Search

Abstract Background A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods. Methods This retrospective cohort study involved 71,682 commercially insured older adults aged 55–64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM). Results Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM. Conclusions Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

Background & Aims: The hospital frailty risk score (HFRS) identifies older patients at risk of poor outcomes and may have value in cirrhosis. We compared the Charlson (CCI), Elixhauser (ECI), and cirrhosis (CirCom) comorbidity indices with the HFRS in predicting outcomes for cirrhosis hospitalisations. Methods: Using the National Inpatient Sample (quarter 4 of 2015–2019), we analysed cirrhosis hospitalisations. For each index, we described the prevalence of comorbid conditions and inpatient mortality. We compared the ability of CCI, ECI, CirCom, and HFRS to predict inpatient mortality. Raw and adjusted models predicting inpatient mortality were compared using the area under the receiver operating characteristic curve and the Akaike information criterion. Results: The cohort’s (N = 626,553) median age was 61 years (IQR 52–68 years), 60% were male, cirrhosis was caused by alcohol in 43%, and 38% had ascites. The median comorbidity scores are as follows: ECI 4 (IQR 3–6), CCI 5 (IQR 4–8), and HFRS 5.6 (IQR 3.0–8.6). The most common CirCom score was 0 + 0 (44%). Across the range of values of each index, we observed different mortality ranges: CCI 1.9–13.1%, ECI 3.2–8.7%, CirCom 4.9–13.8%, and HFRS 1.0–15.2%. An adjusted model with HFRS had the highest area under the receiver operating characteristic curve in predicting mortality (HFRS 0.782 vs. ECI 0.689, CCI 0.695, and CirCom 0.692). We observed substantial variation in mortality with HFRS within each level of CCI, ECI, and CirCom. For example, for ECI 4, mortality increased from 0.6 to 16.4%, as HFRS increased from 0 to 15. Conclusions: Comorbidity indices predict inpatient cirrhosis mortality, but HFRS performs better than CCI, ECI, and CirCom. HFRS is an ideal tool for measuring comorbidity burden and disease severity risk adjustment in cirrhosis-related administrative database studies. Impact and Implications: We compared commonly used comorbidity indices to a more recently described risk score (hospital frailty risk score [HFRS]) in patients with cirrhosis using a national sample of hospital records. Comorbid conditions are common in hospitalised patients with cirrhosis. There is significant variability in mortality across the range of each index. HFRS outperforms the Charlson comorbidity index, Elixhauser comorbidity index, and CirCom (cirrhosis-specific comorbidity scoring system) in predicting inpatient mortality. HFRS is a valuable index for risk adjustment in inpatient administrative database studies.

Background: Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research. Objective: To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays. Design: Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems. Participants: Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022. Main Measures: The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022. Key Results: In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th–75th 0.848–0.876, range 0.816–0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th–75th 0.024–0.057, range 0.006–0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction. Conclusions: An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Most of the current public health surveillance methods used in epidemiological studies to identify hotspots of diseases assume that the regional disease case counts are independently distributed and they lack the ability of adjusting for confounding covariates. This article proposes a new approach that uses a simultaneous autoregressive (SAR) model, a popular spatial regression approach, within the classical space‐time cumulative sum (CUSUM) framework for detecting changes in the spatial distribution of count data while accounting for risk factors and spatial correlation. We develop expressions for the likelihood ratio test monitoring statistics based on a SAR model with covariates, leading to the proposed space‐time CUSUM test statistic. The effectiveness of the proposed monitoring approach in detecting and identifying step shifts is studied by simulation of various shift scenarios in regional counts. A case study for monitoring regional COVID‐19 infection counts while adjusting for social vulnerability, often correlated with a community's susceptibility towards disease infection, is presented to illustrate the application of the proposed methodology in public health surveillance. [ABSTRACT FROM AUTHOR]

Standard approaches to comparing health providers' performance rely on hierarchical logistic regression models that adjust for patient characteristics at admission. Estimates from these models may be misleading when providers treat different patient populations and the models are misspecified. To address this limitation, we propose a novel profiling approach that identifies groups of providers treating similar populations of patients and then evaluates providers' performance within each group. The groups of providers are identified using a Bayesian multilevel finite mixture of general location models. To compare the performance of our proposed profiling approach to standard methods, we use patient-level data from 119 skilled nursing facilities in Massachusetts. We use simulated and observed outcome data to explore the performance of these profiling methods in different settings. In simulations, our proposed method classifies providers to groups with similar patients' admission characteristics. In addition, in the presence of limited overlap in patient characteristics across providers and misspecifications of the outcome model, the provider-level estimates obtained using our approach identified providers that under- and overperformed compared to the standard regression-based approaches more accurately. [ABSTRACT FROM AUTHOR]

Background: Overweight and obesity and their consequent morbidities are important worldwide health problems. Some research suggests excess adiposity origins may begin in fetal life, but unknown is whether this applies to infants born preterm. Objective: The objective of the study was to assess the association between small for gestational age (SGA) birth and later adiposity and height among those born preterm. Data sources: MEDLINE, EMBASE and CINAHL until October 2022. Study selection and data extraction: Studies were included if they reported anthropometric (adiposity measures and height) outcomes for participants born preterm with SGA versus non‐SGA. Screening, data extraction and risks of bias assessments were conducted in duplicate by two reviewers. Synthesis: We meta‐analysed across studies using random‐effects models and explored potential heterogeneity sources. Results: Thirty‐nine studies met the inclusion criteria. In later life, preterm SGA infants had a lower body mass index (−0.66 kg/m2, 95% CI −0.79, −0.53; 32 studies, I2 = 16.7, n = 30,346), waist circumference (−1.20 cm, 95% CI −2.17, −0.23; 13 studies, I2 = 19.4, n = 2061), lean mass (−2.62 kg, 95% CI −3.45, 1.80; 7 studies, I2 = 0, n = 205) and height (−3.85 cm, 95% CI −4.73, −2.96; 26 studies, I2 = 52.6, n = 4174) compared with those preterm infants born non‐SGA. There were no differences between preterm SGA and preterm non‐SGA groups in waist/hip ratio, body fat, body fat per cent, truncal fat per cent, fat mass index or lean mass index, although power was limited for some analyses. Studies were rated at high risk of bias due to potential residual confounding and low risk of bias in other domains. Conclusions: Compared to their preterm non‐SGA peers, preterm infants born SGA have lower BMI, waist circumference, lean body mass and height in later life. No differences in adiposity were observed between SGA preterm infants and non‐SGA preterm infants. [ABSTRACT FROM AUTHOR]

Purpose: Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a complex and high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores are used clinically for patient prognostication and outcomes risk adjustment. This study aims to create the first artificial intelligence (AI)-driven ECMO survival score to predict in-hospital mortality based on a large international patient cohort. Methods: A deep neural network, ECMO Predictive Algorithm (ECMO PAL) was trained on a retrospective cohort of 18,167 patients from the international Extracorporeal Life Support Organisation (ELSO) registry (2017–2020), and performance was measured using fivefold cross-validation. External validation was performed on all adult registry patients from 2021 (N = 5015) and compared against existing prognostication scores: SAVE, Modified SAVE, and ECMO ACCEPTS for predicting in-hospital mortality. Results: Mean age was 56.8 ± 15.1 years, with 66.7% of patients being male and 50.2% having a pre-ECMO cardiac arrest. Cross-validation demonstrated an inhospital mortality sensitivity and precision of 82.1 ± 0.2% and 77.6 ± 0.2%, respectively. Validation accuracy was only 2.8% lower than training accuracy, reducing from 75.5% to 72.7% [99% confidence interval (CI) 71.1–74.3%]. ECMO PAL accuracy outperformed the ECMO ACCEPTS (54.7%), SAVE (61.1%), and Modified SAVE (62%) scores. Conclusions: ECMO PAL is the first AI-powered ECMO survival score trained and validated on large international patient cohorts. ECMO PAL demonstrated high generalisability across ECMO regions and outperformed existing, widely used scores. Beyond ECMO, this study highlights how large international registry data can be leveraged for AI prognostication for complex critical care therapies. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Background Prenatal diagnosis of congenital heart disease has been associated with early-term delivery and cesarean delivery (CD). We implemented a multi-institutional standardized clinical assessment and management plan (SCAMP) through the University of California Fetal-Maternal Consortium. Our objective was to decrease early-term (37-39 weeks) delivery and CD in pregnancies complicated by fetal congenital heart disease using a SCAMP methodology to improve practice in a high-risk and clinically complex setting. Methods and Results University of California Fetal-Maternal Consortium site-specific management decisions were queried following SCAMP implementation. This contemporary intervention group was compared with a University of California Fetal-Maternal Consortium historical cohort. Primary outcomes were early-term delivery and CD. A total of 496 maternal-fetal dyads with prenatally diagnosed congenital heart disease were identified, 185 and 311 in the historical and intervention cohorts, respectively. Recommendation for later delivery resulted in a later gestational age at delivery (38.9 versus 38.1 weeks, P=0.01). After adjusting for maternal age and site, historical controls were more likely to have a CD (odds ratio [OR],1.8; 95% CI, 2.1-2.8; P=0.004) and more likely (OR, 2.1; 95% CI, 1.4-3.3) to have an early-term delivery than the intervention group. Vaginal delivery was recommended in 77% of the cohort, resulting in 61% vaginal deliveries versus 50% in the control cohort (P=0.03). Among pregnancies with major cardiac lesions (n=373), vaginal birth increased from 51% to 64% (P=0.008) and deliveries ≥39 weeks increased from 33% to 48% (P=0.004). Conclusions Implementation of a SCAMP decreased the rate of early-term deliveries and CD for prenatal congenital heart disease. Development of clinical pathways may help standardize care, decrease maternal risk secondary to CD, improve neonatal outcomes, and reduce healthcare costs.

BackgroundCurrent smokers undergoing lobectomy are at greater risk of complications than are former smokers. The Society of Thoracic Surgeons (STS) composite score for rating program performance for lobectomy adjusts for smoking status, a modifiable risk factor. This study examined variability in the proportion of current smokers undergoing lobectomy among STS database participants. Additionally, the study determined whether each participant's rating changed if smoking was excluded from the risk adjustment model.MethodsThis is a retrospective analysis of the STS cohort used to develop the composite score for rating program performance for lobectomy. The study summarized the variability among STS database participants for performing lobectomy on current smokers and compared star ratings developed from models with and without smoking status.ResultsThere were 24,912 patients with smoking status data: 23% current smokers, 62% former smokers, and 15% never smokers. There was significant variability among participants in the proportion of current smokers undergoing lobectomy (3% to 48.6%; P < .001). Major morbidity or mortality (composite) was greater in current smokers (12.1%) than in former smokers (8.6%) and never smokers (4.2%) (P < .001). Using the current risk adjustment model, participant star ratings were as follows: 1 star, n = 6 (3.2%); 2 stars, n = 170 (91.4%); and 3 stars, n = 10 (5.4%). When smoking status was excluded from the model, 1 participant shifted from a 2-star to a 3-star program.ConclusionsThere is substantial variability among STS database participants with regard to the proportion of current smokers undergoing lobectomy. However, exclusion of smoking status from the model did not significantly affect participant star rating.

Acquired cardiovascular conditions are a leading cause of maternal morbidity and mortality. A growing number of pregnant women have acquired and heritable cardiovascular conditions and cardiovascular risk factors. As the average age of childbearing women increases, the prevalence of acute coronary syndromes, cardiomyopathy, and other cardiovascular complications in pregnancy are also expected to increase. This document, the third of a 5-part series, aims to provide practical guidance on the management of such conditions encompassing pre-conception through acute management and considerations for delivery.

BackgroundPatient surveys are the primary tool to measure patient experiences of care. Caution must be taken when analyzing these data, as responses can be influenced by factors that do not reflect the quality of care received.ObjectivesTo provide a practical overview of adjusting patient experience survey results to address bias related to patient case-mix, extreme response tendency, and mode of survey administration.Research designWe discuss options for adjustment for biases in how people respond to patient experience surveys.ResultsCase-mix adjustment (CMA) aims to compare provider performance that would have been observed if all providers had treated the same set of patients by removing the effects of patient characteristics that vary across providers. Extreme response tendency can bias the measurement of the disparities in patient experiences even after typical CMAs, since differences in patients' use of extreme response options may affect patient experience scores when they have a skewed distribution. Survey mode may affect scores for the provider entity being evaluated (eg, hospital) more than CMA if survey mode differs at the provider level.ConclusionsIt is best practice to evaluate known source of bias when analyzing patient experience surveys. Failure to adjust for patient case-mix, extreme response tendency, and survey mode in patient experience surveys may lead to erroneous comparisons of providers.

Background Cardiac sympathetic denervation (CSD) has been used as a bailout strategy for refractory ventricular tachycardia (VT). Risk of VT recurrence in patients with scar-related monomorphic VT referred for CSD and the extent to which CSD can modify this risk is unknown. We aimed to quantify arrhythmia recurrence risk and impact of CSD in this population. Methods and Results Adjusted competing risk time to event models were developed to adjust for risk of VT recurrence and sustained VT/implantable cardioverter-defibrillator shocks after VT ablation based on patient comorbidities at the time of VT ablation. Adjusted VT and implantable cardioverter-defibrillator shock recurrence rates were estimated for the subgroup who subsequently required CSD after ablation. The expected adjusted recurrence rates were then compared with the observed rates after CSD. Data from 381 patients with scar-mediated monomorphic VT who underwent VT ablation were analyzed, excluding patients with polymorphic VT. Sixty eight patients underwent CSD for recurrent VT. CSD reduced the expected adjusted VT recurrence rate by 36% (expected rate of 5.61 versus observed rate of 3.58 per 100 person-months, P=0.01) and the sustained VT/implantable cardioverter-defibrillator shock rates by 34% (expected rate of 4.34 versus observed 2.85 per 100 person-months, P=0.03). The median number of sustained VT/implantable cardioverter-defibrillator shocks in the year before versus the year after CSD was reduced by 90% (10 versus 1, P

ObjectiveThis study aimed to describe two cases of acute respiratory distress syndrome (ARDS) secondary to novel coronavirus disease 2019 (COVID-19) in pregnant women requiring extracorporeal membrane oxygenation (ECMO), and resulting in premature delivery.Study designThe clinical course of two women hospitalized with ARDS due to COVID-19 care in our intensive care (ICU) is summarized; both participants provided consent to be included in this case series.ResultsBoth women recovered with no clinical sequelae. Neonatal outcomes were within the realm of expected for prematurity with the exception of coagulopathy. There was no vertical transmission to the neonates.ConclusionThis case series highlights that ECMO is a feasible treatment in the pregnant woman with severe COVID-19 and that delivery can be performed safely on ECMO with no additional risk to the fetus. While ECMO carries its natural risks, it should be considered a viable option during pregnancy and the postpartum period.Key points· COVID-19 may present with a more severe course in pregnancy.. · ECMO may be used in pregnant woman with severe COVID-19.. · Delivery can be performed on ECMO without added fetal risk..

BackgroundDespite increasing neonatal antibiotic stewardship efforts, understanding of interhospital variation in neonatal antibiotic use is limited.MethodsA retrospective cohort study was conducted among primarily academically affiliated hospitals participating in the Vizient Clinical Database/Resource Manager. Neonatal discharges were identified by admission age

Hepatocellular carcinoma (HCC) is a highly morbid and prevalent cancer globally. While high quality evidence for mortality benefit of HCC surveillance is lacking, early detection of HCC is likely beneficial as prognosis is highly correlated with tumor stage. High risk populations, including patients with cirrhosis and subgroups with Hepatitis B, should undergo surveillance with ultrasound ± alpha-fetoprotein (AFP) at 6-month intervals. In addition, emerging data suggest that patients with Hepatitis C cirrhosis who achieve sustained virologic response should continue surveillance. Further research is needed to determine the value of surveillance in patients with nonalcoholic fatty liver disease in the absence of cirrhosis or with advanced fibrosis of other etiologies. Newer biomarkers and models such as Lens culinaris agglutinin-reactive fraction of AFP, des-γ-carboxy prothrombin, and the GALAD score are increasingly utilized in the diagnosis and prognostication of HCC. The role of these biomarkers in surveillance is still under investigation but may potentially offer a more practical alternative to traditional image-based surveillance. Despite recommendations from multiple professional society guidelines, many at-risk patients do not receive HCC surveillance due to barriers at the patient, clinician, and health care system levels. Strategies such as implementing patient navigation services, educating clinicians about surveillance guidelines, and creating automated outreach systems, may improve surveillance rates and ultimately reduce morbidity and mortality from HCC.

Background: Historical reports suggest that infants born small for gestational age (SGA) are at increased risk for high blood pressure (BP) at older ages after adjustment for later age body size. Such adjustment may be inappropriate since adiposity is a known cause of cardiovascular and metabolic disease. Objectives: To assess the association between SGA births and later BP among preterm births, considering potential background confounders and over‐adjustment for later body size. Methods: A database search of studies up to October 2022 included MEDLINE, EMBASE and CINAHL. Studies were included if they reported BP (systolic [SBP] or diastolic [DBP]) (outcomes) for participants born preterm with SGA (exposure) or non‐SGA births. All screening, extraction steps, and risk of bias (using the Risk of Bias In Non‐randomised Studies of Interventions [ROBINS‐I] tool) were conducted in duplicate by two reviewers. Data were pooled in meta‐analysis using random‐effects models. We explored potential sources of heterogeneity. Results: We found no meaningful difference in later BP between preterm infants with and without SGA status at birth. Meta‐analysis of 25 studies showed that preterm SGA, compared to preterm non‐SGA, was not associated with higher BP at age 2 and older with mean differences for SBP 0.01 mmHg (95% CI −0.10, 0.12, I2 = 59.8%, n = 20,462) and DBP 0.01 mm Hg (95% CI −0.10, 0.12), 22 studies, (I2 = 53.0%, n = 20,182). Adjustment for current weight did not alter the results, which could be due to the lack of differences in later weight status in most of the included studies. The included studies were rated to be at risk of bias due to potential residual confounding, with a low risk of bias in other domains. Conclusions: Evidence indicates that preterm infants born SGA are not at increased risk of developing higher BP as children or as adults as compared to non‐SGA preterm infants. [ABSTRACT FROM AUTHOR]

Background: Comorbidity indices such as Charlson's (CCI) and Elixhauser's (ECI) are used to adjust the patient's care, depending on the severity of their condition. However, no study has compared these indices' ability to predict nursing-sensitive outcomes (NSOs). We compared the performance of CCI and ECI in predicting NSOs in gastric cancer patients' gastrectomy. Methods: Gastric cancer patients with gastrectomy, aged 19 years or older and admitted between 2015 and 2016, were selected from the Korea Insurance Review and Assessment Service database. We examined the relationships between NSOs and CCI or ECI while adjusting patient and hospital characteristics with logistic regression. Results: The ECI item model was the best in view of the C-statistic and Akaike Information Criterion for total NSO, physiologic/metabolic derangement, and deep vein thrombosis, while the Charlson item model was the best for upper gastrointestinal tract bleeding. For the C-statistic, the ECI item model was the best for in-hospital mortality, CNS complications, shock/cardiac arrest, urinary tract infection, pulmonary failure, and wound infection, while the CCI item model was the best for hospital-acquired pneumonia and pressure ulcers. Conclusions: In predicting 8 of 11 NSOs, the ECI item model outperformed the others. For other NSOs, the best model varies between the ECI item and CCI item model. [ABSTRACT FROM AUTHOR]

Background: A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods. Methods: This retrospective cohort study involved 71,682 commercially insured older adults aged 55–64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM). Results: Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM. Conclusions: Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US. [ABSTRACT FROM AUTHOR]

ObjectiveTo evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients.DesignRetrospective cohort study.SettingEight tertiary-care referral general hospitals in California.MethodsWe used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment.ResultsFor these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15-1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, -25%; IQR, -20% to -29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%-105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, -15%; IQR, -14% to -21%) and decreased the SIR at all hospitals (median, -8%; IQR, -4% to -11%).ConclusionsFor tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.

Introduction: Syncope is common among emergency department (ED) patients with acute pulmonary embolism (PE) and indicates a higher acuity and worse prognosis than in patients without syncope. Whether presyncope carries the same prognostic implications has not been established. We compared incidence of intensive care unit (ICU) admission in three groups of ED PE patients: those with presyncope; syncope; and neither.Methods: This retrospective cohort study included all adults with acute, objectively confirmed PE in 21 community EDs from January 2013–April 2015. We combined electronic health record extraction with manual chart abstraction. We used chi-square test for univariate comparisons and performed multivariate analysis to evaluate associations between presyncope or syncope and ICU admission from the ED, reported as adjusted odds ratios (aOR) with 95% confidence intervals (CI).Results: Among 2996 PE patients, 82 (2.7%) had presyncope and 109 (3.6%) had syncope. ICU admission was similar between groups (presyncope 18.3% vs syncope 25.7%) and different than their non-syncope counterparts (either 22.5% vs neither 4.7%; p

Background:About 5.8 million people die each year as a result of injuries, and nearly 90% of these deaths occur in low and middle-income countries (LMIC). Trauma scoring is a cornerstone of trauma quality improvement (QI) efforts, and is key to organizing and evaluating trauma services. The objective of this review was to assess the appropriateness, feasibility, and QI applicability of traditional trauma scoring systems in LMIC settings. Materials and methods:This systematic review searched PubMed, Scopus, CINAHL, and trauma-focused journals for articles describing the use of a standardized trauma scoring system to characterize holistic health status. Studies conducted in high-income countries (HIC) or describing scores for isolated anatomic locations were excluded. Data reporting a score's capacity to discriminate mortality, feasibility of implementation, or use for QI were extracted and synthesized. Results:Of the 896 articles screened, 336 were included. Over half of studies (56%) reported Glasgow Coma Scale, followed by Injury Severity Score (ISS; 51%), Abbreviated Injury Scale (AIS; 24%), Revised Trauma Score (RTS; 19%), Trauma and Injury Severity Score (TRISS; 14%), and Kampala Trauma Score (7%). While ISS was overwhelmingly predictive of mortality, 12 articles reported limited feasibility of ISS and/or AIS. RTS consistently underestimated injury severity. Over a third of articles (37%) reporting TRISS assessmentsobserved mortality that was greater than that predicted by TRISS. Several articles cited limited human resources as the key challenge to feasibility. Conclusions:The findings of this review reveal that implementing systems designed for HICs may not be relevant to the burden and resources available in LMICs. Adaptations or alternative scoring systems may be more effective. PROSPERO registration number:CRD42017064600.

Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. $\mathtt {SAS}$ programs to implement these newly proposed hospital compare models were thoroughly developed. The $\mathtt {SAS}$ programs are freely available via a GitHub (https:\\www.GitHub.com) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.

In the last few decades, the impact of chronic health conditions on health systems, as well as on the quality of life, frailty, and dependence of those affected, has been brought to light. The objective of this study was to describe the population care needs of highly complex chronic patients (HCCPs). Methods: An epidemiological observational study was conducted. Results: A total of 13,262 patients were identified, 51% of which were elderly women. Among all patients, 84.4% had received a nursing assessment related to health patterns. Three diagnoses were established in 25% of the sample: readiness for enhanced health management, impaired skin integrity, and risk for falls. There were significant differences according to age, most importantly in terms of impaired skin integrity (39% of patients under 80 years old). Risk for falls, social isolation, situational low self-esteem, chronic low self-esteem, impaired home maintenance, anxiety, ineffective health management, ineffective coping, impaired memory, insomnia, and self-care deficits were more common in those living alone. A total of 37 diagnoses featured differences according to frailty/dependence. Approximately 23% of HCCPs suffered from frail elderly syndrome. Conclusions: This study presents the most common care needs of HCCPs, describing the sociodemographic profile of this part of the population. The planning of HCCP care varies in nature. Factors such as the dependence level and frailty of these people should be taken into consideration.

The United States population is aging and increasing in comorbidities, and patient care is accordingly growing increasingly complex. Complexity impacts patterns of resource consumption, adverse event and medical error rates, health-related quality of life, physician burnout, and more. Tools capturing complexity can be of benefit in the modern value-based reimbursement landscape and have been well studied in specialties other than dermatology. In this report, we describe the validation of a tool specific to outpatient dermatologic care that captures the complexity of clinical visit medical decision making. We performed a cross-sectional retrospective study to determine the inter-rater reliability and face validity of the tool. By objectively grading a clinical encounter based on clinical complexity, there is increased awareness of opportunities to improve clinical care, and the allocation of health care costs and resources within the dermatologic community can be better assessed. [ABSTRACT FROM AUTHOR]

The National Quality Forum (NQF) evaluates healthcare performance measures for endorsement based on a broad set of criteria. We extracted data from NQF technical reports released between spring 2018 and spring 2019. Measures were commonly stewarded by federal agencies (44.29%), evaluated for maintenance (67.14%), classified as outcome (42.14%) or process (39.29%) measures, and used a statistical model for risk adjustment (48.57%). For 80% of the measures reviewed, a patient advocate was present on the reviewing committee. Validity was evaluated using face validity (65.00%) or score-level empirical validity (67.14%), and reliability was frequently evaluated using score-level testing (71.43%). Although 91.56% of all reviewed measures were endorsed, most standing committee members voted moderate rather than high support on key assessment criteria like measure validity, measure reliability, feasibility of use, and whether the measure addresses a key performance gap. Results show that although the Consensus Development Process includes multidisciplinary stakeholder input and thorough evaluations of measures, continued work to identify and describe appropriate and robust methods for reliability and validity testing is needed. Further work is needed to study the extent to which stakeholder input is truly representative of diverse viewpoints and improve processes for considering social factors when risk adjusting. [ABSTRACT FROM AUTHOR]

Objectives: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. Design: Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts. Generalized additive models (GAM) with features designed to mitigate biases introduced from documentation of admission diagnosis, Glasgow Coma Scale (GCS), and extreme vital signs were developed using clinical features representing the first 24 hours of ICU admission. Setting: eICU Research Institute database derived from ICUs participating in the Philips eICU telecritical care program. Patients: A total of 572,985 adult ICU stays discharged from the hospital between January 1, 2017, and December 31, 2018, were included, yielding 509,586 stays in the final cohort; 305,590 and 203,996 in development and validation cohorts, respectively. Interventions: None. Measurements and Main Results: Model discrimination was compared against Acute Physiology and Chronic Health Evaluation (APACHE) IVa/IVb models on the validation cohort using the area under the receiver operating characteristic (AUROC) curve. Calibration assessed by actual/predicted ratios, calibration-in-the-large statistics, and visual analysis. Performance metrics were further stratified by subgroups of admission diagnosis and ICU characteristics. Historic data from two health systems with abrupt changes in Glasgow Coma Scale (GCS) documentation were assessed in the year prior to and after data shift. CCOPM outperformed APACHE IVa/IVb for ICU mortality (AUROC, 0.925 vs 0.88) and hospital mortality (AUROC, 0.90 vs 0.86). Better calibration performance was also attained among subgroups of different admission diagnoses, ICU types, and over unique ICU-years. The CCOPM provided more stable predictions compared with APACHE IVa within an external cohort of greater than 120,000 patients from two health systems with known changes in GCS documentation. Conclusions: These mortality risk models demonstrated excellent performance compared with APACHE while appearing to mitigate bias introduced through major shifts in GCS documentation at two large health systems. This provides evidence to support using automated capture rather than trained personnel for capture of GCS data used in benchmarking ICUs on mortality performance. [ABSTRACT FROM AUTHOR]

We propose a model for risk adjustment, in the context of IFRS 17, for surrender risk. Surrender rates are assumed to follow a stochastic process, underpinned by data. The distribution of the present value of future individual cash flows is calculated. Using well-known techniques from the theory of convex ordering of stochastic variables, we present closed formula approximations of risk measures, such as quantiles, for the total portfolio. These formulas are easy to program and enable an insurance company to calculate its risk adjustment without time-consuming simulations. [ABSTRACT FROM AUTHOR]

In the last few decades, the impact of chronic health conditions on health systems, as well as on the quality of life, frailty, and dependence of those affected, has been brought to light. The objective of this study was to describe the population care needs of highly complex chronic patients (HCCPs). Methods: An epidemiological observational study was conducted. Results: A total of 13,262 patients were identified, 51% of which were elderly women. Among all patients, 84.4% had received a nursing assessment related to health patterns. Three diagnoses were established in 25% of the sample: readiness for enhanced health management, impaired skin integrity, and risk for falls. There were significant differences according to age, most importantly in terms of impaired skin integrity (39% of patients under 80 years old). Risk for falls, social isolation, situational low self-esteem, chronic low self-esteem, impaired home maintenance, anxiety, ineffective health management, ineffective coping, impaired memory, insomnia, and self-care deficits were more common in those living alone. A total of 37 diagnoses featured differences according to frailty/dependence. Approximately 23% of HCCPs suffered from frail elderly syndrome. Conclusions: This study presents the most common care needs of HCCPs, describing the sociodemographic profile of this part of the population. The planning of HCCP care varies in nature. Factors such as the dependence level and frailty of these people should be taken into consideration. [ABSTRACT FROM AUTHOR]

Many regulated health insurance markets include risk adjustment (aka risk equalization) to mitigate selection incentives for insurers. Empirical studies on the design and evaluation of risk‐adjustment algorithms typically focus on mandatory health insurance schemes. This paper considers risk adjustment in the context of voluntary health insurance, as found in Chile, Ireland, and Australia. In addition to the challenge of mitigating selection by insurers, regulators of these voluntary schemes have to deal with selection by consumers in and out of the market. A strategy for mitigating selection by consumers is to apply some form of risk rating. Our paper shows how risk adjustment and risk rating interact: (1) risk rating reduces the need for risk adjustment and (2) risk adjustment reduces premium variation across rating factors, thereby increasing incentives for consumers to select in and out of the market. [ABSTRACT FROM AUTHOR]

Clinical trial designs targeting patient subgroups with certain genetic characteristics may enhance the efficiency of developing drugs for cardiovascular disease (CVD). To evaluate the extent to which genetic knowledge translates to the CVD pipeline, we analyzed how genomic biomarkers are utilized in trials. Phase II and III trial protocols for investigational new drugs for CVD and risk factors were evaluated for prospective and exploratory genomic biomarker use; drug targets were evaluated for the presence of evidence that genetic variations can impact CVD risk or drug response. We identified 134 programs (73 unique drug targets) and 147 clinical trials. Less than 1% (n = 1/147) trials used a genomic biomarker prospectively for in-trial enrichment despite 32% (n = 23/73) of the drug targets having evidence of genetic variations. Additionally, 46% (n = 68/147) of the trials specified exploratory biomarker use. The results highlight an opportunity for more targeted CVD drug development by leveraging genomic biomarker knowledge.

ObjectiveRegionalization directs patients to high-volume hospitals for specialized care. We investigated regionalization trends and outcomes in pediatric cardiac surgery.Data sources/study settingStatewide inpatient data from eleven states between 2000 and 2012.Study designMortality, length of stay (LOS), and cost were assessed using multivariable hierarchical regression with state and year fixed effects. Primary predictor was hospital case-volume, categorized into low-, medium-, and high-volume tertiles.Data collection/extraction methodsWe used Risk Adjustment for Congenital Heart Surgery-1 (RACHS-1) to select pediatric cardiac surgery discharges.Principal findingsIn total, 2841 (8.5 percent), 8348 (25.1 percent), and 22 099 (66.4 percent) patients underwent heart surgeries in low-, medium-, and high-volume hospitals. Mortality decreased over time, but remained higher in low- and medium-volume hospitals. High-volume hospitals had lower odds of mortality and cost than low-volume hospitals (odds ratio [OR] 0.59, P

Cardiac rehabilitation (CR) is an evidence-based intervention that uses patient education, health behavior modification, and exercise training to improve secondary prevention outcomes in patients with cardiovascular disease. CR programs reduce morbidity and mortality rates in adults with ischemic heart disease, heart failure, or cardiac surgery but are significantly underused, with only a minority of eligible patients participating in CR in the United States. New delivery strategies are urgently needed to improve participation. One potential strategy is home-based CR (HBCR). In contrast to center-based CR services, which are provided in a medically supervised facility, HBCR relies on remote coaching with indirect exercise supervision and is provided mostly or entirely outside of the traditional center-based setting. Although HBCR has been successfully deployed in the United Kingdom, Canada, and other countries, most US healthcare organizations have little to no experience with such programs. The purpose of this scientific statement is to identify the core components, efficacy, strengths, limitations, evidence gaps, and research necessary to guide the future delivery of HBCR in the United States. Previous randomized trials have generated low- to moderate-strength evidence that HBCR and center-based CR can achieve similar improvements in 3- to 12-month clinical outcomes. Although HBCR appears to hold promise in expanding the use of CR to eligible patients, additional research and demonstration projects are needed to clarify, strengthen, and extend the HBCR evidence base for key subgroups, including older adults, women, underrepresented minority groups, and other higher-risk and understudied groups. In the interim, we conclude that HBCR may be a reasonable option for selected clinically stable low- to moderate-risk patients who are eligible for CR but cannot attend a traditional center-based CR program.

Abstract Background Hospital-specific template matching (HS-TM) is a newer method of hospital performance assessment. Objective To assess the interpretability, credibility, and usability of HS-TM-based vs. regression-based performance assessments. Research design We surveyed hospital leaders (January-May 2021) and completed follow-up semi-structured interviews. Surveys included four hypothetical performance assessment vignettes, with method (HS-TM, regression) and hospital mortality randomized. Subjects Nationwide Veterans Affairs Chiefs of Staff, Medicine, and Hospital Medicine. Measures Correct interpretation; self-rated confidence in interpretation; and self-rated trust in assessment (via survey). Concerns about credibility and main uses (via thematic analysis of interview transcripts). Results In total, 84 participants completed 295 survey vignettes. Respondents correctly interpreted 81.8% HS-TM vs. 56.5% regression assessments, p

Nowadays, statistical process control has been widely used to monitor processes in various fields. To monitor processes with a large number of zero observations by control charts, the zero‐inflated Poisson (ZIP) model has been adopted. Due to the heterogeneity of each sample in the process, several factors have been taken into account to predict values of two parameters in the ZIP model by risk adjustment. Instead of considering two parameters to be constant directly, risk‐adjusted ZIP control charts can provide more reasonable monitoring results than traditional ones. However, existing methods ignored the interaction between parameters in the ZIP model, which leads to some risk‐adjusted control charts unable to accurately estimate parameters to provide effective monitoring results. To address this problem, this paper presents a generalize likelihood ratio (GLR) based control chart to better monitor the risk‐adjusted ZIP process with EWMA scheme, which can detect the random shift in both parameters efficiently. In the simulation study, the proposed control chart is compared with another two existing control charts and shows superior performance on detecting various types of shifts in parameters. Finally, the proposed control chart is applied to the Hong Kong influenza datasets and the flight delay datasets to illustrate its effectiveness and utility. [ABSTRACT FROM AUTHOR]

Background Emergency surgery, compared with planned surgery, is strongly associated with increased risks of adverse postoperative outcomes owing to the short time available for diagnostic procedures, patient optimization, and surgical intervention in patients presenting with physiological derangement. Patients and methods A prospective cohort study was performed that included 118 patients. They were divided into two groups according to the outcome: group M and group S. A number of predictors were stratified into preoperative, intraoperative, and postoperative predictors and correlated with mortality. Results The most frequently encountered intraoperative finding was viscus perforation (27.1%), followed by appendicitis (25.4%). There was a mortality incidence of 31.35%. Septic shock was the most common cause of death (64.7%). Mortality was strongly correlated with a number of predictors, including age, BMI, diabetes, hypertension and liver disease, systolic blood pressure on presentation and after patient optimization, respiratory rate, Glassgo coma scale (GCS), urine output, acid-base derangement, presence of multiorgan dysfunction especially renal and cardiovascular, and coronavirus disease infection. Intraoperative and postoperative predictors included severe hypotension after induction, urine output, contamination, bleeding and need for blood transfusion, need for ICU admission, need for hemodynamic support/mechanical ventilation, need for dialysis, contractility less than 40%, postoperative lactate levels, and development of complications. Duration of symptoms, history of surgery within 30 days, length of trial of conservative management if indicated, operative time, and presence of malignancy were not shown to be significant predictors. Conclusion Executing the study has assisted in highlighting care processes that need improvement and that could be focused upon. Furthermore, it proved to be a reliable tool to be used for auditing purposes that allows risk adjusted measurement of the quality of care hence providing standardization of medical care. [ABSTRACT FROM AUTHOR]

Introduction: Value‐based purchasing requires accurate techniques to appropriately measure both outcomes and cost with robust adjustment for differences in severity of illness. Traditional methods to adjust cost estimates have exclusively used administrative data derived from billing claims to identify comorbidity and complications. Transplantation uniquely has accurate national clinical registry data that can be used to supplement administrative data. Methods: Administrative claims from the Vizient, Inc, Clinical Data Base (CDB) were linked with clinical records from the Scientific Registry for Transplant Recipients for 76 liver and 109 kidney transplant programs. Using either or both datasets, we fitted a regression model to the total direct cost of care for 16,649 kidney and 6058 liver transplants. Results: The proportion of variation explained by these risk‐adjustment models increased significantly when combined administrative and clinical data were used for kidney (administrative only R2=.069, clinical only R2=.047, combined R2=.14, p <.0001) and liver (administrative only R2=.28, clinical only R2=.25, combined R2=.33, p <.0001). Conclusion: Incorporating accurate clinical data into risk‐adjustment methodologies can improve risk adjustment methodologies; however, as majority of variation in cost remains unexplained by these risk‐adjustment models further work is needed to accuracy assess transplant value. [ABSTRACT FROM AUTHOR]

Background: Current methods of categorising the International Statistical Classification of Diseases and Related Health Problems (ICD) have limitations when deciphering administrative data and monitoring healthcare outcomes. These include many-to-one relationships, non-linear sequencing, collinearity, and ambiguous miscellaneous (residual) codes. Objective: Describe novel methodology for clinically meaningful categorisation of 12th Edition of ICD Version 10 Australian modification (ICD-10-AM). Setting: State of Victoria (Australia), population of 6.6 million with over 3 million separations per annum. Method: Diagnosis codes from ICD-10-AM were aggregated into Clinical Diagnosis Group (CDG) sets according to clinical features and associated risk of in-hospital death and complications. Residual codes were excluded. Administrative data from July 2020 to June 2023 were interrogated to ascertain frequency of diagnoses captured by CDG sets. Results: 12,716 (87.9%) of 14,470 total ICD-10-AM codes were aggregated into 406 CDG sets; mean 32 (range 1–288) codes per set. One thousand seven hundred fifty-three (12.1%) were excluded (not allocated): 775 (5.4%) residual codes; 702 (4.9%) indicating reason for healthcare encounter; and 276 (1.9%) ill-defined clinical symptom codes. Over 36-months, 11.8 million separations were coded with 11,898 (82.2%) unique ICD-10-AM diagnoses, including 10,721 (90.1%) present in a CDG set. Of the 8571 (59.2%) codes associated with death or complications, 7813 (91.2%) were present in a CDG set. Conclusion: The CDG list provides a clinically meaningful method of categorisation and interrogating datasets based on ICD-10-AM and complements existing methods. [ABSTRACT FROM AUTHOR]

There is concern that basing healthcare budgets on risk adjustment estimates derived from historical utilisation data may reinforce patterns of unmet need. We propose a method to avoid this, based on a measure of how closely local health organisations align resources to the needs of their populations. We refer to this measure as the 'responsiveness of expenditure to need' and estimate it using national person-level data on use of acute hospital and secondary mental health services in England. We find large variation in responsiveness in both services and show that higher expenditure responsiveness in mental health is associated with fewer suicides. We then re-estimate the national risk-adjustment model removing the data from the organisations with the lowest expenditure responsiveness to need. As expected, higher need individuals are estimated to have higher expenditure needs when less responsive organisations are removed from the estimation of the risk-adjustment. Removal of organisations with below-average responsiveness results in the neediest deciles of individuals having an extra £163 (7%) annual need for acute hospital care and an additional £79 (27%) annual need for mental health services. The application of this approach to risk adjustment would result in more resources being directed towards organisations serving higher-need populations. • We develop a novel measure, responsiveness of expenditure to need. • We use person-level data to estimate health organisation variation in responsiveness. • We quantify the association of responsiveness with population health outcomes. • Results in more resources directed to higher need groups. [ABSTRACT FROM AUTHOR]

Prospective payments for health care providers require adequate risk adjustment (RA) to address systematic variation in patients’ health care needs. However, the design of RA for provider payment involves many choices and difficult trade-offs between incentives for risk selection, incentives for cost control, and feasibility. Despite a growing literature, a comprehensive framework of these choices and trade-offs is lacking. This article aims to develop such a framework. Using literature review and expert consultation, we identify key design choices for RA in the context of provider payment and subsequently categorize these choices along two dimensions: (a) the choice of risk adjusters and (b) the choice of payment weights. For each design choice, we provide an overview of options, trade-offs, and key references. By making design choices and associated trade-offs explicit, our framework facilitates customizing RA design to provider payment systems, given the objectives and other characteristics of the context of interest. [ABSTRACT FROM AUTHOR]

ObjectivesTo explore effects of disease prevalence adjustment on ambulatory care-sensitive hospitalization (ACSH) rates used for quality comparisons.Data sources/study settingCounty-level hospital administrative data on adults discharged from German hospitals in 2011 and prevalence estimates based on administrative ambulatory diagnosis data were used.Study designA retrospective cross-sectional study using in- and outpatient secondary data was performed.Data collectionHospitalization data for hypertension, diabetes, heart failure, chronic obstructive pulmonary disease, and asthma were obtained from the German Diagnosis Related Groups (DRG) database. Prevalence estimates were obtained from the German Central Research Institute of Ambulatory Health Care.Principal findingsCrude hospitalization rates varied substantially across counties (coefficients of variation [CV] 28-37 percent across conditions); this variation was reduced by prevalence adjustment (CV 21-28 percent). Prevalence explained 40-50 percent of the observed variation (r = 0.65-0.70) in ACSH rates for all conditions except asthma (r = 0.07). Between 30 percent and 38 percent of areas moved into or outside condition-specific control limits with prevalence adjustment.ConclusionsUnadjusted ACSH rates should be used with caution for high-stakes public reporting as differences in prevalence may have a marked impact. Prevalence adjustment should be considered in models analyzing ACSH.

BackgroundPatients with severe chronic obstructive pulmonary disease (COPD) are at a higher risk for postoperative respiratory complications. Despite the benefits of a minimally invasive approach, laparoscopic pneumoperitoneum can substantially reduce functional residual capacity and raise alveolar dead space, potentially increasing the risk of respiratory failure which may be poorly tolerated by COPD patients. This raises controversy as to whether open techniques should be preferentially employed in this population.MethodsThe American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database from 2011 to 2014 was used to examine the clinical data from patients with COPD who electively underwent laparoscopic and open colectomy. Patients defined as having COPD demonstrated either functional disability, chronic use of bronchodilators, prior COPD-related hospitalization, or reduced forced expiratory reserve volumes on lung testing (FEV1

Abstract Aims Some risk assessment tools have been developed to categorize mortality risk in heart transplant recipients, but it is unclear whether these tools can be used interchangeable in different transplant regions. Methods and results We performed a retrospective single‐centre study in 1049 adult German heart transplant recipients under jurisdiction of Eurotransplant. Univariable and multivariable Cox regression analysis was used to generate a risk scoring system. C‐statistics were used to compare our score with a US score and a French score regarding their ability to discriminate between 1 year survivors and non‐survivors within our study cohort. Of 38 parameters assessed, seven recipient‐specific parameters [age, height, dilated cardiomyopathy (DCM), ischaemic cardiomyopathy (ICM), total bilirubin, extracorporeal membrane oxygenation (ECMO), and biventricular assist device/total artificial heart (BVAD/TAH) implant], one donor‐specific parameter (cold ischaemic time), and one recipient‐independent and donor‐independent other parameter (late transplant era) were statistically significant in predicting 1 year mortality. The initial score was generated by using the regression coefficients from the multivariable analysis as follows: 1.70 * ln age − 4.0 * ln height − 0.9 * diagnosis (= 1 if diagnosis = DCM) − 0.67 * diagnosis (= 1 if diagnosis = ICM) + 0.33 * ln total bilirubin + 1.74 * ln cold ischaemic time + 0.98 * mechanical circulatory support (MCS) implant (= 1 if MCS implant = ECMO) + 0.47 * MCS implant (= 1 of MCS implant = BVAD/TAH) − 0.66 * transplant era (= 1 if transplant era = 2017–2018). The initial score was converted into the Bad Oeynhausen (BO) score as a positive integer variable by means of the following formula: BO score = (initial score + 8) * 3. In patients scoring 2 to