Anesthesia & Analgesia:
Critical Care, Trauma, and Resuscitation: Research Report
The Urine Protein NGAL Predicts Renal Replacement Therapy, but Not Acute Kidney Injury or 90-Day Mortality in Critically Ill Adult Patients
Nisula, Sara MD*; Yang, Runkuan MD†; Kaukonen, Kirsi-Maija PhD‡; Vaara, Suvi T. PhD*; Kuitunen, Anne PhD†; Tenhunen, Jyrki PhD†§; Pettilä, Ville PhD*; Korhonen, Anna-Maija PhD*; The FINNAKI Study Group
From the *Department of Surgery, Division of Anesthesia and Intensive Care Medicine, Intensive Care Units, Helsinki University Central Hospital, Helsinki; †Department of Intensive Care Medicine, Critical Care Medicine Research Group, Tampere University Hospital, Tampere, Finland; ‡Department of Epidemiology and Preventive Medicine, Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia; §Department of Surgical Sciences/Anesthesiology and Intensive Care, University of Uppsala, Uppsala, Sweden.
Accepted for publication January 16, 2014.
Published ahead of print May 7, 2014
Funding: Clinical Research funding (EVO) TYH 2010109/2011210 and T102010070 from Helsinki University Hospital (VP), and grants from the Finnish Society of Intensive Care, the Academy of Finland (MK), the Juselius Foundation (VP) and the Päivikki and Sakari Sohlberg Foundation (VP), and the Finnish Society of Anesthesiologists (SN). Laboratory analyses were funded by a grant from the Academy of Finland (MK) and external funding for Critical Care Medicine Research Group (JT).
The authors declare no conflicts of interest.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s web site.
Reprints will not be available from the authors.
Address correspondence to Sara Nisula, MD, Department of surgery, Intensive Care Units, Division of Anaesthesia and Intensive Care Medicine, Institution: Helsinki University Central Hospital, Box 340, 00029 HUS, Finland. Address e-mail to email@example.com.
BACKGROUND: Urine neutrophil gelatinase-associated lipocalin (uNGAL) is increasingly used as a biomarker for acute kidney injury (AKI). However, the clinical value of uNGAL with respect to AKI, renal replacement therapy (RRT), or 90-day mortality in critically ill patients is unclear. Accordingly, we tested the hypothesis that uNGAL is a clinically relevant biomarker for these end points in a large, nonselected cohort of critically ill adult patients.
METHODS: We prospectively obtained urine samples from 1042 adult patients admitted to 15 Finnish intensive care units. We analyzed 3 samples (on admission, at 12 hours, and at 24 hours) with NGAL ELISA Rapid Kits (BioPorto® Diagnostics, Gentofte, Denmark). We chose the highest uNGAL (uNGAL24) for statistical analyses. We calculated the areas under receiver operating characteristics curves (AUC) with 95% confidence intervals (95% CIs), the best cutoff points with the Youden index, positive likelihood ratios (LR+), continuous net reclassification improvement (NRI), and the integrated discrimination improvement (IDI). We performed sensitivity analyses excluding patients with AKI or RRT on day 1, sepsis, or with missing baseline serum creatinine concentration.
RESULTS: In this study population, the AUC of uNGAL24 (95% CI) for development of AKI (defined by the Kidney Disease: Improving Global Outcomes [KDIGO] criteria) was 0.733 (0.701–0.765), and the continuous NRI for AKI was 56.9%. For RRT, the AUC of uNGAL24 (95% CI) was 0.839 (0.797–0.880), and NRI 56.3%. For 90-day mortality, the AUC of uNGAL24 (95% CI) was 0.634 (0.593 to 0.675), and NRI 15.3%. The LR+ (95% CI) for RRT was 3.81 (3.26–4.47).
CONCLUSION: In this study, we found that uNGAL associated well with the initiation of RRT but did not provide additional predictive value regarding AKI or 90-day mortality in critically ill patients.
Neutrophil gelatinase-associated lipocalin (NGAL) is a protein stored in neutrophil granules and synthesized in various human tissues, including epithelial cells in the kidney, trachea, lungs, stomach, and colon.1 NGAL’s expression is generally upregulated in cells that are “under stress.” Plasma NGAL (pNGAL) and especially urine NGAL (uNGAL) concentrations rapidly increase after acute tubular damage in the kidney.2,3 Extensive research has been performed to confirm the predictive value of NGAL in diagnosis and outcome of acute kidney injury (AKI).4
NGAL has high sensitivity and specificity to predict AKI in children and cardiac surgery patients, which are characterized by intact baseline kidney function and an identifiable insult to the kidney.5–7 In mixed critically ill populations, however, the ability of NGAL to predict AKI is not as straightforward due to confounding factors such as infections and other acute events.8–11 NGAL has an important role in bacterial infections,12 which are common among critically ill patients and can confound its value.13,14
Based on previous reports, both pNGAL and uNGAL seem to have some value in predicting the need for renal replacement therapy (RRT) in critically ill patients.10,11,14 However, NGAL’s ability to predict intensive care unit (ICU) or hospital mortality has been poor.15 In addition, there are no studies reporting NGAL’s value in 90-day mortality prediction.
Accordingly, in this study, we tested the primary hypothesis that uNGAL levels are associated with RRT in a large heterogeneous group of adult critically ill patients. In addition, we assessed the association of NGAL with AKI and 90-day mortality in the same group of patients.
We collected urine samples from ICU patients from the first half of the prospective, observational, multicenter FINNAKI study (September 1 to December 1, 2011), which reported the incidence risk factors, and 90-day mortality of patients with AKI in Finland.16 The FINNAKI study included: (1). all emergency ICU admissions, and (2). elective admissions with an ICU stay of >24 hours. The study excluded patients aged younger than 18 years, readmitted patients, who received RRT during their previous admission, elective ICU patients, with a length of stay of <24 hours if discharged alive, patients receiving chronic dialysis, organ donors, patients with no permanent residency in Finland or insufficient language skills, patients transferred between study ICUs if included in the study for 5 days, and intermediate care patients. The FINNAKI study protocol has been published in detail.16 For this study, we chose a random sample of patients for analysis by selecting random laboratory sample boxes from storage without any knowledge of patient characteristics and outcome.
We defined AKI using the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines using both daily serum creatinine (Cr) and hourly urine output measurements.17 We evaluated the highest AKI stage during the first 3 ICU treatment days. We defined baseline serum Cr as the latest value from the previous year excluding the last week preceding admission. If baseline Cr was not available, we estimated it by the Modification in Diet in Renal Disease equation assuming a glomerular filtration rate of 75 mL/1.73 m2.18 We defined sepsis (systemic inflammatory response syndrome and known or probable infection) using American College of Chest Physicians/Society of Critical Care Medicine guidelines.19
The Ethics Committee of the Department of Surgery in Helsinki University Hospital gave approval for the study and for the use of a delayed consent regarding clinical and outcome data and laboratory samples. A written informed consent was obtained from all patients or their next of kin. We collected patient demographics, medical history, severity scores, length of stay, physiologic data, and hospital mortality from the Finnish Intensive Care Consortium prospective database (Tieto Ltd, Helsinki, Finland), and with a study-specific case report form. The Finnish Intensive Care Consortium database is compiled from data automatically collected from electronic clinical information systems and from data entered manually. Data are validated by automated filters and trained personnel before being submitted to the database. We monitored the reliability and completeness of the data collected with the case report form by visiting 8 randomly chosen study sites using a structured monitoring plan. We screened the patients’ AKI and RRT status for 3 days in the ICU. We obtained the 90-day mortality of the study patients from the Finnish Population Register Centre.
Measurement of NGAL
We collected and aliquoted urine samples at ICU admission at 12 and 24 hours. The ICU admission sample was taken immediately after admission or at 2 hours at the latest. The samples were stored in −80°C until assayed with the NGAL ELISA Rapid Kit (BioPorto® Gentofte, Denmark) in duplicate according to the manufacturer’s instructions by author RY (Tampere University Hospital, Tampere, Finland). RY was blinded to patient information. The validated ELISA method used shows good intra- and interassay precision (median coefficient of variations of dilutions corresponding to 10%, 30%, 60%, and 90% of the range of optical densities of the calibration curve, CV% <5% (intra) and CV% <10% (inter)).20 The kits have a measurement range between 10 and 1000 ng/mL. We registered out-of-range values as the highest or lowest value (10 or 1000 ng/mL). For each patient, we chose the highest uNGAL value (uNGAL24) for predictive calculations to improve the sensitivity.
We presented the data as medians with interquartile ranges or as absolute numbers (percentage with 95% confidence intervals, [CIs]). We compared nonparametric data with the Mann-Whitney U test and categorical variables with the χ2 test or Fisher exact test, when appropriate. We calculated areas under receiver operating characteristics (ROC) curves (AUCs) with 95% CIs. We identified the best cutoff points with 95% CIs for uNGAL24 with the Youden index and calculated sensitivity, specificity, and positive likelihood ratios (LR+), using these cutoff points. We performed the analyses first for all patients. We performed sensitivity analyses excluding septic patients, patients who fulfilled criteria for AKI or with early RRT on admission day, and patients lacking a true baseline Cr.
We calculated the continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) for uNGAL regarding each end point to evaluate its additional predictive value. The NRI is described as the percentage of patients whose risk of outcome stratification improves by adding the biomarker in question and is determined by calculating the sum of differences in proportions of patients moving up minus the proportion moving down for patients who develop the event, and the proportion of patients moving down minus the proportion moving up for patients who do not develop the event.21 NRI and IDI are recommended and sensitive tools for detecting additional benefit of a predictive marker.22 Their sensitivity exceeds beyond changes in areas under receiver operating characteristics curves (AUC). IDI is calculated with the same principle as NRI using the changes in the model-based probabilities. For the purpose of calculation of NRI and IDI, we constructed multivariate logistic regression models (Supplemental Digital Content 1, Table 1, http://links.lww.com/AA/A902) for the 3 separate end points (AKI, RRT, and 90-day mortality). We chose the variables for the models by exploring possible association with AKI, RRT, and 90-day mortality in univariate models (Table 1). Finally, we included significant variables from the univariate analyses (P < 0.05) to the multivariate logistic regression models (variable selection with enter-method) performed without, and then with, the highest uNGAL value. Our sample size calculations based on 95% CIs for AUC as in a previous similar study,23 and using numbers of patients with AKI, RRT, and 90-day deaths from the whole study cohort,16 indicated that a sample size of 1000 patients would yield clinically precise AUCs with 95% CIs <0.10 with our primary end point (RRT). We calculated the Youden index and cutoff points with MedCalc version 12.7.2 (MedCalc Software, Belgium) and all other analyses with SPSS version 19 (SPSS, Chicago, IL).
We analyzed at least 1 uNGAL within the first 24 hours for 1042 patients. Figure 1 illustrates the study patients according to their AKI, RRT, and 90-day mortality status. Patient characteristics are presented in Table 2. The available numbers (percentages) of samples from each time point were 965/1042 (93%) at 0 hour, 669/1006 (67%) at 12 hours, and 817/848 (96%) at 24 hours, including all patients still present in the ICU. The median (interquartile range) uNGAL24 was 102 (28–535) ng/mL. The uNGAL24 was below the detection limit in 107 (10.3%) patients and exceeded the upper limit in 184 (17.7 %) patients. In more than half of the patients (56%), the highest uNGAL value was from the admission sample (0 hour).
The incidence of AKI (95% CI) was 379/1042, 36.4% (33.4%–39.4%). Of 1042 patients, 168 (16.1%) had stage 1, 81 (7.8%) had stage 2, and 130 (12.5%) had stage 3 AKI according to the KDIGO criteria. Of the AKI patients, 178/379 (47.0%) developed their AKI on day 1, 162/379 (42.7%) on day 2, and 39/379 (10.3%) on day 3. The AUC (95% CI) of uNGAL24 in predicting AKI occurring during the 3 first days of ICU treatment was 0.733 (0.701–0.765), and the best cutoff value (95% CI) for uNGAL24 was 157 (73–225) ng/mL. The continuous NRI for AKI was 56.9% (IDI 0.071) (Supplemental Digital Content 2, Table 2, http://links.lww.com/AA/A903).
Of the 1042 patients, 83 (8.0%, 95% CI, 6.3%–9.6%) received RRT. Of those 83, RRT was initiated on the first day in the ICU for 38/83 (45.8%) patients. The number of patients with RRT initiated on days 2 and 3 were 30/83 (36.1%), and 15/83 (18.1%), respectively. The initial RRT modality was continuous therapy for 48/83 (57.8%) patients and intermittent (IRRT) in 33/83 (39.8%) patients. The uNGAL24 was associated with the initiation of RRT with an AUC (95% CI) of 0.839 (0.797–0.880) with the best cutoff value (95% CI) of 449 (219–538) ng/mL. The NRI for RRT was 56.3% (IDI 0.022) and LR+ (95% CI) 3.81 (3.26–4.47).
The 90-day mortality (95% CI) was 225/1042, 21.6% (19.0%–24.1%). The AUC (95% CI) for NGAL for prediction of 90-day mortality was 0.634 (0.593–0.675), with a best cutoff value (95% CI) of 229 (76–988) ng/mL. NRI was 15.3% (IDI <0.001) for 90-day mortality.
The sensitivity, specificity, best cutoff values, and LR+ for uNGAL24 regarding each end point, namely AKI, RRT, and 90-day mortality, are presented in Table 3. Table 3 also includes sensitivity analyses excluding patients with sepsis, patients with AKI or RRT on day 1, and patients without known baseline Cr. uNGAL concentrations at different time points according to diagnosis of AKI, RRT, and 90-day mortality are presented in Figure 2.
The details of the predictive models constructed to evaluate additional diagnostic value (NRI and IDI) of uNGAL24 are presented in Table 1 (univariate models), and in the online supplemental material Table 1 (multivariate models). Online supplemental material Figure 1 (Supplemental Digital Content 3, http://links.lww.com/AA/A904) illustrates the predictive model-based ROC AUCs with and without uNGAL24 according to AKI, RRT, and 90-day mortality. NRIs and IDIs for uNGAL24 for predicting AKI, RRT, and 90-day mortality are listed in online supplemental material Table 2.
In this large multicenter prospective study in critically ill adult patients, we found that the highest uNGAL during the first 24 hours (uNGAL24) of ICU admission was only moderately associated with AKI (according to KDIGO) (AUC 0.733). The AUC of uNGAL for predicting RRT was relatively good (0.839), but predictive value of 90-day mortality was poor (AUC 0.634).
NGAL is produced in the kidney in response to tubular injury and is mainly secreted into urine. These findings provide biological plausibility for uNGAL as an AKI biomarker.3 However, despite ongoing large-scale research, definitive proof of NGAL’s role in predicting kidney injury is still lacking.
The most promising results regarding the ability of NGAL to predict AKI are from single-center studies on adults and children undergoing cardiac bypass (with AUCs of 0.960, 0.99, and 0.88).5,6,24 A meta-analysis conducted in 2009, however, reported an AUC of only 0.775 for NGAL for prediction of AKI among postcardiac surgery patients.4 More recently, the largest study to date in cardiac surgery (1219 patients) again reported a less optimistic result (AUC 0.67) and consequently challenged the ability of uNGAL to predict AKI in this population.25
The performance of NGAL concerning AKI in more heterogeneous ICU populations has been inconsistent (AUCs ranging from 0.54 to 0.92) as indicated by a recent clinical review.15 A systematic review and meta-analysis from 2009 report a relatively good AUC (0.815) for NGAL across settings in predicting AKI. However, this result is affected by the inclusion of studies done among children, cardiac surgery patients, and after administration of radio-contrast media. In critically ill patients, the combined AUC in this meta-analysis decreased to 0.728.4 In our multicenter study using the newest KDIGO criteria for AKI, we found uNGAL’s association with AKI to be comparable to this analysis (AUC 0.733). Moreover, we performed several sensitivity analyses to correct for possible bias from (1) septic infection, (2) early AKI or initiation of RRT, or from (3) estimated baseline Cr and found no increase in the predictive power of NGAL. Recently, another large multicenter study including 744 critically ill patients had comparable findings regarding uNGAL using AKI KDIGO stages 2 and 3 as an end point.26 Also, most recently, a prospective study of 102 mixed critically ill patients found lack of predictive power in both pNGAL and uNGAL concerning AKI.27 Thus, the predictive value of uNGAL regarding developing AKI seems to be inadequate for clinical use in the adult critical care setting.
In this study, uNGAL24 had the strongest association regarding RRT (AUC 0.839). This finding is in line with results from the 2 systematic reviews.4,15 The former reported a combined AUC of 0.782 for pNGAL and uNGAL for RRT prediction,4 and the latter AUCs ranging from 0.7328 to 0.89.10 Our results are also comparable with that of a previous study of pNGAL in a mixed ICU setting (AUC 0.82,).11 In our study, uNGAL gave additional value to prediction of RRT judged by positive likelihood ratios of 3.81 (lower 95% CI, 3.26) for all, and 7.79 (lower 95% CI, 5.72) for nonseptic patients (Table 3).
To our knowledge, only ICU and hospital mortality have been assessed in previous studies with NGAL, although long-term mortality has been considered a more reliable end point in randomized controlled trials.29 The largest NGAL study in a mixed ICU setting before the current study (comprising 632 adult patients) reported only a modest AUC of 0.64 for uNGAL in predicting hospital mortality.10 More positive data were reported by Doi et al.9 (AUC 0.827 for uNGAL). In a systematic review, a combined AUC for NGAL to predict hospital mortality in all patients was 0.706.4 In the FINNAKI study, our end point was 90-day mortality. Consequently, we found that in this heterogeneous population of critically ill patients, uNGAL24 had poor predictive value (AUC 0.634) for 90-day mortality. The unaltered predictive model-based ROC AUCs with and without uNGAL (online supplemental material Figure 1) and a low continuous NRI (15.3%) further support our finding. Since the true optimal cutoff value of uNGAL for each end point is unknown, the detected NRIs may overestimate performance. Our result is in concordance with a recent systematic review15 and a prospective study in a general ICU environment.27 Thus, uNGAL cannot be used to predict mortality in adult critically ill patients.
NGAL’s inadequate performance in predicting AKI is not surprising, given the generally accepted idea that the syndrome of AKI encompasses a widely varying pathophysiology. Also, recent data support the hypothesis that there are different molecular forms of NGAL, with current commercial assays being unable to distinguish among these forms of NGAL originating from different tissues.27 Furthermore, the known limitations in Cr-based AKI definitions in identifying early damage in the kidneys might lead to “false positive” NGAL values as suggested by a recent systematic review on NGAL.15 Moreover, despite the fairly promising AUC of 0.839 (lower 95% CI, 0.797) for predicting RRT in this study, transformation of this result into clinical practice is complicated. Except for life-threatening indications for RRT, there are currently no uniform criteria for RRT initiation.17 Furthermore, data on the most beneficial timing of RRT initiation are lacking.30 At this stage, uncertainty remains regarding the use of NGAL for judgment of initiation or withdrawal of RRT.
Our study has some limitations. First, due to the large size of the FINNAKI study (3093 admissions), we did not analyze uNGAL from all study patients for logistic, economic, and futility reasons. After sample size calculations, we therefore analyzed samples from >1000 patients from the first half of the study. These patients were randomly chosen from all samples using complete sampling boxes with consecutive patients from the same hospitals without any selection by age, diagnosis, outcome, RRT, or presence of AKI. We performed sample size calculations based on our primary end point, RRT. Judging by number of end points in the other outcomes as end points (AKI 379, 36.4% and 90-day deaths 225, 21.6%), the sample size was considered adequate for these analyses as well. Second, due to analysis of the highest NGAL value from the first 24 hours, some of the study patients might have developed AKI or been initiated with RRT at the time of sample collection. Therefore, we performed a sensitivity analysis excluding the patients who met the end point on day 1. Third, for logistic reasons, we were not able to centrifuge the samples before freezing them at −80°C. It has been reported that centrifugation does not significantly impact uNGAL values, although those samples with higher white blood cell count have higher NGAL values.31 Fourth, some samples were stored at −80°C for up to 6 months before analysis. Data suggest, however, that uNGAL is stable after storage at −80°C for several months.20,32,33 Fifth, we did not correct for urine Cr as suggested by some study results.34 Sixth, we could obtain actual baseline serum/plasma Cr values for 64% of patients and estimated those lacking a baseline Cr with Modification in Diet in Renal Disease calculation. Seventh, we registered out-of-range values as the highest or lowest value (10 or 1000 ng/mL) being unable to redilute and reassay these samples. However, this should not have any influence on the calculations of predictive value. Eighth, we did not validate the prognostic multivariable models constructed for this study, because an external independent patient cohort would be required for a precise result. Finally, for logistic reasons, some samples were missed during collection, but the percentage of collected samples at admission and 24 hours was high (>90%). An obvious strength of our study is that it was a prospective, multicenter study among heterogeneous critically ill patients. The largest sample size analyzed so far was adequate for clinically precise calculations of additional predictive value of uNGAL. In addition, we were able to use the most recent KDIGO guidelines17 for staging of AKI to enable further comparison of results with other studies.
In conclusion, in this study, we found that uNGAL measured with a commercially available immunoassay agreed with RRT quite well but did not have adequate value associated with AKI during the first 3 days or prediction of 90-day mortality in this large heterogeneous group of critically ill patients with or without sepsis. Our findings raise serious concerns regarding the clinical usefulness of uNGAL for detection of AKI and prediction of long-term outcome in adult critically ill patients.
The FINNAKI Study Group
Central Finland Central Hospital: Raili Laru-Sompa, Anni Pulkkinen, Minna Saarelainen, Mikko Reilama, Sinikka Tolmunen, Ulla Rantalainen, Marja Miettinen. East Savo Central Hospital: Markku Suvela, Katrine Pesola, Pekka Saastamoinen, Sirpa Kauppinen. Helsinki University Central Hospital: Ville Pettilä, Kirsi-Maija Kaukonen, Anna-Maija Korhonen, Sara Nisula, Suvi Vaara, Raili Suojaranta-Ylinen, Leena Mildh, Mikko Haapio, Laura Nurminen, Sari Sutinen, Leena Pettilä, Helinä Laitinen, Heidi Syrjä, Kirsi Henttonen, Elina Lappi, Hillevi Boman. Jorvi Central Hospital: Tero Varpula, Päivi Porkka, Mirka Sivula Mira Rahkonen, Anne Tsurkka, Taina Nieminen, Niina Prittinen. Kanta-Häme Central hospital: Ari Alaspää, Ville Salanto, Hanna Juntunen, Teija Sanisalo. Kuopio University Hospital: Ilkka Parviainen, Ari Uusaro, Esko Ruokonen, Stepani Bendel, Niina Rissanen, Maarit Lång, Sari Rahikainen, Saija Rissanen, Merja Ahonen, Elina Halonen, Eija Vaskelainen. Lapland Central Hospital: Meri Poukkanen, Esa Lintula, Sirpa Suominen. Länsi Pohja Central Hospital: Jorma Heikkinen, Timo Lavander, Kirsi Heinonen, Anne-Mari Juopperi. Middle Ostrobothnia Central Hospital: Tadeusz Kaminski, Fiia Gäddnäs, Tuija Kuusela, Jane Roiko. North Karelia Central Hospital: Sari Karlsson, Matti Reinikainen, Tero Surakka, Helena Jyrkönen, Tanja Eiserbeck, Jaana Kallinen. Satakunta Hospital district: Vesa Lund, Päivi Tuominen, Pauliina Perkola, Riikka Tuominen, Marika Hietaranta, Satu Johansson. South Karelia Central Hospital: Seppo Hovilehto, Anne Kirsi, Pekka Tiainen, Tuija Myllärinen, Pirjo Leino, Anne Toropainen. Tampere University Hospital: Anne Kuitunen, Ilona Leppänen, Markus Levoranta, Sanna Hoppu, Jukka Sauranen, Jyrki Tenhunen, Atte Kukkurainen, Samuli Kortelainen, Simo Varila. Turku University Hospital: Outi Inkinen, Niina Koivuviita, Jutta Kotamäki, Anu Laine. Oulu University Hospital: Tero Ala-Kokko, Jouko Laurila, Sinikka Sälkiö. Vaasa Central Hospital: Simo-Pekka Koivisto, Raku Hautamäki, Maria Skinnar.
Name: Sara Nisula, MD.
Contribution: This author drafted the manuscript, performed the statistical analyses, and participated in the design and data gathering of the study.
Attestation: This author approved the final manuscript.
Name: Runkuan Yang, MD.
Contribution: This author performed the laboratory analyses and reviewed the manuscript.
Attestation: This author approved the final manuscript.
Name: Kirsi-Maija Kaukonen, PhD.
Contribution: This author participated in the design and coordination of the study and revised the manuscript.
Attestation: This author approved the final manuscript.
Name: Suvi T Vaara, PhD.
Contribution: This author participated in the design and data gathering of the study and helped to draft the manuscript and to perform the statistical analyses.
Attestation: Suvi Vaara attests to the integrity of the original data and the analysis reported in this manuscript and approved the final manuscript.
Name: Anne Kuitunen, PhD.
Contribution: This author participated in the data gathering of the study, coordinated the laboratory analyses, and revised the manuscript.
Attestation: This author approved the final manuscript.
Name: Jyrki Tenhunen, PhD.
Contribution: This author participated in the design of the study and data collection and revised the manuscript.
Attestation: This author approved the final manuscript.
Name: Ville Pettilä, PhD.
Contribution: This author was the principal investigator of the FINNAKI study, participated in the design and coordination of the study, and helped to draft the manuscript and to perform the statistical analyses.
Attestation: Ville Pettilä is the archival author and approved the final manuscript.
Name: Anna-Maija Korhonen, PhD.
Contribution: This author participated in the design and coordination of the study, collected study data, and helped to draft the manuscript.
Attestation: This author approved the final manuscript.
Name: The FINNAKI Study group.
Contribution: The study group members all participated in the local study coordination and data gathering.
This manuscript was handled by: Avery Tung, MD.
We are grateful for all the members of the FINNAKI study group in participating hospitals and Tieto Healthcare & Welfare Ltd for database management.
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