Risk-Adjusting Hospital Inpatient Mortality Using Automated Inpatient, Outpatient, and Laboratory Databases

Escobar, Gabriel J.; Greene, J; Scheirer, Peter; Gardner, Marla N.; Draper, David; Kipnis, Patricia

doi:10.1097/mlr.0b013e3181589bb6

Cited by 281 publications

(383 citation statements)

References 31 publications

Supporting

Mentioning

380

Contrasting

Order By: Relevance

“…13,[19][20][21][22][23] In addition, we calculated the Laboratory-based Acute Physiology Score (LAPS) to measure the severity of illness. 24,25 To create diagnostic variables, each admission's primary ICD-9 code was classified using the Healthcare Cost and Utilization Project database. 26 For this analysis, we included patients admitted with any of the 10 most frequent reasons for hospitalization at Weiler hospital (congestive heart failure, myocardial infarction, chest pain/other coronary, pneumonia, asthma/COPD, diabetes, arrhythmia, sepsis, gastrointestinal bleeding, and HIV).…”

Section: Admission-level Covariatesmentioning

confidence: 99%

Increased Risk of Mortality among Patients Cared for by Physicians with Short Length-of-Stay Tendencies

Southern

Arnsten

2015

J GEN INTERN MED

View full text Add to dashboard Cite

Section: Admission-level Covariatesmentioning

confidence: 99%

Increased Risk of Mortality among Patients Cared for by Physicians with Short Length-of-Stay Tendencies

Southern

Arnsten

2015

J GEN INTERN MED

View full text Add to dashboard Cite

“…These scores are used for the over 3 million patients in this hospital network and have similar predictive power as the APACHE and SAPS scores with c statistic in the 0.88 range (Zimmerman et al 2006, Moreno et al 2005. See Escobar et al (2008) for further description of these severity scores.…”

Section: Datamentioning

confidence: 99%

The Impact of Delays on Service Times in the Intensive Care Unit

Chan¹,

Farias

Escobar

2017

Management Science

Self Cite

View full text Add to dashboard Cite

Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and further used in making operational decisions for the same. The vast majority of these queueing models assume that the service requirements of a job are independent of the state of the queue upon its arrival. In a healthcare setting, this assumption is equivalent to ignoring the effects of delay experienced by a patient awaiting care. However, it is only natural to conjecture that long delays may have adverse effects on patient outcomes and can potentially lead to longer lengths of stay (LOS) when the patient ultimately does receive care. At a very coarse level, prior research confirms these natural conjectures. This work sets out to understand these delay issues from an operational perspective. In particular, using data of nearly 6,000 Emergency Department (ED) visits, we use an instrumental variable approach to empirically measure how congestion in the Intensive Care Unit (ICU) can lead to delays in boarding from the ED to the ICU and measure the impact on the patient's ICU LOS.Capturing these empirically observed effects in a queueing model is challenging as the effect introduces potentially long range correlations in service and inter-arrival times. As such, we consider the problem of how to incorporate these measured delay effects into a queueing model and characterize approximations to various quantities of interest when the service time of a job is adversely impacted by the delay experienced by that job. Our findings suggest that this delay effect can be substantial and ignoring it when using queueing models to model healthcare delivery systems may result in significant under-provisioning.

show abstract

“…First, selected variables are used to stratify patients into low and high mortality risk groups. Then 14 laboratory values (anion gap; albumin; arterial oxygen, pH, and carbon dioxide; bicarbonate; bilirubin; blood urea nitrogen; creatinine; glucose; hematocrit; sodium; troponin I; white blood cell count) are added to the algorithm to calculate a score 17, 18. For laboratory values that are not available, the algorithm assigns points based upon the patient's stage‐1 mortality risk group (rather than using imputation) 17, 18.…”

Section: Methodsmentioning

confidence: 99%

“…Then 14 laboratory values (anion gap; albumin; arterial oxygen, pH, and carbon dioxide; bicarbonate; bilirubin; blood urea nitrogen; creatinine; glucose; hematocrit; sodium; troponin I; white blood cell count) are added to the algorithm to calculate a score 17, 18. For laboratory values that are not available, the algorithm assigns points based upon the patient's stage‐1 mortality risk group (rather than using imputation) 17, 18. Because LAPS is designed to be used as a variable in a model that includes other patients characteristics when predicting mortality, we developed a generalized estimating equation logistic mortality prediction model using the LAPS score along with age, sex, race, and comorbidities.…”

Section: Methodsmentioning

confidence: 99%

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Lagu

Pekow

Stefan

et al. 2018

JAHA

View full text Add to dashboard Cite

BackgroundComparing heart failure (HF) outcomes across hospitals requires adequate risk adjustment. We aimed to develop and validate a model that can be used to compare quality of HF care across hospitals.Methods and ResultsWe included patients with HF aged ≥18 years admitted to one of 433 hospitals that participated in the Premier Inc Data Warehouse. This model (Premier) contained patient demographics, comorbidities, and acute conditions present on admission, derived from administrative and billing records. In a separate data set derived from electronic health records, we validated the Premier model by comparing hospital risk‐standardized mortality rates calculated with the Premier model to those calculated with a validated clinical model containing laboratory data (LAPS [Laboratory‐Based Acute Physiology Score]). Among the 200 832 admissions in the Premier Inc Data Warehouse, inpatient mortality was 4.0%. The model showed acceptable discrimination in the warehouse data (C statistic 0.75; 95% confidence interval, 0.74–0.76). In the validation data set, both the Premier model and the LAPS models showed acceptable discrimination (C statistic: Premier: 0.76 [95% confidence interval, 0.74–0.77]; LAPS: 0.78 [95% confidence interval, 0.76–0.80]). Risk‐standardized mortality rates for both models ranged from 2% to 7%. A linear regression equation describing the association between Premier‐ and LAPS‐specific mortality rates revealed a regression line with a slope of 0.71 (SE: 0.07). The correlation coefficient of the standardized mortality rates from the 2 models was 0.82.ConclusionsCompared with a validated model derived from clinical data, an HF mortality model derived from administrative data showed highly correlated risk‐standardized mortality rate estimates, suggesting it could be used to identify high‐ and low‐performing hospitals for HF care.

show abstract

Risk-Adjusting Hospital Inpatient Mortality Using Automated Inpatient, Outpatient, and Laboratory Databases

Abstract: Efforts to support improvement of hospital outcomes can take advantage of risk-adjustment methods based on automated physiology and diagnosis data that are not confounded by information obtained after hospital admission.

Cited by 281 publications

References 31 publications

Increased Risk of Mortality among Patients Cared for by Physicians with Short Length-of-Stay Tendencies

Increased Risk of Mortality among Patients Cared for by Physicians with Short Length-of-Stay Tendencies

The Impact of Delays on Service Times in the Intensive Care Unit

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Contact Info

Product

Resources

About