Cardiovascular reactivity to the cold pressor test (CPT) is considered to be a marker for apparent and potential hypertension. We aimed to elucidate the association between the changes in wave intensity (WI) during CPT and hypertension.We recruited 85 volunteers, 33 of whom were hypertensive and 52 normotensive. Using ultrasonic equipment during CPT, we measured carotid arterial WI, which is defined in terms of blood pressure and velocity in the carotid artery.1) increased during CPT in 70.6% of hypertensive individuals, but decreased in 72.6% of normotensive individuals. The chi-square (χ2) test showed that the association between the direction of change in W1 (increase or decrease) and the blood pressure (hypertensive or normotensive) was very strong (P < 0.0001).The peak WI (W1 during CPT is a clear marker to discriminate cardiovascular reactivity that does not vary depending on each investigator’s subjective point of view.Direction of change in W Hypertensive persons are considered to be hyper-reactive to CPT. A widely accepted indicator for reactivity is the amount of increase in systolic blood pressure , 2. HoweSubjects who had a blood pressure lower than 130/80 mmHg were categorized as normotensive. We recruited 52 normotensive subjects (Norm) from among students and staff at Himeji Dokkyo University, Himeji, Japan. We recruited 33 hypertensive subjects (HT) from among outpatients who were diagnosed as hypertensive at Sakakibara Heart Institute, Tokyo, Japan. All subjects had no abnormal ECGs and had normal echocardiographic findings. Baseline characteristics of the subjects are shown in Table Wave intensity (WI) is a hemodynamic index, which is defined asat any site in the circulatory system, where dP/dt and dU/dt are the derivatives of blood pressure (P) and velocity (U) with respect to time, respectively . We deveEach subject was asked to lie in a supine position for 10 min in a quiet room maintained at a temperature of 25℃ (resting state). Blood pressure readings were obtained during this period; the last of these measurements was used for calibrating the diameter change waveform, which is the surrogate for a blood pressure waveform. Then, the subject was asked to immerse one hand to just above the wrist for 2 min in ice water that was being kept at 0–1 °C. During the period of ice water immersion, blood pressure, blood velocity, and WI were obtained from the carotid artery, and ECG was also recorded  and classification of blood pressure  was also analyzed by the χ2 test. The relationship between %change in W1 (%W1) and age and the relationship between %W1 and arterial stiffness were analyzed by linear regression.Data were expressed as percent change, normalized to the index value in the resting state. Comparisons between HT and Norm groups were performed by two-way ANOVA, followed by Bonferroni test. The relationship between gender  and classification of blood pressure  was analyzed by the χ1 increased significantly in HT. On the other hand, W1 decreased significantly in Norm    Fig. , left, w30) Fig. , right.F1 during CPT on β before CPT (resting). The slope of the regression line for Norm does not deviate significantly from zero , while the slope of the regression line for HT deviates significantly from zero .To define arterial stiffness, we used the stiffness parameter β, which is considered to be independent of pressure. Figure 1 (increase or decrease) and with respect to blood pressure (HT or Norm). The χ2 test was used to determine differences in proportions. The association between the direction of change in W1 and the blood pressure was very strong (P < 0.0001).Table Hypertensive persons are considered to be hyper-reactive to the CPT, and hyper-reactivity to the CPT in normotensive persons is hypothesized to be a marker for future hypertension , 2. Howe1 (increase or decrease) and blood pressure (HT or Norm) was very strong (P < 0.0001) (Table 1) strongly correlates with left ventricular Max dP/dt, an index of cardiac contractility [We consider that an index comprised of multiple variables would be more informative than blood pressure alone or velocity alone. We evaluated WI, which is defined in terms of both blood pressure and velocity. Changes in WI during CPT were two directional, i.e., increase or decrease. The association between the direction of change in W1) Table . The maxactility , 5–7. Th1 and age show that there is a tendency for higher age (say age > 60) to be associated with positive values of %W1 both in Fig. 1 in the HT and Norm groups is not significant for age > 60. However, in the figure for the Norm group, the slope of the regression line is significantly positive , while in the figure for the HT group, the slope of the regression line is negative but does not deviate significantly from zero . Thus, %W1 has a tendency to be negative for lower age in the Norm group, while it has no significant association with age in the HT group.Scatter diagrams for the relationship between %W1 on age for the Norm group and that for the HT group in this age range . That for the HT group also does not deviate from zero . Therefore, as far as the age range of 50–80 is concerned, the associations between % W1 and age are not significant in either the Norm or HT group.However, the age ranges of the Norm group and HT group are different (20–80 vs 50–80). To examine whether the above-mentioned tendencies are also observed over the common age range of 50–80, we compared the regression line of %Wnge Fig. . The reg1 during CPT (Fig. 1 during CPT is not associated with β. In HT, however, the amount of %W1 is associated with β. The effects of the above confounding factors on changes in W1 are not consistent. Thus, the mechanisms underlying the two-way changes in cardiac contractility during CPT are largely unknown. Nevertheless, this property of cardiac contractility, i.e., decrease or increase, is a clear marker to discriminate cardiovascular reactivity, which does not vary depending on each investigator’s subjective point of view.We also evaluated the effects of stiffness parameter β of the carotid artery before CPT on %WCPT Fig. . In NormWe did not discuss the ability of hyper-reactivity to CPT for predicting future hypertension since we had no data concerning this issue. Our study was limited by the small sample size. Information about smoking status, diabetes mellitus, dyslipidemia, and obesity was not reported, which might have affected the multivariate analyses of factors in relation to the reactions to CPT. Therefore, our findings must be considered to be preliminary until they are replicated by other large studies that are able to show whether there is really no association between these factors and reaction to CPT.1 during CPT is a marker without ambiguity.Wave intensity, an index of ventriculo-arterial interaction, increases in hypertensive subjects, but decreases in normotensive subjects during the cold pressor test. Direction of change in W


Atrial fibrillation (AF) symptoms may mimic coronary artery disease (CAD) which reflects the difficulties in qualifying AF patients for invasive diagnostics. A substantial number of coronary angiographies may be unnecessary or even put patients at risk of post-contrast acute kidney injury (PC-AKI), especially patients with chronic kidney disease (CKD). We aimed to investigate the hypothesis indicating higher prevalence of PC-AKI in patients with AF scheduled for coronary angiography. The study population comprised of 8026 patients referred for elective coronarography including 1621 with AF. In the comparison of prevalence of PC-AKI in distinguished groups we can see that kidney impairment was twice more frequent in patients with AF in both groups with CKD (CKD (+)/AF (+) 6.24% vs. CKD (+)/AF (−) 3.04%) and without CKD (CKD (−)/AF (+) 2.32% vs. CKD (−)/AF (−) 1.22%). In our study, post-contrast acute kidney disease is twice more frequent in patients with AF, especially in subgroup with chronic kidney disease scheduled for coronary angiography. Additionally, having in mind results of previous studies stating that AF is associated with non-obstructive coronary lesions on angiography, patients with AF and CKD may be unnecessarily exposed to contrast agent and possible complications. Atrial fibrillation (AF) is the most commonly occurring arrhythmia, while coronary artery disease (CAD) is the most common cardiovascular disease and remains the leading cause of death worldwide ,2. Both The chronic kidney disease (CKD) has in common some of the above-mentioned risk factors and its co-occurrence with AF is increasingly prevalent in the general population . CKD is A substantial number of coronary angiographies may be unnecessary or even put the patient at risk of post-contrast acute kidney injury, especially patients with chronic kidney disease (CKD). Therefore, our goal was to investigate the hypothesis indicating higher prevalence of post-contrast acute kidney failure in patients with AF scheduled for coronary angiography. As shown in We reviewed medical records of 26,985 patients hospitalized in the Department of Invasive Cardiology of the Medical University of Bialystok  for coronary angiography between 2007 and 2016. We excluded patients with chronic coronary syndromes (CCS), acute coronary syndromes (ACS), and those referred for scheduled percutaneous coronary intervention (PCI) or angiography before heart valve surgery. Dialysis and missing creatinine values were also the exclusion criteria .Eventually, our final study cohort consisted of 8026 patients. All patients have undergone coronary angiography. During procedures an iodine-containing non-ionic radiocontrast agent was used. All patients had the same strategy for prevention of radiocontrast agent. Coronary angiography was performed according to the Judkins technique . DiagnosCKD-EPI eGFR and creatinine levels were assessed on admission. The term PC-AKI has been used according to the distinction based on the recommendations, which determines the use of the term contrast-induced acute kidney injury (CI-AKI) only when we can pinpoint a causal connection between the administration of contrast media and acute renal impairment as opposed to PC-AKI . For theThe subgroup of patients with atrial fibrillation was defined as the presence of AF on an electrocardiogram during the index hospitalization and/or as indicated by a diagnosis found in medical records. Diagnoses and AF classification were based on physician-assigned diagnoses in the medical records and/or the presence of corresponding ICD-10 codes . Less thDistribution of the variables was assessed with the Kolmogorov-Smirnov test. Data are expressed as means and standard deviations (SD). Relative frequencies are used to present categorical variables.Statistical significance of differences between PC-AKI and non PC–AKI patients were determined using the Student’s T-test and Mann-Whitney test.2 test was used.To compare four groups for non-normal distributed variables we used Kruskal-Wallis test with multiple pairwise comparisons using the Steel-Dwass-Critchlow-Fligner procedure, for categorical variable χp value of less than 0.1 and without a significant multicollinearity effect. The variance inflation factor was used to identify a correlation—and the strength of that correlation—between independent variables. The data are presented as odds ratios with 95% confidence intervals.Multivariable backward stepwise selection logistic regression was used to determine the odds ratio of having post-contrast acute kidney injury. Model included all predictors with a p-value < 0.05 was considered statistically significant. The statistic software Microsoft Excel  and XL Stat  were used.The A total of 8026 patients were qualified for the study, of which more than a half were men (54.06%) with a mean age of 65.26 years (SD = 10.14) . Separatp < 0.001). They more often had atrial fibrillation (37.58% (N = 59) vs. 19.85 (N = 1562), p < 0.001), chronic kidney disease (42.68% (N = 67) vs. 19.85 (N = 1562), p < 0.001), lower mean ejection fraction (41.53 (SD = 16.97) vs. 50.43 (SD = 13.29), p < 0.001), and were also more likely to have significant stenosis (58.6% (N = 92) vs. 39.94 (N = 3144), p < 0.001). In addition, patients with PC-AKI were more often treated with NOAC (p = 0.03) and VKA (p = 0.008) anticoagulants  vs. 53.74 (N = 4229), agulants .p < 0.001). CKD (−)/AF (+) group more often than CKD (−)/AF (−) one had significant stenosis (33.55% (N = 361) vs. 39.3% (N = 2091), p < 0.001). CKD (+)/AF (+) subgroup had the highest concentration of fibrinogen as well as serum creatinine (p < 0.001). Patients without CKD and AF had the highest value of eGFR (p < 0.001).There were substantial differences in terms of clinical characteristics between groups with and without AF and CKD, details are presented in In the comparison of prevalence of PC-AKI in distinguished groups we can see that kidney impairment was twice more frequent in patients with AF in both groups with CKD (CKD (+)/AF (+) 6.24% vs. CKD (+)/AF (−) 3.04%) and without CKD (CKD (−)/AF (+) 2.32% vs. CKD (−)/AF (−) 1.22%) .p < 0.001), atrial fibrillation  and CHF  and coronary artery disease . These results were also dependent on sex   .The incidence of atrial fibrillation has increased over the last years and will continue to increase in the coming ones, giving us an estimated 17.9 million cases in Europe by 2060 ,18. TherWhile the occurrence of AF among patients with coronary artery disease is rather low, reaching up to 5%, the prevalence of CAD among patients with AF can reach higher values ,22. In oDue to intensified symptoms in atrial fibrillation, which are reflected in the EHRA scale, patients at an early stage of the disease are referred to invasive diagnostics—coronary angiography, since symptoms resembling CAD manifest themselves . TraditiOne of the possible complications after coronary angiography is post-contrast acute kidney injury (PC-AKI) and it is known that patients with CKD have a higher risk of this adverse outcome . In addiThere are several large population studies that consistently show a higher prevalence of AF among patients with CKD than in those without kidney disease . Taking Possible underlying mechanisms that may contribute to development of contrast-induced nephropathy in patients with atrial fibrillation may include acute hemodynamic changes at the time of procedure due to sedation, transient atrial stunning, other severe comorbidities, and hypovolemia from fasting state may result in renal hypoperfusion ,40. MechOverall, coronary angiography is the most effective method used to evaluate coronary vessels, especially in patients with acute coronary syndrome. In one of our previous studies, we observed that a substantial number of patients with atrial fibrillation show no significant narrowing of coronary arteries during coronarography . In the In our study post-contrast acute kidney disease is twice more frequent in patients with AF, especially in subgroup with chronic kidney disease scheduled for coronary angiography. Additionally, having in mind results of previous studies stating that AF is associated with non-obstructive coronary lesions on angiography, patients with AF and CKD may be unnecessarily exposed to contrast agent and possible complications.


Objective: To maximize the utilization of potential kidneys, improving perfusion and preservation techniques is necessary.Methods: We investigated the safety and efficacy of retrograde machine perfusion of kidneys from deceased donors. A total of 30 kidneys were included and all the grafts were preserved in the Kidney Transporter machines. A total of 15 kidneys that received retrograde perfusion (RP) were selected as the RP group (n = 15) and their counterparts received standard antegrade perfusion (AP) as the control group (n = 15).Results: All the recipients were followed up for 6 months. Renal resistance in the RP group remained stable during the perfusion. There was no primary nonfunction. No difference in the incidence of delayed graft function was found in both groups . The RP group had lower serum creatinine  and blood urea nitrogen  than that in the AP group at 6 months. Both the groups had comparable estimated glomerular filtration rate and cystatin C within 6 months.Conclusion: This novel technique may be an effective and safe alternative for kidney preservation. Kidney transplantation is the treatment of option for end-stage renal disease (ESRD) . HoweverThe current standard practice of HMP involves perfusion of cold preservation solution into the kidney via a cannula connected to the renal artery. In procurement, we may encounter multiple renal arteries, artery spasm, or intraoperative damage to the arteries. In these circumstances, the conventional antegrade perfusion (AP) is not a proper technique because it might lead to unsatisfied kidney perfusion and inferior clinical outcomes after transplantation –8, even ClinicalTrials.gov ID: NCT04569682). The institutional review board approved the study protocol and authorized data collection and we obtained the consensus from all the participants. All the kidney grafts were procured from donation after brain death (DBD) between January 1, 2020, and August 1, 2020, with conventional perfusion through a lower segment of the abdominal aorta. When the procurement was completed, kidneys were immediately placed in the ice water for vascular clip on the table preparation. All the right renal veins were lengthening with inferior vena cava for surgical convenience. After that, all the kidneys were perfused with HMP in the LifePort Kidney Transporter machines  until operation (n = 15) and the RP group (n = 15). Demographic data of both the donors and recipients, human leukocyte antigen (HLA) mismatch, warm ischemia time (WIT), cold ischemia time (CIT), perfusion time (PT), urine output, DGF , serum creatinine (Scr), blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), and cystatin C (CysC) at postoperative day (POD) 1, 2, 3, 4, 7, 14, 21, 30, 60, 90, 120, 150, and 180 were collected. Ultrasonic arterial resistance 1 week after the operation was compared as well. No executed prisoners were used as donors and participants were neither paid nor coerced in this study.This study is a prospective observation of kidney transplants from deceased donors in West China Hospital, Sichuan University . The technique for RP was described as follows: a catheter was inserted into the renal vein and the RP was performed with a pulsatile flow of kidney preservation solution-1  at 1 to t-test and the Mann–Whitney U test to evaluate the differences between the AP and the RP groups. Categorical data were expressed as frequency and analyzed with the chi-squared test. p < 0.05 was considered as statistically significant. All the statistical analyses were performed using the SPSS software package (version 24)  and the GraphPad Prism version 8 .Continuous data were represented as the mean ± SD and assessed by the Student's The demographic data of donors are given in p = 0.33).The characteristics of recipients are shown in p < 0.01). At the beginning of perfusion (PT = 10 min), the RP group had higher perfusion flow than the AP group , but the RP group had significantly lower terminal perfusion flow . The RP group had lower initial perfusion resistance , while no difference was found between the two groups in terminal perfusion resistance . During the perfusion, the resistance in the RP group was relatively stable (Details of the perfusion parameters are shown in y stable .p = 0.05) and BUN  than the AP group. There was no statistically significant difference between eGFR and CysC in POD 180 (p > 0.05). One clinically suspected acute rejection episode occurred in the RP group and three clinically suspected acute rejection episodes occurred in the AP group and all received methylprednisolone pulse therapy and recovered. There was no significant difference in the length of hospital stay between the two groups . There was no surgical-related complication such as wound infection, urinary leakage, or ureter stricture during the follow-up.All the patients were followed up for 6 months. Postoperative information is given in At 1 week, all the allografts received the evaluation of ultrasound and there was no difference in renal, segmental, interlobar, and arcuate arterial resistance index . In the In this study, we first used RP machine perfusion for the preservation of kidneys from a deceased donor and found that kidneys receiving RP had a comparable incidence of DGF, urine output to the AP perfused allograft. Interestingly, although both the groups had comparable eGFR, we found that allografts perfused by RP had lower Scr and BUN than those receiving AP perfusion.In organ procurement, we may come across renal artery injury, anatomical variation, and malformations of the arteries. These kidneys might not be well perfused through traditional arterial-to-venous perfusion (AP), which might increase the discard rate . Each rein-situ perfusion of dog model for the first time. Until the late 1980s, Rolles et al.  and found that resistance greater than 0.4 was associated with increased graft failure, even not being used for transplantation . In thisThere are several limitations to this study. First, we reported our early experience of RP techniques and the included cases were limited, which may be underpowered to detect the difference between the AR and RP groups. Second, we only included kidneys from DBD donors, and how the RP work in kidneys from cardiac death donor or those with acute kidney injury remained unknown. In addition, after perfusion satisfied by cannulating the renal vein to LifePort, we usually removed the joint part of the renal vein before the operation, which may lead to a shorter renal graft vein and increase the difficulty of vascular anastomosis, especially for retransplant or obese patients, since it has not happened in this study. Finally, we did not obtain biopsy data that whether RP could preserve the microstructure efficiently is still at issue. Finally, due to the short follow-up period, the long-term effect of RP is not clear. Therefore, a prospective trial with a greater number of participants and long-term follow-up is necessary to prove the equivalence or superiority of retrograde machine perfusion in kidney preservation.Machine perfusion with RP technique is effective and safe in preserving kidneys from deceased donors.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of West China Hospital of Sichuan University. The patients/participants provided their written informed consent to participate in this study.JZ conceived and designed the study and prepared the first draft of the manuscript. ZJ performed the statistical analysis. TL analyzed the data. TS interpreted the data and revised the manuscript. All authors approved the final manuscript and contributed intellectually important content of the manuscript.This study was supported by the Natural Science Foundation of China ; 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant No. ZY2016104); the Youth Researcher Funding of Sichuan University (Grant No. 2017SCU11042); the Research Funding of Sichuan Health and Family Planning Commission .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We also assessed theinfluence of high FiO2 on respiratory system mechanics.To compare gas exchange indices behavior by using liberal2protocols to achieve SpO2 88% - 92% or 96%. We assessedoxygenation indices and respiratory system mechanics.We prospectively included consecutive patients aged over 18 years old with adiagnosis of COVID-19 and moderate-severe acute respiratory distresssyndrome. For each patient, we randomly applied two FiO2 strategy (p < 0.05) selected. ThePaO2/FiO2 deteriorated, PA-aO2increased and Pa/AO2 decreased significantly when usingFiO2 to achieve SpO2 96%. Conversely, thefunctional shunt fraction was reduced. Respiratory mechanics were notaffected by the FiO2 strategy.We enrolled 15 patients. All the oxygenation indices were significantlyaffected by the FiO2 strategy.A strategy aimed at liberal oxygenation targets significantly deterioratedgas exchange indices, except for functional shunt, in COVID-19-related acuterespiratory distress syndrome. The respiratory system mechanics were notaltered by the FiOClinical Trials Register: NCT04486729. Almost 25million people were infected worldwide by August 27, 2020, and the number of deathshas risen to more than 820,000.2) criteria, the pathophysiological featuresand clinical course of SARS-CoV-2 may differ substantially from those of classicalARDS.3,4)(Approximately 85% of COVID-19-infected patients admitted to the intensive care unit(ICU) develop severe acute respiratory syndrome (SARS-CoV-2).3) is the oxygenationindex most widely used in daily clinical practice due to its availability and easeof interpretation. Furthermore, the PaO2/FiO2 value is adeterminant to guide the implementation of rescue therapies such as high positiveend expiratory pressure (PEEP), neuromuscular blocking agents,5)( pronepositioning6). The most relevant ARDS clinical trialspublished in the last two decades set FiO2 to obtain a SpO2between 88 - 95%.5-7,9), and all of the patients’ next ofkin signed informed consent forms. This is a preliminary report of clinicaltrial.govNCT number: NCT04486729.We conducted a prospective physiological study in the ICU of 3) and moderate to severe ARDS according to the Berlindefinition.Baseline characteristics and laboratory analysis of all patients were retrieved fromour electronic clinical records. We collected the variables age, sex, number of daysunder invasive mechanical ventilation, Simplified Acute Physiology Score II (SAPSII) at admission, ARDS severity and sequential organ failure assessment (SOFA) scorethe day of enrollment.13), driving airway pressure(ΔPaw), inspiratory esophageal pressure (Pesinsp), expiratory esophageal pressure (Pes exp), driving esophageal pressure(ΔPes), inspiratory transpulmonary pressure using thedirect method (PL-direct insp), expiratory transpulmonary pressure(PL exp), driving transpulmonary pressure (ΔPL)and inspiratory transpulmonary pressure using the elastance-derived method(PL-elas insp) using the formula:We evaluated respiratory system mechanics using a specific device and software connected to a personalcomputer. The flow (F) and volume (Vol) were measured with a flow sensorprovided by the manufacturer that was correctly calibrated. We inserted anesophageal balloon  7 cm in length filled with0.5mL of air. The correct position in the lower third of the esophagus wasconfirmed by the presence of cardiac artifacts and the occlusion test aspreviously described elsewhere.L) were calculated with the following formulas:The respiratory system elastance (Ers), chest wall elastance (Ecw) and lungelastance /FiO2.14) =((Pbarometric - PvH2O) x FiO2) -PCO2/RQ,14)( where Patm is the barometric pressureexpressed in mmHg (760), PvH2O is the partial pressure of water steamexpressed in mmHg (47), PaCO2 is the partial pressure of arterialcarbon dioxide and RQ is the respiratory quotient (0.8). Once PAO2was obtained, we calculated the indices alveolar-arterial oxygen pressuregradient (PA-aO2) and the quotient arterial/alveolar pressure ofoxygen (Pa/AO2).The PaO2)as an acceptable surrogate for mixed venous oxygen saturation: Qs/Qt =(CcO2 - CaO2)/(CcO2/CvO2), whereCaO2, CvO2, and CcO2 are the arterial,venous and capillary oxygen contents, respectively.14) SpO2 and one to obtain aconservative (88 - 92%) SpO2, both periods evaluated on the same day.For randomization, we used the software available on the randomization.comwebsite, and we used closed opaque envelopes. Each phase lasted 10 minutes,based on the study carried out by Cakar et al., in which they showed that 5minutes was enough time to achieve a stable PaO2level.15), extending the time of measurements would haveled to limiting these interventions for longer periods of time, affecting thestandard of care in our unit and the patient´s clinical status.We randomly applied two different FiOwww.rproject.org) and the ggplot2 package.Data are expressed as the mean ± standard deviation (SD) and number(percentage), as appropriate. The Shapiro-Wilk test was used to test normality.One sample Student`s t-test was used to assess the statistical significance ofthe difference between the two conditions when the data were normallydistributed; otherwise, the Wilcoxon test was used. The results with atwo-tailed p ≤ 0.05 were considered statistically significant. Thestatistical analysis was performed with R 4.0.3 , PA-aO2 increased and Pa/AO2decreased  significantly by usingFiO2 to achieve SpO2 96%. Conversely, the functional shuntfraction was reduced .The comparisons between oxygenation indices obtained with liberal versus conservativeoxygenation targets are presented in figure 1. All of the indices were significantlyaffected by FiO2strategies.The variables related to the respiratory mechanics are presented in 2 target considerably affectsthe oxygenation indices, which may have implications not only in severitystratification of ARDS but also in the clinical decision-making process.Our findings show that adopting a liberal SpO6)( In patients with a high percentage of shunt andlow ventilation/perfusion (V/Q) units, increasing the oxygen supply significantlyaffects the gas exchange indices due to the marginal effect on PaO2 ofhigher concentrations of PAO2.16), but PO2 will not becausedeoxygenated blood leaving low V/Q units will mix with oxygenated blood coming fromnormal V/Q units.17) remained unchanged afterincreasing FiO2, which might indicate that atelectasis formation was notsignificant, possibly due to the limited time of exposure as well as the use ofFiO2 lower than 100%. Second, the impairment of the normal mechanismsof hypoxemic vasoconstriction has been proposed as a possible cause to explain theprofound hypoxemia in COVID-19 in the absence of significant alterations ofrespiratory mechanics;21-23) when assessingfunctional shunt with an FiO2 lower than 100%; when pure oxygen was used,the real shunt fraction was only 4%.24)(Our study presents several limitations that must be addressed. First, the smallnumber of patients enrolled in our study does not allow us to make conclusionsregarding the best clinical strategy in terms of outcome benefits. On the otherhand, cardiac output was not monitored during the protocol, and a reduction in thefunctional shunt when FiOA strategy aimed at liberal oxygenation targets significantly deteriorated gasexchange indices, except for functional shunt, compared with a conservative strategyin COVID-19 related acute respiratory distress syndrome during invasive mechanicalventilation. The respiratory system mechanics were not altered by the fraction ofinspired oxygen strategy.


ESRD is considered an irreversible loss of renal function, yet some patients will recover kidney function sufficiently to come off dialysis. Potentially modifiable predictors of kidney recovery, such as dialysis prescription, have not been fully examined.Retrospective cohort study using United States Renal Data System (USRDS) data to identify incident hemodialysis (HD) patients between 2012 and 2016, the first 4 years for which dialysis treatment data is available. The primary outcome was kidney recovery within 1 year of ESRD and HD initiation, defined by a specific recovery code and survival off dialysis for at least 30 days. Patient and treatment characteristics were compared between those that recovered versus those that remained dialysis-dependent. A time-dependent survival model was used to identify independent predictors of kidney recovery.p < 0.001).During the study period, there were 372,387 incident HD patients with available data, among whom 16,930 (4.5%) recovered to dialysis-independence. Compared to non-recovery, a higher proportion of patients with kidney recovery were of white race, and non-Hispanic ethnicity. Both groups had a similar age distribution. Patients with an acute kidney injury diagnosis as primary cause of ESRD were most likely to recover, but the most common ESRD diagnosis among recovering patients was type 2 diabetes (29.8% of recovery cases). Higher eGFR and lower albumin at ESRD initiation were associated with increased likelihood of recovery. When examining HD ultrafiltration rate (UFR), each quintile above the first quintile was associated with a progressively lower likelihood of recovery (HR 0.45, 95% CI 0.43–0.48 for highest versus lowest quintile, We identified non-modifiable and potentially modifiable factors associated with kidney recovery which may assist clinicians in counseling and monitoring incident ESRD patients with a greater chance to gain dialysis-independence. Clinical trials are warranted to examine the impact of dialysis prescription on subsequent kidney function recovery. By definition, a diagnosis of end-stage renal disease (ESRD) indicates that patients have irreversibly lost kidney function to the point of requiring dialysis or transplantation. Yet a proportion of such patients will in fact recover kidney function sufficient to come off dialysis, at least for a period of time – as high as 5–6% of incident ESRD patients in recent years , 2. DialBecause ESRD designation is a clinical determination, it remains prone to some inherent imprecision. Identification of patient factors associated with recovery can help guide both patient and provider expectations and assist in monitoring for recovery. This can be important because kidney function recovery in the dialysis center may not be recognized if not actively searched for. In many dialysis centers, it is not routine to assess residual kidney function and therefore any improvement in kidney function may be missed. Under-recognition of recovery can result in patients remaining on dialysis longer than necessary.Perhaps even more importantly, there is a need to identify clinical practices – including dialysis prescription – that may influence (either positively or negatively) the chances of kidney recovery. The potential negative impacts of hemodialysis therapy on cardiac and neurologic function have been well-described , 4, and The goal of this study was to identify ESRD patient characteristics and hemodialysis prescription factors associated with subsequent kidney function recovery, with the hope of informing current practices as well as laying the foundation for further research into optimizing treatment approaches in this patient population.The United States Renal Data System (USRDS) collects, analyzes, and distributes information about chronic kidney disease (CKD) and ESRD in the United States. The USRDS data sources include Centers for Medicare & Medicaid Services (CMS), the United Network for Organ Sharing (UNOS), and selected clinical and administrative data reported monthly by all Medicare-certified dialysis facilities in the U.S. (CROWNWeb). Data from these sources are merged together to create a treatment history file of ESRD treatment for each patient , 6. ThisWe conducted a retrospective cohort study of incident ESRD patients on dialysis in the U.S. We identified patients with kidney recovery by ESRD initiation year from 1996 through 2016 in order to describe trends. We then focused the cohort analysis on patients starting maintenance hemodialysis (HD) between May 1st, 2012 and May 31st, 2016, representing the initial period for which CROWNWeb treatment data became available. Patient treatments were followed through May 31st, 2017, allowing at least 1-year of follow-up on all patients. Patient demographic information, including age, sex, race, and ethnicity were obtained from USRDS 2017 Standard Analysis Files (SAF). Additional clinical information, such as pre-ESRD laboratory data, insurance coverage, cause of ESRD and pre-ESRD care, were obtained from the ESRD medical evidence Form (CMS Form 2728).Hemodialysis treatment data was obtained from CROWNWeb. Data are reported to CROWNWeb on a once-monthly data and represent the findings from a single treatment, as opposed to the average of all monthly treatments. Dialysis ultrafiltration rate (UFR) was calculated as (pre-HD weight – post-HD weight)/(post-HD weight)/(treatment time) for each month since ESRD initiation. The mean UFR of the first 12 months was grouped into quintiles and used in the descriptive analysis. The monthly UFR was also grouped into separate quintiles and used in the time-dependent model.Our primary outcome was recovery to dialysis independence within 1 year of ESRD initiation. ESRD recovery events were identified by the recovery of kidney function code in the USRDS patient event file, and patients had to survive at least 30 days without receiving additional dialysis or undergoing kidney transplantation. This parameter was an effort to exclude patients that were withdrawn from dialysis. Of note, there is a separate specific code to denote withdrawal from dialysis for palliative care reasons.Patient characteristics were compared between patients that did versus did not recover kidney function to dialysis independence within 1 year. An unadjusted Cox model was conducted for each predictor, with censoring for death.Time dependent Cox regression analysis was performed to estimate the relationship of predictors with the primary outcome of recovery to dialysis independence within 1 year. To account for potential changes in dialysis prescription over the time period, UFR was used as a time-varying covariate. We generated a counting process data, in which each patient is represented by one row, and each row represents a 1 month interval during which the UFR could be changed while all other covariates remain constant.From 1996 through 2016 there were 2,166,429 incident dialysis patients, of whom 104,250 (4.8%) experienced kidney recovery. Over these 2 decades, the crude cumulative incidence of recovery increased gradually from 2.7% in 1996 to a peak of 6.3% in 2010, before decreasing the past several years  and “other cause” . When looking at specific diagnoses, the top 4 diagnoses by proportion of recovery were forms of acute kidney injury (AKI), each with > 20% of patients recovering (Table 6.4% and p < 0.001).Following dialysis initiation, albumin, calcium and hemoglobin were similar between patients that recovered versus did not recover Table . Patientp < 0.001). When categorized into quintiles, the proportion of patients recovering kidney function decreased with increasing quintile of UFR, ranging from 9.1 to 2.8% from lowest to highest quintile respectively  adopted UFR as a reporting quality measure beginning in 2020 . Our finThere are several limitations to our study. Most notably, the observational design and lack of data on residual kidney function and urine volume limit any causal inferences between the observed higher UF rates and lower likelihood of renal recovery. Nonetheless, our findings are plausible and in line with expert guidelines regarding optimal approaches to dialysis prescription in patients with AKI. We encourage dialysis organizations which have access to more granular treatment data to further explore and confirm our study’s findings. Another limitation is the inherent subjectivity in determining ESRD status and assigning a primary cause of this diagnosis. It is not feasible to do a nephrologist-level analysis, but it is conceivable that individual practice patterns could significantly influence recovery rates. Our study period was prior to recent regulatory changes (i.e. passage of the Trade Bill as discussed above) which have potentially significantly changed the approach to ESRD diagnosis in patients with AKI; future analyses are needed to assess the impact of these changes. Lastly, our primary outcome of kidney function recovery may have incomplete ascertainment due to lack of recognition. We hope that the results of this study will increase awareness of the potential for recovery and reduce any missed opportunities.Nearly 1 in 20 incident ESRD patients will recover to dialysis independence within 1 year. In this study we identified several predictors of kidney recovery, including a potential link between higher ultrafiltration rate and reduced likelihood of recovery. Clinicians can use this information to risk stratify patients and consider delaying ESRD designation and/or closer monitoring for recovery, particularly among patients with a primary ESRD diagnosis in an AKI category.


Inflammation and oxidative stress can contribute to the development and progression of heart failure. This study aimed to investigate the association between inflammatory and oxidative stress markers in dogs with congestive heart failure (CHF). Associations between the disease severity marker N-terminal pro-B-type natriuretic peptide (NT-proBNP) and markers of inflammation and oxidative stress were also determined.P = 0.012), white blood cell (P = 0.001), neutrophil (P = 0.001) and monocyte counts (P = 0.001) in patients with CHF compared to control dogs. The concentration of tumor necrosis factor-alpha (TNF-α) was significantly higher in patients with CHF compared to patients without CHF (P = 0.030). No significant difference was found in most of the measured parameters between MMVD and DCM patients, except for glutathione peroxidase (GPX) and NT-proBNP. In patients with CHF, TNF-α correlated positively with malondialdehyde  and negatively with GPX , and interleukin-6 correlated negatively with GPX . NT-proBNP correlated positively with malondialdehyde . In patients without CHF none of the inflammatory and oxidative stress markers correlated significantly. Furthermore, in the group of all cardiac patients, GPX activity significantly negatively correlated with NT-proBNP  and several markers of inflammation, including TNF-α , interleukin-6 , white blood cell , neutrophil  and monocyte counts .Thirty-seven dogs with cardiovascular diseases , myxomatous mitral valve disease, MMVD (21 dogs)) and ten healthy dogs were included in this prospective study. The patients were further divided into groups with (26) and without CHF (11). We found a significantly higher serum concentration of C-reactive protein (Inflammatory and oxidative stress markers are linked in canine CHF patients, but not in patients without CHF. These results suggest complex cross communication between the two biological pathways in advanced stages of CHF.The online version contains supplementary material available at 10.1186/s12917-021-02878-x. Increasing evidence suggests that processes of inflammation are implicated in the pathophysiology and progression of congestive heart failure (CHF) , 2. InflInflammation and oxidative stress play an important role in various features of cardiovascular disease, involving endothelial dysfunction, lipid disorders, and myocardial injury , 2, 16. Congestive heart failure, regardless of etiology, is linked with inflammation and enhanced oxidative stress, whereas short-term inotropic support results in the reduction of inflammatory and oxidative stress markers . BiomarkOur hypothesis was that inflammatory and oxidative stress markers are associated in canine patients with CHF resulting from MMVD or DCM. To test the hypothesis, we measured selected inflammatory and oxidative stress markers. We correlated them with each other, as well as with the disease severity marker N-terminal pro–B–type natriuretic peptide (NT–proBNP).P < 0.001) older compared to control dogs. Furthemore, patients with and without CHF did not differ significantly in age.Forty-seven privately owned dogs were included in the study: 37 of them were cardiovascular patients, and ten of them were control dogs. Cardiovascular patients were further divided into two groups; patients with (26) and without 11) CHF. The progression algorithm of dogs during the inclusion into the study is shown in Fig. 1 CHF. ThP = 0.027) and control dogs (P < 0.001). In addition, dogs with DCM were significantly older compared to control dogs (P = 0.011) and had significantly higher (P < 0.001) weight than MMVD dogs.Age and weight Table  were comConcentrations of IL-6 were excluded from the statistical comparison because they were below the lower level of detection.P = 0.009) and control dogs (P < 0.001); however, NT–proBNP concentration between patients without CHF and control dogs did not differ significantly.The results of inflammatory markers and NT–proBNP are presented in Table P = 0.012). The concentration of TNF-α was significantly (P = 0.030) higher in patients with CHF compared to patients without CHF, while no significant difference was found in comparison to control dogs.Serum CRP concentration was significantly higher in patients with CHF than in control dogs  and patients without CHF . Neutrophil counts were significantly (P = 0.001) higher in patients with CHF than in control dogs. We found significantly (P = 0.020) higher lymphocyte counts in patients with CHF than in patients without CHF. Furthermore, we found significantly higher neutrophil percentages and significantly lower lymphocyte percentages in patients without  and with CHF  in comparison to control dogs.White blood cell and monocyte counts were significantly higher in patients with CHF compared to control dogs (P = 0.042) higher in patients without CHF than in control dogs.Regarding oxidative stress parameters Table , we founP = 0.003) and control dogs (P = 0.003). NT-proBNP concentration was significantly higher in DCM compared to MMVD patients (P = 0.018) and control dogs (P < 0.001), while NT-proBNP in MMVD patients was significantly higher than in control dogs (P = 0.010). On the other hand, we found a significant difference (P < 0.05) between the control dogs and both groups of patients (MMVD and DCM) in WBC, NEUT, MONO and percentages of neutrophils and lymphocytes.Inflammatory and oxidative stress markers and NT-proBNP concentrations were also compared between DCM and MMVD patients and control dogs Tables  and S3. P = 0.014, r = 0.474) and negatively with GPX activities . Furthermore, we found a significant negative correlation  between IL-6 concentrations and GPX activities. Percentages of neutrophils and monocyte counts correlated significantly negatively with GPX activities . On the other hand, percentage of lymphocytes correlated significantly positively with GPX activities , and lymphocyte counts correlated significantly negatively with MDA concentrations . In this group of patients, we found a significant positive correlation between NT-proBNP and MDA concentrations .In CHF patients, TNF-α concentrations correlated significantly positively with MDA concentrations , IL-6 concentrations , WBC , and neutrophil  and monocyte counts . On the other hand, GPX activities correlated significantly positively with percentages of lymphocytes . Furthermore, we found a significant negative correlation  between MDA concentrations and lymphocyte counts. In the group of all pateints, NT-proBNP concentrations correlated significantly negatively with GPX activities  and positively with TNF-α concentrations  and monocyte count .Additionally, we correlated inflammatory and oxidative stress markers and NT-proBNP in the group of all 37 patients and found several significant correlations. Interestingly, GPX activities correlated significantly negatively with several inflammatory markers, including TNF-α concentrations , and soluble receptors of TNF-α correlated significantly positively . Our resIn our study, markers of inflammation and oxidative stress and NT-proBNP correlated significantly in the group of all cardiac patients. Interestingly, GPX activity significantly negatively correlated with several markers of inflammation, such as TNF-α, IL-6, WBC, and neutrophil and monocyte counts. These results suggest the importance of this antioxidant enzyme in cross communication between inflammatory processes and oxidative stress in canine cardiac patients. Furthermore, GPX significantly positively correlated with the percentage of lymphocytes, whereas MDA concentration significantly negatively correlated with lymphocyte count. Additionally, in the group of all patients, we found a significant negative correlation of NT-proBNP with GPX and a significant positive correlation with TNF-α and monocyte count. These results suggest that the severity of the disease is associated with a decrease in GPX activity and increased inflammation. All these correlations further document the cross communication between inflammation and oxidative stress in canine cardiac patients. Similarly, Rubio and colleagues found significant correlations between selected inflammatory parameters and antioxidant biomarkers and echocardiographic variables, suggesting that inflammation and oxidative stress collaborate in the pathogenesis of heart failure .2α-isoprostanes [Although, it was not our major goal, we compared oxidative and inflammatory parameters between DCM and MMVD patients and healthy dogs. Interestingly, we found no significant differences in any of the inflammatory parameters between DCM and MMVD patients, but there was a significant difference in several inflammatory parameters between healthy and both groups of patients  indicatirostanes . Contrarrostanes . In our rostanes . A recenrostanes . As expeOur study has some limitations that should be noted. Sex and age were not matched between our patients and control dogs. Age and/or sex, as well as neuter status, might influence the measured parameters , 45.In conclusion, most of the measured inflammatory markers were significantly higher in canine patients with CHF than in control dogs. Furthermore, we found that the activity of the antioxidant enzyme, GPX, decreased with increasing concentration of inflammatory markers in the group of all cardiac patients. Significant correlations between markers of inflammation and oxidative stress in patients with CHF but not in patients without CHF suggest complex cross communication between the two biological pathways with the development of CHF.In the present study, 88 privately owned dogs were evaluated. Forty-one dogs were excluded due to allergic diseases, cancer, kidney disease, endocrine disorders, or infectious diseases. Thirty-seven patients with either MMVD or DCM and ten healthy control dogs were prospectively recruited. The same cohort of dogs have been used in our previous study . The tenSigned consent was granted by the owners of the dogs. The Ethical Committee of the Ministry of Agriculture, Forestry and Food, Veterinary Administration of the Republic of Slovenia  approved all procedures.Blood samples for all laboratory analyses were collected from fasted dogs. Samples for determination of haematological parameters were analysed within one hour after collection. Serum tubes  stood for 30 min at room temperature before centrifugation for 10 min at 1300×g at room temperature. The serum used for CRP, TNF-α, and IL-6 measurements was frozen at − 80 °C and analysed in batch at the end of the study. Serum biochemical parameters were measured on the day of collection. EDTA tubes  were used for the collection of samples for measurement of plasma NT-proBNP and MDA concentrations. These tubes were centrifuged for 15 min at 1500×g at 4 °C and obtained plasma samples immediately frozen at − 80 °C until analysis. Concentrations of NT-proBNP and MDA were measured in batch at the end of the study. Blood samples for determination of whole blood GPX activity were collected into tubes containing the anticoagulant lithium heparin . Aliquots of heparinized whole blood were prepared and immediately frozen at − 80 °C until analysed in batch.An automated haematology analyser ADVIA 120  was used for haematological measurements. Biochemical parameters  were measured using automated biochemistry analyser RX Daytona . The concentrations of sodium, potassium, and chloride were measured using an electrolyte analyser ILyte .Serum CRP, TNF-α, and IL-6 concentrations were measured with commercially available canine-specific ELISA kits  based on quantitative sandwich enzyme immunoassay procedures. Assays were performed according to the original manufacturer’s instructions with all samples assayed in duplicate. The concentrations of TNF-α and IL-6 in serum were measured directly, whereas a sample dilution of 1:1000 was used for the measurement of CRP, which was taken into account in the calculation of the final results. The ranges of the tests were 3.9–250 pg/mL (sensitivity 2.4 pg/mL) for TNF- α, 31.3–1000 pg/ml (sensitivity 6.1 pg/mL) for IL-6 and 3.1–200 ng/mL (sensitivity 3.1 ng/mL) for CRP. The precision of the assays was monitored using control sera included in the kits with known concentrations of the analytes. Coefficients of variation were 8.9% for TNF- α, 2.75% for IL-6, and 9.8% for CRP.Glutathione peroxidase activity was measured spectrophotometrically with an automated biochemistry analyser RX Daytona  using a commercial Ransel kit , which is based on the method of Paglia and Valentine . Accordi3PO4 and allowed to stand for 15 min. Absolute ethanol (600 μL) was added to each sample, and the samples were centrifuged . In the glass tubes, the supernatant (700 μL) was mixed with 1.5 mL of 0.44 M H3PO4, 1.5 mL of 0.6% thiobarbituric acid (TBA), and 0.3 mL of ultrapure water and heated at 90 °C for 60 min. The MDA-TBA2 adduct was separated using an Agilent HPLC system  equipped with a 1260 Infinity FLD fluorescence detector. The mobile phase consisted of 50 mmol/l of a 65% KH2PO4 buffer (pH 6.9) and 35% methanol. The flow rate of the mobile phase was 1.0 mL/min. A 10-μL aliquot was injected into a reversed-phase HPLC column  and C18 octadecylsilane guard column . The results of the analysis were evaluated using OpenLab CSD Chem Station ed. rev. C.01.05 (35) software.The plasma MDA concentration was analysed according to a method described elsewhere , 49. BriAll frozen EDTA plasma samples were sent on dry ice to IDEXX laboratory  by air transport. Plasma NT–proBNP concentrations were measured in IDEXX laboratory using a second-generation Cardiopet® proBNP enzyme-linked immunosorbent assay . The second-generation canine NT-proBNP assay is a sandwich ELISA with colorimetric end-point detection at 450 nm for quantitative determination of canine NT-proBNP. The second-generation assay eliminates the need for the specialized protease inhibitor blood collection tube by using new antibodies that target a more stable contiguous fragment of NT-proBNP and offers an upper reporting limit of 10000 pmol/L [Commercial software  was used for data analysis. The normality test (Shapiro-Wilk) was used to assess the distribution of the data. Parameters of interest were compared between groups of dogs  using one-way ANOVA and Tukey’s HSD test (Gaussian distribution of the data), or Kruskal-Wallis test followed by Bonferroni’s method for multiple pairwise comparisons (non-Gaussian distribution of the data). An independent t-test (Gaussian distribution of the data) and the Mann-Whitney U test (non-Gaussian distribution of the data) were used to compare age and weight between healthy dogs and all patients, respectively. The results are reported as means and standard deviations (SD) (Gaussian distribution of the data) and as a median and interquartile range . In the groups of patients with and without CHF and the group of all cardiac patients, we performed correlation analyses (non-parametric Spearman’s rank correlation) to assess associations between inflammatory and oxidative stress markers and between NT-proBNP and markers of inflammation and oxidative stress. A probability value of less than 0.05 was considered statistically significant.Additional file 1: Table S1. Baseline demographic characteristics of canine DCM and MMVD patients and control dogs. Table S2. Inflammatory markers  and NT-proBNP concentrations  in DCM and MMVD patients and control dogs. Table S3. Oxidative stress markers (mean ± SD) in DCM and MMVD patients and control dogs.


Urinary stones cause hydronephrosis, which leads to kidney function impairment. The serum creatinine level is frequently used as a marker of kidney function. However, in some patients with hydronephrosis, it does not reflect the kidney function changes in the early stages of kidney stone disease. Neutrophil gelatinase-associated lipocalin (NGAL) is a novel indicator of the kidney function. Previous NGAL-related research has focused on its role in acute kidney injury. This study aimed to determine the usefulness of NGAL as an early marker of the kidney function in patients with urinary stones and hydronephrosis.Eighty-five patients with urinary stones who were admitted to the Shanghai Tenth People's Hospital (USP group) and 65 healthy volunteers (NC group) were recruited. Blood and urine samples collected from the study participants were evaluated using an enzyme-linked immunosorbent assay to determine the NGAL concentration. Data on the height, weight, age, medical history, and blood and urine findings were collected. Computed tomography data were collected from the USP group.P < 0.001). However, no significant differences in the NGAL levels were observed among the USP group members with different degrees of hydronephrosis. Furthermore, no significant between-group differences in the creatinine level or the estimated glomerular filtration rate were observed. The areas under the receiver operating characteristic curves for the serum and urinary NGAL levels with hydronephrosis were 92.03 and 99.54%, respectively. The areas under the receiver operating characteristic curves for the serum and urinary NGAL levels with kidney stones were 85.05 and 91.89%, respectively.Compared to in the NC group, NGAL levels were significantly elevated in the USP group (NGAL is a sensitive indicator of hydronephrosis secondary to urinary stones. Urinary stone disease is very common . UrinaryCurrently, serum creatinine level is one of the main markers of kidney function –8. CreatIn Kjeldsen et al.  discoverMany diseases affect kidney function; however, most do not do so as quickly as AKI. Nonetheless, early detection of kidney function remains important . CurrentIn this study, 85 patients who were hospitalized at the Tenth People's Hospital of Tongji University (between July 2020 and December 2020) for urinary stones were enrolled and categorized into the USP group. In addition, 65 volunteers who underwent an ultrasound examination of the urinary system to rule out the possibility of hydronephrosis and urinary stones were enrolled as healthy controls in the NC group. We took the short diameter of the renal pelvis cavity under ultrasound as greater than 1 cm as the standard for hydronephrosis. The following inclusion criteria were applied: no history of diseases that may impair renal function, such as hypertension and diabetes; no history of acute or chronic renal impairment; no history of kidney surgery; and first identification of kidney stones and hydronephrosis. Patients with urolithiasis underwent Computed Tomography (CT) and the CT data were collected. The following data were collected from the study participants: age, weight, height, eGFR, and medical history. The body mass index (BMI) for each participant was also calculated from their height and weight. This cohort study was following STOBE guidelines and approved by the ethics committee of Shanghai Tenth People's Hospital (approved number was 21K105), and informed consent was obtained from all patients who participated in the study.From each participant, 2 mL of blood and 5 mL of urine were collected for further analysis. The blood samples were allowed to stand at room temperature for 2 h; they were then centrifuged, and the serum was extracted. The serum and urine samples were stored at −80°C for future analysis. Serum creatinine and urea nitrogen levels were measured at the Central Laboratory of the hospital. The serum and urinary NGAL concentrations were measured using the Human NGAL/Lipocalin-2 ELISA Kit .t-test were used to perform the intergroup comparisons. If the measurement data did not follow a normal distribution, the medians (interquartile ranges) and the rank-sum test were used to perform the intergroup comparisons. Count data were compared using the chi-square test. Statistical significance was set at p < 0.05.Statistical analyses were performed using the SPSS 16.0 statistical package, and charts were made using GraphPad Prism 7. If the measurement data were normally distributed, means and standard deviations and the P < 0.001) and urinary NGAL level . However, no significant between-group differences were observed in the sex , height , weight , BMI , serum creatinine levels , serum urea nitrogen concentrations , CKD-EPI (NC 92.30 ± 23.82 mL/(min*1.73 m2), USP 89.36 ± 23.23 mL/(min*1.73 m2), P-value 0.451). The characteristics of the study participants are summarized in Compared with the NC group , the USP group had significantly elevated serum NGAL level  are shown in P < 0.001). However, there were no significant differences in the serum/urinary NGAL levels among the USP subgroups , moderate hydrops group (USP2), and severe hydrops group (USP3). Results indicated significant differences in the serum and urinary NGAL levels between the healthy control group and each USP group  and 99.54% , respectively. For kidney stones, the areas under the ROC curve for the blood and urine NGAL levels were 85.05%  and 91.89% , respectively (A receiver operating characteristic (ROC) curve of the serum and urinary NGAL levels with hydronephrosis and kidney stones was created. Results indicated that for hydronephrosis, the areas under the ROC curve for the blood and urine NGAL levels were 92.03%  in the kidneys. It is a common disease that occurs more frequently in men than in women, and is particularly predominant in young men. Stones can occur in any part of the urinary system, but their formation often initiates in the kidneys. Kidney stones tend to be located in the renal pelvis or calyces, and can be discharged to the ureter or bladder , 27. NeaAt the same time, we also note that there are some limitations in this study. Since this is the first time we have conducted a cohort study on the clinical significance of NGAL in the diagnosis of hydronephrosis caused by urinary calculi, the sample size of this study is small, the detection of urine creatinine level is lacking, and the analysis of NGAL and prognosis in patients with urinary calculi is lacking. We will further improve it in the follow-up cross-sectional study.NGAL is a very sensitive indicator of hydronephrosis caused by urinary stone.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai Tenth People's Hospital. The patients/participants provided their written informed consent to participate in this study.TX, XH, and YX: conception and design. HZ, YG, XX, and XH: data acquisition. HZ and YG: analysis and interpretation of data. TX, HZ, YG, and XX: statistical analysis. XX: drafting of the manuscript. XY: revision of the manuscript. YX: supervision. All authors contributed to the article and approved the submitted version.This study was funded by National Natural Science Foundation of China (Nos. 81971371 and 82101671) and 2019 Scientific Research Funds of Shanghai Tenth People's Hospital (No. 04.03.19.043).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score . TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future. Half of the global burden related to ischemic heart disease occurs within the Asia-Pacific region . PredictThe TIMI risk score is widely used due to its simplicity in calculation and accuracy in STEMI patients. TIMI scoring, unlike the GRACE score, was derived from patients with ST-segment elevation myocardial infarction (STEMI) only . StudiesConventional cardiovascular disease (CVD) risk assessment models assume that risk factors have a linear relationship to clinical outcomes, leading to the oversimplification of a truly complex correlation. There is a need to develop models which consider these multiple risk factors and outcomes, including the use of machine learning (ML) algorithms , 6–8.Current evidence supporting the use of ML over statistically-based models in mortality predictions include Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF). ML has been shown to outperform the conventional risk scoring model in population-specific mortality studies, post-STEMI, in countries like China, Israel and Korea , 7, 9.To our knowledge, the development, and application of ML algorithms to predict short- and long-term mortality post-STEMI in a heterogeneous Asian population has yet to be reported. The study aims to identify factors and develop an ML model risk calculator that predicts short and long-term mortality in a heterogeneous South-East Asian population.We used retrospective data from the Malaysian National Cardiovascular Database (NCVD-ACS) registry collected between 2006 until 2016. The NCVD registry was approved by the Medical Review & Ethics Committee (MREC), Ministry of Health (MOH) Malaysia in 2007 . MREC waived informed patient consent for NCVD. The registry collects data on a standardised set of clinical, demographic, and procedural variables, along with outcomes, for consecutive patients treated at participating institutions , 11. TheAll patients from the ACS registry without exclusion were used including patients who received reperfusion , angiography demonstrating spontaneous reperfusion, or urgent coronary artery bypass grafting (CABG)) for STEMI. In this context, STEMI was defined as persistent ST-segment elevation ≥ 1 mm in two contiguous electrocardiographic leads, or the presence of a new left bundle branch block in the setting of positive cardiac markers. 50 variables from a complete set of data were used in this study based on clinical recommendation. Categories of variables used were sociodemographic characteristics, CVD diagnosis and severity, CVD risk factors, CVD comorbidities, non-CVD comorbidities, biomarkers and medication used. The mortality time frame was calculated from first hospital admission for in-hospital, 30 days and 1-year. Confirmation of deaths was done yearly via record linkages with the Malaysian National Registration Department. The data collected by the registry does not include data on short term complication such as heart failure. The follow-up data points are meant to collect these variables but unfortunately are excessive in terms of missing values and hence we omitted this from the study. We focused our algorithm to policy changing endpoints for example hard endpoints such as death to increase the impact of the study. This is similarly done in other publications , 7, 9.We developed the ML using a complete set of data to ensure the validity of the findings. A total of 27,592 STEMI cases from the registry were collected 12,368 were identified as complete cases . Out of the 12 368 datasets, a total of 6299, 3130 and 2939 complete cases were used for in-hospital, 30-days and 1-year respectively for the model development. This rendered almost 50% complete cases of patients with a full predictor set of 50 variables for each time frame  for the study . StratifPrediction models post-STEMI were developed using three selected ML algorithms. Next, feature selection (see below) was carried out on the ranked variables in an ascending order iteratively . 10-foldSecondary analyses were carried out after adding 15224 missing cases imputed using multivariable imputation using chained equations and predictive mean matching that yields a total of 27 592 cases [Hence the dataset is classified as missing completely at random (MCAR) which is the highest level of randomness and it implies that the pattern of missing value is random and does not depend on any variable which may or may not be included in the analysis. We had complete data for all our outcomes. The models were tested with a similar validation dataset for ML models trained with a complete cases dataset.Supervised classification ML algorithms RF , SVM 1717 and LRFeature selection is the process of ranking variables using classifier specific variable evaluator. RF, SVM and LR variable importance method were used to rank variables importance associated with outcome .Feature reduction involves the elimination of existing variables to a minimal set, which reduces training time, produces better results, and increases the accuracy of results. Next, sequential backward elimination (SBE)  was usedRFE combined with ML classifiers have been used in various clinical dataset successfully –28. We aCalculated TIMI scores were used from the NCVD registry for the validation data performance. TIMI score performance (AUC) was compared with the developed ML-based models using the validation set. We derived a graph to compare performance between ML and TIMI score based on cutoff points applicable in clinical practice and literature . A high Net reclassification improvement (NRI) was used to determine the changes in discrimination between the TIMI risk score for STEMI and ML algorithm. The NRI uses reclassification tables to examine whether there is an additive benefit gained from reclassifying patients using a different approach in mortality assessment. By calculating the NRI, we were able to quantify the degree to which the different mortality risk assessment approaches driving correct movement between categories. An NRI can be interpreted as the percentage by which the net classification has improved by using a new different approach. The NRI was used to evaluate the improvement in classification obtained by comparing the TIMI risk score for STEMI with ML for STEMI .t-test [The results are expressed as mean and SD for continuous variable and as frequencies for categorical variables. Correlation analysis was carried out to identify a significant relationship between variables. Univariate analysis was performed using a Chi-Square test to identify significant variables and a two-sided independent student t-test (p < 0.05). The ML performance was compared using a pair-wise corrected resampled t-test , 34. StaR package (Version 3.5.2) was used in ML algorithm development. Statistical analysis was conducted using Statistical Package for Social Sciences (SPSS) program version 16.0 .A total of 27,592 STEMI patients were identified. Incomplete data made up 55.2% of patients enrolled. Maximal predictive performances on the validation dataset were observed for ML models constructed using reduced and complete sets of variables compared to TIMI risk score using untouched 30% validation dataset . TIMI onSecondary analysis on best ML models (SVMvarImp–SBE–SVM) performance trained with imputed data reported for in-hospital , 30day , and 1-year . Complete case ML model dataset resulted in an almost similar AUC result for in-hospital , 30 days , and 1-year mortality . For in-hospital and 30 days, the imputed and the complete case model is significant . As for 1-year model, it is not statistically significant between imputed and complete case model .RFE and SBE feature selection methods were combined with ML algorithms to construct predictive models with optimal performance (refer to methods). Initial ranking using all 50 variables for best model (SVMvarimp-SBE-SVM) using SVM variable importance is shown in Common predictors observed for in-hospital, 30 days and 1-year mortality across all ML models in this study are . Diuretics were an additional common predictor for the best model (SVMvarImp-SBE-SVM). Age, heart rate and Killip class are identified as common predictors for the best ML model (SVMvarImp-SBE-SVM) in-hospital, 30 days and 1-year against TIMI .Figs In the high-risk group, ML better-predicted mortality in comparison to TIMI for in-hospital death (21.94% vs 16.15%) but similar for prediction for 30 days and 1-year deaths. (25.61% vs 23.15% and 35.71% vs 34.48%).ML predicted a better mortality rate when compared to TIMI in the lower risk group for in-hospital (1.97% vs 2.59%), 30 days (1.73% vs 3.46%) and 1-year (5.05% vs 9.35%).Regarding the NRI for the in-hospital model, the net reclassification of patients improved using the ML produced a net reclassification improvement of 0.20 with p<0.0001 over the original TIMI risk score, that is, a 20% improved classification. NRI for 30 days reported the net reclassification of patients improved using the ML produced a net reclassification improvement of 0.19 with p<0.0001 over the original TIMI risk score, that is, a 19% improved classification. In the 1-year model, the net reclassification of patients improved using the ML produced a net reclassification improvement of 0.14 with p<0.0001 over the original TIMI risk score, that is, a 14% improved classification .Our study is the first to show better short- and long-term mortality prediction using the ML method in a multi-ethnic Asian patient with STEMI. We demonstrated high performance on validation dataset for ML models with a combination of feature selection and classifier algorithms. Overall ML model performed better than TIMI for in-hospital, 30days and 1-year AUC of . SVMvarImp-SBE-SVM for in-hospital, 30 days and 1-year mortality prediction had better performance compared to RF, LR and TIMI scoring as well.The TIMI risk score was originally developed to estimate 30 days mortality risk. In the absence of a more convenient risk score system, it has since been exploited to predict in-hospital, 30 days and 1-year mortality post-STEMI in other Asian countries as well as Mexico , 36–38. TIMI risk score for STEMI consists of the following components: age; systolic blood pressure; heart rate; Killip classification; infarct location or left bundle branch block; a history of diabetes, hypertension, angina pectoris, weight, and time to reperfusion (thrombolysis or pPCI). Previous studies have modified ‘time to reperfusion’ to be ‘door-to-needle’ or ‘door-to-balloon’ time instead of ‘symptom onset to-reperfusion’ time because of inconsistencies in the reporting of symptom onset time . Our stuFeature selection algorithms are essential in mortality prediction. A combination of feature selection methods with classification algorithms resulted in higher performance versus using standalone classifiers . ApplicaWe used univariate analysis to support the relationship between variables selected from ML algorithms and outcomes . Age, heIncorporating variables like having invasive or non-invasive management into the SVMvarImp-SBE-SVM model to predict in-hospital, 30 days and 1-year mortality yield interesting results. Invasive treatment such as PCI received by STEMI patients showed a trend towards better outcomes for in-hospital and 30 days after discharge. Mortality risk at 1-year was reduced by 40% for patients who received PCI compared to those who did not , 45.TIMI and GRACE scores were calculated based on data during an era where early reperfusion therapy and routine use of drug-eluting stents were not common. Non-invasive treatment predictors such as pharmacological therapy , anti-diabetic agents  were selected for in-hospital, 30 days and 1-year mortality prediction in our study. These drugs are often prescribed in the acute setting to augment neurohumoral modulation associated with left ventricular negative remodelling. Being on these medications could signal a sicker ventricle hence the strong association with death.Systolic and diastolic blood pressure were ranked as predictors for in-hospital, 1-year and 30 days models. Cardiogenic shock at presentation increases the risk of death. STEMI patients with cardiogenic shock who survived in-hospital death are at an increased risk of long-term death, probably as a reflection of the severity during initial admission .Other CVD risk such as hypertension, diabetes, smoking and chronic renal disease, were associated with a poorer 1-year outcome. Poorly controlled CVD risk leads to an adverse systemic remodeling, leading to a plethora of cardiovascular conditions including heart failure, stroke, renal failure, and peripheral vascular disease .By having continuous data collection through an electronic health records system, we were able to allow for the adaptation of ML predictive algorithm tailored to patient’s risk grouping. ML methods discussed in this study are needed to rank and select significant risk factors associated with short- and long-term STEMI mortality. Feature selection allows better interpretation of the models by restricting the scope of predictors used, selecting only those clinically relevant.ML models in this study have demonstrated higher performance compared to TIMI scoring that was extrapolated from a Caucasian cohort. Asian patients present at a younger age with acute coronary syndromes. The average age in the GRACE registry was 61, whereas it is 58 in Malaysia and 51 in the Middle East . NumerouData imputation was performed to ensure the validity of the findings. We used multivariable imputation using chained equations and predictive mean matching method for data imputation instead of using machine learning-based method such as missForest in this study. The data imputation method used in this study was selected as recommended in a similar study conducted on the Swedish heart registry dataset that resulted in high model performance . MoreoveThe cross-validation approach used in this study increases the efficacy of the models during model construction as it reduces the risk of model over-fitting. Also, the classification performance is highly influenced by data pre-processing and tuning of algorithms . A pair-ML models in this study were validated with untouched validation data that was not used for model development, to confirm the reliability of the current study. We also demonstrated the ML model using complete sets of variables collected, without a variable selection process that resulted in a similar performance to models with feature selection. This shows that feature selection does not lead to the loss of important prognostic information.Despite a large proportion of missing values in the original dataset, we were still able to apply both TIMI and ML algorithm and compare outcomes. This is likely because we used a hard endpoint of death that is not affected by missing values. Another possibility is that the variables extracted  was sufficient to increase the model’s precision to predict death reliably.Future study will focus on validation of the ML algorithm in real-time involving several local hospitals for continuous assessment of its reliability. Application of ML models that are population-specific together with conventional risk scoring method allows better outcome in mortality prediction, communication and increases awareness of patients that enables behavioural modifications and better management of limited resources by clinicians.This study compared the performance of an ML-based model for in-hospital, 30 days and 1-year with a clinical prognostic model that was designed for 30 days’ mortality. Its robustness would be increased had we included variables and compared them to other scoring systems such as GRACE and the Heart Score. The lack of certain variables precluded this attempt. We recognised that missing variable may result in a bias finding. We attempted to reduce this effect by applying TIMI score and ML-based score to the same population. Selection bias that exists within registries is difficult to control. We hope that future real-world study would validate our findings.In conclusion, our study illustrates the capability of ML algorithms application for feature selection and prediction of in-hospital, 30 days and 1-year population-specific mortality in STEMI patients. ML models that are population-specific demonstrate better performance compared to TIMI score. A combination of feature selection techniques with classification algorithm allows for reliable selection of significant variables and improvement in model predictive performance, with subsequent benefits by allowing effective resource allocation in the management of STEMI patients.S1 FigAbbreviations = ptagenotification = Age, ptrace = Race, ptsex = Gender, smokingstatus = Smoking status, chpt = History of hypertension, cdm = History of diabetes, cpremcvd = Family history of premature cardiovascular disease, cmi = History of myocardial infarction, ccap = Documented CAD, cheartfail = History of heart failure, clung = Chronic lung disease, crenal = Chronic renal disease, ccerebrovascular = Cerebrovascular disease, heartrate = Heart rate, bpsys = Systolic blood pressure, bpdias = Diastolic blood pressure, killipclass = Killip Class, tc = Total cholesterol, hdlc = High-Density Lipoprotein, ldlc = Low-Density Lipoprotein, tg = Triglycerides, fbg = Fasting blood glucose, ecgabnormstylestelev1 = ST segment elevation ≥1mm in ≥2 contiguous limb leads, ecgabnormstylestelev2 = ST segment elevation ≥2mm in ≥2 contiguous frontal leads or chest leads, ecgabnormstylestdep = ST segment depression ≥0.5mm in ≥2 contiguous leads, ecgabnormtypetwave = T-wave inversion ≥1mm, ecgabnormtypebbb = Bundle branch block, ecgabnormlocationil = Inferior leads: II, III, aVF, ecgabnormlocational = Anterior leads: V1 to V4, ecgabnormlocationll = Lateral leads: I, aVL, V5 to V6, ecgabnormlocationtp = True posterior: V1, V2, ecgabnormlocationrv = Right ventricle: ST elevation in lead V4R, fbstatus = Fibrinolytic status, pci = Percutaneous Coronary Intervention, cabg = Coronary artery bypass graft, asa = Aspirin, gpri = GP receptor inhibitor, heparin = Unfractionated heparin, lmwh = Low-molecular-weight Heparin, bb = Beta blockers, acei = ACE inhibitor, arb = Angiotensin II receptor blocker, statin = Statin, lipidla = Other lipid lowering agent, diuretic = Diuretics, calcantagonist = Calcium antagonist, oralhypogly = Oral hypoglycaemic agent, insulin = Insulin, antiarr = Anti-arrhythmic agent.(TIF)Click here for additional data file.S2 FigAbbreviations = ptagenotification = Age, ptrace = Race, ptsex = Gender, smokingstatus = Smoking status, chpt = History of hypertension, cdm = History of diabetes, cpremcvd = Family history of premature cardiovascular disease, cmi = History of myocardial infarction, ccap = Documented CAD, cheartfail = History of heart failure, clung = Chronic lung disease, crenal = Chronic renal disease, ccerebrovascular = Cerebrovascular disease, heartrate = Heart rate, bpsys = Systolic blood pressure, bpdias = Diastolic blood pressure, killipclass = Killip Class, tc = Total cholesterol, hdlc = High-Density Lipoprotein, ldlc = Low-Density Lipoprotein, tg = Triglycerides, fbg = Fasting blood glucose, ecgabnormstylestelev1 = ST segment elevation ≥1mm in ≥2 contiguous limb leads, ecgabnormstylestelev2 = ST segment elevation ≥2mm in ≥2 contiguous frontal leads or chest leads, ecgabnormstylestdep = ST segment depression ≥0.5mm in ≥2 contiguous leads, ecgabnormtypetwave = T-wave inversion ≥1mm, ecgabnormtypebbb = Bundle branch block, ecgabnormlocationil = Inferior leads: II, III, aVF, ecgabnormlocational = Anterior leads: V1 to V4, ecgabnormlocationll = Lateral leads: I, aVL, V5 to V6, ecgabnormlocationtp = True posterior: V1, V2, ecgabnormlocationrv = Right ventricle: ST elevation in lead V4R, fbstatus = Fibrinolytic status, pci = Percutaneous Coronary Intervention, cabg = Coronary artery bypass graft, asa = Aspirin, gpri = GP receptor inhibitor, heparin = Unfractionated heparin, lmwh = Low-molecular-weight Heparin, bb = Beta blockers, acei = ACE inhibitor, arb = Angiotensin II receptor blocker, statin = Statin, lipidla = Other lipid lowering agent, diuretic = Diuretics, calcantagonist = Calcium antagonist, oralhypogly = Oral hypoglycaemic agent, insulin = Insulin, antiarr = Anti-arrhythmic agent.(TIF)Click here for additional data file.S3 FigAbbreviations = ptagenotification = Age, ptrace = Race, ptsex = Gender, smokingstatus = Smoking status, chpt = History of hypertension, cdm = History of diabetes, cpremcvd = Family history of premature cardiovascular disease, cmi = History of myocardial infarction, ccap = Documented CAD, cheartfail = History of heart failure, clung = Chronic lung disease, crenal = Chronic renal disease, ccerebrovascular = Cerebrovascular disease, heartrate = Heart rate, bpsys = Systolic blood pressure, bpdias = Diastolic blood pressure, killipclass = Killip Class, tc = Total cholesterol, hdlc = High-Density Lipoprotein, ldlc = Low-Density Lipoprotein, tg = Triglycerides, fbg = Fasting blood glucose, ecgabnormstylestelev1 = ST segment elevation ≥1mm in ≥2 contiguous limb leads, ecgabnormstylestelev2 = ST segment elevation ≥2mm in ≥2 contiguous frontal leads or chest leads, ecgabnormstylestdep = ST segment depression ≥0.5mm in ≥2 contiguous leads, ecgabnormtypetwave = T-wave inversion ≥1mm, ecgabnormtypebbb = Bundle branch block, ecgabnormlocationil = Inferior leads: II, III, aVF, ecgabnormlocational = Anterior leads: V1 to V4, ecgabnormlocationll = Lateral leads: I, aVL, V5 to V6, ecgabnormlocationtp = True posterior: V1, V2, ecgabnormlocationrv = Right ventricle: ST elevation in lead V4R, fbstatus = Fibrinolytic status, pci = Percutaneous Coronary Intervention, cabg = Coronary artery bypass graft, asa = Aspirin, gpri = GP receptor inhibitor, heparin = Unfractionated heparin, lmwh = Low-molecular-weight Heparin, bb = Beta blockers, acei = ACE inhibitor, arb = Angiotensin II receptor blocker, statin = Statin, lipidla = Other lipid lowering agent, diuretic = Diuretics, calcantagonist = Calcium antagonist, oralhypogly = Oral hypoglycaemic agent, insulin = Insulin, antiarr = Anti-arrhythmic agent.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.