Predictors of Prolonged Length of Stay after Lobectomy for Lung Cancer: A Society of Thoracic Surgeons General Thoracic Surgery Database Risk-Adjustment Model

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.

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Adjusting Client-Level Risks Impacts on Home Care Organization Ranking

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115502 ◽

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.

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A Prolonged Air Leak Score for Lung Cancer Resection: An Analysis of The Society of Thoracic Surgeons General Thoracic Surgery Database

The Annals of Thoracic Surgery ◽

10.1016/j.athoracsur.2019.05.069 ◽

2019 ◽

Vol 108 (5) ◽

pp. 1478-1483 ◽

Cited By ~ 7

Author(s):

Christopher W. Seder ◽

Sanjib Basu ◽

Timothy Ramsay ◽

Gaetano Rocco ◽

Shanda Blackmon ◽

...

Keyword(s):

Lung Cancer ◽

Thoracic Surgery ◽

Air Leak ◽

Cancer Resection ◽

Prolonged Air Leak ◽

General Thoracic Surgery

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

Circulation ◽

10.1161/circ.144.suppl_2.11372 ◽

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.

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