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

2020 ◽  
Vol 7 (6) ◽  
Author(s):  
Chanu Rhee ◽  
Zhonghe Li ◽  
Rui Wang ◽  
Yue Song ◽  
Sameer S Kadri ◽  
...  
Keyword(s):  
Administrative Data ◽  
Clinical Data ◽  
Risk Adjustment ◽  
Severity Of Illness ◽  
Weighted Kappa ◽  
Mortality Rates ◽  
Severe Illness ◽  
Kappa Statistics ◽  
Sepsis Mortality ◽  
Risk Adjustment Model

Abstract 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.

scholarly journals Evaluation of Acute Myocardial Infarction In-hospital Mortality Using a Risk-adjustment Model Based on Japanese Administrative Data

2007 ◽  
Vol 35 (5) ◽  
pp. 590-596 ◽  
Author(s):  
K Hayashida ◽  
Y Imanaka ◽  
M Sekimoto ◽  
H Kobuse ◽  
H Fukuda
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Mortality ◽  
Administrative Data ◽  
Risk Adjustment ◽  
Clinical Performance ◽  
Primary Diagnosis ◽  
Substantial Impact ◽  
Risk Adjustment Model ◽  
Co Morbidity

This study aimed to develop a new risk-adjustment method to assess acute myocardial infarction (AMI) in-hospital mortality. Risk-adjustment was based on variables obtained from administrative data from Japanese hospitals, and included factors such as age, gender, primary diagnosis and co-morbidity. The infarct location was determined using the criteria of the International Classification of Diseases (10th version). Potential co-morbidity risk factors for mortality were selected based on previous studies and their critical influence analysed to identify major co-morbidities. The remaining minor co-morbidities were then divided into two groups based on their medical implications. The major co-morbidities included shock, pneumonia, cancer and chronic renal failure. The two minor co-morbidity groups also demonstrated a substantial impact on mortality. The model was then used to assess clinical performance in the participating hospitals. Our model reliably employed the available data for the risk-adjustment of AMI mortality and provides a new approach to evaluating clinical performance.

scholarly journals 1659. Variation in Identifying Sepsis and Organ Dysfunction Using Administrative Versus Clinical Data and Impact on Hospital Outcome Comparisons

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S49-S50 ◽  
Author(s):  
Chanu Rhee ◽  
Maximilian Jentzsch ◽  
Sameer S Kadri ◽  
Christopher Seymour ◽  
Derek Angus ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Organ Dysfunction ◽  
Clinical Data ◽  
Hepatic Injury ◽  
Claims Data ◽  
Kidney Injury ◽  
Mortality Rates ◽  
Administrative Claims ◽  
Sepsis Mortality ◽  
Incidence And Mortality

Abstract Background Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices may confound efforts to benchmark hospital sepsis outcomes using claims data. Methods We evaluated the sensitivity of claims data for sepsis and organ dysfunction relative to clinical data from the electronic health records of 193 US hospitals. Sepsis was defined clinically using markers of presumed infection (blood cultures and antibiotic administrations) and concurrent organ dysfunction. Organ dysfunction was measured using laboratory data (acute kidney injury, thrombocytopenia, hepatic injury), vasopressor administrations (shock), or mechanical ventilation (respiratory failure). Correlations between hospitals’ sepsis incidence and mortality rates by claims (using “explicit” ICD-9-CM codes for severe sepsis or septic shock) versus clinical data were measured by the Pearson correlation coefficient (r) and relative hospital rankings using either data source were compared. All estimates were reliability-adjusted to account for random variation using hierarchical logistic regression modeling. Results The study cohort included 4.3 million adult hospitalizations in 2013 or 2014. The sensitivity of hospitals’ claims data for sepsis and organ dysfunction was low and variable: median sensitivity 30% (range 5–54%) for sepsis, 66% (range 26–84%) for acute kidney injury, 39% (range 16–60%) for thrombocytopenia, 36% (range 29–44%) for hepatic injury, and 66% (range 29–84%) for shock (Figure 1). There was only moderate correlation between claims and clinical data for hospitals’ sepsis incidence (r = 0.64) and mortality rates (r = 0.61), and relative hospital rankings for sepsis mortality differed substantially using either method (Figure 2). Of 48 (46%) hospitals, 22 ranked in the lowest sepsis mortality quartile by claims shifted to higher mortality quartiles using clinical data. Conclusion Variation in the completeness and accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospital sepsis rates and outcomes. Sepsis surveillance using objective clinical data may facilitate more meaningful hospital comparisons. Disclosures All authors: No reported disclosures.

Abstract 3276: Assessment of Predictive Accuracy of Centers for Medicare and Medicaid Services’ Method to Risk Adjust Patients for Interhospital Comparison of 30-day Mortality Rates

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Todd M Koelling ◽  
Sara Saberi ◽  
Anthony C DeFranco ◽  
Stephen Skorcz ◽  
Cecelia K Montoye ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Adjustment ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Mortality Rates ◽  
Mortality Prediction ◽  
Multivariable Logistic Regression Analysis ◽  
Validation Data ◽  
Data Subset ◽  
Risk Adjustment Model

Introduction : The Centers for Medicare and Medicaid Services (CMS) will initiate public reporting of 30 day death rates for hospitals caring for patients with heart failure (HF). While hospital specific rates will be adjusted for medical comorbidities, it is not known if this method is adequate to allow direct hospital comparisons. Hypothesis : More accurate risk adjustment of patients can be accomplished by including variables that have previously been described to be predictors of mortality in HF. Methods : We assessed the CMS HF risk adjustment model in a population of 3639 patients studied in the Guidelines Applied in Practice (GAP) - HF study. Probabilties for 30 day mortality were calculated for the CMS model. Multivariable logistic regression analysis was then performed with other models including variables (no mVO 2 ) from the Heart Failure Survival Score (HFSS), ADHERE Registry model (ARM), body mass index categories (BMI), pre-admission origin of patient (PAO), and admission in the previous 6 months (AP6). Probabilities of mortality were tabulated. Backward selection was then used to derive the best model from all candidate variables from a derivation data subset, followed by testing of the model on a validation data subset. Area under the curve (AUC) was then compared for each model. Results : The AUCs and 95% CI are shown in the table below. Calculated AUCs for the CMS model and separate models were similar. The best model was defined by the following variables: admission in the previous 6 months, BUN > 43, systolic blood pressure > 115, 35 ≤ BMI < 40, BMI ≥ 40, mean arterial pressure, serum sodium, and PAO as well as the CMS model variables. The AUCs for the derivation and validation sets were both significantly greater than that provided by the CMS model alone. Conclusions : Risk adjustment for the purpose of interhospital comparison of 30-day mortality rates is best performed with models that include clinical admission variables in addition to the medical comorbidites. Comparison of AUCs for 30 day mortality prediction

scholarly journals Adjusting Client-Level Risks Impacts on Home Care Organization Ranking

2021 ◽  
Vol 18 (11) ◽  
pp. 5502
Author(s):  
Aylin Wagner ◽  
René Schaffert ◽  
Julia Dratva
Keyword(s):  
Risk Factors ◽  
Home Care ◽  
Risk Adjustment ◽  
Care Quality ◽  
Assessment Instrument ◽  
Care Organization ◽  
Discrimination Power ◽  
Adjustment Model ◽  
Risk Adjustment Model ◽  
The Impact

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

2020 ◽  
pp. 1-7
Author(s):  
Stephanie M. Cabral ◽  
Katherine E. Goodman ◽  
Natalia Blanco ◽  
Surbhi Leekha ◽  
Larry S. Magder ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Risk Adjustment ◽  
Community Hospital ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Bivariate Analysis ◽  
Patient Admissions ◽  
Clostridioides Difficile ◽  
Risk Adjustment Model

Abstract 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.

scholarly journals Risk Adjustment in Studies Using Administrative Data

2003 ◽  
Vol 29 (2) ◽  
pp. 267-271 ◽  
Author(s):  
M. J. Sernyak ◽  
R. Rosenheck
Keyword(s):  
Administrative Data ◽  
Risk Adjustment

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

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Andy T Tran ◽  
Anthony Hart ◽  
John Spertus ◽  
Philip Jones ◽  
Bryan McNally ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Hospital Mortality ◽  
Risk Adjustment ◽  
Quality Measures ◽  
Coronary Intervention ◽  
Spontaneous Circulation ◽  
Logistic Regression Models ◽  
Adjustment Model ◽  
Risk Adjustment Model ◽  
Hospital Cardiac Arrest

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.

Abstract 16010: Congestive Heart Failure and Coronavirus Disease 2019 Illness Severity

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Max Ruge ◽  
Joanne Michelle D Gomez ◽  
Gatha G Nair ◽  
Setri Fugar ◽  
Jeanne du Fay de Lavallaz ◽  
...  
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Congestive Heart Failure ◽  
Case Fatality ◽  
Severity Of Illness ◽  
Severe Infection ◽  
Severe Illness ◽  
Increased Risk ◽  
History Of ◽  
Male Sex

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has killed hundreds of thousands worldwide. Those with cardiovascular disease represent a vulnerable population with higher risk for contracting COVID-19 and worse prognosis with higher case fatality rates. Congestive heart failure (CHF) may lead to worsening COVID-19 symptoms. However, it is unclear if CHF is an independent risk factor for severe COVID-19 infection or if other accompanying comorbidities are responsible for the increased risk. Methods: From March to June 2020, data was obtained from adult patients diagnosed with COVID-19 infection who were admitted in the Rush University System for Health (RUSH) in Illinois. Heart failure patients, determined by ICD code assignments extracted from the electronic medical records, were identified. Multivariable logistic regression was performed between predictor variables and a composite outcome of severe infection consisting of Intensive Care Unit (ICU) admission, intubation, or in-hospital mortality. Results: In this cohort (n=1136), CHF [odds ratio (OR) 1.02] alone did not predict a more severe illness. Prior myocardial infarction [(MI), OR 3.55], history of atrial fibrillation [(AF), OR 2.14], and male sex (OR 1.55) were all significantly (p<0.001) associated with more severe COVID-19 illness course when controlling for CHF (Figure 1). In the 178 CHF patients, more advanced age (68.8 years vs. 63.8 years; p<0.05) and female sex (54.5% vs. 39.1%; p<0.05) were associated with increased severity of illness. Conclusions: Prior MI, history of AF, and male sex predicted more severe COVID-19 illness course in our cohort, but pre-existing heart failure alone did not. However, CHF patients who are females and older in age are at risk for severe infection. These findings help clinicians identify patients with comorbidities early at risk for severe COVID-19 illness.

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