A nomogram for predicting osteoporosis risk based on age, weight and quantitative ultrasound measurement

Frailty in the prediction of delirium in the intensive care unit: A secondary analysis of the Deli study

BACKGROUND

Delirium is an acute disorder of attention and cognition with an incidence of up to 70% in the adult intensive care setting. Due to the association with significantly increased morbidity and mortality, it is important to identify who is at the greatest risk of an acute episode of delirium while being cared for in the intensive care. The objective of this study was to determine the ability of the cumulative deficit frailty index and clinical frailty scale to predict an acute episode of delirium among adults admitted to the intensive care.

METHODS

This study is a secondary analysis of the Deli intervention study, a hybrid stepped-wedge cluster randomized controlled trial to assess the effectiveness of a nurse-led intervention to reduce the incidence and duration of delirium among adults admitted to the four adult intensive care units in the south-west of Sydney, Australia. Important predictors of delirium were identified using a bootstrap approach and the absolute risks, based on the cumulative deficit frailty index and the clinical frailty scale are presented.

RESULTS

During the 10-mth data collection period (May 2019 and February 2020) 2566 patients were included in the study. Both the cumulative deficit frailty index and the clinical frailty scale on admission, plus age, sex, and APACHE III (AP III) score were able to discriminate between patients who did and did not experience an acute episode of delirium while in the intensive care, with AUC of 0.701 and 0.703 (moderate discriminatory ability), respectively. The addition of a frailty index to a prediction model based on age, sex, and APACHE III score, resulted in net reclassified of risk. Nomograms to individualize the absolute risk of delirium using these predictors are also presented.

CONCLUSION

We have been able to show that both the cumulative deficits frailty index and clinical frailty scale predict an acute episode of delirium among adults admitted to intensive care.

Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data

Optical bone densitometry (OBD) has been developed for the early detection of osteoporosis. In recent years, machine learning (ML) techniques have been actively implemented for the areas of medical diagnosis and screening with the goal of improving diagnostic accuracy. The purpose of this study was to verify the feasibility of using the combination of OBD and ML techniques as a screening tool for osteoporosis. Dual energy X-ray absorptiometry (DXA) and OBD measurements were performed on 203 Thai subjects. From the OBD measurements and readily available demographic data, machine learning techniques were used to predict the T-score measured by the DXA. The T-score predicted using the Ridge regressor had a correlation of r = 0.512 with respect to the reference value. The predicted T-score also showed an AUC of 0.853 for discriminating individuals with osteoporosis. The results obtained suggest that the developed model is reliable enough to be used for screening for osteoporosis.

Identification of combined biomarkers for predicting the risk of osteoporosis using machine learning

Osteoporosis is a severe chronic skeletal disorder that affects older individuals, especially postmenopausal women. However, molecular biomarkers for predicting the risk of osteoporosis are not well characterized. The aim of this study was to identify combined biomarkers for predicting the risk of osteoporosis using machine learning methods. We merged three publicly available gene expression datasets (GSE56815, GSE13850, and GSE2208) to obtain expression data for 6354 unique genes in postmenopausal women (45 with high bone mineral density and 45 with low bone mineral density). All machine learning methods were implemented in R, with the GEOquery and limma packages, for dataset download and differentially expressed gene identification, and a nomogram for predicting the risk of osteoporosis was constructed. We detected 378 significant differentially expressed genes using the limma package, representing 15 major biological pathways. The performance of the predictive models based on combined biomarkers (two or three genes) was superior to that of models based on a single gene. The best predictive gene set among two-gene sets included PLA2G2A and WRAP73. The best predictive gene set among three-gene sets included LPN1, PFDN6, and DOHH. Overall, we demonstrated the advantages of using combined versus single biomarkers for predicting the risk of osteoporosis. Further, the predictive nomogram constructed using combined biomarkers could be used by clinicians to identify high-risk individuals and in the design of efficient clinical trials to reduce the incidence of osteoporosis.

Nomogram to Predict Cadmium-Induced Osteoporosis and Fracture in a Chinese Female Population

Cadmium exposure may increase the risk of osteoporosis. However, there is no quick method to get bone mineral density (BMD) unless dual-energy X-ray absorptiometry (DXA) examinations were performed. In the present study, we aimed to identify associated factors to osteoporosis and fracture in a Chinese female population with cadmium exposure and develop nomograms to predict the risk. A total of 488 women was included in this study. Cadmium in blood (BCd) and urine (UCd) were determined as exposure biomarkers. BMD was determined using single-photon absorptiometry. Urinary N-acetyl-β-d-glucosaminidase (UNAG) and urinary albumin (UALB) were determined as renal function biomarkers. Osteoporosis was defined if T-score < - 2.5. Multiple logistic regression showed that age, BCd, and menopausal status were independent risk factors for osteoporosis. The odds (OR) value was 1.19 (95% confidence interval (CI): 1.14-1.25) for age, 1.05 (95% CI: 1.004-1.10) for BCd, and 4.75 (95% CI: 1.65-13.69) for menopausal status after adjusting with cofounders. Age and UCd were the independent risk factors for bone fracture. Nomograms were developed based on the associated factors. Age was the main determinant for osteoporosis or fracture. Receiver operating curve showed acceptable performance in predicting osteoporosis (area under the curve (AUC) = 0.93, 95CI: 0.90-0.96) and fracture (AUC = 0.67, 95% CI: 0.58-0.75). Linear discriminant analysis (LDA) further showed that 88.9% of osteoporosis and 68.4% of fractures were correctly classified. Our study develops nomograms that may be used to predict cadmium-induced osteoporosis or fracture if BMD data is not available.

Cartilage and subchondral bone distributions of the distal radius: a 3-dimensional analysis using cadavers

OBJECTIVE

To quantify the spatial distributions of cartilage and subchondral bone thickness of the distal radius.

DESIGN

Using 17 cadaveric wrists, three types of 3-dimensional models were created: a cartilage-bone model, obtained by laser scanning; a bone model, rescanned after dissolving the cartilage; and a subchondral bone model, obtained using computed tomography. By superimposing the bone model onto the cartilage-bone and the subchondral bone models, the cartilage and subchondral bone thickness were determined. Measurements along with the spatial distribution were made at fixed anatomic points including the scaphoid and lunate fossa, sigmoid notch and interfossal ridge, and compared at each of these four regions.

RESULTS

Cartilage thickness of the interfossal ridge (0.89 ± 0.23 mm) had a larger average thickness compared to that of the scaphoid fossa (0.70 ± 0.18 mm; p = 0.004), lunate fossa (0.75 ± 0.17 mm; p = 0.044) and sigmoid notch (0.64 ± 0.13 mm; p < 0.001). Subchondral bone was found to be thickest at the scaphoid (2.18 ± 0.72 mm) and lunate fossae (1.94 ± 0.93 mm), which were both thicker than that of sigmoid notch (1.63 ± 1.06 mm: vs scaphoid fossa, p = 0.020) or interfossal ridge (1.54 ± 0.84 mm: vs scaphoid fossa, p = 0.004; vs lunate fossa, p = 0.048). In the volar-ulnar sub-regions of the scaphoid and lunate fossa, the subchondral bone thickened.

CONCLUSIONS

Our data can be applied when treating distal radius fractures. Cartilage thickness was less than 1 mm across the articular surface, which may give an insight into threshold for an acceptable range of step-offs. The combined findings of subchondral bone appreciate the importance of the volar-ulnar corner of the distal radius in the volar locking plate fixation.

Thai Osteoporosis Foundation (TOPF) position statements on management of osteoporosis

The adjusted incidence rate of hip fracture in Thailand has increased more than 31% from 1997 to 2006. Mortality and morbidity after hip fracture are also high. One year mortality after a hip fracture has increased from 18% in 1999 to 21% in 2007. The Thai Osteoporosis Foundation (TOPF) developed the first Clinical Practice Guideline (CPG) in 2002 and keeps updating the CPG since then. This latest version of the CPG is our attempt to provide comprehensive positional statement on the diagnosis, prevention and treatment of osteoporosis in Thailand. The study group who revised this position statement contains experts from the TOPF, Four Royal Colleges of Thailand, includes the Orthopaedic Surgeons, Gynecologists and Obstetricians, Physiatrists, Radiologists and 2 Associations of Endocrinologists and Rheumatologists which have involved in the management of patients with osteoporosis.

A new nomogram score for predicting surgery-related pressure ulcers in cardiovascular surgical patients

The aim of this study was to build a new nomogram score for predicting surgery-related pressure ulcers (SRPU) in cardiovascular surgical patients. We performed a prospective cohort study among consecutive patients with cardiovascular surgery between January 2015 and December 2015. Univariate and multivariate logistic regression was used to analyse the risk factors for SRPU. A nomogram-predicting model was built based on the logistic regression model. Then, calibration and discrimination were tested. A total of 149 patients with cardiovascular surgery were included in the study. Thirty-seven patients developed SRPUs, with an incidence rate of 24·8% (95%CI: 18·1-32·6%). The logistic regression model for predicting SRPU with four risk factors was Logit(P) = (1·861 × VDH, OR 2·174 × CAD, OR 1·747 × TAA) - 0·029 × weight + 0·005 × surgery duration + 1·241 × perioperative corticosteroids administration (P = 0·003, R² = 0·1181). The goodness-of-fit test (Pearson χ² = 150·69, P = 0·217) indicated acceptable calibration, and the C-index (0·725) indicated moderate discrimination. When the probability cut-off is 0·25 (total score 12), the nomogram model has the best sensitivity and specificity in predicting SRPU. We established a new nomogram model that can provide an individual prediction of SRPU in cardiovascular surgical patients. When the probability is more than 0·25 (total score 12), the cardiovascular surgery patients should be considered at high-risk.

Bone health comparison in seven Asian countries using calcaneal ultrasound

BACKGROUND

Bone density measurements by DXA are not feasible for large population studies, whereas portable ultrasound heel scanners can provide a practical way of assessing bone health status. The purpose of this study was to assess bone health in seven Asian countries using heel ultrasound.

METHODS

Stiffness index (SI) was measured and T-scores generated against an Asian database were recorded for 598,757 women and 173,326 men aged over 21 years old using Lunar Achilles (GE Healthcare) heel scanners. The scanners were made available in public centres in Singapore, Vietnam, Malaysia, Taiwan, Thailand, Indonesia and the Philippines.

RESULTS

The mean SI was higher for men than women. In women SI as well as T-scores declined slowly until approximately 45 years of age, then declined rapidly to reach a mean T-score of < -2.5 at about 71-75 years of age. For men, SI as well as the T-score showed a slow steady decline to reach a mean of -2.0 to -2.5 at about 81-85 years. The results for females indicate that there are differences in the rate of decline between countries (significant differences between the slopes at P < 0.05). Vietnam had the fastest decrease for both T-Score and SI, resulting in this population having the poorest bone health of all countries at older ages. The results for males aged 46-85 years indicate that there are no significant differences in the rate of decline between countries for SI and T-Score. In both men and women aged 46-85 years, Vietnam and Indonesia have the lowest SI as well as T-Score for all age groups. For Vietnam and Indonesia, more than 50% of the women could be at risk of having osteoporosis and related fractures after the age of 70, while in Thailand and the Philippines this was >80 years.

CONCLUSIONS

The heel scan data shows a high degree of poor bone health in both men and women in Asian countries, raising concern about the possible increase in fractures with ageing and the expected burden on the public health system.

Risk factors for in-hospital post-hip fracture mortality

INTRODUCTION

Approximately 10% of hip fracture patients die during hospitalization; however, it is not clear what risk factors contribute to the excess mortality. This study sought to examine risk factors of, and to develop prognostic model for, predicting in-hospital mortality among hip fracture patients.

METHODS

We studied outcomes among 410 men and 1094 women with a hip fracture who were admitted to a major-teaching-hospital in Sydney (Australia) between 1997 and 2007. Clinical data, including concomitant illnesses, were obtained from inpatient data. The primary outcome of the study was in-hospital mortality regardless of length of stay. A Log-binomial regression model was used to identify risk factors for in-hospital mortality. Using the identified risk factors, prognostic nomograms were developed for predicting short term risk of mortality for an individual.

RESULTS

The median duration of hospitalization was 9 days. During hospitalization, the risk of mortality was higher in men (9%) than in women (4%). After adjusting for multiple risk factors, increased risk of in-hospital mortality was associated with advancing age (rate ratio [RR] for each 10-year increase in age: 1.91 95% confidence interval [CI]: 1.47 to 2.49), in men (RR 2.13; 95% CI 1.41 to 3.22), and the presence of comorbid conditions on admission (RR for one or more comorbid conditions vs. none: 2.30; 95% CI 1.52 to 3.48). Specifically, the risk of mortality was increased in patients with a pre-existing congestive heart failure (RR 3.02; 95% CI: 1.65 to 5.54), and liver disease (RR 4.75; 95% CI: 1.87 to 12.1). These factors collectively accounted for 69% of the risk for in-hospital mortality. A nomogram was developed from these risk factors to individualize the risk of in-hospital death following a hip fracture. The area under the receiver operating characteristic curve of the final model containing age, sex and comorbid conditions was 0.76.

CONCLUSION

These data suggest that among hip fracture patients, advancing age, gender (men), and pre-existing concomitant diseases such as congestive heart failure and liver disease were the main risk factors for in-hospital mortality. The nomogram developed from this study can be used to convey useful prognostic information to help guide treatment decisions.

The Fracture and Immobilization Score (FRISC) for risk assessment of osteoporotic fracture and immobilization in postmenopausal women--A joint analysis of the Nagano, Miyama, and Taiji Cohorts

INTRODUCTION

We aimed to (i) explore risk factors for major osteoporotic fracture or immobilization; (ii) develop a prediction model that can be used to assess the risk of fracture and immobilization; and (iii) assess external validity of the final model.

METHODS

A total of 1787 postmenopausal Japanese women were followed in a hospital-based cohort study. Endpoints included the annual incidence of major osteoporotic fracture and immobilization. For each endpoint, multivariate Poisson regression models were fitted separately and risk factors were screened through backward variable selection. The predictive accuracy of the final model (FRISC) was evaluated in two independent community-based cohorts.

RESULTS

Over a median follow-up of 5.3 years, a total of 383 major osteoporotic fractures (279 clinical vertebral, 44 hip, 60 distal forearm) and 83 immobilizations occurred in the developmental dataset. Backward variable selection confirmed that the following are risk factors for major osteoporotic fracture: age, weight, prior fracture, back pain, and lumbar bone mineral density (BMD). Age, prior fracture and dementia were significant risk factors for immobilization. Hosmer-Lemeshow tests did not indicate any significant deviation between the observed fracture frequency and prediction from the FRISC in the independent validation dataset. The C statistic for the FRISC was 0.727 (95% confidence interval: 0.660 to 0.794) and was higher than that for BMD alone significantly (p=0.03).

CONCLUSIONS

We developed a novel prediction model for fracture and immobilization, FRISC, and the clinical risk factors in the FRISC allows better identification of populations at high risk of fracture than BMD alone. A web application is available at http://www.biostatistics.jp/prediction/frisc.

Dynamic bone quality: a noninvasive measure of bone's biomechanical property in osteoporosis

We describe a novel approach to characterize bone quality noninvasively, a measurement that quantifies aggregate shock-absorption capacity of load-bearing bones as a measure of mechanical structural integrity during exposure to real-time self-induced in vivo loading associated with heel strike. The outcome measure, damping factor, was estimated at 5 load-bearing anatomical sites: ankle, tibial tuberosity, femoral condyle, lower back (at 3rd lumbar vertebra), and upper back (7th thoracic vertebra) plus the forehead in 67 patients with postmenopausal osteoporosis with and without documented vertebral fractures. The damping value was significantly lower in patients with vertebral fractures compared with those without a fracture (range: -36% to -72%; median: -44%). In these women with osteoporosis, damping factor was able to discriminate between patients with and without vertebral fractures, whereas traditional measures of bone density and biomechanical measures obtained from bone geometry were not significantly different between the groups.

Unplanned admission to intensive care after emergency hospitalisation: risk factors and development of a nomogram for individualising risk

BACKGROUND AND AIMS

Unplanned admission to an intensive care unit (ICU) is associated with high mortality, having the highest incidence among patients who are emergency admissions to the hospital. This study was designed to identify factors associated with unplanned ICU admission in emergency admissions to hospital and develop an absolute risk tool to individualise the risk of an event during a hospital stay.

METHODS

Emergency department (ED) and in-patient hospital data from a large teaching hospital of consecutive admissions from 1 January 1997 to 31 December 2007 aged over 14 years was included in this study. Patient data extracted from 126826 emergency presentations admitted as in-patients consisted of demographic and clinical variables.

RESULTS

During an 11-year period 1582 incident unplanned ICU admissions occurred. Predictors of unplanned ICU admission included older age, being male, having a higher acuity triage category and a history of co-morbid conditions. Emergency department diagnostic groups associated with higher incidence of unplanned ICU admission included: sepsis, acute renal failure, lymphatic-hematopoietic tissue neoplasms, pneumonia, chronic-airways disease and bowel obstruction. The final model used to develop the nomogram had an ROC curve AUC of 0.7.

CONCLUSION

This study identified factors associated with unplanned ICU admission and developed a nomogram to individualise risk prior to a patient being transferred from the ED. This nomogram provides clinicians the opportunity prior to transfer from the ED, to either (1) review the appropriateness of the ward level of planned transfer or (2) flag patients for follow-up on the general ward to assess for deterioration.

Using risk factors and quantitative ultrasound to identify postmenopausal caucasian women at risk of osteoporosis

There is a need to prescreen large numbers of individuals for osteoporosis due to current demands on clinical resources. Some previous attempts to predict individuals at risk have used simple indices based on patient information, or Quantitative Ultrasound (QUS) and have shown good sensitivity but also demonstrated low specificity, which means that many individuals with good bone mineral density were also selected. The aim of this study was to determine if a tool based on a combination of risk factors and QUS measurements could also be made to provide improved specificity. A risk factors measurement questionnaire was created and completed for a sample of Caucasian postmenopausal women (n=235) who had undergone Dual-energy X-ray absorptiometry scanning. QUS measurements were also taken at various skeletal sites. Assessment tools were generated using stepwise regression to predict osteoporosis, evaluated by receiver operating characteristic curves, and assessed using area under the curve values. Specificity values were determined at a sensitivity of 0.90 to establish the comparative utility of each assessment tool. Using only a risk factors model the specificities were 0.28 at the lumbar spine, 0.45 for the femoral neck and 0.68 for the total hip. In a risk factors+QUS data model the specificities measured were 0.44 for the lumbar spine, 0.78 for the femoral neck, and 0.84 for the total hip. These novel assessment tools can identify those with low bone mineral density at a number of skeletal sites and help towards avoiding many unnecessary investigations in the future.

Bone mass and architecture determination: state of the art

Bone fracture occurs when the bone strength (i.e. the ability of the bone to resist a force) is less than the force applied to the bone. In the elderly, falls represent the more severe forces applied to bone. Bone density is a good marker of bone strength, and has been used widely in this respect. Nevertheless, many aspects of bone strength cannot be explained by bone density alone. For this reason there has been increasing interest in studying architectural parameters of bone, beyond bone density, which may affect bone strength. Macro-architectural parameters include e.g. bone size and geometry assessed with techniques such as radiography, dual-energy x-ray absorptiometry (DXA), peripheral quantitative computed tomography (QCT), computed tomography (CT) and magnetic resonance imaging (MRI). Micro-architectural parameters include fine cortical and trabecular structural detail which can be evaluated using high-resolution imaging techniques such as multidetector CT, MRI, and high-resolution peripheral QCT. These techniques are providing a great deal of new information on the physiological architectural responses of bone to aging, weightlessness, and treatment. This will ultimately lead to the prediction of fracture risk being improved through a combined assessment of bone density and architectural parameters.

Using administrative data to develop a nomogram for individualising risk of unplanned admission to intensive care

AIM

Although unplanned admissions to the intensive care unit (ICU) are associated with poorer prognoses, there is no published prognostic tool available for predicting this risk in an individual patient. We developed a nomogram for calculating the individualised absolute risk of unplanned ICU admission during a hospital stay.

METHOD

Hospital administrative data from a large district hospital of consecutive admissions from 1 January 2000 to 31 December 2006 of aged over 14 years was used. Patient data was extracted from 94,482 hospital admissions consisted of demographic and clinical variables, including diagnostic categories, types of admission and time and day of admission. Multivariate logistic regression coefficients were used to develop a predictive nomogram of individual risk to patients admitted to the study hospital of unplanned ICU admission.

RESULTS

A total of 672 incident unplanned ICU admissions were identified over this period. Independent predictors of unplanned ICU admissions included being male, older age, emergency department (ED) admissions, after-hour admissions, weekend admissions and six principal diagnosis groups: fractured femur, acute pancreatitis, liver disease, chronic airway disease, pneumonia and heart failure. The area under the receiver operating characteristic curve was 0.81.

CONCLUSION

The use of a nomogram to accurately identify at-risk patients using information that is readily available to clinicians has the potential to be a useful tool in reducing unplanned ICU admissions, which in turn may contribute to the reduction of adverse events of patients in the general wards.

Assessment of the 10-year probability of osteoporotic hip fracture combining clinical risk factors and heel bone ultrasound: the EPISEM prospective cohort of 12,958 elderly women

This study aimed to develop a hip screening tool that combines relevant clinical risk factors (CRFs) and quantitative ultrasound (QUS) at the heel to determine the 10-yr probability of hip fractures in elderly women. The EPISEM database, comprised of approximately 13,000 women 70 yr of age, was derived from two population-based white European cohorts in France and Switzerland. All women had baseline data on CRFs and a baseline measurement of the stiffness index (SI) derived from QUS at the heel. Women were followed prospectively to identify incident fractures. Multivariate analysis was performed to determine the CRFs that contributed significantly to hip fracture risk, and these were used to generate a CRF score. Gradients of risk (GR; RR/SD change) and areas under receiver operating characteristic curves (AUC) were calculated for the CRF score, SI, and a score combining both. The 10-yr probability of hip fracture was computed for the combined model. Three hundred seven hip fractures were observed over a mean follow-up of 3.2 yr. In addition to SI, significant CRFs for hip fracture were body mass index (BMI), history of fracture, an impaired chair test, history of a recent fall, current cigarette smoking, and diabetes mellitus. The average GR for hip fracture was 2.10 per SD with the combined SI + CRF score compared with a GR of 1.77 with SI alone and of 1.52 with the CRF score alone. Thus, the use of CRFs enhanced the predictive value of SI alone. For example, in a woman 80 yr of age, the presence of two to four CRFs increased the probability of hip fracture from 16.9% to 26.6% and from 52.6% to 70.5% for SI Z-scores of +2 and -3, respectively. The combined use of CRFs and QUS SI is a promising tool to assess hip fracture probability in elderly women, especially when access to DXA is limited.

Quantitative ultrasound does not identify patients with an inflammatory disease at risk of vertebral deformities

BACKGROUND

Previous studies from our group have shown that a high prevalence of vertebral deformities suggestive of fracture can be found in patients with an inflammatory disease, despite a near normal bone mineral density (BMD). As quantitative ultrasound (QUS) of the heel can be used for refined assessment of bone strength, we evaluated whether QUS can be used to identify subjects with an inflammatory disease with an increased chance of having a vertebral fracture.

METHODS

246 patients (mean age: 44 +/- 12.4 years) with an inflammatory disease (sarcoidosis or inflammatory bowel disease (IBD)) were studied. QUS of the heel and BMD of the hip (by dual X-ray absorptiometry (DXA)) were measured. Furthermore lateral single energy densitometry of the spine for assessment of vertebral deformities was done. Logistic regression analysis was performed to assess the strength of association between the prevalence of a vertebral deformity and BMD and QUS parameters, adjusted for gender and age.

RESULTS

Vertebral deformities (ratio of <0.80) were found in 72 vertebrae of 54 subjects (22%). In contrast to the QUS parameters BUA (broadband ultrasound attenuation) and SOS (speed of sound), T-score of QUS and T-scores of the femoral neck and trochanter (DXA) were lower in the group of patients with vertebral deformities. Logistic regression analysis showed that the vertebral deformity risk increases by about 60 to 90% per 1 SD reduction of BMD (T-score) determined with DXA but not with QUS.

CONCLUSION

Our findings imply that QUS measurements of the calcaneus in patients with an inflammatory condition, such as sarcoidosis and IBD, are likely of limited value to identify patients with a vertebral fracture.