Associations between specialty care and improved outcomes among patients with diabetic foot ulcers.

OBJECTIVE: Specialty care may improve diabetic foot ulcer outcomes. Medically underserved populations receive less specialty care. We aimed to determine the association between specialty care and ulcer progression, major amputation, or death. If a beneficial association is found, increasing access to specialty care might help advance health equity. RESEARCH DESIGN AND METHODS: We retrospectively analyzed a cohort of Wisconsin and Illinois Medicare patients with diabetic foot ulcers (n = 55,409), stratified by ulcer severity (i.e., early stage, osteomyelitis, or gangrene). Within each stratum, we constructed Kaplan-Meier curves for event-free survival, defining events as: ulcer progression, major amputation, or death. Patients were grouped based on whether they received specialty care from at least one of six disciplines: endocrinology, infectious disease, orthopedic surgery, plastic surgery, podiatry, and vascular surgery. Multivariate Cox proportional hazard models estimated the association between specialty care and event-free survival, adjusting for sociodemographic factors and comorbidities, and stratifying on ulcer severity. RESULTS: Patients who received specialty care had longer event-free survival compared to those who did not (log-rank p<0.001 for all ulcer severity strata). After adjusting, receipt of specialty care, compared to never, remained associated with improved outcomes for all ulcer severities (early stage adjusted hazard ratio 0.34, 95% CI 0.33-0.35, p<0.001; osteomyelitis aHR 0.22, 95% CI 0.20-0.23, p<0.001; gangrene aHR 0.22, 95% CI 0.20-0.24, p<0.001). CONCLUSIONS: Specialty care was associated with longer event-free survivals for patients with diabetic foot ulcers. Increased, equitable access to specialty care might improve diabetic foot ulcer outcomes and disparities.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems.

MAIN OBJECTIVE: There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic. STUDY DESIGN AND METHODS: University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission. RESULTS AND SIGNIFICANCE: The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months. CONCLUSIONS: Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

Impactability Modeling for Reducing Medicare Accountable Care Organization Payments and Hospital Events in High-Need High-Cost Patients: Longitudinal Cohort Study.

BACKGROUND: Impactability modeling promises to help solve the nationwide crisis in caring for high-need high-cost patients by matching specific case management programs with patients using a "benefit" or "impactability" score, but there are limitations in tailoring each model to a specific program and population. OBJECTIVE: We evaluated the impact on Medicare accountable care organization savings from developing a benefit score for patients enrolled in a historic case management program, prospectively implementing the score, and evaluating the results in a new case management program. METHODS: We conducted a longitudinal cohort study of 76,140 patients in a Medicare accountable care organization with multiple before-and-after measures of the outcome, using linked electronic health records and Medicare claims data from 2012 to 2019. There were 489 patients in the historic case management program, with 1550 matched comparison patients, and 830 patients in the new program, with 2368 matched comparison patients. The historic program targeted high-risk patients and assigned a centrally located registered nurse and social worker to each patient. The new program targeted high- and moderate-risk patients and assigned a nurse physically located in a primary care clinic. Our primary outcomes were any unplanned hospital events (admissions, observation stays, and emergency department visits), count of event-days, and Medicare payments. RESULTS: In the historic program, as expected, high-benefit patients enrolled in case management had fewer events, fewer event-days, and an average US $1.15 million reduction in Medicare payments per 100 patients over the subsequent year when compared with the findings in matched comparison patients. For the new program, high-benefit high-risk patients enrolled in case management had fewer events, while high-benefit moderate-risk patients enrolled in case management did not differ from matched comparison patients. CONCLUSIONS: Although there was evidence that a benefit score could be extended to a new case management program for similar (ie, high-risk) patients, there was no evidence that it could be extended to a moderate-risk population. Extending a score to a new program and population should include evaluation of program outcomes within key subgroups. With increased attention on value-based care, policy makers and measure developers should consider ways to incorporate impactability modeling into program design and evaluation.

Risk from delayed or missed care and non-COVID-19 outcomes for older patients with chronic conditions during the pandemic.

BACKGROUND: During the COVID-19 pandemic, patients with chronic illnesses avoided regular medical care, raising concerns about long-term complications. Our objective was to identify a population of older patients with chronic conditions who may be at risk from delayed or missed care (DMC) and follow their non-COVID outcomes during the pandemic. METHODS: We used a retrospective matched cohort design using Medicare claims and electronic health records at a large health system with community and academic clinics. Participants included 14,406 patients over 65 years old with two or more chronic conditions who had 1 year of baseline data and up to 9 months of postpandemic follow-up from March 1, 2019 to December 31, 2020; and 14,406 matched comparison patients from 1 year prior. Risk from DMC was defined by 13 indicators, including chronic conditions, frailty, disability affecting the use of telehealth, recent unplanned acute care, prior missed outpatient care, and social determinants of health. Outcomes included mortality, inpatient events, Medicare payments, and primary care and specialty care visits (in-person and telehealth). RESULTS: A total of 25% of patients had four or more indicators for risk from DMC. Per 1000 patients annually, those with four or more indicators had increased mortality of 19 patients (95% confidence interval, 4 to 32) and decreased utilization, including unplanned events (-496 events, -611 to -381) and primary care visits (-1578 visits, -1793 to -1401). CONCLUSIONS: Older patients who had four or more indicators for risk from DMC had higher mortality and steep declines in inpatient and outpatient utilization during the pandemic.

Sufficient dimension reduction for populations with structured heterogeneity.

A key challenge in building effective regression models for large and diverse populations is accounting for patient heterogeneity. An example of such heterogeneity is in health system risk modeling efforts where different combinations of comorbidities fundamentally alter the relationship between covariates and health outcomes. Accounting for heterogeneity arising combinations of factors can yield more accurate and interpretable regression models. Yet, in the presence of high-dimensional covariates, accounting for this type of heterogeneity can exacerbate estimation difficulties even with large sample sizes. To handle these issues, we propose a flexible and interpretable risk modeling approach based on semiparametric sufficient dimension reduction. The approach accounts for patient heterogeneity, borrows strength in estimation across related subpopulations to improve both estimation efficiency and interpretability, and can serve as a useful exploratory tool or as a powerful predictive model. In simulated examples, we show that our approach often improves estimation performance in the presence of heterogeneity and is quite robust to deviations from its key underlying assumptions. We demonstrate our approach in an analysis of hospital admission risk for a large health system and demonstrate its predictive power when tested on further follow-up data.

One Quarter of Medicare Hospitalizations in Patients with Systemic Lupus Erythematosus Readmitted within Thirty Days.

OBJECTIVE: Thirty-day hospital readmissions in systemic lupus erythematosus (SLE) approach proportions in Medicare-reported conditions including heart failure (HF). We compared adjusted 30-day readmission and mortality among SLE, HF, and general Medicare to assess predictors informing readmission prevention. METHODS: This database study used a 20% sample of all US Medicare 2014 adult hospitalizations to compare risk of 30-day readmission and mortality among admissions with SLE, HF, and neither per discharge diagnoses (if both SLE and HF, classified as SLE). Inclusion required live discharge and ≥12 months of Medicare A/B before admission to assess baseline covariates including patient, geographic, and hospital factors. Analysis used observed and predicted probabilities, and multivariable GEE models clustered by patient to report adjusted risk ratios (ARRs) of 30-day readmission and mortality. RESULTS: SLE admissions (n=10,868) were younger, predominantly female, more likely to be Black, disabled, and have Medicaid or end-stage renal disease (ESRD). Observed 30-day readmissions of 24% were identical for SLE and HF (p = 0.6), and higher than other Medicare (16%, p < 0.001). Both SLE and HF had elevated readmission risk (ARR 1.08, (95% CI (1.04, 1.13)); 1.11, (1.09, 1.13)). SLE readmissions were higher for Black (30%) versus White (21%) populations, and highest in ages 18-33 (39%) and ESRD (37%). Admissions of Black patients with SLE from least disadvantaged neighborhoods had highest 30-day mortality (9% versus 3% White). CONCLUSION: Thirty-day SLE readmissions rivaled HF at 24%. Readmission prevention programs should engage young, ESRD patients with SLE and examine potential causal gaps in SLE care and transitions.

Thirty-Day Re-observation, Chronic Re-observation, and Neighborhood Disadvantage.

OBJECTIVE: To determine whether neighborhood socioeconomic disadvantage, as determined by the Area Deprivation Index, increases 30-day hospital re-observation risk. PARTICIPANTS AND METHODS: This retrospective study of 20% Medicare fee-for-service beneficiary observation stays from January 1, 2014, to November 30, 2014, included 319,980 stays among 273,308 beneficiaries. We evaluated risk for a 30-day re-observation following an index observation stay for those living in the 15% most disadvantaged compared with the 85% least disadvantaged neighborhoods. RESULTS: Overall, 4.5% (270,600 of 6,080,664) of beneficiaries had index observation stays, which varied by disadvantage (4.3% [232,568 of 5,398,311] in the least disadvantaged 85% compared with 5.6% [38,032 of 682,353] in the most disadvantaged 15%). Patients in the most disadvantaged neighborhoods had a higher 30-day re-observation rate (2857 of 41,975; 6.8%) compared with least disadvantaged neighborhoods (13,543 of 278,005; 4.9%); a 43% increased risk (unadjusted odds ratio [OR], 1.43; 95% CI, 1.31 to 1.55). After adjustment, this risk remained (adjusted OR, 1.13; 95% CI, 1.04 to 1.22). Discharge to a skilled nursing facility reduced 30-day re-observation risk (OR, 0.63; 95% CI, 0.57 to 0.69), whereas index observation length of stay of 4 or more days (3 midnights) conferred increased risk (OR, 1.29; 95% CI, 1.09 to 1.52); those living in disadvantaged neighborhoods were less likely to discharge to skilled nursing facilities and more likely to have long index stays. Beneficiaries with more than one 30-day re-observation (chronic re-observation) had progressively greater disadvantage by number of stays (adjusted incident rate ratio, 1.08; 95% CI, 1.02 to 1.14). Observation prevalence varied nationally. CONCLUSION: Thirty-day re-observation, especially chronic re-observation, is highly associated with socioeconomic neighborhood disadvantage, even after accounting for factors such as race, disability, and Medicaid eligibility. Beneficiaries least able to pay are potentially most vulnerable to costs from serial re-observations and challenges of Medicare observation policy, which may discourage patients from seeking necessary care.

The importance of health insurance claims data in creating learning health systems: evaluating care for high-need high-cost patients using the National Patient-Centered Clinical Research Network (PCORNet).

OBJECTIVE: Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative effectiveness research for high-need high-cost patients, limited access to claims may negatively affect the quality of the matching process. We sought to determine whether the evaluation of programs for high-need high-cost patients required claims data to match cases to comparison patients. MATERIALS AND METHODS: A retrospective cohort study design with multiple measures of before-and-after health care utilization for 1935 case management patients and 3833 matched comparison patients aged 18 years and older from 2011 to 2015. EHR and claims data were extracted from 3 health systems participating in PCORNet. RESULTS: Without matching on claims-based health care utilization, the case management programs at 2 of 3 health systems were associated with fewer hospital admissions and emergency visits over the subsequent 12 months. With matching on claims-based health care utilization, case management was no longer associated with admissions and emergency visits at those 2 programs. DISCUSSION: The results of a PCORNet-facilitated evaluation of 3 programs for high-need high-cost patients differed substantially depending on whether claims data were available for matching cases to comparison patients. CONCLUSIONS: Partnering with learning health systems to rapidly evaluate programs for high-need high-cost patients will require that PCORNet facilitates comprehensive and timely access to both electronic health records and health insurance claims data.

Risk prediction for heterogeneous populations with application to hospital admission prediction.

This article is motivated by the increasing need to model risk for large hospital and health care systems that provide services to diverse and complex patients. Often, heterogeneity across a population is determined by a set of factors such as chronic conditions. When these stratifying factors result in overlapping subpopulations, it is likely that the covariate effects for the overlapping groups have some similarity. We exploit this similarity by imposing structural constraints on the importance of variables in predicting outcomes such as hospital admission. Our basic assumption is that if a variable is important for a subpopulation with one of the chronic conditions, then it should be important for the subpopulation with both conditions. However, a variable can be important for the subpopulation with two particular chronic conditions but not for the subpopulations of people with just one of those two conditions. This assumption and its generalization to more conditions are reasonable and aid greatly in borrowing strength across the subpopulations. We prove an oracle property for our estimation method and show that even when the structural assumptions are misspecified, our method will still include all of the truly nonzero variables in large samples. We demonstrate impressive performance of our method in extensive numerical studies and on an application in hospital admission prediction and validation for the Medicare population of a large health care provider.

Risk of Advanced Neoplasia Using the National Cancer Institute's Colorectal Cancer Risk Assessment Tool.

Background: There is no validated, discriminating, and easy-to-apply tool for estimating risk of colorectal neoplasia. We studied whether the National Cancer Institute's (NCI's) Colorectal Cancer (CRC) Risk Assessment Tool, which estimates future CRC risk, could estimate current risk for advanced colorectal neoplasia among average-risk persons. Methods: This cross-sectional study involved individuals age 50 to 80 years undergoing first-time screening colonoscopy. We measured medical and family history, lifestyle information, and physical measures and calculated each person's future CRC risk using the NCI tool's logistic regression equation. We related quintiles of future CRC risk to the current risk of advanced neoplasia (sessile serrated polyp or tubular adenoma ≥ 1 cm, a polyp with villous histology or high-grade dysplasia, or CRC). All statistical tests were two-sided. Results: For 4457 (98.5%) with complete data (mean age = 57.2 years, SD = 6.6 years, 51.7% women), advanced neoplasia prevalence was 8.26%. Based on quintiles of five-year estimated absolute CRC risk, current risks of advanced neoplasia were 2.1% (95% confidence interval [CI] = 1.3% to 3.3%), 4.8% (95% CI = 3.5% to 6.4%), 6.4% (95% CI = 4.9% to 8.2%), 10.0% (95% CI = 8.1% to 12.1%), and 17.6% (95% CI = 15.5% to 20.6%; P < .001). For quintiles of estimated 10-year CRC risk, corresponding current risks for advanced neoplasia were 2.2% (95% CI = 1.4% to 3.5%), 4.8% (95% CI = 3.5% to 6.4%), 6.5% (95% CI = 5.0% to 8.3%), 9.3% (95% CI = 7.5% to 11.4%), and 18.4% (95% CI = 15.9% to 21.1%; P < .001). Among persons with an estimated five-year CRC risk above the median, current risk for advanced neoplasia was 12.8%, compared with 3.7% among those below the median (relative risk = 3.4, 95 CI = 2.7 to 4.4). Conclusions: The NCI's Risk Assessment Tool, which estimates future CRC risk, may be used to estimate current risk for advanced neoplasia, making it potentially useful for tailoring and improving CRC screening efficiency among average-risk persons.

Radiation Dose Escalation in Esophageal Cancer Revisited: A Contemporary Analysis of the National Cancer Data Base, 2004 to 2012.

PURPOSE: To evaluate the effect of radiation dose escalation on overall survival (OS) for patients with nonmetastatic esophageal cancer treated with concurrent radiation and chemotherapy. METHODS AND MATERIALS: Patients diagnosed with stage I to III esophageal cancer treated from 2004 to 2012 were identified from the National Cancer Data Base. Patients who received concurrent radiation and chemotherapy with radiation doses of ≥50 Gy and did not undergo surgery were included. OS was compared using Cox proportional hazards regression and propensity score matching. RESULTS: A total of 6854 patients were included; 3821 (55.7%) received 50 to 50.4 Gy and 3033 (44.3%) received doses >50.4 Gy. Univariate analysis revealed no significant difference in OS between patients receiving 50 to 50.4 Gy and those receiving >50.4 Gy (P=.53). The dose analysis, binned as 50 to 50.4, 51 to 54, 55 to 60, and >60 Gy, revealed no appreciable difference in OS within any group compared with 50 to 50.4 Gy. Subgroup analyses investigating the effect of dose escalation by histologic type and in the setting of intensity modulated radiation therapy also failed to reveal a benefit. Propensity score matching confirmed the absence of a statistically significant difference in OS among the dose levels. The factors associated with improved OS on multivariable analysis included female sex, lower Charlson-Deyo comorbidity score, private insurance, cervical/upper esophagus location, squamous cell histologic type, lower T stage, and node-negative status (P<.01 for all analyses). CONCLUSIONS: In this large national cohort, dose escalation >50.4 Gy did not result in improved OS among patients with stage I to III esophageal cancer treated with definitive concurrent radiation and chemotherapy. These data suggest that despite advanced contemporary treatment techniques, OS for patients with esophageal cancer remains unaltered by escalation of radiation dose >50.4 Gy, consistent with the results of the INT-0123 trial. Furthermore, these data highlight that many radiation oncologists have not embraced the concept that dose escalation does not improve OS. Although local control, not investigated in the present study, might benefit from dose escalation, novel therapies are needed to improve the OS of patients with esophageal cancer.

Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.

In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well-documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort.

Hospice Enrollment, Local Hospice Utilization Patterns, and Rehospitalization in Medicare Patients.

BACKGROUND: Rehospitalizations are prevalent and associated with decreased quality of life. Although hospice has been advocated to reduce rehospitalizations, it is not known how area-level hospice utilization patterns affect rehospitalization risk. OBJECTIVES: The study objective was to examine the association between hospice enrollment, local hospice utilization patterns, and 30-day rehospitalization in Medicare patients. METHODS: With a retrospective cohort design, 1,997,506 hospitalizations were assessed between 2005 and 2009 from a 5% national sample of Medicare beneficiaries. Local hospice utilization was defined using tertiles representing the percentage of all deaths occurring in hospice within each Hospital Service Area (HSA). Cox proportional hazard models were used to assess the relationship between 30-day rehospitalization, hospice enrollment, and local hospice utilization, adjusting for patient sociodemographics, medical history, and hospital characteristics. RESULTS: Rates of patients dying in hospice were 27% in the lowest hospice utilization tertile, 41% in the middle tertile, and 53% in the highest tertile. Patients enrolled in hospice had lower rates of 30-day rehospitalization than those not enrolled (2.2% versus 18.8%; adjusted hazard ratio [HR], 0.12; 95% confidence interval [CI], 0.118-0.131). Patients residing in areas of low hospice utilization were at greater rehospitalization risk than those residing in areas of high utilization (19.1% versus 17.5%; HR, 1.05; 95% CI, 1.04-1.06), which persisted beyond that accounted for by individual hospice enrollment. CONCLUSIONS: Area-level hospice utilization is inversely proportional to rehospitalization rates. This relationship is not fully explained by direct hospice enrollment, and may reflect a spillover effect of the benefits of hospice extending to nonenrollees.

Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study.

BACKGROUND: Measures of socioeconomic disadvantage may enable improved targeting of programs to prevent rehospitalizations, but obtaining such information directly from patients can be difficult. Measures of U.S. neighborhood socioeconomic disadvantage are more readily available but are rarely used clinically. OBJECTIVE: To evaluate the association between neighborhood socioeconomic disadvantage at the census block group level, as measured by the Singh validated area deprivation index (ADI), and 30-day rehospitalization. DESIGN: Retrospective cohort study. SETTING: United States. PATIENTS: Random 5% national sample of Medicare patients discharged with congestive heart failure, pneumonia, or myocardial infarction between 2004 and 2009 (n = 255,744). MEASUREMENTS: Medicare data were linked to 2000 census data to construct an ADI for each patient's census block group, which were then sorted into percentiles by increasing ADI. Relationships between neighborhood ADI grouping and 30-day rehospitalization were evaluated using multivariate logistic regression models, controlling for patient sociodemographic characteristics, comorbid conditions and severity, and index hospital characteristics. RESULTS: The 30-day rehospitalization rate did not vary significantly across the least disadvantaged 85% of neighborhoods, which had an average rehospitalization rate of 21%. However, within the most disadvantaged 15% of neighborhoods, rehospitalization rates increased from 22% to 27% with worsening ADI. This relationship persisted after full adjustment, with the most disadvantaged neighborhoods having a rehospitalization risk (adjusted risk ratio, 1.09 [95% CI, 1.05 to 1.12]) similar to that of chronic pulmonary disease (adjusted risk ratio, 1.06 [CI, 1.04 to 1.08]) and greater than that of uncomplicated diabetes (adjusted risk ratio, 0.95 [CI, 0.94 to 0.97]). LIMITATION: No direct markers of care quality or access. CONCLUSION: Residence within a disadvantaged U.S. neighborhood is a rehospitalization predictor of magnitude similar to chronic pulmonary disease. Measures of neighborhood disadvantage, such as the ADI, could potentially be used to inform policy and care after hospital discharge. PRIMARY FUNDING SOURCE: National Institute on Aging and University of Wisconsin School of Medicine and Public Health's Institute for Clinical and Translational Research and Health Innovation Program.

Bayesian approach for flexible modeling of semicompeting risks data.

Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the well-known illness-death models to allow flexible random effects to capture heterogeneous association structures in the data. Our extension also represents a generalization of the popular shared frailty models that usually assume that the non-terminal event does not affect the hazards of the terminal event beyond a frailty term. We propose a unified Bayesian modeling approach that can utilize existing software packages for both model fitting and individual-specific event prediction. The approach is demonstrated via both simulation studies and a breast cancer data set analysis.

Hospital-based health care use correlates with incidence of adverse events among elderly Medicare patients treated in adjuvant chemotherapy trials (Alliance 70802).

OBJECTIVE: Medicare claims can be useful in chemotherapy-related comparative effectiveness research (CER) estimating survival, but methods for estimating patients' treatment morbidity are currently lacking. We sought to determine if patients' health care use in the claims is a marker of treatment morbidity. MATERIALS AND METHODS: For 249 elderly Medicare patients with breast or colon cancer who were treated in two adjuvant clinical trials, we merged patients' National Cancer Institute Common Toxicity Criteria for Adverse Events (CTC AEs) trial data with their contemporaneous Medicare claims. We estimated associations of patients' grade ≥3 CTC AE counts and their use of two types of hospital-based health care in claims (i.e., emergency room (ER) visits and hospitalizations). RESULTS: ER visits and hospitalizations were significantly positively associated with grade ≥3 CTC AE counts incurred by patients during the study. Eight percent of patients without any grade ≥3 CTC AEs had one or more hospitalizations during the observation period compared to 43% of patients with three or more grade ≥3 CTC AEs (p<0.01). Those who were hospitalized at least once had more than three times the rate of grade ≥3 CTC AEs (IRR 3.70, 95% CI: 2.53-5.40) compared to those who were not. With each hospitalization, the daily incidence rate of any grade ≥3 CTC AE more than doubled (IRR 2.10, 95% CI: 1.54-2.86). CONCLUSIONS: Because hospitalization is strongly associated with clinically significant toxicity it may be a useful outcome for Medicare claim-based CER comparing treatment morbidity for elderly patients receiving different adjuvant chemotherapy regimens.

A new "Comparative Effectiveness" assessment strategy using the THIN database: comparison of the cardiac complications of pioglitazone and rosiglitazone.

PURPOSE: Examine feasibility of a new strategy to perform Electronic Medical Record database valid Comparative Effectiveness Research (CER), using determination of whether rosiglitazone (ROS) treatment increases Acute myocardial infarction (MI) in comparison to pioglitazone (PIO) as a model question. METHODS: Using the UK The Health Improvement Network Database, a retrospective cohort design replicated the proactive RCT of diabetics with ischemic cardiovascular disease (CVD). Replication studies using PIO or ROS, as well as expanded studies of subjects not requiring CVD, were performed. MI assessment used multiple analytics comparing ROS and PIO exposed patients including: unexposed subjects, the proactive RCT, and directly between ROS to PIO exposed cohorts. RESULTS: PIO replication studies did not affect MI [HR 0.88 (0.49 to 1.42)], but ROS increased MI, with prior event rate ratio (PERR) adjusted HR (which overcomes unmeasured confounding) results of: [HR 1.31 (0.94 to 1.74)] versus proactive RCT [HR 0.83 (0.65 to 1.06)] (p = 0.02). Direct ROS to PIO exposed cohort comparisons yielded PERR adj HR of 1.55 (0.98 to 2.65). By contrast, expanded studies showed no differences between ROS and PIO exposure. CONCLUSIONS: These results provide new insight regarding the effects of ROS and PIO on MI. In a population with established ischemic CVD, ROS increased MI in contrast to PIO; whereas in an unselected population, ROS and PIO have reasonably comparable effects. Most importantly, this study demonstrates the feasibility and advantages of a new strategy to perform reliable "CER" using an EMR database.

Designing cost-efficient randomized trials by using flexible recruitment strategies.

BACKGROUND: Sample size planning for clinical trials is usually based on detecting a target effect size of an intervention or treatment. Explicit incorporation of costs into such planning is considered in this article in the situation where effects of an intervention or treatment may depend on (interact with) baseline severity of the targeted symptom or disease. Because much larger sample sizes are usually required to establish such an interaction effect, investigators frequently conduct studies to establish a marginal effect of the intervention for individuals with a certain level of baseline severity. METHODS: We conduct a rigorous investigation on how to determine optimum baseline symptom or disease severity inclusion criteria so that the most cost-efficient design can be used. By using a regression model with an interaction term of treatment by symptom severity, power functions were derived for various levels of baseline symptom severity. Computer algorithms and mathematical optimization were used to determine the most cost-efficient research designs assuming either single- or dual-stage screening procedures. RESULTS: In the scenarios we considered, impressive cost savings can be achieved by informed selection of baseline symptom severity via the inclusion criteria. Further cost-savings can be achieved if a two stage screening procedure is used and there are some known, relatively inexpensively collected, pre-screening information. The amount of total cost savings are shown to depend on the ratio of the screening and intervention costs. In our investigation, we assumed that: 1) the cost of approaching available subjects for screening is constant, and 2) all variables are normally distributed. There is a need to carry out further investigations with more relaxed assumptions (e.g., skewed data distribution). CONCLUSIONS: As cost becomes a more and more prominent issue in modern clinical trials, cost-saving strategies will become more and more important. Strategies, such as the ones we propose here, can help to minimize costs while maximizing knowledge generation.

Survival in patients with pancreatic cancer after the diagnosis of malignant ascites or liver metastases by EUS-FNA.

BACKGROUND: The expected survival after the EUS-FNA diagnosis of malignant ascites or liver metastases from pancreatic cancer is not known. OBJECTIVE: To report overall and 1-year survival in these patients. DESIGN: Retrospective cohort series. SETTING: Tertiary referral hospital. PATIENTS: Consecutive subjects with newly diagnosed pancreatic cancer from June 1998 and March 2008 in whom EUS-FNA of the liver or ascitic fluid confirmed hepatic metastases or malignant ascites. INTERVENTIONS: Calculation of survival after diagnosis by using the Social Security Death Index. MAIN OUTCOME MEASUREMENTS: Survival after EUS-FNA diagnosis of stage IV pancreatic cancer. RESULTS: EUS-FNA identified liver metastases and malignant ascites from primary pancreatic cancer in 75 and 13 patients, respectively, and all 88 died during follow-up. For all 88 patients, the 1-year survival rate and median survival were 3.4% (95% CI, 1.1%-10.4%) and 82 days (range 2-754 days), respectively. The 1-year survival rates for those with liver metastases (4.0% [95% CI, 1.3%-12.1%]) and for those with malignant ascites (0% [95% CI, 0-24.7%]) were similar (P = 1.0). The median survival for patients with liver metastases of 83 days (range 2-754 days) was similar to that for those with malignant ascites (64 days; range 2-153 days) (P = .13). No clinical variable considered predicted survival of more than, less than, or 3 months. LIMITATIONS: Retrospective series with variable treatment for malignancy. CONCLUSIONS: In patients with pancreatic cancer, identification of malignant ascites or liver metastases by EUS-FNA is associated with a very poor prognosis.

A new probabilistic rule for drug-dug interaction prediction.

An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (-infinity, 1.25), (1.25, 2), and (2, infinity). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K(i) and dose combinations.