The Prognostic Value of Sarcopenia in Acute Myeloid Leukemia Patients and the Development and Validation of a Novel Nomogram for Predicting Survival

Sarcopenia in peripheral arterial disease: Establishing and validating a predictive nomogram based on clinical and computed tomography angiography indicators

Purpose

To establish, validate, and clinically evaluate a nomogram for predicting the risk of sarcopenia in patients with peripheral arterial disease (PAD) based on clinical and lower extremity computed tomography angiography (LE-CTA) imaging characteristics.

Methods

Clinical data and CTA imaging features from 281 PAD patients treated between January 1, 2019, and May 1, 2023, at two hospitals were retrospectively analyzed using binary logistic regression to identify the independent risk factors for sarcopenia. These identified risk factors were used to develop a predictive nomogram. The nomogram's effectiveness was assessed through various metrics, including the receiver operating characteristic (ROC) curve, area under the curve (AUC), concordance index (C-index), Hosmer-Lemeshow (HL) test, and calibration curve. Its clinical utility was demonstrated using decision curve analysis (DCA).

Results

Several key independent risk factors for sarcopenia in PAD patients were identified, namely age, body mass index (BMI), history of coronary heart disease (CHD), and white blood cell (WBC) count, as well as the severity of luminal stenosis (P < 0.05). The discriminative ability of the nomogram was supported by the C-index and an AUC of 0.810 (95% confidence interval: 0.757-0.862). A robust concordance between predicted and observed outcomes was reflected by the calibration curve. The HL test further affirmed the model's calibration with a P-value of 0.40. The DCA curve validated the nomogram's favorable clinical utility. Lastly, the model underwent internal validation.

Conclusions

A simple nomogram based on five independent factors, namely age, BMI, history of CHD, WBC count, and the severity of luminal stenosis, was developed to assist clinicians in estimating sarcopenia risk among PAD patients. This tool boasts impressive predictive capabilities and broad utility, significantly aiding clinicians in identifying high-risk individuals and enhancing the prognosis of PAD patients.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia

BACKGROUND

The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML).

METHODS

952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements.

RESULTS

Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011).

CONCLUSIONS

In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Effect of low skeletal muscle mass on NK cells in patients with acute myeloid leukemia and its correlation with prognosis

The objective of this study was to analyze the correlation between skeletal muscle mass and the distribution of peripheral blood lymphocytes and natural killer (NK) cells, as well as their impact on prognosis in patients with acute myeloid leukemia (AML). A retrospective analysis was conducted on 211 newly diagnosed AML patients, evaluating skeletal muscle index (SMI), NK cell proportion, and absolute value, along with relevant clinical data. Linear regression and Spearman's correlation coefficient were used to assess the relationship between various indicators and SMI, followed by multiple linear regression for further modeling. Univariate and multivariate Cox proportional hazards regression models were used to identify independent predictors for overall survival (OS). Among the 211 AML patients, 38 cases (18.0%) were diagnosed with sarcopenia. Multiple linear regression analysis included weight, fat mass, ECOG score, body mass index, and peripheral blood NK cell proportion, constructing a correlation model for SMI (R2 = 0.745). Univariate analysis identified higher NK cell count (> 9.53 × 106/L) as a poor predictor for OS. Multivariate Cox proportional hazards regression model indicated that age ≥ 60 years, PLT < 100 × 109/L, ELN high risk, sarcopenia, and B cell count > 94.6 × 106/L were independent adverse prognostic factors for AML patients. Low skeletal muscle mass may negatively impact the count and function of NK cells, thereby affecting the prognosis of AML. However, further basic and clinical research is needed to explore the specific mechanisms underlying the relationship between NK cells and SMI in AML.

Prevalence and prognostic value of baseline sarcopenia in hematologic malignancies: a systematic review

Background

The correlation between sarcopenia and hematological malignancy prognosis is still controversial. Design: A systematic review and meta-analysis. Objectives: To explore sarcopenia's prevalence and prognostic value in hematologic malignancies.

Data sources and methods

We searched Embase, MEDLINE, and Cochrane Library through Ovid SP using an appropriate search strategy on August 28, 2022, and updated the search results on January 9, 2023. Study quality was assessed using the Newcastle-Ottawa scale. The pooled prevalence of sarcopenia was calculated with a 95% confidence interval (CI). Relationships between sarcopenia and prognostic value were expressed as hazard ratio (HR) and 95% CI. HR means the probability of something undesirable, i.e., death or disease progression.

Results

The search identified more than 3992 studies, and 21 (3354 patients, median or mean age ranging from 36 to 78 years) were finally included. The risk of bias in the studies was low to medium. All included studies were diagnosed based on low muscle mass (LMM). Muscle mass was assessed mainly through imaging technologies, and different cut-offs were applied to determine LMM. The prevalence of sarcopenia was 44.5%, which could fluctuate by age. Subgroup analysis showed that older people had a higher sarcopenic rate than the non-elderly group. Sarcopenia resulted in an inferior prognosis [overall survival: HR 1.821, 95% CI 1.415-2.343; progression-free survival: HR 1.703, 95% CI 1.128-2.571).

Conclusion

Sarcopenia has a prevalence of over 30% in malignant hematologic patients and is associated with a poorer prognosis. Future studies with a standardized sarcopenia diagnostic criterion were needed to investigate sarcopenia's prevalence and prognostic effects in hematologic malignancies.