Extremely high myoglobin plasma concentrations producing hook effect in a critically ill patient

Kurt-Mangold, Michelle; Drees, Denny; Krasowski, Matthew D.

doi:10.1016/j.cca.2012.08.024

Cited by 13 publications

(9 citation statements)

References 4 publications

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“…So an accumulation of large amount Mb in the distal renal tubule is responsible for AKI as a consequence. Instead of muscle injury, the elevated serum Mb level can also be observed in these following conditions: multi-organ failure, sever infection and major surgery (16). Furthermore, it has been shown that Mb itself is significantly related to poor outcome of diseases such as acute trauma, myocardial infarction, severe sepsis and so on (17,18).…”

Section: Introductionmentioning

confidence: 99%

Predictive utility of postoperative serum myoglobin in acute kidney injury after liver transplantation

Han¹,

Zhang²,

Gao³

et al. 2021

Ann Palliat Med

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Background: Acute kidney injury (AKI) is a common and multifactorial complication after liver transplantation (LT). Myoglobin (Mb) which can be served as O2 storage and delivery depot is present in muscles and cardiac myocytes. Previous studies had shown the close relationship between Mb and AKI.But there is a lack of clinical studies for Mb with the risk of AKI due to LT. This study was performed to determine the association between the serum level of Mb and incidence of AKI in patients underwent LT. Methods:The clinical data of 140 consecutive adult patients who underwent LT at our center from June 2018 to August 2020 were analyzed in this study. One hundred and fifteen patients met the inclusion criteria.The performances of postoperative laboratory variables (including serum Mb) were evaluated. The outcomes after LT, including the duration of intensive care unit (ICU) stay, hospital stay and 28-day mortality, were also measured. Results:We divided 115 patients into AKI group (n=44) and non-AKI group (n=71). Serum Mb on postoperative day 0 (POD0) was significantly higher in AKI group than those in non-AKI group (P<0.001).According to univariate and multivariable logistic regression analysis, the levels of serum albumin (P=0.024), alanine transaminase (P=0.007) and Mb (P=0.006) on POD0 were independently associated with development of new AKI. The area under curve (AUC) of serum Mb after LT immediately had the best value for predicting AKI [AUC: 0.755, sensitivity: 63.6%, specificity: 77.3%, 95% confidence interval (CI): 0.661-0.849], its cut-off value was 957 ng/mL. Conclusions: Postoperative serum Mb was an independent risk factor for new AKI and could increase the accuracy of predicting the occurrence of post-LT AKI.

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Section: Introductionmentioning

confidence: 99%

Predictive utility of postoperative serum myoglobin in acute kidney injury after liver transplantation

Han¹,

Zhang²,

Gao³

et al. 2021

Ann Palliat Med

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“…Dilution protocols were modified following identification of this problem. [17] A protocol for evaluation of possible toxic alcohol or glycol exposure was developed based on osmolal gap. [1819]…”

Section: Resultsmentioning

confidence: 99%

Autoverification in a core clinical chemistry laboratory at an academic medical center

Krasowski

Davis

Drees

et al. 2014

Journal of Pathology Informatics

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Background:Autoverification is a process of using computer-based rules to verify clinical laboratory test results without manual intervention. To date, there is little published data on the use of autoverification over the course of years in a clinical laboratory. We describe the evolution and application of autoverification in an academic medical center clinical chemistry core laboratory.Subjects and Methods:At the institution of the study, autoverification developed from rudimentary rules in the laboratory information system (LIS) to extensive and sophisticated rules mostly in middleware software. Rules incorporated decisions based on instrument error flags, interference indices, analytical measurement ranges (AMRs), delta checks, dilution protocols, results suggestive of compromised or contaminated specimens, and ‘absurd’ (physiologically improbable) values.Results:The autoverification rate for tests performed in the core clinical chemistry laboratory has increased over the course of 13 years from 40% to the current overall rate of 99.5%. A high percentage of critical values now autoverify. The highest rates of autoverification occurred with the most frequently ordered tests such as the basic metabolic panel (sodium, potassium, chloride, carbon dioxide, creatinine, blood urea nitrogen, calcium, glucose; 99.6%), albumin (99.8%), and alanine aminotransferase (99.7%). The lowest rates of autoverification occurred with some therapeutic drug levels (gentamicin, lithium, and methotrexate) and with serum free light chains (kappa/lambda), mostly due to need for offline dilution and manual filing of results. Rules also caught very rare occurrences such as plasma albumin exceeding total protein (usually indicative of an error such as short sample or bubble that evaded detection) and marked discrepancy between total bilirubin and the spectrophotometric icteric index (usually due to interference of the bilirubin assay by immunoglobulin (Ig) M monoclonal gammopathy).Conclusions:Our results suggest that a high rate of autoverification is possible with modern clinical chemistry analyzers. The ability to autoverify a high percentage of results increases productivity and allows clinical laboratory staff to focus attention on the small number of specimens and results that require manual review and investigation.

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“…MYO is a heme protein expressed in cardiomyocytes and skeletal muscle cells (21). Serum MYO levels are elevated in patients with acute myocardial infarction, renal insufficiency, muscle trauma, shock, and severe infection (22,23). Recent studies have shown that serum MYO is strongly correlated with the severity of sepsis, and an increase in the serum MYO levels within 28 days after diagnosis indicates a higher risk for mortality (24).…”

Section: Discussionmentioning

confidence: 99%

Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome

Liu

Zhang

et al. 2022

Front. Med.

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ObjectiveEarly prediction of long-term outcomes in patients with sepsis-induced cardiorenal syndrome (CRS) remains a great challenge in clinical practice. Herein, we aimed to construct a nomogram and machine learning model for predicting the 1-year mortality risk in patients with sepsis-induced CRS.MethodsThis retrospective study enrolled 340 patients diagnosed with sepsis-induced CRS in Shanghai Tongji Hospital between January 2015 and May 2019, as a discovery cohort. Two predictive models, the nomogram and machine learning model, were used to predict 1-year mortality. The prognostic variables used to develop the nomogram were identified based on a forward stepwise binary logistic regression, and the predictive ability of the nomogram was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve. Meanwhile, machine learning (ML) techniques, such as support vector machine, random forest (RF), and gradient boosted decision tree, were assessed mainly by accuracy and AUC. Feature ranking analysis was performed using the ML algorithm. Both nomogram and ML models were externally validated by an independent cohort of 103 patients diagnosed with sepsis-induced CRS between June 2019 and December 2020.ResultsAge, sequential sepsis-related organ failure score (SOFA), serum myoglobin (MYO), vasopressor use, and mechanical ventilation were identified as independent risk factors for 1-year mortality in the nomogram predictive model. In the discovery cohort, the nomogram yielded higher AUC for predicting mortality than did the SOFA score (0.855 [95% CI: 0.815–0.895] vs. 0.756 [95% CI: 0.705–0.808]). For ML, the model developed by RF showed the highest accuracy (0.765) and AUC (0.854). In feature ranking analysis, factors such as age, MYO, SOFA score, vasopressor use, and baseline serum creatinine were identified as important features affecting 1-year prognosis. Moreover, the nomogram and RF model both performed well in external validation, with an AUC of 0.877 and 0.863, respectively.ConclusionOur nomogram and ML models showed that age, SOFA score, serum MYO levels, and the use of vasopressors during hospitalization were the main factors influencing the risk of long-term mortality. Our models may serve as useful tools for assessing long-term prognosis in patients with sepsis-induced CRS.

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Extremely high myoglobin plasma concentrations producing hook effect in a critically ill patient

Cited by 13 publications

References 4 publications

Predictive utility of postoperative serum myoglobin in acute kidney injury after liver transplantation

Predictive utility of postoperative serum myoglobin in acute kidney injury after liver transplantation

Autoverification in a core clinical chemistry laboratory at an academic medical center

Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome

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