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Myelosuppressive chemotherapy is effective for breast cancer but carries a potential risk of febrile neutropenia (FN). Clinical practice guidelines have recommended prophylaxis with granulocyte colony-stimulating factor (G-CSF) to reduce the incidence of FN in patients receiving chemotherapy. We aimed to examine the use of G-CSFs for primary prophylaxis for FN and to see whether it follows the guidelines. In addition, we examined the changes in the use of long-acting and short-acting G-CSFs in patients with breast cancer over the past ten years. This was a retrospective observational real-world study. The data were obtained from the clinical research database of three hospitals affiliated with Taipei Medical University. Patients with breast cancer who initiated their first chemotherapy regimen between January 1, 2011, and December 31, 2020, were identified by the ICD codes and their use of filgrastim or pegfilgrastim was identified by the Anatomical Therapeutic Chemical codes. Whether and how G-CSF was prescribed during the study patients' first chemotherapy regimen was examined, and the annual change in the total number of short- and long-acting G-CSFs prescribed to the study patients from 2011 to 2020 was analyzed. Among the 2,444 patients who were prescribed at least one of the examined 15 breast cancer chemotherapy drugs, 1,414 did not use any G-CSFs during their first chemotherapy regimen while 145 used G-CSFs for primary prophylaxis and 185 for treatment. Among the patients receiving high FN risk regimens, only 8.6% used G-CSF for primary prophylaxis. The average (± SD) number of days for short-acting G-CSF use was 2.3 (± 1.5) days with a median of 2 days. In addition, it was found that there was a significant reduction in long-acting G-CSF use (p = 0.03) whereas the changes in short-acting G-CSF use over time were not significant (p = 0.50). Our study results show that G-CSFs are used for primary prophylaxis in a small percentage of patients with breast cancer and the duration of short-acting G-CSF use is relatively short. Considering the significant clinical and economic impact of FN, it is hoped that the prescription patterns of G-CSFs observed can provide an important reference for future clinical practice and reimbursement policy.

To examine the cost-effectiveness of using granulocyte colony-stimulating factor (G-CSF) for primary or secondary prophylaxis in patients with breast cancer from the perspective of Taiwan's National Health Insurance Administration. A Markov model was constructed to simulate the events that may occur during and after a high-risk chemotherapy treatment. Various G-CSF prophylaxis strategies and medications were compared in the model. Effectiveness data were derived from the literature and an analysis of the National Health Insurance Research Database (NHIRD). Cost data were obtained from a published NHIRD study, and health utility values were also obtained from the literature. Sensitivity analyses were performed to assess the uncertainty of the cost-effectiveness results. In the base-case analysis, primary prophylaxis with pegfilgrastim had an incremental cost-effectiveness ratio (ICER) of NT$269,683 per quality-adjusted life year (QALY) gained compared to primary prophylaxis with lenograstim. The ICER for primary prophylaxis with lenograstim versus no G-CSF prophylaxis was NT$61,995 per QALY gained. The results were most sensitive to variations in relative risk of febrile neutropenia (FN) for pegfilgrastim versus no G-CSF prophylaxis. Furthermore, in the probabilistic sensitivity analysis, at a willingness-to-pay threshold of one times Taiwan's gross domestic product per capita, the probability of being cost-effective was 88.1% for primary prophylaxis with pegfilgrastim. Our study suggests that primary prophylaxis with either short- or long-acting G-CSF could be considered cost-effective for FN prevention in breast cancer patients receiving high-risk regimens.

The latest installment in SLACK's Pocket Guide series, The Pocket Guide to Medical Retina provides essential information on medical retina diseases along with multimodal imaging of each condition, perfect for medical students, residents, fellows, or comprehensive ophthalmologists.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1‐score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1‐score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5‐year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

•This was an observational real-world study to validate and compare existing prediction models in order to identify suitable models for breast cancer patients receiving chemotherapy.•Our results found that using only pretreatment hematology values had low sensitivity and positive predictive value for predicting febrile neutropenia.•The results of this study provide important information for clinicians when selecting models to identify patients at high-risk of febrile neutropenia. Breast cancer patients receiving chemotherapy may develop a serious complication called febrile neutropenia (FN). We aimed to validate and compare three existing FN prediction models for breast cancer patients receiving chemotherapy in Taiwan. This was a retrospective observational real-world study. Data were acquired from the clinical research databases of three study hospitals. Breast cancer patients who have received at least one antineoplastic chemotherapy drug were chosen for the analysis. For evaluating the occurrence of FN, we used both broad (a body temperature above 38°C with an absolute neutrophil count (ANC) below 0.5 × 109/L or a body temperature above 38°C with a diagnosis of neutropenia) and narrow definitions (having both fever and neutropenia diagnoses or having both neutropenia and infection diagnoses). Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each selected FN model. Among the 1903 patients identified, when the broad and narrow definitions of FN were applied, 70 (3.7%) and 60 (3.2%) patients developed FN in the first cycle, respectively. Using the broad FN definition, Aagaard's model was the highest in sensitivity (90.0%), followed by Chantharakhit's (40.0%) and Chen's (7.2%); in specificity, Chen's (93.6%) was the highest. In addition, the accuracy was highest with the Chen model (90.4%). All three models’ PPVs were low, ranging from 0.5% to 4.2%, but all three models’ NPVs were over 96.3%. When the narrow FN definition was used, Chantharakhit's model showed a relatively high improvement in sensitivity (53.3%) and PPV (3.9%) while negligible increases or even slight decreases were seen in the other two models and in the other performance indicators of Chantharakhit's model. The results of this study provide important information for clinicians when selecting models to identify patients at high-risk of FN. As the model performance observed was less than satisfactory, improving the prediction ability of the models is needed.

Guided by a computational docking analysis, about 30 Food and Drug Administration/European Medicines Agency (FDA/EMA)-approved small-molecule medicines were characterized on their inhibition of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). Of these small molecules tested, six displayed a concentration that inhibits response by 50% (IC50) value below 100 μM in inhibiting Mpro, and, importantly, three, that is, pimozide, ebastine, and bepridil, are basic molecules that potentiate dual functions by both raising endosomal pH to interfere with SARS-CoV-2 entry into the human cell host and inhibiting Mpro in infected cells. A live virus-based modified microneutralization assay revealed that bepridil possesses significant anti–SARS-CoV-2 activity in both Vero E6 and A459/ACE2 cells in a dose-dependent manner with low micromolar effective concentration, 50% (EC50) values. Therefore, the current study urges serious considerations of using bepridil in COVID-19 clinical tests.

Severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) and -2 (SARS-CoV-2) are beta-coronaviruses (β-CoVs) that have caused significant morbidity and mortality worldwide. Therefore, a better understanding of host responses to β-CoVs would provide insights into the pathogenesis of these viruses to identify potential targets for medical countermeasures. In this study, our objective is to use a systems biology approach to explore the magnitude and scope of innate immune responses triggered by SARS-CoV-1 and -2 infection over time in pathologically relevant human lung epithelial cells (Calu-3/2B4 cells). Total RNA extracted at 12, 24, and 48 hours after β-CoVs or mock infection of Calu-3/2B4 cells were subjected to RNA sequencing and functional enrichment analysis to select genes whose expressions were significantly modulated post-infection. The results demonstrate that SARS-CoV-1 and -2 stimulate similar yet distinct innate antiviral signaling pathways in pathologically relevant human lung epithelial cells. Furthermore, we found that many genes related to the viral life cycle, interferons, and interferon-stimulated genes (ISGs) were upregulated at multiple time points. Based on their profound modulation upon infection by SARS-CoV-1, SARS-CoV-2, and Omicron BA.1, four ISGs, i.e., bone marrow stromal cell antigen 2 ( BST2 ), Z-DNA Binding Protein 1 ( ZBP1 ), C-X-C Motif Chemokine Ligand 11 ( CXCL11 ), and Interferon Induced Transmembrane Protein 1 ( IFITM1 ), were identified as potential drug targets against β-CoVs. Our findings suggest that these genes affect both pathogens directly and indirectly through the innate immune response, making them potential targets for host-directed antivirals. Altogether, our results demonstrate that SARS-CoV-1 and SARS-CoV-2 infection induce differential effects on host innate immune responses.

Since 2012, several immune checkpoint inhibitors have been approved by the Taiwan FDA for various types of cancer treatment. However, none of them are covered by Taiwan National Health Insurance due to the fact that they are expensive, and there is a lack of clinical evidence as to their effectiveness. This study was aimed toward an exploration of clinical experiences with use of immune checkpoint inhibitors, including indications, prescription types, drug effectiveness, adverse drug event types, and incidence, all of which shall serve as references for future clinical drug use. This is a retrospective study focusing on three immune checkpoint inhibitors (ipilimumab, nivolumab, and pembrolizumab), which are available for cancer treatment in Taiwan. We collected data from medical records for the period from January 1st, 2015 to January 12th, 2017 at National Cheng Kung University Hospital (NCKUH), a medical center in southern Taiwan, and recorded these cases until May 31st, 2017. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method, and adverse drug reaction odds ratios were analyzed using a chi-square analysis. The 50 patients under consideration in this study had used any one of the immune checkpoint inhibitors in NCKUH. Non-small cell lung cancer (n = 24, 48%) accounted for the highest percentage, followed by hepatocellular carcinoma (n = 4, 8%). The median OS was not reached, and the PFS for all immunotherapies was 4.9 months. The median OS period and PFS for non-small cell lung cancer (NSCLC) patients were 13 and 4.9 months, respectively, which were similar to those in many clinical trials. For NSCLC patients, the OS and PFS were only 0.63 and 1.37 months for squamous cell type NSCLC, and for patients who were PD-L1 negative, the OS and PFS were only 11.53 and 2.6 months, respectively. The most common adverse events in this study included fatigue (42%), rashes (22%), nausea (20%), and fever (20%), while one patient developed severe deep venous thrombosis and tissue inflammation, which was not confirmed in previous clinical trials. The histological subtype, the intensity of the PD-L1 expression, and the timing of treatment affected the NSCLC therapeutic results. It is recommended that clinical tests be conducted in order to enhance therapeutic effectiveness. It is expected that more testing, observation-based studies, and research results will validate their efficacy and the tolerance levels of patients.

High-cost orphan drugs are becoming increasingly available to treat rare diseases that affect a relatively small population. Little attention has been given to the prevalence of rare diseases and their health-related economic burden in Taiwan. This study examined the national trends in the prevalence of rare diseases and their health-related economic burden (including medication costs) in Taiwan. Rare disease-related claims data from 2003-2014 (12 years) from the National Health Insurance Research Database were used in this study. We used a time series analysis to assess trends in the yearly rates of treated patients with rare diseases, overall healthcare use, and expenditures, including drugs. During the 12-year study period, the estimated prevalence of rare diseases increased from 10.57 to 33.21 per 100,000 population, an average rate of a 19.46% increase per year. Total health expenditures for treatment of rare diseases increased from US$18.65 million to US$137.44 million between 2003 and 2014, accounting for 0.68% of the total national health expenditures in 2014. Drug expenditures for treatment of rare diseases increased from US$13.24 million to US$121.98 million between 2003 and 2014, which accounted for 71.00% and 88.75% of the health expenditures for patients with rare diseases in 2003 and 2014, respectively. In 2014, we found a 20.43-fold difference in average health expenditures and a 69.46-fold difference in average drug expenditures between patients with rare diseases and the overall population. The prevalence of rare diseases and the related economic burden have grown substantially in Taiwan over the past 12 years, and these trends are likely to continue. Drug expenditures accounted for almost 90% of health expenditures for rare diseases. Further analyses are underway to examine the economic burden of individual rare diseases.