Automated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustment of quality indicators in healthcare. The methods that have been used so far, however, do not account for the fact that patient data are typically nested within hospitals.
Therefore, we aimed to demonstrate how to account for the multilevel structure of hospital data with LASSO and compare the results of this procedure with a LASSO variant that ignores the multilevel structure of the data. We used three different data sets (from acute myocardial infarcation, COPD, and stroke patients) with two dependent variables (one numeric and one binary), on which different LASSO variants with and without consideration of the nested data structure were applied. Using a 20-fold sub-sampling procedure, we tested the predictive performance of the different LASSO variants and examined differences in variable importance.
For the metric dependent variable Duration Stay, we found that inserting hospitals led to better predictions, whereas for the binary variable Mortality, all methods performed equally well. However, in some instances, the variable importances differed greatly between the methods.
We showed that it is possible to take the multilevel structure of data into account in automated predictor selection and that this leads, at least partly, to better predictive performance. From the perspective of variable importance, including the multilevel structure is crucial to select predictors in an unbiased way under consideration of the structural differences between hospitals.

Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery.
This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration.
The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15-0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15-0.42), 0.40% (neurovascular, IQR 0.24-0.78) and 0.49% (trauma neurosurgery, IQR 0.23-1.68).
Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.

UNASSIGNED: Methods for assessing long-term outcome quality of acute care for sepsis are lacking. We investigated a method for measuring long-term outcome quality based on health claims data in Germany.
UNASSIGNED: Analyses were based on data of the largest German health insurer, covering 32% of the population. Cases (aged 15 years and older) with ICD-10-codes for severe sepsis or septic shock according to sepsis-1-definitions hospitalized in 2014 were included. Short-term outcome was assessed by 90-day mortality; long-term outcome was assessed by a composite endpoint defined by 1-year mortality or increased dependency on chronic care. Risk factors were identified by logistic regressions with backward selection. Hierarchical generalized linear models were used to correct for clustering of cases in hospitals. Predictive validity of the models was assessed by internal validation using bootstrap-sampling. Risk-standardized mortality rates (RSMR) were calculated with and without reliability adjustment and their univariate and bivariate distributions were described.
UNASSIGNED: Among 35,552 included patients, 53.2% died within 90 days after admission; 39.8% of 90-day survivors died within the first year or had an increased dependency on chronic care. Both risk-models showed a sufficient predictive validity regarding discrimination [AUC = 0.748 (95% CI: 0.742; 0.752) for 90-day mortality; AUC = 0.675 (95% CI: 0.665; 0.685) for the 1-year composite outcome, respectively], calibration (Brier Score of 0.203 and 0.220; calibration slope of 1.094 and 0.978), and explained variance (R 2 = 0.242 and R 2 = 0.111). Because of a small case-volume per hospital, applying reliability adjustment to the RSMR led to a great decrease in variability across hospitals [from median (1st quartile, 3rd quartile) 54.2% (44.3%, 65.5%) to 53.2% (50.7%, 55.9%) for 90-day mortality; from 39.2% (27.8%, 51.1%) to 39.9% (39.5%, 40.4%) for the 1-year composite endpoint]. There was no substantial correlation between the two endpoints at hospital level (observed rates: ρ = 0, p = 0.99; RSMR: ρ = 0.017, p = 0.56; reliability-adjusted RSMR: ρ = 0.067; p = 0.026).
UNASSIGNED: Quality assurance and epidemiological surveillance of sepsis care should include indicators of long-term mortality and morbidity. Claims-based risk-adjustment models for quality indicators of acute sepsis care showed satisfactory predictive validity. To increase reliability of measurement, data sources should cover the full population and hospitals need to improve ICD-10-coding of sepsis.

Risk-standardized survival rates (RSSR) for in-hospital cardiac arrest (IHCA) have been widely used for hospital benchmarking and research. The novel coronavirus 2019 (COVID-19) pandemic has led to a substantial decline in IHCA survival as COVID-19 infection is associated with markedly lower survival. Therefore, there is a need to update the model for computing RSSRs for IHCA given the COVID-19 pandemic.
Within Get With The Guidelines®-Resuscitation, we identified 53,922 adult patients with IHCA from March, 2020 to December, 2021 (the COVID-19 era). Using hierarchical logistic regression, we derived and validated an updated model for survival to hospital discharge and compared the performance of this updated RSSR model with the previous model.
The survival rate was 21.0% and 20.8% for the derivation and validation cohorts, respectively. The model had good discrimination (C-statistic 0.72) and excellent calibration. The updated parsimonious model comprised 13 variables-all 9 predictors in the original model as well as 4 additional predictors, including COVID-19 infection status. When applied to data from the pre-pandemic period of 2018-2019, there was a strong correlation (r = 0.993) between RSSRs obtained from the updated and the previous models.
We have derived and validated an updated model to risk-standardize hospital rates of survival for IHCA. The updated model yielded RSSRs that were similar to the initial model for IHCAs in the pre-pandemic period and can be used for supporting ongoing efforts to benchmark hospitals and facilitate research that uses data from either before or after the emergence of COVID-19.

In this study, we estimate sex differences in care complexity and cost of cardiac-related procedures in order to demonstrate the importance of sex as a risk adjuster in a hospital payment system. We use individual visit-level data for all adult Israelis who underwent either heart valve surgery (HVS) or coronary artery bypass graft surgery (CABG) during the period 2014-2018 in publicly funded hospitals. We find that women undergoing a cardiac-related procedure are more likely to die during hospitalization, they have longer hospital stays, and overall, they are more likely to be care-complex than men. Furthermore, the cost of the surgery itself is higher for women than for men in the case of HVS (though not CABG), and the cost of the post-operative hospital stay is higher in the case of CABG (though not HVS). It is concluded that sex differences should be considered in the calculation of payment for cardiac-related procedures in order to reduce incentives for selection and reduce unwarranted variation in cardiac-care utilization and medical practice.

Risk-adjustment models are used to predict the cost of care for patients based on their observable characteristics, and to derive efficient and equitable budgets based on weighted capitation. Markers based on past care contacts can improve model fit, but their coefficients may be affected by provider variations in diagnostic, treatment and reporting quality. This is problematic when distinguishing need and supply influences on costs is required.We examine the extent of this bias in the national formula for mental health care using administrative records for 43.7 million adults registered with 7746 GP practices in England in 2015. We also illustrate a method to control for provider effects.A linear regression containing a rich set of individual, GP practice and area characteristics, and fixed effects for local health organisations, had goodness-of-fit equal to R2 = 0.007 at person level and R2 = 0.720 at GP practice level. The addition of past care markers changed substantially the coefficients on the other variables and increased the goodness-of-fit to R2 = 0.275 at person level and R2 = 0.815 at GP practice level. The further inclusion of provider effects affected the coefficients on GP practice and area variables and on local health organisation fixed effects, increasing goodness-of-fit at GP practice level to R2 = 0.848.With adequate supply controls, it is possible to estimate coefficients on past care markers that are stable and unbiased. Nonetheless, inconsistent reporting may affect need predictions and penalise populations served by underreporting providers.

This study addresses the question of how hospitals respond to the cross price incentives inherent in reimbursements based on diagnosis-related groups (DRG). Unique market-wide administrative data allow to exploit a natural experiment in Germany in which the relative attractiveness of greatly divergent reimbursements for clinically similar patients changes in the market for sepsis conditions on January 1, 2010. This natural experiment provides-unintentionally-extra reimbursements in cases in which hospitals reorganize transfers for deceasing patients to other facilities, alter the time of death, the choice of the condition being chiefly responsible for the hospital admission (primary diagnosis), or the intensity of mechanical ventilation. The differences-in-differences results demonstrate that hospitals primarily alter the primary diagnosis. As the choice of the primary diagnosis is the backbone of the design of modern DRG systems, the findings suggest that payment contracts between hospitals and payers based on modern DRG algorithms may not necessarily improve patient welfare.

Most trauma patients admitted to the hospital alive and die later on, decease during the initial care in the emergency department or the intensive care unit (ICU). However, a number of patients pass away after having been discharged from the ICU during the initial hospital stay. On first sight these cases could be seen as \"failure to rescue\" of potentially salvageable patients. A low rate of such patients might be a potential indicator of quality for trauma care on ICUs and surgical wards.
Retrospective analysis of the TraumaRegister DGU® with data from 2015 to 2017. Patients that died during the initial ICU stay were compared to those who were discharged from the initial ICU stay for at least 24 h but died later on.
A total of 82,313 trauma patients were included in the TraumaRegister DGU®. In total, 6576 patients (8.0%) died during their hospital stay. Out of those, 5481 were admitted to the ICU alive and 972 patients (17.7%) were discharged from ICU and died later on. Those were older (mean age: 77 vs. 68 years), less severely injured (mean ISS: 23.1 vs. 30.0 points) and had a longer mean ICU length of stay (10 vs. 6 days). A limitation of life-sustaining therapy due to a documented living will was present in 46.1% of all patients who died during their initial ICU stay and in 59.9% of patients who died after discharge from their initial ICU stay.
17.7% of all non-surviving severely injured trauma patients died within the hospital after discharge from their initial ICU treatment. Their death can partially be explained by a limitation of therapy due to a living will. In conclusion, the rate of such late deaths may partially represent patients that died of potentially avoidable or treatable complications.

Several national studies have demonstrated that rural hospitals successfully deliver high-quality care. Data at the national, regional, institutional, and individual practitioner levels all contribute to understanding of surgical outcomes in the rural setting. Quality metrics should be interpreted within the context of the rural community and outcomes analyzed with relevant risk adjustment for patient factors.

There is growing body of evidence that important patient-, procedure- and pathogen-related factors are linked to higher risks for prosthetic joint infections (PJI) following arthroplasty surgeries. The prior identification and optimization of such risk factors is considered paramount to minimize the incidence of these infections. Without any doubt, antibiotic prophylaxis remains one of the cornerstones among all preventive measures. However, the ideal antibiotic prophylaxis is still in debate and discussions have emerged, whether certain situations deserve adjustments or variations of the standard protocol taking into account antibiotic resistance surveillance data and patient risk factors for infections. This review aims to provide the reader with an overview of possible antibiotic prophylaxis strategies in response to these risks and discusses the clinical experiences so far obtained. We further present preliminary evidence that the use of a reinforced local antibiotic prophylaxis regimen with high-dose dual antibiotic-loaded bone cement may be an effective and easy-to-apply option in patients at high infection risks.