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Examine the associations of sleep measures with kidney function changes over time among individuals from a community-based study.The sample includes 1657 participants (287 with chronic kidney disease [CKD]) in the Multi-Ethnic Study of Atherosclerosis Sleep Cohort (mean age: 57.7 years, male: 46.0%). We examined associations between a large set of sleep variables (polysomnography, actigraphy, and questionnaires) and cardiovascular disease risk factors and changes in estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio over approximately 5 years using high-dimensional regression. We investigated the modifying effect of sleep on the associations between cardiovascular disease risk factors and kidney function.Sleep metrics predicted kidney function decline only among individuals with baseline CKD. Among this group, eGFR decline was associated with decreased stage N3 sleep (0.32 mL/min/1.73 mReduced deep sleep, daytime napping, increased wake bouts, delayed sleep rhythms, and overnight hypoxemia are associated with longitudinal kidney function decline, with effects most apparent in individuals with CKD. Deep sleep, napping, and sleep timing modified the association between hemoglobin A1C and kidney function.

Oral immunotherapy (OIT) leads to suppression of mast cell and basophil degranulation along with changes in the adaptive immune response.This study aimed to determine how rapidly these effects occur during OIT and more broadly, the kinetics of basophil and mast cell suppression throughout the course of therapy.Twenty participants, age 4 to 12 years, were enrolled in a peanut OIT trial and assessed for desensitization and sustained unresponsiveness after 9 months of therapy. Blood was collected 5 times in the first month and then intermittently throughout to quantify immunoglobulins and assess basophil activation by CD63, CD203c, and phosphorylated SYK (pSYK).Twelve of 16 participants that completed the trial were desensitized after OIT, with 9 achieving sustained unresponsiveness after discontinuing OIT for 4 weeks. Basophil hyporesponsiveness, defined by lower CD63 expression, was detected as early as day 90. pSYK was correlated with CD63 expression, and there was a significant decrease in pSYK by day 250. CD203c expression remained unchanged throughout therapy. Interestingly, although basophil activation was decreased across the cohort during OIT, basophil activation did not correlate with individual clinical outcomes. Serum peanut-specific IgGSuppression of basophil activation occurs within the first 90 days of peanut OIT, ultimately leading to suppression of signaling through pSYK.

When an infectious disease recurs, it may be due to treatment failure or a new infection. Being able to distinguish and classify these two different outcomes is critical in effective disease control. A multi-state model based on Markov processes is a typical approach to estimating the transition probability between the disease states. However, it can perform poorly when the disease state is unknown. This article aims to demonstrate that the transition likelihoods of baseline covariates can distinguish one cause from another with high accuracy in infectious diseases such as malaria. A more general model for disease progression can be constructed to allow for additional disease outcomes. We start from a multinomial logit model to estimate the disease transition probabilities and then utilize the baseline covariate's transition information to provide a more accurate classification result. We apply the expectation-maximization (EM) algorithm to estimate unknown parameters, including the marginal probabilities of disease outcomes. A simulation study comparing our classifier to the existing two-stage method shows that our classifier has better accuracy, especially when the sample size is small. The proposed method is applied to determining relapse vs reinfection outcomes in two Plasmodium vivax treatment studies from Cambodia that used different genotyping approaches to demonstrate its practical use.

HIV-infected individuals have a known increased risk of sudden cardiac death (SCD) compared to uninfected individuals. Implantable cardioverter-defibrillators (ICDs) are standard therapy for preventing SCD; however, there is limited data on the outcomes of ICDs in HIV-infected individuals.HIV-infected subjects receive a higher number of appropriate ICD therapies than uninfected controls.This is a retrospective cohort study of 35 consecutive HIV-Infected patients and 36 uninfected controls matched by age, race, and gender who were treated at the University of North Carolina Medical Center in the outpatient or inpatient setting from 2014 to the present and had undergone ICD implantation. For HIV-infected subjects, a multivariate Poisson regression analysis was performed to evaluate the association between covariates and ICD therapies.Among HIV-infected subjects, the mean CD4 count was 582.5 cells/mmHIV-infected subjects had a higher number of appropriate ICD discharge or ATP therapy per person-year than matched uninfected controls.

Summary Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model’s coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus computationally expensive. Here, we propose a computationally efficient test with a closed-form limiting distribution, which allows the parameter being tested to be either sparse or dense. We show that, under certain regularity conditions, the Type-I error of the proposed method is asymptotically correct, and we establish its power under high-dimensional alternatives. Extensive simulations demonstrate the good performance of the proposed test and its robustness when certain sparsity assumptions are violated. We also apply the proposed method to Chinese famine sample data in order to show its performance when testing the significance of gene-environment interactions.

Supplementary Data Online

Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79–1.49) and 1.54 (1.01–2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients.Significance:A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.

Supplementary Text S1. Mathematical model and parameter estimation; Supplementary Table 1. Cohen's d point estimate and 95% confidence interval in effect size tests among patients with PD (Progressive Disease), SD (Stable Disease) or PR (Partial Response) responses; Supplementary Figure 1. The individual tumor growth profiles for all 599 individuals; Supplementary Figure 2. Kaplan-Meier curves of the overall survival starting from last tumor observation for each individual patient. The curves were categorized by evolutionary parameters and the number of cell subclones at diagnosis derived from the evolutionary model.

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it fails to address response heterogeneity across metastases. This study argues that spatiotemporal heterogeneity across metastases and organ-specific response is informative for drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients from five phase III clinical trials. Initially, a metric Gower distance was applied to quantify response heterogeneity across metastases. Next, the spatiotemporal response heterogeneity across anatomic sites, therapies, and KRAS mutation status was assessed and examined for its association with drug efficacy and long-term patient survival. The response of metastatic lesions broadly differed across anatomic sites and therapies. About 60% of patients had at least one lesion respond contrarily from total tumor size. High interlesion heterogeneity was associated with shorter progression-free survival and overall survival. Targeted therapies (bevacizumab or panitumumab) combined with standard chemotherapy reduced interlesion heterogeneity and elicited more favorable effects from liver lesions (P < 0.001) than chemotherapy alone. Moreover, the favorable responses in liver metastases (> 30% shrinkage) were associated with extended patient overall survival (P < 0.001), in contrast to lesions in the lungs and lymph nodes. Altogether, the spatiotemporal response heterogeneity across metastases informed drug efficacy and patient survival, which could improve the current methods for treatment evaluation and patient prognosis.Significance:These findings support the modification of RECIST criteria to include individual lesion response to improve assessments of drug efficacy.

Achieving systemic tumor control across metastases is vital for long-term patient survival but remains intractable in many patients. High lesion-level response heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying lesion-level variability. However, our understanding of lesion-specific heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and progression during treatment, remains rudimentary. This study investigates lesion-specific response heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and progression dynamics, metastatic lesions converge on four phenotypes that vary with anatomical site. Importantly, we find that organ-level progression sequence is closely associated with patient long-term survival, and that patients with the first lesion progression in the liver often have worse survival. In conclusion, our study provides insights into lesion-specific response and progression heterogeneity in mCRC and creates impetus for metastasis-specific therapeutics.

A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria.

Purpose The purpose is to identify predictors of post-induction hypotension (PIH) during general anesthesia in a population of patients with varying degrees of pulmonary hypertension (PH). Methods This is a single-center, retrospective, observational study of perioperative data obtained via electronic health records from patients with PH undergoing surgery over a five-year period. Baseline patient characteristics, peri-induction management variables, and pre-induction mean arterial pressure (MAP) were statistically analyzed using Kruskal-Wallis rank sum tests, Pearson's chi-squared tests, and logistic regression analysis to identify risk factors for PIH. We further assessed the relationship between PH and PIH using propensity score matching. Primary outcomes include a percent decrease in post-induction blood pressure as well as a post-induction nadir with a threshold of 55 mm Hg. Results Eight hundred fifty-seven patients in the cohort stratified by severity of PH reveal that advanced age (p0.001), higher BMI (P = 0.002), higher American Society of Anesthesiologists (ASA) score (P = 0.001), and renal and cardiac comorbidities (P0.001) are associated with PH severity. None of our tested parameters were significantly predictive for PIH in patients with PH. Right heart failure was found to be weakly and non-significantly predictive of PIH in patients with PH (P = 0.052, odds ratio [OR] = 1.116). Diabetes (P = 0.007, OR = 0.919) and maintenance of spontaneous ventilation (P = 0.012, OR = 0.925) were associated with decreased rates of PIH. Conclusion Hypotension after induction of general anesthesia in patients with PH is a serious problem, yet statistically significant risk factors were not identified. History of diabetes and preservation of spontaneous ventilation had a significant but weak effect of decreasing rates of PIH. This pilot study was limited by retrospective design and warrants further analysis with a prospective cohort.

Introduction Alveolar macrophages (AMs) are lung-resident immune cells that phagocytose inhaled particles and pathogens, and help coordinate the lung’s immune response to infection. Little is known about the impact of chronic e-cigarette use (ie, vaping) on this important pulmonary cell type. Thus, we determined the effect of vaping on AM phenotype and gene expression. Aims and Methods We recruited never-smokers, smokers, and e-cigarette users (vapers) and performed research bronchoscopies to isolate AMs from bronchoalveolar lavage fluid samples and epithelial cells from bronchial brushings. We then performed morphological analyses and used the Nanostring platform to look for changes in gene expression. Results AMs obtained from smokers and vapers were phenotypically distinct from those obtained from nonsmokers, and from each other. Immunocytochemistry revealed that vapers AMs had significantly elevated inducible nitric oxide synthase (M1) expression and significantly reduced CD301a (M2) expression compared with nonsmokers or smokers. Vapers’ AMs and bronchial epithelia exhibited unique changes in gene expression compared with nonsmokers or smokers. Moreover, vapers’ AMs were the most affected of all groups and had 124 genes uniquely downregulated. Gene ontology analysis revealed that vapers and smokers had opposing changes in biological processes. Conclusions These data indicate that vaping causes unique changes to AMs and bronchial epithelia compared with nonsmokers and smokers which may impact pulmonary host defense. Implications These data indicate that normal “healthy” vapers have altered AMs and may be at risk of developing abnormal immune responses to inflammatory stimuli.

A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of

Perioperative hypothermia (core temperature36°C) occurs in 50%-to-80% of patients recovering from thoracic aortic surgery, though its effects have not been described fully in this context. The authors, therefore, sought to characterize the incidence of perioperative hypothermia and its association with time from procedure end to extubation in endovascular aortic surgical patients.A retrospective cohort study.At a single academic tertiary center.Patients recovering from thoracic aortic surgery with lumbar drains.None.A total of 196 patients were included in this study, 55 of whom were hypothermic with temperatures35.0°C at the end of surgery. Though the unadjusted time to extubation was not statistically different in the hypothermic group (median 8 minutes, IQR 5-13.5 minutes) compared to the normothermic group (median 7 minutes, IQR 4-12 minutes; p = 0.062), multivariate predictors of increased time from procedure end to extubation included hypothermia (p = 0.011), age (p = 0.009), diabetes (p = 0.015), history of carotid disease (p = 0.040), and crystalloid volume (p = 0.019).Hypothermia in patients recovering from endovascular aortic surgery was associated with prolonged time from procedure end to extubation. Because of the retrospective observational nature of the authors' analysis, it was not possible to determine the extent to which prolonged mechanical ventilation was influenced by low temperature.

Diuretic use may reduce volume-related complications in hemodialysis. We evaluated the efficacy, safety, and tolerability of furosemide in patients with hemodialysis-dependent kidney failure.We conducted an open label, single-arm, 18-week, dose titration pilot study of oral furosemide (maximum dose 320 mg/day) among patients receiving maintenance hemodialysis who reported at least 1 cup of urine output per day. The primary efficacy outcome was an increase from baseline to a specified threshold of 24-hour urine volume, with the threshold based on baseline urine volume (200 ml/dayOf the 39 participants, 28 (72%) received the expected furosemide dose, 3 (8%) underwent dose reduction, 5 (12%) discontinued furosemide without dose reduction, and 3 (8%) underwent dose reduction and subsequently discontinued furosemide. The median (quartile 1, quartile 3) baseline 24-hour urine volume was 290 ml (110, 740), and the maximum, average daily study furosemide dose ranged from 69 mg/day to 320 mg/d. The urine output efficacy outcome was met by 12 (33%), 11 (33%), and 7 (22%) participants at weeks 5, 12, and 18, respectively, in the intention-to-treat analysis, and by 12 (39%), 9 (35%), and 7 (28%) participants at weeks 5, 12, and 18, respectively, in the on-treatment analysis. There were no electrolyte, furosemide level, or patient-reported hearing change safety events.Furosemide was generally safe and well tolerated, but only one-third of participants met the efficacy definition at week 5. The clinical importance of the efficacy findings is uncertain.

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it fails to address response heterogeneity across metastases. This study argues that spatiotemporal heterogeneity across metastases and organ-specific response is informative for drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients from five phase III clinical trials. Initially, a metric Gower distance was applied to quantify response heterogeneity across metastases. Next, the spatiotemporal response heterogeneity across anatomic sites, therapies, and KRAS mutation status was assessed and examined for its association with drug efficacy and long-term patient survival. The response of metastatic lesions broadly differed across anatomic sites and therapies. About 60% of patients had at least one lesion respond contrarily from total tumor size. High interlesion heterogeneity was associated with shorter progression-free survival and overall survival. Targeted therapies (bevacizumab or panitumumab) combined with standard chemotherapy reduced interlesion heterogeneity and elicited more favorable effects from liver lesions (P < 0.001) than chemotherapy alone. Moreover, the favorable responses in liver metastases (> 30% shrinkage) were associated with extended patient overall survival (P < 0.001), in contrast to lesions in the lungs and lymph nodes. Altogether, the spatiotemporal response heterogeneity across metastases informed drug efficacy and patient survival, which could improve the current methods for treatment evaluation and patient prognosis. Significance: These findings support the modification of RECIST criteria to include individual lesion response to improve assessments of drug efficacy.

Many biomedical studies collect data of mixed types of variables from multiple groups of subjects. Some of these studies aim to find the group-specific and the common variation among all these variables. Even though similar problems have been studied by some previous works, their methods mainly rely on the Pearson correlation, which cannot handle mixed data. To address this issue, we propose a latent mixed Gaussian copula (LMGC) model that can quantify the correlations among binary, ordinal, continuous, and truncated variables in a unified framework. We also provide a tool to decompose the variation into the group-specific and the common variation over multiple groups via solving a regularized M-estimation problem. We conduct extensive simulation studies to show the advantage of our proposed method over the Pearson correlation-based methods. We also demonstrate that by jointly solving the M-estimation problem over multiple groups, our method is better than decomposing the variation group by group. We also apply our method to a Chlamydia trachomatis genital tract infection study to demonstrate how it can be used to discover informative biomarkers that differentiate patients.

Achieving systemic tumor control across metastases is vital for long-term patient survival but remains intractable in many patients. High intrapatient heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying intrapatient heterogeneity. However, our understanding of intrapatient heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and relapse during treatment, remains rudimentary. This study investigated intrapatient heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and relapse dynamics, metastatic lesions converged on four phenotypes that varied with anatomical site. Importantly, we found that organ-level relapse sequence was closely associated with patient survival, and that patients with the first relapses in the liver often had worse survival. In conclusion, our study provides insights into intrapatient response heterogeneity in mCRC and creates impetus for metastasis-specific therapeutics.

BACKGROUND: Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer’s disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies. OBJECTIVE: To study the dynamic spreading patterns of neuropathological events in AD. METHODS: We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively. RESULTS: Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity. CONCLUSION: The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner.

Background: We compared women's acceptability of urine and cervico-vaginal sample self-collection for high-risk (oncogenic) human papillomavirus (hrHPV) testing and assessed whether acceptability v...

In patients with Plasmodium vivax malaria treated with effective blood-stage therapy, the recurrent illness may occur due to relapse from latent liver-stage infection or reinfection from a new mosquito bite. Classification of the recurrent infection as either relapse or reinfection is critical when evaluating the efficacy of an anti-relapse treatment. Although one can use whether a shared genetic variant exists between baseline and recurrence genotypes to classify the outcome, little has been suggested to use both sharing and nonsharing variants to improve the classification accuracy. In this paper, we develop a novel classification criterion that utilizes transition likelihoods to distinguish relapse from reinfection. When tested in extensive simulation experiments with known outcomes, our classifier has superior operating characteristics. A real data set from 78 Cambodian P. vivax malaria patients was analyzed to demonstrate the practical use of our proposed method.

Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79–1.49) and 1.54 (1.01–2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients. Significance: A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.

Introduction: Heart failure (HF) exacerbations are often precipitated by dietary noncompliance, which is common around holidays. We examined whether HF hospitalizations would increase after major national holidays. Methods: The ARIC Study performed surveillance of HF admissions among patients age ≥55 years in 4 US communities from 2005-2014. HF admissions were identified by ICD-9 codes and validated by physician review. For each holiday (H), 4 days before the holiday (H-4 to H-1), and 4 days after the holiday (H+1 to H+4), we compared average daily HF admission rates, hospital length of stay (LOS), 28-day mortality, and 1-year mortality to the remaining days in each holiday’s month using Poisson regression using a complex sampling design. Results: ARIC identified 92,354 HF admissions (weighted). Patients admitted in holiday months were 46% male and 71% white, with average age 75 years. Compared to the holiday months, patients admitted on H were older (78 years, p Conclusions: HF admission rates, hospital LOS, and mortality were similar in the 4 days following a holiday compared to the remainder of each holiday’s month. However, the day before and/or day of a holiday were associated with lower HF admission rates, shorter hospital LOS, and lower 28-day mortality.

Background: Interscalene nerve catheters have been proven to be effective in managing pain after rotator cuff repair (RCR) surgery. Liposomal bupivacaine is a newer approved therapy for use around the interscalene brachial plexus, but its analgesic efficacy has limited supporting data in various patient populations. Purpose/Hypothesis: The purpose of this study was to investigate the quality of recovery after arthroscopic RCR in patients who received either single-injection interscalene liposomal bupivacaine or an interscalene peripheral nerve catheter. It was hypothesized that interscalene peripheral nerve catheters would provide more reliable analgesia and improved patient satisfaction 48 hours after surgery. Study Design: Cohort study; Level of evidence, 2. Methods: Enrolled were 93 consecutive patients who underwent arthroscopic rotator cuff surgery at a single ambulatory surgery center between October 2020 and June 2021. Of these patients, 13 were lost to follow-up; thus, 80 patients were included in statistical analysis. One group of patients (n = 48) received a preoperative interscalene nerve block placed with 10 mL 0.5% bupivacaine and 10 mL 1.3% liposomal bupivacaine. The second group (n = 32) received a preoperative interscalene catheter with an initial bolus of 20 mL 0.25% bupivacaine and a 0.2% ropivacaine infusion by an elastomeric pump set at 10 mL/hr for 48 hours. The primary outcome was the difference between preoperative and 48-hour postoperative quality of recovery-15 (QoR-15) scores. Secondary outcomes included visual analog pain scores, opioid use, and patient satisfaction. Complications and adverse effects were also noted. The Kruskal-Wallis test was used to analyze means and standard deviations for continuous endpoints; Fisher exact test was used to analyze counts and proportions for categorical endpoints. Results: The liposomal bupivacaine group had a mean reduction of 3.9 in their postoperative QoR-15 scores, and the catheter group had a mean reduction of 25.1 in their postoperative QoR-15 scores, indicating a significantly worse functional recovery period compared with liposomal bupivacaine within the first 48 hours ( P < .001). Patients who received liposomal bupivacaine also had significantly lower pain scores on the second postoperative day, improved quality of sleep, and improved satisfaction with analgesia ( P < .05 for all). Conclusion: The use of interscalene liposomal bupivacaine demonstrated significantly improved quality of recovery when compared with interscalene nerve catheter after RCR.

In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multi-modality data could deliver better prediction performance than using single modality data. However, one special challenge for using multi-modality data is related to block-missing data. In practice, due to dropouts or the high cost of measures, the observations of a certain modality can be missing completely for some subjects. In this paper, we propose a new DIrect Sparse regression procedure using COvariance from Multi-modality data (DISCOM). Our proposed DISCOM method includes two steps to find the optimal linear prediction of a continuous response variable using block-missing multi-modality predictors. In the first step, rather than deleting or imputing missing data, we make use of all available information to estimate the covariance matrix of the predictors and the cross-covariance vector between the predictors and the response variable. The proposed new estimate of the covariance matrix is a linear combination of the identity matrix, the estimates of the intra-modality covariance matrix and the cross-modality covariance matrix. Flexible estimates for both the sub-Gaussian and heavy-tailed cases are considered. In the second step, based on the estimated covariance matrix and the estimated cross-covariance vector, an extended Lasso-type estimator is used to deliver a sparse estimate of the coefficients in the optimal linear prediction. The number of samples that are effectively used by DISCOM is the minimum number of samples with available observations from two modalities, which can be much larger than the number of samples with complete observations from all modalities. The effectiveness of the proposed method is demonstrated by theoretical studies, simulated examples, and a real application from the Alzheimer’s Disease Neuroimaging Initiative. The comparison between DISCOM and some existing methods also indicates the advantages of our proposed method.

Considerable lesion-specific response heterogeneity exists in metastatic colorectal cancer patients, largely due to organ-specific ecological environments and evolutionary pressures. Metastatic lesions with poor response to therapy often become tumor sanctuary sites, leading to systemic resistance and tumor relapse. To map the lesion-specific response and relapse patterns, we investigated the longitudinal dynamics of individual lesions in metastatic colorectal cancer patients. Tumor longitudinal data in 4,308 colorectal cancer patients with 40,612 individual lesions were collected from eight Phase III trials in Project Data Sphere. First, tumor response dynamics (regression after treatment and progression upon resistance) were characterized using an empirical mathematical model. Next, tumor response time (when the lesion size decreases ≥20% from baseline) and relapse time (when the lesion size increases ≥30% from tumor nadir) were estimated for each individual lesion in patients being treated with bevacizumab, panitumumab, and/or chemotherapy. Random effect cox proportional models were applied to predict lesion-specific response and relapse probabilities and temporal sequence. We then took machine learning algorithm k-means to cluster patients based on their lesion relapse sequence. We found the response probabilities across organs are: Liver > Distal Lymph Nodes (LN) > Abdomen > Spleen > Lung > Regional LN > Adrenal > Muscle/Soft Tissue > Bone > Brain/CNS. Lesion relapse temporal sequence are: Brain/CNS > Liver > Adrenal > Muscle/Soft tissue > Abdomen > Bone > Spleen > Lung > Distal LN > Regional LN. Of note, lesions in the bone, brain, adrenal, and muscle/soft tissues often had low responses and high relapse probabilities, implying the greatest potential as tumor sanctuary sites. Liver, the most common metastatic organ in colorectal cancer, showed highest response rate but high relapse probabilities. Interestingly, the organ-specific response rate and relapse probabilities are respectively in line with drug distribution profiles and organ-specific immune landscape. Organ-specific relapse sequence in each patient is significantly correlated with patient long-term survival (p Citation Format: Jiawei Zhou, Quefeng Li, Amber Cipriani, Yanguang Cao. Mapping lesion specific response and relapse patterns in metastatic colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3789.

Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.

PURPOSE: The use of traditional, complementary, and alternative medicine (TCAM) for cancer may influence the delivery or effectiveness of conventional cancer treatment. In this systematic review, we aimed to 1.) summarize the available prevalence data on traditional medicine use by cancer patients in less developed countries (LDCs), and 2.) stratify the prevalence data by world region and country income level. METHODS: A literature search for cancer, TCAM, and low income (LI) and lower-middle income (LMI) countries was conducted across 5 databases. A total of 2,365 publications were reviewed for eligibility, of which 25 studies met inclusion criteria. RESULTS: The combined sample size was 6,878 cancer patients, with a median of 54.5% reporting the use of TCAM for cancer care. Of the studies providing data on the concomitant use of TCAM and conventional cancer treatment (n = 4,872 cancer patients), a median of 26.7% of participants reported combining the two systems of medicine. CONCLUSION: From the data available, it is apparent that TCAM use among cancer patients in less developed countries is common; however, additional studies are needed to support the safe and effective management of cancer for patients in LI and LMI countries.

Aim: Determine if incentive spirometry (IS) values correlate with postoperative pain control. Design: Prospective observational study. Setting & participants: A total of 100 patients undergoing major abdominal procedures at the University of North Carolina Medical Center. Interventions: Patients studied as a single cohort. All patients received thoracic epidural analgesia preoperatively. Outcome: Preoperative and daily postoperative numeric pain scores, subjective pain description and IS values were collected for all patients. Results: There was a strong correlation with IS values relative to baseline for both the numeric pain scores (p

HIV-infected individuals have a known increased risk of sudden cardiac death (SCD) compared to uninfected individuals. Implantable cardioverter defibrillators (ICDs) are standard therapy for preventing sudden arrhythmic death; however, there is limited data on the use and outcomes of ICDs in HIV-infected individuals. This is a retrospective cohort study of 35 consecutive HIV- Infected patients and 36 uninfected controls matched by age, race, and gender who were treated at University of North Carolina, Chapel Hill Medical Center in the outpatient or inpatient setting from 2014 to the present and had undergone primary or secondary prevention ICD implantation. For HIV-positive subjects, multivariate Poisson regression analysis was performed to evaluate the association between covariates and ICD therapies. Among HIV-infected subjects, mean CD4 count was 582.5 cells/mm3 and 69 % had an undetectable viral load. The median followup was 6.4 years. There was a trend towards longer corrected QT interval at the time of ICD implantation in HIV-infected subjects compared to uninfected controls (469.7 +/- 35.8 vs. 454.7 +/- 35.7 msec, p=0.089). HIV-infected subjects had both a higher number of appropriate ICD shocks or anti-tachycardia pacing (ATP) therapy per person-year as well as a higher number of inappropriate ICD shocks per person-year than uninfected controls (1.512 vs. 0.590 and 0.122 vs. 0.0166 respectively, p

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis.

Introduction: Hospital length of stay with transcatheter aortic valve replacements (TAVRs) has decreased, though the rate of heart block requiring permanent pacemaker (PPM) implantation after TAVR has remained steady. It is unknown whether more patients are being readmitted for PPM after discharge from TAVR. Objective: To explore frequency, timing, and risk factors for PPM implant after TAVR in a nationally representative database. Methods: Patients who underwent TAVR from January 2012 through December 2017 were identified in the Nationwide Readmissions Database (NRD). Smoothing splines and logistic regression were used to analyze trends in length of stay and timing of PPM implantation after TAVR respectively. Multivariable logistic regression analysis was performed to identify risk factors for overall, early (during index hospitalization), and late (during subsequent hospitalization) PPM after TAVR. Results: Of the 62,083 included, 6,817 (11.0%) underwent PPM implantation [6,137 (9.9%) early and 680 (1.1%) late]. Rates of PPM remained stable between 8% and 12.5% with an increasing proportion occurring late (7% in 2012 increasing to 13% in 2017, p < 0.0001 for trend) (Figure 1). Late PPM was associated with atrial fibrillation (p < 0.01), diabetes mellitus (p < 0.001), chronic kidney disease (p < 0.05), second degree AVB (p < 0.001), left bundle branch block (p < 0.001), right bundle branch block (p < 0.001), and bifascicular block (p < 0.001). Conclusions: There has been a significant increase in the proportion of patients requiring readmission for PPM implantation after TAVR. As this high-risk population grows, algorithms for extended in-hospital observation or ambulatory cardiac monitoring post-TAVR are needed to reduce the risk for adverse events.

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it neglects the response heterogeneity across metastases. This study argues that the spatiotemporal response heterogeneity across metastases informs drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients and examined their response heterogeneity. The response dynamics of metastatic lesions varied broadly across anatomical locations and therapies. High inter-lesion heterogeneity is associated with worse survival (p < 0.001), while targeted therapies (bevacizumab or panitumumab) reduced the inter-lesion heterogeneity (p < 0.05) and elicited more favorable effects on liver lesions (p < 0.001) than chemotherapy alone. The responses of liver lesions predicted patient survival more significantly than the lesions in the lungs and lymph nodes. Altogether, the high spatiotemporal heterogeneity across metastases should be integrated into current methods for treatment evaluation and patient prognosis.SignificanceThe spatiotemporal heterogeneity across metastases in response to first-line therapies in colorectal cancer is informative for drug efficacy and patient survival, particularly in targeted therapy. Our findings provide evidence to support the inclusion of individual lesion response in the RECIST to improve the assessment of drug efficacy and patient survival.

The objective of this study is to document the prevalence of traditional, complementary, and alternative medicine (TCAM) use by adult cancer patients at a national teaching hospital in Malawi. We aim to document the products/therapies used, the reason for use, as well as patient-reported satisfaction with TCAM practitioners and modalities.We conducted investigator-administered interviews with adult cancer patients presenting to the Kamuzu Central Hospital (KCH) Cancer Clinic in Lilongwe, Malawi between January and July 2018. The KCH is a national teaching hospital in the capital of Lilongwe, which serves patients with cancer from the northern half of Malawi. Descriptive statistics were used to describe TCAM use and logistic regression was applied to identify predictors of TCAM.A total of 263 participants completed the survey, of which 70% (n = 183) were female and average age was 45 (SD 14) years old. The prevalence of overall TCAM use was 84% (n = 222), and 60% (n = 157) of participants reported combining TCAM with conventional cancer treatment. The majority of patients used TCAM to directly treat their cancer versus for symptom management. Patients reported using faith-based healing (64%, n = 168), herbal medicine (56%, n = 148), diet change (46%, n = 120), and vitamins/minerals (23%, n = 61). Participants reported the highest satisfaction for physicians among practitioners and diet change for modalities. Female gender was found to be a predictor of TCAM with conventional treatment use, no other significant predictors were observed.There is a high prevalence of TCAM use among an adult population with cancer in Malawi, and a wide variety in the TCAM modalities used among patients. Additional studies are needed to identify risks and benefits of TCAM use to assist with policy and public health, patient safety, and holistically address the global burden of cancer.

To identify associations with either early or late permanent pacemaker (PPM) implantation after transcatheter aortic valve replacement (TAVR) in order to develop an easily interpretable management algorithm.Injury to the conduction system after TAVR occasionally requires PPM. There is limited data on how to identify which patients will require PPM, particularly after discharge from index hospitalization after TAVR.All patients having undergone TAVR at the University of North Carolina through August 2019 were identified and records were manually reviewed. Multivariable analyses were performed to identify associations with post-TAVR PPM due to high-degree atrioventricular block (HAVB). Comparisons were made between patients with no PPM (n = 304) and PPM required, stratified into early (during index hospitalization, n = 32) and late (during subsequent hospitalization, n = 11) PPM cohorts.Of the 347 patents included for analysis, 43 (12.4%) underwent post-TAVR PPM. In multivariable regression models, early PPM was associated with baseline bifascicular block (OR: 42.16; p .001), requiring any pacing on first post-TAVR electrocardiogram (ECG) (OR: 31.55; p .001), and valve oversizing15% (OR: 3.61; p .05). Late PPM was associated with baseline right bundle branch block (RBBB) (OR 12.62; p .001) and history of atrial fibrillation/flutter (OR 4.83; p .05).Bifascicular block, any pacing on first post-TAVR ECG, and15% valve oversizing are associated with early PPM, while RBBB and history of atrial fibrillation/flutter are associated with late PPM. We suggest a management strategy for post-TAVR surveillance and management of HAVB.

Study objective Our study sought to determine whether or not nitrous oxide analgesia decreases pain compared to oxygen placebo during external cephalic version for breech presentation. Procedural success may be limited by pain and anxiety. Although neuraxial anesthesia has been studied extensively for these procedures, many centers lack resources for routine use. Nitrous oxide is noninvasive, has minimal side effects and requires limited facilities. We hypothesized that its analgesic properties would decrease pain compared to oxygen placebo during external cephalic version. Design Double-blinded randomized placebo-controlled trial. Setting Labor and delivery triage room. Patients Forty-eight patients, ≥18 years of age, 37-weeks' gestation or beyond, singleton pregnancy, breech presentation, and American Society of Anesthesiology physical status I-III, having an external cephalic version. Interventions Patients undergoing external cephalic version were randomized to receive self-administered 50% nitrous oxide/50% oxygen versus 100% oxygen placebo. Measurements The primary outcome measured was intra-procedural pain. Secondary outcomes were intra-procedural anxiety, patient satisfaction, and procedure difficulty. Main results Forty-eight patients were enrolled; 23 received nitrous oxide and 25 received oxygen. No difference was noted in mean pain scores (nitrous oxide 5.5 ± 2.3, placebo 5.4 ± 2.7, [CI95% = −1.40, 1.51]; P = 0.943) or anxiety scores (nitrous oxide 1.6 ± 2.0, placebo 1.2 ± 1.8, [CI95% = −0.74, 1.45]; P = 0.515). Procedural difficulty (1–10 scale, 1 = very easy, 10 = extremely difficult) was not different between groups (nitrous oxide 6.1 ± 2.2, placebo 6.1 ± 3.2, [CI95% = −1.54, 1.66]; P = 0.944). There was a significant increase in the number of version attempts in the nitrous oxide group (nitrous oxide 3.9 ± 1.9, placebo 2.8 ± 1.4, [CI95% = 0.05, 2]; P = 0.046). Patient satisfaction was significantly lower in the nitrous oxide group (nitrous oxide 4.3 ± 4.0, placebo 6.9 ± 3.6, [CI95% = −4.93, −0.34]; P = 0.025). Conclusion Despite the desirable properties of nitrous oxide, there was no analgesic benefit over oxygen for external cephalic version. Its routine use for these procedures was not supported.

Converging evidence shows that Alzheimer’s disease (AD) is a neurodegenerative disease that represents a disconnection syndrome, whereby a large-scale brain network is progressively disrupted by one or more neuropathological processes. However, the mechanism by which pathological entities spread across a brain network is largely unknown. Since pathological burden may propagate trans-neuronally, we propose to characterize the propagation pattern of neuropathological events spreading across relevant brain networks that are regulated by the organization of the network. Specifically, we present a novel mixed-effect model to quantify the relationship between longitudinal network alterations and neuropathological events observed at specific brain regions, whereby the topological distance to hub nodes, high-risk AD genetics, and environmental factors (such as education) are considered as predictor variables. Similar to many cross-section studies, we find that AD-related neuropathology preferentially affects hub nodes. Furthermore, our statistical model provides strong evidence that abnormal neuropathological burden diffuses from hub nodes to non-hub nodes in a prion-like manner, whereby the propagation pattern follows the intrinsic organization of the large-scale brain network.

Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a survey variable to improve the estimation precision. We consider the generalized regression estimator formulated under the model-assisted framework in which a regression model is utilized to make use of the available covariates while the estimator still has basic design-based properties. The generalized regression estimator has been shown to improve the efficiency of the design-based Horvitz-Thompson estimator when the number of covariates is fixed. In this study, we investigate the performance of the generalized regression estimator when the number of covariates p is allowed to diverge as the sample size n increases. We examine two approaches where the model parameter is estimated using the weighted least squares method when p < n and the LASSO method when the model parameter is sparse. We show that under an assisted model and certain conditions on the joint distribution of the covariates as well as the divergence rates of n and p, the generalized regression estimator is asymptotically more efficient than the Horvitz-Thompson estimator, and is robust against model misspecification. We also study the consistency of variance estimation for the generalized regression estimator. Our theoretical results are corroborated by simulation studies and an example.