Durvalumab Consolidation After Chemoradiotherapy in Elderly Patients With Unresectable Stage III NSCLC: A Real-World Multicenter Study.

BACKGROUND: The PACIFIC trial demonstrated survival benefit of durvalumab after concurrent chemoradiotherapy (CCRT) in unresectable stage III non-small-cell lung cancer. Data on the effectiveness and safety of durvalumab in elderly patients is lacking. METHODS: This retrospective study was conducted between September 2017 and September 2022. Progression-free survival (PFS), overall survival (OS), recurrence patterns, first subsequent treatment after recurrence, factors associated with survival outcomes, and adverse events (AEs) were compared. RESULTS: Of the 286 patients, 120 (42.0%) were ≥ 70 years and 166 (58.0%) were < 70 years. The median PFS (17.7 vs. 19.4 months; P = .43) and median OS (35.7 months vs. not reached; P = .13) were similar between 2 groups. Proportion of patients who completed durvalumab was lower in elderly patients (27.5% vs. 39.2%; P = .040). In elderly patients, ECOG PS 0 or 1 was associated with better PFS, and being male and having received a cisplatin-based regimen during CCRT were factors associated with better and worse OS, respectively. In patients aged < 70 years, a PD-L1 ≥ 50% was associated with improved PFS and OS. Elderly patients experienced more treatment-related AEs, grade 3/4 AEs, permanent discontinuation of durvalumab, and treatment-related deaths. Among the AEs leading to permanent discontinuation or death, pulmonary AE was significantly more common in elderly patients. CONCLUSION: Durvalumab demonstrated similar outcomes in elderly compared to younger patients. However, AEs were more common in elderly patients. Thus, judicious selection of patients and chemotherapy regimens, coupled with careful AE monitoring, are important factors for ensuring optimal durvalumab treatment.

Conversion of the bronchial tree into a conforming electrode to ablate the lung nodule in a porcine model.

BACKGROUND: Radiofrequency ablation (RFA) is one of the treatment options for lung nodules. However, the need for exact delivery of the rigid metal electrode into the center of the target mass often leads to complications or suboptimal results. To overcome these limitations, a concept of conforming electrodes using a flexible material has been tested in this study. METHODS: A bronchoscopy-guided RFA (CAROL) under a temperature-controlled mode was tested in in-vivo and ex-vivo porcine lungs. Gallium-based liquid metal was used for turning the bronchial tree into temporary RF electrodes. A customized bronchoscopy-guided balloon-tipped guiding catheter (CAROL catheter) was used to make the procedure feasible under fluoroscopy imaging guidance. The computer simulation was also performed to gain further insight into the ablation results. Safety was also assessed including the liquid metal remaining in the body. RESULTS: The bronchial electrode injected from the CAROL catheter was able to turn the target site bronchial air pipe into a temporally multi-tined RF electrode. The mean volume of Gallium for each effective CAROL was 0.46 ± 0.47 ml. The ablation results showed highly efficacious and consistent results, especially in the peripheral lung. Most bronchial electrodes were also retrieved by either bronchoscopic suction immediately after the procedure or by natural expectoration thereafter. The liquid metal used in these experiments did not have any significant safety issues. Computer simulation also supports these results. CONCLUSION: The CAROL ablation was very effective and safe in porcine lungs showing encouraging potential to overcome the conventional approaches.

Lung cancer can be treated by inserting a metal device into the lung via the throat and using this to send radio waves into the cancer. However, using a rigid metal device can cause damage to other areas of the lung and can only treat small cancers. Here, we describe an alternative method to treat lung cancers in which liquid metal is used to fill the spaces within the lung closest to the cancer. We demonstrate that this method can be used to treat cancer in a swine model of lung cancer. Given the positive results we obtained, we think this approach should be tested in a clinical trial in human patients with lung cancer, as it might improve cancer treatment.

Initial Tumor Necrosis Factor-Alpha and Endothelial Activation Are Associated with Hemorrhagic Complications during Extracorporeal Membrane Oxygenation.

Studies on inflammatory markers, endothelial activation, and bleeding during extracorporeal membrane oxygenation (ECMO) are lacking. Blood samples were prospectively collected after ECMO initiation from 150 adult patients who underwent ECMO for respiratory failure between 2018 and 2021. After excluding patients who died early (within 48 h), 132 patients were finally included. Their tumor necrosis factor-alpha (TNF-α), tissue factor (TF), soluble thrombomodulin (sTM), and E-selectin levels were measured. A Cox proportional hazards regression model was used to estimate the hazard ratio for hemorrhagic complications during ECMO. The 132 patients were divided into hemorrhagic (n = 23, H group) and non-complication (n = 109, N group) groups. The sequential organ failure assessment score, hemoglobin level, and ECMO type were included as covariates in all Cox models to exclude the effects of clinical factors. After adjusting for these factors, initial TNF-α, TF, sTM, E-selectin, and activated protein C levels were significantly associated with hemorrhagic complications (all p < 0.001). TNF-α, TF, and E-selectin better predicted hemorrhagic complications than the model that included only the aforementioned clinical factors (clinical factors only (area under the curve [AUC]: 0.804), reference; TNF-α (AUC: 0.914); TF (AUC: 0.915); E-selectin (AUC: 0.869)). Conclusions: TNF-α levels were significantly predictive of hemorrhagic complications during ECMO.

Favorable Conditions for the Detection of EGFR T790M Mutation Using Plasma Sample in Patients with Non-Small-Cell Lung Cancer.

BACKGROUND: Detection of the epidermal growth factor receptor (EGFR) T790M mutation using plasma samples has been considered simple and non-invasive, but the relatively high false negative results lead to additional tissue sampling in some patients. Until now, the characteristics of patients who prefer liquid biopsy have not yet been established. METHODS: To evaluate the favorable conditions for the detection of T790M mutations using plasma samples, a multicenter retrospective study was performed between May 2018 and December 2021. Patients whose T790M mutation was detected in a plasma sample were classified as the plasma positive group. Study subjects with a T790M mutation not detected in a plasma sample but only in a tissue sample were grouped as the plasma false negative group. RESULTS: Plasma positive and plasma false negative groups were found in 74 and 32 patients, respectively. As a result, 40% of patients with one or two metastatic organs at the time of re-biopsy had false negative plasma sample results, and 69% of patients with three or more metastatic organs at the time of re-biopsy had positive plasma results. In multivariate analysis, three or more metastatic organs at initial diagnosis were independently associated with the detection of a T790M mutation using plasma samples. CONCLUSION: Our results demonstrated that the detection rate of a T790M mutation using plasma samples was related to the tumor burden, particularly to the number of metastatic organs.

Head-to-Head Comparison between Xpert MTB/RIF Assay and Real-Time Polymerase Chain Reaction Assay Using Bronchial Washing Specimens for Tuberculosis Diagnosis.

BACKGROUND: With the introduction of Xpert MTB/RIF assay (Xpert), its incorporation into tuberculosis (TB) diagnostic algorithm has become an important issue. The aim of this study was to evaluate the performance of the Xpert assay in comparison with a commercial polymerase chain reaction (PCR) assay. METHODS: Medical records of patients having results of both Xpert and AdvanSure TB/NTM real-time PCR (AdvanSure) assays using the same bronchial washing specimens were retrospectively reviewed. RESULTS: Of the 1,297 patients included in this study, 205 (15.8%) were diagnosed with pulmonary TB. Using mycobacterial culture as the reference method, sensitivity of the Xpert assay using smear-positive specimens was 97.5%, which was comparable to that of the AdvanSure assay (96.3%, p=0.193). However, the sensitivity of the Xpert assay using smear-negative specimens was 70.6%, which was significantly higher than that of the AdvanSure assay (52.9%, p=0.018). Usng phenotypic drug susceptibility testing as the reference method, sensitivity and specificity for detecting rifampicin resistance were 100% and 99.1%, respectively. Moreover, a median turnaround time of the Xpert assay was 1 day, which was significantly shorter than 3 days of the AdvanSure assay (p<0.001). CONCLUSION: In comparison with the AdvanSure assay, the Xpert assay had a higher sensitivity using smear-negative specimens, a shorter turnaround time, and could reliably predict rifampin resistance. Therefore, the Xpert assay might be preferentially recommended over TB-PCR in Korean TB diagnostic algorithm.

Atopic asthma as a potentially significant but unrecognized risk factor for Kawasaki disease in children.

OBJECTIVES: Childhood asthma is known to be associated with risks of both respiratory and non-respiratory infections. Little is known about the relationship between asthma and the risk of Kawasaki disease (KD). We assessed associations of asthma status and asthma phenotype (e.g. atopic asthma) with KD. METHODS: We performed a population-based retrospective case-control study, using KD cases between January 1, 1979, and December 31, 2016, and two matched controls per case. KD cases were defined by the American Heart Association diagnostic criteria. Asthma status prior to KD (or control) index dates was ascertained by the two asthma criteria, Predetermined Asthma Criteria (PAC) and Asthma Predictive Index (API, a surrogate phenotype of atopic asthma). We assessed whether 4 phenotypes (both PAC + and API+; PAC + only; API + only, and non-asthmatics) were associated with KD. RESULTS: There were 124 KD cases during the study period. The group having both PAC + and API + was significantly associated with the increased odds of KD, compared to non-asthmatics (odds ratio [OR] 4.3; 95% CI: 1.3 - 14.3). While asthma defined by PAC was not associated with KD, asthma defined by PAC positive with eosinophilia (≥4%) was significantly associated with the increased odds of KD (OR: 6.7; 95% CI: 1.6 - 28.6) compared to non-asthmatics. Asthma status defined by API was associated with KD (OR = 4.7; 95% CI: 1.4-15.1). CONCLUSIONS: Atopic asthma may be associated with increased odds of KD. Further prospective studies are needed to determine biological mechanisms underlying the association between atopic asthma and increased odds of KD.

Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.

BACKGROUND: Clinicians' asthma guideline adherence in asthma care is suboptimal. The effort to improve adherence can be enhanced by assessing and monitoring clinicians' adherence to guidelines reflected in electronic health records (EHRs), which require costly manual chart review because many care elements cannot be identified by structured data. OBJECTIVE: This study was designed to demonstrate the feasibility of an artificial intelligence tool using natural language processing (NLP) leveraging the free text EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines. METHODS: This is a retrospective cross-sectional study using a birth cohort with a diagnosis of asthma at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline-congruent elements by examining care description in EHR free text. RESULTS: Natural language processing algorithms demonstrated a sensitivity (0.82-1.0), specificity (0.95-1.0), positive predictive value (0.86-1.0), and negative predictive value (0.92-1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess because of the complexity and wide variety of descriptions. CONCLUSIONS: Natural language processing technologies may enable the automated assessment of clinicians' documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool to assess and monitor asthma care quality. Multisite studies with a larger sample size are needed to assess the generalizability of these NLP algorithms.

Safety and Diagnostic Yield of Radial Probe Endobronchial Ultrasound-Guided Biopsy for Peripheral Lung Lesions in Patients with Idiopathic Pulmonary Fibrosis: A Multicenter Cross-Sectional Study.

BACKGROUND: Radial probe endobronchial ultrasound-guided transbronchial lung biopsy (RP-EBUS-TBLB) is widely used for diagnosis of peripheral lung lesions (PLLs). To date, there have been no reports regarding the clinical outcomes of RP-EBUS-TBLB for PLLs in patients with idiopathic pulmonary fibrosis (IPF). OBJECTIVES: This study was performed between October 2017 and December 2019 to identify the safety and diagnostic performance of RP-EBUS-TBLB in IPF patients. METHODS: Patients were divided into the usual interstitial pneumonia (UIP) group (n = 39, 4%), the probable UIP group (n = 12, 1%), and the noninterstitial lung disease (non-ILD) group (n = 903, 95%). RESULTS: The diagnostic yield was significantly lower in the UIP group than in the non-ILD group (62% vs. 76%; p = 0.042), but there were no significant differences between the UIP and probable UIP groups (62% vs. 83%; p = 0.293) or the probable UIP and non-ILD groups (83% vs. 76%; p = 0.741). Multivariate logistic analysis showed that the mean diameter of PLLs, positive bronchus sign on CT, and "within the lesion" status on EBUS were independently associated with success of the procedure. Especially, the presence of the UIP pattern on CT (OR, 0.385; 95% CI: 0.172-0.863; p = 0.020) was independently associated with failed diagnosis. Among patients with UIP, "within the lesion" status on EBUS (OR, 25.432; 95% CI: 2.321-278.666; p = 0.008) was shown to be a factor contributing to a successful diagnosis. Overall, there were no significant differences in complication rates among the 3 study groups. CONCLUSION: RP-EBUS-TBLB can be performed safely with an acceptable diagnostic yield, even in patients with IPF.

Identification of asthma control factor in clinical notes using a hybrid deep learning model.

BACKGROUND: There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician's documentation is not feasible using only structured data but requires labor-intensive chart review of electronic health records (EHRs). A certain guideline element in asthma control factors, such as review inhaler techniques, requires context understanding to correctly capture from EHR free text. METHODS: The study data consist of two sets: (1) manual chart reviewed data-1039 clinical notes of 300 patients with asthma diagnosis, and (2) weakly labeled data (distant supervision)-27,363 clinical notes from 800 patients with asthma diagnosis. A context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) was developed to identify inhaler techniques in EHR free text. Both original BERT and clinical BioBERT (cBERT) were applied with a cost-sensitivity to deal with imbalanced data. The distant supervision using weak labels by rules was also incorporated to augment the training set and alleviate a costly manual labeling process in the development of a deep learning algorithm. A hybrid approach using post-hoc rules was also explored to fix BERT model errors. The performance of BERT with/without distant supervision, hybrid, and rule-based models were compared in precision, recall, F-score, and accuracy. RESULTS: The BERT models on the original data performed similar to a rule-based model in F1-score (0.837, 0.845, and 0.838 for rules, BERT, and cBERT, respectively). The BERT models with distant supervision produced higher performance (0.853 and 0.880 for BERT and cBERT, respectively) than without distant supervision and a rule-based model. The hybrid models performed best in F1-score of 0.877 and 0.904 over the distant supervision on BERT and cBERT. CONCLUSIONS: The proposed BERT models with distant supervision demonstrated its capability to identify inhaler techniques in EHR free text, and outperformed both the rule-based model and BERT models trained on the original data. With a distant supervision approach, we may alleviate costly manual chart review to generate the large training data required in most deep learning-based models. A hybrid model was able to fix BERT model errors and further improve the performance.

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study.

Our prior work and the work of others have demonstrated that asthma increases the risk of a broad range of both respiratory (e.g., pneumonia and pertussis) and non-respiratory (e.g., zoster and appendicitis) infectious diseases as well as inflammatory diseases (e.g., celiac disease and myocardial infarction [MI]), suggesting the systemic disease nature of asthma and its impact beyond the airways. We call these conditions asthma-associated infectious and inflammatory multimorbidities (AIMs). At present, little is known about why some people with asthma are at high-risk of AIMs, and others are not, to the extent to which controlling asthma reduces the risk of AIMs and which specific therapies mitigate the risk of AIMs. These questions represent a significant knowledge gap in asthma research and unmet needs in asthma care, because there are no guidelines addressing the identification and management of AIMs. This is a systematic review on the association of asthma with the risk of AIMs and a case study to highlight that 1) AIMs are relatively under-recognized conditions, but pose major health threats to people with asthma; 2) AIMs provide insights into immunological and clinical features of asthma as a systemic inflammatory disease beyond a solely chronic airway disease; and 3) it is time to recognize AIMs as a distinctive asthma phenotype in order to advance asthma research and improve asthma care. An improved understanding of AIMs and their underlying mechanisms will bring valuable and new perspectives improving the practice, research, and public health related to asthma.

A DNA-derived phage nose using machine learning and artificial neural processing for diagnosing lung cancer.

There is a growing interest in electronic nose-based diagnostic systems that are fast and portable. However, existing technologies are suitable only for operation in the laboratory, making them difficult to apply in a rapid, non-face-to-face, and field-suitable manner. Here, we demonstrate a DNA-derived phage nose (D2pNose) as a portable respiratory disease diagnosis system requiring no pretreatment. D2pNose was produced based on phage colour films implanted with DNA sequences from mammalian olfactory receptor cells, and as a result, it possesses the comprehensive reactivity of these cells. The manipulated surface chemistry of the genetically engineered phages was verified through a correlation analysis between the calculated and the experimentally measured reactivity. Breaths from 31 healthy subjects and 31 lung cancer patients were collected and exposed to D2pNose without pretreatment. With the help of deep learning and neural pattern separation, D2pNose has achieved a diagnostic success rate of over 75% and a classification success rate of over 86% for lung cancer based on raw human breath. Based on these results, D2pNose can be expected to be directly applicable to other respiratory diseases.

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.

RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. OBJECTIVES: To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). METHODS: This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. MEASUREMENTS: Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. MAIN RESULTS: Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. CONCLUSIONS: While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02865967.

Clinical course of asymptomatic malignant pleural effusion in non-small cell lung cancer patients: A multicenter retrospective study.

ABSTRACT: The British Thoracic Society guidelines recommend observation for patients with asymptomatic malignant pleural effusion (MPE). However, asymptomatic MPE can become symptomatic. This study examined the clinical course of asymptomatic MPE in patients with non-small cell lung cancer (NSCLC), including the incidence and timing of symptom development of asymptomatic MPE and the associated factors.Retrospective data of 4822 NSCLC patients between January 2012 and December 2017 were reviewed. Symptom development of asymptomatic MPE was defined as the development of symptoms requiring additional treatment, such as insertion of a chest tube, within 1 year in patients who lacked MPE symptoms at the time of diagnosis. Clinical information, pathological parameters, and radiological characteristics were reviewed. Patient data up to 1 year from the initial diagnosis were reviewed.Of 113 patients with asymptomatic MPE, 46 (41%) became symptomatic within 1 year despite appropriate anticancer treatment. The median time to symptom development was 4 months, and 38 patients (83%) developed symptoms within 6 months. Multivariate logistic regression showed that female sex (odds ratio [OR], 0.256; 95% confidence interval [CI], 0.101-0.649; P = .004) and the depth of pleural effusion on initial computed tomography (CT) (OR, 0.957; 95% CI, 0.932-0.982; P = .001) were independently associated with symptom development of asymptomatic MPE.A fraction of 41% of patients with asymptomatic MPE became symptomatic within 1 year. Female sex and larger MPE on initial CT were independently associated with symptom development of asymptomatic MPE.

Long-term Incidence of Kawasaki Disease in a North American Community: A Population-Based Study.

To assess the longitudinal incidence of Kawasaki disease (KD) within the well-defined predominantly White population of Olmsted County, MN. This retrospective cohort study used a population-based medical record linkage system and manual chart reviews to identify children with KD in Olmsted County, MN between January 1, 1979-December 31, 2016. Age- and gender-adjusted incidence rates were calculated using the 2010 U.S. White population. 124 children with KD were confirmed during the study period (median age 3.5, 61% male, 85% White, 9% Asian). The overall age- and gender-adjusted incidence rates for all ages and < 5 years old were 9.8 and 21.4 per 100,000 person-years, respectively. There was an overall increase in incidence up to 1994 followed by plateau, except among children between the ages of 1-5 years. There was also an overall increase in incidence among females compared to males. 24% of children had cardiac complications. While the overall incidence of KD in Olmsted County appears to be stable since 1994, the incidence of KD in subgroups of children 1-5 years old and females seems to have increased. Given the rising trends and one-quarter of children developing cardiac complications, further studies identifying factors driving these trends are warranted.

Predictive Value of 18F-Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Assessment of Occult Lymph Node Metastasis in Non-Small Cell Lung Cancer.

OBJECTIVE: The purpose of the current study was to investigate the diagnostic performance of 18F fluorodeoxyglucose (FDG) positron emission tomography (PET) or positron emission tomography/computed tomography (PET/CT) for the prediction of occult lymph node metastasis (OLNM) in non-small cell lung cancer (NSCLC) patients through a systematic review and meta-analysis. METHODS: The PubMed, Cochrane, and EMBASE database, from the earliest available date of indexing through March 31, 2020, were searched for studies evaluating the diagnostic performance of preoperative 18F FDG PET or PET/CT for the prediction of OLNM in NSCLC patients. RESULTS: Across 14 studies (3,535 patients), the pooled sensitivity for 18F FDG PET or PET/CT was 0.79 (95% CI; 0.70-0.86) with heterogeneity (I2 = 81.5, p < 0.001) and a pooled specificity of 0.65 (95% CI; 0.57-0.72) with heterogeneity (I2 = 93.7, p < 0.001). Likelihood ratio (LR) syntheses gave an overall positive likelihood ratio (LR+) of 2.3 (95% CI; 1.9-2.6) and a negative likelihood ratio (LR-) of 0.32 (95% CI; 0.23-0.44). The pooled diagnostic odds ratio (DOR) was 7 (95% CI; 5-10). The hierarchical summary receiver operating characteristic curve indicates that the area under the curve was 0.77 (95% CI; 0.74-0.81). CONCLUSION: The current meta-analysis showed a moderate sensitivity and specificity of 18F FDG PET or PET/CT for the prediction of OLNM in NSCLC patients. The DOR was low and the likelihood ratio scatter-gram indicated that 18F FDG PET or PET/CT might not be useful for the prediction of OLNM in NSCLC patients and not for its exclusion.

Prone Positioning as a Bridge to Recovery From Refractory Hypoxemia After Oversized Lung Transplant.

BACKGROUND: Size matching is an important challenge in lung transplantation. Although the survival rate after lung transplantation with an oversized allograft was improved, it is associated with substantial immediate postoperative morbidity and mortality. Prone positioning is a rescue therapy showing improved outcomes in acute respiratory distress syndrome. We present a case of immediate postoperative refractory hypoxemia after oversized lung transplantation treated by prone positioning. METHODS: A 62-year-old man was transferred to our hospital by our extracorporeal membrane oxygenation (ECMO) transport team because of acute exacerbation of idiopathic pulmonary fibrosis. He underwent bilateral lung transplantation through bilateral anterior thoracotomy. For size matching between donor and recipient, multiple wedge resection and lingular segmentectomy were performed, but an oversized lung was implanted. On the immediate postoperative day, chest radiography revealed haziness in the left lower quadrant and the patient had an increased O2 requirement; he could not be weaned from venovenous (VV) ECMO. Chest computed tomography revealed left lower lobar atelectasis and primary graft dysfunction. To revert the atelectatic portion, improve ventilation/perfusion mismatch, and avoid high ventilation pressure, we performed the recruitment maneuver. Despite this, his blood gas profile did not improve. Therefore, we applied prone positioning with VV ECMO. After conversion to the prone positioning, the hypoxia corrected and the tidal volume increased. After 20 hours, he was changed to the supine position. Thereafter, arterial blood gas analyses were stable and he could be weaned from ECMO. He was discharged on postoperative day 57 and maintained good respiratory function. CONCLUSIONS: This case demonstrated the safety and feasibility of prone positioning during the immediate postoperative period after lung transplant by bilateral anterior thoracotomy. Prone positioning successfully reversed postoperative atelectasis and improved primary graft dysfunction after oversized lung transplant.

A diagnostic codes-based algorithm improves accuracy for identification of childhood asthma in archival data sets.

OBJECTIVE: While a single but truncated ICD code (493) had been widely used for identifying asthma in asthma care and research, it significantly under-identifies asthma. We aimed to develop and validate a diagnostic codes-based algorithm for identifying asthmatics using Predetermined Asthma Criteria (PAC) as the reference. METHODS: This is a retrospective cross-sectional study which utilized two different coding systems, the Hospital Adaptation of the International Classification of Diseases, Eighth Revision (H-ICDA) and the International Classification of Diseases, Ninth Revision (ICD-9). The algorithm was developed using two population-based asthma study cohorts, and validated in a validation cohort, a random sample of the 1976-2007 Olmsted County Birth Cohort. Performance of the diagnostic codes-based algorithm for ascertaining asthma status against manual chart review for PAC (gold standard) was assessed by determining both criterion and construct validity. RESULTS: Among eligible 267 subjects of the validation cohort, 50% were male, 70% white, and the median age at last follow-up was 17 (interquartile range, 8.7-24.4) years. Asthma prevalence by PAC through manual chart review was 34%. Sensitivity and specificity of the codes-based algorithm for identifying asthma were 82% and 98% respectively. Associations of asthma-related risk factors with asthma status ascertained by the code-based algorithm were similar to those by the manual review. CONCLUSIONS: The diagnostic codes-based algorithm for identifying asthmatics improves accuracy of identification of asthma and can be a useful tool for large scale studies in a setting without automated chart review capabilities.

Expert artificial intelligence-based natural language processing characterises childhood asthma.

INTRODUCTION: The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics. METHODS: Using the 1997-2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC-/NLP-API-)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs). RESULTS: Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC-/NLP-API-. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects. CONCLUSION: Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

Evaluation of factors affecting the visualization of dye after transbronchial dye injection: an animal experiment.

BACKGROUND: Since peripheral lung lesions can be frequently visualized on computed tomography (CT), various methods of localization for thoracoscopic surgery have been developed. In the case of transbronchial dye injection (TDI), there can be difficulties with dye visualization through the thoracoscope depending on early disappearance of the dye due to diffusion before field exposure. Herein, we performed an animal experiment to determine the appropriate dye amount and the duration of visualization. METHODS: Twelve pigs were experimented as following four groups (n=3): group 1 received 0.6 mL of dye; group 2 as 0.8 mL; and group 3 as 1.0 mL, all followed by 2.0 mL of air injection and group 4, with 1.0 mL of dye followed no air injection to evaluate the utility of air injection. The detection, the peak time, the wash-out time were measured. RESULTS: The mean detection times, the peak time, and the mean wash-out times for 0.6, 0.8, and 1.0 mL of dye were not significantly different (P=0.195, 0.092, 0.06). However, regardless of the injected amount, it usually lasts in 2 hours. Comparing with non-air injection group, the peak time and wash-out time were statistically significantly different in injected group; P=0.07 and 0.001. CONCLUSIONS: The marking could be identified clearly at about 2 hours after TDI regardless of the amount of indigo carmine injected. However, in cases with longer duration to exposure, especially in cases with severe adhesions, it might be necessary to discover the mixture of dye which will last longer for visualization of lung nodules.

Predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography for PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

BACKGROUND: The purpose of the current study was to investigate the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC) patients through a systematic review and meta-analysis. METHODS: The PubMed, Cochrane, and EMBASE database, from the earliest available date of indexing through 30 April 2020, were searched for studies evaluating the diagnostic performance of 18F-FDG PET/CT for prediction of PD-L1 expression in NSCLC patients. RESULTS: Across six studies (1739 patients), the pooled sensitivity for 18F-FDG PET/CT was 0.72 (95% CI: 0.58-0.82) with heterogeneity (I2 = 90.9, P < 0.001) and a pooled specificity of 0.69 (95% CI: 0.64-0.74) with heterogeneity (I2 = 77.9, P < 0.001). Likelihood ratio (LR) syntheses gave an overall positive likelihood ratio (LR +) of 2.3 (95% CI: 1.8-2.9) and negative likelihood ratio (LR-) of 0.41 (95% CI: 0.26-0.63). The pooled diagnostic odds ratio (DOR) was six (95% CI: 3-11). Hierarchical summary receiver operating characteristic (ROC) curve indicated that the area under the curve was 0.74 (95% CI: 0.70-0.78). CONCLUSIONS: The current meta-analysis showed a moderate sensitivity and specificity of 18F-FDG PET/CT for the prediction of PD-L1 expression in NSCLC patients. The DOR was low and the likelihood ratio scatter-gram indicated that 18F-FDG PET/CT might not be useful for the prediction of PD-L1 expression in NSCLC patients and not for its exclusion. KEY POINTS: Significant findings of the study The current meta-analysis showed a moderate sensitivity and specificity of 18F-FDG PET/CT for the prediction of PD-L1 expression in NSCLC patients. The DOR was low and the likelihood ratio scattergram indicated that 18F-FDG PET/CT might not be useful for the prediction of PD-L1 expression in NSCLC patients and not for its exclusion. What this study adds This study concluded that the role of 18F-FDG PET/CT in predicting tumor expression of PD-L1 should be further elucidated.