Impact of antibiotic use before definitive concurrent chemoradiation in patients with locally advanced non-small cell lung cancer.

PURPOSE: This study evaluated whether antibiotic treatment before chemoradiotherapy influenced outcomes in patients with locally advanced non-small cell lung cancer (LA-NSCLC). METHODS: The records of LA-NSCLC patients treated with chemoradiotherapy between 2010 and 2017 at West China Hospital of Sichuan University were retrospectively examined together with their antibiotic use (antibiotic type, duration of treatment, and time between discontinuation and chemoradiotherapy). The influence of antibiotics on progression-free survival (PFS) and overall survival (OS) was evaluated with Kaplan-Meier curves and univariate and multivariate Cox regression. RESULTS: Of 522 patients, 176 had received intravenous broad-spectrum antibiotics in the month before chemoradiotherapy. Antibiotic use was linked to both reduced PFS (7.9 vs. 13.4 months, p < 0.001) and OS (20.4 vs. 25.3 months, p = 0.049). Multivariate regression demonstrated that antibiotic treatment was an unfavorable independent prognostic factor for LA-NSCLC patients who received chemoradiotherapy (HR 1.234; 95% CI 1.019-1.494; p = 0.031). Prognosis was also influenced by antibiotic type, length of treatment, and interval between discontinuation and chemoradiotherapy initiation. ß­lactamase inhibitors were found to be the most harmful (median OS for ß­lactamase inhibitors/fluoroquinolones/cephalosporins: 16.5/19.9/25.9 months, p = 0.045). Cutoff values for interval and duration calculated by the X­tile procedure showed that intervals of 7-16 days or durations ≤ 6 days did not significantly affect OS relative to untreated patients (intervals: p = 0.9; duration: p = 0.93). CONCLUSION: Antibiotic treatment for longer than 6 days, especially with ß­lactamase inhibitors, was associated with poor prognosis. Furthermore, delaying chemoradiotherapy for 7-16 days after antibiotic discontinuation may reduce these negative effects.

Survival of Patients With Inoperable Non-Small Cell Lung Cancer With Baseline Severe Pulmonary Dysfunction: Impacts of Thoracic Radiotherapy and Predictive Analysis for Acute Radiation Pneumonitis.

BACKGROUND AND PURPOSE: Currently, there is no standard treatment for patients with lung cancer with deteriorated pulmonary function. In this study, we aimed to assess the efficacy of thoracic radiotherapy for unresectable non-small cell lung cancer (NSCLC) with baseline severe pulmonary dysfunction and severe acute radiation pneumonitis (SARP). METHODS: Patients were categorized into a radiotherapy group and a nonradiotherapy group, followed by analysis of clinical variables. A Cox regression was used to evaluate the impact of various factors on overall survival (OS). Each SARP factor's predictive value was assessed using logistic regression, receiver operating characteristic curve, and Kaplan-Meier analyses. RESULTS: The median OS in the radiotherapy group was 21.6 months vs 8.9 months in the nonradiotherapy group. Cox analysis revealed that chemotherapy (HR, 0.221; 95% CI, 0.149-0.329; P < .001) and radiotherapy (HR, 0.589; 95% CI, 0.399-0.869; P = .008) are independent prognostic factors for the current cohort. The data suggested that the ipsilateral lung V10 (ilV10, the percentage of the lung volume that received more than 10 Gy) was an independent predictor of SARP. CONCLUSIONS: Our findings suggested that thoracic radiotherapy might be associated with clinical benefits to inoperable NSCLC in patients with severe pulmonary dysfunction and that ilV10 may be involved in the prediction of risk for SARP in these patients.

Risk of Tendon Injury in Patients Treated With Fluoroquinolone (FQ) Versus Non-Fluoroquinolone Antibiotics for Community-Acquired Pneumonia (CAP).

BACKGROUND: Fluoroquinolones (FQs) are associated with increased risk of tendon injury but comparative risk versus other antibiotic options for the same indication has yet to be evaluated. OBJECTIVE: Describe the incidence (relative risk) of any tendon injury in patients receiving FQ compared with other (non-FQs) antibiotics for treatment of community-acquired pneumonia (CAP). METHODS: A retrospective propensity score weighted cohort study was performed to evaluate the association between FQ antibiotics and tendon injury risk at 2 time points (within 1 month and within 6 months of use) compared with non-FQ regimens for treatment of CAP. The evaluation was performed using the CCAE (MarketScan Commercial Claims and Encounters) and COB (Medicare Supplemental and Coordination of Benefits) databases from 2014 to 2020. Patients with ICD (International Classification of Diseases) 9/10 coding for outpatient pneumonia who were >18 years and without history of tendon injury were included. Patients with history of tendon injury, who received multiple antibiotic therapies for recurrent pneumonia, or who received both FQ and non-FQ regimens during the study period were excluded. Propensity score weighting was used to adjust for selection bias due to contributing risk factors, including demographics (age, sex), comorbidities (diabetes mellitus, chronic kidney disease), and concurrent medications (corticosteroids). RESULTS: At 1 month, the odds of tendon injury were estimated to be significantly higher (41.9%) in patients receiving FQs compared with those receiving a non-FQ-based regimen (odds ratio [OR] = 1.419, 95% confidence interval [CI] = [1.188-1.698]). The odds of tendon injury were also estimated to be higher (OR = 1.067, 95% CI = [0.975-1.173]) in the FQ population within 180 days, but this effect was not statistically significant. The most frequent sites of tendon injuries were rotator cuff, shoulder, and patellar tendon. CONCLUSIONS AND RELEVANCE: Prescribers should recognize the risk of tendon injury within 1 month of FQ use when considering treatment regimens for CAP and use alternative options with lower risk whenever possible.

Multiple herbicide resistance in a Cyperus difformis population in rice field from China.

Herbicide resistance is rapidly emerging in Cyperus difformis in rice fields across China. The response of a C. difformis population GX-35 was tested against five acetolactate synthase (ALS)-inhibiting herbicides, auxin herbicide MCPA and photosynthesis II (PSII)-inhibitor bentazone. Population GX-35 evolved multiple resistance to ALS-inhibiting herbicides (penoxsulam, bispyribac­sodium, pyrazosulfuron-ethyl, halosulfuron-methly and imazapic) and auxin herbicide MCPA, with resistance levels of 140-, 1253-, 578-, 18-, 13-, and 21-fold, respectively, compared to the susceptible population. In this population, ALS gene expression was similar to that of the susceptible population. However, an Asp376Glu mutation in ALS gene was observed, leading to reduced inhibition of in-vitro ALS activities by five ALS-inhibiting herbicides. Furthermore, CYP71D8, CYP77A3, CYP78A5 and three ABC transporter genes (cluster-14412.23067, cluster-14412.25321, and cluster-14412.24716) over-expressed in absence of penoxsulam. On the other hand, an UGT73C1 and an ABC transporter (cluster-14412.25038) were induced by penoxsulam. Additionally, both over-expression and induction were observed for CYP74, CYP71A1, UGT88A1 and an ABC transporter (cluster-14412.21723). The GX-35 population has indeed evolved multiple herbicide resistance in China. Therefore, a diverse range of weed control tactics should be implemented in rice field.

miRNA microarray profiling in patients with androgenic alopecia and the effects of miR-133b on hair growth.

OBJECTIVE: Androgenetic alopecia (AGA), a common alopecia, is often accompanied by abnormal expression of multiple miRNAs. This study aims to investigate abnormally expressed miRNAs in patients with AGA and their specific molecular mechanism. METHODS: miRNA microarray profiling and qRT-PCR validation were used to screen and verify abnormally expressed miRNAs in patients with AGA. Human hair follicles (HFs) were treated with different concentrations of dihydrotestosterone (DHT, 10-5, 10-6, 10-7 and 10-8 mol/L) for 10 days. The effects of DHT on HF growth, proliferation, and miRNA expression in cultured HFs were investigated using immunofluorescence staining and qRT-PCR. Moreover, human dermal papilla cells (HDPCs) were treated/transfected with a Wnt/ß-catenin pathway activator and/or miR-133b mimic, and then the CCK-8 assay was used to evaluate HDPC proliferation. qRT-PCR and Western blotting were used to measure the expression of Versican, ALP and ß-catenin RESULTS: miRNA microarray profiling identified 43 miRNAs that were significantly differentially expressed in AGA patients, and qRT-PCR verified that 8 miRNAs were significantly differentially expressed. The expression of miR-133b was abnormally high in AGA patients. DHT (10-5 mol/L) inhibited human HF growth and upregulated miR-133b expression, and DHT (10-7 mol/L) induced human HF growth and downregulated miR-133b expression. HDPC proliferation was inhibited, and the expression of ß-catenin was downregulated in the miR-133b mimic-transfected group compared with the control group (P < 0.05). Wnt/ß-catenin pathway activator treatment significantly promoted HDPC proliferation and upregulated the expression of ß-catenin (P < 0.05). In addition, the proliferation of HDPCs was not significantly different between the group cotreated with a Wnt/ß-catenin pathway activator and miR-133b mimic, and the control group (P > 0.05), but the expression of Versican and ALP was suppressed in the cotreatment group (P < 0.05) CONCLUSION: Our data indicated that patients with androgenic alopecia have specific miRNA expression profiles and that the abnormal expression of miR-133b may inactivate the Wnt/ß-catenin pathway and ultimately regulate hair growth.

Activation of Wnt/ß-catenin signaling is involved in hair growth-promoting effect of 655-nm red light and LED in in vitro culture model.

Activation of the Wnt/ß-catenin signaling pathway plays an important role in hair follicle morphogenesis and hair growth. Recently, low-level laser therapy (LLLT) was evaluated for stimulating hair growth in numerous clinical studies, in which 655-nm red light was found to be most effective and practical for stimulating hair growth. We evaluated whether 655-nm red light + light-emitting diode (LED) could promote human hair growth by activating Wnt/ß-catenin signaling. An in vitro culture of human hair follicles (HFs) was irradiated with different intensities of 655-nm red light + LED, 21 h7 (an inhibitor of ß-catenin), or both. Immunofluorescence staining was performed to assess the expression of ß-catenin, GSK3ß, p-GSK3ß, and Lef1 in the Wnt/ß-catenin signaling. The 655-nm red light + LED not only enhanced hair shaft elongation, but also reduced catagen transition in human hair follicle organ culture, with the greatest effectiveness observed at 5 min (0.839 J/cm2). Additionally, 655-nm red light + LED enhanced the expression of ß-catenin, p-GSK3ß, and Lef1, signaling molecules of the Wnt/ß-catenin pathway, in the hair matrix. Activation of Wnt/ß-catenin signaling is involved in hair growth-promoting effect of 655-nm red light and LED in vitro and therefore may serve as an alternative therapeutic option for alopecia.

Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning.

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and ß-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R2 > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics.

Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms.

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO2 array SERS sensor. SERS spectra with concentrations of different variants were collected using a portable Raman system. After appropriate spectral preprocessing, a deep learning algorithm, CoVari, is developed to predict both the viral variant species and concentrations. Using a 10-fold cross-validation strategy, the model achieves an average accuracy of 99.9% in discriminating between different virus variants and R2 values larger than 0.98 for quantifying viral concentrations of the three viruses, demonstrating the high quality of the detection. The limit of detection of the ACE2 SERS sensor is determined to be 10.472, 11.882, and 21.591 PFU/mL for SARS-CoV-2, SARS-CoV-2 B1, and CoV-NL63, respectively. The feature importance of virus classification and concentration regression in the CoVari algorithm are calculated based on a permutation algorithm, which showed a clear correlation to the biochemical origins of the spectra or spectral changes. In an unknown specimen test, classification accuracy can achieve >90% for concentrations larger than 781 PFU/mL, and the predicted concentrations consistently align with actual values, highlighting the robustness of the proposed algorithm. Based on the CoVari architecture and the output vector, this algorithm can be generalized to predict both viral variant species and concentrations simultaneously for a broader range of viruses. These results demonstrate that the SERS + CoVari strategy has the potential for rapid and quantitative detection of virus variants and potentially point-of-care diagnostic platforms.

Epidemiological trends and clinical outcomes of cryptococcosis in a medically insured population in the United States: a claims-based analysis from 2017 to 2019.

Background: Emerging risk factors highlight the need for an updated understanding of cryptococcosis in the United States. Objective: Describe the epidemiological trends and clinical outcomes of cryptococcosis in three patient groups: people with HIV (PWH), non-HIV-infected and non-transplant (NHNT) patients, and patients with a history of solid organ transplantation. Methods: We utilized data from the Merative Medicaid Database to identify individuals aged 18 and above with cryptococcosis based on the International Classification of Diseases, Tenth Revision diagnosis codes from January 2017 to December 2019. Patients were stratified into PWH, NHNT patients, and transplant recipients according to Infectious Diseases Society of America guidelines. Baseline characteristics, types of cryptococcosis, hospitalization details, and in-hospital mortality rates were compared across groups. Results: Among 703 patients, 59.7% were PWH, 35.6% were NHNT, and 4.7% were transplant recipients. PWH were more likely to be younger, male, identify as Black, and have fewer comorbidities than patients in the NHNT and transplant groups. Notably, 24% of NHNT patients lacked comorbidities. Central nervous system, pulmonary, and disseminated cryptococcosis were most common overall (60%, 14%, and 11%, respectively). The incidence of cryptococcosis fluctuated throughout the study period. PWH accounted for over 50% of cases from June 2017 to June 2019, but this proportion decreased to 47% from July to December 2019. Among the 52% of patients requiring hospitalization, 61% were PWH and 35% were NHNT patients. PWH had longer hospital stays. In-hospital mortality at 90 days was significantly higher in NHNT patients (22%) compared to PWH (7%) and transplant recipients (0%). One-year mortality remained lowest among PWH (8%) compared to NHNT patients (22%) and transplant recipients (13%). Conclusion: In this study, most cases of cryptococcosis were PWH. Interestingly, while the incidence remained relatively stable in PWH, it slightly increased in those without HIV by the end of the study period. Mortality was highest in NHNT patients.

Epidemiological trends of cryptococcosis in the US The epidemiology and outcomes of cryptococcosis across the United States have not been recently examined. This study analyzed an insured population from 2017 to 2019 and revealed a relatively stable incidence of cryptococcosis among people with HIV, while concurrently demonstrating a slightly increased incidence among individuals without HIV. Notably, mortality rates were highest among non-HIV-infected and non-transplant patients.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

INTRODUCTION: Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS: This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS: FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS: Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.

The Association between Type of Insurance Plan, Out-of-Pocket Cost, and Adherence to Antihypertensive Medications in Medicare Supplement Insurance Enrollees.

BACKGROUND: Medicare supplement insurance, or Medigap, covers 21% of Medicare beneficiaries. Despite offsetting some out-of-pocket (OOP) expenses, remaining OOP costs may pose a barrier to medication adherence. This study aims to evaluate how OOP costs and insurance plan types influence medication adherence among beneficiaries covered by Medicare Supplement plans. METHODS: We conducted a retrospective analysis of the MerativeTM MarketScan® Medicare Supplement Database (2017-2019) in Medigap enrollees (≥ 65 years) with hypertension. Proportion of days covered (PDC) was a continuous measure of medication adherence and was also dichotomized (PDC ≥ 0.8) to quantify adequate adherence. Beta-binomial and logistic regression models were used to estimate associations between these outcomes and insurance plan type and log-transformed OOP costs, adjusting for patient characteristics. RESULTS: Among 27,407 patients with hypertension, the average PDC was 0.68 ± 0.31; 47.5% achieved adequate adherence. A mean $1 higher in 30-day OOP costs was associated with a 0.06 (95% Confidence intervals [CI]: -0.09 - -0.03) lower probability of adequate adherence, or a 5% (95% C.I.: 4% - 7%) decrease in PDC. Compared to comprehensive plan enrollees, the odds of adequate adherence were lower among those with point-of-service plans (O.R.: 0.69, 95%C.I.: 0.62 - 0.77), but higher among those with preferred provider organization (PPO) plans (O.R.: 1.08, 95%C.I.: 1.01 - 1.15). Moreover, the association between OOP costs and PDC was significantly greater for PPO enrollees. CONCLUSIONS: While Medicare supplement insurance alleviates some OOP costs, different insurance plans and remaining OOP costs influence medication adherence. Reducing patient cost-sharing may improve medication adherence.

Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio.

BACKGROUND: The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients. METHODS: Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis. RESULTS: Our protocol describes the processes and methods for an observational study in the ICU. CONCLUSIONS: This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.

A route planning for oil sample transportation based on improved A* algorithm.

The traditional A* algorithm suffers from issues such as sharp turning points in the path, weak directional guidance during the search, and a large number of computed nodes. To address these problems, a modified approach called the Directional Search A* algorithm along with a path smoothing technique has been proposed. Firstly, the Directional Search A* algorithm introduces an angle constraint condition through the evaluation function. By converting sharp turns into obtuse angles, the path turning points become smoother. This approach reduces the occurrence of sharp turns in the path, resulting in improved path smoothness. Secondly, the algorithm enhances the distance function to strengthen the directional guidance during the path search. By optimizing the distance function, the algorithm tends to prefer directions that lead towards the target, which helps reduce the search space and shorten the overall path planning time. Additionally, the algorithm removes redundant nodes along the path, resulting in a more concise path representation. Lastly, the algorithm proposes an improved step size adjustment method to optimize the number of path nodes obtained. By appropriately adjusting the step size, the algorithm further reduces the number of nodes, leading to improved path planning efficiency. By applying these methods together, the Directional Search A* algorithm effectively addresses the limitations of the traditional A* algorithm and produces smoother and more efficient path planning results. Simulation experiments comparing the modified A* algorithm with the traditional A* algorithm were conducted using MATLAB. The experimental results demonstrate that the improved A* algorithm can generate shorter paths, with reduced planning time and smoother trajectories. This indicates that the Directional Search A* algorithm, incorporating the angle constraint condition in the evaluation function and the direction-guided strategy, outperforms the traditional A* algorithm in path planning and provides better solutions to the existing issues.

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

Racial and ethnic differences in patterns of use and discontinuation of long-acting injectable antipsychotics using Medicaid claims data.

Introduction: In general, racial and ethnic differences exist in antipsychotic prescription practices. However, little is known about such differences between individual long-acting injectable (LAI) antipsychotic formulations, specifically. This study's primary objective was to determine racial and ethnic differences among LAI antipsychotic use. Secondary objectives were to identify if discontinuation rates differed between agents and by race or ethnicity. Methods: International Classification of Diseases, 10th edition (ICD-10) codes were used to identify patients with schizophrenia and related disorders (18-64 years) who received an LAI antipsychotic between 2016 and 2020 using Merative Multi-State Medicaid databases. Using National Drug Code numbers for LAI antipsychotics, pharmacy claims were identified and data analyzed. Cochran-Mantel-Haenszel tests and odds ratio estimators were used to investigate conditional association between race or ethnicity and medication, while controlling for age, sex, health plan, and prescription year. Kaplan-Meier survival curves were examined, and stratified log-rank tests were conducted to compare the time until discontinuation distributions by race or ethnicity. Results: The analysis included 37 712 patients. Blacks received an LAI first-generation antipsychotic more often than Whites (OR: 1.64, 95% CI: [1.56, 1.73], Hispanics (OR: 1.46, 95% CI: [1.21, 1.75]) and others (OR: 1.44, 95% CI: [1.20, 1.73]). Aside from fluphenazine decanoate showing earlier discontinuation rates for Whites over Blacks (P = .02), no significant differences in discontinuation across race or ethnicity were identified. Discussion: Despite no significant differences in second-generation antipsychotic LAI discontinuation rates between Blacks and other racial or ethnic groups, Blacks received second-generation antipsychotic LAIs significantly less often than other groups. Further studies are needed to determine why differences may be occurring.

Evaluation of medication regimen complexity as a predictor for mortality.

While medication regimen complexity, as measured by a novel medication regimen complexity-intensive care unit (MRC-ICU) score, correlates with baseline severity of illness and mortality, whether the MRC-ICU improves hospital mortality prediction is not known. After characterizing the association between MRC-ICU, severity of illness and hospital mortality we sought to evaluate the incremental benefit of adding MRC-ICU to illness severity-based hospital mortality prediction models. This was a single-center, observational cohort study of adult intensive care units (ICUs). A random sample of 991 adults admitted ≥ 24 h to the ICU from 10/2015 to 10/2020 were included. The logistic regression models for the primary outcome of mortality were assessed via area under the receiver operating characteristic (AUROC). Medication regimen complexity was evaluated daily using the MRC-ICU. This previously validated index is a weighted summation of medications prescribed in the first 24 h of ICU stay [e.g., a patient prescribed insulin (1 point) and vancomycin (3 points) has a MRC-ICU = 4 points]. Baseline demographic features (e.g., age, sex, ICU type) were collected and severity of illness (based on worst values within the first 24 h of ICU admission) was characterized using both the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA) score. Univariate analysis of 991 patients revealed every one-point increase in the average 24-h MRC-ICU score was associated with a 5% increase in hospital mortality [Odds Ratio (OR) 1.05, 95% confidence interval 1.02-1.08, p = 0.002]. The model including MRC-ICU, APACHE II and SOFA had a AUROC for mortality of 0.81 whereas the model including only APACHE-II and SOFA had a AUROC for mortality of 0.76. Medication regimen complexity is associated with increased hospital mortality. A prediction model including medication regimen complexity only modestly improves hospital mortality prediction.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020.

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 µg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 µg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.

Involved-field irradiation or elective-nodal irradiation in neoadjuvant chemo-radiotherapy for locally-advanced esophageal cancer: comprehensive analysis for dosimetry, treatment-related complications, impact on lymphocyte, patterns of failure and survival.

Purpose: To compare the differences between involved-field irradiation (IFI) and elective nodal irradiation (ENI) in selecting the optimal target area for neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC). Materials and methods: We retrospectively analyzed 267 patients with LA-ESCC, of whom 165 underwent ENI and 102 underwent IFI. Dosimetry, treatment-related complications, pathological responses, recurrence/metastasis patterns, and survival were compared between the two groups. Results: The median follow-up duration was 27.9 months. The R0 resection rates in the IFI and ENI groups were 95.1% and 92.7%, respectively (p=0.441), while the pathological complete response (pCR) rates were 42.2% and 34.5%, respectively (p=0.12). The ENI group received higher radiation doses to the heart (HV30:23.9% vs. 18%, p=0.033) and lungs (LV30:7.7% vs. 4.9%, p<0.001) than the IFI group. Consequently, the ENI group showed a higher incidence of grade 2 or higher radiation pneumonitis (30.3% vs. 17.6%, p=0.004) and pericardial effusion (26.7% vs. 11.8%, p=0.021) than the IFI group. Post-operation fistulas were observed in 3 (2.9%) and 17 cases (10.3%) in the IFI and ENI groups, respectively (p=0.026). In the multivariate analysis, smoking, positive lymph node involvement (pN+), and anastomotic fistula were independent predictors of overall survival (OS). The pN+ patients exhibited a greater propensity for recurrence compared to pN- patients, especially in the first year of follow-up (6.67% vs. 0.56%, p=0.003). Conclusion: The ENI group had a higher incidence of radiation-induced adverse events compared to the IFI group, likely due to the higher radiation doses to normal tissues. Considering the similar disease-free survival (DFS) and OS rates in the two groups, IFI may be suitable for nCRT in patients with LA-ESCC, although further prospective studies are warranted.

Pericardial irradiation dose may be strongly associated with grade 4 lymphopenia and affect prognosis in patients with locally advanced esophageal cancer receiving definitive concurrent chemoradiotherapy.

BACKGROUND: The immune system may influence prognosis, and lymphopenia is a frequent side effect of concurrent chemoradiotherapy (CCRT). Radical irradiation for locally advanced esophageal cancer (LA-EC) exposes significant vascular and heart volumes. In this study, we hypothesized that lymphopenia is linked to cardiac and pericardial doses and affects patient prognosis. METHODS AND MATERIALS: We identified 190 LA-EC patients who received radical CCRT. Multivariate analysis (MVA) was performed to correlate clinical factors and dosimetric parameters with overall survival (OS). We collected lymphocyte-related variables and ratios before and during CCRT. MVA was performed to correlate hematologic toxicity with OS. The relationship between dosimetric parameters and G4 lymphopenia was determined using logistic stepwise regression. Finally, a nomogram of G4 lymphopenia was developed and validated externally. RESULTS: Median follow-up time for all patients was 27.5 months. On MVA for OS, higher pericardial V30 (PV30 ) was linked to worse survival (HR: 1.013, 95% CI: 1.001-1.026, p = 0.039). The median OS stratified by PV30 > 55.3% and PV30 ≤ 55.3% was 24.0 months and 54.0 months, respectively (p = 0.004). G4 lymphopenia was shown to be linked with worse OS in the MVA of hematological toxicity with OS (HR: 2.042, 95% CI: 1.335-3.126, p = 0.001). Thirty of the 100 patients in the training set had G4 lymphopenia. Logistic stepwise regression was used to identify variables associated with G4 lymphopenia, and the final model consisted of stage-IVA (p = 0.017), platelet-to-lymphocyte ratio during CCRT (p = 0.008), Heart V50 (p = 0.046), and PV30 (p = 0.048). Finally, a nomogram predicting G4 lymphocytopenia were constructed and externally validated. The ROC curve showed an AUC for internal validation of 0.775 and external validation of 0.843. CONCLUSION: Higher doses of pericardial radiation might affect LA-EC patients' prognosis by inducing G4 lymphopenia during CCRT. Further prospective studies are warranted to confirm these findings, especially in the era of immune-checkpoint inhibitor treatment.