Monitoring policy in the context of preventive treatment of cardiovascular disease.

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases.

Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death in the US. Although research has shown that ASCVD has genetic elements, the understanding of how genetic testing influences its prevention and treatment has been limited. To this end, we model the health trajectory of patients stochastically and determine treatment and testing decisions simultaneously. Since the cholesterol level of patients is one controllable risk factor for ASCVD events, we model cholesterol treatment plans as Markov decision processes. We determine whether and when patients should receive a genetic test using value of information analysis. By simulating the health trajectory of over 64 million adult patients, we find that 6.73 million patients undergo genetic testing. The optimal treatment plans informed with clinical and genetic information save 5,487 more quality-adjusted life-years while costing $1.18 billion less than the optimal treatment plans informed with clinical information only. As precision medicine becomes increasingly important, understanding the impact of genetic information becomes essential.

Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering.

In managing patients with chronic diseases, such as open angle glaucoma (OAG), the case treated in this paper, medical tests capture the disease phase (e.g. regression, stability, progression, etc.) the patient is currently in. When medical tests have low residual variability (e.g. empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. This paper presents a framework for handling the latter case. The framework presented integrates the outputs of interacting multiple model Kalman filtering with supervised learning classification. The purpose of this integration is to estimate the true values of patients' disease metrics by allowing for rapid and non-rapid phases; and dynamically adapting to changes in these values over time. We apply our framework to classifying whether a patient with OAG will experience rapid progression over the next two or three years from the time of classification. The performance (AUC) of our model increased by approximately 7% (increased from 0.752 to 0.819) when the Kalman filtering results were incorporated as additional features in the supervised learning model. These results suggest the combination of filters and statistical learning methods in clinical health has significant benefits. Although this paper applies our methodology to OAG, the methodology developed is applicable to other chronic conditions.

Projected increase in obesity and non-alcoholic-steatohepatitis-related liver transplantation waitlist additions in the United States.

Nonalcoholic steatohepatitis (NASH) cirrhosis is the fastest growing indication for liver transplantation (LT) in the United States. We aimed to determine the temporal trend behind the rise in obesity and NASH-related additions to the LT waitlist in the United States and make projections for future NASH burden on the LT waitlist. We used data from the Organ Procurement and Transplantation Network database from 2000 to 2014 to obtain the number of NASH-related LT waitlist additions. The obese population in the United States from 2000 to 2014 was estimated using data from the U.S. Census Bureau and the National Health and Nutrition Examination Survey. Based on obesity trends, we established a time lag between obesity prevalence and NASH-related waitlist additions. We used data from the U.S. Census Bureau on population projections from 2016 to 2030 to forecast obesity estimates and NASH-related LT waitlist additions. From 2000 to 2014, the proportion of obese individuals significantly increased 44.9% and the number of NASH-related annual waitlist additions increased from 391 to 1,605. Increase in obesity prevalence was strongly associated with LT waitlist additions 9 years later in derivation and validation cohorts (R2 = 0.9). Based on these data, annual NASH-related waitlist additions are anticipated to increase by 55.4% (1,354-2,104) between 2016 and 2030. There is significant regional variation in obesity rates and in the anticipated increase in NASH-related waitlist additions (P < 0.01). Conclusion: We project a marked increase in demand for LT for NASH given population obesity trends. Continued public health efforts to curb obesity prevalence are needed to reduce the projected future burden of NASH. (Hepatology 2017).

Characterising 'bounce-back' readmissions after radical cystectomy.

OBJECTIVE: To examine predictors of early readmissions after radical cystectomy (RC). Factors associated with preventable readmissions may be most evident in readmissions that occur within 3 days of discharge, commonly termed 'bounce-back' readmissions, and identifying such factors may inform efforts to reduce surgical readmissions. PATIENTS AND METHODS: We utilised the Healthcare Cost and Utilization Project's State Inpatient Databases to examine 1867 patients undergoing RC in 2009 and 2010, and identified all patients readmitted within 30 days of discharge. We assessed differences between patients experiencing bounce-back readmission compared to those readmitted 8-30 days after discharge using logistic regression models and also calculated abbreviated LACE scores to assess the utility of common readmissions risk stratification algorithms. RESULTS: The 30-day and bounce-back readmission rates were 28.4% and 5.6%, respectively. Although no patient or index hospitalisation characteristics were significantly associated with bounce-back readmissions in adjusted analyses, bounce-back patients did have higher rates of gastrointestinal (14.3% vs 6.7%, P = 0.02) and wound (9.5% vs 3.0%, P < 0.01) diagnoses, as well as increased index and readmission length of stay (5 vs 4 days, P = 0.01). Overall, the median abbreviated LACE score was 7, which fell into the moderate readmission risk category, and no difference was observed between readmitted and non-readmitted patients. CONCLUSION: One in five readmissions after RC occurs within 3 days of initial discharge, probably due to factors present at discharge. However, sociodemographic and clinical factors, as well as traditional readmission risk tools were not predictive of this bounce-back. Effective strategies to reduce bounce-back readmission must identify actionable clinical factors prior to discharge.

Role of Post-Acute Care on Hospital Readmission After High-Risk Surgery.

BACKGROUND: Payment models, including the Hospital Readmissions Reduction Program and bundled payments, place pressures on hospitals to limit readmissions. Against this backdrop, we sought to investigate the association of post-acute care after major surgery and readmission rates. METHODS: We identified patients undergoing high-risk surgery (abdominal aortic aneurysm repair, coronary bypass grafting, aortic valve replacement, carotid endarterectomy, esophagectomy, pancreatectomy, lung resection, and cystectomy) from 2005 to 2010 using the Healthcare Cost and Utilization Project's State Inpatient Database. The primary outcome was readmission rates after major surgery. Secondary outcome was readmission length of stay. RESULTS: We identified 135,523 patients of whom 56,720 (42%) received post-acute care. Patients receiving post-acute care had higher readmission rates than those who were discharged home (16% versus 10%, respectively; P < 0.001). The risk-adjusted readmission length of stay was greatest for patients who received care from a skilled nursing facility, followed by those who received home care, and lowest for those who did not receive post-acute care (7.1 versus 5.4 versus 4.8 d, respectively; P < 0.001). CONCLUSIONS: The use of post-acute care was associated with higher readmission rates and higher readmission lengths of stay. Improving the support of patients in post-acute care settings may help reduce readmissions and readmission intensity.

Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods.

PURPOSE: To generate personalized forecasts of how patients with open-angle glaucoma (OAG) experience disease progression at different intraocular pressure (IOP) levels to aid clinicians with setting personalized target IOPs. DESIGN: Secondary analyses using longitudinal data from 2 randomized controlled trials. PARTICIPANTS: Participants with moderate or advanced OAG from the Collaborative Initial Glaucoma Treatment Study (CIGTS) or the Advanced Glaucoma Intervention Study (AGIS). METHODS: By using perimetric and tonometric data from trial participants, we developed and validated Kalman Filter (KF) models for fast-, slow-, and nonprogressing patients with OAG. The KF can generate personalized and dynamically updated forecasts of OAG progression under different target IOP levels. For each participant, we determined how mean deviation (MD) would change if the patient maintains his/her IOP at 1 of 7 levels (6, 9, 12, 15, 18, 21, or 24 mmHg) over the next 5 years. We also model and predict changes to MD over the same time horizon if IOP is increased or decreased by 3, 6, and 9 mmHg from the level attained in the trials. MAIN OUTCOME MEASURES: Personalized estimates of the change in MD under different target IOP levels. RESULTS: A total of 571 participants (mean age, 64.2 years; standard deviation, 10.9) were followed for a mean of 6.5 years (standard deviation, 2.8). Our models predicted that, on average, fast progressors would lose 2.1, 6.7, and 11.2 decibels (dB) MD under target IOPs of 6, 15, and 24 mmHg, respectively, over 5 years. In contrast, on average, slow progressors would lose 0.8, 2.1, and 4.1 dB MD under the same target IOPs and time frame. When using our tool to quantify the OAG progression dynamics for all 571 patients, we found no statistically significant differences over 5 years between progression for black versus white, male versus female, and CIGTS versus AGIS participants under different target IOPs (P > 0.05 for all). CONCLUSIONS: To our knowledge, this is the first clinical decision-making tool that generates personalized forecasts of the trajectory of OAG progression at different target IOP levels. This approach can help clinicians determine appropriate, personalized target IOPs for patients with OAG.

Predictors and Cost of Readmission in Total Knee Arthroplasty.

BACKGROUND: The Comprehensive Care for Joint Replacement bundle was created to decrease total knee arthroplasty (TKA) cost. To help accomplish this, there is a focus on reducing TKA readmissions. However, there is a lack of national representative sample of all-payer hospital admissions to direct strategy, identify risk factors for readmission, and understand actual readmission cost. METHODS: We used the Nationwide Readmission Database to examine national readmission rates, predictors of readmission, and associated readmission costs for elective TKA procedures. We fit a multivariable logistic regression model to examine factors associated with readmission. Then, we determined mean readmission costs and calculated the readmission cost when distributed across the entire TKA population. RESULTS: We identified 224,465 patients having TKA across all states participating in the Nationwide Readmission Database. The mean unadjusted 30-day TKA readmission rate was 4%. The greatest predictors of readmission were congestive heart failure (odds ratio [OR] 2.51, 95% confidence interval [CI] 2.62-2.80), renal disease (OR 2.19, 95% CI 2.03-2.37), and length of stay greater than 4 days (OR 2.4, 95% CI 2.25-2.61). The overall median cost for each readmission was $6753 ± 175. Extrapolating the readmission cost for the entire TKA population resulted in the readmission cost being 2% of the overall 30-day procedure cost. CONCLUSIONS: A major focus of the Comprehensive Care for Joint Replacement bundle is improving cost and quality by limiting readmission rates. TKA readmissions are low and comprise a small percentage of total TKA cost, suggesting that they may not be the optimal measure of quality care or a significant driver of overall cost.

Variation in readmission expenditures after high-risk surgery.

BACKGROUND: The Hospital Readmissions Reduction Program reduces payments to hospitals with excess readmissions for three common medical conditions and recently extended its readmission program to surgical patients. We sought to investigate readmission intensity as measured by readmission cost for high-risk surgeries and examine predictors of higher readmission costs. MATERIALS AND METHODS: We used the Healthcare Cost and Utilization Project's State Inpatient Database to perform a retrospective cohort study of patients undergoing major chest (aortic valve replacement, coronary artery bypass grafting, lung resection) and major abdominal (abdominal aortic aneurysm repair [open approach], cystectomy, esophagectomy, pancreatectomy) surgery in 2009 and 2010. We fit a multivariable logistic regression model with generalized estimation equations to examine patient and index admission factors associated with readmission costs. RESULTS: The 30-d readmission rate was 16% for major chest and 22% for major abdominal surgery (P < 0.001). Discharge to a skilled nursing facility was associated with higher readmission costs for both chest (odds ratio [OR]: 1.99; 95% confidence interval [CI]: 1.60-2.48) and abdominal surgeries (OR: 1.86; 95% CI: 1.24-2.78). Comorbidities, length of stay, and receipt of blood or imaging was associated with higher readmission costs for chest surgery patients. Readmission >3 wk after discharge was associated with lower costs among abdominal surgery patients. CONCLUSIONS: Readmissions after high-risk surgery are common, affecting about one in six patients. Predictors of higher readmission costs differ among major chest and abdominal surgeries. Better identifying patients susceptible to higher readmission costs may inform future interventions to either reduce the intensity of these readmissions or eliminate them altogether.

A Model to Optimize Followup Care and Reduce Hospital Readmissions after Radical Cystectomy.

PURPOSE: Radical cystectomy has one of the highest readmission rates across all surgical procedures at approximately 25%. We developed a mathematical model to optimize outpatient followup regimens for radical cystectomy. MATERIALS AND METHODS: We used delay-time analysis, a systems engineering approach, to maximize the probability of detecting patients susceptible to readmission through office visits and telephone calls. Our data source includes patients readmitted after radical cystectomy from the Healthcare Cost and Utilization Project State Inpatient Databases in 2009 and 2010 as well as from our institutional bladder cancer database from 2007 to 2011. We measured the interval from hospital discharge to the point when a patient first exhibits concerning symptoms. Our primary end point is 30-day hospital readmission. Our model optimized the timing and sequence of followup care after radical cystectomy. RESULTS: The timing of office visits and telephone calls is more important in detecting a patient at risk for readmission than the sequence of these encounters. Patients are most likely to exhibit concerning symptoms between 4 and 5 days after discharge home. An optimally scheduled office visit can detect up to 16% of potential readmissions, which can be increased to 36% with 1 office visit followed by 4 telephone calls. CONCLUSIONS: Our model improves the detection of concerning symptoms after radical cystectomy by optimizing the timing and number of outpatient encounters. By understanding how to design better outpatient followup care for patients treated with radical cystectomy we can help reduce the readmission burden for this population.

Strategic modeling of the neonatal nurse practitioner workforce.

BACKGROUND: Neonatal nurse practitioners (NNPs) play a vital role in the medical care of newborns and infants. There is expected to be a shortage of NNPs in the near future. PURPOSE: To assess the present NNP workforce and study the impact of potential policy changes to alleviate forecasted shortages. METHODS: We modeled the education and workforce system for NNPs. Forecasting models were combined with optimal decision-making to derive best-case scenario admission levels for graduate and undergraduate programs. DISCUSSION: Under the best-case scenario for the current system, the shortage of NNPs is expected to last 10 years. We analyzed the impact of improving the certification examination passing rate, increasing the annual growth rate of master's programs, and reducing the workforce annual attrition rate. We found that policy changes may reduce the forecasted shortage to 4 years. CONCLUSION: Present forecasts of demand for NNPs indicate that the existing workforce and education system will be unable to satisfy the growing demand. Policy changes may reduce the expected shortage and potentially improve access to care for newborns and infants.

Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity.

We investigate the problem faced by a healthcare system wishing to allocate its constrained screening resources across a population at risk for developing a disease. A patient's risk of developing the disease depends on his/her biomedical dynamics. However, knowledge of these dynamics must be learned by the system over time. Three classes of reinforcement learning policies are designed to address this problem of simultaneously gathering and utilizing information across multiple patients. We investigate a case study based upon the screening for Hepatocellular Carcinoma (HCC), and optimize each of the three classes of policies using the indifference zone method. A simulation is built to gauge the performance of these policies, and their performance is compared to current practice. We then demonstrate how the benefits of learning-based screening policies differ across various levels of resource scarcity and provide metrics of policy performance.

Sharpening the focus on causes and timing of readmission after radical cystectomy for bladder cancer.

BACKGROUND: Readmissions after radical cystectomy are common, burdensome, and poorly understood. For these reasons, the authors conducted a population-based study that focused on the causes of and time to readmission after radical cystectomy. METHODS: Using Surveillance, Epidemiology, and End Results-Medicare data, at total of 1782 patients who underwent radical cystectomy from 2003 through 2009 were identified. A piecewise exponential model was used to examine reasons for readmission as well as patient and clinical factors associated with the timing of readmission. RESULTS: One in 4 patients (25.5%) were readmitted within 30 days of discharge after radical cystectomy. Compared with patients without readmission, those readmitted were similar with regard to age, sex, and race. Readmitted patients had more complications (33.8% vs 13.9%; P< .001) and were more likely to have been discharged to skilled nursing facilities from their index admission (P< .001). The average time to readmission and subsequent length of stay were 11.5 days and 6.7 days, respectively. The majority of readmissions (67.4%) occurred within 2 weeks of discharge, 66.8% had emergency department charges, and 25.9% involved intensive care unit use. Although the spectrum of reasons for readmission varied over the 4 weeks after discharge, the most common included infection (51.4%), failure to thrive (36.3%), and urinary (33.2%) and gastrointestinal (23.1%) etiologies; 95.8% of patients had ≥ 1 of these diagnosis groups present at the time of readmission. CONCLUSIONS: Readmissions after radical cystectomy are common and time-dependent. Interventions to prevent and reduce the readmission burden after cystectomy likely need to focus on the first 2 weeks after discharge, take into consideration the spectrum of reasons for readmission, and target high-risk individuals.

Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma.

PURPOSE: To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). DESIGN: Secondary analyses using longitudinal data from 2 randomized controlled trials. PARTICIPANTS: A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS). METHODS: Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements. MAIN OUTCOME MEASURES: Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression. RESULTS: Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001). CONCLUSIONS: Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.

Improving screening for hepatocellular carcinoma by incorporating data on levels of α-fetoprotein, over time.

BACKGROUND & AIMS: Current screening algorithms for hepatocellular carcinoma (HCC) view each testing interval independently, without considering prior test results. We investigated whether measurements of α-fetoprotein (AFP), over time, can be used to identify patients most likely to develop HCC. METHODS: We performed a nested case-control study using data from subjects in the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis trial; 82 patients with HCC were matched 1:3 to individuals without HCC (controls), using bootstrap methods to ensure similar follow-up times between groups. We assessed the independent association between development of HCC and the following: (1) most recent level of AFP, (2) standard deviation in level of AFP, and (3) rate of increase in AFP using a multiple logistic regression that included patient-specific risk factors such as age, platelet count, and smoking status. RESULTS: In bivariable analysis, all 3 AFP metrics were associated with HCC development; the most strongly associated was the standard deviation of AFP (odds ratio, 1.03 per unit increase in standard deviation; P < .001). Incorporating the standard deviation of AFP and rate of AFP increase, along with patient-specific risk factors, improved the prognostic accuracy to an area under the receiver-operating characteristic curve of 0.81, compared with 0.76 when only the most recent AFP level was used. CONCLUSIONS: Patterns of AFP test results can more accurately identify patients with hepatitis C and advanced fibrosis or cirrhosis most likely to develop HCC, compared with most recent AFP test results.

Maximizing the value of mobile health monitoring by avoiding redundant patient reports: prediction of depression-related symptoms and adherence problems in automated health assessment services.

BACKGROUND: Interactive voice response (IVR) calls enhance health systems' ability to identify health risk factors, thereby enabling targeted clinical follow-up. However, redundant assessments may increase patient dropout and represent a lost opportunity to collect more clinically useful data. OBJECTIVE: We determined the extent to which previous IVR assessments predicted subsequent responses among patients with depression diagnoses, potentially obviating the need to repeatedly collect the same information. We also evaluated whether frequent (ie, weekly) IVR assessment attempts were significantly more predictive of patients' subsequent reports than information collected biweekly or monthly. METHODS: Using data from 1050 IVR assessments for 208 patients with depression diagnoses, we examined the predictability of four IVR-reported outcomes: moderate/severe depressive symptoms (score ≥10 on the PHQ-9), fair/poor general health, poor antidepressant adherence, and days in bed due to poor mental health. We used logistic models with training and test samples to predict patients' IVR responses based on their five most recent weekly, biweekly, and monthly assessment attempts. The marginal benefit of more frequent assessments was evaluated based on Receiver Operator Characteristic (ROC) curves and statistical comparisons of the area under the curves (AUC). RESULTS: Patients' reports about their depressive symptoms and perceived health status were highly predictable based on prior assessment responses. For models predicting moderate/severe depression, the AUC was 0.91 (95% CI 0.89-0.93) when assuming weekly assessment attempts and only slightly less when assuming biweekly assessments (AUC: 0.89; CI 0.87-0.91) or monthly attempts (AUC: 0.89; CI 0.86-0.91). The AUC for models predicting reports of fair/poor health status was similar when weekly assessments were compared with those occurring biweekly (P value for the difference=.11) or monthly (P=.81). Reports of medication adherence problems and days in bed were somewhat less predictable but also showed small differences between assessments attempted weekly, biweekly, and monthly. CONCLUSIONS: The technical feasibility of gathering high frequency health data via IVR may in some instances exceed the clinical benefit of doing so. Predictive analytics could make data gathering more efficient with negligible loss in effectiveness. In particular, weekly or biweekly depressive symptom reports may provide little marginal information regarding how the person is doing relative to collecting that information monthly. The next generation of automated health assessment services should use data mining techniques to avoid redundant assessments and should gather data at the frequency that maximizes the value of the information collected.

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

BACKGROUND: Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. METHODS: Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. RESULTS: The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. CONCLUSION: A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

Estimating the Relationship Between the Symptom-Free Waiting Period and Injury Rates After Return-to-Play from Concussion: A Simulation Analysis Using CARE Consortium Data.

BACKGROUND: A key component of return-to-play (RTP) from sport-related concussion is the symptom-free waiting period (SFWP), i.e., the period during which athletes must remain symptom-free before permitting RTP. Yet, the exact relationship between SFWP and post-RTP injury rates is unclear. OBJECTIVE: We design computational simulations to estimate the relationship between the SFWP and rates of repeat concussion and non-concussion time-loss injury up to 30 days post-RTP for male and female collegiate athletes across 13 sports. METHODS: We leverage N = 735 female and N = 1,094 male post-injury trajectories from the National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research, and Education Consortium. RESULTS: With a 6-day SFWP, the mean [95% CI] rate of repeat concussion per 1,000 simulations was greatest in ice hockey for females (20.31, [20.16, 20.46]) and American football for males (24.16, [24.05, 24.28]). Non-concussion time-loss injury rates were greatest in field hockey for females (153.66, [152.59, 154.74]) and wrestling for males (247.34, [246.20, 248.48]). Increasing to a 13-day SFWP, ice hockey for females (18.88, [18.79, 18.98]) and American football for males (23.16, [23.09, 24.22]) exhibit the greatest decrease in repeat concussion rates across all sports within their respective sexes. Field hockey for females (143.24, [142.53, 143.94]) and wrestling for males (237.73, [236.67, 237.90]) exhibit the greatest decrease in non-concussion time-loss injury rates. Males receive marginally smaller reductions in injury rates for increased SFWP compared to females (OR = 1.003, p ≤ 0.002). CONCLUSION: Longer SFWPs lead to greater reductions in post-RTP injury rates for athletes in higher risk sports. Moreover, SFWPs should be tailored to sport-specific post-RTP injury risks.

Sensitivity and Specificity of the ImPACT Neurocognitive Test in Collegiate Athletes and US Military Service Academy Cadets with ADHD and/or LD: Findings from the NCAA-DoD CARE Consortium.

BACKGROUND AND OBJECTIVE: Computer-based neurocognitive tests are widely used in sport-related concussion management, but the performance of these tests is not well understood in the participant population with attention-deficit/hyperactivity disorder (ADHD) and/or learning disorder (LD). This research estimates the sensitivity and specificity performance of the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) computer-based neurocognitive test in identifying concussion in this population. METHODS: Study participants consisted of collegiate university athletes and military service academy cadets from the National Collegiate Athletic Association-Department of Defense CARE Consortium who completed the ImPACT test between 2014 and 2021. Participants who self-identified as belonging to one of the subgroups of interest (ADHD with or without LD [ADHD:LD+/-], LD with or without ADHD [LD:ADHD+/-], ADHD and/or LD [ADHD a/o LD]) and completed a baseline (1874 ADHD:LD+/-, 779 LD:ADHD+/-, 2338 ADHD a/o LD) or 24-48 h post-concussion (175 ADHD:LD+/-, 77 LD:ADHD+/-, 216 ADHD a/o LD) ImPACT assessment were included. Sensitivity and specificity were calculated using a normative data method and three machine learning classification methods: logistic regression, classification and regression tree, and random forest. RESULTS: Using the four methods, participants with ADHD:LD+/- had sensitivities that ranged from 0.474 to 0.697, and specificities that ranged from 0.538 to 0.686. Participants with LD:ADHD+/- had sensitivities that ranged from 0.455 to 0.688, and specificities that ranged from 0.456 to 0.588. For participants with ADHD a/o LD, sensitivities ranged from 0.542 to 0.755, and specificities ranged from 0.451 to 0.724. CONCLUSIONS: For all subgroups and analytical methods, the results illustrate sensitivity and specificity values below typically accepted levels indicative of clinical utility. These findings support that using ImPACT alone may be insufficient to inform concussion diagnoses and encourages the use of a multi-dimensional concussion assessment.

An Assessment of Ineligible Donor Use in Solid Organ Transplant.

BACKGROUND: In the United States, the demand for organ transplants far outpaces available organs. The use of Organ Procurement and Transplantation Network-defined ineligible donors is an immediate method for increasing donations. However, the use of ineligible donors varies across organ procurement organizations (OPOs), and its association with recipient survival remains unclear. METHODS: We evaluated ineligible donor use from 2008 to 2020 by OPO and its association with graft and recipient survival across demographics. RESULTS: In this study of 297 223 organ donations, 42 184 (14%) did not meet eligibility criteria as defined by the Organ Procurement and Transplantation Network. Log-rank tests on Kaplan-Meier curves suggested differences in graft and patient survival between eligible and ineligible recipients for kidney and liver transplants ( P ≤ 0.01 for all). Recipients of ineligible kidney and liver donations saw a 2.20% and 9.38% decrease in 10-y graft survival probability, respectively. There were no statistically significant graft and patient survival differences for recipients of ineligible heart, lung, and pancreas donations. Multivariate proportional hazard models showed eligibility was associated with kidney, liver, and lung graft survival ( P ≤ 0.02 for all). However, if OPOs increased ineligible donor use to meet the current 75th percentile use rate, there could be as many as 1000 transplants and 6291 life-years gained annually. CONCLUSIONS: Ineligible donor use can provide significant survival benefit for patients who would otherwise never receive a transplant. Methods to reduce regional heterogeneity in ineligible donor use could increase the number of transplants and improve outcomes for waiting patients.