PM2.5 constituents associated with childhood obesity and larger BMI growth trajectory: A 14-year longitudinal study.

BACKGROUND: The association of specific PM2.5 chemical constituents with childhood overweight or obesity (OWOB) remain unclear. Furthermore, the long-term impacts of PM2.5 exposure on the trajectory of children's body mass index (BMI) have not been explored. METHODS: We conducted a longitudinal study among 1,450,830 Chinese children aged 6-19 years from Beijing and Zhongshan in China during 2005-2018 to examine the associations of PM2.5 and its chemical constituents with incident OWOB risk. We extracted PM2.5 mass and five main component exposure from Tracking Air Pollution in China (TAP) dataset. Cox proportional hazards models were applied to quantify exposure-response associations. We further performed principal component analysis (PCA) to handle the multi-collinearity and used quantile g-computation (QGC) approach to analyze the impacts of exposure mixtures. Additionally, we selected 125,863 children with at least 8 physical examination measurements and combined group-based trajectory models (GBTM) with multinomial logistic regression models to explore the impacts of exposure to PM2.5 mass and five constituents on BMI and BMI Z-score trajectories during 6-19 years. RESULTS: We observed each interquartile range increment in PM2.5 exposure was significantly associated with a 5.1 % increase in the risk of incident OWOB (95 % confidence Interval [CI]: 1.036-1.066). We also found black carbon, sulfate, organic matter, often linked to fossil combustion, had comparable or larger estimates of the effect (HR = 1.139-1.153) than PM2.5. Furthermore, Exposure to PM2.5 mass, sulfate, nitrate, ammonium, organic matter and black carbon was significantly associated with an increased odds of being in a larger BMI trajectory and being assigned to persistent OWOB trajectory. CONCLUSIONS: Our findings provide evidence that the constituents mainly from fossil fuel combustion may have a perceptible influence on increased OWOB risk associated with PM2.5 exposure in China. Moreover, long-term exposure to PM2.5 contributes to an increased odds of being in a lager BMI and a persistent OWOB trajectories.

Efficacy of high-dose-rate brachytherapy with different radiation source activities among cervical cancer patients and risk factors for long-term outcomes: A 6-year retrospective study.

PURPOSE: This study aimed to assess the impact of dose rates due to natural decay of Iridium-192 sources and the risk factors of clinical outcomes for cervical cancer patients treated with high-dose-rate (HDR) brachytherapy. METHODS AND MATERIALS: Four ninety-four patients were divided into relatively-high-radioactive (rHR), relatively-medium-radioactive (rMR), and relatively-low-radioactive (rLR) groups for retrospective treatment response comparison. The short-term outcomes were evaluated using the 1-month /3-month follow-up results based on RECIST 1.1. Local recurrence-free survival (LRFS) and metastatic recurrence-free survival (MRFS) were selected as long-term outcomes. A class of transformation models with adaptive lasso was applied to assess the risk factors of long-term outcomes. RESULTS: No significant difference was identified in short- or long-term outcomes of different radioactive groups. Subgroup analyses demonstrated similar findings. In multivariate factor analysis, advanced stage was significantly associated with higher risk of local recurrence and metastatic recurrence (HR = 1.66, 95%confidence interval [CI] = 1.14-2.43, p = 0.008; HR = 1.57, 95%CI = 1.23-2.00, p < 0.001). Significant associations were observed between local recurrence and pathology, and between metastatic recurrence and pre-treatment serum indices, respectively (HR = 8.62, 95%CI = 2.28-32.60, p = 0.002; HR = 1.98, 95%CI=1.20-2.26, p = 0.008). CONCLUSIONS: Overall, there was no significant difference in long- or short-term efficacy of the HDR brachytherapy among the groups with different levels of activity of radiation sources. Stage, pathology, and pretreatment serum indices were crucial factors that affected the long-term outcomes.

Impact of Comorbidity on the Duration from Symptom Onset to Death in Patients with Coronavirus Disease 2019: A Retrospective Study of 104,753 Cases in Pakistan.

(1) Background: The evidence indicates that comorbidities are associated with an increase in the risk of death from coronavirus disease 2019 (COVID-19). It is unclear whether such an association is different for various combinations of chronic disease comorbidities. (2) Methods: From 16 March 2020 to 30 November 2021, 104,753 patients with confirmed COVID-19 from Khyber Pakhtunkhwa Province, Pakistan, were studied to determine the association between comorbidities and the duration from symptom onset to death in patients with COVID-19 by stratifying their comorbidity status. (3) Results: The patients with comorbidities had an 84% (OR, 0.16; 95% CI, 0.14 to 0.17) decrease in the duration from symptom onset to death, as opposed to patients without a comorbidity. Among the patients with only one comorbidity, chronic lung disease (OR, 0.06; 95% CI, 0.03 to 0.09) had a greater impact on the duration from symptom onset to death than hypertension (OR, 0.15; 95% CI, 0.13 to 0.18) or diabetes (OR, 0.15; 95% CI, 0.12 to 0.18). The patients with both hypertension and diabetes had the shortest duration (OR, 0.17; 95% CI, 0.14 to 0.20) among the patients with two comorbidities. (4) Conclusions: Comorbidity yielded significant adverse impacts on the duration from symptom onset to death in COVID-19 patients in Pakistan. The impact varied with different combinations of chronic disease comorbidities in terms of the number and type of comorbidities.

The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model.

PURPOSE: By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations. METHODS: Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed. RESULTS: In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score. CONCLUSION: The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.

Variable selection for mixed panel count data under the proportional mean model.

Mixed panel count data have attracted increasing attention in medical research based on event history studies. When such data arise, one either observes the number of event occurrences or only knows whether the event has happened or not over an observation period. In this article, we discuss variable selection in event history studies given such complex data, for which there does not seem to exist an established procedure. For the problem, we propose a penalized likelihood variable selection procedure and for the implementation, an expectation-maximization algorithm is developed with the use of the coordinate descent algorithm in the M-step. Furthermore, the oracle property of the proposed method is established, and a simulation study is performed and indicates that the proposed method works well in practical scenarios. Finally, the method is applied to identify the risk factors associated with medical non-adherence arising from the Sequenced Treatment Alternatives to Relieve Depression Study.

Prognostic Factors Analysis of Metastatic Recurrence in Cervical Carcinoma Patients Treated with Definitive Radiotherapy: A Retrospective Study Using Mixture Cure Model.

OBJECTIVES: This study aims to identify prognostic factors associated with metastatic recurrence-free survival of cervical carcinoma (CC) patients treated with radical radiotherapy and assess the cure probability of radical radiotherapy from metastatic recurrence. METHODS: Data were from 446 cervical carcinoma patients with radical radiotherapy for an average follow up of 3.96 years. We applied a mixture cure model to investigate the association between metastatic recurrence and prognostic factors and the association between noncure probability and factors, respectively. A nonparametric test of cure probability under the framework of a mixture cure model was used to examine the significance of cure probability of the definitive radiotherapy treatment. Propensity-score-matched (PSM) pairs were generated to reduce bias in subgroup analysis. RESULTS: Patients in advanced stages (p = 0.005) and those with worse treatment responses in the 3rd month (p = 0.004) had higher metastatic recurrence rates. Nonparametric tests of the cure probability showed that 3-year cure probability from metastatic recurrence was significantly larger than 0, and 5-year cure probability was significantly larger than 0.7 but no larger than 0.8. The empirical cure probability by mixture cure model was 79.2% (95% CI: 78.6-79.9%) for the entire study population, and the overall median metastatic recurrence time for uncured patients (patients susceptible to metastatic recurrence) was 1.60 (95% CI: 1.51-1.69) years. Locally advanced/advanced stage was a risk factor but non-significant against the cure probability (OR = 1.078, p = 0.088). The interaction of age and activity of radioactive source were statistically significant in the incidence model (OR = 0.839, p = 0.025). In subgroup analysis, compared with high activity of radioactive source (HARS), low activity of radioactive source (LARS) significantly contributed to a 16.1% higher cure probability for patients greater than 53 years old, while cure probability was 12.2% lower for the younger patients. CONCLUSIONS: There was statistically significant evidence in the data showing the existence of a large amount of patients cured by the definitive radiotherapy treatment. HARS is a protective factor against metastatic recurrence for uncured patients, and young patients tend to benefit more than the elderly from the HARS treatment.

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models.

BACKGROUND: To evaluate the diagnostic value of combinations of tumor markers carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, and CA19-9 in identifying malignant pleural effusion (MPE) from non-malignant pleural effusion (non-MPE) using machine learning, and compare the performance of popular machine learning methods. METHODS: A total of 319 samples were collected from patients with pleural effusion in Beijing and Wuhan, China, from January 2018 to June 2020. Five machine learning methods including Logistic regression, extreme gradient boosting (XGBoost), Bayesian additive regression tree, random forest, and support vector machine were applied to evaluate the diagnostic performance. Sensitivity, specificity, Youden's index, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of different diagnostic models. RESULTS: For diagnostic models with a single tumor marker, the model using CEA, constructed by XGBoost, performed best (AUC = 0.895, sensitivity = 0.80), and the model with CA153, also by XGBoost, showed the largest specificity 0.98. Among all combinations of tumor markers, the combination of CEA and CA153 achieved the best performance (AUC = 0.921, sensitivity = 0.85) in identifying MPE under the diagnostic model constructed by XGBoost. CONCLUSIONS: Diagnostic models for MPE with a combination of multiple tumor markers outperformed the models with a single tumor marker, particularly in sensitivity. Using machine learning methods, especially XGBoost, could comprehensively improve the diagnostic accuracy of MPE.

Stereotactic ablative brachytherapy with or without assistance of 3D-printing templates for inoperable locally recurrent or oligometastatic soft-tissue sarcoma: a multicenter real-world study.

The management of inoperable locally recurrent or oligometastatic soft-tissue sarcoma (STS) remains a clinical challenge. This study aimed to explore the long-term outcomes of stereotactic ablative brachytherapy (SABT) for these patients. Patients diagnosed with inoperable locally recurrent or oligometastatic STS from eight hospitals between 2006 and 2021 underwent iodine-125 (I-125) seed SABT, either with or without the assistance of three-dimensional (3D)-printing templates. The analysis concentrated on several key parameters, including objective response rate (ORR), disease control rate (DCR), local control time (LCT), overall survival (OS), adverse events (AEs), pain relief rate, and performance improvement rate. The ORR and DCR reached 78.3% and 95.0%, respectively. The results of multivariate logistic regression analysis indicated that a smaller tumor volume and a higher treatment dose were significantly associated with complete response (P < 0.001; P=0.036). The 1-, 3-, and 5-year LCT rates were 73.2%, 40.6%, and 37.9%, respectively. The 1-, 3-, and 5-year OS rates reached 83.1%, 50.5%, and 36.1%, respectively. Multivariate analysis revealed that a higher dose, a smaller tumor volume, and utilization of 3D-printing templates were significantly positive prognostic factors of LCT (P=0.006; P=0.007; P=0.034). Moreover, the tumor locations of trunk wall and extremities and lower tumor grade (G1/2) were significantly positive prognostic factors of survival (P=0.008; P=0.002). Pain relief rate was 88.0%, and the performance improvement rate was 46.7%. The AEs were predominantly of grade ≤ 2 and were well-tolerated. SABT seems to be an efficacious and safe alternative therapy for inoperable locally recurrent or oligometastatic STS.

Adverse Events of PD-1 or PD-L1 Inhibitors in Triple-Negative Breast Cancer: A Systematic Review and Meta-Analysis.

(1) Background: This study aimed to develop a comprehensive understanding of the treatment-related adverse events when using PD-1 or PD-L1 inhibitors in triple-negative breast cancer (TNBC). (2) Methods: We conducted a meta-analysis of Phase II/III randomized clinical trials. Studies were searched for using PubMed, Embase, and Cochrane Library from 1 March 1980 till 30 June 2022. Data on adverse events were mainly extracted from ClinicalTrials.gov and published articles. A generalized linear mixed model with the logit transformation was employed to obtain the overall incidence of adverse events across all studies. For serious adverse events with low incidences, the Peto method was used to calculate the odds ratio (OR) and 95% confidence interval (95%CI) in the PD-1 or PD-L1 inhibitors groups compared to the control groups. (3) Results: Nine studies were included in the meta-analysis, including a total of 2941 TNBC patients treated with PD-1 or PD-L1 inhibitors (including atezolizumab, pembrolizumab and durvalumab) and 2339 patients in the control groups. Chemotherapy alone was the control group in all studies. The average incidences of all serious immune-related adverse events of interest (hypothyroidism, hyperthyroidism, pneumonitis, pruritus, rash) were less than 1%, except for adrenal insufficiency (1.70%, 95%CI: 0.50-5.61%) in the PD-1 or PD-L1 groups. PD-1 or PD-L1 inhibitors significantly increased the risk of serious pneumonitis (OR = 2.52, 95%CI: 1.02-6.26), hypothyroidism (OR = 5.92, 95%CI: 1.22-28.86), alanine aminotransferase (ALT) elevation (OR = 1.66, 95%CI: 1.12-2.45), and adrenal insufficiency (OR = 18.81, 95%CI: 3.42-103.40). For non-serious adverse events, the patients treated with PD-1 or PD-L1 inhibitors had higher risk of aspartate aminotransferase (AST) elevation (OR =1.26, 95%CI: 1.02-1.57), hypothyroidism (OR = 3.63, 95%CI: 2.92-4.51), pruritus (OR = 1.84, 95%CI: 1.30-2.59), rash (OR = 1.29, 95%CI: 1.08-1.55), and fever (OR = 1.77, 95%CI: 1.13-2.77), compared with chemotherapy alone. (4) Conclusions: The incidence of serious immune-related adverse events in PD-1 or PD-L1 inhibitors groups is low but significantly higher than in chemotherapy groups. When using PD-1 or PD-L1 inhibitors for the treatment of TNBC, serious pneumonitis, hypothyroidism, ALT elevation, and adrenal insufficiency should be considered. Non-serious adverse events, such as AST elevation, rash, and fever, should also be taken into consideration.

AR Expression Correlates with Distinctive Clinicopathological and Genomic Features in Breast Cancer Regardless of ESR1 Expression Status.

Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with AR negativity in both ER-positive and ER-negative breast cancer, trying to elucidate the molecular correlation between AR and ER. Our results showed that AR negativity was associated with different clinicopathological characteristics and molecular features in ER-positive and ER-negative breast cancer. Moreover, AR-positive breast cancer has better clinicopathological features than AR-negative breast cancer, especially in the ER-negative subtype. These results suggest that the role of AR in ER-negative breast cancer is distinctive from that in ER-positive breast cancer.

Two-Dose Vaccination Significantly Prolongs the Duration from Symptom Onset to Death: A Retrospective Study Based on 173,894 SARS-CoV-2 Cases in Khyber Pakhtunkhwa, Pakistan.

This research was carried out to quantify the duration from symptom onset to recovery/death (SOR/SOD) during the first four waves and the Alpha/Delta period of the epidemic in Khyber Pakhtunkhwa, Pakistan, and identify the associated factors. A total of 173,894 COVID-19 cases were admitted between 16 March 2020 and 30 November 2021, including 458 intensive care unit (ICU) cases. The results showed that the case fatality rate (CFR) increased with age, and females had a higher CFR. The median SOR of ICU cases was longer than that of non-ICU cases (27.6 vs. 17.0 days), while the median SOD was much shorter (6.9 vs. 8.4 days). The SOR and SOD in the Delta period were slightly shortened than the Alpha period. Age, cardiovascular diseases, chronic lung disease, diabetes, fever, breathing issues, and ICU admission were risk factors that were significantly associated with SOD (p < 0.001). A control measure, in-home quarantine, was found to be significantly associated with longer SOD (odds ratio = 9.49, p < 0.001). Infected vaccinated individuals had longer SOD than unvaccinated individuals, especially for cases that had received two vaccine doses (p < 0.001). Finally, an advice on getting full-dose vaccination is given specifically to individuals aged 20-59 years.

Detection of Structural Variations and Fusion Genes in Breast Cancer Samples Using Third-Generation Sequencing.

Background: Structural variations (SVs) are common genetic alterations in the human genome that could cause different phenotypes and diseases, including cancer. However, the detection of structural variations using the second-generation sequencing was limited by its short read length, which restrained our understanding of structural variations. Methods: In this study, we developed a 28-gene panel for long-read sequencing and employed it to Oxford Nanopore Technologies and Pacific Biosciences platforms. We analyzed structural variations in the 28 breast cancer-related genes through long-read genomic and transcriptomic sequencing of tumor, para-tumor, and blood samples in 19 breast cancer patients. Results: Our results showed that some somatic SVs were recurring among the selected genes, though the majority of them occurred in the non-exonic region. We found evidence supporting the existence of hotspot regions for SVs, which extended our previous understanding that they exist only for single nucleotide variations. Conclusion: In conclusion, we employed long-read genomic and transcriptomic sequencing to identify SVs from breast cancer patients and proved that this approach holds great potential in clinical application.

Triple-Negative Apocrine Breast Carcinoma Has Better Prognosis despite Poor Response to Neoadjuvant Chemotherapy.

Apocrine carcinoma is a rare subtype of invasive ductal breast cancer that shows apocrine differentiation and largely triple-negative immunohistology. Triple-negative breast cancers are known to have more aggressive clinical courses. However, unlike most other subtypes, it is reported that triple-negative apocrine carcinoma (TNAC) has a better prognosis. Due to the scarcity of reported studies, our knowledge regarding its clinical behavior, prognosis and response to therapy is very limited. In this study, we retrospectively retrieved 41 triple-negative apocrine carcinoma cases from our breast cancer database, with an average follow-up of 32.8 months. It was found that TNAC had a poorer response to neoadjuvant therapy but a better prognosis than other nonapocrine types of triple-negative breast cancer. Meanwhile, TNAC has a low proliferative nature, as indicated by its low Ki-67 index. An updated analysis of the Surveillance, Epidemiology, and End Results database showed that chemotherapy did not improve breast-cancer-specific survival in TNAC patients. Our results suggest that TNAC is a special subtype of triple-negative breast cancer with a better short-term prognosis despite poor response to neoadjuvant chemotherapy.

Machine learning predicts cancer-associated deep vein thrombosis using clinically available variables.

PURPOSE: To develop and validate machine learning (ML) models for cancer-associated deep vein thrombosis (DVT) and to compare the performance of these models with the Khorana score (KS). METHODS: We randomly extracted data of 2100 patients with cancer between Jan. 1, 2017, and Oct. 31, 2019, and 1035 patients who underwent Doppler ultrasonography were enrolled. Univariate analysis and Lasso regression were applied to select important predictors. Model training and hyperparameter tuning were implemented on 70% of the data using a ten-fold cross-validation method. The remaining 30% of the data were used to compare the performance with seven indicators (area under the receiver operating characteristic curve [AUC], sensitivity, specificity, accuracy, balanced accuracy, Brier score, and calibration curve), among all five ML models (linear discriminant analysis [LDA], logistic regression [LR], classification tree [CT], random forest [RF], and support vector machine [SVM]), and the KS. RESULTS: The incidence of cancer-associated DVT was 22.3%. The top five predictors were D-dimer level, age, Charlson Comorbidity Index (CCI), length of stay (LOS), and previous VTE (venous thromboembolism) history according to RF. Only LDA (AUC = 0.773) and LR (AUC = 0.772) outperformed KS (AUC = 0.642), and combination with D-dimer showed improved performance in all models. A nomogram and web calculator https://webcalculatorofcancerassociateddvt.shinyapps.io/dynnomapp/ were used to visualize the best recommended LR model. CONCLUSION: This study developed and validated cancer-associated DVT predictive models using five ML algorithms and visualized the best recommended model using a nomogram and web calculator. The nomogram and web calculator developed in this study may assist doctors and nurses in evaluating individualized cancer-associated DVT risk and making decisions. However, other prospective cohort studies should be conducted to externally validate the recommended model.

Biomarker Alteration after Neoadjuvant Endocrine Therapy or Chemotherapy in Estrogen Receptor-Positive Breast Cancer.

In estrogen receptor (ER)-positive breast cancer, changes in biomarker expression after neoadjuvant therapy indicate the therapeutic response and are prognostic. However, there is limited information about the biomarker alteration caused by neoadjuvant endocrine therapy in ER-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. We recruited ER-positive/HER2-negative breast cancer patients who received neoadjuvant chemotherapy (NCT), neoadjuvant endocrine therapy (NET), or sequential neoadjuvant endocrine-chemotherapy (NECT) at Peking University Cancer Hospital from 2015 to 2021. A total of 579 patients had paired immunohistochemistry information in both diagnostic biopsy samples and post-neoadjuvant therapy surgical samples. Through a paired comparison of the immunohistochemical information in pre-treatment and post-treatment samples, we found that progesterone receptor (PR) expression reductions were more frequent than ER expression reductions (70.8% vs. 35.2%) after neoadjuvant therapy. The percentage of patients who had a decreased Ki-67 index in the post-operative samples was similar in the three groups (79.8% vs. 79.7% vs. 78.4%). Moreover, PR losses caused by NET were related to low baseline PR expression (p = 0.001), while we did not find a significant association between PR losses and Ki-67 reductions (p = 0.428) or ER losses (p = 0.274). All three types of neoadjuvant therapies caused a reduction in ER, PR, and Ki-67 expression. In conclusion, we found that PR loss after NET was only significantly related to low baseline PR expression, and there is no significant difference in the extent of prognostic factor change including Ki-67 and ER between the PR loss and non-loss groups.

Treatment-Related Adverse Events with PD-1 or PD-L1 Inhibitors: A Systematic Review and Meta-Analysis.

OBJECTIVE: to evaluate the risk of treatment-related adverse events of different severity and different system with PD-1 or PD-L1 inhibitors. METHODS: randomized controlled trials (RCTs) that using PD-1/PD-L1 for cancer treatment were searched in the PubMed, Embase, Cochrane Library, and Web of Science from 1 January 2019 to 31 May 2021. Adverse events data were extracted from clinical trials website or original article by two authors separately. Meta-analysis was used to determine risk ratio (RR) and 95% confidence interval (95% CI) of adverse events in PD-1/PD-L1 inhibitors groups compared to that of control groups. Subgroup analyses were also performed. RESULTS: a total of 5,807 studies were initially identified and after exclusion, 41 studies were included in meta-analysis. All the trials were international multicenter, randomized, phase II/III clinical trials, with the median follow-up of 27.5 months on average. Analysis of all grade adverse events showed that PD-1/PD-L1 inhibitors treatment significantly increased the risk of immune-related adverse events, including pruritus (RR: 2.34, 95% CI: 1.85-2.96), rash (RR: 1.53, 95% CI: 1.25-1.87), ALT elevation (RR 1.54, 95% CI 1.23-1.92), AST elevation (AST: RR 1.49, 95% CI 1.20-1.85), hepatitis (RR: 3.54, 95% CI: 1.96-6.38) and hypothyroid (RR: 5.29, 95% CI: 4.00-6.99) compared with that of control group. Besides that, PD-1/PD-L1 inhibitors were associated with higher risk of adverse events related to respiratory system including cough (RR: 1.33, 95% CI: 1.21-1.48), dyspnea (RR:1.23, 95% CI: 1.12-1.35) and chest pain (RR: 1.26, 95% CI: 1.07-1.47) compared with that of control groups in our meta-analysis and the dyspnea was taken high risk both in all grade and grade 3 or higher (RR: 1.55, 95% CI: 1.13-2.12). The risk of arthralgia was increased with PD-1/PD-L1 inhibitors (RR: 1.27, 95% CI: 1.10-1.47). Although the risk of myalgia was similar with PD-1/PD-L1 inhibitors and control groups, under subgroup analysis, PD-1/PD-L1 inhibitors decreased the risk of myalgia (RR: 0.56, 95% CI: 0.45-0.70) compared with that of chemotherapy. CONCLUSIONS: our results provide clear evidence that the risk of treatment-related adverse events in PD-1 or PD-L1 varies widely in different system. In particular, when using PD-1/PD-L1 inhibitors for oncology treatment, besides the common immune-related adverse events like pruritus, rash, hepatitis, and hypothyroid, the respiratory disorders and musculoskeletal disorders, such as cough, dyspnea, arthralgia, and myalgia, should also be taken into consideration.

Impact of radiation source activity on short- and long-term outcomes of cervical carcinoma patients treated with high-dose-rate brachytherapy: A retrospective cohort study.

OBJECTIVE: High-dose-rate (HDR) afterloading brachytherapy using Iridium-192 source involves large radiation activity varieties due to fast decay. It was unknown but clinically desirable to evaluate its impacts on patient outcomes to support more informed decisions. METHODS: Data of 510 cervical carcinoma (CC) patients were retrospectively included. High-radioactive (HR) and low-radioactive (LR) groups were statistically defined per patient-specific average mean-dose-rate (MDR) of all fractions. The cutoffs were calculated using R-3.6.1 packages based on significance of correlation with binary outcome or survival time. Categorized 1-month and 3-month follow-up results were analyzed as short-term outcomes. Long-term outcomes were evaluated using local recurrence-free survival (LRFS) and metastatic recurrence-free survival (MRFS). Propensity-score-matched (PSM) pairs were generated to reduce bias. RESULTS: The median follow-up time was 47.1 months (interquartile range: 33.9 months-66.4 months), involving MDR varieties of up to 9 folds ranging from 6059.99 cGy/h to 54013.66 cGy/h due to 17 source replacements at intervals ranging from 93 days-199 days. Both short-term (1-month: p = 0.22; 3-month: p = 0.79) and long-term (LRFS: p = 0.10; MRFS: p = 0.46) outcomes showed no significant difference between HR and LR. Subgroup analysis displayed significantly better results in LR for stage I-II (3-month, p = 0.02) and stage II (LRFS, p = 0.04) patients. Both LRFS and MRFS of LR were significantly non-inferior to HR (p ≤ 0.02). CONCLUSIONS: LR is clinically non-inferior or partially superior to HR for CC treatment using HDR, which dispels concerns of potentially undermined patient outcomes when source replacement is delayed.

Different patterns of treatment-related adverse events of programmed cell death-1 and its ligand-1 inhibitors in different cancer types: A meta-analysis and systemic review of clinical trials.

Programmed cell death receptor-1 and its ligand-1 (PD-1/PD-L1) inhibitors have been applied to many cancers, but the difference of treatment-related adverse events (AEs) across cancer types remains unknown. We performed a meta-analysis and systemic review to compare the incidences of commonly reported all-grade AEs across cancer types and found that the most frequent AEs were fatigue, rash/pruritus, loss of appetite/nausea and diarrhea. However, each cancer type also had its higher incidences of AEs involving a relevant system, such as melanoma with epidermal AEs (rash, diarrhea and enterocolitis), lung cancer with dyspnea and pneumonitis, digestive system cancers with amylase and lipase elevation; and renal cell and urothelial cancer with kidney injury (creatinine elevation and proteinuria). However, the incidence of hepatitis did not follow the pattern to show a difference. We did another comparison between PD-1 and PD-L1 inhibitors in lung cancer and urothelial cancer respectively, and found that the risk of most AEs did not differ much, except for more hypothyroidism in PD-1 inhibitors, and more kidney injury in PD-L1 inhibitors. Besides possible immunological mechanisms for treatment-related AEs, the influence of previous radiotherapy and the clinical characteristics of the diseases themselves should also be considered and is worth further investigation. With the result of this meta-analysis, clinicians could estimate the risk of certain AE in certain cancer type, to make treatment options and to customize monitor strategies.

SEMIPARAMETRIC TRANSFORMATION MODELS WITH MULTILEVEL RANDOM EFFECTS FOR CORRELATED DISEASE ONSET IN FAMILIES.

Large cohort studies are commonly launched to study risk of genetic variants or other risk factors on age at onset (AAO) of a chronic disorder. In these studies, family history data including AAO of disease in family members are collected to provide additional information and can be used to improve efficiency. Statistical analysis of these data is challenging due to missing genotypes in family members and the heterogeneous dependence attributed to both shared genetic back-ground and shared environmental factors (e.g., life style). In this paper, we propose a class of semiparametric transformation models with multilevel random effects to tackle these challenges. The proposed models include both proportional hazards model and proportional odds model as special cases. The multilevel random effects contain individual-specific random effects including kinship correlation structure dependent on the family pedigree, and a shared random effect to account for unobserved environment exposure. We use nonparametric maximum likelihood approach for inference and propose an expectation-maximization algorithm for computation in the presence of missing genotypes among family members. The obtained estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Simulation studies demonstrate that the proposed method performs well with finite sample sizes. Finally, the proposed method is applied to study genetic risks in an Alzheimer's disease study.

SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA.

In many clinical studies, patients may be asked to report their medication adherence, presence of side effects, substance use, and hospitalization information during the study period. However, the exact occurrence time of these recurrent events may not be available due to privacy protection, recall difficulty, or incomplete medical records. Instead, the only available information is whether the events of interest have occurred during the past period. In this paper, we call these incomplete recurrent events as repeated current status data. Currently, there are no valid standard methods for this kind of data. We propose to use the Andersen-Gill proportional intensity assumption to analyze such data. Specifically, we propose a maximum sieve likelihood approach for inference and we show that the proposed estimators for regression coefficients are consistent, asymptotically normal and attain semiparametric efficiency bounds. Simulation studies show that the proposed approach performs well with small sample sizes. Finally, our method is applied to study medication adherence in a clinical trial on non-psychotic major depressive disorder.