Intraoperative arterial blood pressure waveform variation predicts short-term acute kidney injury after cardiac surgery

Intraoperative arterial blood pressure waveform variation predicts short-term acute kidney injury after cardiac surgery | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Intraoperative arterial blood pressure waveform variation predicts short-term acute kidney injury after cardiac surgery View ORCID Profile Cheng-Hsi Chang , View ORCID Profile Shen-Chih Wang , Wen-Kuei Chang , View ORCID Profile Chien-Kun Ting , View ORCID Profile Ming-Jen Lu , Chia-Hsun Lin , View ORCID Profile Chai-Hock Chua , View ORCID Profile Hau-Tieng Wu , View ORCID Profile Yu-Ting Lin doi: https://doi.org/10.1101/2025.10.12.25337819 Cheng-Hsi Chang 1 Department of Anesthesiology, Shin Kong Wu Ho Su Memorial Hospital , Taipei, Taiwan 2 School of Medicine, Fu Jen Catholic university , New Taipei City, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cheng-Hsi Chang Shen-Chih Wang 5 Department of Anesthesiology, Taipei Veterans General Hospital , Taipei, Taiwan 6 School of Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shen-Chih Wang Wen-Kuei Chang 5 Department of Anesthesiology, Taipei Veterans General Hospital , Taipei, Taiwan 6 School of Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chien-Kun Ting 5 Department of Anesthesiology, Taipei Veterans General Hospital , Taipei, Taiwan 6 School of Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan 7 Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chien-Kun Ting Ming-Jen Lu 2 School of Medicine, Fu Jen Catholic university , New Taipei City, Taiwan 3 Division of Cardiovascular Surgery, Department of Surgery, Shin Kong Wu Ho Su Memorial Hospital , Taipei, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ming-Jen Lu Chia-Hsun Lin 2 School of Medicine, Fu Jen Catholic university , New Taipei City, Taiwan 3 Division of Cardiovascular Surgery, Department of Surgery, Shin Kong Wu Ho Su Memorial Hospital , Taipei, Taiwan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chai-Hock Chua 3 Division of Cardiovascular Surgery, Department of Surgery, Shin Kong Wu Ho Su Memorial Hospital , Taipei, Taiwan MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chai-Hock Chua Hau-Tieng Wu 4 Department of Mathematics, Courant Institute of Mathematical Sciences, New York University , New York, NY, US MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hau-Tieng Wu Yu-Ting Lin 5 Department of Anesthesiology, Taipei Veterans General Hospital , Taipei, Taiwan 6 School of Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan 7 Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yu-Ting Lin For correspondence: linyuting{at}hotmail.com.tw Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Arterial blood pressure (ABP) waveforms are ever-changing. Variation in waveform morphology (VarM) may reflect the complex interactions of cardiovascular regulatory mechanisms. We investigated the association between VarM and the development of acute kidney injury (AKI) following cardiac surgery. Methods In tahis prospective observational single-cohort study, 101 patients undergoing elective cardiac surgery involving cardiopulmonary bypass (CPB) were enrolled. VarM was quantified from consecutive ABP waveform pulses. Postoperative AKI was defined using serum creatinine levels according to the KDIGO criteria. Results After weaning from CPB, AKI group showed a lower VarM index and a lower trend. In the presence of baseline renal function, EuroSCORE, and CPB duration, VarM provided additional predictive value for identifying patients at risk of AKI (AUROC: 0.775 vs. 0.762). Conclusions VarM derived from the ABP waveform following CPB is associated with the incidence of short-term postoperative AKI. Question Is intraoperative variation of arterial blood pressure waveform morphology (VarM) associated with postoperative acute kidney injury (AKI) in elective cardiac surgery? Findings A lower VarM value and a lower trend after CPB weaning were associated with AKI, and combining VarM to baseline eGFR, EuroSCORE, and CPB time improved discrimination (AUC 0.775) compared with models without VarM and outperformed standard vital-sign metrics. Meaning Beat-to-beat arterial blood pressure waveforms reflect the unique state of the cardiovascular system relevant to AKI after cardiac surgery. Introduction Acute kidney injury (AKI) after cardiac surgery is a common complication associated with significant morbidities and mortalities 1 – 4 . Postoperative AKI risk factors are largely cardiovascular in nature. Elevated blood pressure (BP), high pulse pressure (PP), or hypotension have been linked to AKI 5 – 9 . Although BP has been implicated in AKI pathogenesis, hypotension-avoidance strategy have failed to decrease its incidence 10 , 11 . It is arguable that conventional BP measures familiar to clinicians, including systolic BP (SBP), diastolic BP (DBP) and PP, present only partial information of the arterial blood pressure (ABP) waveform, while its morphology potentially reflects additional cardiovascular properties 12 – 15 . While morphological changes between two consecutive ABP pulses can be subtle, the cumulative pattern over time is complex, and no two waveform pulses are exactly the same 13 , 16 . Growing evidence suggests an interesting fact: greater variability in waveform morphology is associated with more favorable clinical states 12 , 17 – 19 . This variability may reflect the combinatorial action of multiple integrated physiological regulatory mechanisms. In this study, we hypothesized that variation of morphology (VarM) could be associated with AKI following cardiac surgery involving CPB 12 , 20 . Thus, we investigated the association between VarM and postoperative AKI, focusing on two aspects: 1) dynamic changes across surgical phases, 2) the association between VarM and postoperative AKI. Methods Patients This single-center prospective observational study was conducted at Shin Kong Wu Ho Su Memorial Hospital after being approved by the institutional review board (20181205R) and conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and the CONSORT guideline. Patients who underwent elective cardiac surgery involving cardiopulmonary bypass procedures were recruited after written informed consent was obtained from each patient from November 2019 to November 2022. The patients underwent cardiac surgery for either coronary artery bypass graft (CABG), valvular heart disease, or both, by three cardiac surgeons (MJL, CHL, CHC). All patients underwent preoperative creatinine measurements along with other laboratory examinations, as well as measurements on the same day (postoperative day 0, POD0) and from postoperative day 1 (POD1) to day 3 (POD3), in accordance with institutional clinical routine, which also includes direct arterial pressure monitoring via the radial artery and pulmonary artery catheter (or central venous catheter), avoiding aminoglycosides as prophylactic antibiotics. Anesthesia was maintained with desflurane during the operation and mean arterial pressure (MAP) during CPB was maintained between 50 and 70 mmHg. The data collection process was automatic without interference with anesthetic management. Patients requiring renal replacement therapy, pacemaker, intra-aortic balloon pump, or extracorporeal membrane oxygenation support, or presenting with significant arrhythmias (e.g., atrial fibrillation or frequent premature ventricular contractions) were excluded. After the operation, the patients were routinely transferred to the intensive care unit (ICU), where complete blood count (CBC) and blood chemistry panel were examined daily and under the discretion of the physician in ICU. The European System for Cardiac Operative Risk Evaluation (EuroSCORE) was obtained as the baseline condition. Data collection Continuous intraoperative physiological waveform data as well as all numeric data were collected from the patient monitor (IntelliVue MX800, Philips, Amsterdam, Netherlands) using the data collection software ixTrend (ixellence GmbH, Wildau, Germany). All ABP waveform data in the present study were obtained from the direct radial arterial cannulation. The recorded ABP waveforms were uniformly sampled at 125 Hz. The anesthesiologist (CHC) logged the anesthetic events and surgical progress with an automated electronic system. From the continuous waveform data, we focused on specific anesthetic and surgical periods, including 1) the period before anesthetic induction, 2) a 10-minute segment during central venous catheter insertion, 3) a 10-minute segment during vascular graft harvest, 4) the 0-10 minute period after weaning from CPB, 5) the 30-40 minute period after weaning from CPB, 6) the period from post-CPB to 10 minutes after the closure of sternum bone, and 7) the 10-minute period following the completion of sternal closure. Aware of the significance of CPB, we chose several time periods after weaning from CPB. Renal function association and primary outcome Preoperative estimated glomerular filtration rate (eGFR) was used to represent baseline renal function. The primary outcome was postoperative AKI occurring within 2 weeks. AKI was determined according to the modified KDIGO definition 21 : an increase of more than 1.5 times the baseline creatinine or an absolute increase of 0.3 mg/dl. Cases of end-stage renal disease (ESRD) requiring renal replacement therapy before surgery were excluded from the AKI analysis. Quantifying variation of morphology The morphology of ABP waveform is more complex than a single numeric value, so quantifying the waveform morphology is a task of multivariate data analysis 22 , 23 . Each consecutive pulse was automatically identified by the fastest point in the anacrotic (ascending) phase 13 as the fiducial point from the continuous ABP waveform signal. Taking the nadir before the anacrotic of each pulse as the origin, we obtained each successive pulse waveform ( Fig. 1 ). By subtracting each DBP value, we removed the confounding influence since BP was constantly monitored and regulated during the cardiac surgery. Download figure Open in new tab Figure 1. ABP waveform data from two cases before anesthetic induction demonstrate different variations in morphology, with better renal function associated with greater variation. Although this difference may not be readily apparent in the raw waveform tracing (panel A), subtle yet intricate morphologic changes are evident when examined on a per-pulse basis (panel B). From the perspective of waveform morphology, variations in DBP and SBP reflect only partial aspects of VarM. From the collections of consecutive pulses, we used an unsupervised learning algorithm, DDmap 12 , to quantify the waveform variation. This computation method condenses the subtle but intricate waveform morphological change to quantify the waveform variance in a high dimensional space 13 . As we know, the common pulse pressure variation within the respiratory cycle offers assessment of fluid status. This respiratory pulse pressure variation was removed when quantifying the VarM indices 12 . The final output was on a scale of 0-100 by comparing with the baseline waveform data of 101 cases as the reference 24 . Under the hood, this unsupervised learning method used only waveform signal to reveal the inner structure without the knowledge of clinical condition as input 13 , 16 , 22 , 24 . It is worth mentioning the robustness of this method as well 16 . Basic vital parameters and the variation indices Common BP measures including SBP, DBP, MBP, and PP, were derived from each pulse cycle of the ABP waveform. We performed validation analysis on them and their variations. From these BP measures in beat-to-beat format, we obtained their summary-dispersion statistics, including the mean, coefficient of variation (CV), standard deviation (SD), average real variability (ARV), interquartile range (IQR), quartile coefficient of dispersion (QCD) of SBP, DBP, MBP, and PP. Note that the above statistics belong to blood pressure variability (BPV) indices in the literature 9 , 25 , 26 . Though not a blood pressures measure, the pulse rate is vital information and a surrogate of heart rate. We also considered its variation indices as the measures of heart rate variability. These basic parameters serve for the sensitivity analysis. Statistical analysis Spearman’s rank correlation analysis and regression analysis were used for the exploration and association of the perioperative clinical conditions with respect to the mean VarM values of surgical periods. The demographic data and laboratory data were reported as median and interquartile range (IQR). Missing data were addressed using available-case (pairwise) deletion for univariate analyses and complete-case (listwise) deletion for multivariable analyses. Correlation analysis and its 95% confidence interval were validated with bootstrap resampling method without replacement with 100,000 samplings. The performance was assessed with Area under the ROC Curve (AUROC), for which confidence interval curves were evaluated by 100,000 runs of bootstrap resampling as well. The accuracy and precision were derived from the optimal threshold. The AUROC served as an overall performance evaluation while accuracy and precision provide more clinical meaning. We assessed the prediction performance by Leave-one-out cross-validation and four-fold cross-validation with Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance 27 . The null hypothesis was performed with Wilcoxon rank sum exact test and was considered significant when the P value < 0.05. To delineate the individual effect and CPB effect, we used a linear mixed-effects model by treating the individual case as a random intercept. The CPB effect is represented by the transition of surgical step from the central venous access period to the period from post-CPB to 10 minutes after the closure of the sternum bone as individual linear slopes. This model considered dependency on the inter-individual variation and the evolution with the ongoing surgery in the statistical test. The level for each individual case models the unique property of each patient. For the mixed-effects modeling, we calculated the VarM indices as a mean on every tenth part for each surgical period to create repeated measurement conditions for modeling, as well as the slopes. The difference between models was evaluated with an Analysis of variance (ANOVA). The interactions of VarM with the other preoperative factors, eGFR and EuroSCORE, as well as the intraoperative factor, CPB time, were included in mixed-effects modeling also. All the statistical analyses were carried out with R (version 4.5.0; the R Foundation for Statistical Computing). Results Epidemiology From 116 enrolled patients undergoing CABG and/or valvular surgery requiring CPB support, we excluded 15 cases due to suboptimal data quality. We analyzed baseline data from 101 patients (83 males and 18 females; Age 64.6[59,70.0]) and evaluated AKI outcome from 83 non-ESRD cases ( Fig. 2 ). There were 27 AKI cases (32.5%) who presented higher creatinine postoperatively (CreD1: 1.55 [1.31,2.08], CreD2: 1.19 [0.9,1.4], CreD3: 1.2 [0.92,1.42], unit: mg/dL) than 56 non-AKI cases (CreD1: 0.94 [0.84,1.17], CreD2: 0.75 [0.63,0.86], CreD3: 0.75 [0.66,0.89]). Longer CPB time was associated with higher AKI incidence (Spearman’s correlation coefficient (CC) = 0.415[0.200,0.584], p = 0.0001). Valvular procedures involving cardiac arrest is associated with AKI (Spearman’s CC = 0.409 [0.173, 0.603], p = 0.0001). Associations were observed between vasopressor and AKI for the final rate of norepinephrine (Spearman’s CC = 0.262 [0.036,0.477], p = 0.016) and that of dopamine (Spearman’s CC = 0.274 [0.053,0.475], p = 0.012). Download figure Open in new tab Figure 2. Flowchart of patient selection. AKI incidence was higher when valvular procedure was involved. Af, atrial fibrillation. CABG, coronary artery bypass graft. MIDCAB, minimally invasive direct coronary artery bypass. PPM, permanent pacemaker. Overview of surgical periods VarM indices were highest during the baseline period before anesthetic induction and decreased afterward in both the AKI group and non-AKI group ( Fig. 3 ) until CPB. After weaning from CPB, VarM indices increased toward sternal closure (slope =72.24214, p=0.00057). In mixed effects modeling, including individual case as a random effect showed an improvement in model fitting (mixed AIC:1299, regression AIC:1688, ANOVA: p < 2.2 X 10 -16 ), which indicates strong interindividual variation. Meanwhile, the AKI group presented lower VarM as the random effect variable (-141 [-260,20.3]) than the non-AKI group (160 [42, 272], p< 2.2 X 10 -16 ) in this model. This indicates that the inter-individual variation supports AKI prediction. Download figure Open in new tab Figure 3. The group plot shows VarM evolving with the surgery steps and the higher VarM of non-AKI group after CPB. The indices before anesthetic induction (panel A) were highest and decreased as the surgery going on before CPB. After weaning from CPB, the AKI group presented lower VarM than non-AKI group (panel A). Postoperative day 1 change of creatinine exhibited this association (panel B) where Linear regression lines and 95% confidence interval are superimposed on the scatterplots (panel B). On the surgical steps 30 minutes after weaning from CPB to sternal closure, the association with AKI is better than any basic vital measures in terms of AUROC (panel C) and Spearman’s correlation coefficient (panel D). VarM indices during the baseline period presented no association with eGFR (Spearman’s CC=0.196[-0.005, 0.386], p=0.062) and were also not associated with AKI (Spearman’s CC=0.10, p=0.39). After weaning from CPB, the non-AKI group showed higher VarM values than the AKI group (p=0.003) ( Fig. 3A ). The postoperative increase of creatinine is also negatively associated with VarM following CPB weaning ( Fig. 3B ). The period from CPB weaning to sternal closure showed the strongest statistical association of VarM indices with AKI ( Fig. 3C-D ), when compared with other vital parameters. Associations of baseline renal function After weaning from CPB, VarM presented a negative association with baseline eGFR during the first 10 minutes following CPB (Spearman’s CC=-0.519 [-0.656, -0.349], p= 5.77 X 10 -7 ) and the during the weaning-CPB-to-sternal-closure period (Spearman’s CC=-0.345 [-0.552, -0.108], p=0.004) but showed no eGFR association after the sternal closure period ( Fig. 3D ). Associations of postoperative AKI In the period after weaning from CPB to sternal closure, VarM showed a significant association with postoperative AKI (Spearman’s CC=-0.387 [-0.597, -0.152], p=0.0029) ( Fig. 4 ). VarM during the sternal closure period was marginally associated with AKI (Spearman’s CC= -0.30 [-0.52, -0.021], p=0.023). The trends of VarM after weaning from CPB were upward. Download figure Open in new tab Figure 4. Spearman’s correlations to perioperative renal conditions. VarM after weaning from CPB shows AKI association. It showed that the low slope of VarM change across CPB favors AKI (p= 0.00012). The mixed model with patient as a random effect showed strong inter-individual variation (p < 2.2 X 10 -16 ), while the AKI group presented lower VarM (AKI vs non-AKI, p=0.0036). Intraoperative factors associated with AKI include longer bypass time (Spearman’s CC=0.415[0.20, 0.58], p=0.0001) and valvular surgery (Spearman’s CC=0.41 [0.17, 0.60], p=0.00012). The vasopressor dosage rates at the end of surgery showed marginal associations for Norepinephrine (Spearman’s CC=0.26 [0.036,0.477], p=0.016) and on Dopamine (Spearman’s CC=0.27 [0.053,0.47], p=0.012). Other physiological parameters in the corresponding surgical periods, including various BP measures, heart rate, and their derived variations, were not associated with either baseline renal function or AKI. Postoperative AKI was not associated with the baseline renal function (eGFR) (Spearman’s CC=-0.185, p=0.094 [-0.387, 0.0346]). AKI prediction contribution To evaluate the contribution of VarM in the presence of other known risk factors ( Fig. 5 ), the VarM index with the slopes among surgical steps, baseline eGFR, EuroSCORE, and CPB time combined showed better performance (AUC: 0.783, Fig. 5A ) than the model that excluded the slopes among surgical steps of VarM (AUC:0.765, p= 0.00069, Fig. 5B ), which in turn outperformed the combination of eGFR, EuroSCORE, and CPB time ( Fig 5C , AUC: 0.762, p=0.0025). Both VarM and CPB time as the only intraoperative factors combined also achieved good performance with AUC = 0.760 ( Fig. 5D ). In summary, VarM and its changes across surgical steps enhance the predictive performance of other clinical variables for AKI. Download figure Open in new tab Figure 5. Regression analyses of multiple preoperative and/or intraoperative factors on AKI prediction presented with ROC curve, AUC and their 95% confidence interval curves. Combining VarM and CPB time as intraoperative factors with baseline eGFR and EuroSCORE as preoperative factors (panel A) yields the best performance, while VarM without the effect of surgical step (panel B) shows the secondary-best performance. All other factors without VarM (panel C) perform third best, while only intraoperative factors, VarM and CPB time, reach an AUC of 0.760. Basic vital parameters comparison and their variations After weaning from CPB, VarM leads in the AKI prediction performance (AUROC = 0.762) in comparison with other common vital parameters, SBP, DBP, and PP, and their variability indices ( Fig.6 and Supplementary table S1). So is the tendency after the sternal closure (AUROC = 0.692). The best basic vital parameter is SBP measured 10-minute after weaning CPB. (AUROC = 0.646) Download figure Open in new tab Figure 6. In the prediction of postoperative AKI, VarM indices after weaning from CPB (A,B,C) outperform all common vital sign parameters during the period from weaning CPB to after sternal closure. Discussion The main finding is that lower VarM and a decreased trend after weaning from CPB are associated with AKI. These provide additive predictive value for AKI risk. Change within ongoing surgery During surgery, VarM was highest before anesthetic induction and decreased as the operation progressed, while the association with AKI became stronger during the periods after weaning from CPB. This result suggests that VarM reflects pathophysiological processes relevant to AKI. The AKI group presented lower VarM after weaning from CPB than the non-AKI group, as well as the lower slope across periods before and after CPB ( Fig. 3A ). This suggests that VarM could reflect unique clinical conditions pertaining to each surgical period, particularly the CPB effect. Whether there is less stress response, more functional reserve, or better recovery capacity, VarM might reflect the properties of the non-AKI group. This intraoperative change is similar to that observed in our previous study of liver transplant surgery where VarM in the neohepatic phase was associated with better clinical outcomes for the recipient 12 . Since both studies involved major surgery, this suggests that VarM is not specific to a single organ or system. AKI and cardiovascular system VarM predicts AKI better than HR, SBP, DBP, PP and their variability indices (Supplementary table S1). To our knowledge, PP in the post-CPB period is not predictive of AKI in the literature. Mitrev et al. have reported that PP before CPB is predictive, but not in post-CPB period or during the first hour in the ICU 15 . This suggests that VarM carries information that other currently available indices do not. The AKI incidence (31%) in this study is comparable to that in the other research (25-45%) 28 . The intraoperative factors reported in the literature include the vasopressor consumption, the type of surgery, and the duration of CPB 28 – 32 , which were also observed in our results. The pathophysiology of AKI includes an inflammatory response, hypoperfusion, or stress from CPB 33 – 35 , all of which involve the cardiovascular system. Extreme BP values reveal properties of the cardiovascular system: hypotension poses a risk of hypoperfusion 6 , 35 – 38 , while a high BP may reflect arterial stiffness or other harmful conditions 7 . These findings support the association between AKI and indices derived from the cardiovascular system. Clinical perspectives The combination of VarM with other clinical variables, including EuroSCORE, CPB time, and the preoperative eGFR, shows additive performance improvement. This result indicates the unique intraoperative information presented by VarM. Together, these factors providing preoperative and intraoperative information may aid AKI risk assessment before completion of surgery. The notion of ABP waveform morphological variation is different from the BP measures with respect to waveform, such as SBP (maximum), DBP (minimum), MBP (mean). The subtle waveform variation is related to various cardiovascular conditions 13 . While ABP waveform morphology is determined by the physical principles and physiological properties of the cardiovascular system 13 , its variation could be the joint result of multiple regulatory mechanisms constantly adjusting to maintain homeostasis. VarM is the aggregate of their effects. Growing evidence supports its association with clinical conditions, including the outcome of liver transplant surgery and the survival of septic shock 12 , 18 . Even the photoplethysmography appears to possess this clinical value 19 . Our results show improved prediction when combining multiple variables. As anesthesiologists make judgements constantly based on all available information, a real-time VarM index might be useful. Certainly, future studies examining this application are needed. Limitations Several limitations of this study should be mentioned. First, this is the first prospective study of VarM on AKI in cardiac surgery. While more studies are needed for validation, it might be challenging to consider retrospective data because waveform data are not commonly recorded. A large-scale prospective study is therefore mandatory. Second, it is not trivial to elucidate the pathophysiologic mechanism underlying VarM and AKI, as multiple factors and complex interactions are involved. We speculate that including blood and urine biomarkers would shed light on this. How VarM evolves with other clinical data also deserves further exploration. Third, all the surgeries were carried out by 3 experienced cardiac surgeons. While our results benefit from surgical consistency, future studies in different clinical settings or geographic locations would help with generalization. Fourth, our anesthetic management created unique conditions for VarM measurement. The anesthetized patient, close hemodynamic management, and intra-arterial BP monitoring create conditions for waveform analysis. Otherwise, a kinked or a damped ABP tube would inevitably invalidate the VarM measure, so does the motion artifact. Lastly, VarM information is not perceptible to the naked eye, so a device would be necessary in the future. Conclusion Morphological changes in the ABP waveform after weaning from CPB are associated with postoperative AKI in cardiac surgery. Data Availability All data produced in the present study and the R-script for statistical analysis are available upon reasonable request to the authors MISC Funding This work was supported by National Science and Technology Council, Taiwan (NSTC 112-2115-M-075-001-) and Shin Kong Wu Ho-Su Memorial Hospital, Taiwan (Grant No. 2021SKHADR026). The funders had no role in the study design, data collection, data analysis, data interpretation, manuscript writing, or the decision to submit the manuscript for publication. Acknowledgement Footnotes Email: Cheng-Hsi Chang: acetfo{at}gmail.com , Shen-Chih Wang: akkwang{at}gmail.com , Wen-Kuei Chang: wkchang{at}vghtpe.gov.tw , Chien-Kun Ting: ckting2{at}gmail.com , Ming-Jen Lu: M000726{at}ms.skh.org.tw , Chia-Hsun Lin: M000678{at}ms.skh.org.tw , Chai-Hock Chua: M006513{at}ms.skh.org.tw , Hau-Tieng Wu: hauwu{at}cims.nyu.edu , Yu-Ting Lin: linyuting{at}hotmail.com.tw Funding disclosure: This work was supported by the National Science and Technology Council, Taiwan (grant NSTC 112-2115-M-075-001-, to Yu-Ting Lin) and by Shin Kong Wu Ho-Su Memorial Hospital, Taiwan (grant 2021SKHADR026, to Cheng-Hsi Chang). The funders had no role in the study design, data collection, data analysis, data interpretation, manuscript writing, or the decision to submit the manuscript for publication. Conflict of Interest: The authors declare that there are no conflicts of interest to disclose. 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Share Intraoperative arterial blood pressure waveform variation predicts short-term acute kidney injury after cardiac surgery Cheng-Hsi Chang , Shen-Chih Wang , Wen-Kuei Chang , Chien-Kun Ting , Ming-Jen Lu , Chia-Hsun Lin , Chai-Hock Chua , Hau-Tieng Wu , Yu-Ting Lin medRxiv 2025.10.12.25337819; doi: https://doi.org/10.1101/2025.10.12.25337819 Share This Article: Copy Citation Tools Intraoperative arterial blood pressure waveform variation predicts short-term acute kidney injury after cardiac surgery Cheng-Hsi Chang , Shen-Chih Wang , Wen-Kuei Chang , Chien-Kun Ting , Ming-Jen Lu , Chia-Hsun Lin , Chai-Hock Chua , Hau-Tieng Wu , Yu-Ting Lin medRxiv 2025.10.12.25337819; doi: https://doi.org/10.1101/2025.10.12.25337819 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Anesthesia Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9219) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffcb44c0e7a06f7',t:'MTc3OTQ2MjE3MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();