This study aimed to assess the utility of SAFPs, alone or combined with FeNO, to predict a positive MCT in patients in two age groups (<55 and ≥55 years) with asthma‐suggestive symptoms and FEV1 ≥80% predicted.Small airway function parameters (SAFPs) combined with fractional exhaled nitric oxide (FeNO) can predict a positive methacholine challenge test (MCT) for asthma diagnosis. However, their predictive utility in patients with forced expiratory volume in one second (FEVn = 534) and validation cohort (n = 312) in both age groups with FEV1 ≥80% predicted.We enrolled 846 Chinese patients with suspected asthma and standard spirometry, FeNO, and MCT findings. Using the area under the curves (AUCs), the utility of SAFPs, alone or combined with FeNO, for predicting a positive MCT was analyzed in a discovery  were higher than those in patients aged ≥55 years . However, the optimal FeNO value in patients aged <55 years (43 ppb) was lower than that in patients aged ≥55 years (48 ppb). FeNO combined with SAFPs  significantly increased the AUCs in both groups  compared with a single parameter (p < 0.05). These findings were confirmed in the validation cohort. Compared with patients ≥55 years, those aged <55 years had higher and lower optimal cut‐off values for SAFPs and FeNO, respectively. The AUCs of FeNO combined with SAFPs for predicting a positive MCT for asthma diagnosis were significantly higher than those of the individual parameters (p < 0.05) in both age groups.In the discovery cohort, the optimal cut‐off values for predicting a positive MCT in patients aged <55 years (74.2% and 74.9% for forced expiratory flow (FEF)1 should be stratified by age when using SAFPs combined with FeNO to predict a positive MCT.There were age‐group differences in the utility of SAFPs combined with FeNO for predicting a positive MCT. Patients with an asthma‐suggestive history and a normal FEV Airway hyper‐responsiveness (AHR) to MCT is reflective of another aspect of airway lability. A positive MCT with a provocative concentration of inhaled methacholine <16 mg/ml or ≤0.48 mg is highly sensitive for identifying AHR presence to allow asthma diagnosis in patients with typical symptoms suggestive of asthma and preserved baseline pulmonary function (FEV1 >70% pred).Asthma is a common disorder caused by chronic inflammation of the lower respiratory tract. Its diagnosis is based on typical symptoms of wheeze, recurrent breath shortness, chest tightness, and cough, as well as evidence of variable expiratory airflow limitation based on objective pulmonary function tests.1 for early stage asthma diagnosis based on small airway function parameters (SAFPs).25%–75%) and FeNO for predicting AHR presence in patients with cough‐variant asthma was significantly higher than that of the parameters being applied separately ; furthermore, FeNO >43 ppb and FEF25%–75% <78.5% were the optimal cutoff values.Small airways, which are defined by an inner diameter <2 mm, represent a "quiet zone" and provide minimal resistance to airflow in normal adult lungs1 ≥80% predicted. This could contribute to reduced over‐ or underprediction of a positive MCT for asthma diagnosis, as well as the elucidation of the treatment effect assessed by SAFPs and FeNO in patients with asthma with different age ranges.Both SAFPs and FeNO are age dependent22.11 ≥80% predicted. This study was approved by the ethics committee of the Institutional Review Board at Shanghai General Hospital (no. 2020 [30]). All the participants provided informed written consent for study participation.This retrospective, cross‐sectional, multicentered, observational study recruited 846 adult patients with suspected asthma involving recurrent dyspnea, cough, chest tightness, and wheeze for ≥2 months. The recruited patients were referred to the Pulmonary Outpatient Clinic from January 2016 to September 2020. Among the recruited patients, a discovery cohort of 534 patients was enrolled from the electronic medical databases (EMD) of Shanghai General Hospital affiliated to Shanghai Jiao Tong University and Tongji Hospital Affiliated to Tongji University School of Medicine. Furthermore, a validation cohort of 312 patients was enrolled from the EMD of the Second Affiliated Hospital of Zhejiang University School of Medicine. These patients underwent standard spirometry, FeNO, and MCT. The participants in both cohorts were stratified by the previously mentioned cutoff age of 55 years to clarify the effect of age on the predictive value of SAFPs, alone or combined with FeNO, in patients with asthma‐suggestive symptoms and FEV2.21 of ≥80% with spirometric measurement, and having undergone MCT.The inclusion criteria were as follows: age 18–80 years; history of chronic dyspnea, cough, and wheeze for ≥2 months; normal chest X‐ray or computed tomography results; predicted FEV2.32‐agonists, theophylline, anticholinergic agents, and inhaled or oral corticosteroids within the previous 4 weeks; or having comorbid severe systemic diseases, including chronic obstructive pulmonary diseases.The exclusion criteria were as follows: having fever or acute respiratory tract infections within the previous 8 weeks; having taken montelukast, long‐acting β2.4FeNO was measured using NIOX MINO (Aerocrine AB) at a standard flow rate of 50 ml/s following the American Thoracic Society (ATS)/European Respiratory Society (ERS) recommendations.2.5Spirometry tests were performed between 8 and 11 a.m. using an MS‐PFT spirometer (Jaeger or SensorMedics). Spirometry tests followed the standards and recommendations of the ATS/ERS.1, FEV1/FVC (FEV1%), peak expiratory flow (PEF), FEF at 25% of FVC exhaled (FEF25%), FEF at 50% of FVC exhaled (FEF50%), FEF at 75% of FVC exhaled (FEF75%), and FEF between 25% and 75% (FEF25%–75%). Most of these parameters were presented as percentages of predicted values with FEV1/FVC being presented as the absolute value.The following eight pulmonary function parameters were reviewed and analyzed: forced vital capacity (FVC), FEV2.61 was measured at 3 min after each provocation step. Provocative doses that caused a 20% fall in FEV1 (PD20) was recorded; moreover, AHR was defined as positive if PD20 ≤ 0.48 mg.MCT was performed using the Jaeger APS Pro system using a Medic‐Aid sidestream nebulizer with doubling methacholine doses (0.0725–0.48 mg) following the ATS/ERS recommendations.Well‐trained technicians in each center performed FeNO, spirometry, and MCT measurements. An experienced clinician and technician discussed whether the MEFV curves met the ATS/ERS quality criteria for spirometry.2.7The primary outcome was the predictive value of SAFPs, alone or combined with FeNO, for the presence of a positive MCT in asthma diagnosis of the discovery cohort enrolled from two centers located in Shanghai, China. The secondary outcome was the confirmation of the primary outcome in a validation cohort enrolled from a third center located in Hangzhou, China.2.8SD. Nonnormally distributed data were expressed as median and interquartile range. Independent samples were compared using Student's t test (two‐tailed) or Mann–Whitney U test. Count data were presented as the percentage and between‐group comparisons were performed using the χ2. Between‐parameter correlations were analyzed using Spearman's analysis.Data were analyzed for normality of distribution using Kolmogorov–Smirov test. Normally distributed data were presented as mean ± For patients with suspected asthma who presented small airway dysfunction, MCT was considered the gold standard for defining AHR. Logistic regression was applied to determine the impact of continuous test variables with dichotomous state variables. Univariate logistic regression was used to determine the impact of the variables of interest; subsequently, multiple logistic regression was used to check whether the model could be improved. Predictive values of single or combined measurements were calculated by constructing receiver‐operating characteristic (ROC) curves and measuring AUCs.χ2 test based on the method of Hanley and McNeil. Statistical significance was set at p < 0.05.Analyses were performed using SPSS software package version 22.0 (IBM Corp.). ROC curve construction and AUCs comparisons were performed using MediCalc 19.0.4 software. AUCs were compared using the 33.1p < 0.05); however, the average value was within the normal range. Compared with the corresponding values in the negative MCT group, the FEF25%, FEF50%, FEF75%, FEF25%–75% FEV1, and FEV1/FVC, values were significantly lower while the FeNO value was significantly higher , FEF25%–75% , and FeNO .The discovery cohort included 534 adults with symptoms of cough, chest tightness, dyspnea, and wheeze for ≥2 months. Among them, 276 51.69%) patients exhibited a positive MCT. After stratification according to age, 291 and 243 participants were aged <55 and ≥55 years; among them, 148 (50.86%) and 128 (52.67%) participants had a positive MCT, respectively. Table .69% pati3.21, FEV1/FVC, FEF25%, FEF75%, FEF50%, and FEF25%–75%, alone or combined with FeNO, was evaluated using ROC curves. Tables The predictive value of FEV50% and FEF25%–75% were 0.771  and 0.774 (95% CI: 0.736–0.809), respectively, which represented the two largest AUCs for a positive MCT diagnosis in the spirometry measurement  with FeNO. The AUC for FEF50% combined with FeNO was 0.858 (95% CI: 0.826–0.887), which was significantly higher than that of either FEF50% (p < 0.0001) or FeNO alone (p < 0.0001). The AUC for FEF25%–75% combined with FeNO was 0.865 (95% CI: 0.833–0.893), which was significantly higher than that of either FEF25%–75% or FeNO alone , 0.769 (95% CI: 0.711–0.820), and 0.702 (95% CI: 0.640–0.759), respectively. The AUC for FEF50% combined with FeNO was 0.851 (95% CI: 0.800–0.893), which was higher than that for either FEF50% (p = 0.0119) or FeNO (p < 0.001) alone. The AUC for FEF25%–75% combined with FeNO was 0.844 (95% CI: 0.792–0.887), which was significantly higher than that for either FEF25%–75% or FeNO alone , 0.792 (95% CI: 0.740–0.837), and 0.798 (95% CI: 0.747–0.842), respectively. The AUC for the combination of FEF50% and FeNO was 0.865 (95% CI: 0.820–0.902), which was significantly higher than that for FEF50% (p < 0.0001) or FeNO (p = 0.0005) alone. The AUC for FEF25%–75% combined with FeNO was 0.883 (95% CI: 0.841–0.918), which was significantly higher than that for each of them , 0.738 (95% CI: 0.686–0.786), and 0.761 (95% CI: 0.710–0.807) respectively. The AUC for FEF50% combined with FeNO was 0.842 (95% CI: 0.797–0.881), which was significantly higher than that for FEF50% (p < 0.0001) or FeNO alone (p = 0.0003). The AUC for FEF25%–75% combined with FeNO was 0.840 (95% CI: 0.795–0.879), which was significantly higher than that for either FEF25%–75% (p < 0.0001) or FeNO used alone , 0.760 (95% CI: 0.682–0.827), and 0.752 (95% CI: 0.674–0.830), respectively. The AUC for FEF50% combined with FeNO was 0.851 (95% CI: 0.782–0.905), which was significantly higher than each of them used individually (both p < 0.05). The AUC for FEF25%–75% combined with FeNO was 0.842 (95% CI: 0.772–0.898), which was significantly higher than that for either FEF25%–75% (p = 0.0084) or FeNO (p = 0.0171) used alone , 0.728 (95% CI: 0.655–0.794), and 0.768 (95% CI: 0.697–0.830), respectively. The AUC for FEF50% combined with FeNO was 0.842 (95% CI: 0.777–0.893), which was significantly higher than that for FEF50% (p = 0.0007) or FeNO (p = 0.01137) used alone. The AUC for FEF25%–75% combined with FeNO was 0.842 (95% CI: 0.778–0.894), which was higher than each of them used singly , a lower optimal cutoff value of FeNO and a higher AUCs for the combination of SAFPs and FeNO (>0.86); and the predictive value of SAFPs combined with FeNO in both age groups for a positive MCT diagnosis was significantly improved in patients with asthma‐suggestive symptoms and FEV1 ≥ 80% predicted.This study aimed to determine the age effect on the predictive utility of SAFPs, alone or combined with FeNO, for a positive MCT in patients in two age groups  with asthma‐suggestive symptoms and FEV1 . This combination was confirmed to be easier, safer, cheaper, and time‐saving than MCT. However, these studies did not address the age effect on the predictive value of these parameters.In patients with asthma‐suggestive symptoms and normal or near‐normal pulmonary functions values, MCT is appropriate for confirming or excluding an asthma diagnosis.50% and FEF25%–75% for predicting AHR in the discovery cohort did not significantly differ in both age groups . This is consistent with previous results that normal aging contributed to SAD in different subgroups of patients with asthma25%–75% and FEF50% for a positive MCT prediction for asthma diagnosis in patients aged ≥55 years (65.0% for FEF25%–75% and 62.9% for FEF50%) was much lower than that in patients aged <55 years (74.2% for FEF25%–75% and 74.9% for FEF50%), which is consistent with previous findings that the optimal cut‐off value of FEF25%–75% was lower in Chinese adults with CVA (78.5%)25%–75% and FEF50% in older patients with a positive MCT (65.0% for FEF25%–75% and 62.9% for FEF50%) decreased significantly compared with those in patients not stratified by age (75.2% for FEF25%–75% and 73.7% for FEF50%). This is indicative of an overdiagnosis in older patients with asthma if not stratified by age when using FEF25%–75% and FEF50% to predict a positive MCT in patients with suspected asthma, which supports the previous finding that age‐specific reference values should be considered for asthma diagnosis.1, and FEV1/FVC, although it was within the normal range . There were no significant between‐age‐group differences in the FEF25% and PEF  in patients with a positive MCT. Furthermore, we found that the predictive value of FEF25%–75% was lower in elder patients  combined with FeNO for AHR diagnosis was significantly improved in both age groups.The current study showed that FEFp < 0.05), which indicated that airway eosinophilic inflammation might be more severe in younger patients with asthma who may be more sensitive to corticosteroids. This is consistent with previous findings that older patients with asthma had lower FeNO level, which indirectly indicates predominant neutrophilic bronchial inflammation in elderly patients that contributes to greater airflow limitation.FeNO is widely used as a noninvasive biomarker for monitoring airway eosinophilic inflammation and predicting corticosteroid sensitivity in allergic diseases such as asthma.50% (FEF25%–75%) combined with FeNO was 0.858 (0.865), 0.865 (0.883), and 0.851 (0.844) in patients without age stratification, patients aged <55 years, and patients aged ≥55 years, respectively, which was significantly higher than that for either FEF50% (FEF25%–75%) or FeNO used alone. These findings suggest that FEF50% (FEF25%–75%) combined with FeNO could improve the predictive value for AHR diagnosis, which is consistent with a previous study on patients with CVA.50%, 85.94% for FEF25%–75%) and negative predictive value ; in patients aged <55 years, the corresponding values of specificity  and positive predictive value  also improved when SAFPs were combined with FeNO. This indicated that the combined use of optimal cut‐off values of SAFPs and FeNO contributed to improved prediction for a positive MCT detection in patients of both age groups with suspected asthma with FEV1 ≥80% predicted.The AUC for FEFFinally, we validate the findings from the discovery cohort using a validation cohort. This indicates that our findings could be generalized in patients with asthma in other care centers in China.This study has several limitations. First, SAD included airway wall thickening, airway narrowing, and air trapping, and so forth.51 ≥ 80% and an asthma‐suggestive history should be stratified by age (55 years) when SAFPs , alone or combined with FeNO, are used to predict the presence of a positive MCT in asthma diagnosis.Patients with asthma with SAD, which was affected by aging, were more prone to have a positive MCT. Therefore, Chinese patients with FEVThe authors declare that they have no competing interests.Lili Hou, Lei Zhu, and Min Zhang were responsible for conceptualization, design of the study, and drafting the manuscript. Min Zhang and Lei Zhu was responsible for funding acquisition. Lili Hou, Gang Huang, Huahao Shen, and Li Yu were responsible for data collection and interpretation. Lili Hou, Huijuan Hao, and Huahao Shen were responsible for data analysis and interpretation. All authors reviewed the article critically for important intellectual content and approved the final version to be submitted.


In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of mortality and morbidity, for the remaining (high-risk) patients ABP is measured continuously using invasive devices, and derived values are extracted from the recorded waveforms. However, since invasive monitoring is associated with major complications , the ideal ABP monitor should be both non-invasive and continuous. With large volumes of high-fidelity physiological waveforms, it may be possible today to impute a physiological waveform from other available signals. Currently, the state-of-the-art approaches for ABP imputation only aim at intermittent systolic and diastolic blood pressure imputation, and there is no method that imputes the continuous ABP waveform. Here, we developed a novel approach to impute the continuous ABP waveform non-invasively using two continuously-monitored waveforms that are currently part of the standard-of-care, the electrocardiogram (ECG) and photo-plethysmogram (PPG), by adapting a deep learning architecture designed for image segmentation. Using over 150,000 min of data collected at two separate health systems from 463 patients, we demonstrate that our model provides a highly accurate prediction of the continuous ABP waveform (root mean square error 5.823 (95% CI 5.806–5.840) mmHg), as well as the derived systolic (mean difference 2.398 ± 5.623 mmHg) and diastolic blood pressure (mean difference − 2.497 ± 3.785 mmHg) compared to arterial line measurements. Our approach can potentially be used to measure blood pressure continuously and non-invasively for all patients in the acute care setting, without the need for any additional instrumentation beyond the current standard-of-care. Recent studies even suggest that only a few minutes of hypotension in the acute care setting increases the incidence of these complications5. These observations strongly suggest that continuous blood pressure monitoring is critical in the acute care setting to identify periods of hypertension and/or hypotension as early as possible. Today, the gold standard for blood pressure monitoring is the invasive arterial line, a small catheter inserted into an artery, which enables continuous blood pressure monitoring4. However, this technique is highly invasive and is associated with significant complications such as bleeding, hematoma, pseudoaneurysm, infection, nerve damage, and distal limb ischemia7, and thus it is only applied to very high-risk patients.Each year in the United States, 5.7 million patients are admitted to an intensive care unit (ICU) and nearly 50 million patients undergo surgery. Hypotension and hypertension in the ICU and perioperative period are associated with adverse patient outcomes including stroke8. More recently, devices allowing for continuous and non-invasive blood pressure monitoring have been introduced. These devices, however, are sensitive to patient movement, they are expensive, and they cause continuous pressure on the finger that can interfere with blood circulation9. Additionally, the accuracy of the device can deteriorate in patients with severe vasoconstriction, peripheral vascular disease, or distorted fingers due to arthritis10.On the other hand, the widely used non-invasive blood pressure monitoring system using cuff-based devices is both inaccurate and intermittent, only allowing for the monitoring of blood pressure every three or five minutes14 or at the resolution of a heartbeat17, while high-risk patients need to be monitored using the continuous arterial line blood pressure waveform, and thus current methods are not adequate for usage in critical care settings. Moreover, many of the previously-studied cohorts consisted of healthy patients at rest, and therefore the blood pressure variability is not as dynamic compared to patients in the ICU. This calls into question the utility of these approaches in real life settings. Finally, many of the existing methods developed and tested their models in the same set of patients by training the model on an earlier part of each patient record and testing the model on the remaining data. Additionally, the number of patients used was often only several dozen, all coming from the same health system. This calls into question the generalizability of the models to unseen patients. For example, Sideris et al. impute the arterial blood pressure waveform by training the model on the same patient for which arterial blood pressure waveforms are provided18. However, such a scenario is not clinically useful, since applying such an approach will require invasive monitoring of the patients for at least part of the time.The need for measuring the continuous blood pressure non-invasively suggests that the physiological waveform of the blood pressure should be imputed from other data. However, previous attempts at blood pressure imputation have primarily focused on the imputation of discrete systolic and diastolic blood pressure measurements taken intermittently by cuff19), and we adapted it for 1D physiological waveform signals. A key aspect of our preprocessing pipeline includes a manual labeling of PPG quality in a large subset of the training data to improve the signal-to-noise ratio and remove artifacts. The manual labeling was used to train a deep neural network to predict the signal quality of the waveform, resulting in a high-quality preprocessing pipeline that can be used beyond the scope of this study. We demonstrate that the modified 1D V-Net approach provides a highly accurate prediction of continuous arterial blood pressure waveform (root mean square error 5.823 (95% CI 5.806–5.840) mmHg), as well as the derived systolic (mean difference 2.398 ± 5.623 mmHg) and diastolic blood pressure (mean difference − 2.497 ± 3.785 mmHg).In this paper, we present the development, training, and validation of a novel non-invasive and continuous deep learning method for predicting the arterial blood pressure waveform using the ECG waveform, the pulse oximeter (PPG) waveform, and non-invasive blood pressure cuff measurements. These measurements are collected as part of the current standard-of-care, and therefore no additional patient monitoring devices are needed. Our method leverages a well-known deep learning model architecture originally designed for image segmentation  waveforms, arterial blood pressure (ABP) waveforms, and at least one non-invasive blood pressure measurement. After exclusion of patients with insufficient data, the remaining 264 patients were then randomly separated into disjoint training and testing sets, with 175 and 89 patients, respectively (see Methods). The MIMIC dataset was used for primary training of the model. The second cohort of patients consisted of 115 ICU patients  from the UCLA Health hospital system who had ECG, PPG, and ABP waveform records. These patients were randomly separated into two disjoint groups: 28 patients for secondary fine-tuning  of the MIMIC-based model, and 87 patients for testing the method. Summary cohort demographic information is shown in Table Two separate cohorts of ICU patients were used in this study to train and validate the method. The first cohort consisted of randomly sampled ICU patients from the Medical Information Mart for Intensive Care version III (MIMIC-III)The ECG and PPG waveforms, the most recent NIBP measurements, the time since the most recent NIBP measurement, the pulse arrival time, and the heart rate were used as input to a deep learning model that was trained to predict the continuous blood pressure waveform that occurred during the timeframe of the input window. An example window of ECG and PPG waveforms used as input to the algorithm is shown in Fig. One of the unique features of our approach is that it provides a prediction of the continuous blood pressure waveform, and not simply summary statistics such as systolic or diastolic blood pressure in a window. To evaluate the quality of the waveform generated by 1D V-Net, we compared the predicted ABP waveform to the ground-truth ABP waveform obtained from the arterial line. Time was split into windows of constant duration 32 s). For measuring the method performance, we used the root mean square error (RMSE) and the correlation between the true and the predicted signals within each window. As shown in Table  s. For mAs an additional waveform quality analysis, we compared the systolic and diastolic values derived from the arterial line and the predicted ABP waveforms. For each heartbeat in a window, we computed the systolic and diastolic blood pressure, and compared those values to the imputed blood pressure waveform generated by our algorithm. In Fig. Next, we compared the performance of 1D V-Net with the performance of the long short-term memory (LSTM) model described in Sideris et al. and with PPG scaling. PPG scaling uses the PPG waveform as a template shape and scales the magnitude of the PPG signal in a given window to match the most recent systolic and diastolic NIBP measurements. We observe that 1D V-Net achieves the lowest RMSE and the highest correlation across both cohorts  mmHg for MIMIC, 13.111 (95% CI 13.072–13.151) mmHg for UCLA). Both the PPG Scaling and 1D V-Net do see a minor increase in error as time from the NIBP measurement increases . This predicted waveform would allow clinicians to monitor blood pressure continuously in patients that otherwise receive blood pressure measurement intermittently. With this continuous and non-invasive monitoring, clinicians would be able to rapidly identify changes in patient state and intervene, which is crucial when even a short span of hypotension or hypertension can lead to poor health outcomes. Additionally, we have developed a preprocessing pipeline that leverages machine learning to identify windows containing low-quality signals with high precision and sensitivity. The preprocessing pipeline can be generally useful in applications that utilize physiological waveforms.24, resulting in promising applications including diagnosis tools for diabetic retinopathy21, skin cancer22 and arrhythmia detection using electrocardiograms (ECG)24. Notably, the majority of methods using deep learning in medicine have focused on classification problems, such as diagnosis, and not regression. Our approach differs from previous approaches, since it provides regression results as opposed to diagnosis, and to the best of our knowledge, the modified 1D V-Net approach has never been applied to physiological waveforms in this context.Our approach leverages the V-Net deep learning architecture , cardiac output (CO), cardiac power output (CPO), vascular resistance, and pulse pressure variation, which can only be calculated using the ABP waveform, not the beat-to-beat measurements. Measurements like CPO are clinically relevant, and have been shown to be predictive of outcomes such as mortality26. Additionally, the imputed blood pressure waveform can be used as input to predictive algorithms which use the arterial blood pressure waveform as input, such as predicting hypotensive events up to 15 min before they occur27. Another limitation of previous studies is the limited number of patients used to train and evaluate models. The small sample sizes, most often only several dozen patients and from a single health system, calls into question the generalizability of the methods. To demonstrate the wide applicability of our proposed method, we used data from over four hundred patients and two separate health systems to show that our model successfully predicts the continuous blood pressure waveform in new patients.While other computational methods for predicting beat-to-beat blood pressure measurements have been developed, they predict only the systolic/diastolic blood pressure measurements and not the actual 28.This manuscript follows the “Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View”29 matched subset. The 309 patients were randomly divided into a training set  and a testing set . Of these patients, 31 patients from the training set and 14 from the testing set were removed because none of the data were found to pass the quality filtering process (see Dataset Creation), leaving 175 patients in the training set and 89 in the testing set. The second retrospective dataset consisted of 150 randomly sampled UCLA ICU patients with recorded ECG, PPG, and ABP waveforms. The UCLA dataset was divided into 40 patients for calibrating the model, and 110 patients for testing the model. Of these patients, 12 were removed from the calibration set and 23 were removed from the testing set due to issues with waveform data quality, for a final training set of 28 patients and testing set of 87 patients.The first retrospective, de-identified dataset consisted of 309 randomly sampled ICU patients with ECG (lead II) waveforms, photo-plethysmographic (PPG) waveforms, arterial blood pressure (ABP) waveforms, and at least one non-invasive blood pressure measurement from the MIMIC-III waveform databaseThe ABP waveform prediction model was first trained using patients from the MIMIC training set, and model performance results were computed using patients from the held-out MIMIC testing set. Then, the model parameters were fine-tuned using patients from the UCLA calibration set, and model performance results were computed using patients from the held-out UCLA testing set.Cohort demographic data were extracted for the MIMIC patients using the MIMIC clinical database. The features extracted included patient age, height, weight, BMI and sex. For de-identification, patients older than 90 have their age encoded as 300 in the clinical database. Since we do not know the exact age of these patients, we set their age to 90 years old. The same demographic information for the UCLA patients was retrieved from the UCLA Clinical Data Mart, a data warehouse system that extracts data from UCLA's electronic medical record system . As was done for the MIMIC data, any UCLA patients older than 90 years had their age set to a maximum of 90 years for de-identification purposes.In both cohorts, if the signal sampling rate was greater than 100 Hz, each waveform signal was downsampled to 100 Hz. Each signal was low-pass filtered with a cutoff frequency of 16 Hz to remove high-frequency noise. Since the range of the ECG and PPG signals differed for each patient, we scaled each 32-s window by subtracting the running median and dividing by the difference between the upper and lower quartiles.Intermittent non-invasive blood pressure (NIBP) measurements were extracted for each patient from the MIMIC-III database. However, since NIBP measurements were only recorded, on average, once per hour, the frequency of non-invasive blood pressure measurement was insufficient. Therefore, we created derived NIBP measurements by sampling the invasive blood pressure waveform  every 5 min to simulate the frequency of NIBP measurement that would be used when deploying the algorithm in practice. Since the derived non-invasive blood pressure was measured every 5 min, but the waveforms were sampled at 100 Hz, we used the most recent derived NIBP measurement to fill in missing NIBP values. As an additional feature, we also included the time (in milliseconds) from the most recent NIBP measurement to each sample in our input window.30, issues with clock synchronization can cause waveform signals to drift. We corrected for signal drift between the PPG signal and the arterial blood pressure signal by computing the cross-correlation of the PPG signal under consideration with the arterial blood pressure signal. Once the cross-correlation was computed, the location of the highest cross-correlation was used to correct the PPG signal drift by shifting the signal in time, up to a maximum of 4 s in either direction.As mentioned by the authors of the MIMIC datasetAfter completing the above preprocessing steps, we selected valid 32-s windows from the record using a sliding window approach with a 16-s step size. A window size of 32 s was chosen since it is long enough to give temporal context for several heartbeats, yet short enough to ensure that there would be a sufficient number of windows that did not contain artifacts. See Table Each waveform  was checked for signal quality and windows with technical artifacts or invalid parameters were removed. Windows were excluded if they met any of the following criteria for the ECG or PPG data: the variance of the signal was less than a small value (1e−4 for ECG and PPG), the number of peaks in a window was greater than a threshold , or the number of peaks in a window was less than a threshold .31 Python package, the difference between two consecutive systolic or diastolic values was greater than 50 mmHg, the waveform signal was flat , a pulse pressure value in the window was greater than 70 mmHg, the difference between the systolic BP and the most recent NIBP was greater than 40 mmHg, or the time delay between a diastolic blood pressure measurement and the subsequent systolic blood pressure measurement was greater than 0.5 s.For the arterial blood pressure waveform, windows were excluded if they met any of the following criteria: the mean signal value was less than 30 mmHg or greater than 200 mmHg, the maximum signal value was greater than 300 mmHg or less than 60 mmHg, the minimum signal value was less than 20 mmHg, the variance of the signal was less than 80, we could not find any systolic or diastolic blood pressure values using the “find_peaks” function from the scipyIf a window contained signals that passed all of the above criteria, we performed two additional filtering steps, and excluded windows that failed either of these criteria: the number of PPG peaks was different than the number of arterial blood pressure peaks, or the mean absolute time difference between arterial blood pressure peaks and PPG peaks was greater than 0.15 s . Finally, outlier windows were excluded if the mean ECG, PPG, or ABP signal in the window was greater than the 99.9% quantile or less than the 0.01% quantile.Assuming a window under consideration passed the above criteria, it was included in our training or testing dataset. The input features were then scaled to have a mean of zero and standard deviation of one using a running mean and standard deviation.Since the quality of the PPG waveform is crucial to the performance of our algorithm, we developed an additional filtering step to remove windows containing artifacts in the PPG signal. A CNN model was trained to classify four-second PPG windows as “valid” or “invalid” using the PPG waveform as input. To train the model, 4000 four-second PPG windows from the 206 patients in the MIMIC training set were hand-labeled (by B.L.H.) as “valid”  if the window was free of artifacts, or “invalid”  if the window contained artifacts, using visual inspection. From these 4000 windows, we randomly sampled a subset of 100 windows and an expert clinician (M.C.) then labeled the windows to estimate the initial classification quality. In 94% of the sampled windows, the clinician’s classification matched the initial labeling (Cohen’s kappa: 0.857). We then trained a 3 layer CNN model  or unreasonably large (> 1.0 s). Finally, for each window, the median (log-transformed) and standard deviation of the PAT were used as two additional input feature channels. If PAT values were excluded or unavailable, the missing values were imputed using the median observed value.To improve the ABP waveform imputation we included two features derived from the non-invasive signals that have been previously shown to be predictive of blood pressure values: pulse arrival time (PAT) and heart rate (HR)Additionally, the HR was calculated for each window using the PPG signal. The scipy peak finding algorithm was similarly used to detect the PPG systolic peaks. Then, the number of these peaks found in a given window was divided by the window length (in seconds) and multiplied by the number of seconds in a minute to give the resulting HR in beats per minute. The HR was also included as an additional input feature channel for each window. Any missing HR values were imputed using the median observed value.18.The performance of two other methods were used as a comparison: PPG scaling and the LSTM model of Sideris et al.18. These approaches use manual feature extraction17 or learn features from the data using machine learning methods18. Since the PPG waveform is correlated with the ABP waveform, one approach for predicting the ABP waveform uses the PPG waveform as a template shape and scales the magnitude of the PPG signal in a given window to match the most recent systolic and diastolic pseudo-NIBP measurements. Specifically, the PPG waveform is stretched such that the maximum value in the window is equal to the most recent systolic NIBP measurement, and the minimum value in the window is equal to the most recent diastolic NIBP measurement. The PPG window was scaled using the transformationPrevious work has shown the utility of the PPG signal for predicting blood pressure measurements18 proposed training a patient-specific LSTM model (i.e. one model per patient) to impute the ABP waveform, using the PPG signal from the same window of time as input. While the results were promising, a critical issue is that the model requires that each patient first receive invasive blood pressure monitoring so a patient-specific model can be trained. However, this means that only a fraction of the patient population can benefit from such a model (the subpopulation that receives invasive blood pressure monitoring), and only after they have already undergone invasive monitoring. Therefore, the algorithm may not generalize to the majority of the patient population who do not receive invasive monitoring. To fairly compare the LSTM model to our proposed model, we trained the model as described in the paper, using 128 nodes as a default since the number of nodes was not described.Sideris et al.19  remained the same. An additional L2 penalty was added to the activation of the final network layer to force the network to prioritize modification of the PPG scaling residual waveform.We developed a deep learning model that takes as input a window of two signals, ECG and PPG, and several constant values encoded as additional channels by repeating the value for each timestep: the most recent non-invasive systolic, diastolic, and mean blood pressure measurements prior to the window, the time since the most recent NIBP measurement, the median and standard deviation of the pulse arrival time, and heart rate. The model is trained to minimize the residual difference between the PPG scaling method and the true ABP waveform to compensate for the difference in waveform morphology, as well as the change in BP over time. This forces the network to focus on windows where the PPG scaling significantly differs from the ABP waveform, and therefore improve on the PPG scaling method. With enough data and a large enough model, the neural network should be able to similarly learn the scaling method. However, to accelerate the learning process we designed the method to learn the residual error. The model output is a prediction of the residual difference between the continuous ABP waveform and the baseline PPG scaling waveform, and this predicted residual difference is added to the PPG scaling waveform to generate the 1D V-Net waveform prediction. The deep learning model architecture was based on the V-Net CNN architecture, which has been proven to be useful in the field of image segmentationTo train the network, we used a custom loss function consisting of two parts. The first was the mean squared error between the true ABP waveform and the predicted waveform, which forces the network to learn an accurate prediction of the entire waveform. The second part of the loss was the mean squared error between the true and predicted waveforms at the locations of the systolic and diastolic points, to encourage the network to be particularly accurate at these locations.32 and was trained for a maximum of 100 epochs using random weight initialization and the Nadam33 optimizer with default parameters beta1 of 0.9, beta2 of 0.999. The learning rate used was 0.001 with a schedule decay of 0.004, and the mini-batch size was 32. For the last layer of the network, an L2 activation penalty with weight 0.0005 was added. Ten percent of the training data was held out for validation, and these data were used for choosing hyperparameters. If the validation loss did not improve after 8 epochs, the model training process was stopped early.The deep-learning model was implemented using Keras34 as this is the standard method for comparing the agreement of two medical devices. Accuracy and precision of the predictions were described as the mean ± standard deviation of the differences between the predicted and true blood pressure values, and the differences are considered acceptable by the Association for the Advancement of Medical Instrumentation (AAMI) criteria if less than 5 ± 8 mmHg. The method was implemented as follows. For each window under consideration, we extracted the systolic and diastolic blood pressure measurements from the ABP waveform using the peak finding algorithm described previously. These measurements were used as the gold standard reference values. We then used the peak finding algorithm on the DNN-generated waveform to determine the systolic and diastolic blood pressure measurements and used these as the comparison values. If the number of systolic or diastolic points identified by the peak finding algorithm differed between the true and predicted waveforms, we performed local alignment by minimizing the sum of the differences between the indices of the true and predicted waveform points. We then took the difference between the reference blood pressure measurement and the predicted blood pressure measurement pairs, and plotted these differences as a function of the average of the reference and predicted value pairs. The 95% confidence intervals (CI) were calculated using bootstrapping.To evaluate the agreement between the gold standard invasive blood pressure measurements  and the DNN predictions, we used the Bland and Altman methodTwo metrics were used to quantitatively compare the quality of the predicted waveform and the true ABP waveform: root mean square error (RMSE) and correlation. These values were calculated for all windows per patient. The 95% confidence intervals (CI) were calculated using bootstrapping.RMSE was calculated as a function of time from the most recent NIBP measurement. The time between the window and the most recent NIBP measurements were binned into ten second intervals, and for each bin the mean and SD of the RMSE between the true and predicted waveforms was calculated.20. Retrospective data collection and analysis was approved by the UCLA IRB. All research was conducted in accordance with the tenets set forth in the Declaration of Helsinki.The institutional review board (IRB) of the Massachusetts Institute of Technology  and Beth Israel Deaconess Medical Center  approved the use of MIMIC-III for research, and the requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was de-identifiedSupplementary Information.


P < 0.05). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively (P > 0.05). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians' artificial judgments and had great clinical value for the diagnoses and treatments of the disease.This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft's residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians' artificial diagnoses. The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians' artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians' artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than AUCs of doctors' diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with the statistically significant difference ( Now, as sepsis is better understood, it is defined by scholars as a clinical syndrome in which organism's inflammatory response is maladjusted by infection, resulting in severe damage to physiology and organ functions. Sepsis has extremely high morbidity and mortality in intensive care units, and it has become the leading cause of death for critically ill patients . AccordiAcute kidney injury is usually characterized by rapid decline in renal function, which results in acute kidney failure and other organ failure in severe cases. Acute kidney injury can be caused by a variety of factors, which include drug use, ischemia/reperfusion, and infection –17. In rClinically, early diagnoses are often made by creatinine and urine volume detections according to international guidelines, but it is usually unable to making correct diagnoses in time and completely. With the continuous development of image examination, ultrasound image examination is gradually applied in the clinical diagnoses of sepsis complicated with acute kidney injury. Clinical workers are often unable to sum up quantitative and accurate medical information from ultrasonic images by naked eyes. Medical image analyses and processing technologies solve this dilemma and become important helpers of clinical diagnoses –25. The This study was intended to analyze the diagnostic value severe sepsis complicated with acute kidney injury under deep learning-based convolutional neural network, to provide a certain reference for the clinical ultrasound image diagnosis.In this study, 50 patients with severe sepsis complicated with acute kidney injury admitted to hospital from January 10, 2020, to May 10, 2021, were selected as the experimental group. According to the age and gender distribution of these patients, 50 healthy volunteers were also selected as the healthy control group. This study had been approved by ethics committee of hospital, and patients' families had been informed of this study and signed informed consents.Inclusion criteria were as follows. First, patients were diagnosed as sepsis complicated with acute kidney injury according to the diagnostic criteria. Second, patients had signed informed consent forms. Third, patients did not suffer from other serious organ diseases or hereditary diseases. Fourth, patients were not examined for contraindications.The patients met exclusion criteria had severe allergies, other serious underlying diseases, and a history of chronic kidney injury. Moreover, the patients took diuretics for a long time.There were two requirements in the criterion for suspension and elimination. First, patients could not normally complete ultrasound scans. Second, patients who did not comply with the treatments were followed up for index evaluation.nd-4th). The exclusion criterion was the same as those for patients with sepsis complicated with acute kidney injury (2nd-4th). Patients with acute kidney injury complicated with sepsis were discontinued and excluded.For healthy control group volunteers, the inclusion criterion was the same as those for patients with sepsis complicated with acute kidney injury  = b, its derivative was 1. When b < 0, M(b) = 0, its derivative was 0. When b = 0, M of b was equal to 0; its derivative was 0.When o and p were two normal probability distributions. The cross entropy of o was represented by p in the figure. It was shown in the following equation:The cross entropy was used as the loss function, Cross entropy must satisfy the probability distribution function, which were shown in the following equations:In this study, ultrasonic images of three common indicators on account of deep learning were used to evaluate the diagnostic effect of severe sepsis complicated with acute kidney injury. Three common indicators were accuracy, specificity, and sensitivity. Calculation methods were shown in the following equations:In addition, the diagnosis error rate of ultrasonic images on account of deep learning was shown in the following equation:A was true positive, indicating that the diagnosis result was positive, which was actually positive. B was true negative, meaning that the diagnosis was negative, which was truly negative. C was false positive, indicating that the diagnosis was positive, but actually negative. D was false negative, meaning that the actual result was positive, but actually negative. M was the error rate.Receiver-operating characteristic (ROC) curve was used to represent the diagnostic efficiency of the three algorithms and artificial diagnoses of professional physicians. The area under curve (AUC) of ROC was determined according to ROC. Comparative analyses were conducted.s), t test was used to represent measurement data, chi-square (χ2) test was used to represent count data, and P < 0.05 indicated statistical differences.SPSS 24.0 software was used for statistical analyses of data. Data conforming to normal distribution was expressed as mean ± standard deviation (mean ± Ultrasound image examinations were performed on patients with severe sepsis complicated with acute kidney injury. P < 0.05). The sensitivity of three algorithms was 92.3%, 91.8%, and 93.4%, respectively, higher than that 84.5% of the manual diagnosis by specialists significantly (P < 0.05). In terms of specificity, there was no significant difference among the three algorithms and the manual diagnosis (P > 0.05).As shown in P < 0.05). It could be observed in The misjudgment rate of sepsis complicated with acute kidney injury was 5.2%, 6.3%, and 4.9% of the ResNet, GoogLeNet, and DenseNet121 algorithms, respectively. These were all significantly lower than the 14.5% of that by specialists manually, with the difference statistically significant , which were shown in At the same time, training time and the number of parameters of the three algorithms were compared and analyzed; there were no significant differences in training time. However, the number of DenseNet121 and GoogLeNet parameters was significantly lower than that of ResNet algorithm (P < 0.05). However, there were no significant differences in specificity between the two groups (P > 0.05). At the same time, the error rate of the three algorithm models for severe sepsis complicated with acute kidney injury was significantly lower professional doctors' artificial diagnoses (P < 0.05). AUCs of the three algorithms were significantly higher than doctors' diagnosis results (P < 0.05). Compared with clinicians' artificial visual diagnoses, deep learning-based convolutional neural network models could identify ultrasonic image features more accurately and had greater value for the clinical diagnoses of severe sepsis complicated with acute kidney injury.At present, the incidence and mortality of severe sepsis complicated with acute kidney injury are gradually increasing. Clinically, it is very important for the early diagnosis and treatment of sepsis complicated with acute kidney injury. Delays in early diagnoses and treatments lead to the progression of the disease, worsening its progression, causing irreversible damage, and rapidly leading to death. Therefore, accurate diagnoses and examination are extremely important . 50 patiP > 0.05). However, the number of parameters of DenseNet121 and GoogLeNet was significantly lower than that of ResNet (P < 0.05). In addition, the results showed that the error rate of professional physicians in clinical diagnoses of severe sepsis complicated with acute kidney injury was as high as 15.01%. The error rate of DenseNet121, GoogLeNet, and ResNet was 4.98%, 6.75%, and 5.32%, respectively, which were significantly lower than professional physicians' artificial diagnoses (P < 0.05). Le et al. (2021) [36] pointed out after experiments that the accuracy and sensitivity of the diagnosis were higher than that of the doctor as the convolutional neural network was optimized, which is consistent with the results of this study. In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury by ultrasonic image features was higher than the artificial judgment of professional physicians, which had great clinical value for the diagnoses and treatments of the disease. However, the diagnoses of any disease require a comprehensive judgment on account of clinical symptoms, signs, professional experience, and image examination. Therefore, comprehensive evaluation should be combined with many factors in the clinical diagnoses of severe sepsis complicated with acute kidney injury.As a kind of deep neural network, convolutional neural network could transform image data into visual pathological features and obtain information that could not be directly captured by human eyes, which was extremely important in ultrasound clinical diagnoses . In this50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models DenseNet121, GoogLeNet, and ResNet were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians' artificial diagnoses. The results showed that the accuracy and sensitivity of the three algorithms on account of deep learning were significantly higher than professional image physicians' artificial diagnoses. At the same time, the error rate of the three algorithm models for severe sepsis complicated with acute kidney injury was significantly lower than professional doctors' artificial diagnoses. In addition, among the three algorithms, DenseNet121 was easier to achieve convergence, and the number of parameters of DenseNet121 and GoogLeNet was significantly lower than that of ResNet. In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury by ultrasonic image features was higher than professional doctors' artificial diagnoses, which had great clinical value for the diagnoses and treatments of the disease. The shortcomings of this study are that the sample size of the research object is small, and the source is single, which does not have randomness and wide applicability. In the future, multisite, multitype, and large-sample analyses and researches will be considered to provide more practical. In short, the three algorithms under deep learning were more valuable in the diagnosis of severe sepsis complicated with acute kidney injury than the specialists, and they gave important clinical value in the diagnosis and treatment of the disease. This study provided a reference for the imaging diagnosis of sepsis complicated with acute kidney injury.


The receptor of severe respiratory syndrome coronavirus 2 (SARS-CoV-2), angiotensin-converting enzyme 2, is more abundant in kidney than in lung tissue, suggesting that kidney might be another important target organ for SARS-CoV-2. However, our understanding of kidney injury caused by Coronavirus Disease 2019 (COVID-19) is limited. This study aimed to explore the association between kidney injury and disease progression in patients with COVID-19.A retrospective cohort study was designed by including 2630 patients with confirmed COVID-19 from Huoshenshan Hospital  from 1 February to 13 April 2020. Kidney function indexes and other clinical information were extracted from the electronic medical record system. Associations between kidney function indexes and disease progression were analyzed using Cox proportional-hazards regression and generalized linear mixed model.We found that estimated glomerular filtration rate (eGFR) and creatinine clearance (Ccr) decreased in 22.0% and 24.0% of patients with COVID-19, respectively. Proteinuria was detected in 15.0% patients and hematuria was detected in 8.1% of patients. Hematuria , proteinuria , elevated baseline serum creatinine  and blood urea nitrogen , and decrease baseline eGFR  were found to be independent risk factors for disease progression after adjusted confounders. Generalized linear mixed model analysis showed that the dynamic trajectories of uric acid was significantly related to disease progression.There was a high proportion of early kidney function injury in COVID-19 patients on admission. Early kidney injury could help clinicians to identify patients with poor prognosis at an early stage.The online version contains supplementary material available at 10.1186/s12879-021-06576-9. Since December 2019, Coronavirus Disease 2019 (COVID-19) outbreak, caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide pandemic and public health emergency .Existing studies have shown that SARS-CoV-2 virus may specifically attack the kidney, causing acute kidney injury (AKI) . HoweverThis was a retrospective hospital-based cohort study. COVID-19 patients were enrolled from Huoshenshan Hospital, which was one of the largest special hospital of COVID-19, in Wuhan, China. Patients were enrolled from February to April 2020 with the inclusion criteria: at least 18 years old and confirmed with SARS-CoV-2 infection based on positive nucleic acid or antibody detection. Patients with a history of kidney diseases, dialysis or kidney transplantation were excluded from this study ” published by the National Health Commission of China , patientA standardized uniform form was used to collect data. Demographic information (age and sex), clinical symptoms (from the onset to admission), laboratory measurements, medical treatment  and outcomes were extracted through the electronic medical record system. Dynamic kidney laboratory parameters included Scr (Serum creatinine), BUN (blood urea nitrogen), uric acid, eGFR (estimated glomerular filtration rate), and Ccr (creatinine clearance rate) on admission, mid-term hospitalization and the final test before discharge were collected at the same time. Other laboratory measurements included: complete blood count, blood chemical analysis, coagulation testing, hypersensitive C-reactive protein (CRP), brain natriuretic peptide (BNP), procalcitonin, myocardial infarction kinase, routine urine and stool check results. Neutrophil-to-lymphocyte ratio  was used to represent inflammatory markers on admission. The inflammatory markers cut-off point was determined using the X-tile software (version 3.50). All data were independently processed and reviewed by two researchers (Yu Xu and Bin Wang).Serum creatinine (Scr), blood urea nitrogen (BUN), and uric acid (UA) were measured. Additional file eGFR was calculated based on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI serum creatinine) formula, for it was considered more precise and less biased than MDRD equation recommended by National Institute for Health and Care Excellence Clinical Practice Guideline .¯0.411×0.993age (year-old) Male > 80 years old : eGFR=141×[Scr (umol/L) /80]¯1.328×0.993age (year-old) Female ≤ 62 years old : eGFR=144×[Scr (umol/L) /62]¯0.329×0.993age (year-old)  Female > 62 years old: eGFR=144×[Scr (umol/L) /62]¯1.209×0.993age (year-old) Ccr was calculated according to Cockcroft's formula as follow. Ccr = [140 − Age (year-old)] × weight (kg) ÷ [0.818 × Scr (umol/L)] .Male ≤ 80 years old : eGFR=141×[Scr (umol/L) /80]2 or Ccr < 80 min*1.73 m2. Acute kidney injury was determined according to Kidney Disease: Improving Global Outcomes (KDIGO) [In this study, early kidney injury was defined as eGFR < 90 min*1.73 m (KDIGO) .The primary outcome was disease progression, which was defined by the following criteria: (1) Severity of COVID-19 changed from mild/moderate into severe/critical during hospital stay. (2) Death. (3) Transfer to ICU after initial hospital admission (i.e. more than 24 h of admission).Categorical variables were described as frequency and percentage. Continuous variables were described as median and interquartile range (IQR) values. A Cox proportional-hazards regression analysis was used to assess the associations between early kidney function indexes and outcomes. Three methods  in multivariable Cox regression analysis were used to screen potential confounders including age at diagnosis, gender, smoking, clinical types, comorbidity, laboratory tests and treatment. The significant variables were retained as covariates for adjustment in subsequent multivariable Cox regression analysis by entering method. Variance inflation factor (VIF) and the condition index (CI) were used to identify collinearity among the covariates. VIF > 5 and condition index > 10 were considered as collinearity and can not entered the multivariate Cox regression analysis. Rude and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. A generalized linear mixed model analysis was performed to assess the association between dynamic variation of kidney function indexes and outcomes. Statistical analyses were performed using SPSS version 25.0. A two-tailed P-value < 0.05 was considered statistically significant.A total of 2630 hospitalized patients with confirmed COVID-19 were finally enrolled in this cohort study. The age ranged from 18 to 100 years . The median days from the illness onset to admission were 4 days (IQR 0–8). There were 1053 (40.0%) patients who reported more than three symptoms on admission. According to the severity of disease on admission, patients were classified into four clinical types: mild , moderate , severe , and critically ill ; 7% (183) patients were unclassified. Sixty-four patients progressed to critical or severe. 102 patients admitted to ICU during hospitalization. By the end of April 13th, 2539 (96.5%) patients were discharged, 62 (2.4%) patients died, and 29 (1.1%) patients were lost to follow-up  patients, including 517 (19.7%) slight, 92 (3.5%) moderate and 22 (0.8%) severe decrease. On admission, proteinuria and hematuria was found in 15.0% and 8.1% patients, respectively. Both proteinuria and hematuria increased in proportion along with the severity of the disease. It was worth noting that 395 patients with eGFR reduction and 511 patients with Ccr reduction had Scr levels within the normal range. On admission, only 26 patients (0.99%) had an acute kidney injury (AKI) (Table P < 0.001) and 2.16 times (95% CI 1.33–3.51 P = 0.002) more likely to have a disease progression, respectively. The elevated baseline of uric acid was associated with disease progression in univariate Cox analysis . However, the association was no longer significant after adjustments for covariates age, gender, severe status, heart failure, respiratory failure, total protein, alkaline phosphatase, and NLR . BUN and Scr were both significantly associated with disease progression   and multivariate Cox analysis . Decreasing Ccr showed an adverse effect in univariate analysis , but this association was only marginally significant in multivariate analysis with adjusted HR 1.90      during hospitalization in COVID-19 patients were shown in Table 61) Fig. A. But th07) Fig. B. The ur01) Fig. C.Table 3P = 0.170)   Fig. D. PatienIn this study, we found there was a high proportion of early kidney function injury in COVID-19 patients on admission. eGFR and Ccr decreased in 22.0% and 24.0% of patients, respectively. This study also demonstrated a significant association between early kidney function injury and poor outcomes in patients with COVID-19  as one of the following: (1) an increase in serum creatinine by ≥ 0.3 mg/dL (≥ 26.5 µmol/L) within 48 h, (2) an increase in serum creatinine to ≥ 1.5 times baseline within the previous 7 days, (3) urine volume ≤ 0.5 mL/kg/h for 6 h . Due to Even though this cohort study enrolled 2630 hospitalized patients, there were several limitations. First, the clinical data came from a single center, and our findings need to be validated by external cohorts. Second, our study was a real-world study with dataset from clinical practice, it was difficult to avoid information bias caused by missing and inaccurate clinical information. Third, although we attempted to adjust for potential confounders in multivariate analysis, there might be unmeasured or unknown confounding bias in our study.There was a high proportion of early kidney function injury in COVID-19 patients on admission. The early injury in kidney function predicted the poor prognosis of COVID-19. The dynamic changes blood uric acid were associated the poor prognosis. Our findings could help clinicians prevent worse outcome and promote the recovery of patients.Additional file 1: Table S1. Upper and lower limits of kidney function laboratory parameters in male and female patients with COVID-19.Additional file 2: Table S2. Risk factors associated with disease progression in COVID-19 patients in univariate Cox regression analysis.Additional file 3: Table S3. Summary and comparison of recent studies on kidney damage from COVID-19 patients.Additional file 4: Table S4. Risk factor associated disease progression in COVID-19 patients in univariate Cox regression analysis.Additional file 5: Table S5. Collinearity diagnosticsof variables in multivariate analysis.Additional file 6: Table S6.Associations of kidney function indexes with disease progression in COVID-19 patients by multivariate cox regression analyses.


The use of controlled hypotension during neuraxial anesthesia for joint arthroplasty is controversial. We conducted a large institutional database analysis to assess common in-hospital complications and mortality of patients undergoing primary total hip arthroplasty (THA) under controlled hypotension and neuraxial anesthesia.We conducted a large retrospective case control study of 11,292 patients who underwent primary THA using neuraxial anesthesia between March 2016 and May 2019 in a single institution devoted to musculoskeletal care. The degree and duration of various mean arterial pressure (MAP) thresholds were analyzed for adjusted odds ratios with composite common severe complications  as the primary outcome.Sixty-eight patients developed common severe complications (0.60%). Patients with complications were older (median age 75.6 vs 64.0 years) and had a higher American Society of Anesthesiologists (ASA) classification (45.6% vs 17.6% ASA III). The duration of hypotension at various MAP thresholds (45 to 70 mm Hg) was not associated with increasing odds of common severe medical complications.Controlled hypotension (ranging from 45 to 70 mmHg) for a moderate duration during neuraxial anesthesia was not associated with increased odds of common severe complications  among patients receiving neuraxial anesthesia for elective THA. Neuraxial anesthesia has been associated with lower risk of transfusion and decreased short-term complications including venous thromboembolism, decreased length of stay, and facilitating home discharge following total hip arthroplasty (THA) –4. NeuraIn contrast, controlled hypotension during neuraxial anesthesia for joint arthroplasty is usually associated with different pathophysiology. However, its application in orthopedic surgery remains controversial . Our insThe use of IOCH in joint arthroplasty made a significant contribution to the improvement of surgical outcomes, especially during the early practice of joint arthroplasty , 12. WitThis retrospective case control study was approved by the Hospital for Special Surgery institutional review board (IRB# 2018–2235). The study followed STROBE standards . All datAll 13,860 patients who underwent primary, elective, unilateral THA at our institution between March 11, 2016 and May 17, 2019 were identified using the institutional electronic medical record platform . The inclusion criteria included age older than 18, ASA 1–3, received spinal/epidural anesthesia. Patients were subsequently excluded if they underwent a second independent procedure within the same study period (n = 998), if they were younger than 18 years at the time of surgery (n = 13), if they had an ASA class of 4 (n = 21), if they received general anesthesia (n = 960), or if there were >50% of intraoperative blood pressure data missing (n = 576). The final cohort included 11,292 patients .The most common anesthetic for THA in our institution is a combination of neuraxial anesthesia and intravenous sedation. Neuraxial anesthesia was delivered via spinal anesthesia (3600 patients), or epidural with/without spinal anesthesia (7692 patients). Patients often received arterial-line monitor during the procedure (83.9%) to facilitate management of IOCH. The mean arterial pressure (MAP) is managed by balancing the effects of neuraxial anesthesia induced sympathectomy, intravenous fluid volume, and use of inotropes (such as epinephrine infusion and ephedrine). Such approach will provide low pressure low vascular resistance with preserved or even increased cardiac output status . The tarIntraoperative MAP data were defined as blood pressure measurements collected from the anesthesia start time to anesthesia end time. The following standards were applied to blood pressure data cleaning process:If invasive monitoring via arterial line existed, we used MAP measurements at 1-minute intervals. There were 9573 patients with invasive arterial line monitor (84.8%). If arterial line monitoring was not used, regular noninvasive intraoperative blood pressure measurements were used to calculate MAP at 5-minute intervals.Blood pressure recording before anesthesia start time and blood pressure recording after anesthesia end time were removed. MAP<20 mmHg or >200mmHg time points were considered as artificial noise and removed .Time points at which the MAP value was more than 50% different from the previous and subsequent MAP values, were deleted with the assumption of artifact.For invasive monitoring via arterial line recording, if no MAP was recorded, but the time point 1 minute before and 1 minute after were recorded, then the missing MAP value would be recoded using the previous time point value. Similar for noninvasive measurement, if no MAP was recorded, but the time point 5 minutes before and 5 minutes after were recorded, then the missing MAP value will be recoded using the previous time point value. If a MAP value was missing longer than the specified time above, then it would be kept as missing.After the above 4 steps, patients whose electronic medical record MAP data were missing over 50% of total anesthesia time were excluded (n = 576). Although there were handwritten paper records available likely, we elected to remove these patients.Postoperative complications during hospitalization and transferring care to New York Presbyterian hospital were identified from electronic records using the discharge diagnoses described by the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM). Thirty-day mortality data were obtained from institutional quality control board as part of Adult Reconstruction and Joint Replacement quality control requirement. All identified complications were manually confirmed via chart review. Given the small number of complications of interest, and in order to better capture the outcome of interest, we chose a binary any-versus-none collapsed composite outcome as primary outcome . The comThe primary exposure of interest was total time of IOCH. For each patient, the duration of IOCH was defined as the total minutes during which MAP fell below a predetermined threshold, such as MAP < 60 mmHg. A range of thresholds (45–70 mmHg) were tested in sensitivity analysis.Statistical plans were finalized before analysis. Categorical variables were reported as frequency and percentage and analyzed using Chi Square or Fisher exact test across common complication status. Continuous variables were reported as median and interquartile range and analyzed using Mann-Whitney U test across common complication status. Multiple logistic regression models were used to determine adjusted odds ratios (OR) and 95% confidence intervals. Firth’s penalized likelihood was used to reducing small-sample bias in maximum likelihood estimation in the model. The covariables included in the regression model were statistically significant based on univariate analysis by complications of interest. Therefore, the covariables included in the model were age, sex, and ASA classification. IOCH duration and types of anesthesia were forced into the model irrelevant to the univariate analysis output based on the authors’ interest. We also conducted sensitivity analyses with MAP threshold from 45 to 70 mmHg at 1 mmHg interval adjusting for the same covariables in the same primary analysis model. An area under the curve was generated for each logistic regression model to assess the model ability to classify composite common severe complication status. We electively conducted the analysis with broader MAP threshold above 60s mmHg to be more inclusive. All analyses were performed with SAS Version 9.4 . All P-values reported were from two-tailed tests. A p value <0.01 was determined significant.Sixty eight of 11,292 patients (0.60%) developed common severe complications during the period of study . The comPatients who developed common severe complications were older , were more likely non-white , had a higher ASA class , and a longer hospitalization 3 [2.5–5] days versus 2  days . DiabeteThe extent and duration of IOCH were generally limited to the essential portion of the surgery. There were 26.7%, 49.0%, 70.2%, and 84.6% of patients experienced MAP<45, 50, 55, and 60 mmHg respectively. The median duration of these IOCH episodes were 3 , 6 , 14 , and 30  minutes respectively . CompariIn the regression analysis with composite common severe complications as dependent variable, MAP threshold of 60 mmHg applied as an example, and after adjusting for covariables, the OR of composite common severe medical complications was not significantly associated with duration of IOCH . The adjusted odds ratios for complications in older patients , ASA Level III patients  were significantly increased. White patients exhibited lower odds ratio comparing to non-white race . No other covariables had significant effects on the adjusted odds of composite common severe complications .An area under the curve of 0.78 suggested the logistic regression model is effective in distinguishing patients with common severe complications from the ones with no complications. Given the small number of common severe complication events, the number of covariables included in the multiple logistic regression model is limited to avoid overfitting. We also conducted sensitivity analysis with additional independent variables, which did not contradict the results of regression. Therefore, the final model only included ASA classification to represent comorbidity. We also conducted sensitivity analysis on patients with myocardial infarction and stroke as the complication group, and found similar pattern of significance . Similarly, we also conducted sensitivity analysis with the 3 mortality patients included in the complication group, and the results were similar .In subsequent serial logistics regression analyses with MAP threshold ranging from 45 to 70 mmHg at 1 mmHg interval, the duration of hypotension at various MAP thresholds did not increase the odds of common severe complications across various MAP cutoff thresholds from 45 to 70 mmHg .In our study of 11,292 patients undergoing elective THA under neuraxial anesthesia with IOCH ranging from 45 to 70 mmHg, we observed no association between the level of IOCH and the likelihood of common severe complications, including myocardial infarction, stroke, and/or acute kidney injury. To our knowledge, our study is the largest to date assessing the effect of IOCH on the risk of common severe medical complications.IOCH during arthroplasty surgery under neuraxial anesthesia differs from the general concept of intraoperative uncontrolled hypotension. With the use of vasoactive support, as is standard in our practice, cardiac output and thus perfusion can be maintained even in the setting of low blood pressures . UncontrThere is not a clear definition of what constitutes intraoperative hypotension . There aA number of large observational analyses have associated intraoperative MAPs below certain thresholds with higher odds of myocardial injury and mortality. The MAP thresholds used in the studies were <75 mmHg , <60 mmHFutier et al.  conducteIOCH has been in practice for decades and has been predominantly used in specialized institutions focusing on neurosurgery and orthopedic surgery. During joint arthroplasty surgery, we achieve IOCH via a combination of neuraxial anesthesia and a vasoactive pressor agent to achieve and maintain a target blood pressure. Such an approach maintains cardiac output and blood flow to organ systems in a low pressure, low resistance system using epinephrine infusions in the range of 1–4 mcg/minute .Many studies support the practice of IOCH –25. MostThis study is not without limitations. First, it is a retrospective review of data from a single, specialized institution and for a single surgical procedure. This may limit generalizability of our conclusion to other patient populations, practice conditions and/or surgical procedures. Second, we only included patients who received neuraxial anesthesia as the small number who received general anesthesia have not followed the standard of care pathway in our institution and the physiology of hypotension under general anesthesia is distinct from that under neuraxial. Third, although IOCH is standard practice in our institution during primary THA surgery, we could not definitively distinguish between controlled versus inadvertent events in this database. However, a significant incidence of inadvertent hypotension would be expected to increase the rate of adverse outcomes and this was not observed. Fourth, this retrospective study could not account for variation in specific blood pressure targets between different anesthesiologists and among different patients. It must be assumed that surgeons/anesthesiologist teams are balancing concerns regarding patient comorbidities with conditions on the surgical field when determining targets for IOCH. Fifth, there were only sixty eight patients with common severe complications (0.60%) among 11,292 patients. Such low frequency of events limited our ability to analyze other potential interesting confounders. However, we believe that such low incidence of events with our practice is a testimony that IOCH, as practiced in our institution, does not increase the risk of common severe complications. Sixth, duration of IOCH varies across our study cohort. We could not determine whether the durations were all intentional and necessary. It is possible that brief periods of hypotension serve as ischemic preconditioning that protects against subsequent ischemia. Finally, patients were not followed after discharge for the development of common severe complications other than 30-day mortality. It is reasonable to believe that as patients met discharge criteria, the potential impact of IOCH on the development of complications after discharge would be negligible.Our study of 11,292 primary THA patients from a single institution showed that moderate duration of IOCH  across various MAP thresholds (ranging from 45 to 70 mmHg) was not associated with increased odds of common severe in-hospital medical complications, including myocardial infarction, stroke, and/or acute kidney injury. IOCH is a distinct entity from passive, uncontrolled, and inadvertent hypotension and neuraxial-based IOCH could be considered and practiced for a reasonable duration when there are clear clinical indications.


Postoperative reflux can compromise anastomotic healing after Ivor-Lewis oesophagectomy (ILE). We report on Pre-emptive Active Reflux Drainage (PARD) using a new double-lumen open-pore film drain (dOFD) with negative pressure to protect the anastomosis.To prepare a dOFD, the gastric channel of a triluminal tube  is coated with a double-layered open-pore film  over 25 cm. The ventilation channel is blocked. The filmcoated segment is placed in the stomach and the intestinal feeding tube in the duodenum. Negative pressure is applied with an electronic vacuum pump  to the gastric channel. Depending on the findings in the endoscopic control, PARD will either be continued or terminated.PARD was used in 24 patients with ILE and started intraoperatively. Healing was observed in all the anastomoses. The median duration of PARD was 8 days (range 4–21). In 10 of 24 patients (40%) there were issues with anastomotic healing which we defined as “at-risk anastomosis”. No additional endoscopic procedures or surgical revisions to the anastomoses were required.PARD with dOFD contributes to the protection of anastomosis after ILE. Negative pressure applied to the dOFD (a nasogastric tube) enables enteral nutrition to be delivered simultaneously with permanent evacuation and decompression.The online version contains supplementary material available at 10.1007/s00464-021-08933-w. Postoperative reflux (PR) after Ivor-Lewis oesophagectomy (ILE) can have a deleterious effect on anastomotic wound healing and is a known risk of pulmonary aspiration. The main causes of PR are: paralysis, a pressure gradient between positive intraabdominal and negative intrathoracic pressure, and resection of the distal oesophageal sphincter. Until now PR has been drained using nasogastric tubes (NGT) –3. SinceWe describe the novel method of a pre-emptive active reflux drainage (PARD) with continuous negative pressure using a new double-lumen open-pore film drainage (dOFD) in order to completely drain and decompress the gastric conduit with the anastomotic region , 6. ThisFor PARD, we used a dOFD as a NGT Fig. . For conThe dOFD is inserted intraoperatively by endoscopic means directly after suturing the anastomosis. The DE is placed distal to the anastomosis, between the anastomosis and the pylorus. The intestinal feeding tube is directed into the duodenum. Correct placement of the DE is controlled endoscopically and from the open abdomen by the surgeon. The gastric channel of dOFD is then connected to an electronic vacuum pump  and negative pressure  is applied Fig. . To remoThe dOFD is a NGT with active suction. Since the use of a NGT an integral part of operative management after oesophagectomy, IBR approval was not needed. Written consent was obtained from all patients.Between 11/2017 and 8/2020 PARD was used in all patients who underwent ILE in our clinic during this period. The group included 24 patients with ILE for cancer . 18/24 patients had undergone neoadjuvant oncological treatment before the operation. Individual patient data are shown in Table Healing was observed in all the anastomoses (100%). No additional endoscopic interventions or operative revisions on the anastomosis were necessary.2 insufflation under short-acting anesthesia or sedation. Exchange of the dOFD with endoscopic evaluation of the wound situation was performed. PARD was terminated when stable anastomotic healing was confirmed endoscopically. All patients subsequently underwent further endoscopic examinations to confirm this.The application of negative pressure to dOFD resulted in immediate drainage of PR. An endoscopic control was performed for the first time after a median of 4 (2–7) days. All endoscopies were performed with COEndoscopy revealed complete elimination of PR and emptied gastric conduit in all patients. As seen in the removed dOFDs, the pores of the oral part of the DE, which was placed just below the anastomosis, were blocked due to ingested bronchial mucus and saliva. In the distal part of the DE, the pores were patent and functioning for the purposes of active negative pressure drainage. The DE was observed to be saturated with green bilious discolouration descending from distal to proximal Fig. . In all m = 360 (50–950) ml/24 h. Enteral nutrition using the enteral feeding channel of dOFD was started gradually after operation. On two occasions it was observed that tube feeds were aspirated through the dOFD. This was a result of the feeding tube retracting into the stomach. The dOFD was then exchanged for a new one.In total, dOFDs were placed 56 times, with a median of 2 (1–7) per patient. PARD lasted for a median of 8 (4–21) days. The amount of reflux aspirated on the first postoperative day was At the first endoscopic control, in 10/24 (40%) patients, problems with anastomotic healing were observed. The suspicious endoscopic findings were: visible suture material ; widespread ulceration of the adjacent mucosa and epithelium, ischaemic adjacent tissue Fig. . We defiNone of the ten patients with anastomotic problems developed signs of systemic sepsis during PARD. Local infection did not spread extraluminally in any of the patients. None developed mediastinitis. Even those patients with circular anastomotic necrosis or ischaemic gastric sleeve felt unaffected by the complications of internal wound healing. Full enteral nutrition was possible via the feeding tube.For the comfort of our patients, they were allowed to take sips of water during prolonged PARD. This was also completely evacuated via the dOFD. No postoperative pulmonal aspiration occurred during PARD. No PR-induced oesophagitis was found. No other complications related to PARD were observed.Six patients required additional operations . Because of the additional surgeries, PARD was continued for longer periods in these patients. Two of them had an ARA.One patient died 18 days after the operation due to a fulminant pulmonary infection; a relook of the anastomosis revealed no signs of pathological healing.The intrathoracic anastomosis continues to be an anatomically vulnerable weak spot in the early postoperative period following ILE.Several factors are known to influence anastomotic healing. Careful surgical techniques, good tissue perfusion and postoperative intermediate care management are prerequisites . But eveCommonly used NGTs work passively by draining gases and fluids by means of gravity, capillary force or positive pressure. Their benefit is thought to be exerted through decompression of the stomach, thus helping to relieve tension in the anastomotic region. However, their utility is debated controversially .PR flooding the anastomotic region is a frequently observed and well-known phenomenon after ILE. In numerous post-operative endoscopies after ILE, we have observed that even in the presence of a NGT, anastomoses were saturated with reflux . The typThe main mode of action of the PARD method presented here is to prevent digestive reflux-related contamination of the anastomotic tissue in the early postoperative period. PARD using an dOFD eliminates PR permanently. The visible endoscopic evidence is that the anastomotic regions were free from bilious secretions and were whitish in colour. The method simultaneously results in complete decompression of the stomach and anastomotic region, and enteral feeding can also be undertaken.When negative pressure is applied to common NGTs, their few lateral openings are sucked onto the gastric mucosa and blocked off. Open-pore drains used for endoscopic negative pressure therapy (ENPT) are coated with an open-pore material, either open-pore polyurethane foam or the thin double-layered open-pore film . Even ifThe dOFD used in this study is a special type of open-pore film drain (OFD) , 7. The It could be said that an OFD is like an NGT, but with the additional benefit that suction can be applied.The use of dOFD as a new tool for intraluminal ENPT (Endoscopic Negative Pressure Therapy) has been reported to be successful in the treatment of iatrogenic perforations of the oesophagus , BoerhaaCorrect placement of dOFD was ensured with intra-operative endoscopy . The DE In 40% of the patients, wound healing disturbances at the anastomosis were found in the first endoscopic control. With the protection of PARD, patients were clinically unaffected. Four of them developed ischaemic necrosis of the anastomoses and proximal gastric sleeve. Transmural defects were treated by continuing PARD with simultaneous enteral feeding only. All anastomoses healed; three developed short-stretch scarring anastomotic stenosis treated with balloon dilatation. The observed rate of ARA initially appears high , but corWe assume that at least some of the ten patients with ARA would have developed manifest severe anastomotic insufficiency without PARD.A similar observation was made by Neumann et al. They demonstrated successful pre-emptive intraluminal ENPT with OPD-devices in eight patients with postoperative ischaemic oesophageal anastomosis . A polyuGubler et al. reported intraoperative pre-emptive intraluminal ENPT in a first clinical series of 19 patients with 20 anastomoses . They inThe pre-emptive use of intraluminal negative pressure is supported by a study from Scott et al. They demonstrated the use of prophylactic intraoperative intraluminal ENPT in a series of pigs with ILE. After creating the anastomosis, a transmural defect was left which was covered with an intraluminal OPD. Intraluminal ENPT was started intraoperatively. All defects were found to be closed after a treatment period of 5 days .These studies suggests that intraluminal ENPT may have a favorable impact on anastomotic healing in the oesophagus. The two mechanisms of action of intraluminal ENPT are, firstly, sealing the anastomosis, and secondly, drainage of secretions , 11. TheIn this study, our aim was not to cover the anastomotic area with negative pressure drainage, which would be technically very easy with a longer DE. Our goal was to use the PARD method to keep the anastomosis completely and permanently free of digestive secretions. This single measure seems to have a high protective effect on anastomotic healing, even though there are alarming disturbances of wound healing at the anastomosis. This protective effect allows anastomotic healing to occur unperturbed. We conclude from our study that the drainage effect is of particular importance. We consider this observation to be the most significant result of our work. According to our preliminary experience, PARD could be a suitable method to increase patient safety for ILE. It follows that PARD could be a protective method in the sense of real prophylaxis, which supports anastomotic healing on the one hand and, on the other, prevents the serious consequences of a wound healing disorder at the anastomosis, including the complicating and life-threatening consequences of anastomotic insufficiency . In our We would like to mention potential limitations of the study. Firstly, the study has a small sample size. However, it is currently the largest study describing the PARD method for ILE with simultaneous enteral nutrition using the thin dOFD. Our observations and results should be confirmed by other surgical-endoscopic centers. This should be easily reproducible based on the detailed presentation and ease of use of the method. Secondly, this was a retrospective observational study on PARD using a single type of dOFD. The advantages of dOFD are its thin diameter, easy insertion into the drain, and the possibility of simultaneous nutrition. PARD can also be carried out using a single-lumen OFD and OPD or a double-lumen OPD , 25. ProIntraoperative onset PARD with a dOFD for ILE is a simple endoscopic method to prevent PR in the early vulnerable post-operative period at the anastomoses. Negative pressure applied to the dOFD (a nasogastric tube (NGT)) enables enteral nutrition to be delivered simultaneously with permanent evacuation and decompression. PARD seems to have a strong protective effect on anastomotic healing and may reduce the rate of anastomotic insufficiency.Supplementary file1 (MP4 355111 kb)Below is the link to the electronic supplementary material.