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Prognostic nutritional index as a predictive marker for acute kidney injury in adult critical illness population: a systematic review and diagnostic test accuracy meta-analysis

Journal of Intensive Care volume 12, Article number: 16 (2024) Cite this article

Abstract

Background

The prognostic nutritional index (PNI), integrating nutrition and inflammation markers, has been increasingly recognized as a prognostic predictor in diverse patient cohorts. Recently, its effectiveness as a predictive marker for acute kidney injury (AKI) in various clinical settings has gained attention. This study aims to assess the predictive accuracy of the PNI for AKI in critically ill populations through systematic review and meta-analysis.

Methods

A systematic review was conducted using the databases MEDLINE, EMBASE, PubMed, and China National Knowledge Infrastructure up to August 2023. The included trials reported the PNI assessment in adult population with critical illness and its predictive capacity for AKI. Data on study characteristics, subgroup covariates, and diagnostic performance of PNI, including sensitivity, specificity, and event rates, were extracted. A diagnostic test accuracy meta-analysis was performed. Subgroup analyses and meta-regression were utilized to investigate the sources of heterogeneity. The GRADE framework evaluated the confidence in the meta-analysis’s evidence.

Results

The analysis encompassed 16 studies with 17 separate cohorts, totaling 21,239 patients. The pooled sensitivity and specificity of PNI for AKI prediction were 0.67 (95% CI 0.58–0.74) and 0.74 (95% CI 0.67–0.80), respectively. The pooled positive likelihood ratio was 2.49 (95% CI 1.99–3.11; low certainty), and the negative likelihood ratio was 0.46 (95% CI 0.37–0.56; low certainty). The pooled diagnostic odds ratio was 5.54 (95% CI 3.80–8.07), with an area under curve of summary receiver operating characteristics of 0.76. Subgroup analysis showed that PNI’s sensitivity was higher in medical populations than in surgical populations (0.72 vs. 0.55; p < 0.05) and in studies excluding patients with chronic kidney disease (CKD) than in those including them (0.75 vs. 0.56; p < 0.01). Overall, diagnostic performance was superior in the non-chronic kidney disease group.

Conclusion

Our study demonstrated that PNI has practical accuracy for predicting the development of AKI in critically ill populations, with superior diagnostic performance observed in medical and non-CKD populations. However, the diagnostic efficacy of the PNI has significant heterogeneity with different cutoff value, indicating the need for further research.

Background

Acute kidney injury (AKI) is a frequent complication in patients with critically illness, with varying incidences across patient groups. For instance, post-major surgery patients show AKI rates between 3 and 35% [1]; patients with myocardial infarction, with or without percutaneous coronary intervention (PCI), show AKI rates ranging from 7 to 27% [2, 3]; and cardiac surgery participants show AKI rates between 19 and 32% [4, 5]. Multiple factors, such as sepsis, pre-existing diabetes mellitus (DM) or cardiovascular disease, chronic kidney disease (CKD), advanced age, anemia, and hypoalbuminemia, contribute to AKI susceptibility [6,7,8].

The prognostic nutritional index (PNI), initially developed by Buzby [9] and later modified by Onodera and Kosaki [10], is a readily accessible marker evaluating nutritional and inflammatory status. This index is calculated as 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm3). It has been linked to post-operative or peri-treatment morbidity and mortality across various patient groups, including those with various malignancy [11,12,13], heart failure [14], and DM [15]. Subsequent research has identified PNI as a prognostic factor for outcomes or AKI risk assessment in populations with critically illness, including acute coronary syndrome [16,17,18], major abdominal surgery [19], ICU admissions [20], and COVID-19 infection [21]. Kurtul et al. demonstrated that a PNI of less than 38 has 82% sensitivity and 70% specificity for contrast-associated AKI prediction in patients with ST-segment elevation myocardial infarction who underwent percutaneous coronary intervention [16]. Shimoyama showed that a PNI with a cutoff point of 26.08 has 58% sensitivity and 67% specificity for sepsis AKI prediction among enrolled ICU patients with sepsis [22].

Despite the abovementioned studies, a systematic examination of the diagnostic accuracy for PNI as an AKI risk stratification tool is lacking. In the current study, a systematic review and meta-analysis was conducted to (1) analyze the diagnostic accuracy of PNI as an AKI risk stratification tool and (2) determine whether the diagnostic accuracy of PNI could be affected by predefined covariates such as different populations and underlying CKD.

Materials and methods

Literature search strategy

This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Diagnostic Test Accuracy (DTA) Studies [23] (Additional file 1: Table S1). The protocol was registered with PROSPERO (CRD42023456607, registration date: 01 September 2023). Two independent reviewers (J.J. Chen and T.H. Lee) conducted a comprehensive systematic review and searched for articles published until August 07, 2023, in PubMed, MEDLINE, EMBASE, and China National Knowledge Infrastructure (CNKI). The search strategy relied on the use of the following keywords: [acute kidney injury], [acute renal failure], and [prognostic nutritional index]. Detailed search strategies are provided in Additional file 1: Table S2. Review articles were not included in the present analysis; however, the references of these studies were screened for relevant studies. The language or article types had no limitations.

Study eligibility criteria

The titles and abstracts of the studies extracted from the search were independently examined by two reviewers (Chen and Lee), and articles were excluded during initial screening if the titles or abstracts indicated that they were clearly irrelevant to the objective of the current study. The full texts of the relevant articles were reviewed to examine whether the studies were eligible for inclusion.

A study was included if it met the criteria of adult humans as its population and reported the cutoff point of PNI and its diagnostic/predictive ability for AKI development. The other inclusion criteria were that the study reported at least one of the following outcomes of interest:

  1. 1.

    Occurrence rate of AKI with various definitions (either guideline-based AKI criteria, such as Acute Kidney Injury Network [AKIN], Kidney Disease: Improving Global Outcomes [KDIGO], European Society of Urogenital Radiology [ESUR], or predefined AKI criteria by individual studies);

  2. 2.

    Sensitivity and specificity based on a defined cutoff value of PNI for AKI prediction or with a receiver operating characteristic (ROC) curve of PNI for AKI prediction.

A third reviewer (Y.T. Huang) was consulted to reach an agreement through consensus in the case of any disagreement regarding eligibility. Studies were excluded if they were duplicate cohorts or had insufficient information about the outcomes.

Data extraction and outcome measurement

Two investigators (Chen and Lee) independently extracted relevant information from each study. The extracted data elements included the first author, year of publication, study location, study design, patient source (coronary artery disease for percutaneous coronary intervention or cardiac surgery, liver transplant, or sepsis), AKI judgment timing and definition, the measured day of PNI, and whether the study excluded CKD population or not. As for diagnostic test performance, the extracted data included the cutoff value of PNI based on the Youden index or predefined criteria, sensitivity, specificity, number of true positive, number of false positive, and the event number of AKI (Tables 1 and 2). Disagreements about data extraction between the two authors (Chen and Lee) were resolved through discussion with a third author (Huang).

Table 1 The characteristics of the included studies
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Table 2 Diagnostic test performance of prognostic nutritional index for acute kidney injury in enrolled studies
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Data synthesis and analysis

Summary of the effect

The AKI event numbers; total sample size; and true positive (TP), false positive (FP), true negative (TN), and false negative (FN) for PNI as an AKI prediction tool based on Youden index or predefined cutoff value were extracted. We also extracted these information by calculating from the reported sensitivity or specificity, or measured from ROC. The summary measures (pooled sensitivity, specificity, positive likelihood ratio [+ LR], negative likelihood ratio [− LR]) was calculated using a univariate model through the metapro & metabin function. The diagnostic odds ratio (DOR) was calculated using a bivariate model through the metabin function in R “meta” package. A random-effects model with maximum likelihood estimation was used to estimate the between-study variance. This model was selected due to the anticipated heterogeneity among the studies included in this analysis. The heterogeneity is attributed to variations in the study populations (surgical or medical), the procedures received, the baseline underlying diseases, and the different cutoff values for PNI. A summary receiver operating characteristic (SROC) curve with a bivariate model, which was established by the reitsma function in R “mada” package, was used to assess the predictive performance of PNI regarding AKI development.

Analysis of heterogeneity between studies

The threshold effect was examined using Spearman’s correlation coefficient test [24, 25]. A p value ≥ 0.6 indicated considerable threshold effect. If no significant threshold effect was found, subgroup analysis or meta-regression analysis was further performed to explore the sources of heterogeneity. Between-study variance (tau-squared) was evaluated through maximum likelihood estimation, and the result of heterogeneity examination was presented as the I2 index and p value of Chi-squared test. An I2 > 50% indicated substantial heterogeneity. Several potential covariates were identified, including patient population (population: medical vs. surgical patients, procedure: underwent PCI or not, including CKD or not, hypoalbuminemia or not [serum albumin level less than 3.5 g/L], and used AKI diagnosis criteria by KDIGO criteria or others). Subgroup analysis was performed to examine whether a difference existed in the diagnostic performance in different subgroups. Subgroup analysis was performed by employing the subgroup order within the metaprop and metabin functions to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) across various subgroups. The overall PNI diagnostic performance between subgroups was examined by the relative DOR (RDOR). It was calculated using the metareg function in the “meta” package. Diagnostic odds ratio is a measure derived from comparing the odds of positive test outcomes among participants with the target disease to the odds of positive outcomes in those without the target disease [26, 27]. RDOR can be utilized to compare various diagnostic tests or evaluate diagnostic accuracy across different studies [28, 29]. In the subgroup analyses, a coefficient can estimate the change in the log-transformed DOR for studies featuring a specific covariate versus those without. By applying an antilogarithm transformation to this coefficient, yielding the RDOR, we can interpret this RDOR as the mean DOR for studies with a particular covariate relative to those without a particular covariate. An RDOR greater than 1 suggests enhanced diagnostic accuracy in studies that include the specific covariate [30].

Publication bias was assessed using a funnel plot and Deeks test through the metabias function [31]. All analyses were performed using R version 4.2.2 (2022-10-31) [32]. A two-sided p value of < 0.05 indicated statistically significant.

Risk of bias and certainty of evidence assessment

The risk of bias for each included study was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. The judgment of risk of bias in the QUADAS-2 tool is based on four main domains: patient selection, index test, reference standard, and flow and timing [33]. If all signaling questions for a domain receive “yes” responses, the domain is deemed to have a low risk of bias. Conversely, an answer of “no” to any question in a domain indicates a high risk of bias. Two independent reviewers (Chen and Lee) rated high, low, or unclear risk for four domains, and disagreements between the reviewers were resolved by discussion with another author (Huang). The judgment of “applicability” followed the same principle as in the bias section, but no signaling questions were asked. The certainty of evidence for the diagnostic performance of PNI for AKI prediction was evaluated on the basis of the guidelines of the GRADE Working Group methodology [34,35,36].

Results

Search result and study characteristics

A flowchart of the literature search is provided in Additional file 1: Fig. S1. The electronic database search identified 225 potentially eligible studies from PubMed, 83 from EMBASE (Excerpta Medica database), 27 from CNKI, and 23 from MEDLINE. The remaining 301 articles were screened after duplicate articles were removed. After the titles and abstracts were screened, the full texts of 28 studies were reviewed to assess their eligibility. After studies were excluded for various reasons (no outcome of interest [n = 11]; duplication cohort [n = 1], Additional file 1: Table S2), 16 studies (17 separate cohorts) comprising 21,239 patients were included for analysis [16, 17, 22, 37,38,39,40,41,42,43,44,45,46,47,48,49].

The detailed characteristics of the enrolled studies are presented in Table 1. The enrolled patients had a mean age ranging from 52 to 75 years, and the average percentage of female participants was 36.6%. Fourteen of the 16 enrolled studies were retrospective trials, and the remaining two were prospective studies. Nearly half of the enrolled studies (seven out of 16) defined AKI by KDIGO criteria, followed by ESUR post-contrast criteria (n = 4) and the AKIN criteria (n = 3). Five of the 16 studies enrolled patients who underwent cardiac surgery, and the other six studies enrolled patients who underwent PCI.

Prognostic nutritional index for AKI prediction

The diagnostic value, cutoff points, and key results of the enrolled studies are summarized in Tables 1 and 2. The pooled sensitivity and specificity were 0.67 (95% CI 0.58–0.74) and 0.74 (95% CI 0.67–0.80), respectively, with substantial heterogeneity (Fig. 1). The pooled positive LR and negative LR were 2.49 (95% CI 1.99–3.11) and 0.46 (95% CI 0.37–0.56), respectively (Fig. 1). The pooled DOR was 5.54 (95% CI 3.80–8.07), with substantial heterogeneity (Fig. 2A). The area under the curve (AUC) for SROC to summarize the diagnostic accuracy was 0.759 (Fig. 2B). In populations with varying AKI prevalence—low (15%), moderate (25%), and high (40%)—the post-test probabilities of AKI for those with positive PNI results were 30%, 45%, and 62%, respectively; conversely, for individuals with negative PNI results, these probabilities were 8%, 13%, and 23%, respectively (Fig. 3).

Fig. 1
figure 1

Forest plot of prognostic nutritional index diagnostic accuracy: A sensitivity, B specificity, C positive likelihood ratio, and D negative likelihood ratio for acute kidney injury

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Fig. 2
figure 2

Pooled diagnostic odds ratio (A) and SROC curves (B) of prognostic nutritional index for prediction of AKI. AKI: acute kidney injury; SROC: summary receiver operating characteristic

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Fig. 3
figure 3

Fagan’s nomogram for prognostic nutritional index as acute kidney injury prediction marker with pre-test probabilities of 15% (A), 25% (B), and 40% (C)

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Subgroup analysis

Spearman’s correlation coefficient test was conducted with a p value of 0.44, which indicated no significant threshold effect. Subgroup analysis was further performed in population (surgical vs. medical), received procedure or not (PCI vs. non-PCI), baseline CKD (including or excluding CKD population), serum albumin level (hypoalbuminemia vs. not), and used AKI criteria (KDIGO vs. other). The results showed that the PNI had higher sensitivity and negative LR in the medical population and population after excluding patients with CKD (Table 3). The overall diagnostic performance evaluated by RDOR was significantly lower in studies including patients with CKD (RDOR: 0.44, 95% CI 0.22–0.48, p = 0.02, Table 3). The diagnostic performance was not significantly different in populations with hypoalbuminemia or not and the used AKI criteria. In non-CKD individuals, the post-test AKI probabilities for negative PNI results were 6%, 11%, and 19% for pre-test AKI prevalence rates of 15%, 25%, and 40%, respectively (Additional file 1: Fig. S2).

Table 3 Heterogeneity analysis by subgroup analysis and meta-regression for prognostic nutritional index as an acute kidney injury prediction marker
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Risk-of-bias of enrolled studies and certainty of evidence

The publication bias assessment via Deeks’ funnel plot showed no significant publication bias, with a p value of 0.9 (Additional file 1: Fig. S3).

Potential biases were identified using the QUADAS-2 tool. In Domain 1, which assessed patient selection, three studies [39, 40, 43] were marked as having unclear concerns due to the exclusion of specific populations, such as those with heart failure or anemia. One study [22] included patients already diagnosed with septic AKI, raising questions about selection bias. In Domain 2, which evaluated the index test (PNI), two studies [37, 39] used predefined cutoff points, whereas the remaining 14 studies relied on cutoffs defined by the Youden index, categorizing them as high risk in this domain. In Domain 3, which assessed the reference test, one study [22] was flagged for unclear risk. This present study defined the outcome as stage 3 AKI requiring dialysis. Finally, in Domain 4, which focused on patient flow and the timing of outcome assessment, classified five studies as high risk or unclear risk due to inadequate AKI follow-up timing or failure to report specific time periods (Additional file 1: Fig. S4).

The certainty of evidence for the predictive performance of PNI in this meta-analysis was assessed. Due to the lack of predefined cutoff values in most studies, the interpretation of the index test was considered to be biased. As a result, the risk-of-bias domain was ranked as serious concern. Regarding the inconsistency domain, the meta-analysis revealed high heterogeneity. Furthermore, two out of five predefined subgroup analyses demonstrated a significant difference in sensitivity, and none of the five predefined subgroups showed a significant difference in specificity. Consequently, the inconsistency domain was ranked as serious concern. Other three domains (indirectness, imprecision, and publication bias) were ranked as not serious concern. The overall certainty of evidence was low. Table 4 summarizes the results.

Table 4 GRADE certainty of evidence assessment
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Discussion

Our analysis revealed that the PNI has pooled sensitivity and specificity of 0.67 and 0.74, respectively, with an area under the SROC curve of 0.76 for predicting the onset of acute kidney injury (AKI). Additionally, we demonstrated that PNI could serve as an effective risk stratification tool for identifying individuals at low risk for AKI in populations with a relatively low occurrence rate of AKI (Fig. 3). The diagnostic accuracy of our assessment remained consistent across various AKI criteria and two mean serum albumin level groups. Sensitivity appears to be higher in medical patients compared to surgical patients (0.72 vs. 0.55), potentially reflecting a higher baseline postoperative inflammatory status in surgical patients relative to medical patients [50] (26,126,129). Moreover, we found that the diagnostic accuracy is improved in the non-CKD population. It has a relative diagnostic odds ratio (RDOR) of 0.44 for studies including CKD populations versus those excluding CKD populations. The overall diagnostic performance of PNI is comparable to several novel biomarkers, such as serum Neutrophil Gelatinase-Associated Lipocalin (NGAL) (sensitivity 0.76 and specificity 0.79) and Liver-Type Fatty Acid-Binding Protein (L-FABP) (sensitivity 0.69 and specificity 0.81) [51]. The performance of PNI was also similar to the renal angina index, which has a sensitivity of 0.82 and specificity of 0.77 for predicting the onset of AKI post-major surgery [52]. We concluded that, compared to these novel biomarkers, PNI is routinely available without additional cost in daily clinical practice, making it a practical and cost-effective option for AKI risk assessment.

Acute kidney injury, being a complex and multifactorial condition, encompasses exposure risk factors and susceptibility factors. Old age; hypovolemia; anemia; and chronic disease with chronic inflammation, such as CKD, chronic liver disease, and DM, are considered as susceptibility factors [7]. Moreover, low serum albumin level was identified as a risk factor for AKI development in patients with critical illness [53] or those who had cardiac surgery [54]. PNI, created by Buzby et al., merges serum albumin and lymphocytes to indicate nutritional and inflammatory states and stress response, initially aiding in operative risk assessment and preoperative nutrition in gastrointestinal surgery [55,56,57,58]. Several studies [16, 17, 22, 37,38,39,40,41,42,43,44,45,46,47,48,49] have explored the predictive efficacy of PNI in assessing the risk of AKI in diverse clinical populations with critical illness (e.g., cardiac surgery, coronary artery disease, liver transplant, and sepsis), with different AKI criteria and diverse PNI cutoff values. We are the first to conduct a comprehensive meta-analysis examining the diagnostic accuracy of the PNI for AKI prediction. Our analysis demonstrates that the diagnostic accuracy of PNI remains consistent across various disease populations, different AKI criteria, and different levels of baseline serum albumin.

This study has several limitations. First, the risk of bias in the included studies was not negligible, primarily because most did not employ pre-specified PNI cutoff values. Additionally, certain studies specifically excluded conditions commonly observed in patients with critical illness, such as anemia (serum Hb level < 10) [40] and congestive heart failure [39, 40, 42]. Second, the analysis did not account for factors other than malnutrition that may contribute to lymphopenia, such as malignancy or autoimmune disorders [59]. Third, while the subgroup analysis revealed significant heterogeneity between studies involving patients with CKD and those that did not, as well as between medical and surgical cohorts, this high degree of heterogeneity warrants caution in interpreting the findings. Moreover, the absence of large-scale, prospective studies and a standardized PNI cutoff point for predicting AKI and its use as a prognostic marker necessitate further investigation. Finally, the majority of the studies analyzed were retrospective cohorts that used the Youden index to determine cutoff values, leading to a lower level of confidence in the evidence due to heterogeneity.

Conclusion

PNI could serve as a readily available marker for identifying patients with critical illness at low risk for AKI development. It demonstrates enhanced predictive performance, particularly in non-chronic kidney disease and medical populations. Additional trials with larger sample sizes and high-quality study designs are needed, employing predefined cutoff values for more precise assessments, to further elucidate the diagnostic utility of PNI in AKI risk classification within clinical settings.

Availability of data and materials

The datasets used in this meta-analysis are available from the corresponding author on reasonable request.

Abbreviations

ACS:

Acute coronary syndrome

AKI:

Acute kidney injury

AKIN:

Acute Kidney Injury Network

CABG:

Coronary artery bypass graft surgery

CAD:

Coronary artery disease

CKD:

Chronic kidney disease

DOR:

Diagnostic odds ratio

KDIGO:

Kidney Disease: Improving Global Outcomes

LR:

Likelihood ratio

NR:

Non-reported

PNI:

Prognostic nutritional index

RDOR:

Relative diagnostic odds ratio

SROC:

Summary receiver operating characteristic

References

  1. Gameiro J, Fonseca JA, Neves M, Jorge S, Lopes JA. Acute kidney injury in major abdominal surgery: incidence, risk factors, pathogenesis and outcomes. Ann Intensive Care. 2018;8(1):22.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Amin AP, Salisbury AC, McCullough PA, et al. Trends in the incidence of acute kidney injury in patients hospitalized with acute myocardial infarction. Arch Intern Med. 2012;172(3):246–53.

    Article  CAS  PubMed  Google Scholar 

  3. Tsai TT, Patel UD, Chang TI, et al. Contemporary incidence, predictors, and outcomes of acute kidney injury in patients undergoing percutaneous coronary interventions: insights from the NCDR Cath-PCI registry. JACC Cardiovasc Interv. 2014;7(1):1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Warren J, Mehran R, Baber U, et al. Incidence and impact of acute kidney injury in patients with acute coronary syndromes treated with coronary artery bypass grafting: insights from the Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction (HORIZONS-AMI) and Acute Catheterization and Urgent Intervention Triage Strategy (ACUITY) trials. Am Heart J. 2016;171(1):40–7.

    Article  PubMed  Google Scholar 

  5. Englberger L, Suri RM, Li Z, et al. Clinical accuracy of RIFLE and Acute Kidney Injury Network (AKIN) criteria for acute kidney injury in patients undergoing cardiac surgery. Crit Care. 2011;15(1):R16.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Boyer N, Eldridge J, Prowle JR, Forni LG. Postoperative acute kidney injury. Clin J Am Soc Nephrol. 2022;17(10):1535–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chen JJ, Kuo G, Hung CC, et al. Risk factors and prognosis assessment for acute kidney injury: The 2020 consensus of the Taiwan AKI Task Force. J Formos Med Assoc. 2021;120(7):1424–33.

    Article  PubMed  Google Scholar 

  8. Wiedermann CJ, Wiedermann W, Joannidis M. Causal relationship between hypoalbuminemia and acute kidney injury. World J Nephrol. 2017;6(4):176–87.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Buzby GP, Mullen JL, Matthews DC, Hobbs CL, Rosato EF. Prognostic nutritional index in gastrointestinal surgery. Am J Surg. 1980;139(1):160–7.

    Article  CAS  PubMed  Google Scholar 

  10. Onodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 1984;85(9):1001–5.

    CAS  PubMed  Google Scholar 

  11. Mohri Y, Inoue Y, Tanaka K, Hiro J, Uchida K, Kusunoki M. Prognostic nutritional index predicts postoperative outcome in colorectal cancer. World J Surg. 2013;37(11):2688–92.

    Article  PubMed  Google Scholar 

  12. Mirili C, Yilmaz A, Demirkan S, Bilici M, Basol TS. Clinical significance of prognostic nutritional index (PNI) in malignant melanoma. Int J Clin Oncol. 2019;24(10):1301–10.

    Article  CAS  PubMed  Google Scholar 

  13. Ishiguro T, Aoyama T, Ju M, et al. Prognostic nutritional index as a predictor of prognosis in postoperative patients with gastric cancer. In Vivo. 2023;37(3):1290–6.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cheng YL, Sung SH, Cheng HM, et al. Prognostic nutritional index and the risk of mortality in patients with acute heart failure. J Am Heart Assoc. 2017;6(6): e004876.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang J, Chen Y, Zou L, Gong R. Prognostic nutritional index as a risk factor for diabetic kidney disease and mortality in patients with type 2 diabetes mellitus. Acta Diabetol. 2023;60(2):235–45.

    Article  CAS  PubMed  Google Scholar 

  16. Kurtul A, Gok M, Esenboga K. Prognostic nutritional index predicts contrast-associated acute kidney injury in patients with ST-segment elevation myocardial infarction. Acta Cardiol Sin. 2021;37(5):496–503.

    PubMed  PubMed Central  Google Scholar 

  17. Hu Y, Cao Q, Wang H, et al. Prognostic nutritional index predicts acute kidney injury and mortality of patients in the coronary care unit. Exp Ther Med. 2021;21(2):123.

    Article  CAS  PubMed  Google Scholar 

  18. Huang Y, Zhang Q, Li P, et al. The prognostic nutritional index predicts all-cause mortality in critically ill patients with acute myocardial infarction. BMC Cardiovasc Disord. 2023;23(1):339.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sim JH, Jun IG, Moon YJ, et al. Association of preoperative prognostic nutritional index and postoperative acute kidney injury in patients who underwent hepatectomy for hepatocellular carcinoma. J Pers Med. 2021;11(5):428.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Vermeulen KM, Leal LL, Furtado MC, Vale SH, Lais LL. Phase angle and Onodera’s prognostic nutritional index in critically ill patients. Nutr Hosp. 2016;33(6):1268–75.

    Article  PubMed  Google Scholar 

  21. Wang ZH, Lin YW, Wei XB, et al. Predictive value of prognostic nutritional index on COVID-19 severity. Front Nutr. 2020;7: 582736.

    Article  PubMed  Google Scholar 

  22. Shimoyama Y, Umegaki O, Kadono N, Minami T. Presepsin and platelet to lymphocyte ratio predict the progression of septic subclinical acute kidney injury to septic acute kidney injury: a pilot study. BMC Res Notes. 2022;15(1):212.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA. 2018;319(4):388–96.

    Article  PubMed  Google Scholar 

  24. Deville WL, Buntinx F, Bouter LM, et al. Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol. 2002;2:9.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lee J, Kim KW, Choi SH, Huh J, Park SH. Systematic review and meta-analysis of studies evaluating diagnostic test accuracy: a practical review for clinical researchers-Part II. statistical methods of meta-analysis. Korean J Radiol. 2015;16(6):1188–96.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Deeks JJ. Systematic reviews in health care: systematic reviews of evaluations of diagnostic and screening tests. BMJ. 2001;323(7305):157–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982–90.

    Article  PubMed  Google Scholar 

  28. Williams GJ, Macaskill P, Chan SF, Turner RM, Hodson E, Craig JC. Absolute and relative accuracy of rapid urine tests for urinary tract infection in children: a meta-analysis. Lancet Infect Dis. 2010;10(4):240–50.

    Article  PubMed  Google Scholar 

  29. Boyd LNC, Ali M, Leeflang MMG, et al. Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: a meta-analysis of aggregate and individual participant data. EClinicalMedicine. 2023;55: 101747.

    Article  PubMed  Google Scholar 

  30. Rutjes AW, Reitsma JB, Di Nisio M, Smidt N, van Rijn JC, Bossuyt PM. Evidence of bias and variation in diagnostic accuracy studies. CMAJ. 2006;174(4):469–76.

    Article  PubMed  PubMed Central  Google Scholar 

  31. van Enst WA, Ochodo E, Scholten RJ, Hooft L, Leeflang MM. Investigation of publication bias in meta-analyses of diagnostic test accuracy: a meta-epidemiological study. BMC Med Res Methodol. 2014;14:70.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Shim SR, Kim SJ, Lee J. Diagnostic test accuracy: application and practice using R software. Epidemiol Health. 2019;41: e2019007.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.

    Article  PubMed  Google Scholar 

  34. Schunemann HJ, Mustafa RA, Brozek J, et al. GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy. J Clin Epidemiol. 2020;122:129–41.

    Article  PubMed  Google Scholar 

  35. Zhang Y, Coello PA, Guyatt GH, et al. GRADE guidelines: 20. Assessing the certainty of evidence in the importance of outcomes or values and preferences-inconsistency, imprecision, and other domains. J Clin Epidemiol. 2019;111:83–93.

    Article  PubMed  Google Scholar 

  36. Schunemann HJ, Mustafa RA, Brozek J, et al. GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables. J Clin Epidemiol. 2020;122:142–52.

    Article  PubMed  Google Scholar 

  37. Aykut A, Salman N. Poor nutritional status and frailty associated with acute kidney injury after cardiac surgery: a retrospective observational study. J Card Surg. 2022;37(12):4755–61.

    Article  PubMed  Google Scholar 

  38. Dolapoglu A, Avci E, Kiris T, Bugra O. The predictive value of the prognostic nutritional index for postoperative acute kidney injury in patients undergoing on-pump coronary bypass surgery. J Cardiothorac Surg. 2019;14(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Efe SC, Karagoz A, Dogan C, et al. Prognostic significance of malnutrition scores in elderly patients for the prediction of contrast-induced acute kidney injury. Int J Clin Pract. 2021;75(7): e14274.

    Article  CAS  PubMed  Google Scholar 

  40. Gucu A, Ozluk OA, Guvenc O, Sunbul SA, Engin M. The importance of prognostic nutritional index in predicting acute renal failure after on-pump coronary artery bypass operations in patients with insulin-dependent diabetes mellitus. Heart Surg Forum. 2021;24(4):E651–5.

    Article  PubMed  Google Scholar 

  41. Han M, Lee HW, Lee HC, Kim HJ, Seong EY, Song SH. Impact of nutritional index on contrast-associated acute kidney injury and mortality after percutaneous coronary intervention. Sci Rep. 2021;11(1):7123.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hatem E, Aslan O, Demirci EE, Yildirim S. Relationship between prognostic nutritional index and contrast-associated acute kidney injury in patients with non-ST segment elevation myocardial infarction undergoing coronary angiography. Angiology. 2023;74(7):672–9.

    Article  CAS  PubMed  Google Scholar 

  43. Jiing W, Yu H, Jiang J, He L, Zhao D, Yu H. The predictive value of preoperative prognostic nutritional index for postoperative acute kidney injury in 584 patients undergoing cardiac surgery. Chin J Clin Thorac Cardiovasc Surg. 2021;28(4):6.

    Google Scholar 

  44. Lee SI, Ko KP, Choi CH, Park CH, Park KY, Son KH. Does the prognostic nutritional index have a predictive role in the outcomes of adult cardiac surgery? J Thorac Cardiovasc Surg. 2020;160(1):145–53.

    Article  PubMed  Google Scholar 

  45. Li D, Chen Z, He W, et al. The association between nutritional risk and contrast-induced acute kidney injury in patients undergoing coronary angiography: a cross-sectional study. Nutr J. 2022;21(1):56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Min JY, Woo A, Chae MS, et al. Predictive impact of modified-prognostic nutritional index for acute kidney injury within 1-week after living donor liver transplantation. Int J Med Sci. 2020;17(1):82–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Shimoyama Y, Umegaki O, Kadono N, Minami T. Presepsin and prognostic nutritional index are predictors of septic acute kidney injury, renal replacement therapy initiation in sepsis patients, and prognosis in septic acute kidney injury patients: a pilot study. BMC Nephrol. 2021;22(1):219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Xie T, Xin Q, Chen R, et al. Clinical value of prognostic nutritional index and neutrophil-to-lymphocyte ratio in prediction of the development of sepsis-induced kidney injury. Dis Markers. 2022;2022:1449758.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Yuksel Y, Kose S. Prognostic nutritional index predicts contrast-induced nephropathy in patients with acute coronary syndrome. Angiology. 2023;74(8):736–44.

    Article  CAS  PubMed  Google Scholar 

  50. Cui P, Fang X. Pathogenesis of infection in surgical patients. Curr Opin Crit Care. 2015;21(4):343–50.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Pan HC, Yang SY, Chiou TT, et al. Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis. Crit Care. 2022;26(1):349.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bellos I, Pergialiotis V, Kontzoglou K. Renal resistive index as predictor of acute kidney injury after major surgery: a systematic review and meta-analysis. J Crit Care. 2019;50:36–43.

    Article  PubMed  Google Scholar 

  53. Shao M, Wang S, Parameswaran PK. Hypoalbuminemia: a risk factor for acute kidney injury development and progression to chronic kidney disease in critically ill patients. Int Urol Nephrol. 2017;49(2):295–302.

    Article  CAS  PubMed  Google Scholar 

  54. Li Q, Lv H, Chen Y, Shen J, Shi J, Zhou C. Development and validation of a machine learning predictive model for cardiac surgery-associated acute kidney injury. J Clin Med. 2023;12(3):1166.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gradel KO. Interpretations of the role of plasma albumin in prognostic indices: a literature review. J Clin Med. 2023;12(19):6132.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Keller U. Nutritional laboratory markers in malnutrition. J Clin Med. 2019;8(6):775.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Onsrud M, Thorsby E. Influence of in vivo hydrocortisone on some human blood lymphocyte subpopulations. I. Effect on natural killer cell activity. Scand J Immunol. 1981;13(6):573–9.

    Article  CAS  PubMed  Google Scholar 

  58. Benschop RJ, Rodriguez-Feuerhahn M, Schedlowski M. Catecholamine-induced leukocytosis: early observations, current research, and future directions. Brain Behav Immun. 1996;10(2):77–91.

    Article  CAS  PubMed  Google Scholar 

  59. Merayo-Chalico J, Rajme-Lopez S, Barrera-Vargas A, Alcocer-Varela J, Diaz-Zamudio M, Gomez-Martin D. Lymphopenia and autoimmunity: a double-edged sword. Hum Immunol. 2016;77(10):921–9.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We would like to express our gratitude to the English editors at KG Support for their invaluable assistance with language editing.

Funding

Grant-in-aid from National Cheng Kung University Hospital, Tainan, Taiwan (NCKUH-11209002 & NCKUH-11210042). Chih-Hsiang Chang have been supported by grant from Chang Gung Memorial Hospital (CGRPG3N0041)

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Authors and Affiliations

  1. Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Branch, Chang Gung university, Taoyuan, 33305, Taiwan

    Jia-Jin Chen & Chih-Hsiang Chang

  2. Department of Nephrology, Chansn Hospital, Taoyuan City, 33305, Taiwan

    Tao-Han Lee

  3. Education Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan

    Pei-Chun Lai

  4. Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 231, Taiwan

    Che-Hsiung Wu

  5. School of Medicine, Tzu Chi University, Hualien, 970, Taiwan

    Che-Hsiung Wu

  6. Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138, Shengli Road, Tainan, 701, Taiwan

    Yen-Ta Huang

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Contributions

All authors participated in the research and preparation of the manuscript. Methodology: J.J. Chen and Y.T. Huang; original draft writing: J.J. Chen, T.H. Lee, and Y.T. Huang; formal analysis: J.J. Chen, C.H. Chang, and Y.T. Huang; data extraction and search: J.J. Chen, T.H. Lee, and Y.T. Huang; writing—review and editing: P.C. Lai, and P.H. Wu; risk-of-bias and quality assessment: P.C. Lai, P.H. Wu, and Y.T. Huang; project administration: C.H. Chang and Y.T. Huang. All authors read and approved the final manuscript.

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Correspondence to Che-Hsiung Wu or Yen-Ta Huang.

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Supplementary Information

Additional file 1: Table S1.

Checklist for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Diagnostic Test Accuracy (DTA) studies. Table S2. Search strategy for each database. Table S3. Reasons for excluding full-text screening studies. Figure S1. PRISMA flowchart. Figure S2. Fagan’s nomogram for prognostic nutritional index as acute kidney injury prediction marker in non-CKD population with pre-test probabilities of 15% (A), 25% (B), and 40% (C). Figure S3. Deek’s funnel plot. Figure S4. Assessment (A) and summary (B) of risk of bias and applicability concern.

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Chen, JJ., Lee, TH., Lai, PC. et al. Prognostic nutritional index as a predictive marker for acute kidney injury in adult critical illness population: a systematic review and diagnostic test accuracy meta-analysis. j intensive care 12, 16 (2024). https://doi.org/10.1186/s40560-024-00729-z

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Keywords

Journal of Intensive Care

ISSN: 2052-0492