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Deutsches Arzteblatt International Feb 2021
Topics: Humans; Risk Adjustment
PubMed: 33835009
DOI: 10.3238/arztebl.m2021.0056 -
The Medical Clinics of North America Mar 2021Patients with rheumatic disease, including those with systemic lupus erythematous, rheumatoid arthritis, and spondyloarthritis, use total hip and knee arthroplasties at... (Review)
Review
Patients with rheumatic disease, including those with systemic lupus erythematous, rheumatoid arthritis, and spondyloarthritis, use total hip and knee arthroplasties at high rates. They represent a particularly vulnerable population in the perioperative setting because of their diseases and the immunosuppressant therapies used to treat them. Careful planning among internists, medical specialists, and the surgical team must therefore occur preoperatively to minimize risks in the postoperative period, particularly infection. Management of immunosuppressant medications, such as conventional synthetic disease-modifying antirheumatic drugs and targeted therapies including biologics, is one avenue by which this infectious risk can be mitigated.
Topics: Arthroplasty; Humans; Medication Therapy Management; Perioperative Period; Rheumatic Diseases; Risk Adjustment
PubMed: 33589102
DOI: 10.1016/j.mcna.2020.09.011 -
European Journal of Preventive... Nov 2023Many models developed for predicting the risk of cardiovascular disease (CVD) are adjusted for the competing risk of non-CVD mortality, which has been suggested to...
BACKGROUND
Many models developed for predicting the risk of cardiovascular disease (CVD) are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population.
METHODS AND RESULTS
Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART). In 8355 individuals, followed for a median of 8.2 years (IQR 4.2-12.5), two similar prediction models for the estimation of 10-year residual CVD risk were derived: with competing risk adjustment using a Fine and Gray model and without competing risk adjustment using a Cox proportional hazards model. On average, predictions were higher from the Cox model. The Cox model predictions overestimated the cumulative incidence [predicted-observed ratio 1.14 (95% CI 1.09-1.20)], which was most apparent in the highest risk quartiles and in older persons. Discrimination of both models was similar. When determining treatment eligibility on thresholds of predicted risks, more individuals would be treated based on the Cox model predictions. If, for example, individuals with a predicted risk > 20% were considered eligible for treatment, 34% of the population would be treated according to the Fine and Gray model predictions and 44% according to the Cox model predictions.
INTERPRETATION
Individual predictions from the model unadjusted for competing risks were higher, reflecting the different interpretations of both models. For models aiming to accurately predict absolute risks, especially in high-risk populations, competing risk adjustment must be considered.
Topics: Humans; Aged; Aged, 80 and over; Risk Factors; Cardiovascular Diseases; Risk Assessment; Risk Adjustment; Proportional Hazards Models; Heart Disease Risk Factors
PubMed: 37338108
DOI: 10.1093/eurjpc/zwad202 -
BMJ Open Aug 2021Adequate risk adjustment for factors beyond the control of the healthcare system contributes to the process of transparent and equitable benchmarking of trauma outcomes....
OBJECTIVES
Adequate 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.
SETTING
This 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 MEASURES
The 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.
RESULTS
Of 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.
CONCLUSION
Our risk adjustment model of six variables is efficient and can be reliably collected from registries to enhance the process of benchmarking.
Topics: Aged; Australia; Hospitals; Humans; Length of Stay; Male; Registries; Risk Adjustment
PubMed: 34426470
DOI: 10.1136/bmjopen-2021-050795 -
World Neurosurgery Aug 2019With the increasing interest in big data and health services research, use of administrative databases is becoming commonplace in health care studies, including in... (Review)
Review
With the increasing interest in big data and health services research, use of administrative databases is becoming commonplace in health care studies, including in neurosurgery. Administrative data offer the unique advantage of accessing large amounts of information previously collected from a population-based sample with geographic diversity. When using administrative data sets, researchers can benefit from application of risk adjustment instruments, which help stratify patients and tailor the original sample for specific research questions. The Charlson Comorbidity Index and Elixhauser Comorbidity Index are 2 of the most common indices. The Pediatric Medical Complexity Algorithm and Clinical Classification Software are other promising tools. Understanding of these tools may assist neurosurgeons who wish to critically assess research findings relevant to their clinical practice. In this review, an overview is presented of risk adjustment tools commonly used in adult as well as pediatric populations and their history, uses, limitations, and applications in neurosurgical research are summarized.
Topics: Databases, Factual; Datasets as Topic; Health Services Research; Humans; Neurosurgeons; Neurosurgery; Risk Adjustment
PubMed: 31048059
DOI: 10.1016/j.wneu.2019.04.179 -
Health Economics Jul 2022The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially...
The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R and mean squared error with fine data granularity compared to linear regression, while in coarse granularity and poor range of variables scenario no differences were observed. The advantage of ML algorithms is greater in the coarse granularity and fair/rich range of variables set and limited with fine granularity scenarios. The inclusion of detailed morbidity- and pharmacy-based adjustors generally increases fit, although the trade-off of creating adverse economic incentives must be considered.
Topics: Algorithms; Humans; Italy; Linear Models; National Health Programs; Risk Adjustment
PubMed: 35384134
DOI: 10.1002/hec.4512 -
Cardiac Risk Assessment in Liver Transplant Candidates: Current Controversies and Future Directions.Hepatology (Baltimore, Md.) Jun 2021In the changing landscape of liver transplantation (LT), we are now evaluating older and sicker patients with more cardiovascular comorbidities, and the spectrum of... (Review)
Review
In the changing landscape of liver transplantation (LT), we are now evaluating older and sicker patients with more cardiovascular comorbidities, and the spectrum of cardiovascular disease is uniquely physiologically impacted by end-stage liver disease. Cardiac complications are now the leading cause of morbidity and mortality in LT recipients, and the pretransplant risk is exacerbated immediately during the transplant operation and continues long term under the umbrella of immunosuppression. Accurate risk estimation of cardiac complications before LT is paramount to guide allocation of limited health care resources and to improve both short-term and long-term clinical outcomes for patients. Current screening and diagnostic testing are limited in their capacity to accurately identify early coronary disease and myocardial dysfunction in persons with end-stage liver disease physiology. Furthermore, a number of testing modalities have not been evaluated in patients with end-stage liver disease. As a result, there is wide variation in cardiac risk assessment practices across transplant centers. In this review, we propose a definition for defining cardiac events in LT, evaluate the current evidence for surgery-related, short-term and long-term cardiac risk assessment in LT candidates, propose an evidence-based testing algorithm, and highlight specific gaps in knowledge and current controversies, identifying areas for future research.
Topics: Cardiovascular Diseases; End Stage Liver Disease; Humans; Liver Transplantation; Postoperative Complications; Risk Adjustment
PubMed: 33219576
DOI: 10.1002/hep.31647 -
International Journal For Quality in... Nov 2020To develop risk-adjusted models for two quality indicators addressing surgical site infection (SSI) in clean and colorectal surgery, to be used for benchmarking and...
OBJECTIVE
To develop risk-adjusted models for two quality indicators addressing surgical site infection (SSI) in clean and colorectal surgery, to be used for benchmarking and quality improvement in the Spanish National Health System.
STUDY DESIGN
A literature review was undertaken to identify candidate adjustment variables. The candidate variables were revised by clinical experts to confirm their clinical relevance to SSI; experts also offered additional candidate variables that were not identified in the literature review. Two risk-adjustment models were developed using multiple logistic regression thus allowing calculation of the adjusted indicator rates.
DATA SOURCE
The two SSI indicators, with their corresponding risk-adjustment models, were calculated from administrative databases obtained from nine public hospitals. A dataset was obtained from a 10-year period (2006-2015), and it included data from 21 571 clean surgery patients and 6325 colorectal surgery patients.
ANALYSIS METHODS
Risk-adjustment regression models were constructed using Spanish National Health System data. Models were analysed so as to prevent overfitting, then tested for calibration and discrimination and finally bootstrapped.
RESULTS
Ten adjustment variables were identified for clean surgery SSI, and 23 for colorectal surgery SSI. The final adjustment models showed fair calibration (Hosmer-Lemeshow: clean surgery χ2 = 6.56, P = 0.58; colorectal surgery χ2 = 6.69, P = 0.57) and discrimination (area under receiver operating characteristic [ROC] curve: clean surgery 0.72, 95% confidence interval [CI] 0.67-0.77; colorectal surgery 0.62, 95% CI 0.60-0.65).
CONCLUSIONS
The proposed risk-adjustment models can be used to explain patient-based differences among healthcare providers. They can be used to adjust the two proposed SSI indicators.
Topics: Colorectal Surgery; Humans; Logistic Models; Retrospective Studies; Risk Adjustment; Risk Factors; Surgical Wound Infection
PubMed: 32901796
DOI: 10.1093/intqhc/mzaa104 -
American Journal of Infection Control Mar 2021Until recently, there was no national surveillance system for monitoring infection occurrence in long-term care facilities (LTCF) in the United States. As a result,... (Review)
Review
Until recently, there was no national surveillance system for monitoring infection occurrence in long-term care facilities (LTCF) in the United States. As a result, there are no national benchmarks for LTCF infection rates that can be utilized for quality improvement at the facility level. One of the major challenges in the reporting of health care-related infection data is accounting for nonmodifiable facility and patient characteristics that influence benchmarks for infection. The objectives of this paper are to review: (a) published infection rates in LTCF in the United States to assess the level of variability; (b) studies describing facility- and resident-level risk factors for infection that can be used in risk adjustment models; (c) published attempts to risk-adjust LTCF infection rates; and (d) efforts to develop models specifically for risk adjustment of infection rates in LTCF for benchmarking. It is anticipated that this review will stimulate further study of methods to risk-adjust LTCF infection rates for benchmarking that will facilitate research and public reporting.
Topics: Benchmarking; Humans; Infection Control; Long-Term Care; Nursing Homes; Risk Adjustment; United States
PubMed: 32791257
DOI: 10.1016/j.ajic.2020.08.006 -
Infection Control and Hospital... Aug 2021To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection...
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.
Topics: Clostridioides; Clostridioides difficile; Clostridium Infections; Comorbidity; Cross Infection; Electronic Health Records; Hospitals; Humans; Risk Adjustment
PubMed: 33327970
DOI: 10.1017/ice.2020.1344