P2Y6 Receptor Activation Aggravates NLRP3-dependent Microglial Pyroptosis via Downregulation of the PI3K/AKT Pathway in a Mouse Model of Intracerebral Hemorrhage.

Pro-inflammatory signals generated after intracerebral hemorrhage (ICH) trigger a form of regulated cell death known as pyroptosis in microglia. White matter injury (WMI) refers to the condition where the white matter area of the brain suffers from mechanical, ischemic, metabolic, or inflammatory damage. Although the p2Y purinoceptor 6 (P2Y6R) plays a significant role in the control of inflammatory reactions in central nervous system diseases, its roles in the development of microglial pyroptosis and WMI following ICH remain unclear. In this study, we sought to clarify the role of P2Y6R in microglial pyroptosis and WMI by using an experimental mouse model of ICH. Type IV collagenase was injected into male C57BL/6 mice to induce ICH. Mice were then treated with MRS2578 and LY294002 to inhibit P2Y6R and phosphatidylinositol 3-kinase (PI3K), respectively. Bio-conductivity analysis was performed to examine PI3K/AKT pathway involvement in microglial pyroptosis. Quantitative Real-Time PCR, immunofluorescence staining, and western blot were conducted to examine microglial pyroptosis and WMI following ICH. A modified Garcia test, corner turning test, and forelimb placement test were used to assess neurobehavior. Hematoxylin-eosin staining (HE) was performed to detect cells damage around hematoma. Increases in the expression of P2Y6R, NLRP3, ASC, Caspase-1, and GSDMD were observed after ICH. P2Y6R was only expressed on microglia. MRS2578, a specific inhibitor of P2Y6R, attenuated short-term neurobehavioral deficits, brain edema and hematoma volume while improving both microglial pyroptosis and WMI. These changes were accompanied by decreases in pyroptosis-related proteins and pro-inflammatory cytokines both in vivo and vitro. Bioinformatic analysis revealed an association between the PI3K/AKT pathway and P2Y6R-mediated microglial pyroptosis. The effects of MRS2578 were partially reversed by treatment with LY294002, a specific PI3K inhibitor. P2Y6R inhibition alleviates microglial pyroptosis and WMI and ameliorates neurological deficits through the PI3K/AKT pathway after ICH. Consequently, targeting P2Y6R might be a promising approach for ICH treatment.

Utilizing Clinical Trial Data to Assess Timing of Surgical Treatment for Emphysema Patients.

BACKGROUND: Lung volume reduction surgery (LVRS) and medical therapy are 2 available treatment options in dealing with severe emphysema, which is a chronic lung disease. However, or there are currently limited guidelines on the timing of LVRS for patients with different characteristics. OBJECTIVE: The objective of this study is to assess the timing of receiving LVRS in terms of patient outcomes, taking into consideration a patient's characteristics. METHODS: A finite-horizon Markov decision process model for patients with severe emphysema was developed to determine the short-term (5 y) and long-term timing of emphysema treatment. Maximizing the expected life expectancy, expected quality-adjusted life-years, and total expected cost of each treatment option were applied as the objective functions of the model. To estimate parameters in the model, the data provided by the National Emphysema Treatment Trial were used. RESULTS: The results indicate that the treatment timing strategy for patients with upper-lobe predominant emphysema is to receive LVRS regardless of their specific characteristics. However, for patients with non-upper-lobe-predominant emphysema, the optimal strategy depends on the age, maximum workload level, and forced expiratory volume in 1 second level. CONCLUSION: This study demonstrates the utilization of clinical trial data to gain insights into the timing of surgical treatment for patients with emphysema, considering patient age, observable health condition, and location of emphysema. HIGHLIGHTS: Both short-term and long-term Markov decision process models were developed to assess the timing of receiving lung volume reduction surgery in patients with severe emphysema.How clinical trial data can be used to estimate the parameters and obtain short-term results from the Markov decision process model is demonstrated.The results provide insights into the timing of receiving lung volume reduction surgery as a function of a patient's characteristics, including age, emphysema location, maximum workload, and forced expiratory volume in 1 second level.

Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.

OBJECTIVE: This study aims to establish an informative dynamic prediction model of treatment outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect cases when the current treatment plan may not be effective. MATERIALS AND METHODS: We used 122 267 follow-up records from 17 958 new cases of pulmonary TB in the Republic of Moldova. A dynamic prediction framework integrating landmark modeling and machine learning algorithms was designed to predict patient outcomes during the course of treatment. Sensitivity and positive predictive value (PPV) were calculated to evaluate performance of the model at critical time points. New measures were defined to determine when follow-up laboratory tests should be conducted to obtain most informative results. RESULTS: The random-forest algorithm performed better than support vector machine and penalized multinomial logistic regression models for predicting TB treatment outcomes. For all 3 outcome classes (ie, cured, not cured, and died after 24 months following treatment initiation), sensitivity and PPV of prediction models improved as more follow-up information was collected. Specifically, sensitivity and PPV increased from 0.55 to 0.84 and from 0.32 to 0.88, respectively, for the not cured class. CONCLUSION: The dynamic prediction framework utilizes longitudinal laboratory test results to predict patient outcomes at various landmarks. Sputum culture and smear results are among the important variables for prediction; however, the most recent sputum result is not always the most informative one. This framework can potentially facilitate a more effective treatment monitoring program and provide insights for policymakers toward improved guidelines on follow-up tests.

Evaluating Risk-Stratified HPV Catch-up Vaccination Strategies: Should We Go beyond Age 26?

BACKGROUND: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. HPV can cause genital warts and multiple types of cancers in females. HPV vaccination is recommended to youth age 11 or 12 years before sexual initiation to prevent onset of HPV-related diseases. For females who have not been vaccinated previously, catch-up vaccines are recommended through age 26. The extent to which catch-up vaccines are beneficial in terms of disease prevention and cost-effectiveness is questionable given that some women may have been exposed to HPV before receiving the catch-up vaccination. This study aims to examine whether the cutoff age of catch-up vaccination should be determined based on an individual woman's risk characteristic instead of a one-size-fits-all age 26. METHODS: We developed a microsimulation model to evaluate multiple clinical outcomes of HPV vaccination for different women based on a number of personal attributes. We modeled the impact of HPV vaccination at different ages on every woman and tracked her course of life to estimate the clinical outcomes that resulted from receiving vaccines. As the simulation model is risk stratified, we used extreme gradient boosting to build an HPV risk model estimating every woman's dynamic HPV risk over time for the lifetime simulation model. RESULTS: Our study shows that catch-up vaccines still benefit all women after age 26 from the perspective of clinical outcomes. Women facing high risk of HPV infection are expected to gain more health benefits compared with women with low HPV risk. CONCLUSIONS: From a cancer prevention perspective, this study suggests that the catch-up vaccine after age 26 should be deliberately considered.

Neuroendoscopic and microscopic transsphenoidal approach for resection of nonfunctional pituitary adenomas.

BACKGROUND: Nonfunctional pituitary adenoma is a common type of pituitary adenoma, which can lead to headache, visual field disturbance, and cranial nerve damage due to increased tumor volume. Neuroendoscopic and microscopic transsphenoidal approaches have been widely used in the resection of nonfunctional pituitary adenomas. However, the clinical efficacy in neuroendoscopic and microscopic surgery is still controversial. AIM: To explore the clinical efficacy of neuroendoscopic and microscopic transsphenoidal approach for resection of nonfunctional pituitary adenomas. METHODS: We retrospectively analyzed 251 patients with nonfunctional pituitary adenomas; 138 underwent neuroendoscopic surgery via transsphenoidal approach, and 113 underwent microscopic surgery via transsphenoidal approach between July 2010 and September 2015. All patients were followed up for > 6 mo. Gender, age, course of disease, tumor diameter, tumor location, and percentage of patients with headache, visual impairment, sexual dysfunction, and menstrual disorders were contrasted between the two groups to compare the difference of preoperative data. Cure rate, symptom improvement rate, recurrence rate, the postoperative hospital stay, operating time, intraoperative blood loss, and the incidence of postoperative complications were compared in order to evaluate the advantages and disadvantages of neuroendoscopic and microscopic surgery. RESULTS: There was no significant difference in cure rate, symptom improvement rate, and recurrence rate between neuroendoscopy group and microscopy group (82.6% vs 85.8%, P > 0.05; 90.6% vs 93.8%, P > 0.05; 5.1% vs 9.7%, P > 0.05). In the neuroendoscopy group, the postoperative hospital stay was 8.4 ± 0.6 d; operating time was 167.2 ± 9.6 min; intraoperative blood loss was 83.4 ± 9.3 mL, and the rates of diabetes insipidus and electrolyte imbalance were 4.3% and 8.0%, respectively. The corresponding results in the microscopic group were 11.2 ± 0.6 d, 199.7 ± 9.3 min, 138.8 ± 13.6 mL, and 32.7% and 20.4%, respectively. There were significant differences in postoperative hospital stay, operating time, intraoperative blood loss, and the rates of diabetes insipidus and electrolyte imbalance between the two groups (P < 0.05). CONCLUSION: Neuroendoscopic and microscopic transsphenoidal approaches have similar clinical efficacy for the resection of nonfunctional pituitary adenomas. Neuroendoscopic surgery reduces operating time, intraoperative bleeding, postoperative recovery, and complications.

Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration.

While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as rapid response team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations. The objective of this study is to evaluate CEWS-triggered RRT policies based on patient demographics and policy structures. Using retrospective data derived from a subset of electronic health records between December 2015 and December 2016 (6000 patients), we developed a microsimulation model with integrated regression analysis to compare RRT policies on subpopulations defined by age, gender, and comorbidities to find score thresholds that result in the lowest percent of time spent above critical CEWS values. Policies that rely on average scores were more sensitive to threshold changes compared to policies that rely on current value and change in the CEWS. Policy using score threshold 10 provided the lowest percentage of time under the critical condition for majority of subpopulations. The proposed model is a novel framework to simulate individual deterioration patterns and systematically evaluate RRT policies based on their impact on health conditions. Our work highlights the importance of integration of data-driven models into personalized care and represents a significant opportunity to inform biomedical and health informatics research on designing and evaluating EWS-based clinical interventions.

From Data to Improved Decisions: Operations Research in Healthcare Delivery.

BACKGROUND: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. METHODS: Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. EXAMPLES: We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. CONCLUSIONS: There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.

Strategic level proton therapy patient admission planning: a Markov decision process modeling approach.

A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no-shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI).

Analyzing factors associated with women's attitudes and behaviors toward screening mammography using design-based logistic regression.

We examined the factors associated with screening mammography adherence behaviors and influencing factors on women's attitudes toward mammography in non-adherent women. Design-based logistic regression models were developed to characterize the influencing factors, including socio-demographic, health related, behavioral characteristics, and knowledge of breast cancer/mammography, on women's compliance with and attitudes toward mammography using the 2003 Health Information National Trends Survey data. Findings indicate significant associations among adherence to mammography and marital status, income, health coverage, being advised by a doctor to have a mammogram, having had Pap smear before, perception of chance of getting breast cancer, and knowledge of mammography (frequency of doing mammogram) in both women younger than 65 and women aged 65 and older. However, number of visits to a healthcare provider per year and lifetime number of smoked cigarettes are only significant for women younger than 65. Factors significantly associated with attitudes toward mammography in non-adherent women are age, being advised by a doctor to have a mammogram, and seeking cancer information. To enhance adherence to mammography programs, physicians need to continue to advise their patients to obtain mammograms. In addition, increasing women's knowledge about the frequency and starting age for screening mammography may improve women's adherence. Financially related factors such as income and insurance are also shown to be significant factors. Hence, healthcare policies aimed at providing breast cancer screening services to underserved women will likely enhance mammography participation.

Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups.

This study quantifies breast cancer mortality in the presence of competing risks for complex patients. Breast cancer behaves differently in different patient populations, which can have significant implications for patient survival; hence these differences must be considered when making screening and treatment decisions. Mortality estimation for breast cancer patients has been a significant research question. Accurate estimation is critical for clinical decision making, including recommendations. In this study, a competing risks framework is built to analyze the effect of patient risk factors and cancer characteristics on breast cancer and other cause mortality. To estimate mortality probabilities from breast cancer and other causes as a function of not only the patient's age or race but also biomarkers for estrogen and progesterone receptor status, a nonparametric cumulative incidence function is formulated using data from the community-based Carolina Mammography Registry. Based on the log(-log) transformation, confidence intervals are constructed for mortality estimates over time. To compare mortality probabilities in two independent risk groups at a given time, a method with improved power is formulated using the log(-log) transformation.

The association of breast density with breast cancer mortality in African American and white women screened in community practice.

The effect of breast density on survival outcomes for American women who participate in screening remains unknown. We studied the role of breast density on both breast cancer and other cause of mortality in screened women. Data for women with breast cancer, identified from the community-based Carolina Mammography Registry, were linked with the North Carolina cancer registry and NC death tapes for this study. Cause-specific Cox proportional hazards models were developed to analyze the effect of several covariates on breast cancer mortality-namely, age, race (African American/White), cancer stage at diagnosis (in situ, local, regional, and distant), and breast density (BI-RADS( ® ) 1-4). Two stratified Cox models were considered controlling for (1) age and race, and (2) age and cancer stage, respectively, to further study the effect of density. The cumulative incidence function with confidence interval approximation was used to quantify mortality probabilities over time. For this study, 22,597 screened women were identified as having breast cancer. The non-stratified and stratified Cox models showed no significant statistical difference in mortality between dense tissue and fatty tissue, while controlling for other covariate effects (p value = 0.1242, 0.0717, and 0.0619 for the non-stratified, race-stratified, and cancer stage-stratified models, respectively). The cumulative mortality probability estimates showed that women with dense breast tissues did not have significantly different breast cancer mortality than women with fatty breast tissue, regardless of age (e.g., 10-year confidence interval of mortality probabilities for whites aged 60-69 white: 0.056-0.090 vs. 0.054-0.083). Aging, African American race, and advanced cancer stage were found to be significant risk factors for breast cancer mortality (hazard ratio >1.0). After controlling for cancer incidence, there was not a significant association between mammographic breast density and mortality, adjusting for the effects of age, race, and cancer stage.

Modeling the impact of comorbidity on breast cancer patient outcomes.

The objective of this paper is to model the impact of comorbidity on breast cancer patient outcomes (e.g., length of stay and disposition). Previous studies suggest that comorbidities may significantly affect mortality risks for breast cancer patients. The 2006 AHRQ Nationwide Inpatient Sample (NIS) is used to analyze the relationships among comorbidities (e.g., hypertension, diabetes, obesity, and mental disorder), total charges, length of stay, and patient disposition as a function of age and race. A multifaceted approach is used to quantify these relationships. A causal study is performed to explore the effect of various comorbidities on patient outcomes. Least squares regression models are developed to evaluate and compare significant factors that influence total charges and length of stay. Logistic regression is used to study the factors that may cause patient mortality or transferring. In addition, different survival models are developed to study the impact of comorbidity on length of stay with censoring information. This study shows the interactions and relationship among various comorbidities and breast cancer. It shows that certain hypertension may not increase length of stay and total charges; diabetes behaves differently among general population and breast cancer patients; mental disorder has an impact on patient disposition that affects true length of stay and charges, and obesity may have limited effect on patient outcomes. Moreover, this study will help to better understand the expenditure patterns for population subgroups with several chronic conditions and to quantify the impact of comorbidities on patient outcomes. Lastly, it also provides insight for breast cancer patients with comorbidities as a function of age and race.