Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation. However, in order to be clinically acceptable, radiomics models must be trained on large data sets of real-world images. This is challenging as the full 3D segmentation of BM on planning-CT images is time-consuming for radiation oncologists in the clinical context. Often, in the interest of time and given the low doses used in palliative RT, radiation oncologists only delineate treatment field boundaries when treating BM, and they do not fully contour individual BM lesions. As a result, most of the published BM radiomics studies to date were trained and tested with relatively small sample sizes  classifiers have shown great potential for use in the early detection of bone metastases (BM) and in assessing response of BM to radiotherapy (RT)22. ML algorithms can be trained to find relationships between radiomics features and cancer outcomes if provided with sufficient and appropriate data. There are three main steps in the training phase of a typical radiomics study. These include: (1) manual or semi-automated segmentation of regions of interest (ROIs) on patients’ images, (2) feature extraction from the segmented ROIs, and (3) generation of a statistical or ML model to correlate extracted features to each patient’s endpoint data such as their cancer outcome or other clinically-measured biomarkers8.Radiomics is an automated feature generation method for the extraction of hundreds of quantitative phenotype (radiomics features) from radiology images24. Therefore, to achieve a clinically-reliable radiomics model, it is important to study and account for the effect of the variation in feature selection (FS) methods27.In addition to the need for adequate sample sizes, which is the main motivation behind this study, a radiomics model must overcome two important challenges in order to be reliable in a clinical context. First, it must be clinically reproducible. This is challenging because different radiomics studies use different subsets of radiomics features to achieve optimal models. The variations in published feature selection approaches make radiomics models less clinically reproducible20. The feasibility of using radiomics-based ML models to distinguish between benign and malignant bone lesions has been reported in previous studies7. The main details of these studies are summarized in Table Depending on the endpoint of interest, various ML classifiers may be used in a radiomics model. Support vector machine (SVM), Bayesian network (BN), multivariate logistic regression (MLR), k-nearest neighbor (kNN), decision trees (DT), random forests (RF), neural network (NNet), and convolutional neural networks (CNN) are among the ML classifiers that are most commonly used in radiomics-based ML modelsThe radiomics-based ML models listed in Table With the above limitations in mind, in this study, we investigated the feasibility of developing a fast and reliable radiomics-based ML pipeline capable of differentiating between BM and HB in RT planning-CT images of cancer patients using just geometric ROIs centered on expert-identified lesion point locations. We investigated the effect of using ROIs with different sizes and geometric shapes. We also examined the performance of different FS methods and ML classifiers in achieving the optimal BM detection pipeline.The planning-CT images of BM and HB patients used in this study were collected from the Oncology Information System at our institution. Our patient selection procedure is presented in Fig. BM samples were from patients who received palliative RT for a secondary malignant neoplasm of bone in the thoracic spine between January 2016 and September 2019. HB samples were from individuals who received curative RT for non-metastatic lung cancer (as their CT images covered the same anatomy) during this period.In total, we found 189 BM patients , 69 ± 13 years) and 1474 HB patients in our database. To reduce the large imbalance between the number of BM and HB patients, we randomly shuffled the HB sample (in a Microsoft Excel file) and selected the first 170 patients  to include in our study  for lesion identifying. diCOMBINE is an open-source software developed by our group using the Python Flask29 framework for DICOM 3D visualization and lesion point location labeling. The center points of BM lesions were labeled by an expert team comprising one staff radiation oncologist and four radiation oncology fellows. Each expert was asked to label BM center points in one of the five data sets, and a peer expert was tasked with reviewing them and validating the labels. A total of 631 validated BM center points were thus identified in the BM data set. Similarly, the planning-CT images of the HB patients were randomly divided into three sets and were loaded into diCOMBINE for HB labeling. One staff medical physicist and two medical physics graduate students were asked to identify HB points in one of the data sets each. When identifying HB points, the medical physicists were instructed to avoid non-metastatic skeletal complications . An average of four HB points were identified in each planning-CT image. Then, we asked each physicist to independently review and confirm the HB points that one of their peers had labeled. A total of 674 validated HB points were identified in this way. Screenshots of our diCOMBINE 3D lesion labeling web application are presented in Fig. The planning-CT images of the BM patients were randomly divided into five sets using the Python |random.shuffle| module and were loaded into our custom-written 3D DICOM visualization web application diCOMBINE8 for les30 to the maximum size of a spinal vertebra  and five cylindrical along the z-axis (CY) ROIs of various sizes. The characteristics of the ROIs used are specified in Table https://pydicom.github.io/pydicom/stable/) was used to read DICOM CT images and normalize pixel data to Hounsfield Units. Then, the normalized CT slices were stored as 3D raster data using the pynrrd package (version 0.4.2) (https://pypi.org/project/pynrrd/0.4.2/). The pynrrd package was also used to generate 3D binary masks from each of the nine ROIs listed in Table 33 was used to calculate the 3D quantitative radiomics features. For each of the nine ROIs listed in Table 35. We also aggregated radiomics features from multiple ROIs to define four ensemble ROIs, including; (1) E4SP: 428 features extracted from all four spherical ROIs, (2) E4CY: 428 features extracted from the first four cylindrical ROIs, (3) E5CY: 535 features extracted from all five cylindrical ROIs, and (4) E9SC: 963 features extracted from all nine ROIs combined. Our rationale for this approach was that by aggregating features extracted from ROIs with various sizes around the BM centers, we could extract sufficient information about the BMs’ shape, size, and other characteristics and distinguish them from HBs using ML classifiers. Similar feature aggregation approaches were used in other studies37.The pydicom package , fast independent component analysis42 (Fast ICA), zero variance threshold43  logistic regression algorithm, recursive feature elimination with cross-validation45 (RFECV), and decision-tree-based feature selection46 (TREE). For the PCA, motivated by Zack et al.9 we used 20, 24, and 30 features. For the LASSO method, we examined least-squares penalty  feature selection module (https://scikit-learn.org/stable/modules/feature_selection.html). The performance of these FS methods, along with no FS, was then evaluated using 12 supervised ML classifiers.Radiomics calculates hundreds of features from images and some of them are redundant or are not useful for detecting BM48 was used to implement our ML classifiers. We used 12 supervised classification models, including the linear support vector machine49 (L-SVM), SVM with Radial-basis function kernel49 (SVM), Gaussian Naive Bayes50 (NB), K-Nearest Neighbors51 (kNN), Quadratic Discriminant Analysis52 (QDA), Gaussian Process Regression53 (GPR), Decision Tree54 (DT), Random Forest55 (RF), Bagging55, AdaBoost55, Neural network with stochastic gradient-based solver57 (NNet) and NNet with Limited-memory Broyden-Fletcher-Goldfarb-Shanno solver58 (NNet-LBFGS). For both NNet classifiers, we used the rectified linear unit activation function59 (RELU).The Python scikit-learn ML package (version 0.20.4)60 (AUC) for performance evaluation. Also, we reported precision and recall for our best-performing pipeline. Matplotlib (version 3.4.3)61 was used to generate figures.The performance of our radiomics-based ML models were measured using the test data set. The standard error of calculations was reported using 5-fold cross-validation on the training data set. We used the area under the receiver operating characteristic curveThis retrospective study was approved by the Research Ethics Board of the McGill University Health Centre, Montreal, Quebec, Canada, with the waiver of informed consent. We confirmed that all research were performed in accordance with the relevant guidelines and regulations.62. The predictive performance of the different FS methods and ML classifiers was evaluated for each ROI on the test set using the AUC, precision, recall, and F-1 scores. Examples of receiver operating characteristic (ROC) curves are presented in Fig. 62.A JSON file of the metadata of extracted radiomics features is available in the supplementary dataset in our public repositoryAn example of an AUC grid for different combinations of ML classifiers and FS methods is presented in Fig. 62.Two examples of the effect of using geometric ROIs with different sizes and shapes are presented in Fig. Comparing Fig. To visualize the effect of the size of ROI on the performance of our models, in Fig. 62.The grid of the F-1 scores for the best performing FS method (LASSO) is presented if Fig. In this study, we investigated the feasibility of using a single-point-based geometric ROI to develop a radiomics pipeline to distinguish BM and HB locations in planning-CT images of cancer patients with BM. We investigated various FS methods, and ML classifiers using point-based geometric ROIs with various shapes and sizes.63 for use in the clinic. Another limitation of manual lesion segmentation is inter-observer variability, which has been shown to have a significant impact on the performance and reproducibility of radiomics-based pipelines64. Furthermore, manual segmentation tools, designed for radiation therapy treatment planning, intend to load one patient at a time. Therefore, switching between patients is another time-consuming process that slows down the lesion delineation for multiple patients in the research context65.The time and effort needed for manual 3D segmentation of ROI are significant limitations to achieving large real-world image data sets. This, in turn, hinders the generation of generalizable radiomics-based prognostic ML models28) allowed our collaborating radiation oncologists to quickly review planning-CT images of several hundred patients and efficiently identify 676 BM centers. They found diCOMBINE fast and easy to use, allowing each expert to label around 150 lesions per hour. Based on our experts’ anecdotal experience, single-point-based geometric ROI delineation was 10–15 times faster than full manual 3D segmentation. These lesion centers were used to generate ROIs automatically. Defining point-based geometric ROIs, instead of full 3D manual segmentation of the ROIs, allowed us to rapidly generate a large sample set, minimize expert imposed uncertainties, and investigate the effect of the size and shape of the ROIs in the performance of our radiomics models. Besides, our point-based radiomics models will allow us to study the feasibility of building an automated BM-identifying pipeline. To the best of our knowledge, no studies on automated BM delineation have been published previously.Our in-house-developed open-source 3D DICOM visualization and lesion identification tool , applying FS methods in some cases worsened the overall performance of the model. Inversely, for ML classifiers that did not have built-in FS methods , adding FS had a significant effect on the performance of the ML classifier. The effect of the FS method was more significant when working with ensemble ROIs that had many more features. For example, the AUC value for the GPR ML classifier using the E9SC ROI (963 features) improved from 0.52 to 0.97 when the LASSO FS method was used, as shown in Fig. Radiomics extracts hundreds of features from an ROI. However, these features are generally highly correlated and contain much noise. Therefore, it is essential to apply proper FS methods to achieve a robust radiomics-based ML pipeline. Among the seven FS methods we examined in this study, we found that PCA and ICA resulted in lower AUC values than the VT, LASSO, and TREE FS methods. One reason for this difference was that VT, LASSO, and TREE methods automatically defined the optimal number of features, while in PCA and ICA, the number of features was predefined. For highly-correlated features, the optimal number of features (f) is roughly proportional to the square root of the sample size (n)68.Among the ML methods we examined in this study, we found that GPR, NNet, SVM, and RF resulted in the highest AUC values and F-1 scores. We showed that the GPR classifier outperformed the NNet classifier for most ROIs. However, for the ensemble ROIs (in which the number of features was large), GPR required a proper FS method . The dimensionality issue of GPR classifiers and their requirement for FS was also discussed in the literature30. As can be seen in Fig. We found that our radiomics-based ML models performed slightly better on spherical ROIs compared to cylindrical ROIs of similar volumes. More significantly, we found that the smaller ROIs (15 and 20 mm) resulted in better performance compared to the larger ROIs (30 and 50 mm) Fig. . This mi65. Moreover, 3D segmentation of the ROI is a time-consuming bottleneck that likely compelled all the prior studies to train and test their radiomics pipelines with limited sample sizes. Training on a small sample size diminishes the generalizability and clinical applicability of a radiomics pipeline. In comparison, our point-based pipeline allowed us to avoid the labor-intensive manual segmentation step and train and test our pipeline on a large data set. Finally, in this study, we investigated the effects of FS methods, and ML classifiers in achieving the optimal prognostic model using geometric ROIs. To the best of our knowledge, no prior study performed such a comprehensive optimization.Although various radiomics pipelines have been previously developed and reported to classify bone lesions, our radiomics-based ML pipeline, reported here, offers several advantages compared to preceding efforts, mainly in the context of palliative radiotherapy planning. First, we, pragmatically, used planning-CT images of cancer patients for extracting radiomics features, whereas prior studies used hybrid modalities or diagnostic-CT images  were ignored when labeling HB points. A solution for this problem would be using pathology data to identify non-metastatic and metastatic lesions but this would significantly increase the required effort. Third, we used a nearly balanced data set of HB and BM patients in this study. However, having an imbalanced sample ratio is common in many real-world radiation oncology outcome data setsWe demonstrated that our radiomics-based ML models can successfully distinguish between metastatic and healthy bones in planning-CT images using lesion-center-based geometric ROIs. Our results suggest that the GPR classifier with ensemble ROIs is particularly promising for the differentiation of BM and HB. Optimum pipeline performance was obtained using elimination-based FS methods such as LASSO. Our results demonstrate that radiomics features obtained from a lesion-center-based geometrical ROI may be sufficient to train radiomics-based ML classifiers to distinguish between bone lesions when full 3D segmented ROIs are not available. This opens the door to big data artificial intelligence research for cancer patients with BM.


To compare two different forms of mechanical circulatory support (MCS) in patients with complex high-risk indicated PCI (CHIP): the Impella CP system and veno-arterial extracorporeal membrane oxygenation (VA-ECMO).  To prevent hemodynamic instability in CHIP, various MCS systems are available. However, comparable data on different forms of MCS are not at hand.  In this multicenter observational study, we retrospectively evaluated all CHIP procedures with the support of an Impella CP or VA-ECMO, who were declined surgery by the heart team. Major adverse cardiac events (MACE), mortality at discharge, and 30-day mortality were evaluated. p = 0.22). The composite outcome of MACE showed no significant difference . Bleeding complications were higher in the Impella CP group, but showed no significant difference  and occurred more at the non-Impella access site. In-hospital mortality was 7.4% in the Impella CP group versus 14.3% in the VA-ECMO group and showed no significant difference (p = 0.48). 30-Day mortality showed no significant difference .  A total of 41 patients were included, of which 27 patients were supported with Impella CP and 14 patients with VA-ECMO. Baseline characteristics were well-balanced in both groups. No significant difference in periprocedural hemodynamic instability was observed between both groups (3.7% vs. 14.3%;  In patients with CHIP, there were no significant differences in hemodynamic instability and overall MACE between VA-ECMO or Impella CP device as mechanical circulatory support. Based on this study, the choice of either VA-ECMO or Impella CP does not alter the outcome.  In patients with complex coronary artery disease (CAD) or one or more chronic total occlusions (CTO) combined with the presence of comorbidities, determining the optimal revascularization strategy (percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) remains a challenge. These high-risk patients pose an extreme surgical risk and are frequently assigned to medical therapy instead of CABG. However, with the development of new interventional techniques and materials, PCI is a good alternative to CABG and is referred to as complex high-risk indicated PCI (CHIP).During CHIP, hemodynamics can deteriorate because of temporary complete coronary occlusion or profound myocardial ischemia. This could result in loss of cardiac output and flattening of the arterial curve once the ventricle “uncouples” from the aortic pressure with a risk for cardiogenic shock . MechaniThere are several mechanical circulatory support (MCS) systems available, i.e., intra-aortic balloon counterpulsation (IABP), Impella, TandemHeart, and veno-arterial extracorporeal membrane oxygenation (VA-ECMO). These MCS have been widely studied in patients with acute myocardial infarction (MI) complicated by cardiogenic shock and showed conflicting results –8. The mHowever, studies regarding the use of MCS in the setting of CHIP are much less abundant and to our knowledge, no study has compared Impella CP with VA-ECMO in CHIP patients –11. ThisA multicenter comparative observational study was performed including all patients undergoing CHIP with the mechanical circulatory support of either Impella CP or VA-ECMO.In patients who underwent Impella CP support, an Impella catheter was inserted through the femoral artery by using a modified Seldinger technique and forwarded into the left ventricle under fluoroscopic guidance. Closure of the Impella femoral access site was performed with a minimum of two Perclose ProGlide suture systems . Cannulation of the VA-ECMO was performed using open cutdown and decannulation was accomplished through surgical vascular repair of the femoral artery.All patients were discussed in the heart team prior to intervention and complex high-risk PCI was defined as patients with an unprotected left main artery, last patent vessel, or complex 3-vessel coronary artery disease, with a left ventricular ejection fraction (LVEF) of ≤30%–40% and severe comorbidities , renal, pulmonary, or cerebrovascular) that were declined for CABG.Baseline characteristics were collected including age, gender, prior coronary artery disease, prior CABG, diabetes, history of smoking, hypercholesterolemia, hypertension, prior malignant disease, peripheral artery disease (PAD), renal function, and LVEF. Renal function was defined using the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease (CKD). Left ventricular function was divided into four categories normal (LVEF >55%), mildly impaired (LVEF 45–55%), moderately impaired (LVEF 35–44%), and severely impaired (LVEF < 35%).https://syntaxscore.org). The euroSCORE I and II were also calculated for all patients using the online calculator (https://www.euroscore.org/). If there was a CTO lesion present the Japanese-CTO score was calculated using the available online J-CTO calculator (https://www.progresscto.org/cto-scores).The coronary anatomy of both groups was assessed in regard to the target vessel for revascularization, multivessel disease, and the presence of a CTO lesion. For all individual patients, the SYNTAX score and SYNTAX 2 score were calculated using the online Syntax-score calculator  were analyzed for both groups.MACE at 30 days was defined as a composite of all-cause death, myocardial infarction, stroke or transient ischemic attack (TIA), major bleeding events according to the Bleeding Academic Research Consortium (BARC) criteria, repeat revascularization, need for cardiac surgery, limb ischemia, cardiopulmonary resuscitation or acute renal insufficiency. Limb ischemia was assessed by palpation of either the posterior tibial or dorsalis pedis artery of the ipsilateral cannulated femoral artery during the procedure and the occurrence of limb ischemia postprocedure.Major bleeding events postprocedure were defined according to the BARC criteria and events with a BARC 3 or higher were considered significant and included in the analysis .6 units per ml. Transfer to the Intensive Care Unit (ICU) and the mean stay in the ICU as well as on the ward were also analyzed.Deterioration of renal function postprocedurally was the increase of one or more KDIGO stages above baseline. Blood hemoglobin levels in mmol/l were assessed prior to and after the procedure as well as thrombocyte count in 10t-test was performed otherwise a Mann–Whitney U test was performed. Categorial variables were analyzed using a Chi-Square test. A two-tailed p value of < 0.05 was considered statistically significant.Statistical analysis was performed using a Kolmogorov–Smirnov test to assess the normal distribution of continuous data. If there was a normal distribution of the data a Student's The data used in this article are derived from previously published data , 13. BotBetween 2017 and 2020, a total of 41 patients underwent a complex high-risk PCI facilitated by mechanical circulatory support: 27 patients received support with the Impella CP device (between March 2018 and October 2020) and 14 patients received VA-ECMO support (between January 2017 and July 2018). The decision for the use of mechanical circulatory support was made by the heart team.p = 0.44) and the number of men (p = 0.15) did not differ significantly between the groups. Other baseline characteristics were well-balanced in both groups (The mean age (h groups . Most pap = 0.02), but other lesions showed no difference. A majority of the patients in both groups had a CTO (74.1% vs. 71.4%). EuroSCORE, SYNTAX, and J-CTO scores between the two groups did not differ (A left anterior descending(LAD) lesion was significantly more apparent in the Impella group . All hemodynamic unstable patients developed hypotension with a systolic blood pressure < 90 mmHg for more than 30 minutes and this hemodynamic instability was treated successfully with the administration of intravenous vasopressor agents in two patients and the other patient with fluid resuscitation.Hemodynamic instability during the procedure occurred in one patient in the Impella group (3.7%) and in two patients in the VA-ECMO group (14.3%). This difference was not statistically significant (p = 0.59) (p = 0.22). The VA-ECMO patient developed a cardiac tamponade which was successfully evacuated by pericardial drainage. The 6 bleeding complications in the Impella groups had femoral access-site-related bleeding of which 2 patients (7.4%) had significant bleeding from the Impella access site and needed acute vascular surgery to restore the defect. The remaining 4 patients (14.8%) had a grade 3A hematoma at the non-Impella access site but absence of active bleeding. [The composite outcome of MACE was higher in the Impella-facilitated CHIP group but showed no significant difference  . Bleedinleeding. .p=0.48). Thirty-day mortality was higher in the VA-ECMO group but did not reach statistical significance .In-hospital mortality was 7.4% in the Impella CP-assisted patients and 14.3% in the VA-ECMO-assisted patients and showed no significant difference .Revascularization was successful in 25 patients in the Impella group (92.6%) and in 14 patients in the VA-ECMO group (100%) and showed no significant difference (Kidney function deteriorated in 4 patients in the Impella group (14.8%) and 3 patients in the VA-ECMO group (21%).p=0.35) between both groups. The mean length of hospital stay showed no significant difference between the groups but was overall longer in the VA-ECMO group . There was a trend towards more transfer to ICU after Impella in comparison with VA-ECMO .There was no statistically significant difference in regard to hemoglobin levels post PCI . In the 3 patients, where hemodynamic instability during the procedure did occur, the significant drop in blood pressure was resolved with temporary inotropic agents. In comparison with data from the PROTECT II trial, hypotension during support with an Impella 2.5 occurred in 10.2% of the patients whereas with the Impella CP it occurred in only 3.7% [Our study results indicate that in patients with CHIP who underwent either Impella CP or VA-ECMO assisted PCI, no statistically significant difference in the occurrence of periprocedural hemodynamic instability was observed  is a micro-axial pump positioned across the aortic valve which aspirates blood from the left ventricle into the ascending aorta . The effIn our study, the choice of MCS was left to the discretion of the operators, local expertise, and available facilities. Not every patient is suitable for all MCS and the choice depends on patient characteristics such as anatomy and procedural characteristics.In-hospital and 30-day mortality were not different between both groups, but a trend toward higher 30-day mortality was seen with VA-ECMO-assisted PCI. In both groups, 2 patients died during hospitalization. Careful consideration of these patients by several physicians, including the attending intensivist, cardiologist, interventional cardiologist, and anesthesiologist, concluded that all in-hospital deaths were not related to either the PCI itself or the MCS support. All patients were known with end-stage heart failure and several comorbidities and died as a result of multiorgan failure due to persistent cardiogenic shock. In the VA-ECMO group, mortality is up to 30 days extended to 1 patient. The patient who died after discharge died as a result of persistent heart failure.No difference in MACE or bleeding complications between both groups was observed in our study population. There were nonetheless numerically more bleeding complications in the Impella group, mainly driven by femoral access site bleeding at the non-Impella site. In only 2 patients there was significant bleeding at the access site. A possible solution to reduce the number of access site bleedings of the non-Impella site is to use the single-access technique for Impella . Using tThe reason for no access site bleeding complications in the VA-ECMO group might be explained by a controlled insertion and removal of the cannulas. The cannulas of VA-ECMO are inserted and extracted by surgical cutdown and closure of the femoral artery. In comparison, the femoral access site of the Impella in these patients was closed with the ProGlide closure device. Nonetheless, in only 2 patients in the Impella group the bleeding complications were related to bleeding from the Impella access site, so most bleeding complications arose from the non-Impella access site.Limb ischemia did not occur in either group. This illustrates the safety of short-term support of MCS during CHIP in regard to limb ischemia. However, in this semi-elective setting of CHIP, patients with known significant peripheral artery disease which could be compromised during or after the procedure, would not be selected for Impella or VA-ECMO. One patient developed venous thromboembolism several weeks after VA-ECMO cannulation at the venous cannulation site. This was resolved without further consequences by treating this patient with oral anticoagulation.This is to our knowledge the first comparative study between Impella and VA-ECMO in the setting of CHIP. A comparison study between Impella and VA-ECMO in patients with cardiogenic shock showed lower in-hospital mortality, fewer complications, decreased hospital costs, and decreased length of stay in patients supported with Impella in comparison to VA-ECMO. Moreover, there was a higher rate of ischemic stroke and vascular complications in the VA-ECMO cohort . Our stuThis is an observational study and therefore no direct comparison between both groups in a randomized fashion was performed. Data was collected retrospectively with all shortcomings of such. The sample size is relatively small and therefore hypothesis-generating, but may pave the way for future randomized studies.In this observational study, there was no difference in hemodynamic instability and MACE in CHIP with either VA-ECMO or Impella CP device for mechanical circulatory support. Although statistically not significant, bleeding complications were higher in patients supported with the Impella CP, but these were mainly driven by bleeding complications from the non-Impella access site. Based on this study the choice of either VA-ECMO or Impella CP does not alter the outcome. Future research, preferably in a randomized fashion, is needed to establish the most effective, safe, and financially favorable form of MCS in patients undergoing high-risk PCI.


Approximately 30% of childhood cancer survivors (CCSs) will develop chronic kidney disease (CKD) or hypertension 15 to 20 years after treatment ends. The incidence of CKD and hypertension in the 5-year window after cancer therapy is unknown. Moreover, extent of monitoring of CCS with CKD and associated complications in current practice is underexplored. To inform the development of new and existing care guidelines for CCS, the epidemiology and monitoring of CKD and hypertension in the early period following cancer therapy warrants further investigation.To describe the design and methods of the KIdney aNd blooD prESsure ouTcomes in Childhood Cancer Survivors study, which aims to evaluate the burden of late kidney and blood pressure outcomes in the first ~10 years after cancer therapy, the extent of appropriate screening and complications monitoring for CKD and hypertension, and whether patient, disease/treatment, or system factors are associated with these outcomes.Two distinct, but related studies; a prospective cohort study and a retrospective cohort study.Five Ontario pediatric oncology centers.The prospective study will involve 500 CCS at high risk for these late effects due to cancer therapy, and the retrospective study involves 5,000 CCS ≤ 18 years old treated for cancer between January 2008 and December 2020.2 or albumin-to-creatinine ratio ≥ 3mg/mmol. Hypertension is defined by 2017 American Academy of Pediatrics guidelines.Chronic kidney disease is defined as Estimated glomerular filtration rate <90 mL/min/1.73 mProspective study: we aim to investigate CKD and hypertension prevalence and the extent to which they persist at 3- and 5-year follow-up in CCS after cancer therapy. We will collect detailed biologic and clinical data, calculate CKD and hypertension prevalence, and progression at 3- and 5-years post-therapy. Retrospective study: we aim to investigate CKD and hypertension monitoring using administrative and health record data. We will also investigate the validity of CKD and hypertension administrative definitions in this population and the incidence of CKD and hypertension in the first ~10 years post-cancer therapy. We will investigate whether patient-, disease/treatment-, or system-specific factors modify these associations in both studies.Results from the prospective study may not be generalizable to non-high-risk CCS. The retrospective study is susceptible to surveillance bias.Our team and knowledge translation plan is engaging patient partners, researchers, knowledge users, and policy group representatives. Our work will address international priorities to improve CCS health, provide the evidence of new disease burden and practice gaps to improve CCS guidelines, implement and test revised guidelines, plan trials to reduce CKD and hypertension, and improve long-term CCS health. The first 5 years post-therapy may be a critical window to mitigate long-term progression and adverse effects of CKD and hypertension in CCS.Research on long-term effects of cancer therapy on kidney function in CCS has been historically limited by small sample size and suboptimal definitions for CKD and hypertension with resulting uncertainty of the onset, severity, and characteristics of these conditions in CCS.18 lack specific or actionable recommendations for detecting, preventing, or treating CKD and hypertension soon after cancer therapy completion. The “Kidney Disease: Improving Global Outcomes (KDIGO)”20 and Pediatric Hypertension21 guidelines do provide actionable recommendations on kidney disease and BP monitoring, although not specific to CCS. To our knowledge, there have been no attempts to harmonize these guidelines to optimize practice. Moreover, the extent of CKD and hypertension monitoring being performed in CCS during the first 5 years after cancer therapy completion is unknown.Current kidney and BP follow-up guidelines ,To investigate CKD and hypertension epidemiology during the first 5 years after cancer therapy and identify evidence-practice gaps in contemporary screening and prevention of CKD and hypertension, and of complications from CKD and hypertension in CCS.Supplementary Table 1). Both studies will be conducted at 5 pediatric oncology sites in Ontario, Canada: Hamilton, Kingston, London, Ottawa, and Toronto.We will perform 2 related multi-center studies in Ontario, Canada. This pair of studies involves a network of clinicians, researchers, biostatisticians, stakeholders, and patient partners  and the SickKids Research Institute will act as a participating site, study and data coordinating center, and the biorepository. ICES  scientists and analysts will be integral members of the study team, receiving secure study data for linkage and analysis with administrative healthcare data.SickKids will provide comprehensive training on the study protocol via video-teleconference covering data collection, biospecimen collection and processing, standardized physical exams and placement of 24-hour ambulatory blood pressure monitoring (ABPM) devices. The study will be submitted to the Ontario Cancer Research Ethics Board (OCREB) for approval, prior to study start . Upon approval, we anticipate recruitment for the prospective study will begin in 2022, with a recruitment target of 500 patients.Evaluate CKD and hypertension prevalence at 3 and 5 years after cancer therapy and prevalence of 24-hour ABPM abnormalities at 5 years after cancer therapy, in CCS at high risk for late kidney and BP outcomes.Investigate changes in estimated glomerular filtration rate , albuminuria, BP, and of CKD and hypertension prevalence from 3 to 5 years post-cancer therapy in CCS at high risk for late kidney and BP outcomes.Investigate whether AKI occurrence during cancer therapy, cardiometabolic risk factors, and/or patient-, condition-, system-, and treatment-specific factors are associated with and/or modify kidney outcomes in CCS at high risk for late kidney and BP outcomes.A secondary but important aim will be to expand our patient representative committee. We will prioritize ongoing engagement with patient partners to ensure their voices are heard and promoted in research planning. An example of this will be to explore ways of communicating information on long-term kidney and BP outcomes to patients and their families, during cancer therapy.This is a prospective cohort study of ~500 CCS followed in pediatric oncology clinics, including “AfterCare” clinics devoted to follow-up of CCS, across 5 Ontario sites over a period of approximately 3.5 years . Study vThe study will enroll CCS who will be ≤18.9 years old at 3 years ± 6 months after cancer therapy completion and received potentially nephrotoxic therapies . Patients with pre-cancer CKD diagnoses will be excluded. Given the challenges differentiating underlying hypertension and hypertension secondary to cancer diagnosis, patients with pre-cancer hypertension diagnoses will be included. The definition for cancer therapy end may vary based on cancer treatment group. For example, cancer treatment end will be at 2 years after transplant for SCT patients. Detailed eligibility criteria are outlined in Site coordinators will screen CCS at approximately 2 years post-cancer therapy . Eligible participants will be approached for informed consent either virtually or in person. To support recruitment, study posters with contact information will be advertised in clinics.Study visits are scheduled 3- and 5-years post-cancer therapy . For Tor21 Sociodemographics, family history of kidney/BP diagnoses, medications, questionnaires,26 QoL measures,28 a 5 mL blood sample, and a 30 mL urine sample will also be collected at this time.Variables of interest collected at 3, 4, and 5 years after cancer treatment are summarized in At the 4-year visit, participants will be contacted  to maintain correspondence and collect minimal data. Then 2 months before the 5-year visit, participants will be mailed 2 urine cups for first-morning samples and a 24-hour urine collection container with instructions; samples will be collected within a week before 5-year follow-up.29 Families will choose whether to have the ABPM device  placed during the study visit or given instructions on how to place the device and perform ABPM within a week of follow-up. Ambulatory blood pressure monitoring will record wake and sleep BP every 20 and 30 minutes, respectively, and participant diaries will be kept throughout the 24-hour period.30The 5-year visit will mirror the 3-year visit with the addition of: urine samples (2 first-morning and one 24-hour sample) and 24-hour ABPM data for patients 5 years + as per guidelines.31Blood will be centrifuged at 4°C (2000 g × 10 minutes) and serum aliquoted into 1 mL cryovials. Urine will be poured into 2 x 15 mL conical tubes for collection and a dipstick urinalysis. Coordinators will scan specimens into REDCap with barcode readers as they collect/store/ship them for real-time monitoring and quality assurance evaluations. Biospecimens will be stored on-site at −80°C until quarterly shipments to SickKids on dry ice. The central biochemistry lab at SickKids will measure several kidney and cardiovascular markers: serum creatinine (SCr) using isotope-dilution mass spectrometry-traceable assay electrolytes, glucose, cholesterol, triglycerides, serum C-reactive protein, urine albumin to creatinine ratio (ACR), and urine protein to creatinine ratio. Yearly, the Ottawa site biochemistry lab will receive serum shipments for Cystatin C measurement (particle-enhanced turbidimetric immunoassay). Leftover biospecimens will be stored at SickKids at −80°C for repeat measurements as needed and future studies.On top of data collected at recruitment and follow-up, clinical data  will be abstracted from patients’ electronic medical records and entered onto paper case report forms (CRFs). Each site will upload de-identified data forms to a secure REDCap database located on the SickKids server. A SickKids data coordinator will enter these data into REDCap and send monthly queries to sites. In previous work, we found central data entry yields fewer errors and reduces study site burden. At the time of participant recruitment, permission will be sought for future data abstraction from patient charts (up to 10 years) to update existing study records.Only at SickKids, 2 sets of laboratory values will be collected retrospectively, as SickKids only recently (2021) started contributing data to Ontario Laboratories Information System (OLIS): SCr values throughout cancer therapy to define AKI, monthly SCr, urine albumin, and protein values between cancer therapy end and 3-year follow-up and between 3-year and 5-year timepoints.3235 The main patient, system, and treatment factors we will utilize from ICES databases include pre-cancer diagnosis and previously diagnosed cardiac/diabetes/liver disease, dialysis procedures/CKD and/or hypertension diagnoses per administrative healthcare data, birthweight, income quintile, rural vs. urban community, and cancer-variables .Following data collection, a database containing all measurements will be sent to ICES for linkage with administrative healthcare data, as done previously.3638 a database of clinical information for pediatric oncology patients in the 5 Ontario tertiary care centers, providing rich cancer data3943; Discharge Abstract Database (DAD), Same Day Surgery (SDS), and MOMBABY, documenting administrative, clinical, and demographic characteristics of patients44; The Ontario Health Insurance Plan (OHIP), ICES-derived Physicians Database (IPDB), and Canadian Organ Replacement Register (CORR), databases used to track physician billing, procedures, and specialist referrals46; The Registered Persons Database (RPDB), providing demographic information on all Ontarians with health cards47 and when they leave the Ontario healthcare system,48 providing a method to account for some loss to follow-up; and the OLIS, used to collect laboratory data. A detailed list of data collected from each administrative health data source are provided ,provided . These d2; Grade 2 CKD or worse] or albuminuria [ACR >3 mg/mmol] per KDIGO guidelines19  using height-, sex-, and age-adjusted BP percentile tables29 (Supplementary Table 2).The primary outcomes in this study are post-cancer therapy CKD and hypertension. Chronic kidney disease will be defined as low eGFR [eGFR<90 mL/min/1.73 melines19 . Grade 3tables29 . At the Changes in eGFR, albuminuria, and BP percentile from the 3-year to the 5-year visits, persistent CKD, and persistent hypertension at 3- and 5-year visits, and new CKD or hypertension at the 5-year visit will be recorded. Aim 3 outcomes will be the same as those described above.52 Cardiometabolic risk factors will be ascertained at the study visits and will include glucose, body mass index (BMI) z-score , non-fasting lipid profile , and C-reactive protein.Study exposures of interest pertain mainly to aim 3: AKI during cancer therapy and presence of cardiometabolic risk factors. Acute kidney injury will be defined based on KDIGO guidelines for AKI definition, as a ≥50% SCr rise throughout cancer therapy, from baseline, as previously described.53 age at diagnosis, birthweight, sex (gender), income quintile, rural vs. urban, and baseline cardiac, diabetes or liver disease, unless stated otherwise. Sex-stratified analyses will also be conducted. Effect estimates will be described with 95% confidence intervals.Multivariable analyses described below will include variables of cancer diagnosis classified by a previously described classification system,vs. 5- year CKD and hypertension prevalence using the McNemar test, calculate 3- to 5-year eGFR, ACR, and BP percentile change and proportions of participants with persistent CKD, hypertension and status change. We will use mixed effect models (for repeat measures) to evaluate adjusted associations of AKI and cardiometabolic measures with 3- and 5-year outcomes to evaluate if they modify trajectories by including interaction terms for these measures with time in models. We will also evaluate univariable and multivariable associations of AKI and cardiometabolic measures  with individual 3- and 5-year binary outcomes  using log-binomial regression and continuous outcomes using linear regression . In exploratory analyses, we will determine if analyses above differ by treatment groups , treatment combinations , and diagnosis classification53 in univariable analyses, and if event rates allow, in multivariable analyses. Sample size justification is provided in Supplementary Information.Three- and 5-year prevalence of CKD, hypertension, all categorical 5-year ABPM outcomes, and continuous outcome measures  will be calculated. We will compare 3- In a retrospective cohort of Ontario CCS treated for cancer between 2008 and 2020, we will evaluate:1a. Chronic kidney disease screening, complications monitoring  and nephrology referral;1b. Hypertension screening, complications monitoring  and nephrology referral.2. The validity of several pediatric specific CKD and hypertension administrative algorithms3. Chronic kidney disease and hypertension incidence in the ~10 years after cancer therapy.4. Whether patient, disease/treatment or system factors are associated with each of the above outcomes.54 In sensitivity analyses, we will consider including second cancer and relapse as binary variables in the multivariable analyses described below, rather than as censoring events (not described further).The retrospective multi-center cohort study consists of approximately 5,000 CCS treated for cancer across 5 Ontario sites between January 2008 and December 2020. Hypertension outcome analyses will include SickKids patients only (approximately 2500 CCS) due to data collection feasibility. The inclusion and exclusion criteria are shown in Much of the data used will be obtained from ICES databases . Ideal hypertension monitoring will be defined as performance of a 24-hour ABPM and echocardiogram within 6 months of hypertension appearance, as defined in 21Aim 1 primary outcomes include ideal CKD and hypertension monitoring and appropriate nephrology referral. As shown in 5557 eGFR will be calculated with only SCr if cystatin C is unavailable; a sensitivity analysis for patients with both analytes available will be conducted. Should height be unavailable (for pediatric GFR estimation), validated height-independent eGFR equations will be used.50,5860 Hypertension will be defined as 2 or more consecutive hypertensive values, defined in The primary exposures for aim 1 are CKD and hypertension. CKD will be defined in 2 ways, detailed in 62Aim 2  outcomes will be CKD and hypertension, defined using laboratory data and BP measures, respectively. These outcomes will be defined using the strict criteria described above  and considered the “gold standard.” The diagnostic test will be algorithms based on diagnosis and procedure codes available from ICES databases (described in previous work). These CKD and hypertension algorithms in the ICES databases will be evaluated for their ability to identify or detect CKD and hypertension defined by reference standard-methods.vs. without CKD and hypertension to investigate factors related to variation in clinical practice. Using log-binomial regression , we will estimate the yearly change in ideal monitoring. We will use multivariable log-binomial regression with generalized estimating equations to yield adjusted rate ratios of patient, disease, treatment, and system covariates for ideal monitoring. Analyses will be repeated, adjusting for time as well as provider-level and center-level clustering in monitoring practices.We will calculate yearly proportions of patients with CKD and hypertension who undergo ideal monitoring, and appropriate nephrology referral, separately. We will also evaluate individual CKD and hypertension monitoring components . Yearly measurement rates of eGFR and urine protein measures from the end of therapy will also be calculated. We will compare characteristics and monitoring in CCS with Various diagnosis and procedure code-based algorithms for CKD and hypertension will be evaluated for detecting reference-standard-based CKD and hypertension (Aim 2). Algorithms will be evaluated for validity and level of agreement with the following measures: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), kappa (κ) statistic, and area under the receiver operating characteristic (ROC) curve.63 to estimate cumulative incidence  and generate cumulative incidence function curves to understand burden and time of CKD and hypertension onset (aim 3). Censoring will occur at system leave date , relapse, second malignancy, or follow-up end.64 Sample size calculation is provided in supplementary information.We will use a sub-distribution hazards model accounting for competing risk of death (Fine and Gray method)At years 3 and 5, we will host videoconference meetings with investigators, patients, stakeholders, and policy representatives to discuss progress, surveys, and update/unify priorities. We also plan to hold working groups to discuss action plans in response to actual and potential findings , discuss steps to impact CCS guidelines based on burden findings from the prospective study and practice gaps from the retrospective study. We will know normative practice patterns, gaps, and associated factors of CKD and CKD-related complications. Our cohort, data and biobank will open countless avenues of novel cross-disciplinary research endeavors and training. Our team will be ideally placed to inform and apply CCS kidney and BP guidelines within the greater context of CCS health and maximize patient and system beneficial impact from the knowledge we generate.While it is known that CCS are at increased risk of adverse kidney effects, limitations in our understanding of long-term effects of cancer therapy may track as late complications. CKD and hypertension burden in the first 5 years after cancer therapy is unclear, as are current recommendations on follow-up for CCS.65The involvement of all major cancer centers in Ontario, facilitating diverse patient recruitment including a spectrum of therapies is a certain strength of this study. Our use of robust definitions for CKD, hypertension, and AKI than currently available will increase the validity of results and applicability to current clinical practice. To our knowledge, this will be the first study to implement 24-hour ABPM, gold-standard for detection of hypertension in a large cohort of CCS, permitting comprehensive diagnosis of types of hypertension.19 or COG.18 KDIGO provides management objectives for patients with or at risk of CKD, but is not adapted to the CCS health context, whereas COG kidney guidelines are used internationally to identify and manage cancer therapy effects in CCS, but does not provide actionable recommendations on managing kidney complications.19 We believe that data generated from this study may enable improvements in both pediatric oncology and of overall CKD and hypertension guidelines, especially with a targeted KT strategy.A major goal is to describe current management of CKD and hypertension complications in CCS and establish practice gaps in this patient population. When consulting best practices in managing CCS at higher risk for CKD, clinicians may turn to KDIGO6668 Mothers experiencing high stress and/or with residences far from neonatal clinics were less likely to attend follow-up appointments.68 If specific barriers are identified, an informed, targeted approach may lead to improved quality of care and health outcomes. Our aim to validate administrative CKD and hypertension definitions in this population would dramatically enhance understanding of disease burdens in this population, using administrative data.Another strength is our assessment of whether cancer-, patient-, therapy-, and system-specific factors influence kidney and BP outcomes or screening and follow-up practices when treating CCS.vs. non-survivors’ characteristics, conduct sensitivity analyses to assess the impact of differential loss to follow-up, and if needed, propensity scores  can be used in multivariable analyses to mitigate these biases. There may be small sample sizes for subgroup analyses of specific treatments; however, our patient partners stressed these subgroup analyses are priorities.Both studies have limitations. In the prospective study, a proportion of CCS will have a cancer relapse, which may prove difficult to discern between worsening disease- and treatment-associated kidney dysfunction. Survivor bias will be an issue when analyzing risk factors for treatment-associated CKD and hypertension. We can compare survivors’ 15 By design, conclusions in the prospective study only apply to high-risk CCS; however, this is a priority population for evidence-based guideline development and will benefit the most from early CKD and hypertension diagnosis and intervention. Moreover, our high-risk groups definitions are aligned with risk groups delineated by the COG long-term follow-up guidelines, which will enhance future KT. One potential issue is 3 and 5-year prospective study event rates may be lower than expected; however, previous work69 demonstrates high event rates and including only high-risk CCS makes this problem unlikely. If low event rates are found, this will direct future research on the timing of CKD and hypertension onset in CCS. We may not see much change between the 3- and 5-year outcomes, but this first-of-its-kind evaluation of sustained kidney or BP abnormalities will establish rate-of-change data for future research. ABPM will only be done at the 5-year visit to reduce attrition bias. Similar to most albuminuria studies, ACR is susceptible to postural proteinuria and overestimation. This is a challenge in all pediatric studies evaluating proteinuria. We attempt to mitigate this by providing participants with instructions to effectively perform first-morning urine prior to the 5-year visit. We acknowledge that the study aim is descriptive in measuring changes in the outcomes.We acknowledge including study visits before 3 years would provide insight on early post-therapy development of CKD and hypertension. We chose 3-year follow-up for cohort entry to improve feasibility/cost and allow time for resolution of immediate therapy-related effects, while capturing patients early enough to identify early detection and intervention opportunities.70 We will evaluate pre vs. post 2013 eras and will include this variable in analyses if differences are found. The retrospective study is susceptible to surveillance bias; sicker patients are tested more often for CKD, thus included in analyses. We will use this large database to calculate CKD incidence using routinely collected data, but because of ascertainment bias concerns, this was not the primary goal of this study. A possible challenge for the retrospective study relates to using tests  to determine if complication monitoring occurs; it will not be possible to know if a test is specifically done to monitor for CKD. For atypical tests , this is likely not an issue. We also may be unable to infer the specific intentions of clinicians when ordering the test, nor if treatments were given in response to abnormal test results; we can only know whether or not tests were done. In future work, we may determine if treatments were given in response to test abnormalities by incorporating healthcare provider surveys with prospective data collection.In the retrospective study, practice drift may be evident. However, we do not expect this as COG guidelines have not changed significantly in recent editions in 2013.19 and hypertension21 exist but are not adapted to the natural history of CKD and hypertension specifically in CCS. We aim to prospectively study 3-year and 5-year CKD and hypertension outcomes and retrospectively determine whether appropriate screening practices are used for kidney outcomes in CCS. With this information we will be able to identify practice gaps within the current CCS healthcare context in Ontario and inform modifications to current guidelines for monitoring late kidney complications in CCS.72Guidelines to monitor CKDClick here for additional data file.Supplemental material, sj-docx-1-cjk-10.1177_20543581221130156 for KIdney aNd blooD prESsure ouTcomes in Childhood Cancer Survivors: Description of Clinical Research Protocol of the KINDEST-CCS Study by Adree Khondker, Michael Groff, Sophia Nunes, Carolyn Sun, Natasha Jawa, Jasmine Lee, Vedran Cockovski, Yasmine Hejri-Rad, Rahul Chanchlani, Adam Fleming, Amit Garg, Nivethika Jeyakumar, Abhijat Kitchlu, Asaf Lebel, Eric McArthur, Luc Mertens, Paul Nathan, Rulan Parekh, Serina Patel, Jason Pole, Raveena Ramphal, Tal Schechter, Mariana Silva, Samuel Silver, Lillian Sung, Ron Wald, Paul Gibson, Rachel Pearl, Laura Wheaton, Peter Wong, Kirby Kim and Michael Zappitelli in Canadian Journal of Kidney Health and Disease


COVID-19 is a contagious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As humanity continues to fight this virus, it keeps presenting with new manifestations and complications. Platypnea-orthodeoxia syndrome (POS) is a rare clinical syndrome characterized by dyspnea and oxygen desaturation, in the supine to sitting position, which resolves with recumbency and can be diagnosed with a simple pulse oximeter. We report three cases of COVID-19 pneumonia, who developed POS during the recovery phase, in the absence of pulmonary hypertension. Here, we report three cases of POS in the recovery phases of COVID-19, who improved with oxygen and physiotherapy. It is imperative for the clinician to be aware of this condition, which can be easily diagnosed bedside.2 23 mmHg, pO2 48 mmHg. Chest X-ray showed bilateral radio-opacities with lower zone predominance. HRCT-Thorax revealed diffuse ground glass opacities with interlobular septal thickening in both lung fields with a CT severity score [A 50-year-old male presented with high-grade fever and breathlessness for the preceding five days. On examination, he had an oxygen saturation of 84% on room air. Arterial blood gas analysis showed pH 7.43, pCOPatient was diagnosed with severe COVID-19 pneumonia and was shifted to the ICU with high flow oxygen support at 70 L/min using a high flow nasal canula and was treated with parentral methyl prednisolone 1–2 mg/kg intravenous in two divided doses for 10 days and parentral enoxaparin 0.5 mg/kg per dose subcutaneously 12 hourly for 2 weeks. After 2 weeks, repeat COVID-19 RT-PCR turned negative and his inflammatory markers decreased to almost normal levels. As his oxygen saturation was about 90% on room air, he was shifted out of ICU and kept on low flow oxygen and bedside chest physiotherapy.During this recovery phase, the patient repeatedly complained of uneasiness and shortness of breath, shortly whenever he tried to sit in his bed. The complaints and findings were consistent and reproducible. His oxygen saturation was checked in the lying and sitting positions, which were 94% and 86%, respectively, confirming a diagnosis of POS.His echocardiography was normal with no evidence of pulmonary hypertension, systolic, and diastolic dysfunction. All chambers were normal with no evidence of any clot or vegetations or any other morphological abnormalities. However, cardiac catheterization could not be performed due to ongoing pandemic. He improved with low flow oxygen at 2–3 l/min and chest physiotherapy for 2 months and was discharged on oxygen saturation > 96% on room air to follow-up in the post-COVID clinic and doing well at present.2 saturation was 74%. ABG analysis at room air revealed: pH 7.43, pCO2 34 mmHg, pO2 45 mmHg, Chest X-ray showed bilateral opacities with lower zone predominance and HRCT-Thorax revealed diffuse areas of ground glass opacities and non-specific interstitial pneumonia  and oxygen desaturation (orthodeoxia) in the supine to sitting position, which resolves quickly with recumbency. This drop in saturation is defined as a drop in PaOThe primary etiologies of POS can be broadly classified as intra-cardiac or extra-cardiac. A patent foramen ovale (PFO) is the most common cause, followed by atrial septal defect (ASD) in the presence of pulmonary hypertension . Extra-c, or it may be the result of myocardial dysfunction [While the commonest mechanism of POS in cardiac pathologies is an inter-atrial shunt, the patho-physiology of respiratory disorders differs. The proposed mechanism of POS in COVID-19 is gravitational dependent shifting of blood to the lower zones, leading to wasting of perfusion as posterior and lower parenchymal involvement is common in COVID-19 pneumonia, causing V/Q mismatch. Alternatively, it may be due to diminished perfusion in the lower zones, due to vasculopathy and coagulopathy , or it mfunction , 8. FactPOS in our patients was reversible with oxygen and physiotherapy, in concordance with few other reported cases from the country –8. ThesePOS should be considered as a cause of respiratory impairment in patients of COVID-19 pneumonia after ruling out other causes. This case series has been reported to emphasize that POS may not be as uncommon as thought to be and high level of suspicion should be kept in COVID-19 patients, requiring prolonged oxygen support, especially those having positional exacerbation of dyspnoea.


Vaccine hesitancy is considered a major barrier to achieving herd immunity against COVID-19. While multiple alternative and synergistic approaches including heterologous vaccination, booster doses, and antiviral drugs have been developed, equitable vaccine uptake remains the foremost strategy to manage pandemic. Although none of the currently approved vaccines are live-attenuated, several reports of disease flares, waning protection, and acute-onset syndromes have emerged as short-term adverse events after vaccination. Hence, scientific literature falls short when discussing potential long-term effects in vulnerable cohorts. The COVAD-2 survey follows on from the baseline COVAD-1 survey with the aim to collect patient-reported data on the long-term safety and tolerability of COVID-19 vaccines in immune modulation. The e-survey has been extensively pilot-tested and validated with translations into multiple languages. Anticipated results will help improve vaccination efforts and reduce the imminent risks of COVID-19 infection, especially in understudied vulnerable groups.The online version contains supplementary material available at 10.1007/s00296-022-05157-6. It is unclear when, if and how natural regression of the ongoing coronavirus disease 2019 (COVID-19) pandemic, which has been a cause of unprecedented global morbidity and mortality, will be achieved. Moreover, the progression of COVID-19 to a chronic endemic illness is likely to increase the burden of emerging problems such as post-COVID-19 syndrome . VaccineScientific ingenuity combined with global collaboration has accelerated the development, licensure, and dissemination of SARS-CoV-2 vaccines at an unprecedented pace. The milestone of 10 billion inoculations worldwide was already achieved in January 2022  althoughSeveral health organizations have recently authorized booster vaccine doses for previously vaccinated patients . HoweverRecent data from the ongoing COVID-19 vaccination in autoimmune disease (COVAD) study  and otheWhile newer antiviral agents, such as Molnupiravir, Paxlovid, and Sotrivimab, are currently in use, their efficacy in patients with specific autoimmune diseases is yet to be ascertained . DespiteThe paucity of evidence-based literature on the short-term safety of COVID-19 vaccines in patients with autoimmune diseases aroused distrust amongst these individuals despite the widely-asserted safety of vaccines . To addrThereafter, the COVAD-2 survey was formulated with a similar methodology for its development, dissemination, and data collection as the baseline COVAD-1 survey to build on the information previously collected while preserving the original core item set. COVAD-2 will thus serve as a follow-up survey for past respondents and a standalone survey for new participants. The COVAD-2 e-survey has been vetted and extensively pilot-tested by a group of international experts including rheumatologists, neurologists, and internists. It has further undergone testing in patients and lay public and medical students to ensure the questions are simple and easy to understand. It is being translated into several languages and will then be hosted on the online platform surveymonkey.com followed by dissemination by the international expert COVAD study team on social media platforms  and among patient groups in the out patient clinics. Special efforts are being made to cover regions of the world that were relatively under-represented in the current literature and COVAD-1 survey, including Africa, South America, and several countries in Asia.This ongoing patient-reported study follows the predefined guidelines for survey-based reporting and is conceptualised to gather global data on the long-term efficacy of vaccines , 23. MorData will be downloaded from surveymonkey.com in Microsoft Excel format and imported into statistical analysis packages for further analysis. Descriptive statistics will be used with intergroup comparisons ad hoc using Stata. Open-ended responses will be qualitatively analysed with manual allocation to pre-existent or new categories while incomplete responses will be excluded. The anonymized dataset will be open to future analysis of proposed hypotheses, research questions, and study designs approved by collaborators of the COVAD steering committee for scientific validity and feasibility.Results will be disseminated in select peer-reviewed journals, on virtual platforms, and at academic conferences. A summary of the study outcomes in plain language will be provided to the respondents upon request. All information about the project, such as study design, project administration, and publication preprints, will be made available upon request. At the conclusion of the study, recruitment materials, project destination links, and online survey media will be deactivated or removed. All data will remain confidential with the principal investigator for a period of five years.Although the switch to remote consultations during the COVID-19 pandemic has widened the communication gap between healthcare providers and patients, it is imperative to address patient concerns and adverse events following vaccination as gathered by this self-reported survey. The results will help improve vaccination efforts and provide future policymakers with valuable data to formulate immunisation guidelines and reduce the imminent risks of COVID-19 in high-risk immunosuppressed groups that are currently understudied. Collaborative efforts toward rebuilding public confidence and community engagement with research-backed communication are essential for improving vaccine acceptance among those now holding views of vaccine hesitancy. The COVAD-2 survey thus follows a similar methodology for its development, dissemination, and data collection as the baseline COVAD-1 survey and aims to promote evidence-based health literacy in immunologically vulnerable populations.Supplementary file1 (DOCX 51 KB)Supplementary file2 (PDF 897 KB)Below is the link to the electronic supplementary material.


Is a single question about cannabis use, used in routine primary care with results documented in electronic health records, an accurate screening test for past-year cannabis use disorder?In this diagnostic study of 1688 adult patients, the Single-Item Screen–Cannabis (SIS-C) had excellent performance characteristics as a screening test for cannabis use disorder. Report of any past-year cannabis use balanced sensitivity and specificity.These findings suggest that the brief SIS-C is a valid screen for cannabis use disorder and could be easily integrated with screening for other behavioral health conditions in primary care. This diagnostic study evaluates the Single-Item Screen–Cannabis (SIS-C), a single-question measure to assess frequency of cannabis use in routine primary care, to detect past-year cannabis use disorder (CUD). Cannabis use is prevalent and increasing, and frequent use intensifies the risk of cannabis use disorder (CUD). CUD is underrecognized in medical settings, but a validated single-item cannabis screen could increase recognition.To evaluate the Single-Item Screen–Cannabis (SIS-C), administered and documented in routine primary care, compared with a confidential reference standard measure of CUD.This diagnostic study included a sample of adult patients who completed routine cannabis screening between January 28 and September 12, 2019, and were randomly selected for a confidential survey about cannabis use. Random sampling was stratified by frequency of past-year use and race and ethnicity. The study was conducted at an integrated health system in Washington state, where adult cannabis use is legal. Data were analyzed from May 2021 to March 2022.The SIS-C asks about frequency of past-year cannabis use with responses  documented in patients’ medical records.Diagnostic and Statistical Manual, Fifth Edition (DSM-5) Composite International Diagnostic Interview–Substance Abuse Module (CIDI-SAM) for past-year CUD was completed on a confidential survey and considered the reference standard. The SIS-C was compared with 2 or more criteria on the CIDI-SAM, consistent with CUD. All analyses were weighted, accounting for survey design and nonresponse, to obtain estimates representative of the health system primary care population.The Of 5000 sampled adult patients, 1688 responded to the cannabis survey (34% response rate). Patients were predominantly middle-aged , female or women , non-Hispanic , and White . Approximately 6.6% of patients met criteria for past-year CUD. The SIS-C had an area under receiver operating characteristic curve of 0.89  for identifying CUD. A threshold of less than monthly cannabis use balanced sensitivity (0.88) and specificity (0.83) for detecting CUD. In populations with a 6% prevalence of CUD, predictive values of a positive screen ranged from 17% to 34%, while predictive values of a negative screen ranged from 97% to 100%.In this diagnostic study, the SIS-C had excellent performance characteristics in routine care as a screen for CUD. While high negative predictive values suggest that the SIS-C accurately identifies patients without CUD, low positive predictive values indicate a need for further diagnostic assessment following positive results when screening for CUD in primary care. To our knowledge, no study has tested the validity of cannabis screens administered in routine care and documented in the electronic health record (EHR). Optimal screening tools for general medical settings typically include fewer than 4 items,STARD) reporting guideline and took place at Kaiser Permanente Washington (KPWA), an integrated health care system providing health insurance and medical care in Washington state, where adult cannabis use is legal. KPWA conducts annual population-based screening for behavioral health conditions  in primary care using a 7-item questionnaire, with results documented in the EHR.27 A single item, the SIS-C (described in the Measures subsection), asks patients about the frequency of past-year cannabis use. Responses to the SIS-C trigger additional assessment for CUD, guiding clinical decision-making.This prospective diagnostic study follows Standards for Reporting of Diagnostic Accuracy  and ensured 35% of the sample were individuals who belonged to minoritized racial and ethnic groups to obtain representation from important subgroups.Adult patients (aged ≥18 years) at KPWA who completed the SIS-C in primary care between January 28 and September 12, 2019 (N = 108 950) were eligible to be sampled for a confidential cannabis survey  or by telephone (34%). Participants acknowledged informed consent online or by telephone prior to the survey and received $20 compensation.37 The KPWA Health Research Institute Institutional Review Board approved this study with waivers of consent (to identify eligible sample), consent documentation (for survey respondents), and HIPAA authorization.The study sample included 1688 primary care patients who completed the survey, representing a 34% response rate, consistent with current health survey research.Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) CUD based on demonstrated feasibility of administration.39 The 15-item CIDI-SAM provides a diagnosis and scaled score of CUD severity (0-11), reflecting the number of DSM-5 CUD criteria met. Any past-year CUD (mild to severe) was defined as 2 or more CUD criteria endorsed on the CIDI-SAM; moderate to severe past-year CUD was defined as 4 or more CUD criteria, consistent with the DSM-5.6 The first 2 survey questions asked about frequency and recency of cannabis use. Patients who reported no past-year use on both did not complete the CIDI-SAM to minimize assessment burden (n = 94). These patients were assigned a CIDI-SAM score of zero.The Composite International Diagnostic Interview Substance Abuse Module (CIDI-SAM) was selected as the reference standard for 40 and scored from 0 to 4 points. The term marijuana was not defined and could include medical and nonmedical use—relevant in cannabis-legal settings where medical authorization by a clinician is not required. The SIS-C was embedded in the 7-item behavioral health questionnaire,41 self-administered on paper during the study period. An electronic flag prompted administration of the screen after check-in if patients had not been screened in the past year, and a medical assistant entered responses into the EHR before the physician visit.The SIS-C, offered as part of routine primary care, asked about frequency of past-year cannabis use (“How often in the past year did you use marijuana?”) with response options , adapted from the third question of the World Health Organization’s Alcohol Use Disorders Identification Test,42 and with different lived experiences . This included age , sex or gender , race , and ethnicity (Hispanic or non-Hispanic). Due to small sample sizes and/or low prevalence of CUD in certain subgroups, we combined age groups 50 and race and ethnicity 51 for subgroup analyses. Socioeconomic status was approximated using insurance status from enrollment records, marital status, education, employment, and type of residence reported on survey. Mental health and substance use disorder diagnoses were based on International Classification of Disease, Tenth Revision codes documented in the EHR or insurance claims in the year prior to survey completion.Demographic information collected from patients and documented in the EHR by the health system at the time of sampling was used to approximate the social conditions that shape the health of patients at different developmental stages53 We calculated nonresponse weights using logistic regression to estimate inverse probabilities of survey nonresponse based on sociodemographic characteristics.54 We multiplied sampling weights and nonresponse weights to obtain estimates representative of the KPWA primary care population.55 Comparisons of the eligible primary care population, eligible survey sample, nonrespondents, respondents, and the weighted primary care sample have been previously reported.35All analyses were weighted to account for stratified random sampling and nonresponse. Specifically, we calculated sampling weights by taking the inverse probability of being sampled within 10 sampling strata resulting from the 5 cannabis screen responses and indicator for patients who belong to minoritized racial and ethnic groups.57 We depicted receiver operating characteristic (ROC) curves graphically and estimated area under the curves (AUCs). ROC curves provide a useful summary of the overall discriminatory power of a screening test.56 AUCs can range from 0 to 1.0, with 0.8 to 0.9 considered excellent performance and greater than 0.9 considered outstanding.59 We estimated 95% CIs for weighted AUC estimates using nonparametric bootstrapping with 10 000 replications.60We described characteristics of the sample, including the frequency of each response option on the SIS-C. We compared the SIS-C with the reference standard of any CUD and moderate to severe CUD. To assess screening test performance characteristics, we estimated sensitivity (true-positive rate) and specificity (true-negative rate) for each cut point on the SIS-C.60To determine whether the SIS-C performs differently across sociodemographic subgroups, we plotted ROC curves stratified by age, sex or gender, and race and ethnicity. We evaluated differences between AUCs across subgroups and bootstrapped 95% CIs for differences; 95% CIs that did not contain zero indicated statistically significant between-group differences.61 Using Bayes theorem,61 we modeled the probability a patient has CUD if the SIS-C is positive and the probability a patient does not have CUD if the SIS-C is negative across a range of prevalence estimates for CUD (<0.5%-30%)12 to understand performance when applied to different populations. Analyses used Stata version 15.1 (StataCorp),62 R version 4.0.2 , and Excel version 2202 (Microsoft Corp) and were conducted from May 2021 to March 2022.Although positive and negative predictive values (indicating the probability of a condition given a positive screening result and absence of a condition with a negative screening result) are often reported in validation studies, these postscreening probabilities are highly dependent on the prevalence of the condition in the screened population.DSM-5 CUD and 1.9% for moderate to severe CUD. Characteristics stratified by CUD are available in eTable 1 in the The SIS-C had excellent performance characteristics as a screen for any past-year CUD , with anThe probability of any past-year CUD based on a positive SIS-C  varied across the range of population-based prevalences and screening thresholds . If the The SIS-C had outstanding performance characteristics for past-year moderate to severe CUD, with an AUC of 0.95  . Report The probability of past-year moderate to severe CUD based on a positive or negative SIS-C screen varied across the range of population-based prevalences and screening thresholds . ProbabiThere were statistically significant but small differences in the performance of the SIS-C across age and race and ethnicity. Full results appear in the eAppendix, eFigure 2, and eTable 2 in the DSM-5 CUD based on the confidential CIDI-SAM reference standard, slightly higher than national survey estimates.11 The SIS-C had excellent performance characteristics as a screening test for any past-year CUD and outstanding performance characteristics for moderate to severe CUD. Report of any past-year cannabis use balanced sensitivity and specificity for any CUD, whereas report of monthly or more frequent cannabis use balanced sensitivity and specificity for moderate to severe CUD.This study evaluated the screening performance of the SIS-C, a single-item cannabis screen administered and documented in the EHR as part of routine primary care in a US state where adult cannabis use is legal. Among primary care patients, 6.6% met criteria for past-year 67 few are single-item,31 no single-item screens are specific to cannabis use, and none have been validated when administered during routine care and documented in the medical record  provide more detail but may not be practical for use in routine care due to their length (>4 items)70 or appropriate due to validation only in people who use cannabis .72While there are several substance use screens validated in general adult patient populations,l record . Existin31 and its performance for identifying moderate to severe CUD was stronger.28 Approximately 90% of KPWA primary care patients are screened annually with the SIS-C, demonstrating routine use is feasible and clinically useful.This is the first study, to our knowledge, to evaluate the performance characteristics of any substance use screen when used in routine clinical care. Patients may respond differently to substance use screens when administered in clinical settings—where clinicians will see results in the medical record—compared with when administered in confidential research settings. It is promising, therefore, that the performance of the EHR-documented SIS-C for any CUD was comparable with the performance of single-item drug screens validated in research settings,73 the lowest threshold on the SIS-C (any use) may be preferred. Performance characteristics were also less strong for Hispanic patients relative to non-Hispanic White patients. Hispanic patients may underreport cannabis use to avoid repercussions74 stemming from intersecting cannabis and anti-immigration stigma.48The SIS-C performed well across all groups based on age, sex or gender, and race and ethnicity, but performance characteristics were less strong for younger and middle-aged adults relative to older adults. Because young adults have a higher prevalence of CUD and may be more susceptible to risks of CUD,12; whereas a higher threshold, such as daily use, may be appropriate for general medical settings.10 Threshold selection also depends on how a positive screen will be used, the costs of false-positive results, and the benefits of true-positive results.76 Costs of screening include time and resources to administer further assessment and inappropriate diagnostic labeling of patients.34 Benefits of screening include identifying at-risk patients for prevention and harm reduction, clinician recognition of underlying reasons for cannabis use 77 and treatment options.19 Prioritizing a sensitive threshold may be appropriate as part of behavioral health screening in primary care settings when the screen is followed by low-cost, low-burden, nonstigmatizing symptom assessment and discussion of symptoms.41 Prioritizing a specific threshold that minimizes false positive screens might be more appropriate in resource-constrained settings or those in which a positive screen results in referral.57Selection of a SIS-C screening threshold for detecting CUD will depend on the prevalence of CUD in the setting where screening is taking place and resources for follow-up assessment and care. Although any use and monthly use were the optimal screening thresholds for identifying any CUD and moderate to severe CUD, respectively, applying Bayes theorem, we found that the probability a patient with a positive screen has CUD was low when the underlying prevalence of CUD in the screened population was less than 8%. A lower threshold, such as any use, may be appropriate for some settings  and populations  expected to have a higher prevalence of CUDDSM-5 criteria will be important for diagnosis of CUD.78 Furthermore, the SIS-C provides a starting point for asking patients about cannabis use to support clinicians in exploring reasons for use—including medical reasons79—and discussing benefits and risks of use.80 Finally, high negative predictive values suggest that the SIS-C accurately identifies patients without CUD so that patients who screen negative need no further evaluation.The SIS-C is not a replacement for assessment of CUD symptoms or for making a diagnosis. Many—or most—patients who screen positive on the SIS-C will not meet criteria for CUD, as reflected by low positive predictive values. This is common when the underlying prevalence of the screened condition is low. Follow-up administration of longer assessments using 39 in-person administration and urine drug screening were not feasible. We used the entire survey sample, assuming no CUD criteria for respondents who indicated no past-year use on 2 different questions, to increase representation across the spectrum of cannabis use and minimize spectrum bias.81 This approach could introduce measurement error, but we expected minimal bias, as participants indicated no past-year cannabis use twice. The CIDI-SAM asked about “cannabis” use, whereas the SIS-C asked about “marijuana”; it is unclear whether patients interpreted these terms synonymously or whether they considered medical use and/or other cannabis products in their response on the SIS-C. Only 34% of invited patients completed the survey. Although lower than desired, this response rate is consistent with industry averages35 and reflects a national trend of declining response rates.82 Consequently, responses from a small number of patients may have contributed disproportionately to analyses due to weighting for oversampling design and nonresponse.83 These weights were estimated using measured factors and cannot account for unmeasured factors; however, we found patient characteristics of the weighted sample were similar to the eligible primary care population35 and other KPWA patients overall.3 We were unable to conduct subgroup analyses for all age and race subgroups due to small sample sizes. Additionally, this study was conducted in a state with legal cannabis use and among patients who were largely White, commercially insured, and with high socioeconomic status, potentially limiting generalizability. Future studies are needed to evaluate the SIS-C in settings where adults may experience legal consequences for cannabis use.This study has limitations. While the CIDI-SAM is considered a criterion-standard measure of CUD,41 making screening for CUD feasible in primary care.This study found that the SIS-C—a self-administered, single-item screen for CUD—had excellent performance characteristics when used in routine primary care in a setting with legal cannabis use. The screening thresholds can be tailored to patient populations and the needs and preferences of health settings. The SIS-C can be easily integrated with other behavioral health screening,


Several studies showed an association between lower respiratory tract symptoms (LRS) and exposure to the 9/11 terrorist attack. However, few studies have examined the long-term impact of natural disasters on those with prior respiratory distress. The present study aims to assess the impact of Hurricane Sandy on persistent LRS among people exposed to the World Trade Center (WTC) terrorist attack. The analytic sample consisted of WTC Health Registry enrollees who completed survey waves 1, 3, and 4 and the Hurricane Sandy Survey and did not report LRS before the WTC terrorist attack. The log binomial was used to assess the association between the impact of Hurricane Sandy and persistent LRS. Of 3277 enrollees, 1111 (33.9%) reported persistent LRS post-Sandy. Participants of older age, males, lower household income, current smokers, and those with previous asthma were more likely to report persistent LRS. In separate adjusted models, multiple Sandy-related inhalation exposures (relative risk (RR): 1.2, 95% CI: 1.06–1.37), Sandy-related PTSD , and Sandy LRS  were associated with persistent LRS post-Sandy. Our findings suggest that respiratory protection is important for everyone performing reconstruction and clean-up work after a natural disaster, particularly among those with previous respiratory exposures. Lower respiratory symptoms (LRS) were one of the most common conditions reported following disaster-related environmental hazards such as the World Trade Center (WTC) attacks on 11 September 2001 . A previPersistent LRS has been associated with several negative outcomes, such as increased risk of hospitalization  and decrDespite research exploring the relationship among PTSD, Hurricane Sandy environmental conditions, and LRS, the literature on long-term LRS due to Hurricane Sandy is limited, particularly among those exposed to 9/11. The present study is a continuation of work conducted by Gargano et al.  with theThe World Trade Center Health Registry (Registry) was established in 2002 to monitor the physical and mental health of over 71,000 individuals exposed to the 9/11 terrorist attacks. The Registry includes rescue and recovery workers, lower Manhattan residents living south of Canal Street, schoolchildren, building occupants, passers-by, and area workers south of Chamber Street. The Registry conducts surveys to monitor the long-term health impacts of 9/11, beginning with the baseline survey conducted in 2003–2004 and follow-up surveys conducted in 2006–2007 (wave 2), 2011–2012 (wave 3), 2015–2016 (wave 4), and 2020–2021 (wave 5) [Wave 3 data collection ended on 7 March 2012, just months before Hurricane Sandy. The 43,134 enrollees who completed wave 3 and lived in the tri-state metropolitan area constituted the sampling frame for the Hurricane Sandy survey. The survey included all 4435 enrollees who lived in the Sandy inundation zone, defined by the Federal Emergency Management Agency (FEMA) Modeling Task Force; a random sample of 4435 enrollees who were not from the FEMA-defined inundation zone was selected as a comparison group. Beginning on 28 March 2013, the 8870 enrollees were invited via email or mail to complete a survey regarding their physical and mental health and their Sandy-related exposure. A total of 4558 completed surveys were received (2443 from the inundation zone and 2115 from the non-inundation zone) by 7 November 2013. The Registry and Hurricane Sandy studies were approved by the New York City Department of Health and Mental Hygiene’s Institutional Review Board.The current study included enrollees who completed the Hurricane Sandy survey as well as Registry waves 1, 3, and 4, and had not reported LRS before the WTC terrorist attack, resulting in a final analytic sample of 3277.LRS were defined as wheezing, persistent cough, or shortness of breath reported on at least one day of the 30 days preceding survey completion, excluding symptoms related to a cold, flu, or seasonal allergies . LRS wasWe investigated three types of Hurricane Sandy-related inhalational exposures as in the previous study : (1) recSandy-related PTSD was assessed on the Hurricane Sandy survey. It was measured using a version of the PTSD checklist Specific 17 (PCL-17), a 17-item self-reported symptom scale which referred specifically to the events of Sandy and corresponded to the DMV-IV criteria for PTSD  ,11. RespSocio-demographic information , smoking history, body mass index, and self-reported history of clinician-diagnosed asthma were obtained from Registry survey data, and all the socio-demographic data were categorized in a manner consistent with a previous study of the health effects of Hurricane Sandy .Chi-square tests were used to test the statistical significance of associations between LRS and socio-demographic characteristics, PTSD, and Hurricane Sandy exposure. Because the prevalence of LRS at wave 4 is in excess of 10%, the log binomial model with copy method , adjusteOver one-third (33.9%) of the sample reported at least one LRS on the wave 4 survey. Among them, 46.0% reported one LRS symptom, 26.4% reported two LRS symptoms, and 13.6% reported three LRS symptoms. Participants of an older age, male gender, lower household income, current smokers, and those with a previous asthma diagnosis were associated with at least one LRS at wave 4 in bivariate analysis. Compared to those without Sandy-related PTSD, a higher proportion of those with Sandy-related PTSD reported persistent LRS (61.8% vs. 31.7%). Moreover, a higher proportion of those who reported previous LRS also reported persistent LRS on the wave 4 survey (50.1%) compared with those who did not report previous LRS (13.1%).In the models assessing wave 4 persistent LRS, we discovered that the interaction between Sandy-related LRS and prior LRS, as well as the interaction between Sandy-related PTSD and prior LRS, were significant. Therefore, Conversely, the association between Sandy-related exposures and persistent LRS among those with prior LRS was attenuated compared to those without prior LRS. For example, in fully adjusted models, enrollees with Sandy-related PTSD had 1.26 times the risk of LRS at wave 4 (95% CI 1.15–1.38) compared to those without Sandy-related PTSD, and those with Sandy-related LRS had 1.57 times the risk of persistent LRS (95% CI: 1.42–1.74) compared to those without Sandy-related LRS.This study evaluated LRS symptoms three to four years after Hurricane Sandy. We found that multiple Sandy inhalation exposures  were associated with persistent LRS. Sandy-related PTSD and Sandy-related LRS were also significantly associated with persistent LRS in separate models. In addition, we found the interaction between Sandy-related PTSD and prior LRS and the interaction between Sandy-related LRS and previous LRS were significant. Specifically, the magnitude of Sandy-related PTSD and Sandy-related LRS risk for persistent LRS was greater among those without prior LRS than those with prior LRS.The current study found that any one of three Sandy inhalation exposures  was not statistically associated with LRS three to four years after Sandy, while multiple Sandy inhalation exposures were associated with long-term persistent LRS. A previous study found that each of the three Sandy respiratory exposures was independently associated with LRS a few months after Sandy . The curHurricane Sandy-related PTSD and LRS more strongly impacted persistent LRS symptoms in wave 4 among those with no previous LRS symptoms. It is unclear why this association was not consistent across sub-groups. However, this finding is similar to previous reports studying mental and behavioral health outcomes in this cohort, which found that Hurricane Sandy was more likely to negatively impact future health among those who had not previously reported psychological conditions. For example, Hurricane Sandy exposure was statistically significantly associated with re-experiencing 9/11 PTSD symptomology among those without PTSD symptoms in wave 3; however, no association was found among those who reported PTSD symptoms in the third survey wave . SimilarExposure to multiple sources of potential respiratory irritants after Hurricane Sandy, including mold/damp environments, irritants from reconstruction, and diesel exhaust, was still associated with an increased risk of LRS three to four years after the hurricane, although the effect of individual inhalation exposures on the LRS decreased over time. In addition, Sandy-related LRS and Sandy-related PTSD had a prolonged impact on the enrollee’s health, and this effect was strongest among those without previous LRS symptoms. Our findings highlight the additional LRS risk among enrollees exposed to both Hurricane Sandy and the WTC disaster. This work suggests respiratory protection is important among those performing reconstruction and clean-up work after a natural disaster, particularly among those with previous adverse respiratory exposures.