TRENDS IN CHOLESTEROL AND LIPOPROTEINS ARE ASSOCIATED WITH ACUTE RESPIRATORY DISTRESS SYNDROME INCIDENCE AND DEATH AMONG SEPSIS PATIENTS.

ABSTRACT: Objective: Compare changes in cholesterol and lipoprotein levels occurring in septic patients with and without acute respiratory distress syndrome (ARDS) and by survivorship. Methods: We reanalyzed data from prospective sepsis studies. Cholesterol and lipoprotein levels were analyzed using univariate testing to detect changes between septic patients with or without ARDS, and among ARDS survivors compared with nonsurvivors at enrollment (first 24 h of sepsis) and 48 to 72 h later. Results: 214 patients with sepsis were included of whom 48 had ARDS and 166 did not have ARDS. Cholesterol and lipoproteins among septic ARDS versus non-ARDS showed similar enrollment levels. However, 48 to 72 h after enrollment, change in median total cholesterol (48/72 h - enrollment) was significantly different between septic ARDS (-4, interquartile range [IQR] -23.5, 6.5, n = 35) and non-ARDS (0, -10.0, 17.5, P = 0.04; n = 106). When compared by ARDS survivorship, ARDS nonsurvivors (n = 14) had lower median total cholesterol levels (75.5, IQR 68.4, 93.5) compared with ARDS survivors (113.0, IQR 84.0, 126.8, P = 0.022), and lower median enrollment low-density lipoprotein cholesterol (LDL-C) levels (27, IQR 19.5-34.5) compared with ARDS survivors (43, IQR 27-67, P = 0.013; n = 33). Apolipoprotein A-I levels were also significantly lower in ARDS nonsurvivors (n = 14) (87.6, IQR 76.45-103.64) compared with ARDS survivors (130.0, IQR 73.25-165.47, P = 0.047; n = 33). At 48 to 72 h, for ARDS nonsurvivors, median levels of low-density lipoprotein cholesterol (9.0, IQR 4.3, 18.0; n = 10), LDL-C (17.0, IQR 5.0, 29.0; n = 9), and total cholesterol (59.0, 45.3, 81.5; n = 10) were significantly lower compared with ARDS survivors' (n = 25) levels of low-density lipoprotein cholesterol (20.0, IQR 12.0-39.0, P = 0.014), LDL-C (42.0, IQR 27.0-58.0, P = 0.019), and total cholesterol (105.0, IQR 91.0, 115.0, P = 0.003). Conclusions: Change in total cholesterol was different in septic ARDS versus non-ARDS. Total cholesterol, LDL-C, and apolipoprotein A-I levels were lower in ARDS nonsurvivors compared with survivors. Future studies of dysregulated cholesterol metabolism in septic ARDS patients are needed to understand biology and links to potential therapies.

HDL and persistent inflammation immunosuppression and catabolism syndrome.

PURPOSE OF REVIEW: This study reviews the mechanisms of HDL cholesterol immunomodulation in the context of the mechanisms of chronic inflammation and immunosuppression causing persistent inflammation, immunosuppression and catabolism syndrome (PICS) and describes potential therapies and gaps in current research. RECENT FINDINGS: Low HDL cholesterol is predictive of acute sepsis severity and outcome. Recent research has indicated apolipoprotein is a prognostic indicator of long-term outcomes. The pathobiologic mechanisms of PICS have been elucidated in the past several years. Recent research of the interaction of HDL pathways in related chronic inflammatory diseases may provide insights into further mechanisms and therapeutic targets. SUMMARY: HDL significantly influences innate and adaptive immune pathways relating to chronic disease and inflammation. Further research is needed to better characterize these interactions in the setting of PICS.

Lipid and Lipoprotein Dysregulation in Sepsis: Clinical and Mechanistic Insights into Chronic Critical Illness.

In addition to their well-characterized roles in metabolism, lipids and lipoproteins have pleiotropic effects on the innate immune system. These undergo clinically relevant alterations during sepsis and acute inflammatory responses. High-density lipoprotein (HDL) plays an important role in regulating the immune response by clearing bacterial toxins, supporting corticosteroid release, decreasing platelet aggregation, inhibiting endothelial cell apoptosis, reducing the monocyte inflammatory response, and inhibiting expression of endothelial cell adhesion molecules. It undergoes quantitative as well as qualitative changes which can be measured using the HDL inflammatory index (HII). Pro-inflammatory, or dysfunctional HDL (dysHDL) lacks the ability to perform these functions, and we have also found it to independently predict adverse outcomes and organ failure in sepsis. Another important class of lipids known as specialized pro-resolving mediators (SPMs) positively affect the escalation and resolution of inflammation in a temporal fashion. These undergo phenotypic changes in sepsis and differ significantly between survivors and non-survivors. Certain subsets of sepsis survivors go on to have perilous post-hospitalization courses where this inflammation continues in a low grade fashion. This is associated with immunosuppression in a syndrome of persistent inflammation, immunosuppression, and catabolism syndrome (PICS). The continuous release of tissue damage-related patterns and viral reactivation secondary to immunosuppression feed this chronic cycle of inflammation. Animal data indicate that dysregulation of endogenous lipids and SPMs play important roles in this process. Lipids and their associated pathways have been the target of many clinical trials in recent years which have not shown mortality benefit. These results are limited by patient heterogeneity and poor animal models. Considerations of sepsis phenotypes and novel biomarkers in future trials are important factors to be considered in future research. Further characterization of lipid dysregulation and chronic inflammation during sepsis will aid mortality risk stratification, detection of sepsis, and inform individualized pharmacologic therapies.

Glucagon Resistance and Decreased Susceptibility to Diabetes in a Model of Chronic Hyperglucagonemia.

Elevation of glucagon levels and increase in α-cell mass are associated with states of hyperglycemia in diabetes. Our previous studies have highlighted the role of nutrient signaling via mTOR complex 1 (mTORC1) regulation that controls glucagon secretion and α-cell mass. In the current studies we investigated the effects of activation of nutrient signaling by conditional deletion of the mTORC1 inhibitor, TSC2, in α-cells (αTSC2KO). We showed that activation of mTORC1 signaling is sufficient to induce chronic hyperglucagonemia as a result of α-cell proliferation, cell size, and mass expansion. Hyperglucagonemia in αTSC2KO was associated with an increase in glucagon content and enhanced glucagon secretion. This model allowed us to identify the effects of chronic hyperglucagonemia on glucose homeostasis by inducing insulin secretion and resistance to glucagon in the liver. Liver glucagon resistance in αTSC2KO mice was characterized by reduced expression of the glucagon receptor (GCGR), PEPCK, and genes involved in amino acid metabolism and urea production. Glucagon resistance in αTSC2KO mice was associated with improved glucose levels in streptozotocin-induced ß-cell destruction and high-fat diet-induced glucose intolerance. These studies demonstrate that chronic hyperglucagonemia can improve glucose homeostasis by inducing glucagon resistance in the liver.

How do physician demographics, training, social media usage, online presence, and wait times influence online physician review scores for spine surgeons?

OBJECTIVEThe purpose of this study was to assess the impact of certain demographics, social media usage, and physician review website variables for spine surgeons across Healthgrades.com (Healthgrades), Vitals.com (Vitals), and Google.com (Google).METHODSThrough a directory of registered North American Spine Society (NASS) physicians, we identified spine surgeons practicing in Texas (107 neurosurgery trained, 192 orthopedic trained). Three physician rating websites (Healthgrades, Vitals, Google) were accessed to obtain surgeon demographics, training history, practice setting, number of ratings/reviews, and overall score (January 2, 2018-January 16, 2018). Using only the first 10 search results from Google.com, we then identified whether the surgeon had a website presence or an accessible social media account on Facebook, Twitter, and/or Instagram.RESULTSPhysicians with either a personal or institutional website had a higher overall rating on Healthgrades compared to those who did not have a website (p < 0.01). Nearly all spine surgeons had a personal or institutional website (90.3%), and at least 1 accessible social media account was recorded for 43.5% of the spine surgeons in our study cohort (39.5% Facebook, 10.4% Twitter, 2.7% Instagram). Social media presence was not significantly associated with overall ratings across all 3 sites, but it did significantly correlate with more comments on Healthgrades. In multivariable analysis, increasing surgeon age was significantly associated with a lower overall rating across all 3 review sites (p < 0.05). Neurosurgeons had higher overall ratings on Vitals (p = 0.04). Longer wait times were significantly associated with a lower overall rating on Healthgrades (p < 0.0001). Overall ratings from all 3 websites correlated significantly with each other, indicating agreement between physician ratings across different platforms.CONCLUSIONSLonger wait times, increasing physician age, and the absence of a website are indicative of lower online review scores for spine surgeons. Neurosurgery training correlated with a higher overall review score on Vitals. Having an accessible social media account does not appear to influence scores, but it is correlated with increased patient feedback on Healthgrades. Identification of ways to optimize patients' perception of care are important in the future of performance-based medicine.

Analysis of Internet Review Site Comments for Spine Surgeons: How Office Staff, Physician Likeability, and Patient Outcome Are Associated With Online Evaluations.

STUDY DESIGN: Observational study. OBJECTIVE: To evaluate how online patient comments will affect website ratings for spine surgeons. SUMMARY OF BACKGROUND DATA: With the ever-growing utilization of physician review websites, healthcare consumers are assuming more control over whom they choose for care. We evaluated patient feedback and satisfaction scores of spine surgeons using comments from three leading physician rating websites: Healthgrades.com, Vitals.com, Google.com. This is the largest review of online comments and the largest review of spine surgeon comments. METHODS: From the North American Spine Society (NASS) membership directory, 210 spine surgeons practicing in Florida (133 orthopedic trained; 77 neurosurgery trained) with online comments available for review were identified, yielding 4701 patient comments. These were categorized according to subject: (1) surgeon competence, (2) surgeon likeability/character, (3) office staff, ease of scheduling, office environment. Type 1 and 2 comments were surgeon-dependent factors whereas type 3 comments were surgeon-independent factors. Patient comments also reported a score (1-5), 5 being the most favorable and 1 being the least favorable. RESULTS: There were 1214 (25.8%) comments from Healthgrades, 2839 (60.4%) from Vitals, and 648 (13.8%) from Google. 89.9% (4225) of comments pertained to surgeon outcomes and likeability (comment type 1 and 2), compared with 10.1% (476) surgeon-independent comments (comment type 3) (P < 0.0001). There was a significantly higher number of favorable ratings associated with surgeon-dependent comments (types 1 and 2) compared with surgeon-independent comments (type 3). Surgeon-independent comments were associated with significantly lower scores compared with comments regarding surgeon-dependent factors on all review sites. CONCLUSION: Spine surgeons are more likely to receive favorable reviews for factors pertaining to outcomes, likeability/character, and negative reviews based on ancillary staff interactions, billing, and office environment. Surgeons should continue to take an active role in modifying factors patients perceive as negative, even if not directly related to the physician. LEVEL OF EVIDENCE: 3.

How social media, training, and demographics influence online reviews across three leading review websites for spine surgeons.

BACKGROUND CONTEXT: The future of health care is consumer driven with a focus on outcome metrics and patient feedback. Physician review websites have grown in popularity and are guiding patients to certain health-care providers, for better or worse. No prior study has specifically evaluated Internet reviews of spine surgeons, determined if social media (SM) correlates with patient reviews, or evaluated Google as a physician review website. PURPOSE: This study aimed to evaluate patient satisfaction scores for spine surgeons in Florida using leading physician ratings websites. STUDY DESIGN: A retrospective study was carried out. SAMPLE POPULATION: The sample comprised spine surgeons with a review on Healthgrades.com (HG), Vitals.com (V), or Google.com (G) online rating websites as of August 17, 2017. OUTCOME MEASURES: Number of ratings, number of comments, overall rating, patient-reported wait times, physician website presence, and physician SM presence were the outcome measures. METHODS: Using the directory of registered North American Spine Society physicians, we identified all spine surgeons practicing in Florida (137 orthopedic trained; 78 neurosurgery trained). Surgeon demographics and ratings data were collected from three physician rating websites (HG, V, G) from July 19, 2017 to August 17, 2017. Using only the first 10 search results from Google.com we then identified if the surgeon had accounts on Facebook (FB), Twitter (TW), or Instagram (IG). RESULTS: Nearly every surgeon in this cohort had either an institutional or personal website (98.1%), and 38.6% had at least one SM outlet of our three reviewed. Both personal and institutional website presence significantly correlated with higher G scores. Spine surgeons with a searchable account on FB, TW, or IG made up 35.4%, 10.2%, and 0.5% of the cohort, respectively. Surgeons with an SM presence had a significantly higher number of ratings and comments on HG, V, and G, but not overall scores. In multivariable analysis, only V showed a significant inverse correlation between overall score and age, private institution, and orthopedic surgery training. Wait times >30 minutes were significantly associated with worse overall scores across all three review sites. Overall ratings between HG, V, and G all had significantly positive correlations on Pearson correlation analysis. CONCLUSION: Social media presence correlates with patient communication in the form of number of ratings and comments, yet does not impact overall scores, suggesting social media may influence patient feedback. Longer wait times are indicative of lower scores across all three platforms. Overall ratings from all three websites correlate significantly with each other, indicating agreement between physician ratings across different platforms. Understanding the factors that optimize a patient's overall experience with a physician is an important and emerging outcome measure for the future of patient-centered health care.

Fuzzy ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular data set.

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.