Low Skeletal Muscle Area at the T12 Paravertebral Level as a Prognostic Marker for Community-Acquired Pneumonia

Association between remote resistance exercises programs delivered by a smartphone application and skeletal muscle mass among elderly patients with type 2 diabetes- a retrospective real-world study

Objective

We aimed to explore the relationship between remote resistance exercise programs delivered via a smartphone application and skeletal muscle mass among elderly patients with type 2 diabetes, utilizing real-world data.

Methods

The resistance exercises were provided through Joymotion®, a web-based telerehabilitation smartphone application (Shanghai Medmotion Medical Management Co., Ltd). The primary outcome was the changes in skeletal muscle index (SMI) before and after the remote resistance exercises programs. The secondary outcomes were changes in skeletal muscle cross-sectional area (SMA), skeletal muscle radiodensity (SMD) and intermuscular adipose tissue (IMAT).

Results

A total of 101 elderly patients with type 2 diabetes were analyzed. The participants had an average age of 72.9 ± 6.11 years for males and 74.4 ± 4.39 years for females. The pre- and post-intervention SMI mean (± SE) was 31.64 ± 4.14 vs. 33.25 ± 4.22 cm2/m2 in male, and 22.72 ± 3.24 vs. 24.28 ± 3.60 cm2/m2 in female respectively (all P < 0.001). Similarly, a statistically significant improvement in SMA, IMAT, and SMD for both male and female groups were also observed respectively (P < 0.001). Multiple linear regression models showed potential confounding factors of baseline hemoglobin A1c and duration of diabetes with changes in SMI in male, while hemoglobin A1c and high density lipoprotein cholesterol with changes in SMI in female.

Conclusion

Remote resistance exercises programs delivered by a smartphone application were feasible and effective in helping elderly patients with type 2 diabetes to improve their skeletal muscle mass.

Acute muscle wasting is associated with poor prognosis in older adults with severe community-acquired pneumonia

PURPOSE

To investigate the impact of acute muscle wasting on 90-day mortality in older patients with severe pneumonia using ultrasound and chest computed tomography (CT).

METHODS

Quadriceps muscle layer thickness was measured via ultrasound on days 1, 7, and 14, and cross-sectional area of the erector spinae muscle was assessed using chest CT on days 1 and 14 in patients aged ≥ 65 years old. The primary outcome was all-cause 90-day mortality. Receiver operating characteristic curves were conducted for muscle loss to predict 90-day mortality. Cox proportional hazard models and Kaplan-Meier survival curves were employed to evaluate the association between muscle loss and 90-day mortality.

RESULTS

Sixty-two patients were enrolled with median age of 80.2 years, 29 (46.8%) were men and 28 (45.2%) patients died. Muscle mass measured using ultrasound and CT decreased significantly from baseline to day 14 in the non-survivor group. Muscle loss assessed by ultrasound (with minimum and maximum pressure) and CT independently predicted all-cause 90-day mortality (adjusted hazard ratios = 1.497, 1.400 and 1.082; P < 0.001, P = 0.002, and P = 0.004; respectively), and cutoff values of muscle loss were 0.34 cm, 0.11 cm and 4.92 cm2, correspondingly. A higher muscle loss had an increased risk of 90-day mortality.

CONCLUSIONS

Acute muscle wasting assessed by ultrasound and chest CT persisted for 14 days and was an independent predictor of adverse outcomes in older patients with severe pneumonia. A greater decline in muscle mass was associated with a higher 90-day mortality risk.

Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia

RATIONALE AND OBJECTIVES

The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves.

RESULTS

In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012).

CONCLUSION

Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia.

Erector spinae muscle-based nomogram for predicting in-hospital mortality among older patients with severe community-acquired pneumonia

BACKGROUND

No multivariable model incorporating erector spinae muscle (ESM) has been developed to predict clinical outcomes in older patients with severe community-acquired pneumonia (SCAP). This study aimed to construct a nomogram based on ESM to predict in-hospital mortality in patients with SCAP.

METHODS

Patients aged ≥ 65 years with SCAP were enrolled in this prospective observational study. Least absolute selection and shrinkage operator and multivariable logistic regression analyses were used to identify risk factors for in-hospital mortality. A nomogram prediction model was constructed. The predictive performance was evaluated using the concordance index (C-index), calibration curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis.

RESULTS

A total of 490 patients were included, and the in-hospital mortality rate was 36.1%. The nomogram included the following independent risk factors: mean arterial pressure, peripheral capillary oxygen saturation, Glasgow Coma Scale score (GCS), lactate, lactate dehydrogenase, blood urea nitrogen levels, and ESM cross-sectional area. Incorporating ESM into the base model with other risk factors significantly improved the C-index from 0.803 (95% confidence interval [CI], 0.761-0.845) to 0.836 (95% CI, 0.798-0.873), and these improvements were confirmed by category-free NRI and IDI. The ESM-based nomogram demonstrated a high level of discrimination, good calibration, and overall net benefits for predicting in-hospital mortality compared with the combination of confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65), Pneumonia Severity Index (PSI), Acute Physiology and Chronic Health Evaluation II (APACHEII), and Sequential Organ Failure Assessment (SOFA).

CONCLUSIONS

The proposed ESM-based nomogram for predicting in-hospital mortality among older patients with SCAP may help physicians to promptly identify patients prone to adverse outcomes.

TRIAL REGISTRATION

This study was registered at www.chictr.org.cn (registration number Chi CTR-2300070377).

Relationship between Muscle Mass, Bone Density and Vascular Calcifications in Elderly People with SARS-CoV-2 Pneumonia

BACKGROUND

Little is known about the changes in organs and tissues that may make elder patients more vulnerable to acute stressors such as SARS-CoV-2 infection.

METHODS

In 80 consecutive elderly patients with SARS-CoV-2 infection, we evaluated the association between the descending thoracic aorta calcium score, L1 bone density and T12 skeletal muscle density measured on the same scan by high-resolution computed tomography.

RESULTS

At median regression, the ln-transformed DTA calcium score was inversely associated with L1 bone density (-0.02, 95%CI -0.04 to -0.01 ln-Agatston units for an increase of 1 HU) and with T12 muscle density (-0.03, -0.06 to -0.001 ln-Agatston units for an increase of 1 HU). At penalized logistic regression, an increase of 1 ln-Agatston unit of DTA calcium score was associated with an OR of death of 1.480 (1.022 to 2.145), one of 1 HU of bone density with an OR of 0.981 (0.966 to 0.996) and one of 1 HU of muscle density with an OR of 0.973 (0.948 to 0.999). These relationships disappeared after correction for age and age was the stronger predictor of body composition and death.

CONCLUSIONS

Age has a big effect on the relationship between vascular calcifications, L1 bone density and T12 muscle density and on their relationship with the odds of dying.