Predictive factors associated with induction-related death in acute myeloid leukemia in a resource-constrained setting

Mendes, Fernanda Rodrigues; Silva, Wellington F; Melo, Raphael da Costa Bandeira de; Silveira, Douglas R. A.; Velloso, Elvira Deolinda Rodrigues Pereira; Rocha, Vanderson; Rego, Eduardo Magalhães

doi:10.1007/s00277-021-04687-6

Cited by 13 publications

(9 citation statements)

References 32 publications

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“…Kirkizlar et al found that in leukaemia patients colonised with VRE, a low neutrophil count and coinfection were associated with increased mortality [ 96 ]. Mendes et al included VRE in a bivariable screening but discarded the factor as it did not meet the criterion of p < 0.1 [ 55 ]. Ornstein et al found that leukaemia patients with a VRE bacteraemia at the induction of chemotherapy had poorer survival than patients with other bloodstream infections [ 97 ].…”

Section: Resultsmentioning

confidence: 99%

Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models

et al. 2023

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Background Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, better models of clinical outcomes building on current knowledge are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aimed to review multivariable models of resistant infections/colonisations and corresponding mortality, what risk factors have been investigated, and with what methodological approaches. Methods We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistance, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection/colonisation or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, microbial aetiology, and methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools. Results Two searches yielded a total of 27,151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five per cent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 7 events per variable. An in-depth example of vancomycin-resistant enterococci was reported. Conclusions We found the current research to be heterogeneous in the approaches to studying this topic. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature are urgent.

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Section: Resultsmentioning

confidence: 99%

Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models

et al. 2023

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“…Kirkizlar et al found that in leukaemia patients colonised with VRE, a low neutrophil count and coinfection were associated with increased mortality (92). Mendes et al included VRE in a bivariable screening, but discarded the factor as it did not meet the criterion of p<0.1 (51). Ornstein et al found that leukaemia patients with a VRE bacteraemia at the induction of chemotherapy had poorer survival than patients with other bloodstream infections (93).…”

Section: Resultsmentioning

confidence: 99%

“…In total, there were 23/144 (16%) studies of patients with solid cancers , 65/144 (45%) studies of patients with haematological cancers (41-105), and 56/144 (39%) studies with patients of both or unspecified cancer types (106-161). Most studies selected (39/144, 27%) reported and modelled several bacteria and/or fungi that were tested for resistances towards several antimicrobials (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(106)(107)(108)(109)(110)(111)(112)(113)(114)(115). Eight of 144 (6%) reporting and modelling several microorganisms focused only on Gram-negative bacteria (54)(55)(56)(57)(58)(116)(117)(118)) and 1/144 (1%) focused only on fungi (59).…”

Section: Studies Selectedmentioning

confidence: 99%

“…In total, there were 23/144 (16%) studies of patients with solid cancers , 65/144 (45%) studies of patients with haematological cancers , and 56/144 (39%) studies with patients of both or unspecified cancer types . Most studies selected (39/144, 27%) reported and modelled several bacteria and/or fungi that were tested for resistance towards several antimicrobials [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][45][46][47][48][49][50][51][52][53][54][55][56][57][110][111][112][113][114][115][116][117][118][119]. Eight of 144 (6%) reporting and modelling several microorganisms focused only on Gram-negative bacteria [58][59][60][61][62][120][121][122] and 1/144 (...…”

Section: Study Characteristicsmentioning

confidence: 99%

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Clinical outcomes of antimicrobial resistance in cancer patients: A systematic review of multivariable models

Danielsen

Franconeri

Page

et al. 2022

Preprint

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Background Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, a better understanding of the risk factors are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aims to review what risk factors for resistant infections and deaths due to resistant infections have been investigated, and with what methodological approaches. Methods We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistant infections, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, the microbial aetiology, methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools. Results Two searches yielded a total of 27151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five percent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 6.9 events per variable. An in-depth example of vancomycin-resistant enterococci was reported. Conclusions We found the current research on this topic to be heterogeneous, in both the methodological and epidemiological approach. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature is urgent.

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“…To predict patients at high risk for D30 death, various factors are suggested. Mendes et al suggested that age, Gram-negative bacterial infection, monocytic AML, increased C-reactive protein (CRP), and adverse genetic risks were associated with an increased risk of 60-day mortality [ 18 ]. Sasaki et al suggested the following prognostic factors for D30 death after intensive chemotherapy (CTx): age, ECOG performance status, infection at diagnosis (pneumonia), cytogenetics (complex karyotype), total bilirubin, and creatinine and uric acid [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Predicts 30-Day Outcome among Acute Myeloid Leukemia Patients: A Single-Center, Retrospective, Cohort Study

Lee,

Han,

Kim

et al. 2023

JCM

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Acute myeloid leukemia (AML) is a clinical emergency requiring treatment and results in high 30-day (D30) mortality. In this study, the prediction of D30 survival was studied using a machine learning (ML) method. The total cohort consisted of 1700 survivors and 130 non-survivors at D30. Eight clinical and 42 laboratory variables were collected at the time of diagnosis by pathology. Among them, six variables were selected by a feature selection method: induction chemotherapy (CTx), hemorrhage, infection, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase. Clinical and laboratory data were entered into the training model for D30 survival prediction, followed by testing. Among the tested ML algorithms, the decision tree (DT) algorithm showed higher accuracy, the highest sensitivity, and specificity values (95% CI) of 90.6% (0.918–0.951), 70.4% (0.885–0.924), and 92.1% (0.885–0.924), respectively. DT classified patients into eight specific groups with distinct features. Group 1 with CTx showed a favorable outcome with a survival rate of 97.8% (1469/1502). Group 6, with hemorrhage and the lowest fibrinogen level at diagnosis, showed the worst survival rate of 45.5% (25/55) and 20.5 days. Prediction of D30 survival among AML patients by classification of patients with DT showed distinct features that might support clinical decision-making.

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Predictive factors associated with induction-related death in acute myeloid leukemia in a resource-constrained setting

Cited by 13 publications

References 32 publications

Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models

Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models

Clinical outcomes of antimicrobial resistance in cancer patients: A systematic review of multivariable models

Machine Learning Predicts 30-Day Outcome among Acute Myeloid Leukemia Patients: A Single-Center, Retrospective, Cohort Study

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