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  1. Ab Rasid AM, Muazu Musa R, Abdul Majeed APP, Musawi Maliki ABH, Abdullah MR, Mohd Razmaan MA, et al.
    PLoS One, 2024;19(2):e0296467.
    PMID: 38329954 DOI: 10.1371/journal.pone.0296467
    The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model's performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.
    Matched MeSH terms: Logistic Models
  2. Song J, Shin SD, Jamaluddin SF, Chiang WC, Tanaka H, Song KJ, et al.
    J Neurotrauma, 2023 Jul;40(13-14):1376-1387.
    PMID: 36656672 DOI: 10.1089/neu.2022.0280
    Abstract Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.
    Matched MeSH terms: Logistic Models
  3. Desnita R, Surya DO
    Med J Malaysia, 2023 Jul;78(4):523-525.
    PMID: 37518926
    INTRODUCTION: One of the common problems in patients with diabetes mellitus is a decrease in balance stability. A decrease in balance stability will result in functional limitations, an increased risk of falling and injury and a decrease in patient productivity. This study aimed to analyse the factors associated with functional balance in diabetes mellitus patients in Padang, Indonesia.

    MATERIALS AND METHODS: This research design is crosssectional. The number of samples in this study was 132 diabetes mellitus patients. Chi-square test and binary logistic regression were used to examine the factors associated with functional balance in diabates mellitus patients.

    RESULTS: Factors associated with functional balance in diabetes mellitus patients were age.

    CONCLUSION: This study highlights that age, gender and degree of neuropathy are significant factors associated with functional balance in diabetes mellitus patients. Nurses must enhance health education about prevention and risk factors that affect functional balance in diabetes mellitus patients.

    Matched MeSH terms: Logistic Models
  4. Ibrahim N, Foo LK, Chua SL
    PMID: 36833984 DOI: 10.3390/ijerph20043289
    Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.
    Matched MeSH terms: Logistic Models
  5. Jani P, Mishra U, Buchmayer J, Maheshwari R, D'Çruz D, Walker K, et al.
    World J Pediatr, 2023 Feb;19(2):139-157.
    PMID: 36372868 DOI: 10.1007/s12519-022-00625-2
    BACKGROUND: Globally, are skincare practices and skin injuries in extremely preterm infants comparable? This study describes skin injuries, variation in skincare practices and investigates any association between them.

    METHODS: A web-based survey was conducted between February 2019 and August 2021. Quantifying skin injuries and describing skincare practices in extremely preterm infants were the main outcomes. The association between skin injuries and skincare practices was established using binary multivariable logistic regression adjusted for regions.

    RESULTS: Responses from 848 neonatal intensive care units, representing all geographic regions and income status groups were received. Diaper dermatitis (331/840, 39%) and medical adhesive-related skin injuries (319/838, 38%) were the most common injuries. Following a local skincare guideline reduced skin injuries [medical adhesive-related injuries: adjusted odds ratios (aOR) = 0.63, 95% confidence interval (CI) = 0.45-0.88; perineal injuries: aOR = 0.66, 95% CI = 0.45-0.96; local skin infections: OR = 0.41, 95% CI = 0.26-0.65; chemical burns: OR = 0.46, 95% CI = 0.26-0.83; thermal burns: OR = 0.51, 95% CI = 0.27-0.96]. Performing skin assessments at least every four hours reduced skin injuries (abrasion: aOR = 0.48, 95% CI = 0.33-0.67; pressure: aOR = 0.51, 95% CI = 0.34-0.78; diaper dermatitis: aOR = 0.71, 95% CI = 0.51-0.99; perineal: aOR = 0.52, 95% CI = 0.36-0.75). Regional and resource settings-based variations in skin injuries and skincare practices were observed.

    CONCLUSIONS: Skin injuries were common in extremely preterm infants. Consistency in practice and improved surveillance appears to reduce the occurrence of these injuries. Better evidence regarding optimal practices is needed to reduce skin injuries and minimize practice variations.

    Matched MeSH terms: Logistic Models
  6. Yuan CJ, Varathan KD, Suhaimi A, Ling LW
    J Rehabil Med, 2023 Jan 09;55:jrm00348.
    PMID: 36306152 DOI: 10.2340/jrm.v54.2432
    OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.

    SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.

    METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.

    RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.

    CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

    Matched MeSH terms: Logistic Models
  7. Law ZK, Appleton JP, Scutt P, Roberts I, Al-Shahi Salman R, England TJ, et al.
    Stroke, 2022 Apr;53(4):1141-1148.
    PMID: 34847710 DOI: 10.1161/STROKEAHA.121.035191
    BACKGROUND: Seeking consent rapidly in acute stroke trials is crucial as interventions are time sensitive. We explored the association between consent pathways and time to enrollment in the TICH-2 (Tranexamic Acid in Intracerebral Haemorrhage-2) randomized controlled trial.

    METHODS: Consent was provided by patients or by a relative or an independent doctor in incapacitated patients, using a 1-stage (full written consent) or 2-stage (initial brief consent followed by full written consent post-randomization) approach. The computed tomography-to-randomization time according to consent pathways was compared using the Kruskal-Wallis test. Multivariable logistic regression was performed to identify variables associated with onset-to-randomization time of ≤3 hours.

    RESULTS: Of 2325 patients, 817 (35%) gave self-consent using 1-stage (557; 68%) or 2-stage consent (260; 32%). For 1507 (65%), consent was provided by a relative (1 stage, 996 [66%]; 2 stage, 323 [21%]) or a doctor (all 2-stage, 188 [12%]). One patient did not record prerandomization consent, with written consent obtained subsequently. The median (interquartile range) computed tomography-to-randomization time was 55 (38-93) minutes for doctor consent, 55 (37-95) minutes for 2-stage patient, 69 (43-110) minutes for 2-stage relative, 75 (48-124) minutes for 1-stage patient, and 90 (56-155) minutes for 1-stage relative consents (P<0.001). Two-stage consent was associated with onset-to-randomization time of ≤3 hours compared with 1-stage consent (adjusted odds ratio, 1.9 [95% CI, 1.5-2.4]). Doctor consent increased the odds (adjusted odds ratio, 2.3 [1.5-3.5]) while relative consent reduced the odds of randomization ≤3 hours (adjusted odds ratio, 0.10 [0.03-0.34]) compared with patient consent. Only 2 of 771 patients (0.3%) in the 2-stage pathways withdrew consent when full consent was sought later. Two-stage consent process did not result in higher withdrawal rates or loss to follow-up.

    CONCLUSIONS: The use of initial brief consent was associated with shorter times to enrollment, while maintaining good participant retention. Seeking written consent from relatives was associated with significant delays.

    REGISTRATION: URL: https://www.isrctn.com; Unique identifier: ISRCTN93732214.

    Matched MeSH terms: Logistic Models
  8. Naserrudin NA, Jeffree MS, Kaur N, Syed Abdul Rahim SS, Ibrahim MY
    PLoS One, 2022 01 28;17(1):e0261249.
    PMID: 35089931 DOI: 10.1371/journal.pone.0264247
    Every person diagnosed with diabetes mellitus (T2DM) is at risk of developing Diabetic retinopathy (DR). Thus, DR is one of the major chronic microvascular complications of T2DM. However, in Malaysia, research about DR is still scarce. This study aimed to determine the prevalence of DR among diabetic patients across 46 primary healthcare clinics in Sabah, Malaysia. Secondly, it purported to identify the factors influencing the development of DR. This cross-sectional study involved a total of 22,345 Type 2 diabetes mellitus (T2DM) patients in the Sabah Diabetic Registry from 2008 to 2015. Of the 22,345 T2DM patients, 13.5% (n = 3,029) of them were diagnosed with DR. Multiple logistic regression revealed seven major risk factors of DR, i.e. patients with diabetic foot ulcer [aOR: 95% CI 3.08 (1.96-4.85)], patients with diabetic nephropathy [aOR: 95% CI 2.47 (2.13-2.86)], hypertension [aOR: 95% CI 1.63 (1.43-1.87)], dyslipidaemia [aOR: 95% CI 1.30 (1.17-1.44)], glycated haemoglobin [(HbA1c) > 6.5 (aOR: 95% CI 1.25 (1.14-1.38)], duration of diabetes mellitus (T2DM) [aOR: 95% CI 1.06 (1.05-1.07)] and age of patient [aOR: 95% CI 1.01 (1.00-1.02)] respectively. DR is a preventable complication. The effective glycaemic control is crucial in preventing DR. In minimizing the prevalence of DR, the healthcare authorities should institute programmes to induce awareness on the management of DR's risk factors among patient and practitioner.
    Matched MeSH terms: Logistic Models
  9. Bukhari MM, Ghazal TM, Abbas S, Khan MA, Farooq U, Wahbah H, et al.
    Comput Intell Neurosci, 2022;2022:3606068.
    PMID: 35126487 DOI: 10.1155/2022/3606068
    Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.
    Matched MeSH terms: Logistic Models
  10. Philip N, Lung Than LT, Shah AM, Yuhana MY, Sekawi Z, Neela VK
    BMC Infect Dis, 2021 Oct 19;21(1):1081.
    PMID: 34666707 DOI: 10.1186/s12879-021-06766-5
    BACKGROUND: Leptospirosis is a re-emerging disease with vast clinical presentations, that ranges from subclinical or mild to severe and fatal outcomes. Leptospirosis can be managed well if diagnosed earlier, however, similar clinical presentations by several other febrile illnesses or co-infections, and laboratory diagnostic challenges due to the biphasic nature of the illness, often result in mis- or underdiagnosis, thereby lead to severe illness. Identification of clinical predictors for the severe form of the disease plays a crucial role in reducing disease complication and mortality. Therefore, we aimed to determine the clinical predictors associated with severe illness among leptospirosis patients from Central Malaysia through a prospective multicenter observational study.

    METHODS: A prospective multicenter observational study was performed on patients admitted for clinically suspected leptospirosis. Three hospitals namely Hospital Serdang, Hospital Tengku Ampuan Rahimah and Hospital Teluk Intan were included in the study. Among a total of 165 clinically suspected leptospirosis patients, 83 confirmed cases were investigated for clinical predictors for severe illness. Qualitative variables were performed using χ2 and the relationship between mild and severe cases was evaluated using logistic regression. Multivariable logistic regression was used to predict the independent variable for severity.

    RESULTS: Among the 83 patients, 50 showed mild disease and 33 developed severe illness. The mean age of the patients was 41.92 ± 17.99 and most were males (n = 54, 65.06%). We identified mechanical ventilation, acute kidney injury, septic shock, creatinine level of > 1.13 mg/dL, urea > 7 mmol/L, alanine aminotransferase > 50 IU, aspartate aminotransferase > 50 IU, and platelet  50 IU and platelet 

    Matched MeSH terms: Logistic Models
  11. Islahudin F, Lee FY, Tengku Abd Kadir TNI, Abdullah MZ, Makmor-Bakry M
    Res Social Adm Pharm, 2021 10;17(10):1831-1840.
    PMID: 33589374 DOI: 10.1016/j.sapharm.2021.02.002
    BACKGROUND: An adherence model is required to optimise medication management among chronic kidney disease (CKD) patients, as current assessment methods overestimate the true adherence of CKD patients with complex regimens. An approach to assess adherence to individual medications is required to assist pharmacists in addressing non-adherence.

    OBJECTIVE: To develop an adherence prediction model for CKD patients.

    METHODS: This multi-centre, cross-sectional study was conducted in 10 tertiary hospitals in Malaysia using simple random sampling of CKD patients with ≥1 medication (sample size = 1012). A questionnaire-based collection of patient characteristics, adherence (defined as ≥80% consumption of each medication for the past one month), and knowledge of each medication (dose, frequency, indication, and administration) was performed. Continuous data were converted to categorical data, based on the median values, and then stratified and analysed. An adherence prediction model was developed through multiple logistic regression in the development group (n = 677) and validated on the remaining one-third of the sample (n = 335). Beta-coefficient values were then used to determine adherence scores (ranging from 0 to 7) based on the predictors identified, with lower scores indicating poorer medication adherence.

    RESULTS: Most of the 1012 patients had poor medication adherence (n = 715, 70.6%) and half had good medication knowledge (n = 506, 50%). Multiple logistic regression analysis determined 4 significant predictors of adherence: ≤7 medications (constructed score = 2, p 

    Matched MeSH terms: Logistic Models
  12. Nettemu SK, Nettem S, Singh VP, William SS, Gunasekaran SS, Krisnan M, et al.
    Int J Implant Dent, 2021 06 10;7(1):77.
    PMID: 34109477 DOI: 10.1186/s40729-021-00315-0
    AIM: This study was to evaluate the association between peri-implant bleeding on probing in peri-implant diseases and its association with multilevel factors (site specific factors, implant factors, and patient level factors).

    METHODOLOGY: A cross-sectional study involved consented adult patients with ≥ 1 dental implant. Two calibrated operators examined the patients. BoP was outcome variable and peri-implant gingival biotype was principal predictor variable. The effects of site, implant, and patient level factors on BoP were assessed using a multilevel logistic regression model.

    RESULTS: Eighty patients for a total of 119 implants and 714 sites were included in the study. Bleeding on probing was observed in 42 implants (35.29%) with a significant higher risk observed in presence of gingival recession, thin peri-implant gingival biotype, duration of implant placement, smokers, and male patients.

    CONCLUSION: Peri-implant bleeding on probing was associated with site specific, implant, and patient level factors.

    Matched MeSH terms: Logistic Models
  13. Kong YL, Anis-Syakira J, Jawahir S, R'ong Tan Y, Rahman NHA, Tan EH
    BMC Public Health, 2021 Jun 01;21(1):1033.
    PMID: 34074275 DOI: 10.1186/s12889-021-11022-1
    BACKGROUND: The increase in the elderly population, chronic and degenerative diseases, as well as accidents at work and on the road in Malaysia would result in an increased demand for informal care. This paper aimed to determine the associated factors of informal caregiving and its effects on health, work and social activities of adult informal caregivers in Malaysia.

    METHODS: The data from the 2019 National Health and Morbidity Survey (NHMS), a nationwide cross-sectional survey with a two-stage stratified random sampling design, was used in this research. The study included respondents who were 18 years and older (n = 11,674). Data were obtained via face-to-face interviews using validated questionnaires. Descriptive and complex sample logistic regression analyses were employed as appropriate.

    RESULTS: 5.7% of the adult population were informal caregivers. Provision of informal care were significantly associated with the female sex (OR = 1.52, 95% CI [1.21, 1.92]), those aged 36-59 years (OR = 1.61, 95% CI [1.15, 2.25]), and those who reported illness in the past 2 weeks (OR = 1.79, 95% CI [1.38, 2.33]). The risk of having their health affected were associated with female caregivers (OR = 3.63, 95% CI [1.73, 7.61]), those who received training (OR = 2.10, 95% CI [1.10, 4.00]) and those who provided care for 2 years or more (OR = 1.91, 95% CI [1.08, 3.37]). The factors associated with the effects on work were ethnicity, received training and had no assistance to provide the care. In terms of effect on social activities, female caregivers (OR = 1.96, 95% CI [1.04, 3.69]) and caregivers who received training were more likely (OR = 2.19, 95% CI [1.22, 3.93]) to have their social activities affected.

    CONCLUSION: Our study revealed that sex, age, and self-reported illness were factors associated with being an informal caregiver in Malaysia. Informal caregivers faced effects on their health, work, and social activities which may be detrimental to their well-being. This understanding is crucial for planning support for caregivers.

    Matched MeSH terms: Logistic Models
  14. Sajid MR, Muhammad N, Zakaria R, Shahbaz A, Bukhari SAC, Kadry S, et al.
    Interdiscip Sci, 2021 Jun;13(2):201-211.
    PMID: 33675528 DOI: 10.1007/s12539-021-00423-w
    BACKGROUND: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms.

    METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.

    RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.

    CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.

    Matched MeSH terms: Logistic Models
  15. Hung TH, Hsieh TT, Shaw SW, Kok Seong C, Chen SF
    J Diabetes Investig, 2021 Jun;12(6):1083-1091.
    PMID: 33064935 DOI: 10.1111/jdi.13441
    AIMS/INTRODUCTION: The association between gestational diabetes mellitus (GDM) and adverse maternal and perinatal outcomes in twin pregnancies remains unclear. This study was undertaken to highlight risk factors for GDM in women with dichorionic (DC) twins, and to determine the association between GDM DC twins and adverse maternal and perinatal outcomes in a large homogeneous Taiwanese population.

    MATERIALS AND METHODS: A retrospective cross-sectional study was carried out on 645 women with DC twins, excluding pregnancies complicated by one or both fetuses with demise (n = 22) or congenital anomalies (n = 9), who gave birth after 28 complete gestational weeks between 1 January 2001 and 31 December 2018. Univariable and multiple logistic regression analyses were carried out.

    RESULTS: Maternal age >34 years (adjusted odds ratio 2.52; 95% confidence interval 1.25-5.07) and pre-pregnancy body mass index >24.9 kg/m2 (adjusted odds ratio 2.83, 95% confidence interval 1.47-5.46) were independent risk factors for GDM in women with DC twins. Newborns from women with GDM DC twins were more likely to be admitted to the neonatal intensive care unit (adjusted odds ratio 1.70, 95% confidence interval 1.06-2.72) than newborns from women with non-GDM DC twins. Other pregnancy and neonatal outcomes were similar between the two groups.

    CONCLUSIONS: Advanced maternal age and pre-pregnancy overweight or obesity are risk factors for GDM in women with DC twins. Except for a nearly twofold increased risk of neonatal intensive care unit admission of newborns, the pregnancy and neonatal outcomes for women with GDM DC twins are similar to those for women with non-GDM DC twins.

    Matched MeSH terms: Logistic Models
  16. Lee ZY, Hasan MS, Day AG, Ng CC, Ong SP, Yap CSL, et al.
    PMID: 34021917 DOI: 10.1002/jpen.2194
    BACKGROUND: Nutrition risk, sarcopenia, and frailty are distinct yet inter-related. They may be due to suboptimal or prevented by optimal nutrition intake. The combination of nutrition risk (modified nutrition risk in the critically ill [mNUTRIC]), sarcopenia (SARC-CALF) and frailty (clinical frailty scale [CFS]) in a single score may better predict adverse outcomes and prioritizing resources for optimal nutrition (and exercise) in the intensive care unit (ICU).

    METHODS: This is a retrospective analysis of a single-center prospective observational study that enrolled mechanically ventilated adults with expected ≥96 hours ICU stay. SARC-F and CFS questionnaires were administered to patient's next-of-kin and mNUTRIC were calculated. Calf-circumference was measured at the right calf. Nutrition data was collected from nursing record. The high-risk scores (mNUTRIC ≥5, SARC-CALF >10 or CFS ≥4) of these variables were combined to become the NUTRIC-SF score (range: 0-3).

    RESULTS: Eighty-eight patients were analyzed. Multiple logistic model demonstrated increasing mNUTRIC score was independently associated with 60-day mortality while increasing SARC-CALF and CFS showed a strong trend towards higher 60-day mortality. Discriminative ability of NUTRIC-SF for 60-day mortality is better than it's component (AUROC 0.722, 95% confidence interval [CI] 0.677-0.868). Every increment of 300 kcal/day and 30 g/day is associated with a trend towards higher rate of discharge alive for high [≥2; Adjusted Hazard Ratio 1.453 (95% CI 0.991-2.130) for energy, 1.503 (95% CI 0.936-2.413) for protein] but not low (<2) NUTRIC-SF score.

    CONCLUSION: NUTRIC-SF score may be a clinically relevant risk stratification tool in the ICU. This article is protected by copyright. All rights reserved.

    Matched MeSH terms: Logistic Models
  17. Lo TS, Ng KL, Lin YH, Hsieh WC, Kao CC, Tan YL
    Int Urogynecol J, 2021 May 18.
    PMID: 34003308 DOI: 10.1007/s00192-021-04757-3
    INTRODUCTION AND HYPOTHESIS: Our primary objective was to study outcomes of patients with intrinsic sphincter deficiency (ISD) following mid-urethral slings (MUS) at 1-year. Our secondary objective was to delineate factors affecting success in these patients.

    METHODS: Six hundred eighty-eight patients who had MUS between January 2004 and April 2017 were reviewed retrospectively; 48 women were preoperatively diagnosed with ISD. All completed urodynamic studies and validated quality-of-life (QOL) questionnaires at baseline and 1 year. Primary outcomes were objective and subjective cure of stress incontinence, defined as no involuntary urine leakage during filling cystometry and 1-h pad test < 2 g and negative response to Urogenital Distress Inventory-6 Question 3. Ultrasound was performed to determine tape position, urethral mobility and kinking at 1 year.

    RESULTS: Women with ISD had significantly lower objective and subjective cure rates of 52.1% and 47.9%, respectively, compared to an overall of 88.2% and 85.9%. QOL scores significantly improved in those with successful surgeries. The sling type did not make a difference. Multivariate logistic regression identified reduced urethral mobility [OR 2.11 (1.24-3.75)], lower maximum urethral closure pressure (MUCP) [OR 1.61 (1.05-3.41)] and tape position [OR 3.12 (1.41-8.71)] to be associated with higher odds of failed slings for women with ISD.

    CONCLUSIONS: Although there are good overall success in women undergoing MUS, those with ISD have significantly lower cure rates at 1 year. Factors related to failure include reduced urethral mobility, low MUCP and relative tape position further away from the bladder neck. Optimal management of patients with ISD and reduced urethral mobility remains challenging.

    Matched MeSH terms: Logistic Models
  18. Teoh SL, Ngorsuraches S, Lai NM, Chaiyakunapruk N
    Value Health Reg Issues, 2021 May;24:167-172.
    PMID: 33714105 DOI: 10.1016/j.vhri.2020.09.003
    OBJECTIVES: Globally, nutraceuticals have been increasingly used. Nevertheless, the consumer preferences for nutraceuticals have not been quantitatively investigated. This study used discrete choice experiment (DCE) to examine consumer preferences and willingness to pay for nutraceuticals.

    METHODS: Four attributes (ie, the scientific proof of effectiveness, the scientific proof of safety, the source of recommendation, and cost) were identified from a systematic review and focus group interviews. They were used to develop a DCE questionnaire. Consumers at community pharmacies in Malaysia were asked to respond to 8 DCE choice sets. A conditional logit model was employed to obtain the relative importance of each attribute and to estimate respondents' WTP for nutraceuticals.

    RESULTS: A total of 111 valid responses were analyzed. A negative constant term in the developed model indicated that generally the respondents preferred not to use nutraceuticals before they considered the study attributes. The respondents preferred nutraceuticals with no side effect, clear evidence of effectiveness, and recommendation of a healthcare professional. The respondents were willing to pay $252/month more for nutraceuticals proven with no side effect than for those without proof of safety, and $102/month more for nutraceuticals proven with clear effectiveness than for those without proof of effectiveness.

    CONCLUSIONS: Consumers weighed relatively high on the availability of safety and effectiveness proofs when they chose nutraceuticals. The study highlights on the crucial need to inform consumers using clinical evidences of nutraceuticals as the information is highly preferred by consumers.

    Matched MeSH terms: Logistic Models
  19. Judge C, O'Donnell MJ, Hankey GJ, Rangarajan S, Chin SL, Rao-Melacini P, et al.
    Am J Hypertens, 2021 04 20;34(4):414-425.
    PMID: 33197265 DOI: 10.1093/ajh/hpaa176
    BACKGROUND: Although low sodium intake (<2 g/day) and high potassium intake (>3.5 g/day) are proposed as public health interventions to reduce stroke risk, there is uncertainty about the benefit and feasibility of this combined recommendation on prevention of stroke.

    METHODS: We obtained random urine samples from 9,275 cases of acute first stroke and 9,726 matched controls from 27 countries and estimated the 24-hour sodium and potassium excretion, a surrogate for intake, using the Tanaka formula. Using multivariable conditional logistic regression, we determined the associations of estimated 24-hour urinary sodium and potassium excretion with stroke and its subtypes.

    RESULTS: Compared with an estimated urinary sodium excretion of 2.8-3.5 g/day (reference), higher (>4.26 g/day) (odds ratio [OR] 1.81; 95% confidence interval [CI], 1.65-2.00) and lower (<2.8 g/day) sodium excretion (OR 1.39; 95% CI, 1.26-1.53) were significantly associated with increased risk of stroke. The stroke risk associated with the highest quartile of sodium intake (sodium excretion >4.26 g/day) was significantly greater (P < 0.001) for intracerebral hemorrhage (ICH) (OR 2.38; 95% CI, 1.93-2.92) than for ischemic stroke (OR 1.67; 95% CI, 1.50-1.87). Urinary potassium was inversely and linearly associated with risk of stroke, and stronger for ischemic stroke than ICH (P = 0.026). In an analysis of combined sodium and potassium excretion, the combination of high potassium intake (>1.58 g/day) and moderate sodium intake (2.8-3.5 g/day) was associated with the lowest risk of stroke.

    CONCLUSIONS: The association of sodium intake and stroke is J-shaped, with high sodium intake a stronger risk factor for ICH than ischemic stroke. Our data suggest that moderate sodium intake-rather than low sodium intake-combined with high potassium intake may be associated with the lowest risk of stroke and expected to be a more feasible combined dietary target.

    Matched MeSH terms: Logistic Models
  20. Muthanna FMS, Karuppannan M, Hassan BAR, Mohammed AH
    Osong Public Health Res Perspect, 2021 Apr;12(2):115-125.
    PMID: 33980002 DOI: 10.24171/j.phrp.2021.12.2.09
    Objective: Fatigue is the most frequently reported symptom experienced by cancer patients and has a profound effect on their quality of life (QOL). The study aimed to determine the impact of fatigue on QOL among breast cancer patients receiving chemotherapy and to identify the risk factors associated with severe fatigue incidence.

    Methods: This was an observational prospective study carried out at multiple centers. In total, 172 breast cancer patients were included. The Functional Assessment of Chronic Illness Therapy-Fatigue Questionnaire was used to measure QOL, while the Brief Fatigue Inventory (BFI) was used to assess the severity of fatigue.

    Results: The total average mean and standard deviation of QOL were 84.58±18.07 and 4.65±1.14 for BFI scores, respectively. A significant association between fatigue and QOL was found in linear and multiple regression analyses. The relationships between fatigue severity and cancer stage, chemotherapy dose delay, dose reduction, chemotherapy regimen, and ethnicity were determined using binary logistic regression analysis.

    Conclusion: The findings of this study are believed to be useful for helping oncologists effectively evaluate, monitor, and treat fatigue related to QOL changes.

    Matched MeSH terms: Logistic Models
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