Comparison of machine learning approaches for positive airway pressure adherence prediction in a veteran cohort

BackgroundAdherence to positive airway pressure (PAP) therapy for sleep apnea is suboptimal, particularly in the veteran population. Accurately identifying those best suited for other therapy or additional interventions may improve adherence. We evaluated various machine learning algorithms to predict 90-day adherence.MethodsThe cohort of VA Northeast Ohio Health Care system patients who were issued a PAP machine (January 1, 2010–June 30, 2015) had demographics, comorbidities, and medications at the time of polysomnography obtained from the electronic health record. The data were split 60:20:20 into training, calibration, and validation data sets, with no use of validation data for model development. We constructed models for the first 90-day adherence period (% nights ≥4 h use) using the following algorithms: linear regression, least absolute shrinkage and selection operator, elastic net, ridge regression, gradient boosted machines, support vector machine regression, Bayes-based models, and neural nets. Prediction performance was evaluated in the validation data set using root mean square error (RMSE).ResultsThe 5,047 participants were 38.3 ± 11.9 years old, and 96.1% male, with 36.8% having coronary artery disease and 52.6% with depression. The median adherence was 36.7% (interquartile range: 0%, 86.7%). The gradient boosted machine was superior to other machine learning techniques (RMSE 37.2). However, the performance was similar and not clinically useful for all models without 30-day data. The 30-day PAP data and using raw diagnoses and medications (vs. grouping by type) improved the RMSE to 24.27.ConclusionComparing multiple prediction algorithms using electronic medical record information, we found that none has clinically meaningful performance. Better adherence predictive measures may offer opportunities for personalized tailoring of interventions

Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients

ABSTRACT Background: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for highrisk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient'

Nitrous oxide may not increase the risk of cancer recurrence after colorectal surgery: a follow-up of a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Even the best cancer surgery is usually associated with minimal residual disease. Whether these remaining malignant cells develop into clinical recurrence is at least partially determined by adequacy of host defense, especially natural killer cell function. Anesthetics impair immune defenses to varying degrees, but nitrous oxide appears to be especially problematic. We therefore tested the hypothesis that colorectal-cancer recurrence risk is augmented by nitrous oxide administration during colorectal surgery.</p> <p>Methods</p> <p>We conducted a 4- to 8-year follow-up of 204 patients with colorectal cancer who were randomly assigned to 65% nitrous oxide (n = 97) or nitrogen (n = 107), balanced with isoflurane and remifentanil. The primary outcome was the time to cancer recurrence. Our primary analysis was a multivariable Cox-proportional-hazards regression model that included relevant baseline variables. In addition to treatment group, the model considered patient age, tumor grade, dissemination, adjacent organ invasion, vessel invasion, and the number of nodes involved. The study had 80% power to detect a 56% or greater reduction in recurrence rates (i.e., hazard ratio of 0.44 or less) at the 0.05 significance level.</p> <p>Results</p> <p>After adjusting for significant baseline covariables, risk of recurrence did not differ significantly for nitrous oxide and nitrogen, with a hazard ratio estimate (95% CI) of 1.10 (0.66, 1.83), <it>P </it>= 0.72. No two-way interactions with the treatment were statistically significant.</p> <p>Conclusion</p> <p>Colorectal-cancer recurrence risks were not greatly different in patients who were randomly assigned to 65% nitrous oxide or nitrogen during surgery. Our results may not support avoiding nitrous oxide use to prevent recurrence of colorectal cancer.</p> <p>Implications Statement</p> <p>The risk of colorectal cancer recurrence was similar in patients who were randomly assigned to 65% nitrous oxide or nitrogen during colorectal surgery.</p> <p>Trial Registration</p> <p>Current Controlled Clinical Trials NCT00781352 <url>http://www.clinicaltrials.gov</url></p

Another Shipment of Six Short-Period Giant Planets from TESS

We present the discovery and characterization of six short-period, transiting giant planets from NASA's Transiting Exoplanet Survey Satellite (TESS) -- TOI-1811 (TIC 376524552), TOI-2025 (TIC 394050135), TOI-2145 (TIC 88992642), TOI-2152 (TIC 395393265), TOI-2154 (TIC 428787891), & TOI-2497 (TIC 97568467). All six planets orbit bright host stars (8.9 <G< 11.8, 7.7 <K< 10.1). Using a combination of time-series photometric and spectroscopic follow-up observations from the TESS Follow-up Observing Program (TFOP) Working Group, we have determined that the planets are Jovian-sized (R$_{P}$ = 1.00-1.45 R$_{J}$), have masses ranging from 0.92 to 5.35 M$_{J}$, and orbit F, G, and K stars (4753 $<$ T$_{eff}$ $<$ 7360 K). We detect a significant orbital eccentricity for the three longest-period systems in our sample: TOI-2025 b (P = 8.872 days, $e$ = $0.220\pm0.053$), TOI-2145 b (P = 10.261 days, $e$ = $0.182^{+0.039}_{-0.049}$), and TOI-2497 b (P = 10.656 days, $e$ = $0.196^{+0.059}_{-0.053}$). TOI-2145 b and TOI-2497 b both orbit subgiant host stars (3.8 $<$ $\log$ g $<$4.0), but these planets show no sign of inflation despite very high levels of irradiation. The lack of inflation may be explained by the high mass of the planets; $5.35^{+0.32}_{-0.35}$ M$_{\rm J}$ (TOI-2145 b) and $5.21\pm0.52$ M$_{\rm J}$ (TOI-2497 b). These six new discoveries contribute to the larger community effort to use {\it TESS} to create a magnitude-complete, self-consistent sample of giant planets with well-determined parameters for future detailed studies.Comment: 20 Pages, 6 Figures, 8 Tables, Accepted by MNRA

An Information-Theoretic Measure for Balance Assessment in Comparative Clinical Studies

Limitations of statistics currently used to assess balance in observation samples include their insensitivity to shape discrepancies and their dependence upon sample size. The Jensen&ndash;Shannon divergence (JSD) is an alternative approach to quantifying the lack of balance among treatment groups that does not have these limitations. The JSD is an information-theoretic statistic derived from relative entropy, with three specific advantages relative to using standardized difference scores. First, it is applicable to cases in which the covariate is categorical or continuous. Second, it generalizes to studies in which there are more than two exposure or treatment groups. Third, it is decomposable, allowing for the identification of specific covariate values, treatment groups or combinations thereof that are responsible for any observed imbalance

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling.

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states

No difference in pain reduction after epidural steroid injections in diabetic versus nondiabetic patients: A retrospective cohort study

Background and Aims: Diabetes affects peripheral and central neurons causing paresthesia, allodynia, hyperalgesia, and spontaneous pain. However, the effect of diabetes on response to epidural steroid injection (ESI) remains unknown. We hypothesized that diabetic patients receiving ESI will have different pain scores compared to nondiabetic patients. We tested a secondary hypothesis that pain reduction differs at different levels of hemoglobin A1c (HbA1c) for patients with diabetes. Material and Methods: Data from 284 consecutive patients given ESIs for radiculopathy were obtained via a manual review of electronic medical records. We initially compared diabetic and nondiabetic groups with respect to balance on baseline demographic and morphometric characteristics. Next, a linear regression model was developed to evaluate the association between existing diabetes and postinjection reduction in pain scores. And finally, we univariably characterized the association between HbA1c and pain reduction. Results: After exclusion of nine patients, 275 patients were analysed, including 55 (20%) who were diabetic. Pain reduction after ESI was comparable in diabetic and nondiabetic patients (Wald test P = 0.61). The degree of pain reduction generally decreased with the level of HbA1c until reaching HbA1c levels of approximately 7.5%, after which point it stayed fairly constant. Conclusion: There was no difference in pain reduction after ESIs comparing diabetic with nondiabetic patients; however, for diabetic patients, pain reduction may decrease with uncontrolled diabetes determined by high HbA1c values, thus suggesting pain physicians to take an active role in guiding their patients to have their blood glucose levels better regulated to improve outcomes of their ESIs

Risk-period-cohort approach for averting identification problems in longitudinal models.

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period-cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005-2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies