Outcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome  after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.We retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.In 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.Our results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance. Mechanical thrombectomy (MT) is the most effective treatment for patients with acute ischemic stroke (AIS) due to a large vessel occlusion of the anterior circulation . While tOne way to address this problem is to utilize artificial intelligence, in particular machine learning (ML) approaches. These models are potentially superior to conventional linear or logistic regression models as they excel at finding complex and non-linear relationships across a multitude of prognostic variables. Specially, artificial neural networks and methods of tree-boosting are promising tools in this regard . Recent Applying this methodology to outcome prediction after MT, multiple prognostic variables can be used representing the clinical course of stroke patients: baseline clinical variables , MRI varThe outcome and potential benefit of MT are usually assessed after 3 months with the modified Rankin Scale (mRS) in a dichotomized analysis: 0–2 is defined as favorable outcome and the remaining 3–6 as unfavorable outcome. Patients with a predicted favorable outcome will undoubtedly undergo MT. However, the remaining group of patients with unfavorable outcome is highly heterogeneous, ranging from outcomes of mRS of 3 (moderate disability) to 6 (death).Therefore, it may also be reasonable to find prognostic factors for a poor outcome   and poor outcome (mRS 5 and 6 vs. 1–4) with multiple prognostic variables grouped in three sets: baseline clinical, MRI–derived, and MT-associated variables.The data that support the findings of this study are available from the corresponding author upon reasonable request.The study protocol for this retrospective analysis of our prospectively established stroke database was approved by the ethics committee of Heidelberg University and patient-informed consent was waived.p = 0.83).We identified patients with AIS due to an occlusion of the middle cerebral artery in the M1 or M2 segment or the distal terminus of the internal carotid artery who were treated with MT between 03/2009 and 09/2018; 95/210 patients were treated between 2010 and 2013, and the remaining 116/210 between 2014 and 2018. Between these two groups, there was no significant outcome difference after 3 months . The attending neurologist and interventional neuroradiologist decided on treatment on a case-by-case basis. Only patients with a completed MRI protocol and outcome assessment at 3 months were included.Baseline clinical and imaging parameters are given in In a routine clinical setting, MR images were acquired on 3 Tesla MRI systems . Imaging protocol included diffusion-weighted, FLAIR, susceptibility weighted and T2-weighted sequences, non-contrast time-of-flight, and contrast-enhanced angiography as well as dynamic susceptibility contrast perfusion-weighted imaging. The imaging protocol has been published previously and is included in the ® . ADC maps were automatically calculated from DWI images with different b-values. For perfusion imaging, automatic motion correction was applied. The arterial input function (AIF) was detected automatically. In two cases, the automatically detected AIF was manually corrected. Tmax maps were calculated using a block-circulant singular-value decomposition (cSVD) deconvolution algorithm. Diffusion lesion volumes [ADC value threshold of ≤620 × 10−6 mm2/s  and perfusion MRI images were post-processed with Olea Sphere−6 mm2/s ] and Tma−6 mm2/s ] were sehttps://github.com/prediction2020/explainable-predictive-models). Details on the technical implementation have been described in open-access publications previously , with baseline clinical and MRI variables combined (A+B), and finally with all baseline clinical, MRI and thrombectomy-associated variables combined (A+B+C).For each of the three scenarios, we trained models for both prognostic dichotomization paradigms I and II with favorable (mRS ≤ 2) and poor outcome (mRS 5 or death), respectively. This yielded six different scenarios in total see .Multicollinearity was estimated using the variance inflation factor (VIF) .We utilized all seven available ML algorithms from the framework to provide a comprehensive coverage of various ML methods. The more traditional techniques were represented by three algorithms: A generalized linear model (GLM), which for dichotomous outcomes is equivalent to a plain logistic regression, and two regularized variants, a Lasso algorithm with L1 regularization and an ElasticNet with L1 and L2 regularization.Further, ML algorithms included tree boosting (Catboost implementation), a Support Vector Machine Classifier (SVMC), Naive Bayes (NB), and a Multilayer Perceptron (MLP).The data comprising the given clinical parameters and outcomes were randomly split into training and test sets in a corresponding 4:1 ratio. Due to slight imbalance with respect to the outcome measures (127/210 patients with favorable outcome in paradigm I and 49/210 patients with poor outcome in paradigm II), random sub-sampling of the majority class was employed for the training sets. Test sets were stratified to follow the original imbalanced ratio to represent real distribution of our patient outcomes in model testing. In total, there were only 11 missing values in the data set. Missing values were imputed by mean/mode imputation. Non-categorical features—both in training and test sets—were standardized to zero-mean and unit variance based on training set statistics. Models were trained and best parameters were selected using 10-fold cross validation over an extensive grid of hyperparameters for each model. Parameter ranges were initially taken from the public repository referenced under the heading “ML framework”, and then refined taking run times of experiments into consideration. The used ranges are included in For performance measures, we report results as the median over the test sets of the 200 shuffles. Model performance was primarily assessed by area-under-the-curve (AUC) measure resulting from receiver-operating-characteristic (ROC) analysis. Accuracy, balanced class accuracy, precision, recall, f1 score, negative predictive value, and specificity measures for each model are included in the We used SHapley Additive exPlanations (SHAP) scores to rate the importance of included clinical features for all seven models. More detailed explanation of the technique can be found in . The absIn total, 236 patients met the inclusion criteria, and 26 patients were excluded due to motion artifacts on MRI images or because no accurate AIF could be obtained, resulting in the final number of 210 patients. Median mismatch ratio was 4.6 (2.3–8.4). In 154 patients (73%), successful reperfusion (TICI 3 or 2b) could be achieved. Median time to TICI was 492 min. In prediction paradigm I, 83/210 patients (39%) had a favorable outcome (mRS 0–2). In prediction paradigm II, 49/210 patients (23%) had a poor outcome (mRS 5–6).In this study, 168/210 patients (80%) had a target MRI mismatch [according to the EXTEND-IA study criteria , prediction was slightly better for poor outcome (II A) than for favorable outcome (I A). The smallest difference in AUC between I A and II A was 0.02 for GLM and the largest 0.05 for Lasso logistic regression. Only SVMC showed comparable results.Adding MRI-derived parameters (scenario I A+B and II A+B) did not change the prediction performance for both paradigms. This was consistent for all algorithms in both scenarios I A+B and II A+B.Finally, adding thrombectomy-associated parameters—extent and speed of recanalization—(I A+B+C and II A+B+C) improved the prediction performance noticeably across many algorithms for the favorable outcome paradigm (I A+B+C). Prediction for the poor outcome paradigm with all variables (II A+B+C) remained approximately stable; only the Catboost and SVMC algorithm showed a slight improvement .To summarize, prediction for the poor outcome paradigm II remained comparable on a relatively high level across all three prediction scenarios . Contrariwise, prediction for the favorable outcome paradigm I improved noticeably when thrombectomy-associated parameters were added (I A+B+C). The final performance for the last scenario with all predictive variables included (I A+B+C and II A+B+C) was comparable for both the favorable and poor paradigms.Feature importance values for each scenario and each algorithm are displayed in For the favorable outcome paradigm I , the mosFor the poor outcome prediction paradigm , we founTo summarize, for the prediction of either good or poor outcome, age, premorbid mRS, and baseline NIHSS were important, with mTICI score as an additional relevant feature from the third scenario. ADC and Tmax volume were more important for the favorable than for the poor outcome paradigm. Information about i.v. thrombolysis was only important for the favorable outcome paradigm.In this study, we examined ML-based outcome prediction models for patients with stroke who underwent MT. We compared prediction of poor outcome mRS 5 or 6 vs. 0–4) and favorable outcome (mRS 0–2 vs. 3–6) measured at 3 months post stroke. These prediction paradigms have direct implications for clinical decision-making by predicting an outcome of no or only slight disability on the one hand and severe disability or death on the other. In particular, the definition of favorable outcome is generally accepted and was applied in large prospective studies. We chose different combinations of prognostic variables that were deliberately limited to those most commonly used in stroke practice and literature , 6, 7 an vs. 0–4 Our main finding suggests that prediction of poor outcome may possibly be based on clinical baseline variables only and set a rather high benchmark in the first prediction scenario. The predictive performance did not improve by adding target MR-mismatch and recanalization-related parameters. In contrast, prediction of favorable outcome did improve significantly by adding speed and extent of recanalization compared to using baseline clinical variables only.In contrast to previous studies, the main strength of the presented work is the direct comparison of two different outcome prediction paradigms. The choice of how to dichotomize mRS for outcome prediction has clinical relevance: The standard dichotomization with 0–2 vs. 3–6 as used in the large randomized clinical trials puts an emphasis on patients with favorable outcomes. Dichotomizing mRS 0–4 vs. 5–6 focuses on patients with a very high degree of dependency or mortality after MT.Our exploratory analysis revealed that patients with expected poor outcome could already be captured by clinical baseline variables before thrombectomy. The prediction scenario with baseline clinical variables only was already close to the final performance when mismatch and recanalization information was added. In those patients, withholding MT based on the clinical baseline variables could be the consequence; however, these implications should be verified in larger, prospective studies  and withThis was in contrast to the prediction of favorable outcome, where the prediction with baseline clinical variables only was lower but could be considerably improved by adding information about the speed and extent of reperfusion. However, because of the retrospective and thus exploratory nature of our study, our results should be interpreted with caution with regards to clinical treatment decisions. Nonetheless, they warrant further exploration in prospective studies to confirm our findings. Within such prospective data, the proposed paradigms could then be used to estimate individualized chances for either poor or favorable outcome before and after therapy. A similar approach using two models for an individualized prognosis of the same outcome variable is proposed by Debs et al. .This performance pattern was accompanied by complimentary information from the feature importance analysis: For poor outcome, age, stroke severity (NIHSS) and degree of disability before stroke (premorbid mRS before stroke), and the time from stroke onset to imaging were the most important baseline variables. For favorable outcome, the most important predictive variables were also age, NIHSS at onset, and the mRS before stroke, but additionally, the speed and extent of recanalization  were paramount. NIHSS was a more important predictor for final outcome than the ischemic lesion volume before therapy. This could be due to lesion location in eloquent brain regions where a smaller infarct causes comparably more severe clinical symptoms. Also, the final ischemic lesion volume after therapy might improve prediction but was not available to be included in our models.Across all models, the maximum predictive value was an AUC of 0.73 for both favorable and poor outcome with regards to model performance and feature rankings. Our findings are comparable to previous studies applying ML algorithms: Hammam et al.  found a Interestingly, prediction for either poor or favorable outcome did not improve by including MR-mismatch variables. These results need to be interpreted together with the characteristics of the cohort: Most patients included had a target mismatch as defined in the inclusion criteria for the EXTEND-IA study  with an Despite these findings, the overall performance of the ML models tested in our study could be improved. Considering the potential power of ML algorithms to extract patterns, our findings suggest that important variables for outcome prediction might not be included in today's clinical decision-making. It is conceivable that there are so far unknown or undetectable variables. This warrants further studies including more and new prediction variables and biomarkers as well as direct integration of multimodal imaging and clinical information.A deep learning model including raw imaging data and not derived variables might extract further, previously unknown predictive information. For example, these models might be able to account for inherent errors in the definition of infarct core  or indivFinally, our results show that the predictive value can differ significantly between two different dichotomization paradigms or different “cutoffs” (mRS 0–2 vs. 3–6 and mRS 5/6 vs. 0–4). Unnecessary dichotomization of the mRS can be suboptimal . ResearcOur study has some limitations. It is based on a relatively small and retrospective patient cohort. Thus, our results must be understood as an exploratory analysis for future research.Our data reach back to 2009. This might have influenced outcome due to improvements in thrombectomy technique and accelerated workflows. However, there was no significant outcome difference between patients treated 2009–2013 vs. 2014–2018.Our results suggest that a prediction of poor outcome (mRS of 5 or 6) after MT can be based on clinical baseline variables only. Speed and extent of thrombectomy did not seem to influence poor outcome but were important for favorable outcome (mRS 0-2). The predictive value of a target MR mismatch with smaller infarct core and larger penumbra was not relevant and could be already captured by clinical baseline variables. However, our sample does not allow a conclusion about the predictive value in patients without target MR mismatch.The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request.The studies involving human participants were reviewed and approved by Ethics Committee of Heidelberg University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.MMu, AP, CW, MMö, PR, SN, and MB acquired the data and organized the database. VM, AH, EZ, and DF performed and developed the machine learning and statistical analysis. MMu wrote the first draft of the manuscript. VM, AH, and DF wrote sections of the manuscript. All authors contributed to the conception, design of the study, manuscript revision, read, and approved the submitted version.VM reported receiving personal fees from ai4medicine outside the submitted work. AH reported receiving personal fees from ai4medicine outside the submitted work. DF reported receiving grants from the European Commission Horizon2020 PRECISE4Q No. 777107, reported receiving personal fees from and holding an equity interest in ai4medicine outside the submitted work. There is no connection, commercial exploitation, transfer, or association between the projects of ai4medicine and the results presented in this work. SN received unrelated fees for consultancy from Brainomix and Boehringer Ingelheim, payment for lectures including service on speakers' bureaus from Pfizer, Medtronic, and Bayer AG. MB received unrelated grants from Siemens, grants and personal fees from Novartis, grants from Stryker, grants from DFG, personal fees from Merck, personal fees from Bayer, personal fees from Teva, grants and personal fees from Guerbet, personal fees from Boehringer, personal fees from Vascular Dynamics, personal fees from Grifols, and grants from the European Union, all outside the submitted work. MMö received unrelated Board Membership from Codman; consultancy from Medtronic, MicroVention, and Stryker; payment for lectures including service on speakers bureaus' from Medtronic, MicroVention, and Stryker. PR received unrelated grants for consultancy from Boehringer and lecture fees from Bayer, Boehringer Ingelheim, BMS, Daichii Sankyo, and Pfizer. The remaining 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.


Patients of both CF and HighBP groups had high systolic blood pressure during the first 12 h of life and ventricular hypertrophy at early echocardiography. Cardiac failure occurred at a median age of 14 h (IQR = 6–24) and was followed by a drop in systolic and diastolic blood pressure. Acute kidney injury was more frequent  and severe (p <0.001) in the CF group than in the HighBP group. The mortality rate in the CF group was 40%. Factors associated with CF were twin anemia-polycythemia sequence (p = 0.012), very preterm birth (p = 0.040), and polycythemia (p = 0.002). Conclusion: One-third of recipient twins born without prenatal FSLC developed life-threatening cardiac failure during the first 24 h of life.Background: This paper’s intent is to describe the neonatal hemodynamic characteristics of recipient twins of monochorionic pregnancies complicated with twin-to-twin transfusion syndrome (TTTS), born without prenatal fetoscopic selective laser coagulation (FSLC). Methods: Retrospective analysis of hemodynamic characteristics was performed during the first five days of life of recipient twins from untreated TTTS. Results: Forty-two recipient twins were included and divided into three groups: no hemodynamic impairment (NoHI) ( Twin-to-twin transfusion syndrome (TTTS) is an unbalanced transfer of volemia through placental intertwin anastomoses and occurs in 10 to 20% of monochorionic diamniotic pregnancies, with a fetal mortality approaching 90% and a morbidity in survivors of approximately 50% if left untreated . The effDue to the hemodynamic imbalance and associated prematurity, neonates with TTTS not treated with FSLC are at high risk for neonatal mortality, neonatal morbidity including necrotizing enterocolitis, severe brain injury, polycythemia, and long term neurodevelopmental delay ,11,12,13A monocentric cohort study was conducted over a 9-year period in a single referral tertiary medical center for high-risk pregnancies and TTTS. All consecutive cases of newborns with untreated TTTS admitted to the neonatal intensive care unit (NICU) between 1 January 2011 and 31 December 2019 were considered for the study. Inclusion criteria were: inborn recipient twin from a multiple pregnancy with TTTS (isolated or associated with Twin Anemia Polycythemia Sequence (TAPS)  or intraClinical and biological data were collected retrospectively from medical records using a case report file with predefined parameters that combined prenatal, perinatal, and neonatal relevant information. Postnatal data were collected for each case until the fifth day of life or death.Prenatal data included gestational age estimated by first trimester ultrasound, Quintero staging of TTTS , intrautPostnatal cardiac ultrasound assessment included diastolic interventricular septum diameter (IVSd) and left ventricular systolic function in the parasternal long axis view  . VentricUrine output was measured by weighing diapers every 3 h and expressed as median and quartiles by a period of 6 h. Acute kidney injury (AKI) was defined according to the neonatal modified KDIGO definition  . InternaMedications included antihypertensive treatment, fluid expansion, and inotropic support. Inotropic support was defined by any inotrope drug administered during the first 5 days of life (dobutamine or milrinone regardless of dose and duration of treatment).The protocol for the management of recipient twins was as follows. An initial echocardiographic assessment was performed as soon as possible after admission (preferably within the first two hours of life) and repeated regularly depending on the hemodynamic status of the infants. Heart rate, blood pressure, and urine output were recorded every 3 h. Hemodilution by partial exchange transfusion was indicated if the hematocrit at birth was above 65%. Nicardipine was used for high systolic blood pressure in combination with ventricular hypertrophy on echocardiography. Dobutamine was used as the first-line treatment for left ventricular dysfunction at echocardiography. Milrinone was used in place of dobutamine when the left ventricular dysfunction persisted despite treatment with nicardipine and dobutamine. Cautious fluid expansion with saline solution (10 mL/kg) was performed when echocardiography showed evidence of preload dependence . Blood p < 0.05 was considered significant.Quantitative values were expressed as median and interquartile range and compared using the Mann–Whitney test. Qualitative variables were expressed as percentages and compared using Fischer’s exact test. Ordinal qualitative variables were compared using the Kruskall–Wallis test. A value of During the 9-year study period, 393 newborns with TTTS were born at our institution. Of the 43 recipient twins who had not benefited from FSLC and were considered for the study, 42 were included . The stuThe NoHI group had no hemodynamic disorder. Their clinical characteristics differed from those of the two other groups by a higher rate of prenatal death of the donor twin, a higher gestational age at birth, and a higher birth weight .p = 0.023) (p = 0.019).Comparison of the other two groups  showed that they had similar clinical characteristics at birth  . During = 0.023) . The medBetween 18 and 48 h of life, systolic and diastolic blood pressure values were significantly lower in the CF group than in the HighBP group. During this period, systolic and diastolic blood pressure in the CF group were approximately at the mean and at the 30th percentile of normal values for gestational age, respectively. p < 0.001) and also more severe . Urine output was similar in both groups at 12 h of life but decreased to less than 1 mL/kg/h between 18 and 36 h of life in the CF group, whereas it remained above 2 to 3 mL/kg/h in the HighBP group , emergency cesarean section for fetal rescue (p = 0.020), very preterm birth (p = 0.040), low APGAR score at 1 and 5 min (p = 0.020), polycythemia (p = 0.002), and hematocrit greater than 65% (p = 0.047). All infants with hematocrit upper than 65% received hemodilution.Search for associated factors of CF was performed by comparing the characteristics of the CF group with those of the NoHI and the HighBP groups combined . The facp = 0.042, p = 0.03), received a higher dose of dobutamine (p = 0.006), and were treated more frequently with both milrinone and dobutamine (p = 0.01)  of the CF patients died during the first 5 days of life, all because of severe cardiac failure. The median time of death was 47 h (IQR = 26–65). There was no significant difference in clinical characteristics, blood pressure profiles, urine output, or biological indices between the CF infants who died and those who survived . In cont = 0.01) .By describing three groups of recipient twins born after a pregnancy with TTTS based on their hemodynamic characteristics, we clarify the data from the literature that seem to be contradictory. Indeed, some authors did not find a significant higher risk of high blood pressure in recipients, nor did they observed higher levels of endothelin in the cord blood . For othIn our study, we described, in a sufficiently large cohort of patients, that there are three different postnatal hemodynamic outcomes, each of them concerning roughly one-third of the recipients not treated by FSLC. One of the groups, born less premature, exhibited neither hypertension nor hemodynamic failure, likely reflecting a less active TTTS. In this group, half of them were born from a pregnancy characterized by the prenatal death of the donor twin , suggesting that the loss of the co-twin had cured the TTTS.The other two groups had significant hemodynamic disorders characterized by high systolic blood pressure and ventricular hypertrophy, consistent with fetal descriptions. During pregnancy with TTTS not treated by FSLC, the recipient fetus suffers from an increased preload with an absolute increase in circulating volume and cardiac output . IncreasOne-third of the infants developed postnatal cardiac failure. The first hours of life of the infants in this group are characterized by systolic hypertension and ventricular hypertrophy, as was also observed in the HighBP group. Interestingly, only subtle early clinical differences existed between the HighBP and CF groups. They had similar high systolic blood pressure, antihypertensive treatment, and ventricular hypertrophy, but the latter developed earlier in the CF group. The CF group subsequently developed a ventricular dysfunction leading to a drop in both systolic and diastolic blood pressure and to a consequent acute renal injury. This relative hypotension raises the question of the blood pressure target in this population. Blood pressure may need to be maintained at the 95th percentile, and nicardipine dose reduced if progression to hypotension occurs. Short-term renal dysfunction has been described in both laser and non-laser patients [Identification of risk factors for postnatal cardiac failure in recipient twins has never been reported. We found that TAPS and polycythemia were associated with hemodynamic failure. Whether hyper-viscosity or the severity of exchange between twins, resulting in higher circulating volume, is responsible for the reduction in myocardial compliance remains unclear. Surprisingly, but in agreement with the literature, we found no association between the severity of TTTS, as determined by Quintero grading and the risk of cardiac failure. This may be because Quintero grading only partially quantifies cardiac involvement and functional cardiovascular changes .We acknowledge that our study has several limitations. The retrospective design has its own limitations. The ultrasound measurements were performed by the physicians on duty with a lack of standardization and which were not uniformly recorded in the medical records; however, distributive, hypovolemic and obstructive shocks from other causes were systematically excluded. We did not measure biochemical markers such as natriuretic peptide, endothelin, and troponin to better investigate cardiac involvement and damage.In conclusion, three groups of recipient twins born after a pregnancy with TTTS not treated by FSLC are described on the basis of their hemodynamic characteristics. Approximately one-third of them developed life-threatening cardiac failure within the first 24 h of life. Associated TAPS and polycythemia are risk factors for cardiac failure. Forty percent of patients with cardiac failure died within the first five days of life, despite early antihypertensive treatment and high doses of inotropes. Close clinical and echocardiographic hemodynamic monitoring should be initiated immediately after birth in these high-risk infants. Additional postnatal studies with biological markers are needed to better explore and predict the postnatal hemodynamic outcomes.


Prediction of disease progression at all stages of chronic kidney disease (CKD) may help improve patient outcomes. As such, we aimed to develop and externally validate a random forest model to predict progression of CKD using demographics and laboratory data.2 and a urine albumin-to-creatinine ratio (ACR) available were included from Manitoba and 107,097 from Alberta. We considered >80 laboratory features, including analytes from complete blood cell counts, chemistry panels, liver enzymes, urine analysis, and quantification of urine albumin and protein. The primary outcome in our study was a 40% decline in eGFR or kidney failure. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and calibration using plots of observed and predicted risks.The model was developed in a population-based cohort from Manitoba, Canada, between April 1, 2006, and December 31, 2016, with external validation in Alberta, Canada. A total of 77,196 individuals with an estimated glomerular filtration rate (eGFR) > 10 ml/min per 1.73 mThe final model achieved an AUC of 0.88 (95% CI 0.87–0.89) at 2 years and 0.84 (0.83–0.85) at 5 years in internal testing. Discrimination and calibration were preserved in the external validation data set with AUC scores of 0.87 (0.86–0.88) at 2 years and 0.84 (0.84–0.86) at 5 years. The top 30% of individuals predicted as high risk and intermediate risk represent 87% of CKD progression events in 2 years and 77% of progression events in 5 years.A machine learning model that leverages routinely collected laboratory data can predict eGFR decline or kidney failure with accuracy.  CKD currently affects >850 million adults worldwide and is associated with increased morbidity and mortality and high health care costs.The Kidney Failure Risk Equation is an internationally validated risk predictieon equation that accurately predicts the risk of progression to kidney failure for an individual patient with CKD. However, this equation has important limitations in that it applies only to later stages of CKD (G3–G5) and considers only the outcome of kidney failure requiring dialysis. In earlier stages of CKD, kidney failure is a rare event, even if progression to a more advanced stage is not. In these early stages, a decline in GFR of 40% is both clinically meaningful to patients and physicians and allows sponsors to design feasible randomized controlled trials at all stages of CKD.Accurate models to predict a 40% decline in eGFR or the composite outcome of kidney failure or 40% decline in eGFR that can be applied to patients at all stages of CKD (G1–G5) are needed. When these models are based on laboratory data, they can be used through electronic health records or laboratory information systems, and are not subject to variability in coding, often found with CKD and its complications. We present here the derivation and external validation of new laboratory-based machine learning prediction models that accurately predict 40% decline in eGFR or kidney failure in patients with CKD G1 to G5.The development cohort was derived from administrative data in Manitoba, Canada (population 1.4 million), using data from the Manitoba Centre for Health Policy. The Manitoba Centre for Health Policy is a research unit within the Department of Community Health Sciences at the University of Manitoba that maintains a population-based repository of data on health services and other social determinants of health covering all individuals in the province. We identified all adult (age 18+ years) individuals in the province with an available outpatient eGFR test between April 1, 2006, and December 31, 2016, with valid Manitoba Health registration for at least 1-year preindex. eGFR was calculated from available serum creatinine tests using the CKD-Epidemiology Collaboration equation.The validation cohort was derived from the Alberta Health database. This database contains information on demographic data, laboratory data, hospitalizations, and physician claims for all patients in the province of Alberta, Canada (population 4.4 million). Regular laboratory coverage for creatinine measurements and ACR/PCR values is complete from 2005; however, additional laboratory values are fully covered only from 2009 onward. As such, we identified a cohort of individuals with at least 1 calculable eGFR, valid health registration, and an ACR (or imputed PCRs) value starting from April 1, 2009, to December 31, 2016. We randomly sampled one-third of the external cohort to perform the final analysis to reduce computation time. Patients with a history of kidney failure  were excluded.This study was reviewed and approved by the institutional ethics review board at the Universities of Manitoba  Alberta (Pro00053469), and Calgary (REB16-1575). Informed consent was not required as all data were provided deidentified using a scrambled personal health information number.All models included age, sex, eGFR, and urine ACR as described previously. Baseline eGFR was calculated as the average of all available outpatient eGFR results beginning with the first recorded eGFR during the study period and moving forward to the last available test in a 6-month window and calculating the mean of tests during this period. The index date of the patient was considered the date of the final eGFR in this 6-month period.et al.In addition to the previously described variables, we also evaluated the utility of additional laboratory results from chemistry panels, liver enzymes, and complete blood cell count panels for inclusion in the random forest model based on associations found in previous studies of models predicting CKD progression.All laboratory data included were extracted from the Shared Health Diagnostic Services of Manitoba Laboratory Information System, and any values recorded during a hospitalization event as determined by a linkage to the Discharge Abstract Database were not included (inpatient tests). For the validation cohort, Alberta Health laboratory data were extracted from the Alberta Kidney Disease Network. Of the 18 laboratory tests used in the Manitoba model, 16 were also regularly collected by the Alberta Kidney Disease Network. The unavailable tests (aspartate aminotransferase and gamma glutamyl transferase) were treated as missing data.2. Dialysis was defined as any 2 claims in the Manitoba Medical Services database for chronic dialysis, and transplant was defined as any 1 claim in the Manitoba Medical Services database for kidney transplant or a hospitalization in the Discharge Abstract Database with a corresponding procedure code for kidney transplantation . An overview of tariff codes identifying dialysis and transplant is provided in The primary outcome in our study was a 40% decline in eGFR or kidney failure. The 40% decline in eGFR was determined as the first eGFR test in the laboratory data that was 40% or greater in decline from the baseline eGFR, requiring a second confirmatory test result between 90 days and 2 years after the first test unless the patient dies or experiences kidney failure within 90 days after the first test result revealing a 40% or greater decline. Therefore, a patient experiencing a single eGFR representing a 40% decline and dying within 90 days is treated as an event, or if they experience kidney failure in that period.Using laboratory creatinine measurements as described for the Manitoba cohort described previously, 40% decline in eGFR was identified. Kidney failure was defined similarly, but with minor adaptations necessitated by a structurally different administrative data set . Chronictune.rfsrc function using comparisons of the maximal size of the terminal node and the number of variables to possibly split at each node to the out-of-bag error rate from the Random Forest for Survival, Regression, and Classification package.Baseline characteristics for the development  and external validation cohorts were summarized with descriptive statistics. We developed a random forest model using the R package Fast Unified Random Forest for Survival, Regression, and Classification using a survival forest with right-censored data.In addition, we assessed sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for the top 10%, 15%, and 20% of patients at highest estimated risk (high risk), including for the bottom 50%, 45%, and 30% at lowest risk (low risk). These metrics were assessed at 2 and 5 years. A visualization of the risk of progression versus predicted probability was plotted for 2 and 5 years. Using the final grown 22-variable forest, variable importance of included parameters was evaluated, with results for the 5 most influential variables presented.To evaluate robustness, we evaluated the model in subpopulations of the testing and validation cohorts for the 5-year prediction of the primary outcome defined by CKD stage and the presence or absence of diabetes.For sensitivity analyses, we considered 2 comparator models. (i) We evaluated a Cox proportional hazards model using a guideline-based definition of risk using the 3-level definition of albuminuria and 5 stages of eGFR as categorical predictors as a comparator (heatmap model).Analysis was performed using R Version 4.1.0. Statistical significance was a priori identified using an α = 0.05.For the development cohort (training and testing), we had a total sample size of 77,196, allocating 54,037 to the training data set (70%) and 23,159 to the testing data set. A total of 321,396 individuals were identified in the validation cohort, with a random subset of 107,097 selected for evaluation. Detailed overview of the cohort selection process for both the development and validation cohorts is provided in 2 and median urine ACR of 1.1 mg/mmol. Of the patients, 48% were male, 45% had diabetes, 70% had hypertension, 5% had a history of congestive heart failure, 4% a prior stroke, and 3% a prior myocardial infarction (similar between the testing and training cohorts). The validation cohort was slightly younger, with a mean age of 55.5 years, mean eGFR of 86.0 ml/min per 1.73 m2, and median ACR of 0.8 mg/mmol. The validation cohort had a higher proportion of male patients (53%), 41% of patients had diabetes, 51% hypertension, 5% a history of congestive heart failure, 5% a prior stroke, and 5% a prior myocardial infarction. An overview of baseline descriptive statistics is provided in The mean age of the development cohort was 59.3 years, with a mean eGFR of 82.2 ml/min per 1.73 mIn the random forest model with 22 variables, when evaluated in the testing cohort, we found an AUC of 0.90 (0.89–0.92) for 1-year prediction of the primary outcome and 0.84 (0.83–0.85) for 5-year prediction. The Brier score was 0.02 (0.01–0.02) for 1-year prediction of the primary outcome and 0.07 (0.06–0.09) for 5-year prediction. AUCs and Brier scores for years 1 to 5 are presented in We observed excellent calibration at both 2 and 5 years a in bothWe evaluated statistics on sensitivity, specificity, and PPV in high-risk patients . For prediction of the primary outcome at 2 years, we found that patients in the top decile (14% 2-year risk threshold) had a sensitivity of 58%, a specificity of 92%, and a PPV of 25%. Similarly, for the top 15% of patients (10% 2-year risk threshold), we found a sensitivity of 69%, specificity of 87%, and PPV of 20%. For the top 20% of patients (7% 2-year risk threshold) sensitivity was 76%, specificity was 83%, and PPV was 16%. Using a 30% threshold to identify high- and intermediate-risk patients, we would have identified 87% of individuals with an event in 2 years and 77% within 5 years.In the low-risk patients, we found that the bottom 50% of patients (1.95% 2-year risk threshold) had a sensitivity of 94%, specificity of 52%, and NPV of >99%. For the lowest 45% of risk scores (1.61% 2-year risk threshold), sensitivity was 95%, specificity was 47%, and NPV was >99%. Last, for the lowest 30% of risk scores (0.85% 2-year risk threshold), we found a sensitivity of 97%, a specificity of 31%, and an NPV >99%. We also considered these statistics for the prediction of the outcome at 5 years and found similar accuracy .Table 3OUrine ACR (including converted PCRs) was the most influential variable in the random forest model, followed by eGFR, urea, hemoglobin, age, serum albumin, hematocrit, and glucose. An overview of model inputs ranked by importance is detailed in 2 had similar outcomes to the internal testing cohort  for 1-year prediction declining to 0.84 (0.84–0.85) for 5-year prediction, with Brier scores of 0.01 (0.01–0.01) at 1 year and 0.04 (0.04–0.04) at 5 years . The extg cohort . Similarg cohort .P < 0.001, In the comparator analysis, the heatmap model performed worse than the 22-variable random forest model in the development cohort  with greater accuracy than current standard of care or commercially available models that test for novel biomarkers and use machine learning methods.Previous investigators have developed prediction models for the progression of CKD to kidney failure and for intermediate outcomes that include incident CKD, rapid decline in kidney function, or 40% decline in eGFR.Our machine models using a random forest seem to perform better than a commercially available machine learning model .2), but at high risk of progression in the next 5 years. Given the effect of interventions such as SGLT2i on the slope of eGFR in this population, it is possible that these patients may be able to forestall or prevent the lifetime occurrence of kidney failure entirely versus delaying the time to dialysis if the interventions are implemented later in course of disease.There are important clinical and research implications of our proposed models: From a clinical perspective, physicians can use our tool in the office to identify patients who are early in their course of CKD  with accuracy for all patients with CKD. These models can have important clinical and research benefits and should be further externally validated and implemented in health care settings.NT reports receiving grants, personal fees, and other from Tricida Inc.; grants and personal fees from Astra Zeneca Inc., Janssen, and Bayer; personal fees from Otsuka Inc., Boehringer Ingelheim/Eli Lilly, and Roche; other from PulseData and Mesentech; personal fees and other from Renibus, outside the submitted work; serving as the founder of Klinrisk and Clinpredict Inc., in which Klinrisk develops models for CKD progression and Clinpredict works on implementation of models in electronic health records; serving as scientific advisor to PulseData Inc., in which PulseData develops customized machine models for adverse outcomes in kidney disease. TF reports receiving personal fees from Strategic Health Resources, Quanta Dialysis Technologies Ltd., and Baxter Canada outside the submitted work; and personal fees from Clinpredict Inc. MMS received speaker’s fees from AstraZeneca outside the submitted work. PK reports receiving personal fees and other from Quanta Dialysis Technologies Ltd. outside the submitted work. CR reports receiving personal fees from Otsuka and Bayer and personal fees and other from Health Logic Interactive outside the submitted work.


The mean blur rate (MBR) of the stage 3 CKD group was significantly lower than that of the no-CKD group (p < 0.015). The total retinal flow index (TRFI) in the stage 3 CKD group was significantly lower than that of the no-CKD group (p < 0.002). Multiple regression analysis demonstrated that CKD stage was independently associated with MBR  and TRFI . No significant differences were observed in external diameter, lumen diameter, wall thickness, and wall to lumen ratio among the groups. These results indicated that the ONH MBR and TRFI as assessed by LSFG decreases in diabetic patients with stage 3 CKD, but the arterial diameter measured by adaptive optics imaging does not change, suggesting that impaired renal function may be associated with decreased retinal blood flow in early-stage diabetic retinopathy.This study investigated the effect of renal dysfunction categorized by the stage of chronic kidney disease (CKD) on the retinal microcirculation assessed by laser speckle flowgraphy (LSFG) and retinal artery caliber measured by adaptive optics imaging in diabetic patients particularly the early stage of retinopathy and nephropathy. We divided the patients with diabetes into three groups based on the CKD stage (non-CKD ( The incidence and morbidity of diabetes mellitus have increased in the world. Diabetic retinopathy (DR) is one of the main causes of blindness, and about 40% of patient with diabetes develop diabetic nephropathy (DN), causing end-stage renal disease in developed countries . The retThere have been some reports that changes in the caliber of retinal vessels are related to chronic kidney disease (CKD) using direct ophthalmoscopic examinations ,6. HowevLaser speckle flowgraphy (LSFG)  can evaluate the ocular blood flow ,10,11. TAlthough there have been studies of retinal and choroidal hemodynamics in diabetic patients with LSFG, it is not known whether impaired retinal microcirculation is associated with renal dysfunction in diabetic patients. Recently, it was reported that CKD is significantly associated with ocular disorders such as cataract and DR , indicatTherefore, the purpose of this study was to examine the effect of renal dysfunction categorized by the stage of CKD on the retinal microcirculation assessed by LSFG and retinal artery caliber measured by AO imaging in diabetic patients, especially in the early stage of retinopathy and nephropathy.The study was conducted retrospectively and cross-sectionally at the Nagoya University Hospital, and the procedures used were approved by the Ethics Committee of the Nagoya University Hospital (2016-0028-8183) . This study conformed to the tenets of the Declaration of Helsinki. Patient consent was waived due to the retrospective nature of the study.This study enrolled 115 consecutive Japanese diabetic patients from April 2016 to March 2018. Diabetes was diagnosed in patients based on the criteria of the American Diabetes Association .Urinary albumin excretion is expressed as an albumin/creatinine ratio (ACR) (mg/g creatinine). DN was staged based on analysis of spot urine. That is, stage 1 DN , ACR < 30 mg/g creatinine; stage 2 DN , 30 < ACR < 300 mg/g creatinine; stage 3 DN , ACR > 300 mg/g creatinine  . If the 2, there was no CKD.We measured the serum creatinine level within 4 h after fasting venous blood collection. We also evaluated renal function based on the estimated glomerular filtration rate (eGFR) calculated using published formulas . We clasIn the current study, we enrolled diabetic patients with no or minimal diabetic retinopathy and no albuminuria or microalbuminuria. We excluded patients who had macroalbuminuria or proteinuria, poorly controlled diabetes , uncontrolled hypertension , severe anemia , or other kidney diseases and those who underwent hemodialysis.For ophthalmologic evaluation, slit-lamp examinations and indirect ophthalmoscopy were used. After the pupils were dilated, an ophthalmologist assessed the retinopathy. We excluded patients with poor visual acuity (<20/25), high intraocular pressure (IOP) (>20 mmHg), moderate-to-severe nonproliferative and proliferative diabetic retinopathy, any macular abnormalities, topical anti-glaucoma treatment, anti-VEGF therapy or steroid at last 1 year before the examinations, or axial length (AL) > 26.5 mm . This stLSFG-NAVI was used to evaluate the relative ocular blood flow using mean blur rate (MBR) ,24,25. TWe evaluated the relative flow volume (RFV) index using the LSFG-NAVI. The retinal vascular MBR always contains a background of choroidal blood flow. However, the effect of choroidal blood flow can be reduced by manually selecting a region of interest centered on a region where retinal vessels are dominant and subtracting the background choroidal blood flow from the overall MBR. The resulting LSFG value is the RFV.A total RFV index of the major retinal vessels around the ONH can be semi-automatically calculated as TRFI from the sum of retinal arteries and retinal veins. Rectangular regions were set around each major blood vessel around the ONH, and arterial and venous TRFIs were calculated after semi-automatic identification of arteries and veins . If therAn adaptive optics (AO) camera  was used to obtain images of the retinal structures for clinical research . The AO In the present study, arterioles in zone B, 0.5 to 1 disc diameter away from the optic disc edge, were photographed as the vascular measurement area , and the2 test for categorical variables to compare between groups. After one-way ANOVA, post hoc comparisons were performed using the Tukey–Kramer procedure. We used Spearman’s rank correlation coefficient tests to evaluate the correlation coefficients between the variables and used multiple stepwise regression analysis to investigate the association between blood flow parameters and other variables. IBM SPSS Statistics for Windows, v.23 , was used for statistical analyses. A probability (p) value < 0.05 was set as the significance level.The value of each parameter was expressed as the mean ± standard deviation. We used one-way ANOVA for continuous variables and the xn = 54; CKD stage 1 + 2, n = 20; CKD stage 3, n = 41). No patient had stage 4 or 5 CKD. p = 0.001), serum creatinine (p < 0.001), and eGFR (p < 0.001) among the groups.The diabetic patients were divided into three groups based on the CKD stage . The TRFI in the stage 3 CKD group was significantly lower than that of the no-CKD group (p < 0.002). There were no significant differences in the ONH MBR in the tissue area and the choroidal MBR among the groups.In the no-CKD group, the ONH MBR in the vessel, the ONH MBR in the tissue, the choroidal MBR, and the TRFI were 42.85 ± 7.48, 11.33 ± 1.82, 8.50 ± 2.77, and 2768.53 ± 752.08 , respectively . In the Clear vascular images were successfully acquired for 36 of 54 eyes in the no-CKD group, 15 of 20 eyes in the stage 1 + 2 CKD group, and 28 of 41 eyes in the stage 3 CKD group. Clear images could not be obtained for the remaining eyes because of poor eye fixation. Vascular measurements for all subjects are shown in p < 0.001), duration of DM , eGFR , CKD stage , and DBP . The TRFI correlated with age , duration of DM , eGFR , and CKD stage .The results of single linear regression analysis are displayed in Multiple regression analysis showed that CKD stage was independently associated with ONH MBR-vessel and TRFI .Our results showed that ONH MBR-vessel and TRFI were significantly reduced in the stage 3 CKD group compared with the no-CKD group. In vascular measurements of the retinal artery by AO imaging, no significant differences were observed in external diameter, lumen diameter, wall thickness, and wall to lumen ratio among the groups. Multiple regression analysis showed that CKD stage was a common independent factor associated with ONH MBR-vessel and TRFI in subjects.In this study, retinal blood flow velocity and volume was decreased in the stage 3 CKD group, while the vascular parameters in retinal artery determined by AO imaging were not significantly different among the groups. There have been several reports on the association between CKD and retinal vessel ,30,31,32The Cardiovascular Health Study , the BeaThe measurement of vessel diameter by a fundus camera reflects the blood column surrounded by the plasma edge stream and may underestimate the actual inner diameter , and theSince it has been reported that retinal and glomerular capillaries in end-stage diabetic retinopathy and nephropathy have a thickened basement membrane , it is tShiba et al. used LSFG to show that, in diabetic patients without retinopathy, BOS  in the ONH was lower than in normal eyes, indicating greater stress on the vessel wall during blood pressure changes . NagaokaAlthough DR and the preclinical morphologic changes of DN are associated closely , about oNagaoka et al. reported that, using laser Doppler velocimetry, the retinal blood flow in the retinal arterioles decreased in patients with stage 3 CKD compared to patients without CKD , suggestIn this study, blood flow was lower in the stage 3 group than in the no-CKD group due to a decrease in blood flow velocity, although there was no change in arterial diameter. There are several considerable reasons for this. First, endothelial function in retinal vessels should be reduced. CKD has been associated with mild inflammation, platelet activation, and decreased vascular endothelial cell function . In addiSecond, blood viscosity is increased in patients with CKD. According to the Hagen–Poiseuille law, increased blood viscosity results in reduced blood flow and increased vascular resistance in the renal vascular beds . SugimorIn addition, increased blood viscosity decreases blood flow and oxygen supply to retinal ganglion cells  and is aThere are several limitations in the present study. First, because the design of the study was cross sectional with a small patient population, a prospective study is required to detect the interaction of the pathogenesis between retinopathy and nephropathy in diabetes with a larger patient population. Second, the average age of the patients in the stage 3 CKD group was higher than that of the patients in the other CKD groups. Because we previously reported that ONH MBR decrease with aging , it is aIn conclusion, our results showed that the ONH MBR-vessel and TRFI by LSFG decreases in patients with diabetes with stage 3 CKD, but the arterial diameter measured by AO camera imaging does not change, indicating that impaired renal function may be associated with decreased retinal blood flow in early-stage diabetic retinopathy. LSFG should be a useful method to detect early changes in retinal microcirculation effected by renal dysfunction.


Despite preventive strategies and major advances in pharmacological treatment, the prevalence of hypertension has been increasing . To curbLi C. et al. identified the role of impaired gut health in hypertension. They found elevated intestinal barrier serum markers diamine oxidase, lipopolysaccharide, and D-lactate in hypertensive patients compared to normotensives. The patients with longstanding hypertension history (≥20 years), poor control of diastolic blood pressure, and cardiac and renal complications had elevated diamine oxidase and lipopolysaccharide, while patients on multidrug therapy for hypertension had higher D-lactate levels. This study suggests the association of the intestinal barrier with hypertension.The pathogenesis of hypertension cannot be entirely elucidated by genetics and conventional risk factors . There iLi T. et al. found that the AB blood group patients have an increased risk of preeclampsia (PE), and the A blood group is associated with early-onset PE. Clinicians could proactively manage pregnant women with these blood groups who have raised blood pressure and are potentially at high risk of developing PE.In another study on the association of the ABO blood group with preeclampsia, Ojji et al. found the selective beta-1 blocker nebivolol has significant antihypertensive efficacy and satisfactory tolerability in reducing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Blacks with stage I hypertension residing in sub-Saharan Africa, indicating the potential use of nebivolol as monotherapy in grade 1 hypertension or as the fourth drug in multitherapy after maximum doses of a combination of long-acting calcium channel blockers, thiazide diuretics, and renin-angiotensin-aldosterone blockers have been used.Besides accumulating new evidence, the preexisting evidence applicability in different sets of populations could also be helpful. Sun et al. investigated the effect of an incremental rise in the risk of cardiovascular diseases (CVD) with increasing SBP in older adults above 65 years of age, having SBP between 90 and 129 mm Hg. The authors found a non-linear association between normal range SBP levels and all-cause and cardiovascular death. Also, when SBP <115 mmHg, the risk of mortality gradually increased with decreasing SBP, and when SBP ≥115 mmHg the risk of mortality gradually increased with SBP level. The findings suggest that SBP level 110–120 mmHg could be optimal, and the SBP level in the normal range might have a dose-response relationship with all-cause and cardiovascular mortality.Yang et al. examined the relationship between SBP and in-hospital deaths in Type -A Aortic dissection (AAD) patients and found a non-linear correlation between admission SBP and in-hospital mortality of AAD patients when SBP was ≤120 mmHg. A SBP ≤ 120 mmHg negatively correlated with in-hospital mortality and a 10 mmHg rise in admission SBP was found to be associated with 12% lower odds of in-hospital mortality.Fu et al. identified two sub-phenotypes in masked hypertension (MHT); the low-risk metabolic syndrome (MetS) and the high MetS  sub-phenotype. The authors found a significant correlation between sub-phenotypes and clinical results and the sub-phenotypes have different metabolic characteristics and prognoses. For patients with MHT and high-risk MetS, antihypertensive therapy alone may be insufficient. In addition to antihypertensive treatment, the management of the metabolic disorder in MHT populations may be also considered in the future.Visco et al. retrospectively analyzed the database of 177 essential hypertensives and found an independent positive correlation of serum uric acid (sUA) with left ventricular mass index (LVMi) which is a common complication of hypertension and increases the risk of cardiovascular morbidity. The results showed that the sUA above 5.6 mg/dl predicts larger LV sizes. The results suggest hyperuricemia is an early marker of increased left ventricular mass that can be used to identify a hypertensive population with target organ damage.Meng and Xu stated that the ideal blood pressure for the treatment and management of Atrial Fibrillation (AF) is not clear. The eight major hypertension guidelines that were updated between 2016 and 2019 vary in the recommended target BP for preventing new-onset AF. The authors expressed that the ideal approach to assess the impact of hypertension on AF in large population-based cohort studies remains unclear because of the increased beat-to-beat BP variability in the presence of AF which may affect BP estimation and thus the assessment of the correlation between BP and AF. Also, the insufficient sensitivity in detecting sporadic arrhythmias  by intermittent monitoring adds to the complexity of accuracy in answering the question. The authors suggested the use of a continuous rhythm monitoring strategy to overcome the limitations of intermittent monitoring in assessing the rate of incident AF and help evaluate the real effect of hypertension on AF.Meier et al. evaluated whether the financial incentives are more effective than evidence-based educational feedback reports in the increasing proportion of patients with diabetes mellitus meeting specific quality indicators (QIs). The authors found that the intervention group achieved an improvement of 2% in the proportion of patients achieving the recommended blood pressure target levels, whereas the proportion of patients receiving annual HbA1c measurements remained stable. After the withdrawal of financial incentives for the General Practitioners after 12 months, some quality of life indicators (QIs) still improved, but there was a decrease in QI achievement rates after 18 months suggesting that the positive effects of time-limited financial incentives eventually diminish.In summary, the articles on this Research Topic discussed some pertinent issues in the field of hypertension. The association of the intestinal barrier with hypertension, the association of hyperuricemia with left ventricular mass, and the AB blood group with preeclampsia was revealed. These findings will help clinicians identify the at-risk population and treat them proactively for the prevention and improved management of hypertension and related cardiovascular disease. The articles also discussed treating masked hypertension based on subphenotype in patients having associated metabolic syndrome and treating hypertension with nebivolol in sub-Saharan Africa. These findings could prove useful in hypertension treatment in specific populations.The non-linear association of normal range systolic BP with all-cause mortality and cardiovascular death in older patients above 65 years of age and the non-linear association of SBP with in-hospital mortality of type A Aortic Dissection will help in determining an optimal BP target in these populations.The suggested use of intermittent monitoring for assessing the rate of incident AF in hypertensives will help in assessing the association between blood pressure and incident AF. This will help in reaching the consensus on target BP for preventing new-onset AF. Lastly, a positive effects of time-limited financial incentive to general practitioners (GPs) was found on the proportion of patients achieving the recommended blood pressure target levels. After the withdrawal of financial incentives for the GPs after 12 months, some quality of life indicators (QIs) still improved, but there was decrease in QI achievement rates after 18 months, suggesting that 1 year might be too short to observe the full effect of such interventions and the positive effects of time-limited financial incentives eventually wane.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.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.


Blood pressure variability (BPV) is an important risk factor for cardiovascular events in hemodialysis patients. We sought to determine the impact of BPV on hemodialysis access thrombosis.We enrolled 1,011 prevalent hemodialysis patients from 12 hemodialysis centers since January 2018 and followed them until December 2020. Predialysis blood pressure (BP) was assessed at 12-week intervals. The coefficient of variation derived from 36 consecutive BP measurements was used as the metric for variability. The primary outcome was incident hemodialysis access thrombosis. Linear regression models were used to assess factors associated with BPV at baseline. Kaplan-Meier curves of the time until vascular access events were drawn and log-rank tests were calculated. Cox proportional hazards models were performed to assess the association of BPV with incident vascular access events.The average coefficient of variance for systolic BPV was 10.9%. BPV was associated with age, body mass index, mean BP, diabetes, coronary and peripheral artery disease, history of access dysfunction, graft access, intradialytic hypotension, and use of antihypertensive medications. There were 194 access thrombosis events and 451 access stenosis events during a median follow-up period of 30 months. After adjustment of potential confounding factors, BPV was associated with increased risk of access thrombosis . The patients in the highest BPV quartile had 2.45 times the risk of thrombosis . The association was independent of average BP, intradialytic hypotension, and comorbidities. Similar trends of association were found in the subgroups analyzed. Comparative analysis using a time-varying variable model and different metrics of BPV showed consistent results.Our findings underscored the impact of BP fluctuation on vascular access thrombosis. Hemodialysis access dysfunction continues to be a major source of morbidity and mortality in patients with end-stage renal disease . ThromboHypertension is a well-known risk factor for vascular diseases in the general population. Nonetheless, most studies found the association of vascular access thrombosis with hypotension –7. BloodBoth short-term  or long-term (day-by-day or visit-by-visit) BP variability (BPV) are informative measures to predict vascular events . The effWe hypothesized that higher visit-to-visit BPV would increase the risk of vascular access thrombosis. Therefore, we conducted a prospective study to investigate the relation between BPV and dialysis access thrombosis.ClinicalTrials.gov identifier: NCT04692636) designed to investigate clinical factors related to cardiovascular events of maintenance hemodialysis patients. The study participants were recruited from January 1, 2018 to December 31, 2018, from 12 hemodialysis centers in the Hsinchu district of Taiwan. The Hsinchu district is composed of 1 city and 13 townships, with a total population of about 1,000,000. Four of the 12 hemodialysis centers are hospital-based centers and eight of them are dialysis clinics. The inclusion criteria were as follows: (1) age between 18 and 90 years old, (2) maintenance dialysis of more than 6 months, and (3) no hospitalization during the previous 3 months. For the analysis of hemodialysis access outcomes in this study, the following exclusion criteria were used during the exposure period: (1) use of central venous catheters in dialysis, (2) death, facility transfer, or modality change, (3) missing dialysis session, and (4) missing BP data > 10%  in which vital signs and dialysis parameters from each dialysis session are stored digitally. Dialysis accesses were followed at all respective hemodialysis centers under the same protocol, and were referred to our angiographic unit for evaluation or management under the same criteria. Events of dialysis access were collected by reviewing the dialysis records, and angiography or surgery reports at a 3-month interval. Subjects were followed until December 31, 2020, and censored at the time of death, kidney transplant, or transfer to a non-study center. The study was approved by the Institutional Review Board and all patients signed an informed consent to participate in the study.y-axis) against mean systolic BP (x-axis) with the parameter d estimated from the curve [VIM = (SD/meanx)] , during, and after  each hemodialysis session, in a seated position, using automated oscillometric devices as per the dialysis unit routine. BP values were extracted from a computerized database every 90 days. We calculated the baseline mean BP and BPV using all the pre-dialysis BP measurements from week 1 to the end of week 12. During the follow-up period, we assessed the mean BP and BPV every 12 weeks. Allowable ranges were 50–300 mm Hg for systolic BP and 10 to 150 mm Hg for diastolic BP. When a value was outside the range, the data point was coded as missing. We only analyzed patients with BP data of more than 30 occasions to maintain excellent reproducibility of BPV data . The BPV/meanx)] . We usedThe following domains of covariates were assessed over the first 12 weeks as the baseline level and updated every 12 weeks: demographic and anthropometric, clinical, dialysis , laboratory, and medication. Demographic and anthropometric domains include age, sex, and body mass index. Clinical domains included diabetes, coronary artery disease, cerebrovascular disease, heart failure, and history of vascular access dysfunction. To account for other comorbidities, a comorbidity index previously validated in dialysis patients was calculated . DialysiThe surveillance protocol of dialysis access included physical examination, dynamic venous pressure monitoring at each dialysis session and dialysis dose, transonic examination of access flow (if available) at monthly interval. Patients were referred to our angiographic unit for evaluation on the basis of the following criteria: (1) clinical manifestations suggesting vascular access dysfunction ; (2) a reduction in flow rate of >25% from baseline; (3) total access blood flow <500 ml/min by ultrasound dilution method; and (4) increased venous pressure during dialysis (dynamic venous pressure exceeding threshold level measured three consecutive times); and (5) unexplained decrease (>0.2 unit) in the delivered dialysis dose. Our primary outcome of interest was the time to the first vascular access thrombosis, defined as the access that had clotted without blood flow. Information of vascular access thrombosis was obtained from documentation in the dialysis records, angiography reports, or surgery reports. The secondary outcome was the time to the first vascular access stenosis, defined as the anatomical stenosis of more than 50% by angiography with corresponding clinical or hemodynamic abnormalities of the referring criteria. Because the patient may change vascular access throughout the follow-up period, the only outcome of first vascular access was used for analysis. In the primary baseline model analysis, vascular access outcomes were defined as the first event that occurred from week 13 to the end of the study or censoring. In the companion timing-varying analyses, vascular outcomes were defined as the first event within 12 weeks after the end of the 12-week exposure period .a priori confounders. Significance thresholds of 0.1 in the univariate linear regression model were selected as potential confounding factors in multivariate models. Proportional hazard assumption was checked graphically using the log-log plot and was found to be acceptable for the factors of interest. Subgroup analyses were performed on patients defined by age, sex, systolic BP, diabetes, coronary artery disease, intradialytic hypotension, vascular access, and institution. These interaction of these factors on the associations between BPV and thrombosis events were assessed by adding interaction terms in the model. We considered two-tailed P values < 0.05 as statistically significant. All analyses were performed using the SPSS software .We reported patients’ baseline characteristics across quantiles of systolic BPV. We assessed factors associated with BPV at baseline using linear regression models. Kaplan-Meier curves of the time until vascular access events were drawn and log-rank tests were calculated. Cox proportional hazards models were performed to assess the association of BPV with incident vascular access events. Three sets of analyses were examined to access the relationship between BPV and vascular access events. The primary analysis describes the relationship between baseline BPV (derived from a 12-week exposure period) and vascular access outcomes. The first sensitivity analysis used time-varying BPV  as well as other time-varying covariates for analysis. The second sensitivity analysis excluded thrombosis or stenosis events during hospitalization in order to eliminate the hemodynamic effect due to acute illness. Univariate analyses were done before proceeding to multivariate models. Unadjusted and adjusted models with hazard ratios (HRs) and 95% CI were presented per 1-SD increase of BPV. Age, sex, and mean SBP were considered as potential via central vein catheters and 49 patients for other reasons, the final cohort included for analysis consisted of 1,011 patients . Other metrics of BPV, such as SD, ARV, and VIM, were provided in P < 0.001]. When BPV was stratified into quartiles, the highest BPV quartile had 1.60 times  risk of stenosis than the lowest quartile (P < 0.001). When BPV was stratified into quartiles, the highest BPV quintile had 2.45 times the risk of thrombosis  than the lowest quartile, with a graded effect among the quartiles  had a higher risk of access thrombosis . The BP quartile . After mquartile . Baselinquartile . In univuartiles . After muartiles . The relBecause the risk of events differed significantly between arteriovenous fistula (AVF) and AVG, we analyzed the association of BPV and thrombosis by access types . After aTo account for the change of BPV over time, we analyzed the association of BPV and dialysis access outcomes by treating BPV as a time-dependent variable, rolling over 12 weeks. The effect of BPV on vascular access outcomes, regardless of stenosis or thrombosis, was similar to that observed in the baseline BPV model . To accoVisit-to-visit BPV is emerging as a relevant risk factor for cardiovascular events in dialysis and non-dialysis patients , 25, 26.A low-flow state is a critical determinant for access thrombosis. Arterial BP is the primary driving force of access blood flow. Low BP has been shown to decrease access flow and increase thrombosis events. In the absence of structural abnormalities, low BP accounted for at least 20–40% access thrombosis . In prevPrevious studies of the relationship between BP and vascular access focused on short-term variability or instability during dialysis. For example, a secondary analysis of the Hemodialysis (HEMO) Study revealed frequent episodes of intradialytic hypotension associated with AV fistula thrombosis. Two recent retrospective studies also found that the variability of intradialytic BP can predict access thrombosis , 14. NonThere were other possible reasons for the association between BPV and thrombosis. Baseline BPV was associated with risk factors of arterial stiffness, such as aging, obesity, diabetes, and vascular diseases . Diabetevia these common pathogenic pathways as well  was used to estimate BPV based on data from a digitized dialysis information system. Although such a high-density measurement is not easy for non-dialysis patients, it can be obtained from regular dialysis sessions without additional loadings on the staff. Home BP is addressed in recent guidelines for BPV assessment. Nonetheless, device standardization, measurement, timing, and data transformation are all potential obstacles to the application of home BP in clinical practice. In contrast, using clinic BP in dialysis centers is more feasible and the prognostic value was well-documented in previous studies –11. We uOur study has several strengths. It was conducted in a typical clinical setting using a representative community-based cohort, with characteristics comparable to that of a nationwide registry. The detailed time-varying patient-level data on key variables were available, including fluid removal and anti-hypertensive medications, detailed ascertainment of comorbidities using medical records, and prospective ascertainment of vascular access events using dialysis records or angiographic reports. It is a multi-center study with a longitudinal study design and a long-term follow-up period, allowing us to collect repeated BP measurements, relevant confounders, and reliable outcomes. Unlike most studies that focused on a certain BPV indicator, we used several BPV indices to strengthen our findings. We used high-density measurements, which rendered a stable and reproducible BPV.Some limitations should be addressed. Our sample was relatively small compared to nationwide registries or claims databases. From the research point of view, VIM is an ideal approach to exclude the influence of usual BP. However, in terms of clinical decision making, a CV may be a more practical option. Although the use of medications was adjusted, we did not have a direct assessment of medication adherence. BPV measurements were performed as part of routine clinical practice and we did not standardize the device for BP measurement. Different devices used for BP measurement could be a source of extra variability. The validity for lower-density BP measurements was not clear. A high-density BPV measurement is laborious and the assistance of information technology is important in clinical practice. We did not examine variability of diastolic BP or mean BP because studies on diastolic BPV are relatively few and have conflicting results , 28, 40.post hoc analyses of previous studies suggested that certain classes of antihypertensive medicines were more effective on BPV than others (Given the high cost and prevalence of vascular access thrombosis, finding modifiable risk factors and implementable strategies may improve patient outcomes and healthcare burden. The current practice already dictates the association of BP variability and instability with mortality and cardiovascular events. Our results expanded the role of BPV in vascular access thrombosis events, either in fistulas or grafts. The impact of BPV is more pronounced in dialysis patients with normal BP. Uncertainty regarding appropriate BP targets in hemodialysis patients continues, and whether higher BP targets are warranted in patients at risk of access thrombosis deserved further investigation. Evidence derived from n others . DefinitThe 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 National University Hospital, Hsinchu Branch, IRB. The patients/participants provided their written informed consent to participate in this study.M-YH and C-CW: research idea and study design. M-YH, S-YC, Chi-HC, and Chiu-HC: data acquisition. M-TL, S-YC, and C-CW: data analysis/interpretation. M-TL and S-YC: statistical analysis. C-CW: supervision or mentorship. All author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate.


AGTR1) is related to essential hypertension (EH), and left ventricular hypertrophy (LVH) and arterial stiffness are common complications of EH. This study aimed to explore the association between AGTR1 genotype and LVH and arterial stiffness in EH patients.AT1 receptor gene (AGTR1(rs4524238) was assessed using a gene sequencing platform. The outcomes were LVH and arterial stiffness.A total of 179 EH patients were recruited in this study. Oral exfoliated cells were collected from each patient, and the genetic polymorphism of AGTR1 genotype of GG  and 65 were with AGTR1 genotype of GA or AA . Patients with AGTR1 genotype of GG were more likely to have LVH  and arterial stiffness . The AGTR1 polymorphism frequency was in accordance with Hardy–Weinberg equilibrium (P = 0.291). The multivariate logistic regression showed that AGTR1 genotype of GA or AA was independently associated with lower risk of LVH  and arterial stiffness  after adjusting for gender, age, and diabetes.Among 179 patients, 114 were with EH patients with the AGTR1 genotype of GA or AA were at lower risk for LVH and arterial stiffness than those with the GG genotype. Hypertension is one of the most common chronic diseases in China. According to the China Hypertension Survey, the number of adults with hypertension is approximately 245 million . TherefoProlonged poor blood pressure control in patients with hypertension may cause damage to target organs such as blood vessels, heart, brain, and kidneys. Left ventricular hypertrophy (LVH) is a manifestation caused by prolonged increased cardiac workload and is most commonly associated with hypertension. Studies have shown that the prevalence of hypertension combined with LVH is as high as 57.5% . ArteriaAGTR1 gene polymorphisms are closely associated with the development of arterial stiffness by acting in combination with other gene polymorphisms or alone . All enrolled patients understood the study content and signed the written informed consent.Oral exfoliated cells from all patients were collected and placed in collection tubes. DNA extraction was performed using the TIANamp Blood DNA Kit  according to the manufacturer’s instructions, and the obtained genomes were accurately quantified using the Qubit® dsDNA HS Assay Kit. Polymerase chain reaction (PCR) Table  was follCarotid-femoral pulse wave velocity (cf-PWV) was measured using the CompliorsP arterial pulse wave velocity detector. All patients were asked to refrain from smoking, drinking, and eating for 3 h before the examination. The examination room was quiet with comfortable temperature. Patients were instructed to rest in the supine position in the examination room for 15 min. The vertical distance from the carotid artery to the body surface of the femoral artery was measured with a tape measure and entered into the computer. The pressure transducer was then placed on the right carotid and femoral arteries with the most pronounced pulsation, and the instrument automatically detected the cf-PWV value. The above operations were performed by qualified personnel with uniform training. Higher cf-PWV values indicated higher arterial stiffness. According to the 2018 ESC/ESH Guidelines for the management of arterial hypertension and the Task Force for the management of arterial hypertension of the European Society of Cardiology (ESC) and the European Society of Hypertension (ESH), all participants were divided into 2 classes: class 1 (cf-PWV > 10 m/s); class 2 (cf-PWV ≤ 10 m/s).3- LVDd3] + 0.6, body surface area (BSA) = 0.0061 × height + 0.0128 × weight − 0.1529, and left ventricular mass index (LVMI) (g/m2) = LVM/BSA calculated according to the Devereux correction formula. The diagnostic criteria for LVH were LVMI > 115 g/m2 (men) and > 95 g/m2 (women) [Left ventricular end-diastolic dimension (LVDd), septal thickness (LVST), and left ventricular posterior wall thickness (LVPWT) were measured by the same technician using color Doppler ultrasound. Left ventricular mass (LVM) (g) = 0.8 × 1.04 × [(LVST + LVPWT + LVDd) (women) .The outcomes in this study were LVH and arterial stiffness (cf-PWV). The demographic information , clinical characteristics  was used for data analysis. The continuous data were expressed as mean ± standard deviation (SD) and compared by t-test. Categorical data were expressed as n (%) and compared by the chi-square test. Hardy-Weinberg equilibrium was tested by the fit chi-square test. Multivariate logistic regression analysis was used to explore the association between AGTR1 genotype with LVH and arterial stiffness after adjusting for gender, age, and diabetes (Insignificant factors had been omitted after preliminary statistics). A two-sided P < 0.05 was considered statistically significant.A total of 179 patients were finally included in this study. Among them, 114 were with AGTR1 genotype of GG , and 65 patients were with of GA or AA genotype .P = 0.008), higher SBP , higher Csbp , higher TG , and higher Scr  compared to GA or AA genotype. Patients with GG genotype were more likely to have LVH  and arterial stiffness  (Table P = 0.291)  and arterial stiffness  after adjusting gender, age, and diabetes  genotype of GA or AA might be at lower risk for LVH and arterial stiffness compared to the GG genotype. And G allele of AGTR1 appears to be more strongly associated with SBP, which is consistent with the study of Felder et al. . On the Long-term poor blood pressure control may lead to target organ damage and increased cardiac load, which in turn leads to LVH. LVH is not only an adaptive response to hemodynamic changes in hypertension patients but also a risk factor for adverse cardiovascular events. This study showed that the GG genotype of the AGTR1 gene was the most common genotype in patients with hypertension and LVH, and the distribution of AGTR1 polymorphisms was significantly different in the LVH and non-LVH patients. A previous study showed that LVMI was associated with the AGTR1 A1166C polymorphism, which might be mediated by different expressions of AGTR1 as modulated by microRNA-155 . BesidesThe assessment of arterial stiffness is important for preventing and treating hypertension and developing complications. The volume of blood injected into the aorta during systole generates a wave (pulse wave) that circulates through the arterial system at a certain velocity. Cf-PWV measurement has now become the “gold standard” for noninvasive assessment of arterial stiffness, where cf-PWV > 10 m/s is considered a conservative estimate of changes in aortic function in middle-aged hypertension patients. This study showed an association between AGTR1 and arterial stiffness, and patients with the AGTR1 genotype of GG were more likely to suffer from arterial stiffness. Marcin et al. reported that the AGTR1 genotype was not related to vascular stiffness; however, other studies confirmed that AGTR1 gene polymorphism is closely associated with the occurrence of arterial stiffness by combining with other gene polymorphisms or acting alone , 27. ThiThere are several limitations to this study. First, due to the small sample of the study population, with only 2 patients with the AA genotype, additional sample size is needed in this study to clarify the relationship between different AGTR1 genotypes and their complications. Second, this study only included EH patients, so reported results could not be generalized to all patients with hypertension.In conclusion, EH patients with the AGTR1 genotype of GA or AA might be at lower risk for LVH and arterial stiffness compared to the GG genotype. EH patients with AGTR1 genotype of GG need examination for LVH and arterial stiffness to receive appropriate treatment.