Sarcopenia, frailty and cachexia patients detected in a multisystem electronic health record database

Using machine learning to detect sarcopenia from electronic health records

Introduction

Sarcopenia (low muscle mass and strength) causes dysmobility and loss of independence. Sarcopenia is often not directly coded or described in electronic health records (EHR). The objective was to improve sarcopenia detection using structured data from EHR.

Methods

Adults undergoing musculoskeletal testing (December 2017-March 2020) were classified as meeting sarcopenia thresholds for 0 (controls), ≥1 (Sarcopenia-1), or ≥2 (Sarcopenia-2) tests. Electronic health record diagnoses, medications, and laboratory testing were extracted from the Indiana Network for Patient Care. Five machine learning models were applied to EHR data for predicting sarcopenia.

Results

Of 1304 participants, 1055 were controls, 249 met Sarcopenia-1 and 76 met Sarcopenia-2. Sarcopenic participants were older, with higher fat mass, Charlson Comorbidity Index, and more chronic diseases. All models performed better for Sarcopenia-2 than Sarcopenia-1. The top performing models for Sarcopenia-1 were Logistic Regression [area under the curve (AUC) 71.59 (95% confidence interval [CI], 71.51-71.66)] and Multi-Layer Perceptron [AUC 71.48 (95%CI, 71.00-71.97)]. The top performing models for Sarcopenia-2 were Logistic Regression [AUC 91.44 (95%CI, 91.28-91.60)] and Support Vector Machine [AUC 90.81 (95%CI, 88.41-93.20)]. For the best Logistic Regression Model, important sarcopenia predictors included diabetes mellitus, digestive system complaints, signs and symptoms involving the nervous, musculoskeletal and respiratory systems, metabolic disorders, and kidney or urinary tract disorders. Opioids, corticosteroids, and antihyperlipidemic drugs were also more common among sarcopenic participants.

Conclusions

Applying machine learning models, sarcopenia can be predicted from structured data in EHR, which may be developed through future studies to facilitate large-scale early detection and intervention in clinical populations.

Sarcopenia definition, diagnosis and treatment: consensus is growing

Sarcopenia is a skeletal muscle disorder that commonly occurs with advancing age as well as with a number of long-term conditions. Recognition in clinical practice is relatively recent but important because of the association between sarcopenia and a range of adverse effects on health including impaired mobility, increased morbidity and mortality. Originally characterised as loss of muscle mass, the definition has evolved to focus on loss of skeletal muscle function, particularly strength, through a number of international definitions such as that of the European Working Group on Sarcopenia in Older People most recently revised in 2019. Progress in the decades ahead is likely to be seen with regard to use of routine health data, prescription of resistance exercise, translation of biology and epidemiology into first in man studies for new treatments, and focus on sarcopenia in low and middle-income countries. Immediate next steps include the newly formed Global Leadership Initiative on Sarcopenia to develop international consensus on definition and diagnosis.

Cachectic muscle wasting in acute myeloid leukaemia: a sleeping giant with dire clinical consequences

Acute myeloid leukaemia (AML) is a haematological malignancy with poor survival odds, particularly in the older (>65 years) population, in whom it is most prevalent. Treatment consists of induction and consolidation chemotherapy to remit the cancer followed by potentially curative haematopoietic cell transplantation. These intense treatments are debilitating and increase the risk of mortality. Patient stratification is used to mitigate this risk and considers a variety of factors, including body mass, to determine whether a patient is suitable for any or all treatment options. Skeletal muscle mass, the primary constituent of the body lean mass, may be a better predictor of patient suitability for, and outcomes of, AML treatment. Yet skeletal muscle is compromised by a variety of factors associated with AML and its clinical treatment consistent with cachexia, a life-threatening body wasting syndrome. Cachectic muscle wasting is associated with both cancer and anticancer chemotherapy. Although not traditionally associated with haematological cancers, cachexia is observed in AML and can have dire consequences. In this review, we discuss the importance of addressing skeletal muscle mass and cachexia within the AML clinical landscape in view of improving survivability of this disease.

Clinical practice of Best Practice Nursing Care Standards for Older Adults with Fragility Hip Fracture: A propensity score matched analysis

BACKGROUND

Fragility hip fracture (FHF) is a significant cause of morbidity and mortality in older adults. In 2018, Best Practice Nursing Care Standards for Older Adults with Fragility Hip Fracture (NSOF) were released by The International Collaboration of Orthopaedic Nursing (ICON). However, there are only limited clinical data about the application of this standard in clinical practice in China.

AIMS

To determine the clinical practice effect of the NSOF.

METHODS

A retrospective single-centre cohort study was performed from January 2016 to June 2020. Patients were divided into the standardized nursing care group (SN group) and the conventional nursing care group (CN group) depending on whether they were cared for according to the NSOF criteria. The propensity score matched (PSM) analysis was conducted in this study. The perioperative and follow-up outcomes between the two groups were analyzed.

RESULTS

A total of 204 patients diagnosed with FHF were included in the study. After a 1:1 matching, 56 cases were identified in the SN group as well as the CN group. Patients in the SN group had significantly shorter preoperative wait times for surgery (17.4 ± 4.6 vs. 24.4 ± 7.6 h, p < 0.05) and a higher proportion of individuals performing exercise within 24 h after surgery (94.6% vs. 66.1%, p < 0.05). Notably, patients in the SN group also had a significantly shorter length of stay than those in the CN group (9.4 ± 3.1 vs. 14.2 ± 5.1 days, p < 0.05). At the 6-month follow-up, the incidence of refracture was significantly lower (3.6% vs. 14.3%, p < 0.05), and the timed up and go mobility index was improved in the SN group compared to the CN group (20.3 ± 1.7 vs. 24.6 ± 2.2 s, p < 0.05).

CONCLUSION

This study showed that application of the NSOF resulted in a significant improvement in the treatment of older adults patients with FHF.

Applying Administrative Data-Based Coding Algorithms for Frailty in Patients With Cirrhosis

Frailty is a powerful prognostic tool in cirrhosis. Claims-based frailty scores estimate the presence of frailty without the need for in-person evaluation. These algorithms have not been validated in cirrhosis. Whether they measure true frailty or perform as well as frailty in outcome prediction is unknown. We evaluated 2 claims-based frailty scores-Hospital Frailty Risk Score (HFRS) and Claims-Based Frailty Index (CFI)-in 3 prospective cohorts comprising 1100 patients with cirrhosis. We assessed differences in neuromuscular/neurocognitive capabilities in those classified as frail or nonfrail based on each score. We assessed the ability of the indexes to discriminate frailty based on the Fried Frailty Index (FFI), chair stands, activities of daily living (ADL), and falls. Finally, we compared the performance of claims-based frailty measures and physical frailty measures to predict transplant-free survival using competing risk regression and patient-reported outcomes. The CFI identified neuromuscular deficits (balance, chair stands, hip strength), whereas the HFRS only identified poor chair-stand performance. The CFI had areas under the receiver operating characteristic curve (AUROCs) for identifying frailty as measured by the FFI, ADL, and falls of 0.57, 0.60, and 0.68, respectively; similarly, the AUROCs were 0.66, 0.63, and 0.67, respectively, for the HFRS. Claims-based frailty scores were associated with poor quality of life and sleep but were outperformed by the FFI and chair stands. The HFRS, per 10-point increase (but not the CFI) predicted survival of patients in the liver transplantation (subdistribution hazard ratio [SHR], 1.08; 95% confidence interval [CI], 1.03-1.12) and non-liver transplantation cohorts (SHR, 1.13; 95% CI, 1.05-1.22). Claims-based frailty scores do not adequately associate with physical frailty but are associated with important cirrhosis-related outcomes.