Abstract 11372: A Risk-Adjustment Model for Patients Presenting to Hospitals with Out-of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction

Background: Given the diversity of patients resuscitated from out-of-hospital cardiac arrest (OHCA) complicated by STEMI, adequate risk adjustment is needed to account for potential differences in case-mix to reflect the quality of percutaneous coronary intervention. Objectives: We sought to build a risk-adjustment model of in-hospital mortality outcomes for patients with OHCA and STEMI requiring emergent angiography. Methods: Within the Cardiac Arrest Registry to Enhance Survival, we included adult patients with OHCA and STEMI who underwent angiography within 2 hours from January 2013 to December 2019. Using pre-hospital patient and arrest characteristics, multivariable logistic regression models were developed for in-hospital mortality. We then described model calibration, discrimination, and variability in patients’ unadjusted and adjusted mortality rates. Results: Of 2,999 hospitalized patients with OHCA and STEMI who underwent emergent angiography (mean age 61.2 ±12.0, 23.1% female, 64.6% white), 996 (33.2%) died. The final risk-adjustment model for mortality included higher age, unwitnessed arrest, non-shockable rhythms, not having sustained return of spontaneous circulation upon hospital arrival, and higher total resuscitation time on scene ( C -statistic, 0.804 with excellent calibration). The risk-adjusted proportion of patients died varied substantially and ranged from 7.8% at the 10 th percentile to 74.5% at the 90 th percentile (Figure). Conclusions: Through leveraging data from a large, multi-site registry of OHCA patients, we identified several key factors for better risk-adjustment for mortality-based quality measures. We found that STEMI patients with OHCA have highly variable mortality risk and should not be considered as a single category in public reporting. These findings can lay the foundation to build quality measures to further optimize care for the patient with OHCA and STEMI.

Risk Adjustment Model for Preserved Health Status in Patients With Heart Failure and Reduced Ejection Fraction: The CHAMP-HF Registry

Background: Health status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College of Cardiology/American Heart Association Task Force proposed using the proportion of patients with preserved health status as a quality measure but not as a performance measure because risk adjustment methods were not available. Methods: We built risk adjustment models for alive with preserved health status and for preserved health status alone in a prospective registry of outpatients with HF with reduced ejection fraction across 146 US centers between December 2015 and October 2017. Preserved health status was defined as not having a ≥5-point decrease in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 1 year. Using only patient-level characteristics, hierarchical multivariable logistic regression models were developed for 1-year outcomes and validated using data from 1 to 2 years. We examined model calibration, discrimination, and variability in sites’ unadjusted and adjusted rates. Results: Among 3932 participants (median age [interquartile range] 68 years [59–75], 29.7% female, 75.4% White), 2703 (68.7%) were alive with preserved health status, 902 (22.9%) were alive without preserved health status, and 327 (8.3%) had died by 1 year. The final risk adjustment model for alive with preserved health status included baseline Kansas City Cardiomyopathy Questionnaire Overall Summary, age, race, employment status, annual income, body mass index, depression, atrial fibrillation, renal function, number of hospitalizations in the past 1 year, and duration of HF (optimism-corrected C statistic=0.62 with excellent calibration). Similar results were observed when deaths were ignored. The risk standardized proportion of patients alive with preserved health status across the 146 sites ranged from 62% at the 10th percentile to 75% at the 90th percentile. Variability across sites was modest and changed minimally with risk adjustment. Conclusions: Through leveraging data from a large, outpatient, observational registry, we identified key factors to risk adjust sites’ proportions of patients with preserved health status. These data lay the foundation for building quality measures that quantify treatment outcomes from patients’ perspectives.

Development and validation of a risk-adjustment model for mortality and hospital length of stay for trauma patients: a prospective registry-based study in Australia

ObjectivesAdequate risk adjustment for factors beyond the control of the healthcare system contributes to the process of transparent and equitable benchmarking of trauma outcomes. Current risk adjustment models are not optimal in terms of the number and nature of predictor variables included in the model and the treatment of missing data. We propose a statistically robust and parsimonious risk adjustment model for the purpose of benchmarking.SettingThis study analysed data from the multicentre Australia New Zealand Trauma Registry from 1 July 2016 to 30 June 2018 consisting of 31 trauma centres.Outcome measuresThe primary endpoints were inpatient mortality and length of hospital stay. Firth logistic regression and robust linear regression models were used to study the endpoints, respectively. Restricted cubic splines were used to model non-linear relationships with age. Model validation was performed on a subset of the dataset.ResultsOf the 9509 patients in the model development cohort, 72% were male and approximately half (51%) aged over 50 years . For mortality, cubic splines in age, injury cause, arrival Glasgow Coma Scale motor score, highest and second-highest Abbreviated Injury Scale scores and shock index were significant predictors. The model performed well in the validation sample with an area under the curve of 0.93. For length of stay, the identified predictor variables were similar. Compared with low falls, motor vehicle occupants stayed on average 2.6 days longer (95% CI: 2.0 to 3.1), p<0.001. Sensitivity analyses did not demonstrate any marked differences in the performance of the models.ConclusionOur risk adjustment model of six variables is efficient and can be reliably collected from registries to enhance the process of benchmarking.

Adjusting Client-Level Risks Impacts on Home Care Organization Ranking

Quality indicators (QIs) based on the Resident Assessment Instrument-Home Care (RAI-HC) offer the opportunity to assess home care quality and compare home care organizations’ (HCOs) performance. For fair comparisons, providers’ QI rates must be risk-adjusted to control for different case-mix. The study’s objectives were to develop a risk adjustment model for worsening or onset of urinary incontinence (UI), measured with the RAI-HC QI bladder incontinence, using the database HomeCareData and to assess the impact of risk adjustment on quality rankings of HCOs. Risk factors of UI were identified in the scientific literature, and multivariable logistic regression was used to develop the risk adjustment model. The observed and risk-adjusted QI rates were calculated on organization level, uncertainty addressed by nonparametric bootstrapping. The differences between observed and risk-adjusted QI rates were graphically assessed with a Bland-Altman plot and the impact of risk adjustment examined by HCOs tertile ranking changes. 12,652 clients from 76 Swiss HCOs aged 18 years and older receiving home care between 1 January 2017, and 31 December 2018, were included. Eight risk factors were significantly associated with worsening or onset of UI: older age, female sex, obesity, impairment in cognition, impairment in hygiene, impairment in bathing, unsteady gait, and hospitalization. The adjustment model showed fair discrimination power and had a considerable effect on tertile ranking: 14 (20%) of 70 HCOs shifted to another tertile after risk adjustment. The study showed the importance of risk adjustment for fair comparisons of the quality of UI care between HCOs in Switzerland.

Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

Objective: To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment. Patients: All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018. Methods: Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities. Results: At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change. Conclusions: Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

786. Diagnostic stewardship of C difficile PCR testing with two step algorithm- A rural community hospital experience

Background An estimated 15% of hospitalized patients are asymptomatic carriers of C. diff. Inappropriate testing can lead to over diagnosis, treatment, isolation & substantial financial penalties. Ours is a rural 310 bed hospital with nurse driven C. diff test ordering protocol. Due to inadvertent test ordering, we had an uptick in the HO-CDI incidence with rates as high as 0.94 per 1000 patient days in 2017. In order to streamline testing, we initiated an infection preventionist(IP) led diagnostic stewardship program which was implemented in two phases in 2017-2019 Methods The phase 1 involved daily review by IPs regarding the legitimacy of PCR order for minimum 3 loose stools in 24 hours, use of laxatives, presence of symptoms.There were concerns nationally that then CDI risk adjustment model from NHSN in 2017 does not optimally account for the impact of specific CDI testing methods used by individual hospitals on CDI SIRs. Hence, in Jan 2018 NHSN’s MDRO/CDI Protocol stated “Results of the final test that are placed in the patient’s medical record should be used to determine whether event meets the CDI LabID defn”.This led to phase 2 in mar 2019 which involved two step testing which started with C diff PCR assay with positive test reflexed to the toxin A/B assay. Results During the first phase, and a full year of the protocol in 2018, the number of completed PCR tests decreased to 626 (compared to 940 PCR tests in 2016) with an 34% decrease. In the year following implementation of the Diagnostic Stewardship, HO CDI decreased from 60 in 2017 to 43 events in 2018 with a reduction of 28%. Subsequently, HO CDI further decreased in 2019 to 28 with a reduction of 35%. Since the start of the project in 2017, HO CDI have decreased 54% in total. The reduction in 314 C diff PCR tests in the first year[2017-2018] led to a savings of $8300 in lab supplies. No readmissions with C difficile infection documented within 30 days on patients who did not meet the criterion for testing. Significant decrease in the usage of C difficile antibiotics. After the start of the two step test, we have seen a precipitous drop in our HO-CDI rates to less than 0.3 per 1000 pt days by the end of 2019. Quarterly comparison of HO CDI incidence for 2017-2020 HO CDI incidence before and following phase 1 and phase 2 interventions C. difficile antibiotic use trends during intervention period Conclusion IP run diagnostic stewardship programs with two step tests are highly successful in streamlining testing and in discriminating infection from colonization Disclosures All Authors: No reported disclosures

Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons

Background A reliable risk-adjusted sepsis outcome measure could complement current national process metrics by identifying outlier hospitals and catalyzing additional improvements in care. However, it is unclear whether integrating clinical data into risk adjustment models identifies similar high- and low-performing hospitals compared with administrative data alone, which are simpler to acquire and analyze. Methods We ranked 200 US hospitals by their Centers for Disease Control and Prevention Adult Sepsis Event (ASE) mortality rates and assessed how rankings changed after applying (1) an administrative risk adjustment model incorporating demographics, comorbidities, and codes for severe illness and (2) an integrated clinical and administrative model replacing severity-of-illness codes with laboratory results, vasopressors, and mechanical ventilation. We assessed agreement between hospitals’ risk-adjusted ASE mortality rates when ranked into quartiles using weighted kappa statistics (к). Results The cohort included 4 009 631 hospitalizations, of which 245 808 met ASE criteria. Risk-adjustment had a large effect on rankings: 22/50 hospitals (44%) in the worst quartile using crude mortality rates shifted into better quartiles after administrative risk adjustment, and a further 21/50 (42%) of hospitals in the worst quartile using administrative risk adjustment shifted to better quartiles after incorporating clinical data. Conversely, 14/50 (28%) hospitals in the best quartile using administrative risk adjustment shifted to worse quartiles with clinical data. Overall agreement between hospital quartile rankings when risk-adjusted using administrative vs clinical data was moderate (к = 0.55). Conclusions Incorporating clinical data into risk adjustment substantially changes rankings of hospitals’ sepsis mortality rates compared with using administrative data alone. Comprehensive risk adjustment using both administrative and clinical data is necessary before comparing hospitals by sepsis mortality rates.