episodes) in transitional home care programs in Ontario. We classified care episodes into established RUG-III/HC groups based on clients' clinical and functional characteristics and calculated four case-mix indices to describe care relative resource utilization in the study sample. Using these indices in linear regression models, we evaluated the degree to which the RUG-III/HC system can be used to predict care resource utilization. RESULTS: A majority of transitional home care clients are classified as being Clinically complex (41.6%) and having Reduced physical functions (37.8%). The RUG-III/HC groups that account for the largest share of clients are those with the lowest hierarchical ranking, indicating low Activities of Daily Living limitations but a range of Instrumental Activities of Daily Living limitations. There is notable heterogeneity in the distribution of clients in RUG-III/HC groups across transitional care programs. The case-mix indices reflect decreasing hierarchical resource use within but not across RUG-III/HC categories. The RUG-III/HC predicts 23.34% of the variance in resource utilization of combined paid and unpaid care time. CONCLUSIONS: The distribution of clients across RUG-III/HC groups in transitional home care programs is remarkably different from clients in long-stay home care settings. Transitional care programs have a higher proportion of Clinically complex clients and a lower proportion of clients with Reduced physical function. This study contributes to the development of a case-mix system for clients in transitional home care programs which can be used by care managers to inform planning, costing, and resource allocation in these programs.

Patient-level predictors of temporal regularity of primary care visits.

BACKGROUND: Patients with chronic diseases should meet with their primary care doctor regularly to facilitate proactive care. Little is known about what factors are associated with more regular follow-up. METHODS: We studied 70,095 patients age 40 + with one of three chronic conditions (diabetes mellitus, heart failure, chronic obstructive pulmonary disease), cared for by Leumit Health Services, an Israeli health maintenance organization. Patients were divided into the quintile with the least temporally regular care (i.e., the most irregular intervals between visits) vs. the other four quintiles. We examined patient-level predictors of being in the least-temporally-regular quintile. We calculated the risk-adjusted regularity of care at 239 LHS clinics with at least 30 patients. For each clinic, compared the number of patients with the least temporally regular care with the number predicted to be in this group based on patient characteristics. RESULTS: Compared to older patients, younger patients (age 40-49), were more likely to be in the least-temporally-regular group. For example, age 70-79 had an adjusted odds ratio (AOR) of 0.82 compared to age 40-49 (p < 0.001 for all findings discussed here). Males were more likely to be in the least-regular group (AOR 1.18). Patients with previous myocardial infarction (AOR 1.07), atrial fibrillation (AOR 1.08), and current smokers (AOR 1.12) were more likely to have an irregular pattern of care. In contrast, patients with diabetes (AOR 0.79) or osteoporosis (AOR 0.86) were less likely to have an irregular pattern of care. Clinic-level number of patients with irregular care, compared with the predicted number, ranged from 0.36 (fewer patients with temporally irregular care) to 1.71 (more patients). CONCLUSIONS: Some patient characteristics are associated with more or less temporally regular patterns of primary care visits. Clinics vary widely on the number of patients with a temporally irregular pattern of care, after adjusting for patient characteristics. Health systems can use the patient-level model to identify patients at high risk for temporally irregular patterns of primary care. The next step is to examine which strategies are employed by clinics that achieve the most temporally regular care, since these strategies may be possible to emulate elsewhere.

Predictors of amounts of child and adolescent mental health service use.

The aim of this study was to build evidence about how to tailor services to meet the individual needs of young people by identifying predictors of amounts of child and adolescent mental health service use. We conducted a secondary analysis of a large administrative dataset from services in England was conducted using the Mental Health Services Data Set (years 2016-17 and 2017-18). The final sample included N = 27,362 episodes of care (periods of service use consisting of at least two attended care contacts and less than 180 days between care contacts) from 39 services. There were 50-10,855 episodes per service. The descriptive statistics for episodes of care were: Mage = 13 years, SDage = 4.71, range = 0-25 years; 13,785 or 50% male. Overall, there were high levels of heterogeneity in number of care contacts within episodes of care: M = 11.12, SD = 28.28, range = 2-1529. Certain characteristics predicted differential patterns of service use. For example, young people with substance use (beta = 6.29, 95% CI = 5.06-7.53) or eating disorders (beta = 4.30, 95% CI = 3.29-5.30) were particularly more likely to have higher levels of service use. To build on this, evidence is needed about predictors of child and adolescent mental health treatment outcome and whether the same characteristics predict levels of improvement as well as levels of service use.

Construction and application of a nursing human resource allocation model based on the case mix index.

BACKGROUND: The case mix index (CMI) may reflect the severity of disease and the difficulty of care objectively, and is expected to be an ideal indicator for assessing the nursing workload. The purpose of this study was to explore the quantitative relationship between daily nursing worktime (DNW) and CMI to provide a method for the rational allocation of nursing human resources. METHODS: Two hundred and seventy-one inpatients and 36 nurses of the department of hepatobiliary surgery were prospectively included consecutively from August to September 2022. The DNW of each patient were accurately measured, and the CMI data of each patient were extracted. Among 10 curve estimations, the optimal quantitative model was selected for constructing the nursing human resource allocation model. Finally, the applicability of the allocation model was preliminarily assessed by analyzing the relationship between the relative gap in nursing human resources and patient satisfaction, as well as the incidence of adverse events in 17 clinical departments. RESULTS: The median (P25, P75) CMI of the 271 inpatients was 2.62 (0.92, 4.07), which varied by disease type (F = 3028.456, P < 0.001), but not by patient gender (F = 0.481, P = 0.488), age (F = 2.922, P = 0.089), or level of care (F = 0.096, P = 0.757). The median (P25, P75) direct and indirect DNW were 76.07 (57.98, 98.85) min and 43.42 (39.42, 46.72) min, respectively. Among the 10 bivariate models, the quadratic model established the optimal quantitative relationship between CMI and DNW; DNW = 92.3 + 4.8*CMI + 2.4*CMI2 (R2 = 0.627, F = 225.1, p < 0.001). The relative gap between theoretical and actual nurse staffing in the 17 clinical departments were linearly associated with both patient satisfaction (r = 0.653, P = 0.006) and incidence of adverse events (r = - 0.567, P = 0.021). However, after adjusting for other factors, it was partially correlated only with patient satisfaction (rpartial = 0.636, P = 0.026). CONCLUSION: The DNW derived from CMI can be used to allocate nursing human resources in a rational and convenient way, improving patient satisfaction while ensuring quality and safety.

Process data analytics for hospital case-mix planning.

The composition and volume of patients treated in a hospital, i.e., the patient case-mix, directly impacts resource utilisation. Despite advances in technology, existing case-mix planning approaches are mostly manual. In this paper, we report on a solution that was developed in collaboration with the Queensland Children's Hospital for supporting its case-mix planning using process mining. We investigated (1) How can process mining capabilities be used to inform hospital case-mix planning?, and (2) How can process data be used to assess hospital capacity assessment and inform hospital case-mix planning? The major contributions of this paper include (i) an automated workflow to support both process mining analysis, and capacity assessment, (ii) a process mining analysis designed to detect process performance and variations, and (iii) a novel capacity assessment model based on limiting-resource saturation.

Predictors of child and adolescent mental health treatment outcome.

BACKGROUND: To examine the predictors of treatment outcome or improvement in mental health difficulties for young people accessing child and adolescent mental health services. METHODS: We conducted a secondary analysis of routinely collected data from services in England using the Mental Health Services Data Set. We conducted multilevel regressions on N = 5907 episodes from 14 services (Mage = 13.76 years, SDage = 2.45, range = 8-25 years; 3540 or 59.93% female) with complete information on mental health difficulties at baseline. We conduct similar analyses on N = 1805 episodes from 10 services (Mage = 13.59 years, SDage = 2.33, range = 8-24 years; 1120 or 62.05% female) also with complete information on mental health difficulties at follow up. RESULTS: Girls had higher levels of mental health difficulties at baseline than boys (ß = 0.28, 95% CI = 0.24-0.32). Young people with higher levels of mental health difficulties at baseline also had higher levels of deterioration in mental health difficulties at follow up (ß = 0.72, 95% CI = 0.67-0.76), and girls had higher levels of deterioration in mental health difficulties at follow up than boys (ß = 0.09, 95% CI = 0.03-0.16). Young people with social anxiety, panic disorder, low mood, or self-harm had higher levels of mental health difficulties at baseline and of deterioration in mental health difficulties at follow up compared to young people without these presenting problems. CONCLUSIONS: Services seeing higher proportions of young people with higher levels of mental health difficulties at baseline, social anxiety, panic disorder, low mood, or self-harm may be expected to show lower levels of improvement in mental health difficulties at follow up.

The development of one-day surgical care in Hungary between 2010 and 2019.

BACKGROUND: The constant increase in the utilization of one-day surgical care could be identified since more than a decade in most of European countries. Initially, according to the international rankings, the exploitation of one-day surgery in Hungary was not really significant. In 2010, the Hungarian policy makers intended to increase one-day surgical care as a priority strategy. The aim of our study was to analyze the evolution of the Hungarian one-day surgical care during the last decade in DRG- based performance financing system in Hungary. METHODS: The dataset of the research was provided by the National Health Insurance Fund Administration of Hungary. The most important indicators related to the one-day surgical care were compared to inpatient care (market share, number of cases, and DRG cost-weights). To discover the impact of one-day surgical care to the utilization of inpatient treatment, the number of hospitalized days was also analyzed. RESULTS: Between 2010 and 2019, the market share of one-day surgical cases increased from 42, to 80%. Simultaneously the constant increase of one-day surgical cases, the number of hospitalized days were decreased in inpatient care by 17%. The value of Case Mix Index has also increased, approximately by 140%, which could confirm that more complex interventions are being conducted in one-day surgical care as well. CONCLUSIONS: Due to the comprehensive health policy strategy related to the dissemination of one-day surgical care in Hungary, several important performance indicators were improved between 2010 and 2019. Given that Hungary belongs to the low- and middle-income countries, the results of the study could be considerable even in an international comparison.

A Composite Risk Score for Capsule Complications Based on Data from the Swedish National Cataract Register: Relation to Surgery Volumes.

PURPOSE: To investigate case mix in relation to capsule complication, possible associations between case mix and operation volume, and change in case mix over time. DESIGN: Register-based study. PARTICIPANTS: Swedish patients who underwent cataract surgery between 2007 and 2016. METHODS: Demographics and data on ocular comorbidity, intraoperative difficulties, and capsule complications were registered from 2007 to 2016 and analyzed retrospectively in relation to coded data on individual surgeons' operation volume. Single factor analysis and logistic regression were performed, and a composite risk score was created. MAIN OUTCOME MEASURES: Risk of capsule complication, given as adjusted and composite odds ratio in relation to cataract surgery volume. RESULTS: Preoperative and intraoperative variables significantly associated with capsule complications were best-corrected visual acuity (BCVA) ≤0.1 (decimal, adjusted odds ratio [aOR], 1.82; P < 0.001); pseudoexfoliation (PEX) (aOR, 1.53; P < 0.001); sight-threatening ocular comorbidity other than age-related macular degeneration (AMD), diabetic retinopathy, glaucoma, or cornea guttata (aOR, 1.35; P = 0.006); use of Trypan blue (aOR, 1.76; P < 0.001); mechanical pupil dilation (aOR, 1.36; P = 0.024); and iris hooks at the rhexis margin (aOR, 6.99; P < 0.001). The composite risk score was 3.09 ± 6.40 (mean ± standard deviation) for patients with capsule complication and 1.28 ± 1.66 for uncomplicated procedures (P < 0.001). High-volume cataract surgeons (≥500 procedures yearly) had a significantly lower composite risk score (mean risk score ≤1.28; range, 1.01-2.02) compared with low- and medium-volume cataract surgeons (1.34 ± 0.56; range, 1.00-4.55 and 1.49 ± 0.58; range, 1.01-5.19), respectively. During the period 2007-2016, the proportion of patients aged >88 years, patients with BCVA ≤0.1, and patients with intraoperative difficulties decreased. CONCLUSIONS: Case mix, as calculated from a composite risk score based on preoperative and intraoperative parameters registered in the National Cataract Register (NCR), may contribute to the decrease in capsule complications from 2007 to 2016 and the lower complication rate observed in cases managed by high-volume cataract surgeons.

Determinants of self-reported functional status (EPIC-26) in prostate cancer patients prior to treatment.

PURPOSE: The self-reported functional status (sr-FS) of prostate cancer (PCa) patients varies substantially between patients and health-care providers before treatment. Information about this issue is important for evaluating comparisons between health-care providers and to assist in treatment decision-making. There have been few reports on correlates of pretherapeutic sr-FS. The objective of the article, therefore, is to describe clinical and sociodemographic correlates of pretherapeutic sr-FS, based on a subset of the TrueNTH Global Registry, a prospective cohort study. METHODS: A total of 3094 PCa patients receiving local treatment in 44 PCa centers in Germany were recruited between July 2016 and April 2018. Multilevel regression models were applied to predict five pretherapeutic sr-FS (EPIC-26) scores based on clinical characteristics (standard set suggested by the International Consortium for Health Outcomes Measurement), sociodemographic characteristics, and center characteristics. RESULTS: Impaired pretherapeutic sr-FS tended to be associated with lower educational level and poorer disease characteristics-except for "urinary incontinence" which was only associated with age. Notably, age was a risk factor ("urinary incontinence," "urinary irritative/obstructive," "sexual") as well as a protective factor ("hormonal") for pretherapeutic sr-FS. Pretherapeutic sr-FS varies little across centers. CONCLUSIONS: Pretherapeutic sr-FS varies by clinical patient characteristics and age as well as by socioeconomic status. The findings point out the benefit of collecting and considering socioeconomic information in addition to clinical and demographic patient characteristics for treatment decision-making and fair comparisons between health-care providers.

In the eye of the storm: impact of COVID-19 pandemic on admission patterns to paediatric intensive care units in the UK and Eire.

BACKGROUND: The coronavirus disease-19 (COVID-19) pandemic had a relatively minimal direct impact on critical illness in children compared to adults. However, children and paediatric intensive care units (PICUs) were affected indirectly. We analysed the impact of the pandemic on PICU admission patterns and patient characteristics in the UK and Ireland. METHODS: We performed a retrospective cohort study of all admissions to PICUs in children < 18 years during Jan-Dec 2020, using data collected from 32 PICUs via a central database (PICANet). Admission patterns, case-mix, resource use, and outcomes were compared with the four preceding years (2016-2019) based on the date of admission. RESULTS: There were 16,941 admissions in 2020 compared to an annual average of 20,643 (range 20,340-20,868) from 2016 to 2019. During 2020, there was a reduction in all PICU admissions (18%), unplanned admissions (20%), planned admissions (15%), and bed days (25%). There was a 41% reduction in respiratory admissions, and a 60% reduction in children admitted with bronchiolitis but an 84% increase in admissions for diabetic ketoacidosis during 2020 compared to the previous years. There were 420 admissions (2.4%) with either PIMS-TS or COVID-19 during 2020. Age and sex adjusted prevalence of unplanned PICU admission reduced from 79.7 (2016-2019) to 63.1 per 100,000 in 2020. Median probability of death [1.2 (0.5-3.4) vs. 1.2 (0.5-3.4) %], length of stay [2.3 (1.0-5.5) vs. 2.4 (1.0-5.7) days] and mortality rates [3.4 vs. 3.6%, (risk-adjusted OR 1.00 [0.91-1.11, p = 0.93])] were similar between 2016-2019 and 2020. There were 106 fewer in-PICU deaths in 2020 (n = 605) compared with 2016-2019 (n = 711). CONCLUSIONS: The use of a high-quality international database allowed robust comparisons between admission data prior to and during the COVID-19 pandemic. A significant reduction in prevalence of unplanned admissions, respiratory diseases, and fewer child deaths in PICU observed may be related to the targeted COVID-19 public health interventions during the pandemic. However, analysis of wider and longer-term societal impact of the pandemic and public health interventions on physical and mental health of children is required.

Methods and measures to quantify ICU patient heterogeneity.

Patients in intensive care units are heterogeneous and the daily prediction of their days to discharge (DTD) a complex task that practitioners and computers are not always able to solve satisfactorily. In order to make more precise DTD predictors, it is necessary to have tools for the analysis of the heterogeneity of the patients. Unfortunately, the number of publications in this field is almost non-existent. In order to alleviate this lack of tools, we propose four methods and their corresponding measures to quantify the heterogeneity of intensive patients in the process of determining the DTD. These new methods and measures have been tested with patients admitted over four years to a tertiary hospital in Spain. The results deepen the understanding of the intensive patient and can serve as a basis for the construction of better DTD predictors.

Funnel plots of patient-reported outcomes to evaluate health-care quality: Basic principles, pitfalls and considerations.

A funnel plot is a graphical method to evaluate health-care quality by comparing hospital performances on certain outcomes. So far, in nephrology, this method has been applied to clinical outcomes like mortality and complications. However, patient-reported outcomes (PROs; eg, health-related quality of life [HRQOL]) are becoming increasingly important and should be incorporated into this quality assessment. Using funnel plots has several advantages, including clearly visualized precision, detection of volume-effects, discouragement of ranking hospitals and easy interpretation of results. However, without sufficient knowledge of underlying methods, it is easy to stumble into pitfalls, such as overinterpretation of standardized scores, incorrect direct comparisons of hospitals and assuming a hospital to be in-control (ie, to perform as expected) based on underpowered comparisons. Furthermore, application of funnel plots to PROs is accompanied by additional challenges related to the multidimensional nature of PROs and difficulties with measuring PROs. Before using funnel plots for PROs, high and consistent response rates, adequate case mix correction and high-quality PRO measures are required. In this article, we aim to provide insight into the use and interpretation of funnel plots by presenting an overview of the basic principles, pitfalls and considerations when applied to PROs, using examples from Dutch routine dialysis care.

Assessing the performance of a method for case-mix adjustment in the Korean Diagnosis-Related Groups (KDRG) system and its policy implications.

BACKGROUND: To evaluate the performance of the patient clinical complexity level (PCCL) mechanism, which is the patient-level complexity adjustment factor within the Korean Diagnosis-Related Groups (KDRG) patient classification system, in explaining the variation in resource consumption within age adjacent diagnosis-related groups (AADRGs). METHODS: We used the inpatient claims data from a public hospital in Korea from 1 January 2017 to 30 June 2019, with 18 846 claims and 138 AADRGs. The differences in the total average payment between the four PCCL levels for each AADRG was tested using ANOVA and Duncan's post hoc test. The three patterns of differences with R-squared were as follows: the PCCL reflected the complexity well (valid); the average payment for PCCL 2, 3, and 4 was greater than PCCL 0 (partially valid); the PCCL did not reflect the complexity (not valid). RESULTS: There were 9 (6.52%), 26 (18.84%), and 103 (74.64%) ADRGs included in the valid, partially valid, and not valid categories, respectively. The average R-squared values were 32.18, 40.81, and 35.41%, respectively, with an average R-squared for all patterns of 36.21%. CONCLUSIONS: Adjustment using the PCCL in the KDRG classification system exhibited low performance in explaining the variation in resource consumption within AADRGs. As the KDRG classification system is used for reimbursement under the new DRG-based prospective payment system (PPS) pilot project, with plans for expansion, there should be an overall review of the validity of the complexity and rationality of using the KDRG classification system.

Risk Prediction of Cognitive Decline after Stroke.

BACKGROUND AND PURPOSE: Cognitive decline is one of the major outcomes after stroke. We have developed and evaluated a risk predictive tool of post-stroke cognitive decline and assessed its clinical utility. METHODS: In this population-based cohort, 4,783 patients with first-ever stroke from the South London Stroke Register (1995-2010) were included in developing the model. Cognitive impairment was measured using the Mini Mental State Examination (cut off 24/30) and the Abbreviated Mental Test (cut off 8/10) at 3-months and yearly thereafter. A penalised mixed-effects linear model was developed and temporal-validated in a new cohort consisted of 1,718 stroke register participants recruited from (2011-2018). Prediction errors on discrimination and calibration were assessed. The clinical utility of the model was evaluated using prognostic accuracy measurements and decision curve analysis. RESULTS: The overall predictive model showed good accuracy, with root mean squared error of 0.12 and R2 of 73%. Good prognostic accuracy for predicting severe cognitive decline was observed AUC: (88%, 95% CI [85-90]), (89.6%, 95% CI [86-92]), (87%, 95% CI [85-91]) at 3 months, one and 5 years respectively. Average predicted recovery patterns were analysed by age, stroke subtype, Glasgow-coma scale, and left-stroke and showed variability. DECISION: curve analysis showed an increased clinical benefit, particularly at threshold probabilities of above 15% for predictive risk of cognitive impairment. CONCLUSIONS: The derived prognostic model seems to accurately screen the risk of post-stroke cognitive decline. Such prediction could support the development of more tailored management evaluations and identify groups for further study and future trials.

What Metrics Accurately Reflect Surgical Quality?

Surgeons are increasingly under pressure to measure and improve their quality. While there is broad consensus that we ought to track surgical quality, there is far less agreement about which metrics matter most. This article reviews the important statistical concepts of case mix and chance as they apply to understanding the observed wide variation in surgical quality. We then discuss the benefits and drawbacks of current measurement strategies through the framework of structure, process, and outcomes approaches. Finally, we describe emerging new metrics, such as video evaluation and network optimization, that are likely to take on an increasingly important role in the future of measuring surgical quality.

Arteriovenous Fistula Use in the United States and Dialysis Facility-Level Comorbidity Burden.

RATIONALE & OBJECTIVE: Patients with multiple comorbid conditions are less likely to use an arteriovenous fistula (AVF) for hemodialysis vascular access. Some dialysis facilities have high rates of AVF placement despite having patients with many comorbid conditions. This study describes variation in facility-level use of AVFs across the facility-level burden of patient comorbid conditions. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: Medicare patients receiving hemodialysis for 1 year or more in US dialysis facilities. PREDICTORS: Facility-level burden of patient comorbid conditions; patient characteristics. OUTCOMES: Odds of AVFs versus other access types; facility-level use of AVFs. ANALYTICAL APPROACH: Facility-level comorbidity burden was calculated by summing individual comorbid conditions, determining the average per patient, then defining 11 groups based on facility percentile ranking. Generalized estimating equations with a logit link were used to estimate the odds of AVF placement at the patient level. For the facility-level analysis, a generalized estimating equation model with the identity link was fit to characterize the percentage of AVF use at each facility. RESULTS: Overall, AVF use was 65.8% in 315,919 prevalent hemodialysis patients among 5,813 facilities. After adjustment for patient characteristics, AVF use was 0.27, 0.30, 1.05, and 1.74 percentage points lower than the median among facilities in the 61st to 70th, 71st to 80th, 81st to 90th, and 91st to 99th percentiles of comorbidity, respectively, and 0.42, 0.63, 1.34, and 1.90 percentage points higher than the median among facilities in the 31st to 40th, 21st to 30th, 11th to 20th, and 1st to 10th percentiles of comorbidity, respectively. Facilities in the greater than 99th percentile of comorbidity burden had AVF use that was 3.47 percentage points lower than the median. Facilities in the less than 1st percentile of comorbidity burden had AVF use that was 2.64 percentage points greater than the median. LIMITATIONS: Limited to Medicare dialysis-dependent patients treated for 1 year or more. CONCLUSIONS: After adjustment for patient characteristics, we found small differences in facility rates of AVF use except in the extremes of high or low levels of comorbidity burden. Our study demonstrates that dialysis facilities with a relatively high patient comorbidity burden can achieve similar fistula rates as facilities with healthier patients. Although high comorbidity burden does not explain low facility AVF use, additional study is needed to understand differences in AVF use rates between facilities with similar comorbidity burdens.

Impact of a care delivery redesign initiative for vascular surgery.

OBJECTIVE: A hospital-wide quality improvement process through a care delivery redesign (CDR) was initiated to improve patient care efficiency, clinical documentation, and length of stay (LOS). The impact of CDR was assessed through LOS, unplanned readmission rates, and hospital financial metrics. METHODS: The CDR team consisted of the Chief of Vascular Surgery, inpatient nurse practitioner, dedicated case manager, clinical documentation improvement specialist, and vascular surgery residents and faculty. The nurse practitioner facilitated patient care coordination, resident system-based education, and multidisciplinary collaboration. Tools created to track performance and to ensure sustainability included daily discussions of patient care barriers and solutions; standardized order sets; a mobile app for residents containing resident service expectations, disease-specific resources, and vascular surgery journal links; and a weekly inpatient tracker showing real-time patient care data. Outcome measures included LOS, case mix index, contribution margin, and unplanned readmissions. Each outcome was determined for all inpatient admissions the year before and the 12 months after CDR was initiated. Outcomes were compared between the two groups. RESULTS: Implementation of CDR resulted in a 23% decrease in LOS (P = .003), reducing the gap to the Centers for Medicare and Medicaid Services geometric mean LOS from 2.1 days to 0.5 day (P < .001). Clinical documentation resulted in an increase in case mix index of 10% (P = .011). The 30-day unplanned readmission rates did not change in the 12 months after CDR was initiated compared with the year before (P = .92). Financial data demonstrated decreased variable cost and increased revenue resulting in a $1.89 million increase in contribution margin. CONCLUSIONS: A CDR predicated on a dedicated service line advanced practitioner, clinical documentation education, weekly service tracker review, and real-time management of system-related barriers to patient care is described. Implementation of the CDR reduced hospital LOS with no change in unplanned readmissions and provided significant financial benefit to the hospital by increasing revenue and decreasing variable cost.

Early administration of glucocorticoid for thyroid storm: analysis of a national administrative database.

BACKGROUND: Thyroid storm is a life-threatening disease with a mortality rate of over 10%. Although glucocorticoids have been recommended as a treatment option for thyroid storm, supportive evidence based on a large-scale clinical research is lacking. The objective of the current study was to evaluate the beneficial effects of glucocorticoids in the treatment of patients with severe thyroid storm. METHODS: A retrospective nationwide cohort study was conducted using a Japanese national administrative claims database. Patients admitted to intensive care units due to severe thyroid storm between the financial years 2013 and 2017 were included in the study. The primary outcome was in-hospital mortality; secondary outcomes were mortality within 30 days and insulin administration during hospitalization. Generalized linear mixed model (GLMM) with maximum likelihood estimation (MLE) and Bayesian estimation using Markov chain Monte Carlo methods (MCMC), in addition to propensity score matching (PSM), were used for statistical analysis. RESULTS: A total of 811 patients were included in the study, of which 600 patients were treated with glucocorticoids, and 211 patients were treated without glucocorticoids. The early administration of glucocorticoids was not associated with a significant improvement in the in-hospital mortality of patients with thyroid storm [adjusted odds ratio (95% confidence interval) = 1.77 (0.95-3.34), 1.44 (1.14-1.93), and 1.46 (0.72-3.00) in the GLMM (MLE), GLMM (MCMC), and PSM, respectively]. The results of mortality within 30 days were almost identical to the results of in-hospital mortality. However, insulin use was significantly higher in the glucocorticoid group. CONCLUSIONS: This analysis of a nationwide administrative database indicates that the administration of glucocorticoids does not improve the survival of patients with thyroid storm.