Confidence score: a data-driven measure for inclusive systematic reviews considering unpublished preprints.

OBJECTIVES: COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain. MATERIALS AND METHODS: We propose a novel data-driven method for assigning weights to individual preprints in systematic reviews and meta-analyses. This weight termed the "confidence score" is obtained using the survival cure model, also known as the survival mixture model, which takes into account the time elapsed between posting and publication of a preprint, as well as metadata such as the number of first 2-week citations, sample size, and study type. RESULTS: Using 146 preprints on COVID-19 therapeutics posted from the beginning of the pandemic through April 30, 2021, we validated the confidence scores, showing an area under the curve of 0.95 (95% CI, 0.92-0.98). Through a use case on the effectiveness of hydroxychloroquine, we demonstrated how these scores can be incorporated practically into meta-analyses to properly weigh preprints. DISCUSSION: It is important to note that our method does not aim to replace existing measures of study quality but rather serves as a supplementary measure that overcomes some limitations of current approaches. CONCLUSION: Our proposed confidence score has the potential to improve systematic reviews of evidence related to COVID-19 and other clinical conditions by providing a data-driven approach to including unpublished manuscripts.

Dynamic prediction modeling of postoperative mortality among patients undergoing surgical aortic valve replacement in a statewide cohort over a 12-year period.

Objective: Clinical prediction models for surgical aortic valve replacement mortality, are valuable decision tools but are often limited in their ability to account for changes in medical practice, patient selection, and the risk of outcomes over time. Recent research has identified methods to update models as new data accrue, but their effect on model performance has not been rigorously tested. Methods: The study population included 44,546 adults who underwent an isolated surgical aortic valve replacement from January 1, 1999, to December 31, 2018, statewide in Pennsylvania. After chronologically splitting the data into training and validation sets, we compared calibration, discrimination, and accuracy measures amongst a nonupdating model to 2 methods of model updating: calibration regression and the novel dynamic logistic state space model. Results: The risk of mortality decreased significantly during the validation period (P < .01) and the nonupdating model demonstrated poor calibration and reduced accuracy over time. Both updating models maintained better calibration (Hosmer-Lemeshow χ2 statistic) than the nonupdating model: nonupdating (156.5), calibration regression (4.9), and dynamic logistic state space model (8.0). Overall accuracy (Brier score) was consistently better across both updating models: dynamic logistic state space model (0.0252), calibration regression (0.0253), and nonupdating (0.0256). Discrimination improved with the dynamic logistic state space model (area under the curve, 0.696) compared with the nonupdating model (area under the curve, 0.685) and calibration regression method (area under the curve, 0.687). Conclusions: Dynamic model updating can improve model accuracy, discrimination, and calibration. The decision as to which method to use may depend on which measure is most important in each clinical context. Because competing therapies have emerged for valve replacement models, updating may guide clinical decision making.

Dynamic logistic state space prediction model for clinical decision making.

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data.

A Conceptual Model for Sources of Differential Selection in Lung Transplant Allocation.

Rationale: In the United States, donor lungs are allocated to transplant candidates on the basis of lung allocation scores (LAS). However, additional factors beyond the LAS can impact who is transplanted, including listing and donor-organ acceptance practices. These factors can result in differential selection, undermining the objectivity of lung allocation. Yet their impact on the lung transplant pathway has been underexplored. Objectives: We sought to systematically examine sources of differential selection in lung transplantation via qualitative methods. Methods: We conducted semistructured qualitative interviews with lung transplant surgeons and pulmonologists in the United States between June 2019 and June 2020 to understand clinician perspectives on differential selection in lung transplantation and the LAS. Results: A total of 51 respondents (30 surgeons and 21 pulmonologists) identified many sources of differential selection arising throughout the pathway from referral to transplantation. We synthesized these sources into a conceptual model with five themes: 1) transplant center's degree of risk tolerance and accountability; 2) successfulness and fairness of the LAS; 3) donor-organ availability and regional competition; 4) patient health versus program health; and 5) access to care versus responsible stewardship of organs. Conclusions: Our conceptual model demonstrates how differential selection can arise throughout lung transplantation and facilitates the further study of such selection. As new organ allocation models are developed, differential selection should be considered carefully to ensure that these models are more equitable.

Underprescribing vs underfilling to oral anticoagulation: An analysis of linked medical record and claims data for a nationwide sample of patients with atrial fibrillation.

BACKGROUND: Oral anticoagulants (OAC) is indicated for stroke prevention in patients with atrial fibrillation (AF) with a moderate or high risk of stroke. Despite the benefits of stroke prevention, only 50%-60% of Americans with nonvalvular AF and a moderate or high risk of stroke receive OAC medication. OBJECTIVE: To understand the extent to which low OAC use by patients with AF is attributed to underprescribing or underfilling once the medication is prescribed. METHODS: This is a retrospective cohort study that used linked claims data and electronic health records from Optum Integrated data. Participants were adults (aged ≥ 18 years) with first AF between January 2013 and June 2017. The outcomes included (1) being prescribed OACs within 180 days of AF diagnosis or not and (2) filling an OAC prescription or not among patients with AF who were prescribed an OAC within 150 days of AF diagnosis. Multivariable logistic regression models were constructed to determine factors associated with underprescribing and underfilling. RESULTS: Of the 6,141 individuals in the study cohort, 51% were not prescribed OACs within 6 months of their AF diagnosis. Of the 2,956 patients who were prescribed, 19% did not fill it at the pharmacy. In the final adjusted model, younger age, location (Northeast and South), a low CHA2DS2-VASc score, and a high HAS-BLED score were associated with a lower likelihood of being prescribed OACs. Among patients who were prescribed, Medicare enrollment (odds ratio [OR] [95% CI] = 2.2 [1.3-3.7]) and having a direct oral anticoagulant prescription (1.5 [1.2-1.9]) were associated with a lower likelihood of filling the prescription. CONCLUSIONS: Both underprescribing and underfilling are major drivers of low OAC use among patients with AF, and solutions to increase OAC use must address both prescribing and filling. DISCLOSURES: Research reported in this study was supported by the National Heart, Lung and Blood Institute (K01HL142847 and R01HL157051). Dr Guo is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK133465), PhMRA Foundation Research Starter Award, and the University of Florida Research Opportunity Seed Fund. Dr Hernandez reports scientific advisory board fees from Pfizer and Bristol Myers Squibb, outside of the submitted work.

Clinical impact of a modified lung allocation score that mitigates selection bias.

BACKGROUND: The Lung Allocation Score (LAS) is used in the U.S. to prioritize lung transplant candidates. Selection bias, induced by dependent censoring of waitlisted candidates and prediction of posttransplant survival among surviving, transplanted patients only, is only partially addressed by the LAS. Recently, a modified LAS (mLAS) was designed to mitigate such bias. Here, we estimate the clinical impact of replacing the LAS with the mLAS. METHODS: We considered lung transplant candidates waitlisted during 2016 and 2017. LAS and mLAS scores were computed for each registrant at each observed organ offer date; individuals were ranked accordingly. Patient characteristics associated with better priority under the mLAS were investigated via logistic regression and generalized linear mixed models. We also determined whether differences in rank were explained more by changes in predicted pre- or posttransplant survival. Simulations examined how 1-year waitlist, posttransplant, and overall survival might change under the mLAS. RESULTS: Diagnosis group, 6-minute walk distance, continuous mechanical ventilation, functional status, and age demonstrated the highest impact on differential allocation. Differences in rank were explained more by changes in predicted pretransplant survival than changes in predicted posttransplant survival, suggesting that selection bias has more impact on estimates of waitlist urgency. Simulations suggest that for every 1000 waitlisted individuals, 12.8 (interquartile range: 5.2-24.3) fewer waitlist deaths per year would occur under the mLAS, without compromising posttransplant and overall survival. CONCLUSIONS: Implementing a mLAS that mitigates selection bias into clinical practice can lead to important differences in allocation and possibly modest improvement in waitlist survival.

Modified Brier score for evaluating prediction accuracy for binary outcomes.

The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer Surveillance Consortium and compare the performance of predicting breast cancer risk using the models with and without its most important predictor.

The development and initial validation of the PROMIS®+HF-27 and PROMIS+HF-10 profiles.

AIMS: Heart failure (HF) is a common and morbid condition impacting multiple health domains. We previously reported the development of the PROMIS®-Plus-HF (PROMIS+HF) profile measure, including universal and HF-specific items. To facilitate use, we developed shorter, PROMIS+HF profiles intended for research and clinical use. METHODS AND RESULTS: Candidate items were selected based on psychometric properties and symptom range coverage. HF clinicians (n = 43) rated item importance and clinical actionability. Based on these results, we developed the PROMIS+HF-27 and PROMIS+HF-10 profiles with summary scores (0-100) for overall, physical, mental, and social health. In a cross-sectional sample (n = 600), we measured internal consistency reliability (Cronbach's alpha and Spearman-Brown), test-retest reliability (intraclass coefficient; n = 100), known-groups validity via New York Heart Association (NYHA) class, and convergent validity with Kansas City Cardiomyopathy Questionnaire (KCCQ) scores. In a longitudinal sample (n = 75), we evaluated responsiveness of baseline/follow-up scores by calculating mean differences and Cohen's d and comparing with paired t-tests. Internal consistency was good to excellent (α 0.82-0.94) for all PROMIS+HF-27 scores and acceptable to good (α/Spearman-Brown 0.60-0.85) for PROMIS+HF-10 scores. Test-retest intraclass coefficients were acceptable to excellent (0.75-0.97). Both profiles demonstrated known-groups validity for the overall and physical health summary scores based on NYHA class, and convergent validity for nearly all scores compared with KCCQ scores. In the longitudinal sample, we demonstrated responsiveness for PROMIS+HF-27 and PROMIS+HF-10 overall and physical summary scores. For the PROMIS+HF overall summary scores, a group-based increase of 7.6-8.3 points represented a small to medium change (Cohen's d = 0.40-0.42). For the PROMIS+HF physical summary scores, a group-based increase of 5.0-5.9 points represented a small to medium change (Cohen's d = 0.29-0.35). CONCLUSIONS: The PROMIS+HF-27 and PROMIS+HF-10 profiles demonstrated good psychometric characteristics with evidence of responsiveness for overall and physical health. These new measures can facilitate patient-centred research and clinical care, such as improving care quality through symptom monitoring, facilitating shared decision-making, evaluating quality of care, assessing new interventions, and monitoring during the initiation and titration of guideline-directed medical therapy.

Identifying Patients at Risk of Acute Kidney Injury Among Medicare Beneficiaries With Type 2 Diabetes Initiating SGLT2 Inhibitors: A Machine Learning Approach.

Introduction: To predict acute kidney injury (AKI) risk in patients with type 2 diabetes (T2D) prescribed sodium-glucose cotransporter two inhibitors (SGLT2i). Methods: Using a 5% random sample of Medicare claims data, we identified 17,694 patients who filled ≥1 prescriptions for canagliflozin, dapagliflozin and empagliflozin in 2013-2016. The cohort was split randomly and equally into training and testing sets. We measured 65 predictor candidates using claims data from the year prior to SGLT2i initiation. We then applied three machine learning models, including random forests (RF), elastic net and least absolute shrinkage and selection operator (LASSO) for risk prediction. Results: The incidence rate of AKI was 1.1% over a median 1.5 year follow up. Among three machine learning methods, RF produced the best prediction (C-statistic = 0.72), followed by LASSO and elastic net (both C-statistics = 0.69). Among individuals classified in the top 10% of the RF risk score (i.e., high risk group), the actual incidence rate of AKI was as high as 3.7%. In the logistic regression model including 14 important risk factors selected by LASSO, use of loop diuretics [adjusted odds ratio (95% confidence interval): 3.72 (2.44-5.76)] had the strongest association with AKI incidence. Disscusion: Our machine learning model efficiently identified patients at risk of AKI among Medicare beneficiaries with T2D undergoing SGLT2i treatment.

Comparable Cardiorenal Benefits of SGLT2 Inhibitors and GLP-1RAs in Asian and White Populations: An Updated Meta-analysis of Results From Randomized Outcome Trials.

BACKGROUND: Whether the cardiorenal benefits of sodium-glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide 1 receptor agonists (GLP-1RAs) are comparable between White and Asian populations remains unclear. PURPOSE: To compare the cardiorenal benefits of SGLT2 inhibitors and GLP-1RAs between White and Asian populations and to compare the cardiorenal benefits between the two agents in Asian patients. DATA SOURCES: Electronic databases were searched up to 28 March 2021. STUDY SELECTION: We included the cardiovascular (CV) and renal outcome trials of SGLT2 inhibitors and GLP-1RAs where investigators reported major adverse CV events (MACE), CV death/hospitalization for heart failure (HHF), or composite renal outcomes with stratification by race. DATA EXTRACTION: We extracted the hazard ratio of each outcome stratified by race (Asian vs. White populations). DATA SYNTHESIS: In 10 SGLT2 inhibitor trials, there was no significant difference between Asian and White populations for MACE (P = 0.55), CV death/HHF (P = 0.87), or composite renal outcomes (P = 0.97). In seven GLP-1RA trials, we observed a similar MACE benefit between Asian and White populations (P = 0.10). In our networkmeta-analysis we found a comparable benefit for MACE between SGLT2 inhibitors and GLP-1RAs in Asian patients. LIMITATIONS: The data were from stratified analyses. CONCLUSIONS: There appear to be comparable cardiorenal benefits of SGLT2 inhibitors and GLP-1RAs between Asian and White participants enrolled in CV and renal outcome trials; the two therapies seem to have similar CV benefits for Asian participants.

A Comparison of Methods to Detect Changes in Prediction Models.

BACKGROUND: Prediction models inform decisions in many areas of medicine. Most models are fitted once and then applied to new (future) patients, despite the fact that model coefficients can vary over time due to changes in patients' clinical characteristics and disease risk. However, the optimal method to detect changes in model parameters has not been rigorously assessed. METHODS: We simulated data, informed by post-lung transplant mortality data and tested the following two approaches for detecting model change: (1) the "Direct Approach," it compares coefficients of the model refit on recent data to those at baseline; and (2) "Calibration Regression," it fits a logistic regression model of the log-odds of the observed outcomes versus the linear predictor from the baseline model (i.e., the log-odds of the predicted probabilities obtained from the baseline model) and tests whether the intercept and slope differ from 0 and 1, respectively. Four scenarios were simulated using logistic regression for binary outcomes as follows: (1) we fixed all model parameters, (2) we varied the outcome prevalence between 0.1 and 0.2, (3) we varied the coefficient of one of the ten predictors between 0.2 and 0.4, and (4) we varied the outcome prevalence and coefficient of one predictor simultaneously. RESULTS: Calibration regression tended to detect changes sooner than the Direct Approach, with better performance (e.g., larger proportion of true claims). When the sample size was large, both methods performed well. When two parameters changed simultaneously, neither method performed well. CONCLUSION: Neither change detection method examined here proved optimal under all circumstances. However, our results suggest that if one is interested in detecting a change in overall incidence of an outcome (e.g., intercept), the Calibration Regression method may be superior to the Direct Approach. Conversely, if one is interested in detecting a change in other model covariates (e.g., slope), the Direct Approach may be superior.

Comparative Safety of Dipeptidyl Peptidase-4 Inhibitors and Sudden Cardiac Arrest and Ventricular Arrhythmia: Population-Based Cohort Studies.

In vivo studies suggest that arrhythmia risk may be greater with less selective dipeptidyl peptidase-4 inhibitors, but evidence from population-based studies is missing. We aimed to compare saxagliptin, sitagliptin, and linagliptin with regard to risk of sudden cardiac arrest (SCA)/ventricular arrhythmia (VA). We conducted high-dimensional propensity score (hdPS) matched, new-user cohort studies. We analyzed Medicaid and Optum Clinformatics separately. We identified new users of saxagliptin, sitagliptin (both databases), and linagliptin (Optum only). We defined SCA/VA outcomes using emergency department and inpatient diagnoses. We identified and then controlled for confounders via a data-adaptive, hdPS approach. We generated marginal hazard ratios (HRs) via Cox proportional hazards regression using a robust variance estimator while adjusting for calendar year. We identified the following matched comparisons: saxagliptin vs. sitagliptin (23,895 vs. 96,972) in Medicaid, saxagliptin vs. sitagliptin (48,388 vs. 117,383) in Optum, and linagliptin vs. sitagliptin (36,820 vs. 78,701) in Optum. In Medicaid, use of saxagliptin (vs. sitagliptin) was associated with an increased rate of SCA/VA (adjusted HR (aHR), 2.01, 95% confidence interval (CI) 1.24-3.25). However, in Optum data, this finding was not present (aHR, 0.79, 95% CI 0.41-1.51). Further, we found no association between linagliptin (vs. sitagliptin) and SCA/VA (aHR, 0.65, 95% CI 0.36-1.17). We found discordant results regarding the association between SCA/VA with saxagliptin compared with sitagliptin in two independent datasets. It remains unclear whether these findings are due to heterogeneity of treatment effect in the different populations, chance, or unmeasured confounding.

Comparison of dynamic updating strategies for clinical prediction models.

BACKGROUND: Prediction models inform many medical decisions, but their performance often deteriorates over time. Several discrete-time update strategies have been proposed in the literature, including model recalibration and revision. However, these strategies have not been compared in the dynamic updating setting. METHODS: We used post-lung transplant survival data during 2010-2015 and compared the Brier Score (BS), discrimination, and calibration of the following update strategies: (1) never update, (2) update using the closed testing procedure proposed in the literature, (3) always recalibrate the intercept, (4) always recalibrate the intercept and slope, and (5) always refit/revise the model. In each case, we explored update intervals of every 1, 2, 4, and 8 quarters. We also examined how the performance of the update strategies changed as the amount of old data included in the update (i.e., sliding window length) increased. RESULTS: All methods of updating the model led to meaningful improvement in BS relative to never updating. More frequent updating yielded better BS, discrimination, and calibration, regardless of update strategy. Recalibration strategies led to more consistent improvements and less variability over time compared to the other updating strategies. Using longer sliding windows did not substantially impact the recalibration strategies, but did improve the discrimination and calibration of the closed testing procedure and model revision strategies. CONCLUSIONS: Model updating leads to improved BS, with more frequent updating performing better than less frequent updating. Model recalibration strategies appeared to be the least sensitive to the update interval and sliding window length.

Mitigating selection bias in organ allocation models.

BACKGROUND: The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions. METHODS: We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots. RESULTS: The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization. CONCLUSIONS: Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.

Comparing medication adherence using a smartphone application and electronic monitoring among patients with acute coronary syndrome.

AIM: The purpose of this study was to determine the extent of agreement between adherence measures obtained using two technological interventions, electronic monitoring (EM) and a smartphone application (App). BACKGROUND: Clinicians, patients, and researchers depend on valid measurements of medication adherence to inform the delivery of preemptive care when needed. Technology is routinely used for monitoring medication adherence in both clinical practice and research, yet there is a dearth of research comparing novel App based approaches to traditional approaches used for assessing medication adherence. METHODS: Adherence rates were captured on both the EM and the App for 3697 daily observations from 44 participants with acute coronary syndrome over 90 days immediately following discharge from acute care. For EM, adherence was measured using EM equipped pill bottles. For the App, adherence was measured by having participants upload daily photos to the App prior to taking their daily aspirin. Agreement was assessed using a Bland-Altman analysis. RESULTS: The mean adherence rate was higher on the App, 92%, than the EM, 78% (p < 0.001). The mean difference in adherence rates between these methods was 14% (95% Confidence Interval: -23%, -5%). CONCLUSIONS: These findings illustrate a lack of agreement between technological interventions used for measuring adherence in cardiovascular patient populations, with higher adherence rates observed with the App compared to EM. These findings are salient given the increased reliance on telehealth due to the ongoing COVID-19 pandemic.

Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery.

BACKGROUND: Electroencephalographic seizures (ESs) after neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates. METHODS: ES prediction models were developed and validated in a multicenter prospective cohort where all postoperative neonates who underwent cardiopulmonary bypass (CPB) also underwent CEEG. RESULTS: ESs occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single-ventricle defect, deep hypothermic circulatory arrest duration, cardiac arrest, nitric oxide, extracorporeal membrane oxygenation, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic, 0.77; Hosmer-Lemeshow, P = .56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, P = .60); however, discrimination was poor (c-statistic, 0.61), and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. By using a cut point that emphasized negative predictive value in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) of neonates with ESs (negative predictive value, 99%; sensitivity, 97%). CONCLUSIONS: In this large prospective cohort, a prediction model of ESs in neonates after CPB had good performance in the derivation cohort, with an NB in DCA. However, performance in the validation cohort was weak, with poor discrimination, poor calibration, and no NB in DCA. These findings support CEEG of all neonates after CPB.

Evaluation of COVID-19 Testing Strategies for Repopulating College and University Campuses: A Decision Tree Analysis.

PURPOSE: The optimal approach to identify SARS-CoV-2 infection among college students returning to campus is unknown. Recommendations vary from no testing to two tests per student. This research determined the strategy that optimizes the number of true positives and negatives detected and reverse transcription polymerase chain reaction (RT-PCR) tests needed. METHODS: A decision tree analysis evaluated five strategies: (1) classifying students with symptoms as having COVID-19, (2) RT-PCR testing for symptomatic students, (3) RT-PCR testing for all students, (4) RT-PCR testing for all students and retesting symptomatic students with a negative first test, and (5) RT-PCR testing for all students and retesting all students with a negative first test. The number of true positives, true negatives, RT-PCR tests, and RT-PCR tests per true positive (TTP) was calculated. RESULTS: Strategy 5 detected the most true positives but also required the most tests. The percentage of correctly identified infections was 40.6%, 29.0%, 53.7%, 72.5%, and 86.9% for Strategies 1-5, respectively. All RT-PCR strategies detected more true negatives than the symptom-only strategy. Analysis of TTP demonstrated that the repeat RT-PCR strategies weakly dominated the single RT-PCR strategy and that the thresholds for more intensive RT-PCR testing decreased as the prevalence of infection increased. CONCLUSION: Based on TTP, the single RT-PCR strategy is never preferred. If the cost of RT-PCR testing is of concern, a staged approach involving initial testing of all returning students followed by a repeat testing decision based on the measured prevalence of infection might be considered.

Risk factors for 30-day readmission in adults hospitalized for pulmonary hypertension.

Readmissions for pulmonary hypertension are poorly understood and understudied. We sought to determine national estimates and risk factors for 30-day readmission after pulmonary hypertension-related hospitalizations. We utilized the Healthcare Cost and Utilization Project Nationwide Readmission Database, which has weighted estimates of roughly 35 million discharges in the US. Adult patients with primary International Classification of Disease, Ninth Revision, Clinical Modification diagnosis codes of 416.0 and 416.8 for primary and secondary pulmonary hypertension with an index admission between 2012 and 2014 and any readmission within 30 days of the index event were identified. Predictors of 30-day readmission were identified using multivariable logistic regression with adjustment for covariates. Results showed that the national estimate for Primary Pulmonary Hypertension vs Secondary Pulmonary Hypertension-related index events between 2012 and 2014 with 30-day readmission was 247 vs 2550 corresponding to a national readmission risk estimate of 17% vs 18.3%, respectively. The presence of fluid and electrolyte disorders, renal failure, and alcohol abuse were associated with increased risk of readmission in Primary Pulmonary Hypertension, while factors associated with Secondary Pulmonary Hypertension readmissions included anemia, congestive heart failure, lung disease, fluid and electrolyte disorders, renal failure, diabetes, and liver disease. The median cost of Primary Pulmonary Hypertension admissions and readmissions were $46,132 (IQR: $25,384-$85,647) and $41,604.50 (IQR: $22,481.50-$84,420.50), respectively. The median costs of Secondary Pulmonary Hypertension admissions and readmissions were $34,893 (IQR: $19,670-$66,143) and $36,279 (IQR: $19,059-$74,679), respectively. In conclusion, approximately 19% of Primary Pulmonary Hypertension and Secondary Pulmonary Hypertension hospitalizations result in 30-day readmission, with significant costs accrued during the index hospitalization and readmission. With evolving clinical terminology and diagnostic codes, future study will need to better clarify underlying factors associated with readmissions amongst pulmonary hypertension sub-types, and identify methods and procedures to minimize readmission risk.

Testing small study effects in multivariate meta-analysis.

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias, and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error rates with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed tests with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the Cochrane Database.