1243 ijanaesth Indian Journal of Anaesthesia Indian J Anaesth Wolters Kluwer -- Medknow Publications PMC12965424 12965424 12965424 41799243 10.4103/ija.ija_1519_25 Comparison of the Hypotension Prediction Index and pulse pressure variation-guided haemodynamic management for intra-operative hypotension during kidney transplant: A randomised controlled trial Aditya Ashish S 1 Kajal Kamal 1 ✉ Sethi Sameer 1 Premkumar Madhumita 1 Naik Naveen 1 Sharma Ashish 2 1 Department of Anaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, India 1 Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India 2 Renal Transplant Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India ✉ Address for correspondence: Dr. Kamal Kajal, Postgraduate Institute of Medical Education and Research, Chandigarh, Pin - 160 012, India. E-mail: kamal.kajal@gmail.com 30 1 2026 70 Suppl 1 S50 S50–S58 7 3 2026 Copyright: © 2026 Indian Journal of Anaesthesia This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License (CC BY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Abstract Background and Aims: Intra-operative hypotension (IOH) adversely affects renal graft and recipient outcomes. The Hypotension Prediction Index (HPI), a machine learning-based algorithm, predicts IOH. This study compared HPI- versus pulse pressure variation (PPV)-guided haemodynamic management in kidney transplantation, hypothesising that HPI guidance would reduce intra-operative hypotension. Methods: In this single-centre randomised controlled trial, 82 patients with end-stage renal disease (ESRD) undergoing live-related kidney transplantation under general anaesthesia were randomised to HPI-guided ( n = 41) or PPV-guided ( n = 41) haemodynamic management. The primary outcome was the time-weighted average area under the threshold (TWA-AUT) of mean arterial pressure (MAP) <65 mmHg. Secondary outcomes included absolute area under the threshold (AUT), the number and total duration of hypotensive episodes per patient, and the proportion of patients with MAP <50 mmHg. Delayed graft function, serum creatinine on post-operative day 3, hospital stay, and 90-day mortality were also compared. Results: The results were analysed for 82 patients. The median [interquartile range (IQR)] TWA-AUT <65 mmHg was 0.10 mmHg [0.04–0.17] in the HPI group and 0.16 mmHg [0.06–0.32] in the PPV group, with a median difference of –0.05 mmHg [95% confidence interval (CI): –0.18 to 0.01; P = 0.09]. The median AUT was 24.8 mmHg·min in the HPI group and 43.3 mmHg × min in the PPV group ( P = 0.10). Hypotensive events occurred in 47% of patients in the HPI group versus 61% in the PPV group ( P = 0.16). No significant differences were observed in the secondary outcomes between the groups. Conclusion: HPI-guided haemodynamic management did not significantly reduce the time-weighted average of intra-operative hypotension during renal transplant surgery compared to PPV-guided haemodynamic management. Keywords: Goal-directed therapy, haemodynamic monitoring, hypotension prediction index, intra-operative hypotension, pulse pressure variation, renal transplant status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2025 Nov 11; Revised 2026 Jan 14; Accepted 2026 Jan 14; Issue date 2026 Jan. INTRODUCTION Kidney transplantation is the most frequently conducted solid organ transplant globally and continues to be the definitive treatment option for end-stage renal disease (ESRD).[ 1 ] Following graft implantation, kidney perfusion relies solely on the recipient’s mean arterial pressure (MAP) due to loss of autoregulation. Brief episodes of intra-operative hypotension (IOH) can significantly impair the graft and recipient outcomes.[ 2 ] The total duration of hypotension during surgery is particularly critical as even brief repeated episodes can lead to cumulative ischaemic injury. In this context, the Hypotension Prediction Index (HPI) offers a novel approach for predicting IOH. The HPI software uses a machine learning algorithm that evaluates the arterial pressure waveform to anticipate hypotension (defined as MAP <65 mmHg for at least 1 minute, up to 15 min in advance).[ 3 ] The HPI is a unitless score ranging from 1 to 100, with higher values indicating a greater probability and shorter time to IOH.[ 4 ] Several randomised controlled trials (RCTs) and a recent systematic review have underscored the clinical benefits of HPI in enhancing intra-operative haemodynamic stability.[ 5 , 6 , 7 , 8 ] Recent RCTs in non-cardiac surgery compared FloTrac-based goal-directed therapy (GDT) with and without additional HPI monitoring and found significantly fewer hypotensive episodes with HPI guidance.[ 9 , 10 ] A retrospective study in kidney transplant recipients found less IOH with HPI-guided management versus FloTrac alone, although the difference was not statistically significant.[ 11 ] In GDT, haemodynamic management is guided not only by MAP but also by dynamic parameters such as pulse pressure variation (PPV), stroke volume variation (SVV), dynamic arterial elastance, and the HPI, along with static measures including cardiac index (Ci) and systemic vascular resistance (SVR).[ 12 , 13 , 14 ] This RCT evaluated whether HPI-guided management, compared with PPV-guided therapy, reduces the incidence and duration of IOH during live-donor kidney transplantation. The primary outcome was the time-weighted average of the area under the threshold (TWA-AUT) MAP <65 mm Hg. Secondary outcomes included the absolute area under the threshold (AUT), number and total duration of hypotensive episodes per patient, and number of patients with MAP <50 mmHg. Additional intra-operative variables recorded included cold ischaemia time, intravenous fluid administration, urine output, and blood loss. Post-operative outcomes, such as delayed graft function (DGF), serum creatinine on post-operative day 3, length of hospital stay, and 90-day mortality, were also compared between the groups. METHODS This randomised, controlled, single-centre trial was conducted between April 2023 and October 2024 at a tertiary-care academic hospital. Ethical approval was obtained from the Institutional Ethics Committee (approval no. INT/IEC/2022/SPL-969). The trial was prospectively registered with the Clinical Trials Registry-India (CTRI/2023/03/050702) on 15/03/2023. The study was conducted in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines. Written informed consent was obtained from all participants. The procedures followed were in accordance with the Declaration of Helsinki (2013, World Medical Association) and Good Clinical Practice guidelines. Patients aged >18 years with ESRD scheduled to undergo living-related kidney transplantation under general anaesthesia were included. Exclusion criteria were refusal of consent, procedures without controlled ventilation or under regional anaesthesia, contraindications to invasive arterial blood pressure (BP) monitoring, atrial fibrillation, pregnancy, massive ascites, underlying respiratory failure defined as forced expiratory volume in one second to forced vital capacity ratio (FEV 1 /FVC) <70% and forced expiratory volume in one second (FEV 1 ) <50%, ongoing active infection, deceased-donor renal transplantation, heart failure (New York Heart Association class IV), known clinically significant intracardiac shunts, severe valvular heart disease, and dilated cardiomyopathy. Anaesthesia and protocol-based interventions were performed by attending anaesthesiologists trained in goal-directed haemodynamic management. Patients were randomised in a 1:1 ratio to either the Hypotension Prediction Index (HPI) group ( n = 41) or the pulse pressure variation (PPV) group ( n = 41) using computer-generated random numbers generated by an independent investigator. Allocation concealment was achieved using sequentially numbered, opaque, sealed envelopes, which were opened on the day of surgery by a person not involved in patient care. Owing to the nature of the intervention, the attending clinicians were not blinded; however, patients and the data analyst were blinded to group allocation. Both groups received therapy according to predefined haemodynamic algorithms [ Figure 1 ]. Figure 1 Algorithm for intra-operative haemodynamic management. SVV: Stroke volume variation; SVR: Systemic vascular resistance; BSS: Balanced salt solution; CO: Cardiac output; PPV: Pulse pressure variation; aPPV: Augmented pulse pressure variation; MAP: Mean arterial pressure; HPI: Hypotension prediction index Patients were continuously monitored in the operating room using multi-channel monitors (Datex-Ohmeda S/5 Avance) for heart rate (HR), BP, electrocardiography, and peripheral oxygen saturation. General anaesthesia was induced with intravenous fentanyl (2 µg/kg) and propofol (2–3 mg/kg), followed by atracurium (0.5 mg/kg) to facilitate tracheal intubation. Anaesthesia was maintained with isoflurane [minimum alveolar concentration (MAC) 1–1.3] in a 50% air–oxygen mixture under controlled positive-pressure ventilation, targeting an end-tidal carbon dioxide concentration of 32–36 mmHg. Radial arterial cannulation was performed, avoiding limbs with arteriovenous fistulae. All transplant procedures were performed by the same surgical team. During induction, patients received a 100 mL bolus of Plasma-Lyte A (Baxter India Pvt. Ltd., Gurgaon, India), followed by a maintenance infusion at 50 mL/h. A mandatory 500 mL bolus was administered during vascular anastomosis in both groups to optimise graft perfusion, with additional fluids administered as per the respective group algorithms. After induction, advanced haemodynamic monitoring was initiated using the HemoSphere monitoring platform (Edwards Lifesciences, Irvine, CA, USA). Patients were connected either to a FloTrac sensor, which provided continuous measurements of MAP and PPV, or to an Acumen IQ sensor, which displayed MAP, SVV, SVR, and Ci. Intra-operative management in both groups was guided by a pre-defined treatment algorithm [ Figure 1 ]. In the HPI group, haemodynamic management was guided by the HPI software integrated within the HemoSphere platform and connected to the Acumen IQ arterial pressure transducer. Management decisions were based on HPI values, in conjunction with Acumen-derived parameters (MAP, SVV, SVR, and Ci), according to the pre-defined algorithm [ Figure 1 ]. Interventions were initiated when MAP fell below 65 mmHg or HPI exceeded 85, with the aim of maintaining MAP ≥65 mmHg and HPI ≤85. Hypotension was managed according to SVV and SVR values. Patients with SVV >13% and SVR >800 received 200 mL fluid boluses, whereas those with SVV >13% and SVR <800 received fluids along with phenylephrine (50–100 µg). For SVV <13% with SVR <800, phenylephrine was administered. When SVV <13% with SVR >800, management was guided by changes in cardiac output, and ephedrine was administered if cardiac output decreased by ≥10%. In the PPV group, haemodynamic management was based on continuous PPV and MAP measurements obtained from the FloTrac sensor, according to the corresponding algorithm. Hypotension, defined as MAP <65 mmHg, was treated with 200 mL fluid boluses when PPV was >13% or with phenylephrine (50–100 µg) when PPV was <9%. For PPV values between 9% and 13% (grey zone), the tidal volume was temporarily increased from 8 to 12 mL/kg, with the respiratory rate adjusted to maintain minute ventilation (augmented PPV). After 2 minutes, if PPV was ≥17%, a 200 mL fluid bolus was administered; if PPV remained <17%, phenylephrine was used. MAP and HPI values were continuously recorded using the Acumen IQ sensor and the HemoSphere monitoring platform. All haemodynamic variables, including calibrated systolic, diastolic, and mean arterial pressuress, were stored as 20-second averaged values and exported via universal serial bus for offline analysis. Hypotension was defined as MAP <65 mmHg. The AUT was calculated as the integral of the product of MAP magnitude and duration for values below 65 mmHg. The TWA-AUT was calculated by dividing the AUT by the total monitoring time. Each dataset was assigned a unique device-generated identification code, and analyses were performed by an investigator blinded to group allocation. After graft reperfusion, 100 mL of 20% mannitol and 20 mg of furosemide were administered as per institutional protocol to optimise graft perfusion, followed by ureteroneocystostomy, with confirmation of urine output. Intra-operatively, hourly urine output and blood loss were replaced with equal volumes of Plasma-Lyte A. Residual neuromuscular blockade was reversed, and tracheal extubation was performed according to standard criteria. Post-operatively, patients were managed in the transplant intensive care unit (ICU), where immunosuppressive therapy and antihypertensive medications were administered as per institutional protocol. Intravenous fluids were administered hourly for the first 48 hours to maintain urine output. Post-operative outcomes included DGF, serum creatinine on post-operative day 3, ICU and hospital durations of stay, and 90-day mortality. DGF was defined as the requirement for dialysis within the first post-operative week. Sample size was calculated based on prior data estimating that the mean TWA-AUT MAP in the control group was <65 mmHg [standard deviation (SD) 0.51].[ 6 , 15 ] A 75% reduction in TWA-AUT, corresponding to a mean value of 0.12 mmHg in the HPI group, was considered clinically relevant. With a two-sided alpha level of 0.05 and 90% power, a total of 74 patients were required to detect this difference using a two-sample t -test. Allowing for a 10% dropout rate, the final target sample size was 82 patients. Continuous variables are presented as mean (SD) or median [interquartile range (IQR)], depending on the distribution of the data. The normality of the data was assessed using the Kolmogorov–Smirnov test. Comparisons between two independent groups were performed using the unpaired t -test or the Mann–Whitney U test, as appropriate. Median differences with 95% confidence intervals (CIs) were calculated using the Hodges–Lehmann method. Categorical variables are presented as numbers (percentages) and were compared using Pearson’s Chi-square test or Fisher’s exact test, as appropriate. Statistical analysis was performed using R software (R Foundation for Statistical Computing, Austria; version 4.1.2) and Acumen Analytics software (Edwards Lifesciences). All analyses were two-tailed, and a P value < 0.05 was considered statistically significant. RESULTS One hundred patients with ESRD scheduled for renal transplantation were assessed for eligibility. Sixteen patients were excluded due to atrial fibrillation ( n = 2), severe valvular heart disease ( n = 3), dilated cardiomyopathy ( n = 4), severe tricuspid regurgitation (TR) ( n = 3), refusal to participate ( n = 2), or logistical reasons ( n = 2). The remaining 86 participants were randomised into the HPI group ( n = 43) or the PPV group ( n = 43). Two patients in each group were excluded from the final analysis, resulting in a total of 82 participants (HPI: n = 41; PPV: n = 41) included [ Figure 2 : Study flow chart]. Figure 2 Study flow chart. Group HPI: Hypotension Prediction Index group; Group PPV: Pulse pressure variation group; DCMP: Dilated cardiomyopathy; TR: Tricuspid regurgitation The mean (SD) age of participants was 33 (11) years in the HPI group and 32 (11) years in the PPV group. The majority of participants in both groups were males. More than 70% of participants had a history of hypertension. Participant demographics and perioperative variables are summarised in Table 1 . There were no significant differences between the HPI and PPV groups in cold ischaemia time, intra-operative blood loss, or urine output after reperfusion. Table 1 Demographics and perioperative variables Demographic HPI group ( n =41) PPV group ( n =41) P Age; years, mean (SD) 33 (11) 32 (11) 0.51 Gender Male, n (%) 35 (85.4%) 33 (80.5%) 0.77 Female, n (%) 6 (14.6%) 8 (19.5%) BSA; m 2 , mean (SD) 1.61 (0.22) 1.59 (0.22) 0.41 BMI; kg.m -2 , mean (SD) 21.26 (3.62) 20.04 (4.20) 0.53 Comorbidities Hypertension, n (%) 31 (72.09%) 29 (70.73%) 0.89 Diabetes mellitus, n (%) 7 (16.28%) 9 (21.95%) 0.51 Perioperative Variables Cold ischaemia time; minutes, mean (SD) 53 (10) 55 (11) 0.362 Blood loss; mL, mean (SD) 345 (84) 319 (76) 0.166 Intra-operative urine output after reperfusion; mL, mean (SD) 376 (218) 391 (211) 0.703 Continous variables as mean (SD); Categorical variables as percentage are presented as, number (percentage). BSA: Body surface area; BMI: Body mass index; SD: Standard deviation; n : Number of patients The median TWA-AUT of mean MAP <65 mmHg during the monitoring period was 0.10 mmHg (IQR: 0.04–0.17) in the HPI group and 0.16 mmHg (IQR: 0.06–0.32) in the PPV group, with a median difference of –0.05 mmHg (95% CI: –0.18 to 0.01; P = 0.09). The median monitoring duration was 271 minutes (IQR: 228–281) in the HPI group and 249 minutes (IQR: 214–300) in the PPV group. The Hodges–Lehmann estimator for the difference in monitoring time was –5.6 minutes (95% CI: –46 to 36.3; P = 0.75). The median AUT was 24.8 mmHg × minutes (IQR: 11–42) in the HPI group and 43.3 mmHg × minutes (IQR: 17–117) in the PPV group, yielding a median difference of –14.3 mmHg × minutes (95% CI: –49.3 to 2.6; P = 0.10) [ Table 2 , Figures 3a and b ]. Table 2 Comparison of HPI and PPV groups on cumulative hypotension and postoperative outcomes Primary Outcome HPI group ( n =41) PPV group ( n =41) Mean or Median difference (95% CI) P TWA-AUT (MAP<65 mmHg); mmHg, median [IQR] 0.1 [0.04-0.17] 0.16 [0.06-0.32] -0.05 [-0.18 to 0.01] 0.09 Monitoring time; min, median [IQR] 271 (228-281] 249 [214-300] -5.6 [-46 to 36.3] 0.75 Secondary Outcomes AUT (MAP <65 mmHg); mmHg x min, median [IQR] 24.8 [11-42] 43.3 [17-117] -14.3 [- 49.3 to 2.6] 0.10 Patients with hypotensive events, n (%) 20 (47%) 25 (61%) 0.16 Hypotensive events per patient, median [IQR] 0 [0-2] 1 [0-3] -9.6 (-1 to 4.2) 0.20 Duration of hypotensive events per patient; min, median [IQR] 0 [0-11] 2.8 (0-11) -1 (-3.7 to 3.8) 0.07 Percentage of time with MAP <65 mmHg, median [IQR] 3 [1-4] 3 [1-11] -0.6 (-6 to 0.7) 0.33 Patients with severe hypotensive events (MAP <50 mmHg), n (%) 2 (4.7) 4 (9) 0.37 Perioperative Fluids and Vasopressors Phenylephrine Dose; μg, median [IQR] 150 [138-200] 16 (39%) 200 [162-300] 18 (43%) -50 [-150 to 450] 0.09 Ephedrine Dose; mg, median [IQR] 5 [5-10] 8 (19.5%) - - Intra-operative fluid; mL, mean (SD) 1903 (434) 1796 (337) 0.151 Post-operative outcomes Delayed graft function, n (%) 1 (2.4%) 2 (4.8%) - 0.55 POD 3 creatinine; mg/dL, median [IQR] 1.3 [1-1.8] 1.3[1-1.5] - 0.68 Post-operative ICU stay in days, median [IQR] 5 [5-10] 5 [5-6] - 0.26 Hospital Stay in days, median [IQR] 10 [7-12] 8 [7-10] - 0.13 90-day Mortality, n (%) 0 1 (2.4) 0.31 Values are presented as mean (SD), median [IQR], or number (percentage). Median differences and their 95% confidence intervals were calculated with the Hodges–Lehmann method. AUT: Area under threshold; TWA-AUT: Time-weighted average of AUT; POD: Post-operative day; SD: Standard deviation; IQR: Interquartile range; CI: Confidence interval; n : Number of patients; MAP: Mean arterial pressure; ICU: Intensive care unit Figure 3 Comparison of TWA-AUT and AUT between PPV and HPI groups. (a) TWA-AUT. (b) AUT. Statistical significance is determined using the Mann–Whitney test. AUT = area under threshold; TWA-AUT = time-weighted average of AUT Hypotensive episodes occurred in 20 patients (47%) in the HPI group and 25 patients (61%) in the PPV group ( P = 0.16). The median number of hypotensive events per patient was 0 (IQR: 0–2) in the HPI group and 1 (IQR: 0–3) in the PPV group, with a median difference of –0.6 events (95% CI: –1 to 4.2; P = 0.20). The median duration of hypotensive events per patient was 0 minutes (IQR: 0–11) in the HPI group versus 2.8 minutes (IQR: 0–11) in the PPV group, with a median difference of –1.0 min (95% CI: –3.7 to 3.8; P = 0.07). The percentage of time spent with MAP <65 mmHg was similar between the groups: 3% (IQR: 1–4) in the HPI group and 3% (IQR: 1–11) in the PPV group, with a median difference of –0.6% (95% CI: –6 to 0.7; P = 0.33) [ Table 2 ]. There were no significant differences between the HPI and PPV groups with respect to DGF, serum creatinine on post-operative day 3, ICU or hospital length of stay, and 90-day mortality [ Table 2 ]. DISCUSSION In this prospective randomised study, HPI-guided haemodynamic management did not reduce the cumulative amount of IOH compared to PPV-guided management, as measured by the TWA-AUT for MAP <65 mmHg. Intra-operative fluid administration, urine output, serum creatinine at 72 hours, and DGF were also comparable between the groups. Goyal et al .[ 11 ] conducted a retrospective observational study in 28 kidney transplant recipients with dilated cardiomyopathy, comparing HPI-guided management with FloTrac-based therapy. Consistent with our findings, they reported no statistically significant difference in cumulative hypotension between the groups, with TWA-AUT values of 0.10 and 0.15 mmHg, respectively. In our study, the median TWA-AUT was similarly comparable between the HPI and PPV groups (0.10 vs. 0.16 mmHg). In non-cardiac surgeries, the median TWA-AUT in control groups has been reported to be very low (0.14 mmHg), which may explain why HPI has not consistently demonstrated clinical benefits in reducing IOH.[ 16 ] In our study, the control group also exhibited a low median TWA-AUT of 0.16 mmHg. The minimal hypotension observed under PPV guidance may explain why HPI did not confer a distinct advantage. In contrast, another study in non-cardiac surgery reported a higher median TWA-AUT of 0.30 mmHg, nearly twice that observed in our control group.[ 17 ] This variation may be attributable to patients with ESRD frequently having resistant hypertension, rendering MAP <65 mmHg an unreliable marker of hypotension for this population. Furthermore, both HPI and PPV groups in our trial were managed with strict, protocolised strategies, and clinicians were specifically instructed to prevent episodes of low BP. The results of the present study differ from earlier reports that concluded HPI monitoring significantly reduces IOH.[ 5 , 6 , 15 ] The control groups in these studies were managed using only conventional arterial lines, without access to advanced haemodynamic parameters such as SVV, stroke volume (SV), or SVR. In a prior trial of major non-cardiac surgery, HPI monitoring independently reduced hypotension compared with FloTrac-guided therapy (TWA-AUT 0.19 vs. 0.66 mmHg), likely due to more frequent and timely norepinephrine administration in the HPI group.[ 18 ] However, this study did not incorporate additional parameters such as pressure change over time (dP/dt) (an indicator of cardiac contractility) or dynamic arterial elastance (Eadyn) (reflecting arterial tone and pressure responsiveness to volume). A subsequent study compared conventional FloTrac-guided therapy with an HPI-based algorithm that included dP/dt and Eadyn as additional parameters and found that, in patients undergoing major gynaecologic oncologic surgery, the HPI-based approach, combined with a GDT protocol, reduced IOH.[ 9 ] Nevertheless, the incremental benefit attributable to HPI itself remains unclear as the intervention group received more comprehensive haemodynamic monitoring than the control group. It is important to recognise that GDT encompasses a wide range of haemodynamic strategies, each defined by specific target variables and intervention thresholds for fluids, vasopressors, and inotropes.[ 19 , 20 , 21 ] In our study, both groups were treated with comprehensive GDT protocols that incorporated PPV, MAP, SVV, SVR, and Ci, thereby enabling timely and informed therapeutic decisions. This may explain the absence of significant differences in vasopressor requirements and intra-operative fluid administration between the two groups. Similarly, Wijnberge et al .[ 15 ] reported that the use of the HPI reduced the incidence of IOH without altering crystalloid, colloid, or vasopressor use, including norepinephrine. Variations in patient populations, surgical complexity, and baseline risk profiles may account for the discrepancies observed across studies. Although HPI can predict impending hypotension, effective prevention still depends on prompt clinical intervention. The reliability of the HPI algorithm depends on the quality of the arterial waveform signal and may be compromised by signal artefacts, arrhythmias, or waveform damping. Furthermore, its performance in patient populations underrepresented in the algorithm’s development remains unclear, and the system’s proprietary nature limits interpretability. False-positive alerts generated by the HPI may contribute to alert fatigue and unnecessary interventions, and their effectiveness can vary depending on the specific monitoring platform and clinical environment.[ 15 , 17 ] Therefore, in this study, the lack of a significant difference in IOH between the groups may be attributed to the structured, protocol-driven management applied to both cohorts, which likely minimised the potential benefits of HPI-guided intervention. This study has several limitations. First, a standard target MAP of 65 mmHg was applied to all participants, regardless of baseline BP. Given that patients with ESRD often exhibit resistant hypertension and typically require multiple antihypertensive agents, this uniform approach may not accurately reflect their unique haemodynamic needs. Second, patients with elevated cardiovascular risk profiles were excluded, limiting the generalisability of our findings to renal transplant recipients with significant cardiovascular comorbidities, who may experience greater haemodynamic instability during surgery. Third, advanced haemodynamic metrics such as dP/dt and Eadyn were not incorporated. The absence of these parameters may have limited the potential benefits observed in the HPI-guided management group. CONCLUSION In conclusion, HPI-guided haemodynamic management alone did not significantly reduce the time-weighted average of IOH during renal transplant surgery. Larger multi-centre studies are warranted to evaluate its impact on hypotension-related graft dysfunction in both deceased and living-donor kidney transplant recipients. Presentation at conferences/CMEs and abstract publication This study was presented at the American Society of Anesthesiologists conference in October 2024 at Philadelphia. Statement on data sharing De-identified data may be requested with reasonable justification from the authors (email to the corresponding author) and shall be shared after approval as per the authors’ Institution policy. Disclosure of use of artificial intelligence (AI)-assistive or generative tools The authors disclose that artificial intelligence (AI)–assistive tools were used for language editing and grammatical improvement only. The authors take full responsibility for the content, accuracy, and integrity of the manuscript. Declaration of use of permitted tools The authors declare the use of standard permitted tools, including reference management software and statistical analysis software, in the preparation of this manuscript. Conflicts of interest There are no conflicts of interest. Acknowledgment The authors acknowledge Dr. Varun Mahajan, Assistant Professor, Department of Anaesthesia and Intensive Care, PGI Chandigarh for his help in writing the manuscript. Funding Statement This work was supported by funds as Intramural grant (sanction order no. IM/299/03-03-23-0797) from Post Graduate Institute of Medical Education and Research, Chandigarh, India. REFERENCES 1. Franjić S. Kidney transplantation is the most common form of solid organ transplantation; the main indication is end-stage renal failure. J Urol Res. 2023;10:1142. 2. Sicova M, McGinn R, Emerson S, Perez P, Gonzalez R, Li Y, et al. Association of intraoperative hypotension with delayed graft function following kidney transplant: A single-centre retrospective cohort study. Clin Transplant. 2024;38:e70000. doi: 10.1111/ctr.70000. 3. Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018;129:663–74. doi: 10.1097/ALN.0000000000002300. 4. Davies SJ, Vistisen ST, Jian Z, Hatib F, Scheeren TWL. Ability of an arterial waveform analysis–derived hypotension prediction index to predict future hypotensive events in surgical patients. Anesth Analg. 2020;130:352–9. doi: 10.1213/ANE.0000000000004121. 5. Schneck E, Schulte D, Habig L, Ruhrmann S, Edinger F, Markmann M, et al. Hypotension prediction index–based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: A single-centre feasibility randomized blinded prospective interventional trial. J Clin Monit Comput. 2020;34:1149–58. doi: 10.1007/s10877-019-00433-6. 6. Tsoumpa M, Kyttari A, Matiatou S, Tzoufi M, Griva P, Pikoulis E, et al. The use of the hypotension prediction index integrated in an algorithm of goal-directed haemodynamic treatment during moderate- and high-risk surgery. J Clin Med. 2021;10:5884. doi: 10.3390/jcm10245884. 7. Murabito P, Astuto M, Sanfilippo F, La Via L, Vasile F, Basile F, et al. Proactive management of intraoperative hypotension reduces biomarkers of organ injury and oxidative stress during elective non-cardiac surgery: A pilot randomized controlled trial. J Clin Med. 2022;11:392. doi: 10.3390/jcm11020392. 8. Li W, Hu Z, Yuan Y, Liu J, Li K. Effect of hypotension prediction index in the prevention of intraoperative hypotension during non-cardiac surgery: A systematic review. J Clin Anesth. 2022;83:110981. doi: 10.1016/j.jclinane.2022.110981. 9. Frassanito L, Giuri PP, Vassalli F, Piersanti A, Garcia MIM, Sonnino C, et al. Hypotension prediction index–guided goal-directed therapy and the amount of hypotension during major gynaecologic oncologic surgery: A randomized controlled clinical trial. J Clin Monit Comput. 2023;37:1081–93. doi: 10.1007/s10877-023-01017-1. 10. Šribar A, Jurinjak IS, Almahariq H, Bandić I, Matošević J, Pejić J, et al. Hypotension prediction index–guided versus conventional goal-directed therapy to reduce intraoperative hypotension during thoracic surgery: A randomized trial. BMC Anesthesiol. 2023;23:101. doi: 10.1186/s12871-023-02069-1. 11. Goyal VK, Shekhrajka P, Mittal S, Bhardwaj M. Impact of FloTrac versus hypotension prediction index–guided haemodynamic management on intraoperative hypotension in kidney transplantation: A retrospective observational study. Indian J Anaesth. 2025;69:496–501. doi: 10.4103/ija.ija_927_24. 12. Kumar L, Rajan S, Baalachandran R. Outcomes associated with stroke volume variation versus central venous pressure–guided fluid replacement during major abdominal surgery. J Anaesthesiol Clin Pharmacol. 2016;32:182–6. doi: 10.4103/0970-9185.182103. 13. Derichard A, Robin E, Tavernier B, Costecalde M, Fleyfel M, Onimus J, et al. Automated pulse pressure and stroke volume variations from radial artery: Evaluation during major abdominal surgery. Br J Anaesth. 2009;103:678–84. doi: 10.1093/bja/aep267. 14. Kannan G, Loganathan S, Kajal K, Hazarika A, Sethi S, Sen IM, et al. The effect of pulse pressure variation compared with central venous pressure on intraoperative fluid management during kidney transplant surgery: A randomized controlled trial. Can J Anaesth. 2022;69:62–71. doi: 10.1007/s12630-021-02130-y. 15. Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, et al. Effect of a machine learning–derived early warning system for intraoperative hypotension versus standard care on depth and duration of intraoperative hypotension during elective non-cardiac surgery: The HYPE randomized clinical trial. JAMA. 2020;323:1052–60. doi: 10.1001/jama.2020.0592. 16. Maheshwari K, Shimada T, Yang D, Khanna S, Cywinski JB, Irefin SA, et al. Hypotension prediction index for prevention of hypotension during moderate- to high-risk non-cardiac surgery. Anesthesiology. 2020;133:1214–22. doi: 10.1097/ALN.0000000000003557. 17. Salmasi V, Maheshwari K, Yang D, Mascha EJ, Singh A, Sessler DI, et al. Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after non-cardiac surgery: A retrospective cohort analysis. Anesthesiology. 2017;126:47–65. doi: 10.1097/ALN.0000000000001432. 18. Yoshikawa Y, Maeda M, Kunigo T, Sato T, Takahashi K, Ohno S, et al. Effect of using hypotension prediction index versus conventional goal-directed haemodynamic management to reduce intraoperative hypotension in non-cardiac surgery: A randomized controlled trial. J Clin Anesth. 2024;93:111348. doi: 10.1016/j.jclinane.2023.111348. 19. Jessen MK, Vallentin MF, Holmberg MJ, Bolther M, Hansen FB, Holst JM, et al. Goal-directed haemodynamic therapy during general anaesthesia for non-cardiac surgery: A systematic review and meta-analysis. Br J Anaesth. 2022;128:416–33. doi: 10.1016/j.bja.2021.10.046. 20. Deng C, Bellomo R, Myles P. Systematic review and meta-analysis of the perioperative use of vasoactive drugs on postoperative outcomes after major abdominal surgery. Br J Anaesth. 2020;124:513–24. doi: 10.1016/j.bja.2020.01.021. 21. Saugel B, Thomsen KK, Maheshwari K. Goal-directed haemodynamic therapy: An imprecise umbrella term to avoid. Br J Anaesth. 2023;130:1–3. doi: 10.1016/j.bja.2022.12.022.