# Hemolysis Index, Carboxyhemoglobin, and Methemoglobin for the Early Identification of Patients at Risk for Cardiac Surgery–Associated Acute Kidney Injury

Emmanuel Golinvaux, Thomas Goronflot, Julien Cadiet, Thomas Sénage, Bertrand Rozec, Elodie Boissier, Edith Bigot-Corbel, Karim Lakhal

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Emmanuel Golinvaux, Thomas Goronflot, Julien Cadiet, Thomas Sénage, Bertrand Rozec, et al. Hemolysis Index, Carboxyhemoglobin, and Methemoglobin for the Early Identification of Patients at Risk for Cardiac Surgery–Associated Acute Kidney Injury. Journal of Cardiothoracic and Vascular Anesthesia, 2024, 38 (12), pp.3086-3093. 10.1053/j.jvca.2024.09.143. hal-05095645

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https://nantes-universite.hal.science/hal-05095645v1

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Original Article

# Hemolysis Index, Carboxyhemoglobin, and Methemoglobin for the Early Identification of Patients at Risk for Cardiac Surgery—Associated Acute Kidney Injury

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Emmanuel Golinvaux, MD*, Thomas Goronflot, MSc†,
Julien Cadiet, MD*, Thomas Senage, MD, PhD‡,§,
Bertrand Rozec, MD, PhD*,||,
Elodie Boissier, Pharm D¶,
Edith Bigot-Corbel, Pharm D, PhD**, Karim Lakhal, MD*,¹

**Objectives:** Hemolysis is a contributor to CS-AKI. Biochemistry analyzers provide a hemolysis index to quantify in vitro hemolysis, a condition that can, for example, affect the accuracy of potassium concentration measurements. We aimed to assess whether the postoperative plasma level of the hemolysis index (HI<sub>postoperative</sub>) could aid the early recognition of patients at risk for cardiac surgery-associated acute kidney injury (CS-AKI) and also to evaluate other hemolysis indicators: plasma carboxyhemoglobin (COHb<sub>postoperative</sub>) and methemoglobin (MetHb<sub>postoperative</sub>).

**Design:** One-year retrospective study.

**Setting:** University hospital.

**Participants:** Patients undergoing elective cardiac surgery.

**Interventions:** None.

**Measurements and Main Results:** In 1090 patients, the median HI<sub>postoperative</sub> was higher in patients who developed CS-AKI compared to patients who did not (11 mg/dL [interquartile range (IQR), 5-38 mg/dL] v 7 mg/dL [IQR, 3-16 mg/dL]; p < 0.001). HI<sub>postoperative</sub> refined the early recognition of CS-AKI: the area under the precision-recall curve (AUPRC) for HI<sub>postoperative</sub> was 37% (95% confidence interval [CI], 31%-42%), whereas the AUPRC associated with no discriminative power, equal to the prevalence of CS-AKI in the whole population, was 21%. Among the 611 patients with measurements for all 3 biomarkers, the AUPRC of HI<sub>postoperative</sub> was higher than that of COHb<sub>postoperative</sub> or MetHb<sub>postoperative</sub> (+6.6% and +7.4% respectively; p < 0.0001 for both). Unlike COHb<sub>postoperative</sub> or MetHb<sub>postoperative</sub>, the incorporation of HI<sub>postoperative</sub> into a model (trained on a sample then validated in another sample) of CS-AKI early recognition significantly enhanced its performance, with a +1.9% (95% CI, 1.6%-2.1%) increase in AUPRC (p < 0.0001).

Presented at the Annual Congress of the French Society of Thoracic and Cardiovascular Surgery (Société Française de Chirurgie Thoracique et Cardio-Vascular), Saint Malo, France, 2022.

Support was provided solely from institutional and/or departmental sources. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

¹Address correspondence to Karim Lakhal, MD, Service d'Anesthésie-Réanimation, Hôpital Laënnec, CHU, Nantes Cedex 1 F-44093, France

E-mail address: lakhal_karim@yahoo.fr (K. Lakhal).

<footnote>*Service d'Anesthésie-Réanimation, hôpital Laënnec, Centre Hospitalier Universitaire, Nantes, France</footnote>

<footnote>†Pôle Hospitalo-Universitaire 11, Santé Publique, Clinique des données, Nantes Université, CHU Nantes, INSERM CIC 1413, Nantes, France</footnote>

<footnote>‡Service de Chirurgie Cardiaque, hôpital Laënnec, Centre Hospitalier Universitaire, Nantes, France</footnote>

<footnote>§INSERM 1246, Study of Perinatal, Paediatric and Adolescent Health: Epidemiological Research and Evaluation (SPHERE) unit, Centre National de la Recherche Scientifique (CNRS), Université de Nantes, Nantes, France</footnote>

<footnote>||Institut du Thorax, INSERM, CNRS, Université de Nantes, Nantes, France</footnote>

<footnote>¶Laboratoire d'Hématologie, hôpital Laënnec, Centre Hospitalier Universitaire, Nantes, France</footnote>

<footnote>**Laboratoire de Biochimie, hôpital Laënnec, Centre Hospitalier Universitaire, Nantes, France</footnote>

<footnote>¶Laboratoire d'Hématologie, hôpital Laënnec, Centre Hospitalier Universitaire, Nantes, France</footnote>

<footnote>**Laboratoire de Biochimie, hôpital Laënnec, Centre Hospitalier Universitaire, Nantes, France</footnote>
**Conclusions:** Elevated HI<sub>postoperative</sub> represents an early alert signal for the development of CS-AKI. Our findings support the incorporation of HI<sub>postoperative</sub>, a readily available biomarker, into predictive scores of CS-AKI.
© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)

**Key Words:** acute kidney injury; renal replacement therapy; biological markers; creatinine/blood; cardiac surgery; cardiopulmonary; hemolysis

Cardiac surgery-associated acute kidney injury (CS-AKI) is frequent and is associated with poor outcomes.¹,² Predicting and recognizing CS-AKI early is crucial for mitigating its impact.

Hemolysis is one of the contributors to CS-AKI.¹ Cardiac surgery under cardiopulmonary bypass (CPB) may induce mechanical hemolysis by red cell direct trauma and release of gaseous microbubbles from degassing. Hemolysis also can stem from the use of venting and suction devices, from erythrocyte washing, and from the transfusion of cell-salvaged blood.¹⁻³ Once mechanisms that scavenge plasma free hemoglobin (fHb) are overwhelmed, fHb may damage the kidneys.¹,⁴ Thus, there is a rationale for quantifying hemolysis. However, fHb measurement via the conventional spectrophotometry technique lacks automation, is cumbersome, is typically performed only during working hours, and its protracted turnaround time precludes its use as an early indicator of significant hemolysis. Therefore, fHb monitoring is incompatible with timely implementation of nephroprotective measures.⁵,⁶

The plasma hemolysis index (HI) could be an appealing alternative to fHb. For each routine measurement of various biological parameters, HI is systematically displayed by most clinical chemistry analyzers to assess the reliability of measurements potentially biased by hemolysis related to sample collection, transport, and processing.⁷ The turnaround time for HI is short, not exceeding that of serum potassium level, for example. A close relationship between fHb and HI has been reported.⁸⁻¹⁰ Thus, HI may be helpful, not only as an in vitro tool for the detection of preanalytical issues, but also as a gauge for in vivo hemolysis. Whether HI can provide a valuable alert signal that a patient is at risk of CS-AKI is unknown. To the best of our knowledge, only 1 preliminary study, not devoid of important limitations, addressed this issue and reported an encouraging performance of HI in the early detection of moderate-to-severe CS-AKI.¹¹

Plasma carboxyhemoglobin (COHb) and methemoglobin (metHb) levels also have been proposed as markers of hemolysis.¹²⁻¹⁴ These levels can be measured with commonly used devices for blood gas analysis. Whether an association exists between COHb or metHb level and the occurrence of CS-AKI is unclear, however.

The present study aimed to assess whether there is an association between the early postoperative level of HI (HI<sub>postoperative</sub>) and the subsequent development of CS-AKI. Data were obtained from routine biological analysis, including HI<sub>postoperative</sub> measurement, performed 2 to 7 hours after the completion of cardiac surgical procedures at the study institution. Throughout the article, the subscript “postoperative” refers to

a measurement performed within this timeframe. Associations between CS-AKI and each of HI<sub>postoperative</sub>, COHb<sub>postoperative</sub>, and MetHb<sub>postoperative</sub> were compared. Finally, whether the adjunction of these biomarkers to a model of early postoperative recognition of CS-AKI enhanced the model’s performance was assessed.

## Methods

### Ethical Considerations

The study was approved by the relevant institutional review board, the Groupe Nantais d'Ethique dans le Domaine de la Santé (2021.02.19; chairperson Paul Barriere). Patient written consent was waived for this retrospective study. Eligible patients were identified via the comprehensive institutional database for adult cardiac surgery (CORDABASE), which was approved by the French data protection authority.

### Patients

The study cohort comprised adult patients who underwent elective cardiac surgery under CPB between January and December 2018 at the study institution. Patients who underwent emergency surgery (including cardiac transplantation) were excluded. Also excluded were patients who underwent renal replacement therapy (RRT) in the week before the surgery, those who received preoperative and/or postoperative extracorporeal life support, and those who either died or lacked plasma creatinine (pCr) concentration measurements within the first 2 postoperative days. Eligible patients were identified via the comprehensive institutional database for adult cardiac surgery, which was approved by the data protection authority.

### Perioperative Care

The CPB systems used were Sorin or Terumo with their respective oxygenators, Sorin Inspire 6F or Terumo CapioxFX 25. Cardioplegia was performed with CP1B or Plegisol solution at the surgeon's discretion. A Sorin XTRA Cell Saver autologous blood recovery system was used. Total intravenous anesthesia (sufentanil and propofol), atracurium, cefuroxime, and unfractioned heparin were administered. Fluids used for volume expansion were crystalloids (0.9% saline or Ringer lactate) and gelatin solutions. No patient received hydroxyethyl starch. The CPB prime solution was Ringer lactate. All patients were transported to the intensive care unit (ICU) immediately after completion of the surgical procedure.
## Definition of CS-AKI

CS-AKI was defined as stage ≥1 of the Kidney Disease Improving Global Outcome (KDIGO) guidelines: an increase in pCr of ≥26.5 µmol/L (≥0.3 mg/dL) and/or ≥1.5 times the baseline pCr within the first 2 postoperative days. Stage ≥2 (moderate-severe CS-AKI) was defined as a doubling of baseline pCr and/or the initiation of RRT within the first 2 postoperative days.¹⁵

Preoperative pCr (ie, the closest measurement before the surgical procedure) was baseline pCr. All pCr available measurements within the 2 postoperative days were analyzed for the CS-AKI outcome, and the greatest change in pCr was considered. Urine output was not collected.

## Measurement of pCr and HI<sub>postoperative</sub>

Blood (in a lithium heparin tube; BD Vacutainer) was sampled via an arterial catheter at 2 to 7 hours after completion of the surgical procedure. If 2 measurements of a parameter were available within this timeframe, then the closest measurement to postoperative hour 6 was analyzed. At the central laboratory, measurements began within 20 minutes after sampling. Tubes were centrifuged at 2200 × g for 10 minutes before analysis of the plasma supernatant. A Cobas 6000 (Roche) analyzer was used to measure pCr and plasma urea concentration, using an enzymatic assay (creatininase and urease; Roche), and HI<sub>postoperative</sub> determination. HI<sub>postoperative</sub> was calculated according to absorbance measurements at different wavelengths (main wavelength of 570 nm and secondary wavelength of 600 nm) and expressed in mg/dL. Biological parameters were extracted from an institutional biomedical data warehouse.¹⁶

## Statistical Analysis

Categorical variables were summarized as count and percentage and compared between groups of interest using either the $\chi^2$ test or the Fisher exact test when the former was inapplicable. Continuous variables were summarized as mean ± standard deviation (SD) if normally distributed or median and interquartile range (IQR) otherwise. Comparisons between groups were made using the Student t test and the Mann-Whitney-Wilcoxon test. A p value < 0.05 was considered significant. The percentage of missing data was presented systematically.

To meet the study objective, a predictive modeling framework was used for early detection of the occurrence of all-stage CS-AKI within the first 48 postoperative hours. For early recognition, information collected preoperatively, intraoperatively, and postoperatively before postoperative hour 7 was used. Predictors were selected based on the Boruta algorithm and clinical expertise. Various models were trained on a training sample to select the most efficient and parsimonious model, based on the area under the precision-recall curve (AUPRC). AUPRC was chosen owing to the imbalanced rate of patients developing CS-AKI and those who did not.¹⁷

Performance was assessed using a validation sample, and the final model was the one with the best AUPRC.

To evaluate whether any of the variables of interest significantly enhanced model performance, the AUPRC was calculated before and after this variable was removed from the set of predictors in the final model. A total of 2000 bootstrapped samples from the validation sample were used to compute the 95% confidence interval (CI). Details of the predictive modeling framework are available in Appendix A.

Analyses were performed with R version 4.3.118 (R Core Team) and MedCalc version 20.013 (MedCalc Software).¹⁸ This manuscript is in accordance with the TRIPOD statement for the reporting of studies of diagnostic accuracy (Appendix A, Table A1).¹⁹

## Study Size

The sample comprised more than 1,000 patients included over a 1-year period. This was adequate not only for generating the training set, but also for creating a validation set. This approach ensured a less biased estimate of model performance while maintaining satisfactory precision, as evidenced by the range of CIs.

## Results

A total of 1090 patients were analyzed for HI<sub>postoperative</sub> (Table 1). Reasons for noninclusion are depicted in Figure 1. CS-AKI occurred in 224 patients (20.6%), including 154 (68.8%) in stage 1, 35 (15.6%) in stage 2, and 35 (15.6%) in stage 3. RRT was required in 27 patients (2.5%) during the first postoperative week.

As shown in Table 1, patients who developed CS-AKI had a longer ICU stay, were more frequently women, had a higher body mass index and baseline pCr level, and were more likely to have preoperative atrial fibrillation or flutter, vascular disease, and to undergo combined surgical procedures (e.g., valve replacement and coronary artery bypass grafting). They also had longer procedures, more frequently required inotropic and vasopressor drugs, needed more postoperative blood transfusions, and more often had a history of cardiac surgery.

Between the end of the surgical procedure and postoperative hour 48, a median of 5 (IQR, 4-7) pCr measurements were available per patient. HI<sub>postoperative</sub> was measured at a mean of 4.7 ± 1.2 hours after completion of the surgical procedure. HI<sub>postoperative</sub>, COHb<sub>postoperative</sub>, and MetHb<sub>postoperative</sub> data were available for 611 patients. CS-AKI occurred in 113 (18.5%) of these patients. These 3 biomarkers were measured at a mean of 4.5 ± 1.1 hours after the end of the surgical procedure.

### Crude Association between CS-AKI and HI<sub>postoperative</sub>, COHb<sub>postoperative</sub>, or MetHb<sub>postoperative</sub>

In the entire study cohort (n = 1,090), HI<sub>postoperative</sub> was higher in patients who developed CS-AKI compared to those who did not (median, 11 [IQR, 5-38] mg/dL v 7 [IQR, 3-16])
<table><caption>Table 1
Patient characteristics</caption><thead><tr><th>Characteristic</th><th>No CS-AKI (N = 866)</th><th>CS-AKI (N = 224)</th><th>p value</th><th>Missing values, %</th></tr></thead><tbody><tr><td>Age, y, mean ± SD</td><td>67 ± 11</td><td>69 ± 11</td><td>0.053</td><td>0</td></tr><tr><td>Female sex, n (%)</td><td>221 (26)</td><td>42 (19)</td><td>0.043</td><td>0</td></tr><tr><td>Height, cm, mean ± SD</td><td>169 ± 9</td><td>169 ± 8</td><td>0.80</td><td>0</td></tr><tr><td>Weight, kg, mean ± SD</td><td>77 ± 15</td><td>80 ± 16</td><td>0.006</td><td>0</td></tr><tr><td>Body mass index, mean ± SD</td><td>26.9 ± 4.5</td><td>28.0 ± 4.9</td><td>0.002</td><td>0</td></tr><tr><td>Preoperative comorbidities, n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Arterial hypertension</td><td>490 (57)</td><td>134 (60)</td><td>0.43</td><td>0</td></tr><tr><td>Smoking</td><td>375 (43)</td><td>88 (39)</td><td>0.31</td><td>0</td></tr><tr><td>Dyslipidemia</td><td>375 (43)</td><td>89 (40)</td><td>0.37</td><td>0</td></tr><tr><td>COPD</td><td>34 (4)</td><td>8 (4)</td><td>0.96</td><td>0</td></tr><tr><td>Atrial fibrillation or flutter</td><td>45 (5)</td><td>35 (16)</td><td>&lt;0.001</td><td>3.4</td></tr><tr><td>Vascular disease</td><td>171 (20)</td><td>143 (64)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Preoperative LVEF, %, mean ± SD</td><td>60 ± 10</td><td>58 ± 9</td><td>0.06</td><td>0.2</td></tr><tr><td>Preoperative pCr, µmol.L<sup>-1</sup>, median (IQR)</td><td>81 (70-94)</td><td>90 (75-103)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Preoperative eGFR, mL/min/1.73 m<sup>2</sup>, mean ± SD</td><td>84 ± 29</td><td>80 ± 36</td><td>0.091</td><td>0.1</td></tr><tr><td>Preoperative hemoglobin, g/dL, mean ± SD</td><td>13.9 ± 1.4</td><td>13.7 ± 1.7</td><td>0.24</td><td>0.7</td></tr><tr><td>Preoperative platelets count, G/L, median (IQR)</td><td>223 (186-264)</td><td>218 (184-260)</td><td>0.55</td><td>0.8</td></tr><tr><td>History of cardiac surgery, n (%)</td><td>22 (3)</td><td>15 (7)</td><td>0.004</td><td>0</td></tr><tr><td>Surgical procedure, n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>CABG</td><td>359 (42)</td><td>62 (28)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Valvular surgery</td><td>282 (33)</td><td>56 (25)</td><td>0.036</td><td>0</td></tr><tr><td>Other surgical procedure</td><td>138 (16)</td><td>63 (28)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Combination of procedures</td><td>87 (10)</td><td>43 (19)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>CPB time, min, median (IQR)</td><td>97 (75-120)</td><td>129 (100-165)</td><td>&lt;0.001</td><td>1.9</td></tr><tr><td>Cross-clamp time, min, median (IQR)</td><td>74 (55-97)</td><td>96 (73-128)</td><td>&lt;0.001</td><td>2.1</td></tr><tr><td>Intraoperative transfusion, n (%)</td><td>136 (16)</td><td>44 (20)</td><td>0.19</td><td>0</td></tr><tr><td>Intraoperative and/or postoperative inotropic drug use, n (%)</td><td>99 (11)</td><td>90 (40)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Intraoperative and/or postoperative vasopressive drug use, n (%)</td><td>121 (14)</td><td>73 (33)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Postoperative (2 days) transfusion, n (%)</td><td>67 (8)</td><td>53 (24)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>HI<sub>postoperative</sub>, mg/dL, median (IQR)</td><td>7 (3-16)</td><td>11 (5-38)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>HI<sub>postoperative</sub> &gt;20, n (%)</td><td>169 (20)</td><td>88 (39)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>pCr<sub>postoperative</sub>, µmol.L<sup>-1</sup>, median (IQR)</td><td>80 (68-93)</td><td>116 (97-134)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>Hemoglobin<sub>postoperative</sub>, g/dL, median (IQR)</td><td>11.9 (11.0-13.0)</td><td>11.6 (10.4-12.7)</td><td>&lt;0.001</td><td>0.9</td></tr><tr><td>Platelets<sub>postoperative</sub>, G/L, mean ± SD</td><td>171 ± 50</td><td>158 ± 53</td><td>0.001</td><td>1</td></tr><tr><td>PT<sub>postoperative</sub> &lt;60%, n (%)</td><td>86 ± 9</td><td>84 ± 12</td><td>0.001</td><td>2.7</td></tr><tr><td>pCr at day 2, µmol/L, median (IQR)</td><td>69 (58-81)</td><td>139 (97-209)</td><td>&lt;0.001</td><td>0.6</td></tr><tr><td>RRT during the ICU stay, n (%)</td><td>2 (0)</td><td>25 (11)</td><td>&lt;0.001</td><td>0</td></tr><tr><td>ICU length of stay, d, median (IQR)</td><td>1 (1-2)</td><td>3 (1-6)</td><td>&lt;0.001</td><td>0</td></tr></tbody></table>

The subscript “postoperative” refers to a measurement performed within the timeframe of 2 to 7 hours after the end of the surgical procedure. Abbreviations: CABG, coronary artery bypass graft; COPD, chronic obstructive pulmonary disease; CS-AKI, cardiac surgery-associated acute kidney injury; eGFR, estimated glomerular filtration rate (CKD-epi equation); HI, hemolysis index (mg/dL); ICU, intensive care unit; IQR, interquartile range; LVEF, left ventricular ejection fraction; pCr, plasma creatinine; RRT, renal replacement therapy.

mg/dL; p < 0.001). HI<sub>postoperative</sub> refined the early recognition of CS-AKI; the AUPRC for HI<sub>postoperative</sub> was 37% (95% CI, 31%-42%), whereas the AUPRC associated with no discriminative power, equal to the prevalence of CS-AKI in the whole population, was 21%.

In the 611 patients in whom all 3 biomarkers were measured, COHb<sub>postoperative</sub> and MetHb<sub>postoperative</sub> also were higher in patients who developed CS-AKI compared to those who did not (mean, 1.47 ± 0.34% v 1.33 ± 0.38% [p = 0.0003] and 1.26 ± 0.26% v 1.17 ± 0.25% [p = 0.0002], respectively). HI<sub>postoperative</sub> had a higher observed crude predictive power compared to MetHb<sub>postoperative</sub> and COHb<sub>postoperative</sub>, with AUPRC values of 32.5% (95% CI, 25.4%-41.1%), 25.9% (95% CI, 20.6%-33.1%), and 25.1% (95% CI, 21.2%-30.7%), respectively (p < 0.0001 for both) (Fig 2), whereas the

AUPRC associated with no discriminative power (equal to the prevalence of CS-AKI in this subgroup) was 18.5%.

## Modeling for CS-AKI Early Recognition

During the model’s training phase, the variables listed in Table 1 (collected preoperatively, intraoperatively, and postoperatively up to the 7th postoperative hour) were entered into the Boruta algorithm, which identified 16 predictors as significantly associated with the occurrence of CS-AKI; these predictors are listed in Appendix A, Table A2. Unlike HI<sub>postoperative</sub>, neither COHb<sub>postoperative</sub> nor MetHb<sub>postoperative</sub> was retained in this phase. Second, the clinical expert group retained the following predictors in the final model: change from baseline pCr to pCr<sub>postoperative</sub>, known vascular disease, plasma
<figure><img src="image_3.png" /><figcaption>Fig 1. Study flowchart. ECLS, extracorporeal life support; HI<sub>postoperative</sub>, plasma hemolysis index measured 2 to 7 hours postoperatively; COHb<sub>postoperative</sub>, plasma carboxyhemoglobin measured 2 to 7 hours postoperatively; MetHb<sub>postoperative</sub>, plasma methemoglobin measured 2 to 7 hours postoperatively. Missing data for COHb<sub>postoperative</sub> and MetHb<sub>postoperative</sub> were mostly due to lack of measurement, lack of transfer of the measurement from the point-of-care device to the central laboratory, and/or lost data.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig 2. Crude performance of each hemolysis biomarker in predicting cardiac surgery-associated acute kidney injury (CS-AKI). AUPRC, area under the precision-recall curve; HI<sub>postoperative</sub>, postoperative plasma hemolysis index; COHb<sub>postoperative</sub>, postoperative plasma carboxyhemoglobin; MetHb<sub>postoperative</sub>, postoperative plasma methemoglobin; CI, confidence interval. The subscript “postoperative” refers to a measurement performed within the timeframe 2 to 7 hours after the end of the surgical procedure. The precision-recall curve illustrates the performance of each biomarker in predicting CS-AKI. Precision is the ratio of true positive predictions to the total predicted positives, emphasizing the accuracy of positive predictions. Recall, also known as sensitivity, represents the ratio of true positive predictions to the total actual positives, highlighting the model’s ability to capture all positive instances. The precision-recall curve provides a comprehensive view of the trade-off between precision and recall across different thresholds. A biomarker associated with a curve closer to the top-right corner indicates superior predictive performance. HI<sub>postoperative</sub> refines the early recognition of CS-AKI; the AUPRC for HI<sub>postoperative</sub> was 37% (95% confidence interval, 31%-42%), whereas the AUPRC associated with no discriminative power, equal to the prevalence of CS-AKI in the whole population, was 21%. This figure also highlights that the association between HI<sub>postoperative</sub> and the development of CS-AKI could be stronger than that of COHb<sub>postoperative</sub> and MetHb<sub>postoperative</sub> with CS-AKI, respectively.</figcaption></figure>
urea<sub>postoperative</sub>, CPB time, age, and HI<sub>postoperative</sub>. Following the training phase, random forest was selected as the best-performing model (data not shown). Figure 3 illustrates the performance of the final model, as indicated by its AUPRC. The incorporation of HI<sub>postoperative</sub> was associated with significantly enhanced model performance: AUPRC increased significantly by 1.9% (95% CI, 1.6%-2.1%). Table 2 translates the results of predictive modeling into more clinical terms. Considering 100 patients as representative of the source population, when HI<sub>postoperative</sub> was incorporated into the model, more than 3 patients become true positives (ie, correctly classified as about to develop CS-AKI) rather than false negatives, while 1 patient became a true negative rather than a false positive.

## Early Recognition of Stage 2 or 3 CS-AKI (Rather Than All-Stage CS-AKI) with HI<sub>postoperative</sub>

The median HI<sub>postoperative</sub> value was higher in the 70 patients (6%) who developed stage 2-3 CS-AKI compared to the 1020 patients (94%) who did not (27.5 [IQR, 6.3-62.8] mg/dL v 7.0 [IQR, 3.0-17.0] mg/dL; p < 0.0001). The predictive models were trained and tested in the same manner as for the primary analysis (all-stage CS-AKI v no CS-AKI); however, the final model's performance in predicting stage 2-3 CS-AKI was not as strong as its performance in predicting all-stage CS-AKI (AUPRC between 36.5% and 58.3%).

## Discussion

The main finding of the present study is that HI<sub>postoperative</sub>, a readily available biomarker, exhibited a modest yet significant association with the subsequent development of CS-AKI. In contrast to COHb<sub>postoperative</sub> and MetHb<sub>postoperative</sub>, the

incorporation of HI<sub>postoperative</sub> into a model for CS-AKI early recognition enhanced the model's performance.

Some previous studies have assessed whether a biomarker of hemolysis, mostly fHb, can be used to discriminate patients at risk for CS-AKI. Encouraging findings were reported.<sup>4,20-25</sup> However, as mentioned above, fHb is unsuitable for early detection of hemolysis and thus for the timely implementation of nephroprotective measures.<sup>7</sup> Haptoglobin testing, another approach for hemolysis quantification, suffers from a substantial rate of false negativity for the detection of hemolysis.<sup>7</sup> As reported both elsewhere<sup>12,13</sup> and in the present study, COHb<sub>postoperative</sub> and MetHb<sub>postoperative</sub> also are of limited value for CS-AKI early recognition. Hence, HI<sub>postoperative</sub> appears to be an appealing alternative for quantifying the intensity of in vivo (ie, intravascular) hemolysis. Its measurement is inexpensive and fully automated, has a short turn-around time, requires a low blood volume, and, importantly, is already available in most hospitals, measured by modern analyzers to assess the reliability of some parameters (eg, potassium level) that may be impacted by in vitro hemolysis (ie, hemolysis related to sampling, transport, and processing).<sup>7</sup> Thus, HI is already measured each time a biochemical measurement is performed, but values mostly are either not provided to or overlooked by clinicians. The agreement between HI and fHb is high.<sup>7-10</sup>

So, is HI<sub>postoperative</sub> a valuable tool for the early recognition of CS-AKI? Apparently, only 1 study to date has assessed the ability of HI to identify patients at risk for moderate-severe CS-AKI, and encouraging performance was reported.<sup>11</sup> The limited size of that preliminary study (82 patients) precluded drawing any firm conclusions, however. The present study of 1,090 patients confirms the value of HI<sub>postoperative</sub> in identifying patients at risk for CS-AKI. However, although a significant association between HI<sub>postoperative</sub> and CS-AKI was observed, HI<sub>postoperative</sub> alone was unable to reliably

<figure><img src="image_5.png" /><figcaption>Fig 3. The model's performance in predicting cardiac surgery-associated acute kidney injury (CS-AKI) with and without incorporation of the postoperative (2-7 hours after the end of the procedure) plasma hemolysis index (HI<sub>postoperative</sub>). AUPRC, area under the precision-recall curve; CI, confidence interval. This figure highlights that the incorporation of HI<sub>postoperative</sub> was associated with significant enhancement of the model's predictive performance.</figcaption></figure>
<table><caption>Table 2
Model performance in predicting CS-AKI with and without the incorporation of HI<sub>postoperative</sub></caption><thead><tr><th>Parameter</th><th>Estimated predictive performance of the model without HI<sub>postoperative</sub>, %</th><th>Change in performance after incorporation of HI<sub>postoperative</sub>, % (95% CI)</th><th>Clinical consequence of incorporating HI<sub>postoperative</sub> in the model, considering 100 patients representative of the source population</th></tr></thead><tbody><tr><td>Accuracy</td><td>89.7</td><td>+1.8 (1.7-1.9)</td><td>Nearly 2 fewer cases of misclassification</td></tr><tr><td>Sensitivity (ie, recall)</td><td>60.7</td><td>+3.5 (3.2-3.8)</td><td>More than 3 additional patients who developed CS-AKI correctly identified</td></tr><tr><td>Specificity</td><td>97.2</td><td>+1.4 (1.2-1.5)</td><td>More than 1 additional patients who did not develop CS-AKI correctly identified</td></tr><tr><td>Positive predictive value (ie, precision)</td><td>85.0</td><td>+3.2 (2.9-3.4)</td><td>More than 3 additional patients who were predicted to develop CS-AKI actually did so</td></tr><tr><td>Negative predictive value</td><td>95.3</td><td>+1.1 (1.0-1.2)</td><td>More than 1 additional patient who was predicted to not develop CS-AKI actually did not</td></tr></tbody></table>

Abbreviations: CI, confidence interval CS-AKI, cardiac surgery-associated acute kidney injury; HI<sub>postoperative</sub>, postoperative (2-7 hours after the end of the procedure) plasma hemolysis index.

differentiate patients who subsequently developed CS-AKI from those who did not. This is not surprising, as hemolysis is not the sole contributor to CS-AKI; some patients may develop CS-AKI but not experience hemolysis.¹ Conversely, some patients may tolerate a high degree of hemolysis without developing CS-AKI, owing to the absence of other renal insults. Importantly, the incorporation of HI<sub>postoperative</sub> into a model for CS-AKI early recognition improved the model's performance, which was remarkably high. Thus, the incorporation of HI<sub>postoperative</sub> into routine decision making processes and risk-stratification scores for CS-AKI is recommended.

Major strengths of the present study are its size, its novelty, the very limited amount of missing data, and the use of a model that was trained and internally validated. Additionally, a precision-recall analysis was used instead of the more common receiver operating characteristic approach. This analysis is more appropriate when dealing with imbalanced datasets,¹⁷ as was the case with the present data; patients who did not develop CS-AKI significantly outnumbered those who did.

## Study Limitations

This study has several limitations, starting with its retrospective design. Nonetheless, it benefited from the routine prospective collection of perioperative data, which is nearly comprehensive in the setting of cardiac surgery. Indeed, the rate of missing values was low overall (Table 1), but COHb<sub>postoperative</sub> and MetHb<sub>postoperative</sub> data were available in only 611 out of 1,090 patients for whom HI<sub>postoperative</sub> data were available.

Second, the present findings need external validation in a different case mix and ideally with another device for HI<sub>postoperative</sub> measurement. Third, HI<sub>postoperative</sub> performance could be worse in the event of significant blood sampling-related hemolysis. This is more likely to occur if venipuncture is used for blood sampling

rather than an arterial catheter (which is the preferred technique in the aftermath of cardiac surgery) or if the preanalytic time is projected. Sampling via an arterial catheter is likely to optimize the positive predictive value of HI<sub>postoperative</sub> to detect hemolysis, whereas its negative predictive value is already close to its maximal value.⁷

Fourth, as in several studies, CS-AKI was defined solely with the pCr criterion, as urine output was omitted.¹,² Beyond the fact that collecting precise urine output over 6, 12, and 24 hours is cumbersome for such a large study, urine output assessment can be misleading; confounders such as diuretic use or hypovolemia (oliguria being the appropriate response without renal damage) are common in the setting of cardiac surgery.

Fifth, the predictive model does not include certain parameters associated with hemolysis that were not collected, such as the diameter of the CPB circuit, the use of pressure infusion bags for blood transfusion, the level of bag pressure, and parameters related to the intraoperative blood salvage system (eg, circuit diameter, suction power, amount of blood treated, type of treatment). Consequently, an elevated HI could simply be a marker of prolonged hemorrhagic surgery, which might have led to more intensive use of autologous or homologous transfusions. Notably, the duration of CPB (which is often extended during prolonged hemorrhagic surgeries) and HI<sub>postoperative</sub> were independently associated with CS-AKI. This suggests that regardless of the duration of surgery, an elevated HI<sub>postoperative</sub> itself is associated with CS-AKI. However, caution should be exercised before inferring a causal relationship between elevated HI and the occurrence of CS-AKI. Instead, elevated HI<sub>postoperative</sub> should be considered an indicator of renal risk, potentially not solely related to hemolysis.

Finally, this study was not designed to determine the optimal timing for biomarker measurement. Intraoperative HI may
be associated with even better performance than that seen for $HI_{postoperative}$.<sup>11</sup>

In summary, the present findings encourage the incorporation of $HI_{postoperative}$, a readily available biomarker, into routine decision making processes and future risk-stratification scores for CS-AKI.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### CRediT authorship contribution statement

**Emmanuel Golinvaux:** Writing – original draft, Investigation, Data curation, Conceptualization.
**Thomas Goronflot:** Writing – review & editing, Validation, Methodology, Formal analysis, Data curation.
**Julien Cadiet:** Writing – review & editing, Supervision, Project administration, Investigation, Conceptualization.
**Thomas Senage:** Writing – review & editing, Investigation.
**Bertrand Rozec:** Writing – review & editing, Validation.
**Elodie Boissier:** Writing – review & editing, Investigation, Data curation.
**Edith Bigot-Corbel:** Writing – review & editing, Validation, Project administration, Investigation.
**Karim Lakhal:** Writing – original draft, Validation, Project administration, Methodology, Investigation, Formal analysis.

### Data Availability

The datasets used and/or analyzed in the current study are available from the corresponding author on reasonable request.

### Supplementary materials

Supplementary material associated with this article can be found in the online version at doi:10.1053/j.jvca.2024.09.143.

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