Cross-cultural adaptation and psychometric validation of point-of-care outcome assessment tools in Chinese palliative care clinical practice.

BACKGROUND: A standardized national approach to routinely assessing palliative care patients helps improve patient outcomes. However, a quality improvement program-based on person centered outcomes within palliative care is lacking in Mainland China. The well-established Australian Palliative Care Outcome Collaboration (PCOC) national model improves palliative care quality. This study aimed to culturally adapt and validate three measures that form part of the PCOC program for palliative care clinical practice in China: The PCOC Symptom Assessment Scale (PCOC SAS), Palliative Care Problem Severity Scale (PCPSS), Palliative Care Phase. METHODS: A study was conducted on cross-cultural adaptation and validation of PCOC SAS, PCPSS and Palliative Care Phase, involving translation methods, cognitive interviewing, and psychometric testing through paired assessments. RESULTS: Cross-cultural adaptation highlighted the need to strengthen the link between the patient's care plan and the outcome measures to improve outcomes, and the concept of distress in PCOC SAS. Analysis of 368 paired assessments (n = 135 inpatients, 22 clinicians) demonstrated that the PCOC SAS and PCPSS had good and acceptable coherence (Cronbach's a = 0.85, 0.75 respectively). Palliative Care Phase detected patients' urgent needs. PCOC SAS and PCPSS showed fair discriminant and concurrent validity. Inter-rater reliability was fair for Palliative Care Phase (k = 0.31) and PCPSS (k = 0.23-0.30), except for PCPSS-pain, which was moderate (k = 0.53). CONCLUSIONS: The Chinese version of PCOC SAS, PCPSS, and Palliative Care Phase can be used to assess outcomes as part of routine clinical practice in Mainland China. Comprehensive clinical education regarding the assessment tools is necessary to help improve the inter-rater reliability.

Assessment highlights need for improvement in standards of development of core outcome sets for rare genetic diseases.

OBJECTIVES: A rare disease is classified as such if it affects less than one person in 2,000. The Core Outcome Set STandards for Development (COS-STAD) is a set of standards that represent the minimum recommendations to be considered in the process of core outcome set (COS) development. The aim of this study was to provide a baseline assessment of COS development standards for rare genetic diseases. STUDY DESIGN AND SETTING: Core Outcome Measures in Effectiveness Trials (COMET) database contains nearly 400 published COS studies according to the latest systematic review. Studies focusing on COS development for rare genetic diseases were eligible for inclusion and were assessed by two independent evaluators. RESULTS: Nine COS studies were included in the analysis. Eight different rare genetic diseases were investigated. None of the studies met all the standards for development. The number of standards met ranged from 6 to 10, and the median was 7. CONCLUSION: This study is the first study to assess COS-STAD for rare genetic diseases, and it highlights a great need for improvement. First in terms of numbers of rare diseases considered for COS developments, second in methodology, particularly regarding the consensus process, and third in reporting of the COS development studies.

Digital health technology derived measures: Biomarkers or clinical outcome assessments?

Digital health technologies (DHTs) present unique opportunities for clinical evidence generation but pose certain challenges. These challenges stem, in part, from existing definitions of drug development tools, which were not created with DHT-derived measures in mind. DHT-derived measures can be leveraged as either clinical outcome assessments (COAs) or as biomarkers since they share properties with both categories of drug development tools. Examples from the literature indicate a variety of applications for DHT-derived data, including capturing disease physiology, symptom tracking, or response to therapies. The distinction between the categorization of DHT-derived measures as COAs or as biomarkers can be very fine, with terminology variability among regulatory authorities. This has significant implications for integration of DHT-derived measures in clinical trials, leading to confusion regarding the evidence required to support these tools' use in drug development. There is a need to amend definitions and create clear evidentiary requirements to support broad adoption of these new and innovative tools. The biopharma industry, the technology sector, consulting businesses, academic researchers, and regulators need a dialogue via multi-stakeholder collaborations to clarify questions around DHT-derived measures, to unify definitions, and to create the foundations for evidentiary package requirements, providing a path forward to predictable results.

How is the Societal Perspective Defined in Health Technology Assessment? Guidelines from Around the Globe.

Some researchers have argued that the aim of an economic evaluation should be to offer guidance on resource allocation based on public interest from a societal perspective. The application of a societal perspective in health technology assessment (HTA), while common in many published studies, is not mandated in most countries, and there is limited discussion on what the societal perspective should encompass. This study aimed to systematically compare and contrast the HTA guidelines in different countries. HTA methods guidelines were identified through international HTA networks, such as the Professional Society for Health Economics and Outcomes Research (ISPOR) and Guide to Economic Analysis Research (GEAR). The respective HTA agencies were grouped into two categories: well-established and newly developed, based on the establishment date. Data extracted from the guidelines summarised the methodological details in the reference cases, including specifics on the societal perspective. The database search yielded 46 guidelines, and 65% explicitly considered the societal perspective. The maturity of these agencies is reflected in their attitudes towards the societal perspective; the societal perspective is defined in 73% of the guidelines of well-established agencies and only 56% of those of newly developed agencies. The guidelines from multipayer healthcare systems are more likely to consider the societal perspective. Although most guidelines from the well-established agencies recommend the inclusion of a societal perspective, the types of costs and consequences that should be included and the recommended approaches to valuing them are variable. The direct costs to family and carers were included in 73% of the societal perspective definitions, while non-health outcomes were considered in only 40%. Most HTA guidelines lack clear guidance on what to include under specific perspectives. Considering the recent advancements in economic evaluation methods, it is timely to rethink the role of the societal perspective in HTA guidelines and adopt a more comprehensive perspective to include all costs and consequences of healthcare services.

Randomized Trials With Repeatedly Measured Outcomes: Handling Irregular and Potentially Informative Assessment Times.

Randomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e., irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e., informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions.

R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 9.

In this latest update we highlight a recent International Society of Pharmacoeconomics and Outcomes Research Good Practice Report on machine learning (ML) for health economics and outcomes research. We specifically discuss use cases of ML that offer opportunities in the generation of evidence using real-world data, including improvements in the identification of study cohorts, confounder identification and adjustment and estimating treatment effect heterogeneity. Barriers to the wider adoption of ML methods are also discussed.

Outcome Assessment in Parkinson Disease Prevention Trials: Utility of Clinical and Digital Measures.

BACKGROUND AND OBJECTIVES: The prodromal phase of Parkinson disease (PD) is accompanied by subtle clinical signs that are not sufficient for diagnosis but could potentially be measured in the context of clinical trials of therapies intended to delay or prevent more definitive clinical features. The objective of this study was to review the available literature on the presence and time course of subtle motor features in prodromal PD in the context of planning for possible clinical trials. METHODS: We reviewed the available literature based on expert opinion. We considered a range of outcomes including measurement of clinical features, patient-reported outcomes, digital markers, and clinical diagnosis. RESULTS: We considered these features and measures in the context of patient stratification, intermediate outcomes, and clinically relevant end points, including phenoconversion. DISCUSSION: Substantial progress has been made in understanding how motor features evolve in the period immediately before a PD diagnosis. Digital measures hold substantial progress for measurement precision and may be additionally relevant because they can be used in naturalistic environments outside the clinic. Future studies should focus on advancing digital sensor technology and analysis and developing methods to implement available methods, particularly determination of a clinical diagnosis of PD, in a clinical trial context.

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities.

OBJECTIVES: Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods. METHODS: We used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use. RESULTS: We found that the measurement of core elements of value-health outcomes and cost-are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians' productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians' workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians' work can improve productivity but may reduce skills. CONCLUSIONS: The potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.

A Comparison of Home Health Utilization, Outcomes, and Cost Between Medicare Advantage and Traditional Medicare.

BACKGROUND: Home health use is rising rapidly in the United States as the population ages, the prevalence of chronic disease increases, and older Americans express their desire to age at home. Enrollment in Medicare Advantage (MA) plans rather than Traditional Medicare (TM) has grown as well, from 13% of total Medicare enrollment in 2004 to 39% in 2020. Despite these shifts, little is known about outcomes and costs following home health in MA as compared with TM. OBJECTIVE: The objective of this study was to measure the association of MA enrollment with outcomes and costs for patients using home health. DESIGN: This was a retrospective cohort study. PARTICIPANTS: Patients enrolled in plans offered by 1 large, national MA organization and patients enrolled in TM, with at least 1 home health visit between January 1, 2017, and June 30, 2018. EXPOSURE: MA enrollment. MAIN MEASURES: We compared the intensity of home health services and types of care delivered. The main outcome measures were hospitalization, the proportion of days in the home, and total allowed costs during the 180-day period following the first qualifying home health visit during the study period. KEY RESULTS: Among patients who used home health, our models demonstrated enrollment in MA was associated with 14%, and 6% decreased odds of 60- and 180-day hospitalization, respectively, a 12.8% and 14.7% decrease in medical costs exclusive and inclusive of home health costs, respectively, and a 0.27% increase in the proportion of days at home during the 180-day follow-up, equivalent to an additional half-day at home. There were few differences in home health care delivered for MA and TM [mean number of visits in the first episode of care (17.1 vs. 17.3) and mean visits per week (3.2 vs. 3.3)]. The mean number of visits by visit type and percent of patients with each type was similar between MA and TM as well. CONCLUSIONS: Compared with enrollment in TM, enrollment in MA was associated with improved patient-centered outcomes and lower cost and utilization, despite few differences in the way home health was delivered. These findings might be explained by structural components of MA that encourage better care management, but further investigation is needed to clarify the mechanisms by which MA enrollment may lead to higher value home health care.

Ability of the Functional Assessment in Acute Care Multidimensional Computerized Adaptive Test (FAMCAT) to Predict Discharge to Institutional Postacute Care.

OBJECTIVE: To characterize the ability of the patient-reported Functional Assessment in Acute Care Multidimensional Computerized Adaptive Test (FAMCAT) domains to predict discharge disposition when administered during acute care stays. DESIGN: Cohort study. Logistic regression models were estimated to identify the ability of FAMCAT domains to predict discharge to an institution for postacute care (PAC). SETTING: Academic medical center. PARTICIPANTS: Patients admitted to general medicine services from June 2016 to June 2019 (n = 4240). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE(S): Discharge to an institution. RESULTS: In this sample, 10.5% of patients were discharged to an institution for rehabilitation versus home. FAMCAT domain scores were highly predictive of discharge to institutional PAC. Daily Activity and Basic Mobility domains had excellent discriminative ability for discharge to an institution (c-statistic, 0.83 and 0.87, respectively). In best fit models accounting for additional characteristics, discrimination was outstanding for Daily Activity (c-statistic, 0.91; 95% confidence interval, 0.89-0.94) and Basic Mobility (c-statistic 0.92; 95% confidence interval, 0.89-0.94). CONCLUSIONS: The FAMCAT Daily Activity and Basic Mobility domains demonstrated excellent discrimination for identifying patients who discharged to an institutional setting for rehabilitation and outstanding discrimination when adjusted for salient patient factors associated with discharge disposition. Estimates obtained in this investigation are comparable to the best discrimination achieved with clinician-rated measures to identify patients who would require institutional PAC.

Impact of MELD 30-allocation policy on liver transplant outcomes in Italy.

BACKGROUND & AIMS: In Italy, since August 2014, liver transplant (LT) candidates with model for end-stage liver disease (MELD) scores ≥30 receive national allocation priority. This multicenter cohort study aims to evaluate time on the waiting list, dropout rate, and graft survival before and after introducing the macro-area sharing policy. METHODS: A total of 4,238 patients registered from 2010 to 2018 were enrolled and categorized into an ERA-1 Group (n = 2,013; before August 2014) and an ERA-2 Group (n = 2,225; during and after August 2014). A Cox proportional hazards model was used to estimate the hazard ratio (HR) of receiving a LT or death between the two eras. The Fine-Gray model was used to estimate the HR for dropout from the waiting list and graft loss, considering death as a competing risk event. A Fine-Gray model was also used to estimate risk factors of graft loss. RESULTS: Patients with MELD ≥30 had a lower median time on the waiting list (4 vs.12 days, p <0.001) and a higher probability of being transplanted (HR 2.27; 95% CI 1.78-2.90; p = 0.001) in ERA-2 compared to ERA-1. The subgroup analysis on 3,515 LTs confirmed ERA-2 (odds ratio 0.56; 95% CI 0.46-0.68; p = 0.001) as a protective factor for better graft survival rate. The protective variables for lower dropouts on the waiting list were: ERA-2, high-volume centers, no competition centers, male recipients, and hepatocellular carcinoma. The protective variables for graft loss were high-volume center and ERA-2, while MELD ≥30 remained related to a higher risk of graft loss. CONCLUSIONS: The national MELD ≥30 priority allocation was associated with improved patient outcomes, although MELD ≥30 was associated with a higher risk of graft loss. Transplant center volumes and competition among centers may have a role in recipient prioritization and outcomes. CLINICAL TRIAL NUMBER: NCT04530240 LAY SUMMARY: Italy introduced a new policy in 2014 to give national allocation priority to patients with a model for end-stage liver disease (MELD) score ≥30 (i.e. very sick patients). This policy has led to more liver transplants, fewer dropouts, and shorter waiting times for patients with MELD ≥30. However, a higher risk of graft loss still burdens these cases. Transplant center volumes and competition among centers may have a role in recipient prioritization and outcomes.

Volume of frail patients predicts outcome in frail patients after cardiac surgery.

OBJECTIVE: Recent data from major noncardiac surgery suggest that outcomes in frail patients are better predicted by a hospital's volume of frail patients specifically, rather than overall surgical volume. We sought to evaluate this "frailty volume-frailty outcome relationship" in patients undergoing cardiac surgery. METHODS: We studied 72,818 frail patients undergoing coronary artery bypass grafting or valve replacement surgery from 2010 to 2014 using the Nationwide Readmissions Database. Frailty was defined using the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnoses indicator. Multilevel logistic regression was used to assess the independent effect of frailty volume by quartile on mortality, surgical complications, failure to rescue, nonhome discharge, 30-day readmissions, length of stay, and hospital costs in frail patients. RESULTS: In comparing the highest volume quartiles with the lowest, both overall cardiac surgical volume and volume for frail patients were significantly associated with shorter length of stay and reduced costs. However, frailty volume was also independently associated with significantly reduced in-hospital mortality (odds ratio, 0.79; 95% confidence interval, 0.67-0.94; P = .006) and failure to rescue (odds ratio, 0.83; 95% confidence interval, 0.70-0.98; P = .03), whereas no such association was seen between overall volume and either mortality (odds ratio, 0.94; 95% confidence interval, 0.74-1.10; P = .43) or failure to rescue (odds ratio, 0.98; 95% confidence interval, 0.83-1.17; P = .85). Neither frailty volume nor overall volume showed any significant relationship with the rate of 30-day readmissions. CONCLUSIONS: In frail patients undergoing cardiac surgery, surgical volume of frail patients was a significant independent of predictor of in-hospital mortality and failure to rescue, whereas overall surgical volume was not. Thus, the "frailty volume-outcome relationship" superseded the traditional "volume-outcome relationship" in frail patients with cardiac disease.

Developing a measure of overall intensity of injury care: A latent class analysis.

BACKGROUND: While injury is a leading cause of death and debility in older adults, the relationship between intensity of care and trauma remains unknown. The focus of this analysis is to measure the overall intensity of care delivered to injured older adults during hospitalization. METHODS: We used Centers for Medicare and Medicaid Services Medicare fee-for-service claims data (2013-2014), to identify emergency department-based claims for moderate and severe blunt trauma in age-eligible beneficiaries. Medical procedures associated with care intensity were identified using a modified Delphi method. A latent class model was estimated using the identified procedures, intensive care unit length of stay, demographics, and injury characteristics. Clinical phenotypes for each class were explored. RESULTS: A total of 683,398 cases were classified as low- (73%), moderate- (23%), and high-intensity care (4%). Greater age and reduced injury severity were indicators of lower intensity, while males, non-Whites, and nonfall mechanisms were more common with high intensity. Intubation/mechanical ventilation was an indicator of high intensity and often occurred with at least one other procedure or an extended intensive care unit stay. CONCLUSION: This work demonstrates that, although heterogeneous, care for blunt trauma can be evaluated using a single novel measure. LEVEL OF EVIDENCE: For prognostic/epidemiological studies, level III.

Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis.

Importance: COVID-19 has disproportionately affected racial and ethnic minority groups, and race and ethnicity have been associated with disease severity. However, the association of socioeconomic determinants with racial disparities in COVID-19 outcomes remains unclear. Objective: To evaluate the association of race and ethnicity with COVID-19 outcomes and to examine the association between race, ethnicity, COVID-19 outcomes, and socioeconomic determinants. Data Sources: A systematic search of PubMed, medRxiv, bioRxiv, Embase, and the World Health Organization COVID-19 databases was performed for studies published from January 1, 2020, to January 6, 2021. Study Selection: Studies that reported data on associations between race and ethnicity and COVID-19 positivity, disease severity, and socioeconomic status were included and screened by 2 independent reviewers. Studies that did not have a satisfactory quality score were excluded. Overall, less than 1% (0.47%) of initially identified studies met selection criteria. Data Extraction and Synthesis: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Associations were assessed using adjusted and unadjusted risk ratios (RRs) and odds ratios (ORs), combined prevalence, and metaregression. Data were pooled using a random-effects model. Main Outcomes and Measures: The main measures were RRs, ORs, and combined prevalence values. Results: A total of 4 318 929 patients from 68 studies were included in this meta-analysis. Overall, 370 933 patients (8.6%) were African American, 9082 (0.2%) were American Indian or Alaska Native, 101 793 (2.4%) were Asian American, 851 392 identified as Hispanic/Latino (19.7%), 7417 (0.2%) were Pacific Islander, 1 037 996 (24.0%) were White, and 269 040 (6.2%) identified as multiracial and another race or ethnicity. In age- and sex-adjusted analyses, African American individuals (RR, 3.54; 95% CI, 1.38-9.07; P = .008) and Hispanic individuals (RR, 4.68; 95% CI, 1.28-17.20; P = .02) were the most likely to test positive for COVID-19. Asian American individuals had the highest risk of intensive care unit admission (RR, 1.93; 95% CI, 1.60-2.34, P < .001). The area deprivation index was positively correlated with mortality rates in Asian American and Hispanic individuals (P < .001). Decreased access to clinical care was positively correlated with COVID-19 positivity in Hispanic individuals (P < .001) and African American individuals (P < .001). Conclusions and Relevance: In this study, members of racial and ethnic minority groups had higher risks of COVID-19 positivity and disease severity. Furthermore, socioeconomic determinants were strongly associated with COVID-19 outcomes in racial and ethnic minority populations.

Questionnaires vs Interviews for the Assessment of Global Functional Outcomes After Traumatic Brain Injury.

Importance: An interview is considered the gold standard method of assessing global functional outcomes in clinical trials among patients with acute traumatic brain injury (TBI). However, several multicenter clinical trials have used questionnaires completed by a patient or caregiver to assess the primary end point. Objective: To examine agreement between interview and questionnaire formats for assessing TBI outcomes and to consider whether an interview has advantages. Design, Setting, and Participants: This cohort study used data from patients enrolled in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) project from December 2014 to December 2017. Data were analyzed from December 2020 to April 2021. Included patients were aged 16 years or older with TBI and a clinical indication for computed tomography imaging. Outcome assessments were completed using both an interview and a questionnaire at follow-up 3 and 6 months after injury. Exposures: Traumatic brain injury of all severities. Main Outcomes and Measures: Ratings on the Glasgow Outcome Scale-Extended (GOSE) administered as a structured interview rated by an investigator and as a questionnaire completed by patients or caregivers and scored centrally were compared, and the strength of agreement was evaluated using weighted κ statistics. Secondary outcomes included comparison of different sections of the GOSE assessments and the association of GOSE ratings with baseline factors and patient-reported mental health, health-related quality of life, and TBI symptoms. Results: Among the 3691 eligible individuals in the CENTER-TBI study, both GOSE assessment formats (interview and questionnaire) were completed by 994 individuals (26.9%) at 3 months after TBI (654 [65.8%] male; median age, 53 years [IQR, 33-66 years]) and 628 (17.0%) at 6 months (409 [65.1%] male; median age, 51 years [IQR, 31-64 years]). Outcomes of the 2 assessment methods agreed well at both 3 months (weighted κ, 0.77; 95% CI, 0.73-0.80) and 6 months (weighted κ, 0.82; 95% CI, 0.78-0.86). Furthermore, item-level agreement between the 2 methods was good for sections regarding independence in everyday activities (κ, 0.70-0.79 across both time points) and moderate for sections regarding subjective aspects of functioning such as relationships and symptoms (κ, 0.41-0.51 across both time points). Compared with questionnaires, interviews recorded more problems with work (294 [30.5%] vs 233 [24.2%] at 3 months and 161 [26.8%] vs 136 [22.7%] at 6 months), fewer limitations in social and leisure activities (330 [33.8%] vs 431 [44.1%] at 3 months and 179 [29.7%] vs 219 [36.4%] at 6 months), and more symptoms (524 [53.6%] vs 324 [33.1%] at 3 months and 291 [48.4%] vs 179 [29.8%] at 6 months). Interviewers sometimes assigned an overall rating based on judgment rather than interview scoring rules, particularly for patients with potentially unfavorable TBI outcomes. However, for both formats, correlations with baseline factors (ρ, -0.13 to 0.42) and patient-reported outcomes (ρ, 0.29 to 0.65) were similar in strength. Conclusions and Relevance: In this cohort study, GOSE ratings obtained by questionnaire and interview methods were in good agreement. The similarity of associations of the ratings obtained by both GOSE methods with baseline factors and other TBI outcome measures suggests that despite some apparent differences, the core information collected by both interviews and questionnaires was similar. The findings support the use of questionnaires in studies in which this form of contact may offer substantial practical advantages compared with interviews.

Effect of Financial Incentives for Process, Outcomes, or Both on Cholesterol Level Change: A Randomized Clinical Trial.

Importance: Financial incentives may improve health behaviors. It is unknown whether incentives are more effective if they target a key process (eg, medication adherence), an outcome (eg, low-density lipoprotein cholesterol [LDL-C] levels), or both. Objective: To determine whether financial incentives awarded daily for process (adherence to statins), awarded quarterly for outcomes (personalized LDL-C level targets), or awarded for process plus outcomes induce reductions in LDL-C levels compared with control. Design, Setting, and Participants: A randomized clinical trial was conducted from February 12, 2015, to October 3, 2018; data analysis was performed from October 4, 2018, to May 27, 2021, at the University of Pennsylvania Health System, Philadelphia. Participants included 764 adults with an active statin prescription, elevated risk of atherosclerotic cardiovascular disease, suboptimal LDL-C level, and evidence of imperfect adherence to statin medication. Interventions: Interventions lasted 12 months. All participants received a smart pill bottle to measure adherence and underwent LDL-C measurement every 3 months. In the process group, daily financial incentives were awarded for statin adherence. In the outcomes group, participants received incentives for achieving or sustaining at least a quarterly 10-mg/dL LDL-C level reduction. The process plus outcomes group participants were eligible for incentives split between statin adherence and quarterly LDL-C level targets. Main Outcomes and Measures: Change in LDL-C level from baseline to 12 months, determined using intention-to-treat analysis. Results: Of the 764 participants, 390 were women (51.2%); mean (SD) age was 62.4 (10.0) years, 310 (40.6%) had diabetes, 298 (39.0%) had hypertension, and mean (SD) baseline LDL-C level was 138.8 (37.6) mg/dL. Mean LDL-C level reductions from baseline to 12 months were -36.9 mg/dL (95% CI, -42.0 to -31.9 mg/dL) among control participants, -40.0 mg/dL (95% CI, -44.7 to -35.4 mg/dL) among process participants, -41.6 mg/dL (95% CI, -46.3 to -37.0 mg/dL) among outcomes participants, and -42.8 mg/dL (95% CI, -47.4 to -38.1 mg/dL) among process plus outcomes participants. In exploratory analysis among participants with diabetes and hypertension, no spillover effects of incentives were detected compared with the control group on hemoglobin A1c level and blood pressure over 12 months. Conclusions and Relevance: In this randomized clinical trial, process-, outcomes-, or process plus outcomes-based financial incentives did not improve LDL-C levels vs control. Trial Registration: ClinicalTrials.gov Identifier: NCT02246959.

Association of the All-Patient Refined Diagnosis-Related Groups Severity of Illness and Risk of Mortality Classification with Outcomes.

OBJECTIVES: Diagnosis-related groups (DRGs) is a patient classification system used to characterize the types of patients that the hospital manages and to compare the resources needed during hospitalization. The DRG classification is based on International Classification of Diseases diagnoses, procedures, demographics, discharge status, and complications or comorbidities and compares hospital resources and outcomes used to determine how much Medicare pays the hospital for each "product/medical condition." The All-Patient Refined DRG (APR-DRG) incorporated severity of illness (SOI) and risk of mortality (ROM) into the DRG system to adjust for patient complexity to compare resource utilization, complication rates, and lengths of stay. METHODS: This study included 18,478 adult patients admitted to a tertiary care center in Lubbock, Texas during a 1-year period. We recorded the APR-DRG SOI and ROM and some clinical information on these patients, including age, sex, admission shock index, admission glucose and lactate levels, diagnoses based on International Classification of Diseases, Tenth Revision discharge coding, length of stay, and mortality. We compared the levels of SOI and ROM across this clinical information. RESULTS: As the levels of SOI and ROM increase (which indicates increased disease severity and risk of mortality), age, glucose levels, lactate levels, shock index, length of stay, and mortality increased significantly (P < 0.001). Multiple logistic regression analysis demonstrated that each unit increase in ROM and SOI level was significantly associated with an 11.45 and a 10.37 times increase in the odds of in-hospital mortality, respectively. The C-statistics for the corresponding models are 0.947 and 0.929, respectively. When both ROM and SOI were included in the model, the magnitudes of increase in odds of in-hospital mortality were 5.61 and 1.17 times for ROM and SOI, respectively. The C-statistic is 0.949. CONCLUSIONS: This study indicates that the APR-DRG SOI and ROM scores provide a classification system that is associated with mortality and correlates with other clinical variables, such as the shock index and lactate levels, which are available on admission.

Associations Between Socioeconomic Status, Patient Risk, and Short-Term Intensive Care Outcomes.

OBJECTIVES: To investigate the association of socioeconomic status as measured by the average socioeconomic status of the area where a person resides on short-term mortality in adults admitted to an ICU in Queensland, Australia. DESIGN: Secondary data analysis using de-identified data from the Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation linked to the publicly available area-level Index of Relative Socioeconomic Advantage and Disadvantage from the Australian Bureau of Statistics. SETTING: Adult ICUs from 35 hospitals in Queensland, Australia, from 2006 to 2015. PATIENTS: A total of 218,462 patient admissions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The outcome measure was inhospital mortality. The main study variable was decile of Index of Relative Socioeconomic Advantage and Disadvantage. The overall crude inhospital mortality was 7.8%; 9% in the most disadvantaged decile and 6.9% in the most advantaged decile (p < 0.001). Increasing socioeconomic disadvantage was associated with increasing severity of illness as measured by Acute Physiology and Chronic Health Evaluation III score, admission with a diagnosis of sepsis or trauma, cardiac, respiratory, renal, and hepatic comorbidities, and remote location. Increasing socioeconomic advantage was associated with elective surgical admission, hematological and oncology comorbidities, and admission to a private hospital (all p < 0.001). After excluding patients admitted after elective surgery, in the remaining 106,843 patients, the inhospital mortality was 13.6%, 13.3% in the most disadvantaged, and 14.1% in the most advantaged. There was no trend in mortality across deciles of socioeconomic status after excluding elective surgery patients. In the logistic regression model adjusting for severity of illness and diagnosis, there was no statistically significant difference in the odds ratio of inhospital mortality for the most disadvantaged decile compared with other deciles. This suggests variables used for risk adjustment may lie on the causal pathway between socioeconomic status and outcome in ICU patients. CONCLUSIONS: Socioeconomic status as defined as Index of Relative Socioeconomic Advantage and Disadvantage of the area in which a patient lives was associated with ICU admission diagnosis, comorbidities, severity of illness, and crude inhospital mortality in this study. Socioeconomic status was not associated with inhospital mortality after excluding elective surgical patients or when adjusted for severity of illness and admission diagnosis. Commonly used measures for risk adjustment in intensive care improve understanding of the pathway between socioeconomic status and outcomes.

Assessment of 2-Year Neurodevelopmental Outcomes in Extremely Preterm Infants Receiving Opioids and Benzodiazepines.

Importance: Extremely preterm (EP) infants frequently receive opioids and/or benzodiazepines, but these drugs' association with neurodevelopmental outcomes is poorly understood. Objectives: To describe the use of opioids and benzodiazepines in EP infants during neonatal intensive care unit (NICU) hospitalization and to explore these drugs' association with neurodevelopmental outcomes at 2 years' corrected age. Design, Setting, and Participants: This cohort study was a secondary analysis of data from the Preterm Erythropoietin Neuroprotection (PENUT) Trial, which was conducted among infants born between gestational ages of 24 weeks, 0 days, and 27 weeks, 6 days. Infants received care at 19 sites in the United States, and data were collected from December 2013 to September 2016. Data analysis for this study was conducted from March to December 2020. Exposures: Short (ie, ≤7 days) and prolonged (ie, >7 days) exposure to opioids and/or benzodiazepines during NICU stay. Main Outcomes and Measures: Cognitive, language, and motor development scores were assessed using the Bayley Scales of Infant Development-Third Edition (BSID-III). Results: There were 936 EP infants (448 [48%] female infants; 611 [65%] White infants; mean [SD] gestational age, 181 [8] days) included in the study, and 692 (74%) had neurodevelopmental outcome data available. Overall, 158 infants (17%) were not exposed to any drugs of interest, 297 (32%) received either opioids or benzodiazepines, and 481 (51%) received both. Infants exposed to both had adjusted odds ratios of 9.7 (95% CI, 2.9 to 32.2) for necrotizing enterocolitis and 1.7 (95% CI, 1.1 to 2.7) for severe bronchopulmonary dysplasia; they also had a longer estimated adjusted mean difference in length of stay of 34.2 (95% CI, 26.2 to 42.2) days compared with those who received neither drug. After adjusting for site and propensity scores derived for each exposure category, infants exposed to opioids and benzodiazepines had lower BSID-III cognitive, motor, and language scores compared with infants with no exposure (eg, estimated difference in mean scores on cognitive scale: -5.72; 95% CI, -8.88 to -2.57). Prolonged exposure to morphine, fentanyl, midazolam, or lorazepam was associated with lower BSID-III scores compared with infants without exposure (median [interquartile range] motor score, 85 [73-97] vs 97 [91-107]). In contrast, BSID-III scores for infants with short exposure to both opioids and benzodiazepines were not different than those of infants without exposure. Conclusions and Relevance: In this study, prolonged combined use of opioids and benzodiazepines was associated with a risk of poorer neurodevelopmental outcomes as measured by BSID-III at 2 years' corrected age.