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Background Intraductal papillary mucinous neoplasm (IPMN)–derived pancreatic ductal adenocarcinoma (PDAC) is resected at smaller sizes compared with its biologically distinct counterpart, pancreatic intraepithelial neoplasia (PanIN)–derived PDAC. Thus, experts proposed T1 sub-staging for IPMN-derived PDAC. However, this has never been validated. Methods Consecutive upfront surgery patients with IPMN-derived PDAC from 5 international high-volume centers were classified by the proposed T1 sub-staging classification (T1a ≤0.5, T1b >0.5 and ≤1.0, and T1c >1.0 and ≤2.0 cm) using the invasive component size. Kaplan-Meier and log-rank tests were used to compare overall survival (OS). A multivariable Cox regression was used to determine hazard ratios (HRs) with confidence intervals (95% CIs). Results Among 747 patients, 69 (9.2%), 50 (6.7%), 99 (13.0%), and 531 patients (71.1%), comprised the T1a, T1b, T1c, and T2-4 subgroups, respectively. Increasing T-stage was associated with elevated CA19-9, poorer grade, nodal positivity, R1 margin, and tubular subtype. Median OS for T1a, T1b, T1c, and T2-4 were 159.0 (95% CI = 126.0 to NR), 128.8 (98.3 to NR), 77.6 (48.3 to 108.2), and 31.4 (27.5 to 37.7) months, respectively (P  < .001). OS decreased with increasing T-stage for all pairwise comparisons (all P  < .05). After risk adjustment, older than age 65, elevated CA19-9, T1b [HR = 2.55 (1.22 to 5.32)], T1c [HR = 3.04 (1.60 to 5.76)], and T2-4 [HR = 3.41 (1.89 to 6.17)] compared with T1a, nodal positivity, R1 margin, and no adjuvant chemotherapy were associated with worse OS. Disease recurrence was more common in T2-4 tumors (56.4%) compared with T1a (18.2%), T1b (23.9%), and T1c (36.1%, P  < .001). Conclusion T1 sub-staging of IPMN-derived PDAC is valid and has significant prognostic value. Advancing T1 sub-stage is associated with worse histopathology, survival, and recurrence. T1 sub-staging is recommended for future guidelines. [ABSTRACT FROM AUTHOR]

When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis). Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ? Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vs observed mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vs adjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment. Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and ≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9% of hospitals. The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model may aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis. [ABSTRACT FROM AUTHOR]

Background: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-adjusted CUSUM program (DL-CUSUM) to predict and monitor in-hospital mortality in real time after liver transplantation. Methods: Data from 1066 individuals with 66,092 preoperatively available data point variables from 2004 to 2019 were included. DL-CUSUM is an application to predict in-hospital mortality. The area under the curve for risk adjustment with Model of End-stage Liver Disease (D-MELD), Balance of Risk (BAR) score, and deep learning (DL), as well as the ARL (average run length) and control limit (CL) for an in-control process over 5 years, were calculated. Results: D-MELD AUC was 0.618, BAR AUC was 0.648 and DL model AUC was 0.857. CL with BAR adjustment was 2.3 with an ARL of 326.31. D-MELD reached an ARL of 303.29 with a CL of 2.4. DL prediction resulted in a CL of 1.8 to reach an ARL of 332.67. Conclusions: This work introduces the first use of an automated DL-CUSUM system to monitor postoperative in-hospital mortality after liver transplantation. It allows for the real-time risk-adjusted monitoring of process quality. [ABSTRACT FROM AUTHOR]

When monitoring risk in public health, count data commonly exhibit an excessive number of zero, and the zero‐inflated Poisson (ZIP) model is often used to fit this type of data. Most previous methods for monitoring of the ZIP model have focused on the changes in the location parameter and the existence of the scale parameter and usually assumed that the scale parameter is zero in the H0 stage. However, in an objective environment, data often have certain fluctuations, meaning that the scale parameter always exists. Therefore, it is more meaningful to monitor the changes in the scale parameter on top of the predefined baseline than to monitor its existence. In this study, we derive a score test statistic based on the generalized Henderson's joint likelihood function, construct a risk‐adjusted exponentially weighted moving average (EWMA) control chart to monitor the variability of the random effects variance component in the ZIP mixed‐effects model. And the convergence property of the score test statistic is proved through derivation, which shows that the new method has theoretical reliability. The simulation results Indicate that when the scale parameter has different predefined baselines and different variation amplitudes, the proposed method is more effective than the existing RA‐ZIP and PR‐ZIP control charts. In addition, the proposed method is applied to real data from a Hong Kong hospital for online influenza surveillance to demonstrate its practicability. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the variation in COVID-19 inpatient care mortality among hospitals reimbursed by the Unified Health System (SUS) in the first two years of the pandemic in São Paulo state and make performance comparisons within periods and over time. Methods: Observational study based on secondary data from the Hospital Information System. The study universe consisted of 289,005 adult hospitalizations whose primary diagnosis was COVID-19 in five periods from 2020 to 2022. A multilevel regression model was applied, and the death predictive variables were sex, age, Charlson Index, obesity, type of admission, Brazilian Deprivation Index (BrazDep), the month of admission, and hospital size. Then, the total observed deaths and total deaths predicted by the model's fixed effect component were aggregated by each hospital, estimating the Standardized Mortality Ratio (SMR) in each period. Funnel plots with limits of two standard deviations were employed to classify hospitals by performance (higher-than-expected, as expected, and lower-than-expected) and determine whether there was a change in category over the periods. Results: A positive association was observed between hospital mortality and size (number of beds). There was greater variation in the percentage of hospitals with as-expected performance (39.5 to 76.1%) and those with lower-than-expected performance (6.6 to 32.3%). The hospitals with higher-than-expected performance remained at around 30% of the total, except in the fifth period. In the first period, 64 hospitals (18.3%) had lower-than-expected performance, with standardized mortality ratios ranging from 1.2 to 4.4, while in the last period, only 23 (6.6%) hospitals were similarly classified, with ratios ranging from 1.3 to 2.8. A trend of homogenization and adjustment to expected performance was observed over time. Conclusion: Despite the study's limitations, the results suggest an improvement in the COVID-19 inpatient care performance of hospitals reimbursed by the SUS in São Paulo over the period studied, measured by the standardized mortality ratio for hospitalizations due to COVID-19. Moreover, the methodological approach adapted to the Brazilian context provides an applicable tool to follow-up hospital's performance in caring all or specific-cause hospitalizations, in regular or exceptional emergency situations. [ABSTRACT FROM AUTHOR]

The optimal time for total knee arthroplasty (TKA) requires a balance between patient disability and health state to minimize complications. While chronological age has not been shown to be predictive of complications in elective surgical patients, there is a point beyond which even optimized elderly patients would be at increased risk for complications. The purpose of this study was to examine the impact of chronological age on complications following primary TKA. Using an administrative database, the records of 2,129,191 patients undergoing elective unilateral TKA between 2006 and 2021 were reviewed. The primary outcomes of interest were cardiac and pulmonary complications, and their relationship to the Charlson-Deyo Comorbidity Index (CDI) and chronological age. Secondary outcomes included risk of renal, neurologic, infection, and intensive care utilization postoperatively. The results were analyzed using a graphical method. The impact of chronological age as a modifier of overall risk for complications was modeled as a continuous variable. An age cutoff threshold of 80 years was also assigned for clinical convenience. The risk of complications correlated more closely to the CDI (odds ratio (OR) 1.37 to 2.1) than chronological age (OR 1.0 to 1.1) across the various complications [Table 1. However, beyond age 80 years, the risks of cardiac, pulmonary, renal, and cerebrovascular complications were significantly increased for all CDI categories (OR 1.73 to 3.40) compared to patients below age 80 years [Table 2] [Figures 1A and 1B]. Chronologic age can impact the risk of complications even in well-optimized elderly patients undergoing primary TKA. As arthroplasty continues to transition to outpatient settings and inpatient denials increase, these results can help patients, physicians, and payors mitigate risk while optimizing the allocation of resources. [ABSTRACT FROM AUTHOR]

Introduction Racial minorities have been found to have worse health care outcomes, including perioperative adverse events. We hypothesized that these racial disparities may be mitigated in a military treatment facility, where all patients have a military service connection and are universally insured. Materials and Methods This is a single institution retrospective review of American College of Surgeons National Surgical Quality Improvement Program data for all procedures collected from 2017 to 2020. The primary outcome analyzed was risk-adjusted 30-day postoperative complications compared by race. Results There were 6,941 patients included. The overall surgical complication rate was 6.9%. The complication rate was 7.3% for White patients, 6.5% for Black patients, 12.6% for Asian patients, and 3.4% for other races. However, after performing patient and procedure level risk adjustment using multivariable logistic regression, race was not independently associated with surgical complications. Conclusions Risk-adjusted surgical complication rates do not vary by race at this military treatment facility. This suggests that postoperative racial disparities may be mitigated within a universal health care system. [ABSTRACT FROM AUTHOR]

We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Our data are a random 10% sample (553,675 observations) from the Finnish population in 2017. Using annual healthcare cost in 2017 as a response variable, we compare the performance of Random forest, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) to linear regression. As machine learning methods are often seen as unsuitable in risk adjustment applications because of their relative opaqueness, we also introduce visualizations from the machine learning literature to help interpret the contribution of individual variables to the prediction. Our results show that ensemble machine learning methods can improve predictive performance, with all of them significantly outperforming linear regression, and that a certain level of interpretation can be provided for them. We also find individual-level socioeconomic variables to improve prediction accuracy and that their effect is larger for machine learning methods. However, we find that the predictions used for funding allocations are sensitive to model selection, highlighting the need for comprehensive robustness testing when estimating risk adjustment models used in applications. [ABSTRACT FROM AUTHOR]

BACKGROUND: Risk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models. METHODS: We analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO). RESULTS: HGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features. CONCLUSIONS: In this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.

Abstract Objective To evaluate the variation in COVID-19 inpatient care mortality among hospitals reimbursed by the Unified Health System (SUS) in the first two years of the pandemic in São Paulo state and make performance comparisons within periods and over time. Methods Observational study based on secondary data from the Hospital Information System. The study universe consisted of 289,005 adult hospitalizations whose primary diagnosis was COVID-19 in five periods from 2020 to 2022. A multilevel regression model was applied, and the death predictive variables were sex, age, Charlson Index, obesity, type of admission, Brazilian Deprivation Index (BrazDep), the month of admission, and hospital size. Then, the total observed deaths and total deaths predicted by the model’s fixed effect component were aggregated by each hospital, estimating the Standardized Mortality Ratio (SMR) in each period. Funnel plots with limits of two standard deviations were employed to classify hospitals by performance (higher-than-expected, as expected, and lower-than-expected) and determine whether there was a change in category over the periods. Results A positive association was observed between hospital mortality and size (number of beds). There was greater variation in the percentage of hospitals with as-expected performance (39.5 to 76.1%) and those with lower-than-expected performance (6.6 to 32.3%). The hospitals with higher-than-expected performance remained at around 30% of the total, except in the fifth period. In the first period, 64 hospitals (18.3%) had lower-than-expected performance, with standardized mortality ratios ranging from 1.2 to 4.4, while in the last period, only 23 (6.6%) hospitals were similarly classified, with ratios ranging from 1.3 to 2.8. A trend of homogenization and adjustment to expected performance was observed over time. Conclusion Despite the study’s limitations, the results suggest an improvement in the COVID-19 inpatient care performance of hospitals reimbursed by the SUS in São Paulo over the period studied, measured by the standardized mortality ratio for hospitalizations due to COVID-19. Moreover, the methodological approach adapted to the Brazilian context provides an applicable tool to follow-up hospital’s performance in caring all or specific-cause hospitalizations, in regular or exceptional emergency situations.

Abstract We developed and validated the Influenza Severity Scale (ISS), a standardized risk assessment for influenza, to estimate and predict the probability of major clinical events in patients with laboratory-confirmed infection. Data from the Canadian Immunization Research Network’s Serious Outcomes Surveillance Network (2011/2012–2018/2019 influenza seasons) enabled the selecting of all laboratory-confirmed influenza patients. A machine learning-based approach then identified variables, generated weighted scores, and evaluated model performance. This study included 12,954 patients with laboratory-confirmed influenza infections. The optimal scale encompassed ten variables: demographic (age and sex), health history (smoking status, chronic pulmonary disease, diabetes mellitus, and influenza vaccination status), clinical presentation (cough, sputum production, and shortness of breath), and function (need for regular support for activities of daily living). As a continuous variable, the scale had an AU-ROC of 0.73 (95% CI, 0.71–0.74). Aggregated scores classified participants into three risk categories: low (ISS

Abstract Background In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany. Methods The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models. Results Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p

We developed and validated the Influenza Severity Scale (ISS), a standardized risk assessment for influenza, to estimate and predict the probability of major clinical events in patients with laboratory-confirmed infection. Data from the Canadian Immunization Research Network’s Serious Outcomes Surveillance Network (2011/2012–2018/2019 influenza seasons) enabled the selecting of all laboratory-confirmed influenza patients. A machine learning-based approach then identified variables, generated weighted scores, and evaluated model performance. This study included 12,954 patients with laboratory-confirmed influenza infections. The optimal scale encompassed ten variables: demographic (age and sex), health history (smoking status, chronic pulmonary disease, diabetes mellitus, and influenza vaccination status), clinical presentation (cough, sputum production, and shortness of breath), and function (need for regular support for activities of daily living). As a continuous variable, the scale had an AU-ROC of 0.73 (95% CI, 0.71–0.74). Aggregated scores classified participants into three risk categories: low (ISS < 30; 79.9% sensitivity, 51% specificity), moderate (ISS ≥ 30 but < 50; 54.5% sensitivity, 55.9% specificity), and high (ISS ≥ 50; 51.4% sensitivity, 80.5% specificity). ISS demonstrated a solid ability to identify patients with hospitalized laboratory-confirmed influenza at increased risk for Major Clinical Events, potentially impacting clinical practice and research. [ABSTRACT FROM AUTHOR]

Risk adjustment systems, which reallocate funds among competing health insurers, often use risk adjustors that are based on utilization. The level of utilization that triggers an adjustor—the utilization threshold—is frequently chosen implicitly and uniformly. I study utilization thresholds empirically in the setting of the US Marketplaces. I demonstrate how an explicit choice of such thresholds, tailored to each adjustor, may improve the prediction fit of the risk adjustment system and decrease the incentives to game it. Using simulations, I find that a single alternative threshold may improve the prediction fit in some disease groups by up to 14 percent. A choice of multiple utilization thresholds, guided by a regression tree algorithm, may further improve fit while taking into account the effect on gaming incentives. [ABSTRACT FROM AUTHOR]

Background: In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany. Methods: The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models. Results: Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men. Conclusions: Our data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting. [ABSTRACT FROM AUTHOR]

Healthcare quality measures are statistics that serve to evaluate healthcare providers and identify those that need to improve their care. Before using these measures in clinical practice, developers and reviewers assess measure reliability, which describes the degree to which differences in the measure values reflect actual variation in healthcare quality, as opposed to random noise. The Inter-Unit Reliability (IUR) is a popular statistic for assessing reliability, and it describes the proportion of total variation in a measure that is attributable to between-provider variation. However, Kalbfleisch et al. (Health Services and Outcomes Research Methodology, 18, 215–225, (2018)) have argued that the IUR has a severe limitation in that some of the between-provider variation may be unrelated to quality of care. In this paper, we illustrate the practical implications of this limitation through several concrete examples. We show that certain best-practices in measure development, such as careful risk adjustment and exclusion of unstable measure values, can decrease the sample IUR value. These findings uncover potential negative consequences of discarding measures with IUR values below some arbitrary threshold. [ABSTRACT FROM AUTHOR]

To build the Neiman Imaging Comorbidity Index (NICI), based on variables available in claims datasets, which provides good discrimination of an individual's chance of receiving advanced imaging (CT, MR, PET), and thus, utility as a control variable in research. This retrospective study used national commercial claims data from Optum's deidentified Clinformatics Data Mart database from the period January 1, 2018 to December 31, 2019. Individuals with continuous enrollment during this 2-year study period were included. Lasso (least absolute shrinkage and selection operator) regression was used to predict the chance of receiving advanced imaging in 2019 based on the presence of comorbidities in 2018. A numerical index was created in a development cohort (70% of the total dataset) using weights assigned to each comorbidity, based on regression β coefficients. Internal validation of assigned scores was performed in the remaining 30% of claims, with comparison to the commonly used Charlson Comorbidity Index. The final sample (development and validation cohorts) included 10,532,734 beneficiaries, of whom 2,116,348 (20.1%) received advanced imaging. After model development, the NICI included nine comorbidities. In the internal validation set, the NICI achieved good discrimination of receipt of advanced imaging with a C statistic of 0.709 (95% confidence interval [CI] 0.708-0.709), which predicted advanced imaging better than the CCI (C 0.692, 95% CI 0.691-0.692). Controlling for age and sex yielded better discrimination (C 0.748, 95% CI 0.748-0.749). The NICI is an easily calculated measure of comorbidity burden that can be used to adjust for patients' chances of receiving advanced imaging. Future work should explore external validation of the NICI. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: By assessing longitudinal associations between COVID‐19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic‐era hospital quality benchmarking requires risk‐adjustment or stratification for hospital‐level characteristics. Methods: We used hospital‐level data from the US Department of Health and Human Services including weekly total hospital and COVID‐19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID‐19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID‐19 patients), High (10%–19%), Moderate (5%–9%), and Low (<5%) COVID‐19 stress. We assessed longitudinal hospital‐level COVID‐19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core‐based statistical area (CBSA) rurality. Findings: Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID‐19 patients was 6.7%(3.6%–13.0%). 80,268/213,383 (38%) hospital‐weeks experienced Low COVID‐19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID‐19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. Conclusions: US hospitals experienced similar COVID‐19 census burdens across multiple institutional characteristics. Evidence‐based inclusion of pandemic‐era outcomes in hospital quality reporting may not require significant hospital‐level risk‐adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID‐19 census burdens and may merit hospital‐level risk‐adjustment considerations. [ABSTRACT FROM AUTHOR]

BACKGROUND: Injured children initially treated at trauma centers with high emergency department (ED) pediatric readiness have improved survival. Centers with limited resources may not be able to address all pediatric readiness deficiencies, and there currently is no evidence-based guidance for prioritizing different components of readiness. The objective of this study was to identify individual components of ED pediatric readiness associated with better-than-expected survival in US trauma centers to aid in the allocation of resources targeted at improving pediatric readiness. METHODS: This cohort study of US trauma centers used the National Trauma Data Bank (2012-2017) matched to the 2013 National Pediatric Readiness Project assessment. Adult and pediatric centers treating at least 50 injured children (younger than 18 years) and recording at least one death during the 6-year study period were included. Using a standardized risk-adjustment model for trauma, we calculated the observed-to-expected mortality ratio for each trauma center. We used bivariate analyses and multivariable linear regression to assess for associations between individual components of ED pediatric readiness and better-than-expected survival. RESULTS: Among 555 trauma centers, the observed-to-expected mortality ratios ranged from 0.07 to 4.17 (interquartile range, 0.93-1.14). Unadjusted analyses of 23 components of ED pediatric readiness showed that trauma centers with better-than-expected survival were more likely to have a validated pediatric triage tool, comprehensive quality improvement processes, a pediatric-specific disaster plan, and critical airway and resuscitation equipment (all p < 0.03). The multivariable analysis demonstrated that trauma centers with both a physician and a nurse pediatric emergency care coordinator had better-than-expected survival, but this association weakened after accounting for trauma center level. Child maltreatment policies were associated with lower-than-expected survival, particularly in Levels III to V trauma centers. CONCLUSION: Specific components of ED pediatric readiness were associated with pediatric survival among US trauma centers. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

摘要： 推进医保支付方式改革是医保高质量发展的需要，更是减轻人民群众就医负担、增进民生福祉的需要。近年来，我国多元复合式医保支付方式改革取得积极进展，同时也对医保基金管理和医疗机构管理提出了更高要求。在医疗健康大数据背景下，临床预测模型被成熟应用于药物试验等领域，在量化评估患者所患疾病风险程度、医疗资源消耗强度等方面展现出较好的能力。本文旨在借鉴医保风险调整机制和风险预测模型相关国际经验，研究临床预测模型应用于医保支付方式改革下疾病诊断相关分组和医保偿付风险调整的可行性，为进一步提高医疗机构服务质量，提升医保基金使用效能提供参考。 Abstract: Advancing the reform of medical insurance payment methods is not only a necessity for the high-quality development of medical insurance, but also a requirement for alleviating the burden of people’s medical expenses and enhancing their well-being. In recent years, China has made positive progress in the reform of diversified and composite medical insurance payment methods, which has also raised higher requirements for the management of medical insurance funds and medical institutions. Against the backdrop of health big data, clinical prediction models have been maturely applied in areas such as drug trials, demonstrating good capabilities in quantitatively assessing the risk level of patients’ diseases and the intensity of medical resource consumption. This paper aims to draw lessons from international experiences in medical insurance risk adjustment mechanisms and risk prediction models, explore the feasibility of applying clinical prediction models to diagnosis related groups and medical insurance reimbursement risk adjustment under the reform of medical insurance payment methods, and provide corresponding references for further improving the quality of medical institution services and enhancing the efficiency of medical insurance fund utilization.

Abstract Since their inception, small area studies intended to measure health system performance have been challenged by concerns that regional variation in health care may be primarily explained by differences in patient health risk. Controlling for regional population differences depends on appropriate risk adjustment, but the adequacy of the methods used in early analyses was contested. A novel response to these concerns was the development of end-of-life cohorts by Dartmouth Atlas investigators. These were used initially to control for differences in population health status in studies investigating relative efficiency across regions. Later, they became useful for studying hospital-level variation in chronic illness care, and for measuring utilization and patient experiences at the very end of life. Altogether, end-of-life cohorts have been invaluable for clarifying the contribution of health system and provider factors to health care variation and outcomes.

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Copyright of Zeitschrift für Gerontologie und Geriatrie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many community-rated health insurance markets include risk equalization (also known as risk adjustment) to mitigate risk selection incentives for competing insurers. Empirical evaluations of risk equalization typically quantify selection incentives through predictable profits and losses net of risk equalization for various groups of consumers (e.g. the healthy versus the chronically ill). The underlying assumption is that absence of predictable profits and losses implies absence of selection incentives. This paper questions this assumption. We show that even when risk equalization perfectly compensates insurers for predictable differences in mean spending between groups, selection incentives are likely to remain. The reason is that the uncertainty about residual spending (i.e., spending net of risk equalization) differs across groups, e.g., the risk of substantial losses is larger for the chronically ill than for the healthy. In a risk-rated market, insurers are likely to charge a higher profit mark-up (to cover uncertainty in residual spending) and a higher safety mark-up (to cover the risk of large losses) to chronically ill than to healthy individuals. When such differentiation is not allowed, insurers face incentives to select in favor of the healthy. Although the exact size of these selection incentives depends on contextual factors, our empirical simulations indicate they can be non-trivial. Our findings suggest that – in addition to the equalization of differences in mean spending between the healthy and the chronically ill – policy measures might be needed to diminish (or compensate insurers for) heteroscedasticity of residual spending across groups. [ABSTRACT FROM AUTHOR]

Since their inception, small area studies intended to measure health system performance have been challenged by concerns that regional variation in health care may be primarily explained by differences in patient health risk. Controlling for regional population differences depends on appropriate risk adjustment, but the adequacy of the methods used in early analyses was contested. A novel response to these concerns was the development of end-of-life cohorts by Dartmouth Atlas investigators. These were used initially to control for differences in population health status in studies investigating relative efficiency across regions. Later, they became useful for studying hospital-level variation in chronic illness care, and for measuring utilization and patient experiences at the very end of life. Altogether, end-of-life cohorts have been invaluable for clarifying the contribution of health system and provider factors to health care variation and outcomes. [ABSTRACT FROM AUTHOR]

Background: Inpatient falls in hospitals are an acknowledged indicator of quality of care. International comparisons could highlight quality improvement potential and enable cross-national learning. Key to fair cross-national comparison is the availability of a risk adjustment model validated in an international context. This study aimed to 1) ascertain that the variables of the inpatient fall risk adjustment model do not interact with country and thus can be used for risk adjustment, 2) compare the risk of falling in hospitals between Switzerland and Austria after risk adjustment. Methods: The data on inpatient falls from Swiss and Austrian acute care hospitals were collected on a single measurement day in 2017, 2018 and 2019 as part of an international multicentre cross-sectional study. Multilevel logistic regression models were used to screen for interaction effects between the patient-related fall risk factors and the countries. The risks of falling in hospital in Switzerland and in Austria were compared after applying the risk-adjustment model. Results: Data from 176 hospitals and 43,984 patients revealed an inpatient fall rate of 3.4% in Switzerland and 3.9% in Austria. Two of 15 patient-related fall risk variables showed an interaction effect with country: Patients who had fallen in the last 12 months (OR 1.49, 95% CI 1.10–2.01, p = 0.009) or had taken sedatives/psychotropic medication (OR 1.40, 95% CI 1.05–1.87, p = 0.022) had higher odds of falling in Austrian hospitals. Significantly higher odds of falling were observed in Austrian (OR 1.38, 95% CI 1.13–1.68, p = 0.002) compared to Swiss hospitals after applying the risk-adjustment model. Conclusions: Almost all patient-related fall risk factors in the model are suitable for a risk-adjusted cross-country comparison, as they do not interact with the countries. Further model validation with additional countries is warranted, particularly to assess the interaction of risk factors "fall in the last 12 months" and "sedatives/psychotropic medication intake" with country variable. The study underscores the crucial role of an appropriate risk-adjustment model in ensuring fair international comparisons of inpatient falls, as the risk-adjusted, as opposed to the non-risk-adjusted country comparison, indicated significantly higher odds of falling in Austrian compared to Swiss hospitals. [ABSTRACT FROM AUTHOR]

Risk‐adjusted control charts have been widely used in monitoring surgical quality in detecting risks of surgical performance. Most of the previous approaches focus on shifts in the location parameter as well as the existence of the scale parameter, which cannot get the full measure of the scale parameter under different levels. Ignoring the magnitude of the scale parameter, the monitoring methods cannot detect different variations of surgical mortality that is measured by scale parameter and required to reflect surgical quality improvement. The method of detecting variations in surgical quality is of interest in surgical quality improvement. This paper uses a new weighted h‐likelihood method to obtain a weighted score test for the surgical risks from the logistic model. Then an exponentially weighted moving average chart can be constructed to monitor the changes in the variance of risks, which could be of interest in practical surgical monitoring programs. Simulation results indicate that the proposed approach performs more efficiently than existing methods under various magnitudes of shifts in scale parameters on top of different pre‐set threshold stability. In addition, the application of the proposed method to real surgical data from the Surgical Outcome Monitoring and Improvement Program in Hong Kong shows the improvement and deterioration in a hospital's outcomes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Current metrics used to adjust for case mix complexity in congenital cardiac catheterization are becoming outdated due to the introduction of novel procedures, innovative technologies, and expanding patient subgroups. This study aims to develop a risk adjustment methodology introducing a novel, clinically meaningful adverse event outcome and incorporating a modern understanding of risk. METHODS: Data from diagnostic only and interventional cases with defined case types were collected for patients =18 years of age and =2.5 kg at all Congenital Cardiac Catheterization Project on Outcomes participating centers. The derivation data set consisted of cases performed from 2014 to 2017, and the validation data set consisted of cases performed from 2019 to 2020. Severity level 3 adverse events were stratified into 3 tiers by clinical impact (3a/b/c); the study outcome was clinically meaningful adverse events, severity level =3b (3bc/4/5). RESULTS: The derivation data set contained 15 224 cases, and the validation data set included 9462 cases. Clinically meaningful adverse event rates were 4.5% and 4.2% in the derivation and validation cohorts, respectively. The final risk adjustment model included age <30 days, Procedural Risk in Congenital Cardiac Catheterization risk category, and hemodynamic vulnerability score (C statistic, 0.70; Hosmer-Lemeshow P value, 0.83; Brier score, 0.042). CONCLUSIONS: CHARM II (Congenital Heart Disease Adjustment for Risk Method II) risk adjustment methodology allows for equitable comparison of clinically meaningful adverse events among institutions and operators with varying patient populations and case mix complexity performing pediatric cardiac catheterization. [ABSTRACT FROM AUTHOR]

Several years ago, the US News and World Report changed their risk-adjustment methodology, now relying almost exclusively on chronic conditions for risk adjustment. The impacts of adding selected acute conditions like pneumonia, sepsis, and electrolyte disorders ("augmented") to their current risk models ("base") for 4 specialties--cardiology, neurology, oncology, and pulmonology--on estimates of hospital performance are reported here. In the augmented models, many acute conditions were associated with substantial risks of mortality. Compared to the base models, the discrimination and calibration of the augmented models for all specialties were improved. While estimated hospital performance was highly correlated between the 2 models, the inclusion of acute conditions in risk-adjustment models meaningfully improved the predictive ability of those models and had noticeable effects on hospital performance estimates. Measures or conditions that address disease severity should always be included when risk-adjusting hospitalization outcomes, especially if the goal is provider profiling. [ABSTRACT FROM AUTHOR]

BackgroundRisk factors common to nursing home (NH) residents are potentially not fully captured by the Hospital Readmissions Reduction Program (HRRP). The unique challenges faced by hospitals that disproportionately serve NH residents who are at greater risk of readmissions have not been studied.MethodsUsing 100% Medicare Provider Analysis and Review File and the Minimum Data Set from 2010-2013, we constructed a measure of hospital share of NH-originating hospitalizations (NOHs). We defined hospital share of NOHs as the proportion of inpatient stays by patients aged 65 or older who were directly admitted from NHs. To evaluate the impact of the share of NOHs on readmission penalties, we categorized hospitals into quartiles according to their share of NOHs and estimated the differences in the adjusted penalties across hospital quartiles after accounting for hospital characteristics, market characteristics and state fixed effects. We repeated the analyses for the penalties incurred in each year between 2015 and 2019.ResultsHospitals varied substantially in the share of NOHs (median [interquartile range], 11.3% [8.2%-15.1%]), with limited variation over time. In 2015, hospitals in the highest quartile of NOH received on average 0.58% Medicare payment reduction compared to 0.44% reduction among those in the lowest quartile (32.9% higher penalties, p

Background/objective: Laparoscopic surgery for rectal cancer is challenging for novice surgeons because it requires a sharp dissection in a narrow pelvis with visual limitations. Therefore, this study aimed to analyze the learning curve and clinical outcomes of laparoscopic surgery for rectal cancer performed by a novice surgeon en route to becoming an expert. Methods: In total, 119 patients who underwent laparoscopic surgery for rectal cancer performed by a single surgeon between June 2010 and December 2019 were analyzed. A single hybrid model based on the operative time, open conversion, complications, and resection margin involvement was generated to assess the success of laparoscopic surgery. Furthermore, the learning curve was evaluated using the risk-adjusted cumulative sum (RA-CUSUM) method. Results: The learning period was categorized into three phases according to the RA-CUSUM method (phase 1, 1st–33rd cases; phase 2, 34th–84th cases; and phase 3, 85th–119th cases). Tumor size (p = 0.004), distal resection margin (p = 0.003), and the number of harvested lymph nodes (p

Objectives: Adjusting for potential confounders is crucial for producing valuable evidence in outcome studies. Although numerous studies have been published using the Korea National Health Insurance Claim Database, no study has critically reviewed the methods used to adjust for confounders. This study aimed to review these studies and suggest methods and applications to adjust for confounders. Methods: We conducted a literature search of electronic databases, including PubMed and Embase, from January 1, 2021 to December 31, 2022. In total, 278 studies were retrieved. Eligibility criteria were published in English and outcome studies. A literature search and article screening were independently performed by 2 authors and finally, 173 of 278 studies were included. Results: Thirty-nine studies used matching at the study design stage, and 171 adjusted for confounders using regression analysis or propensity scores at the analysis stage. Of these, 125 conducted regression analyses based on the study questions. Propensity score matching was the most common method involving propensity scores. A total of 171 studies included age and/or sex as confounders. Comorbidities and healthcare utilization, including medications and procedures, were used as confounders in 146 and 82 studies, respectively. Conclusions: This is the first review to address the methods and applications used to adjust for confounders in recently published studies. Our results indicate that all studies adjusted for confounders with appropriate study designs and statistical methodologies; however, a thorough understanding and careful application of confounding variables are required to avoid erroneous results.

BackgroundThe Centers for Medicare and Medicaid Services (CMS) hierarchical condition category (HCC) coding is a risk adjustment model that allows for the estimation of risk-and cost-associated with health care provision. Current models may not include key factors that fully delineate the risk associated with spine surgery.ObjectiveTo augment CMS HCC risk adjustment methodology with socioeconomic data to improve its predictive capabilities for spine surgery.MethodsThe National Inpatient Sample was queried for spinal fusion, and the data was merged with county-level coverage and socioeconomic status variables obtained from the Brookings Institute. We predicted outcomes (death, nonroutine discharge, length of stay [LOS], total charges, and perioperative complication) with pairs of hierarchical, mixed effects logistic regression models-one using CMS HCC score alone and another augmenting CMS HCC scores with demographic and socioeconomic status variables. Models were compared using receiver operating characteristic curves. Variable importance was assessed in conjunction with Wald testing for model optimization.ResultsWe analyzed 653 815 patients. Expanded models outperformed models using CMS HCC score alone for mortality, nonroutine discharge, LOS, total charges, and complications. For expanded models, variable importance analyses demonstrated that CMS HCC score was of chief importance for models of mortality, LOS, total charges, and complications. For the model of nonroutine discharge, age was the most important variable. For the model of total charges, unemployment rate was nearly as important as CMS HCC score.ConclusionThe addition of key demographic and socioeconomic characteristics substantially improves the CMS HCC risk-adjustment models when modeling spinal fusion outcomes. This finding may have important implications for payers, hospitals, and policymakers.

Depression is a prevalent and debilitating mental health condition that poses significant challenges for healthcare providers, researchers, and policymakers. The diagnostic coding specificity of depression is crucial for improving patient care, resource allocation, and health outcomes. We propose a novel approach to assess risk-adjusted coding specificity for individuals diagnosed with depression using a vast cohort of over one million inpatient hospitalizations in the United States. Considering various clinical, demographic, and socioeconomic characteristics, we develop a risk-adjusted model that assesses diagnostic coding specificity. Results demonstrate that risk-adjustment is necessary and useful to explain variability in the coding specificity of principal (AUC = 0.76) and secondary (AUC = 0.69) diagnoses. Our approach combines a multivariate logistic regression at the patient hospitalization level to extract risk-adjusted probabilities of specificity with a Poisson Binomial approach at the facility level. This method can be used to identify healthcare facilities that over- and under-specify diagnostic coding when compared to peer-defined standards of practice. [ABSTRACT FROM AUTHOR]

Background: The Georgia Quality Improvement Program (GQIP) surgical collaborative participating hospitals have shown consistently poor performance in the post-operative sepsis category of National Surgical Quality Improvement Program data as compared with national benchmarks. We aimed to compare crude versus risk-adjusted post-operative sepsis rankings to determine high and low performers amongst GQIP hospitals. Patients and Methods: The cohort included intra-abdominal general surgery patients across 10 collaborative hospitals from 2015 to 2020. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) sepsis definition was used among all hospitals for case abstraction and NSQIP data were utilized to train and validate a multivariable risk-adjustment model with post-operative sepsis as the outcome. This model was used to rank GQIP hospitals by risk-adjusted post-operative sepsis rates. Rankings between crude and risk-adjusted post-operative sepsis rankings were compared ordinally and for changes in tertile. Results: The study included 20,314 patients with 595 cases of post-operative sepsis. Crude 30-day post-operative sepsis risk among hospitals ranged from 0.81 to 5.11. When applying the risk-adjustment model which included: age, American Society of Anesthesiology class, case complexity, pre-operative pneumonia/urinary tract infection/surgical site infection, admission status, and wound class, nine of 10 hospitals were re-ranked and four hospitals changed performance tertiles. Conclusions: Inter-collaborative risk-adjusted post-operative sepsis rankings are important to present. These metrics benchmark collaborating hospitals, which facilitates best practice exchange from high to low performers. [ABSTRACT FROM AUTHOR]

Objective: To determine whether county‐level or patient‐level social risk factors are associated with patient‐reported outcomes after total knee replacement when added to the comprehensive joint replacement risk‐adjustment model. Data Sources and Study Setting: Patient and outcomes data from the Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement cohort were merged with the Social Vulnerability Index from the Centers for Disease Control and Prevention. Study Design: This prospective longitudinal cohort measured the change in patient‐reported pain and physical function from baseline to 12 months after surgery. The cohort included a nationally diverse sample of adult patients who received elective unilateral knee replacement between 2012 and 2015. Data Collection/Extraction Methods: Using a national network of over 230 surgeons in 28 states, the cohort study enrolled patients from diverse settings and collected one‐year outcomes after the surgery. Patients <65 years of age or who did not report outcomes were excluded. Principal Findings: After adjusting for clinical and demographic factors, we found patient‐reported race, education, and income were associated with patient‐reported pain or functional scores. Pain improvement was negatively associated with Black race (CI = −8.71, −3.02) and positively associated with higher annual incomes (≥$45,00) (CI = 0.07, 2.33). Functional improvement was also negatively associated with Black race (CI = −5.81, −0.35). Patients with higher educational attainment (CI = −2.35, −0.06) reported significantly less functional improvement while patients in households with three adults reported greater improvement (CI = 0.11, 4.57). We did not observe any associations between county‐level social vulnerability and change in pain or function. Conclusions: We found patient‐level social factors were associated with patient‐reported outcomes after total knee replacement, but county‐level social vulnerability was not. Our findings suggest patient‐level social factors warrant further investigation to promote health equity in patient‐reported outcomes after total knee replacement. [ABSTRACT FROM AUTHOR]

Laparoscopic surgery for rectal cancer is challenging for novice surgeons because it requires a sharp dissection in a narrow pelvis with visual limitations. Therefore, this study aimed to analyze the learning curve and clinical outcomes of laparoscopic surgery for rectal cancer performed by a novice surgeon en route to becoming an expert. In total, 119 patients who underwent laparoscopic surgery for rectal cancer performed by a single surgeon between June 2010 and December 2019 were analyzed. A single hybrid model based on the operative time, open conversion, complications, and resection margin involvement was generated to assess the success of laparoscopic surgery. Furthermore, the learning curve was evaluated using the risk-adjusted cumulative sum (RA-CUSUM) method. The learning period was categorized into three phases according to the RA-CUSUM method (phase 1, 1st–33rd cases; phase 2, 34th–84th cases; and phase 3, 85th–119th cases). Tumor size (p = 0.004), distal resection margin (p = 0.003), and the number of harvested lymph nodes (p < 0.001) significantly increased with the learning period. The time to tolerable soft diet became shorter according to the learning period (p = 0.017). Advanced T stage (p = 0.024) and adjuvant chemotherapy (p = 0.012) were more common in phase 3. This study suggested that the initial technical competence of laparoscopic surgery for rectal cancer was acquired in the 33rd case. Technical mastery was achieved in the 84th case. After mastering the technique, the surgeon tended to challenge more advanced cases, however, the complication rates did not increase. [ABSTRACT FROM AUTHOR]

Purpose: Presence of venous vascular invasion is a criterion of intermediate risk of recurrence in papillary thyroid carcinoma (PTC). However, the presence and type of vascular invasion (lymphatic or venous) is often underreported and its impact on PTCs without other risk features remains unknown. The aim of this study was to evaluate the impact of both lymphatic and venous invasion on the risk of recurrence/persistence on otherwise low-risk PTCs. Methods: Retrospective study including patients with otherwise low-risk PTCs but with vascular invasion, diagnosed between 2013 and 2019. The persistence/recurrence during the follow-up was evaluated. Pathology was reviewed to confirm the presence of lymphovascular invasion and determine the type of invasion. Results: A total of 141 patients were included. Lymphovascular invasion was confirmed in 20.6%. After surgery, 48.9% (N = 69) of the patients received radioactive iodine (RAI). The median follow-up time was 4 [3–6] years. Overall, 6 (4.2%) patients experienced persistent/recurrent disease in the neck, including 3 with lymphovascular invasion, confirmed as "only lymphatic". Overall, patients with tumors harboring lymphovascular invasion had sensibly more persistent/recurrence disease compared with those without lymphovascular invasion (10.3% vs 2.7%, p = 0.1), especially in the subgroup of patients not treated with RAI (20% vs 1.6%, p = 0.049) [OR 15.25, 95% CI 1.24-187.85, p = 0.033]. Conclusion: Lymphovascular invasion, including lymphatic invasion only, is associated with a sensibly higher risk of persistent/recurrent disease in otherwise low-risk PTCs, namely in patients not treated with RAI. Lymphatic invasion could have a role in risk-stratification systems for decision making. [ABSTRACT FROM AUTHOR]

BackgroundOutcome and utilization quality measures are adjusted for patient case-mix including demographic characteristics and comorbid conditions to allow for comparisons between hospitals and health plans. However, controversy exists around whether and how to adjust for social risk factors.ObjectiveTo assess an approach to incorporating social risk variables into a pediatric measure of utilization from the Pediatric Quality Measures Program (PQMP).MethodsWe used data from California Medicaid claims (2015-16) and Massachusetts All Payer Claims Database (2014-2015) to calculate health plan performance using measure specifications from the Pediatric Asthma Emergency Department Use measure. Health plan performance categories were assessed using mixed effect negative binomial models with and without adjustment for social risk factors, with both models adjusting for age, gender and chronic condition category. Mixed effects linear models were then used to compare patient social risk for health plans that changed performance categories to patient social risk for health plans that did not.ResultsOf 133 health plans, serving 404,649 pediatric patients with asthma, 7% to 13% changed performance categories after social risk adjustment. Health plans that moved to higher performance categories cared for lower socioeconomic status (SES) patients whereas those that moved to lower performance categories cared for higher SES patients.ConclusionsAdjustment for social risk factors changed performance rankings on the PQMP Pediatric Asthma Emergency Department Use measure for a substantial number of health plans. Some health plans caring for higher risk patients performed more poorly when social risk factors were not included in risk adjustment models. In light of this, social risk factors are incorporated into the National Quality Forum-endorsed measure; whether to incorporate social risk factors into pediatric quality measures will differ depending on the use case.

BACKGROUND: A recently developed obstetrical comorbidity scoring system enables the comparison of severe maternal morbidity rates independent of health status at the time of birth hospitalization. However, the scoring system has not been evaluated in racial-ethnic and socioeconomic groups or used to assess disparities in severe maternal morbidity. OBJECTIVE: This study aimed to evaluate the performance of an obstetrical comorbidity scoring system when applied across racial-ethnic and socioeconomic groups and to determine the effect of comorbidity score risk adjustment on disparities in severe maternal morbidity. STUDY DESIGN: We analyzed a population-based cohort of live births that occurred in California during 2011 through 2017 with linked birth certificates and birth hospitalization discharge data (n=3,308,554). We updated a previously developed comorbidity scoring system to include the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modifications diagnosis codes and applied the scoring system to subpopulations (groups) defined by race-ethnicity, nativity, payment method, and educational attainment. We then calculated the risk-adjusted rates of severe maternal morbidity (including and excluding blood transfusion-only cases) for each group and estimated the disparities for these outcomes before and after adjustment for the comorbidity score using logistic regression. RESULTS: The obstetric comorbidity scores performed consistently across groups (C-statistics ranged from 0.68 to 0.76; calibration curves demonstrated overall excellent prediction of absolute risk). All non-White groups had significantly elevated rates of severe maternal morbidity before and after risk adjustment for comorbidities when compared with the White group (1.3% before, 1.3% after) (American Indian-Alaska Native: 2.1% before, 1.8% after; Asian: 1.5% before, 1.7% after; Black: 2.5% before, 2.0% after; Latinx: 1.6% before, 1.7% after; Pacific Islander: 2.2% before, 1.9% after; and multi-race groups: 1.7% before, 1.6% after). Risk adjustment also modestly increased disparities for the foreign-born group and government insurance groups. Higher educational attainment was associated with decreased severe maternal morbidity rates, which was largely unaffected by comorbidity risk adjustment. The pattern of results was the same whether or not transfusion-only cases were included as severe maternal morbidity. CONCLUSION: These results support the use of an updated comorbidity scoring system to assess disparities in severe maternal morbidity. Disparities in severe maternal morbidity decreased in magnitude for some racial-ethnic and socioeconomic groups and increased in magnitude for other groups after adjustment for the comorbidity score.

Abstract Background In Israel, internal medicine admissions are currently reimbursed without accounting for patient complexity. This is at odds with most other developed countries and has the potential to lead to market distortions such as avoiding sicker patients. Our objective was to apply a well-known, freely available risk adjustment model, the Elixhauser model, to predict relevant outcomes among patients hospitalized on the internal medicine service of a large, Israeli tertiary-care hospital. Methods We used data from the Shaare Zedek Medical Center, a large tertiary referral hospital in Jerusalem. The study included 55,946 hospitalizations between 01.01.2016 and 31.12.2019. We modeled four patient outcomes: in-hospital mortality, escalation of care (intensive care unit (ICU) transfer, mechanical ventilation, daytime bi-level positive pressure ventilation, or vasopressors), 30-day readmission, and length of stay (LOS). We log-transformed LOS to address right skew. As is usual with the Elixhauser model, we identified 29 comorbid conditions using international classification of diseases codes, clinical modification, version 9. We derived and validated the coefficients for these 29 variables using split-sample derivation and validation. We checked model fit using c-statistics and R2, and model calibration using a Hosmer–Lemeshow test. Results The Elixhauser model achieved acceptable prediction of the three binary outcomes, with c-statistics of 0.712, 0.681, and 0.605 to predict in-hospital mortality, escalation of care, and 30-day readmission respectively. The c-statistic did not decrease in the validation set (0.707, 0.687, and 0.603, respectively), suggesting that the models are not overfitted. The model to predict log length of stay achieved an R2 of 0.102 in the derivation set and 0.101 in the validation set. The Hosmer–Lemeshow test did not suggest issues with model calibration. Conclusion We demonstrated that a freely-available risk adjustment model can achieve acceptable prediction of important clinical outcomes in a dataset of patients admitted to a large, Israeli tertiary-care hospital. This model could potentially be used as a basis for differential payment by patient complexity.

Background: Weaning rate is an important quality indicator of care for patients with prolonged mechanical ventilation (PMV). However, diverse clinical characteristics often affect the measured rate. A risk-adjusted control chart may be beneficial for assessing the quality of care. Methods: We analyzed patients with PMV who were discharged between 2018 and 2020 from a dedicated weaning unit at a medical center. We generated a formula to estimate monthly weaning rates using multivariate logistic regression for the clinical, laboratory, and physiologic characteristics upon weaning unit admission in the first two years (Phase I). We then applied both multiplicative and additive models for adjusted p-charts, displayed in both non-segmented and segmented formats, to assess whether special cause variation existed. Results: A total of 737 patients were analyzed, including 503 in Phase I and 234 in Phase II, with average weaning rates of 59.4% and 60.3%, respectively. The p-chart of crude weaning rates did not show special cause variation. Ten variables from the regression analysis were selected for the formula to predict individual weaning probability and generate estimated weaning rates in Phases I and II. For risk-adjusted p-charts, both multiplicative and additive models showed similar findings and no special cause variation. Conclusion: Risk-adjusted control charts generated using a combination of multivariate logistic regression and control chart-adjustment models may provide a feasible method to assess the quality of care in the setting of PMV with standard care protocols.

BackgroundPatients with ST-elevation myocardial infarction (STEMI) complicated by an out-of-hospital-cardiac-arrest (OHCA) may vary widely in their probability of dying. Large variation in mortality may have implications for current national efforts to benchmark operator and hospital mortality rates for coronary angiography. We aimed to build a risk-adjustment model of in-hospital mortality among OHCA survivors with concurrent STEMI.MethodsWithin the Cardiac Arrest Registry to Enhance Survival (CARES), we included adults with OHCA and STEMI who underwent emergent angiography within 2 hours of hospital arrival between January 2013 and December 2019. Using multivariable logistic regression to adjust for patient and cardiac arrest factors, we developed a risk-adjustment model for in-hospital mortality and examined variation in patients' predicted mortality.ResultsOf 2,999 patients (mean age 61.2 ± 12.0, 23.1% female, 64.6% white), 996 (33.2%) died during their hospitalization. The final risk-adjustment model included higher age (OR per 10-year increase, 1.50 [95% CI: 1.39-1.63]), unwitnessed OHCA (OR, 2.51 [1.99-3.16]), initial non-shockable rhythm [OR, 5.66 [4.52-7.13]), lack of sustained pulse for > 20 minutes (OR, 2.52 [1.88-3.36]), and longer resuscitation time (increased with each 10-minute interval) (c-statistic = 0.804 with excellent calibration). There was large variability in predicted mortality: median, 25.2%, inter-quartile-range: 14.0% to 47.8%, 10th-90th percentile: 8.2 % to 74.1%.ConclusionsIn a large national registry, we identified 5 key predictors for mortality in patients with STEMI and OHCA and found wide variability in mortality risk. Our findings suggest that current national benchmarking efforts for coronary angiography, which simply adjusts for the presence of OHCA, may not adequately capture patient case-mix severity.

BackgroundHealth status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College of Cardiology/American Heart Association Task Force proposed using the proportion of patients with preserved health status as a quality measure but not as a performance measure because risk adjustment methods were not available.MethodsWe built risk adjustment models for alive with preserved health status and for preserved health status alone in a prospective registry of outpatients with HF with reduced ejection fraction across 146 US centers between December 2015 and October 2017. Preserved health status was defined as not having a ≥5-point decrease in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 1 year. Using only patient-level characteristics, hierarchical multivariable logistic regression models were developed for 1-year outcomes and validated using data from 1 to 2 years. We examined model calibration, discrimination, and variability in sites' unadjusted and adjusted rates.ResultsAmong 3932 participants (median age [interquartile range] 68 years [59-75], 29.7% female, 75.4% White), 2703 (68.7%) were alive with preserved health status, 902 (22.9%) were alive without preserved health status, and 327 (8.3%) had died by 1 year. The final risk adjustment model for alive with preserved health status included baseline Kansas City Cardiomyopathy Questionnaire Overall Summary, age, race, employment status, annual income, body mass index, depression, atrial fibrillation, renal function, number of hospitalizations in the past 1 year, and duration of HF (optimism-corrected C statistic=0.62 with excellent calibration). Similar results were observed when deaths were ignored. The risk standardized proportion of patients alive with preserved health status across the 146 sites ranged from 62% at the 10th percentile to 75% at the 90th percentile. Variability across sites was modest and changed minimally with risk adjustment.ConclusionsThrough leveraging data from a large, outpatient, observational registry, we identified key factors to risk adjust sites' proportions of patients with preserved health status. These data lay the foundation for building quality measures that quantify treatment outcomes from patients' perspectives.