Marker chromosome is a strong poor prognosis factor after allogeneic HSCT for adverse‐risk AML patients

Fuse, Kyoko; Tanaka, Tomoyuki; Shibasaki, Yasuhiko; Furukawa, Tatsuo; Narita, Miwako; Sone, Hirohito; Masuko, Masayoshi

doi:10.1111/ejh.13495

Cited by 2 publications

(3 citation statements)

References 15 publications

(61 reference statements)

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“…Furthermore, patients with altered airway structures and diminished immune defenses are susceptible to pulmonary infections and more vulnerable to cardiovascular complications. 24 Consequently, healthcare professionals are encouraged to offer active health education to patients, advise them to quit smoking two to three weeks before the surgery, and encourage appropriate physical activity to enhance cardiopulmonary function. Patients should be closely observed for signs of pulmonary distress and laboratory indicators, and prophylactic antimicrobial drugs should be administered to deter pulmonary infections.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, patients with altered airway structures and diminished immune defenses are susceptible to pulmonary infections and more vulnerable to cardiovascular complications. 24 Consequently, healthcare professionals are encouraged to offer active health education to In recent times, certain researchers have expressed optimism that alternative methods to troponin testing could potentially curtail healthcare expenditures. However, this approach has led to a proportionate escalation in misdiagnoses and heightened mortality risk among patients.…”

Section: Discussionmentioning

confidence: 99%

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Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Liu¹,

Chen²,

Tian³

et al. 2023

IJGM

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Purpose The occurrence of myocardial injury, a grave complication post complete mesocolic excision (CME), profoundly impacts the immediate and long-term prognosis of patients. The aim of this inquiry was to conceive a machine learning model that can recognize preoperative, intraoperative and postoperative high-risk factors and predict the onset of myocardial injury following CME. Patients and Methods This study included 1198 colon cancer patients, 133 of whom experienced myocardial injury after surgery. Thirty-six distinct variables were gathered, encompassing patient demographics, medical history, preoperative examination characteristics, surgery type, and intraoperative details. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor algorithm (KNN), were employed to fabricate the model, and k-fold cross-validation, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed to evaluate it. Results Out of the four predictive models employed, the XGBoost algorithm demonstrated the best performance. The ROC curve findings indicated that the XGBoost model exhibited remarkable predictive accuracy, with an area under the curve (AUC) value of 0.997 in the training set and 0.956 in the validation set. For internal validation, the k-fold cross-validation method was utilized, and the XGBoost model was shown to be steady. Furthermore, the calibration curves demonstrated the XGBoost model’s high predictive capability. The DCA curve revealed higher benefit rates for patients who underwent interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.74, which indicated that the XGBoost prediction model possessed good extrapolative capacity. Conclusion The myocardial injury prediction model for patients undergoing CME that was developed using the XGBoost machine learning algorithm in this study demonstrates both high predictive accuracy and clinical utility.

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Section: Discussionmentioning

confidence: 99%

“…Furthermore, patients with altered airway structures and diminished immune defenses are susceptible to pulmonary infections and more vulnerable to cardiovascular complications. 24 Consequently, healthcare professionals are encouraged to offer active health education to In recent times, certain researchers have expressed optimism that alternative methods to troponin testing could potentially curtail healthcare expenditures. However, this approach has led to a proportionate escalation in misdiagnoses and heightened mortality risk among patients.…”

Section: Discussionmentioning

confidence: 99%

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Liu¹,

Chen²,

Tian³

et al. 2023

IJGM

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“…In AML, Bochtler et al (2017) reported that marker chromosomes could arise from CCRs and predict adverse prognosis [ 154 ]. Marker chromosomes were also suggested to be a risk classification factor for AML with adverse cytogenetics [ 163 ].…”

Section: Mechanisms and Consequences Of Cinmentioning

confidence: 99%

Chromosomal Instability in Acute Myeloid Leukemia

Lisboa

Brofman

Schmid-Braz

et al. 2021

Cancers

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Chromosomal instability (CIN), the increasing rate in which cells acquire new chromosomal alterations, is one of the hallmarks of cancer. Many studies highlighted CIN as an important mechanism in the origin, progression, and relapse of acute myeloid leukemia (AML). The ambivalent feature of CIN as a cancer-promoting or cancer-suppressing mechanism might explain the prognostic variability. The latter, however, is described in very few studies. This review highlights the important CIN mechanisms in AML, showing that CIN signatures can occur largely in all the three major AML types (de novo AML, secondary-AML, and therapy-related-AML). CIN features in AML could also be age-related and reflect the heterogeneity of the disease. Although most of these abnormalities show an adverse prognostic value, they also offer a strong new perspective on personalized therapy approaches, which goes beyond assessing CIN in vitro in patient tumor samples to predict prognosis. Current and emerging AML therapies are exploring CIN to improve AML treatment, which includes blocking CIN or increasing CIN beyond the limit threshold to induce cell death. We argue that the characterization of CIN features, not included yet in the routine diagnostic of AML patients, might provide a better stratification of patients and be extended to a more personalized therapeutic approach.

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Marker chromosome is a strong poor prognosis factor after allogeneic HSCT for adverse‐risk AML patients

Cited by 2 publications

References 15 publications

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Chromosomal Instability in Acute Myeloid Leukemia

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