Predictive implications of albumin and C-reactive protein for progression to pneumonia and poor prognosis in Stenotrophomonas maltophilia bacteremia following allogeneic hematopoietic stem cell transplantation

Harada, Kaito; Sekiya, Noritaka; Konishi, Tatsuya; Nagata, Akihito; Yamada, Yuta; Takezaki, Toshiaki; Kaito, Satoshi; Kurosawa, Shuhei; Sakaguchi, Masahiro; Yasuda, Shunichiro; Sasaki, Shugo; Yoshioka, Kosuke; Watakabe-Inamoto, Kyoko; Igarashi, Aiko; Najima, Yuho; Hagino, Takeshi; Muto, Hideharu; Kobayashi, Takeshi; Doki, Noriko; Kakihana, Kazuhiko; Sakamaki, Hisashi; Ohashi, Kazuteru

doi:10.1186/s12879-017-2745-6

Cited by 20 publications

(12 citation statements)

References 33 publications

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“…Albumin (ALB) tends to decrease in levels during acute infections and is a strong indicator of outcome in infectious diseases. The relationship between low serum albumin level and high mortality has been reported in several studies [15,16].…”

Section: Introductionmentioning

confidence: 78%

The impact of procalcitonin to C-reactive protein ratio (PCR) kinetics as a predictor of mortality in nosocomial bloodstream infections

Hakyemez

Aslan

Durdu

2020

Preprint

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Purpose: To investigate the combination of serum C-reactive protein (CRP) and procalcitonin (PCT) kinetics in predicting mortality in nosocomial blood stream infections (BSIs).Materials and Methods: We retrospectively reviewed the medical records of patients ≥ 18 years of age with nosocomial BSIs hospitalized in intensive care units (ICU). Clinical, microbiological and biochemical data were compared in patients who survivors and deaths. Binary logistic regression analyses were used to identify independent risk factors. The kinetic changes were defined the as difference between level on 5th day and level at 1st day of BSI.Results: Of the 84 included patients, 49 (58.4%) had survivors and 35 (41.6%) had deaths. In univariate analysis, renal disease (p=0.007), cardiac disease (p=0.042), septic shock (p=<0.001), SOFA (p=<0.001) and APACHE-II (p <0.001), ΔCRP (p=0.004), ΔPCT (p=<0.001), and ΔPCR (p=0.025) were significantly higher in non-survivors. In the logistic regression analysis, APACHE-II score (OR=1.46, 95% CI=1.20-1.78, p <0.001), ΔCRP (OR=1.18, %95 CI =1.04-1.34, p=0.009), ΔPCT (OR=0.87, 95% CI=0.79-0.95, p=0.001), and ∆PCR (OR=36.78, 95%C = 4.52-299.01, p=0.001) were independent predictors of 28-day mortality.Conclusions: The ∆PCR kinetic was a strong independent predictor of mortality in nosocomial BSIs in ICUs.

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Section: Introductionmentioning

confidence: 78%

The impact of procalcitonin to C-reactive protein ratio (PCR) kinetics as a predictor of mortality in nosocomial bloodstream infections

Hakyemez

Aslan

Durdu

2020

Preprint

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“…In our study, the relative importance of albumin in predicting aGvHD was about 41%. Similarly, previous studies have also emphasized on the importance of the albumin level for predicting aGvHD [38][39][40], and low amounts of albumin alone and without dependence on other predictors affect overall mortality of aGvHD patients [41]. The second predictor for aGvHD is uric acid with the importance of 15.1%.…”

Section: The Most Important Predictors For Agvhdmentioning

confidence: 88%

An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation

et al. 2021

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Background: Acute graft-versus-host disease (aGvHD) is a complex and often multisystem disease that causes morbidity and mortality in 35% of patients receiving allogeneic hematopoietic stem cell transplantation (AHSCT). Objective: This study aimed to implement a Clinical Decision Support System (CDSS) for predicting aGvHD following AHSCT on the transplantation day. Material and Methods: In this developmental study, the data of 182 patients with 31 attributes, which referred to Taleghani Hospital Tehran, Iran during 2009–2017, were analyzed by machine learning (ML) algorithms which included XGBClassifier, HistGradientBoostingClassifier, AdaBoostClassifier, and RandomForestClassifier. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, and specificity. Using the machine learning developed model, a CDSS was implemented. The performance of the CDSS was evaluated by Cohen’s Kappa coefficient. Results: Of the 31 included variables, albumin, uric acid, C-reactive protein, donor age, platelet, lactate Dehydrogenase, and Hemoglobin were identified as the most important predictors. The two algorithms XGBClassifier and HistGradientBoostingClassifier with an average accuracy of 90.70%, sensitivity of 92.5%, and specificity of 89.13% were selected as the most appropriate ML models for predicting aGvHD. The agreement between CDSS prediction and patient outcome was 92%. Conclusion: ML methods can reliably predict the likelihood of aGvHD at the time of transplantation. These methods can help us to limit the number of risk factors to those that have significant effects on the outcome. However, their performance is heavily dependent on selecting the appropriate methods and algorithms. The next generations of CDSS may use more and more machine learning approaches.

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“…In our study, the incidence of bacteremia was 51 (0.6%) of 8484 patients with the median age of 42 years. [ 21 ]…”

Section: Discussionmentioning

confidence: 99%

Resurgence of global opportunistic multidrug-resistant Stenotrophomonas maltophilia

Gupta

Kale

Khillan

2018

Indian Journal of Critical Care Medicine

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Context:Stenotrophomonas maltophilia is a known nosocomial pathogen which is intrinsically resistant to multiple antibiotics. In India, S. maltophilia infection has only few case reports.Aim:To determine the incidence of S. maltophilia infection from clinical isolates based on the specimen type, antibiotic susceptibility pattern, and impact on outcome.Settings and Design:One-year retrospective study was done at a tertiary liver care center.Methods:Patients with S. maltophilia isolation in clinical samples were selected. Serial levels of serum procalcitonin and total leukocyte count were recorded. Environmental surveillance was done from the wards of S. maltophilia isolation as part of routine practice.Statistical Analysis:Continuous data were compared using Kruskal–Wallis test/Mann–Whitney test. The categorical data were compared by Chi-square/Fisher's exact test, wherever necessary. Besides this, an appropriate analysis like survival was carried out at the time of data analysis.Results:One hundred isolates were obtained from eighty patients of six wards. The greatest number (44/100, 44%) were from the Liver Coma Intensive Care Unit and the lowest (3/100) from the day care. Isolation from the respiratory samples was 1.32% and bloodstream infection 0.6%. Of 100 isolates, 12 (12%) were resistant to both trimethoprim–sulfamethoxazole and levofloxacin.Conclusion:S. maltophilia was effectively isolated from the hospital environment, with two of hand impression and three of water samples’ positive. Patients with respiratory infection had most S. maltophilia isolates. Antibiotic susceptibility revealed more resistance than reported in this region.

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Predictive implications of albumin and C-reactive protein for progression to pneumonia and poor prognosis in Stenotrophomonas maltophilia bacteremia following allogeneic hematopoietic stem cell transplantation

Cited by 20 publications

References 33 publications

The impact of procalcitonin to C-reactive protein ratio (PCR) kinetics as a predictor of mortality in nosocomial bloodstream infections

The impact of procalcitonin to C-reactive protein ratio (PCR) kinetics as a predictor of mortality in nosocomial bloodstream infections

An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation

Resurgence of global opportunistic multidrug-resistant Stenotrophomonas maltophilia

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