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Background Contemporary pulmonary embolism (PE) research, in many cases, relies on data from electronic health records (EHRs) and administrative databases that use International Classification of Diseases (ICD) codes. Natural language processing (NLP) tools can be used for automated chart review and patient identification. However, there remains uncertainty with the validity of ICD-10 codes or NLP algorithms for patient identification. Methods The PE-EHR+ study has been designed to validate ICD-10 codes as Principal Discharge Diagnosis, or Secondary Discharge Diagnoses, as well as NLP tools set out in prior studies to identify patients with PE within EHRs. Manual chart review by two independent abstractors by predefined criteria will be the reference standard. Sensitivity, specificity, and positive and negative predictive values will be determined. We will assess the discriminatory function of code subgroups for intermediate- and high-risk PE. In addition, accuracy of NLP algorithms to identify PE from radiology reports will be assessed. Results A total of 1,734 patients from the Mass General Brigham health system have been identified. These include 578 with ICD-10 Principal Discharge Diagnosis codes for PE, 578 with codes in the secondary position, and 578 without PE codes during the index hospitalization. Patients within each group were selected randomly from the entire pool of patients at the Mass General Brigham health system. A smaller subset of patients will also be identified from the Yale-New Haven Health System. Data validation and analyses will be forthcoming. Conclusions The PE-EHR+ study will help validate efficient tools for identification of patients with PE in EHRs, improving the reliability of efficient observational studies or randomized trials of patients with PE using electronic databases.

Allergy information is often documented in diverse sections of the electronic health record (EHR). Systematically reconciling allergy information across the EHR is critical to improve the accuracy and completeness of patients' allergy lists and ensure patient safety. In this retrospective cohort study, we examined the prevalence of incompleteness, inaccuracy, and redundancy of allergy information for patients with a clinical encounter at any Mass General Brigham facility between January 1, 2018 and December 31, 2018. We identified 4 key places in the EHR containing reconcilable allergy information: 1) allergy modules (including free text comments and duplicate allergen entries), 2) medication laboratory tests results, 3) oral medication allergy challenge tests, and 4) medication orders that have been discontinued due to adverse drug reactions (ADRs). Within our cohort, 718,315 (45.2% of the total 1,588,979) patients had an active allergy entry; of which, 266,275 (37.1%) patient's records indicated a need for reconciliation.

Allergy information is often documented in diverse sections of the electronic health record (EHR). Systematically reconciling allergy information across the EHR is critical to improve the accuracy and completeness of patients’ allergy lists and ensure patient safety. In this retrospective cohort study, we examined the prevalence of incompleteness, inaccuracy, and redundancy of allergy information for patients with a clinical encounter at any Mass General Brigham facility between January 1, 2018 and December 31, 2018. We identified 4 key places in the EHR containing reconcilable allergy information: 1) allergy modules (including free text comments and duplicate allergen entries), 2) medication laboratory tests results, 3) oral medication allergy challenge tests, and 4) medication orders that have been discontinued due to adverse drug reactions (ADRs). Within our cohort, 718,315 (45.2% of the total 1,588,979) patients had an active allergy entry; of which, 266,275 (37.1%) patient’s records indicated a need for reconciliation.

Early prediction of clinical deterioration such as adverse events (AEs), improves patient safety. National Early Warning Score (NEWS) is widely used to predict AEs based on the aggregation of 6 physiological parameters. We took the same parameters as the features for AE prediction using deep learning algorithms (AEP-DLA) among hospitalized adult patients. The aim of this study is to get better performance than traditional naïve mathematical calculations by introducing novel vital sign data preprocessing schemes. We retrospectively collected the data from our electronic medical record data warehouse (2007 ~ 2017). AE rate of all 99,861 admissions was 6.2%. The dataset was divided into training and testing datasets from 2007–2015 and 2016–2017 respectively. In real-life clinical care, physiological parameters were not recorded every hour and missed frequently, for example, Glasgow Coma Scale (GCS). The expert domain suggested that missed GCS was rated as 15. We took two strategies (stack series records and align by hour) in the data preprocessing and tripling the values of negative samples for class balancing (CB). We used the last 28 hours’ serial data to predict AEs 3 hours later with Random Forest, XGBoost, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). It is shown that CNN with CB and align by hour got the best results comparing to the other methods. The precision, recall and area under curve were 0.841, 0.928 and 0.995 respectively. The performance of the model is also better than those proposed in the published literatures.

Purpose: Previous deep learning studies on optical coherence tomography (OCT) mainly focused on diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance. Design: Cross-sectional study. Participants: A total of 3,618 central fovea cross section OCT images from 1,475 eyes of 964 patients. Methods: We retrospectively collected 7,652 OCT images from 1,197 patients. From these images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model. Diagnostic results by four board-certified non-retinal specialized ophthalmologists on the testing dataset were compared with those generated by the DL model. Main Outcome Measures: We calculated for the derived DL model the following characteristics: sensitivity, specificity, F1 score and area under curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of the DL model was finally compared with that of non-retinal specialized ophthalmologists. Results: Regarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics in performance: sensitivity: 98.7%, specificity: 98.0%, and F1 score: 0.945. The accuracy on the training dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1% (95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the average non-retinal specialized ophthalmologists. Conclusions: An ophthalmologist-level DL model was built here to accurately identify ERM in OCT images. The performance of the model was slightly better than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist clinicians to promote the efficiency and safety of healthcare in the future.

ObjectiveTo develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon from clinical notes to support PASC symptom identification and research.MethodsWe identified 26,117 COVID-19 positive patients from the Mass General Brigham’s electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). The PASC symptom lexicon incorporated Unified Medical Language System® (UMLS) Metathesaurus concepts and synonyms based on selected semantic types. The MTERMS natural language processing (NLP) tool was used to automatically extract symptoms from a development dataset. The lexicon was iteratively revised with manual chart review, keyword search, concept consolidation, and evaluation of NLP output. We assessed the comprehensiveness of the lexicon and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus.ResultsThe PASC symptom lexicon included 355 symptoms consolidated from 1,520 UMLS concepts. NLP achieved an averaged precision of 0.94 and an estimated recall of 0.84. Symptoms with the highest frequency included pain (43.1%), anxiety (25.8%), depression (24.0%), fatigue (23.4%), joint pain (21.0%), shortness of breath (20.8%), headache (20.0%), nausea and/or vomiting (19.9%), myalgia (19.0%), and gastroesophageal reflux (18.6%).Discussion and ConclusionPASC symptoms are diverse. A comprehensive PASC symptom lexicon can be derived using a data-driven, ontology-driven and NLP-assisted approach. By using unstructured data, this approach may improve identification and analysis of patient symptoms in the EHR, and inform prospective study design, preventative care strategies, and therapeutic interventions for patient care.

The National Early Warning Score (NEWS) is an early warning system that predicts clinical deterioration. The impact of the NEWS on the outcome of healthcare remains controversial. This study was conducted to evaluate the effectiveness of implementing an electronic version of the NEWS (E-NEWS), to reduce unexpected clinical deterioration. We developed the E-NEWS as a part of the Health Information System (HIS) and Nurse Information System (NIS). All adult patients admitted to general wards were enrolled into the current study. The “adverse event” (AE) group consisted of patients who received cardiopulmonary resuscitation (CPR), were transferred to an intensive care unit (ICU) due to unexpected deterioration, or died. Patients without AE were allocated to the control group. The development of the E-NEWS was separated into a baseline (October 2018 to February 2019), implementation (March to August 2019), and intensive period (September. to December 2019). A total of 39,161 patients with 73,674 hospitalization courses were collected. The percentage of overall AEs was 6.06%. Implementation of E-NEWS was associated with a significant decrease in the percentage of AEs from 6.06% to 5.51% (p = 0.001). CPRs at wards were significantly reduced (0.52% to 0.34%, p = 0.012). The number of patients transferred to the ICU also decreased significantly (3.63% to 3.49%, p = 0.035). Using multivariate analysis, the intensive period was associated with reducing AEs (p = 0.019). In conclusion, we constructed an E-NEWS system, updating the NEWS every hour automatically. Implementing the E-NEWS was associated with a reduction in AEs, especially CPRs at wards and transfers to ICU from ordinary wards.

Graphical abstract, Objective To develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon (PASCLex) from clinical notes to support PASC symptom identification and research. Methods We identified 26,117 COVID-19 positive patients from the Mass General Brigham’s electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). PASCLex incorporated Unified Medical Language System® (UMLS) Metathesaurus concepts and synonyms based on selected semantic types. The MTERMS natural language processing (NLP) tool was used to automatically extract symptoms from a development dataset. The lexicon was iteratively revised with manual chart review, keyword search, concept consolidation, and evaluation of NLP output. We assessed the comprehensiveness of PASCLex and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus. Results PASCLex included 355 symptoms consolidated from 1,520 UMLS concepts of 16,466 synonyms. NLP achieved an averaged precision of 0.94 and an estimated recall of 0.84. Symptoms with the highest frequency included pain (43.1%), anxiety (25.8%), depression (24.0%), fatigue (23.4%), joint pain (21.0%), shortness of breath (20.8%), headache (20.0%), nausea and/or vomiting (19.9%), myalgia (19.0%), and gastroesophageal reflux (18.6%). Discussion and Conclusion PASC symptoms are diverse. A comprehensive lexicon of PASC symptoms can be derived using an ontology-driven, EHR-guided and NLP-assisted approach. By using unstructured data, this approach may improve identification and analysis of patient symptoms in the EHR, and inform prospective study design, preventative care strategies, and therapeutic interventions for patient care.

Background Chronic active antibody-mediated rejection is a major etiology of graft loss in renal transplant recipients. However, there is no consensus on the optimal treatment strategies. Methods Computerized records from Taichung Veterans General Hospital were collected to identify renal transplant biopsies performed in the past 7 years with a diagnosis of chronic active antibody-mediated rejection. The patients were divided into two groups according to treatment strategy: Group 1 received aggressive treatment (double filtration plasmapheresis and one of the followings: rituximab, intravenous immunoglobulin, antithymogycte globulin, bortezomib, or methylprednisolone pulse therapy); and group 2 received supportive treatment. Results From February 2009 to December 2017, a total of 82 patients with biopsy-proven chronic antibody mediated rejection were identified. Kaplan-Meier analysis of death-censored graft survival showed a worse survival in group 2 (P = 0.015 by log-rank test). Adverse event-free survival was lower in group 1, whereas patient survival was not significantly different. Proteinuria and supportive treatment were independent risk factors for graft loss in multivariate analysis. Conclusions Aggressive treatment was associated with better graft outcome. However, higher incidence of adverse events merit personalized treatment, especially for those with higher risk of infection. Appropriate prophylactic antibiotics are recommended for patients undergoing aggressive treatment.

Key Points Question Can a deep learning algorithm applied to clinical notes detect evidence of cognitive decline before a mild cognitive impairment (MCI) diagnosis? Findings In this diagnostic study, using clinical notes on 2166 patients preceding an MCI diagnosis, a deep learning algorithm was trained and validated for detecting cognitive decline using data sets with and without keyword filtering. The model trained in the data set with keyword filtering performed satisfactorily in the data sets without keyword filtering. Meaning The results of this study suggest that a deep learning model can detect evidence of cognitive decline from notes preceding an MCI diagnosis, potentially facilitating earlier detection of cognitive decline in electronic health records., Importance Detecting cognitive decline earlier among older adults can facilitate enrollment in clinical trials and early interventions. Clinical notes in longitudinal electronic health records (EHRs) provide opportunities to detect cognitive decline earlier than it is noted in structured EHR fields as formal diagnoses. Objective To develop and validate a deep learning model to detect evidence of cognitive decline from clinical notes in the EHR. Design, Setting, and Participants Notes documented 4 years preceding the initial mild cognitive impairment (MCI) diagnosis were extracted from Mass General Brigham’s Enterprise Data Warehouse for patients aged 50 years or older and with initial MCI diagnosis during 2019. The study was conducted from March 1, 2020, to June 30, 2021. Sections of notes for cognitive decline were labeled manually and 2 reference data sets were created. Data set I contained a random sample of 4950 note sections filtered by a list of keywords related to cognitive functions and was used for model training and testing. Data set II contained 2000 randomly selected sections without keyword filtering for assessing whether the model performance was dependent on specific keywords. Main Outcomes and Measures A deep learning model and 4 baseline models were developed and their performance was compared using the area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Results Data set I represented 1969 patients (1046 [53.1%] women; mean [SD] age, 76.0 [13.3] years). Data set II comprised 1161 patients (619 [53.3%] women; mean [SD] age, 76.5 [10.2] years). With some overlap of patients deleted, the unique population was 2166. Cognitive decline was noted in 1453 sections (29.4%) in data set I and 69 sections (3.45%) in data set II. Compared with the 4 baseline models, the deep learning model achieved the best performance in both data sets, with AUROC of 0.971 (95% CI, 0.967-0.976) and AUPRC of 0.933 (95% CI, 0.921-0.944) for data set I and AUROC of 0.997 (95% CI, 0.994-0.999) and AUPRC of 0.929 (95% CI, 0.870-0.969) for data set II. Conclusions and Relevance In this diagnostic study, a deep learning model accurately detected cognitive decline from clinical notes preceding MCI diagnosis and had better performance than keyword-based search and other machine learning models. These results suggest that a deep learning model could be used for earlier detection of cognitive decline in the EHRs., This diagnostic study assesses the ability of a deep learning model to detect evidence of cognitive decline before a diagnosis of mild cognitive impairment using clinical notes from electronic health records.

Aim This was a nationwide study by National Health Insurance Research Database to investigate the risk of urinary tract cancers (UTCs) for renin-angiotensin-aldosterone system inhibitors including spironolactone. Methods A total of 32 167 UTC patients with hypertension were enrolled in the National Health Insurance program between 2005 and 2011. Results Among different subclasses of renin-angiotensin-aldosterone system inhibitors, the adjusted odds ratio (OR) for UTC risk was 1.00 [95% confidence interval (CI) = 0.96-1.04] in angiotensin-converting enzyme inhibitors, 1.22 (95% CI = 1.18-1.26) in patients who received angiotensin II receptor blockers, 0.91 (95% CI = 0.87-0.96) in spironolactone. Spironolactone is associated with a significantly lower risk of prostate cancer (adjusted OR = 0.88, 95% CI = 0.82-0.94) in the male patients. A similar trend was observed in the female patients for the risk of bladder cancer (adjusted OR = 0.81, 95% CI = 0.72-0.92). Conclusion Our findings show that a lower risk of UTCs significantly associated with spironolactone in patients.

The protocol for data retrieval, data cleaning, and data analysis for the paper: Investigating the accuracy of National Early Warning Score for predicting clinical deterioration in different disease categories and time frames

SummaryBackground: As a result of the disease‘s high prevalence, chronic kidney disease (CKD) has become a global public health problem. A clinical decision support system that integrates with computer-interpretable guidelines (CIGs) should improve clinical outcomes and help to ensure patient safety.Objectives: The openEHR guideline definition language (GDL) is a formal language used to represent CIGs. This study explores the feasibility of using a GDL approach for CKD; it also attempts to identify any potential gaps between the ideal concept and reality.Methods: Using the Kidney Disease Improving Global Outcomes (KDIGO) anemia guideline as material, we designed a development workflow in order to establish a series of GDL guidelines. Focus group discussions were conducted in order to identify important issues related to GDL implementation.Results: Ten GDL guidelines and 37 archetypes were established using the KDIGO guideline document. For the focus group discussions, 16 clinicians and 22 IT experts were recruited and their perceptions, opinions and attitudes towards the GDL approach were explored. Both groups provided positive feedback regarding the GDL approach, but raised various concerns about GDL implementation.Conclusions: Based on the findings of this study, we identified some potential gaps that might exist during implementation between the GDL concept and reality. Three directions remain to be investigated in the future. Two of them are related to the openEHR GDL approach. Firstly, there is a need for the editing tool to be made more sophisticated. Secondly, there needs to be integration of the present approach into non openEHR-based hospital information systems. The last direction focuses on the applicability of guidelines and involves developing a method to resolve any conflicts that occur with insurance payment regulations.

Background: Patients with polycystic kidney disease (PKD) might have a risk of cardiovascular diseases because several cardiovascular risk factors are occasionally associated with PKD patients. Data on the association between PKD and the risk of cardiovascular events, including acute coronary syndrome (ACS), stroke, and congestive heart failure (CHF), are scant. Methods: Patients aged ≥20 years who were newly diagnosed with PKD (International Classification of Diseases, Ninth Revision, Clinical Modification codes 753.12 and 753.13) between 2000 and 2011 were selected as a PKD cohort (N = 5157). The association between PKD and cardiovascular events was analyzed. Results: We randomly selected a comparison cohort of people without PKD, who were frequency-matched by sex, age, and index date of diagnosis. At the end of 2011, the PKD cohort had a 1.40-fold greater incidence of ACS compared with the comparison cohort (8.59 vs. 6.17 per 1000 person-years), in addition to a 1.40-fold greater incidence of stroke, a 1.49-fold greater incidence of CHF, and a 1.64-fold greater incidence of mortality. Conclusions: This retrospective cohort study shows that patients with PKD have an increased risk of cardiovascular events including ACS, stroke, and CHF as well as mortality, particularly in younger patients. Early identification is necessary to attenuate the risk of cardiovascular complications in patients with PKD.

High fidelity simulation-based teaching has played an important role in medical education, especially in anesthesiology and emergency. But there is not any currently validated scoring system or prediction model for high fidelity simulation. We will develop a validated prediction model to enhance the efficiency and validation of clinical training with high fidelity simulation.

Glomerulus is an important component in human kidney. The appearance of the glomeruli on light microscopic image can provide abundant information for disease diagnosis. Due to the importance of glomeruli on accurate renal disease diagnosis, this paper proposes an automatic method to detect glomeruli in light microscopy images with Periodic Acid Schiff (PAS) or hematoxylin and eosin (H&E) stains at 100x, 200x, or 400x optical magnification. The faster region-based convolutional neural network (R-CNN) is applied to the detection task. The proposed detection approach performs an end-to-end glomerulus detection without any a priori information of the stains and magnifications of the images. The training dataset contains 2,511 images with 3,956 glomeruli. The test dataset contains 482 images with 563 glomeruli. The recall and precision of the test result are 91.54% and 86.50%, respectively, which shows the effectiveness of the proposed detection method.

Successful renal transplantation (RT) improves quality of life and patient survival. Advances in immunosuppressants for RT have improved the prevention and treatment of acute rejection as well as reduced the risk of chronic graft damage, but immunodeficiency may render patients vulnerable to opportunistic infections. We conducted this study to compare the difference in tuberculosis (TB) infection rates between a single institution and a national database of RT recipients in Taiwan. There were 153 patients with TB (3.2%) among 4,835 RT recipients in the database during the period 2000-2009, with a higher prevalence of men (P = .018) and diabetes patients (P = .029). In our institution's registry, 33 patients (2.7%) developed 35 episodes of TB infection among 1,209 RT recipients, but there were no significant differences in general characteristics among different subgroups. Interestingly, the use of cyclosporine was significantly more frequent in RT recipients with TB than in those without in both the national database and in our institution. In contrast, TB infection was negatively correlated with the use of tacrolimus (TAC) and mycophenolate (MPA). RT recipients with TB infection had poor survival (P = .0013) and low graft survival (P = .0003). Taken together, analyses of the national database and the RT patients in our institution revealed that the use of long-term cyclosporine-based immunosuppressive agents was associated with a greater risk of developing post-transplantation TB compared with that of other immunosuppressive agents, but the chronicity and accumulation effect of TAC and MPA should be observed despite the negative correlation found herein. In conclusion, post-transplantation TB is a serious health threat and one of the major causes of death among RT recipients, and a high index of suspicion to ensure early diagnosis and prompt initiation of treatment for TB is crucial. The use of optimal immunosuppressive agents to minimize acute rejection, monitoring of high-risk recipients, prompt diagnosis, and appropriate treatment are required to manage TB infection in endemic areas such as Taiwan.

Metabolic syndrome (MS) is a common complication in renal transplant (RTx) recipients. This study aimed to explore the alterations and interrelationship of various adipokines in RTx recipients with and without MS.RTx recipients followed at our hospital were randomly selected for the cross-sectional study of MS. The modified Adult Treatment Panel III criteria adopted for Asian populations were used to define MS. Overnight fasting blood samples were obtained for determination of adipokines, including adiponectin, leptin, resistin, and visfatin. Univariate and multivariate logistic regressions were performed to determine parameters that were associated with serum adipokine levels. Pearson correlation analysis was performed between adipokines.A total of 280 RTx recipients were enrolled for the study. Seventy-three cases (26.1%) fulfilled the criteria of MS. A significantly higher serum leptin level was found in MS patients (16.61 ± 13.90 vs 8.00 ± 7.42 μg/mL; P.0001). There was no significant difference in serum levels of adiponectin, resistin, and visfatin between the 2 groups. Serum adiponectin level was positively correlated with serum resistin (r = 0.422; P.0001) and visfatin levels (r = 0.224; P.0001). Serum resistin level was positively correlated with serum visfatin level. All but serum visfatin level were negatively correlated with estimated glomerular filtration rate. Univariate logistic regression revealed the following variables to be associated with serum leptin level: metabolic syndrome, sex, body weight, waist circumference, body mass index (BMI), hypertension, serum creatinine, fasting blood sugar, HbA1c, serum triglyceride, and uric acid. Multivariate analysis revealed that sex, body weight, BMI, and serum creatinine were associated with serum leptin level.Compared with RTx recipients without MS, patients with MS were associated with significantly higher serum leptin levels and similar adiponectin, resistin, and visfatin levels. A close interrelationship was also found in the serum levels of these adipokines.

Background: The blood and membrane contact during dialysis may elicit an immune reaction. The current study looked at the impact of different dialyzers on blood levels of cytokines. Methods: During the first month, randomly selected patients were treated with one dialyzer (PF-170H) and then crossed over to another dialyzer (FLX-18GW) during the next month. Pre- and postdialysis blood samples were assayed for interleukin (IL)-6, IL-10 and IL-18. Results: A significant drop of postdialysis systolic blood pressure (pre vs. post 156.4 ± 31.8 vs. 143.1 ± 24.8 mm Hg, p = 0.014) and diastolic pressure (80.7 ± 12.7 vs. 73.4 ± 10.9 mm Hg, p = 0.002) were found when patients were dialyzed with PF-170H. A significant increase of postdialysis IL-18 levels was found in both groups (pre vs. post 605.5 ± 278.6 vs. 690.6 ± 315.3 pg/ml, p = 0.016, for PF-170H and 556.4 ± 231.0 vs. 647.3 ± 282.6 pg/ml, p = 0.067, for FLX-18GW). There was a positive correlation between IL-6 and IL-10 levels (p < 0.0001). Conclusion: We demonstrated a significant increase of postdialysis serum IL-18 level when either dialyzer was used. There is a strong correlation between serum levels of IL-6 and IL-10.

Induction therapy with interleukin-2 receptor antagonist (IL2RA) is widely used for renal transplant recipients and this study aimed to examine the impact of IL2RA among Chinese renal transplant recipients. Two hundred and thirty-eight Chinese renal transplant recipients aged 18-65 years at the Taichung Veterans General Hospital from January 2004 to July 2009 were retrospectively studied to assess the influence of IL2RA on biopsy-proven acute rejection (BPAR) within 1 year. Secondary outcomes included acute rejection rate in the first 3 months, delayed graft function, post-transplant diabetes mellitus, and malignancy. Cox proportional hazard analysis was used for multivariate analysis. Of all the patients, 116 received IL2RA (basiliximab, n = 44; daclizumab, n = 72) and 122 had no induction therapy. The mean follow-up duration was 43.3 months (range, 1-79 months). Overall, 227 (95.4%) patients completed the 12-month follow-up period with a functioning graft. No difference of BPAR was observed between the two groups and the secondary outcomes were also similar. After adjusting potential covariates with Cox regression, IL2RA use still provided no benefit on BPAR. In conclusion, there is no benefit of IL2RA in decreasing BPAR was observed in our study. Routine use of IL2RA for adult Chinese kidney transplant recipients may not be as effective as we thought before. More research is still needed to elucidate the effect of IL2RA among Chinese kidney transplant recipients.

Mycobacterium tuberculosis strain W06, analyzed by molecular methods, was classified as a modern Beijing M. tuberculosis strain, the most predominant strain in Taiwan. To our knowledge, this is the first draft genome announcement of a Beijing M. tuberculosis strain in Taiwan.